Next Article in Journal
Characteristics of Unorganized Hydrogen Sulfide Dispersion for Industrial Building Layout Optimization
Previous Article in Journal
Effects of Precipitation Change and Nitrogen and Phosphorus Additions on Traits and Abundance of Potentilla anserina in an Alpine Meadow
Previous Article in Special Issue
A Quantizing Method for Atmospheric Environment Impact Post-Assessment of Highways Based on Computational Fluid Dynamics Model
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 13
Issue 11
10.3390/atmos13111821
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu

by
Shenghao Huang
1,
Han Wang
1,2,
Dan Wu
3,
Rongjiang Ma
1,
Liangliang Sun
1 and
Mengsi Deng
1,*
1
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2
Graduate School of Tangshan, Southwest Jiaotong University, Tangshan 063000, China
3
School of Architecture, Southwest Jiaotong University, Chengdu 610031, China
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(11), 1821; https://doi.org/10.3390/atmos13111821
Submission received: 18 September 2022 / Revised: 24 October 2022 / Accepted: 27 October 2022 / Published: 2 November 2022
(This article belongs to the Special Issue Aerosols in Residential, School, and Vehicle Environments)
Browse Figures
Versions Notes

Abstract

:
Urbanization promotes the development of the subway system, and the particulate matter (PM) concentrations inside have received increasing attention. This study first measured the dynamic PM2.5 and PM10 concentrations in a metro carriage in Chengdu and explored the dominant influencing factors. The personal inhalation intakes of different routes were evaluated. The results showed that the in-carriage PM2.5 and PM10 concentrations ranged from 11 to 74 μg/m3 (mean: 36.7 μg/m3) and 13 to 89 μg/m3 (mean: 40.1 μg/m3), respectively. When the train passed from the overground to underground, the in-carriage PM2.5 and PM10 concentrations increased by 30.4% and 32.9%, respectively. No specific linear relationship between passenger number and in-carriage PM concentrations was found. In-carriage PM concentrations decreased after the carriage doors were opened on the platforms. PM2.5 inhalation intakes ranged from 1.08 to 9.52, with a mean of 4.24 µg. For the passengers with the same age and sex, the average inhaled PM2.5 intake in the metro carriage on the route with more underground platforms was higher. This study not only revealed the PM characteristics in the Chengdu metro system for the first time, but also provided guidelines for reducing the in-carriage PM concentrations to build a healthier travel environment.

1. Introduction

Rapid urban population growth brings with it severe ground traffic congestion, which increases citizens’ commuting time and causes environmental pollution [1,2,3]. The development of the metro system can effectively alleviate this issue in many countries, especially in China [2,4,5,6]. By the end of 2020, the metro system existed in 45 cities in China, resulting in a total operating mileage of 6280.8 km and an annual passenger number of 17.6 billion [7]. Obviously, the metro system, an essential travel tool, has been an inseparable part of urban daily life of citizens. Compared with a normal indoor environment, the metro system has many specific characteristics, including relative closure, limited ventilation, and special sources of heavy metals, making it a unique microenvironment [8]. Exposure to such an environment when traveling is drawing increasing attention.
As a dominant air pollutant species, particulate matter (PM) can cause diverse respiratory and cardiovascular diseases [9,10,11,12,13]. Revealing the characteristics of PM in the metro system has been a hot issue. Prior studies have shown that, with few exceptions, PM concentrations in metro systems were much higher than those measured in ambient air [14,15,16]. The sources of PM in metro systems were related to brake emissions, wheel-rail abrasion products, and the outdoor contribution to the platform ambient air [17]. However, passengers spend more time in the metro carriages than on the platforms when they take the subway [18,19]. Gao et. al. demonstrated that platform and air outside the carriage are two sources of PM2.5 in metro carriages. During operation and braking, PM2.5 on the platform produced by the friction between the wheel and the track entered the metro carriage. Meanwhile, PM2.5 in the ambient air outside the carriage could enter the inside through the ventilation system [20]. Thus, it is more important to explore the PM characteristics in the metro carriage and its impact on human health.
Investigations of PM concentrations in the metro carriage have been conducted in the US [21,22], Korea [23,24], Spain [25], Turkey [26], Italy [27], and China [28,29]. Some but not all of these have tried to explore the dominant influencing factors. Wang et al. [30] found that the change in the driving condition could cause in-carriage PM2.5 concentration variations. Nevertheless, he did not mention the in-carriage passenger as a variable that might influence the in-carriage PM2.5 levels. Thus, when studying the influence of the driving conditions, the impact of the passenger should be excluded. Zheng et al. [29] reported that the in-carriage PM2.5 concentration had no relationship with the passenger number in Hong Kong, but the experiments were conducted in two different metro lines. The difference in ambient PM concentrations around the two trains and the varied ventilation efficiencies of the two metro air conditioning systems might lead to additional influences on the in-carriage PM2.5 concentration. When exploring the influence of the factor, an experiment should be carried out in the same carriage of the same type of train on the same route in the same position so as to eliminate the aforementioned differences and to more accurately reflect the relationship between passenger number and PM2.5 concentration. On the other hand, a positive relationship between passenger number and in-carriage PM2.5 concentration was found by Xu et al. [31]. The opposite results mean that the correlation between the in-carriage PM2.5 concentration and the passenger number is still unclear. In addition, the research about the influence of train door operation (door opening and closing) on in-carriage PM concentrations was quite limited. Consequently, the impacts of these factors on in-carriage PM concentrations should be further investigated.
Although the long-term health effects of exposure to pollutants during travel have not been specifically determined, it is often assumed in a health impact assessment that the impacts of changes in inhaled pollutants during regular (commuter) travel, as a percentage of daily inhaled pollutants, are similar to the impacts of changes in the proportion of long-term exposure levels [32,33]. Exposure to high concentrations of in-carriage PM with more heavy metal elements, organic compounds, and transition metal elements might cause more severe adverse health effects [34]. Hence, it is important to evaluate the inhalation intake of PM in the metro carriage. The inhalation intakes of PM in some forms of public transport such as bike, taxi, and bus have been investigated [35,36,37,38], but few studies have assessed the inhalation intake of in-carriage PM [39,40,41]. Shen and Gao [40] calculated a volunteer’s inhalation intake of PM in the metro carriage, but they did not estimate the results in terms of the conditions of different sexes, ages, and routes. Lacking these variables in the evaluation is not conducive to understanding the impact of PM on passengers’ health for such a popular mode of transportation.
Chengdu, the economic and cultural center of Southwest China, has a population density of 1460 persons/km2 [42]. The Chengdu metro system has been rapidly developed in the most recent 10 years. By September 2021, the mileage of the Chengdu metro line ranked third in China. However, investigations into PM in the Chengdu metro System are still scarce. In this study, we first measured the PM2.5 and PM10 levels in the metro carriage in Chengdu to: (1) explore the impacts of some influencing factors, including driving routes (overground route/underground route), passenger number, and the opening and closing of the metro carriage door; (2) quantify the in-carriage personal inhalation intakes of PM in the Chengdu metro system, and finally (3) provide recommendations to reduce the in-carriage PM concentrations and inhalation intake.

2. Methodology

2.1. Measurement Site Description

To conduct the measurement, a metro line in Chengdu was selected; the line began operation in December 2019 with a total length of 49.02 km. It comprises 42 platforms (5 of them were overground platforms and the others were underground platforms). The train, equipped with an aluminum alloy body, is 185 m long and 3 m wide, and consists of 8 marshaling A-type carriages. There are 5 doors and 4 benches on each side of a single standard carriage. The maximum speed achieved is 80 km/h.
Our measurement campaign was conducted in a typical middle carriage of the trains on the chosen metro line (the fifth carriage) from 19 April to 25 June 2021 (longitude 104°066′–104°105′ E, latitude 30°75′–30°83′ N). The experiment involved five overground platforms (P1, P2, P3, P4, and P5) and five underground platforms (P6, P7, P8, P9, and P10) (Figure 1). The route sections between the two consecutive platforms were named from R1 to R9.

