PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Urban road traffic noise interventions: a method to quantify theireffects on annoyance and sleep disturbance at a small scale
Maud DOHMEN1; Maarten HORNIKX2; Irene VAN KAMP312
3

Nelissen Ingenieursbureau b.v., The Netherlands

Eindhoven University of Technology, The Netherlands

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

ABSTRACTRoad traffic is the main source contributing to noise exposure in urbanized areas. With the rapid urbanizationand increasing attention on the way sound can affect health and wellbeing, the question is how to shield
ourselves from harmful effects. In the most common noise intervention studies only large-scale interventions,like noise barriers along a highway, are assessed. The effects on health and wellbeing are based on roughestimations of sound reduction. In this study, the effects of a small scale intervention on annoyance and sleep
disturbance will be investigated, considering relative changes in dose-response relationships to account for
local effects. It will give more insight into the subtle changes due to interventions on a local scale, providing
more detail additional to existing conventional studies.
To develop this methodology an intervention study in Rotterdam was taken as a test case. The location and its
intervention were modelled using a 3D geometrical acoustics software (Olive Tree Lab). The predicted noise
reduction due to the interventions, as well as the predicted health effects, were compared with measured data
and local surveys. The results shed a light on the suitability of this approach to predict health effects due tointerventions at a local scale.
Keywords: Intervention, Mitigating Measures, Exposure Response Functions, Health effects, Annoyance,
Sleep disturbance

1. INTRODUCTION
In 2014, 54% of the world population lived in urban areas. It is predicted that by 2050 this will
increase to 66% (1). This increasing urbanization comes with an increase of infrastructure andindustrial areas in and around cities, creating major sound sources that can affect a large number ofpeople. Possible effects of noise on people can be: hearing damage, sleep disturbance, annoyance,increased risk to get cardiovascular diseases, increased blood pressure and cognitive impairment. The
world health organization (WHO) estimated in 2011 that 1.0-1.6 million healthy life years are lost due
to noise exposure (2).
Road traffic is the main source contributing to noise exposure in urbanized areas. To shield
ourselves from the harmful effects, interventions are needed. The available studies on the effect ofinterventions on levels from road traffic noise and health effects are limited due to the difficulty of
doing measurements before and after the interventions. When available, studies mainly focus onlarge-scale interventions, like barriers along a highway. These areas are more likely to be less densely
populated than inner city areas, where the rapid urbanization will play a major role. Also, the effect on
health and wellbeing is often based on rough estimations of sound reduction (3).
In this study, the focus is shifted to small scale urban areas, investigating the effect of noiseinterventions on annoyance and sleep disturbance. By combining detailed acoustic calculations withlocal survey data, the aim is to provide a method to study interventions on a local scale and get more
information of their effect on sound, health and wellbeing, and to provide a methodological base forfurther research.
1

m.dohmen@nelissenbv.nl
m.c.j.hornikx@tue.nl
3
irene.van.kamp@rivm.nl
2

411

The study is unique in its approach to develop an assessment method combining 3D acoustic
modeling and available exposure response functions (ERFs) from literature. For this study a test caseis needed, a location where an intervention has been performed and health and wellbeing data arerecorded. With these health data and acoustic data (measured and calculated) insight can be given into
the applicability of generalized ERFs.

2. TEST CASE
The following criteria were used for the selection of a Dutch inner-city test case: an intervention
was performed at the location; survey data on health and wellbeing was available before and after theintervention and road traffic is the main source of noise. The chosen location was the Strevelsweg (seeFigure 1), an intervention study in Rotterdam performed by the Municipal Health Service ofRotterdam (GGD) (4). This case pertains a street where the asphalt was covered with a noise reducing
top layer (Dutch: Dunne Geluidsreducerende Deklaag (DGD)), the goal being to lower the noise levelon the facades and thereby reducing negative health effects. Before and after the intervention, healthand wellbeing surveys have been carried out by the GGD. The location is characterized by a wide mainroad with closed building blocks and smaller, secondary roads located parallel to it.

Figure 1 – Test case, Strevelsweg (indicated in red) Rotterdam, The Netherlands © OpenStreetMap
contributors.

