A08:
NOISE

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0123

Inter-Wind – an Interdisciplinary Analysis of Wind Turbine Noise

Gundula HUEBNER1; Florian Johannes Yanic MUELLER2; Johannes POHL3
1

Martin-Luther-University Halle-Wittenberg; MSH Medical School Hamburg, Germany
2

3

MSH Medical School Hamburg, Germany

Martin-Luther-University Halle-Wittenberg; MSH Medical School Hamburg, Germany

ABSTRACTSound emissions of wind turbines play an important role for their acceptance. In order to understand theimpact of these emissions on residents it is important to measure it accurately. Often this is done with a singleitem measure of annoyance. With the Annoyance-Stress-Scale a more nuanced approach to this problem ispresented [1]. An empirical application in an interdisciplinary research project on noise annoyance of windturbines is provided: In the proximity of a wind farm in southern Germany residents were interviewed along
a standardised questionnaire (n = 148) and were able to use a noise reporting app to document their annoyancein real-time and synchronous with sound, ground motion, and meteorological measurements. The app data(n = 11) indicate higher complaint rates in the early morning, evening and night hours. Changes in rotation
rate, as well as high rotation rates are associated with resident complaints. Both rotation configurations occur
under different wind directions, and affect residents in different situations–with regard to location, day time,and activity.
Keywords: Sound, Annoyance, Acceptance

1. SOUNDS OF WIND TURBINES – ANNOYANCE AND ACCEPTANCE
The consequence of climate change become increasingly evident. Even more, the need for climate
protection as well as energy independence from finite resources is demonstrated by ongoing wars. In
democratic societies, the energy transition from finite to sustainable resources needs to include
citizens, especially the local residents living in the vicinities of renewable energy plants.
So far, wind energy seems to be the most important pillar of the energy transition. The expected
or experienced annoyance of WT noise [1,2,3] is one of the major acceptance factors of residents’
acceptance. The worries about the effects of noise emissions can initiate negative social dynamics
early on and can result in long-term opposition [4, 5]. Consequently, the understanding of how windturbine sounds become annoying noise is of high relevance. Over the last decade, this relevance ismirrored in the increasing research on wind turbines emissions and their impact on residents [e.g., 1,
3, 6, 7, 8, 1, 9, 10, 11, 12, 13, 14, 15].
While epidemiological studies assess if wind turbines do have negative health effects on residents[e.g., 12, 13, 16], laboratory studies mainly investigate acoustical characteristics of wind turbine
sound linked to annoyance [e.g.,17, 18, 19]. Both approaches provide most relevant information, yet,field studies are also needed to uncover the complexity of realistic settings. The prevalent field studies
rely on subjective annoyance evaluations of residents, predominantly [e.g., 1, 3, 6, 7, 9, 10, 11, 15,20]. To overcome the shortcomings of subjective evaluations, recently some studies combined
objective with subjective measures, assessing residential complaints in tandem with wind turbine1

florian.mueller@medicalschool-hamburg.de

emissions [8, 14, 21, 22]. Still, open questions remain concerning specific annoying situations – aprecondition to derive effective mitigation measures.
To provide a better understanding of specific annoying situations the present interdisciplinary
study combines a real-time noise reporting app with synchronous objective data.

2. DETECTING NOISE ANNOYANCE SITUATIONS – SUBJECTIVE AND
OBJECTIVE MEASURESTo detect noise impacts of wind turbines, epidemiological, laboratory and field studies are
conducted. Epidemiological studies analyse if wind turbines do have negative effects on residents,
investigating if diseases, e.g. cardiovascular diseases or diabetes, comparatively occur more often in
the proximity of wind turbines [12, 13, 16]. These studies are important to understand stress effectsof wind turbines, yet they do not allow conclusions about which circumstances exactly lead to noise
annoyance and symptoms. Laboratory studies mainly investigate acoustical characteristics of windturbine sounds that can be linked for example to annoyance and sleep disturbances [17, 18, 19, 23,24]. However, everyday circumstances, including geography, housing, weather, sleeping conditions
as well as the context of a given wind farm (i.e. the process of wind farm planning and construction)
are not considered. Field studies are designed to uncover the complexity of everyday circumstances[e.g., 1, 3, 6, 7, 9, 10, 11, 15, 20]. However, these studies mostly rely on subjective evaluations ofresidents. Only in recent years a handful of studies were conducted that combine objective withsubjective measures, where residential complaints were assessed in tandem with wind turbine
emissions [8, 14, 21, 22].
One of the first combined-assessment studies used diary observations by annoyed residents, andmeasurements of acoustics and vibrations inside and outside residential buildings [21]. Access tointernal data and shutdown time periods enabled the assignment of measurement data to turbines andthe measurement of background emissions. Although the study set a good example for subsequent
research, the investigated hub height (69 m) is below today's standard and the results could differ
considerably for today's wind turbines.
More recent studies combined written noise diaries with recordings of sound pressure level, windspeed and direction [e.g., 8] or a smartphone app with calculated sound pressure levels, simulated
meteorological data and operating data of the wind turbines [e.g., 14]. [22] simultaneously assessed
acoustic, ground motion and meteorological measurements. However, additional interviews withresidents were conducted but not synchronous to the measurements. Thus, no direct link between
factors influencing annoyance and wind turbine operation could be established. A shortcoming of thestudy from [8] is the small sample size of two residents, which questions the generalizability. Finally,[14] reached a larger sample (n = 68) but used calculated data and had their participants use the app
in fixed locations. Likely, spontaneous annoyance responses, which are of essence in a real-lifescenario, were left under detected by this approach.
Based on the prevalent research, the present study aims to further develop a reliable approach forthe evaluation of annoyance together with acoustics, ground motion and meteorological measurements
as well as operational data [see 25].

3. METHOD
3.1 Location and Participants
The location chosen was the southern Germany in municipalities of Kuchen, in the Swabian Alb.
The local wind farm Tegelberg consists of three wind turbines (hub height 139 m, rotor
diameter 120 m, 2.78 MW rated power). Overall, 148 residents of the wind farm were recruited forinterviews (mean age M = 62.55 years; SD = 11.72, range = 24–83 years). A larger proportion ofmales participated (68.6 %). None of the participants received financial benefits from the local windfarm or was working in the wind energy industry. Out of the total sample, a smaller proportion of 46
participants agreed to use a real-time annoyance app, an even smaller group (n = 11) actually did so.
3.2 Procedure and Assessments
In a first step, standardized questionnaire telephone interviews were conducted with the 148
residents in the second half of 2020. The questionnaire included 311 items adopted from previous
studies on stress effects of wind turbine emissions [e.g., 1, 26], topics were, for example, annoyanceconcerning wind turbine emissions, including somatic and psychological symptoms associated withwind turbine noise as well as the description of specific noise situations, and a range of moderators,e.g., attitude towards the local wind farm, perception of the planning process.
Participants were invited to use an app to report noise occurrences at any time, about a third(31.08 %) agreed to use it. These 46 participants had a higher level of noise annoyance (M = 3.05,
SD = 0.96) compared to the non-user participants (M = 1.44, SD = 1.39, large effect size). This
indicates a higher interest of those more strongly annoyed by wind turbine sounds .
In the second step, the usage of the app enabled us to set the participants’ experiences in relation
to simultaneous acoustic, ground motion, and meteorological measurements at different sites as well
as operational data. Additionally, we had the chance to measure acoustic and ground motions insideand outside at the residencies of four strongly annoyed residents [see 25]. This approach allowed fora differentiated analysis of annoying situations. Finally, 11 participants used the app in the same period
in which the objective measurements were taken.

4. RESULTS
From the 148 participants 62.8 % (n = 93) heard wind turbine sounds. As typical annoying
situations most commonly eastern winds, i.e. from the direction of the wind farm, (by 25.4 %), highwind speeds (62.7 %), and wet weather (53.1 %) were mentioned. Additionally, fog (22.4 %), and frost(18.4 %). The same pattern emerged for the 11 app users as for the overall sample: High wind speeds(72.7 %), and wet weather (36.4 %) were mentioned most often, with eastern (40 %) and western winds(40 %). Consequently, the 11 app users seem to represent the overall sample.
Between October and December 2020, the 11 users made 104 noise reports in the app. As shownin Table 1, noise reports coincided predominantly with two pattern s of rotational speeds of the windturbines, i.e., a pattern of a constant and high rotation rate (33.7 % of noise reports) and a highvariability of rotational speeds across the three turbines during the hour of the report (27.9 %).
Table1
Patterns of rotational speed during resident noise reports. For each noise report, six 10 min intervals
are considered: the 10 min interval of the report and the five preceding intervals.
Rotational patterns
Frequency%
Constant medium rotational speed (7 – 10 rpm) of all 3 WTs
10
9.6
Constant high rotational speed (10 – 12.5 rpm) of at least 1 WT
35
33.7
Low variability (SD < 1)
17
16.3
SD 1 – 2
11
10.6
SD 2 – 3
2
1.0
High variability (SD > 3)
29
27.9
Total
104
100
In Table 2 the patterns are contrasted in detail with regard to the measuremen t data and the app
reports by residents. The patterns occurred under different weather conditions. High rotational speeds
were mostly associated with the main wind direction, west wind (60 %), while the wind directions
were less consistent for high variability. The average wind speed and turbulence intensity were higher
for high rotational speeds. The shear coefficient α indicates that there were larger differences in windspeed between air layers in different heights (3 m – 100 m) for the pattern of high variability.
While sound pressure levels (SPL) were, on average, higher during high rotational speeds, both at
the wind farm and the residential sites, the variability of SPL was, as expected, significantly higher
for the high variability pattern. For both patterns ground motions measured at the residential s ites
were far below the perception threshold, which is considered to be at 100 µm/s [27].
Residents reported a moderately high level of annoyance during both patterns. Differences in theirreports pertained to the predominant description of sounds (rotor / aircraft sounds vs. swooshing anddroning) as well as the annoying situation: High variability was associated with nighttime situations
inside when respondents tried to fall asleep, while high rotational speeds additionally affected daytime
and leisure activities outside.

Table 2
Selected characteristics of the most common pattern of rotational speeds with regard to weather
conditions, acoustic and ground motion data, and subjective reports
Constant highHigh variability Cohen’s drotational speed(M, SEM / %)(M, SEM / %)–Wind direction
East

8.6 %

17.2 %South-east

14.3 %

27.6 %

South

2.9 %

13.8 %South-west

11.4 %

17.2 %

Wind speed

60.0 %
8.64 m/s (0.24)

17.2 %
7.17 m /s (0.30)

0.98

Shear coefficient α

0.374 (0.030)

0.445 (0.033)

0.67

Turbulence intensity I

0.148 (0.009)

0.118 (0.011)

0.94Sound pressure levels (L Aeq 20 – 10.000 Hz) at
wind farm

56.14 dB(A)(0.35)

50.73 dB(A)(0.65)

1.88

Sound pressure levels (L Aeq 20 – 10.000 Hz)
outside residential building

45.94 dB(A)(1.05)

41.87 dB(A)(0.89)

0.96

SD of LAeq 20 – 10.000 Hz at wind farm

0.57 (0.05)

5.64 (0.58)

2.02

Maximum ground motion in residential buildings(vertical component: 12 – 45 Hz)

20.05 µm/s (6.40)

10.15 µm/s
(1.84)

ns.

Noise annoyance (0 = “not at all” – 4 = “very”)
Sound description

2.70 (0.30)

3.10 (0.15)

ns.
–

20.0 %

20.7 %

Aircraft sounds 22.9 %17.2 %

West

Sounds ascribed to rotor blades
SwooshingDroning22.9 %
11.4 %

20.7 %
34.5 %–

Location during report
InsideOutside53.3 %
46.7 %

96.3 %
3.7 %–Daytime22:00 – 06:00

34.3 %

48.3 %

06:00 – 14:00

42.9 %

3.4 %

14:00 – 18:00
18:00 – 22:00

5.7 %
17.1 %

0.0 %
48.3 %–

Disturbed activity
Falling asleep

3.1 %40.7 %

Sleeping 21.9 %25.9 %
Leisure time 28.1 %11.1 %
Note: ns. = non-significant difference, M = Mean, SD = Standard Deviation, SEM = Standard error of
the Mean.

5. Summary and Discussion
The results confirm that the combination of objective measurements, operational data andsubjective reports enhances the understanding of annoying wind turbine sounds. It corroborates earlier
findings that SPL alone cannot adequately explain wind turbine noise annoyance, as annoyance canoccur even at low SPLs. The use of the real-time app enables a differentiated analysis of which
phenomenon can cause the nuisance even at low SPL: The data provide empirical evidence that a highvariability of the rotational rate is related to the annoyance even in case of lower SPL. That phenomena
appears especially during night hours–when background noise becomes lower, e.g., traffic noise (seepresentation of Laura Gaßner and colleagues). One possible explanation might be that a change insound quantity and quality draws the attention of residents. In contrast, during the day, annoyancefrom constant high rotational speed seem more likely. Additionally, fog and high humidity in general
seem to foster annoyance. From these results, technical mitigation measures can be derived, theeffectiveness of which should then be evaluated in further field experiments.
From a practical perspective, there are also further possibilities for utili zation: While themeasurements in- and outside at the four residential buildings provided important information
regarding sound (see presentation of Esther Blumendeller and colleagues) and vibrations, they did notyield additional results concerning the characterization of specific noise annoying situation.
Accordingly, it can be assumed that the combination of the objective data and the subjective app
reports differentiated the specific annoyance situation sufficiently. When it comes to determining thecause of wind turbine noise on site, the use of the app with the affected persons and its combination
with the objective data could offer an analysis approach and starting points for mitigation measures.
In practice, this procedure should be much easier to implement than measurements at single residentialbuildings. How valid such an approach would be should be investigated in further field studies.
However, the present findings should also be viewed critically. Because whet her the residents werereally not annoyed at the times when they did not send an app report or just could not or did not want
to use the app remains an open question. To overcome this shortcoming, we plan combined
spontaneous as well as fixed app usages in the next step in the Inter-Wind project.
Overall, the inter- and transdisciplinary field experiments approach seems promising to further
enlarge the understanding of annoyance due to wind turbine sounds. An important contribution for asocially acceptable expansion of sustainable wind power use.
ACKNOWLEDGEMENTS
We thank the local authorities of Kuchen for their support, as well as the Stadtwerke SchwäbischHall, the KWA Contracting AG for WT access and data. Furthermore, we acknowledge the support oflocal residents and thank Esther Blumendeller, Laura Gaßner, and Maayen Wigger for providing
acoustic, ground motion, and meteorological data. The presented data are part of the interdisciplinary
project Inter-wind, supported by the Federal Ministry for Economic Affairs and Climate Action on thebasis of a decision by the German Bundestag (grant 03EE2023A-D).

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PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0346

Assessment of community responses to noise from German
onshore wind turbines
Dirk SCHRECKENBERG1; Stephan GROSSARTH1
1

ZEUS GmbH, Centre for Applied Psychology, Environmental and Social Research, Germany

ABSTRACT
Within five exposed areas in Germany, the effects of wind turbine (WT) noise on annoyance and disturbanceswere assessed, with a total of 463 participants living at a distance to the nearest WT of up to 5 km. Exposureresponse analysis was carried out for the percentage of people highly annoyed (HAV) by WT noise withannoyance assessed according to ISO/TS 15666. With a correlation of r = .26, the effect size of the noise
rating level Lr,24hrs (LAeq for daytime and nighttime) on WTN annoyance is small. Other contextual factors
contribute to the explanation of the annoyance. Particularly, these are the visual impacts of the WT, lack ofrecreation, reduced sojourn quality outdoors, and negative attitudes towards the local WT. The results further
indicate that the WT amplitude modulations seem to affect the noise annoyance. Following suggestions fromthe literature, a composite annoyance score based on factor analysis, including annoyance due to noise, visual
impacts, and stress-related responses regarding WT-induced lack of recovery outdoors, was constructed andrelated to the distance to the nearest WT. Results of exposure-response analysis regarding the composite scoreare presented at the conference.
Keywords: Wind turbine noise, annoyance, community response, visual effects

1. INTRODUCTION
In order to reduce further global negative consequences of climate change and to allow energy
consumption independent from fossil fuels, there is a need to enforce energy transition to renewableenergy. For this, as in many other countries, the German government plans to considerably extend thenumber of wind turbine (WT) installations on land and the number and size of areas designa ted forconstructing WT. These sites are mostly located in rural areas, where relatively few residents live
nearby. Nevertheless, on a local level, in the vicinity of areas dedicated to WT construction or nearalready installed WT, residents and local stakeholders often complain and oppose to the construction
and use of the WT. Among others, noise from WT (WTN), visual characteristics of the WT, andencroachment on nature and species conservation at the rural site of the WT installation belong to themost mentioned reasons for opposition against WT (1). To address complaints regarding WTN andvisual WT features and to support local acceptance of WT, values of the minimum distance of WT toresidential buildings are set for the construction of WT by the responsible authorities. Examples are
the Bavarian 10H-regulation (minimum distance being 10 times the height of the WT) (2), or theminimum distance set to 1000 m as in the German Federal Bill to increase and accelerate the expansionof onshore wind turbines (3). In Austria, minimum distances vary between counties, for example, 100
to 1000 m depending on the maximum capacity of the WT (Upper Austria), or 750 to 2000 m
depending on the land us (Lower Austria). In Denmark, a minimum distance of four times the totalheight of the WT is mentioned, in France a minimum distance of 500 m (4). All these minimum
distances are set more or less arbitrarily, without clear evidence of whether these distances (and whichof them) allow for protection against aversive WT effects such as annoyance due to WTN. Suchevidence could be provided by information on the annoyance due to WT related to distance.
In this study, we studied the effects of noise from onshore WT on annoyance and disturbance. We
analyzed the exposure-response relationship for noise annoyance and investigated further contextual
factors contributing to annoyance. We further combined several judgments of annoyance and stress1

schreckenberg@zeusgmbh.derelated disturbance responses to WT to a combined annoyance score and analyzed the relationshipbetween the percentage of people highly (in total/combined) annoyed by WT and the distance between
respondents' home addresses and the nearest WT.

2. METHODS
2.1 The study sites
The survey was conducted in the vicinity of five WT locations in Germany, taking different
topographical conditions into account.Residential areas up to a distance of 5 km to the nearest WT
were defined as study sites. Table 1 shows the key characteristics of the WT locations.
Table 1 – Characteristics of wind turbine study areas (SA) of the German WTN study
SA 1

SA 2

SA 3

SA 4

SA 5

Location in Germany

Central

West

North

East

South

Number of wind turbines

3

1

6

21

10

Wind turbine power class2 – 3 MW3 MW

2 – 2.5 MW

2.5 – 3 MW

2.5 MW

Wind turbine rotor diameter

80 – 100 m

c. 130 m

90 – 100 m

110 – 120 m

c. 120 m

Wind turbine hub heightc. 140 m

c. 135 mc. 100 m

c. 140 m

c. 140 m

Wind turbine location

Open field

Edge ofOpen field

Open field

ForestVery hilly

Flat

Flat

Hilly

350 – 450 m

200 – 400 m

7 – 14 m

40 – 60 m

280 – 400m

380 m

345 m

7m

50 m

340 m

400 – 410 m

320 m

10 – 14 m

35 – 55 m

360 – 380 m

forestTopography

Slightly
hilly

Surrounding elevation
above sea level
Immission location
elevation above sea level
Wind turbine elevation
above sea level

2.2 Sample
Address data of each adult living within 5 km to the nearest WT were obtained from the local
registry office. A random sample was drawn, and the selected people were contacted by mail,informing them about the study and Privacy poliy and inviting them to participate in the survey. All
in all, a number of 468 participants were surveyed. For five participants, the noise rating levels were
not available due to a lack of clarity about the geo-coordinates of their home addresses. Thus, theexposure-response analysis refers to a total of 463 subjects.
2.3 Assessment of WTN exposure
The source-specific WTN was modeled for the home address of each participant. The calculations
were done according to the German interim procedure for WTN rating level prediction (5, 6). The
calculations were carried out using the Soundplan software package (version 8.2). Noise rating levels(continuous sound levels LAeq ) were calculated for daytime (6 a.m. to 10 p.m.) and nighttime (10 p.m.
to 6 a.m.). As for the modeling, a continuous 24-hours WT operation was assumed. For each address,
the rating level for daytime (L r,day) is equivalent to the rating level for nighttime (Lr,night ). Therefore,
in the exposure-response analysis, the exposure to WTN is expressed in terms of a 24-hours noise
rating level Lr,24hrs.

2.4 Assessment of community responses to WT
In the telephone interviews and online questionnaire, long-term annoyance due to noise from WTwithin the past 12 months was assessed by means of a 5-point rating scale ('ICBEN scale', 7). TheWTN annoyance was surveyed in total, and separately for outdoors and indoors.
The perceived disturbances due to WTN were assessed regarding the disturbance of activities at
daytime, such as spending time and relaxing outdoors (on the terrace, the balcony, in the garden),communicating indoors and outdoors, watching TV, listening to the radio, having a phone call,socializing at home. The intensity of disturbance was assessed using the verbal categories of the
ICBEN 5-point rating scale. WTN sleep disturbance was assessed as the mean of responses on one ofthe verbal categories of the ICBEN 5-point scale referring to disturbances when falling asleep, during
sleep at night, and sleeping in.
To assess the visual annoyance due to WT, the respondents judged the annoyance owing to thevisibility of the WT, the shadowing, the aviation obstruction lighting, the rotary movements, and the
WT's effects on the landscape.
Attitudes towards the WT that reflect a lack of recreation and reduced sojourn quality outdoors
were assessed by the degree of agreement to statements that WT operation hinders spending timeoutside at home and that WT operation makes recreation outdoors more difficult. The degree ofagreement was assessed using a 5-point verbal rating scale from 1 (not true) to 5 (very true). Twoother WT attitude scores based on the agreement to items on the same 5 -point scale refer to negative
consequences of WT operation (e.g. mismatch with landscape, land depreciation) and positive
implications of WT operation (new jobs, reduction in electricity costs). Also, socio-demographics,
such as gender, age, and socioeconomic status were assessed. The whole questionnaire and main
survey results are described in (8).
2.5 Procedure
A sample was drawn within each study area based on the registry office data. An invitation letter
was sent to each sampled person. The surveys in the first two study areas (SA1 and SA2) were carriedout within the time period from November 2018 to July 2019. In SA3 to SA5, the surveys took place
between June 2020 and April 2021, interrupted by lock-down periods due to the COVID-19 pandemic.
All surveys were conducted as telephone interviews or by filling in the questionnaire online. After thesurveys were completed, the noise rating level as well as the distance to the nearest WT in meters
were assessed for each participants’ address.

3. RESULTS
3.1 Descriptives
Survey and exposure data are available for 463 participants; 51.6% of them are female. The age
ranged from 19 to 91 years (M = 59, SD = 13.6).
The participants were exposed to The average WTN exposure was 31.3 dB Lr,24hrs (SD = 5.5) witha minimum of 17 dB and a maximum of 43 dB. The distance to the nearest WT ranged from 645 to
4099 m (M = 1902.3, SD = 664.3). Descriptives of the community responses to WT are presented inTable 2.
Table 2 – Descriptives (percentage, mean (M), standard deviation (SD)) of community responses to WT
Variable

Rating scale
1

2

3

4

5

M

SD

not at all1

slightly1

mode-

very1

extremely1

not2

little2

rately1, 2rather2

very2

WTN annoyance - total1

66.3

13.4

7.1

6.5

WTN annoyance – outdoors1

6.7

1.74

1.24

65.4

13.9

8.4

6.1

6.3

1.74

1.22

WT disturbs outdoor stay2

48.3

17.4

14.1

11.0

9.1

2.15

1.37

Disturbance of relaxation

61.8

16.8

9.1

8.0

4.3

1.76

1.17

outdoors1
Communication disturbance

67.8

14.5

8.4

6.3

3.0

1.62

1.07

35.9

19.7

15.5

13.7

15.1

2.52

1.47

WT visibility disturbs1

60.4

15.6

10.4

6.1

7.6

1.85

1.27

WT shadowing disturbs1

80.7

9.0

5.0

2.6

2.6

1.37

0.90

WT aviation obstruction

66.7

11.7

7.7

5.5

8.4

1.77

1.29

72.9

10.6

7.8

4.1

4.5

1.57

1.09

40.1

18.7

17.1

11.3

12.8

2.38

1.43

81.8

8.0

5.8

2.8

1.5

1.34

0.83

WTN disturbs night sleep1

86.1

6.1

3.7

2.8

1.3

1.27

0.77

WTN disturbs sleeping in1

85.5

6.5

4.1

3.3

0.7

1.27

0.75

outdoor1
WT makes loc. recreation
outdoors difficult2

lighting disturbs1
WT rotary movements
disturb1
WT effect in the landscape isannoying1
WTN disturbs when falling
asleep1

3.2 Exposure-response relationships for annoyance and sleep disturbanceExposure-response relationships were estimated for the percentage of persons highly annoyed(%HA V ) and the percentage of persons highly sleep disturbed (%HSD V*) by WTN (Figure 1).

Figure 1 –Percentage of persons highly annoyed (%HAV, left) and highly sleep disturbed (%HSDV*, right)
due to WTN against the noise rating level Lr,24hrs
According to the ISO/TS 15666:2021 definition of high annoyance, a cut-off value of 60% of the
response scale was used. That is, persons in the upper two categories of the ICBEN 5-point scale(upper 40% of the scale) are defined as being highly annoyed (HA V). Similarly, high sleep disturbanceis defined on the basis of response score values lying in the upper 40% range of the response scale.
Note that in this study, the sleep disturbance score is the mean of responses on a 5-point scale of three
items, with a score value ranging from 1 (not at all disturbed) to 5 (extremely disturbed). Therefore,
sleep disturbance score values greater than 3.6 form the upper 40% of the combined scale and definehigh sleep disturbance (HSD V*). Figure 1 shows, as expected, higher %HA V outdoors against outdoor
WT L r,24hrs than %HA V indoors against outdoor WT L r,24hrs . The exposure-response relationshipfor %HA V due to WTN in total is similar to that for %HA V outdoors. The %HSD-curve turns out to be
flatter and lower than the %HA V -curve indoors.
Table 3 – Correlations r between community responses to WT, WTN exposure, and distanceCategory

Items

WTN

Lr,24hrs

annoyance

Distance(m)- totalWTN

WTN annoyance - total

-

0.263

-0.183annoyance

WTN annoyance - outdoors

0.910

0.272

-0.194WTN annoyance - indoors

0.752

0.219

-0.154WTN

Disturbance of relaxation outdoors

0.830

0.266

-0.193

disturbanceCommunication disturbance outdoors

0.802

0.283

-0.181WTN

WTN disturbs conversation or phone calls

0.435

0.139

-0.074

disturbanceWTN disturbs listening to radio/music, watching TV

0.390

0.124

-0.084indoors

WTN disturbs reading, concentration

0.458

0.126

-0.084WTN disturbs relaxation at home

0.443

0.127

-0.068WTN disturbs socializing, having visitors

0.398

0.130

-0.076WTN sleep

WTN disturbs when falling asleep

0.645

0.154

-0.100disturbanceWTN disturbs when sleeping in the night

0.583

0.158

-0.113WTN disturbs when sleeping in

0.620

0.189

-0.141WTN sleep disturbance (Score)

0.631

0.181

-0.125Lack of

WT disturbs outdoor stay

0.624

0.096

-0.006

recreation,

WT makes local recreation outdoors difficult

0.617

0.140

-0.078WT

WT shadowing disturbs indoors

0.421

0.061

-0.079annoyance

WT visibility disturbs

0.636

0.031

-0.005visual

WT shadowing disturbs

0.574

0.068

-0.055features

WT aviation obstruction lighting disturbs

0.589

0.094

-0.073

WT rotary movements disturb

0.702

0.146

-0.084

WT effect on landscape is annoying

0.616

0.064

0.000

Attitudes

Positive implications of local WT operation-0.307

0.010

0.004

towards local

Negative consequences of local WT operation

0.442-0.1980.164

outdoorssojournqualityoutdoors

WT
Correlation coefficients highlighted in red are statistically not significant.

The linear relationship between WTN rating level and WTN annoyance is low to moderate (0.21 <r < .27). This indicates a small effect size of WTN exposure on WTN annoyance. The correlation
between WT noise exposure and WTN sleep disturbance is even lower (r < 0.20; Table 3).
Further analysis indicates that WTN annoyance is related to other community responses to WTN
or to the local WT in general. Among these are the perceived WT sound characteristics, particularly"whooshing," which is often understood as the subjective perception of amplitude modulations (AM)of WT sound. The stronger respondents agree to WTN as being characterized by 'whooshing', the morethey are annoyed by WTN (r = .414, p < .01). In addition, the average WTN annoyance in the fivestudy areas corresponds with the frequency of occurrence of detected, stable (periodic) AM in theseareas. This again hints at the association between AM and WTN annoyance (see also 8, 9).
All items referring to disturbances and impairments owing to features of the local WT (noise andother features) correlate in the range of 0.39 ≤ r ≤ 0.91 with the total WTN annoyance (Table 3).
Except for annoyance judgments due to visual WT features, all other WT responses are weakly
correlated with WTN exposure (0.09 ≤ r ≤ 0.28). Only the WTN annoyance, the WTN disturbances
outdoors, and the WT-associated sleep disturbance correlate weakly with the distance to the nearest
WT (-0.19 ≤ r ≤ 0.10). Attitudes towards local WT operation show a moderate association with WT
annoyance (positive attitudes: r = -0.31, negative attitudes: r = 0.44). The negative attitudes in addition
correlate both with WT noise exposure (r = -0.20) and with WT distance (r = 0.16).
Table 4 – Factor loadings of items reflecting judgments of local WT features
Annoyance

DisturbanceSleep distur-

Items(outdoor) (F1)indoor (F2)bance (F3)

WT effect on the landscape is annoying

0.907

WT visibility disturbs

0.898

WT rotary movements disturb

0.846

WT makes local recreation outdoors difficult

0.805

WT aviation obstruction lighting disturbs

0.793

WTN annoyance - total

0.792

Communication disturbance outdoor

0.739

Disturbance of relaxation outdoors

0.731

WTN annoyance - outdoors

0.713

WT disturbs outdoor stay

0.710

WT shadowing disturbs

0.504

WTN disturbs socializing, having visitors

0.967

WTN disturbs reading, concentration

0.944

WTN disturbs listening to radio/music, watching TV

0.934

WTN disturbs relaxation at home

0.842

WTN disturbs conversation or phone calls

0.837

WTN disturbs when sleeping in the night

1.015

WTN disturbs when sleeping off

0.925

WTN disturbs when falling asleep

0.899

WTN annoyance - indoors

0.465

WT shadowing disturbs indoors

0.305

0.223

0.135

The correlation coefficients support suggestions to develop a composite WT annoyance score ( 10,11) comprising several features of WT (10) as well as stress-related responses (WTN disturbances;11). Accordingly, such a composite score was calculated. First, the factor structure of the items listed
in Table 3 (except the positive and negative attitudes towards WT) was analyzed by means of factor
analysis (principal axis analysis). A Promax rotation was chosen to allow for correlated sub-concepts(factors). The results of the factor analysis yield three factors, altogether explaining 71% of thevariance. Two factors include WT disturbance indoors (F2) and WT sleep disturbance (F3). The first
factor (F1) comprises assessments of WTN annoyance and WTN disturbances outdoors, annoyancedue to visual WT features, and judgments on the lack of recreation and reduced sojourn quality
outdoors due to neighboring WT (Table 4). The composite WT annoyance score developed is the meanof all items included in factor F1 (Cronbach's alpha = 0.95) with a value range from 1 to 5 and higher
values indicating higher annoyance. Those participants with composite annoyance scores > 3.4 (upper
40% of the composite annoyance scale) were defined as being highly annoyed (%HA V*,composite ).
3.3 Annoyance and disturbance responses related the distance to WT
Figure 2 shows the results of logistic regression models on the probability of being highly annoyed
and sleep disturbed, respectively, as predicted by the distance to the nearest local WT for thecomposite WT annoyance, WTN annoyance, and WTN sleep disturbance. Following theEnvironmental Noise Guidelines of the World Health Organization (WHO, 12) 10 %HA defines thebenchmark of a relevant risk increase. Applying this benchmark to the exposure-response curves in
Figure 3 reveals that a distance to the nearest local WT between approximately 1200 m (for composite
WT annoyance) and 1600 m (for WTN annoyance) corresponds with the threshold for the relevant
increase in the risk of high annoyance. For HSD, for which WHO defines a benchmark of 3%, thecorresponding distance would be about 2400 m.

Figure 2 – Probability of being highly annoyed (HAV,noise HAV*,composite) and sleep disturbed (HSDV*)
predicted by the distance (m) to the nearest local WT

4. CONCLUSIONSWith a total of 463 participants in a socio-acoustic survey conducted around five WT locations,the impact of WTN on residents' annoyance and disturbances were investigated. The correlation
between the noise rating level and WTN annoyance was low to moderate, indicating an exposureresponse relationship for WTN annoyance with a low effect size. Other WT responses strongly
associated with WTN annoyance are WTN disturbances and reduced sojourn quality outdoors,annoyance due to visual WT features, and the perceived lack of outdoor recreation due to neighboring

WT. These WT responses were summarized to a combined score of WT annoyance following
respective suggestions in previous research on responses to WT and WTN. The percentage of persons
highly annoyed by WT (in total/combined; (%HA V*,composite ) was related to the distance between
respondents' home address and the nearest WT. Applying the WHO benchmark for high (noise)annoyance as relevant health risk (10 %HA) (12) to the distance-annoyance curves in this study resultsin maximum distance values between approximately 1200 m to 1600 m depending on the kind of WT
annoyance considered.

ACKNOWLEDGEMENTS
The study was commissioned by the German Environment Agency (UBA), project number (FKZ)
3717 43 110 0.

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7. International Organization for Standardization. ISO/TS15666:2021. Acoustics - Assessment of noise
annoyance by means of social and socio-acoustic surveys. Geneva, Switzerland: ISO; 2021.
8. Schmitter S, Alaimo Di Loro A, Hemmer D, Schreckenberg D, Großarth S, Pörschmann C, Kühner T.
Noise effects of the use of land-based wind energy. Texte 70/2022. Dessau-Roßlau: Umweltbundesamt;
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9. Pörschmann C, Großarth S, Arend JM, Schmitter S, Schreckenberg D, Wunder K. Amplitude modulations
increase annoyance due to wind turbine noise immission. Proc INTER-NOISE 2021; 1-5 August 2021;
Washington DC, USA 2021. p. 4048-57. URL: https://doi.org/10.3397/IN-2021-2589
10. Michaud DS, Marro L, McNamee J. Derivation and application of composite annoyance reaction
construct based on multiple wind turbine features. Can J Public Health 2018;109:242-251.
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12. World Health Organization (WHO). Environmental noise guidelines for the European region.
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https://www.euro.who.int/__data/assets/pdf_file/0008/383921/noise-guidelines-eng.pdf

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0347

Reflections about the assessment of short-term noise annoyance
Dirk SCHRECKENBERG1; Julia KUHLMANN1; Christin BELKE1; Sarah Leona BENZ1
1

ZEUS GmbH, Centre for Applied Psychology, Environmental and Social Research, Germany

ABSTRACT
Most socio-acoustic surveys on community responses to noise refer to retrospective judgments of long-term
noise annoyance. The technical specification ISO/TS 15666 includes recommendations for the assessment oflong-term noise annoyance. In recent years, more research focussed on assessing acute or short-term noise
annoyance. These studies address a broad range of noise-related topics such as repeated measurements ofannoyance at different times of a day, annoyance due to rare noise events (e.g. shooting noise) and to relatively new noise sources, for which no results on long-term annoyance exist, yet (e.g. low sonic boom). In
soundscape research, the assessment of acute sound perception is more common and guided by ISO standards
and technical specifications. In this contribution, we reflect on methodological issues of short-term noise
annoyance assessment, and the relation to long-term noise annoyance. We propose guidance on studying
community short-term responses to noise in socio-acoustic surveys.
Keywords: Noise annoyance assessment, short-term noise annoyance, experience-sampling, in-situ assessment

1. INTRODUCTION
Noise annoyance belongs to the most important and prevailing health-related outcomes of environmental noise and, therefore, is one of the most studied noise response in socio -acoustic surveys.
According to estimations of the World Health Organization about 1.6 million disability -adjusted live
years (DALYs) get lost in West Europe due to environmental noise with 654'000 DALYs accounted
for by noise annoyance (1). General exposure-response functions for the percentage of people highly
annoyed due to environmental noise (mostly due to transportation noise) have been developed andupdated since the 1970s (2-3). International recommendations and technical specifications for theassessment of noise annoyance have been published in order to allow for study results on noise an noyance being internationally comparable (4).
All these research efforts refer to long-term noise annoyance, i.e. noise annoyance that is assessedin socio-acoustic surveys as quantified retrospective judgments on a rating scale referring to a past
period of time (e.g. the last 12 months).
In recent years, an increasing number of socio-acoustic field studies have been published referring
to noise annoyance as a response to a single event or to a short time period of s pecific days understudy (e.g. one hour, past night). This is the short-term noise annoyance (5-6). Of course, the shortterm noise annoyance assessment has a long tradition in laboratory studies where participants respondto reproduced sounds. In this contribution, we refer to short-term annoyance assessment in field studies outside the laboratory. The assessment of the perception of the acoustic environment in -situ(soundwalks, behavioral observations, 7) is quite common in soundscape research. Similarly, walking
interviews are used for the assessment of children's perception of place and its impact on the children's
well-being. For example, within the ongoing European project Equal-Life (8) walking interviews areused for the assessment of preschool children's perception of their neighbourhood including socialand physical aspects, among them environmental noise.
Methods of data collection in soundscape research including methods for assessments in-situ are
specified in the technical specification ISO/TS 12913-2 (9). To the knowledge of the authors, for theassessment of short-term annoyance no such standards or technical specifications exist.
1schreckenberg@zeusgmbh.de; kuhlmann@zeusgmbh.deIn this contribution, we discuss definitional and methodological issues of short-term noise annoyance and its assessment. A promising way of studying short-term annoyance within a socio-acoustic
survey is by means of mobile devices and applications. Such studies mainly follow the experiencesampling method (ESM, 10). The use of the ESM approach for the assessment of short-term or eventrelated noise annoyance is reflected upon and some guidance is proposed.

2. DEFINITION OF NOISE ANNOYANCE
2.1 Long-term noise annoyance
According to the systematic review on environmental noise annoyance (3) prepared for the Environmental Noise Guidelines of the World Health Organization (WHO, 11) (environmental) noise annoyance "as observed in surveys is a retrospective judgment, comprising past experiences with a noise
source over a certain time period." In this review, the authors explicitely consider the noise annoyance
response as related to long-term exposure. Accordingly, in the WHO environmental noise guidelines'annoyance' refers to long-term noise annoyance (11, p. 11). The review (3) further defines the multifaceted noise annoyance response as usually containing three elements. These elements are:(1) a behavioral element: an often repeated disturbance due to noise (repeated disturbance ofintended activities, e.g., communicating with other persons, listening to music or watching
TV, reading, working, sleeping), and often combined with behavioral responses in order tominimize disturbances;(2) an emotional/attitudinal response (anger about the exposure and negative evaluation of thenoise source); and(3) a cognitive response (e.g., the distressful insight that one cannot do much against this unwanted situation).
Many researchers regard long-term noise annoyance as a stress response involving the perceptionof noise as an environmental threat and behavioral, affective, and cognitive responses to it (e.g. 12).
2.2 Short-term noise annoyance
For short-term annoyance, no clear, consistent or standardized definition is known to the author s,
particularly regarding the time period "short-term". In laboratory studies, 'short-term annoyance' refers to judgments of annoyance to sounds respondents are exposed to continuously or in terms of
distinct single events during the experiment. The annoyance as assessed in the laboratory has also
been called 'functional annoyance', 'acute annoyance', or 'activity disturbance' ( 13).
In socio-acoustic field studies, short-term annoyance is mostly described or defined by the(short) time period the annoyance response refers to, which is regarded as being closer to the event
compared to retrospective long-term annoyance ratings that explicitely refer to an integrative gen eral reaction to a noise experienced in a (residential) environment (4). The referenced situation ortime frame of the short-term annoyance assessment in field studies varies from single-event judments (e.g. 14) to annoyance ratings referring to a time period of one or a few hours, days, or a few
weeks (15). According to (16) long-term annoyance and short-term annoyance represent the same
concept and share the same causes and characteristics beside their different time frames. However,this seems not to be true in any case. First, short-term annoyance and long-term annoyance correlatemoderately (r ~ .50; 5-6) with each other. Then, correlations with coping behavior and contextual
factors are different and sometimes opposite in sign (17), indicating different mechanisms of responding to and coping with noise in-situ and on a long-term level. Both acoustic as well as nonacoustic factors correlate with short-term and long-term annoyance. However, the association withnoise exposure seems to be stronger for short-term than long-term annoyance (5-6). Whereas among
the non-acoustic co-determinants personal and social factors (attitudes, dispositions) seem to be important predictors of long-term annoyance, they seem to be less relevant on a short-term level where
mainly situational factors, i.e. factors that characterize the context of the noise situation, have an influence on annoyance (6). In line with these differences between short-term and long-term annoyance, researchers point out that (the failure of) short-term coping strategies referring to 'here andnow' differ from (the failure of) long-term coping strategies, the latter one involving perceived (lossof ) control as built up through repeated experience (18).

