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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2020, Vol. 14 Issue (2) : 31    https://doi.org/10.1007/s11783-019-1210-8
REVIEW ARTICLE
Excitation-emission matrix (EEM) fluorescence spectroscopy for characterization of organic matter in membrane bioreactors: Principles, methods and applications
Jinlan Yu1, Kang Xiao1,2,3(), Wenchao Xue4, Yue-xiao Shen5, Jihua Tan1, Shuai Liang6, Yanfen Wang1,2, Xia Huang3,7
1. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
2. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
3. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
4. School of Environment, Resources and Development, Asian Institute of Technology, Klong Luang, Pathumthani 12120, Thailand
5. Department of Civil, Environmental, and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA
6. College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, China
7. Research and Application Center for Membrane Technology, School of Environment, Tsinghua University, Beijing 100084, China
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Abstract

• Principles and methods for fluorescence EEM are systematically outlined.

• Fluorophore peak/region/component and energy information can be extracted from EEM.

• EEM can fingerprint the physical/chemical/biological properties of DOM in MBRs.

• EEM is useful for tracking pollutant transformation and membrane retention/fouling.

• Improvements are still needed to overcome limitations for further studies.

The membrane bioreactor (MBR) technology is a rising star for wastewater treatment. The pollutant elimination and membrane fouling performances of MBRs are essentially related to the dissolved organic matter (DOM) in the system. Three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy, a powerful tool for the rapid and sensitive characterization of DOM, has been extensively applied in MBR studies; however, only a limited portion of the EEM fingerprinting information was utilized. This paper revisits the principles and methods of fluorescence EEM, and reviews the recent progress in applying EEM to characterize DOM in MBR studies. We systematically introduced the information extracted from EEM by considering the fluorescence peak location/intensity, wavelength regional distribution, and spectral deconvolution (giving fluorescent component loadings/scores), and discussed how to use the information to interpret the chemical compositions, physiochemical properties, biological activities, membrane retention/fouling behaviors, and migration/transformation fates of DOM in MBR systems. In addition to conventional EEM indicators, novel fluorescent parameters are summarized for potential use, including quantum yield, Stokes shift, excited energy state, and fluorescence lifetime. The current limitations of EEM-based DOM characterization are also discussed, with possible measures proposed to improve applications in MBR monitoring.

