Zamyadi, A. | Search bioRxiv

bioRxiv2025

Exploring use of ozone nanobubbles for removal of cyanobacteria and co-occurring antimicrobial resistance genes in water supply and reuse systems

Harmful cyanobacterial blooms present persistent risks to both drinking water security and wastewater reuse, driving the need for advanced treatment strategies. Treatment barrier(s) need to be capable of simultaneously controlling cyanobacteria, cyanotoxins, and co-occurring contaminants like bloom-associated antimicrobial resistance genes (ARGs) particularly in the case of recycling treated wastewater. Ozone nanobubble technology has emerged as a promising innovation, offering extended oxidative stability and enhanced interfacial reactivity compared to conventional ozonation. Hence this research objectives were to (a) assess the removal performance of ozone nanobubbles in eliminating cyanobacteria and their co-occurring contaminants in comparison to conventional ozone systems, and (b) investigate the repeatability of the results in varying background water qualities, ozone decay and the potential for by-products formation Ozonation using nanobubbles enhanced oxidation performance by 19%-34% in the drinking water reservoir compared to conventional ozonation while keeping the ozone concentration below 2mg/l. Lower oxidation efficiencies were observed in treated wastewater compared to drinking water sources, reflecting the higher content of organic matter and suspended solids, and oxidant demand characteristic of recycled water systems. Despite these challenges, ozone nanobubbles consistently outperformed conventional ozonation in reducing both cyanobacterial biomass and cell viability, underscoring their potential as an advanced "polishing" step for algal management in wastewater reuse applications. By exploring fate of ARGs alongside cyanobacteria and toxin removal, this work extends beyond traditional ozonation trials. It provides valuable field-based evidence that bridges the divide between laboratory efficacy and full-scale operational performance. Future studies should build on this by exploring combined or sequential treatment barriers that enhance DNA degradation, thereby addressing both cellular and genetic risks in water supply and reuse systems. Observing the action of nanobubbles under dynamic, real-world water quality conditions is currently challenging; however, this studys novel field trials demonstrate potential nanobubble applications and provide valuable insights to guide future investigations. The results reinforce the broader applicability of ozone nanobubble technology for multi-target contaminant control in water reservoirs.

microbiology↗

bioRxiv2024

Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types

Algae and cyanobacteria are microorganisms found in almost all fresh and marine waters, where they can pose environmental and public health risks when they grow excessively and produce blooms. Accurate identification and quantification of these microorganisms are vital for ecological research, water quality monitoring, and public health safety. However, traditional methods of manually counting and morphologically identifying these microorganisms are time-consuming and prone to human error. Application of the machine learning-driven Fast Segment Anything Model (FastSAM), an image segmentation model, automates and potentially enhances the accuracy and efficiency of cell identification and enumeration from microscopic images. We assessed FastSAM for algal cell image segmentation, and three clustering evaluation metrics. Segmentation of microscopic images of algal and cyanobacterial cells in water and treated wastewater samples using the Convolutional Neural Network based FastSAM algorithm demonstrated benefits and challenges of this machine learning-driven image processing. Notably, the pre-trained algorithm segmented entire elements in all microscopic images used in this study. Depending on the shape, 50-100% similarity was observed between machine-based segmentation and manual validation of all segmented elements, with 100% of single cells being correctly segmented by FastSAM. The performance of clustering metrics varied between 57-94% with the Spectral Angle Mapper achieving the most accurate performance, 84-94%, compared to the manually chosen clustering benchmarks. Cyanobacterial and algal communities are biologically diverse and have ecological significance. The application of image clustering techniques in studying their cell shapes marks an important advancement in microbial ecology and environmental monitoring. As technology progresses, these methods will become increasingly utilised to decipher the complex roles that algae and cyanobacteria play in our ecosystems supporting mitigation and public health protection measures.

microbiology↗