Bridging the Subjective-Objective Soundscape Gap: A Multi-Seasonal Framework for Prioritized Optimization in Urban Parks Under Traffic Noise

Lihua Yin^1,2, Huimin Du¹, Mingming Zhang³, Wenbo Zhang¹, Yuchuan Cao⁴, Shixuan Liu¹, Xinxin Li^1,2, Ming Chen⁵

^z,2 Xinxin Li

lixx@hust.edu.cn

¹ School of Architecture and Urban Planning of Huazhong University of Science and Technology, Wuhan 430074, China

² Hubei Engineering and Technology Research Center of Urbanization, Wuhan 430074, China

³ Qiaokou District Urban Management and Law Enforcement Bureau, Wuhan 430074, China

⁴ College of Horticulture & Forestry Sciences of Huazhong Agricultural University, Wuhan 430070, China

⁵ College of Landscape Architecture and Art of Fujian Agriculture and Forestry University, Fujian 350002, China

Abstract

The soundscape of urban parks is increasingly impaired by traffic noise. However, there remains a lack of systematic quantitative analysis methods between objective acoustic parameters and public perceptions, resulting in a disconnect between objective measurements and subjective experiences.Taking Wuhan Daijiahu Park as the study area, we conducted multi-seasonal measurements of sound pressure levels and subjective assessments via soundwalks and introduced the Subjective-Objective Soundscape Variance (SOSV) index and an adapted Importance-Performance Analysis (IPA) to identify priority areas for optimization. Results showed significant seasonal variations, with traffic noise dominating except in summer when cicada sounds became prevailing. Equivalent continuous A-weighted sound pressure level (LAeq) correlated with distance to the 3rd Ring Road rather than to railways. SOSV analysis revealed that vegetation, water features, and enclosed spaces positively influence subjective evaluations. The adapted IPA classified 42.6% of the park as high-priority for improvement. This framework provides practical strategies for soundscape optimization in urban parks.The study explores landscape factors influencing soundscape discrepancies under complex traffic impacts, providing guidance for urban park soundscape planning.

Keywords

Soundscape

Urban park

Subjective-Objective Soundscape Variance (SOSV)

Spatio-Temporal variation

Adaptive transformation of the IPA Method

Introduction

The soundscape of urban parks plays a crucial role in improving environmental quality, promoting mental health and well-being, and fostering a sense of belonging and satisfaction(Kang et al. 2016; Jiang et al. 2022). Soundscape refers to the acoustic environment perceived by humans in specific conditions, which serves as a key dimension of ecological service functions(Meng and Kang 2016). The spatial characteristics, acoustic properties, and psychological factors of soundscape are all important elements influencing people's acoustic perception in urban parks(Liu and Kang 2015; Bahali and Tamer-Bayazit 2017; Ou et al. 2017). With the continuous advancement of urbanization, the issue of soundscape quality in urban parks along transportation trunks has become particularly prominent, and the demand for improving urban acoustic environment has become increasingly pressing. As a vital component of urban ecosystems, urban parks play an irreplaceable role in maintaining ecological balance while providing leisure, entertainment, and social venues for local residents(Harvey et al. 2015). Furthermore, soundscape quality could directly affect visitors' satisfaction(Liu et al. 2018).

Noise has been identified as the second most significant environmental risk to health(Affairs 2019). Transportation system is the primary source of urban environmental noise, with most residents exposed to excessive traffic noise(Khomenko et al. 2022). Research on the soundscape of urban parks, particularly in areas adjacent to traffic, has shifted from noise control to the optimization of soundscape resources. Traffic noise (with an equivalent continuous sound level, LAeq > 65 dB) significantly reduces visitors’ satisfaction(Liu et al. 2014). Although sound pressure levels (SPL) are measurable and clearly correlated with the impacts of noise, they could only explain 30% of these influences(Van Renterghem 2019). Natural or green spaces, in addition to creating pleasant soundscapes themselves, may mitigate influence of traffic noise through various mechanisms(Van Renterghem 2019). Therefore, it is essential to adopt landscape-based approaches to reduce traffic noise in urban parks and improve acoustic environment.

Since the concept of soundscape emerged, the description, perception, and evaluation of it have remained significant research topics, with the number of studies in related fields growing steadily(Ren 2023). Seasonal changes, differences in daytime periods, and geographical diversity could substantially affect acoustic environment(Goel et al. 2018). However, most existing studies lack seasonal or diurnal data, making it difficult to capture the dynamic changes in soundscapes and leading to doubts about the universality of optimization strategies. Research on soundscape evaluation has shifted from a paradigm based on physical acoustic measurement to one driven by perception. Early studies relied on objective acoustic environment, analyzing them through physical measurements, soundscape maps, and other similar methods(Brambilla et al. 2013). Due to their limited capacity to explain perceptual differences, a multi-dimensional integrated evaluation system has gradually been developed, which includes four aspects: 1) Subjective perception tools: Methods such as the semantic differential method and the Importance-Satisfaction (I-S) model have become two core approaches(Alvarsson et al. 2010; Ou et al. 2017); 2) Spatio-temporal dynamic analysis: Subjective acoustic environment are analyzed using interviews, questionnaires, or measurements of psychological and physiological indicators(Kang and Zhang 2010). By combining soundwalks with Kriging interpolation, the laws of soundscape zoning are revealed; 3) Technological innovation: Technologies like VR/eye-tracking are used to quantify audio-visual interactions, AI models are also applied to analyze non-linear relationships(Liu and Kang 2018); 4) Cutting-edge focus on intelligent dynamic regulation: Examples include mobile sensing platforms, soundscape masking optimization, and AI-generated soundscapes(Jo and Jeon 2020). While research methods for subjective and objective soundscape evaluation are relatively mature, a dual analytical framework for the objective-subjective coupling of soundscapes has rarely been employed.

