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Journal of Environmental Management 105 (2012) 12e20

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Journal of Environmental Management

journal homepage: www.elsevier .com/locate/ jenvman

Spatio-temporal variability of tastes and odors of drinking water withina distribution system

Francois Proulx a, Manuel J. Rodriguez b,*, Jean B. Sérodes c, Christian Bouchard c

a Environmental Department, Municipality of Quebec, Quebec City, Canada G1N 3X6b École Supérieure d’Aménagement du Territoire, Université Laval, Quebec City, Canada G1K 7P4cCivil Engineering Department, Université Laval, Quebec City, Canada G1K 7P4

a r t i c l e i n f o

Article history:Received 10 February 2011Received in revised form8 February 2012Accepted 2 March 2012Available online 16 April 2012

Keywords:Drinking waterDistribution systemTasteOdorT&O compounds2 MIBGeosminAlgaeSpatio-temporal variation

* Corresponding author. Tel.: þ1 418 656 2131x893E-mail addresses: francois.proulx@ville.qu

manuel.rodriguez@esad.ulaval.ca (M.J. Rodriguez)(J.B. Sérodes), christian.bouchard@gci.ulaval.ca (C. Bou

1 Flavor is a combination of taste and odor percept

0301-4797/$ e see front matter � 2012 Elsevier Ltd.doi:10.1016/j.jenvman.2012.03.006

a b s t r a c t

The threshold of human perception in the detection of tastes and odors (T&O) relating to compounds indrinking water is variable. For example, chlorine can be detected at the ppm level and geosmin can beperceived at the ppt level. In this paper, sensory tests (using a human panel), physicochemical analyses(total and free residual chlorine, temperature, metals, geosmin, and 2-methylisoborneol (2MIB)) andmicrobiological analyses (algae, Actinomycetes and heterotrophic plate count) were performed for watersamples collected during a seventeen-month period at ten different locations of a municipal distributionnetwork of Quebec City (Canada). The results showed that different flavors1 assessed by a panel andaggregated into global flavor intensity (GFI) vary considerably spatially and seasonally. Multipleregression analysis showed that the factors best explaining the variability of GFI are (in order) the season,the location, the concentration of total residual chlorine and the presence of cyanobacteria. Results alsodemonstrate that chlorine has a masking effect on other T&O.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

In the last decade, problems of tastes and odors (T&O) indrinking water have drawn the attention of several authors(Bruchet et al., 1995; Bruchet, 1999; Khiari et al., 2002; Dietrichet al., 2004a) who have made significant efforts to identify T&Ocompounds and determine their sources. T&O compounds areperceived by consumers in a wide range of concentrations varyingfrom mg/l for chlorine to ng/l for geosmin, for example (Watsonet al., 2000; Mackey et al., 2004).

Despite more stringent regulations governing drinking water,improvements in quality control of drinking water in the distri-bution system and efforts expended to improve watershedmanagement to ensure greater drinking water safety, the pop-ulation is increasingly concerned about the quality of tap water(Doria, 2010). T&O in distributed water influences the perception of

3, fax: þ1 418 656 2018.ebec.qc.ca (F. Proulx),, jean.serodes@gci.ulaval.cachard).ion.

All rights reserved.

risk by consumers (McGuire, 1995; Jardine et al., 1999; Grondinet al., 1996; Levallois et al., 1999) even if T&O compounds do notrepresent health risks (Jardine et al., 1999). In addition, T&O canalso have an influence on behavior and the use of drinking wateralternatives (Doria, 2006; Jones et al., 2007; Proulx et al., 2010a).However, it has been difficult to fully incorporate these T&Oconcerns into regulations. For example, in the USA, regulationsstate that drinking water should not have a flavor greater than a 3-threshold odor number (TON). In Canada, drinkingwater guidelinesonly mention that flavor should be “inoffensive” in drinking water(Federal-Provincial Subcommittee on Drinking Water, 1996).Moreover, little is know about the relationship between specificwater parameters and the factors that influence perception,resulting in calls for more research in this area (Doria, 2010).

