A04:
ENVIRONMENTAL ACOUSTICS

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0254

Present and future of wind turbine noise assessment
Hyosung SUN1
1

Korea Environment Institute, Korea

ABSTRACTAs interest in wind power generation increases due to the expansion of policies to respond to climatechange and the promotion of eco-friendly energy policies, social interest in the environmental impact
related to the installation and operation of wind power generation has become a big issue. In particular, notonly interest in methods to reduce noise generation of wind power generation, but also measures to prevent
noise effects of wind power generation including low-frequency noise in advance occupy an important field.
While the plans to develop wind power farms are increasing due to the demand for new and renewableenergy, the role of environmental impact assessment to prevent noise effects from wind power generation inadvance is also becoming more important. Based on the analysis of the current state of evaluating the noise
impact of wind power generation, including low-frequency noise, in the environmental impact assessment
stage of wind power generation, the purpose of this study is to propose a future vision that can prevent thenoise effect of wind power generation in the development planning stage of wind power generation inadvance.
Keywords: Wind power generation, Noise, Environmental impact assessment

1. INTRODUCTION
The demand for new and renewable energy is increasing as part of the response to climate change andenvironmentally friendly energy policy, the interest in wind power generation is increasing. Due to thistrend, the number of environmental impact assessments due to new wind power development plans isincreasing, and the importance of the environmental impact of wind power generation installation andoperation is gradually increasing. Among the various environmental impacts that are being addressed in theenvironmental impact assessment stage for the promotion of the wind power development plan, the noise
effect from wind power generation is emerging as an important issue. In particular, the degree of theinfluence of the low-frequency noise generated by the rotation of wind turbines on the human body is being
focused on. In this study, the current status analysis was conducted to evaluate the noise (including
low-frequency noise) impact of wind power generation for the environmental impact assessment of thewind power development plan. Based on the identification of the problems based on the analysis of thecurrent situation, the improvement measures were proposed to strengthen the role of environmental impact
assessment to prevent the noise damage and civil complaints of the wind power development plan inadvance.

2. ENVIRONMENTAL IMPACT ASSESSMENT OF WIND POWER GENERATIONIn the environmental impact assessment stage of the wind power development plan, the noise assessment
according to the operation of wind power generation can be largely divided into current status survey,
impact prediction, environmental target standard setting, and reduction measures. In the current statussurvey stage, the distribution of facilities around the wind power development area is identified and thebackground noise level including low-frequency noise is measured. In the impact prediction stage, the noise
impact on the facilities around the wind power development area is evaluated using the acoustic power
level of the wind power generator and the noise prediction model. In the stage of setting environmental
target standards, the standards included in related laws and regulations are used to prepare the basis fordetermining whether noise is affected by wind power generation. In the reduction measures stage, thevarious measures to reduce noise damage and civil complaints are established when the noise prediction
1

hssun@kei.re.kr

result from wind power generation exceeds the standard.

3. NOISE EVALUATION IMPROVEMENT PLAN IN WIND POWER GENERATION
In order to accurately grasp the noise impact of wind power generation in the environmental impact
assessment stage of the wind power development plan, it is necessary to secure the reliability of the noise
prediction results. In order to predict the noise impact of wind power generation, it is required to secure thenoise prediction model that can consider the noise propagation characteristics of wind power generation as
well as the acoustic power level data for the wind power generators to be installed in wind power
development areas. In particular, in the case of low-frequency noise, in order to apply the evaluation unit
and the criteria prescribed by the Ministry of Environment, it is necessary to build the database for the
acoustic power level of the wind turbine according to each frequency band. In addition, it is required toderive the noise impact range according to the operation of wind power generation through the developmentof the noise prediction model that can consider the propagation characteristics of low-frequency noise, andto utilize it in reviewing the adequacy of the wind power development plan from the viewpoint of noise.

ACKNOWLEDGEMENTS
This research is based on the findings of the research project “Development of Integrated Decision
Support Model for Environmental Impact Assessment Project”(2022-003) which was conducted by Korea
Environment Institute (KEI), financially supported from “ICT-based Decision Support System
Development Project for Environmental Impact Assessment”(20200029990007) of Korea Environmental
Industry & Technology Institute (KEITI) funded by Korea Ministry of Environment (MOE).

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

ABS-0457

The Development of Innovative Mitigation Measures in Hong Kongfor Reduction of Outdoor Noise Impacts
David YEUNG1; Calvin CHIU1; Tony CHENG1; Louisa CHEUNG1; Gary YUEN1
1

Ramboll Hong Kong Limited, Hong Kong

ABSTRACT
Hong Kong is a densely populated area with a continuous housing demand. Residential development is facing
excessive noise nuisances, causing a need for innovative measures that not only fulfilling regulatorycompliance but also improve the living conditions and wellbeing of the occupants. Acoustic window has beensuccessfully incorporated into the building design for private sector projects. Together with a series of
acoustic laboratory test, following the ISO procedures, conducted prior to occupation, the design performanceof these acoustic windows has been well established. Meanwhile, site constraints in some unique projectsituations demand for further enhancement of the acoustic performance. We established acoustic window
design in a project that be able to mitigate the combined effects of three noise sources including road traffic,
railway and fixed industrial installations. Each one of these sources has different operation patterns andcharacteristic, hence an acoustic performance design to allow for the worst possible scenario for all theemitters is necessary. The design is proven to be a success by gone through comprehensive desk top design,
follow by thorough laboratory test and last step on-site field measurement. The design can then be widelyadopted by environmental professionals for mitigating noise impact for many future projects.
Keywords: Innovative, Laboratory Test, Performance

1. INTRODUCTION
Hong Kong is one of the most densely populated urban cities in the world. With enormous
population packed in an area which dominated by hills, land supply is scarce for residential
developments. It is a common situation that new residential developments were found to be adjacent
to main traffic, such as highways and railways, such that the residents would suffer road traffic andrailway noise impacts unavoidably. Despite the potential traffic and railway noise impacts, industrial
noise contributed by nearby community infrastructures such as rooftop chillers and VRVs,transformers in substation and ventilation shafts would also exert adverse impact to the living standardof the residents.
In this regard, various noise mitigation measures have been proposed in residential developments
to mitigate the nearby road traffic, railway and industrial noise impacts in Hong Kong throughout
decades. These includes building layout designs such as building setback and single aspect, andapplication of architectural fins, noise barriers and canopies. However, restricted by the siteparameters including site boundaries and the limited surrounding space, the captioned traditional
mitigation measures are not always applicable.
In the past decade, the application of innovative noise mitigation measures including baffle-type
acoustic windows and baffle-type acoustic balconies (hereafter as AW(BT) and AB(BT)) have beenarisen. Comparing to typical windows and balconies, AW(BT) and AB(BT) generally have higher
noise reductions with respect to all three noise natures. Without sacrificing much ventilation
performance, the AW(BT) and AB(BT) are solutions to the noises under different circumstances.
In this paper, the designs of baffle-type acoustic windows and balconies are demonstrated. A casestudy which focuses on the application of AW(BT) and AB(BT) in a residential development in Hong
Kong with respect to road traffic, railway and industrial noise are then followed. The case study
includes methodology of the laboratory test for noise reduction of the measures, and corresponding
result analysis. The performance of AW(BT) and EAB(BT) are also compared to those applied in other1

gyuen@ramboll.com

residential developments.

2. BACKGROUND
2.1 AW(BT) and AB(BT) Design
Figure 1 shows the schematic design of AW(BT) and AB(BT). The design of AW(BT) and AB(BT)based on the addition of indoor glass panel in the indoor environment behind the outer opening of atypical window or balcony system, such that a gap is created between the indoor glass panel and theouter façade system. Under this setting, intruding outdoor sound wave needs to travel a longer
transmission path before entering the indoor environment. With the sound energy directly reflecting
to outdoor and dissipated through the gap by the application of sound absorption material at the gap,
the experienced noise level in indoor is diminished. Former research has proved the addition of indoor
glass panel would increase the noise mitigation measures noise performance (1, 2).

Figure 1 – Schematic Design of AW(BT) and AB(BT)The proposed AW(BT) consists of a side-hung window, with an outer opening area of 0.96 m 2 . The
gap width between the indoor glass panel and the outer façade system is 100mm, while an overlapping
length measured from the clear outer opening to the end of the indoor glass panel of 300mm isprovided. The proposed AB(BT) is based on the combination of balcony, utility platform and airconditioner platform (a 3-in-1 balcony). The depth of the 3-in-1 balcony is 1.45 m, with 1.1m glassbalustrade surrounding the balcony and utility platform area. The air-conditioner is surrounded bylouver on the air-conditioner platform. Outer sliding door is provided for indoor access, which has anouter opening area of 2.39 m2 . The gap width of the AB(BT) is 100mm and the overlapping length is
500mm. Table 1 shows the physical parameters of the AW(BT) and AB(BT).
Sound absorption materials including the micro-perforated absorber panel (hereafter as MPA panel)
installing on the indoor glass panel, and perforated sound absorptive panel (hereafter as SAM panel)installing at gap on wall structure and façade mullion adjacent to the outer opening are used todissipate the intruding sound energy and thus enhance the overall noise performance of AW(BT) andAB(BT) systems (3, 4).
In the case study presenting in the rest of the chapter, one AW(BT) design and one AB(BT) design
were proposed in a residential development. Referring to Figure 1, the proposed AW(BT) consists of
the MPA panel and SAM panel at gap. For the proposed AB(BT), MPA panel will also be applied.
Apart from the application of SAM panel at gap, SAM panel are also applied on the balcony ceiling,
and on the balcony sidewall (if present) as well.
For some of the testing scenarios of AW(BT), vertical acoustic fins of SAM panel installed on
certain length of concrete structure are applied adjacent to the AW(BT) and AB(BT). These testing
scenarios are designed to find out the total noise performance of the vertical acoustic fins and theAW(BT) and AB(BT).
Table 1 – Physical parameters of AW(BT) and AB(BT)
Outer
Specimen

Outer

Outer

Opening Opening

OpeningGap
WidthOverlapping

Balcony

BalconyRoom

LengthDepth

Balustrade

Area

Width

HeightArea

AW(BT)

0.7 m

1.37 m

0.96 m2

100mm

300mm

--

--

4.3 m2AB(BT)

0.9 m

2.65 m2.39 m2

100mm

500mm

1.45 m

1.1 m

7.6 m2Height2.2 Residential Development Background
The residential development in the case study is located at the northwest of New Territories inHong Kong. The residential development is located adjacent to an overhead railway station. The
railway track aligns parallelly with the site boundary would be the railway noise source. Traffic noise
is contributed by two local roads and a highway, surrounding the residential development. Several
industrial infrastructures are also found surrounding the development, including chillers located on
the roof top of the railway station and nearby commercial building, and transformer in nearby
substation.

3. LABORATORY TEST SETTING AND METHODOLOGY
3.1 Receiver Room Setting
A full scale 1:1 AW(BT) and AB(BT) model are employed in the laboratory test. The specimens
are installed as a façade of the receiver room. The mock-up receiver room make reference to the actualroom at the residential development which AW(BT) and AB(BT) are applied. The indoor condition ofmock-up flat is unfurnished with gypsum board walls and ceiling. The floor is finished with vanishedplywood. No carpet and furniture were provided in the mock-up room. In order to well demonstrate
the sound field and hence the resultant noise levels at particular points in the receiver rooms, the room
area is referenced to that of the actual room of the residential development which applied with theAW(BT) and AB(BT). No opening was allowed in the mock-up room, except windows and entrance
door. The envelop was sealed with silicon sealant for any gap to cut off noise flanking paths.
According to the above, the mock-up flats had similar interior noise environment with actual flat.
3.2 Sound Source Setting
The setting of the sound source refers to the actual alignment and relationship between the noise
sources and the sensitive receivers of the residential development. Multiple loudspeakers were set ina “line source” manner to mimic the road traffic, railway and industrial noise source surrounding theresidential development. Totally 7 Individual but electrically connected loudspeakers systems arealigned to simulate the line source, which have a separation of 2m between each of them. The lengthof the line sources is kept as 12m. Inclination angle of ~22˚ to 27˚ between the noise sources and thesensitive receivers were well demonstrated in the laboratory test by elevating the mock-up room to acertain level to make the same elevation angle to the sound sources. For the horizontal orientation,
totally five line sources are used as the sound sources aiming to the mock-up room façade, to stimulate

0°, +45°, -45°, +90° and -90° noise source in the real situation.

Figure 2 – Sound Source Orientation in Laboratory Testing
Random white noise with a continuous frequency spectrum is generated by the sound source system.
3.3 Microphone setting
The indoor microphone setting had made reference to ISO-16283:2016 “Acoustic – Field
measurement of sound insulation in buildings and of building elements”. Two outdoor microphones
were set to acquire the outdoor noise levels at particular locations. One was set at the middle of thesound source and the specimen, to ensure a steady sound energy output from the sound source
throughout the testing. Another outdoor microphone was set at 1 meter away from the any building
structure, to record the outdoor noise levels for following result analysis. For AB(BT), thismicrophone was set 1 meter away from the balcony balustrade.
3.4 Measurement Methodology
Sound level meters (SLMs) were used to record the noise levels at all destinated locations. The
SLMs for the measurement were checked by acoustic calibrator before start and end of measurements
for each testing scenario. The measurements were made in 1/3 octave bands with center frequencies
from 100 Hz to 5000 Hz. The background noise measurement was conducted with a duration of 1
minute to record the outdoor and indoor background noise levels, with using both the outdoor
microphone and indoor microphones respectively. Noise checking measurement was then conducted
for at least 1 minute to verify that the sound power level of the loudspeaker would be adequate andconsistent to produce the white noise with sound pressure level in the receiving room which exceeds
the background noise level by 10dB at minimal.
The main noise reduction measurement of the NMM was then followed. Measurements for thenoise levels with the loudspeaker operating were taken simultaneously outside the façade and in thereceiving room. The minimum measurement duration at each measurement point was 15s. L 10 levelof the measurement period was obtained for calculation and determination of the performance ofAW(BT) and AB(BT). There were 3 sets of measurements for each testing scenario and the average
SPL was calculated to ensure the reproducibility.

4. RESULT ANALYSIS AND DISCUSSION
In the aim of assessing the road traffic noise performance of the AW(BT) and AB(BT), the outdoor
and indoor noise levels are used to calculate the noise performance of the proposed noise mitigation
measure designs. In-out difference is used as the indicator of the noise performance of the proposed
mitigation measures.
4.1 Road Traffic Noise Performance
For road traffic noise, the in-out differences of AW(BT) and AB(BT) are given by:

=
−
(1)
,
where IODrt represents the in-out difference of AW(BT) or AB(BT) regarding road traffic noise indB(A), SPLoutdoor indicates the sound pressure levels recorded at the outdoor microphone location,and SPLindoor,avg indicates the average indoor noise levels calculated from those recorded by the indoor
microphones.
A single number rating to indicate the traffic noise reduction was determined according to thenormalized traffic noise spectrum defined in BS EN 1793-3 “Road Traffic Noise Reducing Devices –Test Method for Determining the Acoustic Performance – Part 3: Normalized Traffic Noise Spectrum.”The summary of IOD rt of AW(BT) and AB(BT) under different testing scenarios is given in Table
2 below:
Table 2 – Summary of In-out difference of AW(BT) and AB(BT) regarding Road Traffic NoiseSpecimen

Horizontal

Fin/Sidewall

Orientation

Length0˚AW(BT)
+90˚

-90˚
0˚

AB(BT)+90˚-90˚

IODrt, dB(A)

--

15.7

1m

17.4

1.5 m

17.2

2m

17.4

--

16.9

0.75 m

20.0

1m

20.3

1.5 m

20.2

--

14.8

--

19.0

1.45 m

19.5

--

16.5

1.45 m

16.3

--

14.4

1.45 m20.9

For AW(BT), a significant difference in road traffic noise performance can be found from thetesting results. The IOD rt of AW(BT) in +90˚ is higher than that in 0˚, while that in -90˚ is the lowest
among the three angles. The reasons behind probably would be the difference in the travelling distanceof the sound wave entering from the outdoor to the indoor environment. In the testing scenario with+90˚ orientation, before entering the indoor, the sound wave probably needs to be reflected by theside-hung window, and experience several reflection and potential absorption on the MPA panel
applied on the indoor glass panel. Sound energy entering the indoor is expected to be dissipated tocertain level, and reveal a higher noise performance of the AW(BT) than in 0˚ orientation. The indoor
environment may have much exposure to the outdoor in -90˚ as the sound wave could be directly
entering the indoor with less reflection, thus less sound energy is dissipated, resulting in higher indoor
noise levels.
The installation of vertical acoustic fins adjacent to AW(BT) has different additional performanceto the specimens in different horizontal orientation. In 0˚, the addition of 1m acoustic fin provide an
additional performance of around 2dB(A) to the AW(BT). The acoustic fin may have shielded off
certain sound energy from several loudspeakers at the near side of the array. In +90˚, the additional
performance of acoustic fin elevated to at least 3dB(A) as sound energy from more loudspeakers isshielded off by the acoustic fin. However, it is found that there is no significant increase in the
additional performance of acoustic fin if the acoustic fin length increased. The deviation of IOD rt

between testing scenarios with different acoustic fin within the same orientation angle is only ranged
from 0.2-0.3 dB(A).
For AB(BT), the difference in IODrt with respect to the changes of horizontal orientation isdifferent to that of AW(BT). The IODrt of AB(BT) is 19.0 dB(A) in 0˚, which is the highest among
the three angles. The IODrt of AB(BT) in +90˚ and -90˚ are 16.5 and 14.4 dB(A) respectively. The
reduction in road traffic noise performance of the AB(BT) in +90˚ may regard to the high option of
direct transmission path of sound wave, such that less energy is reflected or dissipated by absorption
through the gap of the system. Comparing to glass balustrade, the louvre surrounded the airconditioner platform has a worse shielding effect. In the -90˚ orientation, with the sound source
located at the near side of the air-conditioner platform, much sound energy is expected to enter thebalcony area than in 0˚ orientation. This reduces the road traffic noise performance of the AB(BT).
The additional sidewall at the balcony area of the AB(BT) enhanced its IOD rt in all horizontal
orientations. An additional performance of 0.5 dB(A) is provided by including the sidewall in 0˚orientation. There is no significant difference in additional performance brought by the sidewall
addition in +90˚. The addition of sidewall reveals the highest additional performance of over 6dB(A)in -90˚.
4.2 Railway Noise Performance
The calculation of the in-out difference of AW(BT) and AB(BT) regarding railway noise is similar
to that regarding road traffic noise. However, in accordance with local statutory requirement, themaximum recorded indoor noise levels are selected to represent the indoor noise levels, such that:
=
−
(2)
,
where IODrw represents the in-out difference of AW(BT) or AB(BT) regarding railway noise indB(A), SPLoutdoor indicates the sound pressure levels recorded at the outdoor microphone location,and SPL indoor,max indicates the maximum indoor noise levels selected from those recorded by the indoor
microphones.
Local railway noise spectrum acquired from the on-site measurement is used to insert a weighting
to the IODrw, such that the outcoming IOD rw would be better demonstrate the performance of thespecimens under the local railway noise.
Table 3 – Summary of In-out difference of AW(BT) and AB(BT) regarding Railway NoiseSpecimen

AW(BT)AB(BT)

Horizontal

Fin/Sidewall

Orientation

Length

0˚

--

17.5

+45˚

--

17.6

--

17.7

1m

20.9

2m

21.2

-45˚

--

16.3

-90˚

--

16.2

0˚

1.45 m21.3+45˚

1.45 m

18.9+90˚

1.45 m

17.1-45˚

1.45 m

23.4-90˚

1.45 m22.7+90˚

IODrw, dB(A)The behavior of IOD rw is different than that of IODrt of AW(BT). Viewed in indoor, the IOD rw of
the AW(BT) when the sound source is located at the front and at the right-handed side (at the side of
the opened side-hung window) is ranged from 17.5 to 17.7 dB(A). When the sound source is locatedat the other side of the balcony, a reduction in IOD rw of around 1.3 – 1.5 dB(A) is found.

The additional performance brought by the adjacent acoustic fins to the AW(BT) in +90˚ is over
3dB(A). Again, same as the behavior found regarding the road traffic noise, the increase in acoustic
fins have no significant effect on the additional performance to the AW(BT).
With the sidewall installed at the balcony area of the AB(BT), the difference in IOD rw of thespecimen in different horizontal orientation matches the findings regarding the road traffic noise.
Viewed in indoor, with the sidewall installed at the balcony area shielding the sound wave comingfrom the left-handed side, the IOD rw would be higher in horizontal orientation -45˚ and -90˚.
Regardless of the location of the sound source at left-handed or right-handed side, the IOD rw decreases
with the horizontal orientation increases.
4.3 Industrial Noise Performance
Same calculation as for railway noise performance is used to calculate the in-out difference (IODin )of the industrial noise performance of the specimens. The IOD in of the AW(BT) and AB(BT) are
weighted by using the local industrial noise spectrum acquired by the surrounding industrial noise
sources. Generally, the industrial noise performance of both AW(BT) and AB(BT) are less than theirrailway noise performance of around 2 dB(A). Regardless the difference in IOD in contributed bydifferent local industrial noise spectrum in the calculation, the IOD in behavior with respect to thedifference in orientation and addition of acoustic fin is similar to that of IOD rw.

5. CONCLUSIONSThe performance of proposed AW(BT) and AB(BT) is studied through 1:1 full scale mock-up
laboratory test. The laboratory test set up made reference to the actual spatial relationship between
the noise sources and the sensitive receivers. International standard has been followed for the test
methodology, including the microphone placement and calculation of resulting noise performance ofthe specimen.
In-out difference is used as the indicator of AW(BT) and AB(BT). From the test results, it isconcluded that the horizontal orientation of the sound source to the testing specimen has a significant
effect on the noise performance of the mitigation measures, due to the changes of the travelling
distance of sound wave into the indoor environment and the shielding of nearby structure. Testing
scenario with acoustic fins installed adjacent to the AW(BT) opening has carried out. The addition of
acoustic fin has boosted the performance of the AW(BT), but there is no significant increase in the
additional performance when the acoustic fin length increases from 1m.
In the future, more testing scenarios will be carried out in order to study the correlation between
the noise performance of AW(BT) and AB(BT) and other parameters including changes in gap width,
overlapping length, outer opening width and length, and the combination of application of different
acoustic material. If possible, on-site measurement of the proposed AW(BT) and AB(BT) would be
carried out to study the difference in noise performance of the specimen from the on-site measurement
and from the 1:1 mock-up laboratory test.

REFERENCES
1. Tong Y.G., Tang S.K. A study on acoustical protection effects of plenum windows [J], J.Acoustic.Soc.Am,
2012, 131, 3530.
2. Tong Y.G., Tang S.K. Plenum window insertion loss in the presence of a line source - A scale model study[J], J.Acoust.Soc.Am., 2013, 133, 1458 - 1467.
3. Kang J., Brocklesby M.W., Feasibility of applying micro-perforated absorbers in acoustic window system[J], Appl.Acoust., 2005, 66, 669-689.
4. Yu X., Lau S.K., Cheng L., Cui F., A numerical investigation on the sound insulation of ventilation
windows [J], 2017, Appl.Acoust., 2017, 117, 113 - 121.

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

ABS-0471

2000 land-use regressions for road traffic noise predictions – howsample selection affects extrapolation weights
Jeroen STAAB 1,2,*, Arthur SCHADY 3, Kathrin WOLF 4, Sahar BEHZADI 4, Marco DALLAVALLE
4,5
1, Matthias WEIGAND 1,6, Tobia LAKES 2,7, Hannes TAUBENBÖCK 1,6

German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Germany
2
3

4

Humboldt-University Berlin, Geography Department, Germany

German Aerospace Center (DLR), Institute of Atmospheric Physics (IPA), Germany

Helmholtz Zentrum München GmbH - German Research Center for Environmental Health, Institute of
Epidemiology, Germany

5

Ludwig-Maximilians-Universität München, Institute for Medical Information Processing, Biometry andEpidemiology, Department of Epidemiology, Germany

6
7

University of Würzburg, Institute of Geography and Geology, Department of Remote Sensing, Germany

Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Germany

ABSTRACTThe awareness that noise exposure is critical for human health is growing around the globe, and land-use
regressions (LURs) are becoming a popular tool for producing noise exposure maps. One important factor
for noise emissions is road traffic. The propagation in this regard is determined by the spatial layout of roadinfrastructure and the surrounding environment, respectively. LURs use geostatistical models and allow to
extrapolate microphone measurements. In this study, we investigated whether models are prone to sampling
artifacts. We used yearly averaged Lden simulations, compliant to the European noise directive 2002/49/EG,
as input for 2000 virtual field campaigns. We permuted different sampling schemes (random, systematic,
stratified) and sizes (n = 50, 100, 200, 500 to 1000) 100 times. The overall model performances varied
substantially between 0.61 – 0.95 for R², 1.94 – 7.46 dB(A) for mean absolute error and 2.47 – 10.03 dB(A)for root mean squared error. Comparing the eventual model terms using variance analyses (ANOVA), we
found significant differences between the sampling schemes for traffic information and land cover (e.g.
vegetated surfaces) features. Simultaneously, less than half of the LURs’ weights differed significantly
depending on the sampling size. Thus, our experiments give an in-depth view on the mechanics of LUR andtheir sensitivity with respect to sampled training data.
Keywords: Traffic noise, Exposure Assessment, Sensitivity Analysis

1

INTRODUCTION

Modern society beholds ongoing technological developments, increased wealth and mobility. With
respect to road traffic noise, the WHO considers yearly averaged levels above 53dB(A) being
unhealthy (1). To investigate the health impact of noise, two approaches exist (2): first, controlled
lab experiments, where the biophysical reactions of human bodies to noise exposure can beinvestigated. It is obvious though, that this is impractical for large scale and long -term studies. Thus,
second, scholars aim to extract information on the noise exposure by the addresses of study
participants (3–5). To do so, microphones could be deployed in the field; however, this approach islimited due to costs and spatiotemporal limitations. In Europe, thanks to the European Noise Directive(6, also referred to as END), simulated Lden levels are available for urban agglomerations with apopulation larger than 100,000 (6). This END compliant data can be utilized for health studies (e.g.
5,7). Other health studies investigate the impact of noise relative to noise exposures mapped using* Jeroen.staab@dlr.deland-use regressions (LUR) (e.g. 8).
LURs use geospatial information describing the surroundings; indicators such as distance to roads,
built-up morphology and close by vegetated areas are used as predictors of a statistical model, e.g.
least-squares (3,9–13). Simplified, noise N is a function of noise emissions E and subsequentinteractions I when propagating though the environment. After fitting the model, each predictor is
supplemented with a weight w, such that the complete term may be denoted as N = w EE + w I I. In aprevious study, we have investigated the impact of variances within the response variable and therespective spatial transferability (14). Thereby, we permuted sample sizes (n = 50, 100, 200, 500 to1000) and sampling schemes (random, systematic, stratified) 100 times and have shown respective
sampling artifacts. However, the produced 2000 regressions terms themselves had not yet been
investigated. It stands to reason, that with each respective sampling experiment, an individual
configuration of weights was produced. We hypothesized to see distinct variations in the respective
terms again. In particular, though, we assumed that the weights for some predictors are more prone tosampling artefacts than others.
As the maps produced with LURs are used for exposure assessments eventually, knowing theirrobustness is of high relevance with respect to their confidence. Also, having the most imp ortant
predictors identified, computational costs could be eventually reduced for large-scale applications.
Therefore, in this study, we investigated the regression terms thoroughly. After visualizing thevariations, ANOVA was used to pin point the most concerned predictors.

2

DATA & METHODS

Large scale noise mapping is often limited by the respective data availability and computational
resources (15). Mechanically, LURs models are, however, computationally comparable toconventional geostatistical modelling and, using satellite data, have been successfully deployed for
air quality assessment on national scales (16–18). Analog, we strived for Earth observation data andother public geodata as input for our model. Methodically, this study continued upon (14); thus, theutilized input data and original experimental setup are only summarized. The focus of the study is put
to the extension of the methodical framework - investigating the regressions’ weights. In complianceto the END, all geospatial operations were conducted on a 10 x 10 Meter resolution.
Spatially, we focused our investigations on the German city Koblenz. We did so, as the city
provided us with an area-wide, highly resolved noise map for training and validation. In 2022,
Koblenz had 113,844 inhabitants living on 105 km² (19). The city has heterogenous types of built-up
structures, ranging from a historic city center, industrial areas and low dense suburban formations. At
the same time, with both, its rivers and forests, it features different natural environments embodied ina fluvial shaped topography.
2.1 Predictor Variables
Of all noise emitting sources, road traffic is affecting most people in Europe (20). Along thispriority, information regarding its respective emissions is available in public datasets. The
OpenStreetMaps (OSM) project holds information on the spatial layout of streets attributed with street
type, number of lanes, speed limits and sometimes even road surface materials. Using ANOVA, we
had previously shown that the road types Motorway, Trunk, Primary, Secondary, Tertiary and
Residential have distinct mean noise levels on the roads themselves and can be used as proxy in LURs
(14). After tunnels were excluded (as in 21), we integrated the road infrastructures in to our LUR in
two fashions: First, as ushered by Harouvi et al. (12), the log-transformed distance to the next road of
the respective type was used. Second, with respect to cumulated road exposures, the road length
multiplied with their lanes within a given radius (9–11,13,21–23) was summarized. Based on
preceding literature (3,9–11,13,21–23) and in relation to the logarithmic behavior of noise propagation(24), we used systematically scaled moving window radii ranging between 12.5 and 1600 Meters.
Once emitted, a soundwave propagates through the environment. In an urban context, buildings
and their respective volume therefore are essential (17,25,26). Heutschi et al. (27), for example, stressthe unfavorable effects of dense street canyons. Opting for the highest data quality, building footprints
and their respective height (Level-Of-Detail-1, LoD1) were retrieved from the Federal Agency for
Cartography and Geodesy (28). The topographic position index was computed according to Weiss (29).
Positive values depict superior locations with a lot of air volume available, while negative values
occur when a pixel is lower than its surrounding. With respect to scope, the same moving window
radii were chosen as defined above. Analog, the surrounding topography was integrated into thegeostatistical model based on satellite derived height information. During data harmonization, theoriginal EU-DEM (30) was rescaled to 10 x 10 Meter using bilinear interpolation.
Last but not least, propagating sound interacts with the ground surface. Plain and solid surfaces
help noise traveling over long distances, while soft and porous material have sound absorbing effects(31–33). That said, we had previously produced a remote-sensing-derived land-cover classification(34), including seven land-cover classes expected to be relevant for noise mapping: These are artificial
land such as paved roads and built-up areas, water areas, open soil as most commonly found on
agrarian fields and opencast mines, as well as four classes differentiating vegetative biomass and its
seasonality. Although this level of detail is not included in END compliant maps, we stress the provencorrelation of biomass and sound attenuation (33,35). During data harmonization, we computed therespective fraction for each land-cover class using the moving window radii described above.
As computing multiple, systematically scaled radii introduced strong correlating covariates, weselected the most relevant scale a priori following the descriptions of Ragettli et al. (21) and Liu etal. (23). Eventually, 21 features were considered in our experimental setup (see Table 1).
Table 1 – Overview of investigated parameters, their source and properties
SourceFeatureMotorway

Attribute

Units

Min.

Max.

Proximitylog(m)

0

3.99

Length800m

017,609Proximitylog(m)

03.744

Length400

m

0

9,343

Proximitylog(m)

0

3.67

Length100

m

0

1,762

Proximitylog(m)

0

3.65

Length1600m

016,990

Proximitylog(m)

0

3.48

Length1600m

028,002

Proximitylog(m)

03.30

Length800m

021,673

Trunk

Primary
OSM
Secondary

Tertiary

Residential
BKG

LoD1

TPI800

/

−1.77

44.20

Copernicus

DEM

TPI1600/−114.62121.66

Artificial land

Mean800

%

0

94.28

Open soil

Mean1600

%

0

1.32

High, seasonal veg.

Mean800

%

0.25

96.67

High, perennial veg.

Mean1600

%

0.01

16.96

Low, seasonal veg.

Mean1600

%

0.04

72.53

Low, perennial veg.

Mean800

%

0

66.01

Water areas

Mean400

%

0

3.99

Weigand et. al. (34)

2.2

Experimental Setup

2.2.1 Sampling Noise Simulation Data
Conventionally, LURs are used to extrapolate in-situ measurements (3,9–13,21–23). Reviewing
the respective literature though showed sample sizes ranged from 40 (Girona in 10) to 1296 (Shanghaiin 3) and sample location was more often stratified (3,9,10,13,21–23) than random (12). Stratification
can be applied e.g. by using land-use classes as strata (13) or based on population (as in 3,22).
Theoretically, also systematic, grid based, sampling could be conducted as well. As we want to assessin this study the uncertainties within the regression’s terms introduced by varying sampling designs,
we reproduced this range using an END compliant map as reference. Such maps are produced using
highly accuracy engineering methods to simulate noise emission and its propagation using ray -tracing
simulations (36,37). Also, the source specific map allows controlling the emitters in our experiments.
For our study area, the road L den 2017 ranged from 12.8 to 88.3 dB(A).
Although, by using an existing map, no new exposure information is produced, subsampling it
repeatedly allowed investigating the LUR models thoroughly. We varied the sample sizes from n being
50, 100, 200, 500 or 1,000 and compared four sampling schemes (random, systematic, stratified Lden,
stratified Urb.Atl. , see Figure 1a). As strata for the latter we used Lden classes or 22 different LU/LC
classes defined by the Urban Atlas (38) respectively. Each configuration was repeated 100 times suchthat in total 2,000 different sample sets, further referred to as virtual field campaigns, were conducted.
Looking at the overall Koblenz data set first, a mean L den value of 51.0 (standard deviation = 11.1)
was computed by an engineering bureau. Compared thereto, Figure 1b depicts that both stratified
approaches tend to have had higher mean values, stratified Lden in particular. True for all sampling
schemes and also important for this study as well, small sample sizes tend ed to vary more over the
100 repetitions. Staab et al. (14) had consequently used a two-sided t-test to determine if the virtual
field campaigns were representing the total population well. Almost all sample size and repetitions
were significant (p > 0.05) for systematic sampled data sets, but most random and smaller
stratified Urb.Atl. sample sets could be considered representative as well (Figure 1c).
2.2.2 Modeling
While some modern machine learning methods have more predictive power, others excel with theirinterpretability (39). That said, we chose linear least squares regression in order to investigate themodels’ weights depending on our various sampling experiments. At the same time, their low
computational costs suited well to our experiments being repeated 2000 times. Most important for
putting our findings into context though, linear regressions are commonly deployed in the noise
mapping context (3,9–13). As the prerequired assumptions for least squares were met by most
predictors, and keeping the focus on sampling artefacts, we selected a consistent feature set and did
not consider forwards- (as in 3,10,11,13,21,40) or backwards-selecting implementations (9,12,22,23).
Eventually, each models’ coefficient of determination (R2 ), root mean squared error (RMSE) andmean absolute error (MAE) was computed (see summary in Table 2). Again, a high varianceintroduced by sampling artefacts was found. With regard to sampling scheme, we found that random
and systematic sampling resulted in lower R² compared to stratified sampling approaches. Higher
RMSE and MAE scores stand out at stratified Lden only. Overall, R² tends to decrease at larger sample
sizes. It ranged between 0.61 at random Sampling, n = 500 and seed 68 and 0.95 for stratified Lden
sampling, n = 50, seed = 50. Vice versa, RMSE and MAE both tend to be lowest at small sample sizes
but increases steadily (for details see 14).
2.3 Regression Term Assessments
To deepen our knowledge on LUR artefacts introduced by different sampling settings, we took a
specific look into the regression terms. With each virtual field campaign, an individual model with its
respective term was produced. Following a deductive approach, first the regression terms itself areinspected, before an analysis of variance (ANOVA) confirms the observed trends.
Within the term, for each selected variable, an individual weight (also known as estimate) and p value was computed. These weights were then compared across the sampling configurations, byaggregating them. For visualization, we used violin charts highlighting the most frequent estimates,
further annotated with quartiles and mean values. We expected to see higher variances at smaller
sample sizes, converging later. Making a priori assumptions concerning the effect of different
sampling schemes though was difficult. Having had a look at the mean L den values of stratified Lden,
we could only assume the respective regression terms aligning to very loud levels such as emitted inclose vicinity to road infrastructure.

Figure 1 – Summary of different sampling schemes (a, where colored background refers to Lden and Urban
Atlas classes) and respective average Lden per sample (b) compared to overall data set (dashed line) and its
standard deviation (yellow to purple colors), using a two-sided t-test eventually (c).
Second, we particularly highlighted systematic differences based on the sampling design. We
therefore utilized a multivariate analyses of variance (ANOVA) using sample size and sampling
scheme as grouping variables. For each predictor it compared, whether the 100 guessed estimates per
repetition had different means depending on the sampling design. A p -value below the confidence
interval of 0.05 was interpreted as being significant. That means, that changing sample size or
sampling scheme respectively would lead to a different estimate for this specific predictor. An ad -hoc
Tukey test was computed where necessary, to pin point exceptional differences.
Table 2 – Statistical summary of accuracy metrics across all 2,000 experiments
Min

1st Qu.

MedianMean

3rd Qu.

Max

R²

0.60840.69940.75930.76180.8086

0.9517

RMSE

2.474

5.473

6.119

6.132

6.637

10.031

MAE

1.938

4.031

4.460

4.582

4.882

7.462

3

RESULTSWith this study, we aimed at deepening the previous experiments by Staab et al. (14) using END
compliant maps for spatial extrapolations. We did so by investigating 2,000 LUR models produced tounderstand model variabilities as a function of sampling configurations.
3.1 Mean LM weights
Due to the intuitive interpretability, we scrutinized the intercept of the models first. Spread along
the Y Axis, Figure 2 shows the estimated intercept value in dB(A). With values ranging between -2.1
and 329.0, these were on average - 153.2 dB(A) -, relatively high. Starting from here, the predicted
noise levels will be lower when adding the rest of the term. At a sample size of 50, these estimates
were found to highly vary and one can barely see differences between the colored sampling schemes.
If at all, random sampled field campaigns lead to on average higher intercept estimates (mean 157.1)
compared to systematic (mean 148.4), stratified Lden (mean 153.8) and stratified Urb.Atl. (mean 142.5)
experiments. With increased sample sizes though, the variance decreased and this observation
solidified. Id est, at sample sizes of 1,000, the mean estimate is 7.7 dB(A) higher for stratified Lden and-5.3 dB(A) lower at stratified Urb.Atl. compared to random sampling (mean 152.9dB(A); 153.7 dB(A)for systematic sampling). With the related ANOVA, we only found significant differences in regards
to sampling scheme. The Tukey test pointed out that these differences stand out for stratified Urb.Atl. incomparison to the other three approaches, as well as significant differences between estimates derived
from stratifiedLden and systematic field campaigns.

Figure 2 – Violin plots showing most frequent (width) estimated intercept value (Y axis) at different sample
sizes (X axis) and sampling schemes (color). Vertical lines depict quartiles whereas cross (x) shows mean.
Looking at the estimated weights for our road variables (Figure 3), variance decreased at larger
samples sizes, too. The distinct information was most visible at sample size of 1000. Thus, we focus
in the following on describing these results in particular: Beginning with the log transformed distanceto the closest road (Figure 3, left side), the mean estimate was negative for motorways, trunks,
primary-, secondary and residential roads. This result is plausible as it would have predicted high Lden
values at close vicinity to these roads and the emitted sound levels decrease naturally over larger
distances. In the following, we will refer to this effect as ‘geometrical attenuation’. Physically, thiseffect is by nature constant and independent of the source. As part of a linear regression model though,the different estimates in the term rather correspond to respective emission levels of the source itself.
A glimpse at the labeled Y-Axis of Figure 3 shows that the estimates for log-transformed distance tomotorways were lowest. With respect to residential roads though, this rational was inverse and theestimates tended to even be positive. Looking at the mapped data, we could see this road type wasmost frequently appearing in residential areas. We thus comprehend a close vicinity to residential
roads to proxy quiet areas and interpreted the estimate as confirming successful noise reduction

Figure 3 – Violin plots showing most frequent (width) estimated values (Y axis) for road variables (logtransformed distance to closest road left and summed road length right) at different sample sizes (X axis)
and sampling schemes (color). Vertical line
management (e.g. speed limits), whereas increased noise levels were found at larger distances toresidential areas only. For a better readability, we will reference hereto as the ‘residential area effect’.
Comparing the different sampling schemes next, we again saw conformity between random andstratified sampling, but distinct deviations for the two stratified schemes. Looking at stratified Lden
first, the geometric attenuation effect was stronger compared to random sampling for motorways,
primary- and tertiary roads and less with respect to trunks and secondary roads. Also, the residential
area effect was most significant here. Estimates derived from samples using the stratified Urb.Atl.
scheme showed a comparably mellow residential area effect and less geometric attenuation for allroad types but motorways. Generally spoken, the variance analyses and the subsequent Tukey test
confirmed significant different estimates for most road types and each potential comparison but
random and stratified sampling. The only exception hereto was found for primary roads, where theestimates of stratified Lden were similar to the two.
Furthermore, observable trends for cumulated road lengths solidified at larger sample sizes. It isnotable though, that the variances for stratified Lden experiments was generally bigger compared to theother 1,400. With respect to the Y Axis of Figure 3, right side, readers need to aware of the predictor
units (Meters) relative to radius and the respective value range (c.f. Table 1). Significant differences
can be seen for the estimates derived regarding the cumulated motorway length within an 800 Meter
radius. Systematic and random sampling both tended to have slightly higher values compared to thetwo stratified approaches. Looking at Trunk Length400 next, stratified Lden ’s mean was -0.00006 at n =1000, while positive for the others (random = 0.00027, systematic = 0.00024, stratified Urb.Atl.=
0.00028). Having investigated the mean estimates with respect to primary roads next, the values atstratified Urb.Atl were significantly higher compared to the ones derived using the other schemes. The
mean estimates for Secondary Length1600 was highest at stratified Urb.Atl. sampling (mean = -0.00027 at n
= 1000), while the Tukey test depicted the others to be compared thereto significantly lower. For
tertiary roads, the mean estimates at n = 1000 were -0.00042 and -0.00043 at random and stratified
sampling, but as low as -0.00062 stratified Lden and only -0.00024 using stratified Urb.Atl. . Last but notleast, with respect to the sum of residential roads length within an 800 Meter radius, stratified Lden
stood out. Here, the mean estimate at n = 1000 was 0.0005, while only about half that much at theother three sampling configurations.
Reviewing the estimated weights for the nine environmental predictors (Figure 4), variance wasdecreasing at larger samples sizes again. It is interesting to note though, that the variance tended tobe higher for stratified Lden in general though. The estimates regarding the topographical position index
derived from the built-up model (LoD1 TPI800 ) tended to be rather negative. This corresponds to soundpressure levels being predicted to be higher in street canyons. Vice versa though, the TPI derived fromthe digital elevation model at a 1,600 Meter radius (DEM TPI1600 ) has slight positive values for random
and systematic sampling. The comparably larger positive values for stratified Lden would have led tohigher predicated noise levels in superior locations. Interestingly, only statified Urb.Atl. showed adistinct tendency for negative estimates, such that noise levels were higher in valleys. The mean
estimates with respect to the surrounding landcover fractions of artificial land were very close to zero.
With respect to these fractions ranging between 0 and 100 percent, a weight of -0.044, such as foundat n = 1000 for both stratified approaches equally, led to a reduction of -4.4 dB(A) in the extreme
cases of fully impervious surfaces (e.g. in the city center). An increased amount of open soil within
the range of 1,600 Meters led to increased noise levels. Although physically, such materials have asound absorbing effect, comparing it to the mapped data, we could see that this land cover class wasmost commonly located in disperse industrial and agricultural areas such as found along the majorroads. High, seasonal and low, perennial vegetation both tended to decrease noise predictions, while
the estimates were inconsistent for high, perennial vegetation and gravitated to be positive for low,seasonal vegetation. It was previously discussed though (14), that vegetation does only play a minor
role in END compliant noise mapping. Thus, drawing conclusions from these estimates is difficult.
Most probably, high vegetation factions rather indicated periphery. The estimates with respect to water
areas within a neighboring scope of 400 Meters inclined to be positive for all schemes but
statified Urb.Atl. sampling. This is logical, as water surfaces in general allow sound travel further
distances.
3.2 LM Terms ANOVA
Summarizing the conducted ANOVA, differences in sampling scheme and sample size were pin
pointed (Figure 5). Where p is below 0.05, a significant difference was found to at least one group.
Looking at the estimated intercept for example, p < 0.001 stresses a highly significant differenceFigure 4 – Violin plots showing most frequent (width) estimated intercept value (Y axis) for environmental
predictors at different sample sizes (X axis) and sampling schemes (color). Vertical lines depict quartiles
whereas cross (x) shows mean.
between the sample schemes, but not with respect to sample size (p = 0.101) and the ad-hoc Tukeytest has highlighted, where such differences occurred. In summary, it is shown that almost every
estimate was influenced by the chosen sample scheme. Nevertheless, with respect to sample size, thiswas true for approximately every second estimate only.

4

DISCUSSION

In this study, we utilized 2,000 virtual field campaigns to investigate sampling effects on theeventual regression terms in LURs. Using ANOVA, we proved significant biases by sampling scheme

Figure 5 – Plot showing p-value (X axis) of ANOVA investigating estimates of predictors (Y axis)
depending on the grouping variables sampling scheme and sample size (shape). Gray vertical line denoting
0.05 threshold for variable significance.
for almost all predictors and the same was true for nine of 22 predictors with respect to sample size.
However, the interpretation of these results also requires a discussion of the methodologicalbackground.
First of all, the use of END compliant road traffic noise simulations from the city of Koblenz as
training data kept the emitting sources constant and allowed for a rigorous repetition of drawing
samples. We are aware though, that these samples differentiate from in -situ microphone measurements
in regards to sematic content, observation period and expenditures when collecting very larg e sample
sizes They can, for example, also measures soundscapes (c.f. 41). Also, the physical reachability ofsample locations may be blocked due to access restrictions or the natural landscape. With respect tothe investigated sampling schemes, we did only compare the three basic types identified. Populationweighted location-allocation, as conducted by Ragettli et al. (21), may be allocated into the group of
stratified sampling, but was not reproduced here to limit the extent of conducted experiments.
Nevertheless, we want to stress its semantic relevance for epidemiological studies.
Second, the 105 km² test site, Koblenz, is relatively small. So far, we argued, that with its
heterogeneous urban morphology and landscape structure, it is an interesting test site for our
experiments. Looking at the high variances of small sample sizes (i.e. n = 50), we assume the observed
effects were stressed by this heterogeneity. While we thus overall promote sample sizes should be aslarge as possible, a limited scope of sample might be sufficient for small and homogenous researchareas. This can particularly be true, if an appropriate sampling scheme was chosen, representing the
population well.
Third, a comprehensive data set was compiled spanning twelve road traffic noise emission
variables and nine predictors depicting the natural and built-up environment. However, the various
scales selected using univariate regression models (as suggested by Ragettli et al. (21) and Liu et al.
(23)), may have been biased by multifactorial relationships (14). Furthermore, no additional feature
selection, as in forwards- or backwards selective regression, has been conducted, which may havepreserved some covariances within the experiment. While we are aware these are decreasing theinterpretability of the terms, a reduction was difficult to place within the setup as the selected
predictors would possibly change between each iteration. Another approach would be switching toadvanced machine learning models as presented by Liu et al. (23), but computing 2,000 random forests

would have bound large computation resources. That said, the insights of this study allow the sampled
data set constant in future studies and focus on such developments.

