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Evaluating integrity of distribution infrastructure based on potential

water quality changes

Imran, S. A.; Sadiq, R.; Kleiner, Y.
http://web-d.cisti.nrc.ca/npsi/jsp/nparc_cp.jsp?lang=frhttp://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=frhttp://web-d.cisti.nrc.ca/npsi/jsp/nparc_cp.jsp?lang=enhttp://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=frhttp://web-d.cisti.nrc.ca/npsi/jsp/nparc_cp.jsp?lang=enhttp://web-d.cisti.nrc.ca/npsi/jsp/nparc_cp.jsp?lang=fr


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E v a l u a t i n g i n t e g r i t y o f d i s t r i b u t i o ni n f r a s t r u c t u r e b a s e d o n p o t e n t i a l w a t e r

q u a l i t y c h a n g e s

NRCC - 4 9 2 5 0

Im ran , S .A . ; Sad iq , R . ; K le i ne r , Y .

A version of this document is published in / Une version de ce document se trouve dans:AWWA 2007 Research Symposium, Reno, Nevada, March 2, 2007, pp. 1-13

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Evaluating Integrity of Distribution Infrastructure Based on Potential

Water Quality Changes

Syed A. Imran , Rehan Sadiq and Yehuda Kleinera b c

aResearch Officer, Center for Sustainable Infrastructure Research, Regina

b, cResearch Officer, Urban Infrastructure Program, Ottawa

Institute for Research in Construction, National Research Council Canada

Abstract

Integrity of water distribution infrastructure can be defined as its ability to

transport water in acceptable quantity and quality, and with minimal interruption. Water

distribution infrastructure is a complex network of pipes and appurtenances constructedof different materials, at different times and using different manufacturing processes.

Changes in water chemistry (quality) can potentially impact the distribution infrastructureby affecting pipe inner surfaces, which are in contact with the water. Similarly, the

chemical properties of these inner surfaces can impact water chemistry. This coupling

(circular) effect is often not completely understood and is difficult to de-couple.

In this paper we propose an innovative but simple framework, called hierarchicalrelational model (HRM), by which utilities can evaluate the impact of changes in

treatment processes to the existing distribution infrastructure. Conflicting water qualityimpacts on different distribution materials can also be identified using the HRM. The

framework proposed in this paper is intended as proof of concept and can be furtherrefined to incorporate more complex real systems. Two case studies illustrate theapplication of the model.

Key words: Water quality, distribution system, infrastructure integrity, hierarchical

relational model (HRM),

Background

A typical water distribution network is a complex system. Pipes could be ofdifferent materials including cast iron, ductile iron, steel, copper, lead, galvanized steel,

reinforced concrete, asbestos cement, thermoplastics including polyvinyl chloride (PVC),chlorinated polyvinyl chloride (CPVC), high density polyethylene (HDPE),

polybutadiene and composites such as glass fiber-reinforced plastic (GFRP).

Additionally, there are a number of ancillary components like coatings, gaskets, o-rings,fittings, valves and solders, as well as pipe liners (cement, epoxy, polymeric, calcite) that

are in contact with water. Even components with the same material can behave

differently due to variability in past operational conditions, such as differential corrosion,formation of biofilms on the interior of the pipe [1], [2].

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The quality of water traveling through an aging (deteriorating) distributioninfrastructure can decrease due to uptake of contaminants intruding the system through

compromised components, permeation, leaching and internal corrosion. Though

considerable research exists, that focuses on the deterioration of water quality throughdistribution infrastructure, however the converse phenomenon of deterioration of

distribution infrastructure due to changes in water chemistry has not been studiedextensively. Water quality failures observed in distribution system are often an indicatorof the long-term deterioration of the distribution infrastructure.

In this paper, integrity of distribution infrastructure is defined as its ability to

transport water in acceptable quantity and quality without causing any structural orfunctional failure of its components. In this paper, we define structural failure as the

inability of the infrastructure to transport the desired quantity of water (water loss

through leaks and breaks, reduced carrying capacity etc.). We further define functionalfailure as the inability of the infrastructure to transport water while maintaining the

desired quality. Structural failures in the distribution network often lead to functional

failures (e.g., intrusion of contaminants through broken or compromised components).