3. ASSESSMENT OF NOISE ANNOYANCE
3.1 Assessment of long-term noise annoyance
Since the first field studies on community responses to noise, long -term noise annoyance has beenassessed in various forms: as response to a single question on a rating scale, either verbal or numericalwith different numbers of categories, as a sum or mean score of ratings on noise -induced disturbancesof activities, as dissatisfaction with noise, affectedness by noise, or as distraction, anxiety, anger,
exhaustion, frustration, disappointment and fear. Other forms of annoyance assessments that wereused are free verbal descriptions, or semantic differential (19). The different methods of annoyanceassessment turned out to be a challenge for meta- and re-analyses in terms of identifying the variable(s) in original studies that could be used as a general annoyance judgment and be compared andpooled with similar annoyance data of other studies. Based on recommendations of the International
Commission on Biological Effects of Noise (ICBEN) the ISO/TS 15666 (2003, revised in 2021) specified the assessment of long-term noise annoyance in the home environment (4). According to (4) averbal 5-point rating scale and a numerical 11-point scale should be used for the annoyance judgments.
The recommended time period is "12 months or so", encouraging survey participants to give theirgeneral reactions to a stable acoustical environment at home, thus integrating their experiences overdifferent times and locations at home. However, as dicussed in (4) a shorter time period may be appropriate, if the study refers to a particular time, or the acoustic environment has recently changed, orthe study is on the evaluation of the impact of a noise interven tion.
3.2 Examples of short-term noise annoyance assessment
Whenever noise annoyance is studied in laboratory experiments, it is usually short-term noise annoyance referring to the time period of the laboratory session and to the sound respondents are exposed
to during the experiment. There are numerous laboratory studies assessing short-term annoyance.
In field studies, the assessment of short-term noise annoyance depends on the situation and thetime frame the annoyance refers to. One study (16) introduced the term short-term annoyance in amodel on the potential impact of nocturnal aircraft noise as the annoyance felt the day after a disturbed
night, which might either be an accumulation of the annoyance felt during the night or a judgment
resulting from the perception that the sleep has been disturbed. In several studies on the impact ofnoise on sleep, the authors followed the approach of assessing short-term noise annoyance in themorning as referring to the time period of the passing night (e.g. 20-21). Similar to the assessment oflong-term noise annoyance, rating scales are used for the collection of the judgments of the degree of
nocturnal short-term annoyance.
Short-term annoyance has been studied and assessed referring to the annoyance due to noise at
specific days. For example, in a field experiment systematically controlling helicopter noise, partici pating residents judged their daily helicopter noise annoyance in evening phone interviews ( 22). In
this study, the reason for studying short-term annoyance in a field experiment instead of long-term
annoyance in a socio-acoustic survey was that helicopter types, numbers of flights and their noiselevels could not be identified in existing communities in sufficient combination and because it was
not feasible to estimate long-term helicopter noise exposure due to daily variations and the unscheduled nature of helicopter flights.
Another example of a field experiment where short-term annoyance was assessed at daytime on
specified days is a study on the impact of seaplanes on peoples' perceptions of their experience on
Green Island – a popular tourist destination predominantly accessed by ferry (23). During the threedays study the number of seaplanes were systematically varied and the noise levels of the planes aswell as the background noise levels measured. Visitors of the island filled in a short questionnaire on
their peceptions after their island visit on the return ferry. Another, more recent example of assessing
short-term noise annoyance as the annoyance on specific days is the daily summa ry survey of the
Quiet Supersonic Flights Test (QSF18 tests) in Galverston, Texas, on the cumulative assessment ofannoyance due to F-18 low boom dive maneuvers on certain days (14). The daily summary survey
was carried out together with a single-event survey during the same days.
Other socio-acoustic surveys refer to the term 'short-term annoyance' when studying noise annoyance in the course of the day and repeatedly asking for annoyance judgments related to time periods
over the day (e.g. one hour). Examples are surveys on source-specific transportation noise in thecourse of the day, where mobile devices were used to notify respondents several times per day (fromabout 2 – 5 times per day to hourly at daytime) on several consecutive days (3 to 10 days) to answer

a short questionnaire about the annoyance within the last hour (5-6, 24).
In single event-related surveys annoyance is assessed repeatedly on consecutive days – nowadays
by means of mobile devices such as smartphones or tablets – referring to the moment when filling-in
the questionnaire or to a single sound event occurring within a defined (short) time period prior to themeasurement of the participant's response. The annoyance assessment is either done self-initiated, forexample, by pressing a button on a mobile device whenever respondents felt unbearable noise annoyedby an overflight (25), or by means of a palm-top computer (26) whenever hearing an annoying sound.
Another approach is to notify participants several times per day (via a phone call or a message on amobile device) in fixed intervals, at fixed times or by random, requesting to judge their current activities, (source-specific) annoyance and disturbance (5, 14).
In summary, depending on the research question, the short-term annoyance assessment in socioacoustic field studies seems to be adequate in situations in which the noise events of the source ofinterest occur irregularly and are of low number per day making it difficult to integrate the noise
experience with the noise over a period of several months (e.g. helicopter noise, shooting noise, motorcycle noise). In addition, short-term annoyance assessments can be useful in situations in which
short-term variations in noise annoyance are of interest (e.g. diurnal variations), when the noise sourceis new and no long-term experience with it exist (e.g. low sonic booms of new shaped supersonicaircrafts). When annoyance is assessed in field experiments in which the occurrence of noise eventsare manipulated in number, intensity and/or time and the effects of these manipulations are of interest,
short-term annoyance assessments is usually the best option. These involve studies on the impact ofnoise interventions as well. Studies in which respondents' short-term annoyance judgements are assessed repeatedly over a period of time mostly– implicitely or explicitely – follow the approach ofexperience sampling.
3.3

The approach of experience sampling in noise annoyance research

3.3.1 Definition of the ESM approach
The Experience Sampling Method (ESM; 10) is a method for the assessment of event-related, acuteor short-term self-reports. ESM involves the repeated measurements of human-beings' daily-life experiences, perceptions, or behavior in-situ in different moments across a period of time (e.g., at different times of day, on several different days, etc.). ESM is a specific form of a diary study in which
respondents repeatedly fill in a questionnaire in response to given signals or notifi cations. The repeated assessment of momentary experience, perception, mood, or behavior allows minimizing a recall bias that might occur in retrospective surveys. Diary methods such as ESM allows "capturing life
as it is lived" (27). In addition to other (paper-and-pencil) diaries, according to the ESM approach,
respondents are alerted to report their acute or very recent short -term experience (e.g. within the lasthour) 27). The alert or notification can be set by random, in fixed intervals, or at fixed times. Nowadays, in ESM studies make use of mobile devices such as smartphones or tablets for the assessment.
For the explicit assessment of short-term annoyance, ESM has been applied, for example, in fieldstudies on annoyance due to different sources of environmental noise (28), due to aircraft noise (5-6),
due to road traffic and railway noise (24), and due to low sonic boom (14).
3.3.2 Methodological issues of ESM studies on short-term noise annoyance
For the design and implementation of ESM studies the following checklist is proposed in a practicalguide to ESM (29): (1) Decide whether you need experience-sampling; (2) determine your resources;(3) set study parameters; (4) choose software and equipment; (5) implement security measures; (6)implement the study; (7) data issues.
Decide whether you need experience-sampling.
As ESM is time- and resource-intense for researchers and participants, it has to be carefully considered to conduct a ESM study or not. Whether the annoyance assessment is to be prioritized overlong-term annoyance assessment depends on the research question, on the exposure, and its occurrence as mentioned in section 3.2. Even if short-term annoyance is the outcome of interest, dependingon the research question it might not be necessary to assess the annoyance repeatedly.
Determine your resources.
Among others, the resources depend on the team strength, the financial capacity to give incentives
to the participants, and to use the desired devices for data collection (mobile devices provided to thepartipcants, use of participants' own devices, paper-pencil instruments). Similarly, it has to be clarified,whether resources for repeated noise exposure estimation referring to the same time period or event

as the short-term annoyance assessment is available.
Set study parameters.
This includes the type of sampling protocol and the sampling period. Three types of sampling
protocols can be distinguished: interval-contingent, signal-contingent, and event-contingent. In noise
research, the interval-contingent protocol would mean, for example, that the annoyance is assessedat fixed times during the day referring to the prior time interval since the previous judgment. It is notnecessary to define the whole time period since the previous assessment as the interval to which thejudgment refers to. For example, to minimize the daily burden for participants, in an ongoing study
on short-term annoyance due to motorcycle noise, we asked participants in one week on five consecutive days in 2-hours-intervals (e.g. even hours) to assess their hourly noise annoyance. The week
after, we asked respondents in another 2-hours-interval block (uneven hours) to assess their hourly
annoyance, so that over a period of two weeks all hours within the defined daily time period arecovered. To reduce bias, the starting hour (even or uneven) is permuted between participants. Signalcontingent protocols involve judgements in response to a signal, usually referring to acute experi ences right at the moment the signal occurs or to on-going behavior with signals often given by random
throughout the day and unpredictable for respondents. The method seems to be suitable for the assessment of noise responses to sources with a higher number of noise events per day, i.e. more continuous
sound, e.g. road traffic noise. In most cases, interval-contingent and event-contingent protocols also
include signals or notifications, however, these notifications are not set by random and serve as reminders to fill-in the questionnaire that refers to prior time periods or events and not to the corresponding current momentary situation. In noise annoyance research, signal-contingent protocols arewell suited when the perception of the acute acoustic environment or the noise annoyance duringongoing activities are of interest. Event-contingent protocols refer to reports of experience following
immediately or within a short-time after a specific event of interest. In noise annoyance research,these protocols are suitable when the single noise event the annoyance judgment is supposed to refer
to is distinct and detectable from the background noise. The single -event survey on annoyance due tosonic booms in the QSF18 study is an example for a ESM survey following an event-contingent protocol, whereas the daily summary survey in the same study is a n adaptation of an interval-contingent
protocol (14).
Decisions on the sampling period refer to questions of how many times per day or how many days
participants report their experiences. According to (29), the decisions are based on four considerations: (a) the number of observations needed for a stable estimate of a particular phenomenon withina person; (b) the naturalistic incidence of target events and states; (c) the burden to participants; and(d) anticipated compliance. The first consideration refers to the sample size. Within statistical poweranalyses the minimum sample-size can be estimated, based on knowledge from previous studies about
the effect sizes and the statistical method chosen for the analysis. For ESM data, multilevel modeling(with measurements and persons being defined as different levels) is an appropriate approach for thedata analysis, e.g. for modeling the exposure-response relationship for short-term noise annoyance.
Usually, for multilevel modelling, the minimum sample size estimation is done by means of simulation(Monte-Carlo simulation) of the model and the estimates (30). A simplified way of estimating thesample size in studies with repeated measurments such as ESM surveys is the calculation of the effective sample size (ESS), which reflects the total number of independent observations. The independent observations are the product of number of subjects and number of measurements per subject
adjusted for the correlation of the repeated measurements within the subjects (31). This correlation isindicated by the intra-class correlation (ICC). However, the ESS assumes perfect data collection
within a random sample taking not into account response rates < 100%, stratified sampling or factorial
study designs and item non-response. Therefore, further considerations have to be made in addition
to the ESS calcluations to decide on the final minimum sample size.
Regarding the naturalistic incidence of target events and states, for ESM surveys in noise research
this means that the number of measurements per day in combination with the type of the sampling
protocol should fit with the occurrence of the noise exposure.
The burden to participate and the anticipated compliance is a big issue in ESM surveys. Participants' perceived burden, their compliance and careless responding varies with the questionnaire lengthand frequency of measurements per day (32-33). As result of a meta-analysis, in non-clinical settingsa sampling interval of an estimated 2-3 times a day interval is most efficient in regards to the overall
compliance (33). Other authors recommend a sampling frequency of no more than 6 times per day
over a period of 3 weeks or more unless the reports are short and additional incentives are provid ed(34). The compliance is positively associated with incentives given to the participants and with fixed
survy time points (predictability) compared to measurements by random (35). Regarding incentives,
micro-compensations (small incentives per completed sampling during the study) seem to be more
effective in terms of compliance than a higher payment at the end of the study (36). When comparing
the role of the length of the questionnaire and the frequency of sampling , studies show that a higher
number of items in a questionnaire weakens the data quality in terms of higher burden and carelessresponding and less compliance stronger than a higher frequency of measurements ( 32). There is no
general rule for the ideal duration of an ESM survey and the number of days respondents participate.
Again, this depends on the number of items (length of the questionnaire), frequency of measurements,
the intrinsic motivation (depending on the topic) and incentives given. In general, drop-outs increase
with study duration (36). Regarding research on short-term annoyance, study durations vary between
four days (e.g. 6) and 3-4 weeks (e.g. 37).
Choose software and purchase equipment
In recent years the number of ESM studies taking advantage of the use of mobile devices(smartphones, tablets) has increased (36). Purchasing the devices and handing them out to the participants has the advantage that the researcher has control over the software to be used and – after comprehensive and careful testing – over the functionality of the software and the compatibility between
soft- and hardware. However, purchasing the devices easily can get quite cost-intensive. As nowadays
mobile devices are widely used, participants' own devices can be used in an ESM study. The question
arises then, whether existing ESM software is to be used or whether the software should be selfdeveloped. In both cases, a decision has to be made on the operation system (OS) of the mobile device
under which the ESM application should run. Commercial applications exist that are specifically tailored to the ESM approach. However, often they are available only for one OS (either for Android or
iOS devices) leading to exclusions of potential participants that do not use the OS for which t he ESM
application is developed. And, there might be specific research questions and sampling protocols of
the ESM survey for which existing software does not fulfill the requirements. In such cases the ESM
application has to be self-developed. To not exclude potential participants the application should be
developed for different plattforms, i.e., for example, for Android and iOS as the most widely used OS.
Yet, this means an extended effort in software development and testing of the application. A soluti on
could be to develop a web applications which is OS independent, although this requires the mobile
devices to be online all the time. More recently, cross-platform programming languages have beendeveloped that allow producing an application able to run on multiple systems without having to adjust
to OS specifics and, thus, could facilitate the ESM application development for par ticipants own devices (38).
Implementation of security measures
The implementation of security measures refers to protecting devices in case they are handed out
by the research team to participants. We will not discuss this further in this paper as at least for noise
annoyance research, we expect the use of participants own devices in ESM studies to catch on.
Implementation of the study
The implementation of the ESM survey involves the configuration of a pilot study with thoroughextensive testing of the procedure and the application functionality, an easy-to-read documentation
with step-by-step guides for the installation and use of the ESM application and the whole study
procedure. A dedicated email/message system or hotline should be established in case participants run
into issues during the study.
Data issues
As in every survey, the ESM data collection and storage have to follow applicable data protection
regulations. The loss of data should be prevented by making sure data is being saved even when there ’s
no connection to the internet. All data should be uploaded as soon as possible to a protected, main tained server. Before starting the data analysis, the data has to be cleaned and tested for plausibility
and repetitive answers. ESM survey data and corresponding acoustic data have to be merged andchecked. A data analysis protocol should include guidance on how to handle missing data and thestatistical method to be applied to the data. Besides descriptives, multi-level models are recommended
for exposure-response analysis on noise annoyance.

4. CONCLUSIONSIn research on community responses to noise, the number of studies assessing short-term annoyance has
increased. Owing to technological development mobile devices are used to assess annoyance that is closer tothe event then the assessment of long-term noise annoyance in retrospective socio-acoustic surveys. The ESM
approach is a common approach in human science for the assessment of momentary or short-term experiences
and is increasingly used in soundscape research and the research on short-term noise annoyance. In thiscontribution, some methodological issues of using the ESM approach for short-term annoyance have been
discussed and guidance for the study of short-term responses to noise in the field proposed.

ACKNOWLEDGEMENTS
This study is part of the conceptual work of the WP1 ('Mechanisms') of the EU project Equal-Life(Early Environmental quality and life-course mental health effects). Equal-Life has received funding
from the European Union’s Horizon 2020 research and innovation programme under grant agreement
No 874724.

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PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0827

Being a Fair Neighbor – Development of the Aircraft Noise related
Fairness Inventory (fAIR-In)
Dominik Hauptvogel1; Tobias Rothmund2; Dirk Schreckenberg3, Susanne Bartels1
1

German Aerospace Center, Institute of Aerospace Medicine, Sleep and Human Factors Research, Linder Höhe,
51147 Cologne, Germany
2

Institute for Communication Science, Friedrich-Schiller University Jena, Ernst-Abbe-Platz 8, 07743 Jena,
Germany

3

ZEUS GmbH, Zentrum für Angewandte Psychologie, Umwelt- und Sozialforschung, Sennbrink 46, 58093,
Hagen, Germany

ABSTRACT
Community annoyance due to aircraft noise is influenced to a large extent by non -acoustic factors,
such as social factors and attitudes that are related to the noise source and one’s own perceived control
over the noise and noise source. Important non-acoustic factors relate to the perceived fairness of the
distribution of aircraft noise, the noise management processes, the information provided by the nois esource and the interaction between the residents and the noise source. This contribution introduces
the concept of perceived fairness in the context of aircraft noise, and describes the content andpsychometric quality of a multi-facet questionnaire developed and validated to measure perceived
fairness in the context of aircraft noise management. A total of 1,367 people living in the vicinity ofdifferent airports in Germany were investigated in an online study. The aim is to gain a deeper
understanding of the impact of fairness on the response to aircraft noise and to identify starting points
for measures aimed at managing the response to aircraft noise by involving communities andstakeholders. By this, this contribution follows a new approach of a comprehensive noise annoyanceassessment that considers the response to noise within context.
Keywords: Aircraft Noise Annoyance; Fairness; Psychometric Questionnaire

1. INTRODUCTION
The detrimental long-term effects of aircraft noise on the human organism have already been
demonstrated in a large number of studies. These include adverse effects on quality of life, wellbeing,mental health (for an overview, see1), sleep (2), cognition (3), cardiovascular and metabolic system(4) and annoyance (5). Annoyance can be seen not only as a result of noise but also as a mediator ofother health effects. The reason for this is that the experience of annoyance can be seen as a stressresponse, which releases further negative processes in the human body through the release of cortisol(6, 7).
Guski already described in 1999 that only one third of the experience of annoyance is caused bynoise itself (8). At least one additional third is due to so-called non-acoustic factors such as attitude,
expectation, situational and personal factors (9).
As described in the review by Hauptvogel et al. (2021), a fundamental aspect of many of these
non-acoustic factors is the perception of social justice or fairness (10). These concepts have been
1
2
3

Dominik.Hauptvogel@dlr.de, Susanne.Bartels@dlr.de
Tobias.Rothmund@uni-jena.de

Schreckenberg@zeusgmbh.deextensively examined in the organizational context in regard to the acceptance of outc omes and thedevelopment of trust (11). These findings suggest that also in the context of aviation noise research,
the explicit implementation of justice and fairness aspects inherit the potential to improve theeffectiveness of measures by the airport. Therefore, it could reduce annoyance from aircraft noise andfurthermore increase the acceptance of aviation and the local airport.
However, there is currently no psychometric instrument that can assess the various aspects offairness in the context of aircraft noise research. Such an instrument would allow researchers to further
research the potential of fairness in the context of aviation noise research. Furthermore, having a
psychometric instrument to measure these fairness facets can serve as a foundation for further targeted
efforts to improve the neighborliness between residents and airports. Moreover, this psychometricquestionnaire can also be used to accompany interventions and serve as an evalua tion tool.
The aim of this study is therefore to investigate the theoretical dimensionality of fairness in thecontext of aviation noise research and to test the validity of the newly developed Aircraft Noise related
Fairness Inventory (fAIR-In).
As this study is part of a dissertation, the results of the research will be published separately ingreater depth. Therefore, at this point in time, the results can only be presented superficially.

2. Method
The study was conducted in online format with residents around Cologne-Bonn, Düsseldorf andDortmund airports. The study regions were selected in such a way that both areas with a higher
exposure (< 55 dB Lden) affected by aircraft noise and regions that are lower affected (> 55 dB Lden)
are included. The noise pollution was determined from the freely available environmental noise maps
for North Rhine-Westphalia published by the Ministry for the Environment, Nature Protection andTransport of the State of North Rhine-Westphalia (12).
A total of 99,921 flyers were sent out via mailshot between September and October 2021, of which
44,134 in areas with high noise exposure and 55,787 in areas with lower noise exposure. After sortingout all incomplete responses, 1,367 complete datasets remained for the further analysis. The complete
data set was randomly divided into two equal parts for performing the item selection and for theassessment of the model quality.

3. Measures
3.1

Fairness Items

The items used to measure fairness were largely taken from the seminal works in fairness research, whichwere modified to apply to the airport aspect (13-20). Further items were derived out of focus groups,
conducted with airport residents in the vicinity of Cologne-Bonn Airport in 2019 (21). Table 1 shows anexample of a statement for each fairness facet and its subcategories. The subjects could answer on a 5-point
Likert scale, from 1 = do not agree to 5 = strongly agree.
Table 1: Examples of items measuring fairness in the context of aviation noise research
Fairness Facet

Distributive

Subscale

Item example

EquityThe airport brings me more advantages than disadvantages

Equality

Due to the different approach and departure directions of the aircraft, the noise

Fairness

pollution is evenly distributed among the residents.
Need

The approach and departure directions are set in such a way that those in need ofprotection, such as children or sick people, are affected as little as possible byaircraft noise.
Process control

Before decisions are made on aircraft noise, I have the opportunity to make myviews known to those responsible.

Decision control

When decisions are made about aircraft noise, I can influence the outcome of thedecision-making process.

Procedural

Bias suppression

The airport tries to make decisions in an unbiased and neutral manner.

RepresentativenessAll parties who are affected are involved in decisions relevant to aircraft noise.

Consistency

Residents cannot understand why different rules apply at different airports, e.g. on

Fairnessnight rest times or flight bans.
Accuracy

In the decision-making processes, those responsible often make decisions on thebasis of incorrect information.

Informational
Fairness

Correctability

I have the possibility to take action against decisions that I think are wrong.

Truthfulness

The airport is honest about its plans for the future.

Justification

The airport explains and justifies decisions relevant to aircraft noise in detail.

Propriety

The airport strives for an exchange with noise-affected residents that is conducted
on an equal footing.

Interpersonal
Fairness

Respect

3.2

Other measures

The exchange between the airport and local residents is respectful.

To measure the validity of the fAIR-In, additional scales were presented to the respondents foranswering. These were scales measuring interpersonal trust (KUSIV3 (22)), political confidence andinfluence perception (PEKS (23)), sensitivity to injustice (USS-8 (24)), perception of control (IE-4(25)), political cynicism (KPZ (23)) and sensitivity to noise (LEF-K (26)). Furthermore, thewillingness to protest in the context of the aircraft noise issue, ai rcraft noise annoyance (27) and theacceptance of the airport and air traffic were surveyed.

4. Results
4.1

Item selection

A total of 68 items were developed to reflect the various aspects of fairness in the field of aircraft
noise. Care was taken to ensure that the various items also reflect the subcategories in the respective

fairness facets (e.g. bias supression and consistency in procedural fairness).
In a first step, items were sorted out on the basis of poor item characteristics (item difficulty, low
discriminatory power) and poor reliability characteristics within the scales and subscales. In a second
step, an explorative factor analysis was carried out to exclude further items that had very low or
unclear factor loadings on the four postulated fairness facets (distributive, procedural, informational
and interpersonal fairness).
This resulted in a reduction in the number of items from 68 to 32.
4.2

Confirmatory Factor Analysis

There have always been discussions about dimensionality in the history of fairness research (for an
overview see 11). The problem that the different aspects of fairness are highly correlated made itdifficult to distinguish between the different facets. Thus, a four-factor solution was discussed, withspecific effects for distributive, procedural, informational and interpersonal fairness. But a one -, twoor three-factor solution was also frequently debated.
The item structure that had been determined was then tested with a confirmatory factor analysis.
Based on the analyses of Colquitt in 2001 (28), four different models were compared. Model 1was thefour-factor model, which distinguishes between the different facets of distributive, procedural,
informational and interpersonal fairness. Model 2 was a three-factor model that combines the facetsof informational and interpersonal fairness under the aspect of interactional fairness. Model 3 was a
two-factor model that distinguishes between distributive fairness and procedural fairness. Here, theaspects of interactional fairness are assigned to procedural fairness. Finally, model 4 was calculated,which combines all items under the global aspect of fairness and no longer differentiates between thevarious facets.
The calculations showed that Model 1, the 4-factor model, with separate factors for distributive,
procedural, informational and interpersonal fairness, is as expected significantly better than the othermodels.
Further results of this confirmatory factor analysis, with the associated structural equation model,
are published in detail in the external paper, as the results are used for a dissertation.

5. Conclusion and Outlook
This study had two main objectives. The first was to investigate the theoretical dimensionality offairness in the context of aircraft noise research. Secondly, the validity and goodness of the newly
developed FAIR-In questionnaire was to be assessed.
Residents around Cologne-Bonn, Düsseldorf and Dortmund airport were examined in a large-scale
online survey. Data from 1,367 were included in the analysis.
The number of items capturing the various fairness aspects in the context of the aircraft noise
debate was narrowed down. In a confirmatory factor analysis, different models were compared andthe general model fit was ascertained.
Looking towards the future, it is desirable that the results of this study have two major impacts.
On the one hand, it should make it possible for future research on aircraft noise issues to take theaspect of fairness further into account and to include it in future studies on non-acoustic factors.
Secondly, it is desirable that airports recognize the relevance of a fair, neighbourly relationshipbetween their residents and themselves and use the questionnaire to gain an overview of the current
perception of residents. The results can then be used to drive such efforts in a targeted and measurable
way and enable a monitoring as well as an evaluation during and after any intervention.

6. Limitations
Regarding the limitations of the study, it should be noted that the study was conducted during theCorona crisis, when flight traffic was severely curtailed. This was not addressed in the questions andmay have distorted the current perception of the airport and its activities.
Furthermore, for reasons of data protection, no specific addresses of the participants were collected,
but only a distinction was made between high- and low-noise areas. This means that the individual
influence of aircraft noise can only be roughly determined.

ACKNOWLEDGEMENTS
This study was conducted within the EU-project ANIMA (Aviation Noise Impact Management
through Novel Approaches) which has received funding from the European Union’s Horizon 2020
research from 2017 to 2021 and innovation programme under grand agreement No 769627.
Special thanks for the support of Dr. Marie-Therese Schmitz from the Working Group Biostatisticsof the Institute of Medical Biometry, Informatics and Epidemiology at the University of Bonn , who
supported the calculation of the confirmatory factor analysis.

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Guski R, Schreckenberg D, Schuemer R. WHO environmental noise guidelines for the European region: A

systematic review on environmental noise and annoyance. International Journal of Environmental Research andPublic Health. 2017;14(12):1539.
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Babisch W. The noise/stress concept, risk assessment and research needs. Noise & Health. 2002;4(16):1.

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Babisch W, Pershagen G, Selander J, Houthuijs D, Breugelmans O, Cadum E, et al. Noise annoyance—a

modifier of the association between noise level and cardiovascular health? Science of the total environment.
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Guski R. Personal and social variables as co-determinants of noise annoyance. Noise & Health.

1999;1(3):45.
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Guski R, Felscher-Suhr U, Schuemer RJJos, vibration. The concept of noise annoyance: how international

experts see it. 1999;223(4):513-27.
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Hauptvogel D, Bartels S, Schreckenberg D, Rothmund T. Aircraft Noise Distribution as a Fairness

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Colquitt JA, Conlon DE, Wesson MJ, Porter CO, Ng KY. Justice at the millennium: a meta-analytic review

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Bies RJ. Interactional justice: Communication criteria of fairness. Research on negotiation in organizations.

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Bartels S. Aircraft noise-induced annoyance in the vicinity of Cologne/Bonn Airport-The examination ofshort-term and long-term annoyance as well as their major determinants: Technische Universität; 2014.
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PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0784

Designing a composite indicator for sleep with emphasis on children
Dick BOTTELDOOREN , Timothy VAN RENTERGHEM , Luc DEKONINCK , Kerstin PERSSON
1

1

1

WAYE , Michael SMITH , Peter LERCHER , Angel DZHAMBOV , Maarten HORNIKX , Michail
2

2

3

3,7

4

EVANGELOS TERZAKIS , Charlotte CLARK and Maria FORASTER
4

1

2

5

6

Information Technology - WAVES, Ghent University, Belgium

Department of Occupational and Environmental Medicine, School of Public Health and Community
Medicine, , University of Gothenburg, Sweden

3

Institute for Highway Engineering and Transport Planning, Graz University of Technology, Austria
4

Building Physics and Services, Department of the Built Environment, Eindhoven University ofTechnology, Netherlands
5

St George’s, University of London, UK
6

7

ISGlobal, ISGlobal, Spain

Department of Hygiene, Faculty of Public Health, Medical University of Plovdiv, Bulgaria

ABSTRACTThe role of sleep in the development of a child’s brain has been widely acknowledged and its effect on mental
health later in life has been hypothesized. Both quantity and quality of sleep are affected by environmental
factors. For adults, several studies have shown an association between noise exposure indicators and sleepand exposure-effect relationships have been derived for several indicators for sleep such as change in heartrate, motility, biomarkers, sleep EEG, and self-report.
In general, the relationship between components of the exposome (integrating e.g. bedroom orientation, noise
events, background hum, social environment) and sleep has been explored using purely data-driven
techniques. This approach ignores the potential benefit from transfer of knowledge of previous research into
the analysis, which has been shown to be very fruitful in many domains. Hence, we propose to combine priorknowledge in a composite indicator for the component of exposome that affects sleep. Such an indicator
allows to incorporate complex and non-linear interactions and accounts for changing vulnerability with age.
This contribution will present such a composite indicator and the models needed to calculate it. The potential
superior performance of this composite indicator over conventional approaches could be validated on cohort
sleep data..
Keywords: Sleep, Exposome, Indicator

1. INTRODUCTION
The neighbourhood physical environment is an important determinant of children's sleep quality
and sleep duration. Studies on transportation noise have shown well-known direct short-term effects
on sleep, annoyance, and cognition in both, adults and children. The long-term effects on children andadolescents are less investigated, although some evidence points to effects of impaired sleep on
cognitive, mental and physical health outcomes (adiposity). However, these studies have inherent
shortcomings. The hitherto used exposure characterisation is very crude (average sound levels) andhave not considered the role of the soundscape and the wider interaction with the neighbouring built
environment. Moreover, existing studies have methodological shortcomings, as analyses related to thepotential mediating role of sleep impairment on health outcomes were not seriously considered.
Therefore, in the framework of Equal-Life, we initiated an attempt to improve on the prediction of
sleep disturbance and potential long-term health effects in children and adolescents with an improved
exposure characterisation. The development of this sleep disturbance indicator is a first step.

2. A SLEEP DISTURBANCE INDICATOR
2.1 Sound events – probability for sleep disturbance of children
The indicator for sleep disturbance is grounded in laboratory and field results on sleep disturbanceby sound events. Hence, the definition of an event is considered first.
The start of a sound event is detected when the indoor loudness exceeds a given threshold,N1sec,indoor > T NA and at the same time loudness in increasing, dN>0. Masking is implicitly included in
the calculation of loudness and hence an additional criterion based on relative level like in thecalculation of the intermittency ratio [1] is avoided. The event is thought to continue until the N 1sec
drops 5 dB below its maximum level or when the level drops below the 1 5-minute 90 percentile valueof loudness, N 90 .
Once an event is detected, the probability of sleep disturbance PSD is calculated. Sleep disturbancecan be detected via noise induced cardiac response [2], changes in sleep stages [3], etc. For the
purpose of constructing the indicator, all these outcomes are pooled and the main trends with respect
to exposure are extracted: (1) probability of sleep disturbance shows an S-like curve with a thresholdaround 35 dBA (1-second average) and a saturation point between 60 and 75 dBA; (2) the type ofsound plays a role and recognition may play a role, yet the strongest evid ence is found for an increasein probability of sleep disturbance with rise time [4] [5]; (3) spectral content may be important but
as most studies consider A-weighted levels rather than loudness, this evidence isinconclusive [4] [6] [7]. Based on the above 𝑃𝑆𝐷 = 𝑓(𝑁, 𝑑𝑁) is proposed. The pattern of subsequent
noise events may be important for determining sleep disturbance and Markov -style models have been
proposed to account for this [8]. When the indicator is calculated based on measurements or a traffic
microsimulation [9], this approach could allow for observing the effect of platooning or traffic lights,
but as most simulations will treat vehicle passages as a random, Poisson process, preference is given
to treating sleep disturbing events as independent and hence:
𝑇𝑘

𝑖−1

𝑃𝑆𝐷,𝑇𝑘 = ∑ 𝑃𝑆𝐷,𝑖 ∏(1 − 𝑃𝑆𝐷,𝑗 )
𝑖=1

𝑗=1

where |Tk| is the time duration of sleep epochs, which is set to 10 minutes, inspired by [10].
In children, loudness perception itself may be deviant from loudness percep tion in adults, yet there
is little direct evidence on this, neither from EEG based studies nor from behavioral research. Yet it
is known that hearing threshold peaks at 6 to 8kHz in young children rather than at 500 Hz to 4kHz
in adults. Moreover pure-tone hearing threshold is higher in young children [11].
There is some evidence that the effect threshold for sleep disturbance may increase with decreasing
age although there are some differences depending on the outcome where EEG based s leep stages
show a stronger dependence than cardiovascular response [12] [13].
Considering the above and in absence of further evidence, an increase in effect threshold of 10 dBfor the age range [0y,3y] and of 5 dB for the age range [3y,6y] is used as a working hypothesis.
2.2 Sound Insulation – a coping mechanism
Sound measurements are often conducted outside at the one hand because of privacy reasons andpractical considerations, and at the other hand because of the uncertainty in the origin of the soundmeasured indoors. Simulations result in outdoor levels that need correcting to account for soundinsulation of the bedroom and orientation of the bedroom in relation to the source. Hence, somehypotheses have to be made regarding this insulation and orientation.
In [14], it was found that sleep disturbance of children correlates better with the equivalent noiselevel at the least exposed façade. This may reflect the choice of parents to orient the children ’s
sleeping room towards the quiet side as a form of coping. In [15] it was found that sound insulationof the façade with windows closed depends on the level outside. This is partly due to a coping
mechanism but in Switzerland (and other countries) the insulation of new buildings is related to the
outdoor level.
Taking this evidence together, several forms of coping, all depending on average outdoor noiselevels (closing windows, insulating, reorienting bedrooms), are summarized in an assumed frequency dependent sound insulation index D(f) loosely fitted on data from [15], is given by:
𝐷(𝑓) = min(58; 31 ∗ 𝑙𝑜𝑔10(150 + 𝑓) − 30) + 0.55 ∗ 10 ∗ 𝑙𝑜𝑔10 (10𝐿𝑛𝑖𝑔ℎ𝑡35
10 + 1010 ) − 60

2.3 Diurnal patterns
There exists a complex interplay between the diurnal pattern of exposure and the sensitivity tosleep disturbance. To account for the changing sensitivity over the night, a weighting w k is introducedin the calculation of the nightly accumulated sleep disturbance index (SDI) where k refers to the sleep
time interval. This weight is linearly increasing from 5:00 until 7:00 from 1 to 2 to account for thelack of bed time that will allow to catch up on deep sleep after being woken by an early morning noise
event. The instantaneous probability of noise related sleep disturbance is expected to be relevant if it
exceeds a natural sleep disruption probability, Pnat,Tk , which is kept independent on the time of thenight Tk and fixed at 0.1 in the current implementation of the model.
1
𝑆𝐷𝐼 =∑ 𝑅𝑒𝐿𝑢(𝑃𝑆𝐷,𝑇𝑘 − 𝑃𝑛𝑎𝑡,𝑇𝑘 )𝑤𝑘|𝑇𝑠𝑙𝑒𝑒𝑝 |𝑘𝜖𝑇𝑠𝑙𝑒𝑒𝑝Children sleep longer and usually during earlier hours of the night when traffic noise is still high.
In the age dependent definition of the SDI, this is accounted for by adjusting the duration of the night
to 14 hours for age range [0y,3y], 12 hours for [3y,6y], 10.5 hours for [6y,12y], 9 hours for [12y,18y].
Sleep patterns may change with age making children of different ages more vulnerable to soundevents that occur at different times during the night.

3. EXAMPLE RESULTS
3.1 SDI versus measured Lnight
For strategic noise mapping, the European Commission proposed the equivalent noise level during
night hours at the facade further averaged over a whole year, L night . To explore the relationship between
the proposed indicator, SDI, and this European common indicator, two measurement sets are analyzed:
a set of measurements in the city of Barcelona, mainly focused on problem areas and spanning several
weeks; a set of population exposure oriented measurements in the city of Gothenburg containing 3 to
4 week night measurements (Tuesday night through Friday night) at every location. Figure 1 showsthat although a general trend between both indicators is visible, SDI varies strongly between locations
where the same Lnight is measured and moreover can vary significantly by nights. SDI also increases
with age, but not to the same extend in all locations, showing that in general the difference insensitivity is more important than the effect of longer sleeping time.
3.2 SDI using indoor versus outdoor measurements
For the measurement campaign in Gothenburg, the SDI calculated on the basis of indoor
measurements and compared to SDI calculated based on outdoor measurements and assumed soundinsulation taking into account coping (Figure 2). SDI based on indoor measurements is higher than
SDI based on outdoor measurements. This could be expected as indoor sounds are not only caused byoutdoor sources. One could however also suspect an overestimate of the sound insulation while
calculating SDI based on outdoor measurements.
3.3 SDI based on traffic noise modelling versus measurements
To model the sleep disturbance index, instantaneous (per second) spectral (per 1/3 octave band)sound levels are needed. For road traffic noise, a model was constructed that simulates such
instantaneous levels based on traffic estimates on all roads near the observation point. Contributions
from road traffic at larger distance is added as a time-invariant level. This model and its limitations
will be discussed elsewhere.
In addition, detailed traffic data on all roads in a region is hard to obtain. Hence, a proxy for traffic
intensity based on Open Street Map road categorization and calculated edge betweenness wasintroduced. This model will be discussed elsewhere.
Figure 3 shows a comparison between SDI, either based on simulated or measured environmental
noise levels at 15 locations in Barcelona. For all age categories a linear trend can be fitted on therelationship between measurement-based and simulation-based SDI. For older age categories,
although the r 2 indicating the linearity of the trend is similar, yet for older age groups SDI seems
under-estimated. This can probably be explained by their sleep pattern that concentrates more on thequietest hours of the night where traffic noise models less accurately predict individual noise events.
Moreover, in a city like Barcelona, at inner city locations recreat ional noise – including additional
local traffic – might play a role in the measurements resulting in a higher SDI.

The reader is reminded that the traffic estimation underlying modelled traffic noise is solely based
on topography of the city and model constants that are location independent and does not include any
local traffic measurements.

Figure 1 – Calculated SDI versus Lnight averaged over the whole measurement period, for longmeasurement campaigns in Barcelona (upper) and 3 to 4 day measurements in Gothenburg (lower)

Figure 2 –SDI for 12 to 18 year old children calculated based on outdoor measurements versus SDI basedon indoor measurements; each color corresponds to a location

Figure 3 –SDI for four age categories calculated based on outdoor measurements versus SDI based on atraffic noise model.

4. DISCUSSION AND CONCLUSIONS
The indicator for sleep disturbance by sound that is proposed in this paper, relies as much as
possible on the available knowledge from both field studies and lab research, while differentiating as
much as possible between age groups of children. As literature does not always report consistentfindings and as calculation feasibility has to be considered, some choices needed to be made. The
calculations presented in the previous section reflect the consequences of these decisions.
For young children, based on literature, we assumed an increase in effect threshold of 10 dB for
the age range [0y,3y] and 5 dB for the age range [3y,5y]. At the same time, due to their typical sleep ing
hours, these young children may be exposed to higher environmental noise levels whil e sleeping. For
the locations investigated, in Barcelona and Gothenburg, the diurnal exposure pattern seems to be lessimportant than the assumed threshold shift and hence SDI is lower for the youngest age group. For
the [3y,5y], in some cases SDI might be higher than for their older peers. The reader should be awarethat SDI assesses sleep disturbance by noise and not the impact this might have on development at
different ages.
Although the SDI increases with L night as expected, it does not correlate strongly with this common
indicator. For age categories [12y,18y] and [6y,12y] the r 2 of a linear regression between the calculated
SDI and Lnight on 370 nights of measurements in Gothenburg, is 0.13. For age category [3y,6y] it isslightly higher, 0.15 but for age category [0y,3y] it remains as low as 0.08. The latter is easily
explained by the sleeping time extending beyond the 8 hours standard night. Although this analysis
shows the complementarity of the SDI to L night , it does not prove that this additional information isuseful for predicting the impact of environmental sound on sleep quality. Ongoing research will
investigate this more thoroughly.
A good indicator should be easy to obtain not only from measurements but also from simulations.
For a complex indicator such as the proposed SDI, this might be a challenge. Hence a traffic noisemapping model capable of simulating instantaneous spectra, has been used to predict SDI. When
compared to measurements, the model predicts somewhat lower values for SDI, wh ich is to be
expected as only road traffic sound sources are modelled.
In this paper we assumed a sound insulation of buildings and an orientation of the bedroom that
could reflect coping. This assumption could not be fully validated through measurements.
Nevertheless, SDI calculated on the basis of synchronous measurements outdoors and indoors at 8
locations were shown. SDI based on indoor measurements generally exceeds the one based on outdoor
measurements combined with the assumed insulation. There are two clear reasons for this: additional
indoor sounds may be present including the sound of the child itself, specifically during the first hoursof the assumed sleeping time; and the overestimation of sound insulation. At this point of the analysis,no distinction could be made between both. Nevertheless, for locations where both estimates do notline up during different measurement nights, the former is a more likely explanation.
In summary, the proposed sleep disturbance indicator shows some basic logics, and the developed
framework is sufficiently flexible to use actually measured sound, either outdoors or indoors, but also
calculated noise exposure maps. Although the developed SDI combines advanced knowledge on sleep
disturbance in response to environmental noise exposure, its improved prediction capabilities shouldstill be proven by sleep cohort studies. In addition, there might be a need to further tune the model
parameters.

ACKNOWLEDGEMENTS
We acknowledge the funding from the European Union’s Horizon 2020 research and innovation
programme for the project “Equal life”, as part of the European Human Exposome Network, under
grant agreement No 87474.

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Systematic Review on Environmental Noise and Effects on Sleep, International Journal ofEnvironmental Research and Public Health.

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M. G. Smith, M. Younes, D. Aeschbach, E. M. Elmenhorst, U. Müller, and M. Basner, Traffic NoiseInduced Changes in Wake-Propensity Measured with the Odds-Ratio Product (ORP), Science of the
Total Environment 805, (2022).

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Spectrum of Road Traffic Noise on Sleep: A Polysomnographic Study, J Acoust Soc Am 147, (2020).

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M. G. Smith, M. Ögren, J. Ageborg Morsing, and K. Persson Waye, Effects of Ground-Borne Noisefrom Railway Tunnels on Sleep: A Polysomnographic Study, Building and Environment 149, (2019).

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Medical Decision Making 30, (2010).

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B. de Coensel, T. de Muer, I. Yperman, and D. Botteldooren, The Influence of Traffic Flow Dynamics

on Urban Soundscapes, Applied Acoustics 66, (2005).
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Differ between Children with and without Obstructive Sleep Apnea, Sleep 32, (2009).