2.2. Monitoring Instruments and Experiment Design

PM2.5 and PM10 were measured simultaneously using two laser dust samplers (DustTrak 8530, TSI Inc., Shoreview, MI, USA), with a measurement range of 0.001–400 mg/m3 and an accuracy of 0.001 mg/m3. A carbon dioxide recorder (HOBO MX1102, Onset Computer Corp., Bourne, MA, USA) was used to measure the CO2 concentrations, with measurement ranges of 0–5000 ppm. The recording intervals of the DustTraks and HOBO were set at 1 s and 10 s, respectively. The two DustTraks were conducted with zero calibration using a high-efficiency particulate air (HEPA) filter and flow adjustment using a calibrated rotameter, and the particulate-battering hammer inside was cleaned with ethanol before each measurement. Furthermore, the PM2.5 and PM10 of the local air quality monitoring station data were found to be 1.03–1.26 (mean: 1.15) times larger than the real-time DustTrak data. In addition, the real-time PM data were corrected according to the gravimetric method using the 37 mm Teflon filter in the DustTrak under some conditions, and the correction coefficients were close to the results from the monitoring station. Therefore, as shown in Equation (1), a reasonable correction coefficient of 1.15 was applied for the dynamic data correction in this study. Before each experiment, we placed the HOBO in an unmanned environment to measure the atmospheric carbon dioxide concentrations. The measured concentration was compared with the recommended atmospheric value (400 ppm) by the manufacturer, and similar results could reflect the measurement accuracy of the sensor. This operation procedure is the standard calibration procedure in the instruction manual.
C c a l i b r a t i o n = A × C m e a s u r e m e n t ,
where Ccalibration and Cmeasurement are the calibrated and measured PM concentrations (μg/m3), respectively. A is the correction coefficient.
Three sub-campaigns were undertaken: (1) the underground/overground campaign, which explored the effects of driving routes on the in-carriage PM2.5 and PM10; (2) the passenger number campaign, to reveal the effect of passenger number on the in-carriage PM2.5 and PM10, and (3) the door opening/closing campaign, for exploring the effect of the door opening process on in-carriage PM concentrations.

2.3. Monitoring Process

In each campaign, two anti-static tubes were connected to the two DustTraks, respectively (Figure 2a,b). The air inlets of the tubes were fixed at a height of 1.5 m from the ground, in the breathing zone of the sitting passengers. The details of the campaigns were introduced below.
When exploring the PM difference between the underground route and the overground route, the operator entered the fifth carriage on platform P1 or P10, which is the start or the end of the measurement route. All the instruments were turned on and then put at the corresponding position (Mp_a in Figure 3). To minimize the effect of passengers on the experiment, the measurement campaign was carried out from 22:00 to 23:00, during which time there were almost no passengers in the metro carriage. A measurement lasting about 20 min was conducted on six days, including weekdays (Tuesday, Wednesday, Thursday, and Friday) and weekends (Saturday and Sunday). Therefore, there were six measurements in this sub-campaign.
For exploring the influence of passenger number on the PM in the carriage, 12 measurements were conducted in four time zones in terms of the passenger flow in the metro carriage. The period of 17:30–19:00 is peak-hour, and the periods of 10:00–11:30, 14:00–15:30, and 21:30–23:00 are off-peak-hour. During each measurement, the number of passengers in the measured carriage was recorded when the train kept running between every two consecutive platforms, which eliminated the influence of the train type, service life, and driving route differences. In all measurements (including peak and off-peak periods), the train was not overcrowded. Therefore, the number of passengers could be accurately counted. The distance between the passengers and the measurement point was 0.5–8.0 m, which prevented the possibility of direct disturbance by a certain individual and aided in representing the in-carriage PM levels. In addition, all 12 measurements were conducted during the days with similar atmospheric PM concentrations in order to avoid the influence of fluctuations by the surrounding PM concentrations (Table A6). The ambient PM concentrations [43] around the measurement route were obtained from nearby air quality monitoring stations before each experiment. The distances between the monitoring sites and the metro line are 5.8 km and 9.2 km, respectively. The measurements were conducted in Sichuan Basin, and the wind speed ranged from light air (level 1) to gentle breeze (level 3) [44]. It has been reported that a wind speed of less than level 4 has little effect on PM concentrations [45]. Meanwhile, the difference caused by the wind direction could be negligible because the wind direction was mainly northeast and southwest, accounting for 71% of the time during the measurement period based on the historical weather forecast data [44]. Hence, the PM data from the monitoring site can be representative in a small area.
To determine the effect of door opening and closing on in-carriage PM concentrations, the in-carriage measurement was continuously conducted near the door (Mp_b in Figure 3) for about 20 min (route: from P1 to P10), and the measurement involving a complete train stop and start process lasted for 3 min on each metro platform (Mp_c in Figure 3). The time points for when the door was opened and closed were recorded. Triplicate measurements were carried out in an off-peak period (21:00–23:00) to reduce the impact of passenger numbers.

2.4. Inhalation Intake Evaluation Method

We evaluated the inhalation intake of PM2.5 in the carriage for a further understanding of the impact of in-carriage PM2.5 on human health, which can be calculated using Equation (2).
Inhalation   intake = ∫ t 1 t 2 C ( t ) ⋅ I R ( t ) ⋅ d t ,
where C(t) is the real-time exposure concentration at the time point of t (µg/m3); t1 and t2 are the start and end time points of exposure (min), respectively, and IR(t) is the inhalation rate (IR) at the time point of t (m3/min) [39].
IR was influenced by many factors such as age, sex, and weight [46]. Hence, two sexes (male and female) and five age stages (children (0–7), juvenile (7–16.5), youth (16.5–45), middle-aged (45–65), and elderly (65–96)) were involved in this study. The corresponding IR (Table A1) was determined according to a previous study conducted by Brochu et al. [47]. Owing to the different PM2.5 concentrations on varied routes, four routes were considered in this study (Table A2), all of which had five platforms but different distributions between the overground and underground platform numbers. Routes a and d consisted of five overground platforms and five underground platforms, respectively. The underground platforms accounted for 2/5 and 3/5 for Routes b and c, respectively (Figure 4). The data of the 10 above measurements on different days were used to evaluate passenger inhalation intakes. There were 40 scenarios for each day in this evaluation. Although the length of the period of each measurement was not very long, it could represent the actual commuting period spent in the metro carriage. Moreover, the in-carriage PM concentrations used to estimate the inhalation intake were repeatedly measured, which could effectively reflect the PM levels in the metro carriage when passing through different routes. Therefore, the inhalation intake calculated from the measured PM concentrations and measurement periods would be representative.

2.5. Data Analysis

An independent sample t-test was used to compare the differences in in-carriage PM2.5 and PM10 concentrations between the overground and underground air. The paired sample t-test was adopted to evaluate the significance of the in-carriage PM2.5 and PM10 concentration difference between the door opening and closing conditions. A one-way analysis of variance (ANOVA) was used to determine the differences in inhalation intakes under varied PM2.5 inhalation conditions. Pearson correlation coefficient was used to examine the correlation between the in-carriage PM concentrations and passenger number. All statistical significances were accepted at p < 0.05.