5

CONCLUSION

Lately, LURs are a trending topic in noise mapping for large-scale areas. With our previous study
investigating spatial transferability (14) and the methodical addition on the regression terms
themselves investigated in this study, we begin to understand the inside mechanics of LUR. By
comparing 2,000 sample data sets, we have seen significant changes in the eventual regression terms.
Some studies though use supervised LUR (e.g. 3), defining a terms direction a priori, e.g. logtransformed distance to road should have a negative estimate and is excluded from the model elsewise.
Rationally, this approach makes sense. With our experiments, we found regression weights varying
across the zero line at smaller sample sizes particularly. Additionally, particular considering thesurrounding environment at an extended radius may introduce proxy biases as well. Most important
though, our experiments showed that the chosen sampling scheme can bias the prediction. Onlysystematic sampling was found to retrieve robust sample sets unbiased by the sample size. We
therefore conclude that previously published studies have produced valuable noise maps with respect
to their research aims. However, based on the findings of our study we want to stress that theinterpretation of the respective terms and cross comparisons in general, requires to take the effects ofthe sampling schemes into account. We conclude that only a systematic analysis will allow, theestimated weights to highlight factors relevant in urban planning.

ACKNOWLEDGEMENTS
This study was mainly funded by the German Federal Environmental Foundation (DBU), theproject “Noise2NAKO AI ” (funding code ZT-I-PF-5-42) granted by the Helmholtz Association and the“Digitaler Atlas 2.0.” project funded by the German Aerospace Center. The funding sponsors had norole in the design of the study, the interpretation, the process of writing of the manuscript, or thedecision to publish the study. We want to furthermore thank the authorities of Koblenz for sharing thesimulated noise levels. Further, both, the EU-DEM and the Urban Atlas were produced with fundingby the European Union (https://land.copernicus.eu). The LoD1 model was provided by the German
Federal Agency for Cartography and Geodesy (BKG, 2015). The administrative borders of the Rhine
valley were provided by the Netherlands-palatinates Office for Land Surveying and Geoinformation(www.lvmergeo.rlp.de). Last but not least, we acknowledge Nils Karges for his support to this work.

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34. Weigand M, Staab J, Wurm M, Taubenböck H. Spatial and semantic effects of LUCAS samples on fullyautomated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs
Geoinformation. 2020 Jun 1;88:102065.
35. Reethof G. Effect of Plantings on Radiation of Highway Noise. J Air Pollut Control Assoc. 1973 Mar
1;23(3):185–9.
36. Garg N, Maji S. A critical review of principal traffic noise models: Strategies and implications. Environ
Impact Assess Rev. 2014;46:68–81.
37. Steele C. A critical review of some traffic noise prediction models. Appl Acoust. 2001;62(3):271–87.
38. European Environment Agency. Urban Atlas 2018 — Copernicus Land Monitoring Service. 2020.
Available from: https://land.copernicus.eu/local/urban-atlas/urban-atlas-2018
39. Carvalho DV, Pereira EM, Cardoso JS. Machine Learning Interpretability: A Survey on Methods andMetrics. Electronics. 2019 Jul 26;8(8):832.
40. Raess M, Brentani A, Ledebur de Antas de Campos B, Flückiger B, de Hoogh K, Fink G, et al. Land use
regression modelling of community noise in São Paulo, Brazil. Environ Res. 2021;199:111231.
41. Karges N, Staab J, Rauh J, Wegmann M, Taubenbock H. Soundscapes on edge - The real-time machinelearning approach for measuring Soundscapes on resource-constrained devices. PROCEEDINGS of the24th International Congress on Acoustics; 2022 28.10; Gyeongju, Korea.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0525

Acoustic Performance of Noise Reducing Devices Installed on the
Highway Noise Barriers
Chulhwan Kim1; Hyejin Kang2; Woongyong Lee3
1~3

Korea Expressway Corporation Research Institute, Korea

ABSTRACT
Various types of noise reducing devices(NRDs) which installed on the noise barriers for getting more noise
shielding effect have been installed in Korean highways from late 1990’s. But, it still doubtful for their realeffect in practical use, so a reasonable and practical methods to evaluate the noise reducing effect of NRDs
have been investigated in the outdoor condition. In the test method, estimation range of evaluating area wasconsidered to 30 meters behind the barrier and 6 meters above the top of the barrier in diffraction area. And
also weighting frequency spectra and evaluating range were considered for actual application of highway
fields. To eliminate the noise reducing effect of the NRD’s height itself, the source and measuring points
were adjusted as high as the NRD’s height.
Keywords: Noise Reducing Device(NRD), Noise Barrier, Highway Noise

1. INTRODUCTION
Installing of noise barriers has been the most common method for abating highway traffic noises
to the road side residential area, and after the report about edge potential concept of a noise barrier by
Fujiwara[1], various types of noise reducing device(NRD) called ‘Noise reducer’ have been suggested
for getting more shielding effect of noise barriers. More than 10 types of NRD products commerciallyused and developed in Korea, and figure 1 show several samples of them. But, NRDs had been still
doubtful for their real performance, because there no appropriate method to determine the acoustic
performance. So authors suggested for having reasonable and practical method to estimating the noise
shielding effect of NRDs, i.e. estimating area, weighting spectrum and frequency range in the outdoor
conditions. For this the weighting frequency spectrum and range were considered for the actual
application of the highway noise abatements.

Figure 1. Samples of NRD installed on Korea highway

2. RESEARCH OVERVIEW
Conventional method for estimating acoustic performance of NRDs were near field test in indoor
condition, so the report of estimation result showed extremely high performance in common sense.
So, authors have suggested a practical method and figure 2 shows scheme of test facility andmeasuring positions for performance evaluation. In test facility, 30 meters of measuring range from abarrier and measuring points for performance evaluating of NRDs defined as (1) Under the line ofsight, (2) At least 30 meters from a barrier, (3) Above the reference level. Finally, from the results,
1

c.h.kim@ex.co.kr
hjin@ex.co.kr
3
knulp@ex.co.kr
2

estimation with highway spectrum weighting is more severe than estimation with A-weighting
spectrum and A-weighting spectrum may not be suitable to estimate of the NRD’s shielding effect for
the highway noise abatement.

Figure 2. Scheme of test facility and measuring positions for performance evaluation

3. SUMMARY
Summarizing the test method for estimating the acoustic performance of NRDs installed on Korean
highways, fest and evaluation method of NRD’s acoustic performance is considered for the practicaluse on highways. By the results, measuring range behind barrier and below the line of sight should be
30 meters at least, and getting closer to the barrier the performance of NRDs could be better than far
from the barrier in a diffraction area. In the weighting frequency spectrum, the results by A-weighting
were better than the results by highway noise spectrum weighting. The results are seemed that the
highway spectrum weighting is more practical than A-weighting for estimating the acoustic
performance of NRDs. Summarizing the results of the study three main points can be suggested asfollowings,(1) The receiving area is involved 30 meters behind a barrier at least.
(2) The real highway noise frequency spectrum of Korea was used for the frequency weighting.
(3) The frequency-range for evaluation as a overall level is 250 to 4kHz on octave band center
frequency (or, 200 to 5kHz on 1/3 octave band center frequency).

REFERENCES
1. K. Fujiwara, et al., “Sound Shielding Efficiency of a Barrier with a Cylinder at the Edge”, Noise Control
Engineering Journal, 37(1) 1991. p. 0000-0000.
2. D. Kim, et al., “A study on the Computation and Application of Sound Power Level for Road Traffic
Noise”, Proceedings of the KSNVE Annual Spring Conference 2004, p. 527-534
3. T. Okubo, et al., “Procedures for determining the acoustic efficiency of edge-modified noise barriers”,
Applied Acoustics, 68, 2007. p. 797-819.
4. F. Asdrubali, “On the Experimental Evaluation of the Performances of Noise Barrier Diffracting Devices”,
ACTA ACUSTICA, 93, 2007. p. 659-669
5. K. Yamanoto, et al., “MEASURMENTS OF NOISE REDUCTION BY ABSORPTIVE DEVICES
MOUNTED AT THE TOP OF HIGHWAY NOISE BARRIERS”, Proceedings of inter-noise 95, 1995. p.
389-392.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0526

Measuring Height Consideration of the Pass-by Noise at Highway
Roadside
Chulhwan Kim1; Woongyong Lee2; Hyejin Kang3
1-3

Korea Expressway Corporation Research Institute, Korea

ABSTRACT
Selection of measuring point is very important on road noise measuring because of the noise levels become
different according to measuring distance and height. But, it is not easy to decide the measuring point on
Korea highways for noise level monitoring because most of highway roads in Korea have banking or cutting
geometric structures unlike that of American or European highways. In this study, noise measuring heights
and methods have been considered at highway roadside for assessing the radiating noise from the road. For
the study, noise measured from the height of 1m, 3m, 5m, 7m at highway roadside have been compared anda reasonable method has been suggested for assessing noise from the highway.
Keywords: Road Traffic Noise, Measuring Height, Highway Noise Abatement

1. METHOD
In the evaluating of pavement noise, the CPX(Close Proximity) noise measurement is very popularand convenient but, this method lacks the measurement of the noise except from the tire-road noise,so the results of measurements could not be used for environmental noise abatements. In this study,measured road noises by measuring at the roadside were reviewed and summarized. Using 128 data
which measured at the height of 1m, 3m, 5m and 7m were analyzed. The difference between the
average of the remaining height noise levels and the noise level for each height except for a height of
1 m with a large degree of dispersion was not considered.

Figure 1. Measuring height of roadside noise in the study

1c.h.kim@ex.co.kr
knulp@ex.co.kr
3hjin@ex.co.kr
2

2. RESULT
Figure 2. show result of the study. Noise level difference measured between 1m and 3m height is
1.2dB, 3m and 5m height is 0.7dB, 5m and 7m height is 0.2dB respectively. In the result, noise
difference measured between 1m and 3m is the largest, 5m and 7m is the smallest, especially, noise
difference measured between 1m and 3m shows very scattered value regarding measuring sites. Also,
difference noise level between averaged noise level of 3, 5, 7m height and 3m, 5m, 7m respectively
shows the smallest value on noise difference between averaged noise level of 3, 5, 7m height and 5m.

Figure 2. Noise level differences with measuring heights

3. SUMMARY
For evaluating highway noise measuring at the roadside, result show that the noise from 1m height
is not reasonable as a representative value for its scattering characteristics from the measuring
circumstance, and noise from 5m height is the most reasonable for representing highway roadsidenoise in the circumstance is not good condition for measuring 3, 5, 7m height noise and averaging its
value.

REFERENCES
1. ISO 11819-1. 1997. Acoustics – Measurement of the influence of road surface on traffic noise – Part 1:
Statistical Pass-By method.
2. AASHTO Designation: TP 98-13. 2015. Standard Method of Test for Determining the Influence of Road
Surface on Vehicle Noise Using the Statistical Isolated Pass-By (SIP) Method.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0527

Case Study of Estimating Acoustic Performance for an Air
Ventilation Type Noise Barrier on Highway Roadside
Chulhwan Kim1; Hyejin Kang2; Woongyong Lee3
1~3

Korea Expressway Corporation Research Institute, Korea

ABSTRACT
An air ventilation type noise barrier has been suggested for relieving wind-load to the barrier. But, becauseof the ventilating slits of the barrier, the noise shielding performance is doubted by related authorities. In thisstudy, noise shielding performance of an air ventilation type noise barrier is tested and estimated by in-situ
method on the highway roadside.
Keywords: Road Traffic Noise, Air Ventilation Type Noise Barrier, Noise Shielding Performance

1. INTRODUCTION
The noise shielding performance of an air ventilation type noise barrier is tested and estimated byin-situ method on the highway roadside. For the study, a test site is selected at the same site ofconventional noise barrier with 500m-length and 5m-height on the highway. In middle part with 30mof the conventional barrier, and the air ventilation type barrier settled for test. Because of the lengthof test barrier with 30m-length, measuring points is set by adjacent the barrier. Figure 1 shows the testsite for the study and installed air ventilation type noise barrier.

Figure 1. Test site and installed air ventilation type noise barrier

2. METHOD
Figure 2 shows test site with estimating positions for air ventilating noise barrier and scheme of
the measuring points(M/Ps). Shielding performance of the air ventilation type noise barrier isevaluated by relative value with that of conventional noise barrier, i.e., [Evaluating Value] Air ventilation
type = [Shielding Performance] Air ventilation type - [Shielding Performance] Conventional type , and [Shielding
Performance]Air ventilation type = [Noise Levels at M/Ps]Without barrier - [Noise Levels at M/Ps]Air ventilation
type , [Shielding Performance] Conventional type = [Noise Levels at M/Ps]Without barrier - [Noise Levels at
M/Ps]Conventional type respectively.
1c.h.kim@ex.co.kr
hjin@ex.co.kr
3
knulp@ex.co.kr
2

Figure 2. Test site with measuring positions and scheme of measuring points

3. RESULT
Figure 3 show the noise shielding performance of conventional and air ventilation type noisebarrier respectively at the measuring points. The performances of conventional barrier show 15.0 ~18.2 dB and air ventilation type barrier show 10.4 ~ 11.6 dB at the measuring points respectively
showed in Figure 2. And Table 1 shows the relative noise shielding performance of an air ventilation
type barrier to the conventional barrier as -4.6 ~ -6.6 dB respectively, -6.1 dB as averaged.

Figure 3. Noise shielding performance of conventional and air ventilation type barrier
Table 1. Estimation results of an air ventilation type noise barrier
M.Ps

No.1

No.2

No.3

No.4

Ave.

Relative
Performance

-6.6

-7.4

-4.6

-5.8

-6.1

4. SUMMARY
Noise shielding performance of an air ventilation type noise barrier is estimated by in-situ methodon the highway roadside. And the results of the study can be summarized as followings,(1) For the 4 M/Ps behind of conventional and air ventilation type noise, the shielding effect of air
ventilation type noise barrier is 4.6~7.4 dB lower than that of conventional type noise barrier.
(2) In designing the noise barrier with air ventilation type for abating highway noise, transmission
loss must be considered in noise assessment calculation.

REFERENCES
1. “Acoustics - In-situ determination of insertion loss of outdoor noise barriers of all types”, ISO 10847,
1997.

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

ABS-0596

Session: A04-01 “Noise Assessment and Policy”
The Acoustic Windows and its Practice Note – The Sustainable
traffic noise mitigation means in Hong Kong
Maurice YEUNG1, King Kwong IU2East and South-East Asia Regional Chapter, Acoustical Society of America.
Abstract
Hong Kong is a very small but hyper-dense city with over 7 million living in 1,100 sq. km inwhich 85% of land is hilly area. Like other metropolitan cities, Hong Kong is facing significant roadtraffic noise problems. To make Hong Kong a live-able place with a good acoustic environment, theGovernment departments, namely Environmental Protection Department, Housing Department andBuilding Department joined force since 2009 and developed the innovative “Acoustic Windows”
which has the unique characteristics of offering high degree of noise reduction of up to 8 dB(A) andat the same time allowing natural ventilation. As at today, this innovative noise mitigation approachhas been adopted in some 150 development projects and over 100000 residents are benefitted. In 2020
and 2021, Environmental Protection Department issued Practice Note providing guidelines topractitioners and professionals in evaluating noise reduction performance, in designing and applying“Acoustic Windows” in housing development projects. This paper will discuss the operational
approach of “Acoustic Windows” and how this is perceived by practitioners and professionals.
Opportunities are taken to review the effectiveness of “Acoustic Windows” and to explore whether
there are ways to further improve its noise reduction ability.
Keywords: Traffic noise, acoustic environment, housing development, acoustic windows

1. INTRODUCTION
Hong Kong is one of the most densely populated metropolitan cities and is facing severe andpervasive road traffic noise problems. Due to a host of factors like limited habitable land, the need toprovide accommodation for large population and concentrated transport networks to support economic
growth, many residential developments are built near heavily trafficked expressways and roads which
result that about 900,000 people are exposed to road traffic noise at levels higher than 70 dB(A)L 10 (1hour) [1][2]. Public concern about the noise environment and its impact on the residents’ quality oflife is on the rise. Legislative Council members called for case conferences to discuss if solutions
could be in place to reverse the situation. The Administration is actively carrying out action plans tominimize the noise exposure.
The Hong Kong Special Administrative Region (HKSAR) Government is addressing traffic
noise through a 4-pronge approaches, namely, legislative control of vehicle emission similar to thoseof international standards; redressing past problems by retrofitting noise barriers and noise absorbing

1
2

mklyeung@yahoo.com
kkiu@wha.com.hk

road surfacing on suitable existing highways; preventing replica of noise situations by proactively
taking part in planning of new highways and new residential developments, and engaging partnershipwith research bodies and public in exploring and developing innovative means. For the penultimate
part which is the most difficult as many residential dwellings are built alongside the maintransportation networks to satisfy the housing demands of the communities. Many conventional noise
mitigation measures like barriers and enclosures built next to highways, building setbacks andextended podia were common approaches that have been used in Hong Kong. However, thesemeasures may not always be applicable because of space limitation, safety, visual intrusion, landownership etc. While double-glazing is commonly found in some other countries, the warm and humid
climate in Hong Kong makes it very expensive for residential use and energy-wise unfriendly due tothe need to provide air-conditioning for a “closed-window” environment and it will practically deprive
the residents of an “open-window” life style. Hence, specially designed window systems with acoustic
treatment have become an alternative way to reduce road traffic exposure and impact in residential
units. In view of sustainability, specially designed window systems which can offer good soundinsulation and at the same time allowing acceptable level of natural ventilation is needed.

2.

THE INNOVATIVE ACOUSTIC WINDOWSThe Innovative Acoustic Windows is a “think out-of-box” solution offering high degree of noise
reduction and at the same time allowing natural ventilation. In the past, windows would need to beclosed to shut-off environmental noise from outside of residential flats affecting residents. The
innovative ideas of shutting off noise from outside of residential flats while providing naturalventilation and creating a preferred form of sustainable living and such “open window living
environment” becomes trendy building designs. It basically comprises two layers of windows. An
additional window layer is introduced to the conventional side-hung window in staggering position.
The outer window is a conventional push-pull type window whilst the inner one is a half-size sliding
window. By properly position the openings of inner window with the outer window, it can reducenoise entering indoor while allowing air flow into the room via the air gap between the two layers of
windows (Figure 1) [3].

Figure 1 – an artistic image showing the working
mechanism of acoustic window

The exploration and development of the innovative acoustic windows are the joint professional
efforts of Environmental Protection Department (EPD) and Housing Department (HD) of the HKSAR
Government. The former is the main brain and action team in safeguarding the acoustic environmentof Hong Kong people while the latter is the team responsible for designing, construction and deliveryof residential developments for the public. Following some successful laboratory tastings in 2009,
mock-up housing flats were built fitted with conventional windows and the innovative acoustic
windows in housing site fronting a heavily trafficked highway. Various technical aspects including
noise reduction ability of acoustic windows, maintenance and operational related issues, compatibilityof housing flat designs etc. were studied. Natural ventilation to the occupants is nonetheless animportant element, therefore Building Department (BD) of the HKSAR Government, the respective
authority was invited to take part in providing necessary views as well as advice. Throughcomprehensive measurements and actual site visits by different categories of professionals involved
as well as the concerned district councilors, the mock-up flat tests demonstrated the innovative
acoustic windows design a very successful one. It gives an 8 dB(A) improvement for the occupants intheir acoustic environment [4].

Figures 2 & 3 – sound measurement equipment set up at external façade and inside mock-up flats forevaluation of noise reduction ability of acoustic windows

3.

EVALUATION OF NOISE REDUCTION EFFECTIVENESS

Noise reduction effectiveness of “Acoustic Windows” 3 is expressed in Relative Noise
Reduction (RNR) which is the IN-OUT difference between the averaged noise levels (in terms of L10 )(outdoor vs. indoor) obtained in actual site measurements or in laboratory measurement 4 of theresidential unit in concern with conventional window and that of “Acoustic Windows”:
RNR = ANRAW - ANRCW [dB(A) L10(1hr)]
Where

(1)

RNR = relative noise reduction
ANRAW = Adjusted noise reduction with “Acoustic Windows”(Operating in acoustic mode meeting ventilation Requirements stated in PNAPAPP 130 of the Building (Planning) Regulations (B(P)R) [5])ANRCW = Adjusted noise reduction with “Conventional Window”(Meeting BD’s natural ventilation requirement in which the openable area of outer
window is not larger than 1/16 of the floor area of the room)

To obtain necessary planning approval, Noise Impact Assessment Study would be conducted
and submitted to the planning authority. Through this study, traffic noise impacts on the buildings
under planning would be assessed following the procedures in “Calculation of Road Traffic Noise”[6] and mitigations measures would be incorporated into the building designs meeting the requirementof 70 dB(A)L10 (1 hour). EPD, being the noise authority of the HKSAR Government, has its duty in
scrutinizing all the noise assessments. While “Acoustic Windows” and its noise reduction
effectiveness are relatively new, EPD accepts the following methods to obtain the noise reduction
effectiveness:- Noise measurements in as built flats.
-

Noise measurements in mock-up rooms on actual or other sites.

-

Noise measurements in laboratory set up with noise mitigation measures.

3“Acoustic Windows (Baffle Type)” is the official name of “Acoustic Windows” used in “Practice Note on
Application of ACOUSTIC WINDOWS (BAFFLE TYPE) in Planning Residential Developments against
Road Traffic Noise Impact”
4

Adjusted with traffic noise spectrum according to the BS EN 1793-2:2012 if source of traffic noise is not fromthe actual road traffic but by means of a line of speakers.

-Numerical analysis or-

Ray-tracing simulation.

Each of the above methods has its own advantages and disadvantages. It is not the intention ofthis paper to go through each method in details. However, it is useful to grossly outline some factualissues for information. For the first two methods, it would be very costly and timing wise rather
difficult to manage in the context of entire project development program, nevertheless, these would
give more reliable and accurate results. For the third one, cost is less than the first two, but it seems
to be more useful on relative comparison of noise reduction only and that the uncertainty is muchhigher than the first two. The last two methods seem to be commonly used for prediction andestimation, but all the results would need careful calibration by mock-up tests; and the uncertainty is
the highest as compared with the others.
To large extent, the EPD needs to consider a pragmatic and practicable approach so thatprofessionals can adopt and make best use of this innovative acoustic designs so that future residents
would be no longer affected by serious traffic noise impact.

4.

THE PRACTICE NOTES & ITS PRACTICABILITY

The EPD issued “Practice Note on Application of ACOUSTIC WINDOWS (BAFFLE TYPE) in
Planning Residential Developments against Road Traffic Noise Impact” (Practice Note) [7] in August2021. This Practice Note is developed to provide guidelines to practitioners and professionals in
evaluating noise reduction performance of Acoustic Windows. This also aims at providing technicalinformation in designing and in applying Acoustic Windows against road traffic noise impact inplanning residential developments in Hong Kong. Besides giving the design feature of Acoustic
Windows, it outlines a possible design of Acoustic Windows for 8m2 habitable room which arebasically dining room, living room or bedroom. Accordingly, professionals should take note of thefollowing key points of designs:-

The resultant net opening after aligning the outer openable window and the inner sliding
window should meet the natural ventilation requirement in PNAP APP-130 of the Building(Planning) Regulations (B(P)R).
The minimum gap difference between 2 window layers should not be less than 100mm; andThe inner sliding window and outer window set should form an overlapping length of notless than 100mm.

The Practice Note outlines in the annex how the positions of Acoustic Windows with respect tothe dominant traffic noise source; the coupling with 1.5m architectural fin which is one of the most
adopted in buildings for noise mitigation in Hong Kong; and the effect of including sound absorption
material on the top and 2 vertical sides of window frame of Acoustic Windows.
This Annex is very useful as it shows that a simple case of adopting Acoustic Windows as
designed parallel to dominant traffic noise line source would have an improvement of 6 dB(A) c.f. theconventional window configuration. If sound absorption material is added, additional 1.5 dB(A)improvement can be assumed. That means if the assessed traffic noise impact of the original design is77.9 dB(A), one can adopt the acoustic window with sound absorption materials to meet the 70 dB(A)requirement under the HKPSG. Obviously, if acoustic window is installed at certain angle incident todominant traffic noise source, there would be additional 1-2 dB(A) reduction depending on the angle
of incident. As illustrated, one can adopt acoustic window with sound absorption material couplingwith 1.5m architectural fin, an unmitigated level of 80.9 dB(A) can be managed to meet 70 dB(A)requirement.
The Practice Note is clear in concept as well as in the application and provides good guidance
to practitioners and professionals. Undoubtedly, it can be adopted in designing flats deploying acoustic
windows as mitigation measures. However, there are always rooms for improvement.

5.

ROOMS FOR FURTHER IMPROVEMENTThe Practice Note provides design of Acoustic Windows adopted in 8m2 habitable room only.
Considerations should be given to include the designs for adopting in other room sizes like 6m2 , 10m2 ,14m2 , 18m2 etc. which are also commonly found in the residential developments in Hong Kong.
For 8m2 room, there could be different combinations of aspect ratio of length and depth of room,
for instance, 4 x 2 m2 , 2.83 x 2.83 m2 etc. Would the aspect ratio affect the RNR? For compiling thisPractice Note, the EPD must have collated very good information or have conducted large numbersof in-situ measurements or by means of laboratory testings. It would be useful if information
pertaining to the above questions or concerns could be included in the Practice Note. Similarly, there
is no mention of the height of room. It could be the assumption that the habitable room in Hong Kong
may largely be restricted to certain height. Nevertheless, this is again the useful technical information
to the practitioners and professionals.
It is commonly known that if absorptive materials or surfacing can be added to inside surfaces
like walls, floor or ceiling of habitable rooms, the RNR could be increased. Hence, it would also be
useful if the Practice Note can include such information.

6.

CONCLUDING REMARKSAt the preparation of this paper, over 100 residential development projects adopted thisinnovative concept of Acoustic Windows protecting future residents from the severe traffic noise
impact. We all visualize and experience the brilliant effect of the innovative Acoustic Windows. We
hope that EPD would further publish further revisions to this Practice Note with extensions to cover
more room sizes and inclusion of more technical materials. This would certainly not only be welcomedby the practitioners and professionals in Hong Kong, but also by all residents as they would be
provided a better living acoustical environment.

REFERENCES
1. An Overview of Noise Pollution and Control in Hong Kong, Noise, web page of Environmental
Protection Department, HKSAR Government
2. Noise section of Website of Hong Kong Planning Standards and Guidelines. Planning Department, theGovernment of Hong Kong Special Administrative Region; 2014.
3. Tackling Traffic Noise Through Plenum Windows – An Application in Hong Kong, Inter-Noise 2014,
Melbourne, Australia
4. Acoustical Design and Performance Evaluation of the Acoustic Window, Final Report, Term Traffic andEnvironmental Consultancy Services 2010 – 2012 for Hong Kong Island and Kowloon East Region,
Hong Kong Housing Authority
5. PNAP APP-130, Lighting and Ventilation Requirements – Performance-based Approach, PracticeNote for Authorized Persons, Registered Structure Engineers and Registered GeotechnicalEngineers, December 2016
6. Calculation of Road Traffic Noise, Department of Transport, Welsh Office, 1998
7. Practice Note of Application of INNOVATIVE NOISE MITIIGATION DESIGNS in Planning Private
Residential Developments against Road Traffic Noise Impact. Environmental Protection Department,
the Government of Hong Kong Special Administrative Region; 2021

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

ABS-0601

How Acoustic Louvre Can Enhance the Noise Reduction
Performance of Acoustic Balcony – a Case Study
King Kwong IUA 1, Kit Wong A 2, Daniel HUNGB 3, Andy CHUNGB 4,
Maurice YEUNGB 5, Alex WongC 6
A

Westwood Hong & Associates Ltd, B Macau Instituto de Acústica,C

Supreme Acoustics Research Ltd

Abstract
A balcony in a residential flat provides residents an outdoor living space and also upgrades theoverall exterior design of the flat. However, balcony becomes an opening for noise penetrating into
the residential flat, particularly if it locates next to road where noise pollution is an issue affecting
residents. Whilst traffic noise links to health effects like high blood pressure and psychological s tress,
blocking it is more than just about the residents’ comfort. Research through innovative balcony
designs, adopting absorptive materials or features at suitable parts of balcony conducting in Hong
Kong gives it a facelift making it an innovative and sustainable form of residential flat design for
mitigating road traffic noise in Hong Kong. This paper discusses the operational approach of“Acoustic Balcony” and how combination of different features like swing balcony door, absorptive
linings, acoustic louvre etc. would offer up to 8 dB(A) reduction through a case study. Discussion ismade on how acoustic louvre could overcome stringent regulatory requirements of ventilation
permeable parapet wall and at the end helping to achieve the noise planning cr iteria. Opportunities
are taken to review the effectiveness of “Acoustic Balcony” and to explore whether there are ways tofurther improve its noise reductions.
Keywords: Traffic noise, acoustic environment, residential development, acoustic balcony, acoustic
louvre1. INTRODUCTION
Hong Kong is one of the most densely populated metropolitan cities and is facing severe andpervasive road traffic noise problems. Due to a host of factors like limited habitable land, the needto provide accommodation for large population and concentrated transport networks to support
economic growth, many residential developments are built near to heavily trafficked expressways andkkiu@wha.com.hk
kitwong@wha.com.hk
3
danielhkhung@gmail.com
4
ac@moiacoustics.org
5
mklyeung@yahoo.com
6
hk@supremeacoustics.com
1
2

roads which result that some 900,000 people [1] are exposed to road traffic noise at levels hig her than
70 dB(A)L10(1 hour) [2]. Public concern about the noise environment and its impact on the residents’quality of life is on the rise. Legislative Council members called for case conferences to discuss if
solutions could be in place to reverse the situation. The Administration is actively carrying out actionplans to minimize the noise exposure. On road surfacing material, the Administration has alreadyadopted low noise material for our new high speed (70km/hr or above) roads. Proactive works arebeing done in planning of new land uses and new roads to avoid causing unacceptable level of roadtraffic noise to the future residents.
Among the measures mentioned above, noise barriers or enclosures seem to be the most effective
in reducing noise so far as direct line of sight of traffic is screened from residential flats. The
cityscape of Hong Kong is basically high-rise in nature and blocks and blocks of tall buildings can befound on both sides of roads (Figure 1). This means that very tall barrier or even enclosures areneeded to bring noise down to acceptable level. However, in many cases site constraints render
erection of noise barriers or enclosure impracticable; or only relatively small noise barrier or enclosure
could be erected, and residents are still subject to high level of road traffic noise. Also, to someextent, noise barriers or enclosures are being seen as causing visual intrusion and they are very costly.
Besides, some of the residents affected by barriers objected to its erection because of reasons likeblocking of view, affecting rents and values, etc. On this front, addition measures such as building
setback; building block designs and orientation; podium design and internal flat layout designs would
be necessary. These measures are to be applied on case-by-case merits and there are limitations andexisting constraints in applying these measures. Some residents still cannot be benefited by thesemeasures. More efforts would be needed to explore if special d esign of windows or balcony could be
adopted to offer noise mitigation.

2.

THE INNOVATIVE ACOUSTIC BALCONY

Balconies are façade devices, which could be the alternative for tackling traffic noise pollution.
Noise entering a balcony can broadly be divided into a direct / diffracted component and a reverberant
component (Figure 1) [3]. The effect of balcony screening on direct / diffracted component depends
on factors, including building orientation, relative distance and height from the noise sources, formand extent of parapet walls and the size of window or door openings behind the balcony. With careful
adjustment of these factors to suit a particular situation, a specially designed acoustic balcony could
offer screening effect for noise sources affecting a residential site. The screening effect would beenhanced if sound absorptive material is added on balcony ceiling to reduce the reverberant
component (Figure 2). In additional, balcony is considered a type of environmental -friendly measuresand accordingly not more than 50% of the area of the balcony is to be exempted from gross floor areaand site coverage calculations provided certain criteria and conditions are complied with [4]. In the
super high density urban area like Hong Kong, Acoustic Balconies would sometime use in conjunction
with air-conditioner platforms (AC platform) (Figure 3). It would not only provide one access for
all, but it would also make best utilization of the very limited outdoor space adhered to the residential
flats. In any event, this portrait is a very special and unique external feature of residential buildingsin Hong Kong.

Figure 1 – Residential buildings in close
proximity to trunk road in Hong Kong

Figure 2 – Schematic representation of
the reception points and images
received in a balcony

Figure 3 – Combination of balcony and airconditioner platform commonly found in Hong
Kong

3.

EVALUATION OF NOISE REDUCTION EFFECTIVENESS
Noise reduction effectiveness of “Acoustic Balconies” is expressed in Relative Noise Reduction (RNR)

which is the IN-OUT difference between the averaged noise levels (in terms of L10) (outdoor vs. indoor)
obtained in actual site measurements or in laboratory measurement 7 of the residential unit in concern withconventional window and that of “Acoustic Balconies” [5]:RNR = ANRAB - ANRCW [dB(A) L10(1hr)]
Where

(1)

RNR = relative noise reduction
ANRAB = Adjusted noise reduction with “Acoustic Balconies”(Operating in acoustic mode meeting ventilation Requirements stated in APP 130)ANRCW = Adjusted noise reduction with “Conventional Window”(Meeting BD’s natural ventilation requirement in which the openable area of outer
window is not larger than 1/16 of the floor area of the room)

4.

THE CODE OF
DEPARTMENT

PRACTICE FOR SAFETY (2019)BY BUILDINGIn September 2019, Building Department of the HKSAR Government issued “Code of Practice on
Design for Safety – External Maintenance” [6] in which a set of requirements is imposed on balcony
combined with AC platform. One of the requirements is that any screens provided to separate theAC platform from the balcony should not be higher than 1.6m and should have permeability not lessthan 70%. Permeability means the visual and physical porosity of the screens. This porosity would
be no doubt cause deterioration of noise reduction of acoustic balcony if it is to be combined with airconditioner. This causes challenges to acoustic professional to come to play to reverse thedeterioration brought by the screen of porosity.

5. THE CASE STUDY
The objective of the study is to provide in-situ evaluation of the Relative Noise Reduction (RNR) between

7

Adjusted with traffic noise spectrum according to the BS EN 1793-3:1998 if source of traffic noise is not fromthe actual road traffic but by means of loudspeakers emitting white noise.

Acoustic Balcony with AC platform screened by metal grille and that screened by acoustic louvre.

Noisemeasurements were conducted in an open-air test site at Supreme NAP Acoustics (Huizhou) Ltd Laboratory
located at Huizhou China with area of over 800 m2. The test ground was hard paved with concrete. There
was no substantial building, structure or obstacle which might interfere with the measurement result.
The Acoustic Balcony with AC platform was installed to the mock-up flat, named as ‘Space Shuttle Beta’,
having internal room dimensions of 3.42 m (W) x 4.1 m (D) x 3.0 m (H). The mock-up flat was installed
on a specially designed steel structure which could be rotated to suit the specified horizontal incident angles.
‘Space Shuttle Beta’ could be raised to have its floor level at height above the ground from 1.8 to 5.3 m.
The open space in front of the mock-up flat was large enough for the line source in length of 21 m to rotatefrom 0 deg. to 90 deg. horizontal incident angles when the ‘Space Shuttle Beta’ was kept at a fixed position.
The indoor condition of mock-up flat was unfurnished with gypsum board walls and ceiling. The floor was
finished with varnished plywood board.
The acoustic balcony had parapet wall in height of 1450 mm and a sidewall. The overall dimensions were2540 (W) x 1300 (D) mm including the AC platform in size of 1100 (W) x 1300 (D) mm.

The balcony

door was of clear opening in size of 1025 x 2210 mm. For ventilation purposes, the parapet of the AC
platform was 50 mm thick air grille or acoustic louvre with 70% permeability (Figure 4).
The acoustic balcony was specially designed to incorporate a combination of mitigation features for the
purpose of noise reduction. The full-height side wall and balcony ceiling were lined with sound absorptive
material of noise reduction coefficient at least 0.7. An additional sliding glass panel lined with transparent
micro-perforated absorber of noise reduction coefficient at least 0.45 was installed inside the room at a
distance of 100 mm from the door opening.
Noise measurements were conducted with reference to the global loudspeaker method stated in
ISO16283-3:2016, loudspeakers were placed in positions outside the mock-up flat at a distance from the
façade with the angle of elevation to outdoor microphone at 30 deg. When the line source was located in
front of the mock-up flat, this was referred to as 0 deg horizontal angle.

Measurements were also takenwith the line source located to the side of the AC platform at horizontal angles of -30 deg. (Figure 5). Whitenoise was generated by the loudspeakers during noise measurements.

The outdoor noise level wasmeasured with a microphone set at 1.0 m from the balcony parapet and 1.0 m above the floor level of the
mock-up room.

Six indoor measurement positions were selected inside the mock-up flat which were

distributed within the maximum permitted space throughout the room.
During the measurements, the door was opened outward to the direction of the side wall with the sliding
glass panel in closed position overlapping the opening by 100 mm to form a plenum design. This would allow
the air to pass through for natural ventilation purpose while the noise entering indoor would be reduced.
The measurements were conducted in dB(A) noise level in terms of L10 in 1/3 octave bands for the mock-up
flat installed either with the acoustic balcony or conventional window.
Based on the noise measurements results for both indoor and outdoor noise, evaluation of noise reduction
abilities of acoustic balcony with air grille and with acoustic louvre were performed following the equation
expressed in section 3. The results showed that the acoustic balcony installed with 50 mm thick acoustic
louvre replacing the air grille, RNR increased from 11 dB(A) to 12 dB(A) for line source at horizontal angles

of 0 deg with elevation angle of 30 deg. When the line source located at horizontal angle of 30 deg withelevation angle of 30 deg., RNR increased from 8 dB(A) to 10 dB(A). The measurements showed that there
was benefit in improving RNR for 1 to 2 dB(A) by using acoustic louvre. Further improvement was expected
if the thickness of acoustic louvre could be increase to 100 mm.

Mock-up flat
Air grille/
Acoustic louvre

Figure 4 – Space Shuttle Beta – Mock-up Flat for Acoustic Balcony and Window Testing

Acoustic Louvre/
Air Grille

Figure 5 – Sketch showing Acoustic Balcony and Horizontal Incident Angles

6. CONCLUDING REMARKSAs shown above, the specially designed balconies with careful incorporation of acoustic louvre
were proved to be effective in addressing the noise problems at the same time complying the practicenotes of Hong Kong Building Department. The acoustic louvre would not only resolve the noise
issue brought by porosity allowing adequate natural ventilation, it also provide opportunity to have agood design from aesthetic aspect. With the provision of such innovative specially designed

balconies, developers can optimize the development potential of residential buildings next to noisy
transportation lines. The results can be productively used for the benefit of Hong Kong as a whole.
It is anticipated that developers may also like to consider adopting this type of specially designed
balconies, which can reduce the noise from 10 to 12 dB(A), in the near future.

ACKNOWLEDGEMENTS
Authors would like to acknowledge support from the Macao Institute of Acoustics.

REFERENCES
1. An Overview of Noise Pollution and Control in Hong Kong, Noise, web page of Environmental
Protection Department, HKSAR Government
2. Noise section of Website of Hong Kong Planning Standards and Guidelines. Planning Department, theGovernment of Hong Kong Special Administrative Region; 2014.
3. Adopting Specially Designed Balconies to Achieve Substantial Noise Reduction for Residential
Buildings, InterNoise 2016, Hamburg, Germany
4. Green and Innovative Buildings, Joint Practice Note No. 1, Building Department, Lands Department andPlanning Department, Hong Kong Special Administrative Region Government
5. Practice Note of Application of INNOVATIVE NOISE MITIIGATION DESIGNS in Planning Private
Residential Developments against Road Traffic Noise Impact (Version 2), Environmental Protection
Department, the Government of Hong Kong Special Administrative Region; December 2021
6. Code of Practice on Design for Safety – External Maintenance, Building Department, the Governmentof Hong Kong Special Administrative Region; September 2019

ABS-0089

Properties of the transmission line matrix model for outdoor soundpropagation: Numerical dispersion effects
Quentin GOESTCHEL(1) * , Gwenaël GUILLAUME(1)† , David ECOTIERE(1)‡ , Benoit GAUVREAU(2)§(1) UMRAE, CEREMA, Univ Gustave Eiffel, F-67035 Strasbourg, France(4) UMRAE, Univ Gustave Eiffel, CEREMA, F-44344 Bouguenais, France

ABSTRACTTime-domain numerical methods are attractive tools for the prediction of outdoor sound propagation as they
are able to handle most of the physical phenomena involved in environmental acoustics. The transmission line
matrix (TLM) method is considered for modelling sound fields in complex outdoor environments but it remains
relatively poorly documented. In this paper, the space-time integration scheme of the method and a stability
analysis are provided. The review of the model highlights similarities with a finite difference scheme andleads to a more robust understanding of its link with the wave equation using Taylor expansions. The stability
analysis allows the numerical dispersion relation of the model to be formulated and the associated errors to bequantified. Two different numerical experiments are presented to characterize precisely the limitations of themethod applied to outdoor sound propagation. To conclude, the results are analyzed, highlighting the effects ofthe propagation distance and the spectral distribution of the simulated sources.

Keywords: Numerical Methods, Outdoor Sound Propagation, Numerical Dispersion

1

INTRODUCTION

Human-induced noise disturbances have a negative impact on public health and biodiversity [1]. Sound pollution
is not limited to urban environments and reaches natural areas. However, the impact of noise pollution on living
beings and their environment is not well quantified and this lack of knowledge could be overcome by predicting
long-range outdoor sound propagation. From frequency domain methods to time domain methods, the study andimprovement of numerical acoustic model have recently been developed with the available computing power.
Compared to other time-domain numerical methods, the transmission line matrix method (TLM) has notbeen documented extensively [2]. Initially introduced to model electromagnetic fields, the TLM model and its
formalism appeared in the early 1970s [3]. Subsequently, the flexibility of the method has been demonstrated
by applying it to various fields [4].
In acoustics, the TLM model successfully simulates indoor propagation and the theoretical basis of themethod has been relatively well studied [5]. For outdoor scenarios, the solver seems to tackle with most of
the difficulties usually involved in modelling inhomogeneous media. Application cases for urban areas or evenforest-like environments have been developed and compared to the experimental results [6, 7, 8]. From a computational point of view, an OpenCL™ implementation has been optimized to run on parallel graphic processing
units [9]. Finally, complex features such as impedance boundary conditions or meteorological effects have been
introduced into the TLM model in order to model more realistic environments [10].
However, no dispersion error analysis of the method has been undertaken yet. Therefore, this article aims
to explore the potential effects of the numerical dispersion inherent in the TLM and the limitations it may* quentin.goestchel@cerema.fr† gwenael.guillaume@cerema.fr‡ david.ecotiere@cerema.fr§ benoit.gauvreau@univ-eiffel.fr

imply. The following study focuses on long-range outdoor noise predictions and the simulation of reflective
boundary conditions. They are complex situations to model and therefore the most likely to expose limitationsof numerical models.
A d-dimensional formalism of the numerical integration scheme under the TLM is given in the next section,
together with a short stability analysis theoretically introducing the dispersion error. The second section ofthis paper presents the virtual experiments performed to demonstrate the effect of numerical dispersion on themodelled sound pressure fields. Finally, the results are partially presented to give an overview of the limitationsof the TLM model when applied to long-range outdoor acoustics.

2

NUMERICAL METHOD

2.1 Origin
The particularity of the TLM is that it is derived from Huygens’ principle for wave propagation and from anelectro-acoustic analogy [11]. The property of a wavefront to be recursively discretized in secondary sources is
exploited and applied to a regular structured mesh. The sound propagation is then modelled as pressure pulses,
propagating along transmission lines. This division of the propagation mechanism is the TLM basis. It allows
the analogy between the progression of a sound wave and the diffusion of pulses between the nodes of a mesh.
2.2 Formalism in a nutshellIn the following, a syntax corresponding to a d-dimension spatial generalization of the TLM model is adopted tobe consistent with previous works [12]. The theoretical aspects discussed below intentionally avoid the iterative
process of the TLM pulses since it is already detailed in the literature [13]. The spatial domain is subdivided
into a Cartesian mesh with a step ∆` such as xd = jd ∆`, jd ∈ Z and the time is decomposed into steps suchas tn = n∆t, n ∈ N. The nodes on the grid are located by the index vector: r = ( j1 , . . . , jd ). The core of themodel is that each node in the volume receives and emits incident and scattered pulses instantaneously, at eachtime step n. These pressure pulses are travelling along transmission lines m connecting the nodes. From theirmodelling, it is possible to calculate n Pr , an approximated value of the exact acoustic pressure p (x1 , . . . , xd ,tn )taken at the point (x1 , . . . , xd ) at time tn .
2.3 Link with the wave equation
Starting from the pressure pulses equations of the method, the corresponding numerical pressure scheme can be
expressed [13]:n+1 Pr + n−1 Pr =


1 d 
∑ n P( j1 +δm1 ,..., jd +δmd ) + n P( j1 −δm1 ,..., jd −δmd ) ,
d m=1

(1)

δ being used here as the Kronecker delta. To find out the order of approximation of this scheme, and to retrieve
the wave equation, Taylor expansions can be used [11] and Eq. (1) becomes:
 4

∂2p
∆`
2
2
2
−
c
∇
p
=
O
∆t
+
O
,
(2)
TLM
∂t 2
∆t 2
∆`
with cTLM = √. It is now possible to recognize the wave equation and to observe that the TLM model is ad∆tsecond-order approximation method in time, and space. Another interesting observation is that the TLM solves
the wave equation only if the condition c2TLM = c20 is fulfilled. Developing this condition gives:
c0 ∆t
1
=√ ,
∆`
d
which corresponds with the CFL criterion of the so-called finite difference Leap-Frog scheme [2].

(3)

2.4 Numerical dispersion
To evaluate the model stability, the numerical dispersion relation of the method can be written:cos(ω∆t) =1 d∑ cos(km ∆`), ∀∆t, ∀∆`.
d m=1

(4)

This equation implies that the TLM method is unconditionally stable in the homogeneous non-dissipativecase. However, this relation also shows that the model presents numerical dispersion in the main directionsπof the grid (for instance in the 2D case θ = β , β ∈ Z). To illustrate this phenomenon, the dispersion error
2
is represented as a function of the angle θ between the plane wave vector k and the horizontal direction of
the mesh. The same reasoning was done for an inhomogeneous environment and an overview of the results
is given for a case with linear vertical gradients of temperature (∂ T /∂ z= ± 0, 35 Co .m−1 ) and wind speed(∂ w/∂ z= ± 0, 2 s−1 ) [13]. Figure 1 shows that the dispersion error is maximal at the bottom part of the grid in
the downward case.

Figure 1. Modeling of downward refraction conditions for N = 5: relative errors on the phase speed [%]according to z and θ .

The values of the local errors are acceptable for N=5 and negligible for N=10 and they are similar in the
upward case. However, it is necessary to study the impact of local errors on a larger scale to analyse the effectof the propagation of dispersion errors. Moreover, it is known that dispersion affects the group speed more thanthe phase speed [14], so the following section aims to analyze its effects on this quantity through a numericallong-range propagation experiment.

