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Journal of Occupational and Environmental Hygiene
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Assessment of Noise Exposure During Commuting in the
Madrid Subway
M. Tabacchi a , I. Pavón a , M. Ausejo a , C. Asensio a & M. Recuero a
a Technical University of Madrid, Center for Applied Acoustic Nondestructive Evaluation
(CAEND), Madrid, Spain
Available online: 10 Aug 2011
To cite this article: M. Tabacchi, I. Pavón, M. Ausejo, C. Asensio & M. Recuero (2011): Assessment of Noise Exposure During
Commuting in the Madrid Subway, Journal of Occupational and Environmental Hygiene, 8:9, 533-539
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Journal of Occupational and Environmental Hygiene, 8: 533–539
ISSN: 1545-9624 print / 1545-9632 online
Copyright c
2011 JOEH, LLC
DOI: 10.1080/15459624.2011.600237
Assessment of Noise Exposure During Commuting
in the Madrid Subway
M. Tabacchi, I. Pav ´
on, M. Ausejo, C. Asensio, and M. Recuero
Technical University of Madrid, Center for Applied Acoustic Nondestructive Evaluation (CAEND), Madrid,
Spain
Because noise-induced hearing impairment is the result not
only of occupational noise exposure but also of total daily noise
exposure, it is important to take the non-occupational exposure
of individuals (during commuting to and from their jobs, at
home, and during recreational activities) into account. Mass
transit is one of the main contributors to non-occupational
noise exposure. We developed a new methodology to estimate
a representative commuting noise exposure. The methodol-
ogy was put into practice for the Madrid subway because of
all Spanish subway systems it covers the highest percentage
of worker journeys (22.6%). The results of the application
highlight that, for Madrid subway passengers, noise exposure
level normalized to a nominal 8 hr (LEx,8h-cj) depends strongly
on the type of train, the presence of squealing noise, and
the public address audio system, ranging from 68.6 dBA to
72.8 dBA. These values play an important role in a more
complete evaluation of a relationship between noise dose and
worker health response.
Keywords commuting noise, non-occupational noise exposure,
subway noise
Correspondence to: Mattia Tabacchi, Universidad Polit´
ecnica de
Madrid, CAEND-UPM-CSIC, Acoustics, Calle de Serrano 144,
28006 Madrid, Spain; e-mail: mtabacchi@i2a2.upm.es.
INTRODUCTION
It is well known that a very noisy place can generate physical
harm, such as hearing impairment.(1–3) To make a proper
evaluation of the effect of workplace noise exposure on worker
health, it is necessary to take into account not only occu-
pational noise exposure but non-occupational noise exposure
as well (e.g., during recreational activities, commuting, and
so on).(4) Almost all occupational laws(5,6) and international
standards(7,8) are more concerned with protecting workers from
the risks related only to exposure to noise at work, while
failing to take into account the assessment of a more complete
noise risk that results from non-occupational activities, like
commuting.
There are scant scientific articles on the subject.(9) Few
studies that use dosimetry measurements to estimate the noise
exposure associated with daily activities conclude that one
of the primary sources of non-occupational noise exposure is
mass transit.(10–13) In fact, noise exposure can reach 79.9 dBA
in a bus,(14) 80.8 dBA in an aircraft,(15) 73.7 dBA in a commuter
train,(16) and 79.3 dBA in a subway,(16) Other studies(17–20) on
subway noise levels can be found, but the lack of a common
environment and measurement method prevents a proper com-
parison.
Our study addresses this issue by defining a new method-
ology to assess commuting noise exposure and by studying
its contribution to the total daily noise exposure. The method-
ology was applied to the Madrid subway because of all the
Spanish subway systems it covers the highest percentage of
worker journeys (22.6%)(21) and because a recent study of
the Madrid subway(22) states that the majority of subway
passengers (59.35%) use this means of transport for getting
to work.
METHODOLOGY
We used the following methodology to assess a complete
commuting noise exposure:
1. Measurement of passenger noise exposure in the whole
transport system.
2. Assessment of a model to predict passenger noise expo-
sure according to the passenger’s journey, with statistical
analysis.
3. Estimation of commuting noise exposure by applying
the previous prediction model to representative com-
muting journeys.