3. Results and Discussion

3.1. PM2.5 and PM10 Concentrations in the Carriage

The PM2.5 and PM10 concentrations of all the measurements involved five overground platforms (P1, P2, P3, P4, and P5) and five underground platforms (P6, P7, P8, P9, and P10) which are summarized in Table 1. All the in-carriage PM2.5 and PM10 concentrations ranged from 11 to 74 µg/m3 and from 13 to 89 µg/m3, respectively. The average PM2.5 concentration accounted for 91.5% of the average PM10 concentration, indicating that PM2.5 is the major part of the particles in the metro carriage. The average PM2.5 concentration in all measurements (36.7 µg/m3) was lower than the 24 h mean concentration limit in residential areas and public transport (75 µg/m3) specified in the Ambient Air Quality Standards [48], but higher than the recommended 24 h level (15 µg/m3) specified in the air quality guidelines proposed by the World Health Organization [49].
As shown in Table 1, the average in-carriage PM2.5 concentration in this study was lower than those in Nanchang [50], Taipei [51], Shanghai [31], Seoul [23], Athens L2 [52], Oporto LA [52], Istanbul [26], and Mexico [53], higher than those in Tianjin [54], Barcelona L10 [25], and Los Angles [21], and close to that in New York [55]. Moreover, the PM10 concentrations measured in the metro carriage on all routes were lower than those in Tianjin [54], Taipei [51], Seoul [23], and Rome [27], and higher than that in Los Angles [21].
Table
Table 1. Average PM2.5 and PM10 concentrations in metro carriages worldwide.
Table 1. Average PM2.5 and PM10 concentrations in metro carriages worldwide.
CityMeasurement YearPM2.5 (µg/m3)PM10 (µg/m3)Reference
MeanRangeMeanRange
Chengdu (all routes)202136.711–7440.113–89This study
Tianjin202123-69-[54]
Nanchang201917972–516--[50]
Taipei2016472–112553–135[51]
Shanghai201384---[31]
Seoul2004–2005126115–13631229–359[23]
Barcelona L1020142620–31--[25]
Athens L22014125---[52]
Oporto LA54---
Rome2010--275-[27]
Istanbul2007–20087322–241 [26]
Los Angeles (underground)2010243–62316–107[21]
New York20083934–44--[55]
Mexico20026131–99--[53]
The metro lines in Seoul have been in operation for a long time, and the PM yield was related to the aging of facilities in the subway system and the dispersion of inorganic metal dust generated by the friction between trains and wheels during normal operation [23]. This could contribute to the fact that the in-carriage PM concentration in the Seoul metro system was higher than that in Chengdu metro system. On the other hand, the red and gold subway line in Los Angeles [21] and the metro line 10 in Barcelona [25] were comparatively new, with more advanced technology, and therefore, their ventilation system and braking technology were more effective than the older one. Meanwhile, the average ambient PM2.5 concentration in Los Angeles (20 µg/m3) was lower than that in Chengdu (Average, 30.5 µg/m3) during the measurement [21]. This could be the reason for why the in-carriage PM concentrations in these cities were lower than those in Chengdu. These studies revealed that in-carriage PM concentrations were affected by many factors such as the monitoring conditions (i.e., measurement time, place, equipment, and the year of subway construction), wheel materials, ventilation levels, and braking systems [23,25,56,57]. The metro line in this study, currently one of the newest metro lines in Chengdu, has an efficient ventilation system and an advanced braking system, which might lead to the fact that the PM levels in this metro line were relatively low among different subway systems worldwide.

3.2. Influencing Factors

3.2.1. Underground and Overground

PM concentrations were affected by the driving routes. As shown in Figure 5, there was only one person in the carriage when the metro train passed through the seven routes involving R1, R2, R3, R4, R5, R6, and R7, and the average passenger number was three and five when passing through R8 and R9, respectively. Therefore, the CO2 level dominantly influenced by human breath remained almost unchanged during the measurement, indicating that the influence of the passenger number can be ignored. There was a significant average PM concentration difference between the overground (PM2.5: 33.9; PM10: 38.6 µg/m3) and underground (PM2.5: 44.2; PM10: 51.3 µg/m3) routes (PM2.5: p = 0.002; PM10: p < 0.05). As the metro train ran from the overground route to the underground route, the PM2.5 and PM10 levels in the carriage increased by 30.4% and 32.9%, respectively.
Figure 6 presents the dynamic in-carriage PM concentrations for the overground and underground conditions for six days. When the metro train ran from R1 to R5, the in-carriage PM concentrations showed a slightly decreasing trend during each measurement. As the metro train passed from R5 to R9, the in-carriage PM concentrations increased rapidly to reach a peak in R8, and then decreased.
Kam et al. [21] have demonstrated that in-carriage PM levels in the overground environment were remarkably affected by the atmospheric surroundings. The main external influencing factor for the in-carriage PM concentrations was the air in the tunnel when the metro train was on the underground route [51]. Poor ventilation contributed to PM accumulation in the tunnel [20,50], and suspended particles would enter the carriage through the train cracks and the mechanical ventilation system [58]. An additional measurement in the tunnel exhaust and the ambient environment on another platform showed that the PM2.5 concentration in the tunnel was much higher than that in the ambient (85.6 μg/m3 in the tunnel and 27.8 μg/m3 for the ambient). Meanwhile, the metro train was exposed to a relatively clean atmospheric environment when running on the overground route in this study. These are the main reasons behind the fact that, in-carriage, the PM concentrations on the underground route were higher than those on the overground route. This result was contrary to the study of Xu et al. [31], who found that the in-carriage PM2.5 concentration increased as the metro train passed from underground to overground. The reason for this might be that the outdoor PM2.5 concentrations in Chengdu (PM2.5: 10–57 µg/m3) were much lower than those in Shanghai (PM2.5: 133 µg/m3) during the experiment [31].

3.2.2. Passenger Number

Table 2 presents the correlation between the passenger number and the PM concentrations in the metro carriage when the train passed through eight different routes. R1, R2, R3, and R4 were overground routes, and R6, R7, R8, and R9 were underground routes. During the measurements, the in-carriage passenger number ranged from 1 to 38. The correlation coefficients of the passenger number and PM2.5 ranged from −0.343 to 0.067, and the significance (Sig.) ranged from 0.275 to 0.963 (>0.05). The correlation coefficients of the passenger number and PM10 ranged from −0.548 to −0.143 and the Sig. ranged from 0.065 to 0.657 (>0.05). The results indicated that the PM2.5 and PM10 concentrations had weak correlations with the passenger number in the metro carriage. Some additional measurements on other routes of the same Chengdu Metro Line (R10, R11, and R12) during peak hours when in-carriage passenger number ranged from 43 to 70 also presented the same conclusion (Table A3 and Table A4). However, Ren et al. [54] and Gao et al. [20] found that the in-carriage PM concentrations were linearly correlated with the passenger number.
The opposite conclusion in this study has several possible explanations. First, all passengers wore masks during our measurement period, owing to COVID-19, which was different from some previous studies [20,31,59] and might have prevented the formation and growth of particles related to suspended exhaled droplets [60,61]. Moreover, Kam et al. [21] and Huang et al. [50] reported that passengers’ activities caused PM that had accumulated over time in the metro carriage to resuspend. In this study, few passengers moved in the carriage when the train was running, which reduced the PM resuspension. Meanwhile, different research methods used in the exploration of the correlation between passenger number and PM concentrations in the metro carriage may have different results due to using data from different metro lines [29] and a limited number of samples [31,54]. The PM atmospheric concentrations around different routes, train models, and years of operation will affect the in-carriage PM concentrations. Therefore, some existing studies, which did not control all of these variables, might limit the exploration of the accurate correlation between the passenger number and the in-carriage PM concentrations. In this study, the number of variables was minimized to make it as close to a univariate experiment as possible, which could directly reflect the influence of passenger number on in-carriage PM concentrations. In addition, the data from 12 measurements were used to conduct the statistical analysis. This relatively large sample size would help to improve the reliability of the results.

