School of Environment, Tsinghua University, Beijing, 100084, China.
Environ Sci Process Impacts. 2015 Feb;17(2):343-50. doi: 10.1039/c4em00580e.
Emergent contamination events have a significant impact on water systems. After contamination detection, it is important to classify the type of contaminant quickly to provide support for remediation attempts. Conventional methods generally either rely on laboratory-based analysis, which requires a long analysis time, or on multivariable-based geometry analysis and sequence analysis, which is prone to being affected by the contaminant concentration. This paper proposes a new contaminant classification method, which discriminates contaminants in a real time manner independent of the contaminant concentration. The proposed method quantifies the similarities or dissimilarities between sensors' responses to different types of contaminants. The performance of the proposed method was evaluated using data from contaminant injection experiments in a laboratory and compared with a Euclidean distance-based method. The robustness of the proposed method was evaluated using an uncertainty analysis. The results show that the proposed method performed better in identifying the type of contaminant than the Euclidean distance based method and that it could classify the type of contaminant in minutes without significantly compromising the correct classification rate (CCR).
突发性污染事件对水系统有重大影响。在检测到污染后，快速分类污染物类型对于修复尝试提供支持非常重要。传统方法通常要么依赖于需要较长分析时间的实验室分析，要么依赖于基于多变量的几何分析和序列分析，而这两种方法都容易受到污染物浓度的影响。本文提出了一种新的污染物分类方法，可以实时区分不同类型的污染物，而不受污染物浓度的影响。该方法量化了传感器对不同类型污染物的响应之间的相似性或差异性。利用实验室污染物注入实验的数据评估了所提出方法的性能，并与基于欧几里得距离的方法进行了比较。利用不确定性分析评估了所提出方法的稳健性。结果表明，与基于欧几里得距离的方法相比，该方法在识别污染物类型方面表现更好，并且可以在不显著降低正确分类率 (CCR) 的情况下在几分钟内对污染物类型进行分类。
