In a time when urban areas continue to expand, environmental noise pollution especially from road traffic remains a big challenge. This study was aimed at using open-source GIS tools to predict road traffic noise pollution using the mid-sized city of Gävle as a case study. The noise indicators measured were the equivalent day (Lday), evening (Levening), nighttime (Lnight), and the equivalent daily average (Lden). Traffic data (composition and flow of vehicles on selected roads), traffic source characteristics (road gradient, road surface type), and buildings (geometry) were integrated into Quantum GIS (QGIS) using the CNOSSOS-EU prediction method packaged in OpeNoise, a QGIS plug-in. The resultant noise levels at receiver points were interpolated using the Inverse Distance Weighting method to create noise maps for the city. The results showed the maximum equivalent day, evening, nighttime predicted noise levels at 85 dB (A), 80 dB (A), 75 dB (A) respectively while the maximum for overall daily average noise level predicted was 85 dB (A). These limits far exceed population exposure threshold limits for the onset of annoyance (55 dB (A)) and sleep disturbance (40 dB (A)). This result is indicative of a poor sound acoustic environment. The pattern of noise level across the city was found to follow street connectivity and traffic intensity. The maximum noise levels were clustered around the highway. Within the city, areas with the highest noise levels were found close to main roads. Residential areas served by service roads were areas with the lowest noise levels. Predicted daytime noise levels (Lday) were compared with 60-second measurements of equivalent noise levels measured at 85 locations during the day in residential and mixed land use areas in the city. The mean of differences between predicted and observed noise levels was found at +1 dB for both residential and areas of mixed land use respectively. Correlation and regression analyses performed for observed and predicted values showed an initial weak positive association with a correlation coefficient of 0.21. However, when outliers were excluded, a correlation coefficient of 0.69 was observed indicating a strong association and linear relationship between the observed and predicted noise levels. Most outliers were underestimations recorded in residential areas at hidden facades. These were attributed to local effects at the measuring locations and assumptions made for building diffraction. The application of the CNOSSOS-EU method in this study did not consider attenuation from ground reflection and terrain effects. Despite these limitations, the results show that the CNOSSOS-EU has good predictive power. However, this study has only been exploratory in nature. It is recommended that further studies be performed with this model as well as in comparison with other models to find the one that best reflects the acoustic environment of the city. A wide application of the CNOSSOS-EU method across several cities will be integral in increasing our understanding of its strengths and weaknesses.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hig-37546 |
| Date | January 2021 |
| Creators | Yeboah, Faustina Lina |
| Publisher | Högskolan i Gävle, Samhällsbyggnad |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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