3

VIRTUAL EXPERIMENTS

The numerical dispersion error is inhomogeneous in a 2D-Cartesian mesh and decreases from the axial direction
to the diagonal direction (Figure 1). To visualise this effect in terms of sound pressure level predictions, thesetups illustrated in Figure 2 are simulated. The most important parameter for the analysis is the number ofpoints per wavelength (Nppw ) which is set at the maximal frequency fmax of the source emission.
3.1 Free-field setup
Visible on Figure 2(a), it is composed of a unique sound source surrounded by two arrays of ten receivers
located along the horizontal and diagonal (θ = π4 ) directions of the mesh.

3.2 Specular reflection setup
Visible on Figure 2(b), it consists in a unique sound source next to a polar matrix of microphones in a domain
with a perfectly reflective ground. It is implemented to emphasize two geometrical parameters: the inclination
angle θ and the distance r between a receiver and the source.

go
n

al

re
c

ei
v

er
s

rmax

D

ia

Receivers

Sourcematrix

SourceHorizontal receivershSurface R=1

(a)

(b)

Figure 2. (a) Dispersion analysis setup. (a) Free-field, source in the center and microphones positioned along
the horizontal and diagonal (θ = π4 ) directions. (b) Specular reflection, source placed at distance h from theground and microphones placed to form a polar matrix.

4

RESULTSThis section presents the analysis of the results for experiment detailed in Section 3 by comparing them toanalytical solutions[13]. For both experiments, the criterion Nppw points per wavelength is respected.
4.1 Free-field
For this experiment, a sound source with a Gaussian temporal profile ( fmax = 2400 Hz) has been used toapproximate a pulse. In Figure 3, the pressure signals at the receivers are displayed according to the reducedrtime (tn − ) which allows to observe the delayed high frequencies along the propagation distance. The effectc0of the dispersion along the horizontal array of microphones is directly noticeable: the results displayed on
Figure 3 show an apparent decrease of the group speed while the propagation distance increases. For a better
visualisation of this delay, the vertical lines on Figure 3 indicate the moment when 95 % of the signal energy
has reached the microphone.

rsr = 45
rsr = 90
rsr = 135
rsr = 180

Pressure [Pa]rsr = 225
rsr = 270
rsr = 315
rsr = 360
rsr = 405
rsr = 450

0.000

0.002

0.004

0.006

0.008

Reduced time [s]0.010

Diagonal
Horizontal
Analytical

0.012

Figure 3. Normalized pressure signals at the microphones as dependent upon reduced time, for N=10 grid points
per wavelength. Vertical lines indicate the arrival of 95 % of the signal energy.

4.2 Specular reflection
A sound source with a windowed sinus temporal profile is used in this experiment as it is pseudo-harmonic.
As shown on Figure 4(a), most of the source energy is focused around the frequency fmax and has a 10-point
spatial discretization. Thus, the incident and reflected wave are more likely to interfere and be affected bynumerical dispersion. With this spectral distribution, significant interference-induced errors are generated (upto 11 dB as shown on Figure 4(b)). Indeed, at some microphones, the energy of the received signal is either
increased or lost due to artificial interference patterns induced by the TLM model. The spectral distribution of
the source has an impact on the spatial distribution of the error: the wider the spectral distribution of the sourceis, the wider the spatial distribution of the error. This is confirmed when sources with a wider distribution areused: the numerical error decreases significantly.
Windowed sinus

1.00

90°

Am litude[-]

0.75

80°
70°

0.50

60°

0.25
0.00

50°

−0.25

11.08
9.85
8.62
7.38
6.15
4.92
3.69
2.46
1.23
0.00

−0.50

40°

−0.75
0.02

t [s]

0.03

0.04

0.05

Energetic fraction [%]Nppw tendance

6

[°]

0.01

error [dB]

−1.00

30°

103
20°

5

Nppw[-]

102

4
3

10°

101

2
1
0
0.0

0.2

0.4

0.6

f/fmax [-]

(a)

0.8

1.0

1.2

0

20

40

rr [m]

60

80

0°
100

(b)

Figure 4. (a) Spectral distribution of a windowed sinus compared to Nppw . (b) Map of the absolute error ε j1 , j2
on SPL attenuation relative to a reference microphone for a windowed sinus. Matrix of 100 × 100 microphones.

5

CONCLUSION

Theoretical and numerical aspects of using the TLM model as a solver for long-range outdoor sound propagation were discussed. The general formulation of the pressure integration scheme was exposed, highlighting
similarities with finite difference methods applied to the wave equation. Thanks to a stability analysis, theinherent anisotropic dispersion of the model has been highlighted. Finally, two numerical experiments were
performed to characterize the effects of dispersion on academic cases of long-range outdoor propagation. From
the results of these experiments, the observed effects on the group speed of the test signals in the main meshdirections must be taken into account when using the TLM. Indeed, this implies a careful choice of simulation
parameters to avoid significant errors in the energy of the resulting signals. The effects of numerical dispersion
according to the spectral distribution of the modelled sources are also noticeable with the appearance of misplaced interference patterns, impacting the sound pressure level predictions. In order to investigate these effects
more thoroughly, it would be relevant to test the model applied to more complex and realistic scenarios, bycomparing it with additional numerical simulations and experimental results from a measurement campaign.

ACKNOWLEDGEMENTS
The authors would like to acknowledge the High Performance Computing Center of the University of Strasbourg for supporting this work by providing scientific support and access to computing resources. Part of thecomputing resources were funded by the Equipex Equip@Meso project (Programme Investissements d’Avenir)
and the CPER Alsacalcul/Big Data.

REFERENCES[1] World Health Organization Regional Office for Europe. Environmental Noise Guidelines for the European
Region. World Health Organization. Regional Office for Europe; 2018.
[2] Cohen GC. Higher-Order Numerical Methods for Transient Wave Equations. vol. 110 of Scientific Computation. Berlin, Heidelberg: Springer Berlin Heidelberg; 2002.
[3] Johns PB, Beurle RL. Numerical Solution of 2-Dimensional Scattering Problems Using a TransmissionLine Matrix. Proceedings of the Institution of Electrical Engineers. 1971;118(9):1203–1203.
[4] de Cogan D, O’Connor WJ, Pulko S. Transmission Line Matrix (TLM) in Computational Mechanics. CRC
Press, editor. CRC Press; 2005.
[5] Kagawa Y, Tsuchiya T, Fujioka K, Takeuchi M. Discrete Huygens’ Model Approach to Sound Wave
Propagation—Reverberation in a Room, Sound Source Identification and Tomography in Time Reversal.
Journal of Sound and Vibration. 1999 Aug;225(1):61–78.
[6] Kagawa Y, Tsuchiya T, Hara T, Tsuji T. Discrete Huygens’ Modelling Simulation of Sound Wave Propagation in Velocity Varying Environments. Journal of Sound and Vibration. 2001;246(3):419–439.
[7] Guillaume G, Picaut J, Dutilleux G. Use of the Transmission Line Matrix Method for the Sound Propagation Modeling in Urban Area. The Journal of the Acoustical Society of America. 2008 May;123(5):3924–
3924.
[8] Chobeau P, Guillaume G, Picaut J, Ecotière D, Dutilleux G. A Transmission Line Matrix Model for Sound
Propagation in Arrays of Cylinders Normal to an Impedance Plane. Journal of Sound and Vibration. 2017
Feb;389:454–467.
[9] Guillaume G, Fortin N. Optimized Transmission Line Matrix Model Implementation for Graphics Processing Units Computing in Built-up Environment. Journal of Building Performance Simulation. 2014
Nov;7(6):445–456.
[10] Guillaume G, Picaut J, Dutilleux G, Gauvreau B. Time-Domain Impedance Formulation for Transmission Line Matrix Modelling of Outdoor Sound Propagation. Journal of Sound and Vibration. 2011
Dec;330(26):6467–6481.
[11] Kagawa Y, Tsuchiya T, Fujii B, Fujioka K. Discrete Huygen’s Model Approach to Sound Wave Propagation. Journal of Sound and Vibration. 1998 Dec;218(3):419–444.
[12] Guillaume G, Aumond P, Gauvreau B, Dutilleux G. Application of the Transmission Line Matrix Method
for Outdoor Sound Propagation Modelling - Part 1: Model Presentation and Evaluation. Applied Acoustics.
2014;76:113–118.
[13] Goestchel Q, Guillaume G, Ecotière D, Gauvreau B. Analysis of the Numerical Properties of the Transmission Line Matrix Model for Outdoor Sound Propagation. Journal of Sound and Vibration. 2022
Aug;531:116974.
[14] Trefethen LN. Group Velocity in Finite Difference Schemes. SIAM Review. 1982;24(2,):113–136.

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

ABS-0152

SOUOND ATTENUATION BY TRUNKS IN THE GROUND WITH THICK
SNOW
Mengmeng LI1,2; Qi MENG1,2; Jian KANG3
1
2

School of Architecture, Harbin Institute of Technology, Harbin 150000, China

Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology,
Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150000, China
3

Institute for Environmental Design and Engineering, The Bartlett, University College of London,
London WC1H 0NN, UK

ABSTRACT
A main way for sound reduction by tree belts is to scatter sound waves by trunks while previous studies ofsound absorption by trunks always contain the ground effects. This study aims to examine the difference ofreverberation time in different kinds of tree belts through eliminating the influence from ground effect. In
order to estimate the scattering of sound waves by trunks, this study investigates sound (Impulse sound)attenuation by three woodlands with same tree species and one woodland with mixed species in the groundwith thick snow. The effect of receiver distance from the sound source was also investigated (5 m,10 m and20m). The results showed that reverberation time can be used to describe the sound propagation over snow
cover through tree belts. The reverberation time in different kinds of forests is different, but the variationshows a similar trend with the increase of frequency. The distance between the receiver and sound source
affects the reverberation time. The species with higher ability of sound scattering can be used in urban green
space to reduce urban noise.
Keywords: Sound propagation, Reverberation time, Tree belts, Ground effect

1. INTRODUCTION
Sound propagation outdoor is obviously affected by the surrounding environment. Compared with
open ground, the effect from tree belts on sound propagation is complex, which includes ground effect,
sound scattering by trunks and leaves, etc.
For the short distance propagation of sound waves in tree belts, multiple scattering is an important
influential factor. Wunderli found that the sound reflection from tree belts mainly occurs in thefringe [1]. Some studies found that the sound attenuation effect is affected by the physical properties(length, width, height, etc.) of tree belts [2-5]. Further studies explored the influence from species, leaf
shape, bark roughness [6-9]. Karbalaei found that the noise reduction effect of arbor shrub mixed forest
land and coniferous broad-leaved mixed forest land is relatively large, by studying the influence oftree belt width and plant species diversity on the noise reduction effect [10] . For tree belts with similar
physical properties, the influence from other factors including plant species and tree age on the noise
reduction effect is important [11-12]. However, ground effect is always incorporated in the process offield investigation.
Furthermore, Aylor found that the influence from ground effect is inevitably involved in the noise
reduction by tree belts in outdoor [13]. By studying the sound propagation over the snow cover, Sidler
found that the sound attenuation effect decreases with the increase of snow surface depth [14]. Albertfurther demonstrated that in the snow, cover parameter associated with snow surface shows dominant
impact, by studying low frequency pulse sound propagation in the forest [15]. Therefore, the test on thenext day of heavy snow can reduce the ground effect on sound propagation through tree belts.
mengmeng.li@hit.edu.cn
mengq@hit.edu.cn
3j.kang@ucl.ac.uk
1
2

Yang et al. found that reverberation time is applicable in the measurement of the sound scattering
effect of a single tree in the open ground [16]. Reverberation time is further authenticated to be
applicable in the forest [17]. This study aims to estimate the scattering of sound waves by trunks, byinvestigating reverberation time by three woodlands with same tree species and one woodland with
mixed species in the ground with thick snow. In this research, the influences from plant species andthe distance between the sound source and receiver on the reverberation time are specified. The
findings can be used to guide the urban tree belt design.

2. METHODOLOGY
2.1 Site Condition
The site in this study should be a forest with no less than 40m in length and 25m in width. The testsite is 121 ° 12 ′ to 127 ° 00 ′ E, 50 ° 10 ′ to 53 ° 33 ′ N, and the average altitude is 573m, located in
the Northeast China. The temperature condition in test is -24℃, and the wind speed is less than 2m/s.
The test is carried out on the day after a heavy snow.
The photo taken in the test is shown in Figure 1, which are Pinus sylvestris seedling, Birch, Shrub,and Forest Mixed with Conifer and Broadleaves, respectively. The parameters representing the trees
in the test site are shown in Table 1.
Table 1 – The parameters representing the trees in the test site
Plant species

Average Breast-height Diameter, cm

Average height, mPinus sylvestris seedling

31.23

3.3

Birch

25.19

6.5

Shrub

19.43

2.8

Mixed with Conifer and Broadleaves

70.57/13.4

11.2/5.5

Pinus sylvestris seedling

Birch

Shrub

Forest mixed with conifer and broadleaves

Figure 1 – Photos of the testing site
2.2 Measurement method
The measuring point arrangement is shown in Figure 2. This research measured the reverberation
time of the microphone when the height is 1.5m, and the distance with the sound source are 5m, 10m
and 20m, separately. The measurement of all settings of distance repeated for 5 times, and the averagevalue is used for analysis.

Figure 2 – Schematic diagram of measuring point arrangement
Because measuring T60 needs a high signal to noise ratio, which is difficult to implement outdoor,
T60 is not measured in this study. T10, T20 and T30 are widely used for outdoor sound propagation,and this study chooses T20 as the index of reverberation time. The site in this study is far away fromurban area and roads, so it is relatively quiet. The SPL of the gun is about 55dB h igher than the
background noise, so it is feasible to measure T20 under this the signal to noise ratio. The software‘DRIAC’ is used to analyze the data.

3. RESULTS
3.1 The variation trend of T20，T20 and T30
Figure 3 shows the variation of T10, T20 and T30 of Birch and Shrub when the distance is 20m.
Though the reverberation time of the two sites is different, the trend of variation with the increase of
frequency is identical, which complies previous findings. The reverberation time measured over thesnow cover can validate that the software ‘DRIAC’ is precise enough in the analysis. In the following
part of this study, T20 is selected to analyze sound propagation over snow cover through tree belts.

Figure 3 – T10, T20 and T30 of Birch and Shrub when the distance between receiver and soundsource is 20m
3.2 The influence from species on T20
Figure 4 shows the spectral characteristics of T20 in site of different plant species. The T20 of all
kinds of tree belts share a similar trend of variation with the frequency. When the frequency is below
200Hz, the T20 of all kinds of tree belts are relatively low. For the condition of the frequency between
200Hz and 400Hz, the T20 of all the tree belts increase significantly with the increase of frequency.
When the frequency is above 400Hz, the relationship between the frequency and T20 of Pinus
sylvestris seedlings and Shrub reaches to a stable trend, and the T20 of Forest mixed with conifer andbroadleaves and Birch decreases slightly with the increase of frequency.

Figure 4 – Spectral characteristics of T20 in site of different plant species
The T20 of Forest mixed with conifer and broadleaves and Birch are largest , which can be causedby the large age, bark roughness and Breast-height Diameter of these two kinds of tree belts. The T20
of Pinus sylvestris seedlings is larger than Shrub. This phenomenon can be caused by two reasons.
First, the trunks of Shrub are small, and cannot insulate sound effectively. Second, the barks of Shrubare smooth, which leads to a lower sound absorption coefficient than Pinus sylvestris seedlings.
3.3 The influence from distance on T20
The spectral characteristics of T20 of Forest mixed with conifer and broadleaves with different
kinds of distance between receiver and sound source is shown in Figure 5. The T20 of different kindsof distance show a similar trend of variation with the increase of frequency. When the distance increase
form 5m to 10m, the change in T20 is negligible.T20 shows an obvious increasing trend, when the
distance increases from 10m to 20m.

Figure 5 – Spectral characteristics of T20 of Forest mixed with conifer and broadleaves withdifferent kinds of distance between receiver and sound source

4. CONCLUSIONSThis research found that the reverberation time of sound propagation over snow cover throughdifferent kinds of tree belts is different, but the reverberation time shows a similar variation trend with
the increase of frequency.
When the plant species is different, the reverberation time varies. In this study, 4 plant species aretested, and Birch and Forest mixed with conifer an broadleaves has the longest reverberation time.

The planting density of Birch is obviously larger than Forest mixed with conifer an broadleaves, which
can demonstrate that forest consisted of trees with smoother barks needs larger planting density inorder to attain a similar reverberation time to forest consisted of trees with rough barks. The
reverberation time of Pinus sylvestris seedlings and Shrub is less, and Pinus sylvestris seedlings has
a longer reverberation time than Shrub, which is caused by the fact that Pinus sylvestris seedlings hastrunk to insulate sound. Moreover, the distance between receiver and sound source can affect thereverberation time, and the reverberation time reach the peak when the distance is 20m in this study.
Plant species, planting density and bark roughness affect the sound propagation outdoor, but theeffect is not quantified. Further studies can measure the sound propagation in more kinds of forestsand find the dominant influential factors.

REFERENCES
1. Wunderli J M, Salomons E M. A model to predict the sound reflection from forests[J]. Acta Acustica
United with Acustica, 2009, 95(1):76-85.
2. Fang C F, Ling D L. Investigation of the noise reduction provided by tree belts[J]. Landscape & Urban
Planning, 2003, 63(4):187-195.
3. Pathak V, Tripathi B D, Mishra V K. Dynamics of traffic noise in a tropical city Varanasi and its abatement
through vegetation[J]. Environmental Monitoring & Assessment, 2008, 146(1-3):67-75.
4. Maleki K, Hosseini S M, Nasiri P. The Effect of Pure and Mixed Plantations of Robinia Pseudoacasia andPinus Eldarica on Traffic Noise Decrease. international journal of environmental sciences, 2010.
5. Lai F O, Ghosh S. Urban cities and road traffic noise: Reduction through vegetation[J]. Applied Acoustics,
2017, 120:15-20.
6. Fan Y, Zhiyi B, Zhujun Z, et al. The investigation of noise attenuation by plants and the corresponding
noise-reducing spectrum[J]. Journal of Environmental Health, 2010, 72(8):8.
7. Reethof G. Effect of Plantings on Radiation of Highway Noise[J]. Journal of the Air Pollution Control
Association, 1973, 23(3):185-189.
8. Reethof G, Mcdaniel OH, Heisler GM. Sound absorption characteristics of tree bark and forest ﬂoor. Usda
Forest Service General Technical Report Ne.1977.
9. Li M, Van Renterghem T, Kang J, et al. Sound absorption by tree bark[J]. Applied Acoustics, 2020,
165:107328.
10. Karbalaei S S, Karimi E, Naji H R, et al. Investigation of the Traffic Noise Attenuation Provided byRoadside Green Belts[J]. FLUCT NOISE LETT, 2015, 2015,14(4)(-):-.
11. Bashir I, Taherzadeh S, Shin H C, et al. Sound propagation over soft ground without and with crops andpotential for surface transport noise attenuation[J]. The Journal of the Acoustical Society of America,
2015, 137(1):154-164.
12. Van Renterghem T, D Botteldooren, Verheyen K. Road traffic noise shielding by vegetation belts oflimited depth[J]. Journal of Sound and Vibration, 2012, 331(10):2404-2425.
13. Aylor D. Noise Reduction by Vegetation and Ground[J]. J. acoust. soc. am, 1972, 51(1B):197-205.
14. Sidler R. A porosity-based Biot model for acoustic waves in snow[J]. Journal of Glaciology, 2015,
61(228):789-798.
15. Albert D G, Swearingen M E, Perron F E, et al. Low frequency acoustic pulse propagation in temperate
forests[J]. Journal of the Acoustical Society of America, 2015, 138(2):735-47.
16. Yang H S, Kang J, Cheal C, et al. 2013. Quantifying scattered sound energy from a single tree by meansof reverberation time. Journal of the Acoustical Society of America 134, 264.
17. Albert D G. Past Research on Sound Propagation through Forests[J]. Past Research on Sound Propagation
Through Forests, 2004.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0221

Comparison of noise assessment models under a semi-infinite thickbarrier configuration: ASJ RTN-Model, Harmonoise and CNOSSOSEU
Takuya OSHIMA1; Fumiya TAKEDA2
1
2

Faculty of Engineering, Niigata University, Japan

Graduate School of Science and Technology, Niigata University, Japan

ABSTRACT
Through Directive 2002/49/EC, the European Union has mandated to its member states to draw strategic
noise maps using an environmental noise assessment model and implement action plans to reduce the noise
exposure of residents in urban areas. To implement a similar noise policy in Japan, a noise assessment model
to draw a noise map needs to be developed first. The ASJ RTN-Models, developed by the Acoustical Societyof Japan, are good candidates but their characteristics with comparison to the European assessment models
need to be investigated. In the current study, the ASJ models are compared with Harmonoise and CNOSSOSEU under the configuration of a semi-infinite thick barrier along a straight source road. In a case of point-topoint propagation to the shadow zone, the energy gain by the reflection at the top of the barrier is found to
be included in the diffraction attenuation in the ASJ models and CNOSSOS-EU, whereas in the groundattenuation in Harmonoise. The equivalent continuous sound pressure level of the ASJ RTN-Model 2018 isfound to be generally 1–2 dB smaller than the other models in the non-shadow zone, whereas 5–10 dB smallerat the shadow zone.
Keywords: Noise assessment model, Point-to-point propagation, Shadow zone

1. INTRODUCTION
Through Directive 2002/49/EC (1), the European Union has mandated to its member states to draw
strategic noise maps using an environmental noise assessment model and implement action plans to
reduce the noise exposure of residents in urban areas. To implement a similar noise policy in Japan, anoise assessment model to draw a noise map needs to be developed first. The ASJ RTN-Models,developed by the Acoustical Society of Japan, are good candidates but their characteristics withcomparison to the European assessment models need to be investigated. In the current study, the ASJ
models, especially focusing on the current model of ASJ RTN-Model 2018, are compared with
Harmonoise (2) and CNOSSOS-EU (3) under the configuration of a semi-infinite thick barrier alonga straight source road. The configuration is chosen because it contains various propagation patterns
in a simple geometry.

2. IMPLEMENTATION OF THE MODELSFor Harmonoise, the Harmonoise Point-to-Point library (4) version 2.022 is used for the calculationof the main parts of the propagation part of the model and a Python implementation is used for thesource model and the auxiliary parts of the propagation model. For CNOOOSO-EU, the native
compiled codes of the CNOSSOS_SOURCEMODEL library (5) version 1.10 and the
CnossosPropagation library (6) version 1.001 are used throughout the calculation. The CNOSSOSEU libraries are based on the original JRC 2012 version (3) of the model. As to the ASJ models, theAcoustical Society of Japan has revised their noise assessment models in a five-year cycle. In thecurrent study, the most recent two revisions of the ASJ models, namely ASJ RTN-Model 2013 (7)(ASJ 2013) and 2018 (8) (ASJ 2018), are chosen as the Japanese models to be investigated. The two
1oshima@eng.niigata-u.ac.jp

revisions differs mainly in the vehicle source strength models and diffraction models. As no opensource implementations of the models are available, the source and propagation parts of the models
are implemented in Cython. The Cython code is then translated into the C language and finallycompiled to a native binary code.

3. CONPUTATIONAL CONFIGURATIONAs shown in Figure 1, A semi-infinite thick barrier of the height ℎ = 5 m and the thickness of 10
m is placed in parallel with the x-axis with a distance of 7.5 m. The end of the barrier is placed at
𝑥 = 0 m. Point sources are placed on the x-axis from 𝑥 = −1050 m to 𝑥 = 1050 m with a 5 m
interval. 637 prediction points are placed in an area from (−50, 10) m to (50, 50) m with an intervalof 2.5 m but excluding the barrier. The height of the receivers is 2 m. Miscellaneous computational
conditions are shown in Table 1. The traffic volumes are 50, 100 and 3 000 per hour for heavy, medium
heavy and lightweight vehicles, respectively. The reflection and absorption coefficients of the barriersurface are 1 and 0, respectively.
y50
Prediction area

20

P2

P1

10 17.5

Sources

7.5

–1050

Barrier(h = 5 m)
x

0

–50

1050
50
Dimensions: [m]Figure 1 – Computational configuration
Table 1 – Miscellaneous computational conditions
Model

Harmonoise

CNOSSOS-EU

Vehicle speed, km/h

ASJ 2013/ASJ 2018

50

Road surface

DAC 11-13

A mix of DAC 11 and SMA 11

Ground surface

Dense grain asphalt

Hard surface
2

Flow resistivity, kPa s/m

20 000 (𝐺 = 0)

20 000Atmospheric condition—

Homogeneous—

Temperature, °C

15Relative humidity, %70
As shown in Table 2, with the combination of the consideration of source directivity, vehicle
running direction, noise assessment model, eight test cases are formed and labeled. The vehicle
running direction only applies to Harmonoise, that has asymmetric source directivity in front and backdirections for heavy vehicles. Lateral diffraction is calculated for CNOSSOS-EU and ASJ 2013, thathave said models.
Table 2 – Test cases
Directivity

Direction

Harmonoise

CNOSSOS-EU

ASJ 2013

ASJ 2018

—

—

HN-N

CN

ASJ13-N

ASJ18-N×+x / –x

HN-P / HN-M

—

—

—

×

—

—

—

ASJ13-D

ASJ18-D

4. RESULTS AND DISCUSSION
Figure 2 shows the distribution of continuous equivalent A-weighted sound pressure levels (𝐿!"# )for all cases. The values and marks indicated in each panel are the minimum and maximum 𝐿!"#values and their positions of appearance, PMin and PMax for the case. In all cases, the effects ofgeometric divergence and diffraction attenuation are dominant at 𝑥 ≤ 0 m and 𝑥 > 0 m, respectively.
The maximum differences of HN-P and HN-M from HN-N are approximately ±0.8 dB at right behindthe barrier. The positions of PMax of HN-P and CN are different from other cases. For the HN-P case,this is because of the asymmetric source directivity of the heavy vehicles specific to Harmonoise. For
the CN case, this is because of the consideration of the lateral diffraction at the end of the barrier.
Furthermore, the 𝐿!"# distribution of the CN case is different from other cases right behind the barrier
and the 𝐿!"# takes by far the highest value at PMin in all models. This is because of the predefined
lower bound value of –25 dB for the diffraction attenuation of the CNOSSOS-EU. In ASJ13-D and
ASJ18-D, the effects of the source directivity noticed by comparing with respective omnidirectional
cases of ASJ13-N and ASJ18-N, are small. This is considered to be because the directivity model in
the ASJ is meant to be used for the prediction of 𝐿!"# at upper floors of high-rise buildings. The 𝐿!"#values of ASJ18-N and ASJ18-D takes approximately 0.5 dB lower values than those of ASJ13-N and
ASJ13-D, respectively. This is because of the revision of a model constant in the source strength
model of the ASJ 2018 from ASJ 2013.

Figure 2 – Distribution of the continuous equivalent noise levels
Figure 3 shows comparison of the 𝐿!"# of the ASJ18-D case with other cases for each prediction
point. The ASJ18-D case is generally smaller by 1–2 dB than the other cases in the non-shadow zone
where 𝐿!"# > 45 dB, mainly because of smaller source strength. The HN-P and HN-M cases takes
approximately 5 dB larger values than the ASJ18-D case for 𝐿!"# < 40 dB and slightly differ eachother because the influence of the source directivity is significant at the shadow zone of the barrier.
The CN case becomes approximately 10 dB larger for 𝐿!"# < 40 dB due to the lower bound of thediffraction attenuation. The CN case also takes larger values for 𝐿!"# > 60 dB due to the effect of
the lateral diffraction. The ASJ13-D case is slightly larger than the ASJ18-D case for 𝐿!"# < 40 dB
due to the effect of the lateral diffraction to the shadow zone.

Figure 3 – Comparison of the ASJ18-D case with other cases

5. CONCLUSIONSThe ASJ models, especially focusing on the current model of ASJ RTN-Model 2018, are comparedwith Harmonoise and CNOSSOS-EU under the configuration of a semi-infinite thick barrier along a
straight source road. The Harmonoise and CNOSSOS-EU models are implemented with the help ofrespective calculation libraries. The ASJ models are implemented in Cython and compiled to nativecodes. The equivalent continuous sound pressure level of the ASJ RTN-Model 2018 is found to begenerally 1–2 dB smaller than the other models in the non-shadow zone, whereas 5–10 dB smaller at
the shadow zone.

ACKNOWLEDGEMENTS
This work was supported by grant-in-aid contracts with Yamaguchi Scholarship Foundation,
Uchida Energy Science Promotion Foundation and Union Tool Scholarship Foundation. This work
was also partially supported by JSPS KAKENHI Grant Number JP18K18897.

REFERENCES
1. Directive 2002/49/EC of the European Parliament and of the Council of 25 June 2002 relating to theassessment and management of environmental noise. Off J Eur Communities. 2002; L 189:12–25.
2. Nota R, Barelds R, Maercke D. Harmonoise WP 3 engineering method for road traffic and railway noise
after validation and fine tuning. Technical Report HAR32TR-040922-DGMR20. Arnhem, The
Netherlands: DGMR; 2005. p. 1-E-21.
3. Kephalopoulos S, Paviotti M, Anfosso-Lédée F. Common noise assessment methods in Europe(CNOSSOS-EU). EUR 25379 EN. Luxembourg, Luxembourg: Publications Office of the European
Union; 2012. p. 1-180.
4. Maercke DV. Programing the Point-to-point Propagation Model. Technical Report HAR34TR-041124CSTB01. Marne-la-Vallée, France: CSTB; 2004. p. 1–77.
5. Verheij TJB. Source modules road - Programming guide. V2012.0816.00.N004. Arnhem, The Netherlands:
DGMR; 2014. p. 1-23.
6. Maercke DV. CNOSSOS-EU Task 2: Propagation software modules user's and programmer's guide.
Marne-la-Vallée, France: CSTB; 2014. p. 1-147.
7. Sakamoto S. Road traffic noise prediction model “ASJ RTN-Model 2013”: Report of the ResearchCommittee on Road Traffic Noise. Acost Sci Technol. 2015;36:49-108.
8. Sakamoto S. Road traffic noise prediction model “ASJ RTN-Model 2018”: Report of the ResearchCommittee on Road Traffic Noise. Acost Sci Technol. 2020;41:529-589.

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

ABS-0294

Overview of Modelling of the Meteorological Effects on
Environmental Noise in Urban Street Canyons
Sophia Julia FERIANI 1; Cheol-Ho JEONG1; Allan Peter ENGSIG-KARUP 2; Finnur PIND 3
1

Acoustic Technology, Department of Electrical and Photonics Engineering, Technical University of Denmark,Denmark
2

Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University ofDenmark, Denmark
3

Treble Technologies, Iceland

ABSTRACT
Urban noise pollution is a harmful environmental stressor for city inhabitants and its impact has
been proven to be harmful for short- and long-term health. Having accurate models to predict andanalyze the noise in ever-growing cities is therefore crucial. If we focus on the scale of city streets,
several wave-based numerical methods have been developed and optimized to predict the soundpropagation in urban street canyons. Some methods are more suited than others to incorporate relevantmeteorological effects such as wind profile, temperature gradient and air absorption into their model.
However, integrating those phenomena comes at a certain computational cost. Assessing in advancethe relevant meteorological effects worth taking into account and only considering those effects in themethod would possibly reduce the computational burden of the modelling.
This paper aims to summarize previous modelling efforts, key effects and relate these to establishedcomputational methods for accounting meteorological conditions inside an urban street canyon. This
overview seeks to identify current limitations in computational modelling with the overall aim to
improve next-generation simulations using advanced wave-based computing methods.
Keywords: DGFEM, Environmental noise, Meteorological effects, Urban Street canyon

1. INTRODUCTION

To reflects real case scenarios, urban sound prediction at the scale of a street should account for
all the wave phenomena, meteorological effects, the complex geometry of streets and the variety ofmaterials of today’s cities. All those considerations make it difficult to get reliable experimental data
so numerical methods can be an efficient tool to estimate the sound exposure level of urban inhabitants.
The Discontinuous Galerkin Finite Element Method (DGFEM) is a wave-based numerical method
already in use for indoor acoustics (1–3) and this study is the first step in the ongoing work of
implementing a DGFEM framework for urban outdoor acoustics. By nature, outdoor acoustic has alarge domain which slows down the computation of the calculations. Moreover, environmental soundpropagates in a medium that is subjected to various meteorological effects. This paper’s goal is toassess which of those effects are negligeable and which are not, in order to simplify future simulationsof urban outdoor acoustics using a wave based DGFEM method.
In a first part, this paper aims at normalizing the way environmental sound propagates in a street
compared to an open field. Physical and geometrical quantities representative of the acoustic
phenomena of sound wave propagation are selected. The geometrical quantities are transformed into
non-dimensional quantities and parameterize with the acoustic quantities into a mathematical equation.
Hence, from a single simulation, the dimensional physical quantities can be scaled up or down quickly
when the dimensional geometrical quantities change.
sjufe@dtu.dk - chje@dtu.dk
apek@dtu.dk
3
fp@treble.tech
12

It has been experimentally shown that meteorological effects have an influence of urban soundpropagation (4,5). In a second part, this paper aims to quantify the impacts of three meteorologicaleffects (temperature gradient, air absorption and wind) in a street canyon using the DGFEM method.

2. NUMERICAL METHOD FOR SOUND PROPAGATION MODELLING
2.1 Choice of the method
Wave-based methods that solve numerically the time-domain wave equation have already beendeveloped for urban outdoor acoustics, notably the Finite-Difference Time-Domain (FDTD) (6,7) and
the Pseudospectral Time-Domain (PSTD) (8,9) methods. Those frameworks are accurate since theyaccount for all the wave phenomena (unlike geometrical acoustics methods), but they cannot handle
complex geometries and can be computationally heavy.
The DGFEM numerical method has been selected because it presents two main advantages
compared to other methods: it is geometrically flexible which is preferable for complex urban outdoor
domains, and it is well-suited for parallelization since elements are decoupled (10). Those arguments
are promising since outdoor acoustics deals with large and complex spatial domains. Once a DGFEM
framework has been thoroughly developed and validated for urban outdoor acoustics, it will becompared to previous methods to evaluate the efficiency and relevance of this new framework for
urban outdoor acoustics. This paper is the starting point of this work.
2.2 Governing equation
The numerical outdoor sound propagation is defined in a cartesian coordinate system (𝑥𝑥, 𝑦𝑦, 𝑧𝑧) in
the domain Ω ⊂ ℝ3. The acoustic pressure is modelled by solving numerically the wave equation in a
two-dimensional plane using the DGFEM method (11):𝜕𝜕 2 𝑝𝑝(𝒙𝒙, 𝑡𝑡)(1)= 𝑐𝑐 2 ∇𝑝𝑝(𝒙𝒙, 𝑡𝑡), 𝑡𝑡 ∈ ℝ+ , 𝒙𝒙 ∈ Ω,𝜕𝜕𝑡𝑡 2
with 𝑝𝑝(𝒙𝒙, 𝑡𝑡) is the acoustic pressure (Pa) at 𝒙𝒙(𝑥𝑥, 𝑦𝑦, 𝑧𝑧) at time 𝑡𝑡 (s) and 𝑐𝑐 is the speed of sound (m/s).
2.3 Geometric considerations
This study is limited to two 2D planes: the XZ-plane (side view) and the XY-plane (top view). The
domain Ω is meshed into triangular elements in 2D and in a shape representative of two sections of asymmetric urban street canyon:

Figure 1: XZ section (side view) with example of a mesh

Figure 2: XY section (top view) with example of a mesh

Four typologies of street canyon ratios are studied, and they are characterized by the street canyon
dimensions in m: 𝐿𝐿𝑥𝑥 for the canyon width, 𝐿𝐿𝑦𝑦 for the canyon half-length and 𝐿𝐿𝑧𝑧 for the canyonheight (also the buildings’ height). For each section, the following ratios are defined:• 𝑅𝑅 = 𝐿𝐿𝑧𝑧 /𝐿𝐿𝑥𝑥 for the XZ section with 𝐿𝐿𝑧𝑧 = 10𝑚𝑚 (Fig. 1),• 𝑅𝑅 = 𝐿𝐿𝑦𝑦 /𝐿𝐿𝑥𝑥 for the XY section with 𝐿𝐿𝑦𝑦 = 10𝑚𝑚 (Fig. 2).
𝑅𝑅 can be equal to either 0.75, 1, 1.5 or 2. Hence, the width of the canyon 𝐿𝐿𝑥𝑥 can be equal torespectively 13.3m, 10m, 7.5m or 5m.

For the XZ section, the source 𝑆𝑆 is positioned on the z symmetry axis of the canyon and 0.05m
above the ground to be coherent with the European Union guidelines for outdoor sound prediction(12). For the XY section, the source is at the center of the canyon.
2.4 Initial and boundary conditions
At the initial time, the sound velocity is equal to 0 𝑚𝑚/𝑠𝑠 in both directions and the sound pressure
is a Gaussian source with a pulse width of 𝜎𝜎 = 0.1 𝑚𝑚 and an amplitude of 1 𝑃𝑃𝑃𝑃 so the sound pressure
for the XZ section and XY section is respectively:(𝑥𝑥−𝑥𝑥𝑠𝑠 )2 +(𝑧𝑧−𝑧𝑧𝑠𝑠 )2(𝑥𝑥−𝑥𝑥𝑠𝑠 )2 +(𝑦𝑦−𝑦𝑦𝑠𝑠 )2(2)
𝑝𝑝𝑆𝑆 (𝑥𝑥, 𝑧𝑧, 𝑡𝑡 = 0) = 𝑒𝑒𝑒𝑒𝑒𝑒 �−� or 𝑝𝑝𝑆𝑆 (𝑥𝑥, 𝑦𝑦, 𝑡𝑡 = 0) = 𝑒𝑒𝑒𝑒𝑒𝑒 �−
�
2
2
𝜎𝜎

𝜎𝜎

For the boundary conditions, the open boundary has an acoustic surface impedance of 𝜌𝜌𝑐𝑐0 (Pa.s/m)
with 𝑐𝑐0 = 343 𝑚𝑚/𝑠𝑠 and 𝜌𝜌 = 1.2 𝑘𝑘𝑘𝑘/𝑚𝑚3 . Whereas, the closed boundaries (ground and building
façades) have an acoustic surface impedance of 5 ∙ 𝜌𝜌𝑐𝑐0 (Pa.s/m) which corresponds to a soundabsorption coefficient of around 0.55.

2.5 Outdoor acoustic DGFEM solver
The DFGEM solver is based on the book from Hesthaven and Warburthon (11)Spatial discretization was done using the meshing tool called distmesh (13). The area of interest is
the rectangular space between buildings so a denser mesh was adopted inside the street canyon close
to the source. The time stepping method used was a low storage 4 th order Runge-Kutta method.
Each element is solved independently based on a piecewise polynomial approximation andcontinuity is weakly enforced across the mesh thanks to a numerical flux. A non-dissipative central
flux is applied; meaning that neighboring elements communicated with each other with a flux integral
along their common edge by taking the average of the two corresponding numerical solutions.
For elements at the edge of the mesh, boundary conditions must be defined since they missneighboring elements with which they can communicate information. Using the numerical flux, theimpedance boundary condition is weakly enforced. The edge at the boundary represents either an open
boundary or close boundary (building façades and ground) so the corresponding acoustic surface
impedance is assigned. This implementation is the simplest reaction model since it assumes that thewave fronts arrive at the boundary with a normal incidence which is not always the case.
For the case where temperature gradient or wind is included in the simulation, the governing
equation (1) is solved with the effective speed of sound: 𝑐𝑐 = 𝑐𝑐0 + Δ𝑐𝑐 with Δ𝑐𝑐 calculated withequations (4) and (5) at the position of the barycenter of the triangular cell for each mesh element.

3. METHODOLOGY
3.1 Acoustic metric
The equivalent sound pressure level 𝐿𝐿𝑒𝑒𝑒𝑒 (dB) is used because this acoustic metric factors in the
total sound energy over a time period 𝑇𝑇. The period 𝑇𝑇 (s) is chosen so that enough time passes forthe sound wave to decay in all receiver points.
1 𝑇𝑇 𝑝𝑝2 (𝒙𝒙𝒊𝒊 , 𝑡𝑡)
𝐿𝐿𝑒𝑒𝑒𝑒 (𝒙𝒙𝒊𝒊 ) = 10 𝑙𝑙𝑙𝑙𝑙𝑙 � �
𝑑𝑑𝑑𝑑�,(3)
2
𝑇𝑇 0 𝑝𝑝𝑟𝑟𝑟𝑟𝑟𝑟
with 𝑝𝑝(𝒙𝒙𝒊𝒊 , 𝑡𝑡) the acoustic pressure as a function of the position 𝒙𝒙𝒊𝒊 (𝑥𝑥, 𝑦𝑦, 𝑧𝑧) of the 𝑖𝑖 𝑡𝑡ℎ receiver point attime 𝑡𝑡 and 𝑝𝑝𝑟𝑟𝑟𝑟𝑟𝑟 the reference sound pressure level (𝑝𝑝𝑟𝑟𝑟𝑟𝑟𝑟 = 20 𝜇𝜇𝜇𝜇𝜇𝜇).

3.2 Parametrization
A base reference case is defined to highlight the sound pressure variation when the domain Ω ischanging geometry from the domain an open field to the domain of a street. As a reference, the caseof an open field with constant and uniform speed of sound is studied at the 51 × 50 receiver points
uniformly distributed in the canyon.

Figure 3: Equivalent sound pressure level 𝐿𝐿𝑒𝑒𝑒𝑒 in section XZ for a) an open field and b) a street canyon in between twobuildings for the 𝑅𝑅 = 1 case c) at the 51 × 50 receiver points

The equivalent sound pressure level 𝐿𝐿𝑒𝑒𝑒𝑒 of the open field case (Fig. 3a) is compared to the levelof the case with buildings (Fig. 3b) at the receiver points 𝑖𝑖 (Fig. 3c): ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖 = 𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 − 𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖,𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 .
Since the energy from the direct wave is cancelled, this relative equivalent sound pressure level ∆𝐿𝐿𝑒𝑒𝑒𝑒correspond to the additional sound energy introduced by the inclusion of buildings in the open field
since the reflections of the sound waves on the buildings’ facades create more sound fluctuations
during the time period at the receivers. As a result, the equivalent sound pressure level is increased at
all points of the domain inside the area of interest.
The domain is normalized into a square grid so that the receivers’ position corresponds to apercentage point of the domain (Fig. 3c). The equivalent sound pressure level of each case ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖 isnormalized and scaled into the square grid.
Then, the physical ( ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖 ) and geometrical ( 𝑅𝑅 and receiver 𝑖𝑖 position) quantities areparameterized into a mathematical relationship to get an equivalent sound pressure level ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖
independent from 𝑅𝑅 i.e., independent from the geometry of the street canyon.

3.3 Impact of the meteorological effects
Three different meteorological effects on the sound pressure level in the domain are considered:
the impact of temperature gradient in a XZ section of a street, the impact of wind blowing parallel thestreet in a XY section and in both sections, the impact of air absorption.
The equivalent sound pressure level 𝐿𝐿𝑒𝑒𝑒𝑒 of the “base” case (street with no meteorological effects)
is compared to the 𝐿𝐿𝑒𝑒𝑒𝑒 of the cases where one meteorological effect is included: ∆𝐿𝐿𝑒𝑒𝑒𝑒 = 𝐿𝐿𝑒𝑒𝑒𝑒,𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 −𝐿𝐿𝑒𝑒𝑒𝑒,𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 . The relative equivalent sound pressure level ∆𝐿𝐿𝑒𝑒𝑒𝑒 can be positive or negative depending onwhether the meteorological effect increases or decreases noise at the receiver point over time.

The sound speed can be represented as a logarithmic function of height because of the airtemperature gradients between the ground and the air above as explained by Salomons (14). To
calculate the speed of sound in the section XZ, the following expression is used:(4)
𝑐𝑐(𝑧𝑧) = 𝑐𝑐0 + Δ𝑐𝑐(𝑧𝑧) = 𝑐𝑐0 + 𝑏𝑏 ∙ ln(z/z0 + 1)
with 𝑐𝑐0 = 343 𝑚𝑚/𝑠𝑠, 𝑏𝑏 ∈ ℝ, 𝑧𝑧0 = 1𝑚𝑚 and 𝑧𝑧 ∈ [0 ; 10] 𝑚𝑚.
For the case where 𝑏𝑏 = ±1 𝑚𝑚/𝑠𝑠, the speed of sound variation over a range of 10m is ∆𝑐𝑐(𝑧𝑧) =ln(11) ≅ 2.4𝑚𝑚/𝑠𝑠. It is possible to approximate the speed of sound as a linear function of temperature:𝑐𝑐(𝜃𝜃) = 331 + 0.6 ∙ 𝜃𝜃 with 𝜃𝜃 the air temperature in ℃ (15). Therefore for 𝑏𝑏 = ±1 𝑚𝑚/𝑠𝑠, the speed ofsound variation in the canyon corresponds to a ±4°𝐶𝐶 temperature variation between the ground andthe top of the canyon, which is a realistic scenario in cities (16–18).
The wind has a complex behavior in urban areas, so the simple situation chosen in this paper is thecase where the wind blows parallel to the buildings of the street canyon in the section XY. It is assumed
that the wind blows the strongest in the center of the street and decreases linearly until the air is stillat the building façades as in the wind tunnel measurement in (19). The time-averaged speed of soundprofile is assumed as follows:
𝑐𝑐(𝑥𝑥) = 𝑐𝑐0 + Δ𝑐𝑐(𝑥𝑥) = 𝑐𝑐0 + 𝑤𝑤 ∙ �|2𝑥𝑥| − 1�
(5)with 𝑐𝑐0 = 343 𝑚𝑚/𝑠𝑠, 𝑤𝑤 ∈ ℝ is the extremum speed of sound (in the middle of the street) and 𝑥𝑥 ∈[−50 ; 50]% of the width of the canyon. In reality, Δ𝑐𝑐(𝑥𝑥) in each element is supposed to be equal tothe projection of the wind vector on the direction of the sound propagation. Equation (5) is therefore
a rough estimate of the effective speed of sound 𝑐𝑐.

To model the air absorption in a realistic case, it is assumed that the air temperature is uniform andequal to 20℃, the atmospheric pressure is equal to 101 325 𝑃𝑃𝑃𝑃 and the relative humidity is equal to
60 %. A post processing algorithm is used by applying a Fast-Fourier Transform on the time-domainsound wave at each receiver position from the base case. Thanks to (20,21), the absorption coefficient
can then be calculated and applied on the signal at each receiver point for the corresponding
frequencies depending on the distance travel by the wave. Then, with an inverse FFT, the signal isbrought back to the time domain attenuated by sound air absorption.

