Method for identifying outliers of soil heavy metal data

Development of a novel and fast XRF instrument for large area heavy metal detection integrated with UAV

The disadvantages of the current chemical and instrumental analysis methods for soil heavy metal pollution are that they have a high detection cost, long cycle times, and may cause secondary pollution. The aims of this study were to improve the rapid detection of soil heavy metal pollution over large areas. This study combined aircraft technology, embedded development, computer software, electronic information, and other technical methods to create a novel solution to the problem, i.e., an integrated unmanned aerial vehicle (UAV) based soil heavy metal pollution rapid detection system (UAV-SHMPRDS) was built. The key technologies required for a rapid detection system were developed, including the development of a hardware system based on a UAV and an X-ray fluorescence spectrum (XRF) analyzer, the design and implementation of a control system software system, and the implementation of a data inversion processing algorithm. Finally, a prototype UAV-SHMPRDS was constructed. Testing showed that the system improved regionalized soil heavy metal pollution detection efficiency. This study provides new solutions for the current problems encountered in the actual rapid detection of soil heavy metal pollution.

Low-cost sensor outlier detection framework for on-line monitoring of particle pollutants in multiple scenarios

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.

Methods for the Identification of Outliers and Their Influence on Exposure Assessment in Agricultural Pesticide Applicators: A Proposed Approach and Validation Using Biological Monitoring

The “patch” approach for skin exposure assessment can easily be combined with biological monitoring in real-life pesticide studies. Nevertheless, this approach is sensitive to outliers, with values markedly deviating from other members of the sample, which can result in a gross overestimation of exposure. This study aimed at developing methods for outlier identification and validating them while using biological monitoring. Twenty-seven workers applying mancozeb in Italian vineyards participated in this study. Their skin exposure was estimated while using the patch methodology, while ethylene-thiourea (ETU) was measured in the 24-h post-exposure urine as a biomarker of exposure. The outliers were detected using methods that were based on the multiplication of the median, the median absolute deviation, and boxplots. The detection rate varied between 2.3% and 17.3%. The estimated median skin exposure of 3.2 μg was reduced to 1.2 μg when the modified Z score was used. The highest reduction in the skin exposure was above 54 μg. The use of the modified Z score for outlier detection resulted in an increase in the correlation coefficient between the skin exposure and the urine ETU levels from 0.46 to 0.71, which suggested the validity of the approach. Future studies should standardize and improve the methods for pesticide exposure and risk assessment.