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Article

Understanding the Role of Visitor Behavior in Soundscape Restorative Experiences in Urban Parks

by
Xuan Guo
1,2,
Si-Yu Jiang
3,4,
Jiang Liu
1,5,*,
Zhu Chen
2 and
Xin-Chen Hong
1,5
1
School of Architecture and Urban–Rural Planning, Fuzhou University, Fuzhou 350108, China
2
Institute of Environmental Planning, Leibniz University Hannover, Herrenhäuser Str. 2, 30419 Hanover, Germany
3
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
4
University of Chinese Academy of Sciences, Beijing 100049, China
5
Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou University, Fuzhou 350108, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(10), 1751; https://doi.org/10.3390/f15101751 (registering DOI)
Submission received: 10 September 2024 / Revised: 29 September 2024 / Accepted: 2 October 2024 / Published: 5 October 2024
(This article belongs to the Section Urban Forestry)
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Abstract

:
The restorative effects of soundscapes on human physical and mental well-being are widely recognized, but their effectiveness is influenced by various factors, including external environments and individual characteristics. Despite being a crucial element in environmental experience, the role of individual behavior in the restorative effects of soundscapes has been insufficiently studied. To address this research gap, we conducted a survey in five parks in Fuzhou, China, using questionnaires to assess visitors’ evaluations of soundscape characteristics, their soundscape restorative experiences, and behavioral characteristics. A total of 419 valid responses were collected. Using these data, we employed a structural equation model and conditional process analysis to explore the interaction between visitor behavior and soundscape experiences. The results show that soundscapes with pleasantness and eventfulness promote static behaviors to a certain degree, while dynamic behaviors are influenced solely by eventfulness soundscapes. In the process by which soundscape characteristics influence restorative experiences, static behavior is the only mediating factor, accounting for 8% of the total effect. Additionally, increased visit intensity enhances the impact of pleasantness soundscape on restorative experiences while weakening the mediating effect of static behavior. These findings provide strong support for relevant design considerations.

1. Introduction

Urban parks are important public relaxation and recreation spaces [1], positively affecting the local ecological environment and enhancing city residents’ health conditions and quality of life [2,3]. As the demand for high-quality public open spaces continues to rise, researchers and designers have sought to enhance visitors’ experience in urban parks from multiple perceptual dimensions [4,5]. Aural perception, as one of the primary human senses [6,7], has gained increasing attention, particularly from the soundscape perspective [8,9]. A significant difference between the concept of soundscape and traditional acoustics is the emphasis on the subjective human perception of the acoustic environment [10], emphasizing how the acoustic environment contributes to human well-being rather than solely on its negative effects [11].
The benefits people acquire from the restoration of experiencing environments are also the key reason for visiting urban parks [12]. Earlier research focused primarily on the restorative effects of visual landscape elements. Still, as studies progressed, it became clear that the quality of the auditory environment also plays a crucial role in human health and well-being [13]. Furthermore, the restorative effects of positive soundscapes are increasingly recognized as an integral part of environmental restoration [11]. Recent research indicates that soundscape restorative experience (SRE) is multi-dimensional, with complex internal mechanisms involving people, places, and their interactions [14]. One of the key aspects is the role of acoustic environmental factors, which have been extensively studied in the field [15]. For example, many studies have investigated the restorative effects of different types of sound and have consistently found that natural sounds such as birdsong, wind, flowing water, and rustling leaves have significant restorative effects [13,16]. In contrast, urban sounds like human activities have varying effects depending on the context [17]. Notably, even natural sounds vary in their restorative effects [17]. For example, a recent study found that wind sounds improved relaxation, as reflected in brain waves, more than birdsong or insect sounds, while birdsong was more effective at improving attentional focus. However, the effect of wind, while significant, was the smallest of the natural sounds studied [18]. Interestingly, another study found that flowing water and birdsong increased pleasantness and relaxation, reduced fatigue, and aided recovery compared to silence, while wind sounds had negligible effects [19]. Additionally, one study investigated whether natural sounds are always beneficial, comparing the restorative effects of four water soundscapes. It found that falling water had the lowest restorative quality compared to streams, springs, and rivers [20]. Besides the type of source, physical and psychoacoustic characteristics also relate to SRE. For instance, one study found that perceived restorative value was positively associated with fluctuation intensity and clarity and negatively associated with loudness and roughness [21]. Another study identified sound level, harmonics, and frequency as significant predictors of perceived recovery potential [22]. Moreover, subjective perceptions of the overall acoustic environment are key factors influencing perceived environmental restoration [23]. For example, it has been suggested that soundscape features that contribute to greater emotional recovery and perceived stress reduction are pleasantness, calm, fun, and naturalness [24]. In addition, structural equation modeling has shown that soundscape pleasantness has a significant impact on perceived recovery [14], a finding confirmed by recent studies [25].
In addition to the direct impact of subjective and objective features of the acoustic environment on SRE, this process is also influenced by contextual factors, with current research focusing on environmental and individual characteristics [26]. There is considerable evidence for the interaction between audio and visual elements in the perceptual recovery process, with significant differences in the recovery effects produced by different combinations of landscape and soundscape elements [27]. For example, recent studies indicate that natural sounds in urban visual environments are more relaxing than urban sounds in natural environments. In addition, in waterfront environments, birdsong is more restorative than the sound of streams in high-rise waterfront areas [28]. Moreover, landscape features can modify the effect of sound on restoration quality. For example, in urban blue spaces, improving the visual quality of the water, increasing the number of boats, and reducing paved areas improved the restorative quality of audio-visual combinations [29]. Individual factors also significantly influence SRE. Both innate characteristics, such as gender and age, and acquired traits, such as noise sensitivity, attitudes towards sound, and current mental state, play a role. For instance, gender significantly affected brainwave changes after exposure to wind sounds [18]. Similarly, a study found that males experienced greater restoration from soundscapes than females [30]. Another study found that demographic factors (e.g., education and age) and psychological states (e.g., stress levels) influenced respondents’ perceptions of bird restoration in urban parks [31]. A previous study found that visitors’ noise sensitivity significantly influenced the restorative effects of soundscapes [32]. Additionally, researchers compared the influence of innate and acquired traits on SRE, finding that acquired traits had a more significant impact on SRE than innate ones [33].
It is important to note that, according to the first part of the International Standards for Soundscapes, context includes the interrelationships between person and activity and place in space and time [8]. It can be found that in addition to places and people, activities are also crucial factors influencing the soundscape experience, which includes the recovery experience [34]. In fact, some earlier studies have already considered the influence of such factors. For example, Payne, one of the first researchers to study the restorative role of soundscapes, noted that activities like interacting with nature, reading, and enjoying food significantly increased SRE in urban parks and that people’s SRE improved as their frequency of park visits increased [32]. In an experiment comparing urban and natural soundscapes, researchers found that restorative effects were more easily perceived when participants were with friends in urban soundscapes [35]. In an interview study with patients with stress-related mental disorders who were treated in a healing garden, it was found that sound influenced their behavior in the garden and, thus, their perception of recovery [36]. Therefore, it is evident that there is a strong association between human behavior and SRE. However, while studies have explored the potential association between the two, there is still a lack of in-depth theoretical examination of how behavior explicitly affects the SRE and its role in this process. To address this research gap, this study conducted a public survey of five urban parks in Fuzhou, China, aiming to explore the intrinsic mechanisms between behavior and SRE. Based on previous research, we propose three main hypotheses to reveal the mechanisms of soundscape characteristics and behavioral factors associated with SRE in urban parks:
(1)
Soundscape characteristics directly affect both visitor behavior and SRE;
(2)
Visitor behavior directly influences SRE and mediates the effects of soundscape characteristics on SRE;
(3)
Tourist visit intensity moderates the effects of soundscape characteristics and behavior on SRE.
A model construction approach, including structural equation modeling and conditional process analysis approach, was adopted to test these hypotheses step by step and to reveal the influence and role of visitor behavior-related factors in SRE in urban parks. The findings can provide new dimensions and perspectives for soundscape restoration theory, enriching the existing theoretical framework and advancing the understanding of the complexity of soundscape restoration experiences and their influencing factors. Furthermore, they also provide valuable insights into the planning and design of urban park landscapes, enabling designers to optimize layouts more effectively, thereby promoting psychological recovery and the overall well-being of residents.

