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A novel approach for the modelling of air quality dynamics in underground railway stations

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[free download on Elsevier until september ] Indoor air quality in subterranean train stations is a concern in many places around the globe. However, due to the specificity of each case, numerous parameters of the problem remain unknown, such as the braking discs particle emission rate, the ventilation rate of the station or the complete particle size distribution of the emitted particles. In this study the problem of modelling PM 10 concentration evolution in relation with train traffic is hence addressed with a particle-mass conservation model which parameters are fitted using a genetic algorithm. The parameters of the model allow to reproduce the dynamics and amplitude of four field data sets from the French and Swedish underground contexts and comply with realistic bounds in terms of emissions, deposition and ventilation rate.
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Transportation Research Part D: Transport and Environment
Volume 56, October 2017, pages 33–42
A novel approach for the modelling of air quality dynamics in underground
railway stations
E. Walther, M. Bogdan
AREP, 16 avenue d’Ivry, 75013 Paris, FRANCE
Indoor air quality in subterranean train stations is a concern in many places around the globe. However, due
to the specificity of each case, numerous parameters of the problem remain unknown, such as the braking discs
particle emission rate, the ventilation rate of the station or the complete particle size distribution of the emitted
particles. In this study the problem of modelling PM10 concentration evolution in relation with train traffic is
hence addressed with a particle-mass conservation model which parameters are fitted using a genetic algorithm.
The parameters of the model allow to reproduce the dynamics and amplitude of four field data sets from the
French and Swedish underground contexts and comply with realistic bounds in terms of emissions, deposition
and ventilation rate.
Keywords: PM10, conservation model, underground air quality
1. Introduction
Indoor air quality in subterranean railway station is
an increasing public health concern. Numerous mea-
surement campaigns and simulations have been un-
dertaken worldwide, e.g. [41] in the Netherlands, [16]
in Japan, in Korea [26] or [7] in Mexico City, as they
help to understand the mechanisms that create such
indoor/outdoor pollution, with the aim to reduce it.
However, these studies mainly measure pollution lev-
els and provide design or operation guidelines with-
out a quantitative analysis of the link between train
traffic and particulate matter concentration.
The clear weekly pattern of PM10 concentration
in subterranean railway stations and the similar be-
haviour of particle concentration evolution and train
movement frequency observed for instance in [9] as
well as in the measurement data exhibited on Figure
1, led us to investigate the modelling of this relation-
Corresponding author
Email addresses: (E. Walther),
(M. Bogdan)
The difficulty of this enterprise resides in the un-
knowns around the two key phenomena:
- the source of particles, divided in direct emission
by abrasion and resuspension of deposited parti-
cles, is unsufficiently characterized,
- the dilution mechanism, which in underground
stations strongly depends on the piston effect
and comfort ventilation. The piston effect is in-
deed responsible for sporadic, violent drafts in
the tunnels [4] and complex, sometimes coun-
terintuitive, air flows on the platforms, with gen-
erated dynamic pressures reaching about thou-
sand Pascal upon arrival in the station [12] and
air velocities of about 1/5th of the train velocity
Different sources have shown that iron is the domi-
nant element in underground stations [11]. In the re-
view by [30] of eight suburban stations over the globe,
particle mass concentration in iron is superior by one
order of magnitude to all other elements. Determin-
ing a value for the direct emission term, i.e. how many
particles are emitted by the components of the trains
Preprint submitted to Elsevier July 27, 2017
that are subject to abrasion (braking system, wheels,
pantograph and catenary) can be achieved by exploit-
ing the maintainer’s parts wearing.
In the underground context, resuspension is more
complicated to evaluate independently of direct emis-
sion, especially without using a tracer such as [29].
However, an attempt was undertaken by [5] for a
Parisian subterranean station. A recent and de-
tailed literature review of progress in experimental
and modeling particle resuspension is presented in
Regarding the ventilation phenomenon, it appears
that it is strongly influenced by wind pressure, air
transfer between tunnels, and the piston effect. The
latter has been studied by [13], whose approach allows
for an estimation of the amount of ventilation due
to train movement. Piston effect is however largely
driven by the pressure drop between the underground
and the exterior environment, which depends on the
geometry of the stations. [34] observed for instance
that the lower the station, the higher the PM concen-
tration. This can be related to the inhibition of ventila-
tion by higher pressure drops in the subway corridors.
An attempt of classification of the stations relating ge-
ometry and air quality was led by [21] for Barcelona’s
Airborne particle dynamics is also influenced by the
particle size distribution. A difference of one order of
magnitude between the particles diameter may result
in more than two orders of magnitude discrepancy of
deposition rates [22, 33]. For the underground con-
text, large particles above 2.5 µm may represent 70% of
the mass concentration [31, 5, 35]. Metallic elements
were found to be dominant during peak hours [14] or
to exhibit a relative abundance depending on the con-
text [34].
Based on these elements, it appears that the mod-
elling options are limited in the subterranean railway
environment. CFD is impeded by the lack of informa-
tion about particle size and distribution as well as the
orders of magnitude of the key phenomena such as re-
suspension and deposition. It is also unclear where
the resuspension occurs, although [21] have observed
that the extremities of platforms exhibit higher PM
concentration levels in comparison with the centre.
The current computing capacity is also a drawback as
it reduces the computable duration of particle disper-
sion to a narrow span that does not allow for the siz-
ing of air quality equipment (for instance ventilation
or filtration), which rely on the daily values of concen-
Given the previous observations, this study is an
attempt of coarse modelling of the PM10dynamics in
underground stations using the approach suggested
by [22], based on the well-mixed volume hypothesis,
with an adaptation to the subterranean railway con-
text. Recently, [40] led a similar study aiming at the
prediction of CO2concentration in the platform and
concourse of a station, linking the measurements of
CO2concentration in the tunnel and an estimation of
the train-induced wind after measurements per [13].
