Related Experiment Video
Updated: Jun 4, 2025
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Mapping environmental noise of Guangzhou based on land use regression models
Guangjun Zheng1, Xia Chen1, Kun Huang2
1Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
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 (LAeq) 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 LAeq 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.
