1
Linking traffic noise, noise annoyance and life
satisfaction: a case study
Abstract: The primary purpose of this study is to explore the link between traffic noise and
overall life satisfaction. While a negative relationship between residential satisfaction and
traffic noise is relatively well established, much less is known about the effect of traffic noise
on overall life satisfaction. We hypothesize that the relationship between traffic noise levels
and life satisfaction is mediated by noise sensitivity, noise annoyance and residential
satisfaction so that the direct relationship between noise exposure and life satisfaction may be
actually attenuated. The empirical model is tested using structural equation modeling. Our
study exploits data on a sample of respondents living in areas with high road-traffic noise and
another sample of respondents living in areas exposed to high levels of rail traffic noise. The
data contained information about average traffic noise levels (Lden) at the address points,
noise annoyance, subjectively perceived noise sensitivity, residential satisfaction and life
satisfaction. The proposed empirical model fits the data on road-traffic noise and rail traffic
noise extremely well. Our results suggest that a link between life satisfaction and traffic noise
exposure exists, however it is mediated by noise annoyance and residential satisfaction, but
not noise sensitivity.
Keywords: traffic noise, noise annoyance, life satisfaction, residential satisfaction
1. Introduction
Environmental noise and particularly persistent and high levels of transportation noise have been shown to
have considerable effects on health and well-being (Berglund, Lindvall, &Schwela, 1999; Berglund,
Lindvall, Schwela, &Goh, 2000; WHO, 2009). According to WHO, one third of EU citizens is annoyed
due to environmental noise and about 25% of EU citizens experience sleep disturbances due to
environmental noise (WHO, 2010). Road-traffic noise seems to be an important source of noise in the EU
as in 2000 some 44% of the citizens of the EU 25 countries were exposed to road-traffic noise levels
exceeding 55dB (Den Boer & Schroten, 2007), a level at which “health effects occur frequently, and a
sizable proportion of the population is highly annoyed and sleep-disturbed” and for which “there is
evidence that the risk of cardiovascular disease increases” (WHO, 2011, p. 58).Railway noise seems to be
a lesser problem than road-traffic noise as only 7% of the EU 25 citizens were exposed to noise levels
exceeding 55 dB in 2000 (Den Boer & Schroten, 2007). Indeed, exposure to the same level of railway
noise is likely to result in less annoyance in comparison to road-traffic and aircraft noise as documented
by exposure-response functions based on meta-analysis of empirical studies of noise annoyance (EU,
2002; H. M. Miedema & Oudshoorn, 2001), possibly with the exception of high-speed trains in Korea and
2
Japan, which produce more noise annoyance at the same noise level (Lim, Kim, Hong, & Lee, 2006;
Morihara, Yeno, & Sato, 2002).
Despite this type of evidence, relatively little is known about how road-traffic and rail-traffic noise affects
overall life satisfaction. The purpose of this paper is to fill this gap by linking together different concepts
that have appeared in empirical literature, proposed a unified model and submit this model to an empirical
test. Specifically, this paper aims to link noise exposure, noise sensitivity, noise annoyance, residential
satisfaction and overall life satisfaction.
1.1. Noise annoyance and noise sensitivity
Environmental noise is unwanted sound caused by emissions from traffic and industrial and recreational
infrastructures, which may cause annoyance and health damage. The noise originating from transport is a
classical nuisance or an externality in economic terms. This is a spill-over effect of an activity which
affects the welfare of others, without being compensated by the producer of such an externality. One of
the effects of noise exposure on the well-being of human consists in noise annoyance defined as "a feeling
ofdispleasure associated with any agent or condition, known or believed by an individual or group to
adversely affect them"(Lindvall & Radford, 1973). According to Miedema, noise annoyance is "a
sensitive indicator of adverse noise effects and by itself means the noise affects people’s quality of life"
(Miedema, 2007). Thus, noise annoyance appears to mediate some of the health effect of noise exposure
(A Fyhri & Klaboe, 2009).
Over the last 40 years a substantial volume of literature has provided evidence on relationships between
level(s) of noise exposure and expected noise annoyance. In a seminal synthesis, Schultz (1978)
translated exposure-response relationships from several social surveys into common day-night average
sound levels and proposed the average of those relationships as a means for predicting community
annoyance from transportation noise. Miedema and Oudshoorn (2001) later developed a more elaborated
model to predict three levels of noise annoyance for road, rail and aircraft noise for two alternative noise
metrics, day-night level (mostly used in the USA) and day-evening-night level endorsed in the EU’s
Environmental Noise Directive. In a recent study for the Danish Ministry of Science, Technology and
Innovation (Pedersen and collaborators 2007) developed logistic functions for annoyance exposureresponse relationships with various covariates representing the effects and parameters of noise sources,
locations, activities, perceived acoustic attributes and non-acoustic factors. These noise exposure
functions, which describe in probabilistic terms the relationship between noise exposure and noise
annoyance, typically consider noise exposure values between 45dB and 75dB in order to avoid
uncertainties.
