An exploratory framework for mapping, mechanism, and management of urban soundscape quality: From quietness to naturalness

Jingyi Wang; Zhen Wang; Chunming Li; Ziyan Yao; Shenghui Cui; Quanjia Huang; Yin Liu; Tingting Wang

doi:10.1016/j.envint.2024.108699

An exploratory framework for mapping, mechanism, and management of urban soundscape quality: From quietness to naturalness

Environ Int. 2024 Apr 25:187:108699. doi: 10.1016/j.envint.2024.108699. Online ahead of print.

Authors

Jingyi Wang¹, Zhen Wang², Chunming Li³, Ziyan Yao¹, Shenghui Cui⁴, Quanjia Huang⁵, Yin Liu⁶, Tingting Wang²

Affiliations

¹ Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China; Xiamen Key Lab of Urban Metabolism, Xiamen, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China.
² School of Statistics - Institute of Quantitative Economics, Huaqiao University, Xiamen 361021, China.
³ Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China; Xiamen Key Lab of Urban Metabolism, Xiamen, China. Electronic address: cmli@iue.ac.cn.
⁴ Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China; Xiamen Key Lab of Urban Metabolism, Xiamen, China.
⁵ Xiamen Environmental Monitoring Station, Xiamen 361021, China.
⁶ Xiamen Research Academy of Environmental Sciences, Xiamen 361021, China.

PMID: 38678937
DOI: 10.1016/j.envint.2024.108699

Abstract

Background: Despite growing attention from researchers and governments, challenges persist in comprehensively assessing urban sound quality by integrating both quietness and naturalness aspects.

Goals: This study aimed to develop an innovative soundscape quality index that concurrently evaluates quietness and naturalness in urban soundscapes. Our objectives included conducting urban soundscape quality mapping, analyzing influential mechanisms, and identifying priority zones for sound environment management.

Approaches: We collected sound pressure level (SPL) and raw audio data, from which we computed a normalized difference soundscape index (NDSI). With a dataset comprising 28 explanatory variables encompassing land use, built environment, vegetation characteristics, and temporal factors, we employed the random forest (RF) model to predict 10 indicators, including eight SPL-related indices, NDSI, and the QNS (quietness and naturalness soundscape) index. Crucially, we utilized SHAP (SHapley Additive exPlanations) values to interpret the RF model.

Findings: Spatial variations in quietness and naturalness were evident, closely associated with road networks and vegetation, respectively, with discernible temporal variations. The top three variables influencing QNS were distance to major roads, normalized difference vegetation index (NDVI), and proportion of tree coverage. Moreover, interaction effects highlighted dual negative or synergistic promoting effects on QNS from factors such as road width, human disturbance, vegetation configurations, and land cover. Notably, these mechanisms were successfully applied to six typical tourist attractions in Xiamen city, where five types of management zones were mapped based on priority considerations of population density and soundscape quality. Interestingly, natural soundscape reserves were highly correlated with city parks, high-risk zones predominantly overlapped with road networks, and potential zones comprised inner communities between streets.

Significance: The framework demonstrated effectiveness in mapping, exploring mechanisms, and guiding management strategies for the urban sound environment.

Keywords: Public health; Random forest; Sound environment management; Sound environment quality; Sound map; Urban sound environment prediction.