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Volume : 44

Part : 2

Proceedings of the Institute of Acoustics

 

 

The role of sound emergence for aircraft noise annoyance

 

Dirk Schreckenberg1, ZEUS GmbH – Centre for Applied Psychology, Environmental and Social Research, Hagen, Germany

 

Christin Belke2, ZEUS GmbH – Centre for Applied Psychology, Environmental and Social Research, Hagen, Germany

 

Jördis Wothge3, German Environment Agency, Dessau-Roßlau, Germany

 

Rainer Guski4, Ruhr University Bochum, Bochum, Germany

 

ABSTRACT

 

The metric sound emergence relates source-specific sound levels to background noise. The approach of subtracting a residual sound level (total sound level without the level of a specific source) from the total sound level follows the definition of sound emergence in ISO 1996-1. Using exposure and survey data of the German NORAH study, the impact of sound emergence on aircraft noise annoyance was analysed. As total background sound levels were not available, information about source-specific and combined sound levels was used instead. That is, the road traffic sound level for daytime (LAeq,day,road) of the home address of survey participants was subtracted from the combined sound level of aircraft and road traffic noise (LAeq,day,air+road) with the combined sound level being a proxy for the total sound level, and the road traffic sound level being a proxy for the residual sound level. The probability of people being highly annoyed by aircraft noise, p(HAV), was predicted by LAeq,day,air and the emergence substitute es(air/road). The results show that particularly in low aircraft noise-exposed areas, p(HAV) is higher for those participants exposed to higher values of es(air/road).

 

1. INTRODUCTION

 

High noise annoyance and sleep disturbances are the most prevalent health outcomes of noise [1]. Numerous field studies have provided exposure-response relationships for the percentage of highly annoyed people (%HA) and for the percentage of highly sleep disturbed people (%HSD). Syntheses, re-analyses and meta-analyses of exposure-response curves relating %HA to continuous sound levels such as Ldn, Lden, or LAeq [2-3] as well as %HSD to Lnight [4-5] show a considerable spread in the exposure-response relationships.

 

As a rule of thumb, up to a third of the variance in noise annoyance is predicted by acoustic factors, specifically by continuous sound level metrics, up to one third is explained by non-acoustic factors, and the remaining variance is unexplained. The latter third comprises measurement error, and probably non-acoustic co-determinants of annoyance not assessed in respective surveys, and additional acoustic characteristics that are not well captured by continuous sound level metrics. These are, for example, event-related metrics such as maximum sound level metrics of single noise events, the number of noise events, or psycho-acoustic sound characteristics [6-7]. For example, for aircraft noise, studies show that adding NX metrics (number of events above threshold x dB) to exposure response models improves the prediction of %HA [8-9].

 

Non-acoustic factors are described by [10] as 'non-Ldn' factors. The reason given is that sometimes those factors modifying exposure-response curves related to continuous sound level metrics are called non-acoustic factors that, however, imply acoustic aspects [10].

 

These factors refer to noise from other sources, the total background noise, the availability of a quiet façade, green space (quiet areas), or the window position at home [11]. Sometimes these factors are indirectly implied, for example, when the degree of urbanisation is identified as a co-determining factor of annoyance [12-13], where the modifying effect on the exposure-response relationship can at least partly be explained by the relationship between the source-specific sound level and the background noise.

 

Several authors point out that the absolute sound level only partly explains the noise annoyance and stress the importance of differential noise metrics that relate the source-specific noise exposure to the background noise [14-16]. An example of such a differential noise metric is the intermittency ratio IR [16]. The IR accounts, whilst being uncorrelated with the Leq, for the frequency distribution of sound events and their emergence from the background. Another metric is sound emergence.

 

According to the ISO 1996-1 standard [17] sound emergence is defined as an "increase in the total sound in a given situation that results from the introduction of some specific sound" ([17], §3.4.7). In other words, the "sound emergence is defined for a specific source as the arithmetic difference between the sound level measured when [the] source is on (overall noise) and the sound level measured when the source is off (background noise)" ([18], p. 3045). In the literature, a variety of metrics relating the sound level of the source of interest to the background noise are used in order to calculate the emergence. Some authors describe the emergence as the difference between peak level metrics of the source of interest (e.g. L1, L5, L10) and the overall background level from all sources except the one under study (e.g. L90, L95, L99) [19-23]. Other, in particular national (French) and international ISO standards, suggest calculating the emergence as the difference between the continuous sound levels of the total environmental sound, including the sound of the source under study, and the background noise, i.e. the continuous sound level of all other sources except the source of interest [14, 18, 24-25]. In this study, we follow the latter definition for the operationalisation of the concept of emergence.

