Aircraft noise exposure has traditionally been assessed using static residential population data and long-term average noise metrics, often overlooking the dynamic nature of human mobility and temporal variations in operational conditions. This study proposes a data-driven framework that integrates high-resolution noise measurements from airport monitoring terminals with mobile phone-derived de facto population estimates to evaluate noise exposure with fine spatio-temporal resolution. We develop hourly noise exposure profiles and quantify the number of individuals affected across regions and time windows, using both absolute counts and inequality metrics such as Gini coefficients. This enables a nuanced examination of not only who is exposed, but when and where the burden is concentrated. At our case study airport, operational runway patterns resulted in recurring spatial shifts in noise exposure. By incorporating de facto population data, we demonstrate that identical noise operations can yield unequal impacts depending on the time and location of population presence, highlighting the importance of accounting for population dynamics in exposure assessment. Our approach offers a scalable basis for designing population-sensitive noise abatement strategies, contributing to more equitable and transparent aviation noise management.
Oh et al. (Tue,) studied this question.
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