Psychophysiological impacts of traffic noises in urban green spaces Boya Yu 1 Beijing Jiaotong University ， school of architecture and design Shangyuancun 3, Haidian distinct, Beijing, China Yuying Chai 2 Beijing Jiaotong University ， school of architecture and design Shangyuancun 3, Haidian distinct, Beijing, China

ABSTRACT The goal of this study is to investigate the psychophysiological effects of traffic sounds in urban green spaces. The experiment took into account four traffic sounds: road, ordinary railway, high-speed train, and tram. The results revealed that traffic sounds had a considerable impact on both psycho- logical and physiological responses. In terms of psychological responses, it was found that the peak sound level performed better than the corresponding sound level in assessing the psychological im- pact of traffic sounds. The noise type and noise level were found to have significant effects on the physiological impacts of traffic noises. The physiological response to high-speed train noise was considerably different from the other three traffic noises. The physiological effects of road traffic noise were discovered to be unrelated to the noise level. In the railway noise groups, however, the change in noise level was observed to cause have an impact on the participants' physiological indi- cators.

1. INTRODUCTION

Urban green spaces provide flora and fauna for cities, offering important spaces for relaxation, recreation, social interaction, and sports. The acoustic environment plays a key role as a component of a positive visitor experience in urban green areas. However, most urban green spaces, as one of the urban open spaces, are frequently affected by traffic noise pollution, which minimizes the positive effects of green spaces. Over 44% of the EU population is regularly exposed to levels over 55 dBA, which was the health risk limitation recommended by WHO.

Numerous studies have revealed the negative effects of traffic noise in urban green spaces. Through the comparison between field measurements and simulation, traffic noise was identified as the dominant component of the green space soundscape 1 . From the view of perception, traffic noises were found to be the least preferred, but to have a dominant position in terms of perceived occurrences or loudness 2 . The annoyance caused by traffic noises further led to a strong impact on the perception of the overall environment, which increased with the noise level 3 . To achieve good soundscape qual- ity in green spaces, a limitation of 50 dBA was suggested by Nilsson et al. 4 . In addition to the per- ceived environment quality, increasing evidence has proved that the traffic noises could also had significant negative effects on recreational activities, restorativeness, and stress recovery 5 .

Besides self-reported subjective assessment, the physiological responses in urban soundscapes were also investigated to reveal the potential effect on health 6–8 . Significant impacts of traffic sound

1 boyayu@bjtu.edu.cn

2 mail2@example.com

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on physiological responses were found in the literature, including electromyography (EMG) 7 , elec- troencephalographic (EEG) indices and heart rate (HR) 9,10 , heart rate variability (HRV), skin con- ductance level (SCL) 11 , and so on 12–15 .

Collectively, the negative effects of traffic noises on urban green space soundscape were outlined by the above studies to a certain extent. However, most of the existing literature mainly focused on the effect of sound level variation of road traffic sound 16 . Very few studies have compared the re- sponses to different traffic sounds, which have been revealed to be different not only in their physical characteristics but also in their impact on people 17,18 .

Therefore, in the present study, we conducted laboratory experiments to measure the psychophys- iological responses under different traffic sound configurations. This paper was organized as follows: The experiment implementations are shown in Section 2. Section 3 shows the results that reveal the influential factors of psychophysiological parameters when exposed to traffic sounds. 2. Methodology

There were 30 subjects ranging in age from 19 to 26 years old participating in the experiment (15 males and 15 females). All of the participants were informed about the aim and protocol of the ex- periment, and they voluntarily participated in the study.

An immersive virtual reality (VR) equipment was used to present a complete and realistic visual environment of an urban green space. As shown in Figure.1, an omnidirectional picture was taken in an actual urban park and played in the head-mounted display system (HTC VIVE Pro EYE).

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Figure 1. The panoramic view of an urban green space. In this study, four traffic sounds (including road traffic sound, conventional train, high-speed train, and tram) were used to generate the sound stimuli. All the sound stimuli were extracted from field recordings collected in Beijing, China. Then 2 min clips were extracted from the field recordings as the experimental stimuli. A continuous 2min clip of road traffic sound was extracted. The railway sounds, on the other hand, were discontinuous, with durations ranging from 15 to 45 seconds. There- fore, a 1 min clip with one train passing by was extracted and then repeated to produce a 2 min experimental stimuli. The sound levels of the stimuli were set at 3 levels in this study, including 45, 55, and 65 dBA. Twelve acoustic stimuli were presented in a random order to each participant. In addition, a 2 min silence was used as the control stimulus to conduct the baseline measurement.

