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

Do we need different aircraft noise metrics to predict annoyance fordifferent groups of people?
Jan SPILSKI1, Kirstin BERGSTRÖM1, Ulrich MÖHLER2, Thomas LACHMANN1,3, Maria KLATTE1
1
2

University of Kaiserslautern, Germany

Moehler + Partner Enginees Munich, Germany
3

Universidad Nebrija Madrid, Spain

ABSTRACTIn the literature, LAeq and Lden are the dominating noise exposure metrics for calculating therelationship between noise and annoyance. However, alternative metrics such as the number of events
above a threshold (NAT), LAmax or Emergence are rarely considered, although they may have thepotential to explain additional variance. This seems necessary because a WHO-Review pointed outthat the average relationship between aircraft noise levels and annoyance raw scores is .44 which
represents an explained variance of only 19.36%. Here, we present a post-hoc analysis of the NORAH
data set and aim to evaluate the adequacy of alternative noise metrics. We calculated regression models
and multilevel analyses with robust estimation algorithms (MLR) by using different noise metrics(LAeq, Lden, LAmax, Emergence, NAT). The analyses were carried out for different groups of people(children, parents, teachers) and different contexts (school: workplace or learning environment,
residential environment). The calculations with different noise metrics showed that the LAeq as asingle noise metric can best predict annoyance. However, for aircraft noise annoyance of children andteachers there was a significant increase in explained variance by including the NAT criterion as asecond noise metric in the models (maximum R² = .684).
Keywords: Aircraft noise, different metrics, Annoyance

1. INTRODUCTION
Environmental noise annoyance is a psychological response to noise and can be viewed as amulti-faceted stress response (1). Besides sleep disturbances, annoyance is one major burden of
disease effect of environmental noise, according the WHO (2)]. Concerning annoyance, different
responses to noise are distinguished including physiological, emotional, cognitive, and behavioral
responses (3).
Guski et al. (4) showed in their WHO-Review that the average association between aircraft noiselevels and annoyance raw scores is .44 (range .21 to .74) which represents an explained variance of
19.36% . This indicates a comparatively low explanation of variance across all the studies included. It
was shown that the association between aircraft noise and annoyance is greater compared to othertraffic noise sources (4]) Previous reviews with focus on children also showed differences in the levelof relationships for different groups of people (e.g., 5). Accordingly, children rate themselves as lessannoyed by traffic noise compared to adults (6). For teachers, on the other hand, there was a muchstronger association between aircraft noise and annoyance (7).
To improve explained variance of aircraft noise annoyance, two perspectives of measurement canbe considered: First, studies could consider adaptions in the measurement of annoyance. Second,studies could use alternative acoustic metrics.
Although transportation noise annoyance is understood as a multi-facetted response, it is currently
predominantly measured with a single item, the so-called ICBEN item – “Thinking about the last (12months or so), when you are here at home, how much does noise from [noise source] bother, disturb, or
annoy you?” - with the verbal marks “not at all,” “slightly,” “moderately,” “very,” “extremely” (8).
In order to achieve a better variance explanation, possibilities of a better annoyance measurement are
discussed (9-11); however, in the field of method development and improvement the focus is mainly

1531

on adults.
Current approaches in the field of annoyance measurement consider the multidimensional constructof annoyance. Thus, Schreckenberg et al. (12) suggest the multi-item noise annoyance scale (MIAS) asa reliable, valid, theory-driven measure to assess annoyance as a response to transportation noise, witha slightly improved variance explanation compared to the ICBEN measurement. Within the frameworkof the NORAH study work package three, annoyance was measured by several items and aggregated toannoying factors. These factors had a high composite reliability (CR between .83 and .93) and averagevariance extracted (AVE between .64 and .73). For this reason, we currently see no possibility of
improving the variance explanation for the NORAH “children” data in this area.
For these reasons, we focus on the perspective of acoustics parameters in order to improve varianceexplanation. Most studies analysed the associations between aircraft noise and annoyance withweighted and unweighted acoustic averaging levels (Lden, LAeq), but less with alternative metrics(but see for a current alternative approach (13)). This neglect may fail to use a potential source formore variance detection, which we will review with our post-hoc analysis of the NORAH data.
Here, we present a further analysis of the NORAH data set, aiming to evaluate the adequacy ofdifferent noise exposure metrics (LAeq, Lden, LAmax, Emergence, NAT55 to NAT80) for analyzing
the association between aircraft noise and annoyance. We focus on differences in the level ofrelationships for different groups of people (children, parents, and teachers) and for different contexts(school: workplace or learning environment, residential environment).

