Relationship between exposure and listening disturbance response due to transportation noise Shigenori Yokoshima 1 Kanagawa Environment Research Center 1-3-39, Shinomiya, Hiratsuka, Kanagawa, 254-0014 JAPAN Makoto Morinaga 2 Kanagawa University 3-27-1, Rokkakubashi, Kanagawa, Yokohama, Kanagawa, 221-8686 JAPAN Sohei Tsujimura 3 Ibaraki University 4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511 JAPAN Koji Shimoyama 4 Aviation Environment Research Center, Organization of Airport Facilitation 1-3-1 Shibakoen, Minato-ku, Tokyo, 105-0011, JAPAN Takashi Morihara 5 National Institute of Technology, Ishikawa College Kitachujo, Tsubata, Ishikawa, 929-0392 JAPAN Takashi Yano 6 Kumamoto University Kurokami 2-39-1, Chuo-ku, Kumamoto, 860-8555 JAPAN

ABSTRACT In the previous paper, we performed secondary analyses using micro-data and established the Japa- nese relationships between sound pressure level from night-time (L night ) and the percentage of highly sleep disturbed people (%HSD) for the following noise source: road traffic, conventional railway and Shinkansen railway noise. Adding the datasets associated with civil and military aircraft noises,

1 yokoshima.7c7q@pref.kanagawa.lg.jp

2 m-morinaga@kanagawa-u.ac.jp

3 sohei.tsujimura.fifty@vc.ibaraki.ac.jp

4 k-shimoyama@aeif.or.jp

5 morihara@ishikawa-nct.ac.jp

6 yano@gpo.kumamoto-u.ac.jp

worm 2022

we successively established the representative relationships between day-evening-night sound pres- sure level (L den ) and the prevalence of highly annoyed people (%HA) due to transportation noise in Japan. This paper continues to focus on listening disturbance caused by transportation noise. Thirty- five datasets, which were provided by Socio-Acoustic Survey Data Archive and derived from the other recent surveys conducted in Japan, were accumulated for the analysis. All the datasets include the following micro-data: demographic factors, exposure, and reaction to disturbance in listening to telephone, television, or radio. Although the question wording and the number of scale points on the evaluation differed among the surveys, we defined the percentage of highly listening disturbed (%HLD) with a 72% cut-off point. We established the relationship between Lden and %HLD by transportation noise. In addition, we discussed the link between listening disturbance and annoyance.

1. INTRODUCTION

The “Environmental Noise Guidelines for the European Region” (Guidelines) [1] published in 2018 showed the recommendation of day-evening-night sound pressure level ( L den ) and night-time sound pressure level ( L night ) for each noise source. Based on the exposure–annoyance relationship obtained by the meta-analysis, the individual recommendation values of L den and L night were set for each of road traffic, railway and aircraft noises.

Moving on to standards for environmental noises in Japan, the Environmental Quality Standards (EQS) are defined as the standards whose maintenance are desirable for the preservation of the living environment and are conducive to the protection of human health. At present, the following three EQSs are legislated: noise (general residential areas and areas facing roads), aircraft noise, and Shinkansen super-express railway noise. Following the release of the WHO guidelines, Ministry of the Environment in Japan has begun discussing whether the recommendation values in the Guidelines can be applied to the EQSs.

Using micro-data derived from social surveys conducted in Japan, we have previously reported a secondary analysis result of the percentage of highly sleep disturbed people (%HSD) as a function of L night for the following transportation noises: road traffic, conventional railway and Shinkansen rail- way noises in Japan [2]. Adding civil and military aircraft noises to the above ground transportation noises, we have established the representative relationship between L den and the percentage of highly annoyed people (%HA) for each transportation noise source [3].

Contributing to the discussion of the revision of the EQSs, we believe that it is necessary to reveal the prevalence not only of annoyance and sleep disturbance but also of other community responses to noise. For example, the reaction of interference with daily lives associated with noise, including listening disturbance, conversation disturbance, rest disturbance, thought disturbance and so forth, were obtained through lots of socio-acoustic surveys conducted in Japan. Among the psychological responses, this paper focuses on listening disturbance, which is regarded as a very important indicator measuring noise impact on human.

