- Email: [email protected]
Prevention of bacterial growth in drinking water distribution systems
~
Pergamon
War. Sci, Tech, Vol. 35. No. 11·12. pp. 283·287. 1997. , 1997 IAWQ. Published by Elsevier Science Ltd Prinled in Great Britain 0273· I 223/97 S17'00 + 0'00
PII: 50273-1223(97)00273-4
PREVENTION OF BACTERIAL GROWTH IN DRINKING WATER DISTRIBUTION SYSTEMS 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 are undoubtedly the subject of the most study. They are also the most closely monitored because of short·term public health risks. A determinist model was developed to predict bacterial growth in the network and to locate the zones where the risks of biological proliferation are the highest. The model takes into account the growth of suspended and fixed bacteria, the consumption of available nutrients in the bulk water and in the biofilm layer. the influence of chlorine residual on the mortality of suspended and fixed biomass. the deposition of suspended bacteria and the detachment of biofilm cells, the influence of temperature on bacterial activity and chlorine decay. The model is constructed using hydraulic results previously generated by PICCOLO, the SAFEGE hydraulic computer model and a numerical scheme to predict bacterial count at each node and on each link of a network. The model provides an effective and easy way to visualise on a computer screen variations in waler quality in the network. The first model calibration was done using data obtained from a pipe loop system pilot. A validation of the model has been carried out by means of measurement campaigns on various real networks. This predictive model of bacterial growth in distribution systems is a unique approach for the study, diagnosis and management of distributed water quality. This tool is helpful for proposing strategies for the management of distribution systems and treatment plants and to define conditions and locations of high bacterial counts in relation to hydraulic conditions. @ 1991 IA WQ. 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 the subject 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 a particular network is subject to biological disorders which can protect pathogenic species (LeChevllllier, 1990; Payment et al., 199 I). The evolution of the bacterial biomass in the network also affects other aspects of 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 kept as 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 during distribution. Mathematical modelling is necessary in order to take all parameters into account in view of the complexity of the different phenomena involved. MATERIALS AND METHODS Model description - a determinist type modelling was developed to predict bacterial variations (viable and total bacteria) during distribution. The model (Figure I) takes into account:
the fate of available nutrients consumed for the growth of suspended and fixed bacteria. These available 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 the action on free and fixed bacteria. The mortality rate takes into account the different forms of chlorine in 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 an equivalent thickness of carbon, has been adopted. This mean it is possible to distinguish between phenomena depending on their locations: reactions in solution, reaction at the waterlbiofl1m surface interface and within the biofilm. The model has been interfaced with PICCOLO software, the SAFEGE hydraulic calculation model (Bos et al., 1989). It is constructed by using hydraulic results previously generated by PICCOLO and a 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 an effective and easy way to visualise on a computer screen water quality variations in the network using a colour 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 work are taken from typical values in the literature. The first model validation was done using data from our pipe loop system. Each loop is modelled as a perfectly mixed reactor (Piriou et ai, 1994). The staging of residence time is obtained by placing several loops in series. During experiments, the pilot facility consisted of three loops in series and the contact time in each loop was 6h. Bacteria concentrations were determined using epifluorescence microscopy after DAPI staining for total bacteria counts and eTC staining for viable bacteria counts. Field validation - a validation of the model was done on a part of the network of the city of Marseille in the south of France. This network is fed by the 5t Bamabe treatment plant. Its production of Im 3/s represents 20% of the total consumption of the city of Marseille. The distribution system is composed of 910 arcs, 40 nodes and reservoirs. Model validation was carried out by means of measurement campaigns from hydrants in the distribution system. Five campaigns were made during a year on 15 sampling sites (Figure 3). Bacteria concentrations were determined using epifluorescence microscopy after DAPI staining for total bacteria counts and eTC staining for viable bacteria counts.
IHYORAl'L1COATA I
IWATER QUALITY PARAMETERS I • nuuwals: (CORDI
• W.t~r toIIsu.. ptton. ·N~.work:
- dlam.Wr and tenlhl or pipe. - tyPf or materi• ." "
PICCOLO • rnlden<:t' tim.
• now utf'
•now dlr.dloa Ind 1a6l:1I11
---
I
• bact.,... roants
• t.mpeontur.
.pH • Chlorint C'Dnc:ftltration
PICCOBIO • fixed and suspended hacteria fate • chlorine decay • COBD fate
PICCOLO gnphl< Interfa.. Water QualIty Mapping
•
Figure 2. Coupling of the model with a hydraulic computer model: PICCOLO.
IJiJM' dilml'tt'f di.trihution
__ (too < I)
<-
~OO)
_(200<1) <-3(0)
(300<0 <--IOOl _ ( -100<1) <-~I _(I»~)
Figure 3. Location of the sampling sites and pipe diameter distribution on the network of Marseille.
