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

Leq + X: Re-Assessment of exposure-response relationships foraircraft noise annoyance and disturbances to improve explained
variance
Julia HAUBRICH1; Sarah BENZ1; Mark BRINK2; Rainer GUSKI3; Ullrich ISERMANN; BeatSCHÄFFER,5; Rainer SCHMID4; Dirk SCHRECKENBERG1; Jean-Marc WUNDERLI5
1
2

5

ZEUS GmbH, Germany

Swiss Federal Office for the Environment, Switzerland
3

Ruhr University Bochum, Germany

4German Aerospace Center, Germany

Empa, Swiss Federal Laboratories for Materials Science and Technology, Switzerland

ABSTRACT
There is evidence from the literature that an increased fraction of people is highly annoyed by aircraft noise
by given average sound levels than in previous decades. Among possible reasons are changes of the air traffic
over the last decades such as the increased number of movements along with a less noisy fleet mix that might
be relevant for noise responses but are not adequately reflected by average sound metrics. In the Leq+X
project, data of two German and two Swiss field studies comprising aircraft noise exposure and annoyance
data of about 37’700 residents living around altogether seven airports are re-analyzed. Among others, theanalyses include mixed-effects multilevel models of the percentage highly annoyed on the Lden, on the LAeq
(day/night), on the LAeq combined with either maximum sound level (Lp,AS,max) or number of events above
threshold (NATLS), and on the Lp,AS,max combined with NATLS for different thresholds. Further models include
the LAeq with non-energy-equivalent parameters k = 20 or 30, which weight the number of events morestrongly than the energy-equivalent LAeq,24h,k=10. In this paper, the methodological approach and first results
will be presented.
Keywords: aircraft noise annoyance, noise metrics, number of events

1. INTRODUCTION
Aircraft noise annoyance is one of the most common negative aspects of (living near) an airport. On
the part of residents, noise annoyance is related to several health and wellbeing issues like sleep
disturbances and cardiovascular diseases (for recent overviews, see, e.g., 1, 2). For airport operators, it
can - at least indirectly, for example through greater and more effective opposition and counteractions
to airport expansion - also have negative economic effects.
Noise annoyance is a complex psychological response to noise. According to Guski and colleagues(3), noise annoyance comprises three elements: (i) the experience of an often repeated noise-related
disturbance and the behavioural response to it, (ii) an emotional/attitudinal response to the sound andits disturbing impact, (iii) the perception of control of the noise situation.
Over the past few years, there has been a growing number of publications showing that the
proportion of people highly annoyed by aircraft noise (%Highly Annoyed, %HA) is higher than would
be expected according to common exposure-response curves (such as the much-quoted one from
Miedema and Oudshoorn, 4). At comparable aircraft sound levels, %HA is usually higher. Evidence1

haubrich@zeusgmbh.de; benz@zeusgmbh.de; schreckenberg@zeusgmbh.demark.brink@bafu.admin.ch3
rainer.guski@rub.de
4
ullrich.isermann@dlr.de; rainer.schmid@dlr.de
5beat.schaeffer@empa.ch; jean-marc.wunderli@empa.ch21523

from a total of 62 studies was summarized in a systematic WHO review and meta-analysis of effects ofenvironmental noise on annoyance by Guski, Schreckenberg, and Schuemer, (3). Some of their main
results are depicted in Figure 1.