4. RESULTS
4.1 Parametrization from open field to street canyon
As an example, the ∆𝐿𝐿𝑒𝑒𝑒𝑒 at the height 𝑧𝑧𝑖𝑖 = 𝑦𝑦𝑖𝑖 = 2𝑚𝑚 (approximately hearing height in a street) for
all R at the line of 51 receivers along the normalized width of the canyon is as follows:

Figure 4: Relative equivalent sound pressure level (dB) for four canyon ratios compared to the open filed case a) for section
XZ at 𝑧𝑧𝑖𝑖 = 2𝑚𝑚 b) for section XY at 𝑦𝑦𝑖𝑖 = 2𝑚𝑚

For both sections, we can see an overall trend in the shape of ∆𝐿𝐿𝑒𝑒𝑞𝑞,𝑅𝑅 for the different R: the relativeequivalent sound pressure is higher at the extremities of the width of the canyon and decreases in the
middle of the canyon, except at 𝑥𝑥 = 0% were the reflected waves are superimposed because the sourceis always at 𝑥𝑥𝑠𝑠 = 0𝑚𝑚 in the simulations. Moreover, it can be observed that ∆𝐿𝐿𝑒𝑒𝑒𝑒,0.75 < ∆𝐿𝐿𝑒𝑒𝑒𝑒,1 <∆𝐿𝐿𝑒𝑒𝑒𝑒,1.5 < ∆𝐿𝐿𝑒𝑒𝑒𝑒,2 (ignoring the edges data points close to the building facades next to 𝑥𝑥 = ±50%).
This observation is coherent since for a high 𝑅𝑅, the street gets narrower so the sound energy from thereflections on the buildings is greater.
The idea of the parametrization is to shift the different curves towards the case where 𝑅𝑅 = 1. Threeparameters need to be taken into account:• ∆𝐿𝐿𝑒𝑒𝑒𝑒,0.75 needs to shift up whereas ∆𝐿𝐿𝑒𝑒𝑒𝑒,1.5 and ∆𝐿𝐿𝑒𝑒𝑒𝑒,2 need to shift down so ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖 needs to be
divided by a function of 𝑅𝑅 where the derivative of this function by 𝑅𝑅 is positive.
• The shift should not be constant along the normalized width (small shift at 𝑥𝑥𝑖𝑖 = ±40%, bigger
shift at 𝑥𝑥𝑖𝑖 = ±10%),• Finally, the shift should grow smaller when ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖 is estimated the end of the canyon since theeffect of the canyon is more pronounced close to the source.
The form of the parametrization equation for each section is chosen to be:∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖,𝑋𝑋𝑋𝑋∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖,𝑋𝑋𝑋𝑋∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖,𝑋𝑋𝑋𝑋 = �1−�2𝑥𝑥 �𝛼𝛼�(1−𝑧𝑧𝛼𝛼) and ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖,𝑋𝑋𝑋𝑋 =
𝛽𝛽
𝛽𝛽
(6)
𝑅𝑅

𝑖𝑖

𝑖𝑖

𝑅𝑅

�1−�2𝑥𝑥𝑖𝑖 � �(1−�𝑦𝑦𝑖𝑖 � )With ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖 the normalized relative equivalent sound pressure level, ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖 the relativeequivalent sound pressure level for the 𝑅𝑅 canyon ratio case, 𝑥𝑥𝑖𝑖 ∈ [−50 ; 50]% of the width of thecanyon, meaning the projection on the x-axis in percentage of the 𝑖𝑖 𝑡𝑡ℎ receiver point and 𝑧𝑧𝑖𝑖 ∈[0 ; 100]% of the height of the canyon for the z-axis and 𝑦𝑦𝑖𝑖 ∈ [0 ; 100]% of the half-length of thecanyon for the y-axis (for section XY, the x-axis is a symmetry axis so only positive y are considered).
|2𝑥𝑥𝑖𝑖 |, |𝑦𝑦𝑖𝑖 | and 𝑧𝑧𝑖𝑖 span from 0 to 1 so those geometric quantities are symmetrical, and they don’t
need to be distinguish on from the other. In each formula in (6), their exponent can therefore be equal

(called 𝛼𝛼 for XZ section and 𝛽𝛽 for XY section).
To figure out the right values of 𝛼𝛼 and 𝛽𝛽, the sum of the distance between the ∆𝐿𝐿𝑒𝑒𝑒𝑒 at each 51
receiver line is calculated at each 50 heights 𝑧𝑧 or length 𝑦𝑦: 𝑆𝑆0.75 = �∑𝑖𝑖 ∆𝐿𝐿𝑒𝑒𝑒𝑒,1,𝑖𝑖 − ∆𝐿𝐿𝑒𝑒𝑒𝑒,0.75,𝑖𝑖 �, 𝑆𝑆1.5 =�∑𝑖𝑖 ∆𝐿𝐿𝑒𝑒𝑒𝑒,1.5,𝑖𝑖 − ∆𝐿𝐿𝑒𝑒𝑒𝑒,1,𝑖𝑖 � and 𝑆𝑆2 = �∑𝑖𝑖 ∆𝐿𝐿𝑒𝑒𝑒𝑒,2,𝑖𝑖 − ∆𝐿𝐿𝑒𝑒𝑒𝑒,1,𝑖𝑖 �. To minimize those sums, a series values from0 to 5 (with a spacing of 0.1) are tested for 𝛼𝛼 and 𝛽𝛽. In Fig.5, 𝛼𝛼 and 𝛽𝛽 are the values where theaverage of the three 𝑆𝑆𝑅𝑅 is minimum (best match of the four curves ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖 in plots like Fig. 4):

Figure 5: Exponent values of 𝛼𝛼 and 𝛽𝛽 for the range of height z and length y considered in the simulations

Using the same example from Fig. 4 at 𝑧𝑧𝑖𝑖 = 𝑦𝑦𝑖𝑖 = 2𝑚𝑚 where 𝛼𝛼 = 2.3 and 𝛽𝛽 = 0.6, the ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖
after using the parametrization equations (6) are transformed thusly:

Figure 6: Relative equivalent sound pressure level (dB) for four canyon ratios compared to the open filed case after
parametrization a) for section XZ at 𝑧𝑧𝑖𝑖 = 2𝑚𝑚 b) for section XY at 𝑦𝑦𝑖𝑖 = 2𝑚𝑚

4.2 Impact of the meteorological effects in the XZ section
The following simulations were done using the case where 𝑅𝑅 = 1 (the width of the canyon is aslarge as the height of the buildings).
4.2.1 Temperature gradientFigure 7: Relative equivalent sound pressure level (dB) for the case with a temperature gradient where a) b=1m/s b) b=-1m/s

The case with a positive temperature gradient (Fig. 7a) creates a “noisier” environment at thepedestrian level than the base case. Whereas the case with a negative temperature gradient (Fig. 57b)creates a “quieter” environment at pedestrian level.
4.2.2 Air absorption

Figure 8: Relative equivalent sound pressure level (dB) for the case with air absorption at 20ºC, 101 325 Pa atmosphericpressure and 60% relative humidity for a) XZ section) XY section

In both cases, the air absorption has the highest impact far away from the source.
4.2.3 Wind

Figure 9: Relative equivalent sound pressure level (dB) for a) downwind situation (w=5 m/s) b) upwind situation (w=-5 m/s)

In both downwind (wind from bottom to top on the street: 𝑤𝑤 = 5𝑚𝑚/𝑠𝑠) and upwind (wind from top
to bottom on the street: 𝑤𝑤 = −5𝑚𝑚/𝑠𝑠) situations, the wind has the largest impact where the wind speed
is highest and further away from the source.

5. DISCUSSION
5.1 Parametrization
The formulas (6) are relationships that give an approximate idea of how the sound’s behaviour istransformed when simple buildings are added to an open field in two dimensions. By modelling thesound propagation of a point source in one single street typology, it is possible to extrapolated what
would become ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖 for a narrower or wider street at any position 𝑖𝑖 in the area of interest.
To assess the impacts of meteorological effects, only one case (here, 𝑅𝑅 = 1) can be computed.

5.2 Meteorological effects
In both XZ and XY sections, the air absorption has a negligeable effect: for all receivers 𝑖𝑖, ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖,𝑋𝑋𝑋𝑋
and ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖,𝑋𝑋𝑋𝑋 is lower than 0.05 dB. It can be explained by the fact that the distances source-receivers
are small, and the fact that the study has been done for lower frequencies. For the XZ section, arealistic temperature gradient of ±4°C creates a relative equivalent sound pressure level of less than
0.1 dB, the maximum ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖,𝑋𝑋𝑋𝑋 been at pedestrian level. Using the parametrization above, theconclusions would be the same for a narrower street. For example, if the street is 5m wide (𝑅𝑅 = 2), in
the worst-case scenario (𝑥𝑥𝑖𝑖 , 𝑦𝑦𝑖𝑖 and 𝑧𝑧𝑖𝑖 are close to 0), ∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑅𝑅,𝑖𝑖 ≅ max (∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖 ) × 𝑅𝑅 < 0.2 𝑑𝑑𝑑𝑑 which isstill negligeable. In future work, it would be reasonable to neglect the effect of air absorption andtemperature gradient in the modelling of outdoor sound propagation in a street canyon.
For the XY section, the effect of the wind is the most important meteorological effect (∆𝐿𝐿𝑒𝑒𝑒𝑒,𝑖𝑖,𝑋𝑋𝑋𝑋 up
to 0.35 dB). In the simulations above, a simple type of wind flow has been chosen so the conclusions
are limited to this elementary scenario.
Those conclusions are in accordance with experimental findings (4,5).

6. FINAL REMARKS

We have presented results from simulation done using a Discontinuous Galerkin Finite Element
method that show that temperature gradients and air absorption are negligeable effects in typical twodimensional scenarios of street canyon. They could therefore be omitted when developing a complete
DGFEM framework for urban outdoor acoustics. Wind is the meteorological effects that has the mostimpact on outdoor sound propagation, but further developments are needed to get a realistic modellingof the sound propagation in a windy street. With the parametrization, the impact of thosemeteorological effects can be modulated according to the geometry of the street.
Simplifying assumptions (normal incidence at the boundary, temperature and wind are simple
function of the position in the canyon, simple rectangular building and flat ground, etc.) were madeat this first stage of the development of a DGFEM framework for urban outdoor acoustics and theyshould be address in the future. In upcoming work, a three-dimensional simulation will be
implemented to get a more comprehensive idea of the impact of meteorological effects in urban noisepropagation.

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ABS-0712

Parallelized FDTD simulations to improve engineering models of urban roadtraffic noise
Hazael MONTANARO(1) , Jonas KNÖPFEL(2) , Kurt HEUTSCHI(1) , Reto PIEREN(1)(1) Empa, Swiss Federal Laboratories for Materials Science and Technology, Dubendorf, Switzerland, hazael.montanaro@empa.ch(2) Grolimund+Partner AG, Zurich, Switzerland

ABSTRACTEngineering methods for urban sound propagation calculations are fast and efficient, but struggle in realistic,
complex and irregular topographies. Wave-based methods model the underlying physics but are computationally expensive. For special applications, wave-based methods can be coupled with engineering models to reduce
modeling uncertainties in the presence of complex geometries like street galleries. For such cases, we develop
a GPU-accelerated 3D Finite-Difference Time-Domain (FDTD) solver supporting arbitrary geometries, locallyreacting frequency-dependent boundary conditions, inhomogeneous perfectly matched layers (PML), and flexible boundary conditions. We apply this solver to improve computations done with an engineering road traffic
noise model. We extract thin cross-sections from road segments to receiver facade points of an urban space in
Switzerland to model a broadband point source propagation. Results at receiver points are expressed as soundpressure level increases in third-octave band spectra with respect to a reference situation where non-groundstructures are modeled as fully absorptive in the normal direction. To couple the results to the engineeringmodel, the derived third octave band levels accounting for complex acoustic phenomena are combined to a total
A-weighted sound pressure level by applying the A-filter and a typical road traffic noise spectrum.

Keywords: finite difference time domain, perfectly matched layer, impedance boundary conditions, roadtraffic noise, engineering model1

INTRODUCTION

Engineering-based models for outdoor sound propagation involve a combination of complex analytical, empiricaland semi-empirical schemes customized to the particular application and source type. They are computationallyefficient but struggle in complex scenarios, in particular in the presence of multiple reflections and irregular
topographies. Wave-based models on the other hand numerically solve the wave-equation and thus intrinsicallymodel propagation phenomena correctly. They are, however, computationally expensive, in particular whenlarge domains and high frequencies are of interest. With this in mind, we introduce a new integrated simulation
framework for wave-based simulations optimized for environmental acoustics applications. The acoustic solver,
Empapu, is supplemented by versatile geometric modeling and pre- and post-processing tools based on Python
and other free software. We demonstrate how it can be coupled with engineering-based models, to help mitigate
their inherent limitations in complex urban landscapes.

2

BACKGROUND

2.1 Model Equations
The acoustic solver solves the following differential equations derived from the continuity and momentum conservation equations∇p = −ρ

∂ v̄
∂t

∂p
= −ρc2 ∇ · v̄
∂t

(1)
(2)

where p is sound pressure in Pa, v̄ is particle velocity in m/s, ρ is media density in kg/m3 , and c is media
speed of sound in m/s.
These equations are subject to boundary conditions which can either be defined as pressure (Dirichlet),velocity (Neumann), or impedance boundary conditions. Frequency-dependent boundary conditions, typical toenvironmental acoustics applications, are expressed as complex impedances and are used to represent different
terrains or surfaces. An example is the Delany-Bazley-Miki model (1) for acoustic impedance"
−0.632−0.632 #
3 f
3 f− j8.43 10(3)
Z = ρ0 c0 1 + 5.50 10σ0σ0where σ0 is the static air flow resistivity in kPa s m−2 representing different porous materials, c0 and ρ0 therespective properties in air, and f is frequency in Hz.
2.2 Finite-difference time-domain method
Equations in Section 2.1 are solved explicitly using a staggered uniform grid finite-difference time-domain(FDTD) scheme of the first order (second order accurate). The pressure field p is defined at integer steps
and the velocity fields vx,y,z at half steps in time and in the main spatial axis and at integer steps in the remaining spatial axes. Where required, integrals are discretized using the trapezoidal rule and missing nodes bylinear interpolation between nearest neighbors.
2.2.1 Update equation
Equations 1 and 2 get discretized tovn,t+.5 = −∆t(pn+1 − p) + vn,t−.5
∆xρ

(4)


−ρc2 ∆tvx+.5 − vx−.5 + vy+.5 − vy−.5 + vz+.5 − vz−.5 + p(5)∆xwhere ∆t is the time step fulfilling the CFL condition (5), ∆x is the spatial step heuristically set to account for
at least 10 points per minimum wavelength of interest in the domain, and the subscripts indicate discrete steps
from the grid point being considered in either space (n for the normal directions: x, y, and z) or time (t).
pt+1 =

2.2.2 Frequency-dependent boundary conditions
Outdoor acoustics typically model medium discontinuities as locally reacting boundary conditions (6). Frequency
domain models such as the Delany-Bazley-Miki model in Equation 3 are not readily translatable to the FDTD
scheme and therefore should be approximated by more suitable functions. Different approaches exist (7–12),
such as modeling the impedance as a spring-mass-dashpot system or a higher order Laurent series or as adigital filter.
We propose fitting any arbitrary frequency-dependent surface impedance Z as a 1st-order rational functionin the Laplace s-domain of the form
P(s) = Z(s)V (s) = ρ0 c0 H(s)V (s)b0 + b1 s
H(s) =
1 + a1 s(6)(7)

where P(s) is the pressure field and V (s) is the scalar velocity field normal to the boundary. H is the transfer
function of a shelf filter with DC gain b0 (s = 0) and high frequency gain b1 /a1 (s → ∞). Parameters a1 , b0 , b1
are fit by using the curve_fit (non-linear least squares) function in SciPy (4) independently on the real andimaginary components of Z on points sampled from logarithmically spaced frequencies. Use of constraints inthe optimizer are avoided by a suitable choice of the initial guess.
Discretizing with the bilinear transform to the z-domain and transforming back to the time domain, we
obtain the following equation
vx,t+.5 =∆tA1 − ∆t2∆t∆x − ∆tcB1
vx,t−.5 −pt−.5 +p∆x + ∆tcB1
ρ∆x + ∆tρcB1
ρ∆x + ∆tρcB1

(8)

where
∆t − 2a1∆t + 2a1
b0 ∆t + 2b1
B0 =∆t + 2a1
b0 ∆t − 2b1
B1 =∆t + 2a1(10)

pt+.5 = ρcB0 vt+.5 + ρcB1 vt−.5 − A1 pt−.5

(12)

A1 =

(9)

(11)

and

Since impedance is a vectorial quantity, it is more convenient to define at velocity nodes. The above equations were derived for a vx node where waves are incident from the left (right going wave). For the reversedboundary, we change the sign of pvx,t+.5 =∆x − ∆tcB1∆tA1 − ∆t2∆tvx,t−.5 −pt−.5 −p∆x + ∆tcB1
ρ∆x + ∆tρcB1
ρ∆x + ∆tρcB1(13)

Analogous equations are derived for boundary conditions defined along the y- and z-directions.
2.2.3 Perfectly Matched Layers
For outdoor sound propagation, special free-field boundary conditions are often needed. Perfectly Matched
Layers (PML) are typically employed at the borders of the domain to allow for outgoing waves to propagate
outwards while limiting back reflections, modeling an infinite domain. We apply the stretched coordinate approach (13) where we assume a superposition of plane waves solution to the wave equation in Section 2.1,
convert it to the frequency domain and, for the axes of interest, change coordinates as shown∂ x 7→ (1 + ( jω)−1 σx )∂ x̃

(14)

where σx is the conductivity profile in the prescribed direction and ω is the angular frequency in rad/s. The frequency domain equations can then be inverse transformed to the time domain where the frequency dependence
disappears. This approach is equivalent to creating an anisotropic lossy impedance with the desired properties.
PMLs need to be derived for all combinations of x, y, and z corresponding to the faces, edges, and corners ofthe domain.
We numerically implemented them using the split-field PML (14) where the pressure field is decomposed
into auxiliary fields fulfillingp = px + py + pz
(15)
and non-zero σ values indicate PML activation in the relevant directions.
As an example, for the PML along the x-direction, we discretize the following equations
∂ px
∂ vx
+ σx px = − ρc2
∂t
 ∂x

∂p
∂ vx
= −ρ
+ σx vx
∂x
∂t

(16)
(17)

Analogous equations are discretized for PMLs in the other directions.
The conductivity profile σx is defined asx
PML m c(m + 1) ln (R0 )(18)
σx (xPML ) = −d2dwhere xPML is the current layer of PML in the x-direction, d is the maximum number of PML layers, m is theorder, and R0 is the reflection from PML at normal incidence. Recommended values for R0 and m are 0.01 and4, respectively (3). These can be further optimized.
The number of PML layers d employed is such that under free field conditions, point recordings near theedge of the domain follow approximately a 6 dB pressure drop per distance doubling from a point source in its
third octave band spectrum in the frequencies of interest (within 5%).
The PML is terminated by an additional layer of nodes which are the domain’s true boundary condition (seeSection 2.2.4). We impose an impedance boundary condition matching the plane wave impedance of air ρ0 c0
vn,t+.5 =∆x − ∆tc∆x − ∆tc
vn,t−.5 −p∆x + ∆tc∆x + ∆tc(19)

where the subscript n indicates the normal direction. Empirically, this gave improved performance when compared to the typical Dirichlet p = 0 or Neumann vn = 0 boundary condition.
We implement an inhomogeneous PML by extending any of the boundary conditions from Section 2.2.2
into the PML. This is used to model an infinitely extending terrain and avoid diffraction effects from boundary
discontinuities.
2.2.4 Domain boundary conditions
The outermost layer of the FDTD domain needs special treatment as the update equation will have missing
terms. These are typically handled with Dirichlet, Neumann or impedance boundary conditions as mentioned
in Section 2.1. Other boundary conditions exist which take advantage of symmetry and can reduce the overall
computational domain. Symmetric, antisymmetric and periodic boundary conditions are achieved by setting theoutermost values in the velocity field to the same value as another grid point in the main computational domain
with the appropriate sign.
2.2.5 Sources
In FDTD, sources are typically modeled according to the Huygen’s principle where any source distribution canbe modeled as a distribution of point sources. These can either be defined as pressure or velocity sources. Hardsources are defined by imposing a value at each time step on a node and soft sources are implemented byadding to the current value at the corresponding node. Source amplitudes are normalized by the voxel size toensure that different grid sizes give equivalent distributions. The corresponding pressure source equation can bederived from the inhomogeneous wave equation and its discretization∂pS+ ρc2 ∇ · v̄ = Ŝ = 3
∂t
∆x
′
pt+1
=


−ρc2 ∆t∆t
vx+.5 − vx−.5 + vy+.5 − vy−.5 + vz+.5 − vz−.5 + p + 3 S
∆x
∆x
∆t
′
= pt+1 + 3 S
pt+1
∆x

(20)
(21)
(22)

′
where S is the source distribution as a function of time and pt+1
is the pressure field considering a soft source.
Hard sources discard the pt+1 term.

3

SIMULATION FRAMEWORK

3.1 Solver
The acoustic solver Empapu was developed in C++ and further accelerated with OpenMP and CUDA to takeadvantage of the parallel nature of the FDTD algorithm. CUDA parallelization was achieved by slicing thecomputational domain into a series of 2D XY slices which were in turn split into the different multiprocessorsof the NVIDIA GPU with each thread handling individual points of the domain. This approach is based on (2),where shared memory (user-handled cache) is optimized for reduced memory latency.
The main limitation of GPU computation is the amount of global memory (in the order of 12–24 GB formodern GPUs) since the entire pressure and velocity fields need to fit in memory and, for 3D problems, grid
sizes grow as ∆x3 . For floating point computation using a modern GPU with 24 GB of memory and an upper
limiting frequency of 2 kHz, we are limited to a domain size of approximately 7 600 m3 . Multi-GPU computation can be achieved as in (2) where the domain is evenly split in the z-direction between GPUs with a single
overlapping layer between ‘adjacent’ GPUs. This can then be deployed in a high performance computing (HPC)
cluster such as the Piz Daint in CSCS, Lugano.
Where hardware acceleration is not available or GPU memory constraints become prohibitive, we also developed a CPU-accelerated solution using OpenMP (15) and normal computer memory. OpenMP parallelizationis achieved by letting each CPU core handle different points in the domain.
3.2 Pre- and post-processing
Input and output files are stored as hdf5 files, a hierarchical data format. Generation, automation, and postprocessing is handled via Python scripts. Domain modeling is handled via the Sketchup (Trimble) software.
Domain discretization — voxeling — is handled via the third party software Sim4Life (Zurich MedTech) while
an in-house solution is being developed. Visualization is done via the Visualization Toolkit (VTK) in the Paraview (Kitware) software environment (16).

4ENGINEERING MODELSFor large-scale sound propagation calculations, engineering models such as ISO 9613-2 (17) are used due totheir efficiency. These models consider the different propagation effects (geometrical spreading, ground effect,
air absorption, shielding by obstacles, or attenuation due to vegetation) by analytical or empirical approaches
that are described by simple formulas. Accounting for reflections is a challenge with these models. While single
reflections at large and flat surfaces can be considered relatively reliably with the mirror source concept, theuncertainty becomes large for multiple reflections at smaller surfaces, possibly in combination with diffraction
at obstacle edges.

5

APPLICATION

We apply our model to augment an engineering model (see Section 4) as a proof-of-concept for cases wherethese models have reached their limits. This is by no means the norm and is not a comment on the accuracyof engineering methods but might be considered in special cases.
We look at a road traffic model in Switzerland to evaluate the added impact of reflections due to non-terrain
structures at receiver points along the facade of buildings of interest (see Figure 1). We explore two distinct
scenarios: (1) a simple case where we expect similar results between both models and (2) a complicated casewith multiple reflections where the engineering model would struggle.
As the volume and frequencies of interest are outside what would fit in modern GPUs, we limit ourselves
to 2D cross-sections of interest (domain size approximately 160 × 20 m2 ). Terrain, buildings, roads, bridges, andnoise barriers are all modeled as frequency-dependent boundary conditions according to Table 1. Parameters
for the noise barriers were fit according to a 7 dBA absorption of a typical porous outdoor absorber. A spatial
resolution of 2 cm was used to be able to fully resolve frequencies up to 2 kHz. The temporal resolution was
set according to a 0.9 CFL criterion. A broadband point source (Gaussian pulse with standard deviation of
4 kHz) was used and placed at 6 cm above the center of the lane of interest and simulated for a full second.
For the simple case, the source is placed in the top lane of the bridge where it has a direct path to the receiverpoints. For the complicated case, the source is placed in the bottom lane in between the street canyon, resulting
in multiple reflections.
For each cross-section, 3 simulations were run. The first one models the ground reflection effect by simulating free-field sound propagation in the presence of a flat terrain 6 cm below the source. The second one isFigure 1. 2D cross section of the investigated road traffic model. Point source is indicated in blue and receiverpoints along facade in red. (Top two) The simple case has a point source in the top lane and the complicatedcase (bottom two) has a point source in the bottom lane within the street canyon. (Top) Evaluated simulationand (bottom) reference simulation pressure field distribution for both cases demonstrating the additional impactof reflections from non-terrain structures outside the direct path from source and receivers.

Table 1. Parameters for different surface boundary conditions defined either by different static air flow resistivities σ0 (in kPa s/m2 ) or frequency-dependent absorption α (in dBA) with respect to a fully reflecting boundary.
Surface

σ0

a1

b0

b1

Terrain200

3.08e-3

7.48e+1

8.25e-3

Road, bridges, buildings

20000

2.62e-3

1.98e+3

1.19e-1

Noise barrier (α = 7 dBA)—

3.00e+1

1.53e+5

3.07e+1

the evaluated simulation, which models the full geometry. The third is the reference simulation which models the full geometry, but with potentially reflecting non-ground surfaces covered with perfect absorbers in thenormal direction (modeled as having the impedance of air). This does not include the building facade with thereceivers or structures in the direct sound path between source and receiver as we are interested in quantifying
the additional impact of back-reflections that wouldn’t be captured accurately by the engineering model.
The ground reflection effect was ascertained by calculating the sound pressure level in the third octave band
spectra at a distance of 1 m from the source. These results were then subtracted from the results at receiverpoints of the evaluated and reference cases and a suitable road emission spectrum was added back. We assume
an emission spectrum according to sonROAD18 (18) for a road with 80 km/h, a neutral pavement with KB=0
and a traffic mix with 92% passenger cars and 8% heavy vehicles. Results at receiver points are then expressedas the combined A-weighted sound pressure level increase in third octave band spectra of the evaluated situation
with respect to the reference situation.
This combined A-weighted level was then compared to calculations with the engineering model. To do so,
we mimicked the FDTD-2D-simulation scenario in the engineering model as an infinitely extended road withconstant geometry in the transverse direction. In this auxiliary engineering model, relevant parameters wereidentified (e.g., porous wall absorption and building heights) and modified to obtain a similar reflection effectas in the FDTD model.
The auxiliary model adaptations were then translated to the full 3D model and the position-dependent receiver level correction for the complete road calculated. Noise maps were then computed with the original 3D
model considering the aforementioned local receiver level corrections.

6

RESULTS

Simulations were run on a standard workstation with an NVIDIA RTX 2080 Ti GPU and each cross section
took around 6 min of simulation time to compute at an average of 10 000 million pressure grid point updates per
second. We observe excellent agreement between the A-weighted sound pressure level increase of the full-wave
simulations and the engineering model for the simple case (within 0.1 dBA).
In the complex cases where the receiver level is dominated by shielded multiple reflections, the engineering model and the wave-based model show significant deviations of around 5 dBA. Total receiver levels arecalculated as the integration over the road discretized by multiple road segments. Since the particular reflectiondominated sections only make up part of the road, for an entire vehicle pass-by these influences are diluted. At
the most exposed receiver points, we found level corrections of 1–2 dBA in relation to the original engineeringmodel calculation.

7

CONCLUSIONSWe show a fully integrated parallelized framework for outdoor acoustic applications which we coupled to anengineering model for improved accuracy and reliability in special cases where traditional approaches reach theirlimits. Current simulations were limited by memory constraints and thus run in 2D which constrains the source
distribution to a 3D coherent line source in the out of plane direction. Other approaches for true point source
propagation in thin cross sections were tested, such as augmenting the 2D plane with PML in the transverse
positive direction and an anti-symmetric boundary condition in the negative direction, however this was still notenough to fully resolve the high frequencies of interest for our given domain size. Development is ongoingon 2.5D simulations supporting frequency-dependent boundary conditions utilizing the approaches from (19)and (20) and on a multi-GPU variant of the solver to offload the work onto an HPC cluster.

REFERENCES
1. Miki Y. Acoustical properties of porous materials-Modifications of Delany-Bazley models. Journal of the
Acoustical Society of Japan (E). 1990;11(1):19-24.

2. Micikevicius P. 3D finite difference computation on GPUs using CUDA. Proceedings of 2nd workshop on
general purpose processing on graphics processing units. 2009 Mar 8. p. 79-84.
3. Zhou D, Huang WP, Xu CL, Fang DG, Chen B. The perfectly matched layer boundary condition for scalar
finite-difference time-domain method. IEEE Photonics Technology Letters. 2001 May;13(5):454-6.
4. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P,
Weckesser W, Bright J, Van Der Walt SJ. SciPy 1.0: fundamental algorithms for scientific computing in
Python. Nature methods. 2020 Mar;17(3):261-72.
5. Courant R, Friedrichs K, Lewy H. On the partial difference equations of mathematical physics. IBM journalof Research and Development. 1967 Mar;11(2):215-34.
6. Van Renterghem T. Efficient outdoor sound propagation modeling with the finite-difference time-domain(FDTD) method: a review. International Journal of Aeroacoustics. 2014 Oct;13(5-6):385-404.
7. Heutschi K, Horvath M, Hofmann J. Simulation of ground impedance in finite difference time domain
calculations of outdoor sound propagation. Acta acustica united with acustica. 2005 Jan 1;91(1):35-40.
8. Richter C, Hay JA, Schönwald N, Busse S, Thiele F. A review of time-domain impedance modelling andapplications. Journal of Sound and Vibration. 2011 Aug 1;330(16):3859-73.
9. Botteldooren D. Finite-difference time-domain simulation of low-frequency room acoustic problems. The
Journal of the Acoustical Society of America. 1995 Dec;98(6):3302-8.
10. Fung KY, Ju H. Time-domain impedance boundary conditions for computational acoustics and aeroacoustics.
International Journal of Computational Fluid Dynamics. 2004 Aug 1;18(6):503-11.
11. Dragna D, Cotté B, Blanc-Benon P, Poisson F. Time-domain simulations of outdoor sound propagation withsuitable impedance boundary conditions. AIAA journal. 2011 Jul;49(7):1420-8.
12. Ostashev VE, Collier SL, Wilson DK, Aldridge DF, Symons NP, Marlin D. Padé approximation in timedomain boundary conditions of porous surfaces. The Journal of the Acoustical Society of America. 2007
Jul;122(1):107-12.
13. Chew WC, Weedon WH. A 3D perfectly matched medium from modified Maxwell’s equations withstretched coordinates. Microwave and optical technology letters. 1994 Sep;7(13):599-604.
14. Berenger JP. A perfectly matched layer for the absorption of electromagnetic waves. Journal of computational physics. 1994 Oct 1;114(2):185-200.
15. Dagum L, Menon R. OpenMP: an industry standard API for shared-memory programming. IEEE computational science and engineering. 1998 Jan;5(1):46-55.
16. Ahrens J, Geveci B, Law C. Paraview: An end-user tool for large data visualization. The visualization
handbook. 2005 Jan;717(8).
17. ISO 9613-2:1996 Acoustics — Attenuation of sound during propagation outdoors — Part 2: General methodof calculation.
18. Heutschi K, Locher B, Gerber M. sonROAD18: Swiss implementation of the CNOSSOS-EU road traffic
noise emission model. Acta Acustica united with Acustica. 2018 Jul 1;104(4):697-706.
19. Narayan JP. 2.5-D numerical simulation of acoustic wave propagation. pure and applied geophysics. 1998
Jan;151(1):47-61.
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PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0331

Active Noise Control Clustering for Acoustic Window
S.K. TANG1,2; P. Y. CHAN2; K.W. MUI2; K.H. CHEUNG3; C.K. LEE3; S.W. TSANG3
1

The University of Hull, Hull, HU6 7RX, United Kingdom

2

The Hong Kong Polytechnic University, Hong Kong, China

3

Environmental Protection Department of Hong Kong SAR Government, Hong Kong, China

ABSTRACTThe method of transfer functions is used experimentally in this study to explore the possibility of improving
the sound transmission loss of acoustic window using active noise control technique. In the experiment, 20
microphones equally spaced at the exit of the acoustic window were used to capture the sound pressures there.
A reference microphone was located at the outermost inlet window frame facing the incoming soundgenerated by a linear loudspeaker array. Nineteen small loudspeakers were lined along the interior perimeterof the acoustic window (except the window bottom) to provide the cancelling sounds. Six error microphones
were chosen from the 20 microphones at the window exit and the sum of the mean squared pressures
measured was minimized. The transfer functions between individual secondary loudspeakers and individual
microphones were measured. Feedback was included in the analysis. The preliminary results show that
control combinations using different number of secondary loudspeakers could be effective within different
frequency ranges. One can also select at least three independent active control clusters to work
simultaneously for noise attenuation from 200 Hz to 4k Hz.
Keywords: Active noise control, Sound transmission, Acoustic window

1. INTRODUCTION
Hong Kong is a congested and densely populated city. Many people live in urban areas becauseof convenience and the demand for residential units in these areas keeps on rising. The congested
urban situation makes noise control a very challenge task as most of the conventional mitigation
measures, such as, extended podia [1], roadside noise barriers/enclosures [2] and setback [1] are all
not applicable due to various restrictions. Space availability is also an important hurdle. Balconies
of high-rise buildings are not good noise screening devices [3] unless they are properly installed withsound absorption material [4]. However, such balcony could result in a reduction of internal living
space area and thus is not very often a preferred solution. Acoustic windows, which offer goodsound insulation and at the same time allow sufficient natural ventilation of the living space, areintroduced in recent years [5] to help relief the housing demand. The additional noise reduction byreplacing a conventional side-hung windows by acoustic windows can be around 4 to 8 dBA. A
simplified schematics of the acoustic window is given in Fig. 1 [6].
As time goes by, residential buildings have to be built in more difficult areas (e.g. areas next tovery busy trunk roads) and more powerful acoustic windows are in need. One way of improving thenoise reduction of an acoustic window without jeopardizing the air movement across the window is
the application of active noise control technique (ANC) [7,8]. In principle, this method is not
restricted by window size and should be very adaptive to different building façade conditions.
However, the simple setup of Tong et al. [7] can only result in limited additional noise reduction
benefit and the working frequency range is also limited.
In this study, the use of active noise control to improve the acoustical performance of acoustic
window is re-visited. The acoustic window is of the dimension similar to those adopted by the
Housing Department of the HKSAR Government [5]. Attempt is made to understand whether
broadband noise reduction can be achieved by clustering sub-ANC systems under ideal and optimalsituation. Experimentally collected transfer functions are used to test the concept.
1

S.Tang@hull.ac.uk

Figure 1 – Schematics of acoustic window [6](provided by Housing Department, HKSAR Government)

2. THE EXPERIMENTAL APPROACH
2.1 The Setup of the Measurement
The experiment was carried out in the building acoustic testing chambers of the Hong Kong
Polytechnic University. It is a dual-chamber design conforms to international testing standard [9]and is isolated structurally from the building where the chamber is housed. The two chambers are also
isolated from each other and the acoustic window tested was installed on the wall separating them(Fig. 2). The source room, where a linear loudspeaker array (~4 m long) was located, was madesemi-anechoic to mimic approximately an outdoor condition. This loudspeaker array was theprimary sound source in the present study. The receiver room was kept reverberant.
The cancelling sounds were produced by nineteen 10-cm aperture loudspeakers installed on themullions and the ceiling of the window void (Fig. 2). A microphone attached to the frame of a sidehung window at the acoustic window inlet was used as the reference of the ANC (Fig. 3a). Twenty
microphones were installed at the window outlet to capture the overall sound levels at the exit plane(Fig. 3b). Some of these microphones were also used as the error microphones where the soundpressures were to be minimized. The performance descriptor was the overall spectral sound pressurelevels obtained from all these 20 microphones.

Figure 2 – The test window and the loudspeaker arrangement

(a) C2 (reference microphone)(b) C5 to C24
Figure 3 – The microphone arrangement
The dimension of the acoustic window used in the present study was similar to that of the acoustic
window adopted in the public rental housing in Hong Kong. Figure 4 illustrates the window
dimension.

Figure 4 – Dimension of the acoustic window used
2.2 The Transfer Function Analysis
Figure 5 illustrates the proposed control system of the present study. For simplicity, the figure
just shows the paths related to the ith error sensor e i. The symbols will be explained in the following
paragraphs.

Figure 5 – The schematics of the control system (Simplified)We did not actually implement the active control physically in this study, and thus the ANC
controller in Fig. 5 was not considered. Instead, we tested the possibility of broadband noise
reduction enhancement using transfer function analysis. Let the suffices r and e denote quantities
related to the reference signal and the error signal respectively, and the suffices p and s quantities
associated with the primary and secondary cancelling sources respectively, the sound reaches the ith
error microphone when N number of secondary sources and the primary source are switched on isNpei ( )  H p ,ei ( ) S p ( )   H s j ,ei ( ) S s j ( )

(1)

j 1

where H denotes transfer function and S the strength of a sound source. In normal practice, S p cannot
be easily determined directly and thus it is included in the analysis via the reference signal which canbe readily measured. Allowing sounds from the secondary sources to reach the reference microphone,
one obtainsN

pr ( )  H p,r ( ) S p ( )   H s j ,r ( ) S s j ( )

(2)

j 1

and thuspei ( ) pei ( )pr ( )

H p ,ei ( )H p ,r ( )N

pr ( )  j 1NH s j ,r ( )H p ,r ( )N

S s j ( )   H s j ,ei ( ) S s j ( )



j 1

 H r ,ei ( )   H s j ,ei ( ) H p ,r ( )  H s j ,r ( )j 1

 H S(()p) ()
sj

p ,r

(3)

r

Suppose there are M error microphones in use, the ANC requires p i() = 0 for all i. There will thenbe M equations like Eq. (3) and the only unknown is the secondary source vector S s. Without loss ofgenerality, one can also normalize p i by pr and Ss by H p,r pr. As there are M equations and N unknown,
unique solution of Ss will only exist when M = N. Least square analysis is used to obtain the solution
otherwise. The overall sound pressure level at the window exit plane obtained using all the 20
microphones (Fig. 3b), even though some of them have been used as the error microphones, is
20

SPL   p 2j ,exit ( ).

(4)

j 1

The performance of the control is described by SPL, which is

SPL  SPLSs 0  SPL.

(5)

For easy reference and presentation hereinafter, the secondary cancelling sound sources arenumbered as shown in Fig. 6.

Figure 6 – Numbering of secondary cancelling loudspeakers

3. RESULTS AND DISCUSSIONSAs there were 19 secondary sources and 20 microphones involved in the experiment, there arethousands of combinations to set up the ANC. In this study, N was fixed at 6 as too many error
microphones are not practical, while M was allowed to vary. The intention here is to test if broadband
noise reduction is possible. Two sets of six error microphones were selected.
The first set of error microphones included C10, C11, C14, C15, C18 and C19. It is intended toforce a quiet zone in the middle of the window exit plane. Figure 7a shows the SPL spectrum(attenuation) with only Loudspeaker 10 was operated. The performance is only satisfactory forfrequencies higher than 500Hz. There are spurious SPL spikes at lower frequencies, but sound is
attenuated on average. It shows that one loudspeaker located in the middle of the overlapping sectionof the window can be useful in the ANC. Figure 7b illustrates the corresponding results when
Loudspeakers 1, 7 and 12 were used. The maximum spectral attenuation is higher, but in this case,the lower frequency performance is much better. One can therefore consider a broadband active
improvement of acoustic window transmission loss by using two separate sub-systems/ANC clusters;
one looks after a particular frequency range. It should be noted that the attenuation is not always
enhanced by including more secondary control sources.
15

15

10

Attenutation (dB)

Attenutation (dB)

10
5
0
-5
-10
-15

5
0
-5
-10
-15
-20

0

500

1000

Frequency (Hz)

1500

2000

0

500

1000

1500

2000

Frequency (Hz)(a) Loudspeaker 10 is ON(b) Loudspeakers 1, 7 & 12 are ON
Figure 7 – Overall attenuation over indoor window outlet(Error microphones C10, C11, C14, C15, C18 and C19)
The second set of error microphones includes C5, C8, C13, C16, C21 and C24. These microphones
are close to the exit window edges. It is interesting to see that while the performance of this setting is
not so satisfactory when less number of control sources are used (Figs. 8a), a 5-control source system
can achieve good sound attenuation at high frequency (Fig. 8b).

20

10

10

Attenutation (dB)

Attenutation (dB)

15

5
0
-5
-10
-15

0
-10
-20
-30

0

1000

2000

Frequency (Hz)

3000

4000

-40

0

1000

2000

3000

4000

Frequency (Hz)(a) Loudspeakers 1, 7 & 12 are ON(b) Loudspeakers 1, 2, 3, 4, 5 & 9 are ON
Figure 8 – Overall attenuation over indoor window outlet(Error microphones C5, C8, C13, C16, C21 and C24)
Although Figs. 7 and 8 cannot give the whole picture of the ANC, they suggest the possibility ofusing clustered sub-ANC systems for broadband acoustic window sound reduction improvement. It
is left to further investigations. The idea of clustering small ANC systems is also suggested by Hu andTang [10] in their noise barrier study.

4. CONCLUSIONSThe possibility of using active noise control strategy to improve the sound transmission loss acrossan acoustic window was studied experimentally in the present investigation. A linear loudspeakerarray was used as the primary sound source and the cancelling sounds were obtained from 19 10-cm
aperture loudspeakers installed along the internal perimeter of the acoustic window (mullions andceiling of window). Transfer functions which describe the propagation of sound from primary andcontrol sources to error and reference microphones were measured and used to estimate theeffectiveness of the active control strategy. The measurements were carried out inside acoustic
chambers and a full size acoustic window was adopted.
Results show that active noise reduction enhancement is possible even with the use of one single
secondary control source installed in the middle of the acoustic window overlapping, when the error
microphones collectively tend to force a quiet zone in the middle of the window exit plane. Low
frequency performance is improved with 3 properly chosen control sources, but there is no evidence
that the performance can further be improved by including more control sources.
It is interesting to note that high frequency performance can be enhanced with 5 properly chosen
control sources are operated with error microphones which tend to produce quiet regions near to theedge of the window exit plane.
The present results, though still preliminary to conclude the whole active control performance,
indicate the possibility of clustering sub-systems consist of different/same error microphones but
different secondary control sources for broadband noise reduction improvement for acoustic window.
Further investigations are required to detail the underlying physics and work out the optimal control
strategy.

ACKNOWLEDGEMENTS
The financial supports from the Environment and Conservation Fund and the Woo Wheelock Green
Fund under Project No. ECF92/2020 are gratefully acknowledged.

REFERENCES
1. Environmental Protection Department, Hong Kong Planning Standards and Guidelines. The HKSAR
Government, Hong Kong, 1993.
2. Kurze UJ, Anderson GS. Sound attenuation by barriers. Appl. Acoust. 1971;4:35-53.
3. Tang SK. Noise screening effect devices of balconies on a building façade. J Acoust Soc Am.
2005;118(1):213-21.

4. May DN. Freeway noise and high-rise balconies. J. Acoust. Soc. Am. 1979;65(3):699-704.
5. Li XL, Tang SK, Yim SYC, Lee RYC, Hung T. Noise reduction of plenum windowson the façade of ahigh-rise residential building next to heavy road traffic. Build. Environ. 2020;186:107353.
6. Tang SK. The use of scatterer arrays to improve the sound transmission loss across plenum windows.
Proc InterNoise 2018; 26-29 August 2018; Chicago, USA 2018, Paper 2053.
7. Tang SK, Tong YG, Tsui KL. Sound transmission across a plenum window with an active noise
cancellation system. Noise Control Eng. J. 2016;64(4):423-31.
8. Huang HH, Qiu XJ, Kang J. Active noise attenuation in ventilation windows. J. Acoust. Soc. Am.
2011;130(1):176-88.
9. ISO 10140-5. Acoustics - Laboratory measurement of sound insulation of building elements - Part 5:
Requirements for test facilities and equipment. ISO 2021.
10. Hu Q, Tang SK. Active cancellation of sound generated by finite length coherent line sources using pistonlike secondary source arrays. J. Acoust. Soc. Am. 2019;145(6):3647-55.

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

ABS-0483

Aircraft noise prediction capability development: An overview offlight profile creation
Linus Yinn Leng ANG 1; Jian Rong YONG 2; Fangsen CUI 3; Hee Joo POH 4
1, 3, 4

Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research(A*STAR), Singapore 138632

2

School of Mechanical and Aerospace Engineering (MAE), Nanyang Technological University (NTU),
Singapore 639798

ABSTRACTThe outbreak of COVID-19 pandemic has led to the implementation of remote work as one of the safe
management measures at workplaces. Consequently, most of the population has been staying home in thedaytime—a period when the volume of air traffic may be higher. For residents staying near to an airport, theirwork concentration may be adversely affected by aircraft noise, not to mention that prolonged exposure could
cause health problems. Hence, it is crucial to consider how aircraft noise can be predicted using noise maps.
Before any noise management measures can be assessed for feasibility, aircraft noise prediction is firstnecessary to unveil how residential areas are affected. This paper presents an overview of the aircraft noise
prediction capability that has been developed based on ECAC Doc. 29. The computation procedure involves
flight profile creation, sound propagation physics correction, and noise map generation. The scope of thispaper is limited to flight profile creation. Critical output parameters were compared against that ofSoundPLAN. Results showed that the developed code can accurately compute both departure and arrival
flight profiles.
Keywords: Aircraft Noise, Environmental Noise, Noise Mapping

1. INTRODUCTION

Following the outbreak of COVID-19 pandemic, workplaces had to quickly implement remote
work as the default arrangement on short notice for employees who were able to do so. Online
collaboration and communication platforms have significantly contributed to the success of remote
work implementation. As a result, employees have been able to still make progress in their work withless risk of being infected by the coronavirus. Naturally, residential areas are now more populated in
the daytime compared to how it was before the pandemic. For areas located close to an airport,
residents may find that their work concentration is being adversely affected by aircraft noise. In thelong run, residents may also face health problems.
Previously, when travel restrictions for non-essential purposes were imposed in Singapore, cargo
and military flights could remain in operation. It may be logical to guess that the decline in passenger
flights would greatly reduce environmental noise. However, it is not the entirely the case. The
permitted flight operations continued to contribute outdoor noise in the daytime, causing disturbanceand annoyance to residents working from home. In some places, the air traffic was even reported tobe more than before. Expectedly, it led to a surge in the number of noise complaints.
In Montreal, Canada, residents participated in an interview to reveal how they were disturbed by
the increase in low-flying sightseeing flights while working from home in the daytime (1). The numberof sightseeing flights increased because some residents resorted to local tourism as a form of vacation.
The noise issue would worsen whenever low-flying helicopters flew over the housing estates. The
Linus_Ang@ihpc.a-star.edu.sg
a190061@e.ntu.edu.sg
3 cuifs@ihpc.a-star.edu.sg
4 pohhj@ihpc.a-star.edu.sg
12

residents eventually had to file complaints to the Government hoping for intervention.
Amid the pandemic, the number of cargo flights has increased to meet international demands forthe transportation of goods, commodities, and especially medical supplies (2). Locally, the Changi
Airport Group (3) reported an 8% increase in the number of cargo flights between January 2021 andNovember 2021 compared to pre-pandemic numbers recorded in 2019. Internationally, theInternational Air Transport Association (4) saw an 8% increase in the number of cargo flights in 2021,
likewise compared to pre-pandemic numbers recorded in 2019. They projected the increase to reach
13% in 2022. Evidently, residents staying near to an airport with heavy cargo air traffic are likely toexperience the same, if not more, amount of noise pollution despite having travel restrictions imposedon passenger flights. Consistent with the published statistics, Zipkin (5) reported an alarming surge
in the volume of noise complaints filed by residents in the United States and Canada due to the risein cargo air traffic.
During takeoff and landing, military aircraft installed with low-bypass turbojet engines can emit
loud noise into the environment—louder than that emitted by civil aircraft. The noise level can beunbearable to residents, adversely affecting their work concentration at that moment. Pre-pandemic,
military flights have been in operation during office hours. Back then, military aircraft noise was notseen as a major concern because majority of the residents were not home in the daytime. Currently,
with remote work arrangement being the norm, residents are now mostly staying home in the daytime
and bearing the brunt of military aircraft noise. In a Honolulu Civil Beat article, Knodell (6) reported
how residents in Oahu, Hawaii, became more aware of military flights after having to stay home inthe daytime due to the ongoing restrictions. At the same time, the nearby airfield increased their air
traffic for training purposes, causing more disturbance to surrounding residents. Formation flights
could take place as frequency as every hour.
As the world progresses beyond the pandemic, recent surveys (7, 8) revealed that the option towork from home may continue to stay in most workplaces. More employees—especially those withchildren and/or aged dependents at home—are keen in having the flexibility to work either physicallyin office or remotely from home (9). Top companies are even considering hybrid work arrangement
as a permanent employment benefit to attract talents (10). Inevitably, residential buildings are
expected to be more populated in the daytime than before to experience aircraft noise.
Before any noise management measures can be assessed for feasibility, aircraft noise prediction is
first necessary to unveil how residential areas around the airport are affected. This paper presents thecurrent progress of the aircraft noise prediction capability that has been developed based on ECAC
Doc. 29 (11). This approach is regarded as one of the best practice models to predict noise maps in apractical and straightforward manner. The computation procedure involves three main steps—flight
profile creation, sound propagation physics correction, and noise map generation. The scope of thispaper is limited to flight profile creation. Eventually, this capability will be integrated to a unified
urban environmental modeling tool for the design of highly livable and sustainable residential towns.
Unlike commercial software (e.g., SoundPLAN), this capability aims to allow reasonably accurate
predictions to be achieved with essential input data from the users. Section 2 presents the computation
procedure used to create the flight profiles. Section 3 compares the computation output between thedeveloped code and SoundPLAN for validation. Lastly, Section 4 concludes with the main findings.

