Behavior of trihalomethanes and haloacetic acids in a drinking water distribution system

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Water Research 38 (2004) 4367–4382

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Behavior of trihalomethanes and haloacetic acids in a drinkingwater distribution system

Manuel J. Rodrigueza,�, Jean-B. Serodesb, Patrick Levalloisc

aEcole superieure d’amenagement du territoire, et developpement regional (ESAD), Universite Laval, 1624 Pavillon Savard, Que.,

Canada G1K-7P4bDepartement de Genie Civil, Universite Laval, Pavillon Pouliot, Que., Canada G1K-7P4

cInstitut National de Sante Publique du Quebec, 2400, Avenue d’Estimauville, Beauport, Que., Canada G1E 7G9

Received 17 December 2003; received in revised form 19 August 2004; accepted 20 August 2004

Abstract

This investigation focused on the seasonal variation and spatial fate of chlorination disinfection by-products

(CDBPs) in a drinking water distribution system located in a region where very significant seasonal variations in water

temperature and surface water quality occur. The analysis of a large number of collected samples showed that the

seasonal and geographical variations of both groups of CDBPs under study—trihalomethanes (THMs) and haloacetic

acids (HAAs)—were particularly important in this region. THM levels in summer and fall were, on average, about five

times higher than in winter, whereas average HAAs in spring were about four times higher than in winter. THMs

increased and stabilized in the extremities of the distribution system, whereas HAAs begin to increase, and then

decrease (mainly due to a reduction of dichloroacetic acid). This decrease was significantly higher in warm waters than

in cold waters, which led to the hypothesis of microbial degradation of HAAs as water approaches the system

extremities. In fact, regression models for the occurrence of both CDBPs showed that the residence time of water was

one important parameter in explaining the fate of both CDBPs. The spatio-temporal portrait of both groups of CDBPs

that was generated demonstrates that, due to their high intra-seasonal changes, the calculation of average annual levels

of these substances for compliance with regulations can vary widely. The results used in the portrait of CDBP behavior

are also relevant in terms of exposure assessment for future epidemiological studies on human reproductive outcomes in

the region.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Trihalomethanes; Haloacetic acids; Chlorination by-products; Water distribution system; Seasonal variations; Location

variations

e front matter r 2004 Elsevier Ltd. All rights reserve

atres.2004.08.018

ing author. Tel.: +1 418 656 2131x8933; fax:

8.

ess: [email protected]

z).

1. Introduction

It is well known that chlorination, the most widely

used strategy for drinking water disinfection, leads to

the formation of potentially harmful by-products (called

disinfection by-products, or DBPs). Chlorination DBPs

(CDBPs) are considered potentially carcinogenic

d.

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ARTICLE IN PRESSM.J. Rodriguez et al. / Water Research 38 (2004) 4367–43824368

(Cantor et al., 1998) and, more recently, have been

associated with adverse reproductive outcomes follow-

ing exposure during pregnancy (Bove et al., 1995; Kallen

and Robert, 2000). Trihalomethanes (THMs) and

haloacetic acids (HAAs) are the most important groups

of CDBPs (others are haloacetonitriles, haloketones and

chloropicrin). THMs include chloroform, bromodi-

chloromethane (BDCM), dibromochloromethane

(DBCM) and bromoform. Total THMs (TTHMs) refers

to the sum of these four substances. HAAs include nine

substances, the most common being dichloroacetic acid

(DCAA) and trichloroacetic acid (TCAA), other com-

pounds, found generally at lower levels, are bromo-

chloroacetic acid (BCAA), dibromoacetic acid (DBAA),

monochloroacetic acid (MCAA) and monobromoacetic

acid (MBAA). In the US, the sum of DCAA, TCAA,

DBAA, MCAA and MBAA is commonly denoted as

HAA5.

Due to their potential link to human health effects,

maximum acceptable concentration (MAC) for THMs

in drinking water have recently been established in

several countries and regions of the world. In most cases

the MAC refers to TTHMs, although the World Health

Organization has published guidelines for each of the

four THM species (World Health Organization, 1994).

Some countries and regions have also established MAC

for HAAs. Canadian drinking water quality guidelines

(Health Canada, 1996) set the MAC for TTHMs at

100 mg/L, while in the province of Quebec—since 2002—

the drinking water quality regulations (DWQR) man-

date that utilities comply with a maximum acceptable

level of 80mg/L for TTHMs, based on the annual

average of four samples collected quarterly (one per

trimester) at the extremity of the distribution system

(Gouvernement du Quebec, 2001). This MAC was

largely inspired by Stage 1 maximum contaminant level

(MCL) for TTHMs of the US Environmental Protection

Agency (USEPA) Disinfectant/Disinfection by-product

(D/DBP) rule (USEPA, 1998). However, no MAC for

HAAs has been established in Quebec’s drinking water

regulations, based on the argument that these substances

have a common origin with THMs. As for TTHMs, the

USEPA did include (in Stage 1 of the D/DBP rule)

MCLs of 60mg/L for HAA5 based on a running annual

average of quarterly samples collected at four distribu-

tion system sites. Stage 2 will favor a locational

approach for both TTHMs and HAA5 monitoring

(USEPA, 2003). The locational running annual average

will require compliance at each distribution system site.

That is, based on quarterly monitoring at four locations,

four separate running annual averages will be computed

(one for each site). Compliance with the MCL will need

to be ensured at each location.

The occurrence of CDBPs in treated and distributed

drinking water varies according to the quality of the

water source and the operations carried out in the

treatment plant. Generally speaking, the main influential

factors are the nature and amount of natural organic

matter (NOM)—in particular humic substances, con-

centrations of bromide (which mainly impact the

speciation of CDBP species), pH of water, water

temperature and residence time of water in the distribu-

tion system (Oxenford, 1996; Krasner, 1999; Rodriguez

and Serodes, 2001). In temperate northern environments

where important seasonal changes in water temperature

and water quality occur, changes to the operational

parameters of the treatment process are necessary,

therefore important variations of CDBP levels can also

occur.

Whereas recent studies have documented the forma-

tion of CDBPs (mainly THMs) using bench-scale

controlled chlorination (Amy et al., 1987; Montgomery

Watson, 1993; Rathbun, 1996; Clark and Sivaganesan,

1998; Rodriguez et al., 2003), there is currently relatively

little information about the impact of seasonal water

quality changes and operational water treatment strate-

gies on the simultaneous occurrence of THMs and

HAAs, and on the preponderance of one or the other in

full-scale distribution systems (Singer et al., 2002;

Obolensky et al., 2002). Most of the available data on

the topic of temperate northern locations involves either

CDBP measurements at single points of distribution

systems, very low sampling frequencies or systems with

very low levels of these compounds. Such studies do not

permit an appropriate analysis of the spatial and

seasonal changes in these compounds. It thus becomes

important to improve the documentation and under-

standing of the variations of CDBPs in this particular

geographical environment, not least because they may

imply variations in human exposure to the contaminants

on both a seasonal and spatial basis—changes resulting

from climatic variations, and changes in exposure

according to the consumer’s location in the distribution

system.

