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Unravelling molecular transformation of dissolved effluent organic matter in UV/H2O2, UV/persulfate, and UV/chlorine processes based on FT-ICR-MS analysis
Abstract
Ultraviolet-based advanced oxidation processes (UV-AOPs) are very promising in advanced treatment of municipal secondary effluents. However, the transformation of dissolved effluent organic matter (dEfOM) in advanced treatment of real wastewater, particularly at molecular level, remains unclear. In this study, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) coupled with multiple statistical analysis were performed to better understand the transformation of dEfOM in UV/H2O2, UV/persulfate (UV/PS), and UV/chlorine treatments. An obvious increase in oxygen content of dEfOM was observed after every UV-AOPs treatment, and the detailed oxygenation processes were further uncovered by mass difference analysis based on 24 types of typical reactions. Generally, UV/H2O2 process was subjected to the most oxygenation reactions with the typical tri-hydroxylation one (+3O), whereas di-hydroxylation reaction (+H2O2) was dominant in UV/PS and UV/chlorine processes. Additionally, the three UV-AOPs shared the majority of precursors, and more proportions of unique products were identified for each process. The precursors with lower H/C and higher aromaticity were readily degraded by UV/chlorine over UV/H2O2 and UV/PS, with the products featuring lower molecular weight. Moreover, dEfOM of high aromaticity tended to produce chlorinated byproducts through addition reactions in chlorination and UV/chlorine processes. Among these UV-AOPs, the highest reduction of both acute toxicity and specific UV absorbance at 254 nm (SUVA254) was observed for UV/chlorine, implying the potential for UV/chlorine process in advanced treatment of wastewater. In addition, acute toxicity was highly correlated with SUVA254 and CHOS compounds. This study is believed to help better understand the different fates of dEfOM in real wastewater during UV-AOPs treatment.
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RICHARDSON, S., PLEWA, M., WAGNER, E., SCHOENY, R., & DEMARINI, D. (2007) · Mutation Research/Reviews in Mutation Research · doi:10.1016/j.mrrev.2007.09.001 · Cited by 3,317
Artificial sweeteners induce glucose intolerance by altering the gut microbiota
Suez, J., Korem, T., Zeevi, D., Zilberman-Schapira, G., Thaiss, C. A., Maza, O., Israeli, D., Zmora, N., Gilad, S., Weinberger, A., et al. (2014) · Nature · doi:10.1038/nature13793 · Cited by 2,067
Yang, Y., Lu, X., Jiang, J., Ma, J., Liu, G., Cao, Y., Liu, W., Li, J., Pang, S., Kong, X., et al. (2017) · Water Research · doi:10.1016/j.watres.2017.03.054 · Cited by 637
Li, W., Jain, T., Ishida, K., & Liu, H. (2017) · Environmental Science: Water Research & Technology · doi:10.1039/c6ew00242k · Cited by 151
Yang, Y., Cao, Y., Jiang, J., Lu, X., Ma, J., Pang, S., Li, J., Liu, Y., Zhou, Y., & Guan, C. (2019) · Water Research · doi:10.1016/j.watres.2018.08.074 · Cited by 120
Cited by
Multiple Roles of Dissolved Organic Matter in Advanced Oxidation Processes
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Huang, X., Ren, X., Zhang, Z., Gu, P., Yang, K., & Miao, H. (2023) · Chemosphere · doi:10.1016/j.chemosphere.2023.139725 · Cited by 20
Metadata
- Authors
- Zhang, B., Wang, X., Fang, Z., Wang, S., Shan, C., Wei, S., & Pan, B.
- Venue
- Water Research
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 199
- Pages
- 117158
- ISSN
- 0043-1354 (ISSN-L)
- OpenAlex
- W3156584055
- Cited by
- 188
- References
- 5
- FWCI
- 7.88
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, B., Wang, X., Fang, Z., Wang, S., Shan, C., Wei, S., & Pan, B. (2021). Unravelling molecular transformation of dissolved effluent organic matter in UV/H2O2, UV/persulfate, and UV/chlorine processes based on FT-ICR-MS analysis. Water Research. https://doi.org/10.1016/j.watres.2021.117158
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