The contribution of this extra exposure to the total daily
noise exposure is analyzed.
Measurements
Equivalent continuous sound pressure level(23) was recorded
each second (LAeq,1s) with type II noise dosimeters (type 4443;
Br¨
uel & Kjaer, Naerum, Denmark) that fulfill the requirements
Journal of Occupational and Environmental Hygiene September 2011 533
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specified in IEC 61252.(24) Research staff carried the dosimeter
in their pockets during measurements, and microphones were
located on the shoulder at a distance of approximately 0.1 m
to 0.3 m from the entrance to the external ear channel.
A systematic sample of the subway system was chosen to
evaluate all the possible sound events to which the passengers
are exposed. To assess commuting noise exposure correctly,
the measurements were taken during rush hours (from 8 to
10 a.m. and from 5 to 8 p.m.) on 15 workdays. During mea-
surements, technicians recorded the subway line, time of day,
duration of measurements, and any unusual acoustic events,
such as musicians, squealing noise, a phone ringing, and so
on.
Noise levels were measured during May and June 2010
in the Madrid subway. The subway system comprises 12
lines totaling 284 km and 282 stations,(22) ofwhich26are
transfer stations where passengers can change from one line to
another without exiting the subway station. Therefore, to have
a more complete assessment of commuting noise exposure,
measurements during these transfers were included in the
estimation. More precisely, these measurements included the
route a technician had to walk between two subway lines in
an underground station and the time spent waiting for the next
train to arrive once the technician reached the new line.
Subway data was collected on each of the 12 Madrid sub-
way lines and almost all the 26 subway transfers, covering
about 78% of the entire subway system. Since the subway
system is mainly underground, all the measurements were
made underground. In the first measurement analysis, all the
corrupted data (e.g., under-range/over-range noise) were ex-
cluded, leaving 15 hours of net data. The duration of the
measurements enabled statistically representative data to be
collected, as passenger noise exposure was measured under the
same noise conditions (e.g., same type of train, PA system, and
so on) at least three times. Although not all subway transfers
could be measured, the small standard error of the mean (SEM)
estimated from all the average equivalent sound pressure levels
(LAeqT) let us assume that no more measurements were needed
(Table I).
Assessment of the Prediction Model
The measurements and research staff notes were analyzed
to determine the noise components that affected passenger
exposure according to the passenger’s journey. For the Madrid
subway, the following noise components were considered:
1. Subway train type. Includes noise generation by type
of engine and tracks. There are seven types of trains in
the Madrid subway system.
2. Squealing noise (1) or not (0). Generated by trains
passing through curves having a closed radius; one of
its main characteristics is the presence of a pure tone
component. Curve squeal is often seen as an erratic
phenomenon that can be strongly influenced by small
operating variations.(25–27)
3. Public address audio system (1) or not (0). Installed
in almost all Madrid subway trains.
Measurement data (LAeq,1s) were put into groups of homo-
geneous observations considering the values of the previous
components (i.e., same type of train, PA system, and squealing
noise). For a better performance, the minimum observation
considered was the journey between two consecutive sub-
way stops. The equivalent continuous sound pressure levels
recorded each second (LAeq,1s) were therefore log-averaged
within these homogeneous observations (LAeq,T). Since the
deviation of these averages (LAeq,T) is quite small (2.6 dBA),
linearity property was assumed for this indicator. Figure 1
shows an example of this where the type of train, squealing
noise, and PA system are the noise components considered.
Table II shows that the average equivalent continuous sound
pressure levels (LAeq,T) range from 76.9 to 82.3 dBA; the
minimum number of homogeneous observations within a noise
component is 5 (in Train Type 5).
Analysis of variance (ANOVA) was used to assess the para-
metric model for the prediction of passenger noise exposure.