Noise annoyance is currently one of the most extensively studied metrics for assessment of environmental
noise impacts on people. The ISO standard 15666:2003 provides a 5-level scale of annoyance (not
annoyed, slightly, moderately, very and extremely) for socio-acoustic and social surveys on noise effects
(for details see method section of this paper).
Noise annoyance is influenced by many factors besides noise exposure, including person-related variables
(age, stress level, duration of exposure to noise, noise sensitivity), house-related variables (floor, number
of windows oriented towards the source of the noise), and the characteristics of noise source there (traffic
3
flow during the day and night) (Jakovljevic, Paunovic, & Belojevic, 2009). Noise sensitivity is probably
the most important non-acoustic factor of noise annoyance (Aslak Fyhri & Klæboe, 2009; Guski, 1999;
Jakovljevic et al., 2009; Job, 1999; H. Miedema & Vos, 2003; Paunović, Jakovljević, & Belojević, 2009;
Weinstein, 1978; Zimmer & Ellermeier, 1999) while socio-demographic factors usually play a minor role
(Schreckenberg, Meis, Kahl, Peschel, & Eikmann, 2010). As a matter of fact, noise sensitivity itself seem
to be independent of noise exposure and rather related to the psychological characteristics of
individuals(Schreckenberg et al., 2010).
1.2. Life satisfaction
Happiness, subjective well-being, life satisfaction, and quality of life are some of the terms that appear
interchangeably in the literature to denote evaluation of one's life as a whole (Frey & Stutzer, 2002; Tella,
MacCulloch, & Oswald, 2003) or specific aspects of life (Andrews & Withey, 1976). The concept of
"overall" life satisfaction that we focus on in this paper originated in psychology and sociology, but
recently has made its way also into economics where it is used as a measure of "experienced utility"
(Kahneman & Sugden, 2005) and applied in a number of empirical studies that aim at monetary valuation
of non-market goods (see, e.g., Praag & Ferrer-i-Carbonell, 2008 for the overview of this approach).
Since "overall" life satisfaction is an evaluation of one's life a whole, basically anything that we can think
of can potentially influence it. Empirical studies show that many economic variables such as income,
unemployment, and inflation are predictive of life satisfaction (Clark & Oswald, 1994; Easterlin, 2001;
Frey & Stutzer, 2002; Tella, MacCulloch, & Oswald, 2001) as well as factors that characterize one's
socio-demographic situation (age, gender, parenthood) and health status (Rehdanz & Maddison, 2008).
Recent research has shown that besides the socio-demographic and economic variables also ambient
environmental quality affects life satisfaction. It has been found that climatic conditions affect life
satisfaction at the country-level aggregation (Rehdanz & Maddison, 2005)and also at the individual level
(Brereton, Clinch, & Ferreira, 2008; Frijters & Praag, 1998). Similarly, air pollution has been found to
affect life satisfaction at the aggregated country level (Welsch, 2002, 2006, 2007) and also at the
individual level (Ferreira, Moro, & Clinch, 2006; MacKerron & Mourato, 2009); one study known to us
also found an effect of perceived air pollution on the individual quality of life (Rehdanz & Maddison,
2008).
Surprisingly little is known about the effects of noise exposure on life satisfaction. As far as we are aware,
only the study by Van Praag and Baarsma (2005) examined and found an effect from exposure to aircraft
noise on individual life satisfaction. Another study has examined the effect of perceived noise-related
adverse effects on life satisfaction (Rehdanz & Maddison, 2008), but found this effect only for some of the
model specifications which may lead one to infer that the effect is not particularly strong.