 

Studies have shown that sound emergence is associated with noise annoyance, indicating higher annoyance with higher levels of emergence. This is, for example, studied for road traffic noise [20], industrial noise [18], and wind turbine noise [26]. Furthermore, for railway noise [23, 27] and aircraft noise [28], the emergence is also an important predictor of self-reported and physiologically measured sleep disturbance. More recently, the impact of sound emergence on hypertension was studied, indicating a significant impact of emergence on reported hypertension for main road traffic noise [29].

 

In this study, we aim to estimate the impact of emergence in addition to the source-specific sound level of aircraft noise on the probability of being highly annoyed due to aircraft noise. The assumption is that for the same source-specific continuous sound level (LAeq,day) the probability of being highly annoyed is higher with higher sound emergence.

 

2. METHODS

 

2.1. Data sets

 

For the analysis of the impact of emergence on aircraft noise annoyance, data of WP1 (Annoyance & health-related quality of life) of the German NORAH study (NOise-Related Annoyance, cognition, and Health) collected at Frankfurt Airport in 2012 was used [30]. NORAH is a research program on the health impact of transportation noise in airport regions, including cross-sectional and longitudinal surveys carried out from 2011 to 2013. During this period, a change in aircraft noise exposure occurred at Frankfurt Airport owing to the opening of a new runway in October 2011 and, at the same time, the implementation of a night flight ban from 11 pm to 5 am. One of the research questions studied within NORAH was the impact of combined noise from aircraft and road traffic noise, and from aircraft and railway noise, respectively, on annoyance and self-reported sleep disturbance [31]. Part of the data used for the combined noise study described in [31] was also used for this study on the impact of emergence. That is, data of the following five NORAH telephone-based and online sub surveys were pooled together:

  • First, data of the second panel wave of a longitudinal panel survey on the impact of the airport expansion on residents' aircraft noise responses was used. This data was collected in 2012, one year after the new runway was opened and the night flight ban was implemented. All data of the panel survey was collected within a study region around Frankfurt Airport defined by aircraft noise exposure in 2011 being greater or equal to 40 dB of the envelope of LAeq at daytime (6 am – 10 pm) and night-time (10 pm – 6 am). The source-specific LAeq,day for both aircraft and road traffic noise ranges from 41 to 61 dB with mean = 51 dB (SDaircraft = 5.3; SDroad = 4.9).
  • Further, data from a cross-sectional survey collected in 2012 focusing on road traffic noise was used. The study selection criterion was the LAeq,24h for road traffic noise being 2.5 dB higher than the LAeq,24h of any of the other two existing transportation noise sources. Across data sets used for this study, the source-specific LAeq,day for aircraft and road traffic noise ranges from 41 to 61 dB for road traffic noise (Mroad = 54 dB; SDroad = 4.7) and to 59 dB for aircraft noise (Mair = 45 dB; SDair = 3.0).
  • In addition, data from another cross-sectional survey conducted in 2012 was used with the LAeq,24h of aircraft and road traffic noise being almost the same within a range of 2.5 dB, whereas the LAeq,24h of the third transportation noise source (railway noise) is below 40 dB. The range in LAeq,day goes from 44 dB to 59 dB for aircraft noise (Mair = 53 dB; SD = 3.0) and from 45 dB to 60 dB for road traffic noise (Mroad = 55 dB; SDroad = 2.5).

 

For all sub-surveys, a stratified random sampling procedure was applied with 2.5 dB classes of LAeq,24h as stratum with the help of address data from the local residents' registration office.

 

The data of the sub-surveys was pooled for analysing the role of emergence with road traffic noise as a substitute for the residual sound (n = 4905) as road traffic noise is regarded as the most prevalent source of environmental noise.

 

2.2. Assessment of aircraft noise annoyance

 

In the sub-surveys, a questionnaire was used assessing residential conditions, responses to transportation noise, attitudes towards the source and towards authorities, noise sensitivity, health-related quality of life, and social demographic variables (age, gender, socio-economic status).

 

Among these, aircraft noise annoyance was assessed by means of the 5-point verbal annoyance scale according to the technical specification ISO/TS 15666 [32], with verbal categories from 1 = 'not at all' to 5 = 'extremely bothered, disturbed, or annoyed'. Persons using the upper two categories 'very' (4) and 'extremely' (5), were defined as being highly annoyed (HAV).