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PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Full paper
ABS-0095

Response to noise and vibration from roads and trams in Norway
Tore F. KILLENGREEN1; Sigmund OLAFSEN1
1

Brekke & Strand akustikk, Norway

ABSTRACTThe human response to physical noise and vibrations is subjective. The noise indicators and limit values are
not a one size fits all framework to predict and mitigate negative human response to noise and vibrations.
Our work and research have gradually evolved starting from noise measurements of increasing complexity
followed by investigations into annoyance. In our experience investigations of response benefit from athorough investigation and detailed description of the physical noise and vibration compiled into a database.
From the database we can investigate the perceived vibroacoustical climate both for individuals and by thecommunity.
The paper will describe the investigations performed for three types of transport noise sources:
1.
The trams of Oslo2.
A highway through a suburb of Oslo
3.
Countryside Norwegian motorways.
All three types of transport noise have been investigated with a combination of field measurements of noisein 1/-3-octave bands and qualitative research interviews.
Keywords: Noise, response, subjective, objective

1. INTRODUCTION
Response is the first word of the title of this paper. Our knowledge of the response come from themaster’s thesis of 4 public health students at NMBU, Ås, Norway:- Vanessa Vea, 2022 (tram) (1)- Karoline Kjær Bakke (2), 2021 and Lykke Michaela Myhrvold, 2022 (3) (highway
through suburb)- Eli Mari Øverdahl, 2021, (motorway) (4)All of these master’s theses are based on semi-structured qualitative research interviews. Thisapproach has been selected because it is a method that gives details about how the neighbors respondto the noise. This includes their description of the noise, the way they perceive annoyance by noiseand how they act to reduce the perceived annoyance due to the noise.
The physical noise has been measured as a representative sample of equivalent level in 1/3 -octave
bands for the outdoor noise. Other information about the physical noise is described when available.
This includes standard calculations of noise or measurements of other noise parameters. The data is
described when available.
Section 2 will present the results for trams while section 3 presents the results for a highway
through a suburb and section 4 will present the results for motorway. A discussion of the particularproperties of each type of noise source will be given in the relevant section. General discussion of theshared challenges for all types of noise sources follows in section 5.
The present paper is a status report and an outline. Further research will follow, possibly along thelines that have been pursued for the metro trains (5). Our aim is a continuous improvement of thedescription of the correlation between the response and the physics of transport noise and vibrations.
1

tkn@brekkestrand.no2. RESULTS FOR TRAMS
The tram in Oslo runs in the city streets close to a large number of dwellings and therefore affecta large number of people. The results for trams will be presented as follows:- Response to tram noise- Response to tram vibrations- Description of tram noise and vibrations
It should be mentioned that comprehensive measurements of outdoor, and indoor, noise andvibrations has been performed in all dwellings.
2.1 Response to tram noise
The subjective response to tram noise has been investigated by Vea (1). 6 persons in 5 h ouseholds
have been interviewed about how they perceive the noise, how it affects their health and what they
can do to reduce the perceived noise. One respondent reported an increase in perceived noise fromtrams. This has been confirmed by the environmental monitoring program (6, the slowly increasing
trend in noise from each tram since 2015 has continued, but this has not yet been published). Those
who have access to an outdoor living area report that they use the area much less than they would liketo due to tram noise.
Several respondents report problems with sleep disturbance due to tram noise. This is not so strange
as the trams of Oslo run for about 21 hours per day with only a 3-hour break at night. One solution is
to close the windows in bedrooms facing the tram line, but this often leads to reduced air quality anda bad indoor climate. There is also a problem with unpredictability of the trams leading to stress. It
also takes a determined effort to concentrate on work for those affected.
Some respondents report that they find the tram noise embarrassing when they entertain guests intheir home.
2.2 Response to tram vibrations
Vibrations from trams do not seem to be a major problem. The results in figure 1come from acontinuation of a simple quantitative survey of perceived vibration originally designed to investigate
vibrations from the metro line of Oslo (7). The results are presented for all the cases where residents
gave their impression about tram vibrations. There are several cases where more than one respondent
has given the same answer for the same vibration level, 10 responses are included in the figure. The
unit v w,95 is a single number rating used in Norwegian assessment of vibrations (8). The limit value of0,3 mm/s have been considered acceptable for new construction, and 0,6 mm/s for existing situations(8).

Perceived vibration level
4

4 : Highly annoyed 3
3 : Annoyed
2 : Somewhat
2annoyed
1 : Not annoyed
0 : No vibrations
1
perceived

Tram

0
0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

vw 95, mm/s

Figure 1, perceived versus measured vibration levels

2.3

Physical tram noise and vibrations

Our general knowledge of tram noise and vibrations comes from a yearly measurement series ofoutdoor tram noise started in 2007 (6). The technology for measuring outdoor and indoor noise andvibrations was originally developed for the metro lines of Oslo. Comprehensive measurements ofoutdoor and indoor vibrations were made for all the houses included in Vea’s study (1).

Figure 2 Tram going through the street Storgata in Oslo

For each house measurements are performed for at least 20 passing trams. Noise is usually
measured in 4 points on the property and tri-axial vibrations is measured in 5 points. In some cases,
noise is also logged every second for a week or so. If applicable, the effect of balconies is alsomeasured (9,10).
All the cases included in the qualitative study (1) comply with the Norwegian regulations forexisting situations (8,11). Two of the cases even comply with guidelines for new construction (12,13),
the other ones are in what is called a “yellow zone” (Lden > 58 dB) where new construction or a new
tramline could be permitted if sufficiently documented noise abatement is included.
2.4 Possible improvement of the situation
The main hope for a better noise situation from trams in Oslo is connected to the new trams. Oslohas ordered 87 new trams to replace the 71 old ones that have been running for decades. The old trams
are supposed to be replaced by 2024 (13).
Some respondents have expressed the wish that the tram reduce the speed past their dwelling. Thiscan hardly be expected, as it would increase travel time and reduce transport capacity.
One respondent complained about a type of noise described as “squeal”. This type of noise was
not observed during the visit to measure noise and vibration. However, a noise log running for 9 days
after the main measurements showed that there were 83 events that could be described as “squeal”
that came from a tram. These events were of two different types. Fortunately, most of them occurredin the afternoon, none at night, but still the noise is so loud as to be expected to be very disturbing,and much louder than was observed during manned measurements.
Some respondents wanted noise barriers. This may be a possible solution in some cases.
Finally, one respondent proposed replacing the trams with buses. This will definitely not happen
as diesel og biogas fueled buses emit more noise and have less transport capacity.
The new trams seem to have the best potential for an improved noise situation. Continued detailed
measurements and careful attention to the wording of complaints combined with continuous
maintenance of the trams and the tracks would seem to be the way forward.

3. RESULTS FOR A SUBURBAN HIGHWAY
Highway 4 through the residential areas Sletteløkka, Veitvet and Linderud is probably the worst
stretch of highway in Norway with regards to noise exposure. Already in 1999 an estimated 367
residences would have equivalent outdoor noise level of Lp,Aeq,
24h ≥ 70 dBA (15). There is a continuous stretch of 4-7 story apartment buildings close to a dual
carriageway. Both the road and the residential buildings are more than 50 years old. The results for asuburban highway will be presented as follows:- Response to road noise- Description of road noise
3.1 Response to road noise – suburban highway
The response to noise was first investigated by Bakke (2). The research was continued by Myhrvold(3). The comments on perceived noise will be sorted according to the work of Myhrvold with some
additional remarks from Bakke. The responses are grouped into 5 parts:
1. This is where we live
2. How do we cope with the noise?
3. Socioeconomic position
4. Changes coming
5. This is how it influences our lives
This is where we live
The respondents describe their situation at home with a focus on noise, vibrations and air pollution
from the road traffic. Many keep the windows closed at all times or have a TV or radio on to maskout the traffic noise. Air pollution is also a severe problem, easily visible on the windows which arestained again soon after cleaning. This is to be expected. However, the respondents also report positive
factors, like the view from Sletteløkka (“Best in town”), good service by public transport, the lawns
and the balconies that could be quality outdoor living areas if the noise is reduced. Repondents alsomention the proximity to “marka”, the large forests surrounding Oslo, and the price of the apartments.
But the total impression is negative for many respondents as the noise weighs more heavily than othe rfactors.
This is how we cope
Masking the traffic noise with radio or TV has been mentioned. One respondent had purchased an
air cleaner, since closing the window did not work sufficiently well to give a good indoor climate.
Some have built an enclosure for the balcony and some use headphones when working from home.
Others cope with complaints to relevant authorities in the hope of noise measures. People haveparticipated in public meetings, they have complained to the national road authorities, some have
involved the politicians of the city of Oslo and the local politicians.
Socioeconomic position
The residents that have been interviewed are better off than many of their neighbors in the area. It
would seem that they have a higher level of education and a higher income. Many other residents havebeen in Norway for a short time. It will be difficult to investigate the opinions of those new to thearea as it takes time for the effects of the noise impact to manifest itself . It should also be noted thatsome of those who live at Sletteløkka have no other choice. Prices of residences in Oslo are too highfor many, and they have to make do with whatever shelter they can afford.
Changes coming
The status quo has been unchanged for decades. The residents simply want a reduction of the noise
and air pollution they are exposed to, and the local authorities aknowledge the situation. However, theNational government is currently not inclined to reduce the capacity or speed limit ofTrondheimsveien since reduced travel time outweigh the inconvenience of noise and air pollution forthe residents. Trondheimsveien was once a road of national importance, and measures to reduce thedemand for transport via this road are not given priority even in long term plans.
This is how it influences our lives
Obviously, noise, air pollution and heavy traffic close to home influences the health and safety of
the inhabitants directly and indirectly. People have reported asthma, increased blood pressure, lack ofsleep are examples of problems that have been reported.
3.2 Description of road noise – suburban highway
The chosen example is the national highway 4 heading north from Oslo. It used to be the mostimportant northbound road, and it still carries the street name “Trondheimsveien”. In the studied area
it is a grade separated dual carriageway with a speed limit of 60 km/h in winter and 70 km/h in summer.
The studied part of the road was built in the 1960s and early 1970s. At that time traffic noise was n ot
a major consideration, and there are apartment blocks as close as 10 meters from the road. The study
started with a consultancy assignment of calculating the noise using standard prediction models (16).

Figure 3, predicted noise map at Sletteløkka, mainly due to highway 4, Trondheimsveien
Measurements of the existing noise in one point were also performed. A representative example isshown in figure 4 showing hourly equivalent levels. Maximal levels are not so interesting in this casesince there are always vehicles passing through the area. This is an illustrative example only.

80

2500

70

2000

60

1500

50

1000

40

500

30

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time of day
Laeq per hourLight vehicles

Number of vehicles per ohur

A-weigthed Leq (dB)

Highway 4 Sletteløkka, 24-hour example, traffic density
vs. noise

Heavy vehicles

Figure 4, noise and traffic at Sletteløkka
3.3 Possible improvement of the situation
The noise calculations show that the residential blocks at Sletteløkka north of the highway have
Lden ≥ 70 dB on the façade towards the highway. Noise barriers aren’t effective for noise reduction
due to the ground rising from the road towards the residences. Better sound insulating windows will
reduce indoor noise levels but is of no avail to reduce outdoor levels. It seems the only effective way
to reduce the noise is to reduce the number of vehicles and the speed limit. This will help, not only
for the residences close to the road but also in the surrounding fores t behind the apartment blocks.
Another possibility is to divert the traffic. Back in 1999 (15) a 2-kilometer-long tunnel for highway 4
past the worst area was suggested. There have also been plans for a diagonal tunnel diverting long
distance traffic to another highway. None of these plans have been realized, and currently it does notseem likely that either plan will be realized.
Probably the only hope for an improvement is a political shift towards reduced road traffic.

4. RESULTS FOR MOTORWAY
The E18 motorway runs along the east coast of southern Norway. The motorway runs through rural
areas, but the noise impact is significant from the 4-lane motorway with a speed limit of 110 km/t.
The results for motorway will be presented as follows:

4.1-

Response to road traffic noise – motorway-

Description of road traffic noise – motorway
Response to road traffic noise – motorway«Verden har ikke våknet, men E18 er våken». The subtitle of Øverdahl’s study of perceived noise from theE18 could be roughly translated as follows:“The world is asleep, but not the E18.”
The title sums up the problem since there’s always traffic on the motorway. The residents in rural
locations expect to live in a quiet area, and they don’t expect noise at night. The affected neighbors
have a consistent feeling that they have somehow been cheated. Some of the respondents have lived
in the area their whole life. Some owners of property along the E18 organized a group that raised thequestion of whether the noise calculations and design of noise abatement measures were correct. The
E18 as a grade separated single carriageway came gradually during the 1980s, but was not perceived
as a severe noise problem at the time. After a new carriageway was added and the speed limit increasedto 110 km/h the 2014 situation was perceived to be substantially changed even though noise barriers
were added as a noise measure. This section is about the perception. Section 4.2 will give possible
physical explanations of why the noise might actually be louder now, even though the predicted values
have not changed.
The respondents say that they spend less time in their outdoor living areas, they do not open their
bedrooms windows at night and they are continuously bothered by the noise when outside. The indoor
noise with windows closed is not considered a problem, although the noise might be audible. The
constructed noise barriers are perceived to be insufficient and unable to deal with the noise impact.
The residents believe that the noise levels are considered acceptable by Norwegian standard
regulations (12), and there is no judicial cause to complain to the relevant authorities. A feeling ofhopelessness has set in for some residents.

Description of road traffic noise – motorway
A graph with hourly equivalent levels for a typical summer day is shown in figure 5 together withthe traffic density on the E18. The picture in figure 6 shows the microphone position at home of one
neighbor of the E18. The microphone is in a free field position 130 meters away from the centerlineof the road. The picture shows a high noise barrier hiding the E18.
4.2

Leq per hour, dBA

60

3000
2500

55

2000

50

1500
1000

45

500

40

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time of day
Leq

Light vehicles

Heavy vehicles

Figure 5 – noise and traffic from rural motorway E18

Figure 6 – microphone measuring noise from the E18

Number of vehicles/hour

E18 Tønsberg - Sandefjord, 24-hour typical sample

The noise barrier is probably designed to compensate for the increase in noise due to higher traffic
speeds and heavier traffic, and it is likely that is has been correctly designed according to regulations
and the standard prediction method (17). This prediction method gives levels that correspond well tomeasurements in almost-worst-case scenarios. However, the prediction method may seriously
overestimate the actual noise that can be measured in many cases (1 8,19). It is quite possible that thelevels measured after the motorway was opened correspond well to the standard predictions. This may
be due to effects of weather. A smaller road without barriers may have a different noise spectrum, and
it is quite possible that the noise level from the E18 before 2014 was below the predictions most of
the time. After 2014 the actual noise level may be higher than before 2014 most of the time even if
the predicted noise level has not changed.
4.3 Possible improvement of the situation
The motorway was opened in 2014 with all the investigations and documentation required by
Norwegian regulations evaluated and approved (11,12). It is unlikely that further noise reduction
measures by the roadside will be carried out. The residents can to some extent improve the situation
with local measures.

5. DISCUSSION
The respondents are not a representative sample of the part of the population bothered by noise.
The general impression is that the respondents have a higher socioeconomic status than the average
population, with a university education being the norm. It has taken some work to become arespondent. On the other hand, this means that the answers will give better guidance. Some of the
response presented gives clues as to how to reduce the impact of transport noise in a realistic way.
The researchers interviewing residents (1,2,3,4) have described how they have carefully done
everything they can to take an objective stance to the interview, realizing that in qualitative research
the researcher is a part of the instrument used.
The results presented are only given as a short summary and a status report. The results are not
likely to be reproduced in another country. Norway is a sparsely populated and rich country, andNorwegians are accustomed to a quiet environment, and many residents are able to pay for noise
measures. But the method with qualitative studies can be transferred to other countries, other noise
sources and other cultures.
The choice of semi-structured qualitative interviews is dictated by matching the detailed
measurements of physical noise. In this paper we have only presented single number ratings of themeasured noise. More detail is available on request. It is our sincere hope to be able to find theobjective parameters that best match the perceived noise, in the same way that we have started tocompare physical noise and vibration with annoyance for Oslo’s metro lines (5). We hope to be able
to do laboratory tests of recorded noise for this purpose. Obviously, the perceived noise annoyance issubjective and will vary between sources and from person to person.
Protection of privacy of the respondents is a challenge. We would like to have direct links between
a detailed description of the noise and the noise as perceived by the individual. Th is would, however,require a much more complicated setup than we have been able to use so far. Our investigations do
not give a simple correlation between noise and annoyance. However, we have been able to pinpoint
the source of noise problems by combining detailed information of the acoustical properties of thenoise with the words people use to describe the noise.

ACKNOWLEDGEMENTS
Our sincere thanks go to Sheri Lee Bastien of NMBU, who has been the university’s supervisor
for the 4 students whose master’s theses in Public Health have been the basis of our knowledge about
the response to noise. Our colleague Teresa Fernandez Espejo has contributed in numerous ways toboth the measurements and analysis throughout a decade.

REFERENCES
1. Vea, V. En kvalitativ studie om opplevelsen av støy fra trikken i Oslo. (A qualitative study about perceived
noise from the tram in Oslo).
Thesis for a master's degree in Public Health at NMBU, Ås, 2022 (in Norwegian)
2. Bakke, K.K. Bråk om støy på Sletteløkka - en kvalitativ studie om veitrafikkstøy i bolig
Thesis for a master's degree in Public Health at NMBU, Ås, 2021 (in Norwegian)
3. Myhrvold, L.M. Opplevelser av veitrafikkstøy fra riksvei 4 Trondheimsveien - en kvalitativ studie.
Thesis for a master's degree in Public Health at NMBU, Ås, 2022 (in Norwegian)
4. Øverdahl, E.M «Verden har ikke våknet, men E18 er våken»En kvalitativ studie om subjektiv opplevelse av veitrafikkstøy fra E18 Tønsberg - Sandefjord.
Thesis for a master's degree in Public Health at NMBU, Ås, 2021 (in Norwegian)
5. Olafsen, S. Metronova – research on physical and perceived noise and vibrations from Oslo’s metro trains,
ICBEN 2021, Stockholm.
6. Olafsen,S; Stensland,A. Tram noise monitoring. Baltic-Nordic Acoustic Meeting, Reykjavik, 2018
7. Olafsen, S. Indoor noise from urban railbound transport. Ph. D. dissertation, Lund, 2016
8. Standard Norge “NS 8176:2017 Vibration and shock – measurements of vibration in buildings from landbased transport, vibration classification and guidance to evaluation of effects on human beings”
9. Rasmussen, K: Sound Field on Balconies, M.Sc. thesis in acoustics, NTNU, 2017
10. Fjeld, Ø: The use of balconies for facade noise attenuation, M.Sc. thesis in acoustics, NTNU, 2020
11. https://lovdata.no/dokument/SF/forskrift/2004-06-01-931,
The Norwegian regulations about pollution, chapter 5 is about noise
12.https://www.regjeringen.no/no/dokumenter/retningslinje-for-behandling-av-stoy-iarealplanlegging/id2857574/
T-1442, The Norwegian guidelines for handling environmental noise in planning
13. Burden of disease from environmental noise: Quantification of healthy life years lost in Europe. World
Health Organization, 201114.https://www.fremtidensbyreise.no/trikkeprogrammet/prosjekter/prosjekt/3087561/anskaffelse-nyetrikker
A description of the new trams in Oslo
15. Olafsen, S. Nordøstkorridoren konsekvensutredning, temarapport støy (estimated noise consequences ofdifferent road system alternatives in northeastern Oslo, in Norwegian), 1999
16. Espejo, Teresa F Rv.4 Sletteløkka, Utendørs støyutredning. Report from assessment of the noise situation
at Sletteløkka and analysis of possible abatement measures (in Norwegian), 2020
17 Road Traffic Noise: Nordic Prediction Method (TemaNord: 1996:525)
18.Olafsen,S Practical tests of the Nordic prediction method for road traffic noise – predicted values
compared to measured values, proceedings Internoise 1993, Leuven
19. Olafsen,S Road and rail traffic noise – the effect of air absorption, proceedings Baltic-Nordic Acoustic
Meeting, Aarhus, 1994

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0391

Combined effects of railway noise and vibrations on annoyances
in detached houses
Shigenori YOKOSHIMA1; Takashi MORIHARA2; Yasunao MATSUMOTO3
1
2

Kanagawa Environmental Research Center, Japan

National Institute of Technology, Ishikawa College, Japan
3

Saitama University, Japan

ABSTRACT
Railway-induced noise and ground-borne vibrations cause large adverse effects on people living in detachedhouses, particularly in Japanese urban districts. Our previous studies revealed that annoyance due to noiseand vibrations differed according to the Shinkansen railway and conventional railway. In addition, we
discussed combined effects of noise and vibrations on annoyance for each railway category. Following thesestudies, we tried to quantify the respective impact of noise and vibration exposures on each annoyance by
the above railway category anew. Datasets used for analysis was compiled through 12 socio-acoustic surveys,
which were conducted over the 30 years in Japan. Prior to the analysis, we developed a causal annoyancemodel. Applying the multiple-group analysis for structural equation modeling to the model, we estimated theweights of noise and vibration exposures on each annoyance. In common with both railway categories, theweight of noise exposure on annoyance due to noise was larger than that of vibration exposure. Regarding
the Shinkansen railway, annoyance due to vibrations was more affected by vibration exposure than by noise
exposure. On the other hand, regarding conventional railway, annoyance due to vibrations was less affectedby vibration exposure than by noise exposure.
Keywords: Railway noise, Railway vibration, Annoyance, structural equation modeling

1. INTRODUCTION
In densely populated areas, problems due to railway-induced noise and ground-borne vibrations
are likely to occur because of close proximity of residences. In particular, the problems are more
pronounced in detached houses than in apartment buildings. In Japan, to preserve living environments
and protect people's health, “Environment Quality Standards for Shinkansen super-express railway
noise” and “Measures for vibration caused by Shinkansen trains urgently required to preserve theEnvironment” are legislated. On the other hand, standards or regulations on existing conventional
railway is not yet legislated.
In 2018 "ENVIRONMENTAL NOISE GUIDELINES for the European Region" (1), recommended
exposure levels for environmental noises to protect population health. For average noise exposure,the Guidelines recommend reducing noise levels produced by railway below 54 dB Lden . However, theGuidelines excluded the Shinkansen railway noise. Using the Socio-acoustic Survey Data Archive(SASDA) (2) and other datasets derived from recent studies in Japan, Yokoshima et al. (3) revealed
higher annoyance due to noise from the Shinkansen railway than that from conventional railway.
Several studies discussed the factors of difference in community response to noise between railway
categories (conventional and the Shinkansen railways). Tamura (4) pointed out that residents had morenegative attitudes to the Shinkansen railway noise than conventional railway noise. Yokoshima et alfocused on the Shinkansen railway and revealed the combined effects of noise and vibrations on
annoyance (5–7). Based on the above findings, to accurately assess each annoyance due to railway
noise and vibrations, it is necessary to consider the railway categories and combined effect.
yokoshima.7c7q@pref.kanagawa.lg.jp
morihara@ishikawa-nct.ac.jp
3ymatsu@mail.saitama-u.ac.jp
12

This study aims at clarifying the difference in the combined effects of noise and vibrations on eachannoyance between the railway categories. In our previous paper, we confirmed annoyance due torailway noise is affected not only by noise exposure but also by vibration exposure regardless of therailway category. Comparing the effects of noise and vibration exposures on noise annoyance, noise
exposure had much larger effect than vibration exposure (8). Subsequently, it was found thatannoyance due to vibrations was also significantly affected by noise and vibration exposures. The
effect of vibration exposure on the annoyance was slightly higher than that of noise exposure (9). In
addition, both studies showed that more residents along the Shinkansen railway are highly annoyed
than those along conventional railway. In the above studies, we focused on the effects of exposureson each annoyance; therefore, no analysis was conducted by incorporating annoyance due to noiseand vibrations into a simultaneous model. In this paper, applying the multiple-group analysis forstructural equation modeling to a causal annoyance model we developed, we estimated the effects ofnoise and vibration exposures on each of individual annoyances.

2. Datasets
2.1 Outline
Tables 1 and 2 show respective datasets for conventional railway (CR) and foe the Shinkansen
railway (SR), which were analyzed in this paper. The datasets were derived from separate surveys,
except for CR05 and SR05 surveys conducted along the Kyushu Shinkansen line. All the datasets were
equipped with noise exposure, ground-borne vibration exposure (only in the vertical direction) andeach annoyance due to railway noise and vibrations (5-point verbal scale).
Several datasets include data on detached house (DH) and apartment building (AB). Most of
detached house and apartment building are made of wood and reinforced concrete, respectively.
Therefore, the level of railway-induced noise in apartment building is lower than that in detachedhouse. Similarly, because of different foundation structure, resident in apartment building is exposed
to lower level of railway-induced vibration than that in detached house. Taking the differences into
consideration, this paper analyzed data on only detached house.
Table 1 – Outline of conventional railway datasets
ID

FY

Location

MethodaAnnoyance

Sample size

CR01

‘97

Kanagawa

Visit-Mail

unbearable

310

CR02

‘02

Kanagawa

Visit-Mail

ICBEN d422

CR03

‘04–‘06

Fukuoka

Visit-Mail

ICBEN

d

653

CR04

‘11

SaitamaVisit-Mail

ICBEN d171

Visit-Visitd559

Kumamoto
a: Distribution-collection method.

CR05‘11 – ‘12

ICBEN

Table 2 – Outline of the Shinkansen railway datasets
ID

FY

Location

Linea

Methodb

Annoyance

Sample size

SR01

’95 – ‘96

Kanagawa

TSL

Visit-Mail

unbearable

709

SR02

’01 – ‘03

Kanagawa

TSL

Visit-Mail

ICBEN

872

SR03

‘03

Fukuoka

SSL

Visit-Mail

ICBEN

358

SR04

‘04

Aichi

TSL

Interview

ICBEN

175

SR05

‘11 – ‘12

Kumamoto

KSL

Visit-Visit

ICBEN

559

SR06

‘13

Kumamoto

HSL

Mail-Mail

ICBEN

294

SR07

‘16
Ishikawa & Toyama
HSL
Mail-Mail
ICBEN
927
a: Tokaido Shinkansen line (TSL), Sanyo Shinkansen line (SSL), Hokuriku Shinkansen line
(HSL), and Kyushu Shinkansen line (KSL).
b: Distribution-collection method.

2.2 Noise and vibration exposures
Noise and vibration exposures were estimated based through on-site measurements in areas along
the railway. A-weighted sound exposure level (LAE) of the pass-by noise was measured or estimatedat several points with different distances from the rail way. Day-evening-night sound pressure level(Lden) was calculated based on the following time category: daytime, 7:00–19:00; evening, 19:00–
22:00; and nighttime, 22:00–7:00. For the datasets where LAeq,24h or day-night sound pressure level(Ldn) was available as noise exposure, we transformed LAeq,24h /Ldn value into Lden value based on theratio of the frequency of service in each time category.
Similarly, the maximum value of “Vibration Level” (JIS C 1510) (10) in the vertical direction(LVzmax ) of the pass-by ground-borne vibration was measured at almost the same points as the noise
measurement. The reference acceleration of the “Vibration Level” is 10 −5 m/s2 . As vibration exposure,maximum-based index (LVmax ) was calculated from the mean value of the top 50% of the measured
LVzmax values. According to the previous findings (11), the following relationship of LVzmax andmaximum transient vibration value (MTVV: m/s 2) (12) were presented for vertical ground-borne
vibration caused by the Shinkansen railway: 10log 10(MTVV/10 -5 )2 = LVzmax + 1.8.
It should be noted that the exposure values were rounded to one decimal place in this paper.

3. Results
3.1 Demographic factors and housing structure
Table 3 shows the frequency distribution of demographic factors and housing structure by therailway category. The parentheses in the table indicate the relative frequency of each railway category.
Overall, there were more female respondents than male respondents. Respondents aged 50 years orolder accounted for more than 70%. This may have been due to the fact that the analysis was limited
to data on detached houses. Therefore, wooden house probably accounts for about 90%.
Table 3 – Frequency distribution of demographic factors and housing structure
Item
GenderAge

Structure

Category

CR

SR

Male

835 (39%)1670 (43%)

Female1256 (59%)

2151 (55%)

No answer

24 (1%)

73 (2%)

less than 30

72 (3%)

156 (4%)30–39

156 (7%)286 (7%)40–49

303 (14%)631 (16%)50–59

477 (23%)899 (23%)60–69

567 (27%)

1060 (27%)

70 or older

521 (25%)810 (21%)

No answer

19 (1%)

52 (1%)

Wood

1879 (89%)

3395 (87%)

Others220 (10%)461 (12%)

No answer

16 (1%)

38 (1%)

3.2 Relationship between noise and vibration exposures
Subsequently, Lden and LVmax values were compared by the railway category. Table 4 shows themean value of Lden according to LVmax values (5-dB intervals from 33 dB to 77 dB) for each railway
category. There are significant differences between railway categories in the LVmax range of 48–52 dB
or above. In the same level of exposure to vibration, the exposure to conventional rail way noise isgreater than that to the Shinkansen railway noise. In other words, the Shinkansen railway brings about
higher level of vibration than conventional railway.

Table 4 – Comparison of the mean value of noise exposure between railway categories
LVmax (dB)

33–37 38–42 43–47 48–52

53–57 58–62 63–67 68–72 73–77

CR/ Lden (dB)

45.5

46.7

48.3

56.1

61.3

65.2

70.1

73.1

79.4

SR/ Lden (dB)

45.9

46.7

47.2

49.2

49.2

51.1

55.6

59.2

61.1

3.3 Structural equation modeling
Structural Equation Modeling (SEM) is a method of multivariate analysis and of identifying causal
relationships between variables. The parameter (regression weight) indicating the degree of causality
is determined by approximating the covariance calculated from the structural model to the covarianceof the observed data. This method is also used in causal analysis of relationship between noise
exposure and the responses.
Figure 1 shows a causal structure model used for the analysis based on the previous findings. Thismodel was constructed using only observed variables without introducing latent variables. The
following variables were used for analysis: Lden and LVmax (1 dB unit), annoyance due to noise andvibrations (AdueN and AdueV: 1 = lowest level of annoyance to 5 = highest level of annoyance),
gender (GND: 0 = male, 1 = female) and housing structure (STR: 0 = Others, 1= Wood).
Variables e0 to e3, which are enclosed in ellipse, mean error variables affecting exogen ous
variables. A single-headed arrow between two boxes represents a causal relation. A double -headed
arrow between two boxes represents a noncausal (unexplained) association.

Figure – 1 A causal structure model
Before the analysis, considering the estimation accuracy of low-/high-level exposure and thecurrent status of the environments, we used the data within the following noise and vibration
exposures: 35 dB ≤ Lden ≤75 dB and 35 dB ≤ LVmax ≤75 dB for the analysis. In addition, missing values
for each variable were excluded for the analysis. As a result, sample sizes of conventional railway andthe Shinkansen railway amounted to be 1824 and 3621, respectively. Moreover, we imposed nocorrelation between the following endogenous variables: Lden and GND, Lden and STR, LVmax and GND,
LVmax and STR, GND and STR; however, we determined correlation between Lden and LVmax on accountof identical noise and vibration sources.
Applying the multiple-group analysis for structural equation modeling to the model, we estimatedthe weights of noise and vibration exposures on each annoyance by the railway category. Table 5shows the standardized regression weights. AMOS 27 was used for the analysis. As the fit indices forthe analysis. the respective values for Root Mean Square Error of Approximation (RMSEA),
Adjusted Goodness of Fit Index (AIC), and Akaike’s Information Criterion (AIC) obtained were 0.050,
0.963 and 196.826.

Table 5 – Estimates of standardized regression weights (SRW) and probabilities (P)
Path

ID

Lden → AdueN

CR

SR

SRW

P

SRW

P

d15

0.387

0.000

0.253

0.000

LVmax → AdueN

d25

0.183

0.000

0.124

0.000

Lden → AdueV

d16

0.372

0.000

0.189

0.000

LVmax → AdueV

d26

0.213

0.000

0.276

0.000

GND → AdueN

d35-0.0290.153-0.0870.000

GND → AdueV

d36-0.0570.004-0.0620.000

STR → AdueN

d45-0.003

0.338

0.030

0.056

STR → AdueV

d46

0.019

0.894

0.036

0.017

Table 5 shows that the weights of d15 (Lden → AdueN), d25 (L Vmax → AdueN), d16 (Lden → AdueV),and d26 (L Vmax → AdueV) are significant and positive for each railway category. Comparing theweights of d15 (Lden → AdueN) and d25 (LVmax → AdueN), d15 is much larger than d25 for bothrailway categories. On the other hand, d26 (LVmax → AdueV) is larger than d16 (L Vmax → AdueN) for
the Shinkansen railway; however, d26 is smaller than d16 for conventional railway.
d35 (GND → AdueN) for the Shinkansen railway and d36 (GND → AdueV) for both railway
categories show significant and negative weights; however, there is no significant weight for d35(GND → AdueN) for conventional railway. Negative weight means that male is suffered from noise
than female. Regarding the housing structure, the weight of d45 (STR → AdueN) is significant at a
5% level for the Shinkansen railway whereas those of d45 and d46 (STR → AdueV) are not significant
for conventional railway. Significant and positive weight of d46 for the Shinkansen railway suggests
the Shinkansen railway-induced vibrations in residences made of wood are larger than otherresidences.

4. Discussion
Annoyance due to noise was affected not only by noise exposure but also by vibration exposure.
Likewise, annoyance due to vibrations was also affected by noise exposure as well as vibration
exposure. Significant and positive impacts of both exposures on each annoyance reveal combined
effect of noise and vibrations on annoyance regardless of the railway category. These results are
consistent with the findings of social surveys: Peris et al. (13) and Yokoshima et al (7).
The impact of noise exposure on annoyance due to noise was larger than that of vibration exposure ,regardless of the railway category. Whereas the impact of vibration exposure on annoyance due tovibrations from the Shinkansen railway was much larger than noise exposure, the impact of vibration
exposure on annoyance due to vibrations from conventional railway was much smaller than noise
exposure. Thus, annoyances due to noise and vibrations from conventional railway are dominated by
the noise exposure. On the other hand, annoyance due to each of noise and vibrations from the
Shinkansen railway are mainly affected by the respective noise and vibration exposures. The
difference can be attributed to different relationship between noise and vibration exposures dependingon the railway category. Higher level of noise from conventional railway brings about significant
effects on the resident’s daily life compared to the vibrations. Therefore, the noise exposure dominates
annoyance due to even vibrations. Conversely, people along the Shinkansen railway can perceive andevaluate the ground-borne vibrations clearly. Thus, the vibration exposure is the determinant ofannoyance due to vibrations. This confirms the above result that annoyance due to vibrations inwooden house was significant for the Shinkansen railway.

5. Conclusions
To clarify the difference in the combined effects of noise and vibration on each annoyance between
the railway categories, we developed a causal model incorporating exposure and annoyance for bothnoise and vibration simultaneously. Applying the multiple-group analysis for structural equation

modeling to the model, we estimated the weights of noise and vibration exposures on each annoyance.
Annoyances due to noise and vibrations from conventional railway are dominated by the noise
exposure. On the other hand, annoyance due to each of noise and vibrations from the Shinkansen
railway were mainly affected by the respective noise and vibration exposures. In the future,developing a revised causal model including the interaction item between noise and vibration
exposures, we applied the multiple-group analysis for structural equation modeling again.

ACKNOWLEDGEMENTS
The present study was supported by Grant-in-Aid for Scientists Research (C) of Japan Society for
the Promotion of Science (No. 18K04472 and 18H01518). The data for this secondary analysis w ere
provided by the Socio Acoustic Survey Data Archive (SASDA), Institute of Noise Control
Engineering/Japan.

REFERENCES
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Guidelines for the European Region; 2018.
2 Yokoshima S, Yano T, Kawai, K, Morinaga, M, Ota A. Establishment of the Socio-Acoustic Survey Data
Archive at INCE/J. Proc INTER-NOISE 2011; 4–7 September 2011; Osaka, Japan 2011.
3 Yokoshima S, Morinaga M, Tsujimura S, Shimoyama K, Morihara T. Representative exposure–annoyance
relationships due to transportation noises in Japan. Int. J. Environ. Res. Public Health 2021;18(20): 10935.
4 Tamura A. Comparison of community response to outdoor noise in the areas along Shinkansen andordinary railroad; Proc INTER-NOISE 1994; 29–31August 1994; Yokohama, Japan, 1994.
5 Yokoshima S, & Tamura A. Combined annoyance due to Shinkansen railway noise and vibration. Proc
INTER-NOISE 2005; 7–10 August 2005; Rio de Janeiro, Brazil, 2005.
6 Yokoshima S, Tamura A. Interactive effects between Shinkansen railway noise and vibration on
annoyance. Proc INTER-NOISE 2006; 3–6 December 2006; Honolulu, Hawaii, USA 2006.
7 Yokoshima S, MoriharaT, Sato T, Yano, T. Combined Effects of High-Speed Railway Noise and Ground
Vibrations on Annoyance; Int. J. Environ. Res. Public Health. 2017;14(8):845.
8 Yokoshima S, Morihara T, Matsumoto, Y. Combined effect of vibrations on railway noise annoyance.
Proc ICA2019; 9–13 September 2019; Aachen, Germany, 2019.
9 Yokoshima S, Morihara T, Matsumoto, Y. Effects of the railway category and noise on vibration
annoyance. Proc ICBEN2021; 14–17 June 2021; Stockholm, Sweden, 2021.
10 Japanese Industrial Standards. JIS C 1510. Vibration level meters; 1995.
11 Shimoyama K, Yokoshima S, Ota A, Morihara T, Tetsuya H, Higashijima D, Yano T. Noise and vibration
analysis of the Tokaido, Sanyo, Nagano, and Kyusyu Shinkansen line (in Japanese). Proc
INCE/J2014;17–18 September 2014; Tokyo, Japan, 2014.
12 International Organization for Standardization. Mechanical vibration and shock – Evaluation of human
exposure to whole-body vibration – Part 1: General requirements, ISO 2631-1; 1997.
13 Peris E, Woodcock J, Sica, J, Sharp. C, Moorhouse A T, Waddington D C. Guidance for new policy
developments on railway noise and vibration; Transportation Research Part A, 2016: 85, 76–88.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0455

An experimental investigation of subjective responses tosimultaneous vibration and noise in buildings induced by traffic
Nguyen Thanh TUNG1; Yasunao MATSUMOTO1; Takashi MORIHARA2; Shigenori YOKOSHIMA3;
Kentaro HAYASHI4
1
2
3

Department of Civil and Environmental Engineering, Saitama University, Japan

Department of Architecture, National Institute of Technology, Ishikawa College, Japan

Environmental Information Division, Kanagawa Environmental Research Centre, Japan
4

Benec Vibration and Sound Institute, Japan

ABSTRACT
Traffic-induced vibration and noise in buildings have adverse effects on the habitability and serviceability ofbuildings. In Japan, vibration and noise in buildings are assessed separately, although there have beenprevious studies conducted in different countries that shows some evidence of the combined effect ofvibration and noise on subjective responses. A laboratory experiment was conducted to investigate thesubjective responses to simultaneous vibration and noise induced by traffic to accumulate data for the
combined evaluation of vibration and noise. Twenty volunteers were exposed to 31 stimuli consisting ofdifferent magnitudes of vibration and noise for each of three sources, i.e., conventional and high-speed
Shinkansen railways, and road traffic. The participants were asked to rate their levels of concern anddiscomfort by the rating scale method. From the experimental results with exposures to vibration alone ornoise alone, the magnitudes of vibration and noise at which comparable subjective responses were raised
were identified. The results with simultaneous vibration and noise exposures suggest that there are combined
effects of vibration and noise on subjective responses when vibration and noise having comparable effects
were presented simultaneously.
Keywords: Subjective responses, simultaneous vibration and noise stimuli, combined effect.

1. INTRODUCTION
Vibration and noise induced by transportation have adverse effects on the residential environment. In
Japan, it was reported that most of the adverse comments on vibrations in residential single-family buildings
were caused by traffic-induced vibration [1]. When people are exposed to traffic-induced vibration inbuildings, in many cases, they are exposed to traffic-induced noise simultaneously. Such simultaneous
vibration and noise exposure can cause some combined effect on subjective responses.
Howarth and Griffin [2] showed that when railway noise and railway-induced building vibration occur
together the overall annoyance depends on the magnitudes of both stimuli, and the total annoyance caused
by a combination of noise and vibration can be determined from a summation of the effects of the individual
stimuli. Paulsen and Kastka [3] exhibited that the assessment of the combined stimuli is dominated by thenoise level, but it is also influenced by the simultaneously perceptible vibration. Yokoshima et al. [4]confirmed the combined effects of vibration/noise exposure on noise/vibration annoyance. Lee and Griffin
11nguyen.t.t.535@ms.saitama-u.ac.jp
ymatsu@mail.saitama-u.ac.jp

[5] have shown that total annoyance caused by combined vibration and noise of high-speed trains wasconsiderably greater than the annoyance caused by noise alone. Huang and Griffin [6] investigated therelative discomfort caused by noise and vibration, and the results showed the “masking effect” of higherlevel noises on the discomfort caused by low magnitudes of vibration and vice versa.
The objectives of this study were to understand the characteristics of subjective responses to simultaneous
vibration and noise and to accumulate knowledge for the evaluation of simultaneous vibration and noise in
the residential environment. A laboratory experiment was conducted and subjective responses tosimultaneous vibration and noise were measured using stimuli based on vibration and noise measured in
actual residential buildings.

