Review

Application of image analysis techniques in activated sludge wastewatertreatment processes

Ewa Liwarska-BizukojcDepartment of Environmental Engineering, Technical University of Lodz, Al. Politechniki 6, 90-924, Lodz,Poland (Fax: +48-42-6313517; E-mail: ewaliwar@p.lodz.pl)

Received 20 May 2005; Revisions requested 5 June 2005; Revisions received 19 July 2005; Accepted 19 July 2005

Key words: activated sludge, flocs morphology, image analysis, wastewater treatment

Abstract

Image analytical techniques have been extensively developed to evaluate complex microbial aggregates suchas sludge flocs and biofilms. This review covers the latest contributions concerning the application of imageanalysis to the activated sludge systems with respect to the most frequently used morphological parametersand relations between them and traditional wastewater treatment parameters. Recent developments haveindicated that image analysis can be successfully used for the quantification of flocs and filamentousbacteria in the operating wastewater treatment plants, which enables prediction of bulking events andpinpoint flocs formation.

Introduction

The characteristics of activated sludge flocs areimportant not only for researchers but also inthe everyday operation of many wastewatertreatment plants (Li & Ganczarczyk 1990, Grijs-peerdt & Verstraete 1997, Contreras et al. 2004).There are two major reasons behind this. First ofall, the activated sludge process is one of themost often used methods for microbiologicaldegradation of the contaminants present inwastewater. Secondly, microbial aggregates, de-noted as flocs, are undoubtedly the major com-ponent of this wastewater purification system.They determine the quality of effluent and theoverall efficiency of wastewater treatment plants.

On the one hand, the activated sludge flocsinfluence the efficiency of wastewater treatmentprocesses due to the impact on substrate transfer,sludge recirculation and separation processes. Onthe other hand, the internal structure and mor-phology of flocs depend on many factors, such assubstrate composition, operational conditions

and the type of the aerated tank. These correla-tions are schematically shown in Figure 1.

The investigations of activated sludge flocsusually concern the following issues: (1) mor-phology, i.e. the size and shape of flocs; (2) com-position of flocs, i.e. exploration of their internalstructure, for example distribution of microbes;(3) the identification of microbial species; (4) spa-tial arrangement of microorganisms.

Recently, a great variety of analytical andmicroscopic techniques have been developed toanalyse activated sludge flocs and biofilms. Inthis study, several latest contributions concerningthe morphology of activated sludge flocs are dis-cussed. A closer look is taken at the definitionand description of the most common morpholog-ical parameters used to quantify the flocs andfilamentous bacteria.

Image analysis of flocs

Image analysis has been widely used to quantita-tively describe many different biological

Biotechnology Letters (2005) 27: 1427–1433 � Springer 2005DOI 10.1007/s10529-005-1303-2

processes with both suspended and immobilisedcultures (Pons & Vivier 1999). Initially, imageanalysis was applied to characterise the morphol-ogy of filamentous species such as fungi andfilamentous bacteria. The application of auto-mated image analysis procedures has subse-quently been extended to mixed cultures ofaerobic and anaerobic sludges (Li & Ganc-zarczyk 1990, Grijspeerdt & Verstraete 1997, Al-ves et al. 2000, da Motta et al. 2001a,b).

Li & Ganczarczyk (1990) analysed stainedmicrotome sections of flocs to determine severalmorphological parameters with the help of imageanalysis. Some authors have used the image anal-ysis of activated sludge, at high magnifications,to detect filamentous bacteria in the sludge(Watanabe et al. 1990). Later, low magnificationmicroscopy (50� or 100�) of unstained or fixedslides combined with image analysis becamemore common to quantify the size and shape ofactivated sludge flocs (Grijspeerdt & Verstraete1997, Alves et al. 2000, da Motta et al. 2001a, b).In this case, sample preparation is a simple andnon-laborious task. Additionally, the applicationof automated procedures makes the measurement

more objective and reproducible, especially incomparison to the traditional microscopic obser-vations. The automated image analysis proce-dures aim at quantification of the size and shapeof activated sludge flocs, however, they do notallow for a detailed identification of the bacterialspecies and a visualisation of filamentous bacte-ria inside the flocs.

