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Prediction and 3D Visualization of Environmental Indicators: Noise and Air Pollution
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Satellite Positioning. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. (Geodesy and Geoinformatics)
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Environmental problems such as noise and air pollution are increasingly catching people’s attention in recent years owing to the industrialization and urbanization all over the world. Therefore it is important to develop effective methods to present information on noise and air pollution to the public. One feasible approach is to carry out prediction based on traffic data and make noise and pollution maps. GIS is a powerful tool for prediction since its spatial analysis function could be used in analysis and calculation. In addition the available GIS platforms also provide visualization functions to display the analysis results in variety of forms, in both 2D and 3D. This thesis uses noise and air pollution as examples to study how to predict noise and pollution from traffic data and how to visualize the predicted pollution information in 3D with the help of the existing visualization technology.

Therefore, the thesis has two objectives. The first objective is focused on prediction of noise and air pollution using existing prediction models based on vehicle speed and traffic volume data. The original spatial road network dataset with traffic information was integrated with GIS and analysis and calculations were carried out. Road Traffic Noise-Nordic Prediction Method is used for predicting traffic noise while ARTEMIS model and OSPM model are applied for traffic air pollution. All analysis and calculations were carried out on virtual receiver points generated on ground surface and over building facades at different heights. The second objective is focused on 3D visualization of the predicted traffic noise and air pollution in ArcScene, Google Earth as well as X3D respectively. In ArcScene the virtual receiver points were visualized in their actual position with different colors representing noise or air pollution level. Then KML files were created from the point shapefiles and imported into Google Earth to show the noise and air pollution level in the virtual city available in Google Earth. Finally one layer of point shapefile was selected as an example to give the 3D scene in X3D. The selected layer of points was first interpolated into a continuous surface and converted into contours. Three types of models were developed in this part. First is to visualize contours in 3D using both colors and heights to show the noise or air pollution levels. Next the interpolated surface was segmented into scattered cells displayed also in colors and heights both representing pollution intensity. The last one is using 3D bars to show noise or air pollution in colors and lengths.

The prediction results shows that the either noise or air pollution in the north part of central Stockholm is much more serious than in south part and the most polluted area appear along the highways. In the same area the pollution levels vary in different heights. The 3D visualization in ArcScene and Google Earth could clearly present the differences. However, so far the visualization in X3D only gives 2D information in 3D, which means although the 3D scenes were created, the height only noise or air pollution on the specific height could be represented. The real 3D representing is still need to be studied. 

Place, publisher, year, edition, pages
2011. , 87 p.
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-91351ISRN: ISRN KTH/GIT/EX--11/011-SEOAI: diva2:509627
Educational program
Degree of Master - Geodesy and Geoinformatics
2011-12-14, 5055, Drottning Kristinas väg 30, Stockholm, 14:00 (English)
Available from: 2016-05-24 Created: 2012-03-13 Last updated: 2016-05-24Bibliographically approved

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