Besides the studies of the effect of noise on overall life satisfaction, there are several empirical studies that
focus on the effect of noise exposure and noise annoyance on satisfaction with some aspects of one's life
(as opposed to satisfaction with life as a whole). Probably the most studied aspect of life satisfaction found
to be related to noise is residential satisfaction or one's satisfaction with the quality of life in a specific
area. A study by Botteldooren et al. (2011) has found the effect of traffic noise exposure on residential
quality. The study by Schreckenberg et al. (2010) has examined the effects of exposure to aircraft noise
4
and related noise annoyance on different aspects of quality of life (i.e., satisfaction with dwelling,
residential area, infrastructure, quietness, and attractiveness of the residential area) and found that there
was a significant negative effect of noise exposure on the attractiveness of the residential area and the total
score of residential satisfaction, and also a significant effect of noise annoyance on satisfaction with
dwelling). A study by Kroesen et al. (2010) has found an effect of exposure to aircraft noise on residential
satisfaction mediated by noise annoyance.
Several other studies have also investigated the effects of noise exposure and noise annoyance on healthrelated aspects of life satisfaction. The study by Shepherd et al. (2010) has found the effect of aircraft
noise, mediated by annoyance, on life satisfaction (or the quality of life as the authors term it), a
multidimensional construct that included physical health, psychological well-being, social relationships
and environmental factors. The study by Dratva et al. (2010) has found an effect from road-traffic noise on
all aspects of health-related quality of life (physical functioning, role-physical, bodily pain, vitality, social
functioning, role-emotional, mental health) except for general health (Dratva et al., 2010).
2. Data and method
2.1. Data
The data exploited in this paper come from a survey conducted in summer 2009 in five cities in the Czech
Republic (Prague, Vysoke Myto, Ceska Trebova, Mnisek pod Brdy, and Koprivnice) using combination of
purposive and stratified random sampling. The five cities were chosen to represent rail and road transport
modes, cities of different sizes and with different transport networks (viz. figure 1).
Based on the noise maps, obtained from National Reference Centre for Community Noise, Transport
Research Centre and AkustikaPraha, we have chosen in each of the cities several localities exposed either
to road-traffic noise or to rail-traffic noise, but not to both. In each of these localities a random sample of
address points was drawn. At each of these addresses, interviewers chose randomly the floor of the house,
specific housing unit and a respondent within that house using Kish sampling method tables (Kish, 1995).
Since we know the address points, we can attribute to each respondent the noise level he or she is exposed
to.
The data were collected using in-person interviews in respondents' homes. In total 609 questionnaires
were collected, out of which 363 in areas exposed to road-traffic noise and 246 in areas exposed to railway
noise. However, for 9 respondents exposed to road noise (all but one from Prague) and 18 respondents
exposed to railway noise (all from Prague), the interviewers failed to report their precise address and
therefore we could not attribute the noise levels for these respondents. In consequence, we included in the
final analysis 354 respondents exposed to road-traffic noise and 228 respondents exposed to railway noise.
The average age of respondent in our sample is 47.4 (min. 18, max. 88) and did not differ across the cities
(F=0.515, df=4, p=0.72) or the type of noise burden, i.e. road vs. railway (F=0.525, df=1, p=0.52). On
average, 6.7% of our respondents had received only elementary education, 30.3% high-school education
without the state leaving exam, 42.1% high-school education with state leaving exam, 4.93% completed
college education and 16.0% had received an education degree; this proportion is similar across the two
types of noise burdens (2=1.956, df=5, p>0.05) but not across the five cities (2=47.6, df=20, p<0.001). A
5
close look at the contingency table (not reported here to save space), reveals that respondents in Prague
were more often university-educated and they were less likely to have elementary education only, and that
respondents in Koprivnice were less likely to have a university degree. These irregularities are given by
the fact that Prague is large city that hosts several universities and attracts university-educated people,
while Koprivnice, a town of 22,800 inhabitants and once an important car-manufacturing centre (Tatra
Koprivnice) which still substantially relies on industrial production. Males represented 58.1% of our
sample and their proportion was similar across the two types of noise burden (2=0.7, df=1, p=0.4) and
across the five cities (2=4.81, df=4, p=0.31).
Figure 1: Towns included in the survey
Note: 1. Mnisek pod Brdy (4,700 inh.), 2. Prague (1.2 million inh.), 3. Vysoke Myto (12,400 inh.),
4. Ceska Trebova (16,000 inh.), 5. Koprivnice (22,800 inh.).
2.2. Measures
The noise data were taken from strategic noise maps drawn up pursuant to EU Environmental Noise
Directive and refer to base year 2006 because newer noise data were available. We made no adjustments
to reflect changes that may have taken place in-between such as change in traffic, installation of noise
barriers, laying of silent pavements, introduction of new speed limits etc. because information concerning
these changes is very scarce. The fact that 3 years has passed between the time of measurement of
objective noise levels and the time of our survey may bias results and we acknowledge this limitation.
