Using bio-based CaCO₃ functionalized sediment to simultaneously remove algae and COD through adsorption and sedimentation in water source reservoirs

Highlights

Authors

Affiliations

Yifan Du

School of Civil Engineering, Chang’an University

Key Laboratory of Environmental Aquatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences

Jinbo Zhao

School of Civil Engineering, Chang’an University

Key Laboratory of Environmental Aquatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences

Qingping Wang

School of Civil Engineering, Chang’an University

Jiacheng Feng

School of Civil Engineering, Chang’an University

Jinyi Qin

School of Civil Engineering, Chang’an University

Key Laboratory of Environmental Aquatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences

Ming Su

Key Laboratory of Environmental Aquatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences

University of Chinese Academy of Sciences

^a School of Civil Engineering, Chang’an University, Xi’an 710064, China.
^b Key Laboratory of Environmental Aquatic Chemistry, State Key Laboratory of Regional Environment and Sustainability, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
^c University of Chinese Academy of Sciences, Beijing 100049, China.

^* Corresponding to: Jinyi Qin (jinyi.qin@chd.edu.cn), Ming Su (mingsu@rcees.ac.cn)

• Bio-CaCO₃-modified sediment traps algae and COD via EPS bridging.
• Nanostructured CaCO₃ forms core–shell clusters with active surface sites.
• Pores and charge heterogeneity promote selective pollutant retention.
• DFT reveals strong EPS–CaCO₃ interaction at molecular level.
• System optimization achieved by RSM and XDLVO energy modeling.