Figure 1: Relationship between Lden and %Highly Annoyed for Aircraft Noise according to different studies
and for different airports in different years; figure taken from the WHO Evidence Review for Environmental
Noise Annoyance from Guski et al. (3). Black line: weighted (according to sample size) average of 12
samples in which the Lden was calculated directly; red line: exposure-response curves according to Miedema& Oudshoorn (4); green line: estimates of Janssen & Vos (5) based on 7 recent aircraft noise examinations.
Figure 1 illustrates that %Highly Annoyed in the data based on the WHO full dataset (black line)
and in the estimates of a more recent study by of Janssen & Vos (5, green line) increases earlier withincreasing L den compared to the much-cited exposure-response curve according to Miedema &Oudshoorn (4, red line). There are also apparent differences between the different samples, each ofwhich relates to a specific airport in a given year.
Among the possible causes for this finding, for example, methodological reasons were discussed.
Janssen et al. (6), however, excluded various methodological aspects as explanatory factors for theincreasing trend in an analysis of 34 studies from the years 1967 to 2005.
Also, the change context at an airport at the time of an investigation, that is, whether it could beclassified as a ‘high rate change’-airport (HRC) or as a ‘low rate change’-airport (LRC) at a given time,
is discussed as another possible cause for the trend recognizable in Figure 1. According to Janssen andGuski (7), a HRC-airport is characterized by the fact that within the last three years, planning
intentions for a long-term increase in flight movements or a relocation of flight routes ha ve been madepublic or still been publicly discussed after such notice. If this criterion does not apply at a given point
in time, an airport will be considered as a LRC-airport at that time. Several studies (7, 8) show that thechange context of an airport bears a significant effect on aircraft noise annoyance, but does not sufficeas the sole explanation for the increase of %Highly Annoyed at comparable continuous sound levels.
As another possible reason for this increase, also an increase in flight movements is discussed. Thisis based on the finding that since the mid-1990s, the characteristics of aircraft noise have changed tothe effect that, on average, airplanes have become quieter, while at the same time there are more flightmovements. Examples of acoustic predictors based on event frequencies include NATLS (‘number
above threshold’; indicating the frequency of exceeding the threshold LS) and Lp,AS,max,ari, indicating
the number of overflights with maximum A-weighted sound pressure levels. For the NAT LS, forexample, in the NORAH study by Schreckenberg et al. (8) correlations with annoyance measures havebeen observed ranging from r=.31 to r=.56 at different airports.
Based on these considerations, the present study investigates whether the predictive power
for %Highly Annoyed can be improved if this criterion is not only predicted by the equivalentcontinuous sound levels but when its underlying parameters - maximum level and frequency of noise

1524

events - are instead or additionally included into the prediction. Due to the differences between
samples, i.e., between different airports in different years, which can be seen in Figure 1, it is also
examined whether specific predictions for each sample or certain sub-samples can improve thepredictive power further. To identify possible relevant sample-related factors, several sample andairport characteristics are considered as potential predictors.

2. PROCEDURE, METHODS
2.1 Data basis, samples and participants
For this study, existing data sets with individual annoyance measures and address-related acoustic
exposure variables from these two Swiss and two German studies are used: ‘Laermstudie (LS) 2000’(9), ‘SiRENE study’ (10), ‘RDF study’ (11), and ‘NORAH study’ (8).
In total, the data of 12 samples were available for the present study, where a sample is meant to be
one survey at one airport at one specific point in time. The total number of participants is N=34250;
17909 (52.3%) female, aged 17-100 years (M=57.5, SD=14.9).
2.2 Acoustic predictors
As acoustic predictors, the parameters listed below were used. These were either ex tracted directlyfrom the data sets of the above-mentioned samples (LAeq , L den) on the basis of the data available fromthe studies, or - for the determination of values for L Aeq,24h,k=20 and L Aeq,24h,k=30 - in some cases
approximations were used (for details see Benz et al., 12).
x LAeq,24h,k=10, LAeq,24h,k=20, LAeq,24h,k=30: equivalent continuous sound level LAeq,k for the 24-hour day
for different equivalence parameters kx

Lden: the day-night-night level as defined in the EU Environmental Noise Directivexlog(NAT24h,50), log(NAT24h,60),log(NAT24h,70), log(NAT24h,80): the logarithmic number of aircraft
noise events with A-weighted maximum sound levels above a threshold of LS decibels („Number
above Threshold“ NATLS)x

LAS,max,ari,50, LAS,max,ari,60, LAS,max,ari,70, LAS,max,ari,80, the arithmetically averaged A-weighted maximum
sound levels (LAS,max,ari,LS ) above these thresholds

2.3 Sample and airport-specific predictors
The following sample and airport-specific predictors were considered: a) the number of flightmovements on an average 24-hour day, b) night-flight rate, c) five year trend of flight movements(increasing or decreasing), d) fleet mix (for the starting movements over the 24 hours of the whole day;cf. 13, 14), e) change context (HRC vs. LRC, see above).
2.4 Assessment of high annoyance
Annoyance was assessed via self-report using the standard 5-point ICBEN noise annoyance scale(all samples except those from the LS2000 study) or the 11-point ICBEN noise annoyance scale.
For the determination of %Highly Annoyed, the upper two values of the 5-level ICBEN scale (4 and
5) were summarized into the category "highly annoyed" (HA); for analyses particularly referring to theLS2000 study, the upper three values on the numerical ICBEN scale from 0 to 10 (8, 9 and 10) wereused instead.
2.5 Method
To investigate whether frequency-based parameters can improve the predictive power of theacoustic predictors, the models presented in Table 1 were examined. Each model includes one
predictor (lines 1-4) or a combination of two predictors (line 5 and beyond). Multilevel analyses werecarried out for each model using the package "lme4" of the statistical software R (15).

