The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.
Monitoring dissolved organic matter in wastewater and drinking water treatments using spectroscopic analysis and ultra-high resolution mass spectrometry
Abstract
Dissolved organic matter (DOM) plays a critical role in determining the quality of wastewater and the safety of drinking water. This is the first review to compare two types of popular DOM monitoring techniques, including absorption spectroscopy and fluorescence excitation-emission matrices (EEMs) coupled with parallel factor analysis (PARAFAC) vs. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), for the applications in wastewater and drinking water treatments. The optical techniques provide a series of indices for tracking the quantity and quality of chromophoric and fluorescent DOM, while FT-ICR-MS is capable of identifying thousands of DOM compounds in wastewater and drinking water at the molecule level. Both types of monitoring techniques are increasingly used in studying DOM in wastewater and drinking water treatments. They provide valuable insights into the variability of DOM composition in wastewater and drinking water. The complexity and diversity of DOM highlight the challenges for effective water treatments. Different effects of various treatment processes on DOM are also assessed, which indicates that the information on DOM composition and its removal is key to optimize the treatment processes. Considering notable progress in advanced treatment processes and novel materials for removing DOM, it is important to continuously utilize these powerful monitoring tools for assessing the responses of different DOM constituents to a series of treatment processes, which can achieve an effective removal of DOM and the quality of treated water.
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Cites
Chen, W., Westerhoff, P., Leenheer, J. A., & Booksh, K. (2003) · Environmental Science & Technology · doi:10.1021/es034354c · Cited by 6,319
Weishaar, J. L., Aiken, G. R., Bergamaschi, B. A., Fram, M. S., Fujii, R., & Mopper, K. (2003) · Environmental Science & Technology · doi:10.1021/es030360x · Cited by 4,580
Helms, J. R., Stubbins, A., Ritchie, J. D., Minor, E. C., Kieber, D. J., & Mopper, K. (2008) · Limnology and Oceanography · doi:10.4319/lo.2008.53.3.0955 · Cited by 2,958
Fluorescence spectroscopy and multi-way techniques. PARAFAC
Murphy, K. R., Stedmon, C. A., Graeber, D., & Bro, R. (2013) · Analytical Methods · doi:10.1039/c3ay41160e · Cited by 2,060
Properties of fluorescent dissolved organic matter in the Gironde Estuary
Huguet, A., Vacher, L., Relexans, S., Saubusse, S., Froidefond, J. M., & Parlanti, E. (2009) · Organic Geochemistry · doi:10.1016/j.orggeochem.2009.03.002 · Cited by 2,032
Stedmon, C. A., Markager, S., & Bro, R. (2003) · Marine Chemistry · doi:10.1016/s0304-4203(03)00072-0 · Cited by 2,019
Dittmar, T., Koch, B., Hertkorn, N., & Kattner, G. (2008) · Limnology and Oceanography: Methods · doi:10.4319/lom.2008.6.230 · Cited by 1,943
From mass to structure: an aromaticity index for high‐resolution mass data of natural organic matter
Koch, B. P., & Dittmar, T. (2006) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.2386 · Cited by 1,661
Zsolnay, A., Baigar, E., Jimenez, M., Steinweg, B., & Saccomandi, F. (1999) · Chemosphere · doi:10.1016/s0045-6535(98)00166-0 · Cited by 1,312
Marine Optical Biogeochemistry: The Chemistry of Ocean Color
Coble, P. G. (2007) · Chemical Reviews · doi:10.1021/cr050350+ · Cited by 1,238
Metadata
- Authors
- Shi, W., Zhuang, W.-E., Hur, J., & Yang, L.
- Venue
- Water Research
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 188
- Pages
- 116406
- ISSN
- 0043-1354 (ISSN-L)
- OpenAlex
- W3084038098
- Cited by
- 299
- References
- 106
- FWCI
- 17.94
- Type
- journal article
- Language
- en
- Open access
- closed
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