The converse phenomenon (functional failure leading to structural failure) is not asfrequent and not as immediate. For example, under certain conditions water chemistry

will affect internal corrosion of metallic pipes or leaching rate of cement-based pipes,

which, given long time-exposures can lead to the weakening of the pipe structure.Consequently, phenomena such as red water, loss of residual and subsequent

microbiological proliferation, when persistent for long durations can be viewed as

indicators of the health of the distribution infrastructure.

Compliance Strategies for Drinking Water Regulations

Traditionally, utilities have managed the mandated compliance levels by

upgrading and optimizing their treatment processes or by changing source-waters [3], [4].

Recent and proposed future regulations are more complex and favor a managed, multiplestrategy approach to maintaining safe drinking water quality. For instance, in a surface

water system overall the risk is managed by balancing short-term (acute) and certainmicrobiological risk with the potential long-term (chronic) and uncertain risk from

disinfection byproducts (DBPs). However, changes in treatment techniques to reduce or

eliminate any contaminant of concern invariably lead to changes in the final waterquality. When this changed water quality is introduced into the distribution system, it

may trigger a cycle of changes, in which the distribution system as well as the final water

quality are likely to be impacted [5]. Therefore, it is necessary to consider these impactson the existing distribution infrastructure when evaluating different compliance

strategies. Historically, the focus of compliance has been on the water quality at the

point of entry to the distribution system, while the impact of water quality on thedistribution infrastructure has largely been ignored.

Deterioration of the Distribution Infrastructure

The impact of water quality on infrastructure surfaces that are in contact with the

finished water is by no means the only (or even the most significant) process that leads tothe overall deterioration of infrastructure components. Other processes including,

structural loading, external corrosion, inadequate operation and maintenance and human

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errors are significant causes of distribution infrastructure failure. However, water qualityinduced deterioration may exacerbate the condition of the pipe and make it susceptible to

other failure mechanisms.

Water quality deterioration in distribution systems is inextricably linked to thecondition of the surfaces in contact with the finished water. Where water quality

problems exist, these can often be attributed to the extent of deterioration of thedistribution infrastructure. Though repair and rehabilitation measures can mitigate waterquality problem in the short-term, an assessment based on the water quality interaction

with distribution infrastructure is essential to realize a long-term solution to infrastructure

deterioration.

Many complex interactions that contribute to this deterioration occursimultaneously between the finished water and the distribution surfaces in contact. For

instance:

Metallic pipe surfaces in contact with water corrode and the corrosionbyproducts are transported and deposited near dead-ends and low-flow

regions.

Biofilms on the inner pipe surface contribute to microbiologically inducedcorrosion (MIC), and in case of slough-off may cause microbiological

proliferation.

Chemicals in the water may degrade or accumulate over time due tointeraction with pipe surfaces and/or bulk reactions.

Depending on the material type, a distribution infrastructure can be divided into

three broad groups - metallic, polymeric and cement-based. Various internal deteriorationmechanisms including physico-chemical based corrosion, microbiologically induced

corrosion (MIC), and leaching (dissolution of material) impact the integrity of these

distribution materials. Though a specific distribution material may have a predominantmechanism of deterioration, all three processes may play some role towards its overall

internal deterioration. Table 1 provides a relative contribution of different deterioration

mechanisms on each type of distribution materials.

Physico-chemical based internal corrosion (henceforth referred to as corrosion)is defined as metallurgy in reverse, where a purified metal or its alloy interacts with the

environment to return to its original more stable state. Three conditions are required inorder for corrosion to proceed; a metallic surface that will corrode, an oxidant that will

oxidize (corrode) the metal to a more stable state and lastly, a medium that will transport

the oxidant to the metal and facilitate further corrosion by moving the corrosion

byproducts away from the corrosion site. All these components are present in a waterdistribution system with metallic pipes. The pipes may corrode both internally and

externally, however, external corrosion is not directly related to the water quality issues

and therefore is not discussed here.

Microbiologically induced corrosion is different from other deterioration

processes because it is caused by the biological activities of microorganisms. Biofilms

(colonies of native microorganisms on the water/ metal interface) could either inhibit

deterioration by providing a protective coating or exacerbate deterioration through

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biological activity. The presence and structure of these biofilms are related to hydraulicconditions, nutrient availability, type and concentration of residual disinfectant and the

roughness of the pipe surface. Many researchers have reported that pipe material seems

to have a significant effect on microbial inactivation by different disinfectants [6].