2. Methods

2.1. Case Study Sites

In this study, five representative urban parks in Fuzhou, China, were selected based on the following criteria: (1) the popularity of the parks in the local area and attractiveness for visitors of different characteristics, ensuring the feasibility of data collection and the representativeness of the sample; (2) the spatial distribution and size of the parks, covering areas from high-density urban centers to the city’s outskirts and suburban areas; and (3) the design style and internal environment of the parks, encompassing a range of types from classical gardens to modern sustainable designs. Based on these criteria, Fuzhou National Forest Park, Wenquan Park, West Lake Park, Jin’an Park, and Zuohai Park were selected as case study parks. These five parks are among the most popular in Fuzhou, attracting a diverse range of visitors, making them suitable for capturing a representative sample of park users. Geographically, the parks are spread across various areas, from the urban core to peripheral regions, representing diverse urban soundscape environments. In terms of design, the parks feature diverse characteristics, including historically significant classical gardens and contemporary parks designed with sustainable concepts. The acoustic environments of these parks are equally varied, ranging from natural soundscapes in forest parks to waterscape features in typical parks and higher levels of technical noise in more urbanized areas. These distinctive urban parks provide a robust foundation for our study.
A preliminary field survey was conducted in September 2020, involving objective acoustic environment measurements and identifying typical sound sources. The objective acoustic environment was measured mainly by monitoring the acoustic environment at the measuring points (see Table 1) using a sound level meter (BSWA308, BSWA Technology Co., Ltd., Beijing, China) during two periods, 8:00–12:00 and 14:00–18:00, with each measurement lasting 3 min. During measurements, the sound level meter was positioned 1.5 m above the ground and equipped with a windshield. Typical sound sources were identified primarily by investigators conducting soundwalks through the park and recording frequently occurring sources. The preliminary field survey results indicated that all five urban parks had rich soundscapes, with LAeq, 3-min (equivalent continuous A sound level) ranging from 43.9 dBA to 67.7 dBA (see Table S1). General information about the five parks is provided in Table 1.

2.2. Questionnaire Design

According to the research objectives, information was collected in four key areas via the questionnaire. The first part is about the social, demographic, and behavioral information of the interviewed park visitors, including gender, age, educational background, occupation, visit frequency, time of stay, as well as the time needed from home to the park, based on previous soundscape studies in urban parks [37,38].
The second part focuses on the soundscape characteristics of the parks. Many studies have explored the classification of soundscape characteristics evaluation. These studies show that the characteristics of soundscape can be classified as pleasantness, eventfulness, familiarity, quietness, appropriateness, and more [39,40,41,42]. Among these, pleasantness and eventfulness are the most widely accepted. Pleasantness is related to how pleasant or unpleasant the acoustics environment is judged. Eventfulness is represented by how eventful or uneventful the acoustic environment is perceived. An eventful environment is busy with human activity, for example, a city center or other sound events produced by non-human agents, whereas an uneventful climate is entirely devoid of human activity [43]. Therefore, this study will evaluate soundscape characteristics based on these two dimensions. Various evaluation indicators exist for these two dimensions [40,44,45,46], and we referred to a previous study with a similar research design [45]. Specifically, soundscape pleasantness was measured using the indicators pleasant, harmonious, and comfortable, while soundscape eventfulness was measured using dynamic, various, and eventful [45]. Both dimensions were evaluated on a seven-point rating scale ranging from “not match at all (1)” to “perfect match (7)”.
The third part is about the visitor’s behavioral types in the parks. Based on the classification methods used in previous studies [47,48,49], activities in urban parks are divided into three categories: static, dynamic, and moving behaviors, each encompassing specific behavior styles. Static behaviors include activities involving little or no movement, such as sitting, resting, or reading. These activities typically involve prolonged exposure to localized soundscapes, enabling deeper interaction with and perception of the acoustic environment. Dynamic behaviors involve activities requiring physical exertion in a localized area, such as dancing, playing, or using fitness equipment. These typically occur in specific areas of the park, meaning that soundscape exposure is more dynamic yet still localized. Moving behaviors, such as strolling or running, cover a larger area and broader scope of activity. These activities result in brief interactions with the soundscape as visitors experience changing auditory environments while moving through different areas. The specific types are shown in Table 2. Interviewees were asked to evaluate the extent to which these activities matched their usual activities in the park, using a seven-point rating scale ranging from “not match at all (1)” to “perfect match (7)”.
The final part is about the soundscape restorative experience of the interviewees. The Perceived Restorativeness Soundscape Scale (PRSS) was first introduced by Payne to specifically evaluate people’s perceived restoration from soundscapes [50]. This scale has been widely applied in soundscape restoration studies, with its validity confirmed [21,31,51]. Therefore, this study employed the PRSS to assess the soundscape restorative experience. Based on the PRSS [50,52], and according to the context-urban parks, a modified version of PRSS was adopted, including 16 items from 5 dimensions, as shown in Table 3. The items were rated on a seven-point rating scale ranging from “strongly disagree (1)” to “strongly agree (7)”.