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Table 1 - Summary of the predictors of the multi-level models
Model group
Lden

Level 1-predictors

Level 2-predictors(acoustic predictors)(Sample and airport-specific predictors)

Lden
LAeq,24h,k=10

LAeqAeq

LAeq,24h,k=20
LAeq,24h,k=30
Lden, log(NAT24h,50)

Lden + log(NATLS)

Lden, log(NAT24h,60)
Lden, log(NAT24h,70)
Lden, log(NAT24h,80)
LAeq,k=10, log(NAT24h,50)

LAeq + log(NATLS)

LAeq,k=10, log(NAT24h,60)
LAeq,k=10, log(NAT24h,70)
LAeq,k=10, log(NAT24h,80)
Lp,AS,max,ari,24h,50. log(NAT24h,50)

Lp,AS,max,ari,LS +Lp,AS,max,ari,24h,60. log(NAT24h,60)

log(NATLS)

Lp,AS,max,ari,24h,70. log(NAT24h,70)(sample)flight movementsnightflight rate
trend
(five year trend of flight movements)
NoiseStarts24(fleet mix)change context(HRC vs. LRC)

Lp,AS,max,ari,24h,80. log(NAT24h,80)
In the first step, it was examined based on the intraclass correlation (ICC, 16), whether the second
level predictors enhance the predictive power significantly. The sample variable, characterized by the
airport and the survey year, served as the classification variable.
Next, each the best model with exactly one predictor and the best model with two predictors wereidentified. The criterion was the goodness of fit of the models (based on the Akaike Information
Criterion, AIC, 17). Finally, these two models were compared statistically with a Chi-Square
Differences Test.
Other outcome measures considered are the odds ratios, each separately for the intercept (the β0 parameter) and the slopes (the β 1 parameter and, if a model has two acoustic predictors, additionallythe β2 parameter).

3. RESULTS
A summary of the results of the multi-level analyses is shown in Table 2.
For all models, the ICC is above .10 (range: .10 - .21), indicating that the 2nd level predictors
should be kept. In the models with one acoustic predictor, the model with L den achieves better goodnessof fit than the various L Aeq models.
For the models with two predictors, the models from the groups L den and log(NAT LS ) as well as
L Aeq,24h,k=10 and log(NAT LS ) are better than those from the group L AS,max,ari,LS and log(NATLS), The bestmodel with two predictors has Lden and log(NAT24h,70 ) as joint predictors.
A Chi-Square Differences Test comparing these two models yields a p-value of p < .001, which ishighly significant, indicating that the more complex model with two predictors yields significantly
better goodness of fit than the model with one predictor.
Overall, thus, the model associated with the best predictive power is the model with the combined
predictors L den and log(NAT 24h,70).

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1527

/

/

/

/

log(NAT24h,50)log(NAT24h,60)

log(NAT24h,70)log(NAT24h,80)log(NAT24h,50)log(NAT24h,60)

log(NAT24h,70)log(NAT24h,80)log(NAT24h,50)log(NAT24h,60)

log(NAT24h,70)log(NAT24h,80)

Lden

LAeq,24h,k=10

LAeq,24h,k=20

LAeq,24h,k=30

Lden

Lden

Lden

Lden

LAeq,24h,k=10

LAeq,24h,k=10

LAeq,24h,k=10

LAeq,24h,k=10

Lp,AS,max,ari,24h,50

Lp,AS,max,ari,24h,60

Lp,AS,max,ari,24h,70

Lp,AS,max,ari,24h,80

AIM

AIM

AIM

AIM

AIM

AIM

FM

AIM

FM

FM

FM

FM

AIM

AIM

AIM

AIM

Model

Best

.1046

.1128

.1251

.1335

.1461

.1367

.1387

.1448

.1210

.1193

.1199

.1212

.2098

.1656

.1443

.1210

ICC

30751.2

29811.729436.629434.629419.5

29387.029382.929418.5

29408.1

29358.5

29368.729406.930557.2

29985.5

29417.7

29417.1

AIC

.158

.208

.230

.216

.190

.192

.216

.188

.209

.214

.213

.209

.154

.152

.146

.151

R

2

Marginal

.189

.240

.269

.260

.238

.237

.264

.236

.264

.264

.262

.264

.356

.299

.255

.246

R

2

Conditional

.67**

.64**

.60**

.62**

.60**

.61**

.57**

.60**

.60***.60***.58***.60**.44***.44***.45***.47***(Intercept)

OR β0

.86*

ns

1,39***

1,81***

2,26***

1,87***

2,19***

2,27***

2,62***

2,15***

2,16***

2,73***

2,42***

2,33***

2,23***

2,25***(Slope)OR β12,08***

2,34***

2,08***

1,70***

ns

1,23***

1,25***

ns

ns

1,26***

1,27***.96***////(Slope)

OR β2

--

--

--

--

--

--

HRC

--

HRC

HRC

HRC

HRC

--

--

--

--

Term(s)Significant.52***.51***.55***.49***.51***

Term(s)sig.

