Noise prediction and environmental noise capacity for urban traffic of Mumbai. (January 2022)

• Record Type: Journal Article
• Authors: Soni, Arti Roshan; Makde, Ketan; Amrit, Kumar; Vijay, Ritesh; Kumar, Rakesh
• Is Part Of: Applied acoustics. Volume 188(2022)
• Publication Date: 2022-01
• Subjects: Calixto model -- Road traffic noise -- Traffic flow -- Mumbai metropolitan region -- Environmental noise capacity; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2021.108516
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: Vehicular traffic noise is inevitable problem having environmental impact in mega cities like Mumbai. In present study, an effort has been made to predict present and future noise as a function of vehicular growth. Further, a new approach have been proposed to estimate environmental noise capacity for the city. Noise was pedicted by classifying vehicles as light and heavy based on thier weights and used as a input to calixto model. The number of vehicles for 2018 were estimated using annual growth rate. The predicted urban noise from Calixto model were validated with real time monitoring results. The analysis revealed that there is variability in traffic flow pattern across the different roads in Mumbai. Location S2, which is in between Kurla and Ghatkopar contributes highest share of vehicle movement resulting in higher noise level (85 dB (A)) among the studied locations. Environmental noise capacity for all the locations are greater than zero except for location S4 (Chembur). The calculated and observed noise levels are found to be in agreement with R 2 value of 0.795 and 0.853 for weekday and weekend respectively. This study will be useful for the policy makers for planning the strategies to mitigate the impacts of increasing urban noise. This shall be a breakthrough in formulating policy especially for a cities like Mumbai having mixed category zones in each location and may be helpful in defining a new approach to estimate ambient noise levels based on land use pattern.