2.3. Data Collection

The investigation was conducted by a group of landscape architecture master’s students between October and November 2020, on days with favorable weather conditions (no rain) from 7:30 to 18:00, encompassing the main period when park visitors were active. As the survey focused on visitors’ experience throughout the park, respondents were randomly selected and interviewed at several vital locations evenly distributed throughout the park. After briefly explaining the purpose of the investigation and their consent, the interviewers instructed them to complete the questionnaire according to their own experience. A total of 482 questionnaires were distributed, and 419 valid responses were received (113 from Fuzhou National Forest Park, 68 from Wenquan Park, 83 from West Lake Park, 101 from Jin’an Park, and 54 from Zuohai Park), resulting in a valid response rate of 86.74%. Sample information is shown in Figure 1. The sample for this study includes respondents of various genders, age groups, and educational backgrounds and is broadly representative. Regarding specific distribution, the respondents are mainly female, aged 25–40, and primarily working people with bachelor’s degrees or equivalent. This distribution closely aligns with sample data from previous studies on urban parks in Fuzhou and effectively reflects the characteristics of the population that frequents these parks [53,54].

2.4. Data Analysis

2.4.1. Reliability and Validity Test of the Data Set

The reliability analysis conducted using statistical analysis software SPSS 25.0 shows that Cronbach’s alpha values for all indicators range from 0.74 to 0.94, exceeding the 0.7 threshold, confirming the strong reliability of the data set. Factor analysis was conducted separately for the three kinds of indicators: soundscape characteristics, behavioral type, and the SRE to assess data validity. The results show that the principal factors and their corresponding items aligned with the questionnaire’s initial design. All factor loading exceeded 0.5, and the cumulative explained variance after rotation ranged from 62.30% to 86.52%, surpassing the minimum standard of 50%. These findings suggest that the data set derived from the questionnaire is valid across all sections. In addition, normality tests showed that the absolute values of skewness (<2.0) and kurtosis (<4.0) for all indicators fell within acceptable ranges, indicating no significant deviation from normality [55].

2.4.2. Model Construction Approach

To test the research hypotheses, we employ a model construction approach. Due to the many variables and hypotheses involved, we use a step-by-step model construction process to eliminate statistically insignificant variables and paths. The entire testing process is divided into three steps, each involving a separate model, as shown in Figure 2:
(1)
Analyze the effects of soundscape characteristics on visitors’ behavior (M1);
(2)
Test the direct impact of visitor behavior on SRE and its mediating role in the relationship between soundscape features and SRE (M2);
(3)
Test the moderating effect of visit intensity on the mediation model (M3). The visiting intensity (VI) is calculated by Equation (1):
VI = TS ∗ VF
where TS is the time of stay, and VF is the visit frequency.
For models M1 and M2, we used structural equation modeling (SEM) for model construction and testing, conducted using the statistical software AMOS 25.0. The SEM consists of two parts: the measurement model and the structural model. We applied confirmatory factor analysis (CFA) for the measurement model to verify whether the relationships between latent and observed variables align with theoretical expectations, ensuring that observed variables accurately measure latent constructs. The validation process consists of two main components. The first is factor loadings, which indicate the correlation between each observed variable and its corresponding latent variable. Estimating factor loadings determines the contribution of each observed variable to its latent variable. Factor loadings are generally required to exceed 0.6, with values closer to 1 indicating stronger associations. The second component is the reliability and validity testing of the measurement model to ensure measurement accuracy [56]. Reliability is assessed using composite reliability (CR) to evaluate the internal consistency of latent variables. A CR value greater than 0.7 indicates good reliability. Validity was assessed primarily through convergent validity and discriminant validity. Convergent validity measures how effectively observed variables reflect latent variables and is assessed using average variance extracted (AVE). An AVE greater than 0.5 indicates good convergent validity. Discriminant validity, which reflects the distinction between different latent variables, is assessed using the Fornell-Larcker criterion [57]. Good discriminant validity is achieved when the square root of the AVE of a latent variable is greater than its correlation with other latent variables. For the structural model, the testing process consisted of the model fit test and the path coefficient significance test. The model fit test assesses the overall suitability of the structural model, primarily using goodness-of-fit indices. This study selected eight commonly used goodness-of-fit indices, with detailed explanations and criteria provided in Table 4. The path coefficient significance test determines whether one latent variable significantly affects another and is assessed using t-values and p-values. The t-value represents the ratio of the estimated path coefficient to its standard error; the larger the t-value, the more significant the path. The p-value represents the probability of statistical significance based on the t-value and is used to determine the significance of the path coefficient. A p-value of less than 0.05 indicates that the path coefficient is significant, meaning the relationship between latent variables is statistically significant. In this study, significance is divided into three levels: p < 0.05 (significant, *), p < 0.01 (highly significant, **), and p < 0.001 (very highly significant, ***). Path coefficients estimate direct causality between latent variables in the structural model. Path coefficients are standardized to compare the magnitude of effects across different paths, typically ranging from −1 to 1. The closer the absolute value is to 1, the stronger the path relationship. In addition, the significance of the mediating effect in model M2 was estimated using the Bootstrap method, which generates standard errors and 95% confidence intervals by repeatedly sampling the raw data and estimating the effect distribution. In this study, 5000 repeated samples were used, and the mediating effect is considered significant when the 95% confidence interval does not include zero.
For model M3, we employed conditional process analysis using the Process 3.3 plug-in for SPSS 25.0. Conditional process analysis is a comprehensive method for simultaneously testing mediation and moderation effects in complex causal models. The analysis introduces a moderating variable to examine whether it significantly affects the path between the independent variable and the mediating or dependent variable. Suppose the regression coefficient of the interaction term (independent variable × moderator variable or mediator variable × moderator variable) is significant in the regression model (tested using t-values and p-values). In that case, it indicates a significant moderation effect.

