Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China.
Environ Sci Pollut Res Int. 2021 Mar;28(12):14943-14956. doi: 10.1007/s11356-020-11709-9. Epub 2020 Nov 21.
Metalworking fluids used in industrial workshops may present a major threat to the health of workers who have been exposed to a high oil mist concentration over a long period of time. Therefore, monitoring the temporal and spatial distribution of particulate matter concentration has great practical significance for the control of oil mist. Traditional particle monitors are generally cumbersome, expensive, and difficult to maintain, which to some extent restricts their extensive use in workshops. Recent years have witnessed tremendous developments in the area of low-cost sensors, which are of great help in obtaining high-density pollution data. In this paper, we evaluate the performance of an inexpensive laser sensor (A4-CG) during long-term oil mist monitoring in a machine shop for the first time. With the use of Lora technology, we developed an online oil mist monitoring network to access real-time concentration, temperature, and humidity information from distributed monitors. According to the results, the sensor data correlated well with measurements by the reference instrument (R = 0.96), which means that the distributed sensor network can accurately detect the concentration level of oil mist in the workshop. In fact, most of the sensors demonstrated stable operation for up to half a year according to cluster analysis, while several sensors exhibited serious data drift. Furthermore, the results indicate that the peak oil mist concentration in most areas during production exceeded the value of 0.5 mg m recommended by NIOSH, and it was found that appropriately lowering the relative humidity can make sampling more accurate, while lowering the temperature can reduce the oil mist concentration in the workshop. Thus, measures to control oil mist such as generation and distribution of pollution sources should be on the agenda.
在工业车间中使用的金属加工液可能会对长时间暴露在高油雾浓度下的工人的健康构成重大威胁。因此，监测颗粒物浓度的时空分布对于控制油雾具有重要的实际意义。传统的粒子监测器通常体积庞大、价格昂贵且难以维护，这在一定程度上限制了它们在车间中的广泛使用。近年来，低成本传感器领域取得了巨大的发展，这对于获取高密度污染数据有很大的帮助。在本文中，我们首次评估了一种廉价激光传感器（A4-CG）在机加工车间进行长期油雾监测的性能。我们利用 Lora 技术开发了一个在线油雾监测网络，以便从分布式监测器获取实时浓度、温度和湿度信息。结果表明，传感器数据与参考仪器的测量结果相关性很好（R = 0.96），这意味着分布式传感器网络可以准确检测车间内油雾的浓度水平。实际上，根据聚类分析，大多数传感器在长达半年的时间内都表现出稳定的运行，而有几个传感器则表现出严重的数据漂移。此外，结果表明，在生产过程中，大多数区域的油雾浓度峰值超过了 NIOSH 建议的 0.5mg/m 值，并且发现适当降低相对湿度可以使采样更准确，而降低温度可以降低车间内的油雾浓度。因此，应将控制油雾的措施（如污染源的产生和分布）提上日程。
