Dynamic modeling for noise mapping in urban areas.

Environmental noise has been a major environmental nuisance in metropolitan cities. To achieve the goal of sustainable community, noise reduction is an important approach. Without systematic noise mapping, the spatio-temporal distribution of noise variations is hard to capture. This study proposes a new methodology framework to combine statistical models and acoustic propagation for dynamic updates of 2D and 3D traffic noise maps by using a limited number of noise sensors in Taipei City based on multisource data including noise monitoring, vehicle detectors, meteorological data, road characteristics, and socio-demographic data. The hourly mean difference between the predicted and measured noise level is within the range of −6.25 dBA to −4.46 dBA in the 2D noise model. For the 3D noise model, the hourly mean prediction error is within the range of 0.02 dBA to 1.93 dBA. Based on the WHO benchmark for excessive road traffic noise, we found at least 30% of inhabitants in Taipei City are exposed to levels exceeding 53 dBA Lden, and >25% are exposed to noise levels exceeding 45 dBA Lnight. The noise maps not only can help identify vulnerable communities to adopt proper approaches for noise reduction but also can remind the residents to take action to improve their quality of life. • Multisource data were used to construct spatiotemporal mapping for traffic noise. • 2D and 3D models were built up for dynamically updating hourly noise levels. • Similarity was used to measure pairwise relationships between two grids. • The vulnerable communities can be identified by the current approach. [ABSTRACT FROM AUTHOR]