The integration between estimating traffic emissions and GIS

• Main Authors: Lin Yung-Chang, 林永章
• Other Authors: Lin Min-Der
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/79462200702901648928

碩士 === 國立中興大學 === 環境工程學系 === 88 === Due to the fact that the air quality in urban areas becomes worse, it is necessary to investigate the relationship between traffic emissions and air quality. The focus of this research is to estimate the temporal and spatial distribution of traffic emissions by using of GIS, and analyze the impact of traffic pollution to air quality. The GIS system developed in this research combines several tools used to estimate traffic emissions, and try to automate the analyzing processes in order to avoid complicated operations. The automatic processes include the calculation of vehicle kilometers traveled, access of database, emissions estimation, dispersion model simulation, simulation of pollutants’ backward trajectory and visualization of the results. For verifying the models, this research uses Taichung City as a case study. The properties of traffic pollution are analyzed from both temporal and spatial point of view. For temporal analysis, the hourly data of highway traffic emissions are used, and the occasions that high pollutant concentrations may occur at the downwind areas can be predicted. For spatial analysis, the grid-based emissions of Taichung are estimated, and the resulting pollutant concentration distribution can be simulated by Caline-4 model. According to the preliminary results of this research, there are higher CO emissions around downtown areas in Taichung, but the areas along highway sustain higher NOx, SOx and TSP pollution. This research also simulates several hypothetical scenarios to demonstrate the decision support abilities of the GIS system. The scenarios include upgrading motorcycles and increasing average traffic speed on city roads. The results show that the major effect of the former is the reduction of TSP emission by more than 10%, while the latter may reduce more than 14% of CO emissions.