Unfortunately, no newer data of comparable quality were available and will only be available after the
6
completion of 2012 strategic noise mapping. Nonetheless, as we argue in the concluding section of this
paper, even with this limitation we think that the data capture the overall trend sufficiently.
To capture noise exposure, we have opted for the 24-hours composite noise indicator Lden that is
nowadays a common reference of objective noise level used in socio-acoustic research (EC, 2002; H. M.
Miedema & Oudshoorn, 2001)and as such provided in strategic noise maps.
To measure noise annoyance, respondents were given the following question: "When you are at home are
you annoyed by road-traffic/ railway noise?" (The source of noise was chosen depending on the principal
noise burden.) A 5-point annoyance scale defined by ISO standard 15666:2003, with the answer
categories "not annoyed", "slightly annoyed", "moderately annoyed", and "very and extremely" was then
used to record respondents' annoyance.
Although various measures of life satisfaction exist, it has been shown that this concept has a high degree
of consistency, reliability, validity and stability over time (Diener, Suh, Lucas, & Smith, 1999). Indeed, it
appears that different measures of life satisfaction do in fact converge and they seem to represent a single
concept; they correlate with physical manifestations of happiness; people who express high life
satisfaction are also more likely to be described as happy by other people and are less likely to commit
suicide (Welsch, 2006, p. 803)
We adopt in the present research a measure of life satisfaction proposed by Cantril (1966), which asks
people how satisfied they are with their life as a whole. This question accompanied with an 11-point scale
has become almost a standard in life-satisfaction research and has been applied in a number of empirical
studies (Carroll, Frijters, & Shields, 2009; Frijters & Praag, 1998; Luechinger, 2009; MacKerron &
Mourato, 2009; Rehdanz & Maddison, 2008) although some researchers may prefer to use a shortened
answer scale (cf. Rehdanz & Maddison, 2005; Welsch, 2006). The validity of this particular measure of
life satisfaction has been has been demonstrated (Frey & Stutzer, 2002; Tella et al., 2003).
There seems to be no agreement whether life satisfaction should be treated as a cardinal or ordinal
measure, though it appears that in many applications accepting the assumption of cardinality or ordinarily
will have negligible effect on empirical results (Ferrer-i-Carbonell & Frijters, 2004). To stay on the safe
side, we decided to treat the variable as ordinal.
Residential satisfaction is measured by asking respondents the following question: "All things considered,
how satisfied are you with the life in the area where you are currently living? (Take into account the area
within walking distance from your home.)". Respondents indicated their answers on a 11-point Likert-type
scale ranging from 0 ("extremely dissatisfied) to 10 ("extremely satisfied").
Noise sensitivity is measured as respondents' perception of their sensitivity to noise. Respondents were
asked the following question: "To what degree are you, in your opinion, sensitive to noise?" Respondents
indicated their answers on a 4-point Likert type scale ranging from 1 ("very sensitive") to 4 ("I am not
sensitive").
2.3. Model
7
Based on the review of previous empirical research we propose the following empirical model (for its
graphical representation in the form of a path diagram see figures 4 or 5 - we propose the same model in
the road- and rail-traffic context). In the model we assume that noise exposure (observed continuous
variable Noise) has an effect on noise annoyance (latent variable Annoy). This hypothesis is supported by
the bulk of the noise annoyance literature discussed earlier. We also assume that noise exposure may have
a direct effect on life satisfaction (latent variable Lsatisf) that is not mediated by Annoy. This hypothesis
reflects the fact that noise annoyance is only one of several adverse effects on human well-being, albeit
one of the most important (cf. WHO, 2011, p. 101). In addition, we assume that Noise may also have an
effect on noise sensitivity (latent variable Sensit) - this would be, for instance, the case when noiseinsensitive people would move into areas with high noise exposure benefiting from their comparative
advantage. We make this assumption is spite of the fact that previous research has not confirmed the effect
of noise exposure on noise sensitivity (see, e.g., Schreckenberg et al., 2010). Nonetheless, we leave this
question open to empirical investigation in our study.
Further, we assume that noise annoyance has an effect on residential satisfaction (latent variable Rsatisf).
This assumption is supported by a wealth of evidence from previous empirical studies (see, e.g.,
Botteldooren et al., 2011; Kroesen et al., 2010; Schreckenberg et al., 2010) . In addition, we assume that
noise annoyance may have direct effect on life satisfaction unmediated by residential satisfaction.