The main purpose of this research is to study the

simultaneous occurrence, in temperate northern envir-

onmental conditions, of the two most prevalent

CDBPs in drinking water—THMs and HAAs—

with an emphasis on their seasonal and spatial evolution

in a water distribution system. Using a portrait of

CDBP occurrence based on the collection a large

number of samples, the factors contributing to the

preponderance of one or the other group will be

identified, by means of descriptive analysis and statis-

tical modeling. Particular attention will be focused on

analyzing the impact that seasonal, intra-seasonal and

spatial variations may have on compliance with

requirements of current and future regulations. The

Canadian water utility under study is located in a region

with considerable temperature changes over the year,

which implies important operational variations in water

treatment.

ARTICLE IN PRESSM.J. Rodriguez et al. / Water Research 38 (2004) 4367–4382 4369

2. Study methodology

2.1. Case of study

The case study was conducted at the largest water

utility of the Quebec City region (Province of Quebec,

Canada). Quebec City which is located in the St.

Lawrence River Valley where the climatic changes over

the year are significant. In the region winters are long

and very cold (average air temperature of �15 1C)

whereas summers are hot (average ambient temperature

of+24 1C) but relatively short. As a result, between

these periods differences in surface water temperature

can be very significant (some years, the difference may

be as great as 25 1C). In addition, the region is

characterized by sudden watershed runoff—associated

with a rapid increase in ambient temperature and with

snow melting in spring—and by frequent rain and a

relatively rapid decay of vegetation (a source of NOM in

water) during fall. All these events contribute to changes

in surface water quality and also require changes in

operations during water treatment.

The utility under study serves about 200,000 inhabi-

tants. The water source is the Saint Charles River

(a small tributary of the St. Lawrence) that is

characterized by highly colored water and relatively

low turbidity. The utility’s treatment process consists of

raw water pre-chlorination, coagulation–flocculation,

sedimentation, filtration, ozonation (for microbial in-

activation), and post-chlorination (for distribution

system protection purposes). A fluoride concentration

of between 1.0 and 1.2mg/L is added near the post-

chlorination point for dental health purposes, and

polyphosphates and lime are added at the extremity of

the plant to protect distribution system pipes from

corrosion. Water is delivered by gravity to most of the

sectors served by the utility. However, a reservoir

located within the distribution system serves about

one-fifth of the population. At the inlet of this tank,

water is rechlorinated in order to maintain sufficient

residual chlorine levels downstream.

2.2. Locations under study and estimation of water

residence time

In order to select the sampling points which represent

spatial variations in water quality—and in particular of

CDBPs—a three-step process was carried out. An initial

selection of 20 locations was made, based on the

historical data on water quality provided by the utility

(some resulting from the their routine monitoring

program) and other data generated in previous research

by the authors (Rodriguez and Serodes, 1999; Serodes et

al., 2001). The expertise of water plant operators also

contributed to the selection. An intensive 3-week

sampling program was conducted to measure free

chlorine at all points to distinguish locations within

the distribution system with different water residence

times; in fact, residual chlorine depletion is closely linked

to water residence time (AWWARF, 1996). This

program led to the selection of ten potential locations

for the study, including the post-chlorination stage.

Estimations of water residence times were undertaken

through tracer studies based on fluoride monitoring

(DiGiano et al., 2000). The tracer studies were not

intended to implement a hydraulic calibration for the

distribution system under study. Instead, they were used

to select specific sampling locations characterized by

different water residence times within the system, and to

investigate the potential impact of water residence time

on the relative variability of CDBPs in the distribution

system. Two 2-day tracer studies were carried out—one

in winter and the other in summer—in order to consider

the possible impacts of seasonal differences in water

consumption and produced flowrates on residence times.

For each tracer study, fluoride dosage in the plant was

switched-off at 6 a.m. A monitoring program for the

decay in fluoride ion (F�) concentration was then

carried out at the ten pre-selected points, throughout

the day and night, during which samples for fluoride

concentration measurement were collected every 15min

at the plant outlet and every hour at the remaining

points within the distribution system. The next day at 6

a.m., about 1.0mg/L of fluoride dose was switched on,

followed by an identical intensive monitoring strategy.

The approximate average water travel time correspond-

ing to a specific location was considered to be the time

lag between the moment at which the fluoride dose was

switched off/on and the time at which fluoride levels

reached 50% of the maximum concentration. The

selection of final five sampling points for detailed

investigation of CDBPs was carried out based on the

following criteria: (1) locations had to represent a

variety of residence times, measured from the outlet of

the treatment plant (when two points had comparable

residence times, one of them was excluded from the

investigation); (2) at least one point had to represent

the treatment plant outlet and other an extremity of the

distribution system; (3) all points had to be accessible for

sampling the same day of the week, throughout the year.

Given these criteria, five distribution system locations

with increasing residence times were selected in order to

monitor CDBPs and other water quality parameters.

The five were located in commercial and municipal

buildings. Two other points were also monitored: the

raw water and clean water (at the point preceding post-

chlorination in order to consider the impact of raw

water pre-chlorination on CDBPs). Fig. 1 is a schematic

(not to scale) representation of the locations of the

points under study and the parameters to be character-

ized in each location. Points P2–P4 represent locations

where water is always provided only by the treatment

ARTICLE IN PRESS

Pre-chlorination

St-Charles river

Post-chlorination

Plant reservoir

Praw

P2

P3

P5

P4

P1

Cl2 THMs HAAs

Cl2

THMs HAAs

Cl2 THMs HAAs

Cl2 THMs HAAs

UV-254 TOC pH Cl2 THMs HAAs

PCT + ozonation

UV-254 TOC pH Turbidity Temperature

Distribution reservoir

P0 UV-254 TOC pH Turbidity Cl2 TemperatureTHMs HAAs

Re-chlorination

Fig. 1. Overview of the system under study and measured water quality parameters (PCT means physicochemical treatment).

M.J. Rodriguez et al. / Water Research 38 (2004) 4367–43824370

plant (plant reservoir) whereas point P5 is a location

where water is always provided only by the distribution

reservoir.

2.3. Sampling strategy

A 14-month sampling program intended to character-

ize the parameters shown in Fig. 1 was carried out (with

a total of 19 sampling dates) between September 2000

and October 2001. In general, samples were collected

every 2 weeks fromMay to September and every 3 weeks

the rest of the year. Sampling was undertaken in the

morning (in general between 10 a.m. and noon). In all

buildings, samples were collected from the faucet of the

washroom nearest the street. Before collecting samples,

the faucet was turned on for about 5min, to ensure that

the water was coming directly from the public distribu-

tion system and not from the building’s plumbing

system. For THM and HAA samples, tap water was

collected in 300mL glass bottles with ground-glass

stoppers. The bottles had been previously washed with

phosphate-free detergent and rinsed with de-ionized

water and ultra-pure water, and placed in an oven at

400 1C for 1 h. Before sampling, a sodium thiosulfate

solution (10%) was added to the bottles (1.5mL) to

eliminate any residual chlorine and to prevent additional

CDBP formation during transportation to the labora-

tory. NOM content was characterized using two

indicators, TOC and UV-254. Samples for measure-

ments of these two parameters were collected in 125mL

plastic bottles. Once collected, samples were carefully

stored in the dark at 4 1C and carried to the laboratory

for analysis. Measurements of free residual chlorine, pH

and temperature were carried out in the field at the same

time as the collection of samples for CDBPs and organic

matter surrogates. It should be noted that for certain

dates, some samples of raw waters were collected to

analyze bromide concentrations; however, levels of

bromide were found to be lower than the detectable

limit for the analytical method.


