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Monitoring biological wastewater treatment processes: recent advances in spectroscopy applications

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Abstract

Biological processes based on aerobic and anaerobic technologies have been continuously developed to wastewater treatment and are currently routinely employed to reduce the contaminants discharge levels in the environment. However, most methodologies commonly applied for monitoring key parameters are labor intensive, time-consuming and just provide a snapshot of the process. Thus, spectroscopy applications in biological processes are, nowadays, considered a rapid and effective alternative technology for real-time monitoring though still lacking implementation in full-scale plants. In this review, the application of spectroscopic techniques to aerobic and anaerobic systems is addressed focusing on UV–Vis, infrared, and fluorescence spectroscopy. Furthermore, chemometric techniques, valuable tools to extract the relevant data, are also referred. To that effect, a detailed analysis is performed for aerobic and anaerobic systems to summarize the findings that have been obtained since 2000. Future prospects for the application of spectroscopic techniques in biological wastewater treatment processes are further discussed.

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Abbreviations

3D-EEM:

Three-dimensional excitation-emission matrix

AD:

Anaerobic digestion

AGS:

Aerobic granular sludge

ALE:

Alginate-like exopolysaccharides

AMPTS:

Automatic methane potential test system II

AnDMBR:

Anaerobic dynamic membrane bioreactor

ANN:

Artificial neural networks

BA:

Bicarbonate alkalinity

BMP:

Biochemical methane potential

BOD5 :

Biochemical oxygen demand

CA:

Cluster analysis

CAS:

Conventional activated sludge

CNOM:

Colored natural organic matter

COD:

Chemical oxygen demand

DCOD:

Dissolved chemical oxygen demand

DM:

Dry matter

DOC:

Dissolved organic carbon

DOM:

Dissolved organic matter

EBPR:

Enhanced biological phosphorus removal

EDAN:

N-(1-naphthyl)ethylenediamine

EDTA:

Ethylenediamine tetraacetic acid

EEM:

Excitation-emission matrix

EPS:

Extracellular polymeric substances

Ex/Em:

Excitation/emission

FIA:

Flow injection analysis

FIR:

Far-infrared

FLC:

Fluorescence

FRI:

Fluorescence regional integration

FT:

Fourier-transform

FTIR:

Fourier-transform infrared

FT-NIR:

Fourier transform near-infrared

GC:

Gas chromatography

GC-MS:

Gas chromatography-mass spectrometry

GDA:

Generalized discriminant analysis

H2S:

Hydrogen sulfide

HS− :

Bisulfide ion

IR:

Infrared

KPLS:

Kernel partial least squares

LC-MS:

Liquid chromatography-mass spectrometry

LDA:

Linear discriminant analysis

LWR:

Locally weighted regression

MBR:

Membrane bioreactors

MIR:

Mid-infrared

ML-PLS:

Multi-layer partial least squares

MLVSS:

Mixed liquor volatile suspended solids

MMC:

Mixed microbial cultures

MWPLS:

Moving window partial least squares

NH4–N:

Ammonia nitrogen

NIR:

Near-infrared

NMR:

Nuclear magnetic resonance

NNPCR:

Nonlinear principal components regression

OLR:

Organic loading rate

OM:

Organic matter

PARAFAC:

Parallel factor analysis

PCA:

Principal component analysis

PHA:

Polyhydroxyacanoates

PHB:

Polyhydroxybutyrates

PLS:

Partial least squares

RF:

Random forest

RVM:

Relevance vector machines

SBR:

Sequencing batch reactor

SG:

Savitzky–Golay

SVM:

Support vector machines

SW-NIR:

Short-wave near-infrared

SWR:

Step-wise regression

TC:

Total carbon

TIC:

Total inorganic carbon

TKN:

Total Kjeldahl nitrogen

TOC:

Total organic carbon

TS:

Total solids

TSS:

Total suspended solids

UV–Vis:

Ultraviolet-visible

VNIR:

Visible-near-infrared

VSS:

Volatile suspended solids

WAS:

Waste activated sludge

WWT:

Wastewater treatment

WWTPs:

Wastewater treatment plants

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Acknowledgements

The authors thank the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684) and the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte. The authors also acknowledge the financial support to Daniela P. Mesquita and Cristina Quintelas through the postdoctoral Grants (SFRH/BPD/82558/2011 and SFRH/BPD/101338/2014) provided by FCT—Portugal.

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  1. Centre of Biological Engineering (CEB), Universidade do Minho, Campus de Gualtar, 4710-057, Braga, Portugal

    Daniela P. Mesquita, Cristina Quintelas, A. Luís Amaral & Eugénio C. Ferreira

  2. Instituto Politécnico de Coimbra, ISEC, DEQB, Rua Pedro Nunes, Quinta da Nora, 3030-199, Coimbra, Portugal

    A. Luís Amaral

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  1. Daniela P. Mesquita
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  2. Cristina Quintelas
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  3. A. Luís Amaral
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  4. Eugénio C. Ferreira
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Correspondence to Daniela P. Mesquita.

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Mesquita, D.P., Quintelas, C., Amaral, A.L. et al. Monitoring biological wastewater treatment processes: recent advances in spectroscopy applications. Rev Environ Sci Biotechnol 16, 395–424 (2017). https://doi.org/10.1007/s11157-017-9439-9

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