It seems however that no study couples the analy-
sis of train traffic and PM10 distribution, as underlined
by [25]. The originality of this work hence relies in the
relation of the PM10 concentration with piston effect,
particle emission and resuspension with train move-
ments. An ordinary differential equation with vari-
able coefficients describes the phenomena. The few
parameters of the equation are then identified versus
available four data sets from the French and Swedish
underground contexts.
2. Measurement data
2.1. Underground PM10 measurements
The experimental data in this work originates from
two main sources :
- Three of Paris subterranean stations, namely
Saint-Michel Notre-Dame, Gare du Nord and La
Défense were part of an air quality measurement
campaign by the French national railway com-
pany SNCF lead in 2005. TEOM devices recorded
the underground air quality over a year. The data
presented here show the year-averaged weekly
- The eight-weeks averaged TEOM measurements
from Arlanda C station in Stockholm retrieved
from [9] were also used.
Despite the numerous unknowns of the under-
ground context, the measurement data on Figure 1
exhibit a strong correlation between train movement
frequency and PM10 concentration: from Monday to
Friday the daily concentration has a similar shape and
decreases during week-end, as does the train move-
ment frequency (see also Figures 6 and 7 for Gare du
Nord and La Défense and the study by [9] for two
Swedish stations.
Additional information may be deduced from the
data: for instance, the overnight peaks circled in red
on Figure 1 stand for the emission of a diesel engine
train passing through the station for overnight work
that occurred during the year of the measurement.
Figure 1: Comparison between PM10 concentrations and train traf-
fic - Saint-Michel station (average 2005 week, SNCF data)
The concentration decrease every night at the end
of service also allows for an estimation of the natural
ventilation rate.
Interestingly, the daily minima on a yearly average
are not only dependent on the train traffic and exhibit
a bell-shaped behaviour (see dotted line on Figure 1).
The same phenomenon can be observed in the mea-
surement campaign done by [5], however, to the best
of the authors’ knowledge, this feature has not been
described previously, and is related to the exterior par-
ticle concentration: on the average Friday early morn-
ing, the minimum PM10 concentration is smaller than
the previous morning, whereas train traffic has the
same intensity.
2.2. Outdoor air PM10 concentration
The average week of outdoor air PM10 concen-
tration for the year corresponding to the measure-
ments was calculated from the hourly air quality open
database [1] at the station of Paris-Centre in 1st dis-
trict, which is about 1 km from Saint-Michel’s under-
ground (the outcome is plotted on Figure 2). On the
2005 average week, one can see by the dotted line on
Figure 2 that the PM10 concentration minima also ex-
hibit a bell-curve behaviour. Concentrations levels are
nevertheless lower than the minima of the concentra-
tion for station Saint-Michel plotted Figure 1.
For Arlanda C station as the outdoor air data was
not available an average outdoor air concentration of
15 [µg/m3] was chosen after the yearly average value
specified in the World Health Organization report [24].
Figure 2: PM10 concentration on the average week (2005 - Paris 1st
arrondissement [1])
3. Physical modelling
Most of the particle matter evolution studies use
a “two compartment model” that includes both the
airborne particles and the particles deposited on the
enclosure’s surfaces. For a stringent closure of the
model, the differential equation quantifying the depo-
sition and resuspension rate on surfaces in the station
would be necessary, such as in [29] or [23]. As esti-
mating the quantity of particles deposited on surfaces
appeared uncertain, surfaces are supposed to be sat-
urated with particles and hence a “one compartment”
model is used, based on the airborne PM10 conser-
vation. Due to the perfect mixing hypothesis, spatial
effects described in [21] for instance, cannot be ob-
served. Coagulation or agglomeration as well as ther-
mal effects on particles are also ignored.
3.1. Presentation of the model
The conservation model proposed in the next para-
graphs relies on a concentration balance in the spirit
of [22] and considers only three main phenomena:
- The emission of particles is modelled with an
“apparent emission” term αthat includes emis-
sion by friction (brakes, catenary. . . ) and particle
resuspension due to train movement.
- Ventilation is composed of the apparent ventila-
tion rate τ0and of the outdoor air βinduced by
the train piston effect.
- Deposition δis chosen in the range calculated af-
ter the procedure detailed in [15] for the settling
of PM10 in the specific subterranean station en-
Other phenomena occurring in polydisperse
aerosols are neglected, e.g. particle size change
by condensation and coagulation as in [27, 28] or
thermal effects.
Cbeing the concentration in PM10, the mass bal-
ance is then expressed as follows:
t=αN2(t)+τ(Cext C)δC(1)
This ordinary differential equation is solved numer-
ically with a semi-implicit Crank-Nicolson second-
order scheme using a time step of one minute. The
initial condition is the measured initial concentration.
The physcial meaning of all terms are explained in fol-
lowing paragraphs.
3.2. Terms of the conservation equation
In the box-model approach, we consider that the
average velocity in the station is proportional to train
movement. The kinetic energy being proportional to
the square of velocity, the mass of resuspended par-
ticles increases with the square of train frequency, as
suggested in [18]. The emission term is then propor-
tional to the apparent emission α[µg/m3] and to the
square of train movement N(t) [1/h] such that the ap-
parent source of particles is α×N2(t). The mixing with
the air pushed into the station from the tunnels is also
included in the apparent emission term.
The ventilation rate τ(t) in air changes per hour
[1/h] is composed of an apparent air change rate τ0in-
cluding mechanical and natural ventilation, plus the
amount of outdoor air brought by the piston effect-
driven ventilation term β×N :
τ(t)=τ0+βN(t) (2)
β[-] is the ratio of the volume of outdoor air
brought by train circulation, divided by the station’s
air volume. In the cases presented in this study, the
geometry of the station and the complex interconnec-
tions with other tunnels at different depths makes it
hard to have an a priori a value of the piston effect per
train. Nevertheless, an order of magnitude of β<1
can be derived from [13] for a station volume of about
10 000 m3. A similar order of magnitude is obtained
from the analytical derivation by [42].
