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Comparison of drinking water treatment process streamsfor optimal bacteriological water quality

Lionel Ho*, Kalan Braun, Rolando Fabris, Daniel Hoefel, Jim Morran, Paul Monis,Mary Drikas

Australian Water Quality Centre, SA Water Corporation, 250 Victoria Square, Adelaide, SA 5000, Australia

a r t i c l e i n f o

Article history:

Received 19 December 2011

Received in revised form

23 April 2012

Accepted 25 April 2012

Available online 4 May 2012

Keywords:

Denaturing gradient gel

electrophoresis (DGGE)

Flow cytometry

Heterotrophic plate count (HPC)

Magnetic ion exchange (MIEX)

Photometric dispersion

analyser (PDA)

Water treatment

* Corresponding author. Tel.: þ61 8 7424 211E-mail address: lionel.ho@sawater.com.a

0043-1354/$ e see front matter ª 2012 Elsevdoi:10.1016/j.watres.2012.04.041

a b s t r a c t

Four pilot-scale treatment process streams (Stream 1 e Conventional treatment (coagu-

lation/flocculation/dual media filtration); Stream 2 e Magnetic ion exchange (MIEX)/

Conventional treatment; Stream 3 e MIEX/Conventional treatment/granular activated

carbon (GAC) filtration; Stream 4 e Microfiltration/nanofiltration) were commissioned to

compare their effectiveness in producing high quality potable water prior to disinfection.

Despite receiving highly variable source water quality throughout the investigation, each

stream consistently reduced colour and turbidity to below Australian Drinking Water

Guideline levels, with the exception of Stream 1 which was difficult to manage due to the

reactive nature of coagulation control. Of particular interest was the bacteriological quality

of the treated waters where flow cytometry was shown to be the superior monitoring tool

in comparison to the traditional heterotrophic plate count method. Based on removal of

total and active bacteria, the treatment process streams were ranked in the order: Stream 4

(average log removal of 2.7) > Stream 2 (average log removal of 2.3) > Stream 3 (average log

removal of 1.5) > Stream 1 (average log removal of 1.0). The lower removals in Stream 3

were attributed to bacteria detaching from the GAC filter. Bacterial community analysis

revealed that the treatments affected the bacteria present, with the communities in

streams incorporating conventional treatment clustering with each other, while the

community composition of Stream 4 was very different to those of Streams 1, 2 and 3. MIEX

treatment was shown to enhance removal of bacteria due to more efficient flocculation

which was validated through the novel application of the photometric dispersion analyser.

ª 2012 Elsevier Ltd. All rights reserved.

1. Introduction minimised in drinking water distribution systems. The effi-

The primary goal of water utilities is to safeguard drinking

water for consumers. Consequently, drinking water must be

of a standard or quality that aligns with many water safety

plans. This involves removing contaminants of concern,

whether they be biological or chemical, and a range of water

treatment methods have been developed over the past

century to ensure that these contaminants are removed or

9; fax: þ61 8 7003 2119.u (L. Ho).ier Ltd. All rights reserve

cacy of these treatment methods is governed by routine

monitoring of specific indicators, including the removal of

pathogenic organisms and chemicals of concern (e.g. disin-

fection by-products, algal toxins, etc.).

Surrogate parameters are generally used to assess the

efficacy of treatment processes. For example, monitoring of

natural organic material (NOM), in particular, dissolved

organic carbon (DOC), colour and UV absorbance, can be used

d.

Fig. 1 e Schematic of the four treatment streams: S1 e

Conventional treatment (coagulation/flocculation/dual

media filtration); S2 e MIEX/Conventional treatment; S3 e

MIEX/Conventional treatment/GAC; S4 e Microfiltration/

nanofiltration.

wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 2 3935

to assess the precursors of disinfection by-products. Likewise,

the general bacteriological quality of drinking water can be

monitored using heterotrophic plate counts (HPC), a method

which has been in use for over a century (Bartram et al., 2003;

Allen et al., 2004; Berney et al., 2008). In recent times, new

detection methods have emerged to evaluate bacteriological

quality in water, includingmeasuring adenosine triphosphate

(ATP) and flow cytometry (FCM) in conjunction with fluores-

cence staining methods (Hoefel et al., 2003; Hammes et al.,

2008; Siebel et al., 2008). These detection methods offer

numerous advantages over the HPC method as they are not

only rapid, accurate and enable high throughput, but they can

also detect bacteria which are non-culturable under the

conditions of the HPC method.