2. METHOD
2.1

Apparatus

A single-axis electrodynamic shaker, Asahi Corporation VSR-S150V, in a laboratory of Saitama
University was used in the experiment. An aluminum-framed seat was mounted on the shaker platform witha size of 800 mm x 800 mm. The vertical acceleration of the seat surface was measured with a uniaxial
piezoelectric accelerometer, RION PV-87, combined with a charge amplifier, RION UV-06. The triaxial
accelerations at the center of the shaker platform were measured with a Vibration Level Meter, RION VM53A, to make sure the seat was rigid enough and cross-axis vibration was negligible. Signals from alltransducers were recorded in a PC-based data acquisition system, National Instruments NIcDAQ-9178 andNI 9234.
Noise stimuli were generated by a PC and presented to a participant through a headphone, SONY WH1000XM4. No monitoring was made for noise stimuli during the experiment.
2.2

Stimuli

Input stimuli presented in the experiment were based on noise and vertical vibration recorded
simultaneously in a residential building near three transportation systems: road traffic (RT), Shinkansen
railway (SR), and conventional railway (CR). Examples of the acceleration time history used in the input
stimuli are shown in Figure 1.

a) Road trafficb) Shinkansen railwayc) Conventional railway

Figure 1. Examples of the acceleration time history of the vertical vibration stimuli
The amplitude of vibration stimuli was modulated to cover the possible range of traffic-induced vibrationin residential buildings. The amplitude of noise stimuli was adjusted such that the range of subjectiveresponses caused by noise stimuli was comparable with that caused by vibration stimuli. Table 1 shows thefive levels of vibration stimuli used in the experiment in the Vibration Dose Value (VDV) with Wb frequencyweighted acceleration, and the six levels of noise stimuli in the sound exposure level, LEA.
Table 1 – Vibration and noise stimuli in all three sources
Road traffic

Level of

Shinkansen railway

Conventional railway

LEA

VDV

LEA

VDV

(dB)

-1.75

(m.s

(dB)

-1.75

(m.s

vibration

VDV

or noise

-1.75

(m.s

V1/N1

0.035

60

0.035

59

0.041

60

V2/N2

0.070

66

0.073

65

0.089

66

V3/N3

0.110

72

0.149

71

0.138

72

V4/N4

0.188

78

0.226

77

0.180

78

V5/N5

0.380

84

0.437

83

0.450

84

N6

)

90

)

89

LEA
)

(dB)

90

For each of the three sources, the combinations of the vibration and noise shown in Table 1 were used as
input stimuli in the experiment. Some combinations were omitted because the effect of noise appeared to be
dominant in a preliminary experiment. In addition, the conditions of vibration only or noise only were used
for the comparison with simultaneous stimuli. The order of presentation of input stimuli was randomized anddiffered for each participant.
2.3

Participants

Twenty Japanese students from Saitama University, consisting of 14 males and 6 females, participated in
the experiment.
2.4

Measurement and analysis of the subjective response

Participants were asked to rate their level of concern and discomfort after each stimulus by using a 7-step
category rating scale with semantic labels [7], in which the label of the top category 7 contains the adverb
corresponding to “Extremely” and the lowest category 1 contains the adverb corresponding to “Not at all”.
The interference of reading activity was also measured by using a similar rating scale with a 5-step category,
although the results of reading activity interference are not discussed in this paper. The participants wereasked to assume that they were in the living room of the building and read a book or magazine (Figure 2).
The data collected from the experiment were analyzed by using some non-parametric statistical tests,
including the Spearman correlation test for proving the correlation between vibration and noise stimuli on
corresponding concern and discomfort, the Wilcoxon signed-rank test to investigate the combined effect ofsimultaneously vibration and noise stimuli. The logistic regression analysis was applied to evaluate the rateof highly concerned/highly uncomfortable people when they were exposed to vibration and/or noise fromtransportation in residential buildings. The term “percent highly annoyed” (%HA) was proposed by Schultz[8] to define the rate of people who were classified in the top two categories of a 7-point scale for measuring
noise annoyance. This paper uses the term “highly uncomfortable” beside the term “highly concerned” andinvestigates the effects of noise or/and vibration stimuli as predictors on “highly concerned” and “highly
uncomfortable” as outcome variables. The statistical software used was R (version 4.1.3).

Figure 2 - A participant during an experiment

3. RESULTS
3.1

Concern due to simultaneous vibration and noise induced by traffic

Figure 3 shows scatter plots of the results of experiment for vibration or noise alone stimuli for road traffic,
as examples. The value in the vertical axis corresponds to each step of the 7-point scale to measure concern.
The darkness of the marker color represents the number of participants for that result. Figure 4 shows
Spearman’s rank correlation coefficients (or rho) between the concern and discomfort caused by vibration ornoise alone exposure and the magnitude of vibration or noise for road traffic, Shinkansen railway, andconventional railway. For the concern, the figure exhibits a strong association between the concern
caused by noise alone (CN) and the sound exposure level (LEA) as well as between the concern causedby vibration alone (CV) and the Vibration Dose Value (VDV) for all three sources. The correlation of
all pairs is statistically significant (p < 0.001). The rho of two variables CN and LEA is highest in thecase of the Shinkansen railway (0.80) and the pair of CV and log 10 VDV is highest for road traffic(0.81).

Figure 3 – Concern due to vibration or noise stimulus alone for road trafficCN: Concern due to noise alone; LEA: Sound exposure level; CV: Concern due to vibration alone; VDV:
Vibration Dose Value; DN: Discomfort due to noise alone; DV: Discomfort due to vibration alone.
Figure 4- Spearman correlation test between subjective response and intensity of stimuli for each source

To investigate the relative importance of noise and vibration, the concern of vibration alone andthe concern of noise alone were compared in each pair by applying the Wilcoxon signed rank test. If
the concern due to noise alone is significantly higher than the concern due to vibration alone, theeffect of noise can be dominant in simultaneous stimuli consisting of that vibration and noise. If, on
the contrary, the concern due to vibration alone is significantly higher than the concern due to noise
alone, the effect of vibration can be dominant in their simultaneous stimuli. If there is no statisticallysignificant difference between the concern due to vibration alone and that due to noise alone, theeffects of vibration and noise on the total concern due to their combination can be comparable. Table
2 shows the results of the Wilcoxon test for road traffic, as an example. The highlighted cells exhibit
the difference between concern due to vibration and noise only was not statistically significant (p 0.05), for which the effects of vibration and noise on the concern of their combinations can becomparable. On the other hand, the remaining cells show the dominant effect of vibration or noise in
the corresponding combinations because the significant difference between concern due to separate
stimuli (p < 0.05) was shown.
Table 2 – p-value in Wilcoxon signed rank test to compare the concern judgments due to vibration alone
and noise alone for road traffic
LEA (dB)

N1

N2

N3

N4

N5

N6

)

60

66

72

78

84

90

V1

0.03

0.028

0.011

X

X

X

X

V2

0.07

0.507

0.097

0.001

X

X

X

V3

0.11

0.066

0.518

0.108

0.024

X

X

V4

0.19

0.001

0.003

0.341

0.923

0.001

X

V5

0.38

1.8E-04

2.7E-04

0.001

0.003

0.575

0.010

-1.75

VDVwb (m.sThe concern due to all simultaneous stimuli and those due to vibration and noise alone werecompared to investigate the combined effects of vibration and noise on the concern. An increase insubjective response to simultaneous stimuli compared with those to vibration and noise alone implies
the combined effect of vibration and noise on the subjective response to simultaneous stimuli. Table
3 shows examples of the results of this comparison for all combinations for road traffic. For example,
the concern due to the simultaneous stimuli of V2N2 (i.e., the combination of V2 and N2) was
significantly higher than the concern due to V2N0 (i.e., the vibration alone at V2) and V0N2 (i.e., thenoise alone at N2) (p < 0.05), so there could have been the combined effect of vibration and noise inthis combination. The simultaneous stimuli in which the effects of vibration and noise werecomparable tended to have the combined effects of two stimuli on concern, except for the cases of
V2N1 and V3N1. The concern of the simultaneous stimuli V1N2 showed a significant difference
Table 3 – Comparison between the concern due to all simultaneous stimuli and the concern due tovibration/noise alone for road traffic
Concern due to combinations

Concern due toConcern due tovibration alone

noise alone

medianmedianp-value

medianp-value

V1N1

2

1

0.008

2

1

V1N2

2

1

0.004

2

0.565

Noise effect is

V2N3

3

2

0.001

3

1

dominant

V3N4

5

2

0.001

3.5

0.003

V4N5

5.5

3

0.002

5

0.666

V5N6

7

5

0.001

6

0.042

V2N1

2

2

0.015

2

0.149

V2N2

3

2

0.002

2

0.034

V3N1

3

2

0.145

2

0.002

Comparable

V3N2

3

2

0.033

2

0.002

effect

V3N3

4

2

0.002

3

0.043

V4N3

4

3

0.078

3

0.005

V4N4

4.5

3

0.009

3.5

0.011

V5N5

6.5

5

0.001

5

0.008

V4N1

3.5

3

0.267

2

4.1E-04

V4N2

4

3

0.308

2

0.001

Vibration effect

V5N1

5

5

1

2

1.8E-04

is dominant

V5N2

6

5

0.22

2

3.4E-04

V5N3

5

5

0.183

3

0.001

V5N4

5.5

5

0.124

3.5

0.001

from the concern of V1N0 but no difference with the concern of V0N2, possibly because the effect of noisein this combination was dominant. When the effect of noise was dominant in the concern due to simultaneous
stimuli, the concern due to simultaneous stimuli tended to be equal to the concern due to noise alone, except
for the combinations V3N4 and V5N6. The effect of vibration in the simultaneous stimuli V5N2 wasdominant so that the significant difference was found only when compared with the concern due to noise
alone. All simultaneous stimuli in which the vibration effect was dominant, the concern of simultaneous
stimuli equals to those of vibration alone. Thus, the combined effect of vibration and noise on concern wasobserved, especially when the effects of vibration and noise were comparable. In the case of the Shinkansen
railway and conventional railway, similar results were observed, although the results are not shown due tothe space limitation.
The effects of noise or/and vibration stimuli on the concern were investigated by using logistic
regression with several models. HC, as defined above, was considered as the binomial outcome with
1 and the responses other than HC as 0. Four models were investigated in this study. For two simple
models, HC due to vibration or noise alone was analyzed with vibration stimuli (20 times logarithmwith base 10 of the Vibration dose value 20log10VDV) or noise (sound exposure level LEA ) as
predictors (or independent variables). The logarithm of the VDV was used to make it comparable tothe logarithmic nature of the sound exposure level [9]. The third model was a multiple logistic
regression model applied for HC due to all stimuli, with vibration and noise as predictors. The fourthmodel involved an interaction term [10] of two stimuli for predicting HC due to all stimuli.
Table 4 shows the result of the logistic regression analysis for predicting HC for the road traffic.
The Odds Ratio (OR) and 95% of confidence interval (95% CI) of the two models for vibration ornoise alone are higher than one. These results indicate that the vibration or noise significantly affects
HC. Figure 5 shows the logistic regression curves to predict the percent highly concerned (%HC) for
noise alone. The result showed that when the intensity of noise increases, %HC increases. For

Sources

RT

Table 4 – Logistic regression analysis for highly concerned (HC) for road traffic
Highly concerned (HC)P
Models
Predictors
OR(Wald’s(95%CI)
test)
1.42
Simple (Vibration
0.003
20log10 VDV(1.13 - 1.79)
stimuli alone)
Simple (Noise stimuli
1.44
LEA< 0.001
alone)(1.21 - 1.71)
1.30
20log10 VDV< 0.001(1.21 - 1.41)
Multiple
1.10
LEA< 0.001(1.06 - 1.14)
1.56
20log10 VDV
0.257(0.72 - 3.34)
Multiple with
1.07
LEA
0.246
(0.95
- 1.20)Interaction term
0.99
20log10 VDV*LEA
0.646(0.99 - 1.01)

Figure 5 – Percent highly concerned due to road traffic noise alone (gray hatching: 95% CI)
multiple-variable models, the OR and 95% CI are higher than one only for the model without theinteraction term of vibration and noise stimuli. The model with the interaction term showed nostatistical significance (p > 0.05, Wald’s test). This may be consistent with the finding by Howarth
and Griffin [2] that the addition of the interaction variable between vibration and noise stimuli to theirregression model did not improve the model of predicting total annoyance. Similar conclusion was
drawn for Shinkansen railway and conventional railway, although, for conventional railway, themodel for vibration stimuli alone was not statistically significant possibly because of only five
responses corresponding to HC.
3.2

Discomfort due to simultaneous vibration and noise induced by traffic

Spearman’s rank correlation coefficients between the discomfort due to vibration or noise stimuli alone(DV or DN ) and the magnitude of vibration or noise for the three sources and corresponding stimuli. The
result of the test is shown in Figure 4. Similar tendency to the concern evaluation was observed in the
figure. All testing pairs were highly correlated and statistically significant (p < 0.001). The
relationship between DV and VDV was highest for road traffic and the relationship between DN, andLEA was highest for Shinkansen railway.
The highlighted cells in Table 5 in the case of the road traffic exhibit the difference between the
discomfort caused by vibration and noise only was not statistically significant (p  0.05), for which
the effects of vibration and noise on the discomfort of their combinations can be comparable. On theother hand, the remaining cells show the dominant effect of vibration or noise in their corresponding
combinations because the significant difference between discomfort due to separate stimuli (p < 0.05)
was shown.
Two simultaneous stimuli V1N2 and V5N1, for example, in Table 6 for the road traffic showedsignificantly higher discomfort when compared with the discomfort due to vibration or noise alone,respectively, because the effect of vibration or noise was dominant. For these simultaneous stimuli,Table 5 – p-value in Wilcoxon signed rank test to compare the discomfort judgments due to vibration alone
and noise alone for the road traffic
LEA (dB)

N1

N2

N3

N4

N5

N6

)

60

66

72

78

84

90

V1

0.03

0.050

0.009

X

X

X

X

V2

0.07

0.457

0.033

0.003

X

X

X

V3

0.11

0.388

0.588

0.020

0.002

X

X

V4

0.19

0.006

0.014

0.557

0.520

0.002

X

V5

0.38

1.9E-04

2.8E-04

0.002

0.030

0.141

0.005

-1.75

VDVwb (m.sTable 6 – Comparison between the discomfort due to all simultaneous stimuli and the discomfort due tovibration/noise alone for road traffic.
Discomfort due to combinations

Discomfort due to

Discomfort due tovibration alone

noise alone

medianmedianp-value

medianp-value

V1N2

2

1

0.023

2.0

0.149

V2N2

3

1

0.001

2.0

0.020

V2N3

3

1

0.001

2.0

0.374

V3N3

3.5

2

0.002

2.0

0.837

V3N4

4

2

0.000

3.0

0.008

V4N5

5

2.5

0.002

5.0

0.463

V5N6

6

4

0.001

6.0

0.124

V1N1

2

2

0.023

2.0

1.000

V2N1

2

2

0.059

2.0

0.507

V3N1

2

2

0.014

2.0

0.013

V3N2

2.5

2

0.003

2.0

0.005

V4N3

4

2

0.022

2.0

0.004

V4N4

4

3

0.008

3.0

0.008

V5N5

6.5

3

3.2E-04

5.0

0.006

V4N1

3

5

0.149

2.0

0.002

V4N2

3

3

0.295

2.0

0.003

Vibration effect

V5N1

5

3

0.200

2.0

1.9E-04

is dominant

V5N2

5

5

0.037

2.0

2.9E-04

V5N3

5

5

0.022

2.0

0.001

V5N4

5.5

5

0.004

3.0

0.001

Noise effect isdominant

Comparable
effectthere was no combined effect of the two stimuli on the discomfort judgments. On the contrary, almost
all simultaneous stimuli in which the effects of vibration and noise were comparable showedsignificant difference in the discomfort compared with the discomfort due to vibration and noise alone(p < 0.05), except for V1N1 and V2N1. The simultaneous stimuli tended to have the combined effecton discomfort especially in case that the two stimuli have comparable effect. In the case of Shinkansen
railway and conventional railway, some simultaneous stimuli induced significantly higher discomfort
than vibration or noise exposure alone.
To evaluate the effect of vibration and/or noise stimuli for predicting the “percent highly
uncomfortable” (%HU), the logistic regression analysis was applied in a similar manner to that
described in the preceding section. The results of the logistic regression analysis for road traffic arepresented in Table 7. Figure 6 shows the logistic regression curves of %HU for noise alone. The
results presented in Table 7 and Figure 6 show that the results of the logistic regression analysis fordiscomfort were similar to those for concern shown in Table 4 and Figure 5, although the model
Table 7 – Logistic regression analysis of being highly uncomfortable in the case of the road traffic
Highly uncomfortable
SourcesModels
Predictors
ORP(95%CI)(Wald’s test)
Simple (Vibration
21.10
0.995
20log10 VDVstimuli alone)(0 - Inf)
Simple (Noise
1.36
LEA< 0.001
stimuli alone)(1.18 - 1.56)
1.27
20log10 VDV< 0.001(1.17 – 1.38)
Multiple
1.11
RT
LEA
< 0.001(1.07 - 1.15)
1.35
20log10 VDV
0.451(0.62 - 2.97)
Multiple with
1.10
LEA
0.124
(0.97
- 1.24)
Interaction term
0.99
20log10 VDV*LEA
0.874(0.99 -1.01)

Figure 6 – Percent highly uncomfortable due to road traffic noise alone (gray hatching: 95% CI)

for vibration stimuli alone in Table 7 was not significant because of only four choices for HU. A better
regression model to predict %HU was the multiple logistic regression model involving vibration andnoise as predictor variables.
A similar analysis was applied for conventional railway. Similar tendency of the results to roadtraffic was observed. For Shinkansen railway, the model for predicting %HU for vibration alone wasstatistically significant.

4. DISCUSSION
Lee and Griffin [5] investigated the effects of vibration and noise on annoyance in buildings during
the passage of a nearby high-speed train in a laboratory experiment with recorded train noise and
20Hz vibration. Their result showed that the total annoyance caused by combined vibration and noise
was considerably greater than the annoyance caused by noise alone at all levels of noise. Although
the combined effect of vibration and noise was observed, the effect of noise at all levels may be notcomparable to the effect of one magnitude of vibration. In this study, significant difference in thesubjective response between simultaneous stimuli and vibration or noise stimulus alone was found formost of the simultaneous stimuli in which the effects of two stimuli were comparable and for a few
of other simultaneous stimuli in which the effect of vibration or noise was dominant. Therefore, thecondition of simultaneous stimuli in which the combined effect is observed needs further investigation.

5. CONCLUSIONSThe effects of simultaneous vibration and noise induced by traffic on subjective responses were
investigated by conducting the laboratory experiment. The following conclusions can be drawn:(1) There were the combined effects of vibration and noise stimuli on concern and discomfort
when participants are exposed to simultaneous stimuli consisting of vibration and noise having
comparable effect on those subjective responses.
(2) The probability of highly concerned and highly uncomfortable responses to all stimuli used inthis experiment are predicted better by the multiple logistic regression model without
considering the interaction term of two stimuli than the model with the interaction term.

ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant Number JP18H1518. We are grateful to Mr.
Atsuki Sato, a former student from Saitama University, for his support of the experiment.

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Science and Business Media Deutschland GmbH, 2010.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0550

METABOLIC AND COGNITIVE CONSEQUENCES OF NOISEINDUCED SLEEP DISTURBANCE
Michael G. SMITH1; Anna NAUSE1; Mathias BASNER2; Sofie FREDRIKSSON1;
Daniel MALMODIN3; Anders PEDERSEN3; Kerstin PERSSON WAYE1;
Christian STRASSBERGER4; Natalia VINCENS1; Magdy YOUNES5; Ding ZOU4
1

Department of Occupational and Environmental Medicine, School of Public Health and Community Medicine,
Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

2

Unit for Experimental Psychiatry, Division of Sleep and Chronobiology, Department of Psychiatry, Universityof Pennsylvania Perelman School of Medicine, Philadelphia PA, USA
3
4

Swedish NMR Centre, University of Gothenburg, Gothenburg, Sweden

Center for Sleep and Vigilance Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg,
Sweden5

Sleep Disorders Centre, University of Manitoba, Winnipeg MB, Canada

ABSTRACT
Epidemiological studies show associations between chronic noise exposure and disease, but the biologicalpathways remain poorly understood. Here we aim to investigate the mechanisms that may link sleep
disruption by environmental noise with the development of disease. In a laboratory sleep study, N=12 young,healthy individuals spent five consecutive nights in acoustically isolated bedrooms. Subjects were exposed
to nocturnal traffic noise of different types (road, rail and air) and noise levels (45-65 dB LAS,max). There werealso nights with continuous pink noise, which may improve sleep by masking the traffic noise that would
otherwise disturb sleep. We measured objective sleep quality and quantity with polysomnography, and selfreported sleep and wellbeing outcomes via questionnaires. We also measured cardiovascular response withelectrocardiography and finger photoplethysmography. To identify neurobehavioural and metabolic
consequences of nocturnal noise, we measured cognitive performance across multiple domains with acomputerised test battery every morning and evening, and collected daily blood samples for metabolomics
analysis. We anticipate that noise-induced sleep disturbance will lead to biological changes that in the longterm may be precursors for cardiometabolic disorders. Analysis is currently ongoing, and results will bepresented at the congress.
Keywords: environmental noise, pathophysiological mechanisms, noise masking

1. INTRODUCTION
Sleep is a highly complex process that is essential for physical and mental function. 1-5
Experimental investigations on sleep loss and sleep fragmentation reveal adverse physiologic changes
that are precursors to disease, including neurodegeneration. 6-13 Accordingly, epidemiologic studies
consistently find associations between chronic short or interrupted sleep and negative health outcomes
including increased risk for obesity, diabetes, hypertension, cardiovascular disease and all -cause
mortality. 14-18 Sleep of sufficient quantity and quality is therefore a key component of health, and its
disruption by environmental stressors, including noise, has important public health implications.
Traffic noise is a highly prevalent environment stressor that contributes to sleep loss andfragmentation. However, reductions in total sleep time and sleep fragmentation by nocturnal traffic
noise are generally less severe than in studies of experimentally -induced sleep restriction.
Nevertheless, sleep disturbance by traffic noise in the long -term may lead to the development ofdisease, particularly cardiometabolic diseases, 19-30 and chronic exposure to nocturnal traffic noise isassociated with many of the same cardiovascular diseases and metabolic disorders linked withrestricted and fragmented sleep. This suggests overlapping mechanistic pathways between long -term
sleep restriction and noise exposure and the genesis of disease, although evidence linking metabolic
outcomes to chronic noise exposure is sparser. 31
One promising non-pharmacological intervention to protect against noise-induced disruption, andpromote sleep generally, is so-called “white noise machines” (WNMs). However, contrary to theirwidespread use, we find that the quality of evidence for the efficacy of WNMs as a sleep aid is very
low. 32 Importantly, WNMs may not only be ineffective as sleep aids, but the noise they introduce may
actively disrupt sleep. 33, 34 Furthermore, it is conceivable that much like the brain, the auditory systemneeds downtime during sleep for recovery, for instance clearing metabolic -by-products accumulated
during waking hours. 1 Precluding such recovery periods with long-term WNM use might contributeto sound-induced auditory fatigue and increased listening effort. 35 As such, it remains an open
question as to whether WNMs can promote sleep per se or whether their use may have adverse
consequences on sleep and auditory function. Furthermore, nothing is k nown regarding theeffectiveness of WNMs for mitigating the sleep-disrupting effects of nighttime traffic.

2. METHODSTo deepen our understanding of experimentally-induced disruption of sleep and changes inmetabolic and cognitive function, and to determine the efficacy of a non-pharmacological sleep aid,
we performed an experimental pilot study. The study was approved by the local ethics committee(Swedish Ethical Review Authority, 2021-06812-01). The study protocol was registered prior to
subject recruitment on ClinicalTrials.gov (NCT05319262). Study subjects provided informed consent
prior to the start of the study, were financially compensated for their participation, and could
discontinue at any time without explanation.
2.1 STUDY PROTOCOL
The study took place in the sound environment laboratory (SEL) at the University of Gothenburg
Department of Occupational and Environmental Medicine. The SEL is a high-fidelity researchlaboratory equipped to simulate a typical apartment, including three individually light -, sound- andvibration-isolated private bedrooms. Ceiling mounted speakers in each room all owed us to create arealistic acoustic environment by transmitting sound exposures from the control room to eachbedroom individually.
The study used a prospective within-subjects cross-over design. Participants spent five consecutive
nights in the SEL, with a sleep opportunity between 23:00-07:00. The first night was a habituation
period to the study protocol and for familiarization with the test procedures. Study nights 2-5 wereexperimental nights and were randomly assigned across participants using a Latin square design to
avoid first-order carryover effects. Each subject was exposed to one night of each of the following:• Quiet night: No noise was played. This night served as a control night to assess individual
baseline sleep, metabolic profile, and cognitive performance;• Traffic noise night: to determine consequences of noise-disrupted sleep;• Pink noise night: To determine impact of continuous pink noise on sleep;• Traffic + pink noise night: simultaneous traffic and pink noise at the same sound pressure levels
as in the traffic-only and pink noise-only nights, to determine sleep-protecting effects of pinknoise in the face of traffic noise.
Study subjects arrived at the SEL by 20:00 each evening. They then completed the Cognition test
battery, 36, 37 comprising of 10 computerized tests covering a range of cognitive domains: sensorimotor
speed, spatial learning and memory, working memory, abstraction and concept formation, spatial
orientation, emotion identification, abstract reasoning, complex scanning and visual tracking, risk
decision making, and vigilant attention. Each night we recorded physiologic sleep with
polysomnography (PSG) and cardiovascular activity with electrocardiography (ECG) and finger
photoplethysmography. Each study morning, subjects provided a 2ml blood sample for metabolomics
analysis, completed Cognition, and completed a short questionnaire including different dimensions of
sleep quality and disturbance in the preceding night, 38 sleepiness, 39 and sleep disturbance by noise. 40
Subjects could follow their normal daytime routine, but were prohibited from alcohol, ingesting
caffeine after 15:00, and napping, confirmed with measures of daytime activity via wrist actigraphy
monitors worn continuously throughout the study. Because sound-induced auditory fatigue may beaffected by noise exposure during participant’s normal routines, 41 subjects wore a noise dosimeter
during the week of the study to record their daytime noise exposure. Because extreme and/or variabledietary behaviour can affect the metabolome/lipoprotein profile, 42 participants were required to eat
the same evening meal on each day of the laboratory study, which was confirmed via a food diary.
2.2 PARTICIPANTS
Twelve healthy participants (mean age 23.6 years, range 21-29 years; 7 females, 5 males) wererecruited via public advertisement around the University of Gothenburg campus and online. They were
habitually good sleepers with a habitual mean bedtime of 22.41 and mean rise time of 07.08, i.e.
closely aligning with the experimental sleep opportunity times. They did not suffer from any sleep
disorder, use any sleep medications or medications with potential side effects impacting sleep, or use
white noise machines or other similar machines or apps to aid their sleep. All participants had normal
hearing, which was assessed via pure tone audiometry to 20 dB HL.
2.3 NOISE EXPOSURE
For traffic noise exposure, we used high fidelity audio recordings of aircraft, road and rail traffic. 43
There were 120 traffic noise events (40 each of road, rail and air), at five different maximum soundpressure levels (45-65 dB L AF,max ) across the night. We randomized the distribution of the length ofintervals between noise events (3 to 5 minutes) and maximum sound pressure levels to obtain an even
distribution throughout the night (23:00-07:00). The same traffic noise audio file was used at the same
sound pressure levels for all nights with traffic noise across all subjects. For the “pink noise” exposure,
we used an audio file of continuous pink noise (20 to 20,000 Hz). This was played at a constant levelof 45 dB LAEq continuously during the night. All sound pressure levels were calibrated to 10 cm above
the pillow in each bedroom prior to the study, so that these levels accurately reflect the noise exposureof the subjects during sleep.

3. RESULTS
Data completeness for all outcomes is summarized in Table 1.Two subjects dropped out after
completing three nights in the study. Acoustic dosimetry data were unavailable for one subject due toa hardware failure. One subject during their sleep removed the PSG sensors part way through onenight. No other data were missing.
Table 1 Data completeness for all outcomes across all 12 subjects and all 5 study nights.
MeasureTotal administrations
expected (n)Data obtained (n)Data completenesscompared to expected

Blood

60

56

93.3%

Polysomnography & ECG

60

55.7

91.1%

Questionnaires - Morning

60

56

93.3%

Questionnaires - Evening

60

56

93.3%

Cognition - Morning

60

56

93.3%

Cognition - Evening

60

56

93.3%

Acoustic dosimetry

60

51

85.0%

Food diary

60

56

93.3%

Actigraphy (in-lab period)

72

67

93.1%

To check subject compliance to the self-regulated lights out time (23:00), we manually scoredactigraphy data during the in-lab study period. Across all subjects and study nights, the mean ±SDlights out time was 22:55±00:13. This indicates generally good adherence to the protocol, although
there were two instances where participants fell asleep rather early at 22:16 and 21:43. There was noevidence of daytime napping in the actigraphy records.

4. DISCUSSION AND CONCLUSIONS
We aimed to investigate the consequences of sleep disturbance by traffic noise , and theeffectiveness of continuous pink noise at mitigating noise-induced sleep disturbance. To this end, ina controlled laboratory setting we collected data across a range of biological, psychological, cognitiveand environmental domains. Study adherence was good, and there was minimal data loss due toparticipant dropout or technical failure. Statistical analysis of collected data is ongoing to address thefollowing research questions:• What are the biological and neurocognitive consequences of noise -disrupted sleep?
• Does pink noise per se improve or disturb sleep, and does pink noise reduce sleep
disturbance by traffic noise
Results of these analyses will be presented at the ICA 2022 Congress.

ACKNOWLEDGEMENTS
We thank Johanna Franzén, Anna Johansson, Agnes Wiberg and Mikael Ögren for their invaluableassistance in performing the study. This work was supported by the University of Gothenburg
Sahlgrenska Academy International Starting Grant (PI: M.G.S.).

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PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0737

Living close to the railway: effects of rail traffic vibration and noise
on mental health
1*

2

1

Natalia VINCENS ; Elise VAN KEMPEN ; Mikael ÖGREN ; Kerstin PERSSON WAYE
1School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Sweden
2

National Institute for Public Health and the Environment (RIVM), Netherlands

ABSTRACTRail traffic is expected to increase in Sweden and in Europe following policy recommendations for amore sustainable transportation model. Still, little is known about the effects of rail traffic vibrationand noise on the mental health of people living close to the railways. Our aim is to investigate therelationship between rail traffic noise, vibration and mental health. The study population (N=5381)
was randomly selected from residents living within 1km of a trafficked railway in Västra Götaland,
Sweden. Survey data was combined with modelled exposures and health re gister data (i.e., ICD10
codes). The study uses a cross sectional design and logistic regression analysis. Preliminary findings
suggest an increase in poor self-rated health with exposure to vibration (OR=1.05 per 0.1 mm/s Vmax;
95%CI 1.02-1.09) and to noise (OR=1.15 per 10 dB Lden ; 95%CI 0.99-1.33). However, there were noclear indications of an association with depression (OR=1.02 per 0.1 mm/s Vmax; 95% CI 0.9 8-1.06);(OR=0.97 per 10 dB L den ; 95% CI 0.83-1.12) or anxiety (OR=1.01 per 0.1 mm Vmax; 95% CI 0.961.05); (OR=0.96 per 10 dB L den ; 95% CI 0.82-1.12). Poor self-rated health but not registereddepression or anxiety was associated with rail traffic vibration and noise.
Keywords: Rail traffic noise, Rail traffic vibration, Mental health, Self-rated health, Depression, Anxiety

1. INTRODUCTION
Rail traffic is increasing in Sweden and in other European countries following policy
recommendations for a more sustainable transportation model. Particularly relevant for health is thegrowing use of freight trains as well as the introduction of high-speed trains; previous studies showthat these trains lead to higher levels of vibration an noise exposure with unfolding health outcomesfor instance increased noise annoyance (1-5).
Mental health can be defined as “a state of mental well-being that enables people to cope with thestresses of life, realize their abilities, learn well and work well, and contribute to their community ”(6).
It entails a complex continuum, with varying degrees of well-being and mental distress. It can thus
include different dimensions for instance well-being and quality of life on one and mental disorders
such as depression and anxiety on the other. Previous studies support effects of rail traffic noise andvibration on several health outcomes including among others annoyance, sleep disturbance anddiabetes (2, 4, 7, 8). Yet, fewer studies have investigated the effects of these exposures in relation tomental health, especially in relation to self-rated health - a comprehensive health indicator that has
been used previously as a predictor of quality of life, including aspects such functional status, coping
mechanisms and well-being dimensions (9-12). It is hypothesized that rail traffic noise and vibration
can affect well-being as well as lead to mental disorders via annoyance and sleep disturbance. Our
aim is thus to investigate the effects of rail traffic noise and vibration on selected indicators of mental
health.

2. METHODS
The study population (N=5381) was randomly selected from residents (18-80 years old) living
within 1km of a trafficked railway in Västra Götaland, Sweden. Survey data was combined withmodelled exposures and health register data (i.e., ICD10 codes) using GIS and personal identification
numbers. The study was conducted in accordance with the Helsinki Declaration and approved by thelocal ethical committee.
2.1 Measurements
Rail traffic vibration was assessed using an empirical calculation scheme based on vibration
measurements and geological data (soil type and depth). Vibration exposure was expressed as the
maximum weighted vibration velocity at the building foundation (Vmax) in mm/s (13). Railway noise
exposure for the most exposed façade was calculated using the Nordic prediction method (14). For
each receiver point, equivalent A-weighted sound pressure level (L Aeq) for the total noise exposure
from freight and passenger traffic was assessed. L den was constructed from the A-weighted equivalentlevel during the day, evening, and night, with a penalty of 5 dB added for the evening period 19:00 to
22:00 and a penalty of 10 dB added for the night period 22:00 to 07:00).
Mental disorders were assessed by registered diagnosis of depression and anxiety (i.e., ICD-10
codes: codes F32, F33 and F41). Well-being was assessed by questionnaire data on self-rated health(i.e., based on the question: “How do you rate your health in the past 12 months?”). For the registereddiagnosis, individuals were considered cases if they had a registered diagnosis at primary care,specialist care or in-patient hospital care settings any time between 2007 and 2017. Self-rated healthwas dichotomized into good SRH (alternative answers: very good and good), and poor SRH(alternative answers: fair, poor and very poor) similar to previous studies (15).
2.2 Analysis
We used logistic regression models in a cross-sectional study design to test for the association
between different indicators of mental health and residential exposure to (i) vibration (Vmax) and (ii)noise (Lden ) separately. Models were adjusted for age and sex. We report ORs and 95% CI. Stata 16
was used for all the analysis.

3. PRELIMINARY RESULTS
The prevalence of depression and anxiety was 10.1% and 9.2% respectively. The prevalence ofpoor SRH was about 10%. Compared to the reference population (about 50% female), overall
individuals with a registered diagnosis of depression and anxiety and the ones reporting poor SRH
were more often female (64%, 68%, 61% respectively). Logistic regression estimates do not support
an association between rail traffic exposures and registered diagnosis of depression and anxiety.
Overall, we observe an increased prevalence of poor SRH in association with higher levels of vibration.
A 0.1mm/s increase in vibration velocity was associated with a 1.05 (95% CI 1.02-1.09) times higher
risk of reporting poor SRH, adjusted to age and sex. A similar pattern was observed in relation tonoise (L den ) (table 1).
Table 1. Logistic regression analysis: mental health indicators in relation to rail traffic noise and vibration,
separately (n=5380).

Noise (per 10 dB Lden)
Vibration (per 0.1 mm/s Vmax)

Depression

Anxiety

Poor SRH

OR (95% CI)

OR (95% CI)

OR (95% CI)

0.97 (0.83-1.12)

0.96 (0.82-1.12)

1.15 (0.99-1.33)

1.02 (0.98-1.06) 1.01 (0.96 – 1.05)

1.05 (1.02-1.09)

Models adjusted for age and sex.

4. Discussion
Our analyses support an association between both rail traffic vibration and noise with poor selfrated health but not registered diagnosis of depression or anxiety. These findings suggest rail traffic
exposures to influence well-being and health in general but not mental disorders. That doesn’tnecessarily mean that there are no effects of rail traffic exposures on mental disorders. It rather points
to a need to further investigate the topic. A recent meta-analysis suggests an effect of aircraft noise
and to a lesser extent of road traffic noise on depression and anxiety. For rail traffic noise, there arestill very few, low-quality studies to draw conclusions (16). Regarding well-being, previous studies
are very scarce (11, 12). A systematic review reports a potential harmful effect of rail traffic noise onself-reported quality of life/health. Yet, the same was not observed in relation to aircraft or road traffic
noise. Importantly, these remarks were based on low quality studies (11). Currently, to the best of ourknowledge there are no other studies on rail traffic vibration and these outcomes.
The mechanisms linking rail traffic exposures with self-rated health but not depression or anxiety
are unclear. It is possible that the stigma associated with mental disorders affects the healthcare
seeking behaviours, the actual diagnosis of depression or anxiety and the registration practices leading
to under-reporting of these disorders (6, 11, 16). Another explanation relates to the complexity of theself-rated health measure that might reflect other health related factors besides well-being and qualityof life, for instance in relation to physical health. Future studies should strive to include otherindicators of mental health, for instance use of medication and other measures of well-being andquality of life (11). In addition, mechanistic studies including further adjustments as well as modelling
potential mediators and/or moderators (e.g., annoyance, sleep disturbance, sex, socioeconomic
position) could assist in clarifying our findings.
We must acknowledge study limitations. There is a risk of selection bias considering thepossibilities of over-representation of participants in the survey who are displeased and maybe
disturbed by the rail traffic. Yet, we have compared the dose response curve of annoyance in relation
to ground-borne vibration in our study with the curve from a Dutch study (17) with good similarities
between these curves (18). This reflects to some extent a good representativeness of our study sample.
Regarding the cross-sectional design, we are aware of the limitation regarding causality inferences,although we find reverse causality less plausible than the direction hypothesized in the study. The
ground-borne vibration estimations have precision limitations. However, calculations and models
have been discussed at length between expertise in The Netherlands and Sweden resulting in greater
knowledge and improved precision. Yet, we are aware that improvements in vibration assessment are
still possible.

ACKNOWLEDGEMENTS
The study was funded by the Swedish Transport Administration and the national research fund
FORMAS Swedish research council for sustainable environment Dnr 942-2016-102. Thanks to Laura
MacLachlan for carrying out the socio-acoustic survey, to Jeong Lim for the preparation of the registerdata and to Arnold Kopman and members from the Swedish Transport Administration for in depth
discussions of vibration exposure models.

REFERENCES
1.

Zhang L, Ma H. Investigation of Chinese residents’ community response to high-speed railway noise.

Applied Acoustics. 2021;172:107615.
2.

Morihara T, Yokoshima S, Matsumoto Y. Effects of noise and vibration due to the hokuriku shinkansen

railway on the living environment: A socio-acoustic survey one year after the opening. International journal ofenvironmental research and public health. 2021;18(15).
3.

Li X, Hu Z, Zou C. Noise annoyance and vibration perception assessment on passengers during train

operation in Guangzhou Metro. Environ Sci Pollut Res. 2021.
4.

Persson Waye K, Smith MG, Hussain-Alkhateeb L, Koopman A, Ögren M, Peris E, et al. Assessing theexposure-response relationship of sleep disturbance and vibration in field and laboratory settings. Environmental
Pollution. 2019;245:558-67.
5.

Morihara T, Yokoshima S, Matsumoto Y, editors. A study on the influence of noise and vibration on theliving environment along the Hokuriku Shinkansen railway2019.
6.

WHO. Mental health: strengthening our response 2022 [Available from: https://www.who.int/news-

room/fact-sheets/detail/mental-health-strengthening-our-response.

7.

Persson Waye K, van Kempen E. Non-auditory effects of noise: an overview of the state of the science ofthe 2017-2020 period.

The 13th ICBEN Congress on Noise as a Public Health Problem; 14-17 June 2021;

Karolinska Institutet, Stockholm2021.
8.

Vincens N, Persson Waye K. Railway noise and diabetes among residents living close to the railways in

Västra Götaland, Sweden: Cross-sectional mediation analysis on obesity indicators. Environmental Research.
2022;212:113477.
9.

Yamada C, Moriyama K, Takahashi E. Self-rated health as a comprehensive indicator of lifestyle-related

health status. Environmental Health and Preventive Medicine. 2012;17(6):457-62.
10.

Simon JG, De Boer JB, Joung IMA, Bosma H, Mackenbach JP. How is your health in general? A qualitative

study on self-assessed health. European journal of public health. 2005;15(2):200-8.
11.

Clark C, Paunovic K. WHO Environmental Noise Guidelines for the European Region: A Systematic

Review on Environmental Noise and Quality of Life, Wellbeing and Mental Health. International journal ofenvironmental research and public health. 2018;15(11):2400.
12.

Clark C, Crumpler C, Notley H. Evidence for environmental noise effects on health for the United Kingdom

policy context: A systematic review of the effects of environmental noise on mental health, wellbeing, quality oflife, cancer, dementia, birth, reproductive outcomes, and cognition. International journal of environmental researchand public health. 2020;17(2).
13.

Ögren M, Ekblad A, Johansson P, Koopman A, Persson Waye K. Ground and Building Vibration Estimationfor Health Impact Research.

Noise and Vibration Mitigation for Rail Transportation Systems. Noise andVibration Mitigation for Rail Transportation Systems2021. p. 487-95.
14.

Naturvårdsverket. Buller från spårburen trafik - Nordisk beräkningsmodell. Rapport 4935. 1999.

15.

Kim D, Subramanian SV, Kawachi I. Social capital and physical health.

Social capital and health:Springer; 2008. p. 139-90.
16.

Hegewald J, Schubert M, Freiberg A, Romero Starke K, Augustin F, Riedel-Heller SG, et al. Traffic Noiseand Mental Health: A Systematic Review and Meta-Analysis. International journal of environmental research andpublic health. 2020;17(17):6175.
17.

Van Kamp I, Van Kempen E, Van Wijnen H, Verjeijen E, Istamto T, Breugelmans O, et al., editors.

Vibrations due to trains: Exposure response relations and the role of contextual and personal factors. Euronoise;
2015.
18.