T&O compounds can originate from natural or anthropogenicsources. They may appear in raw water and wastewater effluents ofindustrial, municipal or individual activities (Young et al., 1996). Forexample, agricultural runoff can lead to surface water eutrophisa-tion and the occurrence of algae, possibly producing odorouscompounds (Watson, 2001). Themajor groups of algae causing T&Oin water are cyanobacteria, chrysophyceae, diatoms, and chlor-ophyceae (Proulx et al., 2010b). T&O compounds may also be

F. Proulx et al. / Journal of Environmental Management 105 (2012) 12e20 13

produced during water treatment processes when molecules inraw water react with ozone or chlorine used as disinfectants(Kenneth et al., 1999). They also may originate from the waterdistribution network, for example, through the leaching ofcompounds from pipe and reservoir materials (Dietrich et al.,2004a), from metabolites of heterotrophic microorganisms suchas Actinomycetes in the network biofilm (Zaitlin and Watson, 2006)and from the reaction of some molecules within the distributionsystem (Kenneth et al., 1999).

Climate can play an important role in T&O occurrence. Incountries where great seasonal variations of temperature occur,physicochemical and microbiological water quality can changedramatically throughout the year. Algae population can growsignificantly during summer, leading to the enhancement ofodorous compound concentration (Westerhoff et al., 2005).Furthermore, during the summer, organic matter and nutrients canbe introduced during storms and this can result in the appearanceof algal blooms (Davies et al., 2004) or the occurrence of hetero-trophic microorganisms (Chang and Jung, 2004).

Sensory analysis is widely used to classify odor sources based ontastes and odors wheel for drinking water (Suffet et al., 1999).However, these tests cannot be used to fully identify and quantifythe compounds responsible for T&O episodes. Today, many chem-ical and microbiological analyses can be performed on watersamples to identify compounds responsible for T&O episodes.However, these analyses are expensive and the link between theT&O compounds is not always easy to establish. Most of theresearch in this field has focused on the identification of sources forT&O compounds, on the measurement of these compounds, theidentification of their relationships with different types of T&O(Young et al., 1999; McGuire et al., 2005) and their removal throughwater treatment processes (Rosenfeldt et al., 2005; Dani et al.,2007). However, little research has been conducted to understandthe spatial and seasonal distribution of T&O compounds withindistribution systems (Maillet et al., 2009; Khiari andWatson, 2007;Dietrich, 2006; Malleret and Bruchet, 2002). The main purpose ofthis study is to explore the relationship between T&O assessed bysensory analysis and of T&O related compounds measured throughchemical analysis. The study also aims to explain, based on statis-tical analysis, the spatial and seasonal variations of T&O.

2. Methodology

2.1. Case under study

The case under study is a distribution system2 supplyingdrinking water to a sector of Quebec City (Canada). This sector isknown as Beauport. Water for Beauport is pumped from theMontmorency River, a tributary of the St. Lawrence River. Thephysicochemical quality of raw water is highly variable throughoutthe year. Water treatment consists of filtration through sand withinthe riverbed, ozonation and, finally, chlorination before distribu-tion. About 70,000 persons are served by the treatment plant. TheBeauport distribution system is an interesting case study of spatialvariations of water quality because the system supplies a largeterritory (74.4 km2) and, given the number of people served, waterresidence times in the network can be relatively long.

Ten sampling points were chosen in six sectors vulnerable to theoccurrence of T&O episodes. These sectors were established usinga methodology based on the study of customer complaints anda survey of customer perception performed throughout the entireterritory of Beauport (Proulx et al., 2007). The territory of Beauport

2 Municipal network.

was divided into 72 geographical units with comparable populationdensities. A multicriteria analysis method (Electre methodology)was used to establish the sectors more vulnerable to degradation interms of tastes and odors. Six vulnerable sectors were establishedusing this method. For each, one to three sampling points wereselected for the purpose of the present study: one sampling pointfor sectors I, II and V; two sampling points for sectors III and IV; andthree sampling points for sector VI).

For each location, monthly-based sampling campaigns werecarried out over 17 months. For each point, samples were analyzedfor microbiological and physicochemical parameters that can beassociated with T&O: heterotrophic plate count (HPC), Actinomy-cetes, algae (cyanobacteria, diatomea, chlorophycea and chrys-ophycea), total and free residual chlorine, temperature, metals(copper, iron, and manganese), geosmin, and 2-methylisoborneol(2MIB). Sensory analyses for T&O were also performed for everycollected sample. Both water quality analyses and sensory analyseswere conducted on 170 collected samples.

2.2. Analytical methods

2.2.1. Microbiological and physicochemical methodsFor the microbiological parameters, sterile polypropylene

bottles containing sodium thiosulfate serving as a quenching agentfor residual chlorine were used for field sampling. Samples wereanalyzed within 24 h of sampling. HPC analyses were performedusing the pour plate method, using 1 ml of sample and 17 ml ofsterile R2A agar in a sterile Petri dish (100 mm). Samples wereanalyzed in duplicate and colonies were counted after 48 h ofincubation at 35 �C (Greenberg et al., 2005).

