PIT: S0273-1223(98)00705-7

~ PergamonWaf. Sci. Tech. Vol. 38, No. 8-9. pp. 299-307. 1998.

IAWQ© 1998 Published by Elsevier Science Ud.Printed in Great Britain. All rights reserved

0273-1223/98 $19'00 + 0'00

MODELLING BACTERIOLOGICALWATER QUALITY IN DRINKING WATERDISTRIBUTION SYSTEMS

P. Piriou, S. Dukan and L. Kiene

Suez-Lyonnaise des eaux, C.I.R.S.E.E., 38 rue du President Wilson, 78230, Le Pecq,France

ABSTRACT

Because on-site experimentation raises numerous problems, the study and the modelling of bacterialregrowth phenomena in drinking water distribution systems has been performed using a pipe loop pilot undervarious operating conditions. As a result. experiments have shown that inlet bacterial counts have littleinfluence on the biofilm behavior which is mainly driven by the amount of available nutrients (BDOC).Biofilm detachment has a significant influence on the increase of suspended bacterial counts with time inrelation to the net growth in the bulk water. All these results have been used to develop and validate adeterministic type of model, calIed PICCOBIO. Some guidelines to achieve water bacteriological stabilityhave been proposed using model simulations. © 1998 Published by Elsevier Science Ltd. AlI rights reserved

KEYWORDS

Bacteria; biofiJm; drinking water; model; pipe loop pilot; regrowth.

INTRODUCTION

Whatever its use is, the water must be available at each point in the network with adequate flow rate andpressure, and must also comply with drinking water quality standards. Of the various causes of water qualitydeterioration in networks, bacteriological parameters are undoubtedly the most closely studied andmonitored, because of the short-term risks regarding public health. Many studies carried out on the bacterialregrowth topic have revealed the ecosystem of drinking water networks. Drinking water contains dissolvedbiodegradable organic molecules which can be consumed by suspended and above all by attached bacteria tosupport their growth, if conditions are favorable (temperature> 16-17°C, low chlorine residual, high nutrientcontents). Attached bacteria can also provide the starting point for a trophic food web leading to thedevelopment of higher-order organisms that are undesirable, e.g. Asellus and Nais (Mouchet and Pourriot,1992). Even if high heterotrophic germ counts do not necessarily give rise to a health risk, they are the signof a particular network which is subjected to biological disorders (Payment et aI., 1991). The presence of asubstantial and active attached biomass can also protect pathogenic micro-organisms (Camper et al., 1996;Quignon et at., 1996), create anaerobic zones (Costerton et at. 1994), lead to the formation of highbiocorrosion zones (Tatnall, 1991) and consume chlorine residual (Kiene et al., 1993).

To predict and control bacterial growth in distribution systems, mathematical modeling is indispensable, inview of the complexity of phenomena involved. To this aim, a model, called PICCOBIO, have been

299

300 P. PIRIOU et al.

developed by the Suez-Lyonnaise des Eaux research center and was previously described elsewhere (Dukanet aI., 1996; Piriou et al., 1996).

In order to validate water quality models, sampling campaigns on various networks are commonly used(Heraud et al., 1997; Piriou et at., 1996; Laurent et al., 1997). However, on-site experimentation raisesnumerous problems: few available and representative sites of access to the network, non steady stateconditions in distribution systems, ... In order to study the phenomena involved in water quality evolutionand calibrate water quality models, pilot pipe loop is useful because it offers the possibility to work inconditions similar to those of an actual network and to control the various parameters which affect waterquality: temperature, residence times, inlet water characteristics, hydraulic conditions.

This article describes the use of a pilot facility to study bacterial regrowth phenomena in distribution systemsand to validate the PICCOBIO@ modeL

Flow velocity.....C_-=-.> Residence time

Bacteria

a.

Figure 1. Phenomena taken into account by the PICCOBIO@ model.

