Elsevier Natural Organic Matter (NOM) in Engineered Aquatic Systems: Advanced Data-Driven Approaches and Climate Change Impact on NOM Characterization and Treatment

Natural Organic Matter (NOM) in Engineered Aquatic Systems: Advanced Data-Driven Approaches and Climate Change Impact on NOM Characterization and Treatment provides an in-depth exploration of Natural Organic Matter (NOM) in drinking water, addressing its sources, occurrence, and environmental impacts, particularly in the context of climate change. It delves into the challenges NOM poses for drinking water treatment, including the formation of disinfection by-products and operational issues like coagulation and membrane treatment. The authors, all experts in the field, review a range of NOM removal technologies, such as coagulation, oxidation, and biofiltration. Special attention is given to high NOM concentrations in the Northern Cape and their implications for water treatment. The treatability of NOM in South African water supplies under changing climatic conditions is examined, highlighting drivers such as temperature, drought, and storm events. Finally, the book explores the potential of using Artificial Intelligence and Machine Learning techniques to optimize NOM treatment processes, offering insights into various models and their applications in managing NOM in drinking water systems. Contains graphs, maps, and illustrations to aid student learning Includes real-life case studies with step-by-step instructions to Natural Organic Matter (NOM) research Provides thorough analysis of trending Artificial Intelligence and Machine Learning methods for water treatment