286
PH. PIRIOU et aI.
I.OE~""T---------------'"
I.OE+():5-!-----..:.~----...!·:..-----1 o
I.OEi04 bac.erillml
I.OE-+m-t--+--+--+--f--+--t--il--t---t
o
+
2
4
6 8 10 12 14 16 18 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 validate the calibration, simulations were perfonned with other set of data obtained from different operating conditions. This model calibration indicated that differences between simulated and measured data were less than 10%. Figure 4 shows the comparison between measurements and simulated data for the total and viable bacteria. Experiments were carried out at 20°C using input water with a BODe concentration of O.45mgll.
. /:
u.N--
=.
._ +--.....".+---+---+---+./-7!"~~ -II
-
No...... borl994 (ll: 8,1 ppm CODD: O,2ppm TO:IJCC
+---+--+-./--"7I'./'-t::---t--
.
1-.-
+--':'+-'"7~-+-"""+--+-.~
-/
• ~-+---+---+---+---+-----.
F~"'uary
lImutatlon
1m
(ll: o,lppm CODD: o,lSppm TO:IO,S"C
.Y"
iI -.
-
./'"
•
•
.
-- -- .-
./'" ~.
~
limulatlun
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 of distribution syste'ms of different sizes and levels of details. The software used in this configuration constitutes an indispensable tool for network bacteriological diagnosis and management. Figure 5 illustrates results obtained on a part of the network of the city of Marseille for two different campaigns. The good relationship obtained between simulated, and measured viable bacteria counts (r 2 0.795, n 15) confirms the accuracy of the model.
=
=
Prevention of baterial growth in drinking water distribution systems
287
CONCLUSION Animating and visualising variations of bacteria counts in distribution systems is a unique approach to study the changes in water quality. This tool is helpful to propose strategies for the management of distribution systems and treatment plants and define the different zones of bacterial regrowth in relation to hydraulics conditions. This model, interfaced with PICCOLO, works in steady-state conditions. The incorporation of the dynamic module in PICCOLO will form the subject of forthcoming work concerning the enrichment of this tool. REFERENCES Bos. M. and Jarrige, P. A. (1989). Mathematical modelling of water distribution networks under steady-state conditions - recent developments, future projects. Aqua. 38. 352-357. Kiene. L.. Lu. W. and Levi. Y. (1993). Parameters governing the rate of chlorine 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. 857862. 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. 224-248 Payment, 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.
Pergamon
War. Sci, Tech, Vol. 35. No. 11·12. pp. 283·287. 1997. , 1997 IAWQ. Published by Elsevier Science Ltd Prinled in Great Britain 0273· I 223/97 S17'00 + 0'00
PII: 50273-1223(97)00273-4
PREVENTION OF BACTERIAL GROWTH IN DRINKING WATER DISTRIBUTION SYSTEMS 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 are undoubtedly the subject of the most study. They are also the most closely monitored because of short·term public health risks. A determinist model was developed to predict bacterial growth in the network and to locate the zones where the risks of biological proliferation are the highest. The model takes into account the growth of suspended and fixed bacteria, the consumption of available nutrients in the bulk water and in the biofilm layer. the influence of chlorine residual on the mortality of suspended and fixed biomass. the deposition of suspended bacteria and the detachment of biofilm cells, the influence of temperature on bacterial activity and chlorine decay. The model is constructed using hydraulic results previously generated by PICCOLO, the SAFEGE hydraulic computer model and a numerical scheme to predict bacterial count at each node and on each link of a network. The model provides an effective and easy way to visualise on a computer screen variations in waler quality in the network. The first model calibration was done using data obtained from a pipe loop system pilot. A validation of the model has been carried out by means of measurement campaigns on various real networks. This predictive model of bacterial growth in distribution systems is a unique approach for the study, diagnosis and management of distributed water quality. This tool is helpful for proposing strategies for the management of distribution systems and treatment plants and to define conditions and locations of high bacterial counts in relation to hydraulic conditions. @ 1991 IA WQ. 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 the subject 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 a particular network is subject to biological disorders which can protect pathogenic species (LeChevllllier, 1990; Payment et al., 199 I). The evolution of the bacterial biomass in the network also affects other aspects of 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 kept as 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 during distribution. Mathematical modelling is necessary in order to take all parameters into account in view of the complexity of the different phenomena involved. MATERIALS AND METHODS Model description - a determinist type modelling was developed to predict bacterial variations (viable and total bacteria) during distribution. The model (Figure I) takes into account:
the fate of available nutrients consumed for the growth of suspended and fixed bacteria. These available 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 the action on free and fixed bacteria. The mortality rate takes into account the different forms of chlorine in 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 an equivalent thickness of carbon, has been adopted. This mean it is possible to distinguish between phenomena depending on their locations: reactions in solution, reaction at the waterlbiofl1m surface interface and within the biofilm. The model has been interfaced with PICCOLO software, the SAFEGE hydraulic calculation model (Bos et al., 1989). It is constructed by using hydraulic results previously generated by PICCOLO and a 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 an effective and easy way to visualise on a computer screen water quality variations in the network using a colour 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 work are taken from typical values in the literature. The first model validation was done using data from our pipe loop system. Each loop is modelled as a perfectly mixed reactor (Piriou et ai, 1994). The staging of residence time is obtained by placing several loops in series. During experiments, the pilot facility consisted of three loops in series and the contact time in each loop was 6h. Bacteria concentrations were determined using epifluorescence microscopy after DAPI staining for total bacteria counts and eTC staining for viable bacteria counts. Field validation - a validation of the model was done on a part of the network of the city of Marseille in the south of France. This network is fed by the 5t Bamabe treatment plant. Its production of Im 3/s represents 20% of the total consumption of the city of Marseille. The distribution system is composed of 910 arcs, 40 nodes and reservoirs. Model validation was carried out by means of measurement campaigns from hydrants in the distribution system. Five campaigns were made during a year on 15 sampling sites (Figure 3). Bacteria concentrations were determined using epifluorescence microscopy after DAPI staining for total bacteria counts and eTC staining for viable bacteria counts.
