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Monitoring the concentration of particle pollutants is very important for industrial production control and workers' health protection. Low-cost sensors are widely used to reduce deployment costs. The outliers in the observed data of pollutant concentration can be eliminated by outlier detection algorithms. However, it is difficult to meet the actual needs of changing working conditions or scene migration in factories by building a single algorithm for specific scenarios. It is a feasible scheme to identify the changing characteristics of data and adaptively adjust the outlier detection algorithm. From the point of view of data characteristics, we creatively match typical data types with high-performance algorithms. The framework proposed in this paper provides a general process including five basic tasks and uses a modular structure to complete the outlier detection target. The actual pollutant data of the workshops are used to evaluate the performance of our framework. At last, we compare eight different strategies under this framework and analyze the contribution of each step to outlier detection from the perspective of algorithm principle. The results show that low-cost sensors following the framework can meet the outlier detection requirements in the field of pollutant monitoring, thus greatly reducing the cost of algorithm selection and data adaptation., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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 2 = 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 -3 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.