Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Geospatial Analysis for Environmental Noise Mapping: A Land Use Regression Approach in a Metropolitan City Publisher Pubmed



Gharehchahi E1, 2 ; Hashemi H1 ; Yunesian M3 ; Samaei M1 ; Azhdarpoor A1 ; Oliaei M4 ; Hoseini M1
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Environmental Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran
  2. 2. Department of Environmental Health Engineering, School of Public Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
  3. 3. Department of Environmental Health Engineering, School of Public Health Institute of Public Health Research, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Department of Occupational Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran

Source: Environmental Research Published:2024


Abstract

Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: Lden and Lnight. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of Lden and Lnight during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in Lden and Lnight, respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for Lden and 4.74 dB(A) for Lnight. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both Lden and Lnight, with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that Lden and Lnight levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe. © 2024
Related Docs
1. A Review of the Studies Investigating the Effects of Noise Exposure on Humans From 2017 to 2022: Trends and Knowledge Gaps, Noise Mapping (2025)
2. Land Use Regression Models for Alkylbenzenes in a Middle Eastern Megacity: Tehran Study of Exposure Prediction for Environmental Health Research (Tehran Sepehr), Environmental Science and Technology (2017)
3. Combination of Artificial Neural Networks and Genetic Algorithm-Gamma Test Method in Prediction of Road Traffic Noise, Environmental Engineering and Management Journal (2015)
4. Long-Term Exposure to Ambient Air Pollution and Autism Spectrum Disorder in Children: A Case-Control Study in Tehran, Iran, Science of the Total Environment (2018)
5. Short-Term Associations Between Daily Mortality and Ambient Particulate Matter, Nitrogen Dioxide, and the Air Quality Index in a Middle Eastern Megacity, Environmental Pollution (2019)
6. Maternal Exposure to Air Pollutants and Birth Weight in Tehran, Iran, Journal of Environmental Health Science and Engineering (2019)
Experts (# of related papers)
View all Related Experts
Masoud Yunesian (4)
Homa Kashani (2)