3. Results

3.1. Effects of Soundscape Characteristics on Behavioral Type

Confirmatory factor analysis was conducted during model building, and observed variables with low factor loading (<0.6) were removed and reanalyzed (see Table S2). The final results of the confirmatory factor analysis for variables in the effect of soundscape characteristics on visitors’ behavioral types (model M1) are shown in Table 5. After adjustment, all observed variables retained in the model have factor loading above 0.6, and all the latent variables have CR > 0.7 and AVE > 0.5, indicating good convergent validity. The discriminant validity test results for variables in model M1 show that all AVE-SR values of the latent variables are greater than their correlation coefficients with other latent variables (Table 6), indicating good discriminate validity. The goodness-of-fit indices of the model M1 were estimated using the maximum likelihood method. The results in Table 7 indicate that all the fit indices meet the fitting standards, confirming good model fit.
Thus, the modified structural equation model of soundscape characteristics affecting visitors’ behavioral types was proposed, as shown in Figure 3, with detailed hypothesis paths testing results provided in Table 8. Soundscape pleasantness had a positive effect on static behavior (β = 0.350, p < 0.001). In contrast, soundscape eventfulness positively influenced both static (β = 0.256, p < 0.05) and dynamic (β = 0.256, p < 0.05) behavior and had no significant effect on moving behavior.

3.2. Effects of Behavioral Type on the SRE

Based on the modified model M1, the effects of soundscape perception and visitors’ behavioral type on the SRE were tested in model M2, with the hypothesis that the two soundscape characteristics indicators have both direct and indirect effects. The indirect effects were achieved through the mediating effect of visitors’ behavioral types. A similar process of confirmatory factor analysis, discriminant validity testing, and goodness-of-fit testing was conducted for model M2, as in model M1, with the relative results shown in Table 9, Table 10 and Table 11, respectively. The modified model M2 is shown in Figure 4, with detailed test results of the hypothesized paths in Table 12. Both soundscape pleasantness and eventfulness had positive effects on the SRE, as well as on static behavior. Besides, static behavior positively impacted the SRE, suggesting that a mediating effect may exist in model M2.
The estimated direct and indirect effects of soundscape pleasantness on the SRE were 0.467 and 0.041, respectively (see Table 13). Furthermore, the results indicated that the estimates for direct and indirect effect paths did not include 0 within the 95% confidence interval, suggesting a partial mediating effect of static behavior in this process. This indicates that higher soundscape pleasantness could directly increase visitors’ SRE and indirectly promote visitors’ static behavior, accounting for 92% and 8% of the total effect, respectively. Regarding soundscape eventfulness, although the mediating effect of static behavior was not statistically significant, as the lower bound of the 95% confidence interval for the indirect effect is exactly 0, it could also contribute about 9% of the total effect.

3.3. Effects of Other Behavioral-Related Characteristics on the SRE

Based on the hypothesis paths with significant coefficients in the model of the mediating effect of behavioral types on the SRE, another behavioral-related factor—visit intensity- was introduced into the model M3 to test its possible moderating effect. It is important to note that the value of soundscape pleasantness was the mean value of its corresponding three indicators: pleasant, harmonious, and comfortable. In contrast, the value of SRE was the sum of all the 16 items. Model 59 in Process 3.3 was chosen for hypothesis testing, as it assumes a moderating effect on both the direct and mediating paths [58], which is consistent with our research hypothesis.
The results in Table 14 indicated that after introducing visit intensity (VI) into model M3, the variable ‘Pleasantness × VI’ showed a significant and positive effect on the SRE (β = 0.10, p < 0.01). In contrast, the variable ‘Static behavior × VI’ showed a significant and negative effect on the SRE (β = −0.11, p < 0.01). This indicates that visit intensity moderates both the direct path of soundscape pleasantness affecting the SRE and the indirect path through static behavior, as shown in the adjusted model M3 (Figure 5). The decomposition diagram of VI’s moderating effect on the effect of soundscape pleasantness on the SRE, as well as static behavior, further shows that as the increase in visitors’ VI, soundscape pleasantness had a greater effect on the SRE. Nonetheless, the mediating effect of static behavior decreased (see Figure 6), as verified by the model testing results (see Table 15).

4. Discussion

4.1. Relationship between Soundscape Characteristics and Behavioral Type

It has been widely verified that human behavioral characteristics are closely related to the perception of soundscape characteristics [59,60,61,62], primarily through mental factors (e.g., visit purpose [10], social interaction), behavioral factors (e.g., visit frequency, type of activity conducted), and environmental factors (e.g., accessibility, crowd density, the existence of certain sounds [63,64,65]). However, limited research has focused on how overall soundscape characteristics are related to human health by influencing behavior. Therefore, this research explored how the two dimensions of soundscape characteristics—pleasantness and eventfulness—affect visitors’ behavioral types in urban parks, shedding light on soundscape design for spaces with different activity types. The results showed that both dimensions of soundscape characteristics promote visitors’ static behavior. The finding is consistent with previous research. For example, introducing music, particularly classical music, into open public space significantly increases the amount of time people spend in the space [66]. It was also found that music-related activities attract people passing by to stand and watch the activities in urban open spaces [34]. Music and music-related sounds in the environment have been shown to enhance the perception of pleasantness and eventfulness in the acoustic environment [67]. This phenomenon may be closely tied to the alignment between soundscape characteristics and behavioral needs. Static behaviors, such as resting and reflecting, typically require a comfortable, quiet environment to facilitate relaxation and recovery, and a pleasant soundscape effectively meets this need. Additionally, static behaviors may involve observing and appreciating the environment, such as viewing landscapes or flora and fauna. In this context, a rich and varied event-based soundscape can add interest and appeal to the environment, further increasing visitors’ engagement and length of stay. Thus, static behaviors are influenced not only by the pleasantness of soundscapes but also by their eventfulness, with both factors contributing to an individual’s overall experience of the environment. Therefore, soundscapes in areas where people engage in static behavior should consider incorporating more natural sounds, such as birdsong and water sounds, which have been shown to enhance pleasant soundscapes [68,69] and focus on improving soundscape quality in noise-polluted parks [70].
As noted in a previous study, music can increase the number of people exercising in urban open spaces [34]. This study also suggests that dynamic behavior is primarily influenced by an eventful soundscape, likely because this behavior is closed tied to physical activity. During dynamic behaviors, individuals’ attention primarily focuses on their motor state and intrinsic physiological feedback. In this context, pleasant soundscapes may be overlooked or have minimal influence on the individual’s behavior. However, eventful soundscapes, associated with energy, vitality, and activity, can more directly influence dynamic behavior. This explains why eventful soundscapes significantly affect dynamic behavior, as such sounds stimulate activity engagement in individuals.
Regarding moving behavior, neither of the two soundscape characteristics dimensions had a significant effect, likely because the purposeful nature of this type of behavior reduces visitors’ sensitivity to their surroundings. During movement, individuals may unconsciously perceive the soundscape as background noise rather than a focus of active attention. As individuals focus on spatial navigation or movement direction, paying less attention to environmental sound characteristics, the soundscape’s influence as a background element may be diminished or ignored.