Leaching is defined as the release of material to water without involving

oxidation-reduction processes. It can take the form of dissolution of the metal-bearingcorrosion scales, monomers from plastics, or calcium from the cement-matrix.

Table 1: Relative Impacts of Different Deterioration Mechanisms on

Different Types of Distribution Materials

*Deterioration Mechanisms

Internal

Corrosion

Microbiologically

Induced Corrosion

Leaching

Metallic

(Iron, copper and lead)Major Unknown Minor

Polymeric

(PVC, PE and PAH)None Unknown Major

WaterDistribut

ion

InfrastructureMa

terial

Cement-based

(AC and CC)Major Unknown Major

AC Asbestos cement, CC Concrete, PE Polyethylene, PAH Polyaromatic

hydrocarbons (bituminous or coal-tar), PVC Polyvinylchloride

Secondary Water Quality Impacts of Treatment Technologies

Treatment process as well as modifications to the treatment process can impactthe distribution system [7]. The primary objective of any treatment technology is to

achieve acceptable removal of the targeted contaminants. However, treatmenttechnologies often cause changes in the ionic content of the water that can lead to adverse

impacts on distribution infrastructure. For instance, an anion exchange resin exchanges

bivalent metallic ions with monovalent sodium ion. In this instance, ion exchangetreatment would be responsible for removing a known inhibitor of corrosion in iron pipes

(calcium and magnesium hardness) with sodium resulting in a possible increase in the

corrosion rate of unlined iron surfaces.

No single unit technology is effective in removing all drinking water

contaminants to a safe drinking level. Treatment technologies that are BAT (bestavailable technology) for a selected contaminant may not be effective in removing other

co-occurring contaminants. Therefore, to achieve simultaneous removal of a wide range

of contaminants, utilities use unit treatment technologies placed in sequential order.

Another complicating factor is the use of different chemicals for similar

objectives. The identification of all secondary changes in water quality is an impossibletask due to the numerous treatment practices. For instance, coagulation may be achieved

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by using alum, ferric chloride or ferric sulfate. Alum and ferric sulfate will increase thesulfate in the finished water relative to the source water, while ferric chloride will

increase the chloride content of the water. Both sulfate and chloride increase the risk of

corrosion of metallic pipes, albeit to different degrees. Coagulation is also accompaniedby a consumption of total alkalinity in the source water. Decreased alkalinity is

detrimental to iron components in the distribution system, but at the same time may bebeneficial in reducing copper corrosion.

Secondary changes induced by the treatment technologies are also dose specific.For instance, depending on the source water quality, different concentration of treatment

chemicals need to be added and thereby result in different concentrations of secondaryions in the finished water.

Proposed Framework - Hierarchical Relational Model (HRM)

Relational Matrix A: Regulations vs. Unit Treatment Processes

As a starting point, a relational matrix of unit treatment processes vs. regulations (A)

was developed, which relates the effectiveness of common unit treatments towardscompliance with drinking water regulations. Each element ij of the matrix A is a

measure of the effectiveness of unit treatment process j (UTj) towards compliance with

drinking water regulation i (Regi). Matrix A was populated with values, based on a

comprehensive literature review, where . A value of +1 indicates the most

effective technology to remove the selected contaminant, while 1 indicates a technologythat might have the most adverse effect on the removal of the selected contaminant. A

value of zero is given to a technology that has no known effect on the removal of a

selected contaminant.

11 + ij

=

IJII

J

J

L

MLMMM

L

L

L

21

22221

11211

IReg

2Reg

1Reg J

UT

2

UT

1

UT

A ; where i= 1, 2,...,I j=1, 2, ...,J (1)

The removal levels attributed to these treatment technologies are the maximumremovals possible under ideal design and operational conditions.

Relational Matrix B: Unit Treatment Processes vs. Total Water Quality Changes

When selecting a unit treatment process to address a regulatory requirement, this

treatment will affect water quality in several ways, some of which intended (primary) andsome unintended (secondary). The magnitude of the secondary water quality changes

depends on a number of factors that include; source water contaminants, dosage of

treatment chemicals, sequence of treatment practices etc. For instance, though

chlorination may be practiced before or after activated carbon filtration, the resultingfinished water quality in either case is quite different.

