Integrated Methodology to Assess Total Energy Consumption in Drinking Water Treatment Plants Based on Raw Water Quality – Application to a Case Study in Portugal

• Innovative, integrated assessment of direct and indirect energy consumption in drinking water treatment using real operational data. • Identification of key water-quality parameters driving energy consumption through advanced statistical tools (PCA and multiple regression). • Demonstration of the critical role of chemical consumption in total energy use. • Proposal of optimisation strategies to enhance treated water quality while significantly reducing resource use (water, chemicals, and energy). • Contribution to operational optimisation, energy planning, and sustainability, supporting the United Nations Sustainable Development Goals (SDGs). As global water demand rises, optimising energy consumption in water treatment processes is essential to improve both environmental sustainability and economic efficiency. This study fills a critical research gap by applying a comprehensive methodology to analyse correlations between energy consumption and raw water quality parameters, aiming to identify drivers of high energy use and optimise drinking water treatment processes. This case study focuses on the Apartadura Drinking Water Treatment Plant (DWTP), which draws water by gravity from the Apartadura reservoir in Portugal. Total energy consumption over the six-year period (2017–2022) was calculated by considering both direct energy, derived from electricity bills, and indirect energy, estimated from reagent dosages, transportation distances, and sludge disposal. The total energy consumption of treated water was 3.6 MJ/m 3 , of which 1.9 MJ/m 3 (54%) corresponded to indirect energy and 1.7 MJ/m 3 (46%) to direct energy. The main contributors to direct energy included ozone generators (180 kWh each), CO 2 recirculation pumps (150 kWh each), and filter backwash pumps (7.7 kWh each). Indirect energy was largely due to the use of calcium hydroxide and carbon dioxide.. Statistical analysis showed that raw water quality parameters, including alkalinity, pH, Langelier Index (LI), manganese (Mn), and total coliforms (TC), significantly influenced energy consumption, primarily by means of adjustments in reagent dosing and process performance. This methodology has proven effective in identifying suboptimal operational patterns, including via remote evaluation. Its broader application provides key advantages like: (1) a reproducible metric for total energy consumption across facilities; (2) integration of water quality and energy data to highlight optimisation opportunities; and (3) support for decision-making regarding energy-efficient and sustainable operation of DWTPs amid climate change challenges. This integrated approach provides a valuable tool for water treatment managers and policymakers to optimise energy consumption and improve sustainability, particularly in regions experiencing climate variability and change