The purpose of this study is to establish a relationship between L den and %HLD for each transpor- tation noise source. This is the first paper establishing exposure–listening disturbance relationship associated with every transportation noise in Japan. This paper has been divided into five parts, in- cluding this introductory section. The second section is concerned with the materials and methodol- ogy used for this study. The third section gives an overview of the exposure–listening disturbance relationship for each of 32 datasets. We established a relationship between L den and estimated %HLD for each transportation noise source in Japan. The fourth section discusses the results briefly. Finally, the conclusion gives a summary and future considerations s of this study. 2. Materials and Methods

2.1. Datasets The 34 datasets used for analyses in this paper are shown in Table 1. These are the same datasets used in the previous paper [3]. Abbreviations of RT, CR, HR, CA, and MA denote respective noises of

road traffic, conventional railway, high-speed railway (Shinkansen railway), civil aircraft, and mili- tary aircraft noises. In addition, CB in the “Survey ID” column denotes the survey carried out in areas exposed to combined noises. Here, military aircraft noise means noises generated by aircraft noise from the Japan Self-Defense Forces and/or U.S. Forces. Sample size shows the number of valid data for analysis available in this paper. It should be noted that the datasets of STM104RT2011 and STM104CR2011 included no response of listening disturbance. Therefore, the sample size for each noise source is 6257, 9742, 6557, 1235, and 4800 for RT, CR, HR, CA, and MA, respectively.

Table 1: Outline of socio-acoustic survey datasets. No Survey ID Mode Survey year Sample size Question Rating

1 JPN002CR1994 CR 1994–1995 1827 SPE SEP (4–7) 2 JPN003RT1994 RT 1994–1995 387 SPE SEP (4) 3 JPN004HR1995 HR 1995–1996 667 SPE COM (2/5) 4 JPN005RT1996 RT 1996 807 SPE SEP (4) 5 JPN006CR1997 CR 1997 196 SPE COM (2) 6 JPN007RT1997 RT 1997–1998 779 SPE SEP (4) 7 JPN009RT1998 RT 1998 310 SPE COM (2) 8 JPN010RT1999 RT 1999–2000 657 GEN COM (2) 9 JPN011RT2000 RT 2000–2006 1337 SPE COM (2) 10 JPN012CR2001 CR 2001 1418 SPE SEP (4–5) 11 JPN013HR2001 HR 2001–2003 1101 GEN COM (2) 12 JPN014CR2002 CR 2002 1549 SPE SEP (5) 13 JPN015HR2003 HR 2003 706 SPE SEP (5) 14 JPN016RT2003 RT 2003–2004 271 SPE SEP (5) 15 JPN017CR2003 CR 2003–2006 1152 SPE COM (2) 16 JPN018HR2003 HR 2003–2006 1025 SPE COM (2) 17 JPN019CA2003 CA 2003–2006 701 SPE COM (2) 18 JPN020MA2003 MA 2003–2006 547 SPE COM (2) 19 JPN021RT2004 (CB) RT 2004–2006 1352 SPE COM (5) 20 JPN021CR2004 (CB) CR 2004–2006 1376 SPE COM (5) 21 JPN022HR2005 HR 2005 175 SPE COM (2) 22 JPN023CA2006 CA 2006 403 SPE SEP (5) 23 JPN024CA1996 CA 1996 131 SPE COM (2) 24 ISK101RT2007 RT 2007 357 SPE SEP (5) 25 KMM102CR2009 CR 2009–2010 570 SPE SEP (5) 26 KMM103CR2011 (CB) CR 2011–2012 992 SPE SEP (5) 27 KMM103HR2011 (CB) HR 2011–2012 1000 SPE SEP (5) 28 STM104RT2011 RT 2011 — — — 29 STM104CR2011 CR 2011 — — — 30 NGN105HR2013 HR 2013 286 SPE SEP (5) 21 JPN106MA2014 MA 2014 4253 SPE COM (2) 32 HKR107HR2016 HR 2016 913 SPE SEP (5) 33 KMM108CR2016 (CB) CR 2016–2017 680 SPE SEP (5) 34 KMM108HR2016 (CB) HR 2016–2017 684 SPE SEP (5)

2.2. Rating of Listening Disturbance As shown in Table 1, listening disturbance due to specific noise source (SPE) was obtained in most of the datasets; however, some of them provided listening disturbance due to general noise (GEN) without specifying noise sources. On the other hand, for the rating of listening disturbance, either separate items for “disturbance in listening to telephone” and “disturbance in listening to television or radio” (SEP) or an item combining listening disturbance for “disturbance in listening to telephone,

television or radio” (COM) were used. The number of scale points used for each item is shown in bracket. As you can see, noise ratings, question wordings, modifiers, descriptors and number of scale points associated with listening disturbance varied by datasets, whereas annoyance was measured with the ICBEN five-point verbal scale in socio-acoustic surveys after 2000.

