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

Effects of aircraft noise and living environment on children´s wellbeing and health
Jan SPILSKI1 Martin RUMBERG1; Martin BERCHTHOLD1, Kirstin BERGSTRÖM1, Ulrich
MÖHLER2, Thomas LACHMANN1 3, Maria KLATTE1
1

2

University of Kaiserslautern, Germany
Moehler + Partner Engineers Munich, Germany
3
Universidad Nebrija Madrid, Spain

ABSTRACT
There are few and inconsistent results suggesting that chronic exposure to aircraft noise is associatedwith poorer quality of life or health in children. Therefore, a recent WHO review pointed out that thecurrent evidence is insufficient. Previous publications mainly analyzed the isolated relationships between
single stressors (e.g., aircraft noise, pollution) and health in children. A combined consideration ofenvironmental stressors and residential environment factors, such as degree of urbanization, access togreen/open space urbanity, and other urban planning data did not occur. In the NORAH study, healthvariables, noise exposure measures, and a variety of potential moderating variables (residential
environments factors) were assessed from second-graders who live in the vicinity of the Frankfurt/Main
airport, Germany. We present a theoretically motivated secondary analysis (stress-diathesis hypothesis) of
the NORAH data set. Post hoc, we linked this data with spatial and urban planning data, to model theimpact of aircraft noise and children´s real life circumstances. We calculated moderated and mediatedregression models and found empirical evidence for mediation effects in the relationship between aircraft
noise exposure, stress response (annoyance) and well-being, moderation effects for health as well as direct
effects of the living environment.
Keywords: Aircraft Noise, Living Environment, Well-Being, Health1.

INTRODUCTION

Both acute noise events and chronic noise exposures can lead to stress reactions (1-3). Chronic noise
exposure leads to a prolonged biological stress response compared to acute noise. This means that therelease of stress hormones (e.g., catecholamine, adrenaline and cortisol) is more prolonged compared to asingle acute noise event. In addition to a biological response, environmental noise can also lead to a
psychological stress response in the form of annoyance. Besides sleep disturbances, annoyance is one
major burden of disease effects of environmental noise, according to WHO (4). If the burdens of stressors
and adverse predispositions to diseases are too high and exceed a critical threshold, symptoms develop or
diseases breaks out. The stress-diathesis hypothesis attempts to explain a disorder or its development as theresult of an interaction between a disposition and a stress-induced burden by life experiences (5).
Therefore, in accordance with Clark & Paunovic (6), we follow the stress-diathesis hypothesis whichsuggests that with increasing exposure to environmental stressors (e.g., aircraft noise) stress responses
increase, and increased stress responses negatively affect well-being and health, because they reinforce
adverse disposition. This indicates that exposure to aircraft noise has an indirect negative effect onchildren's well-being and health, mediated through the mediator aircraft noise annoyance. Based on the
NORAH study, we hypothesize:
H1: Increasing aircraft noise exposure increases the aircraft noise annoyance of children which, in turn,leads to poorer well-being and health in children.