2. METHOD
2.1 Participants
A total of 1,243 second-graders and 84 class teachers (78 female) from 29 primary schools
participated in the NORAH study (14). From a total of 297 schools, 29 schools were selectedbased on extent of aircraft noise exposure. Those schools exposed to the highest amounts ofaircraft noise were selected first. The remaining schools were selected using a -priori direct
matching to avoid potential confounding factors (e.g., socioeconomic status = SES). The schools
were matched by indicators of the pupils´ SES, migration background, and German language
proficiency, according to the headmasters´ reports. For annoyance due aircraft noise at school,
complete data were available for 84 teachers (78 female) and 1,090 children. Mean age forchildren was 8 years and 4 months (SD 5 months).
2.2 Assessment of Noise Exposure
Average aircraft noise levels (school: LAeq, 08-14; home: LAeq, 06-22, Lden, 00-24), maximum
sound levels (school: LAmax, 08-14; home LAmax, 06-22), difference between LAmax and LAeq
(emergence) and number above thresholds (school: NAT, 08-14: home: NAT, 06-22) for bands of fivedB(A) were calculated for the time period of 12 months before data collection was co nducted.
Exposure levels were calculated on the basis of radar data from the Flight Track and Aircraft Noise
Monitoring System (FANOMOS) provided by German Air Traffic Services (for details, see (15)).
Road traffic and railway noise levels were estimated using a combination of information (e.g., traffic
flow data, quantity of train runs) provided by local authorities. Classroom reverberation and insulation
were assessed through screening procedures (15).
2.3 Materials and Procedure
The following description of the procedure has been published elsewhere (e.g. 14, 16). The
questioning of the children was performed in groups of whole classes. Each statement was carefully
explained to the children and practiced with examples. All stateme nts were read aloud by theexperimenter and a combination of picture and number represented the statement. The children were
given a questionnaire to be completed at home by their parents, which contained questions concerning
the annoyance and potential confounding factors (e.g., SES). The teacher questionnaire was given to
the teachers before the children’s testing session and collected immediately after it.
Teachers’ noise annoyance was assessed by a scale (factor) with five statements focused on
annoyance due to aircraft noise at school (composite reliability, CR = .93; average variance extracted,

1532

AVE = .73; Cronbach’s α = .94). The additional statements addressed concrete effects of aircraft noiseon the quality of instruction (e.g., “Due to aircraft noise, I have to interrupt my talk/the discourse fora short time”) and ranged from “not at all annoyed” [1] to “severely annoyed” [5].
Parental noise annoyance was assessed by three statements and a 4-point rating scale ranging from“strongly disagree” to “strongly agree” (CR = .85; AVE = .66; Cronbach’s α = .82).
Children’s annoyance due to aircraft noise at school was assessed through a four-item scale (e.g.,“When learning at school, I am bothered by aircraft noise”). The same applies to the annoyancejudgements for annoyance due to aircraft noise at home. The reliability was good for both scales(school: CR = .88, Cronbach’s α = .81; home: CR = .83, Cronbach’s α = .80). Explained variance wasrelatively high (school: AVE = .64; home: AVE = .71). More details concerning the procedure, thequestionnaires, and statistical examination of psychometrics are provided by Klatte et al. (14, 17).
2.4 Statistical Analyses
To analyze the impact of aircraft noise exposure on annoyance, regression models and multilevel
models (MLA) were realized using Mplus 8 and IBM SPSS 25. Regression analyses were performed
for teacher´s and parents´ annoying judgements and for children’s annoying due aircraft noise at home.
For children’s annoying du aircraft noise at school, we conducted MLA in order to deal with the
hierarchical structure of the data and avoid misspecifications of parameters (e.g., underestimation ofstandard errors) (18). Regression models were adjusted for confounding factors on individual level,
hierarchical models for both levels (level 1: individual, level 2: classes).