There are several examples of commercial im-age analysis software packets which are usuallyoffered by the companies that deliver micro-scopes and imaging systems. Additionally, imageanalysis programmes are elaborated by groups ofscientists and available as a public domain. Here,an example is ImageJ 1.33 (http://rsb.info.-nih.gov/ij/) elaborated by the Research ServicesBranch of National Institutes of Health (USA)and DAIME (Digital Image Analysis in Micro-bial Ecology), created in the Department ofMicrobial Ecology of Vienna Ecology Centre(University of Vienna). DAIME is a novel com-puter programme that integrates digital imageanalysis and 3-D visualisation functions. It cananalyse the digital images from epifluorescencemicroscopes and confocal image stacks (CLSM)

Fig. 1. Correlations between operational conditions, flocs and effluent quality in the activated sludge wastewater treatment system.

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(Daims et al. 2005). It enables exploration of thespatial arrangement of microorganisms, forexample in biofilms and flocs (www.microbial-ecology.net/daime/).

An image analysis procedure can be dividedinto four steps: (1) sample and slide preparation,(2) imaging and grabbing, (3) image processing,(4) image analysis, i.e. measurement of morpho-logical parameters (Russ 1990, Pons & Vivier1999). Within the first stage of image analysisprocedure a suitable slide, either stained or un-stained, vital or fixed, should be prepared. In thenext step, an image is obtained with the help ofan optical, fluorescence or confocal laser scan-ning microscope (Lopez et al. 2005). Later, theimages are taken by means of CCD cameras andsaved on magnetic or optical data carriers withthe use of a relevant software. Image processingis a set of operations which are performed totransform an image in order to enable the mea-surement of the observed objects. Image process-ing also improves the quality of an image byreducing noises, enhancing objects and detectingtheir edges. Basic tools of the image processingare point operations and filtration in the fre-quency and space domain by means of linear andnon-linear filters. The processed image is thensubjected to segmentation and as a result a bin-ary image is obtained. Finally, the size of objectsand other morphological parameters are mea-sured. Figure 2 shows the flowsheet of processingand analysis of a single activated sludge floc.

In order to perform the image analysis proce-dure, a sufficient number of images should betaken. Grijspeerdt & Verstraete (1997) foundthat at least 150 objects, which correspond toabout 10 images, should be analysed in order toobtain the statistically relevant results. da Mottaet al. (2001) considered, however, that the num-ber of examined images should not exceed 70grabbed images because this number is adequateto provide stable results. Liwarska-Bizukojc &Bizukojc (2005) confirmed that the analysis of 40images was sufficient to obtain statistically rele-vant results.

Generally, the image analysis is used forquantification of the morphological parameters ofsludge flocs. However, the image analysis proce-dures combined with molecular biology methodssuch as fluorescent in situ hybridization (FISH)or denaturating gradient gel electrophoresis

(DGGE) allow us to identify the key microorgan-isms as well as to quantify the number of cells.Image analysis, in combination with FISH andenvironmental 16S rRNA libraries, have contrib-uted to the discovery and understanding of theprocesses and microorganisms pertinent to bio-logical wastewater treatment. It applies, forexample, to the anaerobic oxidation of ammo-nium, polyphosphate accumulating organisms(PAO) responsible for biological phosphorus re-moval or various nitrification organisms such asNitrospira sp. (Wilderer et al. 2002). An onlinedatabase for rRNA-targeted oligonucleotideprobes elaborated by Loy et al. (2003) is accessi-ble in the Internet (www.microbial-ecology.net/probeBase). Further information and an overviewof novel molecular biology methods currentlyavailable to investigate microbial aggregates isprovided by Wilderer et al. (2002).

Structure and composition of flocs

Sludge flocs consist mainly of organic matter,which makes about 70% of their dry weight(Hartmann 1992). From the biological point ofview, activated sludge suspension is a complexecosystem, which consists mainly of bacteria andprotozoa (da Motta et al. 2001b). Generally, thefour main constituents can be discriminated inflocs: microorganisms (viable and dead cells),extracellular polymeric substances (mainly carbo-hydrates and proteins), water and inorganic par-ticles (sand). The structure of a typical activatedsludge floc is shown in Figure 3.

Li & Ganczarczyk (1990) investigated indetail the composition and internal structure ofactivated sludge flocs using microtome section-ing, staining and image analysis procedures.Microorganisms, water and extracellular poly-meric substances (EPS) were irregularly dispersedwithin the floc although the cross-sectional mor-phology of the flocs appeared similar. Therefore,flocs could be characterised by the fractal con-cept within a certain size limit. It was also con-firmed that the large amount of extracellularpolymeric substances were present within flocs.Extracellular polymeric substances acted on sub-strates and products transferred to and from themicrobial cells in the flocs. Substances to betransferred have to overcome not only the

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diffusional resistance of water but also of theEPS, which surround most of the microbial cells.The EPS matrix is very heterogeneous and con-sists of a variety of polymeric compounds as car-bohydrates, proteins, lipids and nucleic acids.However, the dominant components are carbo-hydrates and proteins. The presence and compo-sition of EPS deliver data on activated sludgeage and organic loading rates. The total amountof EPS decreases at higher organic loading rates.A large amount of EPS is excreted at low growthstages of microorganisms due to cell autolysis.Also the composition of EPS, especially the pro-tein to carbohydrates ratio depends on sludgeretention time and organic loading. Carbohy-drates are synthesised extracellularly for specificfunctions, while proteins usually come from the

excretion of intracellular polymers or cell lysis(Lee et al. 2003).