7080

However, aircraft noise exposure is only one environmental stressor which can have an impact onchildren’s well-being and health. Thus, both the psychological stress responses (e.g., annoyance) and therelevant diseases can be triggered or promoted not only by noise exposure, but also by other conditions andstress factors. Urban areas differ from suburban and rural areas with respect to the amount of opportunities
and risks for children´s health (7). Other environmental stressors such as toxins, pollutants, other noise
sources and crowding increase with increasing degrees of urbanization (8, 9). In line with this, the degreeof urbanization (measured in terms of outside density for the postcode area) proved a significant predictor
for stress responses and children’s development and health (10-12). However, in less urbanized areas,
aircraft noise has a relatively stronger impact on children’s well-being and health because otherenvironmental stressors are less pronounced and therefore aircraft noise is more prominent. In view of theaccumulation of environmental stressors in urban areas, we hypothesize:
H2: The indirect effect described in H1 is moderated by the degree of urbanization. In less urbanizedareas, aircraft noise has a relatively stronger impact compared to more urbanized areas.
After we have presented a relatively broad construct with the degree of urbanization and possible effects
on children's well-being and health, the proportion of imperviousness space is a more specific predictor.
Imperviousness space very precisely considers a quality aspect of residential environments that is importantfor children. Inhabited areas differ not only in population density but can also vary greatly in terms ofimperviousness space and percentage of accessibility of green spaces (for recreation) and other factors(e.g., bioclimate, air hygiene, building condition, sunlight exposure, ventilation), although urbanization, forexample, has not changed. In this paper we focus on the percentage of imperviousness space and use it as acountervalue to possible undeveloped, ergo natural areas. The literature discusses life environments that
protect against stress or at least stimulate regeneration and moderates stress. Current systematic reviews
report that access to green spaces (conversely, the non-existence of sealed area) has positive effects onchildren's well-being and health (13, 14). To show that the proportion of imperviousness space is notsynonymous with the degree of urbanization (measured in terms of outside density for the postcode area)
and the specific population density in the corresponding 100 x 100 m area (1 ha), we calculated thecorrelations. We found no significant correlations between the population density in Eurostat 100 x 100 m
areas (INSPIRE-Raster, 1 ha, European Commission - EUROSTAT, Luxembourg 2019) and thecorresponding percent of imperviousness (r = - .008, p = .764; 95% C.I.: -.064/ .048). However, asignificant correlation was found for the degree of urbanization (the population density in the postcodearea) (r = -.220, p < .001; 95 % C.I.: -.0272/-.166), and also for the population density in Eurostat 100 x
100 m areas (r = .315, p < .001; 95 % C.I.: .0.264/0.364). That shows that the factors share variance, buthave a substantial part of unique variance that cannot be mutually explained. Therefore, modelling thespecific predictor imperviousness space as a moderator seems to be useful. The existence of natural areas
should reduce stress in children (14). However, we argue that in areas with less imperviousness (manynatural areas) the exposure to aircraft noise appears as a dominant stress factor whereas in areas with highimperviousness the variety of other environmental stressors (e.g., toxins, pollutants) decreases the impact ofaircraft noise as a single stress factor. For this reason, we hypothesize:
H3: The indirect effect described in H1 is moderated by the degree of imperviousness. In lessimpervious areas, aircraft noise has a relatively stronger impact compared to more impervious areas.

2.

METHOD

2.1

Participants

A total of 1,243 second-graders from 29 primary schools participated in the NORAH study. For
information on sampling, see Klatte et al. (15). Mean age for children was 8 years and 4 months (SD 5months). Concerning children's health and well-being as well as living environment at home, complete data
were available for 1,118 children and their parents.
2.2

Assessment of Noise Exposure

Aircraft noise levels (LAeq 06-22) at children's home address for the past 12 months before dataacquisition were calculated based on radar data from the Flight Track and Aircraft Noise Monitoring

7081

System (FANOMOS), provided by German Air Traffic Services. For more details see (16). Road traffic
and railway noise were calculated using a combination of information (e.g., traffic flow data, street types,
proportion of heavy traffic; quantity of train runs, speed and length of the trains) provided by local
authorities.
2.3

Assessment of Living Environment

The moderator urbanization (H2) was operationalized with the key factor density (measured in terms ofoutside density for the postcode area; people per km²). Data of density was available from the German
Zensus 2011 (9 May 2011 (17)) for the postcode areas of all home addresses.
For the investigation of more specific moderating spatial variables, the area in which the children´splaces of residence were located was divided into regular raster cells (standard-hectare grid of European
statistics, INSPIRE grid) with an edge length of 100 m. The raster cells were used for the analysis of thespatial variable imperviousness (H3) in the closest living environment. The imperviousness space data for
the INSPIRE grid were obtained from the European Environment Agency's Copernicus Land Monitoring
Service. Figure 1 shows the geographical distribution of residential addresses and the NORAH study area.

Figure 1 – Distribution of respondent addresses (blue) in the study area (schematic) taken from Möhler et
al. p. 144 (16); map background "© OpenStreetMap-participants
2.4

Materials and Procedure

The following description of the procedure has been published elsewhere (e.g. 15, 18). The questioningof the children was performed in groups of whole classes. Each statement was carefully explained to thechildren and practiced with examples. All statements were read aloud by the experimenter and a
combination of a picture and a number represented the statement. The children took the parent
questionnaire to their parents who filled it out at home. The parental questionnaire contained questions
concerning children’s well-being, health-related outcomes (physical diseases and developmental
abnormalities, see Table 1) and potential confounding factors (e.g., SES).
The scales used in the children questionnaire comprised health-related quality of life [KINDL-R(19)], home environment, annoyance, and noise at home (20). Table 1 shows the items concerning
health-related quality of life and annoyance due to aircraft noise exposure. Principal axis analyses
and confirmatory factor analyses were carried out on the items from the children questionnaire. For
the well-being variables from the children questionnaire, aggregation of items was not justified by
the data, although the scales of the KINDL-R were used. Therefore, well-being analyses were basedon single items. However, the parental assessment of the child's well-being could be aggregated toindex values (physical and psychological well-being; see Table 1). More details concerning theprocedure, the questionnaires, and statistical examination of psychometrics are provided by Klatte etal. (14, 21).

