Small-scale noise mapping for arbitrary regions using open source noise prediction module "NoiseModelling" and global databases

Extensive noise mapping has been crucial in shaping noise mitigation policies; however, assessing noise exposure in different scenarios remains challenging due to static maps that only represent pre-calculated sound levels. To fill this gap, this study introduces a novel noise mapping framework tailored to calculate sound levels in specific environments within small, arbitrary regions. Using the open-source noise prediction tool NoiseModelling and global databases of road networks, buildings, terrain, and population distribution on QGIS, this framework seamlessly integrates noise mapping procedures, including comprehensive data collection, receiver placement, sound level calculation, and visualization. The user-friendly graphical interfaces enable quick and easy generation of noise maps. The approach offers flexibility through customizable geometry and parameter settings to account for variations in noise sources and propagation characteristics, thereby providing stakeholders with valuable insights for policy formulation, environmental impact assessment, and community engagement. This methodology not only improves the accessibility and relevance of noise mapping but also empowers decision makers to implement tailored noise mitigation strategies that promote healthier and more livable environments.