MATERIALS AND METHODS

Model description

A deterministic type of model was developed to described bacterial variations (active and total bacteria)during distribution (Dukan et at., 1996; Piriou et at. 1996). As depicted in Fig. 1, the model takes intoaccount:

The fate of available nutrients consumed for the growth of suspended and attached bacteria. Theseavailable nutrients are determined by the Biodegradable Dissolved Organic Carbon method (BDOC)(Levi and Joret, 1990), assuming that only the carbon fraction limits the growth of bacteria;The influence of temperature on bacterial dynamics;The natural mortality of bacteria by senescence and grazing;The deposition of suspended bacteria and the detachment of attached bacteria;The modeling of the fixed biomass as a layer uniformly distributed over the pipe surface, expressedas an equivalent thickness of carbon. By this way, it is possible to distinguish between phenomenadepending on their locations: reactions in solution, reaction at the waterlbiofilm surface interface andwithin the biofilm.The mortality resulting from the presence of chlorine disinfectant, with a differentiation between theaction on free and attached bacteria. The mortality rate takes into account the different forms ofchlorine in water (HCIO/CIO-) depending on pH ;The chlorine decay kinetics under the influence of pH, temperature, hydraulics and pipe materials(Kiene et al., 1993) ;

Bacterial regrowth 301

The penetration depth of free chlorine into the biofilm which enables the identification of 2 biofilm

layers: a chlori~ated layer and a layer not attained by chlorine. This latter layer is accounted for by

the stronger reSIstance of fixed bacteria in relation to free bacteria against chlorine.

This mod~l has been interfaced with the SAFEGE hydraulic calculation model PICCOLO (Bos and Jarrige,

1989). It IS constructed by using hydraulic results previously generated by PICCOLO and a numerical

sche~~ to describe bacterial counts at each node and on each link of a network, under steady state

COndItIOn.s. Installed on a PC type computer, the model uses the graphic interface of PICCOLO and provides

an eff~ctIve and e~sy way to visualize water quality variations in the network, using a color code for

bactenal count, nutnent concentration and chlorine residual.

Pipe loop experiments

The general design of the pilot meets criteria relating to functional similarities with conditions encountered

in actual networks (e.g. hydraulic conditions and type of materials) (Piriou et ai., 1994).

ContrOl

of water - c:::==~quality

Controlof flow velocity

~_cc_~/

Control ofresidence time

Control of temperature

Quality 2 Quality 3 Quality 4

('---->.....-_~s~(_~s~(_~ftQuality 1

Figure 2. Schematic representation of the pilot facility.

The pipe loop facility is composed of 3 loops (Fig. 2) made of old cast iron recovered on-site. The pilot

facility is supplied from a tank where the injection of chlorine is done before entering in the pilot. The

staging of residence times is obtained by placing several loops in series and enables the observation of water

degradation kinetics for controlled flow conditions (recirculation velocity, residence time). The control of

temperature was performed for each loop, using a heat exchanger associated with a cooling system. Two

types of sampling can be performed on the pilot facility. The water phase can be sampled at the outlet of

each loop. A representative sampling of the inner surface of the pipes is also possible by the introduction of

PVC coupons at the surface of the pipes. Table 1 gives the main characteristics of the pilot facility and also

the operating conditions used during experiments.

Table 1. Main characteristics of the pilot pipe loop

Loop J Loop 2 Loop 3

MaterialDiameterVolumeLengthResidence timeHydraulics (Re / velocity)Tern erature

cast iron150 mrn360 L20m6h

Re=45000 / V=0.3 m.s· 1

20 DC

cast iron100 mrn120 L15 m6h

Re=45000 / V=0.45 m.s· 1

20 DC

cast iron100 mrn120 L15 m6h

Re=45000 / V=0.45 m.s'!20 DC

302 P. PIRIOU et al.

During pipe loop experiments, various water qualities fed the pilot facilities. Table 2 gives the maincharacteristics of the different waters.

