Thesis - Open Access
Master of Science (MS)
Jamie E.L. Spinney
GIS noise mapping noise pollution sound mapping soundscape
Sound is the subjective dimension of what we hear when vibrations reach our ears. Noise, or unwanted sound (mostly human caused), is an objective function of the pressure of those vibrations and is often measured using decibels (dB). Noise is a type of pollution that has both direct and indirect negative impacts on humans, with significant implications for public health plus social, economic, and environmental well-being. Mapping the acoustic landscape (i.e., soundscape) using noise and sound data provides important insights for evaluating, interpreting, understanding, and managing environmental noise. The objectives of this research are threefold; to map the spatial and temporal patterns of the SDSU campus’ soundscape, to identify the dominant sound sources at various locations, especially “problem areas”, and to compare the quality of noise data collected from a smartphone application (SPA) and a traditional digital noise meter (DNM). A SPA and DNM were used to simultaneously collect noise level data at the same collection sites in the field. A digital audio recorder was also used to collect sound data, which were subsequently classified based on their source into one of four different categories: mechanical; natural; human; and, communications. Ordinary kriging was used to interpolate both noise and sound data. A t-test was used to compare the mean noise levels across different time periods and test for significant differences between noise data collected using the SPA and the DNM. Results clearly indicate that mechanical sound sources dominate SDSU’s soundscape. The noise levels captured by the DNM ranged between 43-67, 44-69, and 43-61 dBA during the morning, afternoon, and evening, respectively. Similarly, noise levels captured by the SPA ranged between 44-71, 38-65, and 41-64 dBA during the morning, afternoon, and evening, respectively. The t-test results indicate that mean noise levels measured from these two devices did not exhibit statistically significant differences. Mapping the noisescape and the soundscape allowed the identification of problem areas and it also provided important insights that can be used to mitigate environmental noise issues. The results could also be used to raise awareness of the social, economic, environmental, and public health implications of noise pollution.
Includes bibliographical references
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Parajuli, Sujan, "Noise Mapping of an Educational Environment: A Case Study of South Dakota State University" (2018). Electronic Theses and Dissertations. 2683.