2. COMPUTATION PROCEDURE

In the creation of flight profile, it is required for the user to input the flight procedural steps(departure and/or arrival) of the chosen aircraft. By default, the flight procedural steps are retrieved
from the Aircraft Noise and Performance (ANP) database (12), which is the accompanying opensource aircraft database for ECAC Doc. 29. To further elaborate on the computation procedure, theLockheed C-130 Hercules (C-130H) aircraft was chosen as the example. In practice, aircraft generally
do not make any turns during departure and arrival unless required for special circumstances—avoid
flying past noise-sensitive areas, for example. Hence, the aircraft was assumed to be flying straight.
This section first discusses the creation of departure flight profile (Section 2.1) before moving on to
discuss the creation of arrival flight profile (Section 2.2). Unless otherwise stated, all equations are
given in ECAC Doc. 29.
2.1 Departure Flight Profile Creation
Table 1 shows the default departure procedural steps of the C-130H aircraft retrieved from the ANP
database. These procedural steps describe how the aircraft should be operated by default in the caseof a non-stop flight. In practice, the pilot selects the appropriate engine power, flap angle, andcalibrated airspeed (CAS) to fulfil the conditions in each step (13). Some of the steps involve asignificant change in CAS (> 10 m/s), which, in turn, leads to a huge variation in engine power. As
engine power and slant distance are the two key parameters that determine the noise level at any
observer location, any change in engine power will affect the noise level. This relationship isrepresented by the noise-power-distance (NPD) curves, which are also provided in the ANP database.
Further elaboration is beyond the scope of this paper because NPD curves are only required in thenext step of the computation procedure—sound propagation physics correction.
Table 1 – Default departure procedural steps of the C-130H aircraft retrieved from the ANP database (12).
Last column summarizes the equations used for each step.
ThrustRate of Climb

Ending CAS

Equation(ft)(ft/min)(kt)

Number132.0

1–6

Step Type

1

Takeoff

Max Takeoff

17

2

Climb

Max Takeoff

17

3

Accelerate

Max Takeoff

17

1,423

142.0

10–15

4

Accelerate

Max Climb

ZERO

1,068

162.0

10–15

5

Climb

Max Climb

ZERO

6

Accelerate

Max Climb

ZERO

7

Climb

Max Climb

ZERO

5,500

3, 8, 98

Climb

Max Climb

ZERO

7,500

3, 8, 99Climb

Max Climb

ZERO

10,0003, 8, 9Rating

Flap ID

Ending Altitude

No.

1,000

7–9

3,000

3, 8, 9
1,000

200.0

10–15

2.1.1 Takeoff Ground Roll
Most, if not all, aircraft commence departure with takeoff (Step 1 in Table 1). The flight pathcreation starts at the brake-release point with the aircraft is assumed to be at a complete stop—acommon situation in practice. At the lift-off point, the CAS 𝑉𝑉C,LO can be approximated by(1)
𝑉𝑉C,LO ≈ 𝐶𝐶 ∙ �𝑊𝑊TO
0.5 ; and 𝑊𝑊
where 𝐶𝐶 denotes the aerodynamic coefficient due to the flap setting in kt/lb
TO denotes thegross takeoff weight of the aircraft in lb. Both 𝐶𝐶 and 𝑊𝑊TO are provided in the ANP database. In thiscase, the approximated CAS at the lift-off point 𝑉𝑉C,LO is 132 kt as given in Table 1.
From the brake-release point to the lift-off point, the flight path along the level runway issegmented in a way to ensure that the change in CAS does not exceed 10 m/s. Before the number ofsegments can be determined, it is required to compute the equivalent takeoff ground roll distance 𝑠𝑠TO
by
𝑠𝑠TO =𝐵𝐵 ∙ 𝜃𝜃 ∙ (𝑊𝑊TO⁄𝛿𝛿 )2
𝑁𝑁 ∙ (𝐹𝐹n /𝛿𝛿)(2)

where 𝐵𝐵 denotes the aerodynamic coefficient due to the flap setting under the standard headwind(8 kt) in ft/lb; 𝜃𝜃 denotes the ratio of the air temperature at the given altitude to the standard airtemperature at mean sea level (15°C); 𝛿𝛿 denotes the ratio of the pressure at the given altitude to thestandard pressure at mean sea level (101.325 kPa); 𝑁𝑁 denotes the number of engines; and 𝐹𝐹n⁄𝛿𝛿denotes the corrected net thrust per engine in lbf.
The corrected net thrust per engine (engine power) is computed depending on the type of engines
mounted on the aircraft. For turboprop aircraft (C-130H), the corrected net thrust per engine 𝐹𝐹n⁄𝛿𝛿 iscomputed by𝐹𝐹n⁄𝛿𝛿 =326 ∙ 𝜂𝜂 ∙ 𝑃𝑃P ⁄𝑉𝑉T𝛿𝛿

(3)

where 𝜂𝜂 denotes the propeller efficiency; 𝑃𝑃P denotes the net propulsive power in hp; and 𝑉𝑉T denotes
the true airspeed (TAS) in kt, which can be estimated from the CAS by

(4)𝑉𝑉T = 𝑉𝑉C ⁄𝜎𝜎 0.5
where 𝜎𝜎 denotes the ratio of the air density at the given altitude to the standard air density at meansea level (1.225 kg/m3). Both 𝜂𝜂 and 𝑃𝑃P for the different thrust ratings (Table 1) are provided in the
ANP database.
After computing the equivalent takeoff ground roll distance 𝑠𝑠TO, the number of segments 𝑛𝑛TO canthen be determined by(5)𝑛𝑛TO = int�1 + 𝑉𝑉C,LO ⁄10�
where 𝑉𝑉C,LO denotes the approximated CAS at the lift-off point (kt). To account for the varying
distance covered due to acceleration, the length of every segment 𝑘𝑘 is computed by(2𝑘𝑘 − 1) ∙ 𝑠𝑠TO
(6)
𝑠𝑠TO,𝑘𝑘 =for (1 ≤ 𝑘𝑘 ≤ 𝑛𝑛TO )𝑛𝑛TO ∙ 𝑛𝑛TO
As 𝑘𝑘 increases, the segment length increases. Engine power and CAS are assumed to increase linearly
along the segments (11).

2.1.2 Initial Climb
Upon lift-off, the aircraft enters the initial climb (Step 2 in Table 1) up to an altitude of 1,000 ft.
In this step, the CAS remains constant, but the engine power changes with altitude (Equation 3). The
flight path is segmented in a way to ensure that the change in lateral attenuation is kept within 1.5
dB—a limit established based on past studies (11). To fulfil this criterion, the flight path must be
segmented based on the following list of reference altitudes in ft: [62, 136, 224, 335, 484, 705, 1099,
2000, 4231]. However, it is common for the ending altitude of the initial climb (1,000 ft) to not exactly
match the values within the list. In this case, the list of reference altitudes can be scaled accordingly
by first identifying the reference altitude closest to the ending altitude (1,000 ft) and then apply
𝑧𝑧𝑖𝑖 = 𝑧𝑧e ∙ (𝑧𝑧𝑖𝑖′⁄𝑧𝑧𝑁𝑁′ ) for (1 ≤ 𝑖𝑖 ≤ 𝑁𝑁)(7)′
where 𝑧𝑧𝑖𝑖 denotes the scaled reference altitude; 𝑧𝑧e denotes the original ending altitude; 𝑧𝑧𝑖𝑖 denotes
the altitude at the 𝑖𝑖 th element of the reference list; and 𝑧𝑧𝑁𝑁′ is the altitude at the 𝑁𝑁th element of thereference list that is nearest to 𝑧𝑧e. All arguments are expressed in ft. In the present case where theoriginal ending altitude 𝑧𝑧e is 1,000 ft, the nearest reference altitude 𝑧𝑧7′ is the 7th element (1,099 ft).
Subsequently, the list of scaled altitudes would include 𝑧𝑧1 to 𝑧𝑧7. This scaling method preserves theoriginal ending altitude without any impact on the data in Table 1.
The segment length (ground distance covered) Δ𝑠𝑠 is computed by(8)
Δ𝑠𝑠 = (𝑧𝑧2 − 𝑧𝑧1 )⁄tan 𝛾𝛾̅
where 𝑧𝑧2 and 𝑧𝑧1 denote the ending and starting altitudes of the segment, respectively, in ft; and 𝛾𝛾̅ isthe average climb angle along the segment in rad, which is obtained by������
𝐹𝐹n⁄𝛿𝛿
𝑅𝑅(9)𝛾𝛾̅ = asin �𝐾𝐾 ∙ �𝑁𝑁eng ∙−�����������
𝑊𝑊TO⁄𝛿𝛿 cos 𝜀𝜀where 𝑅𝑅 denotes the ratio of the aircraft’s drag coefficient to the lift coefficient due to the flap setting;
𝜀𝜀 denotes the bank angle in rad; 𝑁𝑁eng denotes the number of engines; and 𝐾𝐾 denotes the speeddependent constant accounting for the effects of climbing into the standard headwind (8 kt). If theaverage CAS along the segment is less than 200 kt, 𝐾𝐾 is equal to 1.01. Or else, 𝐾𝐾 is equal to 0.95.

2.1.3 Accelerating Climb
After the initial climb, the aircraft begins to accelerate up to the ending CAS given in Step 3(Table 1). In this step, the ending altitude is iteratively computed. The same computation procedure
is applied to Steps 4 and 6 (Table 1). To start, ECAC Doc. 29 recommends adding 250 ft to the starting
altitude 𝑧𝑧1 as the initial estimate of the ending altitude 𝑧𝑧2,est. Aircraft performance parameters at theestimated ending altitude are computed accordingly. Subsequently, the segment length (grounddistance covered) is then estimated by𝑠𝑠 = 1.35 ∙2
2− 𝑉𝑉T1
𝑉𝑉T2
𝑎𝑎max − 𝐺𝐺 ∙ 𝑔𝑔

(10)

where 𝑉𝑉T2 and 𝑉𝑉T1 denote the ending and starting TAS of the segment, respectively, in kt; 𝑔𝑔 denotes
the gravitational force (32.174 ft/s 2); 𝐺𝐺 denotes the climb gradient; and 𝑎𝑎max denotes the maximum
acceleration of the segment in ft/s 2 . The climb gradient 𝐺𝐺 is approximated by𝐺𝐺 =𝑅𝑅𝑅𝑅𝑅𝑅
101.28 ∙ ���
𝑉𝑉T

(11)

���
where 𝑅𝑅𝑅𝑅𝑅𝑅 denotes the rate of climb in ft/min (Table 1); and 𝑉𝑉
T denotes the average TAS of thesegment in kt. The maximum acceleration 𝑎𝑎max is determined by������
𝐹𝐹n⁄𝛿𝛿
𝑅𝑅(12)
𝑎𝑎max = 𝑔𝑔 ∙ �𝑁𝑁eng ∙−����������
𝑊𝑊TO⁄𝛿𝛿 cos 𝜀𝜀′After obtaining the estimated 𝑠𝑠 (Equation 10), the ending altitude 𝑧𝑧2,est
is estimated again by′
𝑧𝑧2,est = 𝑧𝑧1 + 𝑠𝑠 ∙ 𝐺𝐺 ⁄0.95(13)′
If �𝑧𝑧2,est− 𝑧𝑧2,est � does not fall within the predefined tolerance, the iteration process (Equations 10–′13) continues with the newly estimated ending altitude 𝑧𝑧2,est. Aircraft performance parameters are′recomputed accordingly. Once �𝑧𝑧2,est − 𝑧𝑧2,est � falls within the predefined tolerance, the current
estimated ending altitude becomes the finalized ending altitude for the step.
Lastly, the flight path is segmented similarly to ensure that the change in CAS does not exceed
10 m/s (Equations 5–6). However, for accelerating climb, the starting CAS 𝑉𝑉C1 must be included in
the computation because it is non-zero. As such, Equation 5 becomes(14)
𝑛𝑛 = int(1 + |𝑉𝑉C2 − 𝑉𝑉C1 |⁄10)
while Equation 6 becomes

𝑠𝑠𝑘𝑘 = [𝑉𝑉C1 + (𝑉𝑉C2 − 𝑉𝑉C1 ) ∙ (𝑘𝑘 − 0.5)] ∙2 ∙ 𝑠𝑠for (1 ≤ 𝑘𝑘 ≤ 𝑛𝑛)(𝑉𝑉𝑛𝑛 ∙ C2 + 𝑉𝑉C1 )(15)

2.1.4 Constant Speed Climb
When the aircraft achieves the required CAS, it stops accelerating. After which, the aircraft climbs
to the ending altitude at constant CAS (Steps 5, 7, 8, and 9 in Table 1). The computation procedure issimilar to that of initial climb (Section 2.1.2) except that no segmentation is required. The reason is
because there is no change in CAS along the flight path (i.e., ΔCAS < 10 m/s). The segment length(ground distance covered) Δ𝑠𝑠 is computed by Equation 8 (together with Equation 9), while the engine
power is determined by Equation 3.

2.2 Arrival Flight Profile Creation
Table 2 shows the default arrival procedural steps of the C-130H aircraft retrieved from the ANP
database. By default, the average descent angle is fixed at -3° (Steps 1–5 in Table 2). The touchdown
distance 𝑠𝑠TD is given as 341.1 ft (Step 5 in Table 2), while the stop distance 𝑠𝑠stop is given as 3069.9
ft (Step 6 in Table 2).
Table 2 – Default arrival procedural steps of the C-130H aircraft retrieved from the ANP database (12).
Last column summarizes the equations used for each step.
Starting AltitudeStarting CAS

Starting Thrust

Equation

(ft)

(kt)

(% Max Thrust)

Number

ZERO

6,000

200.0

14, 16–18

Descend

ZERO

3,000

165.8

14, 16–18

3

Descend

U-INTR

1,500

145.8

14, 16–18

4

Descend

D-35

1,000

135.8

7, 8, 19

5

Land

D-35

6

Decelerate

128.9

40.0

14

7

Decelerate

30.0

10.0

-

No.

Step Type

Flap ID

1

Descend2

14, 202.2.1 Decelerating Descent
Typically, pilots initiate preparation for arrival at an altitude of around 30,000 ft. However, aircraft
noise would only impact the environment when they are closer to the ground. Hence, the arrival flightpath creation usually starts from the altitude of 6,000 ft to the end of runway (11, 12). From Steps 1
to 3 (Table 2), the flight path is segmented via Equation 14 (i.e., ΔCAS < 10 m/s). The engine power
is determined differently (not by Equation 3) depending on the engine type. For turboprop aircraft (C130H), the corrected net thrust per engine 𝐹𝐹n⁄𝛿𝛿 is determined by
𝑊𝑊LD 𝑅𝑅 ∙ cos 𝛾𝛾̅ + sin 𝛾𝛾̅ + 𝑎𝑎⁄𝑔𝑔
𝐹𝐹n⁄𝛿𝛿 =
∙
(16)
𝛿𝛿
𝑁𝑁eng
where 𝑊𝑊LD denotes the gross landing weight of the aircraft in lb; and 𝑎𝑎 denotes the deceleration of
the segment in ft/s2 computed by𝑎𝑎 =(𝑉𝑉G2 ⁄cos 𝛾𝛾̅ )2 − (𝑉𝑉G1 ⁄cos 𝛾𝛾̅ )22 ∙ ∆𝑠𝑠⁄cos 𝛾𝛾̅

(17)where 𝑉𝑉G2 and 𝑉𝑉G1 denote the ending and starting groundspeeds of the segment, respectively, in kt
obtained by
𝑉𝑉C ∙ cos 𝛾𝛾̅
𝑉𝑉G =
− 𝑤𝑤
(18)
√𝜎𝜎

where 𝑤𝑤 denotes the headwind in kt. Equations 16–18 suggest that higher engine power is required
to overcome higher headwind so that deceleration can be maintained. By default, the standard
headwind (8 kt) is considered. In Equation 16, 𝑊𝑊LD is provided in the ANP database.

2.2.2 Final Descent
At the altitude of 1,000 ft, the aircraft enters its final descent (Step 4 in Table 2) to the landing
threshold point of a runway. This point refers to the start of a runway where landing can take place(𝑠𝑠 = 0 in Figure 1). The computation procedure follows that of the initial climb, but in a reverse
manner. To elaborate, the flight path is segmented according to the same list of reference altitudes
given in Section 2.1.2 but sorted in descending order. Therefore, 𝑧𝑧e now denotes the original starting
altitude instead of the ending altitude in Equation 7. The altitude at the landing threshold point can be
determined by trigonometry because descent angle and touchdown distance are both known. In thisstep, the CAS remains constant, while the engine power is computed as an average by���������𝑊𝑊LD ⁄𝛿𝛿
sin 𝛾𝛾̅������
(19)𝐹𝐹n⁄𝛿𝛿 =∙ �𝑅𝑅 +
�
𝑁𝑁eng
1.03

Figure 1 – Schematic diagram of aircraft arriving at the runway after final descent. 𝑠𝑠TD denotes thetouchdown distance in ft; 𝑠𝑠stop denotes the stop distance in ft; and 𝑠𝑠rwy denotes the runway length in ft.
All arguments are known.

2.2.3 Touchdown
After the final descent, the aircraft starts to touchdown from 𝑠𝑠 = 0 to 𝑠𝑠 = 𝑠𝑠TD in Figure 1 (Step 5
in Table 2). Like how the CAS at the lift-off point can be approximated (Equation 1), the CAS at thetouchdown point 𝑉𝑉C,TD can also be approximated by(20)
𝑉𝑉C,TD ≈ 𝐷𝐷 ∙ �𝑊𝑊LD
0.5
where 𝐷𝐷 denotes the aerodynamic coefficient due to the flap setting in kt/lb . For some aircraft,
Equation 20 may underapproximate such that the output is lower than the starting CAS given in Step 6(Table 2). In such cases, the developed code was designed to assign the higher value as the CAS at
the touchdown point 𝑉𝑉C,TD . With the starting and ending CAS, the flight path is then segmented via
Equation 14 (i.e., ΔCAS < 10 m/s). The engine power is set at 40% of the maximum sea level staticthrust (Step 6 in Table 2). For C-130H aircraft, the maximum sea level static thrust is 8,026 lbf asprovided in the ANP database.
2.2.4 Reverse Thrust Activation and Taxiing
From the touchdown point to the taxiing point (Figure 1), the aircraft decelerates linearly to 30 kt,
which is the standard CAS for taxiing (Step 7 in Table 2). Again, the flight path is segmented via
Equation 14 (i.e., ΔCAS < 10 m/s). After covering 10% of the stop distance 𝑠𝑠stop, the aircraft reaches
the reverse thrust activation point where the engine power is set at 20% of the maximum sea levelstatic thrust. This percentage is based on the guideline given in ECAC Doc. 29. To always fulfil thisrequirement, the developed code was written to intentionally segment the flight path at the reverse
thrust activation point. To elaborate, the preceding and succeeding segments would always end andstart at this point, respectively. At the taxiing point, the engine power is set at 10% of the maximum
sea level static thrust (Step 7 in Table 2). Unless otherwise stated, engine power and CAS are assumed
to decrease linearly along the segments (11).
The last part of the arrival process from the taxiing point to the end of runway is represented by
one segment where all aircraft performance parameters remain unchanged. These parameters would
be identical to those computed at the taxiing point.

3. VALIDATION OF COMPUTATION OUTPUT

To pinpoint where should future developmental work be focused on for higher accuracy, thecomputation output was compared against that of SoundPLAN (version 8.2). For consistency, thecomputation procedure in SoundPLAN was set to also follow ECAC Doc. 29 based on a straight flightpath for the C-130H aircraft (11). The critical output parameters include altitude, CAS, engine power,
and ground distance. The first three parameters (i.e., ordinate) are plotted against the ground distance(i.e., abscissa). Sections 3.1 and 3.2 discuss the results comparison for departure and arrival,
respectively.
3.1 Departure
Figure 2 compares the computation output between SoundPLAN and the developed code. Unlike
the developed code, SoundPLAN did not explicitly provide details of all flight segments. Instead, it
provided only the information at the start and end of every procedural step. Owing to this limitation,
the computation output was validated by comparing the parameter values at the start and end of every
procedural step. At these points, the values should be identical (Table 1).

Figure 2 – Comparison of critical output parameters during departure: (a) altitude, (b) CAS, and (c) engine
power. Solid line markers indicate the start and end of the flight segments, while dashed line markers
indicate the start and end of the procedural steps.

Compared to the output of SoundPLAN, the developed code could achieve good accuracy for
altitude, CAS, and ground distance (Figures 2a and 2b). For engine power (Figure 2c), good accuracy
was achieved after Step 5. Before this point, the overall trend was consistently predicted but withdiscrepancies. They were attributed to the consideration of different engine powers during takeoff
ground roll (Step 1 in Table 1) and the iterative process involved in the computation during the firsttwo accelerating climbs (Steps 3 and 4 in Table 1).
In practice, engine power during takeoff is derated by between 10% and 20% to prolong engine
life and reduce noise emission. If considered, ECAC Doc. 29 states that the engine power for theremaining flight must be cutback by 10%. As noise emission increases with engine power, thedeveloped code adopted a conservative approach by considering the lower limit of the percentage
range (10%). Hence, at the brake-release point, the developed code derated the engine power to 90%(7,223 lbf) of the maximum sea level static thrust (8,026 lbf). At lift-off, the engine power wascomputed by Equation 3, derated to 90% (7,918 lbf). In SoundPLAN, the engine power was treatedas constant (7,738 lbf) throughout the takeoff ground roll. Although ECAC Doc. 29 states that engine
power is expected to change along the runway, evidence can be found to support otherwise (14). To
obtain the engine power of 7,738 lbf, SoundPLAN applied the thrust rating for maximum climb(𝑃𝑃P = 3,575 hp) in Equation 3. But Table 1 (Step 1) shows that the thrust rating for maximum takeoff(𝑃𝑃P = 4,205 hp) should be applied, explaining why the developed code computed the lift-off engine
power as 7,918 lbf (90% of 8,978 lbf). And since the lift-off engine power would then be equal to thatat the start of initial climb, the discrepancies in engine power from the brake-release point to the endof initial climb were expected.
Although the developed code adopted the recommended iteration process for every accelerating
climb (Section 2.1.3), discrepancies in CAS and engine power can still be observed between the startof Step 3 and the end of Step 5 (Figures 2b and 2c). Apart from the two accelerating climbs, thetransition zone contributed to the discrepancies too. In practice, the transition zone occurs at somepoint after lift-off to cutback engine power for lower emitted noise and longer engine life (11). Thistransition typically lasts for 3–5 s, covering a ground distance of 1,000 ft. To maintain consistency inmodeling different aircraft, the transition zone was assumed to always take place when the thrust
rating changed from maximum takeoff to maximum climb. The onset of the transition zone is shownin Figure 2c by the abrupt drop in engine power over a ground distance of 1,000 ft.
Owing to the discrepancies in engine power discussed above, other parameters were, in turn,affected. Although insignificant, the total ground distance was underpredicted by 1.4% (205,558 ft
instead of 208,494 ft), suggesting that the aircraft completed its departure slightly earlier than
expected (i.e., reaching the altitude of 10,000 ft). For CAS, the collective influence of the variationin engine power led to the discrepancies from Step 3 to 4 (Figure 2b).
3.2 Arrival
Figure 3 compares the computation output between SoundPLAN and the developed code. Owing
to the limitation as elaborated in Section 3.1, the validation was done by comparing the parametervalues at the start and end of every procedural step. At these points, the values should be identical(Table 2).
Compared to the output of SoundPLAN, the developed code could predict high accuracy for
altitude, CAS, and ground distance (Figures 3a and 3b). In the developed code, a runway length wasconsidered. Hence, the aircraft was predicted to travel farther by 7.5% (126,548 ft instead of
117,736 ft) for one more flight segment after the taxiing point. This last segment represents the aircraft
movement from taxiing point to the end of runway as described in Section 2.2.4.
Figure 3c shows that the engine power was underestimated from 0 ft to 85,865 ft (Steps 1 and 2 inTable 2). The discrepancies could be attributed to the different values applied in Equations 16–18. For
example, at the ground distance of 72,013 ft, the difference in engine power may seem large(Figure 3c). However, it is important to understand the underlying reason before concluding it as adiscrepancy. At this point of the flight segment, the flap setting should be ZERO (Step 2 in Table 2).
Therefore, the developed code computed the engine power based on the same corresponding
aerodynamic coefficient from the preceding step ( 𝑅𝑅 = 0.08), explaining why the engine power
continued to drop linearly following the same trend in the preceding step. In SoundPLAN, only the
information at the start and end of every procedural step was provided. Hence, without theintermediate point, the straight line joining the two procedural steps created the perceived large
difference in engine power. To minimize the remaining discrepancies, further investigation will berequired to verify the computational procedure pertaining to decelerating descent (Section 2.2.1) inthe developed code.

Figure 3 – Comparison of critical output parameters during arrival: (a) altitude, (b) CAS, and (c) engine
power. Solid line markers indicate the start and end of the flight segments, while dashed line markers
indicate the start and end of the procedural steps.
At the touchdown point, the developed code overestimated the engine power (Figure 3c)—3,210
lbf instead of 2,825 lbf. Despite this difference, the developed code followed the recommendation
given in the ANP database in which the engine power was 40% of the maximum sea level static thrust(Step 6 in Table 2). In SoundPLAN, it is unclear why the engine power was 35% of the maximum sea
level static thrust instead. Hence, it is justifiable to preserve the current value (40%) in the developed
code unless there is evidence to support otherwise.

4. CONCLUSION

Owing to hybrid work arrangement, residential buildings are expected to be more populated in thedaytime than before. As more flight operations may take place in the daytime, aircraft noise may cause
residents to lose focus and affect their work quality, not to mention that prolonged exposure could
lead to health problems. To iterate, this work aims to integrate aircraft noise prediction capability(built upon ECAC Doc. 29) to a unified urban environmental modeling tool for the design of highly
livable and sustainable residential towns. This capability differentiates itself from commercial
software by allowing reasonably accurate predictions to be achieved with essential input data fromthe users. This paper has presented how the developed code can create both departure and arrival
flight profiles based on the default procedural steps. The comparison of critical output parameters
between SoundPLAN and the developed code has demonstrated the accuracy of the latter. In
conclusion, both departure and arrival flight profiles can be accurately computed by the developed
code. Future work will focus on developing the algorithm for sound propagation physics correction
before noise levels can be predicted.

ACKNOWLEDGMENTS

The authors would like to thank IHPC for providing support in computational resources and the
project members of IEM2 for their discussions and inputs.

REFERENCES

1. Gyulai L. Oh, the noise! How the pandemic sensitized Montrealers to sound [Internet]. Montreal Gazette.
2021 [cited 8 June 2022]. Available from: https://montrealgazette.com/news/local-news/first-it-wasorange-cones-now-its-air-traffic

2. Twinn I, Qureshi N, Conde M, Guinea C, Rojas D. The impact of COVID-19 on logistics. International
Finance Corporation; 2020.
3. Ong J. Changi Airport sees strong cargo recovery as freight volumes reach pre-Covid-19 levels [Internet].
The
Straits
Times.
2022[cited8June2022].
Availablefrom:https://www.straitstimes.com/singapore/transport/strong-cargo-recovery-at-changi-airport-as-freightvolumes-reach-pre-pandemic-levels
4. Key to air cargo resilience post pandemic: cooperation, safety, sustainability, modernization [Internet].
International Air Transport Association. 2021 [cited 8 June 2022]. Available from:https://www.iata.org/en/pressroom/2021-releases/2021-10-12-01/
5. Zipkin A. As online buying surges, so do noisy cargo flights [Internet]. The New York Times. 2020 [cited
8 June 2022]. Available from: https://www.nytimes.com/2020/04/15/business/cargo-planes-deliveriesnoise.html
6. Knodell K. Military pilots try to keep Hawaii skies quiet. Here's why it's hard [Internet]. Honolulu Civil
Beat. 2021 [cited 8 June 2022]. Available from: https://www.civilbeat.org/2021/04/military-pilots-try-tokeep-hawaii-skies-quiet-heres-why-its-hard/
7. Chew H. IN FOCUS: Thinking out of the cubicle—what lies ahead for hybrid working? [Internet]. CNA.
2021 [cited 8 June 2022]. Available from: https://www.channelnewsasia.com/singapore/office-homefuture-hybrid-work-flexible-wfh-in-focus-225656
8. Malkani D. Going hybrid: The future of work is here [Internet]. Forbes. 2021 [cited 8 June 2022]. Available
from: https://www.forbes.com/sites/forbestechcouncil/2021/06/04/going-hybrid-the-future-of-work-ishere/?sh=37521ef42cb9
9. Boo K. Singapore workers want bosses to meet their flexi-work needs: IPS survey [Internet]. The Straits
Times. 2022 [cited 8 June 2022]. Available from: https://www.straitstimes.com/singapore/jobs/sporeworkers-want-bosses-to-listen-to-their-flexi-work-needs-ips-survey
10. Lee V. How Singapore firms are ramping up employee perks in fight for talent [Internet]. The Straits
Times. 2022 [cited 8 June 2022]. Available from: https://www.straitstimes.com/life/firms-in-singaporeembrace-hybrid-work-employee-perks-in-fight-for-talent
11. ECAC. Doc. 29 Vol. 2: Report on Standard Method of Computing Noise Contours around Civil
Airports—Technical Guide. Paris, France: European Civil Aviation Conference; 2016.
12. Eurocontrol. Aircraft Noise and Performance (ANP) Database [Internet]. Eurocontrol Experimental
Center. 2022 [cited 8 June 2022]. Available from: https://www.aircraftnoisemodel.org/
13. ECAC. Doc. 29 Vol. 1: Report on Standard Method of Computing Noise Contours around Civil
Airports—Applications Guide. Paris, France: European Civil Aviation Conference; 2016.
14. Sherry L. Improving the accuracy of airport emissions inventories using disparate datasets. IIE
Transactions. 2014;47(6):577-585.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022,in Gyeongju, Korea
Extended abstractABS-0085

Reliability evaluation of road traffic noise prediction modelfor highway noise evaluation
Jewon Yoon1; Chulhwan Kim2; Woongyong Lee3; Hyejin Kang4
1

Unison Technology Corporation, Korea

2~4

Korea Expressway Corporation Research Institute, Korea

ABSTRACTThe purpose of this study is to evaluate the reliability of prediction models(KHTN, RLS-90, CRTN,
NMPB-08) that are widely used in road traffic noise analysis. For this purpose, the accuracy and difference
values of the prediction model were analyzed by comparing the measurement values performed at the total of
21 highway sites, reflecting various conditions such as road structure, road pavement type, and noise barrier
installation. In addition, the correlation between commercial programs(SoundPlan, CadnaA) was comparedand reviewed for each of the same prediction models. First of all, as a result of analyzing the accuracy of eachprediction model, KHTN is rated as 92.8% the most accurate based on ±3 dB error range. And CRTN is rated
as 74.0~76.8% the most accurate among prediction models inherent in commercial programs. And as a resultof analyzing the correlation between commercial programs for prediction models, CRTN is 100% highly
correlated and NMPB has the lowest correlation by 69.6%.
Keywords: Highway traffic noise prediction model(KHTN, RLS-90, NMPB-08, CRTN)

1. INTRODUCTION
After 2000s land development for constructing large-scaled apartment houses in Korea and somerevisions of house constructing acts are relieved for house-construction friendly. Because of this,high-rise and large-scales apartment houses are constructed nearby highways. And recently, apartment
house are constructed very variously in it’s shapes, heights and building distance between highways.
For designing the noise mitigation plan of these types of apartment house, 3D noise analysis should be
general because 2D noise analysis cannot consider design factors analysis along the highway. The
purpose of this study is to evaluate the reliability of prediction models(KHTN, RLS-90, CRTN,
NMPB-08) that are widely used in 3D road traffic noise analysis. For this purpose, the accuracy anddifference values of the prediction model were analyzed by comparing the measurement values
performed at the total of 21 highway sites, reflecting various conditions such as road structure, roadpavement type, and noise barrier installation.

2. RESULTS AND DISCUSSION
2.1 Noise measurement on sites
In order to verify the validity of the 3D noise prediction model, field noise measurements were
performed in consideration of various conditions in the area around the highway. The measurement
was conducted at a total of 21 sites(125 points, repeated twice), and the site was selected by different
types of pavement(asphalts, concrete) and road structures(embarkment, flat land, bridges, noisebarrier). For each site, the results were compared by measuring twice at a time interval of 4 hours or
more, and noise measurement was performed at 6 points per site for 30 minutes at the same time, but
an equivalent noise(L Aeq) was calculated after removing unusual noise except highway noise. In
1jwyoon@unisontg.com
c.h.kim@ex.co.kr
3
knulp@ex.co.kr
4hjin@ex.co.kr
2

addition, the traffic volume was measured at the same time as the measurement time period, and thiswas doubled to calculate the traffic volume per hour.
2.2 Noise simulations
To analyze the accuracy of each prediction model for a total of 21 measurement sites, a 3D noise
evaluation model was established for the entire measurement site, and the predictions by KHTN,
CRTN, RLS-90, and NMPB prediction models and measurements were compared. For the commercial
program version, CadnaA used V4.4 and SoundPlan used V7.4.
2.3 Results and discussions
As a result of analyzing the arithmetic mean value of noise level difference between prediction andmeasured values, KHTN is the smallest at 1.8 dB among the prediction models. And, the accuracy ofeach prediction model, KHTN is rated as 92.8% the most accurate based on ±3 dB error range, and
CRTN is rated as 74.0~76.8% the most accurate among prediction models inherent in commercial
programs. And the correlation between commercial programs for prediction models, CRTN is 100%highly correlated and NMPB has the lowest correlation by 69.6%.
Table 1 - Arithmetic mean value of noise level difference between prediction and measured values in [dB]

Prediction model
Total(21 sites, 250 data)
Noise barrier (6 sites, 72 data)
Road side (1 site, 12 data)Road
structure Embarkment (9 sites, 106 data)
Flat land (1 sites, 12 data)
Bridge (4 sites, 48 data)
Concrete (13 sites, 156 data)
Pavement
Asphalt (8 sites, 94 data)*) CA : CadnaA, SP : SoundPlan

KHTN
2007
1.8
1.9
2.9
1.7
1.2
1.5
1.6
2.1

RLS-90CA SP
3.6 4.1
2.7 3.2
0.4 0.3
4.6 4.92.9 2.9
3.5 5.1
3.4 4.2
3.9 4.0

CRTN
CA SP
2.2 2.4
1.7 1.8
3.8 3.6
2.6 3.0
2.0 1.9
1.8 1.7
1.9 2.2
2.7 2.8

Table 2 - Accuracy of prediction model in ±3 dB
RLS-90
CRTN
KHTN
Prediction model
2007
CA
SP
CA
SP
Total(21 sites, 250 data)
92.8 %
48.0 % 40.8 % 76.8 % 74.0 %NMPB-08CA SP
4.2 2.4
3.5 2.2
1.6 2.7
5.8 2.6
2.8 1.1
2.6 2.7
3.4 2.1
5.4 3.0

NMPB-08CA
SP
44.0 % 73.6 %Table 3- Correlation between commercial simulation program(CA & SP) in ±3 dB
Prediction model
RLS-90CRTN
NMPB-08
Total(21 sites, 250 data)
98.8 %
100.0 %
69.6 %(a) KHTN(b) RLS-90(c) CRTN(d) NMPB-08
Figure 1–Comparison of noise level distribution between measured and predicted values

REFERENCES
1. RLS-90, Richtlinien fȕf den Lärmschutz an Straßen(Road noise abatement guidelines);1990.
2. Department of Transport Welsh Office, Calculation of Road Traffic Noise(CRTN);1988.
3. F.Besnard, B.Gauvreau, G.Dutille, M.Bérengier, J.Defrance. Road noise prediction - Noise propagation
computation method including meteorological effects(NMPB 2008), Sétra;2009.
4. User's manual; KHTN, CadnaA, SounPlan

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0137

3D simulation of the effect of a soundproof tunnel made ofmembrane material on noise reduction
Jisu YOO1; Seo Il CHANG2; Myung Hyun NOH3, Jang Bog LEE4
1

University of Seoul, Republic of Korea

2

University of Seoul, Republic of Korea
3

4

POSCO, Republic of Korea

Dong-A Spatial Technology, Republic of Korea

ABSTRACTThe soundproof tunnel blocks direct sound and minimizes reflected sound, resulting in an outstanding noise
reduction effect. However, soundproof tunnels require high installation costs, and only the panels which meet
the acoustic performance standards required for noise barrier walls can be used for the soundproof tunnel andtherefore, the excessive requirement caused heavier and more expensive tunnel than necessary. It is important
to ensure economic efficiency and reduced weight of the soundproof tunnel, but it is difficult to develop thepanel which is both light and economical while providing the same acoustic performance as laminated glass
and polycarbonate used in noise barrier walls. Hence, this study tried to validate marketability by suggestinga fabric membrane as a material for soundproof tunnel panels and evaluating the noise reduction effect, eventhough it does not meet existing acoustic performance standards. The noise reduction effect of the membranestructured soundproof tunnel was investigated using 3D modeling compared to the noise barriers using theexisting panel. Furthermore, field measurements were performed for full-scale membranestructured soundproof tunnel already installed for other purpose, and the suitability of the fabric membrane
as a panel material was demonstrated by comparison to the results of 3D modeling.
Keywords: Road-traffic noise, Soundproof tunnel, Membrane material, 3D modeling, Noise reduction

1. INTRODUCTION
Soundproof tunnel can simultaneously block both reflected and diffracted sounds that thesoundproof wall might generate. Therefore, it is attracting attention as a representative technologythat can maximize the effect of reducing road traffic noise in urban areas, and the demand forsoundproof tunnel installation is gradually increasing in the Republic of Korea. Even though Republicof Korea has the largest soundproof tunnel market in the world, it still has to be improved because thefactors like economic feasibility, stability, and usability haven’t been fully considered [1]. A crucial
issue that needs to be improved is ensuring the economic viability and weight reduction of soundproof
tunnel.
The soundproofing panels, which are currently primarily utilized in soundproofing tunnels, areessentially identical to heavy, thick materials, like laminated glass, which are used to build soundproof
walls. There is also a limit to developing soundproofing tunnels made of new materials or securing
economic efficiency and lightweight because the acoustic performance standards of soundproofing
panels that can be used in soundproofing tunnels in the Republic of Korea are applied the same as
those used for soundproofing walls [2]. On the other hand, attempts are being made to use
soundproofing facilities constructed of different materials than in Republic of Korea, considering theharmony with the landscape.
1dbwltn320@uos.ac.kr
schang@uos.ac.kr
3mnoh@posco.com
4
dast21@dast21.com
2

Therefore, this study suggests a soundproof tunnel made from an economical membrane material
that can minimize the soundproof tunnel's weight, as compared to the main materials of theconventional soundproofing panel (Laminated glass or Polycarbonate, etc.). By comparing the noise
reduction impact of the membrane-structured soundproof tunnel with that soundproofing wall and thesoundproofing tunnel, which was applied laminated glass, the general soundproofing panel material,
the marketability of the invention was confirmed. Additionally, the sound insulation performance was
examined by constructing a real-scale membrane-structured soundproof tunnel and the field
applicability was shown.

2. MATERIALS and METHODS
2.1

Materials and Sound transmission loss

The material of the soundproof tunnel used in this study is a membrane. The membrane material
was polyvinyl fluoride(PVF), and a single layer of 0.8mm was chosen in consideration of the size andtension of the tunnel’s roof area [3]. The membrane material used in this study is shown in Figure 1.

Figure 1 – Membrane material used in this research
The sound transmission loss measurement of the membrane material was measured according toISO10140-2:2021, and the results are shown in Table 1 [4]. It also displays the measurement resultsof the sound transmission loss of the laminated glass used in existing soundproof walls or tunnels.
Table 1 – Sound transmission loss of 3 acoustic panel types
Frequency(Hz)

Laminated glass(8.38mm)

Transparency soundproof
panels(8mm)

Membrane(0.8mm)

100

20.6

20.6

7.9

125

22.5

18.9

11.2

160

24.7

17.4

9.9

200

25.0

21.4

10.6

250

26.1

21.1

14.0

315

27.0

23.3

15.9

400

28.6

24.6

17.6

500

31.8

26.1

19.4

2.2

630

28.2

27.8

19.5

800

31.5

29.4

19.8

1000

32.0

31.8

20.2

1250

29.9

33.3

21.3

1600

27.0

35.1

21.4

2000

28.2

36.7

19.8

2500

31.5

37.9

20.3

3150

31.6

38.0

21.8

4000

35.1

37.2

23.2

5000

39.4

30.5

25.3

3D Modeling conditions

To analyze the noise reduction effect of the membrane structure soundproof tunnel, the ODEON
program was used for 3D modeling [5]. ODEON is a comprehensive software for indoor acoustics,
but it is also feasible to simulate external environments by taking the appropriate measures [6]. In
addition, sound prediction is possible given the 3D model and parameters of the sound source,
receivers, and soundproofing facilities.
To simulate an external environment in ODEON, Room 1 was constructed with lengths of 1,000
meters on all sides and a sound absorption value of 1.0, and a soundproofing facility model was built
inside R1.In addition, the material properties of the sound source, receiver locations, and the noisebarriers were input. Figure 2 shows an example of the 3D model of the noise sources, receivers, andnoise barrier inputted into the ODEON software.

Figure 2 – Membrane material used in this research
Noise source was assumed to be a two-lane road with a total length of 300m, and to simulate it,
the sound power level of the source was calculated through NMPB-Roads-2008, a road traffic noise
prediction model. The calculated noise source was input as a point sound source in the software.
In order to compare the noise reduction effects of soundproofing facilities, three types ofsoundproofing facilities were selected: soundproof wall, existing soundproof tunnel, and membranestructured soundproof tunnel. All soundproofing facilities' height was set to 12m, and length was a
total of 200m.
The height was set to 12m, and all soundproofing facilities were extended a total of 200m.
Laminated glass with a thickness of 8.38mm was used as a noise barrier wall material. 8.38mm thicklaminated glass was applied to the side wall of the existing soundproof tunnel, and 8.00mm thick PC
was applied to the roof in the software. Laminated glass with a thickness of 8.38mm was used as anoise barrier wall material and 8.38mm thick laminated glass was applied to the side wall of theexisting soundproof tunnel, and 8.00mm thick PC was applied to the roof. Figure 3 shows the designof the soundproofing facility inside the ODEON software.

Figure 3 – Cross-sectional shape of noise barriers(Noise barrier wall(left) and Soundproof tunnel(right))
To simulate the environment of an apartment building close to the major road in Seoul, the capitalcity, the receiver building was designed around 27 meters from the centerline of the road. The receiverpoints were positioned at a distance of 1 m from the center of each floor's exterior wall, and thebuilding was supposed to have 20 floors and a height of 54 m.
2.3

Road traffic noise prediction model : NMPB 2008

French engineering model NMPB 2008 was applied as the prediction model for road traffic noise[7,8]. The sound source model of NMPB 2008 is calculated based on the characteristics of each vehicle
type, and the sound power level for each center frequency of the 1/3-octave band between 100 Hz and
5,000 Hz is determined using the following equation [9].

𝐿 /
𝐿 / /
𝑄

𝑗

10𝐿𝑜𝑔 10

/

/

/

+10

/

/

/

R j

: Emission power per meter of lane of the vehicle category for a unit flow rate(1 veh/h): Average hourly flow rate for vehicle category

R(j): Spectral distribution of the emission power
The volume and speed of traffic, the road's inclination, and the road pavements, etc. are the primary
determinants of road traffic noise emission. In this study, the traffic volume and speed used to calculate
the road traffic noise and noise source are day-time data for major 8-lane roads in Seoul in 2017. The
pavement was assumed to be non-drainage pavement, and the road inclination was assumed to be flat.
The traffic flow of 3,855 light vehicles and 292 heavy vehicles per hour is modeled in one lane, and3,444 light vehicles and 259 heavy vehicles per hour are modeled in the other lane. The average speedof both was entered as 83 km/h.

3. RESULTS and DISCUSSION
3.1

Calculation of the sound power level for road traffic noise

Using the road traffic noise prediction formula to calculate the sound power level of the two-laneload, which is the noise source, the light vehicle passing in one lane has a sound power level of 53.1
dB(A) per unit length, whereas the heavy vehicle was calculated as the sound power level of 62.0dB(A)per unit length. The sound power level of the light vehicle passing in the other lane was calculated to
be 54.9 dB(A) per unit length, whereas the sound power level of the heavy vehicle was calculated to
be 63.8 dB(A) per unit length.

