Spatiotemporal dynamics of urban noise complaints link emotion, space, and management pathways

Noise pollution is a critical urban environmental issue with significant public health implications, and public complaints serve as a key indicator of resident dissatisfaction. Emotional analysis has been widely used to evaluate the emotional factors behind public complaints. However, existing studies have focused primarily on atmospheric pollution-related complaints, while little attention has been paid to noise complaints, despite the fact that the emotional descriptions in noise complaints differ notably from those associated with other types of complaints. This study employs sentiment analysis in combination with traditional temporal and spatial analysis to investigate the spatiotemporal patterns of emotions expressed in online noise complaints, using Guangzhou—a city with a high incidence of noise complaints—as a case study. The results revealed two distinct yet interrelated patterns. On one hand, noise complaints exhibit a consistent temporal rhythm: winter and weekdays register the highest volumes, shaped by cultural customs and daily routines, with pronounced surges during both daytime and late-night hours. On the other hand, a pronounced spatial mismatch exists between complaint density and emotional intensity. While complaints are concentrated in the central city, the most acute negative sentiments emerge in newly developing zones characterized by urban-rural and port-industry-city integration. This study not only assists Guangzhou—a typical first-tier city in China—in its noise pollution management, but also highlights that incorporating public sentiment into urban noise management can help uncover latent stress patterns and management blind spots that are often overlooked in conventional approaches.

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Publication Details

Journal
Scientific Reports
Published
2026-08-17
DOI
https://doi.org/10.1038/s41598-026-66256-z
Primary Topic
Noise Effects and Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatiotemporal dynamics of urban noise complaints link emotion, space, and management pathways

Ni-Ping Liu, Xiong Yao, Qiao-Chu Wang, Yi-Peng Jiang et al.
Scientific Reports
Noise Effects and Management
article

Spatiotemporal dynamics of urban noise complaints link emotion, space, and management pathways

Ni-Ping Liu, Xiong Yao, Qiao-Chu Wang, Yi-Peng Jiang, Yang-Sheng Cai, Fang Peng
article en

Abstract

Noise pollution is a critical urban environmental issue with significant public health implications, and public complaints serve as a key indicator of resident dissatisfaction. Emotional analysis has been widely used to evaluate the emotional factors behind public complaints. However, existing studies have focused primarily on atmospheric pollution-related complaints, while little attention has been paid to noise complaints, despite the fact that the emotional descriptions in noise complaints differ notably from those associated with other types of complaints. This study employs sentiment analysis in combination with traditional temporal and spatial analysis to investigate the spatiotemporal patterns of emotions expressed in online noise complaints, using Guangzhou—a city with a high incidence of noise complaints—as a case study. The results revealed two distinct yet interrelated patterns. On one hand, noise complaints exhibit a consistent temporal rhythm: winter and weekdays register the highest volumes, shaped by cultural customs and daily routines, with pronounced surges during both daytime and late-night hours. On the other hand, a pronounced spatial mismatch exists between complaint density and emotional intensity. While complaints are concentrated in the central city, the most acute negative sentiments emerge in newly developing zones characterized by urban-rural and port-industry-city integration. This study not only assists Guangzhou—a typical first-tier city in China—in its noise pollution management, but also highlights that incorporating public sentiment into urban noise management can help uncover latent stress patterns and management blind spots that are often overlooked in conventional approaches.

Scientific Reports
Fuzhou University (CN), Fujian University of Technology (CN)
National Natural Science Foundation of China
Sustainable cities and communities
Openalex Percentile: Top 6%
Noise Effects and Management
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