4.2. Interaction of Soundscape Characteristics and Behavioral Characteristics Affecting the SER

Behavioral characteristics are essential factors influencing the perception of soundscape characteristics, and different behavioral types can lead to varying perceptions of the same soundscapes [32,36]. This research confirms that a pleasant soundscape influences the SRE through the mediating effect of static behavior, which is the only significant path among the four possible mediating effect paths of behavioral types. This result can be explained by the “Attention Restoration Theory” from environmental psychology. According to this theory, environments that effectively restore attention must possess the qualities of being away, extent, fascination, and compatibility [71]. Compatibility is the alignment between the environment’s characteristics and the individual’s needs and goals. Pleasant soundscapes provide a foundation for visitors’ perceptual recovery, while static behaviors allow individuals to engage with the environment for longer periods, thereby enhancing their perceptual and restorative experience of the soundscape. In contrast, although dynamic behaviors also involve environmental interaction, intrinsic stimulation from physical activity may distract individuals from external sounds, diminishing their perception of the soundscape. This may explain why static behaviors exhibited significant mediating effects, whereas dynamic behaviors did not.
For park visitors, visit frequency and time of stay are important behavioral characteristics that reflect visit intensity and their exposure to soundscapes. These factors have been shown in many studies to affect soundscape evaluation, including sound source perception, overall soundscape perception, and the SRE [32,37,72]. More exposure to green spaces can significantly improve mental health, such as reducing the incidence of anxiety and depression, especially during the COVID-19 pandemic [73,74,75]. Our research indicates that visit intensity had a significant positive moderating effect on the process of soundscape pleasantness affecting the SRE, further supporting previous research that higher visit frequency significantly improves the SRE in urban parks [32]. Individuals with higher visit intensity may develop greater sensitivity to pleasant soundscape features (e.g., nature sounds) and more excellent adaptation to negative sounds due to more frequent or prolonged environmental exposure. As individuals become more familiar with the environment, they may focus more on the pleasant aspects of the soundscape, allowing these elements to have a more direct positive impact on the recovery experience [76]. However, higher visit intensity could minimize the mediating effect of static behavior on the SRE. This is likely because individuals with high visit intensity become more familiar with the environment through frequent exposure, influencing their behavioral choices and their perception of the soundscape. Static behaviors may not be necessary for individuals with high visit intensity to achieve restorative effects, as they have already experienced restoration through long-term environmental interactions. In this case, individuals with high visit intensity obtain restoration directly from the environmental soundscape without relying on mediating variables such as static behaviors.

4.3. Theoretical and Practical Implications

The findings of this study provide a broader theoretical framework for soundscape theory and restorative environmental research. By examining the relationship between visitor behavior and soundscape experience in urban parks, this study offers new perspectives on the mediating role of behavior in the restorative effects of soundscapes. Previous studies have focused on the direct effects of acoustic environment characteristics on psychological restoration or on the moderating role of environmental and individual factors in these effects. However, our study shows that different types of behaviors, static, dynamic, and moving behaviors, are affected by different soundscape characteristics, which directly affect an individual’s restorative experience of the soundscape and indirectly affect the relationship between soundscape characteristics and restorative experience. This expands current soundscape theory by showing that static behaviors (e.g., sitting or resting) enable individuals to engage more deeply with the soundscape, enhancing restorative experiences. In contrast, dynamic behaviors (e.g., performing physical activities) influence the soundscape experience differently, highlighting the critical role of behavioral context in shaping auditory experiences. Beyond its theoretical contributions, this study has significant practical implications for urban planners, landscape architects, and policymakers in the design of urban parks and public spaces. The findings suggest that soundscape design should not be uniform across different areas but should be customized to the specific behavioral activities that may occur within each area. To translate these findings into practical design guidelines, we propose the following: First, in areas dominated by static behaviors (e.g., resting or meditation spaces), designers should prioritize pleasing, soothing natural sounds (e.g., birdsong or water sounds) to encourage longer stays and maximize restorative effects. Second, in dynamic activity areas (e.g., exercise zones or fitness spaces), soundscape design should incorporate more energetic and diverse elements to create a stimulating and engaging atmosphere that enhances the user experience. Finally, in areas of movement or transition, soundscape design should maintain fluidity, avoiding abrupt changes in auditory stimuli and ensuring seamless transitions as visitors move between spaces. Understanding these behavioral differences with soundscape needs allows designers and planners to create soundscapes that cater to different visitor needs, thus enhancing public health and well-being.

4.4. Limitations and Future Work

While offering valuable insights into the relationship between visitor behavior and soundscape restorative experiences in urban parks, this study has several limitations that should be acknowledged. First, relying on self-reported data for behavioral analysis may introduce subjective bias. Respondents might overestimate or underestimate their engagement with soundscapes or the extent of their activities due to cognitive biases or social desirability effects. To address this limitation, future research could incorporate objective data collection methods, such as GPS tracking or direct behavioral observation, to accurately represent visitors’ activities and interactions with the soundscape. Second, as a cross-sectional study, this research is limited in its ability to draw causal inferences. While we identified correlations between soundscape characteristics, visitor behavior, and restorative experiences, the direction of causality remains unclear. Longitudinal studies would be valuable in understanding how long-term exposure to specific soundscapes influences behavioral patterns and psychological restoration, potentially offering more robust evidence of causal relationships. Third, this study did not systematically control or consider several environmental factors affecting soundscape perception, such as weather conditions and time of day. These factors may significantly influence how visitors experience and interact with soundscapes. Future studies should aim to account for these variables by collecting data under varying environmental conditions or by controlling for these factors during analysis to improve the external validity of the findings. Additionally, the study was conducted in five urban parks in Fuzhou, China. While these parks provide a rich context for exploring the interaction between soundscapes and visitor behavior, the findings may be influenced by the specific cultural and environmental context of the region. The generalizability of the results to other cultural or geographic settings remains an open question. Future research should examine whether these findings hold true in different cultural or environmental contexts.
In terms of future research directions, several promising avenues can be pursued. First, individual characteristics could be incorporated into research to explore how different types of soundscapes (e.g., natural vs. urban soundscapes) affect different demographic groups’ behavior and restorative experiences. Investigating the interaction of soundscapes with behavior in people of varying age groups, genders, or socioeconomic backgrounds can provide a more nuanced understanding of how different populations engage with and benefit from soundscapes. Second, combining physiological indicators such as heart rate variability or cortisol levels would provide a more complete picture of the restorative effects of soundscapes. These objective indicators of stress and relaxation would complement self-reported data and provide a more comprehensive evaluation of the health benefits of exposure to different soundscapes. Overall, while this study lays a strong foundation for understanding the role of soundscapes in promoting well-being in urban parks, addressing these limitations and pursuing these future research directions would further enrich the field and enhance the practical applications of soundscape design.