The determined sound power level per unit length was finally calculated by adding correction
values based on traffic volume and pavement, and the calculated sound power level of road traffic
noise was input as a point source per unit length of the lane. The point source in the first lane wascalculated as 97.0dB(A) per point source, and the point source in the other lane was calculated as
98.3dB(A).
SoundPLAN8.2, an environmental noise simulation software, was used to validate the calculated
noise level per point source with an error value of 0.
3.2

Comparison of noise reduction effects according to noise barriers

The previously computed noise source was entered into the modeling tool, as well as the shape ofthe soundproofing facilities and the location of the receiver point. By comparing the predicted noiselevel at the receiver when the open area and each soundproofing facility were applied, the noise
reduction effect of each soundproofing facility was evaluated. Figure 4 shows the predicted noiselevels at the receiver for each soundproofing facility.

Figure 4 – Vertical sound pressure level distribution characteristics of each soundproof facility
Comparing the predicted noise level results at the building facade , which is the receiver, when the
open area and the noise barrier wall were applied, the average noise reduction due to the barrier was
calculated to be 9.5 dB(A) from the first to the twentieth floor. The average noise reduction of thelower floors (1st to 6th floors) is 14.1 dB(A), while the average noise reduction of the middle floors(7th to 13th floors) is 12.1 dB(A). However, the average noise reduction of the upper floors(14th to20th floors) is not significantly reduced is 2.9 decibels. Since the noise reduction effect of the noisebarrier wall is limited by its height, it can be confirmed that the noise reduction effect decreases
dramatically as one approaches the higher floor of the building, which functions as a receiver.
Comparing the predicted noise level results at the receiver when the open area and the existingsoundproof tunnel were applied, the average noise reduction from the building's first to twentiethfloors was 11.9 dB(A). The average noise reduction for the lower floors was 15.3 dB(A), the middle
floors were 12.9 dB(A), and the upper floors were 8.0 dB(A) It was confirmed that the noise reduction
effect of the existing soundproof tunnel was more noticeable on the higher floors compared to thenoise barrier wall.
Comparing the predicted noise level results at the receiver when the open area and the membranestructured soundproof tunnel were applied, the average noise reduction from the building's first to
twentieth floors was 10.3 dB(A). The average noise reduction for the lower floors was 13.3 dB(A),the middle floors were 10.6 dB(A), and the upper floors were 7.4 dB(A)As shown in Figure 4.the expected noise level at the receiver on the building facade increases with
the number of floors, then lowers, and then increases again. It is considered that there is a slightdifference due to reflection and diffraction by soundproofing facilities

3.3Sound insulation performance test for real-scale membrane structure soundproof

tunnelIn addition to 3D modeling, a real-scale membrane structure soundproof tunnel was designed, andthe sound insulation performance was evaluated accordingly. Figure 5 shows the installation of thedesigned real-scale membrane structure soundproof tunnel, and the test was conducted in October
2021 at an average temperature of 18.1°C.
The overall design of the test is shown in Figure 5. As the noise source (S), pink noise was
generated by installing 4 omni directional speakers with a height of 1.2 m in the center of the tunnel.
The microphones, which are the receivers, were installed in the tunnel inside (No.1 to No.5) andoutside (No.6 to No.10) at a distance of 1.0 m from the membrane. During the test, both sides of thetunnel were open.

Figure 5 – Mock-up of the soundproof tunnel with membrane material(left) and Experiment designs(right)
The sound insulation performance of the membrane material is the result of subtracting the noiselevel measured by the microphone installed outside the tunnel from the noise level measured by the
microphone installed inside the tunnel. The sound insulation performance of the membrane-structured
soundproof tunnel calculated through field measurements is 15.9dB(A) on average.
In addition, the sound insulation performance of the membrane structure soundproof tunnel at thereal scale was computed using 3D modeling and compared to the results of field measurements. As
the modeling conditions, all conditions except for the noise source were set the same as the actual
scale test. Only the noise source was inputted in the same way as in Sections 2.2 and 2.3, and the set
all conditions were inputted into the ODEON software to analyze the sound insulation performance.
As a result of 3D modeling, the average sound insulation performance of the membrane structure
soundproof tunnel was calculated to be 19.2dB(A), and the results of field measurements andsimulations according to the microphone position are presented in Table 2.
The difference in sound insulation performance of the membrane structure soundproof tunnel
between the on-site experiment and the 3D modeling was around 3.3 dB(A) on average. The reason
for the difference in sound insulation performance is thought to be that other noise sources unrelated
to this study were involved in the field experiment. Therefore, it is determined that the soundinsulation performance of the membrane structure soundproof tunnel measured by on-site tests islower than that calculated through 3D modeling.
Table 2 – Noise level and difference by the location of the microphone
LocationInterior

Measured noise levels throughPredicted noise levels through 3DMock-up Experiment, dB(A)modeling, dB(A)

1-6

2-7

3-8

4-9

5-10

1-6

2-7

3-8

4-9

5-10

84.9

85.5

85.3

85.9

85.0

85.5

85.4

85.2

85.0

84.9

Exterior

69.9

71.2

69.0

68.8

68.4

66.8

66.2

66.6

65.3

65.0

Noise reduction

15.0

14.3

16.3

17.1

16.6

18.7

19.2

18.6

19.7

19.9

4. CONCLUSION
The noise reduction effect of the membrane-structured soundproof tunnel was found to be lesseffective by about 2.0 dB in the lower floors, 2.3 dB in the middle floors, and about 0.6 dB in the highfloors, when compared to the noise reduction effect of the existing soundproof tunnels. It was
confirmed that the noise reduction effect of the membrane structure soundproof tunnel did notdecrease significantly when compared to the existing soundproofing tunnel. In addition, it is assessedthat the membrane structure soundproof tunnel has a significant advantage in that it can compensate
for the economic efficiency and weight, which are considered to be typical limitations of thesoundproofing tunnel [10].
However, the membrane material does not meet the acoustic performance standards required in the
Republic of Korea. Therefore, in order to activate soundproof tunnels with new technologies such as
membrane materials, it is considered necessary to either update the existing sound performancestandards for soundproofing panels or to establish new performance standards that can be
independently evaluated for soundproofing tunnel materials.

REFERENCES
1. Lee, H.G., Kim, J.S., The Effect of noise barrier in reducing road traffic noise and related problems,
Journal of Korean Society of Environmental Engineers, Vol. 26, Republic of Korea, p.1166~1171.
2. KCS 44 80 05, 2016, Korean Construction Specification, Korea Construction Standards Center, Ministryof Land, Infrastructure and Transport.
3. Lee, J.B., 2004, The properties of Architectural Fabrics, Journal of the Korean association for shell andspatial structures, Vol. 4, No. 11.
4. KS F 10140-2, 2021, Acoustics-Laboratory measurement of sound insulation of building elements- Part
2:Measurement of airborne sound insulation. Korean Agency for Technology and Standards.
5. Lai, H., and Hamilton, B., 2020, Computer Modeling of Barrel-Vaulted Sanctuary Exhibiting Flutter Echo
with Comparison to Measurements, Acoustics, Vol. 2, pp.87~109.
6. Odeon A/S, Odeon Room Acoustics Software, Lyngby, Denmark, 2018
7. Francis Besnard, Jean-François Hamet, Joël Lelong, Sonia Doisy, Nathalie Fürst, Emmanuel Le Duc,
Vincent Guizard, and Guillaume Dutilleux., 2011, Road noise prediction, 1 - Calculating sound emissions
from road traffic, Sétra.
8. Francis Besnard, Jérôme Defrance, Michel Bérengier, Guillaume Dutilleux, Fabrice Junker, Benoit
Gauvreau, David Ecotière, Dirk van Maercke, François Abbaléa, Marine Baulac, Bernard Bonhomme,
Hubert Lefèvre, Vincent Steimer, Vadim Zouboff, and Emmanuel Le Duc., 2009, Road noise prediction,
2 -Noise propagation computation method including meteorological effects (NMPB 2008), Sétra.
9. Kim, C. Kang, H, Chang T., and Chun, B., 2017, Model and parameter analysis for highway noise 3D
calculations, Korea Expressway Corporation Research Institute, The Republic of Korea, chap.2, pp.11~14.
10 POSCO, “Development of a new concept soundproofing facility made of a membrane structure using
high-performance steel”, SCRA, Republic of Korea, Tech. Rep. 2021.

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

ABS-0366

Characterization of urban acoustic noise variability using the
exponentially modified Gaussian distribution
Matthew J. KAMRATH1; D. Keith WILSON1; Caitlin E. HAEDRICH2;
Daniel J. BRETON1; Carl R. HART1
1
2

U.S. Army Engineer Research and Development Center, Hanover, NH 03755, USA

Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA

ABSTRACT
Urban acoustic noise levels vary in time and space, and the distribution of these levels can be characterized
by the parameters of a probability density function (pdf). Previously, the exponentially modified Gaussian(EMG) distribution was shown to be a good model for urban acoustic levels because it incorporates randomly
positioned sources and geometrical spreading. The EMG distribution can represent the sound level at thecenter of a circle with N uniformly distributed sources within the circle. The source closest to the receiverproduces the exponential part, and the remaining N-1 sources produce the Gaussian part. In an urban
environment, the closest source could be an adjacent road and the remaining sources could be perceived as
background noise. The EMG parameters represent the mean and variance of the Gaussian distribution and arate parameter for the exponential distribution. Using data collected in the North End of Boston (USA), amaximum-likelihood estimation yields the optimized EMG parameters. This article maps the EMG
parameters and discusses the potential sources for spatial variability in the pdf parameters.
Keywords: urban, noise, statistics

1. INTRODUCTION

Modeling sound levels in urban areas is challenging because they have many sound sources andcomplex geometries. Road noise is often the primary sound source, but it is composed of many moving
vehicles. In addition, rail, airport, construction, pedestrian, fountain, and animal noises also contributeto the overall soundscape. The roads and buildings also complicate the theoretical models because
they reflect and diffract sound.
Much urban acoustic research has focused on estimating noise statistics that are correlated withannoyance. These noise statistics are similar to the long-term weighted-mean sound pressure level(SPL), but they might more heavily weight the evening and nighttime noise. For example, theEuropean Commission developed the Common Noise Assessment Methods in Europe (CNOSSOS EU)(1) to standardize noise maps in the European Union. These noise maps provide a spatial
representation of a noise statistic across an urban area. Relatively little research has been dedicatedto determining the temporal distribution of sound levels at a single location in an urban setting.
García and Faus (2) measured A-weighted noise levels at 50 locations in seven Spanish cities. They
provided representative histograms of a relatively noisy location and a relatively quiet location. At
the noisy location, the A-weighted, equivalent sound level, 𝐿𝐿Aeq , over a 24-hour period was 79.5 dBA,
and the distribution was adequately described by a Gaussian distribution. At the quiet location, theequivalent sound level was 62.9 dBA, but the distribution was not Gaussian with a skewness of 1.2.
For their data, the asymmetry parameter, 𝐴𝐴 = (𝐿𝐿10 − 𝐿𝐿50 ) − (𝐿𝐿50 − 𝐿𝐿90 ), had a mean of 1.657 dBA
and a standard deviation of 2.76 dBA, which indicates that much of their data was positively skewed.
Thus, García and Faus identified that a Guassian distribution was not a good model for a significant
portion of their dataset, but they did not identify a better probability density function (pdf).
Wilson et al. (3) measured the one-third octave SPLs for five minutes at 37 locations in the North
End neighborhood in the city of Boston (USA). They considered Gaussian, compound gamma, andexponentially modified Gaussian (EMG) distributions and concluded that the EMG distribution
performed best overall.

This paper continues to analyze the Boston dataset by mapping the spatial variation of the EMG
parameters and by speculating about the physical causes of the variation. First, Section 2 describes
the EMG distribution. Then, Section 3 briefly overviews of the Boston data collection because the fulldescription is in a previous paper (3). Next, Section 4 illustrates and analyzes the spatial distributionof 𝐿𝐿Aeq and the EMG parameters. It also speculates on the environmental variables that contribute to
the varying EMG parameters. Finally, Section 5 summarizes the key results.

2. THEORYThe EMG distribution describes the sum of two independent random variables. The first variable
is normally distributed with mean 𝜇𝜇 and variance 𝜎𝜎 2 , and the second variable is exponentiallydistributed with rate parameter 𝜆𝜆. The EMG distribution has the following pdf:𝜆𝜆
𝜆𝜆
𝜇𝜇 + 𝜆𝜆𝜎𝜎 2 − 𝑥𝑥
𝑓𝑓(𝑥𝑥|𝜇𝜇, 𝜎𝜎, 𝜆𝜆) = exp � (2𝜇𝜇 + 𝜆𝜆𝜎𝜎 2 − 2𝑥𝑥)� erfc �
�,
2
2
√2𝜎𝜎

where erfc(𝑥𝑥) is the complementary error function
erfc(𝑥𝑥) =2∞

� exp(−𝑡𝑡 2 ) d𝑡𝑡.

√𝜋𝜋 𝑥𝑥The EMG distribution has the following mean, variance, and skewness (4):1𝜇𝜇EMG = 𝜇𝜇 + ,
𝜆𝜆

2
𝜎𝜎EMG
= 𝜎𝜎 2 +1,
𝜆𝜆2𝛾𝛾EMG =

Solving these equations for the EMG parameters yields

2
1 −3/2
�1+
�.
𝜎𝜎 3 𝜆𝜆3
𝜎𝜎 2 𝜆𝜆2

(1)

(2)

(3)

1
𝛾𝛾EMG 1/3
(4)where𝜏𝜏 = 𝜎𝜎EMG �� .
2𝜏𝜏Our original motivation for this distribution was needing to be able to approximate two special
cases. For urban areas with a high traffic flow, García and Faus (2) measured that the levels were
Gaussian distributed. For a simplified model of a vehicle traveling at constant speed on a straight roadwith the receiver at the midpoint of the road, the levels would be exponentially distributed assuming
only geometrical spreading losses and the reference pressure is the pressure when the car is at its
furthest point from the receiver. As Figure 1 illustrates, the EMG nicely interpolates between these
two extreme cases (i.e., a very busy road and an almost desolate road).
Wilson et al. (3) provide a more rigorous derivation of why the EMG distribution could describe
urban noise. They consider a receiver at the center of a circle with one randomly placed source in thecircle. If the source had constant log-power (i.e., log of amplitude squared), then the distribution
would again be exponential. However, if the minimum log-power is normally distributed, then the
resulting distribution would be the EMG distribution. Wilson et al. (3) also provided numericalsimulations that indicate that the EMG distribution is a good approximate for multiple, randomly
positioned sources in a circle assuming only losses due to spherical spreading.
𝜇𝜇 = 𝜇𝜇EMG − 𝜏𝜏,2
𝜎𝜎 2 = 𝜎𝜎EMG− 𝜏𝜏 2 ,𝜆𝜆 =Figure 1 – EMG distribution over a range of parameters that demonstrate that it can represent
Gaussian (blue), exponential (green), and mixed (orange) distributions.

3. PROCEDURE

We consecutively measured the one-third octave SPLs at 37 locations (Figure 2) in the Boston
North End (USA) on 07 June 2018 for five minutes each. Road noise was the dominant noise source,
but construction and pedestrian noise were also important at several locations.
The North End has major roads (N Washington St and Cross St) on the west side with high traffic
volume. Commercial St and Atlantic Ave circle the neighborhood on the north and east sides and havemoderate traffic flow. The traffic flow is low within the neighborhood with Hanover St being the only
street that cuts through the neighborhood.
At each location, a Norsonic Sound Analyzer Nor 140 measured the one-third octave band SPLs
for 6.3 Hz-20 kHz using the fast response setting (125 ms time constant). The sound level meter(SLM) was immediately next to the road on a tripod 1.25 ± 0.05 m above the ground (Figure 3). The
SLM recorded the SPLs every 0.5 s for five minutes.

Figure 2 – Map of the measurement locations and numbered waypoints. Basemap courtesy of ESRI®.

Figure 3 – Experimental setup at the intersection of Commercial St and Clark St (waypoint 107).

4. RESULTS

For brevity, this section will only focus on the EMG parameters for the A-weighted sound levels,
𝐿𝐿A , instead of including the results for some or all of the one-third octave results. A maximum
likelihood approach yielded the EMG parameters. Figure 4 gives the A-weighted, equivalent soundlevel, 𝐿𝐿Aeq , during the five minute measurement at each location. As expected, the locations along thewest side (i.e., Cross St and N Washington St) have the highest 𝐿𝐿Aeq values. The locations near thewharves on the east side have moderate levels. The locations in the interior narrow alleys have thelowest sound levels.
The spatial variation of 𝐿𝐿Aeq is mostly explained by variation in the road traffic volume. The
highest levels near the intersection of Cross St and Hanover St (76 dBA at waypoint 113 and 82 dB at
waypoint 114) are actually due to a combination of both road and construction noise. In the interiorof the neighborhood, there are fewer vehicles and pedestrian noise becomes more important. The
sound levels in the narrow alleys are a combination of noise spilling over from the busier roads andlocal sources (e.g., an air conditioner at waypoint 131) because few vehicles or pedestrians travel
these alleys.

Figure 4 – Map of the A-weighted, equivalent sound level, 𝐿𝐿Aeq , in dBA at each location.
Basemap courtesy of Google Earth Pro®.

Figure 5 provides the EMG parameter 𝜇𝜇 at each measurement location. Recall that 𝜇𝜇 is the meanof the Gaussian part of the EMG distribution, not the mean of the entire EMG distribution. The highest
values are along Cross St, N Washington St, and the beginning of Commercial St (waypoints 113122). The lowest values are in the northeast corner (waypoints 127-133), mostly on the narrow alleys.
For the parameter 𝜇𝜇, the traffic volume is likely the most important environmental factor in an urban
setting. Other sounds sources, like construction sites and pedestrians, can also affect 𝜇𝜇.
The most surprising case for 𝜇𝜇 is at waypoint 107 (circled in white in Figure 5). Here, 𝜇𝜇 is just50 dBA, whereas the adjacent points on Commercial St are 5-10 dBA higher despite all three locations
having similar traffic volume and 𝐿𝐿Aeq values. Maximum likelihood and method of moments
approaches generate very different parameter values, so it is possible that a EMG distribution does
not fit the data well at this location. Perhaps, a longer measurement would have produced a better
agreement at this location. Also, since the exponential part of the EMG distribution dominates at thislocation, the optimization of the 𝜇𝜇 parameter might be less precise.

Figure 5 – Map of the EMG parameter 𝜇𝜇 in dBA at each location.
Basemap courtesy of Google Earth Pro®.
Figure 6 illustrates the variation in the natural logarithm of the EMG parameter 𝜎𝜎 2 , (i.e., thevariance of the Gaussian part of the EMG distribution). A logarithm was used because the values span
multiple orders of magnitude. The largest values are on the north side along Commercial St(waypoints 104, 122-124), and the smallest values are scattered with two on Salem St (waypoints 138-

139), one on Moon St (waypoint 135), and three on the eastern part of Commercial St and Atlantic
Ave (waypoints 106, 107, and 109). The high traffic volume area along Cross St and N Washington St
have moderate values for 𝜎𝜎 2 .
The locations on Salem St and Moon St and waypoint 106 at Commercial St/North St all hadconstruction noise as an important sound source with low to moderate levels of road noise.
Waypoint 109 on Atlantic Ave did not have construction noise but had a semi-truck idling near the
SLM for a significant portion of the measurement. These immobile sources seem to have a smaller
variance, 𝜎𝜎 2 , because the distance to the receiver is constant. The measured level is not constantbecause of the variability of the source and scattering from buildings and turbulence. These
construction sites tend to have a generator producing a 60 Hz tone that is also very consistent (i.e.,
low variance). The low to medium levels of traffic noise allows these stationary sources tomeaningfully contribute to the sound level distribution.
Waypoint 107 had the lowest variance, 𝜎𝜎 2 , but the field notes did not indicate that an immobile
source was nearby. It is not obvious why this site should have such a small value for 𝜎𝜎 2 . Perhaps, theoptimization is not precise at this location because of a poor fit or the exponential part of the EMG
distribution dominates.

Figure 6 – Map of natural log of the EMG parameter 𝜎𝜎 2 at each location.
Basemap courtesy of Google Earth Pro®.

Figure 7 maps 𝜏𝜏, which is the inverse of the EMG parameter 𝜆𝜆. The parameter 𝜏𝜏 was used insteadof 𝜆𝜆 because it seems to be more fundamental in the equation for the EMG parameters [Eq. (4)] andis proportional to skewness. Waypoints 121 and 131 had the lowest values of 𝜏𝜏, possibly due to a
construction site and an air conditioner, respectively. It seems that these immobile sources are more
likely to be Gaussian than the road noise. Waypoint 107 had the highest value of 𝜏𝜏. As discussed
previously, the fit at this location seems to be problematic. Also, it is likely that our five-minutemeasurements (𝑁𝑁 ≈ 600) were not long enough the capture distribution skewness very accurately,
which would lead to inaccurate values for 𝜏𝜏 [Eq. (4)].

Figure 7 – Map of the EMG parameter 𝜏𝜏 = 1/𝜆𝜆 in dBA at each location.
Basemap courtesy of Google Earth Pro®.

5. CONCLUSION

This paper explored the spatial variability of the EMG distribution parameters for the A-weighted
sound levels in the Boston North End neighborhood. An SLM measured the sound levels for fiveminutes at 37 different locations. For most locations, the EMG parameter 𝜇𝜇 was closely linked to thetraffic volume. At some locations, other sources (e.g., construction sites and pedestrians) also
influenced 𝜇𝜇. The presence of immobile sources that were at least as important as the road noise
tended to decrease the EMG parameter 𝜎𝜎 2 . Construction generators/sites were the most common
stationary source. The EMG parameter 𝜆𝜆 seems to be affected by factors beyond the traffic volume.

Perhaps, it is also linked to the intermittency of the traffic or other sources. Measurement intervals
longer than five minutes would likely lead to better information about 𝜆𝜆. A future experiment could
also track the traffic volume and intermittency and correlate them with the EMG parameters.

ACKNOWLEDGEMENTS

This work was supported by the U.S. Army Engineer Research and Development Center,
Geospatial Research Engineering basic research program. Permission to publish was granted bymanagement and security, Cold Regions Research and Engineering Laboratory. Any opinions
expressed in this article are those of the authors and are not to be construed as official positions of thefunding agency of the Department of the Army unless so designated by other authorized documents.

REFERENCES
1. Joint Research Centre, Institute for Health and Consumer Protection, Anfosso-Lédée F, Paviotti M,
Kephalopoulos S. Common noise assessment methods in Europe (CNOSSOS-EU) : to be used by the
EU Member States for strategic noise mapping following adoption as specified in the Environmental
Noise Directive 2002/49/EC. Publications Office; 2012.
2. García A, Faus LJ. Statistical analysis of noise levels in urban areas. Appl. Acoust. 1991; 34(4): 227-247.
3. Wilson DK, Kamrath MJ, Haedrich CE, Breton DJ, Hart CR. Urban noise distributions and the influenceof geometric spreading on skewness. J. Acout. Soc. Am. 2021; 150(2): 783-800.
4. Grushka E. Characterization of exponentially modified Gaussian peaks in chromatography. Anal. Chem.
1972; 44(11): 1733-1738.

ABS-0671

A database of scale model measurements for urban sound propagation in amoving atmosphere and first comparisons with simulations
Matthias Cosnefroy1,2, * , Peng Qin2 , Alessio Ricci2,3 , Stefanie Gillmeier2 , Twan van Hooff2 and Maarten Hornikx2
1 Treble Technologies, Iceland
2 Department of the Built Environment, Eindhoven University of Technology, Netherlands
3 Department of Civil Engineering, KU Leuven, Belgium

ABSTRACTThe ability to predict environmental sound propagation is of paramount importance to the development andevaluation of noise mitigation strategies. In this context, the recourse to full-wave numerical methods is becoming increasingly popular, even for long-range 3D configurations. Such methods consist in directly solving
relevant governing equations of acoustics and do not generally require further modeling assumptions. They
are therefore able to provide a very accurate description of wave phenomena, and can inherently account for
micro-meteorology and/or complex boundaries (absorbing ground, obstacles or buildings). One limitation actually pertains to the determination of the physical input parameters, which can in practice hinder the accuracy ofsimulations; controlled experimental data are thus crucial for validation purposes. The current work aims at presenting and sharing a series of scale model acoustics measurements in a wind tunnel (scale 1:40), with different
downwind conditions representative of the atmospheric boundary layer and with different urban arrangements(solar panel fields and urban canyons). The maximum investigated range is 100 m for frequencies up to 2 kHz,at full scale.

Keywords: Outdoor sound propagation, measurements, wind tunnel, time-domain simulations

1

INTRODUCTION

Accurate prediction of sound propagation outdoors is crucial for a wide range of applications, such as assessing and mitigating noise in semi-open [1] and urban environments [2, 3], or even developing robust sensing
algorithms for defense and security purposes [4, 5]
The recourse to numerical modeling is a popular and convenient alternative to costly and time-consuming
systematic outdoor measurements. Such simulations can however prove challenging due to the combined influence of the ground surface and of the micro-meteorology on sound propagation, especially at long range (up to
thousands of wavelengths). These effects are usually frequency-dependent and can include absorption, reflection
and diffraction from the ground and from obstacles (such as buildings), refraction due to wind and temperature
gradients, and scattering by turbulence. Numerous numerical methods were developed to account for these phenomena. Over the last decades, wave-based methods, in particular, have gained significant traction due to therapid growth in computing power and due to their ability to very accurately solve the linearized Euler equations(LEE) in the time domain with few inherent assumptions; in practice, a limitation of such methods can actuallypertain to the determination of sufficiently accurate physical input parameters.
While the spatio-temporal variability of the acoustic properties of grounds [6, 7, 8] and the influence of urban geometries [9, 10] are reasonably well documented, still too little is known about how well the atmosphericboundary layer should be represented to accurately account for sound propagation over tens to hundreds of* email: mc@treble.techmeters [3, 4, 11], especially in urban environments. This stems from three major cross-disciplinary difficulties:(i) the physical processes associated with sound propagation through the near-surface atmosphere are complex,
with large- and small-scale turbulence responsible for deterministic and stochastic variations of the recorded
acoustics signatures, respectively [12]; (ii) generating (realistic) wind and/or temperature fields to be used ina wave-based acoustic solver requires extensive expertise on computational fluid dynamics; (iii) measuring theproperties of the near-surface atmosphere in situ is difficult and expensive.
The current work presents a series of scale model measurements primarily aimed at providing acoustics
benchmark cases in the presence of wind in urban environments, under well-controlled indoor conditions. The
experiment takes place in a wind tunnel at scale 1:40 with different downwind conditions representative of theatmospheric boundary layer, over different idealized urban geometries consisting of street canyons and solar
panel fields. In the following, Section 2 details the experimental setup, and Section 3 presents measurement
data for a configuration with solar panels with and without wind. A first comparison with a simple analyticalsolution for a configuration with a flat ground surface and without wind is also discussed.

2

MEASUREMENT SETUP

2.1 Sound source and microphone
The frequency range of 50 Hz to 2 kHz is of interest. Considering the experiment is performed at a scale of
1:40, this amounts to frequencies between 2 and 80 kHz at measurement scale,1 where the smallest wavelength
is of the order of 5 mm. An in-house spark source is used to generate pulse trains with sufficient energy within
this frequency range. The time interval between each spark is about 0.5 s, which is long enough to ensure therecorded waveforms do not overlap due to the reflections from the side walls and from the ceiling of the windtunnel (see Figure 1). The individual sparks have a duration of 0.2 ms and are generated at a height of about
15 cm above the ground (6 m at full scale) by applying a high voltage between two electrodes. It was foundnecessary to add a 1 cm thick insulating layer between the source and the metal floor of the wind tunnel toreduce electromagnetic interference. The source is reasonably repetitive up to 80 kHz, with a standard deviationof the order of 1 dB across the whole spectrum (not shown for brevity).
The acoustic signals are recorded with an in-house microphone mounted with a windshield, at a height ofabout 6 cm (2.4 m at full scale), at a sampling frequency of 192 kHz. Separate free-field measurements were
performed prior to the experiment to measure the source characteristics as recorded by the microphone; thisconveniently allows the computation of measured sound pressure levels relative to free field. As illustrated in
the following, a sufficient signal-to-noise ratio is obtained for this combination of source and receiver in mostcases.
2.2 Model configurations
Two sets of small-scale models are used to represent urban canyons and solar panel fields, respectively. For
the former, the buildings consist in four heavy metal beams filled with sand to prevent sound transmission andresonances, with a height of 10 cm (4 m at full scale), as shown on Figure 2. The solar panels were built witha tolerance of 1 mm as inclined plastic planks, with a height of 4.1 cm (1.6 m at full scale), and are supported
by glued triangular feet. The panels are firmly attached to the floor of the wind tunnel with magnets. Both setsof models span the full width of the tunnel (3 meters) to limit the influence of the edge effects. The floor of
the tunnel where the acoustics measurements took place is a flat 3 m by 3 m metal plate (see Figure 1) with abending radius of at most 1 cm. The floor and the models are assumed acoustically rigid.
In total, 54 model configurations were measured, with some redundancies to alleviate potential experimental
difficulties, for several different horizontal source-receiver distances between 1.2 and 2.4 m (48 and 96 m at fullscale). The maximum propagation range considered thus amounts to at most 500 wavelengths. A positioning
uncertainty of at most 1 cm is expected. For the urban canyons, different numbers of buildings with different
separation ratios are investigated. Various numbers and orientations of the solar panels are considered to account1 In the following, most physical quantities are expressed at measurement scale, unless stated otherwise.

Figure 1. Example of measurement configurations with urban canyons (left) and solar panels (right).

Figure 2. Cross section of the models, at measurement scale; left: building, right: solar panel.
for either East-West or South-facing arrangements [1]; both arrangements can be used to optimize solar radiation. Figure 1 shows a configuration example for each set of models. For each configuration, the measurements
are also performed without the models with the exact same source-receiver geometry.
2.3 Wind conditions
The acoustics measurements are performed for each configuration with wind in favorable acoustic conditions(the wind blows from the source towards the receiver) for different reference wind velocities, also including theno-wind case. The wind characteristics in the tunnel were tuned to provide a realistic scaled representation of
the atmospheric boundary layer (ABL). Figure 3 shows the mean vertical wind profiles used for the acoustics
measurements, upstream from the models, for different power settings of the tunnel facility. High-power settings
generate a significant amount of noise; the power is therefore capped at 40% for the longer range configurations
to preserve the acoustic signal-to-noise ratio. This power settings corresponds to a wind blowing on average at
30 km h−1 at a height of 10 m at full scale (0.25 m at measurement scale). This is sufficient to significantly
influence sound propagation, as shown in the following. Note that tape was used to fasten the base of thesource and the microphone to the floor, and to the cables to prevent rattling, as can be seen on Figure 1.
The ambient atmospheric parameters inside the tunnel are also reported before each measurement leg, namely
the temperature, the static pressure, the relative humidity and the air density. The background noise is also measured for 10 seconds for each wind condition prior to each measurement session.

Figure 3. Mean vertical wind profile measured upstream from the models, for different power settings of thetunnel facility (left).
2.4 Post-processing
For each of the 54 model configurations and for each wind strength, the signal recorded by the microphone over
90-second-legs contains about 160 successive shots from the spark source. In post-processing, each individual
propagated waveform is identified and time-windowed to remove the reflections from the side walls and fromthe ceiling of the tunnel; the time interval between each spark is sufficient to ensure the waveforms are well
separated in time. A high-pass filter is also applied below 2 kHz to attenuate some of the background noise.
As a second step, this allows the shots to be aligned in time in order to compute meaningful statistics about
the effects of the wind. This time alignment is performed based on the detection and localization of the maximum of the first peak of each waveform, which is assumed to correspond to the time of arrival (TOA) of thesignal; this is illustrated on Figure 4. The synchronization to the TOA allows the computation of an averaged
waveform [13, 14], where the effects of the mean wind are enhanced compared to the random fluctuations inthe signal due to the background noise and to the small-scale wind turbulence.
For some applications, it can also be interesting to consider the time of emission (TOE) of the source. For
each shot, the TOE can be retrieved thanks to the presence of an electromagnetic pulse associated with the
spark discharge of the sound source (see Figure 4). For some configurations, however, the determination of
the TOE with a one sample accuracy proved difficult, depending on the shape of the electromagnetic pulse. A
synchronization to the TOE can allow the study of the time fluctuations associated with wind turbulence [4].

3

FIRST RESULTS

3.1 Configuration with solar panels
A configuration consisting of 8 South-facing solar panels, as depicted on Figure 5, is now considered for illustration purposes. The horizontal source-receiver distance is 2.4 m (96 m at full scale), with a direct line ofsight between the source and the receiver. Figure 6 shows the corresponding measurement results without wind
and with a strong wind (40 % power, cf. Figure 3) in the time and in the frequency domains for each measured
waveform; the average signal and the average sound level are plotted in black. All the shots are synchronized
to the estimated TOA.2
First of all, in the frequency domain, the red curve indicates an estimation of the (unfiltered) backgroundnoise based on the average power spectral density of a one-second noise recording; this quantity provides an
2 The results of Figure 5 with wind are directly based on Figure 4.

Figure 4. Recorded pulse train for a given model configuration and for a given wind strength, and automatic
estimation of the TOA of each shot; a few shots with a low signal-to-noise ratio are not detected. The locationof the electromagnetic (EM) pulse that can be used for a synchronization to the TOE is also indicated.

Figure 5. Sketch of a South-facing solar panel configuration (at measurement scale). The source and the receiverare denoted S and R, respectively.
upper estimate of the noise floor, since the noise is averaged over a duration much longer than the waveforms
themselves (typically less than 1 ms). It can be observed that the measurements with wind feature a much larger
background noise below 10 kHz, due to the wind tunnel facility and to aerodynamic noise. The backgroundnoise for the higher frequencies mainly consists in electronic noise. The signal-to-noise ratio is still favorable
across the frequency range of interest. Note that in some cases, for some frequencies, the level of the individual
waveforms may be smaller than the background noise; the sound level of the mean waveform (not shown) can
however still carry meaningful information, provided enough realizations are available for accurate denoising.
The influence of the wind on the signals is clear, with a shot-to-shot variability of up to 10–30 dB acrossthe whole frequency range, and with some outliers with a very large amplitude due to sudden wind gusts(i.e., small-scale turbulence). Differences in terms of the mean signal and the mean sound level can also be
observed, which are mainly associated to the mean wind field, responsible for downward refraction, and its
interaction with the panels.
3.2 Comparison with a 3D analytical solution in an empty domain
We now consider a configuration without wind and without any model (flat ground), with the same sourcereceiver geometry as previously (horizontal propagation range of 2.4 m at measurement scale). Figure 7 showsin blue the recorded waveforms as well as the corresponding levels relative to free field. In the time domain,
one can observe a first overpressure associated with the direct wave, and a second overpressure shortly afterFigure 6. Synchronized time signals for the configuration of Figure 5 (top) and corresponding sound levels(bottom), without wind and with wind (40% power). The signals are aligned in time based on the TOA.

associated with the ground-reflected wave; the interaction between the two waves creates an interference pattern,which can be seen in the frequency domain.
A simple analytical solution can be easily computed for this setup by considering a point source and its
image-source below the ground, which is assumed perfectly reflecting. The same ambient atmospheric parameters as for the measurements are used to provide an estimate of the sound speed. The resulting impulse
response function consists of two delayed and scaled Dirac delta functions, which are then convolved with different realizations of the actual source signal generated by the spark source, recorded in free-field conditions.
The frequency-dependent air absorption is then applied to the analytical signals based on the source-receiver
distance and on the atmospheric parameters. The results are plotted in green on Figure 7.
The two sets of signals are both aligned in time based on their respective estimated TOA, for plotting
purposes; the analytical solution is found to arrive about 0.02 ms before the mean measured signal (not shown).
This amounts to an uncertainty on the sound speed of only 0.25 %. One can also observe an almost perfectagreement between the shape of the analytical solution and the measurements, in the time domain and in thefrequency domain. The location and amplitude of the frequency dips due to the interference between the direct
and ground-reflected waves, in particular, are well reproduced.

4

CONCLUSION

Scale model acoustics measurements were performed for different urban arrangements consisting of solar panel
fields and street canyons, with wind conditions representative of the atmospheric boundary layer. In total, 54
configurations were measured at scale 1:40 for different wind strengths. This database will provide benchmark
cases useful for the validation of acoustics solvers in such numerically challenging conditions.
A first comparison with a simple analytical solution was performed in the presence of a flat perfectly reflecting ground surface without wind, and an excellent agreement was found against the measurements. Additional
comparisons with discontinuous Galerkin simulations in more complex configurations will be considered in future work; for comparisons with wind, a joint wind-acoustics methodology will be considered, with wind fields
estimated via computational fluid dynamics (CFD) simulations prior to the acoustics simulations.

ACKNOWLEDGEMENTS
The authors would like to thank Geert-jan Maas, Constant Hak, Gérson Chepuck Fernandes and Ewout Krijgsman for their contribution to this work.

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F. van der Eerden, E. Salomons, and A. Eisses. “Effects of solar panel fields on sound propagation”. In:
Lyon, France: Forum Acusticum, 2020, pp. 857–861.

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S. C. Trikootam and M. Hornikx. “The wind effect on sound propagation over urban areas: Experimental
approach with an uncontrolled sound source”. In: Building and Environment 149 (2019), pp. 561–570.

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M. Hornikx, M. Dohmen, K. Conen, T. van Hooff, and B. Blocken. “The wind effect on sound propagation over urban areas: Predictions for generic urban sections”. In: Building and Environment 144 (2018),
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S. Cheinet, M. Cosnefroy, F. Königstein, W. Rickert, M. Christoph, S. L. Collier, A. Dagallier, L. Ehrhardt,
V. E. Ostashev, A. Stefanovic, T. Wessling, and D. K. Wilson. “An experimental study of the atmosphericdriven variability of impulse sounds”. In: The Journal of the Acoustical Society of America 144.2 (2018),
pp. 822–840.

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A. Dagallier, S. Cheinet, M. Cosnefroy, W. Rickert, T. Weßling, P. Wey, and D. Juvé. “Long-range acoustic localization of artillery shots using distributed synchronous acoustic sensors”. In: The Journal of the
Acoustical Society of America 146.6 (2019), pp. 4860–4872.

[6]K. Attenborough, I. Bashir, and S. Taherzadeh. “Outdoor ground impedance models”. In: The Journal of
the Acoustical Society of America 129.5 (2011), pp. 2806–2819.

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G. Guillaume, O. Faure, B. Gauvreau, F. Junker, M. Bérengier, and P. L’Hermite. “Estimation of impedancemodel input parameters from in situ measurements: principles and applications”. In: Applied Acoustics 95(2015), pp. 27–36.

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D. Dragna and P. Blanc-Benon. “Sound propagation over the ground with a random spatially-varying
surface admittance”. In: The Journal of the Acoustical Society of America 142.4 (2017), pp. 2058–2072.

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M. Hornikx and J. Forssén. “Noise abatement schemes for shielded canyons”. In: Applied Acoustics 70.2
(2009), pp. 267–283.

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T. V. Renterghem and D. Botteldooren. “The importance of roof shape for road traffic noise shielding in
the urban environment”. In: Journal of Sound and Vibration 329.9 (2010), pp. 1422–1434.

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M. Cosnefroy. “Propagation of impulsive sounds in the atmosphere: numerical simulations and comparison
with experiments”. Partly in French. PhD thesis. École Centrale de Lyon, 2019.

[12]

J. M. Noble and H. J. Auvermann. The effects of large and small scale turbulence on sound propagation in
the atmosphere. U.S. Army Research Laboratory, 1995.

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O. Rompelman and H. H. Ros. “Coherent averaging technique: a tutorial review — Part 1: noise reduction
and the equivalent filter”. In: Journal of Biomedical Engineering 8.1 (1986), pp. 24–29.

[14]

B. E. Hurwitz, L.-Y. Shyu, S. P. Reddy, N. Schneiderman, and J. H. Nagel. “Coherent ensemble averaging
techniques for impedance cardiography”. In: Third Annual IEEE Symposium on Computer-Based Medical
Systems. IEEE Comput. Soc. Press, 1990.

Figure 7. Measured pressure signal for each shot (in blue) and 3D analytical solution (in green) for a configuration without wind with a flat ground (no model). The black line indicates the average of the measurements.
Top: in the time domain, with a synchronization based on the TOA; bottom: in the frequency domain.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0947

Synthesizing Sound Sources For Traffic Noise Health Impact
Assesment
Anders GENELL1; Michael SMITH2; Juan Jesus GARCÍA3
1
2

Environmental Research Department, Swedish National Road and Transport Institute, Sweden

Department of Occupational and Environmental Medicine, Sahlgrenska Academy, University of Gothenburg,
Sweden3

NVH Testing and Proving, IDIADA Automotive Technology SA, Spain

ABSTRACT
Within the Horizon 2020 project LEON-T (Low particle Emissions and lOw Noise Tyres), one of the aims is
to investigate the effect on cardiovascular health from heavy vehicle (EU class N2 and N3) tyre noise. Healthissues related to community noise such as road traffic noise are mostly attributed to negative health effects
from sleep disturbance due to noise exposure at nighttime. Within LEON-T a number of experiments on theeffect of tyre noise on sleep will be conducted in the sleep laboratory at Gothenburg University. In order tomaintain control over all properties of the noise stimuli used, a method to synthesize several exposure
scenarios has been developed. Based on single vehicle pass-by recordings or vehicle mounted single wheel
recordings that has undergone thorough psychophysical evaluation, scenarios are constructed allowing forvariation in parameters such as tyre tread pattern design, tyre air cavity resonance, traffic flow properties anddistance between traffic noise source and receiver. The synthesized scenarios are specifically designed inclose cooperation with experts on health effects from noise in order to expose the participants to such stimuli
that provide relevant and valid responses. This paper describes the synthesis process and the related
experiment design decisions.
Keywords: Noise, Health, Synthesis

1. INTRODUCTION
This research is part of the ongoing European project “Low particle Emissions and lOw Noise
Tyres” (LEON-T) financed under the Horizon 2020 programme. The project comprises an answer tothe call “Understanding and mitigating the effects on public health of emerging non-regulated
nanoparticle emissions issues and noise”. In the call, there was a specific item concerning noise andhealth:“Evaluation of traffic noise effect on the cardiovascular system, assessing which type of noise(impulsive or background) has the most consequence on health taking into account sex and genderdifferences when relevant, in order to influence the development methodologies for limiting noise, andto anticipate future legislation and emerging issues.”
Most cardiovascular health issues related to community noise stem from the effect of noise on
sleep, as is shown by the large body of research reviewed e.g. in the WHO report on public health andnoise published in 2018 (WHO, 2018). The focus in the current project is therefore on the effect ofheavy vehicle tyre noise on sleep, and the issue of “impulsive or background” type of noise mentioned
in the call is interpreted as referring to single vehicles passing by close to a dwelling of a continuous
flow of vehicles along a highway at a further distance from the dwelling. The call for proposals also
included an item to “develop innovative tyres of heavy-duty freight transport optimized for low noise,rolling resistance, wear and therefore particles emissions”. One design parameter that is strongly
related to the resulting noise character of tyres is the tread pattern. Depending on the shape of tread1

anders.genell@vti.seblocks and their relative position along the circumference, salient tonal components may be present
in the noise that may cause increase annoyance and might also increase the awakening potential atnight. In addition, the current project aims to develop a composite wheel rather than a tyre, to avoid
the air-filled tyre cavity that will inherently facilitate noise radiation, and to compare the potential ofsuch a design to other design measures such as reduced level and reduced tonality for ordinary tyres .
In order to study the effects of such different traffic flow scenarios and such different design
parameters on sleep, several sleep experiments will be carried out, for which well-defined soundscenarios need to be created.

2. METHOD
The sleep study sound scenarios are created in three steps: first synthesizing tyre sound fromrecordings so that the apparent vehicle speed and position can be varied freely, then combining several
such virtual vehicles into a realistic traffic flow and finally apply a filter corresponding to the façadeof an average dwelling in order to simulate indoor sound as it would be experienced during a normalnight’s sleep.
2.1 Tyre sound synthesis
The mechanism underlying generation of noise from the interaction between a rolling tyre and theroad surface are complex and multifaceted (Sandberg & Ejsmont, 2002). Computational models exist
that can predict radiated noise to a convincing degree, see e.g. (Kropp, 1999), but they do notnecessarily lend themselves to real time synthesis. In the LEON-T project the decision from the onset
was therefore to base stimuli to be used in experiment on recorded sound . Pass-by tyre recordings
were performed by IDIADA in Spain. A large set of heavy duty (C3), medium duty (C2) and passenger
vehicle (C1) tyres were recorded on the IDIADA measurement track. The tyres were mounted on avehicle and driven on a straight track at 50, 70 and 90 km/h, and recordings were done when thevehicle was coasting by the microphone with the engine off to avoid drive train noise. The recordings
were triggered so that each recording was 1.5s in length with the vehicle passing the microphone after
half that time. A thorough psychoacoustical evaluation of the recorded sound has been performed by
INSA Lyon, the results of which will also be presented at ICA 2022 in Gyeongju under the title“Influence of various timbre parameters on the unpleasantness of tyre noise” by T. Marin -Cudraz, J.J.
Garcia, and E. Parizet. One of the factors investigated was tonality calculated using the Sottek Hearing
Model method. Figure 1 shows two different tyres for which the calculated tonality differed greatly,
which also can be inferred by the difference in tread pattern design.

Figure 1. Different tyre tread patterns for two tyres included in the IDIADA pass-by measurements. To the left is a relatively
smooth pattern causing a mostly broad-band noise character. To the right is a coarser pattern with distinct tread blocks causingprominent tonal components in the noise.

As the individual noise recordings were relatively short (1.5s) and since the velocity of the passing
vehicle caused prominent doppler shift as it passed the microphone, relatively little data was available
as basis for synthesis. Different approaches are available when attempting to mimic existing sound,
all with their respective pros and cons. For synthesizing sounds for the sleep study, the choice fell oncreating an impulse response from the recorded sound that could be used for convolving with white
noise to achieve a continuous sound with similar character to the recorded sound. This was simply
done by using the first part of the recording where the doppler shift would be as small as possible,
and where turbulent air flow around the vehicle would be minimal. A physical interpretation might be
to assume that all sound radiates from the vibrating tyre that can be completely described by recording
the response from an impact on the tyre trad, such as hitting it with a hammer. This will of course nottake into account any other mechanism such as air flow around the tyre or non-linearities in the tyrematerials, but for the purposes of creating realistic synthesized tyre sound it is good enough. A tyre
has a reverberation time determined by material properties, air pressure and load among other things,
and can be said to be in the order of a few tenths of a second to one second or perhaps more. A valueof about 0.7s for a freely suspended tyre on a rim can be found in (Abd El-Salam, Gohary, & ElGamal, 2017) as an example. For the sound synthesis the reverberation time was set to 0.5s as areasonable approximation of a tyre in contact with the road and under load. The impulse response wasthen simply created by applying an exponential decay function to the beginning of the recording, as
can be seen in Figure 2.

Figure 2. Example of an impulse response (right) created from a recorded sound (left).