7082

Table 1 – Selected NORAH outcome variables concerning children’s annoyance due to aircraft noise
exposure at home, health-related quality of life, physical diseases and developmental abnormalities.
Outcome variable
Index “Annoyance due
aircraft noise at home”(6 Items)childjudgments

Physical well-being(single variables)
Psychological (mental)
well-being (single
variables)

parental
judgments

Psychometrics/ Questions
CR = .83, α = .80; AVE = .71

Response scale
4-point scale (strongly
disagree, disagree, agree,strongly agree)

PWB 1: Last week I had a headacheand stomach ache.
PWB 2: Last week I felt sluggish andtired.

3-point scale (never,sometimes, often)

MWB 1: Last week I laughed a lot andhad a lot of fun.
MWB 2: Last week I was bored.

3-point scale (never,sometimes, often)e.g., middle ear inflammation, asthma,
neurodermatitis, migraine, attention
disorders, speech and language
Physical diseases anddevelopmental disorders disorders
intake of medically prescribed drugs,
ADS

3-point scale (no, never; yes,once; yes, several times)
2-point scale (no, yes)

Notes. CR = composite reliabilities, α = Cronbach’s α, AVE = average variance extracted

2.5

Statistical Analysis

To model the impact of aircraft noise and children´s real life circumstances, we linked the NORAH datawith spatial and urban planning data. For the main analyses (test of our hypotheses), we used IBM SPSS 25
and the PROCESS 3.3 macro by Hayes (22). The procedure of analysis was as follows. First, we tested the
mediation model for the outcome variables listed in Table 1 (H1: PROCESS Model 4). Second, we
extended the mediation model by the moderators to a moderated mediation model (H2: PROCESS Model8, H3: PROCESS Models 8 & 10) and tested the model for the outcome variables listed in Table 1. All
models were adjusted for confounding factors.

3.

RESULTS

3.1

Aircraft Noise and Living (Residential) Environment

Aircraft Noise. In the NORAH study, aircraft noise levels at home (LAeq 06-22) did not exceed 60 dB
for the majority of children (99.19%) and were thus below the daytime criterion of the German legal
foundation (23). Aircraft noise at home (LAeq, 06-22) ranged from 36.40 to 60.80 dB (M = 49.15; SD =6.16) for a period from 6 a.m. to 10 p.m. for the residential address (06-22).
Urbanization. Given that all children´s home addresses were located in the metropolitan area RhineMain, our sample did not contain children who live in rural areas (see Table 2, last column). Nevertheless,we found a broad variance of outside density for postcode areas (M = 1,552.08, SD = 831.90, Range =446.00-2,890.00 people per km²). Thus, for a better statistical validity (bi-modal distribution of the density
data), we generated a new grouping variable “urbanization” with two levels (medium urban areas and highurban areas). Residential postcode areas with up to 1,495 people per square kilometer were assigned tomedium urban areas and residential postcode areas with more than 2,695 people per square kilometer wereassigned to high urban areas. This classification corresponds exactly to Eurostat's proposal (DEGUBRA,
2011).
Imperviousness: Percentages of sealed areas around the home addresses (INSPIRE grid) ranged from 0
to 100 percent (M = 58.15; SD = 11.59). Figure 2 visualizes the results of the sealed areas as a heat map.
Green, yellow and red marked areas correspond to completely unsealed areas (0 %), 50 % sealed areas and7083

completely sealed areas (100 %), respectively. Black dots represent the distribution of respondent
addresses.

Figure 2 - Heatmap imperviousness, own diagram based on German Federal Office of Statistics(Statistisches Bundesamt), hectares grid, and European Environment Agency (24)
3.2

Relationship Noise, Environment Well-being and HealthResults for H1. We have hypothesized that exposure to aircraft noise has an indirect negative effect onchildren's well-being and health, mediated through aircraft noise annoyance. Hence, we included the
mediator annoyance into a regression model: aircraft noise  annoyance  well-being/ health (see Figure
3).