The physical phenomena driving particle deposi-
tion range from brownian diffusion, turbulent diffu-
sion and gravitational sedimentation, depending on
the particle size. The enclosure overall deposition
loss-rate also depends on its aspect ratio as it is de-
rived from the individual deposition rates of particles
on each type of surface (e.g. vertical walls, floor or
ceiling). The global deposition rate δfor an enclosure
can be computed for still and stirred environments
[33, 15], depending on the friction velocity at the de-
position surface.
A comparison of the deposition rate in a still envi-
ronment between a typical room with a volume V=
4×5×3 [m3] as in [15] and a typical underground
station (V=200 ×20 ×5 [m3]) was done using a fric-
tion velocity of 0.01 [m/s] after the model by [15]. On
Figure 3, one can compare the overall deposition co-
efficient of both enclosures for a particle density of
1 [g/cm3]: with an equal particle density the under-
ground station exhibits a lower deposition rate. This
difference is related to the aspect ratios of the two
spaces: for instance, the relative contribution of the
ceiling surface to the overall deposition coefficient is
higher for the underground than for the typical indoor
(respectively 48 % versus 23 % of the total surface).
In the underground context, the mean density of
particles has been reported to reach 4 [g/cm3] [35, 6,
36]. The overall deposition coefficient δcomputed
with this density presents a shift from 0.2 [µm] to
0.1 [µm] of the particle diameter yielding the lowest
deposition rate (see Figure 3). As a result of the higher
particle inertia, the overall deposition rate is higher
than in the typical indoor environment for particles
above 0.2 [µm].
Figure 3: Comparison of the overall deposition loss rate δfor a typi-
cal indoor room (V=4×5×3 [m3]) and typical underground station
(V=200×20×5 [m3]) after the model by [15] (friction velocity = 0.01
As underground stations are subject to important
air velocities, a correction of the deposition coefficient
is necessary [15]. In the underground context, air ve-
locities reaching 3 [m/s] or more were measured [8]
(the authors’ data exhibit a maximum of no less than
9.5 [m/s] on the platform of Paris’ Saint-Michel sta-
tion). An estimate of the friction velocity can be ob-
tained using the correlation by [37], as described in
[15]. With this approach, supposing that the air ve-
locity in the station reaches 10 [m/s], the friction
velocity attains 0.30 [m/s]. Figure 4 shows the co-
efficient δcomputed for such a high friction velocity
and for a low one, 0.01 [m/s] being considered as a
typical indoor friction velocity [15]. One can observe
that turbulent diffusion drastically increases the de-
position for sub-micrometric particles and shifts the
minimum of δto 0.4 [µm] diameter.
Figure 4: Overall deposition loss rate δin a typical underground sta-
tion for two different friction velocities (ρ=4 [g/cm3]).
Neither the composition nor the particle size distri-
bution was available for the stations considered. In
other stations around the globe, the PM2.5/PM10 ra-
tio ranges from 0.23 to 0.88 [39] depending amongst
other parameters on the train material and the sta-
tion configuration. A previous study by [5] on a sta-
tion of the same railway network has shown that the
PM10 particles prevail in mass during train operation.
Indeed, the ratio PM2.5/PM10 equals 0.3 during daily
train operation and comes back to 1 for the short pe-
riod of absence of circulation over night [32, 5]. A ratio
of 0.28 was also measured by [35] in a Budapest under-
ground station. [31] reported the same phenomenon
in Barcelona underground, with a ratio of 0.3.
Based on these results and in absence of a bet-
ter characterisation of the particle size distribution
for the available data sets, we considered in this first
approach that the predominant particle size affect-
ing the mass concentration was in the coarse mode
[31, 5, 35] (obviously, in terms of particle numbers, the
fine and ultra-fine particles may prevail [19]). In the
later identification process δwill hence be bounded
between 0.002 and 7.2 [h1] as per Figure 4.
4. Identification procedure
4.1. Method and preliminary identification
The model was tested against data sets of three
of Paris underground railway stations (Saint-Michel,
Gare du Nord and La Défense) as well as one station
in Sweden (Arlanda C, Stockholm) with the data from
[9]. Fitting the parameters to the measurement was
not straightforward and three different methods were
tested using the Scilab scientific computing software:
- The Levenberg-Marquardt algorithm [17] proved
to be the fastest in terms of computation time but
also to be very dependent on the initial guess. It
sometimes provided unphysical results (e.g. neg-
ative values of the piston ventilation β).
- A least squares method was much slower and in
many cases the algorithm did not converge.
- The method giving the best results in terms of
computational intensity and diversity of the min-
ima proved to be a genetic algorithm [20].
The genetic algorithm was used with following pa-
rameters: 200 individuals, 50 generations, probability
of crossover of 70 % and mutation rate of 5%. The orig-
inal functions detailed in the documentation [38] for
coding, selection or mutation of the individuals were
Prior to the identification procedure, the correct or-
der of magnitude of natural ventilation has to be es-
timated. This value is computed using the concen-
tration decrease overnight and integrating Equation
1 with N=0 and β=0. The base ventilation rate
τ0[vol/h] that best fits the measurements is then se-
lected. This preliminary calculation also provides an
first guess for the deposition phenomena through the
identification of δ.
After this step, the identification procedure is
launched over parameters α,β,δand τ0.δis bound
by the physical principle of deposition, βand τ0are
allowed to vary by ±25% around their pre-identified
4.2. Results
The identification procedure for Gare du Nord and
Saint-Michel stations gave a correct representation of
the phenomenon, as presented on Figures 5 and 6.