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Matrix B relates the effect of unit technology (UTj) on increase or decrease inselected water quality parameter (WQPk), including both primary and secondary impacts.

Matrix B was populated with values, developed based on a comprehensive literature

review, where . A value of +1 indicates a significant increase while 1

indicates a significant decrease in the selected water quality parameter. A value of zero is

given to a technology that has no known effect on the selected water quality parameter.For instance, the primary impact of ion exchange is the removal of targeted ions from

water and the secondary impact is the removal of non-targeted ions and the addition ofcounter-ions to the water. Changes induced by treatment technologies are dose-specific.

For instance, depending on the source water quality, different concentrations of treatment

chemicals need to be added, and thereby result in different concentrations of secondary

ions in the finished water. The water quality parameters included in B are those known orsuspected to influence the deterioration of distribution infrastructure.

11 + jk

=

JKJJ

K

K

B

L

MLMMM

L

L

L

21

22221

11211

K21

JUT

2UT

1UT

WQPWQPWQP

; where j=1, 2, ...,J k= 1, 2,..., K (2)

Relational Matrix C: Total Water Quality Changes vs. Impacts on Distribution Materials

Water quality impacts on infrastructure surfaces in contact with finished water are

by no means the only (or even the most significant) process that leads to the overall

deterioration of infrastructure components. Other processes including, structural loading,

external corrosion, inadequate operation and maintenance and human errors aresignificant causes of infrastructure failure. However, water quality induced deterioration

may exacerbate the condition of pipes and make it more susceptible to other failure

mechanisms.

Relational matrix (C) relates the effects of changes in selected water quality

parameters (WQPk) on the deterioration of selected distribution system materials (DSM).

The element kl is a measure of the adverse effect that an increase in WQPkhas onDSMl.

These values represent aggregate impacts and should be used for a qualitative evaluation,

rather than a quantitative one. For instance, increasing alkalinity can have a dual effect oniron corrosion. It can decrease risk of corrosion by promoting the formation of stable

siderite and calcite scales on the iron surface. Conversely, the associated increase indissolved solids can help in enhancing the electrochemical conductivity necessary for

corrosion to occur. However, the overall effect of alkalinity is to reduce corrosion andrelease of metallic byproducts and is reflected in a highly positive (+ 0.8) effect element.

Comprehensive relational matrices (A, Band C) were developed based on an extensive

literature survey and expert input from professionals in the field. These matrices will bepublished in an upcoming AwwaRF report [8].

6


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=

KLKK

L

L

C

L

MLMMM

L

L

L

21

22221

11211

L21

KWQP

2WQP

1WQP

DSMDSMDSM

; where k = 1, 2, ..., K l= 1, 2,...,L (3)

Identification of Potential Impacts to Planned Changes

This section introduces a method to identify problems related to the integrity of

distribution systems associated with changing water quality for regulatory compliance. A

hierarchical relational model (HRM) is proposed for this purpose. The HRM is

schematically illustrated in Figure 1. The procedure allows experts in different domains/fields to provide inputs to the relational matrices depending on their expertise. Also

research needs can be identified easily by studying gaps in the effects-matrices.

(Stage 2)

(Stage 1)

(Matrix C)

Total WQ Changes

vs

Impacts on DSM

(Matrix D)

Unit Treatment Processes

vs

Impacts on DSM

(Matrix B)

(Matrix A)

Drinking Water Regulations

vs

Available Treatment Technologies

Unit Treatment Processes

vsTotal WQ Changes

Regs regulations, WQ Water Quality, DSM distribution system material

Figure 1: Structure of Proposed Hierarchical Relational Model (HRM)

In Stage 1, applicable regulations are identified based on source-water characteristicsand contaminants of concern. Using matrix Aas a guide, suitable treatment alternatives

are identified to meet the regulations. The composition operation in Stage 1 represents

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the selection of applicable regulations and treatment alternatives. In Stage 2 (Figure 1)the composition operation is performed using:

CBD = (4)

In this case, represents a composition operation defined by;

==

K

kkljkjl

z1

)( , where j= 1, 2,...,J l = 1, 2, ...,L (5)

The primary and secondary impacts of selected treatment processes are obtained

using matrix B. From matrix C, the most common material(s) of construction in thedistribution infrastructure are selected. In Stage 2, matrices B and C are composed to

yield matrix D, which providesthe estimated impact of the selected treatment practice onthe distribution materials.