While studies have utilised FCMwith fluorescence stains to

characterise bacterial removal through conventional water

treatment and distribution systems (Lebaron et al., 1998;

Rinta-Kanto et al., 2004; Hoefel et al., 2005; Hammes et al.,

2008), few studies, to date, have utilised such an approach to

compare various treatment processes in parallel to assess

their ability to remove bacteria. With many water utilities

commissioning water treatment plants (WTP) that employ

new technologies such as membrane filtration and/or ion

exchange resins (in addition to utilities retrofitting or

upgrading their existing plants), there is a requirement to

validate specific treatments for their bacterial removal

capacity. This can ensure that they adopt the multi-barrier

treatment approach to comply with water safety plan guide-

lines and water quality targets. Such validation studies will

facilitate the design of specific treatment processes for utili-

ties, in addition to optimisation and best management prac-

tices of these processes.

The aim of this study was to evaluate and compare the

quality of potable water produced from four different water

treatment processes in parallel, prior to final disinfection.

Moreover, a major emphasis of this study was to characterise

the bacteriological quality of the product waters from the

various treatments as this can play an important role in

distribution systems including the formation of biofilms

within such systems.

2. Experimental procedures

2.1. Treatment processes

Four different water treatment process streams (full- and

pilot-scale) were designed and/or adapted to generate waters

of variedwater quality from the same sourcewater (see Fig. 1).

The feed water for the streams was supplied from the inlet to

theMt. PleasantWTP in South Australia. This water is sourced

from the River Murray via the Mannum to Adelaide pipeline.

The treatment streams were evaluated from June 2010 to June

2011. Each stream was designed to generate a product flow

rate of 250 L h�1. Details of the treatment streams are

described below:

2.1.1. Stream 1 e conventional treatmentThis pilot-scale conventional treatment stream comprised of

coagulation/flocculation/dual media (sand/anthracite)

filtration, utilising an upflow clarifier and a gravity fed perspex

filter column. The coagulant employed was aluminium

sulphate (alum) as Al2(SO4)3.18H2O. The alum dose ranged

from 20 to 160 mg L�1. Coagulation pH of between 6.0 and 6.5

was maintained through addition of sodium hydroxide or

sodium bicarbonate buffering, depending on source water

alkalinity. In addition, a coagulant aid, either anionic poly-

acrylamide (LT20, BASF Chemicals, Australia) or high molec-

ular weight poly-DADMAC (LT425, BASF Chemicals, Australia)

was also dosed downstream of the coagulant. This process

was selected as a baseline/control as it represents the most

widely applied drinking water treatment process employed in

Australia.

2.1.2. Stream 2 e MIEX/conventional treatmentTreated water from this process stream was sourced directly

from the full-scale WTP at Mt. Pleasant. Full details of this

WTP have been described previously (Drikas et al., 2011).

Briefly, treatment comprised of high rate magnetic ion

exchange contact (MIEX DOC� process, Orica, Australia) for

DOC removal coupled with coagulation/flocculation/dual

media (sand/anthracite) filtration. The average MIEX resin

wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 23936

dose applied during the study was 15 L kL�1. A continuous

stirred-tank reactor with a cone settler operating at 10% resin

regeneration was employed for the MIEX DOC� process. The

primary coagulant used was alum as Al2(SO4)3.18H2O;

however, additional coagulant aids, LT22 and LT425 (BASF

Chemicals, Australia) were also dosed periodically during

coagulation as required. Due to the ability of the MIEX DOC�

process to efficiently remove absorbable organic materials,

the subsequent coagulation treatment is primarily a clarifica-

tion step following the main organic carbon removal by the

MIEX resin. As such, the coagulant demand is reduced leading

to a lower and less variable alum dose range (10e80 mg L�1).