Currently, the key conclusions related to soundscape evaluation and optimization are as follows: 1) Four-season dynamic characteristics: The acoustic diversity index (ADI) of biological sounds in summer is 35% higher than that in winter, with insect sounds reaching their peak proportion in August(Zhao et al. 2022); the pleasantness score of bird songs in spring is the highest (4.5/5), and the sound of falling leaves in autumn can increase the score by 20%(Xiang et al. 2023) ; 2)Daily dynamic characteristics: When the proportion of natural sounds exceeds 40%, the sound comfort score in the early morning is 1.2 points higher than that in the afternoon (on a 5-point scale)(Liu et al. 2019); 3)Soundscape influencing factors and their degrees: In natural sound-dominated areas (e.g., dense forests), the presence of bird songs significantly increases soundscape satisfaction by 23%(Zhao et al. 2018); the soundscape pleasantness score in areas within 50 m of water bodies is 1.5 points higher than that in areas without water bodies, and the restorative effect of dynamic water sounds (such as waterfalls/streams) is 17% higher than that of static water bodies(Ratcliffe et al. 2016); mountainous terrain has a substantial impact on the spatial variation of soundscapes—utilizing height differences in mountains to create sound shadow areas (e.g., leeward slopes) can reduce noise by 5–8 dB(Li et al. 2021). The soundscape satisfaction rate in traffic noise-dominated areas (L10 > 70 dB) is 38% lower than that in natural sound-dominated zones, and the result is related to the linear attenuation of noise with distance from the road.4)Soundscape optimization classification and zoning: Urban park soundscapes are divided into natural-dominated areas, anthropogenic-dominated areas, and mixed areas based on the proportion of sound sources, with the validity of this zoning verified through questionnaires(Jeon and Hong 2015); using principal component analysis (PCA) and K-means clustering, soundscapes are classified into high-quality quiet areas, vibrant entertainment areas, and noise-disturbed areas according to indicators such as pleasantness, sense of event, and sound pressure level(Aletta et al. 2016).

Two critical issues still remain in current soundscape evaluation: the ineffective quantitative evaluation of SOSV, and the lack of implementable pathways in soundscape optimization classification that can guide planning and design. Physical acoustic indicators as well as perceptual dimensions lack dynamic correlation models and fail to account for non-linear relationships(Rey Gozalo et al. 2015). For instance, under the same sound pressure level, the pleasantness brought by natural sounds differs distinctly from that of mechanical sounds(Kogan et al. 2018). Therefore, it is necessary to establish a physics-perception coupling approach to objectively and quantitatively characterize the subjective-objective differences, thereby promoting the optimization and improvement of soundscapes. It is critically significant to conduct research on systematic evaluation, zoned planning, and management of urban park soundscapes.

This study intends to address the following three research questions: 1) What are the spatio-temporal variation characteristics of the subjective and objective soundscapes in urban parks embedded within complex traffic environment? 2) What are the landscape factors influencing the degree of subjective-objective differences and their contribution levels? 3) What is the approach for hierarchical optimization of soundscapes?

Taking Wuhan Daijiahu Park as the research object, this study measured objective SPL on-site and obtained subjective soundscape indices (subjective loudness, sound comfort, and sound satisfaction) through the soundwalk method. Normalized indices were used to analyze the degree of subjective-objective differences and their landscape-influencing factors. Moreover, the IPA method was adapted for application in the hierarchical optimization of urban park soundscapes. The aim is to provide urban planners with scientific bases for designing strategies and formulating noise management and control measures, as well as to enrich the dimensions of soundscape assessment.

Methods

Study Area

Daijiahu Park in Wuhan is surrounded by urban roads on three sides, with high-speed railways, conventional railways, and the city’s 3rd Ring Road running through it. Owing to its complex exposure to multi-source traffic noise, it constitutes a typical research object for exploring the subjective-objective discrepancies in the acoustic environment of urban parks.

Meanwhile, it has also witnessed the industrial transformation of Wuhan, and is popular among citizens. There is an urgent need to address the noise pollution challenges currently confronting the park. In the 1950s, the original site was a natural lake named “Daijiahu”; after 1958, it was converted into a large-scale coal storage yard to serve the production of Wuhan Iron and Steel Factory and nearby industries; in 2005, an urban ecological restoration project was launched on this site, and the planning and design comprehensively considered the relationship between the spaces under the high-speed railway bridges and elevated expressways in the site and urban green spaces(Suligowski et al. 2021); in 2015, it was transformed into “Daijiahu Park”, an ecologically sound space that serves multiple functions, including landscape viewing, sports and leisure, ecological protection, cultural entertainment, and disaster prevention(Fig. 1(b)).

Fig. 1

(a) Location of the research base and functional zoning; (b) Aerial view of the research area

Research Methods

To better understand the soundscape characteristics of Daijiahu Park, the park was divided into 54 grid cells with an equal size of 100 m × 100 m (Hong and Jeon 2017), (Fig. 2), while one representative point was selected within each cell (e.g., near water areas, structures, viaducts, or plant clusters) as the sampling point for objective SPL measurement and subjective soundwalk evaluation.

Fig. 2

Experimental scheme for soundscape data collection

Data Collection and Processing

(1) Four-Season On-Site SPL Measurement

The data collection period for four-season SPL spanned the entire year from July 2023 to July 2024. To minimize environmental interference, measurements were conducted on three sunny days per season with wind speed less than 1 m/s. An AWA5688 sound level meter was used at 54 evenly distributed sampling points in the park to record the equivalent continuous noise level (

$\:{\text{L}}_{\text{Aeq}}$

), within 1 minute. Each sampling point was measured three times repeatedly, and the average data was taken(Wosniacki and Zannin 2021). To reduce noise reflection, the measuring points were set 1.2 m above the ground and at least 1.5 m away from reflective objects. A DISTO laser rangefinder and a handheld GPS were utilized to record the height and latitude/longitude of the measuring points for accurate positioning and subsequent analysis.