=

JLJJJ

L

L

zzzUT

zzzT

zzzT

D

L

MLMMM

L

L

L

21

22221

11211

L21

2U

1U

DSMDSMDSM

; where j=1, 2, ...,J l= 1, 2,...,L (6)

The aggregate impact of the treatment practices, selected in Stage 1, is calculated bysimple arithmetic sum of the individual treatment impacts.

=

===

J

jjL

J

jj

J

jj

L

zzz

DSMDSMDSMD

112

11

21

L

; where j=1, 2, ...,J l= 1, 2,...,L (7)

The algorithm is repeated twice: first for the current (baseline) treatment and second

for the intended change, i.e., alternative treatment. A difference between the two matrices

( ) will give the relative impact of the water quality change on the

distribution system materials. A positive value indicates that water quality does notadversely affect the selected distribution material, a negative value refers to adverse

impacts on the selected distribution material, and zero refers to no change in impact.

baselineealternativDDD =

Basic Assumptions for HRM

In order to evaluate the aggregate impacts of water quality changes on distributionmaterials, the following assumptions are made for simplification:

1. There is a linear correlation between primary and secondary water quality

changes and the treatment technique. Linearity in this case implies that a higherlevel of contaminant in the source-water may require a higher dosage of a

treatment chemical, and subsequently have higher primary and secondary water

quality changes. For instance, this assumption implies that if 2 mg/L of a

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contaminant requires 4 mg/L of a treatment chemical, then 4 mg/L of thecontaminant would require a proportional 8 mg/L of the treatment chemical. In

real systems, the dosage is optimized based on pilot or bench-scale studies and

may not be linear as assumed.

2. Water quality impacts on distribution system are additive. Additivity implies

that the overall water quality impact on any distribution material is the arithmeticsummation of all the potential changes due to individual treatment processes.

3. Water quality impacts are independent of hydraulic regimes prevalent in thedistribution system. This assumption implies that water quality and hydrauliceffects can be separated. However, in actual practice, the type (whether it is in a

storage tank or a pipe) and location (pipes with different flow velocities) of

distribution components determine the rate and magnitude of the deterioration due

to varying hydraulic effects.

4. The water quality impacts are independent of placement of a specific componentwithin the distribution system. However, in actual practice pipes closer and away

from treatment plants or inline chlorine boosters may behave differently.

Application of HRM

The conceptual framework discussed in the previous section is demonstrated withthe help of two hypothetical case studies. Areas of potential concern are obtained by

evaluating the relative impacts of different alternatives to existing (baseline) conditions.

Hypothetical Case Study 1:

Utility X decided to evaluate the impact of changing its secondary disinfectant fromchlorine to chloramines to comply with the Disinfectant/ Disinfection Byproduct Rule(D/DBPR) for TTHMs and HAA5. Though the utility has conducted studies that show

that the change will indeed result in compliance with the D/DBPR, there is some concern

over the impact on the distribution system material. The utility has identified that iron,

copper and lead components comprise more than 90% of its distribution infrastructure.The first step would be to establish a baseline value for current conditions, to which

the proposed changes can be compared. Therefore the rows for TTHM and HAA5 are

activated along with the column for chlorination and chloramination. It is observed thatchloramination has a less adverse DBPs formation impact than chlorination. For the

conditions identified, the resulting matrix Ais:

=

2.0

2.0

tionChloramina

0.15

H

0.1T

C

AA

THM

nhlorinatio

A (8)

Based on the water quality parameters that change during chlorination, the matrix Bfor the current conditions is obtained as follows:

9


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=

7.00.10.11.05.02.03.0

Re

onChlorinati

FormationBiofilmChlorinesidualAmmoniaTDSChlorideAlkalinitypHB (9)

The impact of the water quality on iron, copper and lead is selected.