To measure the physiological responses to traffic sounds, two simple measures were applied: elec- trodermal activity (EDA) and heart rate (HR). The EDA was measured using two electrodes attached to the subject’s index and middle fingers of the non-dominant hand. While the HR was measured by

a photoplethysmography (PPG) sensor attached to the ring finger 19 . As for the psychological attrib- utes, a questionnaire with four attributes was used in this study, including acoustic comfort, noise annoyance, arousal, and pleasant.

The 2 min measured data was divided into 20 s segments to reveal the temporal variations of the physiological index. Then, the mean change of physiological data (EDA and HR) were calculated by comparing with the baseline measurement.

3. Result

3.1. Effect of traffic sounds on psychological responses

The MANOVA analysis was applied to investigate the effects of experiment factors on the sub- ject’s psychological responses, including acoustic comfort, annoyance, arousal, and pleasantness. Four factors were used in the ANOVA analysis, including gender, noise type, noise level, and the interaction of noise type and noise level. Two different noise level indicators, the equivalent noise level (LAeq) and the peak level (LAfmax), were used in two independent MANOVA analyses, as shown in Table 1. The results show that gender only had significant effects on arousal and pleasant evaluations. While acoustic factors, including noise type and noise level, show significant effects on all four evaluation dimensions, Using LAeq as the noise level index (Configuration 1), both the noise type and the noise level show significant influences on participants’ psychological responses. How- ever, when replacing LAeq with LAfmax (Configuration 2), only the sound level shows a significant influence on psychological responses. In both analyses, no significant interaction effects of noise type and noise level were found. Table 1. Results of multivariate test of psychological evaluations to traffic sounds. * and ** represent significant difference at 0.05 and 0.01 level, respectively.

Subjective Configuration 1 Configuration 2 Evaluation Factors Sig. 𝛈 𝒑

𝟐 Factors Sig. 𝛈 𝒑

𝟐 Comfort

0.321 0.00

0.319 0.00 Annoyance 0.090 0.01 0.089 0.01 Arousal 0.006 ** 0.02 0.006 ** 0.02 Pleasant 0.006 ** 0.02 0.006 ** 0.02 Comfort

Gender

Gender

0.002 ** 0.04

0.579 0.01 Annoyance 0.038 * 0.02 0.118 0.02 Arousal 0.050 * 0.02 0.562 0.01 Pleasant 0.047 * 0.02 0.296 0.01 Comfort

Noise Type

Noise Type

0.000 ** 0.37

0.000 ** 0.37 Annoyance 0.000 ** 0.34 0.000 ** 0.34 Arousal 0.000 ** 0.32 0.000 ** 0.32 Pleasant 0.000 ** 0.31 0.000 ** 0.31 Comfort

L Aeq

L Afmax

0.938 0.01

0.584 0.01 Annoyance 0.585 0.01 0.111 0.02 Arousal 0.983 0.00 0.767 0.00 Pleasant 0.740 0.01 0.379 0.01

Noise Type

Noise Type

* L Aeq

* L Afmax

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As indicated by the MANOVA analysis, the noise type also had a significant influence on the subjects’ psychological evaluations. By further pairwise comparison, the psychological impact of tram noise was found to be significantly stronger than that of the other three traffic noises. A grade could be recognized according to the negative effects as: tram > high-speed > conventional train > road, which is contrary to the duration time of the traffic noises. This result explains the difference in two MANOVA analyses with different noise level indicators, in which the peak noise level showed superior performance than the equivalent level in explaining the psychological impact of different traffic noises on people. In this experiment, the major difference between four traffic noises was temporal duration (Road > Conventional > High-speed > Tram). Shorter duration led to a higher peak noise level when the equivalent level was equalized. Therefore, the peak noise level described not only the overall sound level but also the temporal characteristics, which led to superior performance in explaining the psychological responses of participants.

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Figure 2. Effect of sound type on psychological attributes. The vertical bar represents 95% confidence inter-

val. * represents significant differences in pair-wise comparison at 0.05 level.

Evaluation Score Nw Uo @ © GRoad — DConventional High-speed m@Tram= Silence FA q T Het at ‘Comfort Arousal Pleasant

Figure 3. Effect of peak sound level on psychological responses.

As shown in Figure 3, strong linear correlations were found between psychological responses and L Afmax (R 2 : 0.88~0.9). The negative effects of traffic noise continued to worsen as L Afmax increased. Meanwhile, the noise levels for achieving neutral evaluation in each psychological dimension were different. According to the regression equation, the upper limit of L Afmax for avoiding negative eval- uations could be recognized as 60, 69, 65, and 63 dB for comfort, annoyance, arousal, and pleasant evaluation, respectively. These results show that people have a greater tolerance for the evaluation

Subjective Evaluation "2 Conon aAmnoyaned © Arousal m Peat 8 57 E 36 yOOrx 188 3 Namal] 3° as 2 R=0.50 88 yelons 4 yO e1319 a 50 5 6 GO 75 a 450 55 60 6 70 75 80 aS Lafmax Lafmax

of noise annoyance than those on the other three evaluation dimensions. Therefore, these results re- vealed the insufficiency of the questionnaire survey with annoyance as the only evaluation dimen- sion to evaluate the impact of traffic noises on the urban soundscape.