Recordings from the two tyres pictured above were analyzed to identify tonal components to be
used as a factor in the sleep study. Four prominent tonal components were identified by frequency andrelative level, as presented in Table 1.
Table 1. Tonal components of recorded heavy duty tyre with coarse tread pattern.

Frequency [Hz]Relative level [dB]

208

0

392

-7

588

-9

802

-12

The tonal components were the added to the non-tonal synthesized tyre sound by using a sine wave
for each tonal component, setting the relative levels with a simple amplitude factor. As the tonal
character of tyres is often more of a very narrow band noise that a pure tone, frequency modulation
was added to each sine wave to widen the perceived bandwidth. The absolute level of the tonalcomponents is yet to be determined before the first sleep study.
2.2 Synthesizing composite wheel noise
The composite wheel developed within the LEON-T project will, due to natural time constraint
reasons, not be finished until the end of the project, and will thus not be available for noise recording
as a basis for synthesis. There is a prototype composite wheel for passenger cars developed by thesame inventor as is involved in the current LEON-T composite wheel design, Hans-Erik Hansson atEuroturbine AB in Sweden. Rolling noise was recorded using the Close Proximity method (CPX) forthe composite wheel prototype at different speeds (Figure 3).

Figure 3. The composite wheel prototype mounted in the CPX trailer for noise measurement. Picture courtesy of Ulf Sandberg.

Since the recording is of a passenger car wheel, and the stimuli needs to represent heavy duty
vehicles, some assumptions needed to be made. Firstly, the same approach was made for the composite
wheel as for the recorded tyre, i.e. creating an impulse response from the recorded composite wheel.
This is harder interpreted physically since the composite wheel lacks a resonant cavity, but the flexible
spokes or the composite wheel exhibit some resonant behavior that may behave correspondingly.
Secondly, it was the assumed that the vibration wavelengths in a future heavy duty composite wheel
would scale according to the difference in circumference between a passenger car wheel and a heavyduty wheel (which is about 1.6). In practice that meant scaling the impulse response in the time domain
accordingly. Finally, it was assumed that the level difference between a passenger vehicle tyre and thepassenger vehicle composite wheel would be the same as the corresponding difference for heavy -duty
vehicles. While it would be possible to add tonal components to the composite wheel noise as well, it
is considered likely that a wheel designed to reduce noise will have a well-designed tread pattern thatminimized potentially annoying tonal components, and including tonal components would also meana more complex and resource heavy experimental design for the sleep study.
2.3 Dynamic convolution
The music programming language Csound (Lazzarini, o.a., 2016) could be thought of as a Matlab
for sound generation. It has all the tools needed to generate and precisely control any sound and allfunctions are written into a file similar to the Matlab .m-file which is the called by Csound to generatesound, either to a sound file or directly to speakers. In the current project, built in functions forgenerating white noise was used in combination with build in convolution functionality to shape thenoise to mimic tyre noise or composite wheel noise. Since individual vehicles are supposed to be able
to pass at slightly different speeds to mimic a real traffic situation, the impulse response in use iscontinuously scaled in the time domain to simulate difference in frequency content du to variation in
vehicle speed. The level is scaled accordingly by a simple gain function related to velocity. The tonalcomponents added for the tonal tyre scenarios are created using Csound’s sine wave generator withfrequency modulation. Distance dampening is included as a separate gain function calculated fromdistance between the virtual vehicle, due to starting position and velocity, and the virtual listener’sposition. Finally, the built in doppler shift function in Csound is employed using relative velocity
difference between virtual source and virtual receiver.
2.4 Façade filtering
The stimuli should correspond to an indoor setting such as what is experienced when sleeping inan ordinary bedroom. It was decided within the LEON-T project that a 10cm thick concrete wallpartition with a double pane window would represent a majority of urban dwellings in Europe. A
frequency response for the wall partition to be used in the sleep studies within the LEON-T project
can be seen in Figure 4. A corresponding impulse response was created by INSA Lyon and isconvolved with the synthesized tyre noise in Csound to achieve a realistic indoor noise.

Figure 4.

Sound reduction frequency response of a wall partition consisting of 10cm concrete and a double pane window.

Courtesy of Thibaut Marin-Cudraz at INSA Lyon.

2.5 Synthesizing traffic
In order to investigate the influence of single vehicle passes compared to a continuous flow ofvehicles the traffic flow needs to be synthesized. A reliable way of modelling semi-stochastic traffic
is to use a Poisson distribution, which will spread occurrences around a given mean rate (Haight,
1967).

Figure 5. Example of synthesized traffic flow noise. Blue curve represents 50000 vehicles per 24h with 10% heavy duty
vehicles. Red curve represents 2500 vehicles per 24h with 10% heavy vehicles.

Figure 5 shows two examples of traffic flow: 50000 vehicles per 24h (blue) versus 2500 vehicles
per 24h (red). As can be seen, single vehicles are represented as level peaks at certain points in time,and for the higher traffic flow the level is constantly above that of the lower flow be tween theprominent peaks. By choosing the traffic flow parameters, the difference in equivalent noise level isdetermined, whereas the peaks are determined by the loudest vehicles and differ only slightly between
high and low traffic flows. The above example was created in Csound using the built-in Poisson
function to trigger individual synthesized vehicles using slightly varying speed for each vehicle. Bothpassenger vehicles and heavy-duty vehicles were synthesized to mimic a real traffic flow where
passenger vehicles generally outnumber heavy vehicles by 10-20 to 1.

3. CURRENT STATUS
Tyre noise as well as composite wheel noise have been successfully synthesized for use as
individual vehicle noise sources. The realism of the synthesized noise sources has been evaluated by
NVH experts. The individual sources have also successfully been combined into a realistic traffic flow
determined by an average traffic flow rate of number per vehicles per 24h. At the time of writing, thecurrent work is focused on determining what traffic flow values and what levels of tonality should be
used, as well as other specifics about the scenarios to be used in the sleep studies. It is important toensure clear and salient, perhaps even exaggerated differences in the sound characters under study,
since the sleep study will have a limited number of participants, and since the measured effect size islikely to be small. The first sleep study is likely to be designed as a pilot study that will allow further
refinement of the scenario design to ensure the most reliable outcome possib le.

ACKNOWLEDGEMENTS
This research has been funded by the EU Horizon 2020 SOCIETAL CHALLENGES - Smart, Greenand Integrated Transport research program, grant agreement ID: 955387.

REFERENCES

Abd El-Salam, A., Gohary, M. A., & El-Gamal, H. A. (2017). Modal analysis on tire with respect to different
parameters. Alexandria Engineering Journal, 56, 345–357.
Haight, F. A. (1967). Handbook of the Poisson Distribution. New York, NY, USA: John Wiley & Sons.
Kropp, W. (1999). A mathematical model of tyre noise generation. International journal of heavy vehicle
systems, 6, 310–329.
Lazzarini, V., Yi, S., Heintz, J., Brandtsegg, Ø., McCurdy, I., & others. (2016). Csound: A sound and music
computing system. Springer.
Sandberg, U., & Ejsmont, J. (2002). Tyre/Road Noise Reference Book. Kisa, Sweden: Informex.
WHO. (2018). Environmental noise guidelines for the European region. World Health Organization.
Regional Office for Europe.

ABS-0164

Analysing the impact of meteorological parameters on infrasoundfrom on-shore wind turbines
Esther Blumendeller(1) , Po Wen Cheng(1)(1) Stuttgart Wind Energy at University of Stuttgart, Germany, blumendeller@ifb.uni-stuttgart.de

ABSTRACTTo understand the effects of wind turbine (WT) noise on local residents, the Inter-Wind research project analyses annoyance situations of local residents in an interdisciplinary approach. For this purpose, the interaction of
meteorology, sound and ground motion is being investigated in connection to psychological annoyance assessment. Acoustic and seismic measurements were carried out at two wind farms (WF) in southern Germany andin parallel at residential sites in the vicinity of the WF. The focus lies on the low-frequency (20-200 Hz) andinfrasonic (1-20 Hz) range. In addition, meteorological parameters were measured. Especially frequency components due to WT operation below 10 Hz, which are mainly harmonics of the blade passing frequency (BPF),
show dominant peaks in narrow-band spectra that can be assigned to WT operation. Although infrasound is
below the human hearing threshold, it is controversial whether it contributes to annoyance. In order to detect
infrasound, a harmonic of the BPF is used in this contribution to assess the occurrence of infrasound at the WF
and at the residential sites. The occurrence of infrasound is evaluated in relation to meteorological parameterssuch as atmospheric stability, humidity or temperature. In addition, differences in the occurrence of infrasoundfor the measurement sites during two different measurement campaigns, for winter and spring, are compared.
Keywords: Wind turbines, Infrasound, Meteorology

1

INTRODUCTION

Infrasound, here defined for the frequency range 1-20 Hz, is emitted in our environment by many different
sound sources. A distinction can be made between two types. The first is natural infrasound, caused by thunderstorms, waves, wind, etc. The second type is human-made infrasound of technical origin. Here, for example,
vehicles, pumps and wind turbines (WTs) can be mentioned [1]. Since infrasound has very long wavelengths,
the atmospheric attenuation is small over long distances. Nevertheless, it is not clear which factors favour soundpropagation and under which conditions infrasound occurs more frequently. In particular, infrasound caused by
WTs has already been investigated in many studies like [2, 3, 4, 5], with regard to sound generation, measurement methods, propagation and effects on humans. In the frequency spectrum the blade passing frequency(BPF) and higher harmonics can be easily identified as distinct peaks above the background level and clearly
assigned to the WT operation. In a previous study [6], the correlation of WT infrasound in relation to WT operational parameters, such as rotational speed or nacelle position, was investigated. In this study it was found,that infrasound mainly occurred for maximum rotational speeds. Only few publications also cover infrasound in
relation to meteorological parameters. [7] identified a correlation with the shear exponent factor m, which wasused to determine the atmospheric stability. In particular, stable conditions that frequently occur at night, seemto favour infrasound propagation. In order to detect infrasound, a harmonic of the BPF is used in this contribution to assess the occurrence of infrasound at a wind farm (WF). The occurrence of infrasound is evaluatedin relation to meteorological parameters such as atmospheric stability, humidity or temperature.

Figure 1. Map [8] with the locations of the wind turbines of wind farm Tegelberg (stars), and measurement
locations at the wind farm and five locations in the municipality of Kuchen. Circles indicate positions during
measurement campaign 1 in 2020, triangles indicate positions during measurement campaign 2 in 2022.

2

MEASUREMENT CAMPAIGNS AND INSTRUMENTATION

During two measurement campaigns in winter 2020 and spring 2022, acoustic and meteorological data werecollected in the vicinity of a WF. The WF Tegelberg with three GE 2,7 MW WTs is located on the Swabian
Alb in southern Germany close to an escarpment. In total, measurement data of about 103 days are available
from from 2020-10-23 to 2020-12-15 (campaign 1) and 2022-03-24 to 2022-05-11 (campaign 2).
Figure 1 shows a map with the microphone and WT locations. During measurement campaign 1, one
microphone was positioned at WT 1, whereas the other two microphones were positioned outside and insideresidential sites L1-L4 in the municipality of Kuchen in the direct vicinity of the WF. During measurement
campaign 2, the space around WT 2 was chosen as measurement location, where the microphone was positioned
50 m south of the WT. For the second measurement campaign, the microphone was chosen to be placed near the
WT closest to the residents. Since WT 3 was not operating during the planned measurement time period, WT 2
was selected for the measurements at the WF. Location L5, in a closed outdoor swimming pool, was chosen
for the other two microphones, since sites L1-L4 were not available during campaign 2. Here measurements
were conducted throughout the campaign outside and inside a small clubhouse, where WT 2 was also visible.
Although the wooden building construction does not correspond to a typical residential building in Germany, it
can still be used comparatively.
Meteorological parameters such as air humidity and air temperature were measured during both measurement

(a)

(b)

Figure 2. Correlation of humidity and temperature measured at the test site in 3 m height and at the WF in 10 m height during
measurement campaign 1 (a) and 2 (b).

(a)

(b)

Figure 3. Frequency distribution of humidity and temperature measured at the test site in 3 m height and at the wind farm in
10 m height during measurement campaign 1 (a) and 2 (b).

campaign periods close to the microphone in 10 m height. In addition, meteorological measurements wereconducted at a distance of about 2.5 km from the WF at the WINSENT research test site [9]. Humidity,
temperature, wind speed and direction, among other parameters, are captured with various instruments on two
100 m high met masts. Detailed information concerning measurement campaign 1 and instrumentation used inboth campaigns can be found in [10].
Humidity and temperature were measured at the WF location in 10 m height, but also at the WINSENT
test site in five different heights between 3 m and 100 m. Figure 2 (a) and (b) shows that the data collected
at the WF in 10 m height and at the test site at 3 m height correlate well despite the distance. Although themeasurement data from the WF met mast are used in the following, this shows that the values are transferable.
The transferability is used for the relation of acoustic measurement data with stability parameters.
From Figure 3 (a) and (b) it can be seen, that the measurement campaigns were conducted during different
seasons. In winter 2020 (Figure 3 (a)), temperatures between -6◦ C and 18◦ C with a mean value of 4◦ C and amean humidity of 89 % are captured. In spring 2022 (Figure 3 (b)), a mean value of 8◦ C is measured in arange of -5◦ C and 15◦ C and a mean humidity of 71 %.
The temperatures at two different heights of the met mast are used to calculate the temperature gradient γ.
This parameter is used to assess the stability of the atmosphere. In this work, γ with the unit Kelvin/Meter, iscalculated using the temperatures from the test site met mast in 3 m and 96 m height. With positive γ values,
the temperature increases with height by the given value. It indicates little mixing of the air layers and thus
stable atmospheric conditions, whereas negative values indicate unstable atmospheric conditions.

3

TIME SERIES OF METEOROLOGICAL PARAMETERS AND INFRASOUND OCCURRENCES

In [6] infrasound occurrences were identified by detecting the 4th harmonic of the BPF. The prerequisite for a
detection is a peak in the 10 min mean narrow-band spectrum between 2 Hz and 2.5 Hz with a prominenceof 6 dB above the background floor. If these conditions are fulfilled, the sound pressure level of the peak is
captured and an infrasound occurrence identified in this 10 min time period.
Time series of the infrasound occurrence and further parameters are shown in Figure 4 for the first campaign
in 2020 and Figure 5 the second campaign in 2022. From subfigure (a) in both Figures, it can be seen, that
infrasound is most often captured at the WF microphone location, but also at the residential sites.
Both graphs show a positive temperature gradient (temperature inversion), especially at night, and thus stable
atmospheric conditions. The daytime is characterised by increased temperatures as well as reversed temperaturesfrom 3 m to 96 m and thus a negative gradient, indicating unstable atmospheric conditions. These periods aremuch longer in spring 2022.

Figure 4. Data from measurement campaign 1 between 2020-11-13 to 2020-11-19. (a) Sound pressure level of 4th blade
passing frequency harmonic. (b) Rotational speed of wind turbine 1 and humidity measured at the wind farm in 10 m height. (c)Temperature in met mast height of 3 m and 96 m measured at the WINSENT test site and the temperature gradient γ indicating
stable, neutral and unstable atmospheric conditions.

Figure 5. Data from measurement campaign 2 between 2022-04-15 to 2022-04-21. (a) Sound pressure level of 4th blade
passing frequency harmonic. (b) Rotational speed of wind turbine 2 and humidity measured at the wind farm in 10 m height. (c)Temperature in met mast height of 3 m and 97 m measured at the WINSENT test site and the temperature gradient γ indicating
stable, neutral and unstable atmospheric conditions.

In the nighttime of 2020-11-16 and 2020-11-19 (Figure 4, (c)), temperatures at both heights suddenly dropby around 5◦ C, furthermore the temperature gradient decreases, implying neutral conditions. At the same time,
the air humidity increases (Figure 4, (b)), which is caused by the onset of rain. Nevertheless, an increased
occurrence of infrasound was detected during this period, both at the WF and at residential site L2 (Figure 4,(a)). This can be justified with the current rated operation of the WT with maximum rotational speed. As
mentioned before, it was concluded in [6], that infrasound is mainly detected for rated WT operation androtational speeds between 12 rpm and 12.5 rpm.
During the second measurement campaign, shown in Figure 5, infrasound is clearly present in the nighttime periods with stable conditions and maximum rotational speed. An exception is 2022-04-19, were the WT
was shut down. Furthermore, the example day 2022-04-17 shows that even at maximum rotational speed, butchanging atmospheric conditions from stable to unstable, no infrasound is detected in the valley location L5.
From this it could be concluded, that unstable atmospheric conditions might lead to the absence of infrasoundin greater distances from the WF.

4CONCLUSION AND OUTLOOKIn this contribution two measurement campaigns at a WF in complex terrain in southern Germany are briefly
described. Here, sound pressure and meteorological parameters at different heights were captured. It could beshown that meteorological measurement data of temperature and humidity, which were recorded at the WF andin 2.5 km distance at a research test site, are comparable. This also justifies the transferability of parameters
for determining atmospheric stability.
In addition, the occurrence of infrasound induced by WTs was related to meteorological data, in particular
the temperature gradient, which was considered suitable to define atmospheric conditions. Based on this parameter, the atmospheric condition can be categorised as unstable, neutral and stable. The time series showedthat stable conditions occur especially during night, and that infrasound is also detected more frequently then.
However, it was again made clear that there is a strong connection to the rotational speed of the WT. In particular in the valley location, infrasound is detected at the residential sites during rated WT operation. However,unstable atmospheric conditions can result in the absence of infrasound in the valley location at a distance of
1 km, even with maximum rotational rates of the WT.
In further investigations, the relation between temperature gradient with humidity and WT operation will
be examined, as well as the relation to amplitude modulation and annoyance. It seems necessary to consider
infrasonic sound from WTs not isolated with only one, but in relation to several parameters.

ACKNOWLEDGEMENTS
We thank the local authorities of the municipality Kuchen for their support, as well as the Stadtwerke SchwäbischHall and the KWA Contracting AG for providing operating data. Furthermore we thank Philipp Berlinger forsupporting the acoustic measurements and Maayen Wigger for providing meteorological data. We acknowledge
the support of the local residents, who enabled the installation of instruments on their property and within theirhouses. This study is supported by the Federal Ministry for Economic Affairs and Climate Action based on a
resolution of the German Bundestag (grants 03EE2023B).

REFERENCES[1] Ingielewicz R, Zagubień A.Infrasound Noise of Natural Sources in the Environment and Infrasound Noiseof Wind Turbines. Polish Journal of Environmental Studies, 2014, 23(4):1323-1327.
[2] Jakobsen J. Infrasound emission from wind turbines. Journal of Low Frequency Noise Vibration and Active
Control, 24(3), 145–155, 2005,https://doi.org/10.1260/026309205775374451.
[3] Klein L, Gude J, Wenz F, Lutz T, Krämer E. Advanced computational fluid dynamics (CFD)-multi-bodysimulation (MBS) coupling to assess low-frequency emissions from wind turbines. Wind Energy Science,
3(2), 713–728, 2018, https://doi.org/10.5194/wes-3-713-2018.

[4] Maijala P. Infrasound Does Not Explain Symptoms Related to Wind Turbines, 2020.
[5] Hansen C, Hansen K. Recent Advances in Wind Turbine Noise Research. Acoustics, 2(1), 171–206, 2020,
https://doi.org/10.3390/acoustics2010013.
[6] Blumendeller E, Hofsäß M, Goertlitz A, Cheng P W. Impact of wind turbine operation conditions on infrasonic and low frequency sound induced by on- shore wind turbines. J. Phys.s: Conf. Ser. 2265 03204,
2022, https://doi.org/10.1088/1742-6596/2265/3/032048.
[7] Zajamšek B, Hansen K L, Doolan C J, Hansen C H. Characterisation of wind farm infrasound and lowfrequency noise. Journal of Sound and Vibration, 370, 176–190, 2016, https://doi.org/10.1016/j.
jsv.2016.02.001.
[8] OpenStreetMap, https://www.openstreetmap.org, 2022-07-01.
[9] Rettenmeier A, Haizmann F, Musiol F, Wigger M, Trenchev M, Schmidt M. Insights into the Realization of
the Wind Science and Engineering Test Site “WINSENT” in Complex Terrain, 2021. Wind Energy Science
Conference.
[10] Gaßner L, Blumendeller E, Müller F J Y, Wigger M, Rettenmeier ., Cheng P W, Hübner G, Ritter J, Pohl
J. Joint analysis of resident complaints, meteorological, acoustic, and ground motion data to establish arobust annoyance evaluation of wind turbine emissions. Renewable Energy, 188, 1072–1093, 2022, https://doi.org/10.1016/j.renene.2022.02.081.

ABS-0217

Ground motions from wind turbines and their relation to acoustic
emissions and resident annoyance
Laura Gassner(1) , Joachim Ritter(1)(1) Karlsruhe Institute of Technology, Geophysical Institute, Germany, laura.gassner@kit.edu, joachim.ritter@kit.edu

ABSTRACT
Within the interdisciplinary project Inter-Wind we study ground motion signals in the vicinity of two windfarms on the Swabian Alb, a mountain range in Southern Germany. The aim of the project is to understandhow residents are affected by wind turbine (WT) immissions. The wind farms under investigation consist of
three and sixteen WTs, respectively, which are of the same turbine type (GE 2.75-120). Measurements are conducted in the municipalities where residents report annoyance and in the direct vicinity of the WTs, acompaniedby acoustic and meteorological measurements. Additional ground motion measurements are conducted in theforests surrounding the WTs, and inside WT towers.
Signals at constant frequencies below 12 Hz are caused by the eigen modes of the tower and blades. Furthermore, signals at frequencies proportional to the blade passing frequency (BPF) are observed above 12 Hz,
which are also measured in parallel acoustic recordings. These signals relate to the WT generator and possibly
the gearing box. Therefore, similarities in ground motion and acoustic data provide additional information on
signals generated by the WTs and give indications on other noise and vibration sources. Such knowledge iscrucial to design counter-measures with the aim of reducing emissions to support the public acceptance of WTs.
Keywords: Wind turbines, Vibration, Sound

1

INTRODUCTIONWind energy is an important component of the transition to renewable energies, but the installation of WTs
is met with opposition in many regions in Germany. Conservation of nature, the visual impact of the WT
towers, as well as acoustic and vibrational emissions are the main aspects of criticism against wind farms fromresidents. In the research project Inter-Wind we conduct interdisciplinary campaigns aimed at finding reasons forannoyance, related to two wind farms in Southern Germany on the Swabian Alb. During several measurement
campaigns, WT emissions and immissions are measured and quantified to provide a data base for joint research[1].
Three interdisciplinary measurement campaigns were conducted at wind farms Tegelberg (autumn 2020 andspring 2022) and Lauterstein (spring 2021). Surveys found 33 % strongly annoyed residents for wind farm
Tegelberg, while only 8 % were found for wind farm Lauterstein. Therefore, in the following we concentrateon the measurement campaigns at wind farm Tegelberg. While the degree of annoyance related to WTs varies
between residents, there is a more uniform annoyance related to traffic noise. Measurements are thus conductednear the WTs and within the municipality, where a federal road and a major railway line exist.
At the wind farm, ground motion data is dominated by the eigen modes of the WTs at distinct frequencies
below 12 Hz which can be detected up to several kilometers [2, 3]. These signals are of amplitudes well below
the human perception threshold [4, 5], which is considered to be approximately at 100 µm/s. For frequencies
above 12 Hz we observe similarities to acoustic data, which better constrains when WT emissions are present
and how they vary in amplitude. The signals detected are related to the WTs generator and gears, which arealso named by residents when describing perceived WT noise. Additional measurements with ground motion
instruments provide a broad data basis to supplement the acoustic measurements.

2

MEASUREMENT CAMPAIGNSAt first, residents in the municipalities near the wind farm were contacted via letter and then interviewed via
phone calls. Interested residents were offered the use of a noise reporting app through which they could notethe time when they perceived WT noise, the degree of annoyance (between 0 and 4), comments on the kind ofnoise, coping measures taken, etc. Furthermore, for campaign 1 in autumn 2020, four residents were selected
for measurements at their homes. Microphones were placed inside and outside of the residencies and a groundmotion recording instrument was installed outside the houses. These measurements lasted approximately two
weeks each. Further parallel sound and vibration measurements were conducted at the wind farm. Meteorological data was captured within the municipality and at a nearby test site operated by project partners. Data
was provided by the wind farm operators, including rotation rates, wind speeds, and wind directions, registeredat each of the turbines. During campaign 2 in spring 2022, WT 3 (Figure 1) was not in operation, offering aunique opportunity to study the effect of the reduced wind farm operation on noise reports.
2.1 Recording sites
During each campaign, several ground motion recording instruments were installed for parallel measurements inthe municipality and close to the wind farm (Figure 1). One instrument was installed inside the tower of one of
the WTs, in WT 1 for campaign 1 and in WT 2 for campaign 2. Additionally, one instrument was installed near
the WTs in close proximity to the acoustic recording equipment. This was approximately in 145 m distance to
WT 1 for campaign 1 and in 70 m distance to WT 2 in campaign 2. Further instruments were placed in theforest area between the wind farm and the municipality. Two sites were used for both campaigns (with a small
offset), one near the municipal boundary and one at approximately half way between the wind farm and Kuchen(Figure 1). For campaign 2, a continuous measurement site was desired within the municipality. Therefore, thearea of a public swimming pool was selected for the entire period of campaign 2.
2.2 Noise reports
For campaign 1, the app was available from end of August 2020 to early February 2021, for campaign 2 fromearly April to mid May 2022. In order to obtain a wider data basis, residents were encouraged to also makereports when no noise was present in campaign 2. This led to 133 reports without and 55 reports with noisepresent. Figure 2 gives an overview of the daytime distribution of noise reports for campaign 1 and 2. For
campaign 1, only reports starting in October 2020 are considered to match the time period of the measurement
campaign. During campaign 1 most noise reports were registered after 18:00 local time with highest report rates
from 22:00 to 23:00 and from 5:00 to 6:00 local time. In campaign 2 most reports were made from 22:00 to
23:00 local time, with significantly fewer reports during night time.

3

GROUND MOTION DATA EXAMPLES

For the evaluation of recorded ground motion signals we consider power spectral densities (PSD) calculated for
60 s time windows with an overlap of 20 s of the vertical ground motion data. In campaign 1, a sampling
rate of 100 Hz was used and in campaign 2 it was set to 400 Hz to achieve a better comparability to acoustic
signals sampled at 20 kHz. In the following, data examples are shown for ground motion recordings near traffic
ways and a comparison to acoustic measurements.
3.1 Traffic noise
Figure 3 shows data examples from ground motion recording sites in close proximity (approximately 20 m distance) to the railway line and the federal road running through the municipality. The train traffic is characterized
by clear transient signals which occur almost continuously with highest amplitudes (up to 100 µm/s) between
8 Hz and 25 Hz during the day with periods of reduced signals from approximately 0:00 to 3:30 and 23:30
to 24:00 UTC. The road traffic signals exhibit lower amplitudes (up to 25 µm/s) and more continuous signals.
The excited frequency range is 10 Hz to 30 Hz and periods of reduced amplitude occur between 0:00 to 2:00
and 22:00 to 24:00 UTC.

48.66°N

800

Ground motion recording sites
2020/10/20 - 2021/02/05
2022/03/23 - 2022/05/12
Additional acoustic measurement
Indoor measurement
Municipal boundary
Developed area

48.655°N

48.65°N

750

700

650

2

48.645°N

600

3

WF Tegelberg

Kuchen48.64°N

550

Height in m a.s.l.

148.635°N
500

Railway line48.63°N
0 km

0.5 km

1 km

Fed

era
l

450

roa

d

48.625°N

400

9.79°E

9.8°E

9.81°E

9.82°E

9.83°E

Figure 1. Topographic map showing the ground motion recording sites at wind farm Tegelberg and the municipality of Kuchen. Red triangles mark locations of campaign 1, blue triangles those of campaign 2. Small
triangles indicate additional acoustic recording sites, inverted triangles indoor measurement sites. The bold blackline shows the course of the federal road B10 and the black dashed line the course of a major railway line, bothrunning through the municipality. The inset shows the outline of Germany and the state of Baden-Württemberg
with a red marker indicating the project area.

Measurement campaign 2020

50

Number of complaints

Number of complaints

40

30

20

10

0
0
0:0

Measurement campaign 2022

40
30
20
10
0

0

4:0

0

8:0

0
0:0

:00 6:00 0:00
12
1
2

0
4:0

0
0
0
0
8:0 12:0 16:0 20:0

0
0
0:0

15

0
4:0

0
8:0

0.1

0
0
0:0

:00 6:00 0:00
12
1
2

Measurement campaign 2022

Mean rms amplitudes in µm/s

10

0.2

1.5

0
4:0

0
8:0

1
0.5
0
0:0

Near WT 2

Res

1.5

0

:00 6:00 0:00
12
1
2

/s

Mean rms amplitudes in µm/s

20/s

Number of complaints

Figure 2. Hourly distribution(localcampaigntime) of 2020
entries through the noise reportingapp 1for the two measurement
Measurement
Near WT40
0.4
2.5
campaigns at wind farm Tegelberg in autumn 2020 (left) and spring 2022 (right, black bars). For campaign 2
residents were encouraged to also register entries when no noise was noted, the red bars show the distribution2 ofcomplaints with30noise present. The registration period for the0.3
two campaigns was 126 and 35 days, respectively.

0.5

0

4:0

0

8:0

12

Public sw

Figure 3. Time series and spectrograms for one day of data from campaign 1 at a site in approximately 20 m
distance to the railway line (left) and for one day of campaign 2 at a site in approximately 20 m distanceto the federal road (right). Amplitudes for train traffic are higher than those of road traffic, while the roadtraffic signals are more continuous. Time is shown in UTC, local time is UTC+1 (campaign 1) and UTC+2(campaign 2).
3.2 Comparison to acoustic signals
Spectrograms of the vertical ground motion and sound pressure data at 70 m distance to WT 2 show strong
similarities for frequencies up to 150 Hz (Figure 4). Signals at multiples of the BPF related to the WTs
generator and gears (Table 1) can be seen clearly in both datasets. Differences are visible in the signal strengthof the individual signals and in the character of the signals below 12 Hz. In the ground motion data eigen
modes of the WTs at 1.2 Hz, 3.6 Hz and 8.33 Hz can be observed, while in the acoustic data the first eight
harmonics of the BPF can be detected. Data measured at the wind farm provide information about times whenemissions occur and how they vary in amplitude. Although signals within the municipality do not surpass
perception thresholds at the discussed frequencies, the wind farm data can be used to evaluate the noise reportsof residents. Our comparison of acoustic and ground motion measurements shows that the latter can help tofind emitted acoustic signals up to more than 100 Hz. This is helpful, because more ground motion recorders(seismometers) were available during measurements than microphones.
Table 1. Frequencies corresponding to multiples of the BPF for the main rotation rates of 8 rpm and 12.5 rpm,
separated for signals possibly related to the gearing box (left) and the generator (right).
Gears

8 rpm

12.5 rpm

Generator

8 rpm

12.5 rpm

32×BPF12.8 Hz20.00 Hz

46×BPF18.4 Hz28.75 Hz

64×BPF25.6 Hz40.00 Hz

92×BPF36.8 Hz57.50 Hz

96×BPF38.4 Hz60.00 Hz

138×BPF

55.2 Hz86.25 Hz

128×BPF

51.2 Hz80.00 Hz

184×BPF

73.6 Hz115.00 Hz

Figure 4. Spectrograms for one full day of vertical ground motion (left) and sound pressure (right) data of
campaign 2. Instruments were located in approximately 70 m distance to WT 2. From 0:00 to 6:00 UTC and
19:00 to 24:00 UTC WT 2 was at full operation (12.5 rpm), with a reduced rotation rate of 8 rpm from 6:00
to 18:00 UTC (local time is UTC+2). Distinct frequencies at multiples of the BPF, which are common in bothdata sets, are marked on the y-axis, for details on the specific signals see Table 1.

4

SUMMARY AND CONCLUSIONS

Using ground motion recordings, vibrations from WTs and other noise sources like traffic are captured during
two interdisciplinary measurement campaigns at wind farm Tegelberg in Southern Germany. While measured
amplitudes are below the human perception threshold, continuous measurements give indications about the timeof occurrence and relative amplitudes of the different vibrations. These measurements provide additional information to acoustic measurements at the wind farm and within the municipality. At the wind farm, groundmotion spectrograms exhibit strong similarities to sound pressure spectrograms, showing generator and gearing
signals for frequencies up to 150 Hz. In noise reports residents name the generator and gears as noise sources,
coinciding with recorded emissions. Furthermore, annoyance reports can be correlated with wind farm operation
through operating data, to assess the credibility of the registered complaints. Most reports are registered during
the evening (campaign 1 and 2) and early morning hours (campaign 1). Traffic noise from a federal road anda major railway line is reduced at night time while the wind farm operates continuously when favourable meteorological conditions are present. Potentially, with other noise sources reduced, WT sounds are perceived as
especially annoying during the more quiet night hours, even though signal amplitudes are low.

ACKNOWLEDGEMENTS
We thank the local authorities of Kuchen and Degenfeld for their support, as well as the Stadtwerke SchwäbischHall, the KWA Contracting AG and the wpd windmanager technik GmbH for WT access and data. Leon
Merkel, Marie Gärtner and Rune Helk helped to set up and maintain the instruments for the different measurement campaigns. Furthermore, we acknowledge the support of local residents and thank Esther Blumendeller
for providing acoustic data and Florian Müller for providing data on annoyance.
This study is supported by the Federal Ministry for Economic Affairs and Climate Action based on a resolutionof the German Bundestag (grant 03EE2023D).

REFERENCES[1] Gaßner, L., Blumendeller, E., Müller, F. J. Y., Wigger, M., Rettenmeier, A., Cheng, P.W., Hübner, G., Ritter,J., and Pohl, J.: Joint Analysis of Resident Complaints, Meteorological, Acoustic, and Ground Motion Data
to Establish a Robust Annoyance Evaluation of Wind Turbine Emissions, Renewable Energy, 188, 10721093,
https://doi.org/10.1016/j.renene.2022.02.081, 2022.
[2] Nagel, S., Zieger, T., Luhmann, B., Knödel, P., Ritter, J., and Ummenhofer, T.: Ground motions induced bywind turbines, Civil Engineering Design, 3, 73–86, 2021.
[3] Zieger, T. and Ritter, J. R.: Influence of wind turbines on seismic stations in the upper rhine graben, SW
Germany, Journal of Seismology, 22, 105–122, 2018.
[4] DIN 4150-2:1999-06: Erschütterungen im Bauwesen - Teil 2: Einwirkungen auf Menschen in Gebäuden.,
https://www.beuth.de/de/norm/din-4150-2/12168614, [Accessed on 2022-03-04], 1999.
[5] Ratzel, U., Herrmann, L., Westerhausen, C., Bayer, O., Krapf, K.-G., Hoffmann, M., Blaul, J., andMehnert, C.: Tieffrequente Geräusche inkl. Infraschall von Windkraftanlagen und anderen Quellen,
https://pudi.lubw.de/detailseite/-/publication/84558, [Accessed on 2022-03-04], 2016.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0293

Approved devices are indispensable for measurements in theinfrasound frequency range: Effect of membrane defects on
microphone sensitivity
Marvin RUST; Christoph KLING; Christian KOCH
Physikalisch-Technische Bundesanstalt, Germany

ABSTRACTThe proliferation of green energy generation is accompanied by serious concerns about noise emission fromenergy converters. Noise measurements play an important role in the determination of the exposure tohumans. Any results must be legitimate and defendable against critics and the use of approved measurement
devices is indispensable.
To demonstrate the importance of reliable device characterization, a worst-case scenario was created: A noise
measurement was carried out near a wind park using a microphone with a subtle defect intentionally createdby piercing the membrane of an otherwise intact microphone. The perforated system passed the functionality
check with a standard sound calibrator at 1 kHz showing the expected response. However, within an example
measurement sound pressure levels in the infrasound frequency range obtained with the modified device
were found to be around 20 dB lower than reference data, i. e. were wrong. Conclusions on approval strategies
are drawn to avoid the dramatic impact at measurements in the infrasound frequency range.
Keywords: Wind energy converter, Sound measurement, Infrasound

1. INTRODUCTION
Transitioning of European and worldwide economies to carbon-neutral energy production isessential for mitigating climate change and for a sustainable industry production and living.
Renewable energy converters, however, often produce noise specifically at low frequencies including
infrasound. Serious concerns and complaints by those living in the vicinity of planned facilities often
complicate the expansion and development of environmentally friendly energy production. Noise
measurements play an important role in the assessment of the noise emitted whi ch ideally should
provide reliable quantitative data mainly about current exposure situations. Measurements must be
legitimate and defendable against critics, and the use of calibrated and approved measurement devices
is indispensable.
In the audible frequency range, a well-established system for ensuring reliable, traceable, andtrustworthy tests exists, and a comprehensive calibration infrastructure for measurement equipment isestablished. It is carefully balanced between the requirement of complying with the various
international standards and national regulations and the availability of technical equipment andmethodology in measurement practice. The extension to low frequencies, however, has not yet beenrealised, and a calibration service system has not been established, particularly not as a type-approval
infrastructure. This leads to the risk of malfunctioning devices being used in erroneous measurements
deluding reliable results. In general, potential problems are more likely to occur in the infrasoundfrequency region since microphone membrane defects have stronger direct impact on measurements
here than in the audible frequency range.
To investigate the effect of microphone faults on measurement performance in the infrasoundfrequency range mainly for noise measurements in environmental tasks, this study applies a speciallyprepared measurement devices to a common measurement task. A severely limited device with amechanically perforated membrane was prepared, and then applied during a measurement campaign
for the evaluation of infrasound noise from a wind park. Although the microphone was unobtrusive
during a calibrator test at 1 kHz, the infrasound measurement results were more than 20 dB deviating,which underpins the need for specific performance testing and control at infrasound frequencies.

2. EXPERIMENTAL SETUP
Measurements were carried out during a campaign where the comparison between microphone andmicrobarometer measurements was at focus. Details on measurement arrangements, techniques andresults obtained with correctly working microphones were published elsewhere [1]. In this study, themeasurement using a malfunctioning device carried out within the same measurement campaign andunder similar conditions is presented. The results are compared to those of the proven well-behaving
system used in parallel.
The malfunctioning microphone was prepared using an old but flawlessly operating microphone
capsule of type Brüel & Kjær 4133. This capsule was combined with a preamplifier of type Brüel &Kjær 2669 and a power module of type GRAS 12AD to form a microphone set. This microphone set
was calibrated in a frequency range from 2 Hz to 100 Hz. Afterwards, the membrane of th e microphone
capsule was perforated using a hard wire with a diameter of 100 µm. A performance check with asound calibrator of type Brüel & Kjær 4231 confirmed that the microphone was still operational, andno level change could be detected at the calibration frequency of 1 kHz. A proven well behaving
microphone system was assembled using a microphone capsule Brüel & Kjær 4964 in combination
with a GRAS 26AK-S1 preamplifier which was also calibrated to allow a direct comparison of theon-site measurements.
The analog voltage signals of both microphones were simultaneously captured using an analog -todigital converter of type Data Translation DT9857E and saved as .wav files for an offline evaluation.
The raw data were transformed into the frequency domain by the means of a fast Fourier transformand the individual frequency responses of the microphones were corrected using the calibration data
to ensure comparability of the results independent of the different microphone types. For the
malfunctioning microphone, the calibration data from before the perforation preparation were used
for this purpose. In this way, a failure which occurred after the last calibration and which remained
undetected during application, was simulated. In a second step, third-octave levels conforming withIEC 61260-1 were computed to provide an absolute and comparable measure for the sound pressurelevels acquired at the measurement site.

3. RESULTS
The malfunctioning microphone system was applied to regular infrasound measurements near awind park. Following the common best practice for a qualified sound measurement on-site, themeasurement system including the malfunctioning microphone was tested and calibrated with a soundcalibrator at 1 kHz first. The system passed the functionality check showing an expected response(calibration level of 93.9 dB for a sound pressure level input generated by the calibrator of 94.0 dB).

Figure 1: Narrowband spectra (solid lines, frequency spacing d f = 1 / 300 Hz) and third-octave levels(dashed) acquired in the vicinity of a wind park by a correctly functioning microphone and a microphonewith a perforated membrane, distance to the wind park: 480 m, medium wind velocity of about 5 m/s.

In Figure 1an example of a measurement result is given for a measurement position in about 480 m
distance to the nearest wind turbine. Medium wind velocity was measured at the nacelle and Figure 1
depicts the third-octave sound pressure levels along with narrowband frequency spectra. A quite flatcourse of the results using the correctly working microphone was obtained and no coherent signals at
the pass-by frequency were observed. The system comprising the malfunctioning device delivers up
to 20 dB lower values for both output variables, slightly less pronounced in the third -octave results.
This seems to stem from narrow-band signals detected in the frequency range between 8 and 20 Hz
and clearly to be seen in the narrowband spectra. Such components do not occur in the results of thecorrectly working microphone indicating that the malfunctioning device not only delivers too low
power level values but seems also to be more susceptible to disturbances. It should be emphasisedthat all results were obtained with the sound calibrator check indicating no issues with the system.

4. CONCLUSIONS
To illustrate the issue of missing calibration, testing and traceability infrastructure for the
infrasound frequency range, this study demonstrates the relevance and importance of these processes
within an example application. A test experiment using a controlled malfunctioning device was carriedout during a real measurement campaign near a wind park. Although the sensor system behaved
ordinarily during the mandatory preceding calibration test using a common calibrator, themeasurement results exhibited deviations of up to 20 dB at infrasound frequencies. Further
investigation has shown that even small and visually not notable perforations and defects can seriously
hinder measurements at low or infrasound frequencies. In contrast, a careful calibration over the full
frequency range of application would be able to reveal the limitations and could ensure proper
measurements.
The establishment of a metrological basis and traceability at infrasound frequencies is hindered by
the current situation with no specifications for devices and requirements for test procedures existing
to date. A first guideline proposal was prepared during an international European project (see [2]).
This first attempt should be followed up by standardisation work to finally arrive at regulations andtype approval methodologies which allow testing of devices in advance following widely acceptedrules. This is expected to strongly improve the reliability and accuracy of infrasound measurements.
Finally, it is a prerequisite of strengthening the wider acceptance of renewable energy converters
among people which is strongly required to reduce the usage of fossil energy sources and its impact
on climate change.

ACKNOWLEDGEMENTS
This project has received funding from the EMPIR programme co -financed by the Participating
States and from the European Union’s Horizon 2020 research and innovation programme.

REFERENCES
1. Rust M, Koch C. Microphone measurements in the vicinity of wind power plants: Results for the windpark in Gagel, Saxony Anhalt, Germany. PTB OAR 2021, DOI 10.7795/720.20211130,
https://oar.ptb.de/resources/show/10.7795/720.20211130.
2. Kling C, Koch C, Rust M, Barham R, Rodrigues D, Barrera Figueroa S, Sandermann Olsen E
Specifications and testing strategies for measurement devices for noise exposure determination in theinfrasound frequency range. PTB OAR 2021, DOI 10.7795/EMPIR.19ENV03.RE.20210609,
https://oar.ptb.de/resources/show/10.7795/EMPIR.19ENV03.RE.20210609.

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

ABS-0416

An experimental study on the combined effects of noise andvibration from conventional and high-speed railways in Japan andFrance on reading disturbanceTakashi MORIHARA1; Catherine MARQUIS-FAVRE2; Yasunao MATSUMOTO3;
Shigenori YOKOSHIMA4; Kentaro HAYASHI5
1

Department of Architecture, National Institute of Technology (KOSEN), Ishikawa College, Japan2
3
4

University Lyon, ENTPE, LTDS UMR 5513, France

Department of Civil and Environmental Engineering, Saitama University, Japan

Environmental Information Division, Kanagawa Environmental Research Centre, Japan
5

Benec Vibration and Sound Institute, Japan

ABSTRACTIn France, it is generally known that annoyance due to railway noise is lower than annoyance due to roadtraffic noise, while in Japan, many researches have been reported that responses of these annoyances weresimilar or opposite. The authors hypothesize that one of the causes of this difference may be the differencein vibration exposure level from the train pass-bys in the vicinity of dwellings. Using the noise and vibrationof conventional railways and high-speed railways recorded in Japan and France, we conducted anexperimental study on the combined effects of noise and vibration on reading activities. The experiment was
conducted for 30 Japanese participants in a simple anechoic chamber at Ishikawa KOSEN. The stimuli of theexperiment were used in combination two vibrations (peak levels: 65 and 75 dB) with three noises (peaklevels: 50, 60 and 70 dB). This study reports the results of comparing the reading disturbance caused by thedifferent train pass-bys.
Keywords: Noise, Vibration , Combined effect, Activity disturbance, Experiment

1. INTRODUCTION
Based on the knowledge that railway noise has less annoyance than road traffic noise in France, a“railway bonus” is applied with L r = L Aeq – 3 dB to the railway noise of conventional lines. L r is also
used for high-speed trains (TGV), but the “railway bonus” is not applied and its value is set low. In
Japan, many studies have reported that railway noise annoyance is almost same or greater than roadtraffic noise annoyance (1-5). One of the reasons for the difference in knowledge between Japan andEurope is thought to be the influence of vibration from the sound source . Morihara et al (5) reported
that there is a possibility that the magnitude of vibration affect the noise annoyance by a social survey
conducted along the Hokuriku Shinkansen railway. In addition, we have investigated the possibility
that noise and vibration from the Shinkansen railway affect daily activity disturbances throughexperiments (6). This experimental study used noise and vibration data recorded outdoors to create
stimuli with noise (L AS,max ) of 50, 60, 70 dB and vertical vibration (L vz,max ) of 65, 75 dB, for reading
and calculation works.
Shinkansen railways are equipped with soundproof walls and vibration control materials forelevated railway to reduce noise and vibration. On the other hand, conventional railways and high 1

morihara@ishikawa-nct.ac.jp

speed railways in France are built in the suburbs, and in urban areas, the speed is considerably reduced,
and soundproof walls like those in Japan are not installed. Conventional railways in Europe are larger
and heavier than those in Japan, therefore the relationship between noise and vibration that residents
are exposed to is also different from Japan. Furthermore, since this difference is related to the
propagation characteristics of noise and vibration, it is thought that the physical characteristics ofnoise and vibration recorded at the same distance from the track are different.
The purpose of this study is to verify whether there is a combined effect of sound and vibration
caused by high-speed railway and conventional railway recorded at France and Japan on reading
disturbance.