2.4. Analytical procedures

Measurements of fluoride concentrations for tracer

studies were undertaken using the method 4500- F� C

(APHA, AWWA and WPCF, 1995). Measurements of

free chlorine were conducted using the DPD titrimetric

method (Standard method 4500-Cl-F) with a DR-700

colorimeter from Hach. TOC was analyzed using a

Shimadzu TOC analyzer (model 5000). Water pH and

temperature were measured on site using a solid selective

electrode (electrolytic gel). Analysis of bromide was

conducted for samples (ion chromatography) but in all

cases measurements were below the detection limit

(15mg/L).Samples for THM and HAA analysis were extracted

using pentane and methyl-tert-butyl-ether (MTBE),

respectively. For HAA extraction, a surrogate standard

(100 ppm 2,3-dibromopropionic acid in methyl-tert-

butyl-ether, MTBE, HPLC grade) was added to each

sample to monitor method performance. After sample

extraction, analysis for THMs and HAAs was con-

ducted according to EPA methods 551.1 and 552.2,

respectively (USEPA, 1990; USEPA, 1995) by means of

two Perkin Elmer autosystem XL gas chromatographs

with electron capture detectors (GC-ECD). ZB-624 and

ZB-1701 (30m� 0.32mm) Zebron columns were used to

separate THMs and HAAs, respectively. For quality

assurance, field blanks, duplicate samples and injection

and internal standards in each sample were conducted.

For THM species, analytical protocols ensured detec-

tion limits of 0.5 mg/L for chloroform and of 0.3 mg/L for

BDCM, DBCM, and bromoform. For HAA species,

detection limits were 1.2, 1.1, 0.6, 0.9, 1.3 and 0.9 mg/L

Table 1

Characteristics of water quality and operational parameters during th

Jun; Summer: Jul, Aug, Sep; Fall: Oct, Nov, Dec)

Type of water Parameter Season

Winter

Raw water (Praw) pH 7.07 (0.03)

Turbidity (NTU) 1.84 (0.72)

TOC (mg/L) 1.62 (0.38)

UV-254 (cm�1) 0.089 (0.034)

SUVA (L/mg-m) 5.84 (3.06)

Treated water (P0) Temperature (1C) 1.6 (0.3)

pH 7.03 (0.06)

Turbidity (NTU) 0.35 (0.13)

TOC (mg/L) 1.26 (0.18)

UV-254 (cm�1) 0.017 (0.008)

SUVA (L/mg-m) 1.44 (0.70)

Pre-chlorine dose (mg/L) 0.72 (0.10)

Post-chlorine dose (mg/L) 1.17 (0.24)

Note: Standard deviations are shown into parenthesis.

for MCAA, DCAA, TCAA, MBAA, DBAA and

BCAA.

3. Results and discussion

3.1. Indicators for chlorinated DBP precursors

The main operational and measured water quality

characteristics for raw and treated water from the utility

during the period under investigation are presented in

Table 1. This table also shows the specific absorbance.

This parameter represents the ratio UV-254/DOC� 100

(SUVA) and constitutes an indicator of carbon aroma-

city in water (in this case TOC was used in calculating

SUVA because the investigated water has very low

turbidity, DOC representing 95% and more of TOC).

The higher the SUVA, the higher the content of humic

substances. Results show that, as expected, TOC and

UV-254 values in raw water were significantly lower in

winter, when the watersheds of the region are covered

and thus protected from pollution by an ice layer. The

quality of treated water before chlorination is also

variable according to the season, but does not reflect the

same pattern as raw water. For clear water, the greatest

content of organic carbon and the lowest values for UV-

254 (thus the lowest aromacity) were encountered during

fall, whereas the highest aromacity was found during

winter. This pattern could be explained by the relative

efficiency of humic acid removal in fall through physico-

chemical treatment, using high doses of coagulant

(alum). Conversely, in winter the removal of organic

content is less efficient, probably due to the low alum

e period under study (Winter: Jan, Feb, Mar; Spring: Apr, May,

All seasons

Spring Summer Fall

7.34 (0.15) 7.13 (0.18) 6.98 (0.13) 7.13 (0.18)

2.84 (1.47) 4.00 (3.28) 2.62 (1.34) 2.81 (2.08)

3.05 (0.31) 3.90 (1.35) 3.77 (0.80) 3.20 (1.24)

0.146 (0.012) 0.185 (0.059) 0.161 (0.026) 0.154 (0.055)

4.89 (0.28) 4.53 (0.77) 4.31 (0.26) 4.86 (1.50)

13.7 (3.1) 17.9 (3.0) 9.42 (3.4) 12.5 (6.8)

6.74 (0.28) 6.35 (0.32) 6.59 (0.18) 6.59 (0.35)

0.33 (0.21) 0.34 (0.12) 0.22 (0.06) 0.31 (0.15)

1.87 (0.35) 1.89 (0.84) 2.09 (0.12) 1.78 (0.62)

0.018 (0.001) 0.021 (0.005) 0.016 (0.006) 0.016 (0.010)

0.90 (0.14) 0.93 (0.21) 0.69 (0.56) 1.01 (0.48)

1.75 (0.25) 1.98 (0.47) 1.72 (0.30) 1.55 (0.55)

1.35 (0.22) 1.89 (0.20) 1.35 (0.27) 1.54 (0.38)


dose used to remove color and turbidity at very low

temperatures. The variations observed in NOM indica-

tors and temperature may contribute to an explanation

of seasonal variations in chlorinated DBP occurrence.

3.2. Residence time in sampling points

The tracer studies carried out with fluoride permitted

the estimation of typical residence times of water

associated with the chosen sampling points (Table 2).

These are only approximate values because they do not

consider the changes that can occur in the short term

(within 24 h) or over the long term (day-to-day, monthly

and seasonal) due to flowrate variations associated with

changes in water demand. The tracer studies and the

results presented in Table 2 are not consequently useful

to characterize the hydraulics in the distribution system.

But they allow to identify the relative difference of

residence time among the investigated sampling points.

However, it was surprising to find that both tracer

studies (carried out in different seasons) gave compar-

able values for water residence times for both the pre-

selected and the selected points, even though the

produced flowrate by the treatment plant is generally

about 25% higher in summer than in winter. Because the

two tracer studies began at the same hour, they represent

a pattern of water distribution for a specific period of the

day. Nevertheless, these results can be considered as

valid for the order of magnitude of residence times. The

differences in this parameter between sampling locations

will help to explain the spatial variability of CDBPs in

the distribution system.