Finally, our models assume that residential satisfaction has an effect on life satisfaction (latent variable
Lsatisf), this hypothesis is justified by the mere fact that residential satisfaction is one of the aspects of the
overall life satisfaction.
2.4. Procedure
The empirical models are tested using structural equation modeling (Bollen, 1989). SEM is very
advantageous for the purpose of our study specifically because it allows us to examine a relatively
complex model which includes continuous variable Noise and several latent variables indicated by ordinal
outcome variables. As a matter of fact, SEM has been previously used with some success to analyze the
link between noise exposure, noise annoyance and adverse psycho-physical effects of noise
exposure(Aslak Fyhri & Klæboe, 2009).
The core idea of SEM is that it is possible to reproduce a population variance-covariance matrix if we
know the model which correctly explains variation in the data. Although different estimators may be used
to estimate the model parameters, all estimators basically aim to minimize the discrepancy between the
model-implied and empirical variance-covariance matrix of observed variables.
Our model could be expressed as a series of 4 equations for each of the dependent variables. In the
shortened matrix form, these equations can be re-expressed for each individual, i, as:
𝛈𝒊 = 𝐁𝛈𝒊 + 𝛅𝑁𝑜𝑖𝑠𝑒𝑖 + 𝛇𝒊
(1)
where η (4 x 1) is a vector of endogenous latent variables (i.e., Sensit, Annoy, Rsatisf, and Lsatisf), Noise
(a scalar), is the value of environmental noise (Lden) observed for each individual i, B (4 x 4) is a matrix
of regression coefficients among η’s with the diagonal elements equal to zero and I-B, being a non-
8
singular matrix,  (3 x 1) is a parameter vector of coefficients for the regression of latent variables η’s on
Noise, and (4 x 1) is a vector of residuals that are assumed to have multivariate normal distribution. Note
that in the model we fix the  that refers to the influence of Noise on Rsatisf to 0 as we assume that Noise
has no direct effect on Rsatisf (see also figures 4 and 5 where this is expressed in graphical form).
We assume that j-th continuous latent variable, ηj, is measured without a measurement error by only one
observed continuous outcome variable, y, expressed as i-th respondents' rating on j-th Likert-type scale.
The probability of observing i-th respondent indicating values 0,1,...,S on this scale can be expressed using
the following threshold ordered probit model (for technical details of this model see, e.g., B. Muthén,
1983):
𝑃 (𝑦𝑖,𝑗 = 0|𝑖,𝑗 ) = (1𝑗 − 𝑖,𝑗 )
𝑃 (𝑦𝑖𝑗 = 1|𝑖,𝑗 ) = (2𝑗 − 𝑖,𝑗 ) − (1𝑗 − 𝑖,𝑗 )
⋮
𝑃 (𝑦𝑖,𝑗 = 𝑆|𝑖,𝑗 ) = 1 −  (𝑆𝑗 − 𝑖,𝑗 ),
(2)
where  is the standardized univariate normal distribution function and 's are estimated threshold
parameters.
To estimate the model parameters, we use limited-information estimation with robust weighed least square
estimator which avoids demanding numerical integration in the ML estimation, and that is also robust in
presence of non-normal outcome variables (B. Muthén, Du Toit, & Spisic, 1997). The models are
estimated in MPlus, version 6.1 (L. K. Muthén & Muthén, 2010).
3. Results
Sample exposure to traffic noise in terms of Ldvn ranged between 47 and 79dB for road-traffic noise and
between 46 and 72 dB for rail traffic noise. Distributions of noise exposure in the samples exposed to
road-traffic noise and rail traffic noise are displayed in figure 1. As we can see there, the rail traffic sample
has been generally exposed to lower noise levels.
Figure 1: Sample noise exposure (Noise) - road traffic and railway traffic
9
40
30
0
10
20
Abs. frequency
30
20
0
10
Abs. frequency
40
50
Railway (n=223)
50
Road (n=352)
40
50
60
70
80
90
40
50
Ldvn [dB]
60
70
80
90
Ldvn [dB]
Average noise exposure for the two samples is further examined separately for the towns from which the
samples were drawn in table 1. Results of the ANOVA tests conducted separately for road-traffic and rail
traffic samples suggest that there were significant differences in exposure to noise levels between the
towns.