First, the ANOVA assumptions (independence, normality, and
homoscedasticity) were taken into account to check if this
parametric model was suitable for the case study. As the
assumptions were fulfilled, the ANOVA tests were carried
out. Results highlighted that the components considered (type
of train, PA system, and squealing noise) are all statistically
significant, as their p-values are less than 0.05. Applying this
model, the equivalent continuous sound pressure level was
defined as:
LAeq,t ij k =µ+αi+βj+χk+uijk (1)
where µis the average of all the observations (LAeq,T), αis the
effect of the type of train, βis the effect of squealing noise,
and χis the effect of the PA system. Residual values (u)arethe
difference between the model and the measurements. Note that
the subscripts (i,j,k) identify a specific value estimated within
a noise component. For instance, because there are seven
TABLE I. Noise Exposure During Subway Transfers
95% Confidence Interval
Indicator No. of Transfers Mean SD SEM Lower Limit Upper Limit
LAeqT (dBA) 45 78.22.60.477.479.0
Duration (min) 45 4.42.20.33.75.0
534 Journal of Occupational and Environmental Hygiene September 2011
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FIGURE 1. Example of the measurements (LAeq,1s) averaging process within the homogeneous observations (LAeq,T). In Observation 1, the
noise levels are measured in Train Type 3 with squealing noise and PA system. In Observation 2, the noise levels are measured in Train Type 3
with PA system but without squealing noise.
different types of trains, the subscript i(that can vary from
1 to 7 in a discrete interval) will allow identification of each
of the seven values estimated (αi) for this noise component
(Table III). For the effect of squealing noise (β), the subscript
jcan be 0 or 1 depending on whether the squealing noise
appears (1) or not (0). In the same way, for the effect of the PA
system (χ), the subscript kcan be 0 or 1 depending on whether
the PA system is installed (1) or not (0). As the measurements
were undertaken only in the underground part of the subway
system, the model proposed is valid only for this case.
Estimation of Commuting Noise Exposure
To have a more correct estimation of passenger noise ex-
posure, the average noise level (LAeq,t ch) and duration (tch )
of exposure in all the subway transfers were included in the
prediction model described above. Therefore, the equivalent
TABLE II. Noise Exposure [LAeq,T] in Subway Trains
95% Confidence Interval
Noise
Components
No. of
Observations
Total Duration
(min) Mean (dBA) SD (dBA) SEM (dBA)
Lower Limit
(dBA)
Upper Limit
(dBA)
Train type
1 20 182.879.42.80.678.180.6
2 28 174.778.32.80.577.279.3
3 26 160.382.32.70.581.383.3
4852.177.32.50.975.679.1
5544.476.92.51.174.779.1
61081.481.02.70.879.382.7
7850.578.12.60.976.279.9
Squealing noise
0 69 673.977.93.00.477.278.6
13672.480.23.00.579.281.2
PA system
0 31 188.778.03.40.676.879.2
1 74 557.580.13.00.379.480.8
Journal of Occupational and Environmental Hygiene September 2011 535
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TABLE III. ANOVA Model Parameters (dBA)
Variable Mean (µ) Train Type (αi) Squealing Noise (βj) PA System (χk)
Subscript — 1 2 3 4 5 6 7 0 1 0 1
Val u e 79 .00.4–0.83.3–1.8–2.11.9–0.9–1.21.2–1.11.1
continuous sound pressure level for a commuting journey is
henceforth defined as:
LAeq,T e−cj =10
×log N
z=1tz·100.1·LAeq,t ij k −z+M
ch=0tch100.1·LAeq,t ch
N
z=1tz+M
ch=0tch
(2)
where LAeq,t ij k −zis the equivalent continuous sound pressure
level for each of the Nsubway stations considered in a com-
muting journey (excluding the first one), and tzis the average
journey time between two consecutive stations, LAeq,t chis the
equivalent continuous sound pressure level for the Msubway
transfers considered, and tch is the average time spent in a
subway transfer.
To use the prediction model correctly for estimation of
the noise exposure of any journey, it is necessary to know
exactly which values of the noise components considered in
Eq. 1 apply to each subway station of the journey. Moreover,
by taking the equivalent continuous sound pressure level, the
following noise indicators can be calculated:
1. Noise exposure level normalized to a nominal 8-hour
working day (LEx,8h). The level is given by the equa-
tion:(4)
LEx,8h=LAeq,T e +10 log Te
T0(3)
where Teis the effective duration in hours, and T0is the
reference duration (8 hr).
2. Daily noise dose (D(Q)). The dose is given by the
equation:(8)
D(Q)=100
TcTe
0
10LAeq,T e −Lc
qdt (4)
where Tcis the criterion sound duration (8 hr), Tethe duration
in hours, LAeq,Te the sound level, Lcthe criterion sound level,
and qis the parameter that determines the exchange rate (in
Europe, q =10; in USA, q =5/log2).
