Mapping environmental noise of Guangzhou based on land use regression models.

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) 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 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.

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.