3.3. Occurrence of CDBPs

During the period under study, bromoform was the

only product not detectable among the four THMs in

the measured samples (values lower than the detection

limit) whereas among HAA5, only DCAA and TCAA

were identified (referred to as HAA2 in the rest of the

paper). In addition, among the three identified THM

Table 2

Approximate water residence time of water in distribution system loc

Sampling point Tracer studies in winter

Day 1 (h) Day 2 (h

P1 E1 E1

P2 E6 E5

P3 E8 E7

P4 E14 E12

P5 E36a E36a

aThe estimation is less precise than for the other points because ea

maximum concentration in this point was not reached.

compounds (referred to as THM3 in the rest of the

paper), chloroform was the predominant compound,

constituting on average 91% of THM3, whereas DCAA

and TCAA constituted about 51% and about 49% of

HAA2, respectively (with all samples considered to-

gether). These results can be explained primarily by the

very low levels of bromide ion in the waters of the Saint

Charles River, which lead to a very low concentration of

brominated CDBPs. The average concentrations for

both THM3 and HAA2 in the distribution system

(points P1–P5 taken together) were comparable—43.9

and 37.7 mg/L, respectively. This result differs dramati-cally from the documented data obtained in a past

investigation in Canada, where average HAAs in

distribution systems (based on two sampling dates) were

about 30% higher than THMs (Health Canada, 1995).

3.3.1. Seasonal variations

Variations according to the season for both CDBPs

were significant (using the statistical significance level, p,

lower than 0.05, according to the Student–Newman–

Keuls test (SPSS, 1997). As illustrated in Figs. 2a and b,

the highest average THM3 levels in the distribution

system occur during summer and fall. In the summer

period, the average water temperature and chlorination

doses (pre- and post-) were the highest (Table 1). In

addition, the highest raw water TOC was in summer and

the second highest in the fall, with the highest treated

water TOC in the fall. It is interesting to observe that for

summer and fall average THM3 levels were about five

times higher than average concentrations measured

during winter (the highest measured level in the

distribution system occurring in fall, 106mg/L, and the

lowest value obtained in winter, 5.1 mg/L). These

seasonal differences in THMs are considerably higher

than those found in other temperate environments

documented in recent studies in the US and Europe,

where differences between cold and warm waters are

about two-fold (Garcia-Villanova et al., 1997; Chen and

Weisel, 1998; Golfinopoulos, 2000). But values for

THMs are comparable to those obtained in chlorination

ations estimated based on fluoride tracer studies

Tracer studies in summer

) Day 1 Day 2

E45min E1 h

E5 h E5 h

E8 h E8 h

E14 h E15 h

E36 ha E36 ha

ch tracer study had a duration of 16 h, so the 50% of fluoride

ARTICLE IN PRESS

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Wint(a) er Spring Summer Fall

µg/L

Chloroform

BDCM

DBCM

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Winter Spring Summer Fall

µg/L DCAA

TCAA

(b)

Fig. 2. Variations of CDBPs according to seasons; (a) THM species; (b) HAA species (error bars represent 95% confidence intervals).

M.J. Rodriguez et al. / Water Research 38 (2004) 4367–4382 4373

bench-scale experiments using diverse waters from the

region (Rodriguez et al., 2003). Also, as shown in Fig.

2a, the brominated THMs were lower in the spring than

in summer and fall. This could be explained by a

possible decrease of bromide concentration in the raw

water during the spring, associated with the increase of

the Saint-Charles River flowrate.

In the case of HAA2, the highest concentrations in the

distribution system were found during spring—results

about four times higher than those encountered during

winter (the higher measured value obtained in spring,

118 mg/L, and the lowest value obtained in winter,

6.3mg/L). This result also differs from recent studies of

HAA occurrence in the US, where maximum levels were

found during summer, when water temperatures are the

highest. Two possible explanations may exist for the

obtained results: one being that some degradation of

HAAs occurs at higher temperatures (in summer), and

the other being the greater occurrence of precursors

favoring the formation of HAAs instead of THMs

during the spring, when snow melts. It is also worth

noting that DCAA levels were higher than TCAA levels

in winter and spring, whereas TCAA was higher than

DCAA in summer and fall. There are three possible

explanations. The pH of the treated water (Table 1) was

lower in summer and fall and, according to Stevens et al.

(1989), TCAA formation in chlorinated waters is higher

at lower pH, whereas DCAA formation is not as

affected by pH. Another explanation is that NOM

characteristics may change over the year, as DCAA and

TCAA have different precursors (Reckhow and Singer,

1985). Finally, the possible biodegradation of DCAA

(as discussed later in the paper) could change the

preponderance of these two HAAs.

As was the case for THM3, seasonal differences for

HAA2 were much higher than those documented in

recent investigations (Chen and Weisel, 1998). These

results demonstrate the importance of the effect the


significant changes in water temperature in the region

have on seasonal variations of CDBPs. It is, however,

surprising that both the average THM3 and HAA2

observed during fall are comparable with those obtained

in summer, considering the relatively low values of UV-

254 and SUVA of treated water and the relatively low

temperatures during fall. These values suggest that

TOC, for which values are highest in fall for the treated

water, could be a better indicator for the CDBP

precursor in the waters under investigation.

In addition to seasonal variations, the study also

permitted the identification of relatively important intra-

seasonal variations in measured CDBPs in the distribu-

tion system (Fig. 3a), i.e. variations in water samples

collected with intervals of 2–3 weeks. Such variations are

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-02-

06

2001

-03-

06

2001

-04-

06

200

µg/L

THM3 at P

THM3 at P

HAA2 at P1

HAA2 at P4

0.0

0.5

(a)

1.0

1.5

2.0

2.5

3.0

2000

-09-

06

2000

-10-

04

2000

-11-

11

2001

-02-

26

2001

-03-

26

2001

-05

Chl

orin

e do

se a

nd T

OC

(m

g/L

)

Dose (pos

TOC at P0

Temp at P

(b)

Fig. 3. Example for variations over the sampling programme of some

water temperature.

understandable, considering that operational and water

quality parameters affecting the formation and evolu-

tion of CDBPs are also highly variable over the short

term (Fig. 3b). This suggests that quarterly average

values for CDBPs may not be fully representative of that

period. This result could have important implications

for compliance with regulations concerning CDBPs, as

will be discussed later in the paper.

3.3.2. Locational variations

Pre-, post- and re-chlorination of water, as well as

residence time in the distribution system have a

considerable impact on CDBP evolution. Differences

in both CDBP levels according to residence time were

statistically significant (p value o0.05) (Figs. 4a and b).

1-05

-06

2001

-06-

06

2001

-07-

06

2001

-08-

06

2001

-09-

06

2001

-10-

06

1

4

-21

2001

-07-

03

2001

-07-

302

2001

-08-

27

2001

-10-

15

0

5

10

15

20

25

30

Wat

er t

empe

ratu

re (

˚C)

t)

0

measured parameters: (a) CDBPs; (b) chlorine dose, TOC and

ARTICLE IN PRESS

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

P0 P1 P2 P3 P4 P5

µg/L

µg/L

Chloroform

BDCM

DBCM

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

DCAA

TCAA

P0 P1 P2 P3 P4 P5(b)

(a)

Fig. 4. Spatial variations of CDBPs: (a) THM species; (b) HAA species (error bars represent 95% confidence intervals).