(2)

Soundwalk Method

The soundwalk method was adopted in this study to collect and evaluate subjective soundscape data in Daijiahu Park. The soundwalk method is a technique for recording and evaluating the sound environment through on-site walking in a specific environment. Based on the locations of the 54 sampling points in Daijiahu Park and their surroundings, a soundwalk route was planned (Fig. 3(a)). From July 2023 to July 2024, thirty-five scholars with architectural education backgrounds were organized by the research team to conduct soundwalk experiments, which were completed in six sessions. Participants received training before the experiment to understand the study purpose, the soundwalk method, the specific requirements for the questionnaire, and the scoring criteria.

During the soundwalk, a AWA5688 multi-functional sound level meter was employed to record the 1-minute equivalent continuous noise (LAeq) at each sampling point, which was recorded three times continuously, and the arithmetic mean was taken as the average noise SPL of that point. Besides, participants recorded no more than 3 main sound types at each point and scored the subjective loudness(Gale et al. 2021)、soundscape comfort(Yu and Kang 2009)、and soundscape satisfaction(Ma et al. 2021) of each sampling spot using a 5-point Likert scale. The arithmetic mean of three indicators is calculated to ultimately derive the comprehensive score of the soundscape(Aletta et al. 2016).

For the 5-point Likert scale in this experiment, each indicator had five scoring results: 1 point, 2 points, 3 points, 4 points, and 5 points. Subjective loudness corresponded to the five responses of “Noisy”, “Somewhat noisy”, “Neither noisy nor quiet”, “Somewhat quiet”, and “Quiet”; sound comfort corresponded to “Uncomfortable”, “Somewhat uncomfortable”, “Neither uncomfortable nor comfortable”, “Somewhat comfortable”, and “Comfortable”; sound satisfaction corresponded to “Dissatisfied”, “Somewhat dissatisfied”, “Neither dissatisfied nor satisfied”, “Somewhat satisfied”, and “Satisfied” (see Appendix A for details).

Fig. 3

Soundwalk

Data Analysis

(1) Soundscape Distribution Map

In terms of data visualization, this study used ArcGIS 10.7, a geographic information system (GIS) software, for processing: 1) Input the on-site SPL measurement data of all sampling points and their geographic coordinate information; 2) Load the data and ensure settings of the coordinate system is correct; 3) Use the Kriging interpolation method in the Spatial Analyst of the ArcToolBox to perform spatial data interpolation(Bargaoui and Chebbi 2009), and finally generate the soundscape distribution map.

(2)

Subjective-Objective Soundscape Variance (SOSV) Analysis

This study includes both subjective and objective measurements, referring to the objective SPL in on-site sound environment measurement and the subjective soundscape evaluation results. Both are evaluations of the soundscape in Daijiahu Park (the same research object): the former is the SPL measured in the physical environment, and the latter is the comprehensive score of subjective perception. The two have different value ranges; thus, to compare them simultaneously. It is necessary to convert them into quantities within a unified range. Therefore, the subjective and objective data of Daijiahu Park were normalized respectively and converted into values between 0 and 1, which can reduce the subjective and objective evaluation data with different discrete degrees to the same order of magnitude.

For the comprehensive soundscape score, the original data of 1–5 points correspond to better subjective evaluation (a positive indicator). Its normalization formula is as follows:

$\:{\text{m}}^{{\prime\:}}\text{=}\frac{\text{m−}\text{min}\left(\text{m}\right)}{\text{max}\left(\text{m}\right)\text{−min(m)}}$

Among these factors,

$\:\text{m\:}$

represents the comprehensive soundscape score of the sampling point,

$\:\text{m'}\text{}$

represents the soundscape normalized index,

$\:\text{min}\left(\text{m}\right)$

represents the minimum value of the comprehensive soundscape score among the 54 sampling points in Daijiahu Park, and

$\:\text{max}\left(\text{m}\right)$

represents the maximum value of the comprehensive soundscape score among the 54 sampling points in Daijiahu Park.

For the SPL of the acoustic environment, a higher value corresponds to a worse objective evaluation, so it is a reverse indicator. Therefore, reverse normalization is required when normalizing it. The formula for its reverse normalization is as follows.

$\:{\text{n}}^{{\prime\:}}\text{=}\frac{\text{n−}\text{min}\left(\text{n}\right)}{\text{max}\left(\text{n}\right)\text{−min(n)}}$

Among them,

$\:\text{n}$

represents the SPL of the sampling point,

$\:{\text{n}}^{{\prime\:}}$

represents the SPL normalized index,

$\:\text{min}\left(\text{n}\right)$

denotes the minimum SPL among the 54 sampling points in Daijiahu Park, and

$\:\text{max}\left(\text{n}\right)$

stands for the maximum SPL among these 54 sampling points.

Integrating the normalized data with findings on the consistency and causes of subjective-objective measurement discrepancies, this study proposes the concept of SOSV(Song and Zhang et al. 2020).SOSV represents the difference between the normalized result of the subjective soundscape score and that of the objective SPL at a given sampling point. It converts the subjective evaluation and objective SPL from different measurement standards to the same dimensionless standard, which facilitates subsequent comparative analysis and research related to soundscape optimization. Its expression is as follows:

$\:\text{SOSV=}{\text{m}}^{{\prime\:}}\text{−}{\text{n}}^{{\prime\:}}$

Among these factors,

$\:{\text{m}}^{{\prime\:}}$

is the soundscape normalized index of the point, and

$\:{\text{n}}^{{\prime\:}}$

refers to the SPL normalized index of the point. The SOSV of 54 sampling spots in Daijiahu Park across four seasons was calculated respectively. If SOSV > 0, the following is indicated: the landscape design or other aspects of the area elevate visitors’ subjective soundscape evaluation beyond the perception brought by the objective SPL. Summarizing the site design patterns of sampling points with SOSV > 0 in Daijiahu Park across seasons can provide a theoretical basis for subsequent soundscape optimization.