5. Conclusions

The research explores how the interaction between soundscape characteristics and behavioral factors affects the SRE in urban parks. The results indicate that the following:
(1)
In urban parks, visitors’ static and dynamic behavior were associated with different dimensions of soundscape characteristics. Soundscapes with high pleasantness and eventfulness promote static behavior, while only eventful soundscapes promote dynamic behavior. However, soundscape characteristics did not show an apparent effect on moving behavior. Therefore, different soundscape design strategies should be applied to areas targeting different behavioral types in urban parks.
(2)
Soundscape characteristics directly contribute to the SRE and indirectly through the mediating effect of static behavior, which is more evident with soundscape pleasantness than eventfulness. Therefore, enhancing the pleasantness of soundscape in areas conducive to static behavior could be more beneficial for acquiring the SRE in urban parks.
(3)
Visit intensity to urban parks showed a similar moderating effect on the SRE. Specifically, it positively influenced the direct effect of pleasant soundscape on the SRE while reducing the mediating effect of static behavior. The findings further emphasize the importance of exposure to green spaces for health benefits.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f15101751/s1, Table S1: Acoustic parameters of measuring points in five urban parks; Table S2: Preliminary confirmatory factor analysis of model M1, B1: Relaxation and reflection, B2: Contact with nature, B3: Social interaction, B4: Facilities activities, B5: Site activities, B6: Free activities, B7: Stroll, B8: Running, B9: Passing-by.

Author Contributions

Conceptualization, J.L.; methodology, J.L., X.G. and S.-Y.J.; formal analysis, J.L., X.G. and S.-Y.J.; investigation, J.L., X.G., S.-Y.J., and X.-C.H.; writing—original draft preparation, J.L. and X.G.; writing—review and editing, J.L., S.-Y.J., X.-C.H. and Z.C.; supervision, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 52378049 and No. 52208052) and Fujian Natural Science Foundation, China (No. 2023J05108).

Data Availability Statement

The original contributions presented in the study are included in the Supplementary Materials, further inquiries can be directed to the corresponding author.