The pre-chlorination at the raw water point led to about

25% higher levels of HAA2 than THM3. But this

pattern changes after post-chlorination and throughout

the distribution system where, according to residence

time, THM3 levels become comparable to or higher than

HAA2 levels. An observation worth noting is that after

post-chlorination and once the water passes about 5 h in

the distribution system (P2), the concentrations of both

THM3 and HAA2 show their most notable increase. At

the 5-h mark, the average concentrations of THM3 and

HAA2 have increased—about two-fold and about 1.5

times, respectively—in comparison with the water

preceding post-chlorination. However, between about

5 h (P2) and about 14 h (P4) of residence time, the

concentration of THM3 remains stable, whereas the

concentrations of HAA2 decrease significantly, reaching

lower levels than those observed in water preceding pre-

chlorination. This occurs even though, between P2 and

P4, the decrease in average free residual chlorine is

considerable (an average chlorine demand of about

0.35mg/L). It is important to note that, as observed in

Fig. 4b, the decrease in HAA2 levels is basically due to

the decrease in DCAA (a reduction in this compound of

about 60% between P2 and P4) as water residence time

increases within the distribution system, whereas TCAA

concentrations diminished only very slightly (about 15%

on average). Recently, experimental studies have re-

ported the degradation of HAAs over time following

chlorination as being associated with microbial activity

(Williams et al., 1994; Baribeau et al., 2000; Zhou and

Xie, 2002).

Concentrations observed at P5 must be analyzed

separately from those at the other points because, as

shown in Fig. 1, P5 is located downstream from the


reservoir (close to the outlet) at which re-chlorination is

carried out (a dose of about 0.6mg/L applied at the inlet

of the reservoir). The significant increase in both THM3

and HAA2 is undoubtedly associated with the addi-

tional application of chlorine and to the protracted

contact time of chlorine with water within the reservoir

(residence time in the reservoir is approximately 22 h).

However, because sampling was not possible within this

reservoir, the fate of THM3 and HAA2 from the inlet to

the outlet of the reservoir remains unknown.

3.3.3. Effect of water temperature on CDBP variations

The analysis of the geographical variations in both

CDBPs according to water temperature will clarify some

of the results obtained earlier. As observed in Figs. 5a

and b, the portrait of spatial variations in both CDBPs

in the distribution system, but in particular of HAA2, is

closely related to temperature. In warm waters (tem-

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

THM

3 an

d H

AA

2 (µ

g/L

)

T > 15°C

T ≤ 15°

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

P0 P1 P2 P3

P0 P1 P2 P3

TH

M3

and

HA

A2

(µg

/L)

(a)

(b)

Fig. 5. Spatial variations of THM3, HAA2 and free residual chlorine a

species (error bars represent 95% confidence intervals).

perature higher than 15 1C), the decrease of HAA2

according to residence time is considerably higher than

that observed in cold waters (temperature lower than

15 1C), with a very considerable decrease of average

DCAA concentrations: about three times lower at Q4

than at Q2. In addition, it is again surprising to observe

that even with the greater chlorine demand observed

between these two points in warm waters (an average of

about 0.60mg/L), no further formation of THM3

results. Downstream from the distribution reservoir,

the average concentration of HAA2 was, however,

higher in warm water than in cold water (a difference

about 25%).

The previous results—the fact that DCAA concentra-

tions decline with increasing residence time in the

distribution system (in particular at high water tem-

peratures) and that, conversely, they rise following a

relatively high storage time in the reservoir (in both

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Res

idu

al c

hlo

rin

e (m

g/L

)

THM3

HAA2

Residualchlorine

C

P4 P5

P4 P5

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Res

idu

al c

hlo

rin

e (m

g/L

)

THM3

HAA2

Residualchlorine

ccording to water temperature: (a) cold waters; (b) warm waters


cases with the presence of high levels of residual or

chlorine dose)—suggest a microbiological degradation

of this HAA species (Williams et al., 1994; Baribeau et

al., 2000). In effect, the microbial life associated with the

internal surface of the distribution pipe (biofilm) is more

active at high water temperatures and has a greater

impact on the distributed water (bulk water) when pipe

diameters are small (LeChevallier et al., 1996; Rossman

et al., 2001). Consequently, the impact of the potential

occurrence of a reservoir wall biofilm on bulk water

quality—and thus on DCAA degradation—is low.

The observed pattern in the spatial evolution of both

CDBPs leads to a variable correlation between the two

types of compounds, according to temperature and

location. The correlation between THM3 and HAA2

was the highest in cold waters at points closest to the

treatment plant (P1 and P2) and downstream from the

reservoir (P5) (using the correlation coefficient, r, of 0.85

for the three points taken together), and lowest in warm

waters at points approaching the distribution system

extremities (P3 and P4) (ro0:1 for P3 and P4 taken

together).

3.4. Impacts of CDBP variation on regulation compliance

The spatial and seasonal variations of CDBPs that

have been portrayed in the previous sections have

notable implications for compliance with regulations

concerning these substances. In order to comply with the

80mg/L MAC for TTHMs in the 2001 QDWR, utilities

in the province of Quebec will have to take one sample

per quarter at the extremity of the distribution system,

with the time lag between two samples being minimum 2

months. Accordingly, the average of the four measured

Table 3

Comparison of annual average concentrations (lowest and highest v

compliance with the Quebec DWQR criteria for sampling

CDBP Point First quarter (Fall) Second quarter (Winter) Third

Sample Conc.a (mg/L) Sample Conc. (mg/L) Samp

THM3 P4 L 35.2 L 5.1 L

H 48.7 H 20.3 H

P5 L 56.4 L 7.1 L

H 64.3 H 24.4 H

HAA2 P2 L 37.1 L 9.0 L

H 55.3 H 15.9 H

P4 L 14.7 L 6.3 L

H 19.2 H 11.8 H

P5 L 53.6 L 19.1 L

H 66.3 H 25.5 H

Note: L denotes the measured sample with the lowest concentration w

concentration (in all cases, there are at least two months between eacaCDBP concentration.

concentrations is calculated as being the annual average.

However, based on the results of this investigation, it

can be demonstrated that the calculation of the annual

average TTHM concentration that complies with the

specified terms for location and sampling frequency can

result on considerable differences, depending on the

sampling date within a given quarter (Table 3). For

example, the location P4 is considered by the utility

managers as a distribution system extremity (free

residual chlorine being lower than 0.3mg/L throughout

the year). But the calculated annual average of THM3

for this point can differ up to 40%, according to the

selected sample values. The annual THM3 average

calculated for point P5, which is not considered a

system extremity (average free residual chlorine being

higher than 0.5mg/L), may give results considerably

higher than those at point P4. At P5, the differences

between the lowest and highest calculated annual

average of THM3 could be potentially highly significant

(more than 30%).

A similar analysis can be conducted for HAA2 even if

a MAC does not currently exist in the Quebec DWQR

for these compounds. For example in Stage 2 of the D/

DBP rule, the USEPA is currently planning to establish

a MCL for TTHMs and HAA5 based on the location at

which the average concentration in the distribution

system are the highest (among four locations). For the

sector served directly by the treatment plant of the utility

under study, P2 represents the sample point at which the

quarterly annual averages of HAA2 are the highest.

