Multivariate analysis of water quality data for drinking water supply systems

Abstract

Vulnerability of drinking water supply systems (DWSSs) depends on different factors such as failures, loss of security, man-made threats, and the change and deterioration of supply-water quality. Currently, the lifespan of several DWSSs worldwide has been exceeded, exasperating these issues. The monitoring activity and the transparency of information on water availability and quality are becoming increasingly important in accordance with the national regulations and standards, and with guidelines of the World Health Organization (WHO). These activities can be considered as support and guidance tools for identifying health-related risks, for building a safe management of drinking water supply systems, and for improved user confidence in the consumption of tap water. In this context, in the present work an analysis of the quality monitoring data of DWSSs was carried out using multivariate techniques. The analysis considered several chemical–physical parameters collected in the period 2013–2020 for some DWSSs in the Emilia-Romagna region, Italy. Principal component analysis (PCA) and cluster analysis (CA) methods were used to process and reduce the dimensionality of the data, to highlight the parameters that have the greatest influence on the qualitative state of the supplied water and to identify clusters.