3. RESULTS
3.1 Aircraft Noise Exposure
Descriptive statistics for the different noise metrics are shown in Table 1. The aircraft noise
exposure is shown separately for school and home address. A period from 8 a.m. to 2 p.m. is reported
for school (08-14) and a period from 6 a.m.to 10 p.m. for the residential address (06-22).
Table 1 – Descriptive statistics for the different noise metrics (NORAH Study)

LAeq

dB (A)

Lden, 00-24 dB (A)Daytime at school (08-14)

Daytime at home (06-22)

M

SD

Range

M

SD

Range

49.52

6.12

39.10-58.90

49.15

6.16

36.40-60.80

-

-

-

51.74

6.00

40.80-63.60

LAmax

dB (A)

64.12

7.18

50.00-80.00

67.12

7.73

51.00-83.00

EmergencedB (A)

14.92

2.66

9.20-21.30

17.96

2.89

11.20-24.60

NAT (bands) ≥55 <60 dB (A) 22.60 17.15

0.67-66.33

55.28 42.26

0.07-187.00≥60 <65 dB (A) 18.01 17.72

0.03-58.77

45.52 46.52

0.04-165.23≥65 <70 dB (A)

9.95

14.27

0.00-63.61

28.77 42.75

0.01-180.37≥70 <75 dB (A)

3.72

5.55

0.00-22.80

11.60 18.34

0.00-121.21≥75 <80 dB (A)

1.48

3.45

0.00-14.47

3.90

0.00-40.73

8.18

Notes. Emergence school: LAmax (08-14) minus LAeq (08-14) at school; Emergence home LAmax(06-22) minus LAeq (06-22) at home.

3.2 Effects of Aircraft Noise on Children at School and at Home
Multilevel analyses revealed significant associations between aircraft noise exposure at school
and children’s aircraft noise annoyance. Only for NAT ≥ 55 < 60 the association was not significant.
The MLA results for the school context are depicted in Table 2. It can be seen that the size of
associations (unadjusted models) strongly vary from β = .235 to .629 depending on the aircraft noise
metrics. Systematic differences between the annoyance judgements at class level (level 2) were 34%1533

(intraclass correlation, ICC = .34). This means that 34% of the variance in the class average annoyancejudgements is due the class membership. An R² of .396 as in LAeq (unadjusted model) means that
39.6% of 34% can be explained by LAeq - an absolute variance explanation of 13.46%.
The results for the home context are depicted in Table 3. The explained variance was slightly higher
than in the school context, but not for the NAT ≥70 <75 metric. Here, R² can be read directly because
it addresses regression models on individual level. Therefore, the explained variance of the LAeq is forexample R² = .186.
Table 2 - Multilevel Model Parameter Estimates for Effects of Aircraft Noise at School on Children’s
Ratings of Noise Annoyance at School
unadjustedβb (SE)

padjusted
95% CIR²βb (SE)