Actinomycetes were analyzed with a method involving doublelayers of the pour plate method (Greenberg et al., 2005). In the firststep of this method, a bottom layer was made using 17e20 ml ofsterile Actinomycetes isolation agar (AIA, Difco) in a sterile Petridish (100 mm). In the second step, a 5-ml top layer was added overthe bottom layer and made with the following mix: 2 ml of sampleblended with 17 ml of melted sterile AIA and 1 ml of sterilecycloheximide antibiotic solution (0.1%). Colonies were thencounted in the agar body after 7 days of incubation at 28 �C. Eachsample was duplicated.

For the analysis of algae, 10 ml of samples were filtered througha 0.45 mm cellulose membrane (25 mm diameter, Millipore) fol-lowed by the addition of 10 ml of formaldehyde solution (10% v/v).Thereafter, the membrane was placed on a microscope blade andone drop of immersion oil was added to obtain transparency.Afterwards, the algae were analyzed and identified by microscopy(Leitz, Wetzlar) using an identification key (Greenberg et al., 2005).The results are expressed in terms of abundance (organism/ml).Measurements of free and total residual chlorinewere conducted insitu using the DPD colorimetric method. Temperature and pH werealso measured in situ using a solid glass selective electrode coupledwith a temperature probe. For metal analysis, polypropylenebottles were used for sampling. Sample pH was brought to<2 withthe addition of nitric acid. Metals were analyzed within two weeksusing optical inductive coupled plasma (Varian, Vista MPX-OES).

Samples for geosmin and 2MIB were collected in 40-ml glassvials (Greenberg et al., 2005) sealed with Teflon/silicon caps. Theywere analyzed within twoweeks by amethod involving solid phasemicroextraction (SPME) and gas chromatography coupled with iontrap tandem mass spectroscopy (GC/MSMS). Samples wereprepared by adding 5 g of sodium chloride to 15 ml of sample in anairtight 20-ml vial. Afterwards, the samples were heated to 65 �Cand exposed to a preconditioned SPME fiber (50/30 mm DVB/Car-boxen/PDMS StableFlex from Supelco), in headspace, for 30 min atthis temperature. The SPME process was conducted using

Table 1Portrait of water quality parameters during the sampling period.

Parameters Mean Median Minimum Maximum

Total Copper (mg/l) 0.05 0.04 0.01 0.17Total Iron (mg/l) 0.37 0.30 0.08 0.99Total Manganese (mg/l) 0.020 0.014 <0.002 0.16Free residual chlorine (mg/l) 1.15 1.09 <0.03 4.4Total residual chlorine (mg/l) 1.28 1.16 <0.03 4.4Temperature (�C) 11 12 2 22pH 7.0 6.9 6.2 8.5Apparent color (ACU) 13 13 5 30Turbidity (NTU) 0.89 0.88 0.31 2.38Heterotrophic plate count (CFU/ml) 4 0 0 520Actinomycetes (CFU/ml) 0 0 0 252-Méthylisoborneol (ng/l) 16.9 13.2 <4 86.5Geosmin (ng/l) 21 <2 <2 318Total diatomeous algae(org/ml) 86 56 0 537Total green algae (org/ml) 5 1 0 80Total cyanobacteria (org/ml) 13 0 0 182Other algae (org/ml) 34 24 0 211

Note: Statistics consider together all samples collected during the study.

F. Proulx et al. / Journal of Environmental Management 105 (2012) 12e2014

a CombiPal autosampler allowing a completely automated process.Following that, the fiber was heated for 5 min at 250 �C in theinjection port of a gas chromatograph (Varian, 3900) in splitlessmode using a small volume glass insert, where T&O compoundswere desorbed. The GC program temperature was initially 40 �C for2 min, then raised to 250 �C at a rate of 15 �C/min. The compoundswere identified and quantified by MSMS spectrometry technology(Varian Saturn 2100T, Electronic impact, 15 mamp, full scan). Theanalyses were performed with external standard and detectionlimits for geosmin and 2MIB were 2 and 4 ng/l respectively.