(3)

Selection and Contribution Degrees of Landscape Factors

Relevant studies have shown that natural landscape elements can effectively reduce noise and improve soundscape satisfaction. The main factors influencing the subjective-objective soundscape differences are summarized as follows:

Plants: Enhancing audio-visual interactions by increasing the proportion of flowering plants and integrating natural visual elements can divert attention and reduce the perceived intensity of noise(Van Renterghem 2019). Planting bird-friendly plant species—specifically those with tall trunks, straight branches, and yellowish leaves with late abscission in early winter—meets avian colonies’ habitat requirements, while bird vocalizations exert a masking effect.(Zhao et al. 2022). When combined with arbor or shrubs, lawns form a continuous noise attenuation layer with significant noise reduction effects, while single lawn have limited noise reduction capabilities(Van Renterghem 2019).

Water features: Water features exhibit excellent noise masking performance(Zhou et al. 2023). In activity areas (e.g., squares), dynamic water sounds such as waterfalls or streams sounds yield stronger audio-visual synergy than static water bodies, with soundscape satisfaction scores 40% higher than those of static water bodies(Deng et al. 2020).

Facilities: Adding interesting or memorable facilities (e.g., sculptures, decorative ornaments, landscape walls) and interactive sound installations (e.g., piano paths that emit sound when stepped on, sound-controlled fountains) can significantly enhance soundscape satisfaction(Hong and Jeon 2013). Additionally, dynamic soundscape installations such as wind chime matrices and music walls allow for active user participation, which masks traffic noise and increases spatial interest(Mitchell et al. 2022).

Enclosed spaces: Enhancing enclosure through covered trellises or vegetation reduces the propagation of external traffic noise via physical obstruction(Van Renterghem and Botteldooren 2016). The sound pressure level in semi-enclosed spaces of parks is 6.3 dB lower than that in open areas, with a significant improvement in acoustic comfort(Liu et al. 2019). Playing customized natural sounds (e.g., stream sounds, bird songs) in enclosed spaces can further mask noise(Hong et al. 2020).

Topography: The combination of arbor (noise shielding) + shrubs (sound absorption) + ground cover plants (sightline guidance) can simultaneously improve soundscape naturalness and visual aesthetics(Xu and Wu 2021). It also reduces traffic noise through leaf absorption and scattering(Xie et al. 2016; Hao et al. 2021), with more pronounced effects in noise-sensitive areas (e.g., close to railways)(Xu and Wu 2021). Raised terrain combined with planting can be designed in edge zones adjacent to urban roads to form noise barriers. Compared with flat terrain, this design increases low-frequency noise (< 500 Hz) attenuation by 15%, and the noise reduction effect is significant when the terrain slope exceeds 10°(Van Renterghem et al. 2012).

The maximum SOSV value of a site is defined as the highest SOSV value among its four seasonal measurements. According to the calculation of the normalization index, if a maximum SOSV value exists, it should be standardized to 1. Therefore, this study defines the maximum contribution degree as the maximum SOSV value of a given site across the four seasons.

(4)

(4) Adaptive Transformation of the IPA Method Based on Soundscape Research Characteristics

The IPA (Importance-Performance Analysis) method was established by Martila and James in 1977(Martilla and James 1977), It requires respondents to evaluate each indicator of the research object from two dimensions: importance and performance. When the evaluation indicator is satisfaction scores, performance refers to satisfaction evaluation(Yan et al. 2022). The basic principle is to generate a scatter plot, with importance as the X-axis, performance or satisfaction as the Y-axis, and the total mean of each of the two indicators as the dividing point. Each of the four quadrants has distinct characteristics, which can clearly and intuitively reflect the order of priority improvement (Fig. 4(a)). Generally, “Importance” is defined as the significance respondents attribute to a product or service feature during the experience, whereas satisfaction is the degree of pleasure or displeasure respondents feel regarding the actual experience or performance.

The study recognizes the limitations of the "importance-performance" dimension in the classic IPA model when applied to soundscape research. For the first time, it conducts an adaptive transformation of the traditional IPA method to fit the objective-subjective dual analytical framework of soundscapes. Acoustic environment are unique in that both their physical metrics and their perceptual qualities (soundscape evaluation) are equally essential. Therefore, this study applies the IPA framework in a contextual adaptation: the X-axis is redefined as "Physical Prominence", measured by SPL; the Y-axis retains "Subjective Perceptual Performance", measured by subjective soundscape quality evaluation. This study more directly reveals the mismatch between physical stimuli and perceptual responses. Based on this, the study reinterprets the four quadrants of IPA (Fig. 4(b)) to identify key elements in the acoustic environment, comfortable background sounds, and noise sources and areas requiring priority governance.

Fig. 4

Schematic analysis of the adaptive transformation application of the IPA model

Results

Temporal and Spatial Variation Characteristics of Objective Soundscape

In this study, analysis of the annual noise distribution in Daijiahu Park (Fig. 5) revealed that the SPL exhibits significant seasonal variation. In spring, the acoustic environment is relatively quiet, with SPL mostly in the range of 55–62 dB(A), which is attributed to the natural barrier formed by lush vegetation. In summer, the SPL rises to 62.5–70 dB(A), mainly affected by seasonal cicada chirping(Gogala and Riede 1995). Although dense vegetation helps reduce noise, it also provides a suitable environment for cicada reproduction, leading to a relatively increased noise level(Medvedev et al. 2015). The SPL in autumn is similar to that in winter, ranging from 57.5 to 65 dB(A). Among these areas, the region near the Third Ring Road shows a higher SPL, while the ecological zone and camping area far from traffic are relatively quiet (see Appendix Table S1 for details).

Fig. 5

Spatial distribution of SPL across four seasons in Daijiahu Park

Subjective Temporal and Spatial Variation Characteristics of Soundscape

Soundscape Composition Elements

Through field measurements of soundwalks at 54 sampling points across four seasons, three main types of sound at each sampling point were recorded and analyzed to explore the composition of the park’s soundscape (Fig. 6). The soundscape of the park is mainly composed of anthropogenic sounds (52%) and natural sounds (33%), while living sounds account for a relatively small proportion (15%). Among anthropogenic sounds, automobile sounds are dominant (36%), whereas the influence of railway sounds (trains and high-speed rails) is limited (8% and 6%); natural sounds are mainly comprised of bird songs (18%), frog croaks and insect chirps (12%) (Fig. 7(a)). Considering that automobile sounds are the most disliked by tourists, measures should be implemented to reduce the adverse impact of traffic noise and promote preferred acoustic elements (e.g., bird songs and children’s playful sounds) for soundscape quality improvement.

