Investigating the Calco-Carbonic Balance of Drinking Water in Taza Morocco Using Principal Component Analysis and Multiple Linear Regression

Abstract

This study investigates the calco-carbonic balance of drinking water in Taza, Morocco, a critical parameter for ensuring water quality and preserving distribution infrastructure. Using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) with Statistica 12, we examine the interactions between the physicochemical parameters and the Langelier Saturation Index (LSI). The study highlights the difficulty of factors influencing this balance, which is crucial for preventing scaling. Scaling can lead to reduced water flow, decreased energy efficiency, increased maintenance costs, premature equipment wear, and deterioration of water quality. The results identify the parameters impacting this balance, including temperature, total hardness, dissolved oxygen, and pH. PCA enabled us to extract valuable insights from physicochemical analyses, revealing significant correlations between these parameters and suggesting optimization strategies.The predictive model for the Langelier Saturation Index, with a determination coefficient (R² = 0.925) and a standard error (σerr = 0.07), provides a valuable tool for expecting and correcting imbalances, therefore ensuring better management of drinking water quality in Taza.