Figure 5: PM10 model versus measurements for Saint-Michel.
Figure 6: PM10 model versus measurements for Gare du Nord.
For both stations the identification allow to repro-
duce the dynamics and amplitude of PM10 concen-
tration related to train traffic. For Saint-Michel sta-
tion, owing to the overnight construction work (see
2.1), this discrepancy is slightly higher: shortly after
service, the concentration decrease is perturbated by
a passing diesel engine train, which sharpens the dif-
ference between the actual and simulated concentra-
Figures 7 and 8 provide a comparison between the
model and the experiment for stations La Défense and
Arlanda C. Although the dynamics are respected, the
model performs noticeably less accurately for station
La Défense (Figure 7), which we believe is caused by
the geometry of the station: it is probable that the
stations much larger volume and wider openings to-
wards the concourse leads to a different kind of de-
pendance between the average velocity and train traf-
fic. Additionally, these geometric features make the
well-mixed enclosure hypothesis questionable and
may lead to an inappropriate estimation of the piston
The peak hours concentrations of Arlanda C sta-
tion (Figure 8) exhibit a greater discrepancy between
model and measurement. This is probably related to
the fact that the PM10 and train traffic values were av-
eraged over 8 weeks only and thus are less homoge-
neous than measurement data of the three stations in
Paris, for which a yearly average was used.
Figure 7: PM10 model versus measurements for La Défense.
Figure 8: PM10 model versus measurements for Arlanda C.
A summary of the identified parameters are sum-
marized in table 1 where GDN, SHL, LDF and ARL re-
spectively stand for Gare du Nord, Saint-Michel, La
Défense and Arlanda C stations.
α[µg/m3] 2.27 5.30 1.65 9.33
β[-] 0.82 0.54 0.42 0.17
τ0[vol/h] 0.50 0.11 0.48 0.06
δ[h1] 0.06 0.36 0.26 0.14
Mean error [%] 15.8 27.7 13.8 26.1
Mean diff. [µg/m3] 14.1 52.8 10.2 47.2
Table 1: Summary of the parameters α,β,τ0,δidentified for each
station and corresponding mean error.
The simulation results are in good accordance with
the measurements in terms of amplitude and dynam-
ics, using parameters within realistic orders of magni-
tude of deposition, ventilation and piston effect. The
outcome of this study is also an “apparent emission
term αthat mimics the direct emission and the resus-
pension phenomena.
The relative error between the model and the PM10
measurements is plotted over time and day on Fig-
ures 9,10,11,12, respectively for Gare du Nord, Saint-
Michel, La Défense and Arlanda C stations. The max-
imum relative error varies from about 0.3 up to 1 de-
pending on the station and time of the day. One can
observe on the aforementioned figures that the daily
values of concentration are generally closer than the
nightly ones. During the night period, fine particles in
the real aerosol settle slower than coarse ones, which
leads to higher concentrations that the model can-
not represent as it has a constant deposition coeffi-
cient. Diesel engines passing through the station for
overnight work in the tunnels also affect the results,
as explained 2.1.
Interestingly, calculating the slope of the function
PM10 =PM10(Ntrain ) as in [9, 35] seems to be yielding
an accurate first guess of α. However, for all stations,
the model is slightly "ahead" of measurements, which
could imply a delay for example in the mixing process
in the enclosure. Further investigations are to be con-
sidered on this point.
One can notice that the apparent emission term α
of SHL is higher than the one of GDN or LDF stations.
This difference may relate to the fact that SHL station
is much older and hence a higher amount of parti-
cles available for resuspension is deposited on the sur-
faces. Noticeably, the value of αfor Arlanda C is higher
up to a factor 5 compared to the apparent emission
term of the three French stations studied. This may be
Figure 9: Relative error between the model and measurements for
Gare Du Nord station
Figure 10: Relative error between the model and measurements for
Saint-Michel station
related to the different rolling stocks or braking ma-
terial used, however too little information about the
Swedish rail context was at the authors’ disposal to
5. Conclusion
In this paper, a novel approach for the estimation
of the PM10 concentration in underground train sta-
tion is presented. A reduced number of parameters
allow for the representation of the general phenom-
ena driving PM concentration in subterranean railway
context using a mass-conservation model. Consider-
ing that particle emission is proportional to train traf-
fic, the method reproduces the dynamics and ampli-
tude of the PM10 concentration measured in four un-
derground stations in Europe.
Figure 11: Relative error between the model and measurements for
La Défense station
Figure 12: Relative error between the model and measurements for
Arlanda C station
Intrinsically, the model is space and time-averaged,
hence it does not provide an access to the concentra-
tion spatial distribution and requires concentration
measurements prior to being used. The mass con-
centration of other particles size-class was unfortu-
nately not available, which impeded the modeling of
a multi-component aerosol. For instance, having the
PM2.5 concentration measurements would have al-
lowed simulating the difference in dynamics between
the part of the aerosol containing fine-mode particles
(below 2.5 µm) and coarse ones (above 2.5 µm).
This first approach has drawbacks, however it could
provide a reasonably simple way to estimate the level
of particle concentration originating from train circu-
lation and the amount of ventilation or filtration that
would be required to reduce air pollution.
6. Perspectives
It has been shown that the model is tractable to dif-
ferent underground railway stations, which supports
our idea that the number of fitting parameters is suf-
ficient for this type of train stations. We believe it can
be used to generate multiple scenarios with different
ventilation rates, and provide engineers with a way to
quantify the corresponding air quality increase, given
that a new measurement campaign – after implemen-
tation of a solution – can attest the validity of our hy-
potheses and model. Regarding the improvement of
air quality with such technical solutions, the tests lead
by the authors show that ventilation with outdoor air
and filtration of the air inside the station can improve
the underground air quality but only to a limited ex-
tent: the square dependency of the apparent emission
term with train traffic (i.e. air velocity) penalizes the
air quality. For example, a simulated scenario of 10
[vol/h] outdoor air in addition of 2.5 [vol/h] of indoor
air filtration with 90% mass efficiency provided 30%
reduction of the average PM10 concentration.