Figure 6 Composition of the soundscape in Daijiahu Park: (a) overall for four seasons, (b) spring, (c) summer, (d) autumn, (e) winter.

Variation Characteristics of Soundscape in Four Seasons

Soundscape survey data collected from various sampling points across the four seasons (Table 1), were imported into ArcGIS 10.7, along with their coordinates (see Appendix Table S2 for details). Kriging interpolation was then performed to generate the seasonal soundscape maps of Daijiahu Park (Fig. 7).

The comprehensive score of the soundscape quality in Daijiahu Park is 2.7 (out of 5), as the three indicators are all below 3.0, implying that the park’s acoustic environment necessitate urgent improvement. Seasonal evaluation shows that the scores in winter and spring are relatively high (both 2.92), while the lowest data appears in summer (2.32), which may be associated with the cicada chirps. The research results show that the objective acoustic environment quality directly affects tourists’ subjective soundscape evaluation. Therefore, effective measures are suggested to control traffic noise so as to improve the overall quality of the park’s soundscape.

Table 1

Average Soundscape Scores of All Sampling Points in Daijiahu Park
Season	Subjective Loudness	Acoustic Comfort	Acoustic Satisfaction	Comprehensive Score
Spring	2.82	2.94	2.99	2.92
Summer	2.30	2.31	2.34	2.32
Autumn	2.51	2.58	2.59	2.56
Winter	2.75	3.00	3.01	2.92
Four - season Average	2.62	2.72	2.75	2.70

In spring, Daijiahu Park exhibits a favorable overall soundscape, with soundscape indicators exceeding 3.00 points in most areas, which suggests a positive soundscape experience (Fig. 9(a-c)). The lakeside area located in the west part and the central area were rated as "quiet", while the area along the Third Ring Road on the east side was rated as "noisy" due to traffic noise. Spatially, acoustic comfort and satisfaction show a decreasing trend from southwest to northeast. Specifically, while scores peak in the southern area, the lower values recorded in the eastern and northwest corners are primarily attributed to traffic noise.

The overall soundscape of Daijiahu Park is poor in summer. The distributions of subjective loudness, comfort, and satisfaction are similar, forming two regions with high - quality soundscape and two with low - quality soundscape (Fig. 9(d-f)). The soundscape quality at two scenic spots, Time Track and Sunny Lawn, is relatively high. The finding may result from the lush and attractive summer vegetation, which is considered to elevate acoustic satisfaction by improving the visual landscape. In contrast, the soundscape quality at the under - bridge sports field and pavilion is poor, as these locations are close to the Third Ring Road and have high noise levels from recreational activities. Relevant studies have shown that the visual green view index is positively correlated with soundscape pleasure. Particularly when the noise level ranges from 55 to 65 dB(A), vegetation coverage of ≥ 30% can offset the negative impact of noise ,which is consistent with the reasons mentioned above.

In autumn, there is a discrepancy between the soundscape and the sound pressure level distribution depicted in the noise map (Fig. 9(g-i)). The autumn soundscape map shows obvious spatial differentiation: scores exceed 4 points in the west side but are below 2 points in the east side. Except for the area along the Third Ring Road, the park’s soundscape is delightful, which is related to the mild autumn climate and the absence of cicada chirps. The spatial distributions of subjective loudness and acoustic comfort scores are highly consistent: approximately 67% of the area records scores below 2.5 points; conversely, only 20% of the area exceeds 2.8 points. Acoustic satisfaction is relatively high, with approximately 40% of the area scoring over 2.8 points, which may be associated with the advantages of the autumn visual landscape.

The overall soundscape evaluation in Daijiahu Park remains favorable in winter, and the spatial distribution of the three indicators shows clear spatial alignment. (Fig. 9(j-l)). Soundscape scores exceed 3.1 points in the majority of the park, and the lowest score in the worst-performing area is no lower than 1.9 points. Acoustic comfort and satisfaction reach the highest levels in Sunny Lawn, Locomotive Square, and the dense forest area. In contrast, the northwest entrance and the area near the Third Ring Road have lower scores due to the disturbance of traffic noise.

Fig. 7

Spatial distribution of Soundscape across four seasons in Daijiahu Park

Landscape Factors Affecting SOSV and Contribution Degrees

Analysis of the Situation of Points with SOSV

From an annual average perspective, 24 of the 54 sampling points in Daijiahu have a SOSV > 0, meaning nearly half of the sampling points have a better subjective soundscape evaluation than the objective acoustic environment evaluation. Across the four seasons, the number of sampling points with SOSV > 0 in Daijiahu Park varies, with 12, 22, 12, and 15 points in spring, summer, autumn, and winter respectively. This indicates that only a small number of sampling points in the park have a superior subjective soundscape evaluation compared to the objective acoustic environment in each season. Overall, the maximum SOSV value in each season is within 0.3; summer has the largest number of sampling points where the subjective soundscape evaluation is better than the objective acoustic environment, while spring and autumn have the smallest number.

40 sampling points in Daijiahu Park recorded positive SOSV data in at least one season. The number of seasons with SOSV > 0 for these sampling points was counted (Fig. 8). Among them, sampling points No. 18, 23, 39, and 45 have SOSV > 0 in 3 seasons, indicating that the subjective soundscape scores of these points are superior to the objective acoustic environment. These points are distributed across the Time Track scenic spot, dense forest area, and lakeside area. Sampling points No. 1, 14, 15, 19, 20, 21, 22, 26, 27, 28, 34, 42, and 46 have SOSV > 0 in 2 seasons, mostly located across the Time Track area, dynamic leisure area, and lakeside area (see Appendix Table S3 for details). Overall, analyzing and referencing the site environment design of these points is crucial for developing a site design model that could successfully improve the subjective evaluation of the soundscape.