Vincens N, Ögren M, Li H, van Kempen E, Edgren S, Persson Waye K. Epivib: hälsoeffekter av att bo vid

en järnväg. 2021.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0943

Living close to the railway: effects of vibration and noise from rail
traffic on sleep disturbance.
Kerstin Persson Waye1, Huiqi Li1, Elise van Kempen2, Mikael Ögren1, Natalia Vincens1
1

Sound Environmental and Health, School of Public Health and Community Medicine, Institute of Medicine,
University of Gothenburg, Gothenburg, Sweden

2

Centre for Sustainability, Environment and Health, National Institute for Public Health and the Environment(RIVM), The Netherlands

Corresponding author's e-mail address: kerstin.persson.waye@amm.gu.se
ABSTRACT
Background: Railway is considered a sustainable transportation mode, but people living along the
track are exposed to noise and vibration. Limited evidence supports an effect of transportation noise
on several health outcomes and even less is known of the impact of vibration. Vibration is mostly
generated by freight trains, that predominantly traffic the rail during the night-time.
Aim: To investigate the relationship between railway vibration and noise and sleep disturbance.
Methods: The study population (N=7280) was randomly selected from residents living within 1km ofa trafficked railway in Southwest, Sweden. Survey data was combined with modelled exposures. The
study adopts a cross sectional design and used a logistic regression analysis.
Results: A dose effect relationship was found between sleep disturbance and vibration respective
sleep disturbance and noise. The fully adjusted odds ratio for sleep disturbance per 0.1 mm/s Vmax
was 1.39 (95% CI 1.34-1.45) whereas it was 1.21 per 1dB L night . (95%CI 1.18-1.24).
Conclusion: Sleep disturbance was significantly associated with vibration and noise levels. The
proportion of highly sleep disturbed tended to show an increase already from 0.2 mm/s Vmax andfrom 45 dB L night .
Keywords: Sleep, noise, vibration, railway

1. INTRODUCTION
Rail transportation is considered a sustainable mode of transportation, as it in comparison to othermodes of transportation have a small impact on the global climate. It is hence expected to increase
rapidly in the near future. However, rail transportation may still affect the health as people living
along the track are exposed to noise and vibration. Compared to road and air transportation, limitedscientific evidence is available regarding the health impacts due to noise of rail transportation andeven less is known of the impact of vibration due to rail traffic on people’s health. While high noiselevels are predominantly reported from high-speed train and freight trains, vibration is mainly
generated by freight trains. Freight trains may be particularly detrimental for sleep as they
predominantly traffic the rail during the night-time.
While previous studies indicated that rail traffic generated less sleep disturbance and annoyancecompared to other transportation modes (Miedema & Vos, 2007), newer studies point to a higher riskof both outcomes (Basner & McGuire, 2018; Guski, Schreckenberg, & Schuemer, 2017; Zhang & Ma,2021). The importance of further studies of railway and its impact on health was also pointed out inthe review of health effects due to noise in the last International Conference on Biological Effects on
Noise ICBEN 2021 (Persson Waye & van Kempen, 2021).
Frequently occurring sleep disturbances with fragmented and shortened sleep are per se reducing
life quality and health but are also believed to mediate long term h ealth effects due to noise andprobably also vibration. To increase the knowledge on sleep disturbance due to rail traffic, we herepresent a study investigating the impact of noise and vibration on sleep.

2. AIM
To investigate the relationship between railway noise and vibration and sleep disturbance.

3. METHODS
3.1 Study areas and population
The study population (N=7280) was randomly selected from residents living within 1km of atrafficked railway in Southwestern and Central Sweden. Criteria for the selected study areas were: (i)within 1 km of a railway in use, (ii) the railway was trafficked by a minimum of ten passing freighttrains per day and night, (iii) vibration measurements had been carried out in several dwellings, and(iv) no major motorways or air traffic nearby. The study was conducted in accordance with theHelsinki Declaration and approved by the ethical committee in Gothenburg.
3.2. Socio vibro-acoustic study
Between March and June 2017, up to two residents per household, aged 18–80 years old in the
study areas were invited to participate in the study. Postal questionnaires were sent to 35,011
individuals and two reminders were used. By means of the questionnaire we gathered information
about annoyance due to vibrations and noise, sleep disturbance due to vibrations and noise, and other
health aspects, and the determinants of these.
Sleep disturbance due to noise and vibrations was measured with the ICBEN standardized question
for annoyance, being slightly adapted to fit the purpose of measuring s leep. The following wording“Thinking about the last 12 months when you are home (with windows and doors closed) how muchis your sleep disturbed by [noise]/[vibrations] from [passenger trains]/[freight trains]/[fasttrains]/[diesel trains]/[maintenance on railway track]?” Answers were indicated on a 5-point category
scale: not at all, somewhat, rather much, very much and extremely. For the statistical analyses, theanswers on the questions were dichotomized, with very much and extremely sleep disturbed def ined
as being highly sleep disturbed. This paper focus on freight trains and passenger trains.
3.3 Exposure assessment
Noise and vibration exposure due to rail traffic was assessed for each participant by linking homeaddresses using Geographic Information Systems (GIS) to modelled equivalent rail traffic noise levels
and maximum vibration levels.
Modelled railway noise exposure levels for the most exposed façade were calculated using theNordic prediction method revised in 1996 (Naturvårdsverket, 1999). For each receiver point,
equivalent A-weighted sound pressure level (LAeq) for the total noise exposure from freight andpassenger traffic was assessed. Lnight specifically cover the night-time period 22.00 to 07.00.
Reflection loss and/or shadowing of buildings were not included, but this simplification was found to
give acceptable precision.
For the calculations of railway vibration a semi-empirical model was developed based on 829
measurements available from the Swedish Transport Administration in the study area together withgeology data from official maps (Ögren, Ekblad, Johansson, Koopman, & Persson Waye, 2021). The
resulting method categorized all soil types in the area into three classes and then estimated thevibration for different distances to the railway and depths of the main topsoil layer. The presence ofswitches, bridges, tunnels and road underpasses did not contribute significantly to the model accuracy,
and therefore were not included in the final model. Vibration exposure due to rail traffic was expressedas the maximum weighted indoor vibration velocity in mm/s according to the Swedish standard.
3.4 Statistical analyses
The proportion of people with sleep disturbance was calculated for exposure categories. The
vibration exposure categories chosen varied slightly along the exposure range so that we would obtaina sufficient number of people within each category. Thus, the vibration categories for the range 0.1 0.8 mm/s were divided into 0.1 mm/s steps, while for levels above 0.8 to 1.0 the range is given in 0.2

mm/s steps and within the range above 1 mm/s to 1.3 mm/s in 0.3 mm increments. The last category
above 1.3 mm/s includes all exposures above 1.3 mm/s. The noise categorization was done per 5dB
steps. For each category, 95% confidence intervals for the proportion of cases were calculated,
Logistic regression models were used to analyse the relationships between exposures and outcomes .
Unadjusted models were used to calculate the predicted probability for each outcome in relation to
the exposure. The logistic regression models would first predict the odds of having the outcome (e.g.
being highly disturbed by vibration from freight train), and then allow for the calculation of theprobability based on these predicted odds. These values were then plotted to visualize the associations.
In the fully adjusted models, the following adjustments were made: age, gender, level of education,
main employment, household income and number of years living in the dwelling.
Questions on sleep disturbance were asked separately for freight trains and passenger trains while
vibration and noise estimates captured the total train traffic, without division into freight or passenger
trains.

4. PRELIMINARY RESULTS
The distance between the railway track and the study participants' households was on average 411.5
meters. The noise exposure was normally distributed over the population ranging from 27.1 dB L nightto >66 dB Lnight with the highest proportion of people being exposed to noise levels of 40 to 55 dB
L night Vibration exposure varied from 0 to 2.95 mm/s with a skewed distribution. Of the study
population, 14.7% were exposed to more than 0.1 mm/s and 4.2% were exposed to more than 0.4mm/s.
The proportion of sleep disturbed and highly sleep disturbed by noise increased with increasing
noise levels. This was especially noticeable for sleep disturbance by noise from freight trains. For
example, at the exposure category of 55-60 dB L night . the proportion of participants being highly sleep
disturbed by noise from passenger trains was 4% (95% CI: 2.6-5.9) while it was 17.3% (95% CI:14.420.5) from freight trains. The exposure response function based on the predicted probabilities of theunadjusted logistic regression models regarding high sleep disturbance by noise from freight trains
indicates a curve linear growth from 45dB L night with a more rapid growth from 55dB L night . Theinterpretation of the shape of the curve needs to be done with caution given the low number of people
being exposed to lower and higher levels of noise. The association between high sleep disturbance bynoise from freight trains and noise level was statistically significant with a fully adjusted odds ratioof 1.21 per 1 dB L night. (95%CI 1.18-1.24).
Regarding vibration, the proportion of sleep disturbed and highly sleep disturbed increased withthe vibration exposure. This was especially noticeable for sleep disturbance by vibration from freighttrains. For example, at the vibration exposure category of 0.3-0.4 mm/s, the proportion of participants
reporting high sleep disturbance by vibration from passenger trains was 3.4% (95% CI: 0.9-8.5) while
it was 19.3% (95% CI: 12.7-27.6) for freight trains. The increase in proportion of highly sleep
disturbed for freight trains was most clearly seen from 0.1 mm/s to 0.5 mm/s, after which theassociation seemed to flatten out and increase again at levels exceeding 1.3 mm/s. The interpretationof the shape of the derived exposure response curve needs to be done with caution given the low
number of people being exposed to higher levels of vibrations. The association between high sleep
disturbance by vibration from freight trains and vibration level was statistically significant with afully adjusted odds ratio of 1.39 per 0.1 mm/s (95% CI 1.34-1.45).

5. Concluding comment
The study supports an association between both vibration and noise from trail traffic anddisturbed sleep. The disturbance was clearly higher for freight trains as compared to passenger trains.
The exposure response functions need to be corroborated in further studies but indicate an association
between high sleep disturbance already from 0.2 mm/s Vmax and from 45 dB Lnight.
Study limitations adhere to a risk of selection bias of the population due to poor response rate. Yet,when we compare the exposure response curve of annoyance in relation to ground -borne vibration in
our study with the curve from a Dutch study (Van Kamp et al., 2015) we find good similarities between
these curves (Vincens et al., 2021). This indicates that our study sample could be representative of alarger sample. Regarding the cross-sectional design, we are aware of the limitation regarding causality
inferences, although we find reverse causality less plausible than the direction hypothesized in thestudy.
Exposure calculations have precision limitations, this is especially the case for ground vibrations.
The calculations and models have been discussed at length between expertise in The Netherlands andSweden which has resulted in increased knowledge and precision. However, further refinements are
possible. For the estimation of noise exposures, we need to acknowledge that at the lower noise
exposures, other noise sources from e.g. road traffic could affect the results, even though wedeliberately avoided major roads during the study area selection. A general comment is that we should
not exclude the possibilities that a combination of exposures may affect the outcomes, even if these
exposures may involve different sense organs and hence mediated via different biological influencepathways. These possible interactions will be looked at in further analyses.

ACKNOWLEDGEMENTS
The study was funded by the Swedish Transport Administration and the national research fund
FORMAS Swedish research council for sustainable environment Dnr 942-2016-102. Thanks to Laura
MacLachlan for carrying out the socio-acoustic survey and to Arnold Kopman and members from theSwedish Transport Administration for in depth discussions of vibration exposure models.

REFERENCES
Basner, M., & McGuire, S. (2018). WHO Environmental Noise Guidelines for the European Region: A Systematic
Review on Environmental Noise and Effects on Sleep. Int J Environ Res Public Health, 15(3).
doi:10.3390/ijerph15030519
Guski, R., Schreckenberg, D., & Schuemer, R. (2017). WHO Environmental Noise Guidelines for the European
Region: A Systematic Review on Environmental Noise and Annoyance. Int J Environ Res Public Health,14(12). doi:10.3390/ijerph14121539
Miedema, H. M., & Vos, H. (2007). Associations between self-reported sleep disturbance and environmental noisebased on reanalyses of pooled data from 24 studies. J Behavioral sleep medicine, 5(1), 1-20.
Naturvårdsverket. (1999). Buller från spårburen trafik - Nordisk beräkningsmodell. Retrieved from
Persson Waye, K., & van Kempen, E. (2021). Non-auditory effects of noise: an overview of the state of the scienceof the 2017-2020 period. Paper presented at the The 13th ICBEN Congress on Noise as a Public Health
Problem, Karolinska Institutet, Stockholm.
Van Kamp, I., Van Kempen, E., Van Wijnen, H., Verjeijen, E., Istamto, T., Breugelmans, O., . . . Koopman, A.
(2015). Vibrations due to trains: Exposure response relations and the role of contextual and personal
factors. Paper presented at the Euronoise.
Vincens, N., Ögren, M., Li, H., van Kempen, E., Edgren, S., & Persson Waye, K. (2021). Epivib: hälsoeffekter av
att bo vid en järnväg. Retrieved from https://gup.ub.gu.se/publication/313071
Zhang, L., & Ma, H. (2021). Investigation of Chinese residents’ community response to high-speed railway noise.
Applied Acoustics, 172, 107615. doi:https://doi.org/10.1016/j.apacoust.2020.107615
Ögren, M., Ekblad, A., Johansson, P., Koopman, A., & Persson Waye, K. (2021). Ground and Building Vibration
Estimation for Health Impact Research. In Noise and Vibration Mitigation for Rail Transportation
Systems (pp. 487-495).

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0016
Early environmental quality and life-course mental health effects: The Equal-Life project

Irene van Kamp1 and the Equal-Life Team2
1 National Institute for Public Health and the Environment, Netherlands
2

See annex 1.

ABSTRACT
There is increasing evidence that a complex interplay of factors within children's environments, including environmental
noise, contributes to mental ill-health and suboptimal cognitive development later on in life. The concept of the life-course
exposome helps to study the impact of the physical (e.g., acoustic) and social environment on cognitive development andmental health in young people over time. Equal-Life develops and tests combined exposures. Data from eleven studies(N=240.000) linked to exposure data will provide insights and policy guidance into aspects of physical and social
exposures hitherto untapped at different scale levels and timeframes while accounting for social inequities. Exposure
assessment combines GIS-based environmental indicators with omics approaches and new data sources, forming the earlylife exposome. Statistical tools integrate data at different spatial and temporal granularity and combine exploratory machinelearning models with hypothesis-driven causal modeling. Equal-Life contributes to the exposome concept by 1) integrating
the internal, physical and social exposomes, 2) studying life-course effects on a child's development and mental health, 3)characterizing the child's environment at different stages and in different activity spaces, 4) looking at supportive
environments for child development, rather than merely pollutants, 5) combining physical, social indicators with novel
effect markers.
Keywords: Exposome, Children, Early Life Exposures, Mental health, Cognitive development
1.

INTRODUCTION

The places where children are born and grow up in matter, for their development and well-being later on in life.
Most of the time in early life is spent in the dwelling and its immediate vicinity and this comes together with arange of physical and social exposures, both negative and positive. And although we know that the impact of theseis a resultant of a complex interplay between them most environmental research, and this includes noise research,is domain and often source-specific and usually studied independent from the social and policy context.
Health inequalities are growing also in the domain of mental health or at least the awareness of these. Increased
health risks, at least in the western world, can be attributed to an accumulation of unfavorable circumstances in agiven neighborhood: Combined physical and social characteristics, and spatial features distinguish one
neighborhood from the other. These characteristics include aspects as levels of air pollution and noise, the numberof industrial sites, percentage of homeownership, age distribution, and the percentage of lower levels of educationand income, lower-quality housing, higher population density and less green areas, and distinguish suchneighborhoods in an unfavorable sense. Cause and impact are difficult to unravel, and so is the relationship between
the characteristics of the total environment - also referred to as exposome - and health. This is the case for physicaland social aspects but also for a combination of these and the interaction. However, research confirms a link
between education, profession, income, and health. These links are by no means straightforward. Whether healthinequalities can be attributed to a combination of neighborhood characteristics or to the people who live in themis often unclear. The concentration of vulnerable social groups in neighborhoods due to selection mechanisms
might explain the inequalities rather than the actual physical and social environment they share. Also, researchfrom an environmental psychology perspective reveals that environmental stressors do not always have the same
effects on health. Attitudes, expectations, sensitivity to the environment, coping strategies, and the conviction on
the part of individuals that they control their own living environments have been shown to play a crucial role.

1

Irene.van.kamp@rivm.nl

Figure 1: Domains of the exposome
Figure 1 shows the main domains of the exposome concept, offering a framework to study the complex interplay
between physical, social, and spatial characteristics, as described above. The exposome concept was coined in2005 and later expanded to emphasize the importance of human environmental exposures. 1,2 The concept refers to
the totality of exposures from a variety of external and internal exposures over a complete lifetime, from conception
onward. It reinforces the idea that health and its counterpart are the product of the individual's history of exposures,
resources, vulnerability, and coping capacity. Research into the exposome is facilitated by and, maybe, aconsequence of technological developments that complement more traditional epidemiological study designs.3
However, just labeling a study as an exposome study is no guarantee that all relevant exposures are considered.
There is still a tendency to ignore some exposures in favor of others, and in general, we can state that non-chemical
exposures, including noise, tend to be neglected in current exposome research in the field of environment andhealth.4
2.

EQUAL-LIFE APPROACH

Based on a person-context-process-time framework, the European Union's Horizon 2020 Equal-Life project5
addresses the mechanisms deemed relevant for the association between the exposome and child's cognitive
development and mental health.

Figure 1 Perspectives on the fit between children and their environment (adapted from6 )The main aims are to study the long-term effects of the interactions between the child and its environment during
development on mental health and cognitive development and to provide guidance to policymakers to optimize theenvironment of children based on these findings. The environment is hereby broadly defined to include the indoor
en outdoor environmental quality, features of the built and natural environment, social, economic and culturaldimensions, and perceived quality of place and life.
Four objectives are pursued to reach this aim:
Objective #1: Innovate exposure assessment combining aspects of early-life physical environments with social

aspects of the environment. Environments of children and adolescents/young adults are the point of attention
because they have systematically been understudied in this domain.
Objective #2: Define and map the pathogenic and salutogenic environmental factors of mental health and cognitive
development
Objective #3: Compose and explore a set of interventions for different life stages with the purpose of enhancing
the quality of settings and spaces relevant to children's activities in their social context, leading to more healthequity.
Objective #4: Develop evidence-based guidance that anticipates trajectories of changes in health distribution if the
exposome, or its elements, are altered at different levels.
Within Equal-Life, we distinguish between the external exposome, subdivided into the physical exposome, thesocial exposome, and the internal exposome. The physical exposome is directly related to the description of thestate of the environment - such as green spaces, sound, and air quality, and access to high-quality and safe public
spaces and playgrounds. The social exposome focuses on the social and psychosocial environment as well as onsocial (in)equities in mental health and cognitive development. This pertains to (in)equities in exposure distribution
and accumulation across generations, vulnerability to exposures, and potential equity impacts of urban (planning)
policies on different societal groups. The internal exposome is a consequence of external exposome filtered bypersonal vulnerabilities, cognitive processing and lifestyle. It is measured in the person and can include biomarkers(i.e., indicators of exposure, susceptibility or outcome) but can also include processes that happen in the body andthat can be analyzed as a whole. Biomarkers are biological indicators measurable in saliva, hair, blood or urine. A
well-known example is the hormone cortisol as a marker of stress.
Equal-Life's key outcomes are well-being, internalizing symptoms, externalizing behavior, cognitive effects
and diagnosed mental illnesses. Key mechanisms include stress, sleep, restoration, self-regulation, and coping.

3.

METHOD

3.1 Who is in the study
Equal-life makes use of existing data from cohort and school studies and will collect new data to enrich theexposure data in the cohorts, including physical exposure data of high granularity and social data that bettercharacterize the social and societal context and to gain a more in-depth understanding of the mechanisms of sleep,
self-regulation, and stress. We have access to eight European cohorts, one national longitudinal school study, andtwo cross-sectional studies. The cohorts cover seven Western European countries and include at least 226,807
children (mother/child pairs) for whom longitudinal data are available on mental health and cognitive development
endpoints, including biomarkers. The school studies, which are primarily cross-sectional, include an additional
15,866 children. Some of them also provide parental information and retrospective information on perinatal
circumstances. The data cover an age range from prenatal to 24 years.
3.2 What has been measured and will be measured
The cohorts and school studies were selected based on their focus on children and data availability on indicatorsof mental health and/or cognitive development and preferably containing information on the key mechanism
studied (stress, sleep, self-regulation, and restoration). The cohorts and school studies also include data at different
locations and times on a range of exposures, including aspects of the built environment, outdoor and indoor
environmental quality (air pollution, noise pollution, dampness, moulds, light and lifestyle related factors(nutrician, screen time, physical activity etc.). The cohort and school studies available within Equal-Life also
comprise data on sociodemographic characteristics and socioeconomic circumstances at the individual level of thechildren. These data are currently being grouped and described in detail in an overall matrix of exposure data forthe cohorts. This matrix will form the basis for further data harmonization. Exposure will focus on six domains:
outdoor and indoor environmental quality, built environment, natural environment, lifestyle, and social features.
Data will be enriched with features of the built environment and improved models to estimate levels of outdoor
and indoor noise and air exposure.
Apart from secondary analyses, six in-depth studies are designed within the framework of Equal-Life, including
a study into the activity patterns of different social and age groups to investigate what environments children
frequent, how often they go, and how much time they spend there to obtain more precise exposure estimates atplaces other than home or school. A second in-depth study is aimed at examining the influence of real-lifeclassroom scenarios on auditory processing and cognitive performance, with the ultimate aim to improve classroom

acoustics. A third and fourth in-depth study in (pre)school children in Germany and Belgium is aimed at the effectsof physical and social exposures at (pre-)school and at home on children's well-being, cognition, and EEGparameters of auditory processing. And finally, an in-depth study into sleep is being performed in Sweden, whilst
secondary analyses will make use of existing sleep data in the Austrian Alpine study. The main aim of these studies
is to gain more insight into the role of sleep in the association between combined exposures and mental healthindicators and cognitive performance.
3.3 Analyses
Equal-Life combines a non-targeted machine learning approach with hypotheses testing approaches making useof structural equation models and multivariate analyses. In order to get to grips with development in time and therole of exposure at different ages/life phases, trajectories of outcomes will be studied where data are available.

4. CONCLUSION

Mental ill-health is a growing problem in Europe, in general, and in particular among children and adolescents,
or at least the awareness of these problems is increasing and have become more visible, especially during the
pandemic. The life-course exposome concept, referring to the totality of exposures from conception onwards,
might be a way forward to better understand the environmental causes of this trend. The EU-funded Equal-Lifeproject develops and utilizes the exposome concept in an integrated study of the external exposome and measurable,internal biological factors and links those to a child's development and life course mental health. This is done while
using a novel approach combining exposure data to characterize, measure, model, and understand influences andinequities at different developmental stages. The study distinguished itself by paying ample attention to the roleof social factors and their interplay with the physical environment, by its focus on children and adolescents, andby taking mental health and cognitive development as the main outcomes, including non-chemical exposures, suchas environmental noise. The ultimate goal is to provide guidance to (local) authorities on how to develop the best
supportive environments for all children.
ACKNOWLEDGEMENTS

We gratefully acknowledge the contribution of the Equal-Life consortium members and the members of theadvisory committee (www.equal-Life.eu). This project has received funding from the European Union's Horizon
2020 research and innovation program under grant agreement No 874724 and is co-funded by the International
Strategic Research Scheme of the National Institute for Public Health and the Environment in the Netherlands andthe Dutch Ministry of Infrastructure and Water Management Directorate Sustainable Living Environment andCircular Economy. Equal-Life is part of the European Human Exposome Network (EHEN).
REFERENCES

1. Wild CP. Complementing the genome with an "exposome": the outstanding challenge of environmental exposure
measurement in molecular epidemiology. AACR, 2005.
2. Wild CP. The exposome: from concept to utility. International journal of epidemiology 2012;41(1):24-32.
3. Vineis P, Robinson O, Chadeau-Hyam M, Dehghan A, Mudway I, Dagnino S. What is new in the exposome? Environ
Int. 2020 Oct;143:105887. doi: 10.1016/j.envint.2020.105887. Epub 2020 Jun 30. PMID: 32619912. 4. Daiber, Andreas,
et al. (2019) The "exposome" concept–how environmental risk factors influence cardiovascular health. Acta Biochimica
Polonica,, 66.3: 269-283.
5. Van Kamp, Irene, et al. (2022) Early environmental quality and life-course mental health effects: The
Equal-Life project. Environmental Epidemiology, 6.1.
6. Van Kamp I, Leidelmeijer K, Marsman G, de Hollander A. Urban environmental quality and human well-being: Towards
a conceptual framework and demarcation of concepts; a literature study. Landscape and Urban Planning 2003;65(1):518. doi:https://doi.org/10.1016/S0169-2046(02)00232-3.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstract

Annex 1: The Equal-Life Team
Authors

Affiliation

Irene van Kamp
Kim van Wijk
Kerstin Persson Waye
Katja Kanninen
John Gulliver
Gabriele Bolte

Centre for Sustainability. Environment and Health, National Institute for Public Health and the Environment, Bilthoven, Netherlands

Alessandro Bozzon
Achilleas Psyllidis
Hendriek Boshuizen
Jenny SelanderPeter van den Hazel
Marco Brambilla
Marco Balduini
Maria ForasterMaria Klatte
Jan Spilski
Sonja Jeram
Dick BotteldoorenPeter Lercher
Gordana Ristovska
Jaakko Kaprio
Dirk Schreckenberg
Maarten Hornikx
Janina Fels
Miriam Weber

Centre for Sustainability. Environment and Health, National Institute for Public Health and the Environment, Bilthoven, Netherlands
School of Public Health and community medicine, Gothenburg University, Gothenburg, Sweden
A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finlands
Centre for Environmental Health and Sustainability & School of Geography, Geology and the Environment, University of Leicester, UK
Institute of Public Health and Nursing Research, University of Bremen, Bremen, Germany
Faculty of Industrial Design Engineering, Delft University of Technology, Delft, the Netherlands.
Faculty of Industrial Design Engineering, Delft University of Technology, Delft, the Netherlands.
Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
Unit of Occupational Medicine at the Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
International Network on Children’s Health, Environment and Safety, xx, The Netherlands
Data Science Laboratory, Politecnico di Milano, Milan, Italy
Data Science Laboratory, Politecnico di Milano, Milan, Italy
Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
Department of Cognitive and Developmental Psychology Technical University Kaiserslautern, Germany
Department of Cognitive and Developmental Psychology Technical University Kaiserslautern, Germany
National Institute of Public Health, Ljubljana, Slovenia
Department of Information technology, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium
Institute for Highway Engineering and Transport Planning, Graz University of Technology, Graz, Austria
Institute of Public health of the Republic of North Macedonia, Skopje, North Macedonia
Institute for Molecular Medicine Finland, Department of Public Health, University of Helsinki, Helsinki, Finland
Centre for Applied Psychology, Environmental and Social Research (Zeus GmbH ), Bochum, Germany
Built Environment Technical University Eindhoven, Eindhoven, The Netherlands
Hearing Technology and Acoustics, RWTH Aachen University, Aachen, Germany
City of Utrecht, Utrecht, Te Netherlands

Ella Braat-Eggen
Jordi Julvez CalvoCharlotte Clark
Tanja Vrijkotte

Avans University of applied Science, Tilburg, the Netherlands
Institut d'Investigació Sanitària Pere Virgili (IISPV), Barcelona, Spain
Population Health Research Institute, St George's University of London, London, UK
Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam ,The Netherlan

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0775

Environmental exposure to transportation noise during pregnancy and birthoutcomes in NordSOUND – a register-based study in the Nordic countries
Arat Arzua, Skröder Helenaa, Lanki Timob,c, Tiittanen Pekkac, Turunen Anuc, Persson Waye Kerstind,
Roswall Ninae, Sørensen Mettee, Pyko Andreif,a, Ögren Mikaeld, Selander Jennya
a Occupational Medicine, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Swedenb School of Medicine, University of Eastern Finland, Kuopio, Finlandc Environmental Health, Finnish Institute for Health and Welfare, Kuopio, Finlandd School of Public Health and Community Medicine, Gothenburg University, Swedene Danish Cancer Society Research Center, Copenhagen, Denmarkf Center for occupational and environmental medicine, Region Stockholm, Sweden

ABSTRACT
Background: Studies have indicated that environmental exposure to noise during pregnancy may increase
the risk of adverse birth outcomes. However, these associations are not clear particularly for transportationnoise and there is a need for high-quality studies.
Objectives: To assess whether transportation noise exposure during pregnancy is associated with the risk of
small-for-gestational age (SGA), preterm birth (PTB), and low birth weight (LBW).
Methods: Registry-based cohorts from Sweden (n=179,194), Finland (n=138,158) and Denmark(n=177,240) were used, and residential outdoor levels of road and rail traffic noise were modelled using theNordic prediction model. Country-specific results of logistic regression analyses adjusted for potential
confounders (maternal age, parity, marital status, smoking and education, neighborhood income, trafficrelated air pollution, and access to green and blue areas) were combined in meta-analyses.
Results: Preliminary results show that exposure to road traffic noise (per 10 dB increase) was associatedwith a 3% non-significant increased risk of SGA. Exposure to railway noise (per 10 dB increase) was
associated with a significant increased risk of SGA (OR=1.06, 95% CI=1.01, 1.10). No clear associations
were found for PTB and LBW.
Conclusions: Elevated levels of rail traffic noise at the residence during pregnancy may increase the risk of
SGA.
Keywords: birth outcomes, transportation noise, pregnancy

1. INTRODUCTION
Noise is an omnipresent pollutant that has been associated with both auditory and non-auditory
health effects. It has been estimated that at least one million healthy life years are lost every year inWestern Europe due to noise exposure (1).
A few studies have shown increased risk for small for gestation al age (SGA) (2) and low birthweight (LBW) (3) among the offspring of mothers exposed to noise during pregnancy. However, theevidence on the effects of noise on birth outcomes is inconclusive. A systematic review by Hohmann
et al. (4) and a more recent review by Dzhambov et al. (5) found inconclusive results on theassociations between maternal exposure to environmental noise during pregnancy and birth outcomes
including low birth weight (LBW), small for gestational age (SGA) and preterm birth (PTB).
Furthermore, in a literature review by the World Health Organization (WHO), Nieuwenhuijsen et el.
(6) concluded that the number of studies on environmental noise and birth outcomes was small, andthat the quality of evidence generally ranged from very low to low. Therefore, there is a need for high quality studies that have robust and comparable exposure assessment methods, and that disentangle
associations for different sources of noise, while controlling for multiple confounding factors
including air pollution, green space, and blue space.

The objective of this specific study is to assess whether road and rail traffic noise exposures areassociated with adverse birth outcomes namely preterm birth, low birth we ight and small for
gestational age, by using the cohorts from four Nordic countries included in the project.

2. METHODS
Study participants
Data from registries in Sweden (n=179 194), Finland (n= 138 158), Denmark (n=176 695), andNorway (n= 96 407) were used to assess the risk of adverse birth outcomes in relation to residential
transportation noise exposure during pregnancy. The study period ranged from 2004 -2016 and theincluded areas were the metropolitan areas of Stockholm, Gothenburg, Helsinki, Co penhagen, andOslo.
Outcome measure
National population registers were used to collect information regarding the birth outcomes. We
classified singleton births with pregnancy duration of at least 22 weeks into PTB, SGA and LBW. PTB
was defined as <37 full weeks of gestation (<259 days) and LBW as birth weight <2500 g. SGA wasdefined as birth weight less than -2SD of the mean for each gestational week, for boys and girls
separately.
Exposure assessment
Information on addresses during pregnancy was collected from registries and used for modellingof transportation noise. Exposure from different transportation noise sources was calculated as day evening-night noise levels (Lden) using the well-established Nordic prediction method (7). Noiselevels were estimated for each year of the study. The façade noise level within 20 m from theresidential coordinates was used to assess exposure to road and rail traffic noise. Exposure totransportation noise was calculated for the entire pregnancy.
Statistical analysis
Preliminary analyses have been conducted only in Sweden, Denmark, and Finland. Birth outcomes
were analysed in relation to residential noise exposure using logistic regression. Exposure wasincluded linearly for road- or rail traffic but was also analysed in categories for assessment of nonlinear associations. The confounders included were registry-based information on individual and arealevel factors, as well as modelled data at the home address. Covariates were selected a priori and were
included stepwise in different models, and the final model was adjusted for maternal age, parity,
smoking, education, marital status, neighbourhood income (below 20th percentile yes/no), NO2 (PM1in Sweden), access to green and blue areas, aircraft noise (above or below 50 dB) and occupational
exposure to noise (four categories).

3. PRELIMINARY RESULTS
The meta-analyses for linear exposure (by 10 dB increase in noise level) revealed increased risk
for SGA in relation to road traffic noise (OR=1.03, 95% CI=0.99-1.06) as well as railway noise(OR=1.06, 95% CI=1.01, 1.10, per 10 dB increase). As the numbers show, the association was stronger
and significant only for the railway noise. The results did not show any clear associations for PTB or
LBW due to exposure to rail or road traffic noise. There was even a small significant decreased riskof PTB and LBW in relation to road traffic noise, although the risk was not present in all countries
and was possibly due to residential confounding.

4. CONCLUSION
Exposure to elevated levels of residential rail and road traffic noise during pregnancy may increase the riskof SGA indicating an effect of noise on foetal growth. This association was most clear for rail traffic noise.

5. REFERENCES
1. WHO. 2011. Burden of disease from environmental noise. Bonn, Germany.
2. Ristovska et al. Reproductive outcomes associated with noise exposure - a systematic review of theliterature. Int J Environ Res Public Health. 2014;11(8): 7931-7952.
3. Dzhambov et al. Noise exposure during pregnancy, birth outcomes and fetal development: meta-analyses using quality effects model. 2014. Folia Med (Plovdiv) 56(3): 204-214.
4. Hohmann et al. Health effects of chronic noise exposure in pregnancy and childhood: a systematic
review initiated by ENRIECO. 2013. Int J Hyg Environ Health 216(3): 217-229.
5. Dzhambov, A. and Lercher P. Road Traffic Noise Exposure and Birth Outcomes: An Updated
Systematic Review and Meta-Analysis. 2019. Int J Environ Res Public Health 16(14).
6. Nieuwenhuijsen et el. WHO Environmental Noise Guidelines for the European Region: A
Systematic Review on Environmental Noise and Adverse Birth Outcomes. 2017. Int J Environ Res
Public Health 14(10).
7. Bendtsen H. The nordic prediction method for road traffic noise. Science of The Total Environment.
1999. 235:331-338.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS- 0833

The relevance of the number of aircraft events for children’s cognition:
Further evidence from a machine learning algorithm
Jan SPILSKI1, Ruth KOOLE2, Christoph GIEHL1, Hendriek BOSHUIZEN2,
Kirstin BERGSTRÖM1, Thomas LACHMANN1 3, Maria KLATTE1
1

Center for Cognitive Science, University of Kaiserslautern, Germany

2

RVIM, Rijksinstituut voor Volksgezondheid en Milieu , Netherlands

3

Centro de Investigación Nebrija en Cognición (CINC), Universidad Nebrija, Madrid, Spain

ABSTRACT
Chronic aircraft noise impairs children’s cognition. In the literature, LAeq and Lden are the dominating noise
exposure metrics for calculating the relationship between noise and cognition, although other noise metrics
such as the number above threshold (NAT) exist. There is initial evidence from regression models that theNAT metric served as a second (unique) significant predictor over and above LAeq. However, regression
models only consider a limited number of noise metrics due to possible multicollinearity problems. This
necessary selection of variables may have resulted in a bias in the results regarding the relevance of noise
metrics for children’s cognition. In contrast, machine learning (ML) methods are able to consider thecomplete scope of variables in an analysis while dealing with possible multicollinearity of predictors. For
this reason, we re-analyzed previous results of the NORAH study using random forest models (one type ofML) and 65 traffic noise exposure metrics. We found the NAT metric to be an important predictor ofchildren’s reading achievement. We recommend the NAT metric to be considered in the Law for Protection
against Aircraft Noise.
Keywords: NAT, Children, Cognition

1. INTRODUCTION
Children are vulnerable to traffic noise because they are less able to cope with environmental
stressors than adults (1). Previous studies show particularly negative effects of the stressor chronic
aircraft noise exposure on children's cognition (e.g., 2, 6). Clark and Paunovic report in their WHO
review that the evidence regarding negative effects of aircraft noise on reading achievements ofprimary school children can be considered well established (3). However, Thompson et al. (4)concluded in their recent systematic review with meta-analysis that there is only moderate evidence
for an association between aircraft noise and reading achievements of children across studies. In theirmeta-analytic examination of the relationships, only the LAeq was included. Obviously, the LAeq isthe dominant metric in the literature, although other metrics exist that are less rough than an averagelevel and could improve the exposure description. Therefore, Kamrath and Vigeant (5) have demanded
alternative metrics in order to achieve a substantial increase in explained variance of children ’s
cognitive performance.
To achieve an incremental value of other exposure metrics that may predict negative effects ofaircraft noise on children´s cognition, Spilski et al. (7) conducted further analyses of the NORAH dataset, with different aircraft noise exposure metrics. Their previous multi-level regression results (7)confirmed that the number above threshold (NAT) served as a second (unique) significant predictor
over and above LAeq, for the harmful effects of aircraft noise exposure on children’s cognition. This
supports the idea that, when assessing effects of aircraft noise on children ´s cognition, both theaverage of noise intensity (as mirrored by the LAeq), and the number of flight events above a certain
noise threshold (NAT) should be considered.
However, regression models only consider a limited number of noise metrics due to pos sible
multicollinearity problems. For this reason, we could only include a maximum of three aircraft noise
metrics in our prior regression models without having estimation problems ( 7). This necessary
selection of variables may have resulted in a bias in the results regarding the relevance of aircraft
noise metrics for children’s cognition. In contrast, machine learning (ML) methods are able toconsider the complete scope of variables in an analysis while dealing with possible multicollinearity
of predictors. Random forest models (8) are one type of ML. Although these methods can offer
advantages, they have hardly been used in the research context of traffic noise and children ’s cognition.
For this reason, we re-analyzed the NORAH data with a random forest model (one type of ML) and65 traffic noise exposure metrics. We focused on reading as an outcome to show whether the NAT
metric in addition to the LAeq and other traffic noise metrics shows up as a significant noise exposure
metric.

2. Method
2.1 Participants
The present data set was collected within the NORAH study. Detailed information on sampling areprovided by Klatte et al. (6). Mean age for children was 8 years and 4 months (SD 5 months).
Concerning children's cognition, school and living environment, socio factors, complete data wereavailable for 1,118 children and their parents.
2.2 Assessment of Acoustic Metrics
For details about the assessment of traffic noise metrics see Moehler et el. (9). Road traffic andrailway noise levels (LAeq, Lden, LAmax) were estimated for the children's school and homeaddresses using a combination of information (e.g., traffic flow data, number of train journeys) thatwere provided by local authorities. Aircraft noise levels at children's school and home address (LAeq,
Lden, LAmax) and the number above thresholds (NAT) for bands of five dB(A) were calculated forthe time period of 12 months before the data collection was conducted. Descriptive statistics forselected metrics at the home and school address are shown in Table 1. For more information about
room acoustics in the NORAH study, see Klatte et al. (10).
Table 1 - Descriptive statistics for selected traffic noise metrics (NORAH study)
Aircraft
Home

Road traffic

M

Range

M

Range

RailwayM

Range

LAeq, 06-22

dB (A)

49.15

36.40-60.80

53.24 36.20-77.30

45.94

30.00-76.80

LAeq, 22-06

dB (A)

42.47

30.00-55.10

45.05 30.10-67.80

46.58

30.00-78.10

Lden, 00-24

dB (A)

51.74

40.80-63.60

54.86 37.40-78.50

51.24

30.00-84.20

LAmax, 22-06

dB (A)

57.95

39.00-76.00

LAmax, 00-24

dB (A)

62.32 35.00-96.00

61.81

30.00-97.00≥60 <65 dB (A)

45.52

0.04-165.23≥65 <70 dB (A)

28.77

0.01-180.37≥70 <75 dB (A)

11.60

0.00-121.21≥75 <80 dB (A)

3.90

0.00-40.73

Home≥60 <65 dB (A)

4.61

0.00-15.37

Night 22-06≥65 <70 dB (A)

3.05

0.00-14.79

Home
Day 06-22
NAT (bands)

NAT (bands)

School
Day 08-14
NAT (bands)≥70 <75 dB (A)

1.33

0.00-12.31≥75 <80 dB (A)

0.41

0.00-5.31≥50 <55 dB (A)

23.71

4.15-57.75≥55 <60 dB (A)

22.60

0.67-66.33≥60 <65 dB (A)

18.01

0.03-58.77≥65 <70 dB (A)

9.95

0.00-63.61

Notes. Taken from Spilski et al. (12) and Spilski et al. (7). Table 1 shows a sample of selectedtraffic noise metrics. A total of 65 traffic noise metrics was included in the random forest
calculations.

2.3

Tasks, Materials and Procedure

The following description of the procedure has been published elsewhere (e.g., 6). The questioningof the children was performed in groups of whole classes. The scales used in the children questionnaire
comprised health-related quality of life, home and school environment, annoyance, and noise at home,among others. The parental questionnaire contained questions concerning children ’s well-being,living environment, health-related outcomes and socio factors (e.g., SES). Reading was assessed
through a standardized reading comprehension test for primary school children instructed in German
language (11). In addition to reading, the test battery included nonverbal abilities and verbal
precursors of reading acquisition, i.e., story comprehension, phonological awa reness, and rapid accessto phonological representations. More details concerning the procedure, the questionnaires, andstatistical examination of psychometrics are provided by Klatte et al. (10). Population density(urbanization) and average imperviousness (excluding green space) were calculated. For more details
see Spilski et al. (12).
2.4

Statistical Analysis

For statistical analysis, we used the random forest algorithm. The random forest algorithm is asupervised machine learning method based on multiple decision trees (8). Supervised machinelearning refers to the use of labeled training data. The training data are labeled as there is a known
target (e.g., reading comprehension) for each data point. The machine learns to map the known result(target) as best as possible with the available data. For this purpose, many trees are used and not only
one tree.
This supervised machine learning model can be used for both classification and regression
problems, depending on the type of outcome variable. In our analysis, the random forest algorithm
was used for a regression task, since our target reading was measured as a continuous variable (TScore). We included 162 predictor variables (including 65 traffic noise exposure metrics, examples
were given in Table 1) and the target “reading comprehension” in the machine learning model. The
implementation of the random forest algorithm was done in the "randomForest" R package (1 3) incombination with the "iml" (14) and "partykit" (15) R package to obtain additional measures of model
quality.
Feature (variable) importance is the most common used statistic when interpreting random forest
results. The importance of variables can be expressed as mean decrease in prediction accuracy(referred to as "Mean Decrease Accuracy", MDA) when the values of the predictor variable are
randomly permutated in a so-called out-of-bag sample (16). The random forest utilizes bootstrapping
on a subset of variables for each tree. This subset of variables is "in-the-bag" for one tree. Thus, theout-of-bag sample contains the subset of variables not included in the bootstrap sample of one specifictree (17). Since only a subset of the variables is considered at each branch of a decision tree, theinfluence of strongly correlated features can be reduced. Therefore, random forest models can deal
with many correlated traffic noise variables and associated multicollinearity problems. The MDA asa statistical parameter for the interpretation of random forest quantifies the mean increase ofprediction error across all decision trees if the feature (variable) was omitted from the trees .
In this paper we focus on the target reading comprehension and used the MDA as importance metric.
For this purpose, 1,000 randomization trials with a maximum of 5 nodes per tree were performed toobtain robust prediction trees as a result.

