Water Research

Researchers have developed a new framework that uses artificial intelligence to monitor wastewater treatment in real time, ensuring environmental safety while maximizing resource recovery. This “twin transition” approach allows facilities to predict system health and optimize energy use simultaneously.

Data-driven models provide precise prediction on charging-phase of desalination technology building a bridge between data-driven insights and physicochemical principle to lay a strong foundation for system parameter optimization.

Bacteria are good at evolving to evade efforts to destroy it. But building defenses like antibiotic resistance drains limited energy resources, forcing tough survival trade-offs. A recent study has revealed how bacteria in water allocates its limited energy supply. Understanding these hidden energy choices could be key to slowing the spread of antibiotic resistance in aquatic environments.

A research team led by Associate Professor Tomoya Kataoka at Ehime University, in collaboration with Yachiyo Engineering Co., Ltd., Nippon Koei Co., Ltd., and Wageningen University’s Associate Professor Tim van Emmerik, has developed AI software using deep learning to detect, classify, and track plastics flowing in rivers. The system automatically estimates transport volumes from video data, supporting continuous monitoring and advancing understanding of land-to-sea plastic transport. The findings appear in Water Research.

Aeration tanks in wastewater treatment plants are revealed as contributors to the emission and enrichment of airborne antibiotic resistance genes, raising significant public health concerns.