3. THE MODEL
3.1 Acoustic model
The location was modelled in the 3D acoustic software application Olive Tree Lab (OTL) from
Mediterranean acoustics. The advantage of the program is its accuracy, which lies in-between
commonly used noise mapping software and detailed wave-based calculation programs. The order ofreflection and diffraction, atmospheric absorption and turbulence, and Fresnel zone corrections aretaken into account.
The scenario was abstracted to its acoustically essential form. Buildings were modelled only withflat roofs, the ground surface was modeled as acoustically hard and other elements like trees, street
lanterns and parked cars were not taken into account. Road traffic sound sources were represented in
the form of an incoherent line source placed at 0,05m height. The acoustic model was verified withmeasurements, see Section 3.2.
3.2 Prediction model verification
Acoustic measurements were performed on the location to verify the OTL model. Five measuring
points were chosen on both sides of the street (see Figure 2). The choice of locations was based onnoise maps provided by the municipality of Rotterdam (5). At position 1, 4 and 5, low levels of around
40 dB(A) are indicated. However, position 1 and 4 still have a view on the Strevelsweg and thissuggests that there is a possibility for direct sound to reach the position and result in higher soundlevels. Next to measuring point 3, a camera was positioned to record the traffic, which was counted
afterwards to determine the traffic flow. The measurements were performed with a Tascam DR-40
recorder with an external ISEMcon ¼” EMM-7101-CSTB microphone mounted on a 1,5 m tall tripod.
During the recordings, sound events (e.g. people walking past, car door slamming) were noted andlater removed from the sound file using the Audacity software. The cleaned recording was then412

200 mprocessed further in Matlab resulting in a sound pressure level in dB per 1/3 frequency band per
measurement location.

<45 dB(A)
55 dB(A)60 dB(A)>65 dB(A)675 m

Figure 2 – Noise map with measurement positions (5).
3.3 The model
The measurement positions were placed in the model together with two line sources representing
the two traffic lanes. The counted traffic flow is used as input for the traffic sound power spectrum
calculation using the CNOSSOS method (6). The resulting spectrum was used in OTL as a line source.
The results from the measurements were compared to the modeled results of OTL. Settings (e.g.
number of reflections, diffractions etc.) in OTL were adjusted, such that the model are in satisfactory
agreement with the measured results.
From the verification process it was determined that the situation as it was at the time of themeasurement (d.d. 2018) mostly resembled the situation before the intervention, a road surface withregular asphalt. This can be attributed to the fact that the measurements were performed 5 years after
implementation of the noise reducing top layer. It is commonly known that noise reducing top layers
have a short lifespan and therefore it might have been at the end of its performance at the time of themeasurements.
While the calculated noise exposure in Lden ranged from 58 to 77 dB(A), two thirds of the receiver
positions had an Lden of over 70 dB(A). It was observed that receiver positions located further from theline sources experienced a larger deviations in calculation results (up to 5 dB(A)) when calculations
were repeated.
3.4 Acoustic data for the Exposure Response Function (ERF)
To develop a local ERF, a connection needs to be made between survey data on annoyance and sleep
disturbance and the exposure. Exposure is measured in the form of Lden 2 m from the façade at a heightof 4 m. Therefore, the model was expanded with more receivers, placed at the addresses where survey
data were known, see Figure 3. With this model, both the before and after intervention exposure can becalculated. The available survey data were spread over a large area and not all receiver positions withavailable survey data were included in the model. Only the positions that experienced a change insound exposure according to the research by the GGD were included in the model.

Excluded location
Included location

Figure 3 – Positions where survey data was available © OpenStreetMap contributors.

413

4. SURVEY DATA
The survey for the GGD research had a total of 69 respondents spread over multiple zip code areas.
Only 28 respondents could be detected that lived in an area exposed to the noise of the Strevelsweg.
The remaining group was used as a control group.
Within the survey, people were asked to rank their noise sensitivity, rate their satisfaction with theirliving environment on a number of domains including a general rating, noisiness, safety and visualquality. 29% Percent of the people in both the exposed and the control group indicated that they werenoise sensitive. Remarkable was that within the exposed group, less people evaluated theirneighborhood as noisy compared to the control group (56% vs. 78%) whilst the sound levels in the
exposed group are significantly higher than in the control group. Another noteworthy result was thatthe general satisfaction with the environment (rating above 8/10) decreased from 11.5 to 7.7 % after
the intervention. This can be due to the dissatisfaction with the results of the intervention, because only
10% of the people expected a change in noise due to the intervention. Finally, the number of people
experiencing annoyance or sleep disturbance was higher in the control group than in the exposed group.
The number of people (highly) annoyed and (highly) sleep disturbed before and after the intervention
can be seen in Figure 4 and 5. To be classified as highly annoyed or sleep disturbed the rating on the“annoyance/sleep disturbance by road traffic noise” needed to be equal to 8 or higher on a 11-point
scale from 0-10, for annoyed/sleep disturbed the rating needed to be equal to 5 or higher.
Number of people [n]

28
24
20
16

HA before

12

HA after8

A before

4

A after0

Lden categories [dB(A)]