The internal floc structure is also explored withthe help of the most modern microscopic tech-niques. Lopez et al. (2005) evaluated differentmicroscopic techniques from epifluorescencemicroscopy to confocal laser scanning microscopy(CLSM) and two photon excitation laser scanningmicroscopy (TPE-LSM) combined with fluores-cent stains to investigate complex microbialaggregates such as activated sludge flocs (Lopezet al. 2005). The applicability limits of thesemicroscopic techniques were estimated by analy-sing activated sludge samples taken from threedifferent sources after staining with a fluorescentviability indicator. The selection of an appropri-ate microscopic technique depended on the type

g p yp g , g g p

Fig. 2. Image analysis flowsheet for activated sludge flocs.

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of microbial aggregates analysed. Generally, epi-fluorescence and CLSM proved to be sufficient toanalyse the aggregates. However, for flocs of highcell density, only TPE-LSM images revealed theinternal structure of flocs.

Morphological parameters of flocs

The morphological parameters of activatedsludge flocs, which are obtained on the basis ofthe automated analysis of microscopic images,can be divided into two groups. The first groupcovers parameters representing the size of flocs.These are mean projected area, diameter, perime-ter and equivalent circle diameter. Mean pro-jected area (A) is the basic image analysisparameter and is found easily by pixel count andits multiplication by scaling factor. The other sizeparameters are the derivatives of mean projectedarea. For example, equivalent circle diameter(De), which was used by Grijspeerdt & Verstraete(1997) and da Motta et al. (2001a, b), is definedas:

De ¼ 2 �ffiffiffiffiffiffiffiffiffiffiffi

Area

p

r

Taking the size of flocs into account, the threegroups of flocs can be distinguished: small flocs(diameter below 100 lm); mean flocs (diameter

between 100 and 500 lm) and large flocs (diame-ter above 500 lm) (Eikelboom & van Buijsen1992). Additionally, another commonly usedparameter, which describes the size and amount(concentration) of flocs, is the field area (FA).This is the ratio of the area occupied by the flocsdetected in an image to the total image area(Russ 1990).

The second group of morphological parame-ters describe the shape of flocs, mainly with re-spect to their circularity and regularity. Here, themost often used parameters are roundness (RD)or circularity index (Cx). They both indicate towhat an extent the measured floc is similar to thetrue circle. Roundness varies from 0 to 1 and acircle has a RD of one. The circularity index isalso equal to one when the object is a circle,however it increases when the object becomesless circular. This is caused by different expres-sions of these shape factors. Roundness is calcu-lated from the projected area:

RD ¼ 4 �Area

p � LengthCircularity index (Cx) is the ratio of the observedperimeter (P) to the perimeter of a circle of thesame area as the measured one:

Cx ¼Perimeter

2 �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

p �Areap

A next important and popular shape factor is thefractal dimension, which is a measure of theirregularity of the perimeter. The fractal dimen-sion is usually determined with the ‘‘mosaicamalgamation’’ algorithm described by Russ(1990). da Motta et al. (2001b) reported that thefractal dimension decreased before bulking hadstarted.

Image analysis plays an important role in thequantifications of filamentous bacteria. Generally,the amount of filamentous bacteria is estimatedby manual counting under a microscope, which islaborious, time-consuming and can be subjective.da Motta et al. (2001b) developed a general imageanalysis procedure to evaluate the amount of fila-mentous bacteria from the optical microscopyimages. A key parameter expressing the numberof filamentous bacteria within this procedure wastotal filament length per image (Lf). Additionally,a number of filaments per image and surface ratioof filaments per flocs were measured. The total

Fig. 3. The composition of a typical activated sludge floc, im-age taken at magnification of 1000 with phase contrast.