3.2.3. Door Opening and Door Closing

Three periods including the period from the opening to closing of the door, the period before the opening of the door, and the period after the closing of the door were determined, which were the periods from the time point of the door opening to the time point of the door closing, before the door opening point, and after the door closing point, respectively. The last two periods were as long as the first one on the corresponding platform.
When the train arrived at each platform and upon the opening of the doors, the in-carriage PM concentrations decreased (Figure 7). The PM concentrations in the period from the opening to the closing of the door were significantly different compared to those in the period before the opening of the door (PM2.5: p = 0.001; PM10: p < 0.05). The in-carriage PM2.5 concentrations on P2, P3, P4, P5, P6, P7, P8, and P9 decreased from 25.9 to 25.4, 25.2 to 24.8, 23.7 to 23.4, 23.5 to 23.0, 28.1 to 26.7, 35.9 to 35.1, 39.9 to 39.0, and 48.0 to 45.2 (µg/m3), respectively. Similarly, PM10 levels in the same places decreased from 32.3 to 31.9, 33.3 to 32.1, 30.9 to 30.1, 29.9 to 29.7, 34.5 to 33.1, 45.1 to 44.1, 49.9 to 49.5, and 59.5 to 56.7 (µg/m3), respectively. The PM concentrations on the platform were higher than those in the carriage as the train stopped at the overground platforms, but were lower than those in the carriage when the train stopped at the underground platforms, which indicated that the PM concentrations difference between the carriage and the platform was not the dominant factor causing this decreasing trend. The experiment was conducted during off-peak hours, and thus, few passengers entered or left the metro carriages and would not yield a slight air disturbance. Due to the pressure difference between the carriage and the platform caused by the ventilation system inside the train, the air in the carriage would diffuse to the platform. Meanwhile, the in-carriage air supply was at a cleaner level after being filtered by the ventilation system. Opening and closing of doors had a great impact on the pressure difference between indoor and outdoor environments, and the variation of the pressure difference would affect the diffusion velocity of particulate matter [62,63]. That is, the air pressure difference between the carriage and the platform might play an important role on decreasing trends of in-carriage PM concentrations after the carriage doors were opened when the train stopped at the overground platforms. On the contrary, the PM concentrations on the underground platform were lower than those in the carriage owing to the operating ventilation system in the relatively confined underground platforms. Therefore, the synergistic effect of air pressure and concentration differences between the metro carriages and platforms might contribute more significantly to this result.
On the other hand, once the door was closed and the train started running on each platform, the in-carriage PM concentrations increased (Figure 7). The PM concentrations in the period from the opening to closing of the door were significantly different compared to those in the period after the closing of the door (PM2.5: p = 0.004; PM10: p < 0.05). The in-carriage PM2.5 concentration on P2, P3, P4, P5, P6, P7, P8, and P9 increased from 25.4 to 25.7, 24.8 to 25.0, 23.4 to 23.9, 23.0 to 23.8, 26.7 to 27.3, 35.1 to 36.1, 39.0 to 40.0, and 45.2 to 45.7 (µg/m3), respectively. Similarly, PM10 levels in the same places increased from 31.9 to 32.2, 32.1 to 32.7, 30.1 to 30.6, 29.7 to 30.3, 33.1 to 35.0, 44.1 to 45.9, 49.5 to 51.5, and 56.7 to 58.4 (µg/m3), respectively. This might be attributed to the fact that the ambient air had higher PM concentrations than the air supply filtered by a mechanical ventilation system in the metro train, and it infiltrated the carriage from the train cracks when the train started running on overground platforms (P2, P3, P4, and P5). The underground platforms in this experiment were equipped with fully enclosed platform screen doors (PSDs), and the train doors and PSDs were simultaneously opened and closed. After the doors were closed, all the external surroundings of the metro train were underground tunnels. Due to the lack of a mechanical ventilation system in the tunnel, the particles inside were more likely to accumulate and concentrate, leading to a much higher concentration than those on the platform and outdoors [19,24]. Due to restrictions in train operation safety, the measurement of ventilation in the tunnel was not conducted in this study. However, additional measurements of PM in the tunnel exhaust and the ambient environment on another platform were conducted. The results showed that the PM2.5 concentration in the tunnel exhaust was much higher than that in the ambient environment (85.6 µg/m3 in the tunnel and 27.8 µg/m3 for the ambient). High PM concentrations in the tunnel could penetrate into the carriage after the doors were closed and the train started running at underground platforms [58], which might be responsible for an increase of in-carriage PM concentrations (P6, P7, P8, and P9).

3.3. Inhalation Intake

PM2.5 inhalation intakes ranged from 1.08 to 9.52, with a mean of 4.24 µg (Figure 8). There were significant PM2.5 concentration differences between the different age groups (p < 0.05) and routes (p < 0.05). The average youth inhalation intake (6.77 µg) for males on Route d was the highest, and the average children inhalation intake (2.29 µg) for females on Route a was the lowest. The order of the inhalation intakes for different age groups with the same sex on each route was the same as the IR sequence, because the latter was the only variable. Although children and the elderly inhaled the lowest intakes of PM2.5, they were more susceptible to pollution [20,57]; hence, more attention should be paid to the relationship between their inhalation intakes and health. For the passengers with the same age and sex on different days, the average inhaled PM2.5 in the metro carriage in four routes followed the decreasing sequence: d > c > b > a. That the in-carriage PM2.5 concentration on underground routes was higher than that on overground routes led to this sequence. The PM2.5 concentration reached a peak in R8 on 9 of the 10 days selected for evaluation, and its commuting time period was the longest. Therefore, the metro operating agency should pay more attention to the routes such as R8 that caused high PM2.5 inhalation intakes in the metro carriage. The PM10 inhalation intakes presented the same trend.
Exposure period, inhalation rate, and exposure concentration together result in the inhalation intake level [35,37,39]. For people with the same age and sex, using the data measured on May 19 as an example, the exposure period on Route c (686s) was longer than that of Route d (656s). Nevertheless, the latter inhalation intake of PM2.5 was equal to the former. Additionally, the exposure period on Route d (656s) was less than that of Route a (698s), but the former inhalation intake of PM2.5 was higher than the latter. The reason for this was that the average PM2.5 concentration on Route d was higher than those of Routes a and c (Table A5). Moreover, the IR of middle-aged females (14.46 m3/day) was less than that of elderly males (15.25 m3/day), and the exposure duration on Route d was shorter than that of Route a. However, the inhalation intake of PM2.5 for middle-aged females on Route d (6.04 µg) was higher than that of elderly males on Route a (5.95 µg), because the PM2.5 concentration on Route d was higher. It is worth mentioning that the IR in this study might be underestimated sometimes, because the passengers in a metro carriage may stand during peak hours, and the commuting period might be longer than that in this study, indicating the IR would be larger and the exposure period would be longer under some real conditions. As a result, an inhalation intake evaluation of in-carriage PM2.5 over a longer journey on different metro lines should be further explored in the future.