p

95% CIR²

LAeq.629 .052 (.008) < .001 .037/.069 .396

.626 .052 (.009)< .001 .032/.073 .454

LAmax.603 .039 (.007) < .001 .025/.053 .364

.604 .039 (.009)< .001 .022/.056 .442

Emergence.358 .069 (.023)< .01 .025/.114 .128

.362 .070 (.023)< .01 .026/.114 .265

NAT ≥55 <60 .235 .007 (.003)< .01 .002/.013 .055

.146 .004 (.003)> .05 .000/.010 .160

NAT ≥60 <65 .253 .007 (.003)< .01 .002/.012 .064

.186 .005 (.003)< .05 .001/.010 .173

NAT ≥65 <70 .439 .016 (.003) < .001 .009/.023 .193

.448 .016 (.004)< .001 .009/.023 .310

NAT ≥70 <75 .802 .073 (.007) < .001 .059/.087 .643

.832 .075 (.009)< .001 .058/.092 .724

NAT ≥75 <80 .614 .091 (.008) < .001 .075/.107 .377

.749 .110 (.017)< .001 .078/.143 .541

Notes. SE = Standard Error; b = unstandardized coefficient; β = standardized coefficient; 95% CI = 95%
confidence interval. Adjusted for age, gender, socioeconomic status (SES), class SES, class size, classroom
insulation, road-traffic and railway noise at school. ICC = .34 (ICC = intraclass correlation); R² = R² forLevel 2.

Table 3 - Effects of Aircraft Noise at Home on Children’s Ratings of Noise Annoyance at Home
unadjustedβb (SE)

p

95% CI

adjustedR²βb (SE)

p

95% CI

R²

LAeq.411 .077 (.005) < .001 .068/.087 .168

.429 .081 (.006)< .001 .070/.012 .186

Lden

.406 .078 (.005) < .001 .068/.088 .164.425 .082 (.006)< .001 .071/.093 .182

LAmax

.393 .059 (.004) < .001 .051/.067 .154

.404 .060 (.004)< .001 .051/.069 .166

Emergence.179 .072 (.011) < .001 .050/.094 .031

.172 .066 (.012)< .001 .042/.089 .036

NAT ≥55 <60 .149 .004 (.001) < .001 .003/.006 .022

.171 .005 (.001)< .001 .003/.006 .034

NAT ≥60 <65 .244 .006 (.001) < .001 .005/.007 .059

.259 .006 (.001)< .001 .005/.008 .073

NAT ≥65 <70 .261 .007 (.001) < .001 .006/.009 .068

.273 .007 (.001)< .001 .006/.009 .079

NAT ≥70 <75 .316 .020 (.002) < .001 .017/.023 .099

.310 .019 (.002)< .001 .015/.023 .103

NAT ≥75 <80 .318 .045 (.004) < .001 .037/.052 .100

.310 .042 (.004)< .001 .033/.050 .098

Notes. SE = Standard Error; b = unstandardized coefficient; β = standardized coefficient; 95% CI = 95%
confidence interval. Adjusted for age, gender, SES, road-traffic noise, and railroad noise at home. R² =R²corrected (corrected by the number of predictors and sample size).

Overall, for the group of children, we found no consistent evidence that other exposure measuresthan LAeq could better clarify the annoyance judgments. The results are independent of the context(school vs. home).

1534

3.3 Effects of Aircraft Noise on Parents
Adjusted and unadjusted regression models were conducted to test the relationship between
different aircraft noise metrics and annoyance (see Table 4). All noise metrics showed significant
associations with aircraft noise annoyance (all p’s < .001). The explained variance was significantly
higher than in the models calculated for children (ΔR² = .11 for LAeq) and also higher (ΔR² = .07) thanreported by Guski et al. (4). As for children, it was shown for parents that LAeq is the most adequate
noise metric for predicting aircraft noise annoyance in the private context.
Table 4 - Effects of Aircraft Noise at Home on Parent’s Ratings of Noise Annoyance at Home
unadjustedβb (SE)

padjusted

95% CIR²βb (SE)

p

95% CIR²

LAeq.528 .071 (.003) < .001 .064/.078 .278

.513 .071 (.004) < .001 .063/.078 .292

Lden.517 .072 (.004) < .001 .065/.078 .267

.504 .071 (.004) < .001 .063/.079 .283

LAmax.514 .055 (.003) < .001 .049/.060 .264

.493 .053 (.003) < .001 .047/.081 .273

Emergence.259 .074 (.008) < .001 .058/.090 .066

.232 .064 (.009)

