Optimal monitoring locations for identification of ambivalent characteristics of groundwater pollution sources

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.