2.1.3. Stream 3 e MIEX/conventional treatment/GACThe third treatment stream was comprised of the product

water from Stream 2 (described above) with the addition of

two parallel pilot-scale granular activated carbon (GAC) filters

utilising F400 GAC (Calgon Carbon Corporation, USA). F400 is

a bituminous coal-based GAC with effective granule size

0.55e0.75 mm which is commonly applied in water and

wastewater applications for organic contaminant removal.

Filtration was achieved using packed bed columns with

gravity fed empty bed contact times (EBCT) of approximately

14 min at 125 L h�1 for each column.

2.1.4. Stream 4 e microfiltration/nanofiltrationDual pilot-scale membrane filtration consisted of micro-

filtration (MF) pre-treatment for particulate removal using

a single submerged hollow fibre module (Memcor CMF-S

system, USA) followed by a single FILMTEC NF 270-4040

spiral wound nanofiltration (NF) membrane (DOW Chemical

Company, USA). The MF system was operated at 1000 L h�1

with 75% permeate recovery. The NF system operated in

cross-flow configuration at 43% permeate recovery, producing

325 L h�1. Nominal pore size for the MF is reported as 0.2 mm

with the molecular weight cut-off for the NF being 270 Da.

2.2. Analyses

Colour measurements (at 456 nm) were made through a 5 cm

quartz cell using an Evolution 60 Spectrophotometer (Thermo

Scientific, USA) according to a publishedmethod (Bennett and

Drikas, 1993). Results were presented in Hazen units (HU).

Turbidity measurements were conducted on a 2100AN Labo-

ratory Turbidimeter (Hach, USA) with results expressed in

nephelometric turbidity units (NTU).

2.3. Bacterial enumeration

Bacterial enumeration was conducted using HPCs and FCM.

HPCs were performed in accordance with the Australian

Standard AS/NZS 4276.3.1 (Australian Standard, 1995) using

R2A solidmedia (Oxoid, Australia). Dilutions, when necessary,

were performed in maximum recovery buffer (0.1% (w/v)

neutralised bacteriological peptone, 0.85% (w/v) NaCl, pH 7.0).

Incubation was performed using standard conditions of 20 �Cfor 72 h. Results for HPC were presented as colony forming

units per mL (CFU mL�1).

FCM analyses were conducted using a FACSCalibur flow

cytometer (Becton Dickinson, USA) equipped with an air-

cooled 15 mW argon ion laser, emitting at a fixed wave-

length of 488 nm. Fluorescent filters and detectors were all

standard with green fluorescence collected in the FL1 channel

(530 � 30 nm), orange fluorescence collected in the FL2

channel (585 � 42 nm) and red fluorescence collected in the

FL3 channel (>670 nm). Data were analysed using CellQuest�software (Becton Dickinson, USA). Total numbers of bacteria

were enumerated following staining of the bacteria with

SYTO-9 and the BacLight� bacterial viability kit (Molecular

Probes, USA) as described previously (Hoefel et al., 2003).

Results for FCM were presented as cells mL�1.

2.4. Bacterial community analysis

The effects of the different treatment processes on the

bacteria in the raw water was assessed by profiling the

bacterial community composition of the raw water and

product waters using denaturing gradient gel electrophoresis

(DGGE) analyses. Water samples were analysed by FCM and

bacterial numbers adjusted to 2.0 � 106 cells mL�1, with the

exception of treatedwater from Stream 4, which could only be

concentrated to 5.0 � 105 cells mL�1. Duplicate 1 mL samples

from eachwater typewere concentrated by centrifugation, re-

suspended in 5 mM TriseHCl pH 7.5 and subjected to three

cycles of freeze-thawing (liquid N2 and 100 �C). The resultant

DNA was used as a template for universal 16S rDNA gene-

directed nested PCR using the primer sets 27F/1492R and

357F-GC/518R, and the products of the reaction analysed by

DGGE (D-GENE� Gel Electrophoresis System, Bio-Rad, USA) as

reported previously (Hoefel et al., 2005). Positive and negative

controls used in DGGEwere as described by Hoefel et al. (2005).