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filament length (Lf) is the sum of filament lengthof all the individuals present in the image (daMotta et al. 2001a). da Motta et al. (2001a, b) re-ported that an increase of sludge volume indexwas accompanied by the increase of filamentlength per image in pilot-scale experiments as wellas in a large municipal wastewater treatmentplant. The obtained results indicated that imageanalysis could be applied to detect bulking eventsin the wastewater treatment plant.

Quantification of filamentous bacteria

The problems of separating solids from the trea-ted effluent still occur in many wastewater treat-ment plants (Contreras et al. 2004). The majorityof settling failures can be attributed to the bul-king of sludge caused by the excessive growth offilamentous microorganisms. Misbalance betweenthe filamentous bacteria and flocs-forming bacte-ria induces bulking and foaming problems and,as a result, bad quality of the treated effluent.Therefore, a systematic quantification of filamen-tous bacteria with the use of automated imageanalysis procedures can be a useful tool for theclassification of bulking events.

Contreras et al. (2004) used image analysis toquantify the fractions of filamentous microorgan-isms (FM) and non-filamentous microorganisms(NFM) in the activated sludge flocs. In order toclassify the particles as filamentous or not fila-mentous two shape parameters were used: round-ness (RD) and reduced gyration radius (Rg).Roundness is equal to 1 for a circle anddecreases if the object becomes more elongated.The second parameter was reduced gyration ra-dius (Rg). Rg is equal to 0.707 for a circle andthe more elongated the object is, the higher itbecomes. Contreras et al. (2004) gave a detaileddefinition of Rg, which is as follows.

Rg ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Mx2 þMy2

p

ffiffiffiffiffiffiffiffiffi

N=pp

where: N is the object area (pixels); Mx2 and My2

are the central second moments with respect tox-axis and y-axis, respectively.

The calibration of image analysis procedureswas based on pure cultures of Sphaerotilus natans(FM) and strain E932 (NFB). The distributions

of Ro and Rg corresponded very closely both toNFB and FM. For example, area-based histo-grams showed that for NFB roundness and re-duced gyration radius reached maximum atRo=1 and Rg=0.75, respectively. In the case ofFM, the position of the maximum values in thedistribution corresponded to Ro=0.16 and Rg=1.5. It means that both proposed morphologicalparameters can be applied to quantify filamen-tous microorganisms (Contreras et al. 2004).Moreover, it occurred that at low dilution ratesFM dominated, whereas at high dilution rates arapid growth of NFB was observed. It should beadded that the experiments were conducted in acontinuous reactor, where both microorganismscompeted for a single substrate.

Relation between image analysis and traditional

wastewater treatment parameters

The simplest and most often used methods for thebiomass determination in the process of activatedsludge wastewater treatment are volatile sus-pended solids (VSS) or chemical oxygen demand(COD). In spite of their simplicity, these methodsdo not distinguish between living cells, dead bio-mass and nonviable organic particles. Accordingto the literature data, viable biomass in the typicalactivated sludge makes only 5 to 20% of VSS onaverage (Jørgensen et al. 1992, Liwarska-Bizukojc& Ledakowicz 2003). Moreover, these simplestmethods do not allow for the estimation of thequantity of flocs formed or filamentous bacteriapresent in the system. Therefore, new analyticaltechniques, including image analysis, are stillbeing developed for the determination of biomassphysiological state or morphology.

Literature data indicate that some image analy-sis parameters can also be good biomass indica-tors. Moreover, a relation between image analysisparameters and standard parameters of wastewa-ter treatment were established. Grijspeerdt & Ver-straete (1997) found a linear correlation betweenthe field area and activated sludge concentration.This linear correlation was not valid when theactivated sludge was too concentrated, above4 g l)1. Liwarska-Bizukojc & Bizukojc (2005) con-firmed this linear relationship between total sus-pended solids and the field area. Additionally, thelinear relation between the mean projected area of

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flocs and total suspended solids in lab-scale batchexperiments was established (Liwarska-Bizukojc &Bizukojc, 2005). Activated sludge concentrationwithin the batch experiments was below 2 g l)1.

The settleability of sludge depends greatly onthe formation of compact flocs. Ideally, sludgeflocs should be firm and spherical to achieve thebest settling properties. Therefore, finding acorrelation between the morphological parame-ters of flocs and settleability indexes would helpsignificantly in the everyday operation of awastewater treatment plant. Grijspeerdt &Verstraete (1997) indicated that it was possibleto join the morphology of sludge flocs with set-tling properties. However, da Motta et al.(2001a) reported that a global and direct rela-tion between sludge volume index (SVI) andmorphological parameters such as average flocdiameter and roundness within full-scale acti-vated sludge monitoring could not be found.Nevertheless, an increase of SVI was accompa-nied by the increase of filament length perimage, especially in the pilot-scale experiments(da Motta et al. 2001a, b).

