Role of granular activated carbon's physical properties and surface chemistry in sustaining biological activated carbon performance
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
The removal of natural organic matter (NOM) through biofiltration can be significantly influenced by the diversity of acclimated microbes on the substrate surface. In this study, three types of granular activated carbon (GAC), each with different surface functional group distributions, were selected as substrates for biofiltration to explore their effects on microbial and NOM removal in surface water. The results showed that GAC with a higher percentage of carboxyl group achieved better NOM removal efficiency during approximately one year of continuous operation (83.6 ± 1.4 %). Using Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), it was found that coal-based GAC(C-GAC) was more effective at removing biorefractory compounds of NOM, including 42 %, 47 %, and 59 % more lignin, tannins, and condensed aromatic hydrocarbons, respectively. Microbial sequencing analyses revealed that physical and chemical properties of C-GAC favored the acclimation of species involved in the biodegradation of humic substances, such as the family Rhodocyclaceae and the genus Desulfosporosinus. More importantly, microbial interactions and metabolism (related to biodegradation) were found to be strongly influenced by the surface carboxyl group and pore size distribution of GAC, with the influence of surface carboxyl group being particularly critical. This study provides valuable insights into how the physicochemical properties of GAC synergistically govern microbial community dynamics and DOM removal efficiency, laying a foundation for enhancing the performance of biofilters in future water purification and reuse applications.
Date Issued
2025-11-01
Date Acceptance
2025-09-30
Citation
Chemical Engineering Journal, 2025, 524
ISSN
1385-8947
Publisher
Elsevier
Journal / Book Title
Chemical Engineering Journal
Volume
524
Copyright Statement
© 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
169173
Date Publish Online
2025-10-01