The resulting DGGE profiles were analysed using Phoretix

1D version 11.2 (TotalLab, Newcastle upon Tyne, UK) with the

following settings: lanes were identified automatically (lanes

controlling controls were excluded from the analysis), back-

ground subtraction used the rolling ball method with a radius

of 200, bands were manually called, Gaussian peaks were

fitted to bands using the advanced fitting option with manual

adjustment as required, bands were aligned using a synthetic

reference generated by the software and similarity of profiles

was assessed using the UPGMA option.

2.5. Flocculation index determination

The photometric dispersion analyser (PDA 2000, Rank Bros

Ltd., Cambridge, UK), is a laboratory instrument used for

analysis of flowing suspensions (Gregory and Nelson, 1984,

1986). The method employed was similar to Staaks et al.

(2011) with slight modifications. Briefly, the PDA was con-

nected, via flexible tubing, to one jar during jar testing. A

peristaltic pump circulated the sample water at

21.6 mL min�1. The pump was located after the PDA to avoid

deterioration of the flocs. A volume of 1 mm3 of the flowing

suspension is illuminated by a narrow beam of light from

a high intensity light emitting diode at 850 nm wavelength

(Yukselen and Gregory, 2004). The intensity of transmitted

light fluctuates concurrently with the number of particles and

is detected by a sensitive photodiode. The optical signal is

converted to a voltage recorded by a computer equipped with

a data logging system. The resultant PDA output is a graph of

wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 2 3937

the flocculation index (FI) as a function of time. The FI is

a relative value generated from a ratio of the rootmean square

(RMS) and direct current (DC) signals and has been used to

compare and characterise flocculation processes (Gregory and

Nelson, 1984, 1986; Yukselen and Gregory, 2004; Staaks et al.,

2011).

In our study, three key parameters were extracted from the

FI graphs: the initial floc aggregation (IFA), the relative settling

factor (RSF) and the variance. The derivation of these param-

eters has been documented previously (Hopkins and Ducosto,

2003; Staaks et al., 2011). The relevance of these parameters

will be discussed in the following sections.

3. Results and discussion

3.1. Comparison of treatment streams for colour andturbidity reduction

During this study (June 2010eJune 2011) the inlet (raw) water

to Mt. Pleasant WTP and subsequently the pilot-scale

processes were challenged with water which was out of its

usual specification; a consequence of two major water quality

events brought about by large inflows into the MurrayeDarl-

ing Basin from eastern Australia. These flood waters resulted

in large spikes in turbidity with a maximum of approximately

190 NTU, followed by periods of high colour with values in

excess of 100 HU. These events followed a period of extended

drought where river inflows were minimal and source water

quality was relatively stable.

From an operational standpoint, the monitoring of these

two water quality parameters (turbidity and colour) are

generally indicative of how well the treatment processes are

performing; in addition to conforming to appropriate water

quality standards and/or guideline levels. For example,

turbidity has been used as a surrogate for parasites such as

Cryptosporidium and Giardia, while colour is generally regarded

Fig. 2 e Colour measurements before (raw) and after the four tr

Drinking Water Guideline level of 15 HU.

as an aesthetic parameter which can also be used as a surro-

gate for organic matter. To put things into perspective, the

Australian Drinking Water Guideline levels for turbidity and

colour are 0.5 NTU and 15 HU, respectively.

Despite these significant water quality challenges, the

pilot-scale treatment processes were generally efficient in

reducing both the colour and turbidity as shown in Figs. 2 and

3, respectively. For example, colour reduction was consis-

tently high, especially for the advanced multi-stage processes

(Streams 3 and 4) which averaged greater than 98% reduction

over the period. Some difficulty was encountered in main-

taining optimum coagulation conditions throughout the

changing water quality periods, especially when rapid

changes occurred, and this is reflected in the poorer removals

in colour and turbidity by conventional treatment (Stream 1).

