Mapping Noise Pollution Using Modelled and Crowdsourced Urban Noise Data

Abstract

Transport-generated noise pollution significantly burdens population health. Quantifying noise distribution and identifying areas with elevated noise levels is crucial for designing effective policy measures. We apply the CNOSSOS-EU numerical transport noise model to Pōneke Wellington, mapping traffic-induced noise exposure with good spatial coverage from public data. Crowdsourced noise data (CND) maps all noise sources and we spatially compare results. While unsuitable as a standalone method, CND highlights the impact of missing noise sources and offers a limited but promising validation tool for modelled data when alternatives are lacking.