Table 1: Noise exposure (Ldvn) in samples from different towns (means, standard deviations and
ANOVA)
Town
Praha
VysokeMyto
Mnisek pod Brdy
Koprivnice
CeskaTrebova
Total
ANOVA
N
210
39
28
77
-354
Road traffic
M
70.96
63.34
64.47
65.79
-68.48
F=52.76(3), p<0.001
SD
4.35
6.27
4.15
4.37
-5.50
N
117
---111
228
Railway traffic
M
SD
59.97
3.81
------56.04
6.14
58.06
4.45
F=34.285(1), p<0.001
An examination of table 2, which reports average noise annoyance levels in the sub-samples coming from
different towns, reveals that the average reported by respondents in our sample is quite high with values of
4.13 and 3.45 for road-traffic and rail traffic annoyance respectively (where 1 stands for "not annoyed"
and 5 for "extremely annoyed"). Level of annoyance seems to be similar between the different localities
sampled for road-traffic annoyance and also between the localities sampled for rail traffic noise.
Table 2: Average noise annoyance score in sub-samples from different towns (means, standard deviations
and ANOVA test of equality)
10
Town
Praha
VysokeMyto
Mnisek pod Brdy
Koprivnice
CeskaTrebova
Total
ANOVA
N
210
39
28
77
-354
Road traffic
M
4.07
4.08
4.57
4.14
-4.13
F=2.440(3), p=0.064
SD
0.91
1.04
0.92
0.93
-0.93
N
117
---111
246
Railway traffic
M
3.50
---3.36
3.43
F=0.684(1), p=0.409
SD
1.22
---1.25
1.23
Figure 3 displays average noise exposure for different levels of life satisfaction separately for the sample
exposed to road-traffic and rail traffic noise. At first look there appears to be hardly any clear monothetic
relationship, especially when we take into account the estimated confidence intervals of average noise
exposure for each level of life satisfaction.
11
Figure 3: Average noise exposure for each of the life-satisfaction levels (mean and its 95% conf. int.)
65
50
55
60
Ldvn [dB]
70
75
80
Road
n=7
n=9
Very dissatisfied
n=4
n=25
n=22
n=82
n=33
n=60
n=64
2
3
4
5
6
7
8
n=27
n=21
9 Very satisfied
Life satisfaction
65
50
55
60
Ldvn [dB]
70
75
80
Railway
n=6
n=1
Very dissatisfied
n=4
n=11
n=3
n=32
n=24
n=44
n=46
2
3
4
5
6
7
8
n=30
n=27
9 Very satisfied
Life satisfaction
The contention that the statistical relationship between noise exposure and life satisfaction is weak at best
is confirmed in tables 3 and 4, which report very low and statistically insignificant correlations between
noise exposure and life satisfaction in our sample and also very low correlations between noise annoyance
and life satisfaction that are statistically significant (and negative as expected) only in the case of roadtraffic noise.
12
Table 3: Mans, standard deviation, and correlation between observed variables (road traffic)
M
1. Ldvn
68.48
2. Sensit
2.12
3. Annoy
4.12
4. Rsat
6.44
5. Lsat
7.15
*p<.05. **p<.01. ***p<.001.
SD
5.50
0.69
0.93
2.59
2.25
1
1
-.105*
.142**
-.132*
.066
2
3
4
5
1
-.169**
.029
.068
1
-.328**
-.140**
1
.370**
1
Table 4: Means, standard deviation, and correlation between observed variables (railway traffic)
M
1. Ldvn
58.06
2. Sensit
2.20
3. Annoy
3.45
4. Rsat
7.84
5. Lsat
8.00
*p<.05. **p<.01. ***p<.001.
SD
5.44
0.84
1.22
2.17
2.27
1
1
-.095
.172**
-.058
-.065
2
3
4
5
1
-.381**
-.011
-.064
1
-.141*
.020
1
.549**
1
Now we proceed to the estimation of the structural model for the sample of respondents exposed to roadtraffic noise and rail traffic noise. The fit of the models and structural parameter estimates are displayed in
figure 4 for road-traffic noise and in figure 5 for rail-traffic noise. The fit indices for both the models
suggest a very good fit of the models to the empirical data (2(3)<4.3; RMSEA<0.036; CFI>0.99).
However, the fit statistics for the models with low degree of freedom, such as ours, are perhaps of a lesser
theoretical importance than the estimates of structural parameters which are also displayed in figures 4 and
5.
13
Figure 4: Model estimates (road-traffic noise)
Model fit
.937
.986
.180**
Annoy
-.377***
Sensit
.868
Rsatisf
.020 ns
.030**
-.027 ns
.375***
354
32
3
4.346
0.22
0.036
0.992
.851
-.019 ns
Noise
N
Free par.