Fig. 8

Statistical chart of the number of sampling points with SOSV > 0

Analysis of Contribution Degrees of Landscape Factors

A detailed analysis was conducted on the environmental characteristics of sampling points with the maximum SOSV values in each season (Table 2). It was found that these maximum-value points are distributed in water areas, lawns, dense forests area. The shared environmental characteristics of these points—including attractive landscapes, rich vegetation layers, or well-equipped tourist facilities—contribute significantly to enhancing tourists’ subjective perception. Additionally, the relatively high proportion of natural sounds mitigates part of the adverse impact of traffic noise(Brambilla et al. 2013).

Analysis of plant impacts (points No. 28, 50, and 39) shows that the contribution of plants is relatively poor in spring but significant in summer and autumn. Specifically, soundscape quality in dense forest areas improves when supplemented by landmark facilities. Through the analysis of water feature impacts at sampling points No. 45 and 46, it is revealed that in spaces around water areas, their impact on the soundscape is almost consistent, yet there are obvious seasonal differences. This may stem from differences in plant configurations that lead to variations in ornamental value across seasons. Therefore, incorporating plant clusters with seasonal changes around water areas can optimize the soundscape. Analysis of the spatial form at sampling point No. 23, indicates that the enclosed spatial configuration can significantly reduce the sound pressure level and thereby enhance soundscape satisfaction. However, due to the influence of temperature, the effect is poor in winter but relatively remarkable in summer. Moreover, analysis of the topographic characteristics (point No. 22) demonstrates that the soundscape quality was improved more significantly in summer and autumn under the influence of terrain. This is attributed to the lush plant growth in these seasons, combined with the undulating terrain, results in favorable noise reduction effects and soundscape diversity.

Table 2

Contribution Degrees of Landscape Factors at Representative Sampling Points.
Sampling Points	Landscape Factors	SOSV			the Maximum Contribution Degree
28	Plants and Facilities	SOSV>0	Summer	0.30	30%
		SOSV>0	Autumn	0.14
		SOSV<0	Spring	-0.07
		SOSV<0	Winter	-0.31
50	Plants	SOSV>0	Winter	0.25	25%
		SOSV<0	Spring	-0.07
			Summer	-0.09
			Autumn	-0.33
39	Plants	SOSV>0	Summer	0.27	27%
			Winter	0.14
			Summer	0.01
		SOSV<0	Spring	-0.02
45	Water Landscapes	SOSV>0	Spring	0.24	24%
			Summer	0.23
			Autumn	0.02
		SOSV<0	Winter	-0.11
46	Water Landscapes and Facilities	SOSV>0	Summer	0.22	22%
		SOSV>0	Winter	0.07
		SOSV<0	Spring	-0.02
		SOSV<0	Autumn	-0.11
23	Enclosed Spaces	SOSV>0	Spring	0.06	23%
			Summer	0.23
			Autumn	0.11
		SOSV<0	Winter	-0.27
22	Terrain	SOSV>0	Summer	0.21	29%
		SOSV>0	Autumn	0.29
		SOSV<0	Spring	-0.19
		SOSV<0	Winter	-0.14

Hierarchical Approaches for Soundscape Optimization

Analysis of Optimization Classification Results

By applying the transformed IPA (Important Performance Analysis) method adapted to the research on soundscape, it is found that most sampling points in Daijiahu Park are located in the Soundscape Protection Area and Priority Governance Area, while a few of them are in the Low-Priority Area and Resource Utilization Area (Fig. 9). Critically, this distribution signifies that the acoustic environment in approximately half of the park space is poor and in urgent need of improvement.

By marking various types of sampling points on the map of Daijiahu Park, the annual soundscape classification and priority sequence map of governance areas was obtained (Fig. 10).

From the perspective of the annual average soundscape quality, 24 sampling points fall into the Soundscape Protection Area, primarily distributed in the dense forest, lakeside, and lawn areas of the park. This is consistent with the distribution of the aforementioned points with large subjective-objective soundscape differences, and the overall acoustic environment comfort in these areas is favorable. The Priority Governance Area contains 23 sampling points, mainly distributed in the northern entrance area, ring road green space area, and under-bridge sports area of the park. The prominence of noise issues here is primarily due to their proximity to traffic sources. There are 4 sampling points in the Low-Priority Area, with scattered spatial distribution; these areas can be temporarily deferred for management at a later stage. Besides, 3 sampling points in the Resource Utilization Area require the extraction of key soundscape and landscape elements for application in the optimization of other points.

Fig. 9

Annual Average Comprehensive Evaluation of the Soundscape in Wuhan Daijiahu Park

Fig. 10

Hierarchical Map for Soundscape Optimization in Wuhan Daijiahu Park Design Logic of Hierarchical System

Optimization Strategies for Areas at Different Hierarchies

Corresponding optimization strategies, developed based on the characteristics of areas in the soundscape hierarchy, were integrated (Table 3) into the detailed frameworks of "negative design," "positive design," and "zero design" (Ge et al. 2006). Specifically, "positive design" typically adds new elements to the existing soundscape, "zero design" usually maintains the original state, and "negative design" generally removes incompatible elements from the soundscape(Song et al. 2018) .

Table 3

Hierarchical Strategy Table for Sampling Points in Different Soundscape Priority Improvement Zones
Type	Strategy	Detailed Strategy
Soundscape Protection Zone	"Zero - design" strategy: maintain the sounds loved by people in the current Daijia Lake Park, and no design adjustment is required.	It is recommended to adopt the "zero - design" strategy to maintain the status quo.
Priority Governance Zone	1.Negative Design: Mitigates unwanted sounds through noise control, implemented via source reduction, path interruption, or receptor protection. 2.Positive Design: Involves the introduction or enhancement of desired soundscape elements to enrich the auditory environment.	This area needs to focus on the optimal design of the soundscape. While implementing "positive - design", various means of "negative - design" should be used for noise control.
Low Priority Zone	Temporarily ignore and address it at last.	Implement a positive-design strategy to enhance the soundscape in this area, complemented by a zero-design approach for the overall acoustic environment.
Resource Utilization Zone	Extraction of key soundscape elements.	Existing soundscape remains unaffected by ambient noise and requires no priority treatment. The focus should be on enhancing its current attributes.