2. Methods
2.1 Apparatus
The laboratory experiment was conducted in a simplified anechoic room (2,800 × 2,800 mm).
Sound stimuli were presented through two speakers (ECLIPSE TD508MK3) placed in front of theparticipants at ear level. Vertical vibration was generated by a two-axis (vertical and horizontal)
vibration system (San-Esu SPT2DV-9K-12L2-1T) in the room. The seat was 835 mm from the floor,
and its size was 900 × 900 mm. Participants sat on the seat without a backrest or cushion.
2.2 Stimuli
The stimuli used in this experiment were sound and vibration from high-speed railways (TGV) andconventional railways measured in Japan and France, respectively. The noises and vibrations of the
TGV and conventional railways were recorded in Lyon, France in 2019. The noises and vibrations of
the conventional railways were recorded in Ishikawa, Japan in 2020. All stimuli were recorded outdoor
at 12.5m distance from rail and 1.2m height. The recorded stimuli were adjusted to 4 levels of sound(no sound stimulus, 50, 60 and 70 dBs) and 3 levels of vertical vibration (0, 65 and 75 dBs). The
vibration level is defined as 20 times the logarithm to base 10 of the ratio of the root -mean-square
vibration acceleration to the reference acceleration (10 -5 ms -2). Figure 1 shows frequency(a) Noise stimuli (70dB)(b) Vibration stimuli (75dB)
Figure 1 – Characteristics of stimuli (high-speed railway)(a) Noise stimuli (70dB)(b) Vibration stimuli (75dB)
Figure 2 – Characteristics of stimuli (conventional railway)characteristics of sound (70 dB) and vibration stimuli (65 dB) of high-speed railways. High-speed
railway in Japan was Hokuriku Shinkansen railway (HSR, (6)) and it in France was TGV. The
dominant frequency of the TGV sound occurred in the bands at 630 Hz and its vibration occurred in
the bands from 10 to 60 Hz. Frequency characteristics differ from TGV and HSR. The noise recorded
at Ishikawa was slightly stronger in the high frequency range than the noise recorded at Lyon, andboth vibration characteristics were similar.
2.3 Participants
Fifteen men and fifteen women aged 19 or 20 years participated in this study. All participants werediagnosed as having no hearing problems during their regular health check.
2.4 Procedure
Before the start of the experiment, which continued for approximately 1h30m (excluding breaks
every 30 minutes), the experiments were explained to the participants, and then the participants were
instructed to sit on the seat in a comfortable upright posture. All eleven possible paired combinationsof four noise levels and three vibration levels were presented in random order to each participant after
practice. During the experiments, the participants performed the activity tasks of reading magazines.
After one session, the participants answered three questions regarding noise, vibration, activity
disturbance, and annoyance. Table 1 shows the questions and evaluation scales. These evaluation
scales were based on a five-point verbal scale for noise annoyance (7). Since previous our study (6)did not ask about annoyance, this study used three (noisiness, perception of vibration and reading
disturbance) of these four evaluation results for analysis.
Table 1 – Evaluation wordings and scales
Q1. How noisy did you feel during the reading?
A. 1. not at all, 2. Slightly, 3. Moderately, 4. Very, 5. Extremely
Q2. How much did you feel the vibration during the reading?
A. 1. not at all, 2. Slightly, 3. Moderately, 4. Very, 5. Extremely
Q3. How much did you disturb by noise and vibration during the reading?
A. 1. not at all, 2. Slightly, 3. Moderately, 4. Very, 5. Extremely
Q4. How much did you annoy by noise and vibration during the reading?
A. 1. not at all, 2. Slightly, 3. Moderately, 4. Very, 5. Extremely

3. Results
3.1 High Speed railway
This paper presents the results of calculating the average value of three evaluations for eachcombination of stimuli. Figure 3 shows results comparing the results of HSR (6) and TGV. The pointsof markers mean average value of evaluation and bars mean standard deviation. Noisiness of TGV
differed at each of the four stages of sound stimuli, and was not affected by vibration stimuli.
Perception of vibration also differed at each of the three stages of vibration stimuli and was notaffected by noise stimuli. The evaluation of noisiness was higher in TGV than in HSR, and theperception of vibration tended to be higher in HSR than in TGV. The reading disturbance also tended
to increase as the vibration level increased at 50 dB and 60 dB of noise stimuli. However, the resultof tests of between-subjects effects showed that the main effects, noise and vibration, were significant
and the interaction was not significant.
Table 2 shows results of HSR and TGV were compared by Mann-Whitney’s U test. Significant
difference was found only for the evaluation of stimuli combining 70 dB noise and 65 dB vibration,

in other words no significant differences were found between the other evaluations.

(a) Noisiness(b) Perception of vibration(c) Reading disturbanceFigure 3 – Evaluation results for noise and vibration from high-speed railway

3.2 Conventional railway
Figure 4 shows results average value of evaluation to each stage of stimuli on conventional railway.
The differences with noisiness between 50 dB and 60 dB were slightly small, and evaluations ofnoisiness were not affected by vibration stimuli. Perception of vibration also differed at each of thethree stages of vibration stimuli and was not affected by noise stimuli. Both Japan and France, reading
disturbance was significantly affected noise and vibration, respectively. In addition, the interaction
was also significant at a significance level of 5% (Japan) and 1% (France).
Table 3 shows the results of comparing the disturbance between Japan and France. Mann-Whitney
U test showed a significant difference only for the evaluation of 50 dB noise and 75 dB vibration, but
no consistent trend was observed.

(a) Noisiness(b) Perception of vibration(c) Reading disturbanceFigure 4 – Evaluation results for noise and vibration from conventional railway

4. CONCLUSIONS
We verified that the combined effects of noise and vibration were significant for reading
disturbance. It was confirmed that the combined effect of noise and vibration caused by TGV to thereading disturbance was significant. This result was the same with our previous study (6). On the other
hand, from the analysis results of the conventional railway in Japan and France, it was confirmed that
not only main effects but also interactions were significant. It is possible that the differences in the
results were influenced by the physical characteristics of the stimuli. In addition, since the structuresof houses differ between Japan and France, future experiments are expected to take into account thecharacteristics of the sounds and vibrations to which residents are expo sed. When the results werecompared between Japan and France, no systematic difference was found.

ACKNOWLEDGEMENTS
The present study was supported by Grants-in-Aid for Scientists Research of Japan Society for the
Promotion of Science (Nos. 22H01571, 18H01518 and 18K04472).

REFERENCES
1. Kaku J, Yamada I, the possibility of a bonus for evaluating railway noise in Japan, Journal of Sound andVibration 193(1), 445-450, 1996.
2. Yano T, Yamashita T, Izumi K, Comparison of community annoyance from railway noise evaluated bydifferent category scales, J. Sound Vib., 205(4), 505-511, 1997.
3. Morihara T, Sato T, Yano T, Comparison of dose-response relationships between railway and road traffic
noises: The moderating effect of distance, J. Sound Vib., 277, 559-565, 2004.
4. Ota A, Yokoshima S, Kamitani, K, Tamura T, A study on evaluation methods of combined traffic noises,
Part 2: Community response to road traffic and conventional railway noises, Proceedings of Inter-Noise
2006, Honolulu, Hawaii, 3–6, No. 412, 2006.
5. Morihara T, Yokoshima S, Matsumoto Y, Effect of noise and vibration due to the Hokuriku Shinkansen
railway on the libing environment: a socio-acoustic survey one year after the opening, International
Journal of Environmental Research and Public Health, 18, 7794, 2021.
6. Morihara T, Yokoshima S, Matsumoto Y. Experimental study on combined effect of Shinkansen railway
noise and vibration on daily activities: A case of reading and calculation tasks, Acoustical Science andTechnology, 41(3), 607-613, 2020.
7. Fields, J.M, et al., Standardized general–purpose noise reaction questions for community noise surveys:
Research and a recommendation. J. Sound Vib., 242, 641–679, 2001.

482.5-0.6080.543

Wilcoxon W

Z

Asymp. Sig. (2-tailed)0.182-1.334

450.5

240.5

N50V650.543-0.608

736.0

271.0

N50V750.273-1.096

459.0

249.0

N60V0

0.444

-0.766

475.5

265.5

N60V650.983-0.021

509.0

299.0

N60V750.585-0.545

484.0

274.0

N70V0

421.0

886.0

-0.5830.560

Mann-Whitney U

Wilcoxon W

Z

Asymp. Sig. (2-tailed)

N0V65

0.942

-0.073

910.5

445.5

N0V75

0.283

-1.074

850.0

385.0

N50V0

0.987

-0.016

914.0

449.0

N50V650.046-1.999

789.5

324.5

N50V750.103-1.629

814.0

349.0

N60V0

0.296

-1.044

848.5

383.5

N60V650.163-1.397

826.5

361.5

N60V750.011-2.544

389.5

179.5

N70V65

Table 3 – Comparison of Japan and France on the evaluation results of conventional railway by Mann-Whitney U test.

272.5

Mann-Whitney U

N50V0

Table 2 – Comparison of Japan and France on the evaluation results of high-speed railway by Mann-Whitney U test.

0.068-1.828

796.5

331.5

N70V0

0.584

-0.547

484.0

274.0

N70V750.119-1.557

815.5

350.5

N70V650.152-1.434

823.0

358.0

N70V75PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0454

An experimental investigation of the evaluation of subjective
response to simultaneous vertical and horizontal building
vibrations induced by traffic
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

ABSTRACTIn environmental vibration caused by traffic, such as road traffic and railway, vertical vibration is usually
dominant, and the target to control by the Vibration Regulation Law in Japan is vertical vibration only.
However, in modest scale buildings, horizontal vibration at the ground level can be amplified due toresonance of building structure and the magnitude of horizontal vibration can be comparable to that of verticalvibration at higher floors. In such situations, there can be combined effects of vertical and horizontal
vibrations on subjective responses of building occupants. The objective of this study was to understandsubjective responses to simultaneous vertical and horizontal vibrations expected in residential buildings. A
laboratory experiment was conducted to measure subjective responses to simultaneous vertical and horizontal
vibrations based on vibrations recorded in actual residential buildings. It was implied by the experimental
results that combined effect may be present when the response to vibration in one axis is comparable to the
response to vibration in another axis. Evaluation methods to consider such combined effects were
investigated based on the current standardised evaluation methods.
Keywords: Environmental vibration, Subjective responses, Vibration assessment, Dual-axis vibration

1. INTRODUCTION
Vibrations caused by traffic, such as road traffic and railway, in residential environment, vibrationin the vertical axis is usually dominant. In Japan, the applicable statute to problems caused byenvironmental vibration is the Vibration Regulation Law. The target to control by the Vibration
Regulation Law is vertical vibration only. However, in ordinary single-family residential buildings,
the magnitude of horizontal vibration can be comparable to that of vertical vibration at higher floors
due to the vibration amplification caused by resonance of building structure. In such situations, theevaluation of vibration in the vertical axis only as defined by the Vibration Regulation Law may
underestimate the effect of vibration on building occupants. Some current evaluation methods ofwhole-body vibration, such as the method defined in ISO 2631-1 (1), consider the effect of multi-axis
vibrations on the subjective responses of people. Those evaluation methods have been developed
based on the previous studies of the subjective responses to multi-axis whole-body vibration, such as
Fairley and Griffin (2). However, those previous studies focused mainly on the evaluation of vibrationin transportation system, such as vehicles, which occurs at a greater magnitude than environmentalbuilding vibrations.
The objective of this study was to understand subjective responses to simultaneous vertical andhorizontal vibrations expected in residential buildings. A laboratory experiment was conducted tomeasure subjective responses to simultaneous vertical and horizontal vibrations based on vibrations
recorded in actual residential buildings.

1

ymatsu@mail.saitama-u.ac.jp

2. EXPERIMENTAL METHODS
2.1 Apparatus
A horizontal and vertical dual-axis shaker, San-Esu SPRT2DV-9K-12L2-1T, in a laboratory at the
National Institute of Technology, Ishikawa College was used in the experiment. The platform of the
shaker had a size of 0.9 m x 0.9 m, on which a subject was seated directly during the experiment sothat he/she was exposed to vibration in the lateral and vertical directions. The vibration at the platform
was measured by a 3-axis accelerometer, RION PV-83C, with a Vibration Level Meter, RION VM-55.
The acceleration data were stored in a data recorder, RION DA-21, at a sampling rate of 12800
samples/s.
2.2 Participants
Twenty volunteers, ten males and ten females, from students at the Natural Institute of Technology,
Ishikawa College were participated in the experiment. The range of their age was between 19 and 21
years old.
2.3 Input Stimuli
Input stimuli used in the experiment were simultaneous vertical and horizontal vibrations based ona vibration caused by road traffic and a vibration caused by conventional railway, each of which wasmeasured in a single-family residential building. A time history for a duration of 20 seconds was
extracted from the record of each source and used in the experiment. The amplitude of time history ofeach source in each of vertical and horizontal axes was modulated to have 4 different levels at anequal interval in the logarithmic scale corresponding to 6 dB. The lowest magnitude was set at a level
being higher by about 1.4 times, or 3 dB, than the average perception threshold reported by Miwa (3).
In the experiment, simultaneous vertical and horizontal vibrations with a full combination of 4
magnitude levels in each axis and single axis vibrations in each axis were used as input stimuli forboth sources; a participant was exposed to, in total, 48 stimuli, 24 stimuli for each source. The orderof presentation of stimuli was randomised and differed among participants.
Although the target vibration magnitudes were determined as described above, in the experiment,
the motion at the shaker platform could be affected by the dynamic characteristics of the body ofparticipants and unexpected movement of participants. The actual vibrations measured at the platform
were significantly different from the target vibrations in some cases. Therefore, the experimental dataobtained with a vibration magnitude differing from the median magnitude of all participants by 1 dB
or greater were excluded from the analysis and discussion described below. As a result, the data from
13 participants for road traffic and from 16 participants for railway were used. The data from 10
participants were commonly available for both sources. The median vibration magnitudes in terms of
the Vibration Dose Value, VDV, defined in ISO 2631-1 (1) are presented in Table 1. The frequency
weightings applied were in accordance with ISO 2631-1 (1), i.e., Wk weighting for the verticalvibration and Wd for the horizontal vibration, and JIS C 1510 (4). These magnitudes were used in thefollowing analysis and discussion.
2.4 Measurement of Subjective Responses
Subjective responses to input vibrations were measured in terms of “subjective magnitude”,“concern”, and “discomfort” by using seven-step category scales with semantic labels developed by
Hayashi et al. (5). For example, the scale for discomfort consisted of 7 being “extremely
uncomfortable” and 1 being “not at all uncomfortable”, as two extreme categories. Similarly,
appropriate adverbs giving equal intervals in the subjective response scale were assigned to the 5categories between the two extremes. The participants were asked to assume that they were seated ona seat in a living room in their home and rate their subjective responses to an input vibration after
each exposure. They were asked to read a magazine during the exposures and rate “feeling ofdisturbance” to reading caused by input vibrations with a five-step category scale used in Morihara etal. (6). In this report, the results for the discomfort measurements are discussed, as it has been widelyused in the previous studies.

Table 1 – VDV of input vibration stimuli [m/s1.75](a) Frequency weighting in ISO 2631-1
ISO 2631-1

Road traffic

Railway

weighting

Horizontal

VerticalHorizontal

VerticalLevel 10.01620.02540.01160.0199Level 20.02320.04110.02010.0366

Level 30.04180.07440.03600.0665

Level 40.08580.148

0.07260.135(b) Frequency weighting in JIS C 1510
JIS C 1510

Road traffic

Railway

weighting

Horizontal

VerticalHorizontal

VerticalLevel 1

0.02240.01930.01620.0168

Level 20.03230.03100.02810.0284

Level 30.05870.05250.05080.0497

Level 4

0.121

0.104

0.103

0.0982

3. RESULTS
3.1 Subjective responses to single-axis vibrations
Figure 1 plots the median discomfort rating for single-axis vibration exposures against VDV
calculated with the frequency weightings defined in ISO 2631-1 and VDV with the weightings definedin JIS C 1510. It can be observed from the figure that there were differences in the exposure anddiscomfort rating relationship between horizontal and vertical vibrations when the exposure was
evaluated by VDV with ISO weighting (Figure 1(a)). For VDV with JIS weighting shown in Figure1(b), the differences between horizontal and vertical vibrations reduced. This was because thefrequency weighting for the horizontal vibration in JIS C 1510 was 3 dB greater than the weighting
in ISO 2631-1, while the same frequency dependence was expressed in both weightings. The exposureresponse relationships were similar between road traffic and railway for the data obtained in thisexperiment, as observed in Figure 1.

(a) VDV with ISO weighting(b) VDV with JIS weighting

Figure 1 – Median discomfort ratings for single-axis vibrations

Table 2 shows the results of Wilcoxon signed rank test applied to the measured discomfort toinvestigate the difference in discomfort responses between horizontal and vertical vibrations at themagnitude levels used in the experiment. The comparisons resulting in p≥0.1 are indicated with darker
grey hatching and those with 0.05≤p<0.1 are indicated with light grey hatching in the table. It can beinterpreted that, in these combinations, the discomfort response to horizonal vibration was comparable
to the discomfort of vertical vibration. It might be hypothesised that the exposure to simultaneousvertical and horizontal vibrations which induce comparable responses could have some combined
effect on subjective responses, which is investigated in the next section.
Table 2 – p-value from Wilcoxon signed rank test for comparison of discomfort responses
between vertical and horizontal vibrations(a) Road traffic
Vertical, Level 1

Vertical, Level 2

Vertical, Level 3

Vertical, Level 4

Horizontal, Level 1

1

0.773

0.02480.0031

Horizontal, Level 2

0.03690.129

0.05330.0071

Horizontal, Level 3

0.00290.0029

0.200

0.679

Horizontal, Level 40.00160.00230.00640.0070(b) Railway
Vertical, Level 1

Vertical, Level 2

Vertical, Level 3

Vertical, Level 4

Horizontal, Level 1

0.346

0.149

0.00830.0006

Horizontal, Level 2

0.477

0.766

0.07260.0010

Horizontal, Level 3

0.00250.00870.240

0.0021

Horizontal, Level 40.00060.00080.0014

0.792

3.2 Subjective responses to simultaneous dual-axis vibrations
Figure 2 shows the median discomfort ratings for all input stimuli for each source. It can beobserved that, for most simultaneous dual-axis vibrations, the median discomfort rating was equal to
the rating for single-axis vibration in a dominant axis, although there were variations. For example,
for the road traffic vibration shown in Figure 2(a), the median discomfort rating for the simultaneousdual-axis vibrations including the vertical vibration at Level 4 was equal to that for the single-axis
vertical vibration when the horizontal vibration was at Levels 1 and 2. This was possibly because thevertical vibration had a dominant effect on the discomfort response in those simultaneous dual-axis
vibrations. For the road traffic vibration consisting of the vertical vibration at Level 4 and thehorizontal vibration at Level 4, the median discomfort rating was equal to that for the single-axis
horizontal vibration at Level 4, which could have a dominant effect in the simultaneous vibration.
For the simultaneous road traffic vibration consisting of the horizontal vibration at Level 3 and thevertical vibration at Level 4, the median discomfort rating was greater than those for single-axis
vibration in each of the two axes (Figure 2(a)). The difference in these median discomfort ratings
between dual-axis and single-axis vibrations were statistically significant (Wilcoxon singed rank test,
p<0.05). This result may imply that there might be some combined effect of the dual-axis vibrationon the discomfort response. However, the VDV with JIS weighting for the vertical vibration at Level4, 0.104 m/s1.75, was much greater than the VDV for the horizontal vibration at Level 3, 0.0587 m/s1.75,
although the median discomfort ratings for these single-axis vibrations were not significantly different
at a rating of 2. Further investigations are needed to interpret this result.
A similar result was found for the railway vibration consisting of the vertical vibration at Level 3
and the horizontal vibration at Level 3 (p<0.05, Figure 2(b)). The median discomfort rating of thedual-axis vibration at Level 3 in both axes was greater than that of the single-axis vertical vibration
at Level 3 but equal to that of the horizontal vibration at Level 3. The average rating of the same dual-

axis vibration was 2.56, while the average rating of the same horizontal vibration was 1.93, although
the median ratings of those vibrations were equal at 2. That difference might result in the statisticallysignificant difference observed with the median discomfort rating.

(a) Road traffic(b) Railway

Figure 2 – Median discomfort ratings for all input stimuli
Figure 3 shows the relationship between the median discomfort ratings for all stimuli and thecorresponding evaluation by VDV with JIS frequency weighting. To determine the evaluation, thevalue in the maximum axis and the value obtained by combining the values in both axes were
calculated. The use of the value in the maximum axis was recommended in ISO 2631-2 (7). To
combine VDV in the two axes, the following equation was applied:
/
(1)
𝑉𝐷𝑉
𝑉𝐷𝑉
𝑉𝐷𝑉
where the subscript “cmb” represents the combined value, “h” the value in the horizontal axis, and“v” the value in the vertical axis. The method to combine the values in the two axes defined in
Equation (1) was in accordance with the method recommended in BS 6841 (8) for the evaluation ofeffects on health. In addition, Spearman’s rank correlation coefficient ρ is shown in the figure. The
results show that, although the evaluation based on the value in the maximum axis showed strong
correlation with the discomfort rating, the combined value could provide slightly stronger correlation.
It may be worth studying further to investigate a better evaluation method for simultaneous multi-axis
vibration exposures in residential environments.

(a) VDV in maximum axis (ρ=0.892)(b) Combined VDV (ρ=0.902)

Figure 3 – Median discomfort ratings and evaluations by VDV with JIS weighting(ρ: Spearman’s rank correlation coefficient)4. SUMMARY
A laboratory experiment was conducted to investigate the evaluation of subjective responses tosimultaneous vertical and horizontal dual-axis vibrations in buildings induced by traffic. The results
implied that some combined effect of dual-axis vibrations on the subjective responses might be present
when the response to vibration in one axis is comparable to the response to vibration in another axis.
It may be worth considering such combined effect in the evaluation methods, although further study
is required to evaluate the combined effect on subjective responses.

ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant Number JP18H1518. We are grateful to Ms.
Ami Tsuchiya, a former student from National Institute of Technology, Ishikawa College, and Ms.
Yukina Ikeda and Mr. Nozomu Saeda, former students from Saitama University, for their supports inthe experiment.

REFERENCES
1. International Organization for Standardization. Mechanical vibration and shock – Evaluation of human
exposure to whole-body vibration, Part 1: General requirements, ISO 2631-1, 1997.
2. Fairley TE, Griffin MJ. Predicting the discomfort caused by simultaneous vertical and fore-and-aft wholebody vibration, J. Sound Vib., 1988, 124, 141-156.
3. Miwa T. Evaluation methods for vibration effect, Part 1. Measurements of threshold and equal sensation
contours of whole body vertical and horizontal vibrations, Ind. Health, 1967, 5, 183-205.
4. Japanese Standards Association. Vibration Level Meter, JIS C 1510, 1995. (in Japanese)
5. Hayashi K, Matsumoto Y, Higashida T. A questionnaire survey on psychological rating scale for
evaluation of building vibration, J. Env. Eng. (Trans. Arch. Inst. Japan), 2019, 84, 757, 225–234. (inJapanese)
6. Morihara T, Yokoshima S, Matsumoto Y. Experimental study on combined effect of Shinkansen railway
noise and vibration on daily activities: A case of reading and calculation tasks, Acoust. Sci. Tech., 2020,41, 607-613.
7. International Organization for Standardization. Mechanical vibration and shock – Evaluation of human
exposure to whole-body vibration, Part 2: Vibration in buildings (1 Hz to 80 Hz), ISO 2631-2, 2003.
8. British Standards Institution. British Standard Guide to measurement and evaluation of human exposure
to whole-body mechanical vibration and repeated shock, BS 6841, 1987.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0778

Discussion of Infrasound Measurements of a Wind Turbine
Marcus MAEDER 1; Johannes BAUMGART2; Christoph FRITZSCHE3; Steffen MARBURG1
1
2

Technical University of Munich, Germany

Saxon State Ministry for Energy, Climate Protection, Environment, and Agriculture, Germany
3

Saxon State Agency for Environment, Agriculture, and Geology, Germany

ABSTRACTWind turbines in operation produce infrasound due to the rotor blade passing the tower while interacting withthe fluid. This results in a directly measurable periodic pressure fluctuation. At the same time, a measured
stochastic signal is caused by the wind itself. A frequency-domain investigation becomes possible after
Fourier transformation with the evaluation of the power spectrum and the power spectral density, respectively.
Results confirm that the infrasound signals of an investigated wind turbine with an electrical power of
660 kW are, at a distance of 200 m, clearly below the threshold of perception, cf. Baumgart et al. in JSV
2021, 116310. The wind itself contributes the most to the infrasound signal.
In this work, the authors discuss ways for analyzing infrasound measurements and the specific applicationand classification of the subsequent spectral analysis. This way, a consistent framework provides a soundscientific basis for the classification of infrasound by, for example, legal authorities.
Keywords: Infrasound, wind turbines, threshold of perception

1. INTRODUCTIONWind turbines generate a so-called infrasound signal during operation. This is caused by the fluidstructure interaction of the rotating rotor blade and the tower. When a rotor blade passes the tower,
the inclined position deflects the flow, and the pressure fields of the two structures interact [1]. As aconsequence, periodic pressure changes are detectable in the neighborhood. At the same time, thewind causes a stochastic pressure signal. For the characterization of the periodic signal, the Fouriertransform can be used, where the stochastic part can be reduced by sufficient averaging.
The evaluation of the infrasound signal of an actual wind turbine shows levels well below thehuman perception threshold [2]. The major part of the level can be attributed to the wind. According
to the currently accepted state of research, there are no indications for a directly related physicalhealth risk for the residents in the vicinity of wind turbines.
Pilger and Ceranna measured the outdoor pressure signal near a wind turbine in 2004 in their paper
and presented a spectrum with a short section of the time series in the related 2017 publication [3]. In
their work, the amplitudes for interpreting the power spectrum were incorrectly scaled [2]. This wascaused by the use of a power density spectrum [4], which is common in seismology to characterize
background noise, but not in noise assessment.

2. PRESSURE SIGNAL OF A WIND TURBINEAs an example, the half-hour measurement at high wind speeds (more than 10 m/s) is discussed
below. The wind turbine measured is of type Vestas V47 and at the high wind speeds, the generator
with 660 kW of rated electrical power was in operation, which is indicated by the rotational speed(V47 has two electrical generators).
Fluctuations due to the wind characterize the pressure signal (Figure 1). Although the wind speed
decreases towards the ground and becomes zero at the ground itself, this is not the case for the pressurein the boundary layer. As the gradient of the pressure normal to the ground disappears near the ground.
Consequently, the pressure fluctuates in relation to pressure fluctuations far from the ground.
1

Marcus.Maeder@tum.deSuperimposed on this signal is a pressure change recurring every 0.8 s with an amplitude of about
0.1 Pa.

Figure 1: Pressure signal at a distance of 200 m away from a wind turbine at high wind speeds. The data istaken from the work of Pilger and Ceranna [3]. The upper plot covers the entire 30 minutes, the middle one
about 134 seconds, and the lower 10 seconds. Dashed lines mark the areas of the selected section.
The application of a 3rd-order high-pass filter and a cutoff frequency of 0.8 Hz allows us to show
the wind turbine's recurrent pressure signal with a stroboscopic display (Figure 2).

Figure 2: High-pass filtered pressure signal in Pascal near a wind turbine at high wind speeds in stroboscopic
display. The data are taken from the work of Pilger and Ceranna [3]. The signal is plotted vertically for eachtime interval of about 2.3 s duration, which corresponds to the mean time for one complete revolution. The
successive rotations in time are plotted above the abscissa.

3. SPECTRAL EVALUATIONFor classification, the raw data were analyzed again [2]. For the Fourier analysis, the flat-top
window HFT70 [5] with a length of 𝑁𝑁 = 214 = 16384 samples and an overlap of 72.2 % was chosen,
which has a high amplitude accuracy. The original sampling frequency of the recording was 𝑓𝑓𝑠𝑠 =𝑓𝑓
100 Hz and was kept unchanged. This results in a bin width of 𝑓𝑓𝑟𝑟𝑟𝑟𝑟𝑟 = 𝑠𝑠 = 0.0061 Hz. The normalized𝑁𝑁
equivalent noise bandwidth for this window is 3.41 bins.
Figure 3 shows the power spectrum for the three wind situations with low, medium, and high windspeeds. The reference values used are 𝑝𝑝𝑟𝑟𝑟𝑟𝑟𝑟 = 2 ⋅ 10−5 Pa and the frequency of 1 Hz for decibel
22
scaling. Furthermore, the standard definition of sound pressure level, 𝑆𝑆𝑆𝑆𝑆𝑆 = 10 ⋅ log10 �𝑝𝑝𝑟𝑟𝑟𝑟𝑟𝑟
�𝑝𝑝𝑟𝑟𝑟𝑟𝑟𝑟�,without frequency weighting was used.

Figure 3: Power spectrum of the pressure measurement at a distance of about 200 m from a wind turbine.
The input data is taken from the work of Pilger and Ceranna [3]. The turbine was in operation at low speed(20 rpm, u = 5 m/s) or high speed (26 rpm, u = 10 m/s) or at standstill (0 rpm, u < 3 m/s). Illustration from
Baumgart et al. [2] with modifications.

4. DISCUSSION AND CONCLUSION
Numerous technical articles deal with the emitted sound of wind turbines with the impact onhumans, as exemplified by reviews [6-9]. For the effect on humans, the nocebo effect has been
demonstrated [10]. Other extensive studies based on health data in Denmark [11] (and further
publications), field studies in Finland [12], and numerous other studies give no evidence of a negative
effect on physical health. Studies of partial aspects, such as the stimulus of the vestibular organ byinfrasound [13] and a double-blind study with an infrasound source with 80 to 90 dB at 6 Hz [14],also give no indications. For orientation, these values are compared with the levels of the investigatedwind turbine in Figure 4.

Figure 4: Power spectrum analogous to figure 3 but with linear frequency scale. The harmonic components
are summed up and indicated. For comparison, values for the perception threshold [15] and the investigations
with an infrasound source [14] are given. The values shown in the study of Baumgart et al. [2] withmodifications.
In summary, it can be stated that for the aspect of infrasound from wind turbines, there are no
indications of a concrete health hazard for residents.

REFERENCES
1. Yauwenas Y, Zajamšek B, Reizes J, Timchenko V, Doolan CJ, Numerical simulation of blade-passage
noise, The Journal of the Acoustical Society of America. 142 (2017) 1575–1586.
https://doi.org/10.1121/1.5003651.
2. Baumgart J, Fritzsche C, Marburg S, Infrasound of a wind turbine reanalyzed as power spectrum andpowerspectraldensity,
Journal
of
Sound
and
Vibration.
(2021)
116310.
https://doi.org/10.1016/j.jsv.2021.116310.
3. Pilger C, Ceranna L, The influence of periodic wind turbine noise on infrasound array measurements,
Journal of Sound and Vibration. 388 (2017) 188–200. https://doi.org/10.1016/j.jsv.2016.10.027.
4. Pilger C, Ceranna L, Comment on Baumgart et al.: Infrasound of a wind turbine reanalyzed as power
spectrum and power spectral density (JSV, doi: 10.1016/j.jsv.2021.116310, 2021) – Comment on Pilger
and Ceranna: The influence of periodic wind turbine noise on infrasound array measurements (JSV, Vol.
388, pp. 188–200, 2017), Journal of Sound and Vibration. (2021) 116636.
https://doi.org/10.1016/j.jsv.2021.116636.
5. Heinzel G, Rudiger A, Schilling R, Spectrum and spectral density estimation by the Discrete Fouriertransform (DFT), including a comprehensive list of window functions and some new ﬂat-top windows.,(2002) 84.
6. van Kamp I, van den Berg F, Health Effects Related to Wind Turbine Sound, Including Low-Frequency
Sound and Infrasound, Acoust Aust. 46 (2018) 31–57. https://doi.org/10.1007/s40857-017-0115-6.
7. van Kamp I, van den Berg GP, Health effects related to wind turbine sound: an update, 2020.
https://www.rivm.nl/bibliotheek/rapporten/2020-0150.pdf.
8. Hansen C, Hansen K, Recent Advances in Wind Turbine Noise Research, Acoustics. 2 (2020) 171–206.
https://doi.org/10.3390/acoustics2010013.
9. Freiberg A, Schefter C, Girbig M, Murta VC, Seidler A, Health effects of wind turbines on humans inresidential settings: Results of a scoping review, Environmental Research. 169 (2019) 446–463.
https://doi.org/10.1016/j.envres.2018.11.032.
10. Tonin R, Brett J, Colagiuri B, The effect of infrasound and negative expectations to adverse pathologicalsymptoms from wind farms, Journal of Low Frequency Noise, Vibration and Active Control. 35 (2016)
77–90. https://doi.org/10.1177/0263092316628257.
11. Poulsen AH, Raaschou-Nielsen O, Peña A, Hahmann AN, Nordsborg RB, Ketzel M, Brandt J, Sørensen
M, Long-term exposure to wind turbine noise at night and risk for diabetes: A nationwide cohort study,
Environmental Research. 165 (2018) 40–45. https://doi.org/10.1016/j.envres.2018.03.040.
12. Turunen AW, Tiittanen P, Yli-Tuomi T, Taimisto P, Lanki T, Symptoms intuitively associated with windturbineinfrasound,Environmental
Research.
192(2021)
110360.
https://doi.org/10.1016/j.envres.2020.110360.
13. Jurado C, Marquardt T, On the Effectiveness of airborne infrasound in eliciting vestibular-evoked
myogenic responses, Journal of Low Frequency Noise, Vibration and Active Control. 39 (2020) 3–16.
https://doi.org/10.1177/1461348419833868.
14. Ascone L, Kling C, Wieczorek J, Koch C, Kühn S, A longitudinal, randomized experimental pilot study
to investigate the effects of airborne infrasound on human mental health, cognition, and brain structure,
Sci Rep. 11 (2021) 3190. https://doi.org/10.1038/s41598-021-82203-6.
15. Moller H, Pedersen C, Hearing at low and infrasonic frequencies, Noise and Health. 6 (2004) 37–57.

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

ABS-1015
An investigation of the effect of vibration duration on the evaluation of subjective
responses to horizontal building vibration

Kentaro HAYASHI1; Yasunao MATSUMOTO 2; Shigenori YOKOSHIMA3;
Toyohiko HIGASHIDA4123

Benec Vibration and Sound Institute Inc., Japan

Department of Civil and Environmental Engineering, Saitama University, Japan

Environmental Information Division, Kanagawa Environmental Research Centre, Japan
4

Sekisui House, Japan

ABSTRACTThe purpose of this study was to examine the evaluation method based on subjective responses to vibration
caused by external vibration source including road traffic and railways for building habitability. In this paper,
the applicability of the evaluation method considering the effect of vibration duration defined in theinternational standards was examined in a laboratory experiment using an electrodynamics shaker. The input
stimuli were vibration response waveforms measured on various building floors excited by external vibration
sources. The relationship between the evaluations by the international methods of environmental vibrationand the subjective response measured in the experiment seemed to be reasonable with evaluations in which
vibration duration was considered by the fourth power. From the logistic regression analysis, it was confirmedthat it was appropriate to consider vibration duration in the evaluation of the subjective response. However,it was suggested that the effect of vibration duration became relatively small with increasing acceleration
magnitude.
Keywords: Horizontal building vibration, Vibration duration, External vibration sources

1. INTRODUCTION
In urban areas in Japan, houses are built adjacent to main roads and railways. At building sites
close to main roads, vibrations at the natural frequencies of the suspension system of large vehicles,
generally a few Hz, can propagate through the ground. When ground vibration coincides with thenatural frequency of horizontal building vibration, the horizontal vibration is amplified in the building
and can cause vibration problems.
As evaluation methods for non-stationary-like vibrations such as road traffic vibration, ISO 2631
series (1,2) defines the Maximum Transient Vibration Value, MTVV, a method based on the maximum
evaluated value, and the Vibration Dose Value, VDV, a method that considers duration time.
In the previous study (3), a laboratory experiment using an electromagnetic shaker was conducted
to investigate a method for evaluating building habitability based on subjective responses to vibration.
The input stimuli were horizontal vibration of a floor of a building excited by road traffic vibration
and railways. A 7-step category scale was used to measure the subjective response for each input
stimulus. The relationships between subjective responses and the evaluation values of domestic andinternational environmental vibrations were found to be most appropriate for the Vibration Dose Value,
VDV , which considered the effect of vibration duration by its fourth power.
In this study, an experiment was conducted with construction work vibration added to thepreviously reported input stimuli to investigate whether the evaluated value considering the duration
can reasonably evaluate the subjective response as in the previous study. Then, the logistic regression
12
3
4

hayashi-k@benec-vsi.co.jp
ymatsu@mail.saitama-u.ac.jp

yokoshima.7c7q@pref.kanagawa.lg.jp
h-toyo@ga.sekisuihouse.co.jp

analysis was used to understand quantitatively the effect of vibration duration on the evaluation ofsubjective response.

2. METHODS
2.1 Apparatus
The apparatus used in the experiment had the same configuration as the previous study (3). An
electrodynamic shaker, Asahi Corporation VSR-S150H, in a laboratory of Saitama University used in
the experiment can produce horizontal vibration in a single-axis. An aluminum seat mounted on the
shaker platform was set up to expose lateral vibration to a participant. The horizontal acceleration of
the seat surface was measured with a uniaxial piezoelectric accelerometer, RION PV-87. A charge
amplifier, RION UV-06, was used to amplify signals from the accelerometer. The vibration level meter,
RION VM-53A, which had a 3-axis accelerometer and fixed to the shaker platform, was used to ensurethat vibrations in other directions were sufficiently low to have no effect on the evaluation. Signals
from the transducers were recorded in a PC-based data acquisition system, National Instruments
Corporation, NI cDAQ-9178, and NI 9234.
2.2 Participants
Twenty-five participants (14 male and 11 female), with median age 22 years (range 20 - 24 years),
stature 168 cm (range 151 - 179 cm), and weight 56 kg (range 43 -92 kg) participated in the experiment.
They were all students at Saitama University. The experiment was approved by the Human
Experimentation Safety and Ethics Committee of Saitama University. Informed consent to participate
in the experiment was given by all participants.
2.3 Input stimuli
Input stimulus used in the experiment consisted of 14 types of vibration measured in real buildings.
The actual vibrations measured within houses were caused by external sources such as road traffic,
railways, and construction work. The acceleration waveforms of all input stimuli were set to have aduration of about 40 seconds in order to reduce the influence of the time of vibration evaluation on
the subjective response. The magnitude of input stimulus was adjusted so that each type of stimulus
had 7 levels at an equal interval by the logarithmic law. VDV was used to evaluate magnitude because
the previous study (4) showed that the most appropriate correspondence with the subjective responses
was found with the evaluation determined by VDV. The target magnitudes of the input stimuli were
set so that the acceleration amplitude was in the range which c ould occur in an actual building. The
magnitude of each type of input stimulus was adjusted to achieve the target values for four of theseven steps (0.010, 0.015, 0.021, 0.041 m/s 1.75 , or 0.007, 0.010, 0.015, 0.029 m/s 1.75 ). In total, 56 input
stimuli were used in the experiment. Figure 1 shows examples of input acceleration.

(a) Road(b) Conventional railway(c) Shinkansen railway(d) Construction work
Figure 1 – Examples of input vibrations

2.4 Measurement of subjective responses
The participants were asked to assume that they sat down on a seat in a living room. They were
exposed to the input vibrations and asked to rate those vibrations using the rating scale for subjective
magnitude of vibration, concern, and discomfort. Category scales based on the method of
psychological measurement were adopted to rate the subjective vibration discomfort. These rating
scales were set up as 7-step category rating scales with semantic labels, which were Japanese adverbial
expressions, for the evaluation of building vibration. For example, for each subjective scale, the labelof the top category 1 contains the adverb corresponding to “Extremely” and the lowest category 7
contains the adverb corresponding to “Not at all”.

3. RELATIONSHIP BETWEEN THE EVALUATION VALUES AND SUBJECTIVERESPONSES
3.1 Introduction
In order to confirm that the evaluation method taking into account vibration duration can evaluate
subjective responses appropriately, the relationship between evaluation values and subjective
responses measured in the experiment was examined. The evaluation values considered were VDV,
which was reported to have the best correspondence with the subjective response in the previous study(3), and the Maximum Transient Vibration Value, MTVV, which was the evaluation value based on the
maximum value.
3.2

Evaluation method of vibration

3.2.1 Maximum Transient Vibration Value, MTVVThe Maximum Transient Vibration Value, MTVV, defined in ISO 2631-1 and -2 (1,2), was definedas:MTVV = max[ aw (t0 )](1)

 1 t02
aW (t0 ) =   aw ( ) d 1/ 2
 t0 −

(2)Here, the acceleration was frequency-weighted by Wd weighting defined in ISO 2631-1 (1). The
integration time τ was 1.0 s.
3.2.2 Vibration Dose Value, VDVThe Vibration Dose Value, VDV, was defined in ISO 2631-1 and -2 (1,2), was defined as:
T
4
VDV =   aw ( ) d 1/ 4
 0


(3)

3.3 Results
Figure 2 shows the correspondence between the discomfort and the evaluation values. It shows therelationship between the discomfort and MTVV, and VDV. The data shown in the figure are themedians of 25 participants. The horizontal axis of this figure represents the evaluation value, and thenumbers in the vertical axis represents the numbers given to each category of discomfort rating scale.
As the values of both evaluations increased, the subjective response became stronger, and a certain
correspondence was observed.
As described above, the magnitudes of input stimuli were adjusted based on the range that could
be generated by external vibration sources in the actual building and were planned so that the
subjective responses up to the category with the strongest response were obtained. However, as thefigure shows, the top categories were not obtained as median values.

(a) Discomfort scale and MTVV(b) Discomfort scale and VDVFigure 2 – Relation between subjective response and evaluation value(○:Road, ●:Elevated road, □:Conventional railway,■:Shinkansen railway,◇:Freight train, ×:Construction work)

This figure shows that the discomfort response tended to be evaluated better by VDV, which took
into account vibration duration, than to MTVV, which was the maximum evaluation value. ρ in the
figure represents Spearman's rank correlation coefficient between the discomfort response and theevaluation value. In the comparison of these correlation coefficients, VDV had a stronger correlation
than MTVV, indicating that VDV was in a better correspondence with the discomfort response. These
results suggest that, as in the previous study (3), an evaluation value that takes into account the effectof vibration duration can reasonably evaluate subjective responses.

4. EFFECTS OF EXPLANATORY VARIABLES ON THE EVALUATION OF
SUBJECTIVE RESPONSE
4.1 Introduction
It was not clear to what extent acceleration magnitude, duration, and other factors affected thesubjective response. Therefore, a regression model was designed from experimental data to examine
the validity of evaluation values that consider vibration duration.
In this study, a binary logistic regression analysis was employed, in which the top three categoriesof the seven levels of subjective response were defined as strong responses (strong response: 1, notstrong response: 0), which facilitated the interpretation of the regression model. The explanatory
variables in the model were MTVV, as a representation of the maximum value of acceleration, andvibration duration, in addition to a bivariate interaction term. In the logistic regression analysis, theunit of MTVV was converted from m/s 2 to cm/s 2 in order to obtain the odds of the objective variablebeing 1 when the explanatory variable increased by one unit. The vibration duration was defined as
the cumulative time that the Wd -weighted acceleration exceeded a certain reference value. The
reference value was 0.00504 m/s 2 , which was obtained by multiplying the maximum acceleration
value by multiplying by √2 from 0.0036 m/s 2 (r.m.s.), a standard specified in ISO 10137 (5), below
which vibration complaints are unlikely to occur.
The dataset for the logistic regression analysis consisted of 1397 valid data from 56 input stimuli
for 25 participants, excluding those that were distorted by the movement of participants. A binomial
logistic regression equation was used to determine the probability π that the discomfort of theobjective variable is 1. R (version 4.2.1) was used for the logistic regression analysis.
4.2 Results
Table 1 shows the results of the logistic regression analysis. The partial regression coefficients for
all variables were significant (P<0.05), indicating that MTVV, vibration duration and their interaction
were the variables that affected discomfort. The odds ratio for MTVV and vibration duration weregreater than 1, while the interaction term was less than 1.
Equation 4 with the coefficients shown in Table 1 was then used to estimate the probability ofstrong discomfort response, π. Considering the range of experimental conditions which MTVV andvibration duration for each of the 1397 input stimuli, eight levels (0.5 to 4.0 cm/s 2, at 0.5 cm/s 2
intervals) for the MTVV and five levels (1, 5, 10, 20, and 30 s) for the vibration duration were used toestimate the probability of strong discomfort response, π. Figure 3 shows that the results of calculating
π from these levels and the model in Table 1.
Figure 3 indicates that the greater MTVV, the higher the strong response probability. When theprobability of strong response was compared for different vibration durations at the same level of
MTVV, a tendency for a higher response probability was observed for longer vibration durations,indicating quantitatively the relative contributions of the vibration duration and MTVV to thediscomfort response. As MTVV increased, the effect of vibration duration on the discomfort response
became relatively small. This result is obtained from the fact that the partial regression coefficient δ1of the interaction term shown in Table 1 is significantly negative (odds ratio of 95%UCI less than 1).
The odds ratios for the main effects β1 and β 2 are greater than 1, with the odds ratio for β 1 being larger,
indicating that the vibration duration is secondary to the response probability and that the effect ofincreased acceleration response is significant.
Table 1 – Results of logistic regression analysis(*:P<0.05, **:P<0.01, ***:P<0.001)
Estimate

Std. Error

OR

95%CI

α

-5.651

0.418

0.004

[0.002, 0.008]< 2E-16

***

β1

1.085

0.132

2.959

[2.284, 3.834]< 2E-16

***

β2

0.117

0.022

1.125

[1.077, 1.175]

8E-08

***

δ1

-0.045

0.021

0.956

[0.918, 0.996]

0.0300*α: Interceptβ1: Partial regression coefficient of MTVV

β2: Partial regression coefficient of vibration duration

δ1: Partial regression coefficient of interaction between MTVV and vibration duration

Strong response probability [%]

1s

5s

10s

20s

P

30s

70
60

50
40

30
20

10
0
0.5
0.5

1

2
MTVV [cms-2]

3

4

Figure 3 – Estimated result of response probability

55. CONCLUSIONSThis study examined the validity of an evaluation method that takes into account the vibration
duration in the evaluation of horizontal building vibration caused by external vibration sour ces. As aresult of examining the relationship between the evaluation value and discomfort, it was found thatthe method that considered vibration duration had a better correspondence with subjective responsethan the evaluation value based on the maximum value of acceleration. The logistic regression analysis
showed that the vibration duration was a highly significant variable for the discomfort response andthe interaction term between the vibration duration and the maximum acceleration also affected thediscomfort response. These results support the validity of considering vibration duration in theevaluation of subjective response. However, it is suggested that the effect of vibration duration
becomes relatively small as the acceleration response increases.

ACKNOWLEDGEMENTS
This research was partially supported by JSPS Grant-in-Aid for Scientific Research JP18H01518.

REFERENCES
1. ISO 2631-1：1997. Mechanical Vibration and Shock - Evaluation of Human Exposure to Whole-bodyVibration – Part 1: General Requirements. International Standard.
2. ISO 2631-2：2003. Mechanical Vibration and Shock - Evaluation of Human Exposure to Whole-bodyVibration – Part 2: Vibration in Buildings (1 Hz to 80 Hz).
3. Hayashi K, Matsumoto Y, Higashida T. Experimental investigation of evaluation method of horizontal
vibration in building caused by external vibration sources. Proc ICA 2019 AND EAA EUROREGIO;
9-13 September 2019; Aachen, Germany 2019. p. 3637-3642.
4. Hayashi K, Matsumoto Y, Kano H. An Experimental Investigation of Applicability of Evaluation Methods
Considering Duration for Subjective Responses to Horizontal Vibrations. Journal of Environmental
Engineering (Transactions of AIJ), Vol.83, No.743, p.11-19, 2018. (In Japanese)
5. ISO 10137：2007. Bases for design of structures - Serviceability of buildings and walkways against
vibrations