Summary

Microscopic techniques ranging from opticalmicroscopy to confocal laser scanning micros-copy can help to evaluate the morphology,internal structure, identification and spatialarrangement of microorganisms in microbialaggregates such as sludge flocs and biofilms.Low magnification light microscopy for vital un-stained or fixed slides is sufficient to quantifythe size and shape of activated sludge flocs. Thesame image analysis procedures could be appliedto analyse both municipal and industrial acti-vated sludge. The measurement of morphologi-cal parameters of flocs and filamentous bacteriasupply valuable data for everyday operation ofwastewater treatment plants. These data help todetect the bulking events and pinpoint flocs for-mation. Simultaneously, they enable estimationof biomass concentration. Therefore, imageanalysis could be incorporated into control sys-tems for monitoring of a wastewater treatmentplant.

References

Alves M, Cavaleiro AJ, Ferreira EC, Amaral L, Mota M, daMotta M, Vivier H, Pons MN (2000) Characterisation byimage analysis an anaerobic sludge under shock conditions.Water Sci. Technol. 14: 207–214.

Contreras EM, Gianuzzi L, Zaritzky NE (2004) Use of imageanalysis in the study of competition between filamentous andnon-filamentous bacteria. Water Res. 38: 2621–2630.

Daims H, Lucker S, Wagner M (2005) Daime, a novel imageanalysis program for microbial ecology and biofilm research.Environ. Microbial. in press.

da Motta N, Pons MN, Roche N, Vivier H (2001a) Charac-terisation of activated sludge by automated image analysis.Biochem. Eng. J. 9: 165–173.

da Motta M, Pons MN, Roche N (2001b) Automated moni-toring of activated sludge in a pilot plant using imageanalysis. Water Sci. Technol. 43/7: 91–96.

Eikelboom DH, van Buijsen HJJ (1992) Handbuch fur diemikrobiologische Schlammuntersuchung, 3rd edn.Munich: FHirthammer Verlag GmbH.

Grijspeerdt K, Verstraete W (1997) Image analysis to estimatethe settleability and concentration of activated sludge. WaterRes. 31: 1126–34.

Hartmann L (1992) Biologische Abwasserreinigung, 3 Berlin,Heidelberg: Springer Verlag.

Jørgensen PE, Eriksen T, Jensen BK (1992) Estimation ofviable biomass in wastewater and activated sludge bydetermination of ATP, Oxygen Utilization Rate and FDAhydrolysis. Water Res. 26: 1495–1501.

Lee W, Kang S, Shin H (2003) Sludge characteristic and theircontribution to microfiltration in submerged membranebioreactors. J. Membr. Sci. 216: 217–227.

Li DH, Ganczarczyk JJ (1990) Structure of activated sludgeflocs. Biotechnol. Bioeng. 35: 57–65.

Liwarska-Bizukojc E, Bizukojc M (2005) Digital image analysisto estimate the influence of sodium dodecyl sulphate onactivated sludge flocs. Proc. Biochem. 40: 2067–2072.

Liwarska-Bizukojc E, Ledakowicz S (2003) Estimation of viablebiomass in aerobic biodegradation processes of organicfraction of municipal solid waste (MSW). J. Biotechnol. 101:165–172.

Lopez C, Pons MN, Morgenroth E (2005) Evaluation ofmicroscopic techniques (epifluorescence microscopy, CLSM,TPE-LSM) as a basis for the quantitative image analysis ofactivated sludge. Water Res. 39: 456–468.

Loy A, Horn M, Wagner M (2003) ProbeBase-an onlineresource for rRNA-targeted oligonucletide probes. NuleicAcids Res. 31: 514–516.

Pons MN, Vivier H (1999) Biomass quantification by imageanalysis. Adv. Biochem. Eng./Biotechnol. 66: 133–184.

Russ JC (1990) Computer Assisted Microscopy: The Measure-ment and Analysis of Images, New York: Plenum Press.

Watanabe S, Baba K, Matuzaki H, Hiraoka M, Tsumura K(1990) Operation condition diagnosis system for activatedsludge processes based on microorganism image informa-tion. In: Briggs R, ed. Advances in Water Pollution andControl, London: Pergamon Press, pp. 683–690.

Wilderer PA, Bungartz HJ, Lemmer H, Wagner M, Keller J,Wuertz S (2002) Modern scientific methods and theirpotential in wastewater science and technology. Water Res.36: 370–393.

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