3.4. Reduction of In-Carriage PM Concentrations and Inhalation Intakes

According to Equation (2), the inhalation intake of in-carriage PM might decrease with the decrease in PM exposure concentration. PM in the subway tunnel would accumulate with the increase in time and enter the metro carriage through the ventilation system of the train [64]. In addition, outdoor air, platform depth, train speed and frequency, and the operation of the ventilation system also affected the air quality of the subway system. Based on the existing research, it can be found that the PM levels in the carriage are the result of an interaction of multiple factors [57]. Therefore, the treatment of in-carriage particulate matter needs systematic consideration, especially for the underground routes. There are some feasible recommendations. In the initial stage of the subway system project, the design of the platform and the tunnel should be fully considered, namely the installation of screen doors on the platform and the determination of the number and location of piston air shafts in the tunnel, which can enhance the mix of outdoor and tunnel air to decrease the air pollutant concentration in the tunnel. Regular maintenance of the rail during train operation is beneficial for slowing down the friction between rails and wheels so as to reduce the generation of particles. Cleaning the tunnel regularly helps to reduce the particles accumulated in the tunnel. For the ventilation system in the metro carriage, it is necessary to select an efficient filter screen and to regularly clean or replace it.
However, the concrete implementation of the above recommendations cannot be achieved immediately. The health effect of in-carriage PM is more directly related to inhalation intake. Therefore, decreasing the inhaled PM concentration might be an effective way to decrease the health risk, even in an environment with high pollutant concentrations. Wearing masks can filter the particulate matter to clean the inhaled air, especially when passing the underground routes, which might be the most direct and efficient way to reduce the inhalation intake of PM [65].

3.5. Limitations

There were some limitations in this study. In-carriage PM concentrations and PM concentrations on the platform were not measured concurrently due to the limits of the instruments and the experimental operator. However, the large space of the platforms can result in its PM concentrations being stable when the train door is opened. Therefore, the error caused by a non-simultaneous measurement could be acceptable. The selected IR values are physiological daily inhalation rates, which is the average level of the calculated values of healthy normal-weight individuals in a day, including the sleeping period and daytime activities (24 h). Voluntary and involuntary activities during the daytime were performed by individuals in the sitting or standing position, excluding walking. The IR in a sitting position might be higher than that in a sleeping period, but lower than in a standing position. Therefore, the IR values in Table A1 could reflect the IRs of passengers sitting in the metro carriage in this study to some extent, which might lead to some acceptable errors of the inhalation intake. In addition, the commuting period might be longer than that in this study as the urban environment develops. Therefore, more detailed inhalation intake evaluations should be conducted in the future.

4. Conclusions

In this study, the dynamic PM2.5 and PM10 concentrations in the carriage of the Chengdu Metro Line were measured. The effects of the main influencing factors were explored, and the inhalation intakes for sex, age, and route differences were estimated. Based on these results, the following conclusions were drawn:
  • The in-carriage PM2.5 and PM10 concentrations were in the ranges of 11–74 µg/m3 with a mean of 36.7 µg/m3 and 13–89 µg/m3 with a mean of 40.1 µg/m3, respectively.
  • The in-carriage PM concentrations increased when the metro train passed from the overground area to the underground area.
  • Although PM concentrations in the carriage were higher than those on the overground platforms, in-carriage PM concentrations decreased after the door was opened.
  • There was no significant correlation between the passenger number and the in-carriage PM concentrations.
  • The inhalation intake of PM2.5 on the route with more underground platforms was higher than that on the route with more overground platforms.
  • In order to effectively reduce the PM2.5 inhaled by passengers in the metro carriage, the metro operating agency should pay more attention to the routes causing high in-carriage PM concentrations and long commuting time periods.

Author Contributions

Conceptualization, S.H. and M.D.; data curation, S.H. and H.W.; methodology, S.H. and M.D.; Project administration, D.W., R.M. and M.D.; supervision, L.S.; writing—original draft preparation, S.H.; writing—review and editing, S.H., D.W., R.M., L.S. and M.D. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Sciences Foundation of China (No. 52208128), Natural Science Foundation of Sichuan Province (No. 2022NSFSC0978, No. 2022NSFSC0273), and the Fundamental Research Funds for the Central Universities (No. 2682021CX031, No. 2682022CX005).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This study was Sponsored by the National Natural Sciences Foundation of China (No. 52208128), Natural Science Foundation of Sichuan Province (No. 2022NSFSC0978, No. 2022NSFSC0273), and the Fundamental Research Funds for the Central Universities (No. 2682021CX031, No. 2682022CX005). The authors would like to thank Xudong Yang from Tsinghua University for providing the measurement instrument.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A

Table
Table A1. Inhalation rates at different ages.
Table A1. Inhalation rates at different ages.
SexIR (m3/day)
ChildrenJuvenileYouthMiddle-AgedElderly
Male9.0415.6420.3918.4115.25
Female8.5913.3216.4614.4611.51
Table
Table A2. Description of the four routes.
Table A2. Description of the four routes.
RoutesIncluded PlatformsDistance (km)Number of Platforms
OvergroundUnderground
aP1, P2, P3, P4 and P55.5650
bP3, P4, P5, P6 and P75.26332
cP4, P5, P6, P7 and P84.12823
dP6, P7, P8, P9 and P104.14105
Table
Table A3. Correlation coefficients of passenger number and PM in the metro carriage on some additional routes.
Table A3. Correlation coefficients of passenger number and PM in the metro carriage on some additional routes.
RoutesR10R11R12
FactorsRSig.RSig.RSig.
PM2.50.540.460.5550.4450.4480.552
PM100.6360.3640.6850.3150.6420.358
R10 represents route 10; R11 represents route 11; R12 represents route 12.
Table
Table A4. Average PM2.5, PM10 concentrations (µg/m3) and passenger number in the metro carriage on some additional routes.
Table A4. Average PM2.5, PM10 concentrations (µg/m3) and passenger number in the metro carriage on some additional routes.
RoutesR10R11R12
FactorsPM2.5PM10NumberPM2.5PM10NumberPM2.5PM10Number
Measurement 14649.34348515148.551.348
Measurement 247.452.27047.752.76849.253.866
Measurement 351.958.16653.962.16953.358.867
Measurement 444.448.95645.9505847.952.264
Table
Table A5. PM2.5 concentrations of four routes in metro carriage on May 19 (µg/m3).
Table A5. PM2.5 concentrations of four routes in metro carriage on May 19 (µg/m3).
PollutantRouteAverageMedianRange
PM2.5a48.34843–55
b49.15043–55
c52.75245–62
d55.15349–74
Table
Table A6. Date of experiment and evaluation and corresponding ambient atmospheric PM concentrations.
Table A6. Date of experiment and evaluation and corresponding ambient atmospheric PM concentrations.
Experiment and EstimationDateAmbient Atmospheric PM Concentrations (μg/m3)
PM2.5PM10
E14.202341
4.282559
4.292555
4.304782
5.15079
5.25787
E25.151324
5.162240
5.182639
5.262236
6.42048
6.82336
6.113047
6.152233
6.161623
6.171017
6.181935
6.252231
E35.314368
6.13764
6.23761
Estimation of inhalation intakes4.20 *2341
4.28 *2559
4.29 *2555
4.30 *4782
5.1 *5079
5.2 *5787
5.84466
5.105376
5.18 *2639
5.194663
E1: The experiment of exploring the PM difference between the underground route and the overground route. E2: The experiment of exploring the influence of passenger number on the PM in the carriage. E3: The experiment of exploring the effect of door opening and closing on in-carriage PM concentrations. * Reused data.