NAT ≥55 <60.201 .004 (.001) < .001 .003/.005 .040

.184 .004 (.001) < .001 .002/.005 .068

NAT ≥60 <65.295 .005 (.001) < .001 .004/.006 .086

.298 .005 (.001) < .001 .004/.006 .123

NAT ≥65 <70.281 .005 (.001) < .001 .004/.006 .078

.298 .006 (.001) < .001 .004/.006 .122

NAT ≥70 <75.392 .018 (.001) < .001 .015/.020 .153

.377 .017 (.001) < .001 .014/.019 .178

NAT ≥75 <80.480 .048 (.003) < .001 .043/.053 .230

.457 .045 (.003) < .001 .039/.050 .233

< .001 .047/.081 .088

Notes. SE = Standard Error; b = unstandardized coefficient; β = standardized coefficient 95% CI = 95% confidence
interval. Adjusted for age, gender, SES, road-traffic, and railroad noise at home. R² = R²corrected (corrected by the
number of predictors and sample size).

3.4 Effects of Aircraft Noise on Teachers
Again, to test the relationship between different aircraft noise metrics and annoyance, we
calculated adjusted and unadjusted regression models (see Table 5). All noise metrics (except for NAT≥55 <60) showed significant associations with aircraft noise annoyance (all p’s < .001). The explained
variance was markedly higher (ΔR² = .41) than reported by Guski et al. (4). Overall, the NAT ≥70 <75
and the LAeq were the most adequate noise metrics for predicting teachers annoyance due aircraft
noise exposure at school (adjusted model).
Table 5 - Teachers’ Aircraft Noise Annoyance (School Workplace)unadjusted
R²βb (SE)

p

95% CI

R²

LAeq.770 .134 (.012) < .001 .109/.158 .589

.821

.144 (.015)< .001.114/.175

.593

LAmax

.732 .098 (.010) < .001 .078/.118 .530

.764

.101 (.012)< .001.077/.125.541

Emergence.412 .164 (.040) < .001 .084/.244 .160

.420.160 (.042)< .001.077/.244.225

NAT ≥55 <60.286 .018 (.006).005/.030 .071

.191

.011 (.007)> .05-.003/.025 .089

NAT ≥60 <65.544 .032 (.006) < .001 .021/.043 .288

.482

.031 (.006)< .001.018/.044.298

NAT ≥65 <70.610 .045 (.006) < .001 .032/.058 .364

.612

.050 (.007)< .001.035/.064.450

NAT ≥70 <75.773 .146 (.013) < .001 .120/.173 .593

.771

.146 (.015)< .001.116/.176.604

NAT ≥75 <80.606 .184 (.027) < .001 .131/.238 .359

.670

.196 (.033)< .001.130/.261.384

βb (SE)

padjusted< .01

95% CI

Notes. SE = Standard Error; b = unstandardized coefficient; β = standardized coefficient; 95% CI = 95%
confidence interval. Adjusted for class size, classroom insulation, road traffic, and railway noise, noise sensitivityof the teacher. R² = R²corrected (corrected by the number of predictors and sample size).

1535

3.5 Combined Models
In the models aimed to combine the different acoustic metrics, we found that only the inclusion of
the NAT metric leads to a significantly higher variance explanation. The models (see Table 6) revealed
that the NAT metric served as a significant predictor of children’s annoyance as a response to aircraft
noise over and above LAeq. However, this only holds for the NAT ≥70 <75 dB(A) criterion as
exemplified by the comparison between combined model 1 and combined model 2. The inclusion of
the NAT metric leads to an explained variance of R² = .727. Compared to the sole calculation with
LAeq, this corresponds to an increase in explained variance at the class level of ΔR² = .273. Including
the ICC of .34, this corresponds to an absolutely explained variance of R² = .247 = 24.7% (only LAeq
R² = .15 = 15%).
Table 6 - Fully-adjusted Multilevel Models with Inclusion of a Second Aircraft Noise Metric: Effect ofAircraft Noise Exposure at School on Children’s Aircraft Noise Annoyance at Schoolβchildren atschoolb (SE)

p

95% CI

Combined Model 1

R²
.456

LAeq

.595

.050 (.017)< .01.017/.083

NAT ≥65 <70.044.002 (.007)>.05-.012/.015

Combined Model 2

.727

LAeq

.010

.001 (.009)> .05-.017/.019

NAT ≥70 <75.826.074 (.012)< .001.051/.097

Notes. SE = Standard Error; b = unstandardized coefficient; β = standardized coefficient; 95% CI = 95%
confidence interval. Adjusted for age, gender, socioeconomic status (SES), class SES, class size, classroom
insulation, road-traffic, and railway noise at school. ICC = .34 (ICC = intraclass correlation); R² = R² for Level 2.