However, when the sector downstream from the

reservoir is also taken into account, P5 is the point with

the highest annual HAA2 average (Table 3). If the

criteria for TTHMs for compliance with the sampling

alues) of THM3 and HAA2 in selected locations calculated in

quarter (Spring) Fourth quarter (Summer) Annual average

le Conc. (mg/L) Sample Conc. (mg/L) Conc. (mg/L)

29.0 L 37.6 26.7

46.8 H 57.6 43.3

39.7 L 53.5 39.1

42.1 H 101.1 58.0

56.3 L 34.5 34.2

77.9 H 65.8 53.7

46.1 L 32.3 24.9

51.8 H 37.0 29.9

45.3 L 28.3 36.6

106.8 H 96.4 73.8

ithin the quarter whereas H denotes the sample with the highest

h quarterly sample).


frequency of the Quebec DWQR is applied, a notable

difference could result in the calculation of the annual

average of HAA2, depending on the sampling date, for

points P2 and P4. The difference is particularly high for

P5: about 50%.

3.5. Modelling of CDBP variations

Statistical modelling was conducted in order to

identify the most important parameters responsible for

the variations of both groups of CDBPs under study and

to develop multivariate regression equations that can be

used to predict concentrations of these substances. The

development of models consisted of establishing statis-

tical relationships between THM3 or HAA2 concentra-

tions in the distribution system based on data for P1–P4

together (n ¼ 63) and water quality and operational

conditions before post-chlorination (i.e. conditions of

pre-chlorinated water, represented by P0).

Linear and non-linear model structures were tested.

The linear structure is described by the following

equation:

Y ¼X

b0 þXm

i¼1

biX i: (1)

The non-linear model is a transformation of Eq. (1)

within which variables are subjected to Ln–Ln transfor-

mation; it is described by the following equation:

Y ¼ b0Ym

i¼1

Xbi

i : (2)

The non-linear structure has been used (and with very

good results) for modeling TTHMs using data generated

in bench-scale chlorination experiments with waters of

the Quebec region (Rodriguez et al., 2003). Within Eqs.

(1) and (2), Y denotes the variable being modeled (in this

case the concentration of THM3 or HAA2), Xi denotes

the explanatory (or predictive) variables, with m

denoting the number of explanatory variables consid-

ered, b0 the intercept and bi the partial slope coefficients

Table 4

Features of statistical regression models for CDBPs (only variables b

Model Variable significance (p-value)

b0 CDBP0 Ph0 UV0 tr

Linear THM3 o0.01 o0.001 o0.01 o0.01 oHAA2 o0.01 o0.01 o0.1 ns ns

Non-linear THM3 o0.01 o0.001 ns ns oHAA2 o0.001 o0.001 ns ns ns

ns: non significant.

na : non applicable.

Note: Average values of the 2-day seasonal residence time was used w

providing a partial explanation or prediction for the

value of Y. The explanatory parameters being consid-

ered in regression were the water quality parameters

before post-chlorination (at P0): the indicators of NOM

(denoted TOC0, UV-2540 and SUVA0 in mg/L, cm�1

and L/mg-m, respectively), water pH (Ph0), concentra-

tions of CDBPs (that is TTHM0 or HAA20 in mg/L),water temperature (T0 in 1C), post-chlorination chlorine

dose (D, in mg/L) and the estimated residence time of

water (tr, in hours) between the post-chlorination point

and each of sampling points in question (P1, P2, P3 or

P4). The point P5 was not considered in the analysis

because precise data for re-chlorination dose was not

available and because the estimation of residence time

was less precise than for the other points. Models using

chlorine demand from P0 and the relative increase of

CDBPs between this point and the others (instead of

absolute measured concentration) were also tested, with

much less satisfactory results (not shown in this paper).

The parameters of the models shown in Eqs. (1) and

(2) were estimated using the ordinary least squares

(OLS) method which results in a line that minimizes the

sum of squared vertical distances from the observed data

points to the line (Lewis-Beck, 1980; Neter et al., 1990).

Using the stepwise procedure of the statistical software

SPSS (SPSS France, 1997), the method consists of first

classifying the predictor variables according to their

statistical significance (p) and then including one

variable at a time at different steps.

Table 4 presents the results obtained for both linear

and non-linear structures. The criterion used to judge

whether or not to consider selected variables in the

models during the regression stepwise procedure was a

significance level of 10% (po0:10). According to the

adjusted statistical coefficients of determination, R2, the

non-linear structure for HAA2 gave much better results

than the linear structure, whereas for THM3 the linear

model proved moderately more useful. Indeed, the linear

model for HAA2 had a much lower significance than the

other three models (p ¼ 0:002 in comparison with

po0:001 for the others). This result shows that in

eing significant in at least one model are shown)

Statistical coefficients R2

b0 CDBP0 Ph0 UV0 tr

0.1 134.0 1.471 �17.42 �734.4 0.748 0.77

�90.2 0.760 14.93 na na 0.26

0.001 286.9 0.745 na na 0.298 0.68

5.34 0.571 na na na 0.70

ithin the model.

ARTICLE IN PRESS

0.00

10.0

0

20.0

0

30.0

0

40.0

0

50.0

0

60.0

0

70.0

0

00-0

8-06 00

-09-

06 00-10-0

600

-11-

06 00-1

2-06 01

-01-

06 01-0

2-06 01

-03-

06 01-0

4-06 01

-05-

06 01-0

6-06 01

-07-

06 01-0

8-06 01-09-0

601

-10-

06

µg/LFi

eld

Mod

el

0.00

10.0

0

20.0

0

30.0

0

40.0

0

50.0

0

60.0

0

70.0

0

µg/L

Fiel

dM

odel

0.00

10.0

0

20.0

0

30.0

0

40.0

0

50.0

0

60.0

0

70.0

0

µg/L

Fiel

dM

odel

HA

A2

at P

2

TH

M3

at P

2

HA

A2

at P

3

TH

M3

at P

3

HA

A3

at P

4

TH

M3

at P

4

0.00

10.0

0

20.0

0

30.0

0

40.0

0

50.0

0

60.0

0

70.0

0

µg/L

Fiel

dM

odel

0.00

10.0

0

20.0

0

30.0

0

40.0

0

50.0

0

60.0

0

70.0

0

µg/L

Fiel

dM

odel

0.00

10.0

0

20.0

0

30.0

0

40.0

0

50.0

0

60.0

0

70.0

0µg/L

Fiel

dM

odel

00-0

8-06 00

-09-

0600-1

0-06

00-1

1-06 00

-12-

06 01-0

1-06 01

-02-

06 01-0

3-06 01

-04-

06 01-0

5-06 01

-06-

06 01-0

7-06 01

-08-

0601-0

9-06

01-1

0-06

00-0

8-06 00

-09-

0600-1

0-06

00-1

1-06 00

-12-

06 01-0

1-06 01

-02-

06 01-0

3-06 01

-04-

06 01-0

5-06 01

-06-

06 01-0

7-06 01

-08-

0601-0

9-06

01-1

0-06

00-0

8-06 00

-09-

0600-1

0-06

00-1

1-06 00

-12-

06 01-0

1-06 01

-02-

06 01-0

3-06 01

-04-

06 01-0

5-06 01

-06-

06 01-0

7-06 01

-08-

0601-0

9-06

01-1

0-06

00-0

8-06 00

-09-

0600-1

0-06

00-1

1-06 00

-12-

06 01-0

1-06 01

-02-

06 01-0

3-06 01

-04-

06 01-0

5-06 01

-06-

06 01-0

7-06 01

-08-

0601-0

9-06

01-1

0-06

00-0

8-06 00

-09-

0600-1

0-06

00-1

1-06 00

-12-

06 01-0

1-06 01

-02-

0601

-03-

06 01-0

4-06 01

-05-

06 01-0

6-06 01

-07-

06 01-0

8-06

01-09-0

601

-10-

06

Fig. 6. Comparison of field and modeled THM3 and HAA2 in selected locations of the distribution system.


