Chakraborty Anirban, Prakash Om
Civil and Environmental Engineering, Indian Institute of Technology Patna, Bihta, Patna, Bihar, India.
Environ Monit Assess. 2022 Aug 11;194(9):664. doi: 10.1007/s10661-022-10313-3.
Increasing pollution in the environment, particularly for groundwater, has been an issue of great concern for decades. Thus, proper management strategies need to be adopted for reclamation of such polluted groundwater aquifers. Success of these reclamation strategy relies on the precision with which the pollution source characteristics (location of sources, release flux histories, and the starting times of pollutant sources) are identified. In clandestine scenarios of groundwater pollution where neither the location nor starting times of pollutant sources are known, it is impossible to decide where to install a monitoring well. Therefore, an optimally designed pollutant data monitoring plan is needed to reduce the time and cost of monitoring and simultaneously achieve greater accuracy in identification of source characteristics. To address this issue, a principal component analysis (PCA)-based methodology is proposed to design an efficient well network for identifying unknown characteristics of pollutant sources (UCPS). PCA is applied to reduce the dimensionality of a dataset comprising a large number of interrelated variables, thus reducing the uncertainty due to ambivalent source characteristics.
几十年来，环境中污染的不断加剧，尤其是地下水污染，一直是备受关注的问题。因此，需要采取适当的管理策略来修复此类受污染的地下含水层。这些修复策略的成功依赖于对污染源特征（污染源位置、释放通量历史以及污染物源的起始时间）识别的精确程度。在地下水污染的秘密情形中，既不知道污染物源的位置，也不知道其起始时间，就无法决定在哪里安装监测井。因此，需要一个优化设计的污染物数据监测计划，以减少监测时间和成本，同时在识别源特征方面实现更高的准确性。为了解决这个问题，提出了一种基于主成分分析（PCA）的方法，来设计一个有效的井网，以识别污染物源的未知特征（UCPS）。主成分分析用于降低包含大量相互关联变量的数据集的维度，从而减少由于矛盾的源特征导致的不确定性。
