Zheng Guangjun, Chen Xia, Huang Kun, Mölter Anna, Liu Mingliang, Zhou Biying, Fang Zhenger, Zhang Haofeng, He Fudong, Chen Haiyan, Jing Chunxia, Xu Wenbin, Hao Guang
Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou, China.
J Environ Manage. 2025 Jan;373:123931. doi: 10.1016/j.jenvman.2024.123931. Epub 2025 Jan 2.
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.
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.
A total of 100 monitoring sites were randomly selected according to population density. The Equivalent continuous A-weighted sound pressure level (L) 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.
A total of 800 times of measurements were conducted and the average equivalent continuous L 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 R ranging from 0.54 to 0.76, and the R 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.
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.
环境噪声严重影响人们的健康和生活质量，但大城市及夜间的噪声暴露数据匮乏，尤其是在发展中国家。
本研究旨在通过土地利用回归（LUR）模型评估环境噪声水平，并创建广州市高分辨率的白天和夜间噪声地图。
根据人口密度随机选取100个监测点。于2022年12月至2023年5月的工作日，在白天[上午（7:30 - 12:00）、下午（13:00 - 16:30）、傍晚（17:30 - 22:00）]和夜间（23:00 - 7:00）测量等效连续A声级（L），冬季/春季和夏季/秋季在每个监测点测量30分钟。通过监督向前逐步法构建LUR模型，引入地理预测变量来预测噪声暴露水平。采用十倍交叉验证法评估LUR模型的性能。
共进行了800次测量，监测点的平均等效连续L为65.79±7.45 dB(A)。城区的噪声水平高于郊区（66.95±7.37 dB(A) 对 63.08±6.94 dB(A)，P < 0.001）。此外，白天的噪声水平也显著高于夜间（67.18±6.50 dB(A) 对 61.60±7.59 dB(A)，P = 0.001）。开发了四个LUR模型，调整后的R范围为0.54至0.76，十倍交叉验证的R范围为0.61至0.79。兴趣点（POI）、交通相关变量和土地利用是LUR模型中的重要预测因素。分别创建了分辨率为50×50 m的白天和夜间噪声地图。
我们的结果表明，广州白天和夜间的环境噪声超过了世界卫生组织的推荐值。POI、交通相关变量和土地利用是环境噪声水平的主要影响因素。
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