Keywords excitation-emission matrix (EEM)      dissolved organic matter (DOM)      membrane bioreactor (MBR)      fluorescence indicator      characterization method     
Corresponding Author(s): Kang Xiao   
Issue Date: 13 January 2020
 Cite this article:   
Jinlan Yu,Kang Xiao,Wenchao Xue, et al. Excitation-emission matrix (EEM) fluorescence spectroscopy for characterization of organic matter in membrane bioreactors: Principles, methods and applications[J]. Front. Environ. Sci. Eng., 2020, 14(2): 31.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-019-1210-8
https://academic.hep.com.cn/fese/EN/Y2020/V14/I2/31
Fig.1  Schematic diagram of luminescence mechanisms.
Fig.2  Flow chart for EEM data processing.
Fig.3  Collective depictions of fluorescence peaks and wavelength regions according to (a) chemical composition (Coble, 1996; Chen et al., 2003; Ishii and Boyer, 2012; Guo et al., 2018), (b) hydrophobicity (Marhaba et al., 2000; Xiao et al., 2016; Xiao et al., 2018b), and (c) functional behavior (Liu et al., 2011; Xiao et al., 2018a) of DOM in MBRs.
Water quality parameters Water systems Fluorescence peaks and correlation coefficients References
TOC Surface waters, sewage treatment plants and samples of pollution incidents Tryptophan-like T1 (0.88); tryptophan-like T2 (0.80); humic-like A (0.87); humic-like C (0.81) Hudson et al. (2008)
Surface water, groundwater, sewage treatment plants Fulvic-like C2 (0.17-0.76) Cumberland and Baker (2007)
Sewage treatment plant Tryptophan-like T (0.989) Christian et al. (2017)
DOC Surface water, groundwater, sewage treatment plants Humic-like C1 (0.28-0.63) Cumberland and Baker (2007)
Sewage treatment plants (conventional activated sludge treatment) Total tryptophan-like (0.96); total tyrosine-like (0.98) Ignatev and Tuhkanen (2019)
Drinking water treatment plants Humic-like C1 (0.96); humic-like C2 (0.97); humic-like C3 (0.94); protein-like C4 (0.94); humic-like C5 (0.95); humic-like C6 (0.93); protein-like C7 (0.91) Baghoth et al. (2011)
Sewage treatment plants Protein-like C1 (0.85-0.99); humic-like C2 (0.99); humic-like C3 (0.79-0.98); humic-like C4 (0.99) Cohen et al. (2014)
COD Sewage treatment plants (conventional activated sludge treatment) Total tryptophan-like (0.90); total tyrosine-like (0.94) Ignatev and Tuhkanen (2019)
Surface water (Gap River watershed; Korea) Humic-like C1(0.98); humic-like C2 (0.97); tryptophan-like C3 (0.98) Hur and Cho (2012)
Sewage treatment plants Tryptophan-like T1 (0.85) Bridgeman et al. (2013)
Surface water (Tyne catchment in North-East England) Tryptophan-like T ( 0.65) Baker and Inverarity (2004)
Sewage treatment plants Protein-like C1 (0.82-0.99); humic-like C2 (0.91); humic-like C3 (0.96); humic-like C4 (0.80) Cohen et al. (2014)
BOD Surface waters, sewage treatment works and samples of pollution incidents Tryptophan-like T1 (0.91); tryptophan-like T2 (0.85); humic-like A (0.70); humic-like C (0.77) Hudson et al. (2008)
Sewage treatment plants (conventional activated sludge treatment) Total tryptophan-like (0.93); total tyrosine-like (0.96) Ignatev and Tuhkanen (2019)
Surface water (Gap River watershed; Korea) Humic-like C1 (0.95); humic-like C2 (0.94); tryptophan-like C3 (0.95) Hur and Cho (2012)
Sewage treatment plants Tryptophan-like T1 (0.89-0.94) Reynolds and Ahmad (1997)
Sewage treatment plants Tryptophan-like T (0.97) Ahmad and Reynolds (1999)
Sewage treatment plants Tryptophan-like T1 (0.92) Bridgeman et al. (2013)
Surface water (Tyne catchment in North-East England) Tryptophan-like T (0.85) Baker and Inverarity (2004)
Landfill sites (North England) Tryptophan-like T2 (0.94-0.98) Baker and Curry (2004)
Sewage treatment plants Protein-like C1 (0.82)
humic-like C2 (0.72)
Cohen et al. (2014)
Sewage treatment plant Tryptophan-like T (0.971); peak C (0.945) Christian et al. (2017)
TN Surface water (Gap River watershed; Korea) Humic-like C1 (0.951); humic-like C2 (0.927); tryptophan-like C3 (0.950) Hur and Cho (2012)
Sewage treatment plants Protein-like C1 (0.86-0.90); humic-like C2 (0.88); humic-like C3 (0.80); humic-like C4 (0.83) Cohen et al. (2014)
UVA254 Drinking water treatment plants Humic-like C1 (0.89); humic-like C2 (0.91); humic-like C3 (0.88); protein-like C4 (0.92); humic-like C5 (0.89); humic-like C6 (0.91); protein-like C7 (0.86) Baghoth et al. (2011)
Sewage treatment plants Protein-like C1 (0.80-0.92); humic-like C2 (0.75-0.85); humic-like C3 (0.70-0.84); humic-like C4 (0.76) Cohen et al. (2014)
Tab.1  Correlation between water quality parameters and fluorescence parameters (fluorescence peaks and components)
Fig.4  Linkages between EEM properties and DOM properties for MBR studies.
Fig.5  Role of DOM molecules in membrane fouling evolution at different filtration stages.
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