Figure 3 - Hypothesis 1 (Mediation-Model)First, we tested the mediation model with regard to children’s well-being (H1: PROCESS Model 4).
Significant indirect effects were found for physical well-being (PWB 1: a*b = 0.003, 95% C.I.:
0.001/0.006; PWB 2: a*b = 0.006, 95% C.I.: 0.003/0.010) (see table 3). Accordingly, a 10 dB increase inaircraft noise is associated with an increase in annoyance of 0.81 scale points. This effect is passed on tophysical well-being, i.e., higher annoyance leads to higher problems with physical well-being (e.g., moreheadache and stomach ache). The inclusion of the mediator led to a non-significant direct effect (b = 0.003, SE = 0.004, p > .05, 95% C.I.: - 0.011, 0.006), indicating a complete mediation effect. While themeditation effect was statistically robust with respect to physical well-being, the results for psychologicalwell-being were not consistent. Here, we found an indirect effect for the variable “Last week I was bored”(MWB2), but a non-significant indirect effect for the variable “Last week I laughed a lot and had a lot offun” (MWB1) (see Table 3).
After we had examined the mediation effects with regard to well-being, the model was also calculatedwith regard to health variables. We tested H1 for 15 health-related outcomes and developmental
disorder variables (measured by a parental questionnaire), as already listed in Table 3. Contrary tochildren´s well-being, we did not find any effect of aircraft noise exposure on children’s health (neither adirect effect nor an indirect effect, all p’s > .05, all 95% C.I. included 0). Therefore, there was no evidence
that H1 could be maintained for children with regard to the incidence of disease (parental evaluation).

7084

Table 3 – Fully Adjusted Mediation Model for Effects of Aircraft Noise Exposure on Children’s Wellbeing (Hypothesis 1).
βb (SE)

p

95% C.I.

.434
.077
.017
.033

.081 (.005).040 (.017).002 (.003).003 (.001)< .001< .05> .05.071/.091.006/.074-.005/.008.001/.006

a: Aircraft Noise  Annoyanceb: Annoyance  well-being (PWB 2)
c’: Aircraft Noise  well-being (PWB 2)
a*b (indirect effect)
Psychological Well-beinga: Aircraft Noise  Annoyanceb: Annoyance  well-being (MWB 1)
c’: Aircraft Noise  well-being (MWB 1)
a*b (indirect effect)

.434
.118
-.021
.051

.081 (.005).076 (.021)-.003 (.004).006 (.002)< .001< .001> .05.071/.091.035/.118-.011/.006.003/.010

.437
.078
-.097
.056

.081 (.005).042 (.018)-.010 (.003).007 (.002)< .001< .05< .01.070/.091.007/.076-.016/-.003-.003/.010

a: Aircraft Noise  Annoyanceb: Annoyance  well-being (MWB 2)
c’: Aircraft Noise  well-being (MWB 2)
a*b (indirect effect)

.430
.129
.075
.055

.081(.005).081 (.021).009 (.004).007 (.002)< .001< .01< .05.070/.091.041/.121.001/.017.003/.010

Physical Well-beinga: Aircraft Noise  Annoyanceb: Annoyance  well-being (PWB 1)
c’: Aircraft Noise  well-being (PWB 1)
a*b (indirect effect)

Notes. SE = standard error; b = unstandardized coefficient; β = standardized coefficient; 95% C.I.: =95% bootstrapping confidence interval. Adjusted for age, gender, socioeconomic status (SES), road traffic and railway noise at home.

Results for H2. As mentioned before, we extended the mediation model to a moderated mediation
model by including the moderator “degree of urbanization” (H2: PROCESS Model 8). All models were
adjusted for potential confounding factors as already indicated in the notes of Table 3. For a better
understanding, the extension of the model is shown in Figure 4.