Moreover, the model is still dependent on the iden-
tification of the parameters: the source term αand
the dilution one τhaving antagonist effects, one can
find several sets of parameters with different orders
of magnitude that provide a coherent fit on the mea-
sured data. A measurement campaign of the piston
effect in Saint-Michel’s station realised in early 2017
will give the correct order of magnitude of this param-
Considering only coarse particles is also insuffi-
cient for a proper modeling of aerosol behaviour
[33, 22]. Translating the principle of [23] to the
underground environment, an improvement of the
model would be to model the evolution of concen-
tration in the station as a multi-component aerosol.
A permanent measurement station deployed in Paris
Saint-Michel underground station with commitment
to public availability of the results should make this
possible in a near future (see the online PM10 and
PM2.5 data on [2]).
Although the model seems to be able to represent
the dynamics and amplitude of the train-related PM
concentration, it fails to catch the overnight low con-
centration, for instance in Gare du Nord and Saint-
Michel (Figures 5 and 6). Indeed, the nightly min-
ima over the average week exhibit a bell curve that the
model does not manage to reproduce faithfully. We
suppose that this is linked to the slower deposition
rate of fine-mode particles (PM<2.5 µm). An attempt
in this direction is under progress.
A concentration delay at peak hours is also to be
noted and still has to be explained.
The authors would like to express their gratitude to
the SNCF for allowing the use of the PM10 normalized
concentrations presented in this paper and especially
to Dr. Cremezi at SNCF-Direction du Développement
Durable for her expert views and advice on the topic,
as well as to Pr. Gustafsson for providing Arlanda C
station data.
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... In this section, the air quality model presented in [28] serves as a basis for the case study. ...
... In underground stations, a correlation exists between train circulation and particulate matter pollution, as can be observed from Figure 3.22 where the average train traffic and the underground PM 10 concentration are plotted. The PM 10 data set originates from Paris' Gare du Nord station [28] normalised data * . For the sake of the example, the present data set was arbitrarily multiplied by 100 [µg/m 3 ], which will be the peak concentration value. ...
... For the sake of the example, the present data set was arbitrarily multiplied by 100 [µg/m 3 ], which will be the peak concentration value. In the underground stations around the world, the PM 10 10 values are scaled normalised data from [28]) ...
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On one end of the scientific literature, profusion of works about solving partial differential equations exist, however often with a somewhat unfamiliar mathematical formalism. On the other end, volumes dealing with building physics may be either technical about HVAC or rather generic about the equations to be used. Having struggled quite a bit in the past years with the practical implementation of numerical methods in this field, it appeared that putting together the recipes used in a modern programming language could be of interest. Hence the parti pris in this book is to show the link between the governing equations and how to solve them, aiming at a practical use (i.e. ”how to make things work”). It can be seen as a toolbox for simulation engineering, a basis for the illustration of theory or a kick-start for the study of more complex problems. This manual is composed of three chapters, with gradual increase in difficulty: • Chapter 1 succinctly explains the fundamentals of the numerical methods used. • Chapter 2 shows applications of these methods to heat transfer in phase change materials, PID control, indoor air quality and geothermal heat pumps. • Chapter 3 deals with coupled problems and minimisation. Applications to polydispersed aerosols in enclosures, heat and mass transfer in walls and parameter fitting for transient problems are proposed.
... Furthermore, route design optimizations in RSs referring to the use of GA to determine the most efficient and costeffective routes for railway trains are noticed in [7], [136], [139], [147], [151], [159]. The use of GA to optimize the layout, configuration, and operation of railway stations and facilities are the other domains that are discovered [134], [101], [148], [140], [135]. ...
... Citation information: DOI 10.1109/ACCESS.2023.3292790 energy supplementation strategy utilizing regenerative energy of trains in power interruptions [85] Costs energy consumption minimization and cost reduction in a subway ventilation system [92] reducing the operating capacity and costs of compensators [89] Control MPC GA combined with MPC for energy consumption minimization in a subway ventilation system [92] real-time optimal speed control and scheduling with MPC [93] real-time control of a metro reducing wait times by 24.0% and travel times by 5.5% [94] Fuzzy eco-driving and schedule of high speed train for minimizing energy consumption [21] adaptive EMS for a tramway with hybrid ESS [84] optimization of a road-rail intermodal transport system [98] NN train re-scheduling with GA and ANN [47] fault diagnosis of railway rolling bearing with GA and NN [107] optimizing the speed profile of the trains using GA and ANN [108] optimization program of rail profiles [116] SMC speed curve optimization and tracking [70] intelligent control of maglev [121] PID controller design for maglev system [123] vibrations control and active suspension of the train [125] active control strategy on a catenary-pantograph dynamic interaction [126] optimized control for a maglev [127] Design Bogies, rolling stock suspension parameters design for the monorail vehicle [12] bogie frame design [157] Automobile suspension & dynamics analysis of bogie dynamics [130] optimized design of bogie suspension components [131] suspension parameters design of high-speed trains [149] Railway bridge, catenaries modeling and design of a stone masonry railway bridge [132] geometric optimization design of railway catenaries [137] calibration and design of the numerical model for a bowstring-arch railway bridge [150] design of a long-span suspension bridge [154] Multi-modal network designing multimodal freight transport network [128] coordination method for an integrated multimodal transit network [138] Route design designing train routing [7] mass transit route network design [139] routing a metro track inspection vehicle [147] bus bridging route design integrated with metro system [151] Station design designing the support system of a subway station [101] indoor air quality design in subterranean train stations [134] ventilation design in a subway station [140] Network ...