Figure 4 – Number of people (highly) annoyed (A) in the exposed group (n=28), before and after theintervention

Number of people [n]

28
24
20
16

HSD before

12

HSD after

8

SD before

4

SD after0

Ln categories [dB(A)]

Figure 5 – Number of people (highly) sleep disturbed (SD) in the exposed group (n=28), before and after theintervention414

4.1 Analysis and interpretation of exposure response functions
Exposure effect relationships used in decision making processes, like those in literature reviews
preceding the WHO guidelines (the Miedema curves (7, 8)), are aggregated from many (steady state)studies of large groups all over the world. The applicability of these relations to lower scale areas canbe questioned. At low scale, contextual factors like opinions on neighborhood quality, noise sensitivity,
start to play a role in the perception of sound. These contextual factors are ignored in generalized data.
The goal in this study is to see if the generalized curves can be used to obtain accurate results regarding
the prediction of the percentage (highly) annoyed or sleep disturbed. This was done by using survey
data of the Strevelsweg before and after the intervention. These data were compared to the predictedlevels of percentages highly annoyed (HA) and sleep disturbed (SD) of generalized curves at thecorresponding level. An important note is that information on predicted levels of percentages of lower
level annoyance and sleep disturbance is unavailable in the new WHO review on ERFs (9) or recent
Dutch studies.
The survey dataset from the Strevelsweg consisted of 69 respondents, however only 28 where
located directly along the Strevelsweg. Due to the small dataset, the number of people who
experienced high annoyance or high sleep disturbance was limited, see also Figure 4 and 5. In addition,
the majority of the exposed group had a calculated Lden above 70 dB(A) before the intervention as well
as after, the other noise classes were therefore not represented well. The choice was made to not
analyze the percentage HA and HSD but to analyze the percentage A and SD. The sound exposure wasfurther processed in categories of 5 dB(A) instead. The ERFs will be displayed as a function of 5 dB
Lden and Ln categories ranging from 50-80 dB(A).
The downside of this method is twofold, firsly a change in A or SD cannot be read from the curve if
the exposure change is within a Lden or Ln category. Second, the most recent ERFs (d.d. 2018) from the
WHO (9) cannot be compared to the local curves, since these only predict the percentage HA and HSD.
The Miedema curves (d.d. 2001 for annoyance and d.d. 2007 for sleep disturbance) predict percentage
of low annoyance, annoyance and high annoyance as well as for sleep disturbance. These curves may
be older, but are not outdated; they are still used for governmental and research purposes at a European
level and international level.
4.2 Local exposure response functions
In Figure 6 and 7 the ERF, composed of the calculated noise levels and the survey data, before andafter the DGD intervention can be seen for annoyance (A) and sleep disturbance (SD) together with theMiedema curves (7,8) respectively. In both cases the local curves lie underneath the Miedema curve.
The annoyance curve follows the same trend as the Miedema curve up until the 55-60 dB(A) category,after the local curve continues at a more constant level and the Miedema curve increases rapidly. The
local SD curve follows the trend of the Miedema curve quite closely, however the after curve is higher
than the before curve indicating that the sleep disturbance increases after the intervention. However,the shift in the curve is small (2%) and the explanation may lie in the limited data used for fitting the
curve. In the before situation, the number of people SD are 1 and 5 in category 60-65 and 65-70 dB(A)respectively. In the after situation there are 5 people SD in category 60-65 dB(A) and only one incategory 65-70 dB(A). Therefore, in the after situation the maximum is shifted more towards the
middle of the noise exposure scale, moving the curve upwards. In theory these could be the same fivepeople from the before situation, being equally annoyed in spite of the level change.
There are a few remarks which have to be made regarding these graphs. First point concerns thestudy group on which the Miedema curve is based. The exposure levels within this group are mainly
below 60 dB(A), the annoyance response above this level is extrapolated from the response in thelower exposure levels, so this is a mere prediction. The fact that the local annoyance curve follows thetrend of the Miedema curve until 60 dB(A) is promising.
Second point concerns the confidence interval, in the graphs the 95% confidence interval isdisplayed, however a tolerance level can also be used. The tolerance level is higher/wider than theconfidence interval, which makes it more likely that the local curves will fall within the tolerance
limits than in the confidence interval. Unfortunately, applying the tolerance limits for the Miedema
curves in the graphs below was not feasible within this study, due to the small sample size.

415

Figure 6 – Calculated local ERF for annoyance before the placement of the noise reducing top layer (DGD)
and after, compared to the Miedema curve with 96% CI.

