超大规模人工智能数据中心噪声排放：社区健康研究门口的一个特征化不足的暴露问题
Noise Emissions from Hyperscale AI Data Centers: A Poorly Characterized Exposure at the Doorstep of Community Health Research
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中文总结 AI 辅助
针对超大规模AI数据中心噪声暴露数据稀缺且不具可比性的问题，本文综述现有证据并提出基于标准的现场测量与建模协议，以生成频率分辨数据，支撑社区健康与环境正义评估。
中文摘要 AI 辅助
为人工智能工作负载而快速建设超大规模计算设施，已使数据中心噪声成为美国社区反复关注的来源，然而经过校准的、符合标准的声学数据仍然稀缺。本叙述性综述综合了直接测量、监管和法律记录、调查性新闻报道以及相关工程文献，以评估目前已知的信息，并确定进行可信健康推断所需的测量。现有来源报告了住宅区、产权线和近源位置处的异质声学值，单项读数从39到高于70 dB(A)不等。由于位置、度量、持续时间、仪器和来源不同，这些值不能直接比较社区暴露，不能定义合并分布，也不支持与夜间限值的总体比较。在有频谱细节的案例中，除一例外，所有案例均报告了与投诉相关的连续音调噪声，最常见的是接近70-140 Hz的低频内容，而仅使用A加权度量可能低估这些噪声。工程证据进一步表明，冷却架构可能是设施声音排放的主要决定因素；所引用的15-30 dB范围是设备级声功率估计，而非实测的设施或住宅受体差异，并且仍是一个有待比较性现场测量验证的假设。我们提出了一种基于标准的现场测量和建模协议，旨在在指定设施处生成频率分辨的暴露数据。这种暴露特征化是可信评估社区健康影响和环境正义影响所必需的基础。
英文摘要
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.
发表机构
- Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。