=

8.05.08.0

4.04.04.0ChlorineResidual

000

2.02.02.0

8.08.08.0

8.08.08.0

4.06.06.0

FormationBiofilm

Ammonia

TDS

Chloride

Alkalinity

pH

LeadCopperIron

C (10)

Using Equation (5), the resulting impact matrix Dwill be:

=

48.049.060.0

onChlorinati

LeadCopperIronD

onChlorinati (11)

The resulting matrix ( ) establishes the baseline for iron, copper and lead

deterioration in the distribution system. In order to evaluate the relative impact of

chloramination, the relational matrix for chloramination ( ) is obtained in a

similar manner as was done for chlorination.

onChlorinatiD

tionChloraminaD

=

5.00.13.01.05.000tionChloramina

Re mationBiofilmForrinesidualChloAmmoniaTDSChlorideAlkalinitypHB (12)

=

82.037.022.0tionChloramina

tionChloramina

LeadCopperIronD (13)

The difference in the two matrices ( ) yields the relative

impact of the change on the distribution system materials (i.e., iron, copper and lead). It

is concluded that though the change might benefit iron and copper components of the

distribution system, lead components could suffer from increased deterioration.

onChlorinatiationChlora DDD

min =

==

34.012.038.0

onChlorinatitionChloramina

LeadCopperIronDDD (14)

Utility X concludes that further studies are needed to evaluate the impact of thechange on lead components of the distribution system.

10


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Hypothetical Case Study 2:

Consider utility Y that presently practices coagulation with ferric sulfate,

followed by sedimentation and filtration. The filtered water is chloraminated prior to

supply. However, utility Y plans to include nanofiltration after the CSF (coagulation-

settling-filtration) process for further reduction in organic content. Utility Y hasidentified that iron, copper and asbestos cement (AC) are the materials of concern in the

distribution network.

The resulting matrix Bfor the baseline treatment conditions can be obtained:

=

5.000.13.01.05.0000tionChloramina

06.00000000

08.0001.008.05.03.0

HResidual23

2

4

Filtration

nCoagulatio

FormBiofilmNOMClNNHTDSClSOAlkpH

B

n

(15)

The impact of the water quality on iron, copper and asbestos cement is;

=

6.05.08.0

5.05.05.0

1.02.02.0Res.

000

2.02.02.0

6.08.08.0

8.08.08.0

8.08.08.0

8.06.06.0

2

3

2

4

nFormBiofilm

NOM

ClNH

NH

TDS

Cl

SO

Alk

pH

mentAsbestosCeCopperIron

C

(16)

The resulting Dmatrix from the composition shown in Equation (5)will be:

=

1.037.02.0tionChloramina

3.03.03.0

9.004.08.0

Filtration

nCoagulatio

mentAsbestosCeCopperIron

D

(17)

Using Equation (7), the aggregate impacts for the baseline treatment are obtained:

=

72.011.076.0

tionChloramina

mentAsbestosCeCopperIronD

CSF

(18)

Similarly, the aggregate impacts for the addition of membrane process into thetreatment trains are calculated.

=

23.118.175.0

tionChloramina

mentAsbestosCeCopperIronD

ionNanofiltatCSF

(19)

The relative impact of the change is calculated as;

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=

50.029.101.0

tionChloraminationChloramina

mentAsbestosCeCopperIronDD

CSFionNanofiltatCSF

(20)

This indicates that deterioration of asbestos cement components might be aconcern for utility Y. Based on the results the utility Y evaluates the effect of pH

adjustment and the relative impacts are calculated in a similar manner.

=

45.073.177.0

minmin

mentAsbestosCeCopperIronDD

ationChloraCSFationChloraadjustmentpHionNanofiltatCSF

(21)

Based on these results, utility Y decides to conduct further studies to evaluate theeffect of proposed changes on asbestos cement components.

It should be cautioned that the HRM was developed as a concept model and hasall the deficiencies inherent in simplifying highly complex relationships (assumptions of

linearity and additivity). Though this model was evaluated for common scenarios, the use

of this model is dependent on the expertise and experience of decision-makers.

Conclusions

The complex interactions between distribution surfaces and drinking water qualitycan be aggregated to provide a simple decision support tool to help utilities identify

potential issues arising from planned changes in treatment processes to comply with newregulations.

The Hierarchical Relational Model presented in this paper is a proof-of-concept

model at the present stage. The authors realize the complexity involved in making the

model more effective and reliable would require additional research.

Acknowledgements

This work was conducted under a partnership agreement between the NationalResearch Council of Canada (NRC) and American Water Works Association Research

Foundation (AwwaRF). The authors acknowledge the drinking water experts and

professionals who contributed their time and data to this project. The authors

acknowledge the administrative help provided by the Natural Sciences and EngineeringResearch Council of Canada (NSERC).

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