 

2.3. Assessment of noise exposure

 

Transportation noise levels were calculated address- and house front-specific for each residence. For the calculations, the official computation regulations were used, including the German AzB'08 for aircraft sound exposure and the German preliminary computation method for road traffic sound exposure (VBUS) used for the EU noise mapping. Radar data provided by the German air navigation service provider (Deutsche Flugsicherung GmbH, DFS) of the flight movements within 12 months from October 2011 to September 2012 was used as input data for the aircraft noise modelling. The following continuous sound level metrics were calculated: LAeq for 24 hours, day (6 am to 10 pm) and night (10 pm to 6 am), and Lden. These metrics were calculated separately for each source of transportation noise (aircraft, railway, road traffic) and as the energetic sum for the combination of aircraft and road traffic sound exposure as well as aircraft and railway sound exposure [33].

 

In addition, for this study, the sound emergence was assessed following the definition of emergence in ISO 1996-1 [17] as described in [14], whereas

 

 

with e = sound emergence expressed in dB, Ltot = total sound level, and Lres = residual sound level, i.e. the total sound level without the sound level of the source of interest (here: aircraft sound level).

 

Because no information was available regarding the total sound level of all environmental noise sources as well as the residual sound levels, including all background noise sources, the combined sound level of aircraft and road traffic sound exposure was used as a substitute for the total sound level and the road traffic sound level was used as a substitute for the residual sound. This leads to the following substitute for sound emergence e :

 

 

with es = substitute for the sound emergence, Lair+road = energetic sum of the sound levels for aircraft sound Lair, and road traffic sound Lroad as a substitute for Ltot, and Lroad = road traffic sound level as a substitute for Lres (residual sound).

 

In this study, the continuous sound level at daytime (6 am to 10 pm) LAeq,day was used for L.

 

2.3. Data analysis

 

For this study, logistic regression models were calculated in order to predict the probability p of being highly annoyed (HAV) due to aircraft noise with the source-specific LAeq,day and the emergence substitute es(air/road) as continuous predictors, and with the source-specific LAeq,day for subgroups of almost similar size with es being low (< 1.1 dB), moderate (> 1.1 dB to 3.7 dB) and high (> 3.7 dB) The following regression models were calculated:

  • M1: p(HAV) aircraft noise against LAeq,day and es(air/road);

  • M2: p(HAV) aircraft noise against LAeq,day for the subgroup with low es(air/road) (< 1.1 dB);

  • M3: p(HAV) aircraft noise against LAeq,day for the subgroup with moderate es(air/road) (> 1.1 dB to 3.7 dB);

  • M4: p(HAV) aircraft noise against LAeq,day for the subgroup with high es(air/road) (> 1.1 dB to 3.7 dB).

 

The pooled data includes data of persons that are also exposed to the respective third source of transportation noise, i.e. railway noise. Additionally, regression models were calculated with a sub sample of participants with the exposure to railway noise source being below 40 dB LAeq,24h (n = 1558). Thus, with this sub-sample, the models M5 to M8 were calculated, including variables similar to the models M1 to M4.

 

3. RESULTS

 

3.1. Descriptives of the samples

 

In the air/road sample, 52% of participants of n = 4905 are female and 48% male. Participants’ age ranged from 18 to 93 years, with a mean of 54 years (SD = 15.5). In the sub-sample of those subjects with LAeq,24h < 40 dB for railway noise (n = 1558), 54% were female and 46% male. The age ranged from 18 to 90 years (M = 55; SD = 15). In the total sample as well as in the sub-sample, 86% were interviewed by phone, whereas 14% filled in the online questionnaire.

 

Table 1 shows descriptive statistics for the different samples regarding aircraft noise annoyance, exposure to source-specific noise exposure at daytime (LAeq,day ) and the emergence substitute es(air/road).

 

Table 1: Descriptives for aircraft noise annoyance, sound levels for aircraft and road traffic daytime, and emergence substitute.

 

 

3.2. Exposure-response curves for p(HAV) due to aircraft noise

 

Table 2 shows results of two logistic regression analyses for p(HAV) due to aircraft noise with LAeq,day for aircraft noise, the emergence substitute es(air/road), and the interaction between them as predictors. The results of the regression analyses presented in Table 2 show both the LAeq,day for aircraft noise and the emergence substitute es(air/road) being statistically significant predictors of p(HAV). However, the interaction has a statistical influence on p(HAV) as well, indicating that the influence of the emergence substitute is different at different aircraft sound levels. This interaction is shown in exposure response curves derived from subgroup-specific regression analyses for p(HAV) grouped by different categories of emergence substitute (low, moderate, high) – see Figures 1 and 2.

