Comprehensive development of process, hybrid and consequential life cycle inventory models with demonstration in the water industry chemicals sector

Abstract

Water treatment chemicals are pervasive across conventional and modern technologies for water and wastewater treatment operations around the world. While their technical performance has been extensively studied to fulfil a particular purpose (e.g. disinfection or coagulation) their environmental performance has received less attention. The environmental performance of the supply chains for these chemicals can be quantified using life cycle assessment (LCA) in order to understand the potential environmental impacts on decisions associated with operational improvements, new designs or potential future upgrades of water and wastewater infrastructure. However, the methodological challenges associated with developing the life cycle inventory (LCI) data required for robust LCAs can be complex. Challenges which increase the uncertainty of LCA results include multifunctionality, truncated system boundaries and indirect effects depending on the typology of the LCA study undertaken.

This research aims to develop relevant, recent and local data which enable the assessment of the environmental performance of water treatment chemicals under several LCA methodologies used by decision makers. This is achieved through a comprehensive development of process, hybrid and consequential models including assessment of the most important sources of methodological uncertainty associated with them. Firstly, this study investigates chemical consumption trends in the Australian water industry with the objective of identifying the most important chemicals and targets them for assessment. Secondly, this research develops a comprehensive set of process-based LCI models for the prioritised water treatment chemicals, applies different techniques for solving multifunctionality and demonstrates the potential consequences of methodological choices. Thirdly, this thesis develops and demonstrates an input-output model which is subsequently developed into a tiered hybrid model specifically tailored to the chemical industry in Australia. Fourthly, this research explores and demonstrates a consequential methodology to include the uncertainties arising from electricity supply and system expansion through the use of scenario modelling. Overall, this research demonstrates a comprehensive development of process, hybrid and consequential life cycle inventory models for the water industry chemicals sector to enable better environmental decision making.