2.2.2. Sensory analysisSensory analyses were conducted according to a protocol

combining the well-known triangle test (Dietrich et al., 2004b) andflavor profile analysis (FPA) (Greenberg et al., 2005). Three monthsbefore the beginning of sampling campaigns, fifteen persons weretrained to recognize and scale a series of T&O compounds. Amongthem, tenwere selected for their ability to recognize and scale odorand taste correctly.

Water was sampled in a 1-L glass bottle to avoid any air gap andsealed with an airtight glass stopper. Before the panel meeting, thesamples were divided into 2 parts in airtight 500-ml glass bottles,one for odor analysis was maintained at 45 �C in a water bath andthe second for the taste and odor analysis was maintained at 25 �Cin an incubator. Before the analysis, the panelists were asked torefrain from eating or drinking anything, using perfume or smoking2 h before the test. For the first step of FPA, the panelists were askedto taste and smell ten series of water samples. Each series consistedof one sample and two blanks using a reference (activated carbontreated water without taste and odor). The panelists were asked totaste, smell and identify the different samples. This part of the testis an adapted version of triangle test (Dietrich et al., 2004b). In thesecond step, panelists were invited to identify and scale the tastesor odors of the different samples according to FPA test. The panelwas formed by 4 males and 6 females with average age of 39(min ¼ 18, max ¼ 63). For each panel meeting, two control stan-dards for T&O were submitted to panelists in order to evaluate thequality of the analysis. This entire procedurewas carried out for the170 samples collected through the study.

3. Results and discussion

Considerable variability in water quality occurred in the distri-bution system during the period under study, in particular con-cerning residual chlorine, color, turbidity, iron, 2MIB, geosmin andalgae (Table 1). These parameters could be related to the organo-leptic quality of drinking water (Knappe et al., 2004; Lehtola et al.,2004; Piriou et al., 2004). Residual chlorine is generally consideredas a leading cause of customer complaints concerning drinkingwater (Piriou et al., 2004; Montenegro et al., 2009). The 2MIB andgeosmin could be linked to earthy/musty odors in drinking waterand are generally related to the presence of algae and heterotrophicmicroorganisms in drinking water (Watson, 2003; Zaitlin et al.,2003). Color, iron and turbidity are not directly related as such toT&O occurrence but could be related to consumer perceptions ofdrinking water (de Franca Doria et al., 2005).

For the purpose of this study, the results concerning the inten-sity of the various T&O assessed through sensory analysis (by thepanel) were aggregated in order to obtain a global intensity for eachsample analyzed. This aggregation was achieved using a methoddeveloped by Bernal et al. (1999). This method considers themasking effect of some T&O on others, such as chlorine or earthy/musty odors for example (Bernal et al., 1999; Fabrellas et al., 2004).The first step of this methodology was to rank the descriptorswithin seven groups according to the type of T&O issue and to

assign amultiplication factor to each of the seven groups. The sevengroups of tastes and odors considered by Bernal et al. (1999) wereprincipally chlorineous, earthy-musty, medicinal, decaying vege-tation, rubbery, bitter and salty. The multiplication factor wasdesigned to increase progressively with the intensity of T&O. Forthe first five groups, this factor varied from 1 to 5 and for the twoother groups, the multiplicative factors varied from 1.5 to 7 (Bernalet al., 1999). Afterward, a summation of the products of the inten-sities of each descriptor by its factor (Equation (1)) was made toobtain an aggregated intensity called “global flavor intensity” (GFI),as follows,

GFI ¼X

Ii$Fi (1)

where, I represents individual intensity of flavor i assessed by thepanel and F represents a multiplying factor (Bernal et al., 1999)increasing with intensity of flavor i.

For each FPA session, the panelists were asked to choose fromamong three samples (two references sample and an analyzedsample) the one that appeared different. For all analyses, thepanelists were able to recognize the different samples. The flavorsmore frequently detected by the panel in the samples were chlorine(detected in 39% of analyzed samples), earthy-musty (detected in21% of analyzed samples) and metallic (detected in 17% of analyzedsamples).