IHYORAl'L1COATA I
IWATER QUALITY PARAMETERS I • nuuwals: (CORDI
• W.t~r toIIsu.. ptton. ·N~.work:
- dlam.Wr and tenlhl or pipe. - tyPf or materi• ." "
PICCOLO • rnlden<:t' tim.
• now utf'
•now dlr.dloa Ind 1a6l:1I11
---
I
• bact.,... roants
• t.mpeontur.
.pH • Chlorint C'Dnc:ftltration
PICCOBIO • fixed and suspended hacteria fate • chlorine decay • COBD fate
PICCOLO gnphl< Interfa.. Water QualIty Mapping
•
Figure 2. Coupling of the model with a hydraulic computer model: PICCOLO.
IJiJM' dilml'tt'f di.trihution
__ (too < I)
<-
~OO)
_(200<1) <-3(0)
(300<0 <--IOOl _ ( -100<1) <-~I _(I»~)
Figure 3. Location of the sampling sites and pipe diameter distribution on the network of Marseille.
286
PH. PIRIOU et aI.
I.OE~""T---------------'"
I.OE+():5-!-----..:.~----...!·:..-----1 o
I.OEi04 bac.erillml
I.OE-+m-t--+--+--+--f--+--t--il--t---t
o
+
2
4
6 8 10 12 14 16 18 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 validate the calibration, simulations were perfonned with other set of data obtained from different operating conditions. This model calibration indicated that differences between simulated and measured data were less than 10%. Figure 4 shows the comparison between measurements and simulated data for the total and viable bacteria. Experiments were carried out at 20°C using input water with a BODe concentration of O.45mgll.
. /:
u.N--
=.
._ +--.....".+---+---+---+./-7!"~~ -II
-
No...... borl994 (ll: 8,1 ppm CODD: O,2ppm TO:IJCC
+---+--+-./--"7I'./'-t::---t--
.
1-.-
+--':'+-'"7~-+-"""+--+-.~
-/
• ~-+---+---+---+---+-----.
F~"'uary
lImutatlon
1m
(ll: o,lppm CODD: o,lSppm TO:IO,S"C
.Y"
iI -.
-
./'"
•
•
.
-- -- .-
./'" ~.
~
limulatlun
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 of distribution syste'ms of different sizes and levels of details. The software used in this configuration constitutes an indispensable tool for network bacteriological diagnosis and management. Figure 5 illustrates results obtained on a part of the network of the city of Marseille for two different campaigns. The good relationship obtained between simulated, and measured viable bacteria counts (r 2 0.795, n 15) confirms the accuracy of the model.
=
=
Prevention of baterial growth in drinking water distribution systems
287
CONCLUSION Animating and visualising variations of bacteria counts in distribution systems is a unique approach to study the changes in water quality. This tool is helpful to propose strategies for the management of distribution systems and treatment plants and define the different zones of bacterial regrowth in relation to hydraulics conditions. This model, interfaced with PICCOLO, works in steady-state conditions. The incorporation of the dynamic module in PICCOLO will form the subject of forthcoming work concerning the enrichment of this tool. REFERENCES Bos. M. and Jarrige, P. A. (1989). Mathematical modelling of water distribution networks under steady-state conditions - recent developments, future projects. Aqua. 38. 352-357. Kiene. L.. Lu. W. and Levi. Y. (1993). Parameters governing the rate of chlorine 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. 857862. 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. 224-248 Payment, 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.