References

  1. Chang, Y.S.; Jo, S.J.; Lee, Y.-T.; Lee, Y. Population Density or Populations Size. Which Factor Determines Urban Traffic Congestion? Sustainability 2021, 13, 4280. [Google Scholar] [CrossRef]
  2. Jain, A.; Yadav, S.; Vij, S.; Kumar, Y.; Tayal, D.K. A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network. Wirel. Pers. Commun. 2020, 110, 1303–1321. [Google Scholar] [CrossRef]
  3. Li, W.; Feng, W.; Yuan, H.-z. Multimode Traffic Travel Behavior Characteristics Analysis and Congestion Governance Research. J. Adv. Transp. 2020, 2020, 6678158. [Google Scholar] [CrossRef]
  4. Lin, D.; Nelson, J.D.; Beecroft, M.; Cui, J. An overview of recent developments in China’s metro systems. Tunn. Undergr. Space Technol. 2021, 111, 103783. [Google Scholar] [CrossRef]
  5. Pang, L.; Wang, X.; Qu, H.; Li, B.; Tian, Q. Study on forecasting the parameters of gas environment of metro station. Energy Built Environ. 2021, 2, 374–379. [Google Scholar] [CrossRef]
  6. Zhang, W.J.; Zhao, H.; Zhao, A.; Lin, J.Q.; Zhou, R. Current Status, Challenges and Resilient Response to Air Pollution in Urban Subway. Atmosphere 2019, 10, 472. [Google Scholar] [CrossRef] [Green Version]
  7. CAM (China Association of Metros). Statistics and Analysis Report of Urban Rail Transit in China. 2020. Available online: https://www.camet.org.cn/tjxx/7647 (accessed on 12 July 2022).
  8. Salma, I.; Weidinger, T.; Maenhaut, W. Time-resolved mass concentration, composition and sources of aerosol particles in a metropolitan underground railway station. Atmos. Environ. 2007, 41, 8391–8405. [Google Scholar] [CrossRef]
  9. Cheriyan, D.; Khamraev, K.; Choi, J.-H. Varying health risks of respirable and fine particles from construction works. Sustain. Cities Soc. 2021, 72, 103016. [Google Scholar] [CrossRef]
  10. Sinharay, R.; Gong, J.; Barratt, B.; Ohman-Strickland, P.; Ernst, S.; Kelly, F.J.; Zhang, J.; Collins, P.; Cullinan, P.; Chung, K.F. Respiratory and cardiovascular responses to walking down a traffic-polluted road compared with walking in a traffic-free area in participants aged 60 years and older with chronic lung or heart disease and age-matched healthy controls: A randomised, crossover study. Lancet 2018, 391, 339–349. [Google Scholar]
  11. Kim, K.H.; Kabir, E.; Kabir, S. A review on the human health impact of airborne particulate matter. Environ. Int. 2015, 74, 136–143. [Google Scholar] [CrossRef]
  12. Wu, Z.H.; Liu, F.W.; Fan, W.H. Characteristics of PM10 and PM2.5 at Mount Wutai Buddhism Scenic Spot, Shanxi, China. Atmosphere 2015, 6, 1195–1210. [Google Scholar] [CrossRef]
  13. Maciejczyk, P.; Chen, L.C.; Thurston, G. The Role of Fossil Fuel Combustion Metals in PM2.5 Air Pollution Health Associations. Atmosphere 2021, 12, 1086. [Google Scholar] [CrossRef]
  14. Bolourchi, A.; Atabi, F.; Moattar, F.; Ehyaei, M.A. Experimental and numerical analyses of particulate matter concentrations in underground subway station. Int. J. Environ. Sci. Technol. 2018, 15, 2569–2580. [Google Scholar] [CrossRef]
  15. Chen, Y.Y.; Lu, C.Y.; Chen, P.C.; Mao, I.F.; Chen, M.L. Analysis of Aerosol Composition and Assessment of Tunnel Washing Performance within a Mass Rapid Transit System in Taiwan. Aerosol Air Qual. Res. 2017, 17, 1527–1537. [Google Scholar] [CrossRef]
  16. van Drooge, B.L.; Prats, R.M.; Reche, C.; Minguillon, M.; Querol, X.; Grimalt, J.O.; Moreno, T. Origin of polycyclic aromatic hydrocarbons and other organic pollutants in the air particles of subway stations in Barcelona. Sci. Total Environ. 2018, 642, 148–154. [Google Scholar] [CrossRef]
  17. Querol, X.; Moreno, T.; Karanasiou, A.; Reche, C.; Alastuey, A.; Viana, M.; Font, O.; Gil, J.; de Miguel, E.; Capdevila, M. Variability of levels and composition of PM10 and PM2.5 in the Barcelona metro system. Atmos. Chem. Phys. 2012, 12, 5055–5076. [Google Scholar] [CrossRef] [Green Version]
  18. Carteni, A.; Cascetta, F.; Campana, S. Underground and ground-level particulate matter concentrations in an Italian metro system. Atmos. Environ. 2015, 101, 328–337. [Google Scholar] [CrossRef]
  19. Son, Y.-S.; Jeon, J.-S.; Lee, H.J.; Ryu, I.-C.; Kim, J.-C. Installation of platform screen doors and their impact on indoor air quality: Seoul subway trains. J. Air Waste Manag. Assoc. 2014, 64, 1054–1061. [Google Scholar] [CrossRef]
  20. Gao, Y.X.; Chen, F.; Wang, Z.J. The distribution and influential factors of PM2.5 and CO2 in urban rail carriages. Indoor Built Environ. 2019, 28, 1383–1395. [Google Scholar] [CrossRef] [Green Version]
  21. Kam, W.; Cheung, K.; Daher, N.; Sioutas, C. Particulate matter (PM) concentrations in underground and ground-level rail systems of the Los Angeles Metro. Atmos. Environ. 2011, 45, 1506–1516. [Google Scholar] [CrossRef]
  22. Kam, W.; Ning, Z.; Shafer, M.M.; Schauer, J.J.; Sioutas, C. Chemical Characterization and Redox Potential of Coarse and Fine Particulate Matter (PM) in Underground and Ground-Level Rail Systems of the Los Angeles Metro. Environ. Sci. Technol. 2011, 45, 6769–6776. [Google Scholar] [CrossRef] [PubMed]
  23. Kim, K.Y.; Kim, Y.S.; Roh, Y.M.; Lee, C.M.; Kim, C.N. Spatial distribution of particulate matter (PM10 and PM2.5) in Seoul Metropolitan Subway stations. J. Hazard. Mater. 2008, 154, 440–443. [Google Scholar] [CrossRef] [PubMed]
  24. Kim, J.B.; Kim, S.; Lee, G.J.; Bae, G.N.; Cho, Y.; Park, D.; Lee, D.H.; Kwon, S.B. Status of PM in Seoul metropolitan subway cabins and effectiveness of subway cabin air purifier (SCAP). Clean Technol. Environ. Policy 2014, 16, 1193–1200. [Google Scholar] [CrossRef]
  25. Martins, V.; Moreno, T.; Minguillon, M.C.; Amato, F.; de Miguel, E.; Capdevila, M.; Querol, X. Exposure to airborne particulate matter in the subway system. Sci. Total Environ. 2015, 511, 711–722. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Onat, B.; Stakeeva, B. Assessment of fine particulate matters in the subway system of Istanbul. Indoor Built Environ. 2014, 23, 574–583. [Google Scholar] [CrossRef]
  27. Perrino, C.; Marcovecchio, F.; Tofful, L.; Canepari, S. Particulate matter concentration and chemical composition in the metro system of Rome, Italy. Environ. Sci. Pollut. Res. 2015, 22, 9204–9214. [Google Scholar] [CrossRef]
  28. Wang, B.Q.; Liu, J.F.; Ren, Z.H.; Chen, R.H. Concentrations, properties, and health risk of PM2.5 in the Tianjin City subway system. Environ. Sci. Pollut. Res. 2016, 23, 22647–22657. [Google Scholar] [CrossRef]
  29. Zheng, H.L.; Deng, W.J.; Cheng, Y.; Guo, W. Characteristics of PM2.5, CO2 and particle-number concentration in mass transit railway carriages in Hong Kong. Environ. Geochem. Health 2017, 39, 739–750. [Google Scholar] [CrossRef]
  30. Wang, C.-Y.; Lim, B.-S.; Wang, Y.-H.; Huang, Y.-C.T. Identification of High Personal PM2.5 Exposure during Real Time Commuting in the Taipei Metropolitan Area. Atmosphere 2021, 12, 396. [Google Scholar] [CrossRef]
