GIS based spatial noise impact analysis (SNIA) of the broadening of national highway in Sikkim Himalayas: a case study

Polash Banerjee; Mrinal K. Ghose; Ratika Pradhan; Polash Banerjee; Mrinal K. Ghose; Ratika Pradhan

doi:10.3934/environsci.2016.4.714

AIMS Environmental Science

2016, Volume 3, Issue 4: 714-738. doi: 10.3934/environsci.2016.4.714

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Research article Special Issues

GIS based spatial noise impact analysis (SNIA) of the broadening of national highway in Sikkim Himalayas: a case study

1.
Department of Computer Science & Engineering, Sikkim Manipal Institute of Technology, Majitar - 737136, Sikkim, India
2.
Department of Computer Applications, Sikkim Manipal Institute of Technology, Majitar - 737136, Sikkim, India

Received: 30 August 2016 Accepted: 01 November 2016 Published: 09 November 2016

Mountainous areas create a complex and challenging environment to conduct noise impact analysis of development projects. This paper presents a noise impact analysis methodology using Geographic Information Systems (GIS) and Traffic Noise Model (FHWA TNM 2.5) to portray spatial distribution of noise due to the broadening of the national highway in the mountainous terrain of East Sikkim. Two noise level indices viz., Hourly Equivalent Sound Level (Leq(H)) and Day and Night Average Sound Level (Ldn) were calculated for the year 2004 as pre-project scenario, 2014 as project implementation scenario and 2039 as post-project scenario. The overall trend shows that the proportion of area under adverse noise level decreases from pre-project scenario to project implementation scenario. Over the time the adverse noise impact in the post-project scenario reaches very close to pre-project scenario in case of both the noise indices. Overlay analysis of noise based landuse maps over actual landuse map show that non-compliance of noise based landuse will show similar trend. This trend is mainly attributed to traffic composition and highway broadening induced-traffic volume. The study shows that TNM and spatial interpolation of noise data using Empirical Bayesian Kriging (EBK) are reliable tools to perform noise impact analysis in mountainous areas. Multiple regression analysis show that, radial distance and elevation difference of noise receivers from the nearest point in the highway are significant predictors of Leq(H) and Ldn at lower percentage of heavy trucks in traffic composition.
- Geographic information systems,
- traffic noise model (FHWA TNM 2.5),
- noise pollution,
- mountain,
- highway,
- Kriging,
- overlay analysis
Citation: Polash Banerjee, Mrinal K. Ghose, Ratika Pradhan. GIS based spatial noise impact analysis (SNIA) of the broadening of national highway in Sikkim Himalayas: a case study[J]. AIMS Environmental Science, 2016, 3(4): 714-738. doi: 10.3934/environsci.2016.4.714

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Abstract

Mountainous areas create a complex and challenging environment to conduct noise impact analysis of development projects. This paper presents a noise impact analysis methodology using Geographic Information Systems (GIS) and Traffic Noise Model (FHWA TNM 2.5) to portray spatial distribution of noise due to the broadening of the national highway in the mountainous terrain of East Sikkim. Two noise level indices viz., Hourly Equivalent Sound Level (Leq(H)) and Day and Night Average Sound Level (Ldn) were calculated for the year 2004 as pre-project scenario, 2014 as project implementation scenario and 2039 as post-project scenario. The overall trend shows that the proportion of area under adverse noise level decreases from pre-project scenario to project implementation scenario. Over the time the adverse noise impact in the post-project scenario reaches very close to pre-project scenario in case of both the noise indices. Overlay analysis of noise based landuse maps over actual landuse map show that non-compliance of noise based landuse will show similar trend. This trend is mainly attributed to traffic composition and highway broadening induced-traffic volume. The study shows that TNM and spatial interpolation of noise data using Empirical Bayesian Kriging (EBK) are reliable tools to perform noise impact analysis in mountainous areas. Multiple regression analysis show that, radial distance and elevation difference of noise receivers from the nearest point in the highway are significant predictors of Leq(H) and Ldn at lower percentage of heavy trucks in traffic composition.

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