Acknowledgments

The authors appreciate the valuable comments of editors and anonymous reviewers.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Mean values of sound source perception indicators.
Figure 1. Mean values of sound source perception indicators.
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Figure 2. Decomposition diagram for step-by-step model building.
Figure 2. Decomposition diagram for step-by-step model building.
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Figure 3. In the model of soundscape characteristics affecting behavioral types, significant influence is marked with * (p < 0.05) and *** (p < 0.001); solid lines represent significant paths and dashed lines represent non-significant paths.
Figure 3. In the model of soundscape characteristics affecting behavioral types, significant influence is marked with * (p < 0.05) and *** (p < 0.001); solid lines represent significant paths and dashed lines represent non-significant paths.
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Figure 4. In the model of mediating effect of behavioral types on the SRE, significant influence is marked with * (p < 0.05), ** (p < 0.01), and *** (p < 0.001); solid lines represent significant paths and dashed lines represent non-significant paths.
Figure 4. In the model of mediating effect of behavioral types on the SRE, significant influence is marked with * (p < 0.05), ** (p < 0.01), and *** (p < 0.001); solid lines represent significant paths and dashed lines represent non-significant paths.
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Figure 5. The effect of visit intensity on the SRE in the conditional process model M3, where significant influence is marked with ** (p < 0.01) and *** (p < 0.001), and solid lines represent significant paths and dashed lines represent non-significant paths.
Figure 5. The effect of visit intensity on the SRE in the conditional process model M3, where significant influence is marked with ** (p < 0.01) and *** (p < 0.001), and solid lines represent significant paths and dashed lines represent non-significant paths.
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Figure 6. The moderating effect decomposition diagram of visit intensity (VI) on the relationship between soundscape pleasantness and the SRE (a), as well as static behavior and the SRE (b).
Figure 6. The moderating effect decomposition diagram of visit intensity (VI) on the relationship between soundscape pleasantness and the SRE (a), as well as static behavior and the SRE (b).
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Table 1. The general information of five case study parks in Fuzhou, China (source: https://earth.google.com/web/ (accessed on 25 July 2021); https://baike.baidu.com/ (accessed on 1 October 2022)); the red points in the satellite image represent the acoustic measurement sample points.
Table 1. The general information of five case study parks in Fuzhou, China (source: https://earth.google.com/web/ (accessed on 25 July 2021); https://baike.baidu.com/ (accessed on 1 October 2022)); the red points in the satellite image represent the acoustic measurement sample points.
Forests 15 01751 i001Name: West Lake Park
Location: 26°05′33.22″ N, 119°17′23.29″ E
Scale: 42.51 ha
Features: Converted from a classical garden to an urban park in 1914, it is one of the most famous parks in Fuzhou, with a history of nearly 1700 years.
Typical sound sources: Birdsong, Tree rustling, Flowing water, Surrounding speech, Footsteps, Playing children, Sports and fitness, Broadcasting music, etc.
LAeq, 3-min: 52.4–61.9 dBA
Forests 15 01751 i002Name: Zuohai Park
Location: 26°05′53.61″ N, 119°17′6.23″ E
Scale: 35.47 ha
Features: It was built in 1990, and the park’s overall design is based on the theme of “five continents” and incorporates a variety of gardening styles, and a wide range of amusement facilities within the park.
Typical sound sources: Birdsong, Tree rustling, Wind blowing, Surrounding speech, Footsteps, Sports and fitness, Broadcasting music, Construction noise, Traffic noise, etc.
LAeq, 3-min: 49.2–59.7 dBA
Forests 15 01751 i003Name: Wenquan Park
Location: 26°05′48.51″ N, 119°18′46.07″ E
Scale: 10 ha
Features: It was completed and opened in 1997, was designed in a European style, and features various water features throughout, including an artificial lake, fountains, and water-screen, etc.
Typical sound sources: Birdsong, Insects, Flowing water, Fountains, Surrounding speech, Footsteps, Playing children, Sports and fitness, Broadcasting music, etc.
LAeq, 3-min: 54.6–67.7 dBA
Forests 15 01751 i004Name: Fuzhou National Forest Park
Location: 26°08′57.63″ N, 119°17′36.75″ E
Scale: 859.33 ha
Features: It was built in 1960, and it is one of the top ten forest parks in China, rich in flora and fauna, and has both scientific and educational functions in addition to being an urban park.
Typical sound sources: Birdsong, Insects, Tree rustling, Wind blowing, Surrounding speech, Footsteps, Playing children, etc.
LAeq, 3-min: 43.9–65.6 dBA
Forests 15 01751 i005Name: Jin’an Park
Location: 26°05′56.71″ N, 119°20′48.65″ E
Scale: 68.27 ha
Features: It was opened in 2017, and it is a typical practice case of modern urban park concepts and ecological engineering design strategies.
Typical sound sources: Insects, Tree rustling, Wind blowing, Surrounding speech, Playing children, Broadcasting music, Construction noise, Traffic noise, etc.
LAeq, 3-min: 53.7–66.7 dBA
Table 2. Classification of behavioral types in the urban parks.
Table 2. Classification of behavioral types in the urban parks.
TypesSpecific Activities
Static
behavior		B1: Relaxation and reflection, such as sitting, reading, meditation, picnicking, breathing fresh air, etc.
B2: Contact with nature, such as enjoying natural beauty, watching animals and plants, listening to birds and water, etc.
B3: Social interaction, such as chatting, party, playing cards, drinking tea, etc.
Dynamic behaviorB4: Facilities activities, such as activities relying on recreational facilities and fitness facilities
B5: Site activities, such as ball games, dance martial arts, and other fitness activities
B6: Free activities, such as playing, taking photos, taking care of children and other activities
Moving
behavior		B7: Strolling
B8: Running
B9: Passing-by
Table 3. The perceived restorativeness of soundscape scales in urban parks.
Table 3. The perceived restorativeness of soundscape scales in urban parks.
DimensionKeywordItem
Fascination CuriosityThe soundscape in the park awakens my curiosity.
Discover There is plenty for me to discover in this soundscape.
InterestFollowing what is going on in this soundscape really holds my interest.
Being-away BreakSpending time in this soundscape gives me a break from my day-to-day routine.
Free fromWhen I am in this soundscape, I feel free from work and/or responsibilities.
RefugeThis soundscape is a refuge for me from unwanted distractions.
Compatibility AdaptI rapidly adapt to this soundscape in the park.
Do what wantIt is easy to do what I want while I am in this soundscape.
FitBeing in this soundscape fits with my personal inclinations.
AccordanceThere is an accordance between what I like to do and this soundscape.
Coherence CoherentThis soundscape in the park is coherent.
BelongThe existing sounds belong to this soundscape.
Fit togetherThe sounds fit together to form a coherent soundscape.
ExtentExplorationThis soundscape is large enough to allow exploration in many directions.
LimitlessIt seems like the extent of this soundscape is limitless.
SpaciousThis soundscape in the park feels very spacious.
Table 4. Definitions and standards for goodness-of-fit indices in structural equation modeling.
Table 4. Definitions and standards for goodness-of-fit indices in structural equation modeling.
Full NameAbbreviationMeaningStandard for Good Fit