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Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities.
... A substantial measurement campaign of particulate and gas concentrations in 10 stations in Barcelona was led by Moreno et al. [20], and evidenced the inuence of train length, speed, tunnel length and station design on train-induced wind. Monitoring underground particulate concentration in a Parisian station during a week, Walther et al. [21] observed that the PM concentration follows closely the frequency of trains. Targino [5] also reported that a periodicity related to the dynamics of trains serving the station was visible in the pollutants concentrations on the platforms. ...
... Premièrement, la génération de nouvelles particules par les trains. Deuxièmement, le mouvement des trains favorise la remise en suspension de particules plus anciennes déposées sur le sol et les parois des EFS pendant la période d'interruption(Salma et al., 2007 ;, Walther et Bogdan, 2017. A ce sujet, Cartenì et al. (2020) observent que la reprise de la circulation des trains après la période d'interruption nocturne est liée à une multiplication par treize de la concentration massique en PM10 dans le métro de Naples. ...
La qualité de l’air est devenue un enjeu sanitaire majeur dans le monde. Rapportés au voyageur.kilomètre, les transports en commun permettent de diminuer les émissions polluantes mais ils n’en sont pas exempts. Du fait de l’usure de ses composants (roue, rail, ballast, pantographe, caténaire, frein), l’exploitation ferroviaire génère des polluants particulaires. Ceux-ci peuvent s’accumuler dans les enceintes ferroviaires souterraines à cause des effets de confinement. Les études d’impact de cette pollution restant rares, SNCF a engagé dès le début des années 2000 des travaux sur le sujet. En 2016, un programme de caractérisation de la qualité de l’air dans 24 gares souterraines du réseau Transilien a été mis en œuvre. Il est nécessaire de compléter ces connaissances par celle des mécanismes de dispersion des particules émises au freinage en gare souterraine pour implémenter des solutions pérennes. L’objet de cette thèse est donc de caractériser cette dispersion. Pour cela, une approche couplée numérique et expérimentale est proposée. La dispersion est d’abord étudiée grâce à des simulations Euler-Lagrange. La configuration d’étude numérique a ensuite été reproduite en soufflerie. Des campagnes de mesures monophasiques (PIV) et diphasiques permettent ensuite de valider les simulations. De là, les principales régions de l’écoulement responsables de la dispersion des particules de freinage sont identifiées. Les résultats montrent que les structures cohérentes générées par le matériel roulant ont une grande influence sur la dispersion des particules émises lors du freinage. Toutefois elles ne sont pas responsables de la dispersion de ces particules nouvellement émises vers le quai. Enfin, de nombreuses particules se déposent dans la cavité du bogie qui les a émises.
... Literature about particulate matter (PM) in subsurface transport structures concerns mainly the impact of PM exposure on human health (Martins et al., 2015); (Song et al., 2018) ; (Byeon et al., 2015); (Salma et al., 2007)). Only relatively few studies deal with railway-induced PM exposure, either in the area of ground level (Gehrig et al., 2007), or subsurface (Walther and Bogdan, 2017); (Gustafsson et al., 2012)), train stations. These studies discuss concentration levels but do not give any train-specific emission factors. ...
Reliable information concerning rail tunnel dust emissions dependent on parameters such as train type, train speed, operating conditions, etc. is quite rare. Thus, in order to quantify the effect of heavy metals on the environment, specific data is needed when designing air filtration systems inside the tunnel, or in carrying out environmental assessment studies. Detailed investigations were therefore carried out in a test installation in an Austrian rail tunnel. In order to be able to use these data for the long railway tunnels currently under construction, train-specific emission factors were determined. It was found that emissions from freight trains are up to 6.68 times higher than those from passenger trains. Bulk freight transport in open cars produces the highest emissions, while the short regional passenger trains exhibit the lowest emission levels. Chemical analysis of the samples came up with a mass fraction of 60% related to metals, of which, iron is the dominating component. Based on these findings, maintenance intervals for high quality dust filters were derived. In addition, the knowledge gained on train type-dependent emission factors greatly helps to improve upon the very poor data now used in environmental studies, particularly when determining railway particulate matter emissions.
... As people become more and more vigilant, air pollution prediction of the local environment is needed, such as individual streets, buildings, and blocks (Corfa et al. 2004). Railway stations are the most populated regions in most cities, so the air quality in railway stations is becoming a public health concern (Walther and Bogdan 2017). Besides, the railwayassociated mechanical activity can produce harmful particulate matter (Loxham and Nieuwenhuijsen 2019). ...
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Air pollution has a serious negative impact on human health and economic development. Railway stations have the maximum flow of people in a city. It is necessary to provide precise air pollution forecasting for railway station areas. In this study, a spatial ensemble model is proposed to predict hourly PM2.5 concentrations for the Beijing railway station. In the proposed model, the spatial analysis is realized by a spatial feature selection and a spatial ensemble. The spatial feature selection method can recognize the correlated monitoring sites around the Beijing railway station and rank them. An optimization method is utilized to optimize the weighting coefficients of different correlated sites. Base predictors of different correlated sites are integrated according to the weighting coefficients. Besides, a data decomposition method is also utilized to enhance the performance of the spatial model. In this study, data processing methods and spatial analysis methods are combined with each other to build the spatial ensemble model. Four quarters of PM2.5 concentration data are utilized to verify the effectiveness and stability of the proposed model. The proposed spatial ensemble model can outperform other comparison models.