Discussion

Characteristics and Extent of the Impact of Traffic Noise on Subjective and Objective Soundscape Evaluation

The negative dominance of traffic noise is reflected in the linear influence of SPL. In this study, linear regression analysis was conducted to examine the relevance between the average SPL across four seasons and three distance metrics: the shortest distance to roads (including ramps, expressways, etc.), the distance to the main road of the Third Ring Road, and the shortest distance to railway lines (see Appendix Table S4 for details). The acoustic environment of Daijia Lake Park exhibited a significant correlation with the former two distance metrics, but not much with the latter. Specifically, SPL decreased as the distance to roads increased (Table 4). The soundscape perception score showed an obvious correlation with the former two distance metrics (Table 5). This indicates that traffic noise exerts a significant impact on soundscape perception, and the masking effect of vegetation (e.g., trees) in Daijia Lake Park is relatively weak.

This finding is consistent with the conclusions of relevant studies: for every 5 dB(A) increase in traffic noise, the soundscape pleasure decreases by 0.8–1.2 points (on an 11-point scale)(Hong and Jeon 2015). A vegetation belt with an arbor density > 30 trees/100 m² can reduce noise by 4.2 dB and increase the pleasure of the transition zone by 0.6 points(Yu and Kang 2017). However, the arbor density in Daijia Lake Park is relatively low, resulting in a weak noise reduction effect and a slight improvement in soundscape perception.

The negative effects of traffic noise can be mitigated through the following approaches: 1)Sound source masking effect: Social sounds (e.g., human voices in markets) can partially offset the negative perception of traffic noise(Jeon et al. 2018);2)Audio-visual interaction effect: When the Green View Index (GVI) exceeds 40%, the annoyance caused by traffic noise decreases by 35%(Hong et al. 2020)༛3༉Soundscape design: Directional water sound can restore 42% of soundscape perception(Lam et al. 2024).

Table 4

Correlation Analysis Between Traffic Factors and SPL
		LAeq in Spring	LAeq in Summer	LAeq in Autumn	LAeq in Winter
The shortest distance to roads (including ramps, expressways)	Pearson Correlation	-0.677**	0.092	-0.666**	-0.624**
	Significance (Two-Tailed)	< 0.001	0.506	< 0.001	< 0.001
The distance to the main road of the 3rd Ring Road	Pearson Correlation	-0.647**	0.244	-0.629**	-0.567**
The distance to the main road of the 3rd Ring Road	Significance (Two-Tailed)	< 0.001	0.075	< 0.001	< 0.001
The shortest distance to the railway line	Pearson Correlation	0.293*	-0.127	0.252	0.125
The shortest distance to the railway line	Significance (Two-Tailed)	0.032	0.362	0.066	0.366
*Correlation is significant at the 0.01 level (two-tailed test);Correlation is significant at the 0.05 level (two-tailed test)

Table 5

Correlation Analysis Between Traffic and Soundscape Perception
		Subjective Loudness	Sound Comfort	Sound Satisfaction	Comprehensive Score
The shortest distance to roads (including ramps, expressways)	Pearson Correlation	0.646**	0.670**	0.683**	0.676**
	Significance (Two-Tailed)	< 0.001	< 0.001	< 0.001	< 0.001
The distance to the main road of the 3rd Ring Road	Pearson Correlation	0.611**	0.673**	0.654**	0.655**
The distance to the main road of the 3rd Ring Road	Significance (Two-Tailed)	< 0.001	< 0.001	< 0.001	< 0.001
The shortest distance to the railway line	Pearson Correlation	-0.165	-0.230	-0.202	-0.202
The shortest distance to the railway line	Significance (Two-Tailed)	0.232	0.094	0.144	0.144
*Correlation is significant at the 0.01 level (two-tailed test);Correlation is significant at the 0.05 level (two-tailed test)

Soundscape Diversity Effectively Enhances SOSV

Soundscape diversity directly characterizes ecological health, and it is positively correlated with the comfort of soundscapes in urban parks and their SOSV. It also exerts a significant impact on the evaluation of green spaces dominated by traffic noise or natural sounds(Xiang et al. 2024). In urban parks dominated by traffic noise, increasing soundscape diversity—such as using water flow sounds to mask traffic noise—can mitigate noise and enhance the perceived comfort of the soundscape(Hong and Jeon 2013; Cerwen 2016). In areas dominated by natural sounds, natural sounds in urban green spaces can improve soundscape comfort, especially bird songs and the rustle of wind through leaves(Hedblom et al. 2017), thereby increasing residents’ preference for urban parks(Jeon and Hong 2015). The combination of bird songs and water flow sounds can improve satisfaction by 23%(Xiang et al. 2023). Vegetation and water features also shows the ability to enhance soundscape diversity(Song et al. 2018); for instance, selecting mixed tree species characterized by high canopy complexity and vertical heterogeneity in vegetation structure can increase overall species diversity., which in turn boosts soundscape diversity(Zhao et al. 2022; Liu et al. 2024). Creating suitable foraging and habitat environments in green spaces can promote biodiversity, thereby further increasing soundscape diversity(Pijanowski et al. 2011).