Table 2. Main water quality characteristics feeding the pipe loop pilot (average values, n= 10-15 for HPCand total counts, n= 5 for BDOC)

abbreviations DAPI HPC-R2ATCN.mL'1 CFU.mL- I

water qualities

Ozonated waterUP-sand filtered water+PACSand filtered waterGAC filtered waterSand filtered water+C12GAC filtered water+Cl2

03UP-SFW+PAC

SFWGFW

SFW+CI2GFW+CI2

7.5 104± 4 104 1.5 104±104

5103±103 4102±102

2 10s± 3 104 6 103±4 103

9 104± 5 104 2.5 103±103

2 10s± 5 104 60±202 IOs± 7 104 3.5 102±2 102

BDOCL"m.

0.5± 0.10.1± 0.10.3± 0.10.4± 0.1

0.35± 0.10.45± 0.1

Chlorinem .r-' Cl2

oooo

0.7± 0.050.7± 0.05

The pilot facility is used to study water quality evolution under steady state conditions. To this aim, eachexperiment corresponding to a particular water quality, are carried out until reaching steady state conditionfor chlorine residual and bacterial counts (3 to 4 weeks).

Analytical methods

Biofilm removal: Biofilms were removed from their substratum by sonication using a BIOBLOCKSCIENTIFIC Vibracell 72408 Sonicator (400 W) fitted with a 3 mm stepped microtip and operating at 20kHz. Experiments were carried out on 1 cm2 PVC coupons, placed in sterile tubes, maintained in ice, and fedwith 15 mL of normal saline water with sonication characteristics as follows: duty cycle = 25%, sonicationtime = 2 min., sonication power 25%.

HPC-R2A: The culturable bacterial density of each sample was determined by pour plating appropriatedilution of water samples into R2A media (Reasonner and Geldreich, 1985). Colony counts wereenumerated after 7 days at 22°C.

Total counts (TCN): total counts were determined using the DAPI staining procedures according to Porterand Feig (1984).

BDOC determination: The measurement of BDOC is performed on a 300 mL sample of water, according toLevi and Joret (1990).

Free chlorine measurement: The free chlorine concentration was determined using the DPD (N,N-Diethyl-p•phenylene diamine) ferrous ammonium sulphate titration method (APHA, 1981).

RESULTS AND DISCUSSION

Major phenomena influencing bacterial dynamics

Using different experiments and simulations, the influence of water quality parameters on bacterial regrowthand especially biofilm dynamics has been confirmed and described. Measurement data of free and attachedbacteria and also the amount of available nutrients (BDOC) were obtained in steady state conditions fordifferent water qualities under the same operating conditions as previously described. As a result,experiments, performed without chlorine and at 20°C, have showed that:

The biofilm evolution is mainly due to its growth, related to BDOC concentration. In steady state conditions,it was noticed that the inlet suspended HPC seems to have no effect on the increase of attached culturablebacteria (Fig. 3A). Simulations which were performed with PICCOBIO under the same operating conditionsindicate that only high suspended bacterial counts (~ 104 CFU.m1- I) can have a significant impact on thebiofilm behavior.

Bacterial regrowth 303

However, Fig. 3B shows that biofilm activity (CFUffCN) increases with increasing BDOC concentration.This result confinns that the biofilm behavior is mainly driven by its growth and that deposition phenomenacontribute to small amount to its increase. Simulation perfonned under the same operating conditions leadsto similar results (Fig. 3B). It is also noticeable that biofilm activity seems to reach a plateau for BDOChigher than 0.4 -0.5 mg.l- I which may be due to a nutrient uptake rate limitation.

0.5

B

A

106

••• -"---- .... - ... --

...-....-..-•" ... /.,,( .

~" .0.1

I I

102 103 104 lOSbulk water-CFU.mL-l

10210

103

1Z~ 10-1

~ 10-2Ue10-3-&::.s 10-4 +----.----"T--~--,------t~ 0 0.2 0.3 0.4

BDOC (mg.L-l)Figure 3. Impact of inlet water characteristics on biofilm: A Influence of inlet free culturable bacteria ~n atta~hedculturable bacteria - comparison between measurement (average values of more tha,n 10 values) a~d slm~latlon.

B Influence of inlet BDOC on attached bacteria activity (HPC/total counts): comparison between simulatIOn; andmeasurement (average values of more than 10 values).

