PII: 50273-1223(97)00273-4

~ Pergamon War. Sci, Tech, Vol. 35. No. 11·12. pp. 283·287. 1997., 1997 IAWQ. Published by Elsevier Science Ltd

Prinled in Great Britain0273· I223/97 S17'00 + 0'00

PREVENTION OF BACTERIAL GROWTHIN DRINKING WATER DISTRIBUTIONSYSTEMS

Ph. Piriou*, S. Dukan*, Y. Levi* and P. A. Jarrige**

* Lyonnaist! des Eaux. C/RSEE. 38 rut! du President Wilson. 78230 LePeeq. France** SAFEGE. Pare de /'ile. /5-17 rue du Port. 9Z007 Namerre. France

ABSTRACT

Of the many causes of drinking water quality deterioration in distribution systems. biological phenomena areundoubtedly the subject of the most study. They are also the most closely monitored because of short·termpublic health risks. A determinist model was developed to predict bacterial growth in the network and tolocate the zones where the risks of biological proliferation are the highest. The model takes into account thegrowth of suspended and fixed bacteria, the consumption of available nutrients in the bulk water and in thebiofilm layer. the influence of chlorine residual on the mortality of suspended and fixed biomass. thedeposition of suspended bacteria and the detachment of biofilm cells, the influence of temperature onbacterial activity and chlorine decay. The model is constructed using hydraulic results previously generatedby PICCOLO, the SAFEGE hydraulic computer model and a numerical scheme to predict bacterial count ateach node and on each link of a network. The model provides an effective and easy way to visualise on acomputer screen variations in waler quality in the network. The first model calibration was done using dataobtained from a pipe loop system pilot. A validation of the model has been carried out by means ofmeasurement campaigns on various real networks. This predictive model of bacterial growth in distributionsystems is a unique approach for the study, diagnosis and management of distributed water quality. This toolis helpful for proposing strategies for the management of distribution systems and treatment plants and todefine conditions and locations of high bacterial counts in relation to hydraulic conditions. @ 1991 IAWQ.Published by Elsevier Science Ltd

KEYWORDS

Distribution systems; modelling; bacterial growth; biofilms.

INTRODUCTION

Of the many causes of distributed water quality deterioration, biological phenomena are undoubtedly thesubject of the most study and are also the most closely monitored because of short-teon public health risks.Although high heterotrophic bacterial counts do not necessary constitute a health risk, they are a sign that aparticular network is subject to biological disorders which can protect pathogenic species (LeChevllllier,1990; Payment et al., 199I). The evolution of the bacterial biomass in the network also affects other aspectsof distributed water quality. such as tastes and odours, the development macro-invertebrates (Levy, 1990),the ~p~arance of colour and turbidity and the appearance of biocorrosion phenomena (Tatnall, 1991).QualItative management of distribution networks is therefore to ensure that the quality of the product is keptas constant as possible up to the farthest points of the distribution. With this in mind, it is essential to

283

284 PH. PIRIOU et aI.

understand, describe and model the various phenomena which lead to the evolution of water quality duringdistribution. Mathematical modelling is necessary in order to take all parameters into account in view of thecomplexity of the different phenomena involved.

MATERIALS AND METHODS

Model description - a determinist type modelling was developed to predict bacterial variations (viable andtotal bacteria) during distribution. The model (Figure I) takes into account:

the fate of available nutrients consumed for the growth of suspended and fixed bacteria. Theseavailable nutrients are determined by the biodegradable dissolved organic carbon (BDOC) method(Levi and Joret, 1990) assuming that only the carbon fraction limits the growth of bacteria(Le Chevallier et al., 1991);

the influence of temperature on bacterial dynamics;

the natural mortality of bacteria by senescence and grazing;

the mortality resulting from the presence of chlorine disinfectant with a differentiation between theaction on free and fixed bacteria. The mortality rate takes into account the different forms of chlorinein water (HClO/ClO-) depending on pH;

the deposition of suspended bacteria and the detachment of fixed bacteria;

the chlorine decay kinetics under the influence of pH, temperature, hydraulics and pipe materials(Kiene et aL, 1993).

• •

Figure I. Phenomena taken into account by the model.

The modelling of the fixed biomass as a layer uniformly distributed over the pipe surface, expressed as anequivalent thickness of carbon, has been adopted. This mean it is possible to distinguish between phenomenadepending on their locations: reactions in solution, reaction at the waterlbiofl1m surface interface and withinthe biofilm. The model has been interfaced with PICCOLO software, the SAFEGE hydraulic calculationmodel (Bos et al., 1989). It is constructed by using hydraulic results previously generated by PICCOLO anda numerical scheme to predict bacterial count at each node and on each link of a network (Figure 2).Installed on a PC-type computer, the model uses the graphic interface of PICCOLO and provides aneffective and easy way to visualise on a computer screen water quality variations in the network using acolour code for bacterial count, nutrient concentration and chlorine residual.

Prevention of baterial growth in drinking water distribution systems 285

Calibration and field validation of the model - model calibration - most model parameters used in the workare taken from typical values in the literature. The first model validation was done using data from our pipeloop system. Each loop is modelled as a perfectly mixed reactor (Piriou et ai, 1994). The staging ofresidence time is obtained by placing several loops in series. During experiments, the pilot facility consistedof three loops in series and the contact time in each loop was 6h. Bacteria concentrations were determinedusing epifluorescence microscopy after DAPI staining for total bacteria counts and eTC staining for viablebacteria counts.