  31. Xu, B.; Yu, X.; Gu, H.; Miao, B.; Wang, M.D.; Huang, H.Q. Commuters’ exposure to PM2.5 and CO2 in metro carriages of Shanghai metro system. Transp. Res. Part D-Transp. Environ. 2016, 47, 162–170. [Google Scholar] [CrossRef]
  32. de Hartog, J.J.; Boogaard, H.; Nijland, H.; Hoek, G. Do the Health Benefits of Cycling Outweigh the Risks? Epidemiology 2011, 22, S76–S77. [Google Scholar] [CrossRef]
  33. Schepers, P.; Fishman, E.; Beelen, R.; Heinen, E.; Wijnen, W.; Parkin, J. The mortality impact of bicycle paths and lanes related to physical activity, air pollution exposure and road safety. J. Transp. Health 2015, 2, 460–473. [Google Scholar] [CrossRef] [Green Version]
  34. Moreno, T.; Kelly, F.J.; Dunster, C.; Oliete, A.; Martins, V.; Reche, C.; Minguillon, M.C.; Amato, F.; Capdevila, M.; de Miguel, E.; et al. Oxidative potential of subway PM2.5. Atmos. Environ. 2017, 148, 230–238. [Google Scholar] [CrossRef] [Green Version]
  35. Fondelli, M.C.; Chellini, E.; Yli-Tuomi, T.; Cenni, I.; Gasparrini, A.; Nava, S.; Garcia-Orellana, I.; Lupi, A.; Grechi, D.; Mallone, S.; et al. Fine particle concentrations in buses and taxis in Florence, Italy. Atmos. Environ. 2008, 42, 8185–8193. [Google Scholar] [CrossRef]
  36. Li, B.; Lei, X.-N.; Xiu, G.-L.; Gao, C.-Y.; Gao, S.; Qian, N.-S. Personal exposure to black carbon during commuting in peak and off-peak hours in Shanghai. Sci. Total Environ. 2015, 524, 237–245. [Google Scholar] [CrossRef]
  37. Panis, L.I.; de Geus, B.; Vandenbulcke, G.; Willems, H.; Degraeuwe, B.; Bleux, N.; Mishra, V.; Thomas, I.; Meeusen, R. Exposure to particulate matter in traffic: A comparison of cyclists and car passengers. Atmos. Environ. 2010, 44, 2263–2270. [Google Scholar] [CrossRef]
  38. Ramos, C.A.; Wolterbeek, H.T.; Almeida, S.M. Air pollutant exposure and inhaled dose during urban commuting: A comparison between cycling and motorized modes. Air Qual. Atmos. Health 2016, 9, 867–879. [Google Scholar] [CrossRef]
  39. Yu, Q.; Lu, Y.; Xiao, S.; Shen, J.; Li, X.; Ma, W.; Chen, L. Commuters’ exposure to PM1 by common travel modes in Shanghai. Atmos. Environ. 2012, 59, 39–46. [Google Scholar] [CrossRef]
  40. Shen, J.L.; Gao, Z. Commuter exposure to particulate matters in four common transportation modes in Nanjing. Build. Environ. 2019, 156, 156–170. [Google Scholar] [CrossRef]
  41. Ji, W.; Liu, C.; Liu, Z.; Wang, C.; Li, X. Concentration, composition, and exposure contributions of fine particulate matter on subway concourses in China. Environ. Pollut. 2021, 275, 116627. [Google Scholar] [CrossRef]
  42. PGCM (The People’s Government of Chengdu Municipality). Available online: www.chengdu.gov.cn (accessed on 12 July 2022).
  43. The Real-Time Air Quality Reports. Available online: https://www.tianqi.com/ (accessed on 12 July 2022).
  44. CMA (China Meterological Administration). Available online: https://weather.cma.cn/ (accessed on 12 July 2022).
  45. Liu, Q.J.; Cheng, H.X.; Ma, L.; Zhao, H.Q.; Zhang, D.X. Characteristics of Wind Speed on the Southern Slope of Taihang Mountains and Its Influence on Atmospheric Particulate Matter Concentration. Desert Oasis Meteorol. 2022, 16, 96–103. [Google Scholar]
  46. EPA (Environmental Protection Agency). Exposure Factors Handbook. Available online: https://www.epa.gov/expobox/exposure-factors-handbook-chapter-6 (accessed on 12 July 2022).
  47. Brochu, P.; Brodeur, J.; Krishnan, K. Derivation of physiological inhalation rates in children, adults, and elderly based on nighttime and daytime respiratory parameters. Inhal. Toxicol. 2011, 23, 74–94. [Google Scholar] [CrossRef]
  48. GB 3095-2012; Ambient Air Quality Standards. Stand-Ardization Administration of China; Ministry of Environmental Protection of PRC: Beijing, China, 2012.
  49. World Health Organization. WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. World Health Organization. 2021. Available online: https://www.who.int/publications/i/item/9789240034228 (accessed on 12 July 2022).
  50. Huang, S.; Chen, P.X.; Hu, K.Y.; Qiu, Y.C.; Feng, W.W.; Ren, Z.P.; Wang, X.L.; Huang, T.; Wu, D.S. Characteristics and source identification of fine particles in the Nanchang subway, China. Build. Environ. 2021, 199, 107925. [Google Scholar] [CrossRef]
  51. Cheng, Y.H.; Ninh, X.H.; Yeh, S.L. Dominant Factors Influencing the Concentrations of Particulate Matters inside Train Carriages Traveling in Different Environments in the Taipei Mass Rapid Transit System. Aerosol Air Qual. Res. 2019, 19, 1579–1592. [Google Scholar] [CrossRef]
  52. Martins, V.; Moreno, T.; Mendes, L.; Eleftheriadis, K.; Diapouli, E.; Alves, C.A.; Duarte, M.; de Miguel, E.; Capdevila, M.; Querol, X.; et al. Factors controlling air quality in different European subway systems. Environ. Res. 2016, 146, 35–46. [Google Scholar] [CrossRef] [Green Version]
  53. Gomez-Perales, J.E.; Colvile, R.N.; Nieuwenhuijsen, M.J.; Fernandez-Bremauntz, A.; Gutierrez-Avedoy, V.J.; Paramo-Figueroa, V.H.; Blanco-Jimenez, S.; Bueno-Lopez, E.; Mandujano, F.; Bernabe-Cabanillas, R.; et al. Commuters’ exposure to PM2.5, CO, and benzene in public transport in the metropolitan area of Mexico City. Atmos. Environ. 2004, 38, 1219–1229. [Google Scholar] [CrossRef]
  54. Ren, J.L.; He, J.J.; Kong, X.F.; Xu, W.; Kang, Y.T.; Yu, Z.; Li, H.W. A field study of CO2 and particulate matter characteristics during the transition season in the subway system in Tianjin, China. Energy Build. 2022, 254, 111620. [Google Scholar] [CrossRef]
  55. Wang, X.; Gao, H.O. Exposure to fine particle mass and number concentrations in urban transportation environments of New York City. Transp. Res. Part D-Transp. Environ. 2011, 16, 384–391. [Google Scholar] [CrossRef]
  56. Aarnio, P.; Yli-Tuomi, T.; Kousa, A.; Makela, T.; Hirsikko, A.; Hameri, K.; Raisanen, M.; Hillamo, R.; Koskentalo, T.; Jantunen, M. The concentrations and composition of and exposure to fine particles (PM2.5) in the Helsinki subway system. Atmos. Environ. 2005, 39, 5059–5066. [Google Scholar] [CrossRef]
  57. Nieuwenhuijsen, M.J.; Gomez-Perales, J.E.; Colvile, R.N. Levels of particulate air pollution, its elemental composition, determinants and health effects in metro systems. Atmos. Environ. 2007, 41, 7995–8006. [Google Scholar] [CrossRef]
  58. Cha, Y.; Tu, M.; Elmgren, M.; Silvergren, S.; Olofsson, U. Factors affecting the exposure of passengers, service staff and train drivers inside trains to airborne particles. Environ. Res. 2018, 166, 16–24. [Google Scholar] [CrossRef] [PubMed]
  59. Gong, Y.; Zhou, T.; Zhao, Y.C.; Xu, B. Characterization and Risk Assessment of Particulate Matter and Volatile Organic Compounds in Metro Carriage in Shanghai, China. Atmosphere 2019, 10, 302. [Google Scholar] [CrossRef]