Relations between T&O compounds and intensity of specificT&Owere evaluated with one way ANOVA. Before ANOVA analyses,the T&O results were grouped in class of flavors (0e3.5: weak,3.5e7: moderate and >7: strong). Our results showed that theresidual chlorine concentration was statistically related to theintensity of chlorine flavor assessed as a specific component of GFI(F (2,449) ¼ 20.5, p < 0.001). Earthy/musty flavors, also assessed asa specific component of GFI, were related to algae counts and inparticular to diatomeous (F (2,449) ¼ 12.5, p < 0.001), cyanobac-teria (F (2,449) ¼ 2.9, p < 0.05) and green algae (F (2,449) ¼ 9,8,p< 0.001). Algaewere found in treatedwater at the end of spring tothe beginning of fall. On normal treatment conditions, algae arestopped by the filtration of water through the river bed. But, duringthe high demand period (summer), a part of water is pumpeddirectly in the treatment water plant is ozoned and chlorinatedbefore the distribution. No statistical relationship was foundbetween earthy/musty flavors and 2MIB, and geosmin. This result issurprising considering that these compounds are generallyconsidered as metabolites of algae, Actinomycetes and HPC(Watson, 2003; Zaitlin and Watson, 2006). Algae analyses do not

F. Proulx et al. / Journal of Environmental Management 105 (2012) 12e20 15

differentiate between dead, damaged or living cells. 2MIB, geosminor other odorous compounds are generally release in environmentwhen the cells lysis occurs (Proulx et al., 2010b). Moreover someodorous compounds are biodegradable, so their concentrations arenot related to algae count in water (Proulx et al., 2010b). Theseresults could be explained by the presence of other majorcompounds not measured in this study. The detection of Actino-mycetes and HPCwas considered anecdotal regarding the frequencyof their detection in samples (2% and 4% respectively) (Table 1). Thehigh chlorine level observed in the network could limit the pres-ence of Actinomycetes and HPC in the distributed water, but thesemicroorganisms could exist in the biofilm, where they couldproduce 2-MIB and geosmin (Skjevrak et al., 2004). Metallic flavorcould be related to some metals like iron or copper. Thus, moderateassociation was observed with iron concentration (F (2,449) ¼ 3.4,p < 0.1). The median concentrations are 0.30 and 0.04 mg/l forcopper and iron respectively, Omur-Ozbek and Dietrich (2011)found that population flavor thresholds were in the range of0.031e0.05 mg/l for ferrous ion and 0.61 for cuprous ion and cupricion (ferric ion has almost no flavor). Considering oxydoreductionconditions, cupric ion is far more common in the distributionnetwork than cuprous ions.

3.1. Spatial variations of global flavor intensity

This section explains the spatial variations of T&O in the systemunder study. GFI varies from 0 to 18.5 (average of 3.5 and median of2.1) throughout the six sectors of the distribution system (Fig. 1).Sectors in Fig. 1 are ordered according to the methodology used byProulx et al. (2007) for the classification of sectors according totheir a priori vulnerability to tastes and odors. For example, sector Iindicates the sector with the lowest a priori vulnerability to T&Oand sector VI represents the sector with the highest a priorivulnerability to T&O. The results indicate that the value of GFI ishigher when the a priori vulnerability related to T&O in sectors ishigher (with a significant statistical relationship between the GFIand the sectors). Concerning partial GFIs, results indicate thatearthy/musty tastes are also associated with the a priori vulnera-bility to T&O in sectors under study (ANOVA, p< 0.001) as shown inFig. 1. Post hoc tests (Tuckeys) showed a significant differencebetween all sectors under study. For the other flavors, no statisticalassociation was found according to the rank of the six sectors.However, it is interesting to observe that partial GFIs for “other

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6

Sectors under study

Me

an

G

FI

Chlorine flavors Metallic flavors Earthy/musty flavors Other flavors

Fig. 1. Spatial evolution of average GFI (sectors are ordered according to their a priorivulnerability to T&O problems; partial GFIs for each type of flavour and standarddeviation are shown).

flavors” were noticeable only in the four sectors with the highesta priori vulnerability to T&O. Despite the above described results,the variability of T&O compounds such as residual chlorine, algae,2MIB, geosmin and metals did not follow the same pattern as GFIthrough the sectors under study (Fig. 2aed).

However, to observe the influence of water aging in thenetwork, the ten sampling points were classified according towaterresidence time in network. For this purpose, we established thatsampling points located within 3 km of the treatment plant (inhydraulic terms) belonged to the beginning of the network.Sampling points located between 3 and 6 km from the treatmentplant belonged to the middle of the network. Finally, the samplingpoints located at more than 6 km belonged to the end of thenetwork. Results showed that total residual chlorine were statisti-cally associated with the distribution system location (F(2,447) ¼ 77.2, p < 0.001) (Fig. 3). Algae count was significantlyhigher at the end of water network than it was in the beginning (F(2,447) ¼ 2.99, p < 0.05). This result could suggest a slight accu-mulation of algae at the end of the network, where the totalresidual chlorine concentration is low.