Df.
2
P-value
RMSEA
CFI
Lsatisf
Note: Arrows are used for the directed relations between constructs. β coefficients (standardized multiple
regression coefficients) represent their strength. Measurement errors and unexplained proportion of
variance are indicated with arrows without origin. ***p<0.001.**p<0.01.p*<0.05. ns, non-significant pvalue (p>0.05). P-values are listed for parameters in the structural model only as these are of theoretical
significance. Measurement models are not represented in the path diagram.
Figure 5: Model estimates (railway noise)
Model fit
.798
.986
.458**
Annoy
-.132*
Sensit
.979
Rsatisf
.021 ns
Noise
.029*
.106 ns
.732***
-.017 ns
N
Free par.
df.
2
P-value
RMSEA
CFI
228
32
3
0.409
0.938
<0.01
>0.99
.647
Lsatisf
Note: Arrows are used for the directed relations between constructs. β coefficients (standardized multiple
regression coefficients) represent their strength. Measurement errors and unexplained proportion of
variance are indicated with arrows without origin. ***p<0.001.**p<0.01.p*<0.05. ns, non-significant pvalue (p>0.05). P-values are listed for parameters in the structural model only as these are of theoretical
significance. Measurement models are not represented in the path diagram.
We can observe in figures 4 and 5 that the coefficient for the path leading from residential satisfaction to
life satisfaction is always positive and significant, suggesting that higher residential satisfaction leads to
higher life satisfaction. On the other hand, the path from noise annoyance to residential satisfaction is
14
always significant and negative, which can be interpreted in such a way that higher noise annoyance
results in decreased residential satisfaction. The path that leads from noise to noise annoyance is positive
and significant in both models, indicating that exposure to higher noise levels leads likely to higher noise
annoyance. Most interestingly, neither noise nor noise annoyance have any significant direct effect on life
satisfaction when residential satisfaction is controlled; all their effect is mediated by this variable. This
result is very similar for the two models and supports the hypothesis that residential satisfaction mediates
the effect on noise exposure on life satisfaction, at least for traffic noise of similar magnitudes on which
our study focused. We will return to this interesting result in more detail later in the concluding section of
this paper.
Continuing the examination of figures 4 and 5, we see that the path from noise sensitivity to noise
annoyance is positive and statistically significant in both models. This result is quite in line with the
findings of previous empirical studies, which point to the prominent role of noise sensitivity as a factor of
noise annoyance (Guski, 1999; Jakovljevic et al., 2009; Job, 1999; H. Miedema & Vos, 2003; Paunović et
al., 2009; Weinstein, 1978; Zimmer & Ellermeier, 1999). Noise sensitivity seems to be independent from
noise levels, a result that is also in line with previous research (Schreckenberg et al., 2010). In other
words, people living in a noisy area are not less sensitive to noise annoyance than people living in
relatively less noisy places. This result does not support the hypothesis of the mediating role of noise
sensitivity that would develop if less sensitive people move into a high-noise area and more noisesensitive people move out of these areas.
Although both models fit to empirical data quite well and estimates of model parameters are generally in
line with what we can learn from other studies, a closer look at the path diagrams depicted in figures 4 and
5 reveals that both models leave a sizable proportion of variance in dependent variables unexplained
(computed 1-residual variance). This result is not surprising if we take into account the complexity of
such phenomena as noise perception and life satisfaction and the relative simplicity of our model. As a
matter of fact, noise annoyance is a product of such diverse factors as person-related variables (age, years
of employment, stress score, duration of stay at the accommodation during the day), house-related
variables (windows of living room and/or bedroom oriented toward the street, floor), neighbourhoodrelated variables (Leq for day and night, maximal night-time noise level, traffic flow during day and at
night) (Jakovljevic et al., 2009). Also life satisfaction appears to be a phenomenon of considerable
complexityand even relatively elaborate individual-level models explain only between 4 and 9% (Carroll
et al., 2009), between 3 a 5%(Luechinger, 2009), between 8 and 9% (Rehdanz & Maddison, 2008) or
about 9% of the variability in life satisfaction (MacKerron & Mourato, 2009).
4. Conclusions
This study aimed to answer the question of whether road- and rail-traffic noise negatively affects the
overall life satisfaction. Our results support the contention that traffic noise does, indeed, lower people's
life satisfaction. However, the effect of noise on life satisfaction seems to be mediated by at least two
other constructs, noise annoyance and residential satisfaction, which makes the detection of the link
between noise exposure and quality of life non-trivial and which may also obscure the existence of this
relationship.