The datasets analysed in this paper include two-point scale of listening disturbance and others with four-point to seven-point scales. In order to establish the exposure–listening disturbance relationship for each of the 32 datasets, assuming that the ordinal scale between two consecutive ratings is equi- distant regardless of different modifiers and scale points of disturbance, we defined %HLD in the same procedures as %HSD and %HA in our previous studies [2, 3]. Specifically, this study set the following cut-off point: 71% for the seven-point scale; 72% for the five-point and six-point scale; and 75% for the four-point scale. Therefore, we converted each respondent’s disturbance rating into the score corresponding to a category shown in Table 2 for each point scale. In the case of five-point scale, we regarded all the respondents in the top category and 40% of the respondents in the top second category as highly listening disturbed people. Similarly in the case of two-point scale, 56% of the respondents in the disturbed response were also regarded as highly listening disturbed people. In addition, in the case where the listening disturbance was rated using separate items of “disturbance in listening to telephone” and “disturbance in listening to television or radio”, the mean value of each converting score was newly provided to calculate %HLD. These operations yield the average of con- verting score to be equivalent to the %HLD with a cut-off point of around 72% within a range.

2.3. Noise Exposure In this paper, L den was calculated based on the following time category: daytime (from 7:00 to 19:00), evening (from 19:00 to 22:00) and night-time (from 22:00 to 07:00). Exposure data of L den to each respondent was rounded to the nearest integer. L den in some datasets was not directly available. In the corresponding datasets, L den was calculated based on the information on measurements of road traffic or aircraft noise at monitoring points carried out by local governments, railway or aircraft operations, L Aeq values during each time category, and so on. Procedure details of the L den estimates were described in our previous paper [3].

Table 2: Converting scores for %HLD.

Number of point scale Category 1 2 3 4 5 6 7 2-point scale 0 0.56 — — — — — 4-point scale 0 0 0 1 — — — 5-point scale 0 0 0 0.4 1 — — 6-point scale 0 0 0 0 0.7 1 — 7-point scale 0 0 0 0 0 1 1 3. Results

We calculated the relationship between L den in 5-dB steps and %HLD for each dataset. To distinguish L den in 5-dB steps from that in 1-dB steps, the former stands for DENL in this paper. For example, 50 dB DENL ranges from 48 dB to 52 dB L den in 1-dB steps. Taking the estimation accuracy of low- level and high-level exposures and the current status of noise environment into account, we excluded data with DENL ≤ 30 dB or DENL ≥ 80 dB.

Figure 1 (a–e) displays data points, %HLD values in DENL ranges for each dataset by transporta- tion noise. It should be noted that no data point was plotted for DENL ranges of fewer than 25 re- sponses. In addition, we plotted the observed %HLD (black circle) which are derived from aggregated datasets. The black circle for each transportation noise was not plotted in the DENL range s where the corresponding sample size is less than 100 responses.

Moreover, applying a quadratic regression to the relationship between DENL and ob- served %HLD, we drew the modeled exposure–listening disturbance curve (solid line) by transpor- tation noise. Figure 1 (f) compares exposure–listening disturbance curve among the noises.

100

100

JPN002CR JPN006CR JPN012CR JPN014CR JPN017CR JPN021CR KMM2009CR KMM2011CR KMM2016CR JPNCR

JPN003RT JPN005RT JPN007RT JPN009RT JPN010RT JPN011RT JPN016RT JPN021RT ISK2007RT JPNRT

80

80

60

60

%HLD

%HLD

40

40

20

20

0

0

35 45 55 65 75

35 45 55 65 75

DENL (dB)

DENL (dB)

(a) road traffic noise (b) conventional railway noise

100

JPN004HR JPN013HR JPN015HR JPN018HR JPN022HR KMM2011HR NGN2013HR HKR2016HR KMM2016HR JPNHR

100

JPN019CA

JPN023CA

80

80

JPN024CA

JPNCA

60

60

%HLD

%HLD

40

40

20

20

0

0

35 45 55 65 75

35 45 55 65 75

DENL (dB)

DENL (dB)

(c) high-speed railway noise (d) civil aircraft noise

80

100

JPN020MA

RT CR HR CA MA

JPN105MA

80

60

JPNMA

60

%HLD

%HLD

40

40

20

20

0

0

35 45 55 65 75

35 45 55 65 75

DENL (dB)

DENL (dB)

(e) military aircraft noise (f) comparison of exposure–response curve Figure 1: Estimated exposure–listening disturbance relationships (line) derived from aggregated data and data-points derived from micro-data: (a) road traffic noise; (b) conventional railway noise; (c) Shinkansen railway noise; (d) civil aircraft noise; (e) military aircraft; (f) comparison among trans- portation noises. Figure 1 (a–e) were plotted in 5-dB units of the noise level; Figure 1 (f) was drawn by interpolating the noise level in 1-dB units. It should be noted that last four digits of Survey ID are omitted in Figure 1 (a–e).