3.2. Seasonal variations of global flavor intensity

The regionwhere the distribution system under study is locatedis characterized by huge seasonal variations. The climate of QuebecCity area is characterized by long and cold winters (averageambient temperature of about �15 �C) and short, humid and warmsummers (mean ambient temperature of about 18 �C) separated byrainy, temperate autumns and springs. These great seasonal vari-ations contribute to inducing great changes in the physicochemicaland microbiological characteristics of surface water. The seasons inQuebec are not of equal length and do not follow common calendardates. For this study, the real seasonal cut-offs (instead of calendarones) were considered. The distributed water temperature wasused to establish the seasons. The cut-off temperature corre-sponded to the inflexion point of the temperature graph measureddaily in distributed water during the years 2003e2007 by QuebecCity staff.3

GFI varies significantly between the seasons (F (3, 446) ¼ 19.0,p < 0.001) as it can be observed in Fig. 4. These results show thatminimum mean intensities of T&O begin during spring to reachmaximum values during summer. These results follow the trendobserved by water utility managers of Quebec City concerningconsumer T&O related complaints for the network under study(Montenegro et al., 2009). At the end of spring, during the summerand at the beginning of autumn, T&O complaints reach theirmaximum rate (70% of total annual complaints for the Beauportsector). Chlorine and earthy/musty flavors both assessed as specificcomponents of GFI also vary with the season (ANOVA, p < 0.001and p < 0.05 respectively).

Results show that the algae population in distribution systemwater is closely related to seasonal variation (ANOVA, p < 0.001 forall species) (Fig. 5b). During the spring, the ice cover melts, thewater temperature increases and sunlight raises optimum intensityto initiate photosynthesis and thus, further algae growth. Duringtheir growth, algae can, under particular conditions, produce T&Ocompounds such 2MIB and geosmin (Watson, 2003). T&Ocompounds, geosmin and 2MIB also vary through the seasons(Fig. 5c) and their seasonal fate follows the tendency observed forthe GFI, suggesting that these molecules, in particular geosmin(ANOVA, p < 0.001), are generated mainly during the warmer

3 Winter: mid-November to mid-April, spring: mid-April to mid-June, summer:mid-June to end of mid-September and fall: mid-September to mid-November.

20

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Beginning Middle Extremities

p<0.001 p<0.05 p=0.76

p=0.29

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Cu Fe Mn

Fig. 2. Spatial variations of water quality parameters; a) chlorine concentration; b) algae population; c) geosmin and 2MIB concentrations; d) metal concentration in sectors understudy (average values are shown; sectors are ordered according to a priori vulnerability of T&O problems; averages are based on all samples collected at each sector).

F. Proulx et al. / Journal of Environmental Management 105 (2012) 12e2016

seasons of the year. Metal concentrations in drinking water are alsorelated to seasonal variation (ANOVA, p < 0.001 for copper andiron) (Fig. 5d), which could be explained by an increase of the metaldissolution rate in warmer water in summer (Cuppett et al., 2006).Residual chlorine concentrations (free and total) vary moderatelyover the seasons (Fig. 5a). During summer and autumn, managersincrease the chlorine concentration to diminish the potential forbacterial growth. Results showed that total residual chlorineconcentration appeared statistically related to seasons (ANOVA,p < 0.05).

Variations within seasons frommonth to month for GFI and waterquality parameters were also observed, in particular during periods ofsummer and spring (Fig. 6). This figure also shows that spatial differ-ences of such indicators (in this case between sectors I andVI)mayalsovary according to the sampling campaign. Results show that temporalsimilarities from campaign to campaign exist, in particular betweenGFI and chlorine residual. Similar conclusions arise when comparingthe specific sampling locations with the lowest water age with thelocation with the highest water age (Fig. 6).

0

5

10

15

Total residualchlorine x 10

(mg/l)

Total algaex10-1

(org/ml)

2-MIB (ng/l) Geosmin(ng/l)

GFI

p<0.05

Fig. 3. Variations of total residual chlorine, total algae, GFI, 2-MIB and geosminaccording to location in the network (average values are shown; averages values foreach parameter are based on all samples collected at each of the three locations).