3. Results
A total of 162 predictors was included in the random forest analysis. Figure 1 shows the results of
the random forest analysis for the most important 25 predictors. Not surprisingly, cognitive
achievement such as the long-term memory, rapid retrieval of phonological representation andphonological awareness are the most important predictors of reading achievement. In addition tocognitive predictors, education-supportive variables such as the number of books at home,socioeconomic status, parental support for school, and attitudes toward school and reading (Joy at
school, school confidence, I only read when I have to) emerged as additional significant predictors.
The traffic noise related predictors with an influence on the outcome reading achievement arehighlighted in grey. It was found that only the aircraft noise exposure variables (but not road traffic
and railway noise exposure) were identified as an important predictor. In particular, the number offlight events (NAT) emerged as a relevant predictor. Most important, the analysis showed the numberof aircraft noise events between 10 p.m. and 6 a.m. with 65 ≤ noise exposure < 70 dB (A) and with70 ≤ noise exposure < 75 dB (A). Both night-time and daytime metrics are important for predicting
reading comprehension, however, with an emphasis on night-time-related metrics. Interestingly, theLAeq, 05-06 a.m. in the morning off-peak time was found to be important in addition to the NAT
predictors.

Figure 2 - Results of random forest algorithm – Top 25 variable importance plot,
MDA = Mean Decrease Accuracy

4. CONCLUSIONS
While data sets in studies on traffic noise exposure often include a wide range of potential predictor
variables, regression models quickly become a challenge because of statistical limitations like
multicollinearity. For this reason, Spilski et al. (7) could only include a maximum of three aircraft
noise exposure metrics in a multi-level regression model without encountering estimation problems.
With the present paper, we aimed to address this possible bias in the results regarding the relevanceof correlated aircraft noise measures for children's reading achievement. Therefore, we re-analyzed
the NORAH data with a random forest model (one type of ML) and 162 predictor variables (including
65 traffic noise exposure metrics). Cognitive achievement (e.g. rapid retrieval of phonologicalrepresentation), education-supportive variables (e.g., number of books at home, socioeconomic status)
and attitudes toward school and reading (e.g., joy at school) are important predictors of reading
achievement. In addition, eight traffic noise exposure variables were important predictors under the
TOP 25 predictors. Surprisingly, only the aircraft noise exposure metrics were important, but not theroad and railway traffic noise exposure variables.
Further, the NAT metric was the dominating aircraft noise exposure variable as predictor of reading
achievement in primary school children. The night time related metrics NAT ≥65 <70 dB, (A) andNAT ≥70 <75 dB, (A) proved to be the most important. Interestingly, the LAeq, 05-06 a.m. in themorning off-peak time was found to be important in addition to the NAT predictors. These results
suggest that the last sleep phase of primary school children and the number of disturbances caused by
the number of aircraft noise events above thresholds have an impact on reading achievement inchildren. These results indicate that the final sleep phase of primary school children and the numberof potential disturbances (number of aircraft noise events) during fall asleep and sleep phase can havean impact on reading achievement.
In addition, the results show that random forest models can serve as a tool to identify the (relative)
importance of predictors that could not be included in one and the same regression model. To avoid
multicollinearity, a multitude of sequentially conducted regressions would have been necessary, which,however, would have increased type I error (18). However, we also see that random forest models
may not be able to handle very hierarchical data as given for aircraft noise exposure per class.
Furthermore, it seems that mediation relationships as shown by Spilski et al. (7) are not represented
by random forest models. Both of the aforementioned limitations may have resulted in only homebased aircraft noise exposures proving to be important, as there is no hierarchical dependence here
and possibly more direct effects exist. For these reasons, more complex machine learning approaches
such as neural networks should be examined in the future and theory-based analyses should continue.
Nevertheless, we again found that the NAT metric is an important predictor of children's reading
achievement, although we used a more exploratory "unbiased" statistical approach (ML: random
forest). Along with previous results by Spilski et al. (7), this is another indication that the NAT
criterion should be considered more strongly in research and noise protection in the future.

ACKNOWLEDGEMENTS
This study is part of the NORAH research project and the EU Horizon 2020 project EqualLife.
NORAH is commissioned by the Environment & Community Center / Forum Airport & Region,
Kelsterbach, Germany. EqualLife is funded by the EU commission Horizon 2020, grant agreement ID
874583.

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159. (2013).
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Review on Environmental Noise and Cognition. Int. J. Environ. Res. Public Health. 2018; 15, 285(2):123.
4. Thompson R, Smith RB, Karim YB, Shen C, Drummond K, Teng C, Toledano MB. Noise pollution andhuman cognition: An updated systematic review and meta-analysis of recent evidence. Environment
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5. Kamrath M, Vigeant MC. Gaps in the literature on the effects of aircraft noise on children’s cognitive
performance. The Journal of the Acoustical Society of America. 2014; 136(4): 2304.
6. Klatte M, Spilski J, Mayerl J, Möhler U, Lachmann T, Bergström K. Effects of aircraft noise on readingand quality of life in primary school children in Germany: Results from the NORAH study. Environment
and Behavior. 2017; 49(4), 390-424.
7. Spilski J, Bergstroem K., Mayerl J, Moehler U, Lachmann T, Klatte M. Aircraft noise exposure andchildren’s cognition: Evidence for a daytime NAT criterion. Proc Internoise 2017; 27-30 August 2017;
Hong Kong, 2017. p. 457-463.
8. Breiman L.Random Forest. Machine Learning. 2001; 45: 5-32.
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Verkehrslärmwirkungen im Flughafenumfeld: Erfassung der Verkehrsgeräuschexposition [Effects oftraffic noise in areas surrounding airports: Report of methods of noise exposure assessment]. 2014:
available from: URL: www.laermstudie.de/ergebnisse/basismodul-akustik.
10. Klatte M, Bergstroem K, Spilski J, Mayerl J, Meis M. Wirkungen chronischer Fluglärmbelastung auf
kognitive Leistungen und Lebensqualität bei Grundschulkindern Endbericht [Effects of chronic aircraft
noise exposure on cognitive performance and quality of life in primary school children Finalreport]”, NORAH Verkehrslärmwirkungen im Flughafenumfeld–Endbericht. 2014.
11. Lenhard W, Schneider W. Ein Leseverständnistest für Erst- bis Sechstklässler (ELFE 1-6) [Reading
comprehension test for grades 1 to 6]. Goettingen, Germany: Hogrefe; 2006.
12. Spilski J, Rumberg M, Berchterhold M, Bergstroem K, Moehler U, Kurth D, Lachmann T, Klatte M.
Effects of aircraft noise and living environment on children's wellbeing and health. Proc ICA 2019; 0913 September; Aachen, Germany, 2019. p. 7080-7087.
13. Breiman L, Cutler A. Package 'randomForest', v.4.7-1. Breiman and Cutler's Random Forests forClassification and Regression. 2022:
https://cran.r-project.org/web/packages/randomForest/randomForest.pdf (28.04.2022).
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18. Spilski J, Giehl C., Boshuizen H, Wong A, Bergstroem K, Lachmann T, Klatte M. Traffic noise andchildren’s health: New insights from machine learning algorithm? Proc. Internoise 2022; 21-24 August2022; Glasgow, Schottland, 2022.

ABS-0129

Development and evaluation of an artificial ear for quantifying the objective
occlusion effectYu LUAN(1) , Marc-Olivier CYR-DESROCHES(2) , Kévin CARILLO(3) , Olivier DOUTRES(4) , Franck SGARD(5)(1) École de Technologie Supérieure, Montreal (QC), Canada, yu.luan.1@ens.etsmtl.ca(2) École de Technologie Supérieure, Montreal (QC), Canada, mo.c-desroches@softdb.com(3) École de Technologie Supérieure, Montreal (QC), Canada, kevin.carillo.1@ens.etsmtl.ca(4) École de Technologie Supérieure, Montreal (QC), Canada, olivier.doutres@etsmtl.ca(5) Institut de recherche Robert-Sauvé en santé et en sécurité du travail, Montreal (QC), Canada, franck.sgard@irsst.qc.ca

ABSTRACTHearing protectors, such as earplugs remain the last resort to protect workers from occupational noise. However,wearing earplugs is accompanied with discomfort issues, one of which is the occlusion effect (OE) associatedwith the increased auditory perception of the bone-conducted sound at low frequencies when occluding theearcanal. The objective OE is commonly measured by the difference between the tympanic sound pressurelevels in the open and occluded ears when the head is excited by a bone transducer. Artificial test fixtures(ATFs) could serve as useful tools for designing more comfortable earplugs with reduced OE, given their easeof manipulation. However, the existing ATFs dedicated to OE assessments (thus accounting for the outer ear
bone conduction path), do not allow for closely reproducing the phenomena observed on human participants.
Additionally, the numerical models used to design these ears have not been experimentally validated. This work
aims to develop an artificial ear for the objective quantification of OE, which is simple in geometry but realistic
enough to replicate the principal vibroacoustic behavior of the human outer ear. First, it is designed throughfinite element modeling. A corresponding test fixture is then physically fabricated and used for experimental
evaluation of the design.

Keywords: Artificial ear, Occlusion effect, Finite element modeling

1

INTRODUCTION

Hearing protection devices, such as earplugs, remain the last resort for protecting workers exposed to hazardous
noise. However, wearing earplugs is usually accompanied with discomfort issues, which may lead to an improper use and limit their actual efficiency in practice. One of the discomfort issues is the occlusion effect(OE). It commonly refers to the increased auditory perception of the bone-conducted sound at low frequencies(typically below 1 – 2 kHz) when the ear is occluded [1]. The experienced OE can be attributed to the outer,
middle and inner ear bone conduction paths. But as the bone conduction hearing in an occluded ear is dominated by the outer ear path at low frequencies [2], an objective indicator of the OE can be obtained by thedifference between the tympanic sound pressure levels in the open and occluded earcanals of a human subject
when his head is stimulated by a bone transducer (referred to as the objective OE).
It is recognised that the earplug position in the earcanal and the type of earplug have an impact on the OE[1]. However, earplug manufacturers often lack appropriate tools to design their products with the minimum
OE. Instead of the direct measurement on human subjects, acoustic test fixtures (ATFs) could serve as usefultools for assessing the objective OE, better understanding its cause, and even ultimately designing more comfortable earplugs with a reduced experienced OE, given their ease of manipulation. But the most commonly usedcommercial ATFs conforming to the ANSI standard [3] are not supposed to account for the bone conduction.
A few previous studies have attempted to develop virtual and experimental testers for designing ATFs dedi-

cated to assessing the objective OE. Brummund et al. [4] have built a 2D axisymmetric artificial (outer) ear1 tovalidate the OE simulated by a finite element (FE) model. Such a design is simple in geometry and allows for a
low computational cost when performing numerical parametric studies. However, its geometry with an oversized
cartilage part, is not realistic enough to represent a human ear. In addition, due to a lack of time and resources,
this artificial ear has not been fully tested and could not reproduce the loading condition used in the model.
More recently, a more realistic 3D artificial ear has been developed based on a corresponding FE model [5, 6].
Its geometry is reconstructed from the medical images of a real human ear and includes different components,
such as the bone, cartilage and skin. This artificial ear is truncated and adapted to facilitate the experimental
manipulation under similar loading and boundary conditions as the model. But the excitation is put on the bonypart, which is not close to the tests on human subjects where a bone transducer is used to directly stimulate thesoft tissues. Additionally, the design model has not yet been validated since large discrepancies were observed
between the simulated and measured sound pressure levels in both the open and occluded earcanals.
Besides the truncated ears mentioned above, FE models of the entire human head can be found in theliterature [7], which are able to use more realistic anatomies and boundary conditions for capturing the OE
induced by the bone conduction through the head. But on the other hand, the numerical study of Carillo et
al. [8] has recently shown that a truncated ear, under judicious loading and boundary conditions (i.e., radially
excited on its surrounding tissues while partially constrained), can correctly reproduce the vibratory behaviorof the earcanal walls (which is regarded as the source of OE), and thus provide a satisfactory OE prediction
compared to the experimental data on human subjects. However, this is challenging to realize in practice.
The goal of this paper is to develop a simplified artificial ear but adequate for quantifying the OE, and tovalidate the design in a controlled environment. First, the design of the artificial ear is proposed based on a
FE model, which is simple in geometry but accounts for essential components involved in the bone conduction
through the outer ear. Such a design also allows for a vibration excitation directly on the soft tissues. Second,a corresponding prototype is physically fabricated and used to carry out OE measurements in a laboratoryenvironment. The evaluation of the design is achieved by comparing the measured OE on the artificial ear
prototype with i) that simulated using the FE model for two types of occlusion devices under similar conditions
and ii) human test data in the literature.

2

DESIGN AND FABRICATION OF THE ARTIFICIAL EAR

As mentioned in the introduction, the designed artificial ear accounts for important components involved in
the bone conduction through the outer ear, namely the bone, cartilage and soft tissues (see Fig. 1(a)). The
proportion of the volumes of the various components surrounding the earcanal is kept unchanged relative tothe realistic 3D artificial ear proposed by Benacchio et al. [5]. Instead of a curved earcanal, in this paper,
a cylindrical one with a diameter of 6.6 mm and a length of 30 mm is used to simplify the problem. The
eardrum impedance is not accounted for, and the earcanal terminal surface is considered to be acousticallyrigid. Additionally, a miniature microphone is placed at the end of the earcanal to measure the sound pressure
approximately at the “eardrum position” [9]. It is cylindrical in shape whose diameter and length are 2.8 mm
and 2.6 mm, respectively. More information about the geometry of the artificial ear is given in Section 3.2.1.
Loading and boundary conditions are chosen to replicate as closely as possible the ones adopted in the
numerical study of Carillo et al. [8]. More specifically, a parallelepiped shape is used for the artificial ear,which is about 28.4 mm × 38.4 mm × 37 mm in size. The test bench presented in Fig. 1(b) is used for theexperimental evaluation, which includes an electrodynamic shaker connected to a vibrating plate for generating
a vibration excitation perpendicular to the artificial ear and directly on the soft tissues. Moreover, there is aforce sensor which remains motionless and can provide a certain constraint on the bony part of the ear (seealso Section 3.1).
A corresponding prototype of the designed artificial ear is fabricated by the company True Phantom Solutions (Windsor, Canada) using epoxy-based composite material for the bone, and urethane-based soft resins ofdifferent hardnesses for the cartilage and soft tissues (see part 3 in Fig. 1(b)).
1 This study only focuses on the occlusion effect assessed objectively with sound pressures in the earcanal (i.e., outer ear). In the following, if

not otherwise specified, the abbreviation "OE" refers to the objective occlusion effect and the term "artificial ear" to an artificial outer ear.

2

Bone

4

Earcanal
entrance

3

Mic

2

Soft tissues

1

(a)

(b)

Cartilage

Figure 1. (a) Design of the artificial ear (symmetric view); (b) fabricated prototype fixed on the test bench forvalidation: 1) electrodynamic shaker, 2) vibrating plate, 3) artificial ear prototype, 4) force sensor.

3

VALIDATION METHODOLOGY OF THE ARTIFICIAL EAR

The OE is first measured on the fabricated prototype and then compared with that simulated using a corresponding FE model. The experimental procedure and modeling strategy are explained in the two following
subsections, respectively.
3.1 Experimental procedure
The OE measurements are performed in a sound absorbing room. The artificial ear prototype is placed between(and in contact with) the force sensor2 and vibrating plate (see Figs. 1(b) and 2). The upper part of the test
bench on which the force sensor is fixed, is pre-adjusted vertically so that the prototype is subjected to a static
compression rate of about 0.8%. The dynamic mechanical excitation on the artificial ear prototype (throughthe vibrating plate) is produced by the shaker, which is fed with white noise using an audio signal generator(SRC20, Larson Davis Inc., USA). An accelerometer (322A41, Dytran Instruments Inc., USA) is attached to
the vibrating plate for measuring the displacement imposed by the shaker, and a miniature microphone (FG23629-P16, Knowles® , USA) is fixed at the end of the earcanal for recording the sound pressure approximately
at the “eardrum position” (see Fig. 2). The back of the microphone is covered with mounting putty to avoid
the potential influence of sound leaks (not shown in the figure). Additionally, preliminary tests for the open ear
have shown that the sound pressure measured in the earcanal of the artificial ear prototype is at least 10 dB
higher than that recorded on an in-house test fixture of a similar shape which is placed close enough to theformer but not mechanically excited. This ensures that the influence of the external sound field (e.g., ambient
noise, sound radiation of the shaker and scattering from the setup) is relatively negligible.

(a)

(b)

Mic

Earplug

3D printed
deviceAccelerometer

Figure 2. Experimental setup for evaluating the artificial ear design: artificial ear prototype occluded by (a) a“rigid” 3D printed occlusion device and (b) an earplug.
2 In this paper, the force sensor is not used for measurements but rather for providing a certain constraint on the artificial ear. Readers can refer
to Cyr-Desroches’ thesis [10] for additional measurement data.

3

Two types of occlusion devices are tested. First, the OE of a (3D printed) plastic device mimicking an
infinite impedance at the earcanal entrance is measured (see Fig. 2(a)) as it has been investigated numericallyin [8, 11]. This device is much simpler compared to a real earplug and allows for an occlusion of the earcanalwithout touching its lateral walls. The second occlusion device, as presented in Fig. 2(b), is a classical foam
earplug (3M™ , E-A-R™ , USA). For simplicity, only one insertion depth equal to 6 mm is tested, which corresponds to a shallow insertion. The OE of each occlusion device is obtained by the following equation:
H p,u,oc.
(1)
OE = 20 log10
H p,u,op
H p,u,op and H p,u,oc denote the transfer functions between the sound pressure (p) at the “eardrum position” andthe displacement (u) of the vibrating plate in the open and occluded configurations. Each test is repeated threetimes by removing and repositioning the artificial ear prototype and occlusion device in order to account for
the variability related to the mounting conditions (e.g., static compression on the prototype and fitting of theocclusion device).
3.2 Finite element modeling3.2.1 Geometry
The FE model of the designed artificial ear is shown in Fig. 1(a) and aims at predicting the OE assessed inpractice. Besides the artificial ear, the vibrating plate is also included in the model. It has a diameter of 50
mm and a thickness of 6.4 mm (see Fig. 3(a)). The other parts of the test bench are not taken into accountfor simplicity. When simulating the OE of the foam earplug, the latter is considered to be cylindrical in shapewith the same diameter as the earcanal cavity and a total length of 14 mm, neglecting the non-uniform static
deformation of the earcanal walls that might be induced by its insertion. The length of the inserted part is 6
mm which is identical to that chosen for the experiments (see Fig. 3(b)).

Bone

PML (perfectly matched layer)ux = 0 mm
uy and uz = free

Cartilage
Arti cialear

Mic

EarplugVibratingplateSoft tissues

External air

(a)

(b)

ux = 1 mm
uy and uz = 0

Earcanal
cavityfi

Figure 3. FE model of the system: (a) global view; (b) artificial ear wearing an earplug placed on a vibratingplate.
3.2.2 Loading and boundary conditions
The artificial ear together with the vibrating plate is placed inside an external air domain surrounded by aperfectly matched layer (PML), simulating the Sommerfeld condition (see Fig. 3(a)). Loading and boundary
conditions imposed on the artificial ear are chosen to best correspond to the experimental setup (see Fig. 3(b)).
First, a normal dynamic displacement with an amplitude of 1 mm is applied to a circular area of 19 mm indiameter at the center of the vibrating plate bottom surface (in the x direction); the displacement in the yz
plane is blocked. Second, the constraint provided by the force sensor is considered in the model by blocking
the displacement in the x direction on a circular area (with a diameter of 4 mm) at the center of the bonypart top surface; the motion in the yz plane is considered to be free. At the other interfaces between fluid andsolid domains, the fluid-structure coupling boundary condition applies, i.e., continuity of tractions and normal
displacements. Third, the static compression is taken into account indirectly via the mechanical properties of4the soft tissues (see Section 3.2.3). In the specific case of the occluded configuration by the infinite impedance,
an acoustically rigid boundary condition is imposed at the earcanal entrance (not shown in the figure).
3.2.3 Material properties
The earcanal cavity and external air are modeled as compressible perfect gas domains under standard conditions
for temperature and atmospheric pressure. The other components are considered as isotropic elastic solids.
Their mechanical properties (see Table 1) are either characterized using cylindrical specimens on a quasi-staticmechanical analyzer with a static compression rate of 0.8% (soft tissues) or adopted from the literature (bone,cartilage and earplug). Particularly, the Poisson’s ratio of the cartilage is chosen to be equal to that of thesoft tissues as they are fabricated with similar materials [10]. The vibrating plate and microphone are made ofnormal structural aluminum.
Table 1. Mechanical properties of the components in the FE model [10, 12].
Vibrating plate/mic

Bone

Cartilage

Soft tissues

Earplugρ [kg/m3 ]

2700

2267

1075

1007

220

E [kPa]7e7

1.36e7

1650

131.2

100

ν [1]

0.3

0.31

0.478

0.478

0.1

η [1]

0.005

0.01

0.05

0.072

0.5

Table 2. Ranges of the cartilage and soft tissue properties for sensitivity analysis.
Code level

Cartilage

Soft tissues

ρ [kg/m3 ]E [kPa]

ν [1]

η [1]

-1

-

1320

0.382

0.04

0

1075

1650

0.478

0.05

+1

-

1980

0.4999

0.06

-1

-

104.9

0.382

0.058

0

1007

131.2

0.478

0.072

+1

-

157.4

0.49990.086

Additionally, a sensitivity analysis on the properties of the cartilage and soft tissues is carried out with a
one factor at a time technique since they have been shown to have an influence on the OE [13]. Each time, one
parameter is varied while keeping the others at their nominal values given in Table 1. The ranges for the tested
mechanical properties are reported in Table 2. The values for different code levels correspond to a variation of±20% around the nominal ones (i.e., code level 0). Note that for the Poisson’s ratios of the cartilage and soft
tissues, the value 0.4999 corresponds to the code level +1 in order not to exceed the theoretical limit of 0.53 .
In particular, their densities are considered to be constant values and are not included in the analysis.
3.2.4 Meshing and solving
All the domains in the FE model are meshed using 10-noded tetrahedral elements with a meshing criterionof at least 4 elements per wavelength in order to achieve a sufficient accuracy of the solution. Particularly,
the PML is meshed using 15-noded quadratic triangular prisms with 6 elements equally distributed along its
thickness. Simulations are performed with the software COMSOL Multiphysics (v5.6 COMSOL® , Sweden)in the frequency range from 100 Hz to 1 kHz with a 10 Hz step. Third octave band responses are thenderived from narrow band simulation results with an in-house MATLAB routine (MATLAB 2021a, MathWorks,
USA). The sound pressures averaged over the microphone surface are computed for the open and occluded
configurations in order to determine the OE with the transfer functions as indicated by Eq. (1).
3 The built-in "mixed formulation" in COMSOL Multiphysics is used to avoid numerical problems related to quasi-incompressible materials.

5

4

RESULTS AND DISCUSSIONFirst, the simulation and measurement results of the transfer functions between the earcanal sound pressure anddisplacement excitation are compared in Fig. 4, with Figs. 4(a) – 4(c) corresponding respectively to the open
configuration as well as the occluded configurations by the infinite impedance and foam earplug. Results are
displayed in third octave bands in terms of the mean values and 95% confidence intervals for the numericalsensitivity analysis (and for the three repetitions of each experimental test, respectively). Note that due toa low coherence between the signals of the sound pressure and displacement at low frequencies during themeasurements, the comparison is only done for frequencies above 160 Hz. The corresponding OE results areshown in Fig. 5.

(b)

(c)

(a)

Figure 4. Comparison between the simulated and measured transfer functions: (a) open earcanal; (b) earcanaloccluded by an infinite impedance; (c) earcanal occluded by an earplug.

(a)

(b)

Figure 5. Comparison between the simulated and measured OEs: (a) earcanal occluded by an infinite
impedance; (b) earcanal occluded by an earplug.
These results show that i) the mechanical properties of the cartilage and soft tissues have an influence on
the simulated OE (see colored zones, Figs. 4 and 5), which is consistent with Brummund et al.’s finding [13];ii) changes in the mounting conditions can cause non-negligible variability of the measured OE (see blackzones). On the other hand, Fig. 4(a) shows that the simulated transfer function is not in good agreement withthe measured one for the open ear at 400 Hz and above, especially around 630 Hz where a difference ofabout 35 dB can be observed between the simulated and measured means. On the contrary, in the occluded
configurations, the simulation seems to capture the overall tendency of the measured transfer function in a6

satisfactory manner in most of the frequency range concerned (see Figs. 4(b) and 4(c)). Discrepancies can still
be found, such as at frequencies below 250 Hz for the ear occluded by the earplug (see Fig. 4(c)), wherea difference of up to 20 dB is seen between the simulated and measured means. A possible reason for theobserved differences could be the fabrication defects of the artificial ear prototype which can have an impact
on the measured OE but are not taken into account in the simulation. A computed tomography (CT) scan of
the artificial ear prototype shows different fabrication defects, for example i) the earcanal is not completely
cylindrical with the cartilage part not concentric with the earcanal, and ii) there are large bubbles in the bonypart [10]. In addition, there may be other phenomena that are not correctly replicated by the model, such as
the vibration of the test bench itself.
In Figs. 5(a) and 5(b), the most evident discrepancies between the simulation and measurement results of OE
around 630 Hz and 800 Hz could be mainly explained by the differences already observed from the transfer
function results of the open ear in the associated frequency band (see Fig. 4(a)). Moreover, a difference ofup to 20 dB is also seen at frequencies below 250 Hz for the ear occluded by the infinite impedance in
Fig. 5(a), which could be related to the discrepancies of the transfer functions in both the open and occluded
configurations (see Figs. 4(a) and 4(b)). But most simulation results still fall inside the 95% confidence intervalof the measurement data. Particularly, human test data in the literature (displayed as mean ± S.D.) [14] fora shallowly inserted foam earplug is also provided in Fig. 5(b) as the reference for comparison (dashed green
curve). Apart from the inconsistency between the human test data and results of the artificial ear (both numericaland experimental), the latter are found to capture well the decreasing tendency of OE with frequency. Additional
tests not presented here also show that the artificial ear is capable of reproducing the change in OE between
earplug types (i.e., OE difference between a foam and silicone earplug) which has been commonly observed onhuman subjects [10].
In general, a similar overall behavior is observed between the simulated and measured OEs on the artificial
ear for the two occlusion devices studied. Indeed, the agreement between the simulation and measurement
needs to be further improved, and there are several paths to take for the future work. First, the real geometryof the artificial ear prototype can be reconstructed with image processing techniques and used for the simulation.
Second, a method needs to be found for characterizing the real viscoelastic material properties of the artificial
ear components, especially for the cartilage and soft tissues. Third, the potential influence of the vibration of theexperimental setup on the system response needs to be better understood. Finally, improvements are necessary
in order for the designed artificial ear to better capture the OE measured on human subjects.

5

CONCLUSION

This paper proposed the design of an artificial ear for quantifying objectively the OE. From the numerical aspect, it is simple in geometry with a low computational cost for performing simulations. From the experimental
aspect, a first artificial ear prototype has been physically fabricated and used for OE measurements in a laboratory environment. The designed artificial ear was evaluated by comparing the measured OE on the fabricatedprototype with i) that simulated using a FE model for two occlusion devices and ii) human test data in theliterature in the frequency range [160 Hz, 1000 Hz]. A generally comparable behavior is found between thesimulation and measurement on the artificial ear. The causes of discrepancies could be the fabrication defects of
the artificial ear prototype (i.e., system geometry), its material properties and vibration of the test bench, which
are not correctly considered in the model. Both the numerical and experimental OE results obtained on the artificial ear show a similar overall tendency to the human test data. The design needs to be further improved inorder for the artificial ear to better capture the OE observed on human subjects. But despite these limitations,this work has demonstrated the use of an artificial ear, which differs from conventional ATFs and accounts
for the bone conduction through the outer ear for conveniently assessing the OE and studying its associatedbehaviors.

ACKNOWLEDGEMENTS
The authors would like to acknowledge the funding provided by Institut de recherche Robert-Sauvé en santé eten sécurité du travail (No. 2017-0032) and Mitacs Accelerate program (No. IT106403).

7

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[11] Carillo K, Doutres O, Sgard F. Theoretical investigation of the low frequency fundamental mechanismof the objective occlusion effect induced by bone-conducted stimulation. The Journal of the AcousticalSociety of America. 2020;147(5):3476-89.
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du confort acoustique et physique des protecteurs auditifs intra-auriculaires (Design of realistic artificial
ears dedicated to the study of the acoustical and physical comfort of in-ear hearing protectors). Montreal,
Canada: IRSST; 2020. Report R-1106.
[13] Brummund M, Sgard F, Petit Y, Laville F. On the influence of the material properties of the external ear
on occlusion effect simulations. Canadian Acoustics. 2012;40(3):110-1.
[14] Brummund M, Sgard F, Petit Y, Laville F, Nélisse H. An axisymmetric finite element model to study
the earplug contribution to the bone conduction occlusion effect. Acta Acustica united with Acustica.
2015;101(4):775-88.

8

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0362

Hearing threshold for 40-kHz airborne ultrasoundKoichiro DOI1; Hiromu ISHKAWA1; Yoshiki NAGATANI2; Takayuki HOSHI3;
Seiji NAKAGAWA1,4,5
1

Dept. of Medical Eng., Grad. School of Sci. & Eng., Chiba Univ., Japan
1

Development Team, Pixie Dust Technologies, Inc., Japan
3

Board Member, Pixie Dust Technologies, Inc., Japan
4

Ctr. for Frontier Medical Eng., Chiba Univ., Japan

5

Med-Tech Link Ctr., Chiba Univ. Hospital, Japan

ABSTRACT
High-intensity ultrasound around 40 kHz irradiated to the ear is clearly perceived as a high-pitched sound.
However, few studies have been conducted on the perception of airborne ultrasound above 30 kHz, whereas
small numbers of studies have been conducted below 25 kHz. In this study, to examine the basic perceptual
characteristics of high-intensity airborne ultrasound around dozens of kilohertz, hearing thresholds of 40kHz tone burst presented by an ultrasonic speaker array were measured in normal hearing participants. As
results, all of participants perceived 40-kHz airborne ultrasound. The mean hearing threshold was 133.3 ±2.7 dB SPL, much higher than that of 20-25 kHz airborne ultrasound shown in former studies. The results
obtained indicated that the auditory system of the human has sensitivity even to 40-kHz ultrasound to someextent, while sensitivity of the inner ear or transmission characteristics of the middle ear deteriorate as thefrequency increased.
Keywords: Airborne ultrasound, Hearing thresholds, Ultrasonic speaker array

1. INTRODUCTION
Usually, the upper frequency limit of human hearing is believed not to be higher than about 20
kHz. However, Ashihara et al. (2006) reported that a part of normal hearings (18-33 years) can
perceive high-frequency sound up to 24 kHz radiated to the air above 88 dB SPL (1). In addition,
several studies have reported that high-frequency sound up to at least 100 kHz can be perceived clearly
via bone-conduction (2-4). These reports indicate that the inner ear has sensitivity to ultrasound up to
100 kHz to some extent.
On the other hand, Hoshi (2014) reported that high-intensity ultrasound irradiated to the head by
an ultrasonic phased array was perceived as a high-pitched sound (5). In the ultrasonic phased array,ultrasonic waves around 40 kHz are mainly used. In addition, 40 kHz airborne ultrasound is also usedin our daily lives (for example, non-destructive inspection, parking meters, automobile back sonars,etc.). However, a limited number of studies have been conducted on the perception of 40 kHz airborne
ultrasound (6). In this study, to examine the basic perceptual characteristics of airborne ultrasound,hearing thresholds of 40 kHz airborne ultrasound presented by an ultrasonic array speaker were
measured.

2. METHODS
The measurements were conducted in an anechoic room. The stimuli were presented from the left
side of participants. Participants were asked to sit on a chair and adjust the angle of the backrest andposture so that left external acoustic meatus was in the center of the hole of an acrylic plate (Figure1). Prior to the experimental sessions, 40 kHz tone burst with a duration of 550 ms was presented
every 450 ms at a detectable level, and participants were requested to confirm the hearing of thestimulus sound.

Speaker
Acrylic plate

PC
Figure 1 – Experimental settings of the hearing threshold measurement.
Hearing thresholds were measured using a 1 up-2 down three-alternative forced-choice (3AFC)
adaptive procedure with a decision rule that estimated the 70.7% correct point on the psychometricfunction (8).

3. RESULTS
All of participants could perceive 40 kHz airborne ultrasound. The mean hearing threshold was
133.3 dB SPL, and the standard deviation was 2.7 dB SPL. The highest hearing threshold among
participants was 137.6 dB SPL, and the lowest was 125.6 dB SPL.

4. DISCUSSIONS
Air-conducted sounds transmit in the ear canal and reach the inner ear via the middle ear. However,
we consider that ultrasounds will not transmit sufficiently to the inner ear because the middle ear acts
as a low-pass filter (2,4). Therefore, a stimulus sound over a certain sound pressure level is required
to perceive airborne ultrasounds. All of participants could perceive 40 kHz tone bursts as intelligible
sound, and the mean hearing threshold was 133.3 dB SPL. This threshold is higher than that of 20-25
kHz airborne ultrasound shown in the previous studies (20 kHz: about 100 dB SPL, 25 kHz: about
125 dB SPL (6), 24 kHz: above 88 dB SPL (1)). This result indicates the deterioration of reaction
characteristics of the inner ear or transmission characteristics of the middle ear, but further study will
be required for detailed elucidation of perceptual characteristics of 40 kHz airborne ultrasound.

ACKNOWLEDGEMENTS
A part of this work was supported by JSPS KAKENHI Grant Numbers JP19K22950 and
JP20H04497, and a Research Grant for the Next Generation Research Incubator from the Institute for
Global Prominent Research, Chiba University for SN.

REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.

K. Ashihara, K. Kurakata, T. Mizunami and K. Matsushita, “Hearing threshold for pure tones above 20
kHz,” Acoust. Sci. & Tech., 27, 1, 12-19, 2006.
R. J. Pumphrey, “Upper limit of frequency for human hearing,” Nature, 166, p.571, 1950.
R. J. Bellucci and D.E. Schneider, “Some observations on ultrasonic perception in mam,” Ann. Otol.
Rhinol. Laryngol., 71, 719–726, 1962.
J. F. Corso & M. Levine, “Bone-conduction thresholds for sonic and ultrasonic frequencies,” J. Acoust.
Soc. Am., 35, 1738, 1963.
T. Hoshi, “Development of compact ultrasound focusing device for noncontact force radiation,” Proc.
SICE, 50, 7, 543-552, 2014.
J. Herbertz, “Loudness of airborne ultrasonic noise,” Proc. Ultrasonics Int., 83, 226-231, 1983.
I. Itoh, M. Ikeda, K. Sueno, M. Sugiura, S. Suzuki and A. Kida, “Anthropometric study on normal humanauricle in Japan,” J. Otolaryngol. Jpn., 104, 165-174, 2001.
H. Levitt., “Transformed up-down methods in psychoacoustics,” J. Acoust. Soc. Am., 49, 467-477, 1971.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0732

Occupational exposure to noise and myocardial infarction risk one
year later in Sweden.
Claudia LISSÅKER1, Maria ALBIN2, Theo BODIN3, Mattias SJÖSTRÖM4, Jenny SELANDER5
1,2,3,4,5 Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institutet, Sweden
2,3,4 Centre for Occupational and Environmental Medicine, Region Stockholm, Sweden

ABSTRACT
Objective: To explore the association between occupational noise exposure and myocardial infarction (MI)one year later.
Methods: Data came from the Swedish National Cohort on Work and Health (SNOW), comprised of all
individuals born between 1930 and 1990 in Sweden, with demographic, occupational, and outcome data
available from 1968 until 2017. In this study, we included working individuals with at least one occupational
code between 1985 and 2013. These were matched to a job-exposure matrix (JEM) in five categories(LAeq8h): <70, 70-74, 75-79, 80-84, ≥85 dB(A). MI status in the year following exposure was ascertainedusing the patient register. To account for time-varying occupational data, we utilized a discrete-time
proportional hazards model adjusted for individual confounders and other occupational exposures.
Results: Preliminary results show that exposure to over 75 dB(A) of occupational noise is associated with a
14-24% increased risk for MI one year later after adjusting for age, sex, and income.
Conclusion: Exposure to noise was associated with an increased risk for MI one year later after adjusting forindividual confounders among this younger, working population. Additional in-depth analyses are ongoingin which we plan to adjust for other occupational exposures.
Keywords: occupation, myocardial infarction, register data

1.

INTRODUCTION

Ischemic heart disease (IHD) is a leading contributor to the global disease burden (1). Though
there is an overall decreasing trend in the incidence of myocardial infarctions (MI), younger
individuals are not showing similar trends (2). Therefore, it is imperative to identify risk factors forprevention among this group. The work environment is one area that has been implicated in MI
development.
Noise is a common exposure in the occupational setting in both developed and developing
countries. Though the auditory hazards of occupational noise exposures have long been established,studies have shown a possible association with CVD development. According to a meta -analysis, noise
is associated with a 68% increased risk of hypertension and a 34% increase in CVD (3). The WHO
the International Labour Organization (ILO) have recently attempted to summarize the body ofknowledge regarding occupational noise exposure and IHD (4). Though the results corroborate
previous findings of an increased risk of occupational noise on IHD, the authors conclude that qualityof evidence is low, which was partially due to studies being limited to men and differences in exposurelevel cut-offs across studies (4).
One additional limitation of previous research is that, for the most part, it does not consider
concomitant work exposures. Two studies investigated the impact of occupational exposure to bothnoise and job strain (5, 6). Both found an increased risk of MI for those exposed to high levels of both
job strain and noise (5, 6). Neither, however, investigated the independent impact of noise on MI.
This study aims to investigate whether noise exposure affects the incidence of MI one year later ina large, population-based cohort of working individuals in Sweden, while exploring adjustments forindividual, socioeconomic, and other occupational exposures.

1

claudia.lissaker@ki.se

2.

METHODS

2.1
Data sources
For this study, we utilized the Swedish National Cohort on Work and Health (SNOW) cohort, which
was created using the Swedish registers. Data were extracted from the Total Population Register for
all persons born between 1930 and 1990 and residing in Sweden between 1968 and 2017. From thisregister, we obtained data on birth year and month, sex, country of birth, and yearly marital statusamong others.
Individuals were then matched to various other register sources by their personal identification
number. Time span of data varies for each register depending on individual variable availa bility,
however general years are described. From the Income and Taxation Register, we obtained yearly datafrom 1968 until 2017 on total taxable income, income from work, and pension. Data from the National
Patient Registers were used to obtain diagnosis received between 1964 and 2017 from in-and outpatient clinics; however, diagnostic codes from primary care providers are not included in this register.
Finally, data were also matched to the Swedish Census between 1960 and 1990, as well as to theLongitudinal Integration Database for Health Insurance and Labor Market Studies (Swedish acronym
LISA) between 1990 and 2017, to obtain occupational data. From LISA, we also obtained educationaldata.
2.2 Occupational data
Because some JEMs used in this study are not available in all coding systems, we restricted work
exposure data to 1985-2013. Occupational data availability differed based on the source. The census
was collected every 5 years; therefore, to ensure complete working history, we carried forward jo b
codes to all years in between each census. For instance, the job codes reported in 1985 were used forthe period from 1985-1989. Starting in 1997, some occupational codes were already available in LISA,
with more complete data starting in 2005. Additionally, individuals’ job data were not collected yearly.
Therefore, we also had to impute values to obtain complete work history. For LISA, we obtained job
data based on the closest available job code, looking backwards and forwards up to 5 years. To ensurethat job codes were not given to those who did not work, we excluded job codes for those who had no
taxable income reported in the Income and Taxation Register. Lastly, to account for those who werepartially retired, but may still have an occupation reported, we removed job codes for those whose
income from pension accounted for over 50% of their total income.
To estimate noise exposure, we utilized a job exposure matrix (JEM) matched to the occupational
codes from the register. This JEM was developed based on measurements from occupational healthservices, clinics, and large companies throughout Sweden. The original JEM was developed using the
1995 modification of the 1983 version of the Nordic Occupational Classification (NYK83) coding
system and included 321 occupational groups. This version included annual averages of the daily 8 hour equivalent A-weighted sound pressure level in three exposure classes (LAeq8h) encompassing
the time span from 1970 to 2004 and was shown to be valid (Sjöström 2013). It has since been updated
to include newer measurements and was expanded to five exposure classes, <70, 70.74, 75-79, 80-84,≥85 dB(A), available from 1970-2014 in five-year intervals. This JEM was then translated into
different coding systems to reflect the availability of job codes throughout the years in the Swedish
registers.
Occupational information was available from the Swedish Census from 1960 to 1990 and fromLISA from 1997 until 2017. In the census, variations of the NYK coding system were used . In LISA
the Swedish occupational classification versions 1996 and 2012 were available (SSYK96 andSSYK2012, respectively); however, for this study we only use the SSYK96 coding system available
until 2013.
2.3 Outcomes
We obtained outcome data from the National Patient Register. These were coded using theInternational Classification of Diseases, 7 th , 8 th , 9th and 10 th revisions (ICD-7, ICD-8, ICD-9, andICD-10). Codes for MI were extracted starting in 1968 until 2014. For this study, we only included
the first MI occurrence. If an individual had no previous MI history in the register, but were given arecurrent MI ICD code, they were also excluded.
2.4

Covariates

Covariates considered were based on availability in the registers and the logic model specified i n
Teixeira et al (4). Individual confounders considered were age, sex, income, and education. Apartfrom education, all variables were available for the entire study period, with education being available
starting in 1990. Income and education were treated as time-varying confounders.
In addition to individual covariates, we also wanted to explore the independent effect of noise on
MI; therefore, we also included occupational exposures from other JEMs. Occupational exposuresconsidered were physical workload, decision authority, whole-body vibrations, hand-arm vibrations,particles, and chemicals. Based on previous evidence (5, 7-9), we chose to consider physical workload,
decision authority, whole-body vibrations, lead, carbon monoxide, diesel exhaust, polycyclic aromatichydrocarbon (PAH), and welding fumes. Work is ongoing to investigate the possible pathways andpotential interactions between noise and these other occupational exposures.
2.5 Analyses
For each year between 1985 and 2013, we compiled the set of individuals who had no history of
MI up until that point. Thus, once individuals have a MI, they were no longer considered at risk.
Exposure ascertainment occurred on a yearly basis based on available data. The SNOW cohort is an
open cohort; thus, individuals could enter the cohort at any point in the study period. For each year,
MI was ascertained for the year after noise exposure and covariate measurement. In this study,
inclusion criteria were as follows: 16 years of age or older, received some income in the previous year,
have received less than 50% of income from pension sources.
To assess the impact of noise exposure on MI risk, we used a discrete time proportional haza rds
approach. Two models have currently been created, a minimally adjusted model, which includes year
as the underlying time variable and adjusted for individual risk factors (age and gender), and a second
model in which we also adjust for income. We have also created models stratified by sex. Work iscurrently underway to explore how to adjust for other work exposures. All statistical analyses wereconducted using SAS version 9.4 (SAS Institute, Cary, NC, USA).
We obtained approval from the regional ethical review board in Stockholm, Sweden.

