Environment/Climate Change
High-resolution statewide spatial error sound models and unequal environmental burdens among local populations Nina Lee* Nina Lee Lee Lee Brown University
Background: Environmental noise is a pervasive exposure with documented effects on human health, yet population-level assessments are often limited by sparse monitoring data, coarse spatial resolution, and a focus on A-weighted sound. These constraints hinder evaluation of fine-scale exposure variability and environmental justice implications. Methods: We conducted a monitoring campaign across Rhode Island, collecting over 29,000 hours of measurements from 340 locations. Six sound metrics were measured. Measurements were adjusted for temporal and meteorological variability. Statewide parcel-level noise exposure was predicted for ~420,000 land parcels using spatial error models incorporating land use, transportation, and built environment characteristics while accounting for residual spatial autocorrelation. Predicted noise metrics were linked to census block group characteristics from the American Community Survey and evaluated using univariate mixed-effects models with random intercepts for block group. Results: Predicted noise levels exhibited strong spatial patterning, with higher levels in urban areas and along major transportation corridors. Across all sound metrics, higher predicted noise levels were significantly associated with neighborhood characteristics indicative of structural disadvantage, including higher proportions of non-White residents, Black and Hispanic populations, lower income and educational attainment, renter-occupied housing, linguistic isolation, and disability. Parcels located within state-designated environmental justice focus areas experienced significantly higher predicted noise levels across all sound metrics. Conclusions: These state-wide, high-resolution noise models reveal significant social and spatial inequities in environmental noise exposure across an understudied state context. These well-defined exposure surfaces provide a framework for epidemiologic analyses requiring fine-scale exposure assessment.