Detachment is a key phenomenon to explain the increase offree bacteria. Figure 4 shows the relationshipbetween attached bacteria counts of each loop and suspended bacteria counts leaving the different loops.Taking into account the first part of the graph, a linear relationship (r2:: 0.62, n= 11) is noticed between thelog of attached bacteria and the log of outlet suspended bacteria for each loop. For attached bacteria countshigher than 104 CFU.cm-2, no significant increase of suspended bacteria counts is noticed. This plateau levelmay depend on the initial level of suspended bacteria counts in the bulk water. Because of the too shortresidence time in relation to bacteria generation time (doubling times: 1 to 10 days - Maul et aI., 1991), thegrowth of free bacteria cannot explain this increase of free bacterial counts on each loop. Consequently, theincrease of free bacteria with time in networks is mainly due to biofilm growth and detachment.

105

- lOS•

i.~~ . ., •u 104I... • •~-; •~ • •~-=' 103~

102 103 104 lOSbiofilm-CFU.cm-:Z

Figure 4. Influence of attached bacteria on outlet free culturable bacteria (average of more than 10 values).

304 P. PIRIOU et af.

Modeling chlorine bactericidal action in networks. Figure 5 shows an example of experiments performed inthe pipe loop system and compares the evolution of viable suspended bacteria (R2A-HPC) versus residencetime with and without chlorine. In both cases, the initial water quality was GFW and in one case a 0.70 mg.l"I Cl2 chlorine dose was applied at the inlet of the pilot facility. In the presence of chlorine, a signi~icant d~op

of bacterial counts is observed during the first half hour residence time and corresponds to the disaffectionprocess performed in a tank before entering in the pipe loop system. So, it gives an indication of chlorinebactericidal effect on suspended bacteria.

0.4

0.6

0.2

i .. ""f· .... """.... . . .. . .. . ...

""

1+--------r------.-------.-;- 0o 6 12 18

residence time h

100 \

t- - - t--10

_100000..,.---------------.., 0.8-~e 10000~e 1000--

GFW -£J - GFW+CI2 -0- Chlorine

Figure 5. GFW: Comparison of suspended HPC evolution versus residence time with and without chlorine (averageof more than 10 values).

Then, only a slight decrease of HPC is noticed in the first loop, although a 0.40 mg.l- I Cl2 chlorine residualis maintained. When chlorine concentration decreases below 0.20-0.15 mg.l- I C1 2, the HPC increase again toreach similar values to those obtained without chlorine (Fig. 5). This experiment suggests that chlorine hasno long term effect on bacterial dynamics and that attached bacteria may have a stronger resistancecompared to free bacteria in the bulk water against chlorine.

The model takes into account this stronger resistance of attached bacteria by the identification of two layers:a chlorinated layer and a layer not attained by the chlorine, as previously described (Dukan et al., 1996).This modeling approach is in accordance with experiments reported by De Beer et al. (1994) which indicatethat the limited penetration of chlorine into the biofilm is likely to be an important factor influencing thereduced efficiency of this biocide against attached bacteria as compared with its action against planctoniccells.

Model validation in steady state condition

Most of the applied model constants were chosen from typical value given by the literature (Piriou et al.,1996). To confirm the model calibration, simulations are performed with other sets of data than those usedfor the calibration, e.g. different water qualities.

Figure 6A and B show the results of the comparison between simulations and measurement data ofsuspended culturable and total bacteria for two different inlet water qualities. The initial water quality wasSFW in both cases. In case B, chlorine is added at a level of 0.7 mg.l-} C12. For each parameter, thecomparison between measurements and simulations indicates a good agreement between of these data series(Table 3).

Bacterial regrowth 305

A- Chlorine = 0 m I

MODEL VALIDATIONPipe loop experiment:T=20oC, 6 hours/loop,BDOC=0.4 - 0.45 mg/l

-t- -} --1 ~

............. -•

o 6 12 18·d t· (h) 6 B- Chlorine = 0.7 m Ires1 ence Ime 10 r--~=---=::'='::"='::_':':"':"....:::Jt:L.:..----r-0.8

n=-0.6 Q.,S·

0.4~eIJ'Q

0.2 t:-........0. .

