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…mehr
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.
Professor Thabo T.I Nkambule is the Head of the Institute for Nanotechnology and Water Sustainability (iNanoWS) at the university of South Africa, where he is also a professor. He is a C3-Rated researcher by the National Research foundation (NRF) of South Africa. He is responsible for the strategic direction, operations, and consolidation of all the research activities on water and nanotechnology within the institute. He is registered with the South African Council for Natural Scientific Professions (SACNASP) as a professional Natural Scientist (Pr.Sci.Nat.) and a member of the Water Institute of Southern Africa (WISA), the International Water Association (IWA) and the American Chemical Society (ACS). Prof. Nkambule also serves as the current thematic area leader for the Urban Water Cycle and Water Treatment Technologies research niche at iNanoWS. His research interests are in the Urban Water Cycle, Conventional, Advanced and Integrated Water Treatment Technologies, Natural Organic Matter in Engineered Water Treatment Systems and Nanotechnology for Water Treatment. His research focus is specifically on Natural Organic Matter (NOM) in South African waters, studying its characterization, treatability, and method development for effective NOM removal from water.
Inhaltsangabe
1. Freshwater Crises: Drivers to the nascency of the NOM problem 2. Climate Change and drinking water production 3. Natural organic Matter (NOM): A Conundrum for Drinking Water Treatment 4. Characterization of natural organic matter in drinking water: Sample preparation and analytical approaches 5. Overview of NOM removal technologies and implications post drinking water production. 6. High humics impacted NOM concentration and the implications for drinking water treatment. 7. Application of artificial intelligence (AI) and Machine Learning (ML) in drinking water treatment processes: Possibilities for managing NOM treatment
1. Freshwater Crises: Drivers to the nascency of the NOM problem 2. Climate Change and drinking water production 3. Natural organic Matter (NOM): A Conundrum for Drinking Water Treatment 4. Characterization of natural organic matter in drinking water: Sample preparation and analytical approaches 5. Overview of NOM removal technologies and implications post drinking water production. 6. High humics impacted NOM concentration and the implications for drinking water treatment. 7. Application of artificial intelligence (AI) and Machine Learning (ML) in drinking water treatment processes: Possibilities for managing NOM treatment
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