3.3. Modeling spatio-temporal variations of global flavor intensity

The results presented above show that GFI varies seasonally andspatially in distribution systems. For example, seasons have a greatimpact on algae count and other microbiological organismsproducing geosmin, 2MIB and other T&O related compounds,which in turn could produce T&O. On other hand, chlorineconcentration has a considerable influence on T&O. It is alsoinfluenced by spatial location within the distribution system. Toidentify the factors simultaneously explaining the spatial andtemporal variability of GFI, multivariate statistical modeling wasapplied. For this study, the multiple regression model (MR) wasused (Cohen et al., 2003). MR analysis is a well-known modeling

methodology used in many research fields to establish the strengthof a linear relationship between an explained variable and a set ofexplanatory variables (Cohen et al., 2003). Prior to the modelingprocedure, the normality of sample distribution and the homoge-neity of variance residuals, which are required for parametric tests,were tested using ShapiroeWilk and White tests respectively(Belsley et al., 2004). The results demonstrated the normality andthe homogeneity of variance (ShapiroeWilk, p ¼ 0.9924, White,p ¼ 0.5085).

The regression analysis was carried out using the REG procedureof SAS statistical software (version 9.2) with the stepwise selection

0

2

4

6

8

10

12

Spring Summer Fall WinterSeason

Me

an

G

FI

Chlorine flavors Metallic flavors Earthy/musty flavors Other flavors

Fig. 4. Seasonal variation of average GFI (partial GFIs for each type of flavour andstandard deviations are shown; averages are based on all samples collected at eachseason).

F. Proulx et al. / Journal of Environmental Management 105 (2012) 12e20 17

method. A multiple regression model was implemented in order toassess the link between GFI and several parameters. This model wasused in order to test whether or not there was a significant rela-tionship between GFI and some independent variables. Thismethod involves the classification of the explanatory variables(predictors) according to their statistical significance by intro-ducing one variable at a time at different steps. The predictors thatwere considered are: total residual chlorine, total algae count, totaldiatomeous algae count, total green algae count, total cyanobac-teria count, geosmin, 2MIB, iron concentration, color, turbidity and

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a b

Fig. 5. Seasonal variation of water quality; a) chlorine concentration; b) algae population; csamples collected at each season).

temperature, sector number (according to a priori vulnerability toT&O) and seasons. The GFI variable was transformed in order tosatisfy the normality assumptions of the model. Optimal trans-formation was sought in the Box-Cox family, and the resultsshowed that square root transformation of GFI variable gave theoptimal results. Furthermore, the collinearity was examined withthe variance inflation factor and the temporal dependence was alsotestedwith the Durbin-Watson statistic. No problemwas identified.The examination of both season and sector number variablesshowed that the variation between categories of these variableswas not always significant. For this reason, these variables weredichotomized. For the variable ”season”, results revealed that GFIvalues for winter, spring and autumnwere not statistically differentand all these seasons were different from summer. Then, theseasons were grouped into two categories, one representingsummer and one representing all other seasons. Similarly, for thevariable “sector”, results showed that sectors I, II and III werestatistically close but different from sectors IV, V and VI, whichwerealso not statistically different. Thus, two groups were also createdfor this variable; one category for sectors I, II, III and another for thesectors IV, V and VI.

The results obtained for the MLR show that the model was statisti-cally significant (p< 0.001). The following variables had amajor impacton the model: season (p < 0.001), sector under study (p < 0.001),totalresidual chlorine (p< 0.02) and cyanobacteria rate (p< 0.05) (Table 2).The fact that the latter twovariableswere significant in the samemodelwith the season and sector numbermay seem surprising. This could beexplained by water quality variations within specific seasons andspecific sectors (as shown in Fig. 6a and b). These four variables explain27.5%of thevarianceofGFI.Resultsalsoshowthat themodel respondstothe normality assumption and that no collinearity was detected

0

20

40

60

80

100

120

140

160

Spring Summer Fall WinterSeason

Me

an

p

op

ulatio

n (o

rg

/m

l)

Diatomeous algae Green algae Cyanobacteria

0

0,1

0,2

0,3

0,4

0,5

0,6

Spring Summer Fall WinterSeason

Me

an

co

ncen

tratio

n (m

g/l)

Cu Fe Mn

) geosmin and 2MIB concentrations; d) metal concentration (averages are based on all

Fig. 6. a) Temporal variation of average GFI, total residual chlorine and total algae count for sectors I and VI (17 sampling campaigns; averages are based on all samples collected ineach sector for each campaign). b) Temporal variation of average GFI, total residual chlorine and total algae count for locations with the lowest and the highest water ages (17sampling campaigns averages are based on all samples collected in each sector for each campaign).

