Coupling compositional data analysis (CoDA) with hierarchical cluster analysis (HCA) for preliminary understanding of the dynamics of a complex water distribution system: the Naples (South Italy) case study

Abstract

Providing safe tap water has been a global concern. Water scarcity, the ever-increasing water demand, temporal variation of water consumption, aging urban water infrastructure and anthropogenic pressure on the water resources are the greatest challenges in effective water supply. In the present article, the waters exploited to be introduced in a water distribution system (i.e. input water) and tap waters are collected for determination of metal(loid)s, ions and physicochemical parameters. Seasonal variation is observed in the chemistry of the input waters. Further, the annual total dissolved solids (TDS) of the tap waters range from 200 to 1000 mg l⁻¹ which stresses the importance of interconnections between urban water reservoirs for mixing different water types and adjusting water quality. It is complicated in populated cities like Naples with an old water distribution network, which also challenges setting up hydraulic and water quality models. The preliminary data visualization indicates the different chemical characteristics of some samples that are supposed to receive water from the same source. This might explain the difficulties in understanding the network layout in Naples. Thus, the compositional nature of chemical data was considered in hierarchical cluster analysis (HCA) to seasonally study water transfer between urban water reservoirs and define the source of tap water in each city area. The proposed method can preliminarily divide the pipe network into unique clusters and provide an overview of the relationships between different components when representative models cannot be set up due to limited information about network characteristics. Hence, advanced water distribution simulation and management is encouraged.