The noise exposure level normalized to a nominal 8-hr
workday (LEx,8h) is used in European Directive 2003/10/CE(6)
to regulate occupational noise exposure. In fact, according to
Article 3 of this directive, the limits are fixed at:
1. exposure limit values: LEX,8h =87 dB(A)
2. upper exposure action values: LEX,8h =85 dB(A)
3. lower exposure action values: LEX,8h =80 dB(A)(5)
The daily noise dose (D(Q)) is not commonly used in
Europe, but it allows comparison of noise exposure with the
limits established by law. In this case, the criterion sound levels
(Lc) were the lower (80 dBA) and upper (85 dBA) exposure
action values.
After estimating the parameters of Eq. 1, some reasonable
commuting journeys were chosen to estimate commuting noise
exposure. The representativeness of the samples was checked
considering the use of the subway system.
Estimation of a More Complete Total Daily Noise
Exposure
As stated above, transport to work causes an extra noise
exposure that may influence the estimation of total daily noise
exposure. To evaluate it properly, the total sound pressure level
was defined as:
LAeq,T e−total =10·log tw·100.1·LAeq,T e −w+tcj ·100.1·LAeq,T e−cj
tw+tcj
(5)
where LAeq,Te-w is the equivalent continuous sound pressure
level at work, and twis the average working duration, LAeq,Te-cj
is the equivalent continuous sound pressure level in the com-
muting journey, and tcj is the average time spent on the journey
to work and back home.
APPLICATION OF THE METHODOLOGY
As all noise components considered for the Madrid subway
(i.e., type of train, squealing noise, and PA system) are
statistically significant, the ANOVA allowed the parameters of
Eq. 1 to be correctly estimated (Table I). The standard deviation
of the residual uijk is 2.3 dBA, and the mean is 0.0 dBA. As the
residuals represent the difference between the model and the
measurements, it is quite clear that the model is well adjusted
for the case study.
As stated above, to use the model properly, it is essential to
know which noise component of Eq. 1 to use in each subway
station of the journey considered. In this case study, while it
is quite easy to know which type of train passes through a
subway station, we cannot say the same for the PA system and
squealing noise because of their erratic nature. For this reason,
four different scenarios (cases) were considered:
•Case A – PA system is not installed (0), and squealing noise
does not occur (0).
•Case B – PA system is not installed (0), and squealing noise
occurs (1).
536 Journal of Occupational and Environmental Hygiene September 2011
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TABLE IV. Estimation of Commuting Noise Exposure for the Madrid Subway
Case PA SN LAeq,Te-cj (dBA) LEx,8h-cj (dBA) Dose (80) (%) Dose (85) (%)
A00 76.968.67.62.4
B01 79.170.712.64.0
C10 78.970.612.03.8
D11 81.272.820.26.4
•Case C – PA system is installed (1), and squealing noise
does not occurs (0).
•Case D – PA system is installed (1), and squealing noise
occurs (1).
After defining these possible cases and analyzing the
Madrid subway map, 35 reasonable journey samples were
chosen. Their representativeness was checked by three as-
sumptions:
1. Average journey duration is statistically equal to the
duration estimated by the National Statistics Institute.(28)
2. Ratio of the type of lines used by passengers is statis-
tically equal to the ratio estimated by a Madrid Metro
study.(22)
3. Ratio of the number of subway transfers used by pas-
sengers is statistically equal to the ratio estimated by a
Madrid Metro study.(22)
The three foregoing assumptions were checked with a sta-
tistical hypothesis test using t-student.
The first assumption was checked considering as a null
hypothesis that the average journey duration is 71 min (going
to work and back home), as was estimated by the National
Statistics Institute for a Madrid journey to work.(28) As the
significant level (p-value) is 0.34, a null hypothesis shall not
be rejected with at least 95% of confidence level.
The second assumption was checked considering as a null
hypothesis that the average of the differences between the ratio
of the types of lines used by Madrid subway passengers going
to work estimated by Madrid Metro(22) and the ratio calculated
from our samples is zero. As the significant level (p-value) is
0.97, a null hypothesis shall not be rejected with at least 95%
of confidence level.