Field validation - a validation of the model was done on a part of the network of the city of Marseille in thesouth of France. This network is fed by the 5t Bamabe treatment plant. Its production of Im3/s represents20% of the total consumption of the city of Marseille. The distribution system is composed of 910 arcs, 40nodes and reservoirs. Model validation was carried out by means of measurement campaigns from hydrantsin the distribution system. Five campaigns were made during a year on 15 sampling sites (Figure 3). Bacteriaconcentrations were determined using epifluorescence microscopy after DAPI staining for total bacteriacounts and eTC staining for viable bacteria counts.

IIHYORAl'L1COATA I

• W.t~r toIIsu..ptton.·N~.work:

- dlam.Wr and tenlhl or pipe.- tyPf or materi• ." "

IWATER QUALITY PARAMETERS I• nuuwals: (CORDI• bact.,... roants• t.mpeontur..pH• Chlorint C'Dnc:ftltration

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Figure 2. Coupling of the model with a hydraulic computer model: PICCOLO.

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__ (too < I) <- ~OO)

_(200<1) <-3(0)

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Figure 3. Location of the sampling sites and pipe diameter distribution on the network of Marseille.

286 PH. PIRIOU et aI.

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contact time (hours)+ __u'mulated total baderia iimulated viable bacteria

• measured total bacteria 0 measured viable bacteria

Figure 4. Comparison between measured and simulated data for viable and total bacteria (6h11oop, T =20·C.BDDe =O.45mgll).

RESULTS

Model calibration - the fIrst fItting was performed with a set of data to adjust model parameters. To validatethe calibration, simulations were perfonned with other set of data obtained from different operatingconditions. This model calibration indicated that differences between simulated and measured data were lessthan 10%. Figure 4 shows the comparison between measurements and simulated data for the total and viablebacteria. Experiments were carried out at 20°C using input water with a BODe concentration of O.45mgll.

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.-Figure 5. Model validation on the Marseille network: results obtained for IWO different campaigns.

Whole network simuloJions and model validation - the model has also been used to simulate a variety ofdistribution syste'ms of different sizes and levels of details. The software used in this configurationconstitutes an indispensable tool for network bacteriological diagnosis and management. Figure 5 illustratesresults obtained on a part of the network of the city of Marseille for two different campaigns. The goodrelationship obtained between simulated, and measured viable bacteria counts (r2 =0.795, n =15) confirmsthe accuracy of the model.

Prevention of baterial growth in drinking water distribution systems

CONCLUSION

287

Animating and visualising variations of bacteria counts in distribution systems is a unique approach to studythe changes in water quality. This tool is helpful to propose strategies for the management of distributionsystems and treatment plants and define the different zones of bacterial regrowth in relation to hydraulicsconditions. This model, interfaced with PICCOLO, works in steady-state conditions. The incorporation ofthe dynamic module in PICCOLO will form the subject of forthcoming work concerning the enrichment ofthis tool.

REFERENCES

Bos. M. and Jarrige, P. A. (1989). Mathematical modelling of water distribution networks under steady-state conditions - recentdevelopments, future projects. Aqua. 38. 352-357.

Kiene. L.. Lu. W. and Levi. Y. (1993). Parameters governing the rate ofchlorine decay throughout distribution systems. Proc. Am.Wat. Wks. Assn. Conf.. San Antonio. Texas. 503-5 t I.

LeChevallier, M. W. (1990). Coliform regrowth in drinking water: a review. J. Am. Wat. Wks. Assn.• 82. 11.74-81.LeChevallier. M. W.• Schulz, W. and Lee. R. G. (1991). Bacterial nutrients in dnnkmg water.J. Appl. Env. Microbial.• 57. 3. 857­

862.Levi. Y. and Joret. J. C. (1990). Importance of bioeliminable dissolved organic carbon (BDOC) control in strategies for

maintaining the quality of drinking water during distribution. Proc. Am. Wat. Wks. Assn. Conf.. San Diego. 1267-1279.Levy. R. V. (1990). Invertebrates and associated bacteria in drinking water distribution lines. In "Drinking Water Microbiology"

edited by G. A. McFeters. Springer-Verlag pp. 224-248Payment, P.• Richardson. L.. Ewardes., M. 1. and Franco E. (1991). A randomised trial to estimate the risk of gastrointestinal

disease due to the consumption of water meeting the current microbiological standards. Am. J. Pub. Hlth., 81, 703-708.Piriou. P. and Levi, Y. (1994). A new tool for the study of the evolution of water quality in distribution systems: design of a

network pilot. Proc. Am. Wat. Wks. Assn. Conf., New York. 543-555.Stewart. P. S. (1993). A model ofbiofilm detachment. Biotuhnol. Bioeng., 41, 111-117.Tatnall. R. E. (1991). Case history: biocorrosion. In "Bi%uling and Biocorrosion in Industrial Water Systems" edited by H. C.

Flemming and G. C. Geesey. Springer-Verlag.