Figure 7 – Calculated local ERF for sleep disturbance before the placement of the noise reducing top layer(DGD) and after, compared to the Miedema curve with 96% CI.
4.3 Predictions by Exposure Response Functions
The local curves, in theory, can be used to predict the effect of other interventions on health andwellbeing. The method to do this is by looking at the population within one exposure group, e.g. 65-70
dB(A), and see what the curve predicts. In the case of the SD after curve this is 18.1% SD for the A
after curve this is 14.5% A. The same method could be used to verify the results from this study,
however the population is too small to do this. When looking at a specific exposure group, especiallywith lower levels, the groups are small. This can lead to misleading results, e.g. in a group of four
people where one person as an annoyance score above 5 (experiences annoyance) the whole group is
25% annoyed, whilst the curve might suggest a 10% annoyance.
It should be kept in mind that annoyance and sleep disturbance differ per person and the percentage,
which is presented by the curves, should not to be taken as an absolute figure. When analyzing theeffect of an intervention it is better to look at the change in percentage rather than the actual percentage
itself. As an example, a group of 25 people is taken who were in exposure category 65-70dB(A) before
the intervention and in 60-65 dB(A) after the intervention. It is better to say they had a change of 1.2%
in SD and 4.9% in A rather than 16.8% of the group was SD before and 15.6% or 17.4% was A before
and 12.5% after.
When looking at this relative change, it can be seen that the Miedema curve can predict the local
response of SD well, due to the similar slopes. For A this is also true up until 60 dB(A).

416

5. DISCUSSION
The goal of this study was to develop a method to analyze the effect of noise exposure on health andwellbeing on a smaller scale and to see how local ERFs compare to generalized ones. It was found thatgeneralized ERFs as used in current policy documents do not predict the percentage annoyed or sleep
disturbed well in the terms of absolute values, as the local curves, modelled in this study, lie below thegeneralized curve. However, for sleep disturbance, the trend of the Miedema curve and the local curve
was the same, meaning that the relative change in sleep disturbance can be predicted by thegeneralized Miedema curve. Regarding annoyance, a similar relative change was also found until thenoise exposure category 55-60 dB(A). The position of the local curves below the Miedema curves is incontradiction with the findings of the literature study. In intervention studies most local curves werelocated above the Miedema curve and that Miedema under estimated the health effects (10-17).
5.1 Strengths and limitations
The strengths of this study lie with the unique approach of using modelled sound data and survey
data on such a small scale. The method used shows promising results, however there are limitations to
the acoustic model that was used. The acoustic model was made using just one ground type. The
Strevelsweg shows a variation of ground types since in-between the two traffic lanes a strip of grass ispresent, as well as areas of grass are present in the side streets. It was however not possible to take into
account more than one ground type in the modeling program without it producing errors. In addition,
performing calculations with higher order diffractions was not possible without errors, therefore themeasurement positions located in parallel streets to the Strevelsweg have no modeled data. Finally,
there were large difference between identical calculations, although some difference is expected since
it is a random model, deviations of up to 5 dB(A) were found. This makes the modeled data unfit tobase definitive conclusions on. Therefore the exposure levels were clustered into categories of 5dB(A).
The survey data used had its limitations as well. As stated many times already, the database wasvery small due to the low response rate to the survey, however when initially thought to be working
with a population of 69 people, this decreased to 28 when just the people are taken into account for
which the interventions affected the noise levels. But even when the exposed group would have been
larger, the OTL process would also have restricted the size of the modeled location and thus the
exposed group.
Within the survey, it was found that some questions were not asked in the traditional, standardized
way. For example, the annoyance and sleep disturbance due to road traffic was split into road traffic(meaning cars) and scooters. The questions making up the Groningen sleep scale were also notcomplete, some questions from the original protocol had have been left out. There were also adjectives
added to the question relating to the perception of the sound environment. There is little consensus in
the population regarding the meaning of these extra adjectives, therefore they have little added value
to the study.
Finally, the distribution of the participants over the different exposure categories was not even. The
population in the highly exposed group was dominant which makes predicting effects in low exposure
categories nearly impossible because conclusions are based on the information from one or two
people.

6. CONCLUSIONSIn this study it was assessed if it is possible to develop a method to quantify the effects of roadtraffic noise interventions on health and wellbeing on a small urban scale. For this purpose, a 3D
acoustic model is used (Olive Tree Lab), combined with survey data from a study performed by themunicipal health service of Rotterdam (GGD).
The location under investigation was the Strevelsweg in Rotterdam, where a noise reducing top
layer has been applied to the road surface in 2013. Before and after the implementation of theintervention, questionnaire surveys have been performed by the GGD. Using a 3D model of thelocation, the noise levels on the facades of the participants were modeled before and after theintervention. The model was verified with measurements. The results from the survey regarding
annoyance and sleep disturbance related to road traffic noise were combined with the modeled noise
exposure to create local ERFs.

