Effects of Data Sparsity and Spatiotemporal Variability on Hazard Maps of Workplace Noise

Personal sampling, considered a state-of-the-art technique to assess worker exposures to occupational hazards, is often conducted for the duration of a work shift so that time-weighted average (TWA) exposures may be evaluated relative to published occupational exposure limits (OELs). Such cross-shift measurements, however, provide little information on the spatial variability of exposures, except after a very large number of samples. Hazard maps, contour plots (or similar depiction) of hazard intensity throughout the workplace, have gained popularity as a way to locate sources and to visualize spatial variability of physical and chemical hazards within a facility. However, these maps are often generated from short duration measures and have little ability to assess temporal variability. To assess the potential bias that results from the use of short-duration measurements to represent the TWA in a hazard map, noise intensity measurements were collected at high spatial and temporal resolution in two facilities. Static monitors were distributed throughout the facility and used to capture the temporal variability at these locations. Roving monitors (typical of the hazard mapping process) captured spatial variability over multiple traverses through the facility. The differences in hazards maps generated with different sampling techniques were evaluated. Hazard maps produced from sparse, roving monitor data were in good agreement with the TWA hazard maps at the facility with low temporal variability. Estimated values were within 5 dB of the TWA over approximately 90% of the facility. However, at the facility with higher temporal variability, large differences between hazard maps were observed for different traverses through the facility. On the second day of sampling, estimates were at least 5 dB different than the TWA for more than half of the locations within the facility. The temporal variability of noise was found to have a greater influence on map accuracy than the spatial sampling resolution.

个人采样（Personal sampling）作为评估工人职业危害暴露的前沿技术，通常在一个完整工作轮班期间开展，以计算时间加权平均（time-weighted average, TWA）暴露量，并与已发布的职业接触限值（occupational exposure limits, OELs）进行对照评估。然而这类跨班次测量，除非采集的样本量极为庞大，否则几乎无法提供暴露的空间变异性信息。危害地图（Hazard maps），即描绘设施内危害强度的等高线图或同类可视化形式，如今作为定位危害源、可视化设施内物理与化学危害空间变异性的手段愈发普及。但这类地图通常基于短时测量生成，几乎无法评估时间变异性。为评估用短时测量值代表危害地图中时间加权平均暴露量所带来的潜在偏差，研究人员在两处设施中以高空间与时间分辨率采集了噪声强度数据。研究人员在设施内全域布设固定监测器（Static monitors），用以捕获这些点位的时间变异性；而流动监测器（Roving monitors，符合典型危害制图流程）则通过多次遍历设施来捕获空间变异性。随后评估了不同采样技术生成的危害地图之间的差异。结果显示，在时间变异性较低的设施中，基于稀疏流动监测数据生成的危害地图与该设施的时间加权平均危害地图吻合度良好：约90%的设施区域内，估算值与时间加权平均暴露量的偏差不超过5 dB。但在时间变异性较高的设施中，不同遍历流程生成的危害地图间存在显著差异：采样次日，设施内超半数点位的估算值与时间加权平均暴露量的偏差至少达5 dB。研究发现，噪声的时间变异性对危害地图精度的影响大于空间采样分辨率。