Noise Emissions from Hyperscale AI Data Centers: A Poorly Characterized Exposure at the Doorstep of Community Health Research
Abstract
The rapid build-out of hyperscale computing for artificial-intelligence workloads has made data-center noise a recurring source of community concern in the United States, yet calibrated, standards-grade acoustic data remain scarce. This narrative review synthesizes direct measurements, regulatory and legal records, investigative journalism, and relevant engineering literature to assess what is currently known and identify the measurements needed for credible health inference. Available sources report heterogeneous acoustic values at residences, property lines, and near-source positions, with individual readings from 39 to above 70 dB(A). Because location, metric, duration, instrumentation, and provenance differ, these values are not directly comparable community exposures, do not define a pooled distribution, and do not support an aggregate comparison with nighttime limits. Among cases with spectral detail, all but one reported continuous tonal noise associated with complaints, most often low-frequency content near 70-140 Hz, that A-weighted metrics alone may under-represent. Engineering evidence further suggests that cooling architecture may be a major determinant of facility sound emission; the cited 15-30 dB span is an equipment-level sound-power estimate, not a measured facility- or residential-receptor difference, and remains a hypothesis pending comparative field measurements. We propose a standards-based field-measurement and modeling protocol designed to generate frequency-resolved exposure data at named facilities. Such exposure characterization is a necessary foundation for credible assessment of community health effects and environmental justice implications.
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BibTeX
@misc{bray2026noise,
title = {{Noise Emissions from Hyperscale AI Data Centers: A Poorly Characterized Exposure at the Doorstep of Community Health Research}},
author = {Davide Bray and Francesca Dominici},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03975},
eprint = {2610.03975},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03975},
}
RIS
TY - UNPB
TI - Noise Emissions from Hyperscale AI Data Centers: A Poorly Characterized Exposure at the Doorstep of Community Health Research
AU - Davide Bray
AU - Francesca Dominici
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03975
UR - https://arxiv.org/abs/2610.03975
AB - The rapid build-out of hyperscale computing for artificial-intelligence workloads has made data-center noise a recurring source of community concern in the United States, yet calibrated, standards-grade acoustic data remain scarce. This narrative review synthesizes direct measurements, regulatory and legal records, investigative journalism, and relevant engineering literature to assess what is currently known and identify the measurements needed for credible health inference. Available sources report heterogeneous acoustic values at residences, property lines, and near-source positions, with individual readings from 39 to above 70 dB(A). Because location, metric, duration, instrumentation, and provenance differ, these values are not directly comparable community exposures, do not define a pooled distribution, and do not support an aggregate comparison with nighttime limits. Among cases with spectral detail, all but one reported continuous tonal noise associated with complaints, most often low-frequency content near 70-140 Hz, that A-weighted metrics alone may under-represent. Engineering evidence further suggests that cooling architecture may be a major determinant of facility sound emission; the cited 15-30 dB span is an equipment-level sound-power estimate, not a measured facility- or residential-receptor difference, and remains a hypothesis pending comparative field measurements. We propose a standards-based field-measurement and modeling protocol designed to generate frequency-resolved exposure data at named facilities. Such exposure characterization is a necessary foundation for credible assessment of community health effects and environmental justice implications.
ER -