F. Proulx et al. / Journal of Environmental Management 105 (2012) 12e2018

between the variables in themodel (Variance Inflated Factor: VIF¼ 1.01to1.12; target:<10). The temporaldependencewasalso testedusing theDurbineWatson statistic (Harvey and Proietti, 2005) and the resultsshow that model had no temporal dependence.

Table 2Results for the multivariate statistical regression to explain GFI.

Significant variablesin the model

Estimate Standarderror

p

Intercept 1.785 0.137 <0.0001Season

(Summer vs Others)0.714 0.140 <0.0001

Sector number(I, II, III vs IV, V, VI)

�0.503 0.130 0.0002

Total residual chlorineconcentration (mg/l)

�0.196 0.083 0.0189

Total cyanobacteria (org/ml) 0.005 0.002 0.0401

Estimate: Variation of GFI by unit of increase of the explanatory variable. A positivevalue means GFI varies directly and proportionally with the explanatory variable.Standard error: Degree of reliability in the estimation of regression parameters.p: level of significance.

The MLR results show that the more important factors influ-encing the GFI are the season and the sector under study (Table 2).During the summer, the GFI value is higher than in other seasons.As shown earlier, seasons are closely related to algae count (dia-tomeous, green and cyanobacteria) and T&O related compounds(2MIB, geosmin, iron, and total residual chlorine), which havea great influence on GFI. Concerning the sectors under study, themodel shows that GFI values are 34% higher in sectors IV, V and VIthan in other sectors. The two other variables explaining the GFIvariations are total residual chlorine concentrations and cyano-bacteria counts. The GFI values decrease as the total residualchlorine concentration increases. This can be explained by themasking effect of chlorine on other tastes and odors. When totalresidual chlorine disappears in the network, other T&O could beperceived by consumers, which can inflate the partial GFIs associ-ated with compounds other than chlorine. On other hand, the morecyanobacteria counts reach high values, the more the GFI valueincreases. This could be explained by the increase of algal T&Oodorous compounds when the cyanobacteria rate increases.Moreover, as shown earlier, the cyanobacteria rate is related to

F. Proulx et al. / Journal of Environmental Management 105 (2012) 12e20 19

diatomeous and green algae counts that can also produce T&Orelated compounds (Westerhoff et al., 2005).

4. Conclusions

The main results of this study are the following:T&O intensity assessed by flavor panel analysis and aggregated

by a global flavor intensity (GFI) varies spatially and seasonally ina water distribution network.

GFI varies according to sectors ordered according to their a pri-ori vulnerability to T&O problems.

GFI varies with the season and reaches its maximum valueduring summer.

A multiple regression model indicated that the major variablessimultaneously explaining the spatio-temporal variability of T&Oare: seasons, sectors with a priori vulnerability to T&O, residualchlorine and cyanobacteria. Model results indicate that geosminand MIB were not significant variables within the model.

Model results suggest that total residual chlorine has a maskingeffect on T&O other than chlorine related T&O. In fact, T&O otherthan chlorine related T&O appear more clearly when residualchlorine concentrations are low. These results are in accordancewith those obtained by Worley et al. (2003) in experimentalconditions. This study showed that residual chlorine can mask theT&O from geosmin and 2-MIB with concentrations up to 30 ng/l.

The results suggest that sensory analysis can be helpful inidentifying T&O issues in a distribution system. However, FPA istime consuming and requires well-trained panelists for analysis.Other methods such as those using the triangle test, as applied inthis study, require less time and could be applied routinely byoperational staff of drinking water utilities. This test is able todetect variations of drinking water quality throughout the distri-bution network. In addition, chemical analytical methods cannot beused alone to identify a T&O problem.

In future studies, particular attention must be paid to the studyof other T&O compounds, especially those related to the occurrenceof algae and microorganisms like b-cyclocitral, linolenic acid andaldehydes. More studies must be conducted in water distributionnetworks with low residual chlorine concentrations. The bacteria inbiofilm are related to T&O occurrence in water networks (Skjevraket al., 2004). Thus, future work must also focus on the effect of thedistribution pipe biofilm on T&O occurrence and variability.

Acknowledgements

Authors thank the volunteers panelists for their participation tothe sensory analysis study. They also thank Mr Gaétan Daigle, M.Sc.P. Stat, for his valuable support in the statistical analysis.

Appendix. Supplementary material

Supplementary material associated with this article can befound, in the online version, at doi:10.1016/j.jenvman.2012.03.006.

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