  60. Bandiera, L.; Pavar, G.; Pisetta, G.; Otomo, S.; Mangano, E.; Seckl, J.R.; Digard, P.; Molinari, E.; Menolascina, F.; Viola, I.M. Face coverings and respiratory tract droplet dispersion. R. Soc. Open Sci. 2020, 7, 201663. [Google Scholar] [CrossRef] [PubMed]
  61. Kohanski, M.A.; Lo, L.J.; Waring, M.S. Review of indoor aerosol generation, transport, and control in the context of COVID-19. Int. Forum Allergy Rhinol. 2020, 10, 1173–1179. [Google Scholar] [CrossRef]
  62. Li, Z.; Zhang, X.H.; Zhang, W.S.; Zhai, Y.X.; Zhang, H. Parametric Analysis of Particle Penetration under Different Pressure Differences. Build. Sci. 2022, 38, 111–118+260. [Google Scholar]
  63. Zhang, Z.X.; Li, J.Y.; Qi, J.C.; Zhang, J.M.; Zhao, X.Z.; Liu, J.J. Negative Pressure Difference Control in Airtight High-level Bio-safety Laboratory. China Saf. Sci. J. 2010, 20, 116–124. [Google Scholar]
  64. Ji, W.J.; Liu, Z.Z.; Liu, C.H.; Wang, C.W.; Li, X.F. Characteristics of fine particulate matter and volatile organic compounds in subway station offices in China. Build. Environ. 2021, 188, 107502. [Google Scholar] [CrossRef]
  65. Ji, W.J.; Li, X.F.; Wang, C.W. Composition and exposure characteristics of PM(2.5) on subway platforms and estimates of exposure reduction by protective masks. Environ. Res. 2021, 197, 111042. [Google Scholar] [CrossRef]
Atmosphere 13 01821 g001 550
Figure 1. The map of measured routes. P1–P10 represent the 10 platforms measured.
Figure 1. The map of measured routes. P1–P10 represent the 10 platforms measured.
Atmosphere 13 01821 g001
Atmosphere 13 01821 g002 550
Figure 2. Field measurement conditions. (a) Metro platform. (b) Metro carriage.
Figure 2. Field measurement conditions. (a) Metro platform. (b) Metro carriage.
Atmosphere 13 01821 g002
Atmosphere 13 01821 g003 550
Figure 3. Measurement points in all campaigns. Mp_a represents the measurement points when exploring the influences of driving routes and passenger number on PM concentrations in the carriage. Mp_b and Mp_c represent the measurement points in the carriage and on the platform when exploring the influence of the door opening and closing on in-carriage PM concentrations, respectively.
Figure 3. Measurement points in all campaigns. Mp_a represents the measurement points when exploring the influences of driving routes and passenger number on PM concentrations in the carriage. Mp_b and Mp_c represent the measurement points in the carriage and on the platform when exploring the influence of the door opening and closing on in-carriage PM concentrations, respectively.
Atmosphere 13 01821 g003
Atmosphere 13 01821 g004 550
Figure 4. Inhalation intake evaluation scenarios.
Figure 4. Inhalation intake evaluation scenarios.
Atmosphere 13 01821 g004
Atmosphere 13 01821 g005 550
Figure 5. Average in-carriage PM and CO2 concentrations between every two consecutive platforms including overground and underground conditions.
Figure 5. Average in-carriage PM and CO2 concentrations between every two consecutive platforms including overground and underground conditions.
Atmosphere 13 01821 g005
Atmosphere 13 01821 g006 550
Figure 6. Dynamic PM concentrations in the metro carriage in overground and underground conditions on six selected days. (a) Tuesday, 20 April. (b) Wednesday, 28 April. (c) Thursday, 29 April. (d) Friday, 30 April. (e) Saturday, 1 May. (f) Sunday, 2 May.
Figure 6. Dynamic PM concentrations in the metro carriage in overground and underground conditions on six selected days. (a) Tuesday, 20 April. (b) Wednesday, 28 April. (c) Thursday, 29 April. (d) Friday, 30 April. (e) Saturday, 1 May. (f) Sunday, 2 May.
Atmosphere 13 01821 g006
Atmosphere 13 01821 g007 550
Figure 7. Variations of average PM concentrations in the metro carriage when the door was opened and closed on the platforms. (a) Overground platforms. (b) Underground platforms. The orange icons represent the average in-carriage PM concentrations for the period before the opening of the door. The blue icons represent the average in-carriage PM concentrations during the period from opening to closing of the door. The green icons represent the average in-carriage PM concentrations for the period after the closing of the door. Two pink triangles represent the PM concentrations on the platforms (one for PM10 and another for PM2.5).
Figure 7. Variations of average PM concentrations in the metro carriage when the door was opened and closed on the platforms. (a) Overground platforms. (b) Underground platforms. The orange icons represent the average in-carriage PM concentrations for the period before the opening of the door. The blue icons represent the average in-carriage PM concentrations during the period from opening to closing of the door. The green icons represent the average in-carriage PM concentrations for the period after the closing of the door. Two pink triangles represent the PM concentrations on the platforms (one for PM10 and another for PM2.5).
Atmosphere 13 01821 g007
Atmosphere 13 01821 g008 550
Figure 8. PM2.5 inhalation intakes in the metro carriage. Data shown in the box plot are the minimum, maximum, the 1st and 3rd quartile, median (solid line), and arithmetic average values (square).
Figure 8. PM2.5 inhalation intakes in the metro carriage. Data shown in the box plot are the minimum, maximum, the 1st and 3rd quartile, median (solid line), and arithmetic average values (square).
Atmosphere 13 01821 g008
Table
Table 2. The correlation coefficient analysis results between passenger number and average PM concentrations in the same metro carriage on different routes.
Table 2. The correlation coefficient analysis results between passenger number and average PM concentrations in the same metro carriage on different routes.
RoutesR1R2R3R4R6R7R8R9
FactorsRSig.RSig.RSig.RSig.RSig.RSig.RSig.RSig.
PM2.5−0.2430.446−0.1740.590.0670.837−0.0490.879−0.1360.673−0.2160.501−0.3430.2750.0150.963
PM10−0.4190.176−0.3440.274−0.1430.657−0.2170.498−0.2630.408−0.4110.184−0.5480.065−0.1450.654
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Huang, S.; Wang, H.; Wu, D.; Ma, R.; Sun, L.; Deng, M. Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu. Atmosphere 2022, 13, 1821. https://doi.org/10.3390/atmos13111821

AMA Style

Huang S, Wang H, Wu D, Ma R, Sun L, Deng M. Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu. Atmosphere. 2022; 13(11):1821. https://doi.org/10.3390/atmos13111821

Chicago/Turabian Style

Huang, Shenghao, Han Wang, Dan Wu, Rongjiang Ma, Liangliang Sun, and Mengsi Deng. 2022. "Influencing Factors of Particulate Matter Concentration in the Metro Carriage and the Corresponding Inhalation Intake Estimation: A Field Measurement in Chengdu" Atmosphere 13, no. 11: 1821. https://doi.org/10.3390/atmos13111821

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop