Traffic noise has been shown to have negative effects on exposed persons in the communities along highways. Noise from transportation systems is considered a nuisance in the U.S. and the government agencies require a determination of noise impacts for federally funded projects. There are several models available for assessing noise levels impacts. These models vary from simple charts to computer design models. Some computer models, i.e. Standard Method In Noise Analysis (STAMINA), the Traffic Noise Model (TNM) and the UCF Community Noise Model (CNM), have been used to predict geometric spreading, atmospheric absorption, diffraction, and ground impedance. However, they have largely neglected the atmospheric effects on noise propagation in their algorithms. The purpose of this research was to better understand and predict the meteorological effects on traffic noise propagation though measurements and comparison to acoustic theory. It should be noted that this represents an approach to incorporate refraction algorithms affecting outdoor noise propagation that must also work with algorithms for geometric spreading, ground effects, diffraction, and turbulence. The new empirical model for predicting atmospheric refraction shows that wind direction is a significant parameter and should be included in future modeling for atmospheric refraction. To accomplish this, the model includes a "wind shear" and "lapse rate" terms instead of wind speed and temperature as previously needed for input of the most used models. The model is an attempt to explain atmospheric refraction by including the parameters of wind direction, wind shear, and lapse rate that directly affect atmospheric refraction.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-2122 |
| Date | 01 January 2006 |
| Creators | El-Aassar, Ahmed |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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