The equations for estimated %HLD by DENL of each transportation noise are provided in Equa- tions (1) to (5):

RT: %HLD = + 13.510 - 0.757 × L den + 0.96 × L den 2 (1)

CR: %HLD = + 28.581 - 1.735 × L den + 0.988 × L den 2 (2)

HR: %HLD = - 16.763 + 0.270 × L den + 0.967 × L den 2 (3)

CA: %HLD = + 94.070 - 3.967 × L den + 0.902 × L den 2 (4)

MA: %HLD = - 125.313 + 4.512 × L den + 0.918 × L den 2 (5) Comparing the observed %HLD among the datasets by transportation noise in Fig. 1 (a–e), there seems to be noticeable variations. In particular, some datasets show extremely low or high proportion. For example, datasets with high proportion are JPN009RT, JPN010RT and JPN021RT for road traf- fic, JPN021CR and JPN014CR for conventional railway, JPN004HR and JPN015HR for high-speed railway, and JPN023CA for civil aircraft noises. On the other hand, comparing the estimated %HLD, based on modelled curves, among the transportation noises in Fig. 1 (f), military aircraft noise is highest, followed by civil aircraft noise; in contrast, road traffic noise is lowest. 4. Discussion

Fig. 1 (f) shows that the estimated prevalence of highly listening disturbed people due to military aircraft noise was the highest among transportation noises, followed by civil aircraft noise. This was consistent with the prevalence of highly annoyed people revealed in our previous paper [3]. Thus, aircraft noises, which are intermittent noises, can bring about widespread listening disturbance.

Railway noises provide lower prevalence than aircraft noises. Comparing the railway noises, the modeled curve of Shinkansen railway noise almost overlaps with that of conventional railway noise within DENL range of 35 to 65 dB, whereas the prevalence for each railway noise varies widely. The difference in prevalence of highly listening disturbed people among railway categories is different from that of high annoyance. This suggest that there is little difference in a direct auditory effect of listening disturbance induced by pass-by noise between railway categories. Looking at the difference between individual datasets, there is an extreme prevalence of JPN004HR. It is thought that this is led to the excess-response [4] due to 50 km/h speed-up associated with the introduction of a new railcar. On the other hand, datasets derived from the surveys for the planned Shinkansen lines (KMM103HR, HKR107HR2016 and KMM108HR2016) show lower prevalence of highly listening disturbed people. This is probably because trains on the lines run at a low speed near stations.

Moving to road traffic noise, datasets of JPN009RT, JPN010RT, and JPN021RT show relatively higher prevalence. These datasets are delivered from the surveys conducted in Kanagawa prefecture. In addition, the rating is measured on 2-point scale and by synthesized listening disturbance item. Therefore, whether survey areas and evaluation method have an impact on the prevalence is a future subject for further study. 5. CONCLUSIONS

In this paper, using 32 datasets derived from the previous surveys conducted in Japan, the relation- ships between noise exposure and prevalence of highly sleep disturbed people were established for road traffic, railway and aircraft noises. The establishment procedure was done with the same proce- dure as our previous studies. Comparing the estimated prevalence of highly listening disturbed people among the noise sources, military aircraft noise is highest, followed by civil aircraft noise. Railway noises are followed by aircraft noises, and road traffic noise has the lowest prevalence. However, there are noticeable variations among the datasets, even for the same noise source. In the future, to establish the representative accurate exposure-response relationship from secondary analysis, we shall consider the following confounding factors affecting prevalence of highly listening disturbed people: gender, age, survey purpose, rating methods of listening disturbance (question wordings, items, and number of scale points), and so on. In addition, the definition of prevalence of highly listening disturbed people with a cut-off point of 72% is also an important point of discussion.

6. REFERENCES

1. WHO Regional Office for Europe, Environmental Noise Guidelines for the European Region ,

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posure–Annoyance Relationships Due to Transportation Noises in Japan. Int. J. Environ. Res. Public Health , 18(20) , 10935 (2021). 4. Brown, A.L.; van Kamp, I. Response to a change in transport noise exposure: Competing expla-

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