A multi-scale analysis of spatiotemporal variability and environmental influences on urban noise

Urban noise has emerged as a critical environmental stressor affecting public health in densely populated cities. Most existing studies, however, rely on temporally aggregated approaches that obscure how noise-environment relationships vary across seasons and times of day, limiting analytical insight and spatial coverage. This study presents an integrated spatiotemporal framework for urban noise analysis in Seoul, South Korea, using five years of continuous IoT sensor data from 762 monitoring locations. Temporal contexts were defined through Circular K-means clustering, yielding 28 season-by-time-of-day combinations, with meteorological effects explicitly controlled to isolate urban structural contributions. Independent ExtraTrees models were trained for each context and applied to unmeasured locations, with results aggregated to administrative units for neighborhood-scale exposure assessment. Results reveal that noise-environment relationships are not temporally invariant — while road infrastructure consistently ranked as the primary predictor, its relative importance was systematically displaced by commercial activity indicators during evening hours and by topographic features during summer contexts, patterns that a temporally pooled model would fail to detect. Districts with similar mean noise levels exhibited substantially different temporal profiles, underscoring that location alone is insufficient to characterize noise burden. Spatial analysis identified significant neighborhood-scale clustering, with central areas exhibiting both higher exposure and greater temporal variability. These findings demonstrate that context-stratified modeling reveals dimensions of urban noise dynamics that static approaches systematically obscure. The framework is transferable to other data-rich cities seeking evidence-based noise management.