title: A modified Nordic prediction model of road traffic noise in a Taiwanese city with significant motorcycle traffic.
abstract: A prediction model was developed to map road traffic noise in an area with significant motorcycle traffic in Taichung City, Taiwan. This model was modified from the Nordic prediction method by adding three types of traffic flow rates, including heavy vehicles, light vehicles, and motorcycles, as well as local traffic speeds and road characteristics to the calculating equations. The parameters that were input into the equations include traffic flow, vehicle speed, distance from the center of the road, height of the road surface, position and height of the barriers, thickness of the barriers, location of the receiver relative to the surrounding road surface or barriers, reflecting vertical surfaces, type of ground, and height of the buildings. The model was validated by comparing the measured noise levels at 42 sampling sites close to main roads with the predicted values. A significant correlation was found between the predicted and measured noise levels (Pearson correlation coefficient=0.75, p<0.001). The deviation between the predicted and measured noise levels within the range of ±3.5 A-weighted decibel (dB(A)) was 90.5%. The mean difference between the predicted and measured noise levels was 0.9±2.1 dB(A). The modified Nordic prediction model is therefore applicable to estimate the noise exposure in this urban environment in Taiwan.
authors: Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan, ROC.
authors: Ta-Yuan
authors: TY
authors: Chang
authors: Hsiao-Ching
authors: HC
authors: Lin
authors: Wei-Ting
authors: WT
authors: Yang
authors: Bo-Ying
authors: BY
authors: Bao
authors: Chang-Chuan
authors: CC
authors: Chan
mesh_terms: D002947:Cities; D004781:Environmental Exposure; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D009049:Motorcycles; D009624:Noise, Transportation; D013624:Taiwan