Chi-square divided by degrees of freedomχ2/dfMeasures the fit of the model relative to its complexity, considering the ratio of χ2 to degrees of freedomχ2/df < 3 indicates good fit; χ2/df < 5 is considered acceptable
Comparative Fit IndexCFICompares the fit of the proposed model with a null model (assuming no relationships between variables)CFI ≥ 0.95 indicates excellent fit; 0.90 ≤ CFI < 0.95 indicates good fit
Goodness-of-Fit IndexGFIEvaluates the proportion of variance accounted for by the estimated population covariance matrixGFI ≥ 0.90 indicates good fit
Root Mean Square Error of ApproximationRMSEAReflects the approximation error in the model, considering model simplicityRMSEA < 0.05 indicates close fit; 0.05 ≤ RMSEA < 0.08 indicates good fit; RMSEA > 0.10 is poor fit
Normed Fit IndexNFICompares the chi-square value of the model to the null model (assuming no relationships between variables)NFI ≥ 0.90 indicates good fit
Tucker–Lewis IndexTLIAdjusts the comparison between the model and null model, considering model complexityTLI ≥ 0.95 indicates excellent fit; TLI ≥ 0.90 indicates good fit
Incremental Fit IndexIFIMeasures the fit improvement of the proposed model over a null modelIFI ≥ 0.95 indicates excellent fit; IFI ≥ 0.90 indicates good fit
Adjusted Goodness-of-Fit IndexAGFIAdjusts the GFI based on the degrees of freedom in the model, penalizing more complex modelsAGFI ≥ 0.90 indicates good fit
Table 5. Confirmatory factor analysis of model M1 after adjustment, CR: combined reliability, AVE: average variance extraction.
Table 5. Confirmatory factor analysis of model M1 after adjustment, CR: combined reliability, AVE: average variance extraction.
Latent VariablesObserved VariablesFactor LoadingCRAVE
Soundscape pleasantnessPleasant0.910.930.81
Comfortable0.92
Harmonious0.87
Soundscape eventfulnessDynamic0.880.910.77
Various0.87
Eventful0.88
Static behaviorB10.790.820.7
B20.87
Dynamic behaviorB40.880.750.61
B50.66
Moving behaviorB91.001.001.00
Table 6. Discriminant validity test for model M1, soundscape pleasantness (SSP), soundscape eventfulness (SSE), square root of the AVE (AVE-SR).
Table 6. Discriminant validity test for model M1, soundscape pleasantness (SSP), soundscape eventfulness (SSE), square root of the AVE (AVE-SR).
Latent VariablesSSPSSEStatic BehaviorDynamic BehaviorMoving Behavior
SSP0.811
SSE0.8340.769
Static behavior0.5640.5480.699
Dynamic behavior0.2080.2520.1370.605
Moving behavior0.0390.0790.0340.021.00
AVE-SR0.9000.8780.8360.7781.000
Note: The values on the diagonal are the average variance extracted (AVE) for the corresponding latent variables.
Table 7. The goodness-of-fit test of model M1.
Table 7. The goodness-of-fit test of model M1.
Fit Indexχ2/dfCFIGFIRMSEANFITLIIFIAGFI
Fitting standard≤3≥0.9≥0.9≤0.08≥0.9≥0.9≥0.9≥0.9
Conceptual model2.9210.9750.9550.0680.9620.9640.9750.922
Table 8. Path analysis results of the influence of soundscape characteristics on behavioral types, SE: standard error, C.R.: critical ratio.
Table 8. Path analysis results of the influence of soundscape characteristics on behavioral types, SE: standard error, C.R.: critical ratio.
Hypothesis PathβSEC.R.p
Pleasantness → Static behavior0.350.103.38<0.001
Pleasantness → Dynamic behavior−0.010.17−0.040.967
Pleasantness → Moving behavior−0.090.18−0.810.420
Eventfulness → Static behavior0.260.102.480.013
Eventfulness → Dynamic behavior0.260.172.080.037
Eventfulness → Moving behavior0.150.181.390.164
Table 9. Confirmatory factor analysis of model M2.
Table 9. Confirmatory factor analysis of model M2.
Latent VariablesObserved VariablesFactor LoadingCRAVE
Soundscape pleasantnessPleasant0.910.930.81
Comfortable0.92
Harmonious0.87
Soundscape eventfulnessDynamic0.880.910.77
Various0.87
Eventful0.88
Static behaviorB10.780.820.70
B20.89
Dynamic behaviorB4 0.960.770.64
B50.60
Soundscape restorative experienceFascination0.850.950.78
Being-away0.85
Compatibility0.92
Coherence 0.92
Extent0.88
Table 10. Discriminant validity test for model M2.
Table 10. Discriminant validity test for model M2.
Latent VariablesSSPSSEStatic BehaviorDynamic BehaviorSRE
SSP0.811
SSE0.8360.769
Static behavior0.5580.5420.700
Dynamic behavior0.1290.230.1250.641
SRE0.8000.7740.5520.2320.78
AVE-SR0.9000.8770.8370.8000.883
Note: The values on the diagonal are the average variance extracted (AVE) for the corresponding latent variables.
Table 11. The goodness-of-fit test of model M2.
Table 11. The goodness-of-fit test of model M2.
Fit Indexχ2/dfCFIGFIRMSEANFITLIIFIAGFI
Fitting standard≤3≥0.9≥0.9≤0.08≥0.9≥0.9≥0.9≥0.9
Conceptual model2.040.9680.9490.050.9690.980.9840.925
Table 12. Path analysis results of the influence of behavioral types on the SRE, SE: standard error, C.R.: Critical ratio.
Table 12. Path analysis results of the influence of behavioral types on the SRE, SE: standard error, C.R.: Critical ratio.
Hypothesis PathβSEC.R.p
Pleasantness → Static behavior0.350.13.36<0.001
Eventfulness → Static behavior0.250.12.430.015
Eventfulness → Dynamic behavior0.230.084.41<0.001
Pleasantness → SRE0.470.076.50<0.001
Eventfulness → SRE0.310.074.32<0.001
Static behavior → SRE0.120.052.650.008
Dynamic behavior → SRE0.060.021.70.089
Table 13. The mediating effect test results of static behavior in the mechanism of soundscape characteristics affecting the SRE.
Table 13. The mediating effect test results of static behavior in the mechanism of soundscape characteristics affecting the SRE.
Hypothesis PathEffectSEBootstrapping Algorithm
Bias-Corrected 95% CI
LowerUpper
Pleasantness → SREtotal effect0.5080.090.3260.671
indirect effect0.0410.020.0050.099
direct effect0.4670.090.2790.638
Eventfulness → SREtotal effect0.3360.090.1730.506
indirect effect0.030.030.0000.104
direct effect0.3060.080.1630.471
Table 14. The moderating effect test results of visit intensity in the mechanism of soundscape characteristics affecting the SRE.
Table 14. The moderating effect test results of visit intensity in the mechanism of soundscape characteristics affecting the SRE.
VariableStatic BehaviorSRE
βSEtβSEt
Pleasantness0.440.0410.23 ***0.690.0419.61 ***
VI0.130.042.94 **0.110.033.15 **
Pleasantness × VI−0.020.04−0.450.10.042.62 **
Static behavior 0.130.043.6 ***
Static behavior × VI −0.110.42−2.68 **
R0.47 0.77
R20.22 0.6
F39.2 122.22
Note: Significant levels are marked with ** (p < 0.01) and *** (p < 0.001).
Table 15. The effect of soundscape pleasantness on the SRE under different visit intensity (VI) levels, M: mean, SD: standard deviation, SE: standard error, LLCI: lower-level confidence interval, ULCI: upper-level confidence interval.
Table 15. The effect of soundscape pleasantness on the SRE under different visit intensity (VI) levels, M: mean, SD: standard deviation, SE: standard error, LLCI: lower-level confidence interval, ULCI: upper-level confidence interval.
EffectGroupVIEffectSELLCIULCI
Direct effectM − 1SD0.840.60.050.510.69
M2.520.690.040.620.76
M + 1SD4.190.780.050.680.89
Mediating effect through static behaviorM − 1SD0.840.110.040.040.18
M2.520.060.020.020.1
M + 1SD4.190.010.03−0.040.07
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Guo, X.; Jiang, S.-Y.; Liu, J.; Chen, Z.; Hong, X.-C. Understanding the Role of Visitor Behavior in Soundscape Restorative Experiences in Urban Parks. Forests 2024, 15, 1751. https://doi.org/10.3390/f15101751

AMA Style

Guo X, Jiang S-Y, Liu J, Chen Z, Hong X-C. Understanding the Role of Visitor Behavior in Soundscape Restorative Experiences in Urban Parks. Forests. 2024; 15(10):1751. https://doi.org/10.3390/f15101751

Chicago/Turabian Style

Guo, Xuan, Si-Yu Jiang, Jiang Liu, Zhu Chen, and Xin-Chen Hong. 2024. "Understanding the Role of Visitor Behavior in Soundscape Restorative Experiences in Urban Parks" Forests 15, no. 10: 1751. https://doi.org/10.3390/f15101751

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