The third assumption was checked considering as a null
hypothesis that the average of the differences between the ratio
of the number of subway transfers used by passengers going
to work estimated by Madrid Metro(22) and the ratio calculated
from our samples is zero. As the significant level (p-value) is
0.92, a null hypothesis shall not be rejected with at least 95%
of confidence level. As all the assumptions were fulfilled, the
FIGURE 2. Contribution of commuting noise exposure to the total daily noise exposure. A, B, C, and D are the four scenarios considered in
the estimation of commuting noise. PA is public address system; SN is squealing noise.
Journal of Occupational and Environmental Hygiene September 2011 537
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FIGURE 3. Contribution of the commuting noise model deviation to the total daily noise deviation. A, B, C, and D are the four scenarios
considered in the estimation of commuting noise. PA is public address system; SN is squealing noise.
35 commuting journey samples are sufficiently representative.
Commuting noise exposure was therefore estimated for the
four cases described above.
Results of the estimation (Table IV) highlight that the
presence of the PA system and squealing noise (Case D)
provides the highest noise exposure level normalized to a
nominal 8-hr workday (72.8 dBA). The presence of only the
PA system (Case C) or only squealing noise (Case B) seems to
yield almost the same results. In fact, the difference between
these two noise exposure levels is very small (0.1 dBA).
Finally, the importance of adding this extra noise exposure
to assess a more complete total daily noise exposure can be
described properly by considering Eq. 5, where LAeq,Te-w was a
noise level ranging from 60 to 90 dBA, and LAeq,Te-cj depends on
the scenarios chosen (Table IV). After converting the sound
pressure level to the noise exposure level normalized to a
nominal 8-hr workday with Eq. 3, the results (Figure 2) show
that the contribution of the commuting noise to the total noise
exposure (LEx,8h-total) increases toward the lower levels of noise
exposure at work. For instance, the contribution of commuting
noise exposure with a noise exposure at work of 60 dBA is
13 dBA (Case D), while at 80 dBA it is only 1 dBA (Case D).
This example shows the influence of including this extra noise
for a proper evaluation of the total daily noise exposure.
The standard deviation of the total daily noise exposure
(LEx,8h-total) was compared with the deviation of commuting
noise exposure (LEx,8h-cj). To estimate the first indicator, we
considered Eq. 5 where LAeq,Te-w was a noise level ranging from
60 to 90 dBA, and LAeq,Te-cj was set as a normal distribution
with a standard deviation of 2.3 dBA and with the average
depending on the scenarios chosen (Table IV). After converting
the sound pressure level to the noise exposure level normalized
to a nominal 8-hr workday with Eq. 3, we found that total
daily noise exposure (LEx,8h-total) could be approximated to
a lognormal distribution,(29) with a standard deviation that
ranges from 2.25 dBA in Case D at a noise exposure at
work (LAeq,Te-w)of 60 dBA to 0.02 dBA in Case A at a noise
exposure at work (LAeq,Te-w) of 90 dBA (Figure 3). This result
highlights that the model (Eq. 1) standard deviation represents
a small contribution in standard deviation of the total daily
noise exposure at high noise exposure levels at work.
CONCLUSIONS
Our results highlight that for Madrid subway passengers,
commuting noise exposure (LEx,8h-cj) depends strongly on
the type of train, the presence of squealing noise, and the PA
system, ranging from 68.6 dBA to 72.8 dBA. Its contribution to
the total noise exposure (LEx,8h-total) increases toward the lower
levels of noise exposure at work. In fact, the contribution of
the commuting noise exposure with a noise exposure at work
of 60 dBA is 13 dBA (Case D), while at 80 dBA it is only
1 dBA (Case D). Moreover, the standard deviation of the model
proposed represents a small contribution in standard deviation
of the total daily noise exposure, especially at high levels of
noise exposure at work.
This extra non-occupational exposure is essential for having
a more correct assessment of total daily noise exposure and,
consequently, of its effect on worker health. In fact, in many
cases, the addition of this commuting noise exposure could
affect the proper evaluation of the relationship between noise
dose and health response.
538 Journal of Occupational and Environmental Hygiene September 2011
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