"1!

. ········...1

t --1=--- --

+-----.-------+=====-..012 18

residence time (h)

• DAPI measuremento HPC measurement_ chlorine measurement

- chlorine simulation•••• HPC simulation- - DAPI simulation

Figure 6. Model validation with SFW: A suspended HPC and total counts evolution in the pipe loop facility withoutchlorine; B suspended HPC and total counts evolution in the pipe loop facility with chlorine.

SFWSFW+CI

Table 3. Comparison between measurements and simulation: Linear correlation

Ex eriments

Definition of water characteristics to achieve biolo~ical stability

To express the influence of various parameters such as BDOC, temperature or chlorine on bacterial regrowthphenomena, simulations were performed on a straight pipe. The diameter of the pipe was 100 mm and theresidence time of water was 72h. Initial HPC and total counts were 103 CFU.ml- 1 and 105 TCN.ml-1

respectively. For each operating condition, an increase rate was calculated ([maximum value-initialvalue]/initial value) and corresponds to the maximum increase of free bacterial counts obtained during the72h residence time. This increase rate is used to express the potential risk that bacterial regrowth mightoccur.

Because temperature and BDOC are the main parameters influencing bacterial regrowth phenomena, theinteraction of these two parameters was first investigated. Figure 7 shows the influence of BDOCconcentration and temperature on the increase rate previously defined ([maximum value-initial value]/initialvalue). Simulations were performed without chlorine.

According to Figure 7, no significant increase of free cultivable bacteria counts occurs for BDOCconcentrations lower than 0.2 mg.l- l , whatever the temperature is. This value of 0.2 mg.l- l can be proposedas a threshold to achieve biological stability in distribution systems in the absence of chlorine residual. Thesame figure indicates that no significant increase of HPC is noticed for temperatures lower than 15°C forcommon values of BDOC «0.5 mg.l- l ). This value can be proposed as a threshold to achieve biologicalstability in networks. It is also interesting to see that BDOC and temperature need to be taken into accounttogether to define a bacterial regrowth potential risk.

Because chlorine is generally used to control bacterial growth, the interaction of chlorine and BDOC needalso to be investigated. Figure 8 shows the influence of chlorine residuals on the increase rate for different

306 P. PIRIGU et al.

BOOC concentrations when chlorine residuals were maintained at the same level in the whole pipe (nodecay kinetics). Thus, this figure gives indications about the level of chlorine that needs to be maintained toavoid any bacterial growth, depending on the initial amount of available nutrients (BOOC). As a result, it isnoticeable that chlorine effect depends on BOOC. Figure 8 shows that a chlorine concentration of 0.1 mg.l- I

leads to no free HPC increase for a BOOC of 0.5 mg.l- I . Thus, by maintaining a residual higher than 0.1mg.l- I , no significant increase of HPC can be expected in networks (BOOC ~ 0.5 mg.l- l ).

1

100 I------;-~:::;~---=====:::::e:=====l

.! 10ftI...CDII)ftICD...(,)c

Boac =0.20

2522.5150.1 -i-----...--------r------,r-----...-------j

12.5 17.5 20Temperature (OC)

Figure 7. Influence of temperature and BDGC on bacterial regrowth (initial HPC: 103 CFU.ml- I ).

0.3

BDOC=O.5

BDOC=O.3

1000

100CD...ell... 10CDenellCD

1...(JC-

0.1

0.01

0 0.1 0.2chlorine (mgll)

Figure 8. Influence of chlorine on bacterial regrowth for different BDGC concentrations (initial HPC of 103CFU.ml-1, at pH=7 and T=25°C).

CONCLUSION

This work has demonstrated that this pipe loop pilot facility is a useful tool, complementary to fieldexperiments, to study and model water quality evolution in distribution systems.

Concerning bacterial regrowth modeling, the major results are:

a validation of the PICCOBIO® model have been done in controlled conditions which correspondedto various water quality scenario.Biofilm behavior is primarily affected by BOOC rather than inlet HPC counts;

Bacterial regrowth 307

Detachment is one of the key phenomena regarding to bacteriological stability in distributionsystems.