general terms the non-linear structure is able to

adequately identify the spatial evolution of such

substances. Neither chlorine dose nor temperature were

significant variables in any of the models. For both

substances, the concentration of CDBPs already occur-

ring before the post-chlorination point (due to


pre-chlorination) has a significant effect on their fate in

the distribution system. The limited significance of

chlorine dose could be explained by the relatively low

variability of these parameters over the year. The limited

significance of temperature on CDBP levels in the

distribution system could be explained by the fact that

initial levels of CDBPs (occurring before post-chlorina-

tion) are themselves influenced by water temperature (so

these two variables are correlated). In addition, it is

interesting to note that indicators for NOM only

contribute to a moderate degree in seasonal variations

of THM3 and that for the non-linear HAA2 model, this

variable was the only significant one (giving a one-

variable model). This result can be explained by the fact

that the variations in HAA2 levels between P0 and the

next three locations (P1–P3) are relatively minimal.

However, this is not the case for the THM3 linear

model, within which other variables also contribute to

the spatio-temporal fate of this compound. In this model

(which was the best performer of all), the contributions

of the residence time, UV-254 and pH were also

important. Figs. 5 and 6a–d illustrate how the applica-

tion of the linear model for THM3 and the non-linear

model for HAA2 fit relatively well with measured data.

4. Conclusions

This investigation permitted the generation of a

portrait of the simultaneous occurrence of THMs and

HAAs in a distribution system located in a temperate

environment where seasonal variations of surface water

quality and water temperature are considerable. The

analysis of the data obtained from a comprehensive

sampling program involving several points of the utility

(raw water, plant and distribution system) led to the

following conclusions:

(1)
The seasonal variations of both TTHMs and HAAs
in the territory under study are significant and much

higher that those observed in other published studies

involving temperate environments (US and Europe).

(2)
Variations in the two CDBP compounds are not
only important on a seasonal basis but also in the

short term (intra-seasonal variations). This is mostly

associated with the variations of raw and treated

water quality as well as variations in operational

parameters concerning chlorination. Consequently,

for the conditions of the utility under study, the use

of seasonal average values to characterize CDBPs is

not an adequate approach.

(3)
Spatial changes of THMs and HAAs in the
distribution system are important, but the pattern

of evolution is very different for the two CDBPs.

THMs increase and become stable in the distribution

system extremities, but this is not the case for HAAs.

HAA concentrations decrease (in particular DCAA)

approaching the extremities, a phenomenon prob-

ably related to microbiological degradation of these

substances.

(4)
Results showed that the residence time of water
within the distribution system is a significant

contributing variable for spatial variation of THMs.

Consequently, more effort is needed in this domain

to improve the ability to estimate water residence

time, in particular for the estimation of intra-

seasonal, daily and hourly changes. To that end,

the implementation of hydraulic calibrated models

would be very advantageous, in particular if several

sampling points are considered (Clark and Gray-

man, 1998; Walski et al., 2003).

(5)
Re-chlorination of distributed water, followed by
several hours of residence time in a storage tank has

a notable effect on CDBP occurrence. In fact, even

though THMs had increased and stabilized while

HAAs decreased at the extremity, an additional

chlorine application produced a significant increase

of both CDBPs. This result points to the need for an

improved disinfection strategy in distribution system

reservoirs (by using alternative oxidants such as

chloramines and/or optimizing the chlorine dose and

the location at which it is applied).

(6)
This research has important implications for regula-
tion issues. The control of THMs in water utilities

through the establishment of regulations does not

ensure, as stated in Quebec’s DWQR, the control of

the other CDBPs. The establishment of specific

MAC for HAAs must consequently be considered,

based on locational requirements for sampling that

differ from those for THMs. In addition, as

demonstrated in this paper, because the use of single

values of CDBPs are not representative of quarters,

future standards for THMs and HAAs must be

based on quarterly multi-sampling (at least one

monthly sample), preferably with a running annual

average of sample concentrations (as now allowed in

the US).

(7)
The epidemiological implications of this research are
also substantial. Until now, epidemiological studies

aimed at making the link between human health

outcomes (cancer and reproduction) and exposure to

CDBPs in drinking water have not adequately

addressed the geographical and temporal evolution

of these substances in water distribution systems.

Misclassification of exposure according to seasonal,

geographical variation could be important and this

could be biased health effect measures. The con-

sideration of such variations appears necessary to

improving exposure assessment, and this is particu-

larly essential for studies related to reproductive

outcomes where short-term exposure (according to

pregnancy trimesters) is important.


(8)
Those results are particularly pertinent to similar
locations with a temperate northern climate.

Acknowledgements

The authors would like to acknowledge the Environ-

mental Service of Quebec City for its collaboration in

this project, in particular Mr. Francois Proulx. The

authors also thank Janice Pitre, Melanie Huard, Michel

Bisping, Sylvie Drugeon, Danielle Roy and Cynthia

Guay for field and laboratory technical support, as well

as Housseini Coulibaly, Carlos Aparicio, Tarik Sadik

and Stan Ketelers for intensive sampling programs

associated with tracer studies. Financial support for

this project was provided by a Health Collaborative

Grant from NSERC (Canada) and the Canadian

Chlorine Coordinating Committee (C4).

References

Amy, G.L., Chadik, P.A., Chowdhury, Z.K., 1987. Developing

models for predicting trihalomethane formation

potential and kinetics. J. Am. Water Works Assoc. 79 (7),

89–97.

APHA, AWWA and WPCF, 1995. Standard Methods for the

Examination of Water and Wastewater, 19th ed. APHA,

AWWA and WPCF, Washington, DC.

AWWARF, 1996. Characterisation and Modeling of Chlorine

Decay in Distribution Systems. AWWA, Denver, CO.

Baribeau, H., Krasner, S.W., Chinn, R., Singer, P.C., 2000.

Impact of biomass on the stability of haloacetic acids and

trihalomethanes in simulated distribution system. Proceed-

ings of the AWWA Water Quality Technology Conference,

Salt Lake City, Utah.

Bove, F.J., Fulcomer, M.C., Klotz, J.B., Esmart, J., Dufficy,

E.M., Savrin, J.E., 1995. Public drinking water contamina-

tion and birth outcomes. Am. J. Epidemiol. 141 (9),

850–862.

Cantor, K.P., Lynch, C.F., Hildesheim, M.E., Dosemeci, M.,

Lubin, J., Alavanja, J., Craun, G., 1998. Drinking water

source and chlorination by-products I. Risk of bladder

cancer. Epidemiology 9 (1), 21–28.