15
The novelty of the present study consists mainly in the proposing and testing of an empirical model that
links together constructs used in the study of adverse effects of noise exposure (noise annoyance,
residential satisfaction) with the measure of overall life satisfaction. Previous studies dealt only with the
effect of noise exposure and noise annoyance on residential satisfaction, which is only a very specific part
of life satisfaction. Only one previous study dealt with the effect of one particular type of traffic noise,
aircraft noise, on life satisfaction (Van Praag & Baarsma, 2005), while another study dealt with quality of
life and perceived levels of traffic noise (Rehdanz & Maddison, 2008). Therefore the question whether
road- and rail-traffic noise exposure also affects overall life satisfaction remained unanswered.
One of the interesting finding that follows from our results is that noise annoyance mediates most of the
effect of noise exposure on residential satisfaction and life satisfaction. The main adverse effect of roadand rail-traffic noise exposure (in the range of 46 and 79 dB) on the overall quality of life therefore seems
to consist mainly of noise annoyance. There may be additional effects of noise exposure on residential
satisfaction and/or life satisfaction unmediated by noise annoyance, but these are probably smaller than
those mediated through noise annoyance and our study, which exploits two rather small samples of
respondents, is probably not sensitive enough to detect these effects. This finding is quite in line with
results of WHO study that quantifies noise annoyance (and sleep disturbance that some would say is
closely associated with noise annoyance) as by far the largest adverse effects in terms of Disabilityadjusted life years (WHO, 2010).
Of some interest is also the finding that residential satisfaction mediates most of the effect of noise
exposure on the overall life satisfaction. We interpret these results as suggesting that traffic noise exposure
mainly affects residential aspect of life satisfaction, at least for the type and level of noise exposure
targeted in this study. This finding is in line with number of previous studies which, too, found residential
satisfaction to be adversely affected by traffic noise and noise annoyance (Botteldooren et al., 2011;
Kroesen et al., 2010; Schreckenberg et al., 2010). Our findings also attest to importance of the past and
future research that examines effects of noise exposure and noise annoyance on residential satisfaction
because residential satisfaction is, according to our study, a good indicator of those life-satisfaction
aspects that are most affected by traffic noise exposure.
Our study did not confirm the effect of noise exposure on noise sensitivity. Also this result seems to be in
line with the literature (Schreckenberg et al., 2010). Although the cross-sectional evidence exploited in
this study does not allow us to examine whether people get used to traffic noise and become insensitive, or
whether noise-sensitive people have tendency to move out of noise areas, we can tell, based on our results,
that noise sensitivity does not mediate the effect of noise exposure on noise annoyance, although noise
sensitivity by itself affects noise annoyance.
A number of limitations of the present study should be acknowledged. Firstly, our data come from a
survey of a urban population exposed to rather high levels of traffic noise. This fact by itself is likely to
attenuate the correlation between noise exposure (and likely also noise annoyance) on one side and life
satisfaction and residential satisfaction on the other simply because the variance of noise exposure is lower
than would be so in a sample of general population. However, we argue that such a bias does not
compromise our general conclusions about existence of a link between traffic noise exposure and
residential satisfaction. It is quite likely, on the other hand, that in different populations a direct link
(undetected in this study) may exist between noise exposure and life satisfaction.
16
Another limitation of the present study lies in the fact that there is a 3 years gap between collection of
noise exposure data and our survey. Our study most likely underestimated noise exposure because the
traffic increased over those years (especially for the road traffic). However, from what we can learn about
localities that our data come from, we are not aware on any abrupt increase in traffic flow over the three
years. For this reason we believe that effects of noise exposure (but not average level of noise exposure)
are not severely biased in our study.
Our study suffers also from number of limitations that are rather generic to cross-sectional studies.
Specifically, relatively small sample sizes disadvantage our study specifically by decreasing its statistical
power. This may attenuate the correlations between variables. We have also to bear in mind that our data
are cross-sectional and therefore their relevance for the testing of causal hypothesis is lower than that of
longitudinal or experimental data (as suggested by e.g. Baum, Arthurson, & Rickson, 2010; Aslak Fyhri &
Klæboe, 2009). On the other hand, we firmly agree with (Rubin, 1974) that no data can principally prove
causal hypothesis and that different types of data (experimental, observational etc.) differ only in the level
of their relevance for the testing of causal hypothesis.
17
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