Guideline vrlues can be proposed to control bacterial regrowth: T $ 15°C, BDOC $ 0.2 mg.l- l , [C12]$ 0.1 mg.l- .

~ll these results were used to improve and validate the bacterial regrowth model PICCOBIO'. This model,mterfaced to a hydraulic calculation software, has been also successfully used to predict water qualitychanges during distribution in full scale networks.

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APHA (1981). Standard Methods for the Examination of Water and Wastewater, 15th Ed.. APHA-AWWA-WPCF, Washington,DC.

Bos, M. and Jarrige, P. A (1989). Mathematical modeling of water distribution networks under steady state conditions - recentdevelopment future projects. Journal Water SRT - Aqua 38, 352-357.

Camper, A K, Jones, W. L. and Hayes, J. T. (1996). Effect of growth conditions and substratum composition on the persistenceof coliforms in mixed-population biofilms. Applied Environnemental Microbiology 62, 4014-4018.

Costerton, J. W., Lewandowski, Z., DeBeer, D., Caldwell, D., Korber, D. and James, G. (1994). Biofilms, customized microniche.J. Bacteriology 176, 2137-2142.

DeBeer, D., Srinivasan, R. and Stewart, P. S. (1994). Direct measurement of chlorine penetration into biofilms duringdesinfection. Applied Environnemental Microbiology 60(12),4339-4344.

Dukan, S., Levi, Y., Piriou, P., Guyon, F. and Villon, P. (1996). Dynamic modeling of bacterial growth in drinking water network.Water Research 30(9), 1991-2002.

Heraud, J., Kiene, L., Detay, M. and Levi, Y. (1997). Optimised modeling of chlorine residual in a drinking water distributionsystem with a combination of on-line sensors. Journal Water SRT - Aqua 46, 59-70.

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Levi, Y. and Joret, J. C. (1990). Importance of bioeliminable dissolved organic carbon (BDOC) control in strategies formaintaining the quality of drinking water during distribution. Proceeding AWWA WQTC, San Diego, 1267-1279.

Kiene, L., Lu, W. and Levi, Y. (1993). Parameters governing the rate of chlorine decay throughout distribution system.Proceeding A WWA Annual Conference, San Antonio, 503-511

Maul, A, Vagost, D and Block, J. C. (1991). Microbiological analysis in water distribution networks. Ellis Horwood Ed, England.Mouchet, P. and Pourriot, R. (1992). Penetration et developpement de microinvertebres dans les reseaux de distribution d'eau

potable. T.S.M. l'Eau 87(7-8), 353-367.Payment, P., Richardson, L., Ewardes, M. J. and Franco, E. (1991). A randomized trial to estimate the risk of gastro intestinal

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Porter, K G. and Feig, Y. S. (1984). The use of DAPI for identifying and counting aquatic microflora. LimoI. Oceanogr., 25, 943•948.

PirioiJ, P. and Levi, Y. (1994). A new tool for the study of the evolution of water quality in distribution systems: design of anetwork pilot. Proceeding A WWA Annual Conference, New York, 543-555.

Piriou, P., Dukan, S., Levi, Y., Guyon, F. and Villon, P. (1996). Modeling free and fixed bacterial biomass behavior in drinkingwater distribution systems. Revue des Science de l'Eau 3, 381-406.

Quignon, F., Kiene, L., Levi, Y., Sardin, M. and Schwartzbrod, L. (1996). Virus behaviour within a water distribution system.Proceeding fA WQ Health-related Water Microbiology Conference, Mallorca, 32.

Reasoner, D. J. and Geldreich, E. E. (1985). A new medium for the enumeration and subculture of bacteria from potable water.Applied Environnemental Microbiology 1,1-7. .

Tatnall, R. E. (1991). Case history: Biocorrosion, biofouling and biocorrosion in industrial water system, H. C. Fleemmmg and G.C. Geesey (eds). Springer-Verlag, Berlin.