Evaluation of the soundscape of district 2 of Tabriz metropolis using linear regression model

Document Type : Research Paper

Authors

1 Ph.D. Student of Department of Geography and Urban Planning Department, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran

2 Department of Geography and Urban Planning, Faculty of Planning and Environmental Sciences, University of Tabriz , Iran

Abstract

The development of technology and the advancement of science and technology in large and industrial cities have been associated with the destruction of the environment and various pollutions in human societies. One of the most obvious types of pollution is noise pollution, which causes mental and nervous diseases. In this article, with the aim of evaluating the noise pollution and the impact of social, physical and environmental variables in the urban environment, the evaluation of the sound landscape during the peak activity hours of District2 of Tabriz metropolis has been discussed. In terms of research method, this article is a combination of descriptive-analytical, documentary and survey methods. First, district2 was divided into squares of 200×200 meters based on the 1996 ISO standard grid method, and in the center of each square as a sound recording station with a decibel meter (Bentec digital sound meter model GM1357) the level of sound equivalent (Leq) with The sound pressure level of network A was measured with Fast response speed for 3 minutes, in two time periods: morning (8 to 11) and evening (19 to 22) in the months of Tir to Azar 1400 (July to December 2021). The maps of each dependent and independent variables were drawn in GIS software environment and evaluated using multiple linear regression model in SPSS software. The results of this research show that district2 has more noise pollution in the evening than in the morning. At the end, solutions to reduce noise pollution in the area are suggested.

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Journal of Environmental Studies

Articles in Press, Accepted Manuscript
Available Online from 28 January 2024

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