| Title: | Comprehensive understanding of DOM reactivity in anaerobic fermentation of persulfate-pretreated sewage sludge via FT-ICR mass spectrometry and reactomics analysis |
| Author(s): | Liu J; Wang C; Hao Z; Kondo G; Fujii M; Fu QL; Wei Y; |
| Address: | "Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Department of Civil and Environmental Engineering, School of Environment and Society, Tokyo Institute of Technology, 2-12-1-M1-22 Ookayama, Meguro-ku, Tokyo 152-8552, Japan. Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Department of Civil and Environmental Engineering, School of Environment and Society, Tokyo Institute of Technology, 2-12-1-M1-22 Ookayama, Meguro-ku, Tokyo 152-8552, Japan; Department of Civil Engineering, Tsinghua University, Beijing 100084, China. Department of Civil and Environmental Engineering, School of Environment and Society, Tokyo Institute of Technology, 2-12-1-M1-22 Ookayama, Meguro-ku, Tokyo 152-8552, Japan. Electronic address: fujii.m.ah@m.titech.ac.jp. School of Environmental Studies, China University of Geosciences, Wuhan 430074, China. Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: yswei@rcees.ac.cn" |
| DOI: | 10.1016/j.watres.2022.119488 |
| ISSN/ISBN: | 1879-2448 (Electronic) 0043-1354 (Linking) |
| Abstract: | "Understanding the composition and reactivity of dissolved organic matter (DOM) at molecular level is vital for deciphering potential regulators or indicators relating to anaerobic process performance, though it was hardly achieved by traditional analyses. Here, the DOM composition, molecular reactivity and transformation in the enhanced sludge fermentation process were comprehensively elucidated using high-resolution mass spectrometry measurement, and data mining with machine learning and paired mass distance (PMD)-based reactomics. In the fermentation process for dewatered sludge, persulfate (PDS) pretreatment presented its highest performance in improving volatile fatty acids (VFAs) production with the increase from 2,711 mg/L to 3,869 mg/L, whereas its activation in the presence of Fe (as well as the hybrid of Fe and activated carbon) led to the decreased VFAs production performance. In addition to the conventional view of improved decomposition and solubilization of N-containing structures from sludge under the sole PDS pretreatment, the improved VFAs production was associated with the alternation of DOM molecular compositions such as humification generating molecules with high O/C, N/C, S/C and aromatic index (AI(mod)). Machine learning was capable of predicting the DOM reactivity classes with 74-76 % accuracy and found that these molecular parameters in addition to nominal oxidation state of carbon (NOSC) were among the most important variables determining the generation or disappearance of bio-resistant molecules in the PDS pretreatment. The constructed PMD-based network suggested that highly connected molecular network with long path length and high diameter was in favor of VFAs production. Especially, -NH related transformation was found to be active under the enhanced fermentation process. Moreover, network topology analysis revealed that CHONS compounds (e.g., C(13)H(27)O(8)N(1)S(1)) can be the keystone molecules, suggesting that the presence of sulfur related molecules (e.g., cysteine-like compounds) should be paid more attention as potential regulators or indicators for controlling sludge fermentation performance. This study also proposed the non-targeted DOM molecular analysis and downstream data mining for extending our understanding of DOM transformation at molecular level" |
| Keywords: | "Fermentation *Sewage/chemistry *Dissolved Organic Matter Anaerobiosis Mass Spectrometry Fatty Acids, Volatile Dissolved organic matter Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) Machine learning Network Sludge fermentation;" |
| Notes: | "MedlineLiu, Jibao Wang, Chenlu Hao, Zhineng Kondo, Gen Fujii, Manabu Fu, Qing-Long Wei, Yuansong eng England 2022/12/21 Water Res. 2023 Feb 1; 229:119488. doi: 10.1016/j.watres.2022.119488. Epub 2022 Dec 13" |