Chen, W.J., Weisel, C.P., 1998. Halogenated DBP concentra-

tions in a distribution system. J. Am. Water Works Assoc.

90 (4), 151–163.

Clark, R.M., Grayman, M., 1998. Modeling Water Quality in

Drinking Water Distribution Systems. American Water

Works Association, Denver, CO.

Clark, R.M., Sivaganesan, M., 1998. Predicting chlorine

residuals and formation of TTHMs in drinking water.

J. Environ. Eng. 124 (12), 1203–1210.

DiGiano, F.A., Travaglia, A., Zhang, W., 2000. Water age

(Epanet) and mean residence time of fluoride tracer in

distribution systems. Proceedings of the AWWA Water

Quality Technology Conference, Denver, CO.

Garcia-Villanova, R.J., Garcia, C., Gomez, J.A., Garcia, M.P.,

Ardanuy, R., 1997. Formation, evolution and modeling of

trihalomethanes in the drinking water of a town: II, in the

distribution system. Water Res. 31 (6), 1405–1413.

Golfinopoulos, S.K., 2000. The occurrence of trihalomethanes

in the drinking water in Greece. Chemosphere 41 (11),

1761–1767.

Gouvernement du Quebec, 2001. Reglement sur la qualite de

l’eau potable. Gazette officielle du Quebec.

Health Canada, 1995. A national survey of chlorinated

disinfection by-products in Canadian drinking water.

Environmental Health Directorate, Health Protection

Branch.

Health Canada, 1996. Guidelines for Canadian Drinking Water

Quality, sixth ed.

Kallen, B.A.J., Robert, E., 2000. Drinking water chlorination

and delivery outcome—a registry-based study in Sweden.

Reprod. Toxicol. 14 (4), 303–309.

Krasner, S.W., 1999. Chemistry of disinfection by-product

formation. In: Formation and Control of Disinfection by-

Products in Drinking Water. American Water Works

Association, Denver, CO, pp. 27–51.

LeChevallier, M.W., Welch, N.J., Smith, D.B., 1996. Full-scale

studies of factors related to coliform regrowth in drinking

water. Appl. Environ. Microbiol. 62, 2201–2211.

Lewis-Beck, M.S., 1980. Applied Regression: An Introduction.

Sage Publications, Inc., Newbury Park, CA.

Montgomery Watson, 1993. Mathematical Modeling of

the Formation of THMs and HAAs in Chlorinated

Natural Waters. American Water Works Association,

Denver, CO.

Neter, J., Wasserman, W., Kutner, M., 1990. Applied Linear

Statistical Models, third ed. Irwin, Homewood, IL.

Obolensky, A., Shukairy, H., Blank, V., 2002. Occurrence of

haloacetic acids in ICR finished water and distribution

systems. In: McGuire, M.J., McLain, J.L., Obolensky, A.

(Eds.)., Information Collection Rule Data Analysis. Aw-

waRF and AWWA, Denver, CO, pp. 111–140.

Oxenford, J.L., 1996. Disinfection by-products: current prac-

tices and future directions. In: Minear, R.A., Amy, G.L.

(Eds.)., Disinfection by-Products in Water Treatment: The

Chemistry of Their Formation and Control. Lewis Publish-

ers, Florida, USA, pp. 3–16.

Rathbun, R.E., 1996. Trihalomethane and nonpurgeable total

organic-halide formation potentials of the Mississippi River.

Arch. Environ. Contam. Toxicol. 30, 156–162.

Reckhow, D.A., Singer, P.C., 1985. Mechanisms of organic

halide formation during fulvic acid chlorination and

implications with respect to preozonation. In: Joley, R.L.,

et al. (Eds.)., Water Chlorination: Chemistry, Environmen-

tal Impact and Health Effects, vol. 5. Lewis Publishers Inc.,

Chelsea, MI.

Rodriguez, M.J., Serodes, J.B., 1999. Empirical linear and

non-linear modelling of residual chlorine in urban

drinking water systems. Environ. Modelling Software 14

(1), 93–102.

Rodriguez, M.J., Serodes, J.B., 2001. Spatial and temporal

evolution of trihalomethanes in three water distribution

systems. Water Res. 35 (6), 1572–1586.

Rodriguez, M.J., Milot, J., Serodes, J.B., 2003. Predicting

trihalomethane formation in chlorinated waters using

multivariate regression and neural networks. J. Water

Supply Res. Technol.-Aqua 52, 199–215.


Rossman, L.A., Brown, R.A., Singer, P.C., Nuckols, J.R.,

2001. DBP formation kinetics in a simulated distribution

system. Water Res. 35 (14), 3483–3489.

Serodes, J.B., Rodriguez, M.J., Ponton, A., 2001. Chlorcastr:

a methodology for developing decision-making tools for

chlorine disinfection control. Environ. Modelling Software

16 (1), 53–62.

Singer, P.C., Weinberg, H.S., Brophy, K., Liang, L., Roberts,

M., Grisstede, I., Krasner, S., Baribeau, H., Arora, H.,

Najm, I., 2002. Relative Dominance of Haloacetic Acids

and Trihalomethanes in Treated Drinking Water. AwwaRF

and AWWA, Denver, CO.

SPSS France, 1997. SPSS Base 7.5 pour Windows. Manuel

d’utilisation, Boulogne, France, 467pp.

Stevens, A.A., Moore, L.A., Miltner, R.J., 1989. Formation

and control of non-trihalomethane disinfection by-pro-

ducts. J. Am. Water Works Assoc. 81 (8), 54–56.

USEPA, 1990. Determination of chlorination disinfection by-

products and chlorinated solvents in drinking water by

liquid–liquid extraction and gas chromatography with

electron-capture detection. Environmental Monitoring Sys-

tems Laboratory Office of Research and Development,

Method 551.

USEPA, 1995. Determination of haloacetic acids in drinking

water by liquid-liquid extraction and gas chromatography

with electron-capture detection. National Exposure Re-

search Laboratory Office of Research and Development,

Method 552.2.

USEPA, 1998. National primary drinking water regulations:

disinfectants and disinfection byproducts; Final rule.

Federal Register 63 (241), 69390–69476.

USEPA, 2003. National primary drinking water regulations:

Stage 2 disinfectants and disinfection by-products rule;

National primary and secondary drinking water regulations:

approval of analytical methods for chemical contaminants.

Federal Register 68 (159), 49548–49681.

Walski, T.M., Chase, D.V., Savic, D.A., Grayman, W.,

Beckwith, W., Koelle, E., 2003. Advanced water distribu-

tion modeling and management. Haestad Methods, Water-

bury, CT.

Williams, S.L., Williams, R.L., Rinffleisch, D.F., 1994. Dead-

end on haloacetic acids (HAAs). Proceedings of the

American Water Works Association WQTC Conference,

pp. 1053–1058.

World Health Organization, 1994. Guidelines for Drinking

Water Quality, vol. 1, Recommendations, second ed. WHO,

Geneva.

Zhou, H.J., Xie, Y.F.F., 2002. Using BAC for HAA removal—

Part 1: Batch study. J. Am. Water Works Assoc. 94 (4),

194–200.

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Behavior of trihalomethanes and haloacetic acids in a drinking water distribution system