Volume 11, Issue 3 (12-2018)
ijhe 2018, 11(3): 377-390 |
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Jelokhani-Niaraki M, Hajiloo F, Hasanzade L. The use of geographic information systems (GIS) for assessing noise pollution in industrial workplaces: a case study of knitting industry. ijhe 2018; 11 (3) :377-390
URL: http://ijhe.tums.ac.ir/article-1-6076-en.html
URL: http://ijhe.tums.ac.ir/article-1-6076-en.html
1- Department of Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Tehran, Iran , mrjelokhani@ut.ac.ir
2- Department of Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Tehran, Iran
3- Department of Health, Safety and Environment, Faculty of Health, Safety and Environment, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- Department of Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Tehran, Iran
3- Department of Health, Safety and Environment, Faculty of Health, Safety and Environment, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract: (4131 Views)
Background and Objective: Noise pollution causes many physiological, psychological, economic and social effects on human life. This issue is more important in the environment of industrial workplaces. This research aimed to adopt the functions of GIS for evaluating and spatial analysis of noises in industrial environments.
Materials and Methods: At the initial step, the spatial data for industrial halls were collected and stored as map layers into GIS database. Then, the noise pollution data sampled. The data, including the locations and values of sound pressure levels, were used for the relevant spatial analyses.
Results: The analyses included: the estimation of sound pressure levels in different areas of halls and at the given distance from machines, determination of noisy areas, development of sound noise risk map, interpolation of sound pressure level data, prioritization of the sound sources (i.e., machines) for a given point, prediction of sound pressure levels by moving machines, and optimal site selection and distribution of machines. The mean of noise pressure level was 95 dB for knitting hall, 93 dB for spinning hall 1 and 88 dB for spinning hall 2.
Conclusion: GIS plays a key role in the assessment of noise pollution in industrial workplaces. It is an appropriate tool to store, analyze, manage, and present all types of sound pressure spatial data. Specifically, the use of such system provides spatial intelligence and could help monitor, detect, control, and solve real word sound noise pollution issues.
Materials and Methods: At the initial step, the spatial data for industrial halls were collected and stored as map layers into GIS database. Then, the noise pollution data sampled. The data, including the locations and values of sound pressure levels, were used for the relevant spatial analyses.
Results: The analyses included: the estimation of sound pressure levels in different areas of halls and at the given distance from machines, determination of noisy areas, development of sound noise risk map, interpolation of sound pressure level data, prioritization of the sound sources (i.e., machines) for a given point, prediction of sound pressure levels by moving machines, and optimal site selection and distribution of machines. The mean of noise pressure level was 95 dB for knitting hall, 93 dB for spinning hall 1 and 88 dB for spinning hall 2.
Conclusion: GIS plays a key role in the assessment of noise pollution in industrial workplaces. It is an appropriate tool to store, analyze, manage, and present all types of sound pressure spatial data. Specifically, the use of such system provides spatial intelligence and could help monitor, detect, control, and solve real word sound noise pollution issues.
Type of Study: Research |
Subject:
General
Received: 2018/05/19 | Accepted: 2018/09/24 | Published: 2018/12/19
Received: 2018/05/19 | Accepted: 2018/09/24 | Published: 2018/12/19
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