Covid-19 outbreak raised great awareness about ventilation system performance in indoor spaces. Specifically, the HVAC system design and operating parameters, such as air change per hours, air recirculation ratio, filtration device performance, and vents location, play a crucial role in reducing the spread of viruses, molds, bacteria, and general pollutants. Concerning the transportation sector, the SARS-COV-19 outbreak brought a reduction of payload for different carriers. Specifically, this has been particularly severe for the railway sector, where train coaches are typically featured by rather elevated occupancy and high air recirculating ratios. In this framework, to improve the Indoor Air Quality and to reduce the Covid-19 contagion risk into railway carriages, this paper investigates the energy, economic and environmental feasibility of diverse ventilation strategies. To do so, a novel simulation tool for the complete dynamic performance investigation of trains was developed in OpenStudio. Here, the Wells-Riley model was adopted to assess the Covid-19 contagion risk. To prove the capabilities of the proposed approach, a suitable case study related to an existing medium-distance train operating in South/Central Italy is presented. The conducted numerical simulations returns interesting results also providing useful design criteria.
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Quantifying the train-induced wind affecting the climate of subway stations can be applied to improve underground networks air quality. In this paper, numerical simulations of train-induced airflow in a subway station are performed, using a CFD model with dynamic meshing techniques. A preliminary study is done in a double-track tunnel with blockage ratios of 0.30, 0.37 and 0.46 with a train running at constant speed in the order of 10 m/s. The tunnel length necessary to obtain a stable flow around the train body is determined, and this upstream tunnel length is included in a subway station model. Two different architectures and three train speeds are simulated, and the effect of these configurations on the station airflow is evaluated through the air velocity and the mass flow rate at a location on the platform. The results evidence an increase in air circulation with blockage ratio and train speed.
Rolling stock operation generates several metallic particles related to wheel-rail and pantograph-catenary contacts and to the wear of braking components. Despite a composition different from outdoors, particulate matter pollution in underground stations must not be ignored as no consensus exists about its health impact on commuters and workers. Assessment of commuters and workers exposure has then become of major interest. Indeed, depending on the time spent underground, their daily exposure to particles can significantly differ. As spatiotemporal distribution of particulate pollutants can influence personal exposure, in the present work, numerical simulations and wind-tunnel experiments were conducted to assess particle concentrations. The dispersion of brake particles in the wake of a typical double-deck French train within an underground station with a central platform is assessed. Results obtained at a constant train speed show a strong correlation between the wake topology and particle dispersion in relation with the dynamics of vortical structures. Higher particle number concentrations (PNC) are found between the train and the station wall compared to those depicted at the platform side.
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As the number of people using rapid transit systems (subways) continues to rise in major cities worldwide, increasing attention has been given to the indoor air quality of underground stations. This study intended to observe the change of PM distribution by size in an underground station with PSDs installed located near the main road in downtown Seoul, as well as to examine causes for the changes. The results indicate that the PM suspended in the tunnel flowed into the platform area even in a subway station where the effect of train-induced wind is blocked by installed PSDs, as this flow occurred when the PSDs were opened. The results also indicate that coarse mode particles generated by mechanical friction in the tunnel, such as that between wheels and rail, also flowed into the platform area. The PM either settled or was re-suspended according to size and whether the ventilation in the platform area was in operation or if the platform floor had been washed. The ventilation system was more effective in removing PM of smaller sizes (fine particles) while the wash-out performed after train operations had stopped reduced the suspension of coarse mode particles the next morning. Despite installation of the completely sealed PSDs, inflow of coarse mode particles from the tunnel seems unavoidable, indicating the need for measures to decrease the PM generated there to lower subway user exposure since those particles cannot be reduced by mechanical ventilation alone. This research implicate that coarse PM containing heavy metals (generated from tunnel side) proliferated especially during rush hours, during which it is very important to control those PM in order to reduce subway user exposure to this hazardous PM.
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This article presents observations and model simulations of wind and temperature in a subway station. The measurements are taken from an experiment with three ultrasonic anemometers at different levels in the underground facility. The available observations indicate a wind regime with a continuous background flow and train-induced ventilation with very effective air exchange between the platform level and the street level. Model simulations with a resolution of less than 1m were performed with running trains in accordance with a regular timetable. The results demonstrate the applicability of the model to the complex underground geometry. Calculated time series of wind and temperature are very comparable to the observations. The findings of a parameter study indicate the necessity to record all details of train movements in order to define appropriate initial and boundary conditions for the model and to explain the observations correctly.
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From an environmental perspective, the underground metro system is one of the cleanest forms of public transportation in urban agglomerations. Current studies report contradicting results regarding air quality in the metro systems: whereas some reveal poor air quality, others report PM levels which are lower or of the same order of magnitude than those measured in traffic sites above ground level. The present work assesses summer indoor air quality and passenger exposure in the Barcelona metro, focusing on PM levels and their metal contents. In addition, the impact on indoor air quality of platform screen door systems (automated systems consisting of closed rail track and platforms) is evaluated, to determine whether these systems reduce passenger exposure to PM when compared with conventional systems (open tracks and platforms). In the Barcelona metro, PM levels inside the trains in summer are amongst the lowest reported for worldwide metro systems (11–32 μPM2.5 m−3). This is most probably due to the air conditioning system working in all carriages of the Barcelona metro during the whole year. On the platforms, levels were considerably higher, reaching mean levels of 59 and 88 μgPM2.5 m−3 in the new (L9) and old (L3) lines, respectively. PM10 data are also reported in the present study, but comparison with other metro systems is more difficult due to the scarcity of data compared with PM2.5. Results showed clear PM daily cycles, with a drastic increase from 06:00 to 07:00 a.m., a diurnal maximum from 07:00 a.m. to 10:00 p.m., and marked decreases between 10:00 p.m. and 05:00 a.m. The elements with the highest enrichment are those associated with wheel or brake abrasion products (Ba, Fe, Cu, Mn, Cr, Sb, As, Mo, Co, Sr, among others). Laminar hematite (Fe2O3) was the dominant particle type, being mainly originated by mechanical abrasion of the rail track and wheels. Regarding passenger exposure to PM inside the metro system, the contribution of commuting by metro was estimated to account for around 10% of the daily exposure. Finally, we conclude that the implementation of platform screen door systems results in reductions of both PM levels and metal concentrations, but in addition an advanced optimized ventilation system gave even a much higher efficiency in reducing PM exposure to metro commuters. Combining these two features PM exposure levels in the platforms may be reduced down by a factor of 7 with respect the old subway lines in Barcelona.
