Mapping Occupational Hazards with a Multi-sensor Network in a Heavy-Vehicle Manufacturing Facility.
Mapping Occupational Hazards with a Multi-sensor Network in a Heavy-Vehicle Manufacturing Facility.
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在重型车辆制造工厂中使用多传感器网络绘制职业危害图。
DOI：
10.1093/annweh/wxy111
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发表时间：
2019
影响因子：
2.6
通讯作者：
Koehler,Kirsten
中科院分区：
文献类型：
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作者：
Zuidema,Christopher;Sousan,Sinan;Stebounova,LarissaV;Gray,Alyson;Liu,Xiaoxing;Tatum,Marcus;Stroh,Oliver;Thomas,Geb;Peters,Thomas;Koehler,Kirsten
由于它们的小尺寸，低功耗需求和可定制性，低成本传感器可以部署在空间分布在环境中的集合中，称为传感器网络。文献中包含周围环境中此类网络的示例;本文介绍了40节点多危险网络的开发和部署，该网络采用低成本颗粒物传感器（SHARP GP 2 Y1010 AU 0 F）、一氧化碳传感器（Alphasense CO-B4）、氧化气体传感器（Alphasense OX-B421）和重型车辆制造设施中的噪声传感器（内部开发）构建。网络节点与中央数据库进行无线通信，以便以5分钟的间隔记录危害测量结果。在这里，我们报告的时间和空间测量网络，网络测量的精度，并通过8个月的连续部署的网络测量精度相对于现场参考仪器。在典型的生产期间，所有监测器的颗粒物（PM）、一氧化碳（CO）、氧化性气体（OX）和噪声的1小时平均危害水平±标准差分别为0.62 ± 0.2 mg m−3、7 ± 2 ppm、155 ± 58 ppb和82 ± 1 dBA。我们观察到明确的昼夜和每周的时间模式的所有危害和日常，危害特定的空间模式归因于一般的制造过程中的设施。与最高危险等级相关的工艺是机械加工和焊接（PM和噪声）、分段（CO）以及手动和机器人焊接（OX）。网络传感器表现出不同程度的精度与95%的测量之间的三个并列节点在0.21 mg m− 3的PM，0.4 ppm的CO，9 ppb的OX，和1 dBA的噪音。对于PM、CO、OX和噪声，参考直接读取仪器的中位百分比偏倚分别为27%、11%、45%和1%。这项研究表明，成功的长期部署的多危害传感器网络在工业制造环境中，并说明了高的时间和空间分辨率的危险数据，传感器和监测网络的能力。我们发现，网络衍生的危害测量提供了丰富的数据集，全面评估职业危害。我们的网络设置的阶段，在个人层面上与无线传感器网络的职业暴露的表征。
Due to their small size, low-power demands, and customizability, low-cost sensors can be deployed in collections that are spatially distributed in the environment, known as sensor networks. The literature contains examples of such networks in the ambient environment; this article describes the development and deployment of a 40-node multi-hazard network, constructed with low-cost sensors for particulate matter (SHARP GP2Y1010AU0F), carbon monoxide (Alphasense CO-B4), oxidizing gases (Alphasense OX-B421), and noise (developed in-house) in a heavy-vehicle manufacturing facility. Network nodes communicated wirelessly with a central database in order to record hazard measurements at 5-min intervals. Here, we report on the temporal and spatial measurements from the network, precision of network measurements, and accuracy of network measurements with respect to field reference instruments through 8 months of continuous deployment. During typical production periods, 1-h mean hazard levels ± standard deviation across all monitors for particulate matter (PM), carbon monoxide (CO), oxidizing gases (OX), and noise were 0.62 ± 0.2 mg m−3, 7 ± 2 ppm, 155 ± 58 ppb, and 82 ± 1 dBA, respectively. We observed clear diurnal and weekly temporal patterns for all hazards and daily, hazard-specific spatial patterns attributable to general manufacturing processes in the facility. Processes associated with the highest hazard levels were machining and welding (PM and noise), staging (CO), and manual and robotic welding (OX). Network sensors exhibited varying degrees of precision with 95% of measurements among three collocated nodes within 0.21 mg m−3for PM, 0.4 ppm for CO, 9 ppb for OX, and 1 dBA for noise of each other. The median percent bias with reference to direct-reading instruments was 27%, 11%, 45%, and 1%, for PM, CO, OX, and noise, respectively. This study demonstrates the successful long-term deployment of a multi-hazard sensor network in an industrial manufacturing setting and illustrates the high temporal and spatial resolution of hazard data that sensor and monitor networks are capable of. We show that network-derived hazard measurements offer rich datasets to comprehensively assess occupational hazards. Our network sets the stage for the characterization of occupational exposures on the individual level with wireless sensor networks.