The models depicted in Table 7 revealed that the NAT metric served as a significant predictor ofteacher annoyance due to aircraft noise over and above LAeq. The statistical significance for theprediction of annoyance by NAT ≥70 <75 compared to LAeq is quite similar (βLAeq = .466; βNAT
≥70 <75 = .460). The inclusion of NAT ≥70 <75 leads to an explained variance of R² = .684, whichcorresponds to an increase inexplained variance of ΔR² = 9.1%, compared to the sole calculation with
LAeq.

Table 7 – Combined Fully Adjusted Regression Models with a Second Aircraft Noise Metric: Teachers’
Aircraft-Noise Annoyance (School Workplace)teacherβb (SE)

p

95% CILAeq.649.114 (.021)< .001-.071/.156

NAT ≥65 <70.210.017 (.009)< .05.001/.034

LAeq

.466

.082 (.019)< .001.017/.083

NAT ≥70 <75.460.087 (.019)< .001-.012/.015

R²

Combined Model 1.611

Combined Model 2.684

Notes. SE = Standard Error; b = unstandardized coefficient; β = standardized coefficient 95% CI = 95% confidence
interval. Adjusted for class size, classroom insulation, road traffic, and railway noise, noise sensitivity of theteacher. R² = R²corrected (corrected by the number of predictors and sample size).

1536

4. DISCUSSION AND CONCLUSION
In line with prior studies, the NORAH study (14, 16) revealed a significant association between
aircraft noise exposure and aircraft noise annoyance, but with a markedly higher explained variance(ΔR² = .41) than that reported by Guski et al. (4). In the NORAH and other studies in this field, LAeq
was used as parameter of noise exposure. The current study aimed to evaluate the incremental value ofother daytime noise exposure metrics for predicting effects of aircraft noise on annoyance for different
groups of people and contexts. Our analyses confirmed that alternative noise metrics, for example
LAmax, Emergence, and NAT, are significant single predictors of the harmful effects o f aircraft noise
exposure. For each of these metrics, exposure was significantly associated with noise -induced
annoyance for children and teachers during the lessons and also for children and parents at home.
However, further analyses revealed that only the NAT metric served as a second (unique) significant
predictor over and above daytime LAeq. This supports the idea that, when assessing effects of aircraft
noise on annoyance, both the average of noise intensity (as mirrored by the LAeq) and the number offlight events above a certain noise threshold (NAT) should be taken into account. However, thisincremental effect was only apparent for the school context, both for teachers and children, and washighest when the number of flight events in a band of 70 to 75 dB (A) increased. For studies withresidents (children, parents), however, we could not find incremental value of other daytime exposure
metrics We therefore conclude that for assessments of aircraft noise annoyance of teachers andchildren, the NAT criterion should be included in future studies, to improve the prediction of
annoyance, at least in the school context.

ACKNOWLEDGEMENTS
This study is part of the NORAH research project. NORAH is commissioned by theEnvironment & Community Center / Forum Airport & Region, Kelsterbach, Germany.