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In recent years the piston effect in subways has become a topic of interest for researchers and engineers. Many publications have appeared on this topic, but reliable information is scattered and poorly organized. This review paper covers the latest publications on the piston effect in subways. We compile information about the mechanism of the piston effect, evaluate its influence, and describe how it can be effectively utilized.
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Air quality in a subway tunnel has been crucial in most of the subway environments where IAQ could be affected by many factors such as the number of passengers, the amount and types of ventilation, train operation factors and other facilities. A modeling approach has been introduced to manage the general IAQ in a subway station. Field surveys and CO2 measurements were initially conducted to analyze and understand the relationship between indoor and outdoor air quality while considering internal pollution sources, such as passengers and subway trains, etc. The measurement data were then employed for the model development with other statistical information. For the model development, the algorithm of simple continuity was set up and applied to model the subway IAQ concerned, while considering the major air transport through staircases and tunnels. Monitored CO2 concentration on the concourse and platform were correlated with modeling results where the correlation values for the concourse and platform were R2=0.96 and R2=0.75, respectively. It implies that the box modeling approach introduced in this study would be beneficial to predict and control the indoor air quality in subway environments.
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The Barcelona subway system comprises eight subway lines, at different depths, with different tunnel dimensions, station designs and train frequencies. An extensive measurement campaign was performed in this subway system in order to characterise the airborne particulate matter (PM) measuring its concentration and investigating its variability, both inside trains and on platforms, in two different seasonal periods (warmer and colder), to better understand the main factors controlling it, and therefore the way to improve air quality. The majority of PM in the underground stations is generated within the subway system, due to abrasion and wear of rail tracks, wheels and braking pads caused during the motion of the trains. Substantial variation in average PM concentrations between underground stations was observed, which might be associated to different ventilation and air conditioning systems, characteristics/design of each station and variations in the train frequency. Average PM2.5 concentrations on the platforms in the subway operating hours ranged from 20 to 51 and from 41 to 91 μg m− 3 in the warmer and colder period, respectively, mainly related to the seasonal changes in the subway ventilation systems. The new subway lines with platform screen doors showed PM2.5 concentrations lower than those in the conventional system, which is probably attributable not only to the more advanced ventilation setup, but also to the lower train frequency and the design of the stations. PM concentrations inside the trains were generally lower than those on the platforms, which is attributable to the air conditioning systems operating inside the trains, which are equipped with air filters. This study allows the analysis and quantification of the impact of different ventilation settings on air quality, which provides an improvement on the knowledge for the general understanding and good management of air quality in the subway system.
In this study, real-time monitoring campaigns were conducted in two tunnels (Line A and Line B) at a subway station in Shanghai, including temperature, relative humidity, PM1, PM2.5 and PM10, in order to understand the climate and PM characteristics in the transportation microenvironment. In addition, collected floor dust particles in the tunnel were analyzed by ICP for their metal elemental composition. Strong correlations occurred between all PM levels and meteorological parameters in the tunnel of Line A (with platform screen doors), in comparison with the weak correlations between such parameters in the tunnel of Line B (without platform screen doors). PM2.5 and PM10 between peak hours and off-peak hours for both lines presented significant differences (p<0.05), respectively. Nevertheless, PM1 showed a different pattern, with p>0.05 for Line A and p<0.05 for Line B, respectively. In addition, statistical results concluded that PM had an evident weekly variation for both lines. Friday was the highest day of all particulate matters in monitoring periods for both lines. Ratios of PM1/PM10 and PM2.5/PM10 were high when trains were out of service and low when trains were in service. Relative abundance of metal elements detected from floor dust particles proved that floor dust particles in tunnels might be a major source of airborne PM in the subway microenvironments, with Fe as the most abundant metal element, followed by Ca, Al, Mg, Mn, Zn, Cu, Cr, Ni, Pb and Hg.
Measurements of PM10, PM2.5 and PM1 and particle number concentration and size distribution were measured for 24 h on a platform of the Prague underground metro in October 2013. The three PM fractions were analysed for major and minor elements, secondary inorganic aerosols (SIA) and total carbon (TC). Measurements were performed both when the metro was inoperative and closed to the public (referred to as background), and when the metro was in operation and open to passengers. PM concentrations were elevated during both periods, but were substantially increased in the coarse fraction during hours when the metro was in operation. Average PM concentrations were 214.8, 93.9 and 44.8 μg m-3 for PM10, PM2.5 and PM1, respectively (determined gravimetrically). Average particle number concentrations were 8.5 × 103 cm-3 for background hours and 11.5 × 103 cm-3 during operational hours. Particle number concentrations were found to not vary as significantly as PM concentrations throughout the day. Variations in PM were strongly governed by passing trains, with highest concentrations recorded during rush hour. When trains were less frequent, PM concentrations were shown to fluctuate in unison with the entrance and exit of trains (as shown by wind velocity measured on the platform). PM was found to be highly enriched with iron, especially in the coarse fraction, comprising 46% of PM10 (98.9 μg m-3). This reduces to 6.7 μg m-3 during background hours, proving that the trains themselves were the main source of iron, most probably from wheel-rail mechanical abrasion. Other enriched elements relative to background hours included Ba, Cu, Mn, Cr, Mo, Ni and Co, among others. Many of these elements exhibited a similar size distribution, further indicating their sources were common and were attributed to train operations.