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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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