ORNote. ICC: intraclass coefficient. AIM: “augmented intermediate model”, FM: “fullmodel”. AIC: Akaike information criterion. * p<.05 , ** p<.01, *** p<.001; ns: not significant.

Predictor 2

Predictor 1

Table 2 -Summary of the results of the multi-level analyses for the prediction of %HA aircraft noiseTable 2 shows that exactly one level 2-predictor is significant in this model, which is the ‘change
context.’ A look at the odds ratios of the regression parameters shows that the Lden exerts the largest
effect on %HA - the odds ratios for β 1 are higher than those for β2 for log(NAT24h,70 ) (2.15 vs. 1.26,
respectively). The odds ratios for the significant level 2-predictor are about 4x smaller than those for
L den and less than half as big as those for log(NAT24h,70) (β3=.55). Thus, the ‘change context’ implies asignificant additional contribution to the explained variance, but its effect is weaker than that of eitherof the two acoustic predictors.
Figure 2 shows the exposure-response curve for the relationship between L den and % HA for threeclasses of log(NAT24h,70 ), each separately for ‘high-‘ (HRC) and ‘low rate change’-airports (LRC),
respectively. It can be seen that at the LRC-airports, the curves for the high, middle and lower
log(NAT24h,70 )-groups differ mainly in their height, with the curve representing lowlog(NAT24h,70 )-values being above the curve representing middle values at higher L den-values.
At the HRC-airports, however, the curve representing high log(NAT24h,70)-values stands out, inthat it has a much lower slope than the other two.
High-rate change (HRC) airportslog(NAT24h,70) range (HRC)
Min

Max

N

high

>1.30

2.59

5425

mid

>0.29

1.30

5851

low

-2.40

0.29

5891

Σ

17167

Low-rate change (LRC) airportslog(NAT24h,70) range (LRC)
Min

Max

N

high

>1.30

2.51

2579

mid

>0.29

1.30

2639

low

-2,76

0.29

2608

Σ

7826

Figure 2: Exposure-response curve for the relationship between Lden and %HA for HRC airports (upper
panel) and LRC airports (lower panel), separated for three classes of log(NAT24h,70) (see boxes on the right for
detailed information). Dashed lines indicate the upper and lower confidence intervals to the respective curveof the same color. Grayed out are the extrapolated parts of the curves outside the actual range of Lden.

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4. DISCUSSION AND CONCLUSIONSThis research project was based on the assumption that the "aircraft noise annoyance trend ,"showing an increased rate of highly annoyed persons at a comparable continuous sound level, could be
associated with an increase in the frequency of flight movements. It has been postulated that thesimultaneous consideration of a frequency-based predictor - in particular, log(NAT LS) - in combination
with an acoustic predictor - especially L Aeq, Lden or L p,AS,max,ari - yields a better predictive powerof %Highly Annoyed than the respective acoustic predictor on its own.
For this purpose, the goodness of fit and predictive power of different models with single acoustic
predictors and acoustic predictors in combination with frequency-based predictors were comparedwith each other using multi-level analyses. To take into account sample- and airport-specific effects,
additional sample-specific characteristics were investigated as level 2-predictors.
The results show that a combination of L den and log(NAT LS) has better predictive power than models
with a single predictor (Lden or L Aeq, 24h,k with k=10, k=20 or k=30) or a model with a combination of
L Aeq,k=10 and log(NAT LS) or L p,AS,max,ari and log(NAT LS).
In addition, a sample-specific consideration of the effects turns out to be significant. Of the level
2-predictors examined here, the ‘change context’ proved to be significant (‘high rate change’-airport,
HRC, vs. ‘low rate change’-airport, LRC), with %Highly Annoyed significantly higher for HRC.

ACKNOWLEDGEMENTS
The study was funded by the Swiss Federal Office for the Environment (‘Bundesamt für Umwelt, BAFU’),
Bern, Switzerland; and by the Ruhr-University of Bochum, Germany.

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