3.2. Effect of traffic sounds on physiological responses

Table 2 shows the results of MANOVA analysis for physiological responses (EDA and HR). It reveals that both the acoustic factors and the non-acoustic factors have significant effects on the phys- iological impact of traffic noises. The main effect of gender was found to be significant on both EDA and HR. Besides, a significant main effect of the exposure time on HR was also found. As for the acoustic factors, the main effects of noise type and noise level were found to be significant on EDA and HR, respectively. In addition, the interaction between noise type and noise level was observed to be influential on EDA. Table 2. MANOVA analysis of physiological evaluations to traffic sounds. * and ** represent sig- nificant difference at 0.05 and 0.01 level, respectively.

Factor EDA HR F Sig. 𝛈 𝒑

𝟐 F Sig. 𝛈 𝒑

𝟐 Gender 92.27 0.000** 0.041 18.77 0.000** 0.009 Time 0.07 0.996 0.000 4.92 0.000** 0.011 Noise Type 3.11 0.025* 0.004 2.02 0.109 0.003 SPL 1.56 0.209 0.001 3.98 0.019* 0.004 Noise Type* SPL 3.52 0.002** 0.010 1.17 0.321 0.003

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Figure 4 shows the effects of noise type and noise level on physiological responses. By further pairwise comparisons, five significant differences were identified. As for the noise type, all four sig- nificant differences were between high-speed train noise and other traffic noises, including: (1) EDA under high-speed train noise was significantly higher than those under road traffic noise (p = 0.010) and conventional train noise (p = 0.008); (2) HR under high-speed train noise was significantly lower than those under conventional train noise (p = 0.035) and tram noise (p = 0.038). These results indi- cate that the physiological impact of high-speed train noise was significantly different from other traffic noises. As for the noise level, only one pairwise comparison was found to be significant, which indicates that the main effect of SPL on physiological responses was rather limited. A significant increase in HR was found as the noise level increased from 55 dB to 65 dB.

Figure 4. Main effect of noise type and noise level on physiological responses.

a oh L—_ Road GConventional DASdB OS5dB M6SAB] . T on r I I I T EDA HR EDA uR

An explanation for this phenomenon is that the relationship between SPL and physiological re- sponses varied in different noise groups, as indicated by the significant interaction effect in Table 2. Further pairwise comparisons (Manny-U test) were carried out to investigate how physiological re- sponses varied with the increase in noise level in each noise group. As shown in Figure 5, the results varied wildly in different noise groups. As for the road traffic noise, the change in SPL from 45 dB to 65 dB could hardly affect the participant’s physiological responses, including EDA and HR. While significant changes in physiological responses were found with the increase of SPL in all railway noise groups. This result indicated that the effect of noise level on physiological responses depended on the noise type.

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Figure 5. Interaction effect of sound level and sound type on EDA and HR. The vertical bar repre- sents 95% confidence interval. * and ** represents significant differences in the Mann-Whitney U

test at 0.05 level and 0.01 level, respectively. 4. CONCLUSIONS

A laboratory experiment on the psychophysiological impact of traffic sounds on the urban green space soundscape was conducted in this study. The following results were obtained:

1) Significant negative effects of traffic noises on psychological assessment of green space

soundscape were identified, which were affected by both the noise type and the noise level. The peak noise level was found to be superior to the equivalent level in describing the psycho- logical impact of various traffic noises on the urban green space soundscape. 2) Physiological impacts of traffic noises were also found to be associate with the noise type and

the noise level. As for the noise type, the physiological response to high-speed train noise was significantly different from the other three traffic noises. Exposure to high-speed train noise led to lower EDA (compared with conventional train and tram noise) and higher HR (compared with road and tram noise). The relationship between noise level and physiological parameters also depends on the noise type. The physiological impacts of road traffic noise were found to

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be irrelevant to the noise level. On the contrary, the change in noise level was found to be influential on the participants’ physiological parameters in the railway noise group. 5. ACKNOWLEDGEMENTS

We gratefully acknowledge Wu Wei and Li Junjie for their help on laboratory experiments. This research was funded by the National Natural Science Foundation of China (Project No. 51808030). 6. REFERENCES

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