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Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment: a review

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Abstract

Quantitative image analysis techniques have gained an undeniable role in several fields of research during the last decade. In the field of biological wastewater treatment (WWT) processes, several computer applications have been developed for monitoring microbial entities, either as individual cells or in different types of aggregates. New descriptors have been defined that are more reliable, objective, and useful than the subjective and time-consuming parameters classically used to monitor biological WWT processes. Examples of this application include the objective prediction of filamentous bulking, known to be one of the most problematic phenomena occurring in activated sludge technology. It also demonstrated its usefulness in classifying protozoa and metazoa populations. In high-rate anaerobic processes, based on granular sludge, aggregation times and fragmentation phenomena could be detected during critical events, e.g., toxic and organic overloads. Currently, the major efforts and needs are in the development of quantitative image analysis techniques focusing on its application coupled with stained samples, either by classical or fluorescent-based techniques. The use of quantitative morphological parameters in process control and online applications is also being investigated. This work reviews the major advances of quantitative image analysis applied to biological WWT processes.

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Abbreviations

2PLSM:

Two-photon or multiphoton laser scanning microscopy

%Area:

Total aggregates projected area distribution by equivalent diameter ranges

%Nb:

Total number of aggregates distribution by equivalent diameter ranges

A :

Aggregate area

a L :

Aggregates length

a Nb :

Number of aggregates

ANN:

Artificial neural network

AR:

Aspect ratio

A spec < 0.2 mm:

Specific aggregate area for aggregates of D eq < 0.2 mm

A spec ≥ 0.2 mm:

Specific aggregate area for aggregates of D eq ≥ 0.2 mm

B:

Blue level of a pixel

BMP:

Windows bitmap

CA:

Cluster analysis

CARD-FISH:

Catalysed reporter deposition fluorescence in situ hybridization

CCD:

Charge Coupled Device

CF:

Compactness factor

CLSM:

Confocal Laser Scanning Microscopy

CMOS:

Complementary Metal Oxide Semiconductor

COD:

Chemical oxygen demand

Conv:

Convexity

DA:

Discriminant analysis

DAPI:

4’,6’-diamidino-2-phenylindole

D eq :

Aggregate Equivalent diameter

Ecc:

Eccentricity

EF:

Elongation factor

e Fil :

Filamentous fraction

e flocs :

Flocs fraction

e microflocs :

Microflocs fraction

EGSB:

Expanded Granular Sludge Bed

EPS:

Extracellular polymeric substances

Ext:

Extent

FD:

Fractal dimension

ferD:

Feret diameter

FF:

Form factor

FISH:

Fluorescence in situ hybridization

F max :

Maximum feret diameter

fNb:

Filaments number

freefNb:

Free filaments number

GAO:

Glycogen accumulating organisms

HCF:

Heywood circularity factor

HSL:

Hue saturation and lightness channels

JPEG:

Joint photographers expert group format

LD:

Load Disturbance

LfA:

Total filament length per total aggregates projected area

L fi :

Total Filaments length per image

LSM:

Laser scanning microscopy

L spec :

Specific total filament length

M1X, M1Y:

First order moments

M2X, M2Y:

Second order moments

MRI:

Magnetic resonance imaging

N class :

Sum of any individual object/aggregate within a particular class

N obj :

Pixel sum of any individual object/aggregate

OLR:

Organic Loading Rate

P :

Aggregates perimeter

PAO:

Phosphate accumulating organisms

P conv :

Convex envelope perimeter

PCA:

Principal components analysis

PHA:

Poly-β-hydroxyalcanoate

PHB:

Poly-β-hydroxybutyrate

PLS:

Partial least squares regression

PSD:

Pore size distribution

R :

Red level of a pixel

Rec%:

Area recognition percentage

Rb:

Robustness

Rg:

Reduced radius of gyration

RGB:

Red green and blue channels

Ro:

Roundness

SAA:

Specific Acetoclastic Activity

SBR:

Sequencing batch reactor

SDS:

Sodium Dodecyl Sulfate

Sol:

Solidity

SRT:

Sludge retention time

SS:

Suspended Solids

SVI:

Sludge volume index

TA:

Total aggregates area

TIFF:

Tagged image file format

TL:

Total filaments length

TL/TA:

Ratio between total filaments length and total aggregates area

TL/TSS:

Total filament length per total suspended solids

TL/VSS:

Total filament length per volatile suspended solids

TSS:

Total suspended solids

TV:

Total particles volume

UASB:

Up-flow anaerobic sludge blanket

UFBR:

Up flow anaerobic fixed bed reactor

VSS:

Volatile suspended solids

VSS/TA:

Volatile suspended solids per total aggregates projected area

W :

Aggregates width

WWT:

Wastewater treatment

WWTP:

Wastewater treatment plant

X biomass :

Sludge concentration

X ni, y ni :

Coordinates of each objects pixels

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Acknowledgments

The authors acknowledge the financial support to the project PTDC/EBB-EBI/103147/2008 and the grant SFRH/BPD/48962/2008 provided by Fundação para a Ciência e Tecnologia (Portugal).

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Authors and Affiliations

  1. Institute for Biotechnology and Bioengineering (IBB), Centre of Biological Engineering, Universidade do Minho, 4710-057, Braga, Portugal

    J. C. Costa, D. P. Mesquita, A. L. Amaral, M. M. Alves & E. C. Ferreira

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

    A. L. Amaral

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  1. J. C. Costa
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  2. D. P. Mesquita
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  3. A. L. Amaral
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  4. M. M. Alves
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  5. E. C. Ferreira
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Correspondence to E. C. Ferreira.

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Costa, J.C., Mesquita, D.P., Amaral, A.L. et al. Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment: a review. Environ Sci Pollut Res 20, 5887–5912 (2013). https://doi.org/10.1007/s11356-013-1824-5

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