Figure 4 - Hypothesis 2 – (moderated mediation model)
The analysis showed no significant moderator effects on the indirect relationship (i.e., interaction terms
were non-significant, all p > .05, all 95% C.I. included 0). However, the indirect effects (mediation effects)
already shown remained statistically significant for children’s physical and also psychological well-being.
The indirect effect became significant for the psychological well-being variable “Last week I laughed a lot
and had a lot of fun” (MWB 1 medium urban areas: a*b = 0.003, 95% C.I.: 0.001/0.006; high urban areas:
a*b = 0.003, 95% C.I.: 0.001/0.007). Apparently, the inclusion of the moderator reduced error variance, sothat this mediation effect could be statistically supported.
Although we had assumed moderated mediations, we could not statistically confirm them for the wellbeing measures. However, for the health-related outcomes we found a significant moderation by the degreeof urbanization. For example, there was a significant interaction effect on the variable “intake of medicallyprescribed drugs” (W*X: b = - .122, SE = .051, p = .018; 95% C.I.: - .223/-.021). The probability of taking
medically prescribed drugs increased with increasing aircraft noise in areas with a medium degree ofurbanization, but not in high urban areas.
Results for H3. As mentioned before, we extended the moderated mediation model by including thedegree of imperviousness as a second moderator (H3: PROCESS Model 10) as shown in Figure 5. Contrary
to the hypothesis, there were no moderating effects by the degree of imperviousness on the mediation
relationship. However, the analyses showed that the degree of imperviousness is a significant predictor of7085

annoyance (b = .003, SE = .001, p < .05; 95% C.I.: -.223/-.021). In addition to the degree of urbanization,
degree of imperviousness is another significant moderator on the direct relationship of aircraft noise
exposure on children’s health (Z*X: b = -.003, SE = .001, p < .01; 95% C.I.: -.004/-.001). The probabilityof taking medically prescribed drugs increased with increasing aircraft noise in areas with a low degree ofimperviousness (-1SD), but not in medium (0SD) or high degree of imperviousness (+1SD).

Figure 5 - Hypothesis 3 (Moderated-Mediation-Model)

4.

DISCUSSION AND CONCLUSION

In accordance with the stress-diathesis hypothesis (5), the current study found harmful effects of aircraft
noise exposure on children’s well-being and health as well as on annoyance due to aircraft noise exposure.
We found evidence that annoyance mediated the relationship between aircraft noise and children’s wellbeing, so that we could maintain H1 with respect to well-being. However, we found no mediation
relationships with respect to the health-related outcomes recorded by parental judgements.
Contrary to the hypotheses H2 and H3, there were no moderating effects by degree of urbanization anddegree of imperviousness on the mediation relationship between aircraft noise exposure, annoyance due toaircraft noise exposure and children’s well-being. This result was also evident for the health-related
outcomes. As a result, we have to reject H2 and H3.
Interestingly, however, the assumed stronger effect of aircraft noise on health was found for medium
urbanised areas, but not for high urban areas. However, this effect was only observed for the direct effect ofaircraft noise on health and not, as assumed, for the indirect effect. This suggests that aircraft noise has astronger effect for health related outcomes on a lower degree of urbanization, as other stressors are lesspronounced and therefore aircraft noise is more prominent. The same result was also shown for the degreeof imperviousness, according to which the influence of aircraft noise is significant at a lower degree ofimperviousness, but not in higher degrees. The degree of imperviousness seems to be a relevant context
factor for children, which at least has an influence on the perceived annoyance of a child. This may be anindication that further space variables should be included in models for predicting aircraft noise effects inorder to adequately map the living environment. The findings are of relevance for policy of environmental
noise and children’s wellbeing and health.

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

REFERENCES
1. Stansfeld S, Clark C. Health Effects of Noise Exposure in Children. Curr Environ Health Rep 2015;
2(2):171–8.2.
2. World Health Organization (WHO). Guidelines for Community Noise. Geneva, Switzerland: WHO;2000.
3. Zare Sakhvidi F, Zare Sakhvidi MJ, Mehrparvar AH, Dzhambov AM. Environmental Noise Exposure
and Neurodevelopmental and Mental Health Problems in Children: A Systematic Review. Curr
Environ Health Rep 2018; 5(3):365–74.
4. World Health Organization (WHO). Burden of Disease from Environmental Noise. Quantification of
Healthy Life Years Lost in Europe. Bonn, Germany: WHO; 2011.