417

The combination of modelled sound data and survey data on such a small scale is unique and themethod applied in this study shows promising results, however it has shown that a much larger survey
population is needed to verify the results and to find results with meaning. In addition, extensive
measurements data or a higher consistency and applicability of the prediction method are needed toobtain more accurate results.

ACKNOWLEDGEMENTS
This study is carried out under commission of the Ministry for Infrastructure and the Environment
in the Netherlands, department Climate, Air and Noise (KLG) - Now Infrastructure andwatermanagement. M/240039/18/SI. This study was made possible with the help of Mediterranean
acoustics, Tom Koeman (the Municipal Health Service of Rotterdam-Rijnmond (GGD)), Lex Burdorf(Erasmus MC) and Lex Brown (Griffith University). Maps were made available by © OpenStreetMap
contributors
under
the
Open
Database
License
which
can
be
found
at
www.openstreetmap.org/copyright

REFERENCES
1. United Nations, Department of Economic and Social Affairs, Population Division (2014). World
Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352).
2. Fritschi L, Brown AL, Kim R, Schwela DH, Kephalopoulos S, eds. (2011) Burden of disease fromenvironmental noise. Bonn: World Health Organization, ISBN: 978 92 890 0229 5
3. Brown, L., Van Kamp, I. (2017) WHO Environmental Noise Guidelines for the European Region : A
Systematic Review of Transport Noise Interventions and Their Impacts on Health. International journalof environmental health research and public health 14, 873; doi:10.3390/ijerph14080873
4. GGD Rotterdam-Rijnmond, GGD Amsterdam (2015) Ruimtelijk Onderzoek voor betere Advisering op
het gebied van Milieu en gezondheid ROAM, project information excessed via
https://www.academischewerkplaatsmmk.nl/projecten/afgeronde_projecten/2015/roam
5. Gemeente
Rotterdam(2017)
Geluidbelastingkaart
2017,
excessedvia
http://www.si2.nl/eu-kaarten/rdam/2017/geluidsbelastingskaartrtd2017.html.
6. Kephalopoulos, S., Paviotti, M., Anfosso-Ledee, F. (2012) Common Noise Assessment methods in
Europe (CNOSSOS-EU). ISSN 1831-9424
7. Miedema, H.M.E., Oudshoorn, C.G.M. (2001) Annoyance from Transportation Noise: relationships
with Exposure Metrics DNL and DENL and Their Confidence Intervals. Environmental Health Prospect
109:409–416.
8. Miedema, H.M.E., Vos. H. (2007) Associations between self-reported sleep disturbance andenvironmental noise based on reanalyses of pooled data from 24 studies. Behavioral Sleep Medicine
2007;5(1):1-20. Doi:10.1207
9. Guski, R., Schreckenberg, D., Schuemer, R. (2017) WHO environmental Noise Guidelines for the
european Region: A systematic Review on Environmental Noise and Annoyance. International Journalof Environmental Research and Public Health 14, 1539; doi:10.3390/ijerph14121539
10 Brown, A.L. (2015) Longitudinal annoyance responses to a road traffic noise management strategy that
reduced heavy vehicles at night. Journal of the acoustic society of America 137, 165–176.
11 Pedersen, T.H., Le Ray, G., Bendtsen, H., Kraugh, J. (2013) Community response to noise reducing
road pavements. In Proceedings of the INTER-NOISE and NOISE-CON Congress and Conference,Innsbruck, Austria, 15–18 Sepetember 2013.
12 Baughan, C., Huddart, L. (1993) Effects of traffic noise changes on residents‘ nuisance ratings. In
Proceedings of the 6th International Congress on Noise as a Public Health Problem, Noise & Man, Nice,
France, 5–9 July 1993; pp.585–588.
13 Langdon, F., Griffiths, I. (1982) Subjective effects of traffic noise exposure, II: Comparisons of noise
indices, response scales, and the effects of changes in noise levels. Journal of Sound and Vibration, 83,
171–180
14 Öhrström, E. (2004) Longitudinal surveys on effects of changes in road traffic noise-annoyance, activity
disturbances, and psycho-social wellbeing,” Journal of the Acoustic Society of America 115, 719–729
15 Öhrström, E., Skånberg, A. (2000) Adverse health effects in relation to noise mitigation—A longitudinal
study in the city of Göteborg. In Proceedings of the 29th International Congress and Exhibition on
Noise Control Engineering, Nice, France, 27–30 August 2000; pp. 27–30.

418