REFERENCES
1. Guski R, Felscher-Suhr U, Schuemer R. The concept of noise-annoyance: How international experts
see it. Journal of Sound and Vibration 1999; 223(4):513–27.
2. World Health Organization. Burden of disease from environmental noise. Quantification of healthy life
years lost in Europe. WHO: Bonn, Germany; 2011.
3. Stallen PJM. A theoretical framework for environmental noise annoyance. Noise Health 1999; 1(3):69–
80.
4. Guski R, Schreckenberg D, Schuemer R. WHO environmental noise guidelines for the European
region: A systematic review on environmental noise and annoyance. Int J Environ Res Public Health
2017; 14(12).
5. Van Kamp I, Davies H. Noise and health in vulnerable groups: a review. Noise Health 2013;
15(64):153–9.
6. Babisch W, Schulz C, Seiwert M, Conrad A. Noise annoyance as reported by 8- to 14-year-old children.
Environment and Behavior 2012; 44(1):68–86.
7. Ko NWM. Responses of teachers to aircraft noise. Journal of Sound and Vibration 1979; 62(2):277–92.
8. Fields JM, Jong RG de, Gjestland T, Flindell IH, Job RFS, Kurra S et al. Standardized general-purpose
noise reaction questions for community noise surveys: Research and a recommendation. Journal ofSound and Vibration 2001; 242(4):641–79.
9. Taylor SM. A path model of aircraft noise annoyance. Journal of Sound and Vibration 1984;
96(2):243–60.
10. Nguyen TL, Yano T, Yokoshima S, Morihara T. Comparing causal structures of aircraft noise
annoyance in Ho Chi Minh City, Hanoi and Da Nang using structural equation analysis. Proceedings ofthe 11th ICBEN Congress on Noise as a Public Health Problem, 1–5 June 2014; Nara, Japan, 2014,1537

11. Brink M, Schreckenberg D, Vienneau D, Cajochen C, Wunderli J-M, Probst-Hensch N et al. Effects ofscale, question location, order of response alternatives, and season on self-reported noise annoyanceusing ICBEN scales: A field experiment. Int J Environ Res Public Health 2016; 13(11).
12. Schreckenberg D, Belke C, Spilski J. The Development of a Multiple-Item Annoyance Scale (MIAS)
for Transportation Noise Annoyance. Int J Environ Res Public Health 2018; 15(5).
13. Brink M, Schäffer B, Vienneau D, Foraster M, Pieren R, Eze IC et al. A survey on exposure-response
relationships for road, rail, and aircraft noise annoyance: Differences between continuous andintermittent noise. Environ Int 2019; 125:277–90.
14. Klatte M, Sqilski J, Mayerl J, Möhler U, Lachmann T, Bergström K. Effects of aircraft noise on
reading and Quality of life in primary school children in Germany: Results from the NORAH study.
Environment and Behavior 2017; 49(4):390–424.
15. Möhler U, Liepert M, Mühlbacher M, Beronius A, Nunberger M, Braunstein G, Bartel, R.
Verkehrslärmwirkungen im Flughafenumfeld: Erfassung der Verkehrsgeräuschexposition [Effects oftraffic noise in areas surrounding airports: Report of methods of noise exposure assessment]. Retrieved
from http://www.laermstudie.de/ergebnisse/basismodul-akustik/. 2014). Available from: URL:
www.laermstudie.de/ergebnisse/basismodul-akustik.
16. Bergström K, Spilski J, Mayerl J, Möhler U, Lachmann T. & Klatte M. Effects of aircraft noise on
annoyance and quality of life in German children near Frankfurt/Main airport: Results of the NORAH(noise-related annoyance, cognition, and health)-study. Proc Euronoise 2015, 31 May – June 3
Maastricht, Holland, 2015.
17. Klatte M, Bergström J, Spilski J, Mayerl J, & Meis M. Wirkungen chronischer Fluglärmbelastung auf
kognitive Leistungen und Lebensqualität bei Grundschulkindern Endbericht. NORAH
Verkehrslärmwirkungen im Flughafenumfeld–Endbericht, Bd, 1. et al. [Effects of chronic aircraft noise
exposure on cognitive performance and quality of life in primary school children Final report. NORAH
Traffic noise effects in the airport environment Final report. Volume 1]. 2014.
18. Hox JJ. Quantitative methodology series: Multilevel analysis: Techniques and applications. 2nd ed.
New York, USA: Routledge; 2010.

1538