7086

5. Ingram RE, Luxton DD. Vulnerability-Stress Models. In: Development of Psychopathology: A
Vulnerability-Stress Perspective. Thousand Oaks, USA : SAGE; 2005. p. 32–46.
6. Clark C, Paunovic K. WHO Environmental Noise Guidelines for the European Region: A Systematic
Review on Environmental Noise and Quality of Life, Wellbeing and Mental Health. Int J Environ Res
Public Health 2018; 15(11).
7. Cyril S, Oldroyd JC, Renzaho A. Urbanisation, urbanicity, and health: a systematic review of thereliability and validity of urbanicity scales. BMC Public Health 2013; 13(1):513. Available from: URL:
https://bmcpublichealth.biomedcentral.com/track/pdf/10.1186/1471-2458-13-513.
8. Votruba-Drzal E, Miller P, Coley RL. Poverty, Urbanicity, and Children's Development of Early
Academic Skills. Child Dev Perspect 2016; 10(1):3–9.
9. Ferguson KT, Cassells RC, MacAllister JW, Evans GW. The physical environment and childdevelopment: An international review. Int J Psychol 2013; 48(4):437–68.
10. Spilski J, Bergström K, Mayerl J, Möhler U, Lachmann T, & Klatte M. The relationship between
aircraft noise and reading: Mediator and moderator effects. Zürich, Switzerland: ICBEN; June/ 2017.
Proceedings of 12th International Conference on Noise as a Public Health Problem.
11. Gouin M, Flamant C, Gascoin G, Rouger V, Florin A, Guimard P et al. The association of urbanicity
with cognitive development at five years of age in preterm children. PLoS ONE 2015; 10(7):e0131749.
12. Bröms K, Norbäck D, Eriksson M, Sundelin C, Svärdsudd K. Effect of degree of urbanisation on age
and sex-specific asthma prevalence in Swedish preschool children. BMC Public Health 2009; 9:303.
13. McCormick R. Does access to green space impact the mental well-being of children: A systematic
review. Journal of Pediatric Nursing 2017; 37:3–7. Available from: URL:
http://www.sciencedirect.com/science/article/pii/S0882596317301859.
14. Vanaken G-J, Danckaerts M. Impact of green space exposure on children’s and adolescents’ mental
health: A systematic review. Int J Environ Res Public Health 2018; 15(12):2668. Available from: URL:
https://www.mdpi.com/1660-4601/15/12/2668/pdf.
15. Klatte M, Spilski 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.
16. Möhler, U., Liepert, M., Mühlbacher, M., Beronius, A., Nunberger, M., Braunstein, G., ... Bartel, R.
(2014). Verkehrslärmwirkungen im Flughafenumfeld: Erfassung der Verkehrsgeräuschexposition[Effects of traffic noise in areas surrounding airports: Report of methods of noise exposure
assessment]. Retrieved from http://www.laermstudie.de/ergebnisse/basismodul-akustik/
17. Zensus Statistisches Bundesamt. (2014). Zensus 2011: Bevölkerung. Ergebnisse des Zensus 9. Mai
2011. [Zensus 2011: Population, results of Zensus]. Wiesbaden. Retrieved fromhttps://www.zensus2011.de/SharedDocs/Downloads/DE/Pressemitteilung/DemografischeGrundda
ten/xlsx_Bevoelkerung.xlsx?__blob=publicationFile&v=4
18. Bergström K, Spilski J, Mayerl J, Möhler U, Lachmann, T. & Klatte, M., editors. Effects of aircraft
noise on annoyance and quality of life in German children near Frankfurt/Main airport: Results of theNORAH (noise-related annoyance, cognition, and health)-study; Proceedings of Euronoise 2015,31
May – 3 June 2015; Maastricht, Holland 2015.
19. Ravens-Sieberer U, Klasen F, Bichmann H, Otto C, Quitmann J, Bullinger M. Erfassung der
gesundheitsbezogenen Lebensqualität von Kindern und Jugendlichen. Gesundheitswesen 2013;75(10):667–78.
20. Ising, H, Pleines, F, Meis, M. Beeinflussung der Lebensqualität von Kindern durch militärischen
Fluglärm. Umweltbundesamt, Institut für Wasser-, Boden- und Lufthygiene, BaWoLu-Hefte 5. 1999.
21. Klatte M, Bergström K, Spilski J, Mayerl J, Meis M. Wirkungen chronischer Fluglärmbelastung auf
kognitive Leistungen und Lebensqualität bei Grundschulkindern Endbericht. NORAH
Verkehrslärmwirkungen im Flughafenumfeld–Endbericht. 2014.
22. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based
approach. 2 ed. New York, N.Y.: The Guilford Press; 2018.
23. Gesetz zum Schutz gegen Fluglärm in der Fassung der Bekanntmachung vom 31. Oktober 2007
(BGBl. I S. 2550)" [German Law for Protection against Aircraft Noise], Bundesministeriums der Justiz
und für Verbraucherschutz¸30.03.1971.
24. European Environment Agency (EEA) / Copernicus Land Monitoring Service: High Resolution Layer
Imperviousness Degree (IMD) 2012, Copenhagen 20187087