3.

RESULTSA total of 6,675,938 individuals with at least one job code during our study period were includedin this study, of which 3,408,002 (51%) were men. Figure 1 shows the number of individuals at risk
in each exposure year by gender.

5 000 000
4 500 000
4 000 000
3 500 000
3 000 000
2 500 000
2 000 000
1 500 000
1 000 000
500 000
0

Men

Women

Figure 1: Yearly number of participants with an occupational code by sex from 1985-2013.
Table 1 shows the hazard ratios (HR) and 95% confidence intervals (95% CI) for the entire

population and stratified by sex. After adjustment for individual risk factors, exposure to noise levels
between 70-74 dB(A) was associated with an increase of 1.21 (95% CI: 1.18 - 1.23), 75-79 dB(A)with an increase of 1.31 (95% CI: 1.28 - 1.33), 80-85 dB(A) with an increase of 1.30 (95% CI: 1.27 1.33), and 85 dB(A) and over with an increase of 1.30 (95% CI: 1.27 - 1.34). When including income,results were slightly attenuated. Results stratified by sex show that for the highest level ofoccupational exposure, women are at a higher risk than men, even after adjusting for income. Results
are preliminary and current work is ongoing to investigate how to appropriately a djust forsocioeconomic confounders.
Table 1: One year risk of myocardial infarction (MI) according to noise exposure assessed by an job
exposure matrix (JEM) [HR=hazard ratios; CI=confidence intervals.]
Model 1aExposure
NoiseHR

95% CI

Model 2bHR

95% CIAll<70

1 (ref)1 (ref)

70-74

1.21

1.18 - 1.23

1.14

1.12 - 1.16

75-79

1.31

1.28 - 1.33

1.23

1.21 - 1.26

80-84

1.30

1.27 - 1.33

1.22

1.20 - 1.25

≥85

1.30

1.27 - 1.34

1.24

1.20 - 1.27

Men
<70

1 (ref)1 (ref)

70-74

1.24

1.21 - 1.26

1.17

1.15 - 1.20

75-79

1.34

1.31 - 1.37

1.27

1.24 - 1.30

80-84

1.36

1.33 - 1.39

1.28

1.26 - 1.32

≥85

1.31

1.28 - 1.35

1.25

1.22 - 1.29

Women
<70

1 (ref)1 (ref)

70-74

1.11

1.07 - 1.16

1.06

1.02 - 1.11

75-79

1.24

1.18 - 1.31

1.18

1.12 - 1.25

80-84

1.05

1.00 - 1.10

0.96

0.91 – 1.00

≥85

1.53

1.33 - 1.77

1.45

1.26 - 1.67

a Model 1: adjusted for age and sex
b Model 2: adjusted for age, sex, and income

4.

CONCLUSIONSIn this nationwide, prospective cohort of all Swedish individuals, exposure to noise was associatedwith an increased risk with a 14-24% increased risk for MI one year later. Results when stratified bysex show that women exposed to the highest noise category have higher risks than men. This is thefirst large-scale study to investigate the exposure to noise on MI while accounting for sex.
Our preliminary results corroborate previous findings. Teixeira et al obtained an estimate of 1.29
(95% CI: 1.15-1.43) increased risk of incident IHD for noise levels above 85 dB(A) in p rospective
studies (4). Our results are pertinent to MI only and reflect acute exposures to noise since outcome
was ascertained one year later. Additionally, our results apply to younger individuals who received atleast 50% of their income from work in the previous year.
Analyses are ongoing to disentangle the relationships between noise and MI independent of otherwork exposures. Further work is being conducted to investigate the optimal way of adjusting forsocioeconomic status.

ACKNOWLEDGEMENTS
We gratefully acknowledge Anette Linnersjö for her contribution in data extraction. We also would
like to acknowledge Tomas Andersson for help with statistical matters.

REFERENCES
1. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases
and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of
Disease Study 2019. The Lancet. 2020;396(10258):1204-22.
2. Gulati R, Behfar A, Narula J, Kanwar A, Lerman A, Cooper L, et al. Acute Myocardial Infarction in Young
Individuals. Mayo Clinic Proceedings. 2020;95(1):136-56.
3. Skogstad M, Johannessen HA, Tynes T, Mehlum IS, Nordby KC, Lie A. Systematic review of the
cardiovascular effects of occupational noise. Occupational Medicine. 2016;66(1):10-6.
4. Teixeira LR, Pega F, Dzhambov AM, Bortkiewicz A, da Silva DTC, de Andrade CAF, et al. The effect ofoccupational exposure to noise on ischaemic heart disease, stroke and hypertension: A systematic review
and meta-analysis from the WHO/ILO Joint Estimates of the Work-Related Burden of Disease and Injury.
Environ Int. 2021;154:106387.
5. Selander J, Bluhm G, Nilsson M, Hallqvist J, Theorell T, Willix P, et al. Joint effects of job strain androad-traffic and occupational noise on myocardial infarction. Scand J Work Env Hea. 2013;39(2):195203.
6. Eriksson HP, Andersson E, Schiöler L, Söderberg M, Sjöström M, Rosengren A, et al. Longitudinal studyof occupational noise exposure and joint effects with job strain and risk for coronary heart disease andstroke in Swedish men. BMJ Open. 2018;8(4):e019160.
7. Sjögren B, Bigert C, Gustavsson P. The Nordic Expert group for criteria documentation of health risks
from chemicals. 153. Occupational chemical exposures and cardiovascular disease. Arbete och Hälsa.
2020;54:2:1-428.
8. Li J, Loerbroks A, Angerer P. Physical activity and risk of cardiovascular disease: what does the new
epidemiological evidence show? Current opinion in cardiology. 2013;28(5):575-83.
9. Krajnak K. Health effects associated with occupational exposure to hand-arm or whole body vibration.
Journal of Toxicology and Environmental Health, Part B. 2018;21(5):320-34.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0771

Occupational noise exposure and acute effects on pulse rate andblood pressure
Karin GRAHN1,2; Max VIKSTRÖM1; Karin BROBERG1,3; Petter LJUNGMAN1,4; Per
GUSTAVSSON1,2; Petra LINDFORS5; Pernilla WIEBERT1,2; Mattias SJÖSTRÖM1,2; Jenny
SELANDER1
12

Institute of Environmental Medicine, Karolinska Institutet, Sweden

Centre for Occupational and Environmental Medicine, Region Stockholm, Sweden

3

Division of Occupational and Environmental Medicine, Lund University, Sweden
4

Department of Cardiology, Danderyd University Hospital, Sweden
5

Department of Psychology, Stockholm University, Sweden

ABSTRACT
Environmental exposure to noise and particle matter (PM) are risk factors for cardiovascular disease (CVD).
Although there are often higher levels in occupational settings, little is known about noise and particle
exposures at work and CVD risks. We investigated occupational noise and particle exposures and acuteeffects on pulse rate and blood pressure. 46 active, non-smoking, male construction workers were includedin the study. Continuously logged personal exposure measurements of noise (LAmax, LCpeak) and dust of
PM 0.1-10, continuously logged pulse rate and blood pressure measurements were performed for one
working day on each participant. Significant associations were seen between these three exposures and acutechanges in pulse rate. The effects were seen below the Swedish occupational exposure limits for LAmax and
LCpeak, indicating that these limits may not protect from adverse cardiovascular effects. Also, an additiveeffect on pulse rate was seen if simultaneously exposed to noise and dust. No association were found between
these exposures and blood pressure.
Keywords: Noise, Pulse rate, Blood pressure

1. INTRODUCTION
The most common cause of death in the world is cardiovascular disease (CVD). According to theWorld Health Organization (WHO) approximately 18 million deaths were due to CVD in 2019 (1).
However, by addressing modifiable risk factors, for example occupational risk factors, many CVD
can be prevented. Therefore, it is important to identify these risk factors.
Earlier studies have observed an association between environmental transportation noise exposure
and increased incidence of CVD (2). Also, environmental short- and long-term particle exposure havebeen found to contribute to increased CVD-related mortality and morbidity, (3-5). There is strong
evidence for a causal relationship between exposure to particulate matter < 2.5 µm and harmfulcardiovascular effects (4, 6). In occupational settings, exposure levels are often higher than insurroundings and ambient air, but little is known about noise and particle exposure at work and CVD.
The pathophysiological mechanistic pathways suggested between noise exposure and incidence of
CVD indicates that noise is associated with oxidative stress, autonomic imbalance, vascular
dysfunction, and/or metabolic abnormalities (2). For particle inhalation exposure, three main
mechanistic pathways for CVD development have been suggested; inflammation in the systemic
circulation originating from the airways, disturbances in the autonomic nervous system leading to fast
blood pressure or heart rate changes and hypertension development, and uptake of certain particles
into the cardiovascular system thus affecting cardiovascular tissues negatively (6, 7). Different

pathways may be activated due to the composition, chemical characteristics and size of the particles(6).
The main aim of this study was to investigate if occupational exposure to noise during a working
day is associated with acute effects on pulse rate and blood pressure. Also, particle ex posure was
explored.

2. MATERIAL AND METHODS
2.1 Study design and study participants
A study population of active construction workers from noisy, dusty working environments was
selected. To limit confounding from sex differences and smoking, the study was restricted to men who
should not have been smoking tobacco for the last 6 months before the study. To include a stable
exposure pattern and a comprehensive self-reported exposure history and lifestyle evaluation, theparticipants should have worked in the construction industry for the last 6 months. Preferably theyshould talk and read Swedish.
The workers were recruited in late 2018 - spring 2019. We co-ordinated it with the construction
trade union (Svenska byggnadsarbetareförbundet), the association for concrete/stone drillers,demolition and decontamination workers (Branschföreningen för byggnadsberedning), and large
construction companies. The contact persons were sent via regular mail information about the study
and an informed consent form and a questionnaire. The latter should all participating construction
workers fill in before the occasion of sampling. A visit was then booked at the construction site, andwe continuously measured noise- and particle exposure, pulse rate, blood pressure, and recorded sound.
Participation in the study was voluntarily, and agreement with written consent prior to participation
was obtained from all individuals. The Regional Ethics Committee at Stockholm, Sweden (Ref. No.
2019-00208) approved the study.
2.2 Exposure measurements and sampling of blood pressure and pulse rate
Each participant’s exposure to noise and particles, blood pressure, and pulse rate were measured,and sound recorded during one working day at his worksite, The visits took place during March - June
2019.
Noise was measured continuously, and the data was logged every 1 second using Svantek SV 104
noise dosimeter with corresponding software SvanPC++ version 3.3.21. Weighting filter A, detector
type Fast, for maximum sound pressure level (LAmax) in dBA, and weighting filter C for
instantaneous (less 50 µs rise time) sound pressure level (LCpeak) in dBC was used. Sound was also
recorded using the Svantek SV 104 device with corresponding software to be able to analyse the typeof sound causing the effects. The equipment was placed close to each participant’s ear at his shoulder.
In Sweden lunch is not considered as working hours, therefore the recording was paused during lunch
break.
Dust of 0.1-10 PM 0.1-10, was measured continuously and the data logged every 1 minute using
DataRAM pdr-1000AN Monitor, which covers an air range of 0.001 - 400 mg/m 3 concentration. The
device was placed at the back of each participant. The pDR-COM software version 2.10
(ThermoFischer Scientific, USA) was used to transfer data to a computer. The recording was notpaused during lunch break.
Each participant’s usage of hearing protectors and respiratory mask was noted.
Measurements of blood pressure using the device ABPM pulse rate monitor (model ABPM 50,
GIMA S.p.A., Italy) with corresponding software, and pulse rate using the device Polar H10 heart rate
sensor, were measured during the workday, logging data. Blood pressure was measured every 15
minutes and pulse rate every 1 second while the participants were working. They could then use snuff.
In the morning, height and weight were measured. The participants should not have used snuff 1 hourprior to the measurements in the morning.
In order to have the same settings of time, all devices used were calibrated towards the same computer.
2.3 Questionnaire
Medical history and occupational exposure for other cardiovascular toxicants than noise andparticles as well as other factors important of cardiovascular diseases were obtained fromquestionnaires.

2.4 Statistical analyses
Linear regressions were performed to evaluate associations between noise and dust of PM 0.1 -10
exposure and the outcomes blood pressure and pulse rate within each worker. Data were then pooled
between workers. Sensitivity analyses aiming at investigating if any specific type of sound was
associated with pulse rate was performed by stratifying on four types of noise. These four types, set
by some authors, were: sound from machine moving, impulse sound, sound from object moving , andother sound. Interaction analyses between the two different measures of noise and dust of PM 0.1-10
were made to estimate the effect estimates of the blood pressure and pulse rate. Also, correlations
between the different exposures at the same time were calculated.
Statistical analyses were performed with SAS, version 9.4, and StataCorp LLC STATA/SE version
16.1.

3. RESULTS
In total, 28 construction companies were contacted with a response rate of 71 % (20 out of 28). 65
non-smoking, male construction workers were recruited. Of these 65 participants, we simultaneously
measured pulse rate, blood pressure, noise- and dust exposure, as well as recorded sound, in 46
participants. They were 20-65 years of age and included 23 construction carpenters, 19 concrete
workers/stone drillers, 2 plasterers, 1 plumber, and 1 bricklayer. Around 20 000 noise measurements
were logged for each participant.
Results show a clear exposure-response relationship between noise (LAmax and LCpeak,
respectively) and pulse rate, most pronounced in the highest noise group (7.69 beats per minute (bpm)higher pulse rate for exposure > 100 dBA compared to < 75 dB, and 8.6 7 bpm compared to < 80 dBC
respectively). Effects were seen below the Swedish occupational exposure limits for LAmax (115dBA) and LCpeak (135 dBC), respectively (8).
No clear association and no exposure-response was found for blood pressure.
Interaction analyses of the exposures noise (LAmax and LCpeak, respectively) and dust of PM 0.1 10 on the outcome pulse rate showed an additive effect. This indicates that there is a higher influenceon pulse rate if exposed to both noise and dust at the same time.
Correlation between the two types of noise and dust was low (LAmax and dust of PM 0.1-10: 0.190,
and LCpeak and dust of PM 0.1-10: 0.181). This indicates that simultaneous exposure to noise anddust is not common.
In sensitivity analyses stratified by type of sound, we saw no clear results.

4. CONCLUSIONS
Occupational exposure to noise was associated with acute changes in pulse rate, most clearly
indicated in those high exposed to noise compared to the lowest exposed. However, effects were seen
below the Swedish occupational exposure limits for LAmax and LCpeak, indicating that these limits
may not protect from adverse cardiovascular effects. Interaction analyses of noise and dust with pulse
rate also reveal an additive effect, however there was low correlation between the two exposures.
However, this indicates that it is not enough to reduce noise levels to affect pulse rate, reducing dust
exposure is also needed.

ACKNOWLEDGEMENTS
This study was funded by AFA Insurance in Sweden (Grant number 160361) and the Karolinska
Institutet.

REFERENCES
1. WHO. Cardiovascular diseases (CVDs) 2021 [Available from: https://www.who.int/en/news-room/factsheets/detail/cardiovascular-diseases-(cvds).
2. Munzel T, Schmidt FP, Steven S, Herzog J, Daiber A, Sorensen M. Environmental Noise and theCardiovascular System. J Am Coll Cardiol. 2018;71(6):688-97.
3. Rajagopalan S, Al-Kindi SG, Brook RD. Air Pollution and Cardiovascular Disease JACC State-of-theArt Review. Journal of the American College of Cardiology. 2018;72(17):2054-70.
4. Leander K, Frostegård J, Larsson S, Åkesson A, Donat-Vargas C, Håkansson H, et al. Environmental
Exposures and Cardiovascular Disease IMM-rapport nr 1/2019. Institue of Environmental Medicine

(IMM); 2019.
5. Polichetti G, Cocco S, Spinali A, Trimarco V, Nunziata A. Effects of particulate matter (PM10, PM2.5
and PM1) on the cardiovascular system. Toxicology. 2009;261(1-2):1-8.
6. Brook RD, Rajagopalan S, Pope CA, 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, et al. Particulate matter
air pollution and cardiovascular disease: An update to the scientific statement from the American Heart
Association. Circulation. 2010;121(21):2331-78.
7. Franklin BA, Brook R, Pope CA. Air Pollution and Cardiovascular Disease. Current Problems in
Cardiology. 2015;40(5):207-38.
8. Arbetsmiljöverket. Buller. Arbetsmiljöverkets författningssamling (AFS 2005:16). Stockholm 2005. 2005.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0116

Traffic noise radiation suppression in an unbaffled long enclosureusing micro-perforated panel
Weiping YANG1; Yatsze CHOY2; Ying LI3
1, 2, 3

The Hong Kong Polytechnic University, Hong Kong, P. R. China

ABSTRACTSound radiation control of an unbaffled long enclosure using corrugated Z-shaped micro-perforated panel
absorbers (ZMPPAs) is investigated. First, a hybrid model based on the finite element method (FEM) and the
Wiener-Hopf (WH) technique is established to calculate the sound pressure level (SPL) radiated from an
unbaffled long enclosure. Subsequently, an array of ZMPPAs is proposed to absorb higher-order acousticalmodes inside the long enclosure so that the radiated noise can be suppressed. After that, the sound attenuation
performance of ZMPPAs is evaluated and compared with that of corrugated and flat microperforated panel
absorbers (CMPPAs, FMPPAs). The results demonstrate that ZMPPAs can reduce the radiated SPL within awide frequency range which is favorable for the suppression of environmental noise. Finally, the performanceof the ZMPPAs, CMPPAs, and FMPPAs in reducing the radiated noise from an unbaffled long enclosure is
examined using a quasi-2D test rig. The experimental results coincide well with the calculated results which
validates the proposed model and verifies part of the numerical findings.
Keywords: Sound suppression, Unbaffled long enclosure, Micro-perforated panel absorber

1. INTRODUCTION
Sound radiation control of an unbaffled long enclosure has been an important research subject inrecent years as this scenario could be widely seen in the reduction of environmental noise. Helmholtz
resonators (HRs) have been proposed to suppress the sound peaks inside the unbaffled long enclosu re
so that the radiated SPL field around the resonant frequencies of the HRs could be attenuated (1). The
noise reduction performance of an array of HRs, however, is still unsatisfactory which is limited bytheir narrow working frequency bandwidths. Therefore, for the control of noise radiated from a long
enclosure, a simple, compact, and broadband noise suppression device is needed. The feasibility ofapplying two-dimensional hard rough surfaces to attenuate the noise level inside a long enclosure has
been examined by Law et al. (2). It was observed that an average sound reduction of about 3 dB overthe frequency range from 500 Hz to 5000 Hz could be obtained. Aiming at absorbing the higher-orderacoustical modes inside a duct, the interactions between acoustical modes and a perforated liner were
investigated by Eldredge (3). Later, enlightened by the research of Eldredge, broadband acousticalliners with multiple cavity resonances were designed and the acoustical performance of the liners wasinvestigated (4, 5). From above-mentioned findings, acoustical liners consisting of micro-perforated
panel absorbers (MPPAs) are promising noise control devices to attenuate the noise that is consistingof higher-order acoustical modes. Apart from applying multiple cavity resonances, MPPAs integrated
with trapezoidal (6) and L-shaped (7) cavities were also proposed. More acoustical modes that are
initially decoupled with the MPP backed by a rectangular cavity are coupled with the MPP backed bycavities with irregular shapes. Compared with a flat MPPA (FMPPA) backed by constant air gap, theirregular-shaped MPPAs provide more spectral peaks and achieve good absorption performance at thedips in the sound absorption coefficient curve. These designs are from the perspective of the backing
cavity and the incident plane waves are normal to the MPP. However, in pra ctice, the distribution ofsound pressure fields is complex in a duct, enclosure, and cavity system. The amplitudes and incident
angles of sound pressure along MPP surfaces are different. Consequently, the in -situ performance of
w.p.yang@connect.polyu.hk
mmyschoy@polyu.edu.hk
3
carlos.li@connect.polyu.hk
12

the MPPAs is usually inferior to the theoretical results as the normal sound absorption coefficient of
the MPPAs is inapplicable. Hence, to investigate the performance of an MPPA in practical acousticalenvironments, researches on the performance of MPPAs under oblique and random pl ane-wave
incidences have been carried out (8, 9, 10). Results show that shape designs of cavities are not enoughfor a broadband sound absorption performance of an MPPA, especially when the sound waves are
impinging tangentially or at a large incident angle on the flat MPP. To further improve the soundabsorption performance of an MPPA under an oblique plane-wave incidence or in the diffuse field, a
corrugated MPPA (CMPPA) with a sinusoidal MPP profile is proposed (11). Both the plane-wave
incident angle to the local surface and the cavity shape of the MPPA are changed by introducing thecorrugated MPP, which enhanced the sound absorption at the troughs and more spectral peaks can beobserved. A key parameter that influences the sound absorption performance of a CMPPA is thecorrugation depth. The corrugated profile of MPP, however, is determined when the corrugation depthof the MPP and the width of the MPPA are chosen, which lacks flexibility for higher sound absorption
performance. To find a more flexible configuration of the corrugated MPP profile, a Z -shaped microperforated panel absorber (ZMPPA) is proposed here. It is targeted for the absorption of higher-orderacoustical modes inside an unbaffled long enclosure so that the radiated sound field can be attenuatedaccordingly. In addition to the advantages of CMPPAs, a ZMPPA is more versatile, which is promisingfor broadband noise control in large spaces and buildings.
The remainder of this paper is organized as follows. A hybrid model is first proposed to calculate
the sound radiation from unbaffled long enclosures integrated with MPP absorbers. Subsequently, thesound suppression performance of the ZMPPA is investigated and compared with that of FMPPA and
CMPPA. Finally, experimental results are presented to validate the numerical model and examine theperformance of the ZMPPA. Results show that the ZMPPA can attenuate the radiated nois e within awide frequency range which proves that the ZMPPA is a promising noise control device in practicalapplications.

2. HYBRID MODEL
To investigate the sound absorption performance of MPPAs inside a long enclosure, a hybrid
method based on the finite element (FE) method and Winer-Hopf (WH) technique is established. The
schematic diagram of an FE-WH-based hybrid method to predict the sound radiated from an unbaffled
long enclosure integrated with MPPAs is shown in Figure 1. The sound pressure field inside a long
enclosure is obtained by the FEM, while the radiated sound pressure field is determined by the W-H
technique using the calculated pressure and particle velocity at the opening.

Figure 1 – Schematic diagram of a hybrid method to predict sound radiation from an unbaffled
long enclosure with the ground.
The W-H solution for the rigid unbaffled long enclosure with the ground is obtained as follows:

P + ( , h ) =  ( −1)m=0m

f m − i m g m +L ( m )  + ( m )L+ ( )  + ( )2 m ( +  m )++where L ( ) and  ( ) are kernel functions which are expressed as(1)

L+ ( ) =sin ( kh )k   − i ( )   ( ) h  exp ln 


k
 
  


 2  i  i h      i h  exp 1 − C − ln    1 +  exp  −−  kh  2    l =1   l  l (2)

 + ( ) =k + (3)
The far-field directivity pattern of the radiated sound pressure field outside the unbaffled long
enclosure is given by (1)p ( r , ) = iei 4 +e−ikr
P ( −k cos  , h ) eik sin h
2
kr

(4)

From Eqs. (1) and (4), the radiated sound pressure field is determined by the modal response
coefficients of sound pressure and pressure gradient along the horizontal direction. To determine thecoefficients, the continuity relations of sound pressure and pressure gradient at the opening of theunbaffled long enclosure are considered:CC P1   Y1 ( y1 ) Y2 ( y1 )
P  
 2 =      CC Pn  Y1 ( yn ) Y2 ( yn )YmC ( y1 )   g1  
  g2   
YmC ( yn )   g m 

(5)

C
C
V1   Y1 ( y1 ) Y2 ( y1 )V  
 2 =      CC
Vn  Y1 ( yn ) Y2 ( yn )YmC ( y1 )   f1  
  f2   
YmC ( yn )   f m 

(6)

and

where Pn and Vn are the sound pressure and horizontal pressure gradient on meshing nodes ( 0, yn ) along
the enclosure opening. Modal function outside the long enclosure is denoted by YmC which can be obtainedby the Helmholtz equation and the boundary condition on the ground.
Rewriting Eq. (5) and Eq. (6) in matrix form, we have
P=MG

(7)

(8)
where matrices P and V can be obtained using FEM while M can be constructed using Eq. (5). After
solving Eqs. (7) and (8), the modal response coefficients G and F can be obtained. Then, thedirectivity pattern of the radiated sound pressure field can be calculated using Eqs. (1) and (4).
V=MF

3. RESULTS AND ANALYSIS
3.1 Sound reduction performance of ZMPPAAn unbaffled long enclosure of 2 m in length and 0.2 m in height is considered here. A monopole
point source is located at (-0.4, 0.1) m and the volume velocity strength is 0.01 m2 /s. The far-fieldobservation radius is 0.8 m and the observation angle is in the range of [0, 150] degrees. Besides, thespeed of sound and density of the air are 340 m/s and 1.225 kg/m3 , respectively. First, the soundpressure field inside the long enclosure is calculated using the FEM. To ensure the calculation
accuracy of the model, the mesh grids along the opening are refined as presented in Figure 2. Besides,

a geometrical singularity exists at the enclosure edge, where the pressure gradient at this point may
be wrong. Hence, more grids are needed near this point and the horizontal pressure gradient at thispoint is replaced by that of the nearest point in the following calculations. Such replacement may giverise to certain errors of the radiated sound pressure field. however, the direct use of the horizontalpressure gradient at the edge will lead to wrong results.

Figure 2 – Refinement of the meshing grids along the enclosure opening and at the sharp edge.
Comparison of SPLs at receivers outside long enclosures integrated with liners made of FMPPAs,
CMPPAs, and ZMPPAs is presented in Figure 3. At the backside of the long enclosure as presented in
Figure 3 (a) and Figure 3 (b), CMPPA and ZMPPA perform better than the FMPPA almost within thefrequency range considered which indicates that the corrugated MPPAs can absorb more noise than
the flat ones. Besides, ZMPPA performs better than the CMPPA when the frequency is above about
2000 Hz. This is attributed to the Z-shaped MPP profile which can absorb higher-order acousticalmodes efficiently in the middle to high frequency range. In front of the long enclosure, as presented
in Figure 3 (c), CMPPAs and ZMPPAs perform better than FMPPAs. However, ZMPPAs do not
perform better than CMPPAs in the whole frequency range as the sound distribution in front of theenclosure is more complicated than that at the backside of the long enclosure.

Figure 3 – Comparison of SPLs obtained by the liners made of FMPPAs, CMPPAs, and ZMPPAs
at (a) [-0.6, 0.6] m, (b) [0, 0.6] m, and (c) [0.6, 0.6] m.
The directivity patterns of the radiated SPL fields are shown in Figure 4. At 500 Hz, the SPLs of
FMPPA, CMPPA, and ZMPPA are almost the same as the wavelength of the sound is much longer
than the MPP shape size. Low frequency noise is difficult to be absorbed. ZMPPA performs slightly
better than the CMPPA and FMPPA. At 2500 Hz, ZMPPA reduce the SPLs to a low level. The Z-shaped MPP profile exhibit good performance in the absorption of higher-order acoustical modes. As
a result, the radiated SPL outside the long enclosure is reduced significantly.

Figure 4 – Directivity patterns of the radiated SPL fields at (a) 500 Hz and (b) 2500 Hz.
3.2 Mechanism study
To explore the mechanism behind the sound absorption performance of the proposed ZMPPAs. The
sound pressure along the opening and modal response coefficients at 500 Hz and 2500 Hz arepresented in Figure 5 and Figure 6, respectively.

Figure 5 – Sound pressure along the opening and the modal response coefficients at 500 Hz.

Figure 6 – Sound pressure along the opening and the modal response coefficients at 2500 Hz.

At 500 Hz, the absolute sound pressure along the opening is almost the same for MPPAs which arestand at around 0.1 Pa. Besides, the modal response coefficients demonstrates that the first mode is
the dominant mode in these cases. However, the contribution of the mode in the ZMPPA case is smaller
than those of FMPPA and CMPPA cases. As a result, the radiated sound field in ZMPPA case is also
smaller than that of CMPPA and FMPPA cases. At 2500 Hz, multiple higher-order acoustical modes
contribute to the sound field in FMPPA case. After integrating the CMPPA and ZMPPA, the modal
response coefficients reduced a lot. In the ZMPPA case, only the first three modes contribute to theradiated sound pressure field and the amplitudes decrease significantly compared to that in FMPPA
case. Therefore, the radiated sound field in the ZMPPA case is smaller than that in the CMPPA andFMPPA cases, which indicate that the proposed ZMPPA can absorb higher-order acoustical modes.
3.3 Experimental study
The performance of the ZMPPA, CMPPA, and FMPPA in reducing the radiated noise from an
unbaffled long enclosure is examined using quasi-2D experiments. A schematic diagram of the test
rig is presented in Ref. (1). The experiment is carried out in an anechoic chamber of 6 m in length, 6
m in width, and 3 m in height. As the proposed model was established in a 2D configuration, we
designed a quasi-2D test rig accordingly (1, 12). It is constructed by two parallel arranged acrylic
plates of 2.4 m long, 1.2 m wide, and 0.02 m thick. To eliminate the effect of the higher-orderacoustical modes along vertical direction, the distance between the acrylic plates is kept at 0.04 m.
Based on the cut-off rule of a channel, the plates can form a quasi-2D space under about 4250 Hz. A
rectangular duct of 1.2 m long, 0.2 m wide, and 0.04 m high is inserted into the quasi-2D space toform an unbaffled long enclosure. To simulate the infinite region outside the long enclosure, a layerof wedge-shaped Melamine foam is placed at the side openings of the 2D space. The total heights of
the wedges are 0. 2 m. According to the rule of a quarter wavelength, they can achieve non-reflection
boundaries above around 450 Hz. The whole test rig is supported by a frame assembled by aluminum
extrusions. 2 A0 papers printed with the Cartesian and polar coordinate systems are pasted on thebackside of the transparent acrylic plate to locate the measurement points. A Tannoy loudspeakerconnected to a long pipe of 1 m in length and 25 mm in diameter is applied to simulate a monopole
point source. Measurements of the directional characteristics of this source were conducted and it wasobserved that the deviations in all directions were within 1 dB for frequencies above about 200 Hz(13). The harmonic sound source is produced by a signal generator, output by an A/D converter (NI4431), amplified by the power amplifier (LA 1201), and played by the Tannoy loudspeaker. Two
microphones (B&K 4189) are connected to the conditioning amplifier (B&K NEXUS) and the dataacquisition module (NI 9234). They move along the observation radius and collect acoustical signals
every 5 degrees of observation angle. The testing system is controlled by LabVIEW which possesses
the merits of good stability and high real-time performance in the targeted frequency range.

Figure 7 – Experimental setups. (a) a liner made of FMPPAs, (b) a liner made of CMPPAs, and(c) a liner made of ZMPPAs.
Here, 3 types of liners are mounting on the inner wall of the long enclosure as presented from
Figure 7 (a) to Figure 7 (c). Each liner is assembled by four FMPPAs, CMPPAs, and ZMPPAs,
respectively. The cavity width and depth of each unit are 100 mm and 60 mm, respectively. The
corrugation depth and offset distance are 25 mm and 35 mm, respectively. A comparison between theILs obtained by liners made of FMPPAs, CMPPAs, and ZMPPAs is demonstrated in Figure 8. From
500 Hz to 1600 Hz, the ILs are stands at about 5 dB to 8 dB which results from the shallow cavitiesof the MPPAs. Besides, in the frequency range of 1200 Hz to 1700 Hz, the FMPPAs outperform the
CMPPAs and ZMPPAs. The liner made of ZMPPAs starts to show its advantages in the frequency
range between 1700 Hz and 2000 Hz, in which high ILs are achieved. In this frequency interval, thesound field inside the long enclosure become complex. Higher-order acoustical modes can be absorbedby the ZMPPAs.

Figure 8 – Comparison of insertion losses obtained by liners made of FMPPAs, CMPPAs, andZMPPAs.

4. CONCLUSIONSIn this paper, a hybrid method is established to calculate the sound radiated from an unbaffled long
enclosure integrated with MPPAs. A liner consisting of multiple ZMPPAs is proposed to absorb higher
order acoustical modes inside the long enclosure so that the radiated noise can be suppressed. The
sound absorption performance of the ZMPPA is investigated which shows good performance in the
middle to high frequency range. Further modal analysis demonstrates that the proposed ZMPPA canabsorb higher order acoustical modes compared with that of FMPPA and CMPPA which shows great
potential for the reduction of environmental noise. Finally, a quasi-2D experimental study isconducted which validates the proposed modal and verifies the numerical findings.

ACKNOWLEDGEMENTS
The authors would like to acknowledge the funding support from the Research Grants Council,
Hong Kong (PolyU15209520).

REFERENCES
1. Yang, W. P, Choy, Y. S, Wang, Z. B, & Li, Y. (2022). Sound radiation and suppression of an
unbaffled long enclosure using Helmholtz resonators. Mechanical Systems and Signal
Processing, 165, 108408.
2. Law, M. K., Li, K. M., & Leung, C. W. (2008). Noise reduction in tunnels by hard roughsurfaces. The Journal of the Acoustical Society of America, 124 (2), 961-972.
3. Eldredge, J. D. (2004). On the interaction of higher duct modes with a perforated liner systemwith bias flow. Journal of Fluid Mechanics, 510, 303-331.
4. Jing, X., Wang, X., & Sun, X. (2007). Broadband acoustic liner based on the mechanism ofmultiple cavity resonance. AIAA journal, 45(10), 2429-2437.
5. Zhou, D., Wang, X., Jing, X., & Sun, X. (2016). Acoustic properties of multiple ca vity
resonance liner for absorbing higher-order duct modes. The Journal of the Acoustical Societyof America, 140(2), 1251-1267.
6. Wang, C., Cheng, L., Pan, J., & Yu, G. (2010). Sound absorption of a micro -perforated panel
backed by an irregular-shaped cavity. The Journal of the Acoustical Society of America,
127(1), 238-246.
7. Gai, X. L., Xing, T., Li, X. H., Zhang, B., Wang, F., Cai, Z. N., & Han, Y. (2017). Sound
absorption of microperforated panel with L shape division cavity structure. Applied Acoustics,
122, 41-50.
8. Yang, C., Cheng, L., & Pan, J. (2013). Absorption of oblique incidence sound by a finite
micro-perforated panel absorber. The Journal of the Acoustical Society of America, 133(1),201-209.
9. Wang, C., Huang, L., & Zhang, Y. (2014). Oblique incidence sound absorption of parallel
arrangement of multiple micro-perforated panel absorbers in a periodic pattern. Journal ofSound and Vibration, 333(25), 6828-6842.
10. Liu, Y., Zhang, Y., Chen, K., & Ma, X. (2020). Oblique incidence sound absorption of a hybrid
structure using a micro-perforated panel and a planar actuator. Applied Physics Express, 13(5),057001.
11. Wang, C., & Liu, X. (2020). Investigation of the acoustic properties of corrugated microperforated panel backed by a rigid wall. Mechanical Systems and Signal Processing, 140,
106699.
12. Guo, J., Zhang, X., Fang, Y., & Fattah, R. (2018). Reflected wave manipulation by
inhomogeneous impedance via varying-depth acoustic liners. Journal of Applied Physics,
123(17), 174902.
13. Li, K. M., Kwok, M. P., & Law, M. K. (2008). A ray model for hard parallel noise barriers inhigh-rise cities. The Journal of the Acoustical Society of America, 123(1), 121-132.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0279

SOPRANOISE: toward a quick measurement method to verify andmonitor the acoustic performance of noise barriers on site
Massimo Garai 1; Paolo GUIDORZI1; Marco CONTER 2; Andreas FUCHS2
1
2

University of Bologna, Italy

AIT Austrian Institute of Technology GmbH, Austria

ABSTRACTThe European standards EN 1793-5 and EN 1793-6 allow measurements of sound absorption/reflection andairborne sound insulation of noise barriers in-situ, an essential activity for approving noise barriers installed
alongside roads. However, the EN methods require a careful application by expert users, which may limit
their use to few locations. The main objective of the project SOPRANOISE (2019-2022), funded by CEDR,
was the development of a new method that should be quickly applicable to a larger part of the installed noisebarriers in a more manageable way. This paper presents the main results of SOPRANOISE WP4, dealing
with the conception, design, and validation of such simplified quick method. It was necessary to design acompletely new equipment, simpler and lighter than the one for full tests, allowing the use by normal
operators after a short training. The new quick method was validated in the laboratory and under real on-site
conditions, applying both the new quick method and the full EN methods on real noise barriers installed
along motorways. The systematic application of the quick method allows to identify the weak points of thenoise barrier and the appropriate noise barrier fields to be tested with the full EN methods.
Keywords: SOPRANOISE, noise barriers, acoustic performance

1. INTRODUCTION

Noise barriers are extensively used as effective devices to reduce the impact of road traffic noise.
This reduction is quantified by the insertion loss, which depends on the barrier height and length, theenvironment in which the noise barrier is placed (the reciprocal positions of the sound source, receiverand noise barrier, the topography and morphology of the terrain, the local weather conditions, etc.)
and also on the intrinsic acoustic characteristics of the noise barriers components: airborne soundinsulation and sound absorption. For example, a decrease in sound insulation with time can cause adecrease of the insertion loss at receivers close to the noise barrier, due to the energy transmitted
through the barrier and reaching the receiver.
National Road Administrations (NRAs), who write specifications that contractors andmanufacturers of noise barriers products must respect, want that the overall acoustic performances beguaranteed all along the lifetime cycle. Thus, they need methods able to test noise barriers as they are
installed (involving the quality of the products and the workmanship), which means under realconditions alongside roads and following their intended use (i.e.: under direct sound field conditions).
To comply with this requirement, CEN/TC226/WG6 wrote the standards EN 1793-5 (measurementof sound absorption under direct sound field conditions) and EN 1793-6 (measurement of airborne
sound insulation under direct sound field conditions) [1,2]. Only those two standardized methods are
relevant to the intended use of “free standing noise barriers”; they allow measurements almost
everywhere, what is here very relevant while approving or monitoring installed noise barriers. This isalready and increasingly done by NRAs to characterize installed noise barriers. However, EN 1793-5
and EN 1793-6 methods require accurate (and thus lengthy) tests and a good knowledge of the method,i.e., expert users: this can limit their use alongside roads. While always keeping the possibility to use
EN 1793-5 and EN 1793-6 on site, there is a need for new methods that could be easier, faster, andsafer. Some NRAs already undertake in-situ inspections to monitor the integrity of the different parts
1
2

massimo.garai@unibo.it
Marco.Conter@ait.ac.at

of their road equipment. Those visual inspections are the easiest and cheapest tools to investigate
installed noise barriers: implementing such in-situ inspections in a more systematic way, integrating
the acoustic characteristics is a real plus that can save time and money. However, inspections cannot
give quantitative – i.e. objective – results. For quantitative assessments, via measurements, a new“quick method” had to be designed in order to be applicable in a much more systematic and affordable
way than the one allowed by the “full” EN 1793-5 and -6: this has been done successfully in the frameof the SOPRANOISE project and led to the brand new and validated quick “SOPRA” method. Thisnew method is particularly fitted to monitor and maintain noise barriers at all stages of their lifetimeand help NRAs keep the intended performance over time.

2. THE SOPRANOISE 3-STEP APPROACH

Within this research project a new concept of performance assessment has been developed: the socalled SOPRANOISE 3-step approach (see Figure 1). This approach allows to place the right effort
and money to the right level of assessment: from the easiest (but less accurate) way, up to the mostaccurate one (i.e.: the standardized methods EN 1793-5 and EN 1793-6), following an “engineering
progressive approach” [3,4].

Figure 1 - SOPRANOISE progressive 3-steps approach to characterize the intrinsic acoustic characteristics
of installed noise barriers.
The quick method developed in WP4 corresponds to the second step of this 3-steps approach. The
quick method, also called SOPRA method, is intended to reduce the amount of EN 1793-5 and -6
measurements required to qualify a noise barrier. To this aim, the quick method is repeatedly appliedin several locations along the noise barrier under test, giving a reasonable estimate of the noise barrier
performance, and of the related range of variability, even if with an uncertainty that could be greaterthan the one of the full EN standards. Relying on the results of this systematic scan of the noise barrier,two or three sites where to apply the full EN standards could be then selected.
The new quick method differs from the visual inspection method used in step 1 [5,6], because theinspection gives a qualitative assessment, based on an evaluation, while the quick method gives
quantitative indications, based on measured values of the acoustic performance of the noise barrier.
Using these values, it will then be possible to select the best sites for a definitive assessment of thenoise barrier under full test according to EN 1793-5 and EN 1793-6. The quick method differs fromthe full EN standards EN 1793-5 and EN 1793-6 used in step 3 because it is designed for quick andeasy application by generic users, at the price of a possible reduced accuracy compared to that one of
the full EN standards.
In order to understand the usefulness of the quick method, it is necessary to define the goal of theplanned noise barrier investigation. When the goal is the approval of a newly built noise barrier (i.e.,
for legal reasons which require quantified values of the single-number ratings of the intrinsic
characteristics DL RI and DLSI), the only way is to carry out measurements according to the EN 17935 and -6 standards. Since these measurements requires accredited laboratories and can be timeconsuming – especially for long noise barriers – it is advisable to first carry out in-situ inspections
and/or measurements via the quick method. A quick sampling via the quick method allows a fair pre-selection of relevant locations for the actual approval.
If the goal is monitoring an existing noise barrier for maintenance purposes, the quick method canbe routinely applied in several locations along the noise barrier, giving a reasonable estimate of thenoise barrier performance, and of the related range of variability over a large sample of noise barrier
fields.
It is worth noting that for the evaluation of sound absorption properties, it is unavoidable to apply
directly the quick method. For the evaluation of sound insulation properties, the quick method can bepreceded by a visual inspection (step 1), which spots out the defects with negligible consequences forthe insertion loss (green rating), defects which surely have to be repaired (red rating) and defects
which require an actual assessment via acoustic measurements (yellow rating) [5,6]. This caseestablishes the transition to step 2, i.e., the quick measurement method.