 

Table 2: Results of logistic regression of p(HAV - aircraft), on LAeq,day - aircraft, emergence substitute es(air/road), and the interaction between these predictors.

 

 

Figure 1 shows that for the same LAeq,day for aircraft noise, p(HAV) is higher with higher emergence substitute es(air/road) in particular for LAeq,day < 55 dB.

 

This is also true when in addition, railway noise is controlled and below 40 dB LAeq,24h (M5 – M8), see Figure 2. However, the confidence intervals of these models are considerably larger, indicating higher uncertainty in the estimations.

 

 

Figure 1: Exposure-response curves (M2 – M4) for the probability of being highly annoyed, p(HAV), by aircraft noise against LAeq,day (6 am -10 pm) for subgroups with low, moderate and high emergence substitute es (n = 4905).

 

 

Figure 2: Exposure-response curves (M5 – M8) for the probability of being highly annoyed, p(HAV), by aircraft noise against LAeq,day (6 am – 10 pm) for subgroups with low, moderate and high emergence substitute es(air/road) and LAeq,24h ,railway < 40 dB (n = 1558).

 

Finally, we tested whether the impact of the sound emergence with road traffic noise as residual background noise would indicate an impact of the road sound level itself on p(HAV) for aircraft noise. For this, we regressed the p(HAV) on LAeq,day for aircraft noise, LAeq,day for road traffic noise and the interaction between these predictors.

 

It turns out that the road traffic sound level and the interaction with the aircraft sound level do not have a statistical influence on p(HAV) due to aircraft noise (Table 3). This indicates that it is rather the relative acoustic situation, i.e. how aircraft sound levels are related to the background noise (here: road traffic sound levels) than the absolute level of the background sound which affects the aircraft noise annoyance.

 

Table 3: Results of logistic regression of the probability of being highly annoyed due to aircraft noise, p(HAV), on LAeq,day - aircraft, LAeq,day – road traffic, and the interaction between these predictors.

 

 

4. DISCUSSION

 

The regression analyses show a main effect of sound emergence as a predictor for p(HAV) and an interaction with the source-specific LAeq,day .

 

This means that the probability of being highly annoyed, p(HAV), is higher with higher emergence substitute es(air/road). This is in particular true for survey participants exposed to aircraft noise with LAeq,day < 55 dB.

 

The absolute road traffic noise level itself did not significantly affect aircraft noise annoyance, which is in line with previous findings, e.g. [34]. This indicates that it is the relative, differential contribution of the source of interest (here: aircraft noise) against the background noise (here: road traffic noise), which plays a role in the source-specific noise annoyance.

 

The results of this study are in line with results from previous studies that report increasing noise annoyance with rising sound emergence. For example, results from the Alpine study on community response to multiple transportation sound sources (railway, main road, highway) [20] indicate robust exposure-response relationships between sound emergence and the probability of high annoyance for main road traffic, railway, and highway noise. In the Alpine study, sound emergence was defined as the difference between the source event – indicated by L10 for highway, L5 for main roads and L10 for railway and the overall background noise described by the metrics L90 for highway, L99 for main roads and L90 for railway ([20], p. 4).

 

In a laboratory experiment on annoyance due to industrial noise (cooling tower, wind turbine, transformer, power plant turbine), the authors used the French legal definition of sound emergence (similar to this study) to test the impact of the sound emergence [18]. They found a main effect of sound emergence indicating higher noise annoyance when the sound emergence E amounts to 5 dB(A) compared to sound emergence E = 3 dB(A).

 

In line with this, a positive correlation between noise annoyance due to wind turbine noise and sound emergence (defined similar to [18]) was found in another listening test study [26], although the authors report that the source-specific sound pressure level was a better predictor of noise annoyance than the sound emergence in terms of higher explained variance.

 

All three studies did not observe an interaction between the source-specific sound level and the sound emergence in the prediction of annoyance.

 

The impact of sound emergence for aircraft noise annoyance is supported by results of a community response study around the Korean airports Gimpo and Gimhae that show the %HA for aircraft noise to be higher in areas with low background noise levels (measured by LAeq ,1h, res with aircraft sound level excluded) compared to areas with high background noise levels [35].