However, soundscape diversity is not equivalent to the number of sound sources. Its core lies in the balanced distribution of sound source types and the proportion of the dominant sound(Xiang et al. 2025). Among the related indicators, the Soundscape Evenness Index (SEI) exhibits the strongest contribution [58] and demonstrates the same indicative function as well as a positive correlation with SOSV. In this study, sampling points with superior natural environments exhibited higher soundscape diversity and SOSV values, as they contained more soundscape components predominantly composed of natural sounds (e.g., bird songs and water flow sounds). In future park management, visual and auditory data can be integrated to predict soundscape comfort and SOSV, providing intelligent tools for optimizing soundscape diversity. For example, AI technology can be used to automatically identify sound sources and calculate soundscape diversity indices, thereby improving evaluation efficiency(Lam et al. 2024).

Planning and Management of Soundscapes in High-Density Cities

Traditional engineering-based noise reduction measures, such as noise barriers, face three dilemmas in high-density cities: land use constraints, rising costs, and ecological fragmentation(Kang 2017). With the continuous construction of transportation infrastructure in Wuhan, the impact of traffic noise on the city’s open spaces has become increasingly prominent. There is an urgent need to manage the soundscapes of buildings, open spaces, and other urban elements in Wuhan through planning and management approaches. Therefore, soundscape governance in high-density cities must move beyond the industrial logic of "decibel-based control" and shift toward a systematic paradigm of "sound source ecological regulation—cultural value regeneration."To address sound source regulation:Soundscape management units with a radius of 200–500 m can be established. Combined with natural sound masking (e.g., ecological water features, bird-inhabited forest belts), these units can reduce the perceived loudness of traffic noise by 40%(Aletta et al. 2016), An AI-based Soundscape Generation System (AMSS) dynamically adapts natural sounds to mask traffic noise. In an environment with an equivalent continuous A-weighted sound level (LAeq) of 70 dB(A), this system is able to improve restorative perception by 42%(Lam et al. 2024). Furthermore, "marketplace soundscapes" (e.g., voices in morning markets) in high-density neighborhoods elicit strong positive perceptions and should be classified as soundscape resources rather than noise sources—this constitutes an effective approach to enhancing sound perception and soundscape diversity. Meanwhile, developing soundscape cultural maps and identifying social sound heritages requiring protection (e.g., temple fair bells)(Xiang et al. 2024) are of great significance for soundscape governance.

Research Limitations and Futher Research

The study proposes a method for evaluating subjective-objective differences in soundscapes based on acoustic parameters. This method can effectively analyze the quality of the acoustic environment in urban parks, enabling planners to conduct focused optimization and improve parks’ overall soundscape. However, the study has the following limitations: 1) This study only conducts a soundscape analysis of Daijiahu Park, where traffic noise is the dominant sound source, without carrying out comparative verification with multiple samples. Subsequent analytical measurements based on multiple samples can enhance the generalizability of the conclusions. 2) This study does not involve a questionnaire survey on visitors' soundscape perceptions within the park for the time being. Supplementing with the subjective perceptions of different populations and combining them with objective sound pressure levels can further explore the relevant factors and degrees of correlation between human perceptions and the Soundscape Objective Satisfaction Value (SOSV).

Future research should be advanced in the following directions:

1)Construction of multi-scale soundscape maps: Establish a "Wuhan Urban Park Soundscape Database," integrate noise propagation models of various typical parks, and develop graded soundscape maps. Additionally, construct a "noise-vegetation-topography" coupling model to quantify the noise reduction efficiency of vertical green belts under the influence of traffic noise.

2)Advancement of dynamic soundscape governance technologies: Deploy an AI-based Adaptive Soundscape System (AMSS) to real-time monitor LAeq and trigger sound masking (e.g., directional bird calls), with the goal of reducing traffic noise annoyance by 40%.

Conclusions

The SPL of Daijiahu Park in spring, autumn, and winter shows a significant correlation with traffic noise, while the relation between SPL and traffic noise in summer was not conspicuous—this was mainly associated with the cicadas chirp. Overall, the SPL was relatively low in spring.The soundscape of the park was dominated by anthropogenic sounds (52%), with automotive noise comprising 36%. In contrast, railway sounds contributed 8% and 6%, respectively. The subjective soundscape quality of Daijiahu Park requires immediate improvement.

Vegetation, water features, facilities, enclosed spaces, and topography are the main landscape factors influencing SOSV. Different landscape factors exhibit seasonal differences in soundscape optimization. Vegetation shows significant optimization effects in summer and autumn, especially in dense forest areas. The influence of water features on soundscape variation was seasonal, and plant clusters enriched with seasonal diversity could enhance overall soundscape quality. Enclosed space could effectively reduce SPL and improve subjective perception, with notable effects particularly in summer. Topography obviously elevated soundscape quality in summer and autumn, and its effects could be further enhanced when combined with vegetation.

The 4 types of graded optimization areas for the soundscape of Daijiahu Park are Priority Governance Zones, Resource Utilization Zones, Soundscape Protection Zones, and Low Priority Zones. Priority Governance Zones are significantly affected by traffic noise interference. Soundscape Protection Zones has the ability to extract comfortable background sounds and require prioritized protection.The hierarchical optimization framework proposed in this study has been applied to the soundscape improvement suggestions for Daijiahu Park, providing a feasible paradigm for soundscape management of high-density urban parks.

Supplementary Information The online version contains supplementary material available at https://data.mendeley.com/datasets/575pr7fj6n/1

Acknowledgments

We would like to extend our appreciation to all the participants and relevant staff who assisted with this work.

Author contributions MZ contributed to the study’s conception and design. Material preparation, species identification, and data organization were conducted by HD and MZ. Statistical analyses were performed by HD, WZ and SL. The first draft of the manuscript was written by MD, MZ and YC. HY, XL and MC provided critical feedback on previous versions of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China [52208058][52308022][52208058].

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Electronic Supplementary Material

Below is the link to the electronic supplementary material

Supplementary Material 1

Author Contribution

MZ contributed to the study’s conception and design. Material preparation, species identification, and data organization were conducted by HD and MZ. Statistical analyses were performed by HD, WZ and SL. The first draft of the manuscript was written by MD, MZ and YC. HY, XL and MC provided critical feedback on previous versions of the manuscript.

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