Mapping environmental noise of Guangzhou based on land use regression models

Guangjun Zheng; Xia Chen; Kun Huang; Anna Mölter; Mingliang Liu; Biying Zhou; Zhenger Fang; Haofeng Zhang; Fudong He; Haiyan Chen; Chunxia Jing; Wenbin Xu; Guang Hao

doi:10.1016/j.jenvman.2024.123931

Mapping environmental noise of Guangzhou based on land use regression models

J Environ Manage. 2025 Jan 2:373:123931. doi: 10.1016/j.jenvman.2024.123931. Online ahead of print.

Authors

Guangjun Zheng¹, Xia Chen¹, Kun Huang², Anna Mölter³, Mingliang Liu¹, Biying Zhou¹, Zhenger Fang¹, Haofeng Zhang⁴, Fudong He⁴, Haiyan Chen⁵, Chunxia Jing⁶, Wenbin Xu², Guang Hao⁷

Affiliations

¹ Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
² School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou, China.
³ College of Engineering and Architecture, University College Dublin, Belfield, Dublin 4, Ireland.
⁴ Department of Epidemiology and Statistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China.
⁵ Guangzhou Center for Disease Control and Prevention, Guangzhou, China.
⁶ Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China; Guangdong Key Laboratory of Environmental Exposure and Health, Jinan University, Guangzhou, China.
⁷ Department of Epidemiology and Statistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China. Electronic address: haoguang2015@hotmail.com.

PMID: 39752960
DOI: 10.1016/j.jenvman.2024.123931

Abstract

Background: Environmental noise seriously affects people's health and life quality, but there is a scarcity of noise exposure data in metropolitan cities and at nighttime, especially in developing countries.

Objective: This study aimed to assess the environmental noise level by land use regression (LUR) models and create daytime and nighttime noise maps with high-resolution of Guangzhou municipality.

Methods: A total of 100 monitoring sites were randomly selected according to population density. The Equivalent continuous A-weighted sound pressure level (L_Aeq) was measured on weekdays from December 2022 to May 2023 during daytime [morning (7:30-12:00), afternoon (13:00-16:30), evening (17:30-22:00)], and nighttime (23:00-7:00) with 30 min of measurement at each site in winter/spring and summer/autumn. The LUR model was constructed by a supervised forward stepwise method to predict the noise exposure level via introducing the geographic predictor variables. A ten-fold cross-validation method was utilized to assess the performance of LUR models.

Results: A total of 800 times of measurements were conducted and the average equivalent continuous L_Aeq of monitoring sites was 65.79 ± 7.45 dB(A). Urban areas exhibited higher noise levels than suburban areas (66.95 ± 7.37 dB(A) vs. 63.08 ± 6.94 dB(A), P < 0.001). Further, the noise level during the day was also significantly higher than during the night (67.18 ± 6.50 dB(A) vs. 61.60 ± 7.59 dB(A), P = 0.001). Four LUR models were developed with adjusted R² ranging from 0.54 to 0.76, and the R² of the ten-fold cross-validation varied from 0.61 to 0.79. Points of interests (POIs), traffic-related variables, and land use were important predictive factors in LUR models. Noise maps of daytime and nighttime with a resolution of 50 × 50 m were created, respectively.

Conclusion: Our results revealed that daytime and nighttime environmental noise exceeded the recommended values from the World Health Organization in Guangzhou. POIs, traffic-related variables, and land use were the main influencing factors of environmental noise level.

Keywords: Environmental noise; Exposure assessment; Land use regression model; Noise map.