Innovative noise modelling

Source	D3.4 Innovative noise modelling, Gulliver et al, 2024 https://zenodo.org/records/16313125
Level of evidence	Suggestive evidence
What	Report on implementation of the CNOSSOS noise prediction methodology for modelling of road‐transport noise within the Equal‐Life project, including a description of how to generate data for open modelling, specifically : ·      An overview of the metrics and data required for open‐source applications (i.e. harmonised between countries), including open street maps (OSM), meteorological data, and land cover, and implementation of the modelling. ·      A user‐friendly account of the approach to noise modelling  via open data where possible.  This report includes an exemplar application for the ALSPAC cohort in the UK in detail and modelling results from three other cohorts (BREATHE and WALNUTS in Spain, and STARS in Sweden).
Findings	Major roads are largely responsible for exposures at and above 60dB, However much of the population are sheltered from traffic noise exposures by distance and or urban structures. The influence of minor roads is key to higher levels of noise exposure for the majority of the population. There is a substantial exposure misclassification by only focusing on the noise contributions from major roads, which we rectified with our modelling approach. Our approach enables: ·      A reproducible traffic flow noise model ·      Identification of differences in noise levels between the loudest and quietest façades of buildings. ·      Identification of "respite" zones from high noise levels. The scripts to apply the pre‐processing and noise modelling for exposure assessment are in the Appendix of D3.4.