Application of Principal Factor Analysis and Spectral Cluster Analysis for identification of groups of water pollutants: Case Study

Proper identification of groups of water pollutants can be helpful in preventing water pollution and protect vital water resources. In this paper data on chemical industrial wastewater are treated using Principal Factor Analysis (PFA) and Cluster Analysis (CA) to identify of groups of water pollutants. For untypical data Spectral Cluster Analysis (SCA) is conducted. The principal output reports presented in the study consist of the factorization in different coordinate systems and factorization on a plane obtained from the multidimensional scaling for the comparison purposes with the grouping results (PFA), as well as of the illustration of grouping of pollution (SCA). To verify the quality of grouping two quality indexes (grouping measures) were computed: Caliński-Harabasz index and Silhouette index. PFA and SCA produced similar groups of pollutants. The results obtained show that PFA and PCA can be successfully used to monitor the quality of water quality and to organize water supply networks into groups according to similar water quality.