3. MEASURING EQUIPMENT FOR THE QUICK METHOD

The quick method relies on a lightweight equipment that can be brought by one operator in any
location close to a noise barrier [7]. It includes a control and processing unit, a loudspeaker and alinear microphone antenna. The control and processing system, battery operated, substitutes thecomputer used in EN 1793-5 and -6 measurements, being much more lightweight and working
automatically pushing few buttons. University of Bologna (UNIBO) built it around a Teensy 4.1
system (Figure 2); AIT used a Raspberry Pi 4, implementing the same operations.

Figure 2 – The first control and processing unit designed and built at UNIBO.
Both UNIBO and AIT built their lightweight loudspeaker; UNIBO used a SICA Z002601 driver,5” diameter; AIT used an Img Stageline PAB-305/SW, 5” diameter. The performance of theselightweight loudspeakers is comparable to those of the standard loudspeakers used for EN 1793-5 and-6 measurements (Figure 3).
As input sensors, robust and inexpensive electret microphones CMA-4544PF-W have been selected
by UNIBO. Figure 4 shows the magnitude of the frequency response of an electret microphonecompared with that of a PCB microphone adopted for the EN 1793-5 and -6 square array (The curveof the electret microphone has been shifted upward by 11 dB for a better readability). As can be seen,
they are nearly identical in the relevant frequency range (100-5000 Hz in one-third octave bands).
AIT selected digital MEMS-microphones: SPG08P4HM4H-1 by Knowles Electronics LLC, withsimilar performances.
Figure 9 shows microphone antenna, loudspeaker and control device in place for the quick soundreflection index measurements. Microphone M1 is at 1,20 m above ground; the other are spaced
equally spaced with a step of 0,40 m: 1,20 – 1,60 – 2,00 – 2,40 – 2,80 – 3,20 m above ground.

Figure 3 - Impulse response of the of the Zircon© loudspeaker (red line) and the UNIBO lightweightloudspeaker (green line) framed by a 3 ms window (blue line).

Figure 4 - The magnitude of the frequency response of an electret microphone compared with that of a PCB
microphone adopted for the EN 1793-5 and -6 square array. The Y-axis is in dB with an arbitrary reference.
The curve of the electret microphone has been shifted upward by 11 dB for a better readability.

Figure 5 - (not to scale) Sketch of the sound source and the microphone antenna. Left: in front of the roadtraffic noise reducing device under test for sound reflection index measurements. Right: in the free field. 1:
reference surface. 2 Reference height hS [m]. 3 Loudspeaker front panel. 4 Distance between the
loudspeaker front panel and the reference surface, dS [m]. 5 Distance between the loudspeaker front panel
and the microphone antenna, dSM [m]. 6 Distance between the microphone antenna and the reference
surface, dM [m]. 7 Microphone antenna. 8 Noise barrier height, hB [m].

4. MEASURING PROCEDURE

The measuring procedure is borrowed with simplifications and with less changes as possible fromEN 1793-5 and EN 1793-6, which are supposed to be known to the reader. Therefore, the procedure
is summarized here in a compact way, referring to EN 1793-5 [1] and EN 1793-6 [2] whenever possible.
4.1.

Sound absorption

4.1.1. General principle for sound absorption
The methodology assesses indirectly sound absorption by measuring sound reflection, its
complementary characteristics. The sound source emits a transient sound wave that travels past the
microphone antenna position to the device under test and is then reflected on it (Figure 5 left). Eachmicrophone, being placed between the sound source and the device under test, receives both the direct
sound pressure wave travelling from the sound source to the device under test and the sound pressure
wave reflected (including scattering) by the device under test. The direct sound pressure wave can bebetter acquired with a separate free-field measurement keeping the same geometrical setup of soundsource and microphone antenna but without the noise barrier (Figure 5 right). The ratio of the power
spectra of the direct and the reflected components gives the basis for calculating the “quick” soundreflection index.
4.1.2. Measured quantity
The “quick” reflection index RIQ as a function of frequency, in the j-th one-third octave band, is:2∫𝛥𝛥𝑓𝑓 �𝐹𝐹�ℎ𝑟𝑟,𝑘𝑘 (𝑡𝑡)⋅𝑤𝑤𝑟𝑟,𝑘𝑘 (𝑡𝑡)�� 𝑑𝑑𝑑𝑑
𝑛𝑛𝑗𝑗
𝑅𝑅𝐼𝐼𝑄𝑄,𝑗𝑗 = ∑𝑘𝑘=1⋅ 𝐶𝐶𝑔𝑔𝑔𝑔𝑜𝑜,𝑘𝑘 �
� 𝑗𝑗
2
𝑛𝑛𝑗𝑗
∫ �𝐹𝐹�ℎ (𝑡𝑡)⋅𝑤𝑤 (𝑡𝑡)�� 𝑑𝑑𝑑𝑑
1

𝛥𝛥𝑓𝑓𝑗𝑗

𝑖𝑖,𝑘𝑘

𝑖𝑖,𝑘𝑘

(1)

Where: hi,k (t) is the incident reference component of the free-field impulse response at the k-th
measurement point; hr,k (t) is the reflected component of the impulse response taken in front of thesample under test at the k-th measurement point; wi,k (t) is the time window (Adrienne temporalwindow) for the incident reference component of the free-field impulse response at the k-th
measurement point; see EN 1793-5:2016, point 5.5.5 [1]; wr,k(t) is the time window (Adrienne
temporal window) for the reflected component at the k-th measurement point; F is the symbol of theFourier transform; j is the index of the one-third octave frequency bands ([7]between 200 Hz and
5 kHz, where possible); ∆f j is the width of the j-th one-third octave frequency band; k is the
microphone number according to (k = 1, ..., 6); n j is the number of microphone positions on whichto average; Cgeo,k is the correction factor for geometrical divergence at the k-th measurement point.
𝑑𝑑2

𝐶𝐶𝑔𝑔𝑔𝑔𝑔𝑔,𝑘𝑘 = � 𝑟𝑟,𝑘𝑘 �𝑑𝑑𝑖𝑖,𝑘𝑘(2)

Where: d i,k is the distance from the front panel of the loudspeaker to the k-th microphone; d r,k isthe distance from the front panel of the loudspeaker to the source and microphone reference plane andback to the k-th microphone following specular reflection.
For microphone M3, di,5 = dSM = 1,25 m. The spacing between adjacent microphones is s = 0,40 m.
4.1.3. Test arrangement
The quick method is intended to be applied on noise barriers installed alongside roads.
A reference surface for quick sound reflection index measurements is defined: it is an ideal, smoothsurface facing the sound source side of the noise barrier under test and just touching the most
protruding and significant parts of it within the tested area (see Figure 5).
The sound source reference position is a position facing the noise barrier side exposed to roadtraffic noise, located at the reference height hS = 2,00 m and placed so that the horizontal distance ofthe source front panel to the reference surface of the noise barrier is d S = 1,50 m (see [1]).
The microphone antenna reference position for quick sound reflection index measurements is aposition compliant with all the following conditions: i) the microphone antenna is on the noise barrierside exposed to traffic noise; ii) the microphone M3 is located at the reference height hS; iii) theshortest distance of the microphone M3 to the reference surface is d M = 0,25 m (see [1]).
4.1.4. Signal processing
Measurements are done in one-third octave bands between 200 Hz and 5 kHz. The test signal is a
maximum-length sequence (MLS). Any other full period of deterministic, flat-spectrum signal, likeexponential sine sweep (ESS), can be used. The sample rate shall have a value equal or greater than
44,1 kHz. The overall impulse responses consist of a direct component, a component reflected fromthe surface under test and other parasitic reflections. The direct component and the reflectedcomponent from the device under test are extracted from the impulse response at each microphone
position using suitable analysis windows in the time domain. The windowing operations are performed
using the Adrienne temporal window described in EN 1793-5 and keeping the same window widthand positioning rules. One measurement at each of the six microphones of the vertical antenna is done,
independently of the shape and material composition of the noise barrier under test. Then the single
measurements are averaged as per formula (1). The microphones to be considered in the processing
stage as a function of the height of the noise barrier under test are given in Table 1. The microphones
used in the post-processing should be clearly identified in the report, stating their identification label(M1, M2, …, M6). It should be noted that the bottom microphone (M1) is at a height of 1,20 m above
ground. In this position it is influenced by the reflection on the ground and thus the analysis window
should be reduced to avoid the unwanted reflection, inducing a higher low-frequency limit. That’s
why in Table 1 it is always in parentheses. If reputed useful for the particular composition of the noisebarrier under test (e.g., different sound absorbing material at the bottom of the noise barrier), thebottom microphone (M1) can still be used, knowing that the result could be interesting for a relative
comparison of the different barrier fields, but cannot be compared with the full EN measurement.
Table 1 - Microphones to be considered in the post-processing stage as a function of the height of the noisebarrier under test.

4.2.

Barrier height, HB

Microphones

Lowest reliable one-third octave frequency band

HB ≤ 3 m(M1) M2 to M4

315 Hz3 m < HB ≤ 4 m(M1) M2 to M5

250 Hz4 m < HB ≤ 5 m(M1) M2 to M5

250 Hz

5 m < HB ≤ 6 m(M1) M2 to M6

200 Hz

HB > 6 m and over(M1) M2 to M6

200 HzSound insulation

4.2.1. General principle for sound insulation
The sound source emits a transient sound wave that travels toward the device under test and ispartly reflected, partly transmitted and partly diffracted by it. The microphone placed on the otherside of the device under test receives both the transmitted sound pressure wave travelling from thesound source through the device under test, and the sound pressure wave diffracted by the top edge ofthe device under test (Figure 6). If the measurement is repeated without the device under test between
the loudspeaker and the microphone, the direct free-field wave can be acquired (Figure 6). The power
spectra of the direct wave and the transmitted wave give the basis for calculating the “quick” soundinsulation index.
4.2.2. Measured quantity
The “quick” sound insulation index SIQ as a function of frequency, in the j-th one-third octave
band, is:2

∫𝛥𝛥𝑓𝑓 �𝐹𝐹�ℎ𝑡𝑡,𝑘𝑘 (𝑡𝑡)𝑤𝑤𝑡𝑡,𝑘𝑘 (𝑡𝑡)�� 𝑑𝑑𝑑𝑑
𝑆𝑆𝐼𝐼𝑄𝑄,𝑗𝑗 = −10 ⋅ 𝑙𝑙𝑙𝑙 � ∑𝑛𝑛𝑘𝑘=1 𝑗𝑗�2𝑛𝑛∫𝛥𝛥𝑓𝑓 �𝐹𝐹�ℎ𝑖𝑖,𝑘𝑘 (𝑡𝑡)𝑤𝑤𝑖𝑖,𝑘𝑘 (𝑡𝑡)�� 𝑑𝑑𝑑𝑑
1

𝑗𝑗

(3)

Where: h i,k (t) is the incident reference component of the free-field impulse response at the k thscanning point; h t,k (t) is the transmitted component of the impulse response at the kth scanning point;
wi,k (t) is the time window (Adrienne temporal window) for the incident reference component of thefree-field impulse response at the kth scanning point; wt,k (t) is the time window (Adrienne temporalwindow) for the transmitted component at the kth scanning point; F is the symbol of the Fouriertransform; j is the index of the one-third octave frequency bands (between 200 Hz and 5 kHz, where
possible); ∆f j is the width of the j-th one-third octave frequency band; k is the microphone number
according to [7] (k = 1, ..., 6); n = 6 is the number of scanning points.

Figure 6 - (not to scale) Sketch of the sound source and the microphone antenna. Left: in front of the roadtraffic noise reducing device under test for sound insulation index measurements. Right: in the free field. 1:
Sound source reference surface. 2 Reference height hS [m]. 3 Loudspeaker front panel. 4 Distance between
the loudspeaker front panel and the reference surface, dS [m]. 5 Microphone reference surface. 6 Distance
between the microphone antenna and the mic. reference surface, dM [m]. 7 Microphone antenna. 8 Noisebarrier height, hB [m].
4.2.3. Test arrangement
The quick method is intended to be applied on noise barriers installed alongside roads.
A sound source reference surface for quick sound insulation index measurements is defined: it isan ideal, smooth surface facing the sound source side of the road traffic noise barrier under test andjust touching the most protruding and significant parts of it within the tested area (see [2]). A
microphone reference surface for quick sound insulation index measurements is defined: it is an ideal,smooth surface facing the back side of the road traffic noise barrier under test and just touching themost protruding and significant parts of it within the tested area (see Figure 6).
The sound source reference position for quick sound insulation index measurements is a position
facing the noise barrier side exposed to road traffic noise, located at the reference height hS = 2,00 m
and placed so that the horizontal distance of the loudspeaker front panel to the reference surface ofthe noise barrier is d S = 1,00 m (see Figure 6).
The microphone antenna reference position for quick sound insulation index measurements is aposition compliant with all the following conditions: i) the microphone antenna is on the noise barrierback side, not exposed to traffic noise; ii) the microphone (M3 is located at the reference height hS;iii) the shortest distance of the microphone M3 to the microphone reference surface is dM = 0,25 m(see Figure 6). Regarding test arrangement and signal processing, considerations similar to those in
4.1.3 and 4.1.4 apply.

5. LABORATORY TESTS

Among the many laboratory tests done, the one focused on airborne sound insulation is reported
here. In an ideal laboratory setup, it was possible to measure real-size test sample with the EN 17936 method and the new quick method. Artificial defects, impacting on airborne sound insulation, havebeen created in the test noise barriers; it is 4 m in height, made of vertically placed aluminum panels

filled with absorptive material and having height 0,5 m each; the sound source side of the panels is
perforated, while the backside is fully reflective. Horizonal gap between two panels were created,over the full length of the noise barrier field and with variable height: 0 mm gap (to be used as areference); 5 mm gap; 10 mm gap; 20 mm gap; 50 mm gap and 100 mm gap.

Figure 7 – Different gap positions used in the laboratory test for airborne sound insulation, at 200 cm (left)
and 250 cm (right) from the ground.

Figure 8 – Comparison between the results from the 3 centre-column microphones of the EN 1793-6 grid(M2, M5, M8) and the SOPRA-3a quick method microphones (M2, M3, M4), which have exactly the same
nominal position. The comparison is shown for the following gap dimensions: no gap, 5 mm gap, 10 mm
gap and 100 mm gap. The vertical bars represent the uncertainty U95 according to EN 1793-6.
Figure 7 shows two different gap positions used, at 200 cm and 250 cm from the ground. These
results confirm the very good accordance between both methods and show the validity of using the
hardware of the quick measurement system to assess the intrinsic performance of a noise barrier.
Figure 8 shows the comparison between spectral results considering the 3 center-column
microphones of the EN 1793-6 grid (M2, M5, M8) and the “SOPRA-3a” quick method (M2, M3, M4),
which have the same nominal position [7]. The comparison is shown for the following gap dimensions:
0 mm, i.e., no gap, 5 mm gap, 10 mm gap and 100 mm gap, at a height of 2 m above ground. The
vertical bars represent the uncertainty U95 according to EN 1793-6 and is plotted as an additional
information. It should be noted that the uncertainty U95 according to EN 1793-6 is related to theaverage of 9 microphones, while here only the average of 3 microphones is shown.

6. IN-SITU TESTS

The in-situ validation of the quick method by comparison with full EN 1793-5 and -6 methods was
carried out on noise barriers installed along the A22 motorway connecting Northern-Italy to Austria.
Metal barriers and timber barriers have been tested. It has been proved that the quick methods allow
to test many more noise barrier fields at the same time, at the price of a slightly reduced accuracy,compared to the full EN standards: in one day, from about 10 AM to 16 PM, twenty-two quick soundreflection index (RIQ) tests and eleven quick sound insulation index (SIQ) tests have been done. Threemonths before this test, a field of the same noise barrier was measured applying the full EN 1793-5
and -6 procedure with the standard equipment; this measurement has been taken as a reference.

Figure 9 – The quick method in action: microphone antenna and loudspeaker in place along the A22
motorway for the quick sound reflection index measurement on a metal noise barrier.
Figure 10 presents the results of all 22 quick measurements for RI, averaged over the four
microphones M2 to M5 (excluding the bottom microphone, M1, and the top microphone, M6,
according to Table 1), the results of a full EN 1793-5 test done 3 months before and the toleranceinterval defined by adding or subtracting to/from the EN 1793-5 measured value the measurement
uncertainty at 95% confidence level [8]. The overall agreement is very good and within the expanded
uncertainty of EN 1793-5 down to 400 Hz. The mean value of the single-number rating is 9,7 dB, witha standard deviation of 1,55 dB, versus a single-number rating for the full EN measurement of 8,1 dB.
This variability points out the actual differences existing among the different fields of a noise barrierin good conditions, due to the combined effects of manufacturing, installation workmanship, etc. Only
the new quick method, allowing to do multiple measurements in a short time, can give this information.
A visual inspection cannot appreciate this variability: it would conclude that all fields are very similar
and in good order (see Figure 10) and thus should get the same single-number rating.
Figure 11 presents the results of all 11 quick sound insulation index measurements, averaged overthe four microphones M2 to M5 (excluding the lowest microphone, M1, and the highest microphone,M6 as per Table 1), the results of a full EN 1793-6 test done 3 months before and the tolerance interval
defined by adding or subtracting to/from the EN 1793-6 measured value the measurement uncertainty
at 95% confidence level. The overall agreement is very good and generally within the expanded
uncertainty of EN 1793-6 with the exception on field 4.

Figure 10 – Colour lines: RIQ spectra obtained with the quick method on the metal noise barrier for 20
different fields (2 repeated). Average over mic. M2-M5. Black continuous line: result of a previous EN
1793-5 measurement on a single field. Black dashed lines: EN 1793-5 measured value ± the expandedmeasurement uncertainty at 95% confidence level.

Figure 11 – Colour lines: SIQ spectra obtained with the quick method on the metal noise barrier for 10
different fields (1 tested twice). Average over microphones M2-M5. Black continuous line: result of aprevious EN 1793-6 measurement on field n. 1. Black dashed lines: EN 1793-6 measured value plus orminus the expanded measurement uncertainty at 95% confidence level.
The mean value of the single-number rating is 29,4 dB, with a standard deviation of 1,53 dB, versus
a single-number rating for the full EN measurement of 32,0 dB. Again, the quick method spots out theactual differences existing among the different fields of a noise barrier in good conditions, due to the
combined effects of manufacturing, installation workmanship, etc., an information the visualinspection cannot give.

7. CONCLUSIONSThe SOPRANOISE project, funded by CEDR, developed an innovative 3-step approach tocharacterizing noise barriers; it ensures that the respective relevant method is used for the relevant
analysis and allows NRAs to better manage their noise barriers.
SOPRANOISE successfully achieved the design of the brand new quick (SOPRA) method: thismethod allows a quicker and safer procedure than the “full” standardized methods EN 1793-5 and EN
1793-6. It works as a link in between those accurate but long and demanding standards and thesimplest visual inspections. New equipment has been independently designed by the University of
Bologna and the Austrian Institute of Technology and the new quick method has been validated. Boththe procedure and the equipment are simpler and faster than for EN 1793-5 and EN 1793-6 standards,
allowing the use by normal operators after a short training. Laboratory tests proved that there is agood degree of correlation between the quick method and the acknowledged EN standards. In situ
tests proved that the quick method gives reliable and quantitative conclusions on the noise barrier
performances.
The new quick methods developed in the frame of the SOPRANOISE project helps roadauthorities to extend quantitative tests to a larger portion of the noise barrier, as a single applicationof the quick methods is easy and quick. Thus, the quick methods can be routinely applied in several
locations along the noise barrier, giving a reasonable estimate of the noise barrier performance, andof the related range of variability over a large sample of noise barrier fields, even if with an uncertainty
greater than that one of the full EN standards.
In the next future, the work performed in SOPRANOISE will be proposed at the European
standardization committee (CEN) for standardization, while more in-situ tests of the quick method
are already ongoing. Both UNIBO and AIT are working to develop an acceptance criterion for theindividual quick measurements and on a sampling criterion to evaluate the statistical representativityof a large sample of quick measurements in assessing the “average” performance of a long noisebarrier.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the CEDR (Conference of European Directors of Roads) for
supporting and funding this research as part of their Transnational Road Research Program, Call 2018.

REFERENCES

1. EN 1793-5:2016+AC:2018, Road traffic noise reducing devices - Test method for determining theacoustic performance - Part 5: Intrinsic characteristics - In situ values of sound reflection under direct
sound field conditions, Brussels, Belgium, 2018.
2. EN 1793-6:2018, Road traffic noise reducing devices - Test method for determining the acoustic
performance - Part 6: In situ values of airborne sound insulation under direct sound field conditions,
Brussels, Belgium, 2018.
3. Clairbois J.-P., Garai M., Bartolomaeus W., Chudalla M., Strigari F., Conter M., Fuchs A., Nicodème C.,
SOPRANOISE: EU Research on new techniques to characterize Noise Barriers acoustic performances.
Proc. INTER-NOISE 2020; 23-26 August 2020; Seoul, South Korea, 2020.
4. Conter M., Fuchs A. SOPRANOISE – Update and analysis of noise barrier database including new current
results” Proc. INTER-NOISE 21; 1-5 August 2021; Washington, USA 2021.
5. Fuchs A., Wehr R., M. Conter M. Empirical study on the correlation between measurement methods under
diffuse and direct sound field conditions for determining sound absorption and airborne sound insulation
properties of noise barriers. Proc. INTER-NOISE 21; 1-5 August 2021; Washington, USA 2021.
6. Strigari F., Chudalla M., Bartolomaeus W., Conter M., Fuchs A., Garai M., Nicodème C. and Clairbois
J-P. SOPRANOISE – In-situ inspection procedure for airborne sound insulation properties of existing
noise barriers. Proc. Euronoise Conference; 25 – 28 October 2021; Madeira, Portugal 2021.
7. Garai M. and Guidorzi P. SOPRANOISE Deliverable D4.2 - Report on the validation of the new quick
methods in-situ with recommendations for proper use. 2022. https://www.enbf.org/sopranoise.
8. Garai M., Schoen E., Behler G., Bragado B., Chudalla M., Conter M., Defrance J., Demizieux P., GlorieuxC., Guidorzi P. Repeatability and reproducibility of in situ measurements of sound reflection and airborne
sound insulation index of noise barriers. Acta Acustica u. Acustica, 2014; 100: 1186-1201.

PROCEEDINGS of the24th International Congress on Acoustics
Extended abstract
October 24 to 28, 2022, in Gyeongju, Korea

ABS-0444

The Impact On Noise, Noise Disturbance And Health, After The
Introduction Of Electrified Bus Transportation - An Intervention
Study.

Kerstin Persson Waye 1*, Huiqi Li1, Loisa Sandstrom1, Agnes Wiberg1 , Juan Parra 2 and Dag Glebe3
1 School of Public Health and Community Medicine, University of Gothenburg, Sweden
2

RISE, Research Institutes of Sweden, Sweden

3 IVL, Swedish Environmental Research Institute, Gothenburg, Sweden

Corresponding author’s e-mail address: kerstin.persson.waye@amm.gu.se
ABSTRACT
Background: Diesel driven buses generate engine noise predominantly in the low frequency range(<200Hz). The noise may adversely impact health. The impact of noise exposure and human response
was evaluated in an intervention study where hybrid (diesel-electric) buses were replaced by electricbuses.
Method: A survey, 3-6 months before and 6-9 months after the intervention, with a questionnaire sent
to a random 4000 residents (Phase I) and 3333 (Phase II). Half lived along the bus route (exposed),
and half at a further distance (reference). The response rate was 33% and 37%. Noise measurements
indoors were performed in 15 apartments. Noise levels from the total traffic, only busses and low
frequencies were numerically evaluated.
Results: After the intervention, the sound pressure levels (31.5 - 400Hz) indoors decreased in the
exposed areas. Calculated total traffic noise, was unchanged, while there was a reduction of noiselevels if only buses or the low frequencies noise were included. High annoyance, serious insomnia(sleep disturbance, daytime tiredness and energy depletion) and feeling down were significantly
reduced in the exposed population after the intervention, analyses adjusted for confounders.
Conclusion: This study found that electrified public transport in an urban environment lowered thenoise and improved health.
Keywords: Sound, Noise, Insulation, Transmission, Low frequency Noise

INTRODUCTION
Noise pollution is a major health risk and is estimated as the second worst risk factor for publichealth in Europe, after air pollution (1). Prolonged exposure to road transportation noise has
established effects on sleep and myocardial infarction and there is emerging evidence for othercardiovascular outcomes, diabetes and mental health. Noise may also affect us in our daily activities
causing annoyance and stress, affecting mental wellbeing, and how we interact with other people forexample. Future electrification of transportation is believed to reduce the engine noise which could
be health relevant for the public bus transportation and delivery vans travelling at low speed in urban
areas, while noise from tires or the interaction from tires and road surface will still be the dominantsource of noise at higher speeds. Diesel driven buses generate engine noise with predominant soundpressure levels in the low frequency range (<200Hz). Due to its long wave lengths, low frequency

noise propagates over long distances and intrudes also into people’s home with little attenuation by
the façade. The low frequency noise may hence be a risk for adversely impacting people’s health.
Further, people tend to be adversely affected even at very low sound pressure levels of low frequency
noise (2), and due to the proximity of the equal loudness curves at the low frequencies, a small increasein sound pressure level will be perceived as a great change in loudness. Compared to other noises,
there is limited knowledge of the long term health impacts (2) of the exposure to low frequency noise,
how to best assess it indoors (3) or even how to correctly calculate the outdoor propagation.
In 2019 the hybrid fossil fueled buses along one bus route in residential areas in the city ofGothenburg Sweden, were to be replaced by electrified busses. This gave us a unique opportunity toinvestigate the impact of this intervention. We hypothesized that the intervention would lead to an
improved sound environment and especially a reduction of the sound pressure levels in the low
frequency range. We furthered hypothesized that residents’ health would be improved after theintervention.

AIM
The aim of the study is to access the impact of an intervention where fossil fueled hybrid public
transportation buses were replaced by electrified busses and to specifically investigate the impact onnoise, noise disturbance and health among urban residents living along the bus route.

METHODS
3.1 Study population
A socio-acoustic survey was carried out 3-6 months before (Phase I) and 6-9 months after (PhaseII) the intervention. A questionnaire was sent to a randomly selected 4000 residents (Phase I) and 3333
(Phase II) with half of the residents living along the bus route (intervention), and half at a further
distance (reference) in two city districts. The response rate was 33% and 37% in Phase I and Phase II
respectively, making up around 1300 in Phase I and around 1200 in Phase II. The study was conductedin accordance with the Helsinki Declaration and approved by the ethical committee in Gothenburg.
3.2 Noise assessments
Noise measurements indoors were performed using 10 microphones in a bedroom facing the street
in 15 apartments before and after the intervention. A numerical evaluation of the annual average
equivalent A-weighted noise levels against the façade of the surveyed addresses from the buses andthe total traffic was performed for at a broadband spectrum. In particular, the annual average
equivalent sound pressure levels induced by the hybrid and electric buses were numerically assessed
within a low frequency range (31.5 - 100 Hz) at the façade for each address.
3.3 Statistical approach
In the analysis, any change in health outcomes from Phase I to Phase II was compared between theintervention population and the reference population to find out whether the introduction of electrifiedbuses was associated to a change in disturbance, sleep or health, and whether this change was greaterfor the intervention population than for the reference population. The effect on noise exposure wasstudied by comparing whether measured levels of noise and low-frequency noise decreased indoors
and outdoors from Phase I to Phase II. Dose response relationships were calculated for the healthimpacts in relation to the calculated noise levels.
The selection of reference populations in the same residential areas was done to ensure that the
populations and areas were as similar as possible regarding all other factors than the actualintervention. (i.e demographic, socioeconomic). So, if any change in outcome would occur, we could
conclude with a rather high certainty that the change was attributed to the intervention. In addition tothis we also adjusted in statistical analyses for any difference between areas that may have affected
the results.

RESULTS
4.1 Noise levels
Indoor measurements in the intervention area showed that sound pressure levels in the frequency
range of 31.5 - 400 Hz were lower after the introduction of electrified buses. The exact reduction ofsound levels depended on various factors such as the façade construction and the volume of the rooms.
However, a reduction in sound pressure levels had occurred within the frequency range of 31.5 - 80
Hz for all but a few measurements. Numerically evaluated noise levels described in annual average
A-weighted equivalent noise levels in the areas of interest were marginally affected, while annual
average noise levels calculated based on buses alone on the low frequency spectrum were clearlyaffected.
4.2 Health impacts
The proportion of respondents who paid attention to noise and vibration from buses from Phase I
to Phase II was significantly lowered among the intervention population. Noise from buses wasnoticed by 35 percentage points less and vibration was noticed by 15 percentage points less in Phase
II. Furthermore, the proportion who were very or extremely disturbed by noise and low-frequency
noise from buses was significantly reduced in the intervention population, by 17 and 24 percentage
points, respectively. The intervention population was also less disturbed by exhausts from buses andvibrations from buses. Also, severe sleep disturbance by bus noise was significantly reduced in theintervention population, by 6 percentage points. We also found significant improvements in symptoms
related to restoration and mental wellbeing.
There was no difference in people’s response to noise from other traffic. Importantly the
improvements were significantly reduced in Phase II as compared to Phase I, and in the interventionpopulation as compared to the reference-population.
The highest association between dose-response relationships between noise annoyance andnumerically evaluated sound level was found for noise exposure calculated as the frequency range of
31.5 - 100 Hz, and a slightly lower for the equivalent sound level calculated only for buses and lowest
for equivalent sound levels for all traffic.

CONCLUDING COMMENTS
The results from this study give strong indications that the intervention was associated with asignificant reduction of noise, particularly at low frequencies, as well as significant improvements ofpeople’s health.
Given that the results are generalizable and can be replicated, it suggests that we can expect
improvements of the sound environment and presumably also improvements of people’s health inareas with predominant public transportation travelling at low speed, if fossil fueled buses are changed
to electrified transportation. For the large number of people living along highly trafficked road withhigher speed, it is though vital that we succeed to reduce the noise also from tires or road surface tireinteractions, in order to achieve a sustainably healthy environment.

ACKNOWLEDGEMENTS
We gratefully acknowledge the financial contribution from Region Västra Götaland, Departmentof Public Transportation and Infrastructure and Regional Development, and Gothenburg Municipality.

REFERENCES

1. United Nations Environment Programme (2022).Frontiers 2022: Noise, Blazes and Mismatches–
Emerging Issues of Environmental Concern. Nairobi.
2. Persson Waye K. Effects of low frequency noise and vibrations: Environmental and occupational
perspectives. In: Nriagu JO (ed.) Encyclopedia of Environmental Health, volume 2, pp. 240–253
Burlington: Elsevier 2011.
3. Pedersen S, Møller H and Persson Waye K. Indoor measurements of noise at low frequenciesproblems and solutions. J Low Freq Noise Vibr and Active Control, 2007,26, 249-270.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0940

Involving stakeholders in noise research projects: Towards aholistic approach for interaction between scientific and practicalconsiderations
Sarah Leona BENZ1; Dirk SCHRECKENBERG1; Peter van den HAZEL2; Miriam WEBER3; Sammie
JANSEN4; Gordana RISTOVSKA5; Arzu ARAT6; Maddie WHITE7; Igor SPIROSKI5; Irene van
KAMP4
1

Centre for Applied Psychology, Environmental and Social Research (ZEUS), Hagen, Germany
2
3
4

INCHES Network, Ellecom, The Netherlands

City of Utrecht (CoU), Utrecht, The Netherlands

National Institute for Public Health and the Environment (RIVM), The Netherlands

5 Institute of public health of Republic of North Macedonia, Skopje, Republic of North Macedonia
6
7

Karolinska Institute, Stockholm, Sweden

Department of Social Epidemiology, Bremen University, Bremen, Germany

ABSTRACT
Usually, research results inform policy and decision-making after scientific studies have been conducted.
Research results can then be implemented to update noise policy. During the research process itself
information exchange between scientific experts and policy experts is often poor.
The EU funded Equal-Life project (Early Environmental quality and life-course mental health effects) is aninterdisciplinary project focusing on children’s mental health and cognitive development during the life
course. In linking cohort and school study data to physical and social exposures such as environmental noise
in a child’s environment, the complex interplay is studied.
The project includes stakeholder involvement throughout the whole research process to ensure the needs ofstakeholders and to take gaps into account along the way. During the project, a frequent exchange withstakeholders with various backgrounds from policymakers, health care professionals to urban planners takes
place aiming to derive guidance for evidence-based interventions. So far, among other activities, a Delphi
study has been conducted online. Up to 52 stakeholders from 25 countries participated in three rounds of theDelphi study. The aim was to collect needs and research gaps, gather feedback on research questions in the
project and evaluate their importance for stakeholders' work.
Keywords: Environmental noise, policy, stakeholder involvement

1. INTRODUCTION
In a Green Paper of the European Commission (1) on future noise policy, several objectives are
mentioned from improving the data situation on noise studies, better exchange of experience between
the member states and harmonisation of noise assessment. Since then, some progress has been made:the joint WHO Environmental Noise Guidelines for the European Region (2) were developed, a risingnumber of studies on the noise situation regarding different noise sources were conducted and efforts
for harmonisation were made, e.g. regarding impact assessment in the standardisation of instruments
to assess noise annoyance (3,4).
However, noise research is an interdisciplinary research field with multiple relevant perspectives
and points of actions. There is noise impact research with the central aim to assess the response1

benz@zeusgmbh.deprovoked by noise sources and estimate potential adverse effects. There are source-specific technicaland operative approaches to measure, model, and calculate noise emissions to display an authentic
illustration of the noise exposure. Urban planning and land-use planning are juggling with spatialconditions, concurring interests and public authorities dealing with claims arising from high noise
pollution to prompt noise mitigation measures when the exposure exceeds certain thresholds. Lastly,
noise control itself has the purpose to react to high exposure by inventing, implementing and managing
mitigation measures. At best, evaluation research is conducted in order to assess the impact of noiseinterventions on exposed people.
In policy-making, these different perspectives need to be considered to address specific issues.
Therefore, evaluating and including an expert’s knowledge on a holistic level could lead to policy thatis more comprehensive. Valuable input can be further collected from other fields that are indirectly
influenced by noise like health care, public health and urban planning.
Equal-Life is studying the combined impact of physical, social and internal exposures on children’s
cognitive development and life course mental health while taking into account mediating processes
such as sleep, stress and coping (5).
One of the aims of Equal-Life is to implement the project’s results by creating tools supporting
practitioners, policy-makers, etc. targeting to improve children’s development and mental health.
Therefore, one work package is specifically designed to take stakeholders’ expertise into accountthroughout the project. Various formats are deployed to build constant exchange with stakeholders.
Some approaches qualify to be included in noise research projects.
This paper first summarises these methods and will then present the results of the Delphi study.
The Delphi consultation is a multitiered approach to survey a group of experts on a specific topic
aiming to obtain a group opinion. First learnings from the stakeholder involvement that can be
implemented in the process of designing noise policy are discussed.

2. METHODS
Stakeholders invited were from organisations and institutions involved in children’s andadolescents’ (mental) health, research institutes, public health organisations, civil society
organisations, national stakeholders in relevant fields of (mental) health care, urban planning,
environmental institutions at local, national or European levels.
Several methods are used to involve stakeholders in the Equal-Life research project. Firstly,delphi surveys were conducted among the research consortium in the beginning of the project andamong external stakeholders. Stakeholder’s evaluations of needs and gaps regarding environmental
exposures in relation to children’s mental health and development were gathered in a multitiered
process (see Figure 1). The assessment was conducted in several rounds. Each stakeholders’s
evaluation was assessed anonymously and separately. Stakeholders received feedback on the resultsof each round. The aim was to compile responses from the participating stakeholders and reachconsensus among the group.

Figure 1 – Flow-chart depicting the procedure of the Delphi consultation with the stakeholders
Secondly, in-depth interviews with stakeholders were performed using a partly standardized
interview guideline covering topics such as experiences from stakeholder’s work regarding physicaland social factors important for children’s health and development, good examples for interventions
from stakeholder’s work fields, among others. Thirdly, to enable an interactive exchange with andbetween the stakeholders, stakeholder forums are being conducted to discuss policy gaps and needs
in a hybrid format.

In this paper we will further focus on the results of the Delphi consultation among stakeholders.
Invitations to participate in the Delphi survey were circulated to contacts from the work group in
European countries. Contacts were explicitly told that they could also act as multipliers and forward
the invitation.
The Delphi survey was performed in three rounds by means of an online survey be tween November
2020 and July 2021.
1. Round: Gathering needs and identifying gaps regarding factors affecting children’s mental
health and cognitive development from the stakeholders’ view.
2. Round: Rating the importance of different topics differentiated in separate fields ofapplication collected in the 1 st round.
3. Round: Rating of research questions formulated in the project consortium regarding
importance and usefulness for experts working field and other criteria.

3. RESULTS OF THE DELPHI STUDY
Table 1 shows an overview of the number of participants that participated in the Delphi survey
among stakeholders, separately listed for participants from European and Non-European countries.
Table 1 – Number of participants in the Delphi survey among stakeholders in the Equal-Life project
NonEuropean
European
Total
Countries
countries
1. Round34
34
2. Round40
1151
3. Round43
952
52 stakeholders from public health organisations, research institutes, civil society organisations,
national organisations and official authorities, childcare organisations, and urban planners participated.
Results of round 1 are a broad selection of needs and gaps stakeholders collected regarding children’sdevelopment and life course mental health in relation to their physical and social environment.
Referring to noise e.g. the need for noise studies focusing on early life noise exposure (prenatal,
postnatal and childhood), the assessment of noise exposure in school settings with adequate measures
as well as the evaluation of policy interventions that target noise reduction measures were mentioned
by stakeholders. Results of round 2 indicate research priorities to be considered in future research in
the domains of knowledge gaps, relevant exposures, accessibility of data, interventions and policy
making. Noise is e.g. considered relevant regarding the need to adjust environmental pollution
monitoring to account for differential vulnerability of children as well as taking children into accountin urban planning processes (due to their higher vulnerability for the effects of exposures) .
Results of round 3 show the importance and usefulness of the research questions for stakeholders’work such as designing policies, provision of knowledge, raising awareness, and tackling social
inequalities. Two research questions that received high ratings regarding importance for professionals
work and to design policy were e.g. 1) about properties of env ironmental exposures (intensity,
regularity, predictability, duration) that affect mental health and cognitive development outcomes inchildren and 2) about critical windows in the life of a child particularly sensitive to exposures.

4. LEARNINGS SO FAR / CONCLUSION
A large number of stakeholders from different countries and work fields could be won over toparticipate in the Equal-Life project.
In the beginning of the process of involving stakeholders, it is important to formulate goals andthe purpose for the stakeholder involvement to communicate transparently on why to involve them tokeep them motivated and engaged. In our project, by including stakeholders from a variety of work
fields, valuable input was obtained about physical exposures such as noise, air quality or accessibilityof green spaces, social exposures (parental socioeconomic status, migrant background, parenting
practices, and school social environment) and the combined role of these factors for health outcomes.
Vertical (national – regional – local authorities or institutions) and horizontal (between fields)
exchanges give insight in problems and needs that stakeholders deal with. Some topics Some topics

appear to be more important for specific stakeholders, levels or countries depending on the prevailingconditions.
Stakeholder involvement is beneficial for scientists, however feedback loops for stakeholders
about results enable them to reflect on their own views and might broaden their perspectives for theirown work field. Surveys such as the Delphi approach performed online and anonymously can be used
in a low-threshold manner to get a first insight into different perspectives while reaching a broad rangeof people. Subsequent activities can focus on in-depth interactive engagement to delve deeper into a
topic, e.g. by means of a stakeholder forum, and might lead to initiate new collaborations between
stakeholders. Establishing interconnections between different perspectives/working fields enables
tackling environmental problems holistically.
For noise research that is designed to result in practical application, it should be strived for an
early integration of expertise from practice in the process. Considering the link between noise researchand noise policy experts, the involvement of stakeholders into the research process could facilitate
early pairing of research and fields of applications such as noise mitigation and urban planning.
Additionally, stakeholders’ input is part of the data that a projects’ final results are based on.
Our approach of stakeholder involvement could be used in studies where noise is evaluated bydifferent perspectives. In Equal-Life, Delphi consultations were initially used to formulate researchquestions in the project consortium to see where there are similarities and differences among thepartners. The Delphi survey with stakeholders gives the opportunity to compare perspectives ofresearch and practical fields and to sharpen the focus of the project. An example of how research could
inform noise policy is the field of combined noise assessment since increasing urbanization,
increasing numbers of complaints as well as data of noise impact studies support the need for new
approaches for a combined assessment.

ACKNOWLEDGEMENTS
This study is part of the work of the WP8 ('Stakeholder involvement') of the EU project Equal-Life(Early Environmental quality and life-course mental health effects). Equal-Life has received funding
from the European Union’s Horizon 2020 research and innovation programme under grant agreement
No 874724.

REFERENCES
1. Commission of the European Communities. Future Noise Policy. Green Paper. 1996. COM(96) 540 final.
https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:51996DC0540&from=PT
2. World Health Organisation Regional Office for Europe.WHO environmental noise guidelines for theEuropean region. WHO Regional Office for Europe, Copenhagen (Denmark), 2018.
3. Fields, J., De Jong, R., Gjestland, T., Flindell, I., Job, R., Kurra, S., . . . Yano, T. Standardized generalpurpose noise reaction questions for community noise surveys: Research and a recommendation. Journalof Sound and Vibration, Vol 242 (4), 2001, pp 641-679.
4. International Organization for Standardization ISO/TS 15666 (ISO/TS 15666:2003-02).Acoustics –Assessment of noise annoyance by means of social and socio-acoustic surveys. Geneva: ISO TC 43/SC.
5. van Kamp, I., Persson Waye, K., Kanninen, K., Gulliver, J., Bozzon, A., Psyllidis, A., Boshuizen, H.,
Selander, J., van den Hazel, P., Brambilla, M., Foraster, M., Julvez, J., Klatte, M., Jeram, S., Lercher, P.,
Botteldooren, D., Ristovska, G., Kaprio, J., Schreckenberg, D., Hornikx, M., Fels, J., Weber, M., BraatEggen, E., Hartmann, J., Clark, C., Vrijkotte, T., Brown, L., Bolte, G. and Equal-Life team. Early
environmental quality and life-course mental health effects: The Equal-Life project. Environmental
Epidemiology. 2021;6(1):e183. DOI: 10.1097/EE9.0000000000000183.