Lei Li

Micro/nanobubbles (MNBs), due to their unique interface properties, have demonstrated remarkable potential in enhancing advanced oxidation processes (AOPs) in water. These gas-liquid interfaces enhance oxidation efficiency by facilitating oxidant mass transfer and contaminants adsorption. While the role of interface reactions is well-documented, the influence of MNBs-induced effects on contaminant removal mechanisms in aqueous phase reactions remains inadequately understood. To bridge this knowledge gap, an oxygen MNBs/ultraviolet/hydrogen peroxide (O-MNBs/UV/H₂O₂) photooxidation system was developed to explore the role of interface effects in aqueous phase reactions. Model contaminants, including representative natural and synthetic organic compounds commonly found in surface waters, were selected to evaluate the removal performance. The results indicated that the removal efficiency of contaminants was significantly influenced by the electrostatic and hydrophobic properties of the contaminants. Notably, the O-MNBs enhanced activation of H₂O₂ by improving UV-light utilization and oxygen mass-transfer efficiency, leading to increased production of reactive oxygen species (ROS) in the aqueous phase. Meanwhile, surface repulsion induced by electrostatic and hydrophobic forces facilitated the migration of contaminants into the aqueous phase, thereby enhancing their contact with ROS and enabling selective oxidation. These findings reveal that MNB-mediated interface effects can drive the spatial redistribution of contaminants and modulate their oxidation pathways in photooxidation, offering a promising strategy for controlling oxidation reactions and achieving selective removal of contaminants in water treatment applications.

Satellite evidence indicates a global increase in lacustrine algal blooms. These blooms can drift with winds, resulting in significant changes of the algal biomass spatial distribution, which is crucial in bloom formation. However, the lack of long-term, large-scale observational data has limited our understanding of bloom drift. Here, we have developed a novel method to track the drift using multi-source remote sensing satellites and presented a comprehensive bloom drift data set for four typical lakes: Lake Taihu (China, 2011-2021), Lake Chaohu (China, 2011-2020), Lake Dianchi (China, 2003-2021), and Lake Erie (North America, 2003-2021). We found that blooms closer to the water surface tend to drift faster. Higher temperatures and lower wind speeds bring blooms closer to the water surface, therefore accelerating drift and increasing biomass transportation. Under ongoing climate change, algal blooms are increasingly likely to spread over larger areas and accumulate in downwind waters, thereby posing a heightened risk to water resources. Our research greatly improves the understanding of algal bloom dynamics and provides new insights into the driving factors behind the global expansion of algal blooms. Our bloom-drift-tracking methodology also paves the way for the development of high-precision algal bloom prediction models.