Meteorological Factor Using Statistical Analysis At Quarry Site
The noise level produced by engineering equipment is growing in importance with increasing emphasis on the reduction of noise pollution as parts of government effort to improve quality of life, by way of increasing public awareness and also through enforcement of noise level regulations. With the fa...
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2017
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| Online Access: | http://eprints.usm.my/52551/1/Meteorological%20Factor%20Using%20Statistical%20Analysis%20At%20Quarry%20Site_Fatin%20Asyhikin%20Mohd%20Shariff_B1_2017.pdf http://eprints.usm.my/52551/ |
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| Summary: | The noise level produced by engineering equipment is growing in importance with increasing emphasis on the reduction of noise pollution as parts of government effort to improve quality of life, by way of increasing public awareness and also through enforcement of noise level regulations. With the fast rate of industrialization especially for quarry industries which the major benefiting world economic contributor. Some implementation need to be taken in order to achieve an effective noise-control emission so that it can be used to supervise the daily noise exposure level yielding in the operating quarry conveniently especially noise generated by crusher. There are numbers of research work has been done on relationship between meteorological and sound level but there is not much information or resources on noise prediction model specifically. This study seeks to add growing narrative on accessing the sound level of crushing unit in quarry industries and simultaneously to study the possible meteorological factor influencing the noise prediction model. As well as to developed an empirical formula using the statistical approach and determine the principal component for the independent variables. The new prediction noise model is then verified for better performances, to eliminate any uncertainty input and model discrepancies which means to check the difference between the noise prediction model created and the real case study. |
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