 

In a broader sense, as emergence relates the level of a source-specific sound to the background sound from other sources, it refers to the noticeability of the sound. The simple assumption is that being annoyed by sound from a specific source requires the source-specific sound to be noticed, i.e. detected with the total environmental noise [36]. A few decades ago, it was already found that the detectability of a sound assessed within a signal-to-noise approach is in particular important for the prediction of the annoyance of low-level sounds [37]. This is in line with the result of this study that the effect of sound emergence on noise annoyance is stronger at lower levels of aircraft noise exposure. In fact, it seems that the effect changes above LAeq,day = 55 dB, indicating higher aircraft noise annoyance with lower sound emergence. The latter result speaks for a cumulative effect of aircraft and road traffic noise exposure on aircraft noise annoyance in overall higher noise exposed areas.

 

The stronger effect of sound emergence at lower source-specific sound levels is supported by results reported by [38]. The authors found a higher percentage of people highly annoyed by aircraft noise (%HA) when the road traffic noise was lower, in particular, for respondents exposed to aircraft noise levels below approx. 55 to 60 dB LAeq,day . In line with this, below approx. 60 dB LAeq,day for aircraft noise, the authors found %HA for aircraft noise in participants exposed to the difference in LAeq for aircraft minus LAeq for road traffic > 6 dB being higher than for participants exposed to a

difference < 0 dB (indicating the same or higher road traffic sound levels than aircraft sound levels), whereas this result is inverted above 60 dB LAeq,day . However, in the same study, the authors found the highest %HA at a difference in LAeq (aircraft – road) between 0 and 3 dB. The authors found the opposite results for road traffic noise as the source of interest, indicating the impact of sound emergence at low source-specific sound levels to be in particular true for exposure to intermittent sounds such as aircraft sound exposure as compared to exposure to continuous sounds like road traffic sound exposure.

 

It cannot be ruled out that in this study, it is not only the noticeability of the aircraft sound against the road traffic sound as background noise which accounts for the noise annoyance, but also the expectations of the participants living in areas with low road traffic noise. In tendency, the impact of the emergence substitute es(air/road) was confirmed in the sub-sample of participants additionally exposed to railway sound of less than 40 dB LAeq,day,railway, which might indicate that these people live in residential areas of overall low background noise. It seems plausible that these people are aware of living in a rather quiet area and may have formed the expectation not to be exposed to environmental noise. This may have amplified the aircraft noise annoyance in these areas. Unfortunately, no such expectations were assessed in this study. It is up to future research to shed light on the role of non-acoustic, attitudinal factors that cover aspects of the background noise.

 

A limitation of this study is that for the assessment of sound emergence, no information about the total environmental sound level and the residual sound level, including all background noise sources, was available. Therefore, in this study, the difference between the combined sound levels of aircraft and road traffic noise LAeq,day,air+road and sound levels of the road traffic noise (LAeq,day,road) was used as a substitute for the sound emergence.

 

Further, due to the fact that the data from the NORAH study used for the re-analyses in this study was collected mainly for the purpose of a study on the impact of combined noise in selected areas at Frankfurt Airport with differently specified dominance in source-specific sound levels [31], the ranges of aircraft and road traffic sound levels as well as the range of the emergence substitute es(air/road) are restricted. The data includes no situation where es(air/road) is negative, i.e. where the road traffic sound level as background noise is higher than the aircraft sound level. This limits the generalizability of the findings. However, the findings of this study on the impact of sound emergence are promising regarding the improvement of the prediction of noise annoyance even when sound emergence is operationalised by means of a proxy. Future research is encouraged to study the relationship between the sound of the source under study and the background noise for noise annoyance in more detail, comprising non-acoustic factors that refer to residents' perception and expectations related to the living acoustical environment as well.

 

5. CONCLUSIONS

 

With samples of the German NORAH study collected at Frankfurt Airport in 2012, after the expansion of the airport, the impact of sound emergence on the probability of being highly annoyed due to aircraft noise, p(HAV), was studied with road traffic noise as a substitute for the background noise. It could be shown that besides the aircraft sound level LAeq,day the emergence substitute and the interaction between these predictors affect the aircraft noise annoyance. The results indicate that, in particular, in aircraft noise-exposed areas below LAeq,day,aircraft = 55 dB, the probability of being highly annoyed by aircraft noise is higher with higher sound emergence as operationalised with road traffic noise as a substitute for the total background noise (residual sound levels without the aircraft sound level).

 

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1 schreckenberg@zeusgmbh.de

2 belke@zeusgmbh.de

3 joerdis.wothge@uba.de

4 rainer.guski@rub.de