This was in part due to the reactive nature of coagulation

control where decline of treated water quality dictated the

operational changes. During these periods, additional chem-

icals (including the coagulant aids) were dosed to maintain

target pH and floc settleability for acceptable filter run times

but only after water quality showed deterioration, resulting in

the largest span between maximum and minimum reduction

percentages of all the treatments.

3.2. Comparison of treatment streams for removal ofbacteria

The bacteriological quality of the four treated waters was

evaluated using both HPCs and FCM. Results for HPC showed

no clear trends between each of the treatment streams, sug-

gesting that each of the treatment processes were equally

effective in removing bacteria (Fig. 4). Furthermore, large

fluctuations in bacterial numbers in the treated waters were

evident with a numbers ranging from 2 CFU mL�1 up to

w7 � 103 CFU mL�1.

In contrast to the HPC data, FCM analyses of the treated

waters showed more definitive and stable trends between

eatment processes. Dashed line represents Australian

Fig. 3 e Turbidity measurements before (raw) and after the four treatment processes. Dashed line represents Australian

Drinking Water Guideline level of 0.5 NTU.

wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 23938

each of the treatment processes, as shown in Fig. 5. This

highlights the shortcomings of utilising HPCs for monitoring

bacteriological quality, a finding supported by others

(Hammes and Egli, 2005; Berney et al., 2008; Hammes et al.,

2008; Siebel et al., 2008). Many of the authors ascribe the

deficiency of HPCs to human error. For example, the statistical

accuracy of the plating method is dependent upon colonies

being counted between 30 and 300 per plate, and this is

dependent upon the appropriate dilution factor. Hammes

et al. (2008) documented that the standard error of HPC

results was >30% compared with FCM results which were

<5%. Another deficiency and perhaps the biggest drawback of

the HPC method is its selectivity as it is unable to enumerate

viable, non-culturable bacteria, which explains why HPC

Fig. 4 e Heterotrophic plate counts (HPC) before (

results are on average two orders of magnitude lower than

bacterial enumeration by FCM (Siebel et al., 2008). This in part

is due to the nutrient concentrations on conventional HPC

agar plates which can be between 800 and 1000 times higher

than the concentrations detected in drinking water (Berney

et al., 2008; Hammes et al., 2008). The large discrepancy

between HPC and FCM results has led some to suggest for

a reconsideration of existing drinking water guidelines and

legislation (Berney et al., 2008).

The raw water total bacterial count averaged

1.8 � 107 cells mL�1 (minimum ¼ 8.5 � 106 cells mL�1,

maximum ¼ 3.2 � 107 cells mL�1) during the study period, of

which 55% were shown to be active, as determined by FCM.

This number is relatively high in comparison to other water

raw) and after the four treatment processes.

Fig. 5 e Bacterial enumeration by flow cytometry (FCM) before (raw) and after the four treatment processes.

wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 2 3939

sources and may be attributed to the water being sourced

from the River Murray via the Mannum to Adelaide pipeline.

The residence time in this non-disinfected pipeline is between

2 and 3 d prior to the Mt. Pleasant WTP, which subjects the

pipeline to sloughing of biofilm and consequently higher

numbers of bacteria entering the WTP.

The order of effectiveness of the processes based on

removal of total and active bacteria followed the trend:

Stream 4 > Stream 2 > Stream 3 > Stream 1 (see Table 1). As

expected, the advanced multi-stage process of MF/NF was the

superior treatment stream due to its size-exclusion nature

(2.7-log removal of both total and active bacteria). However, an

average number of 4.5 � 104 cells mL�1

(minimum ¼ 8.3 � 103 cells mL�1,

maximum ¼ 2.0 � 105 cells mL�1) was still detected in the NF

treatedwater, even though the nominalmolecular weight cut-

off of the membrane is quoted as 270 Da; approximately

100e10,000 times smaller than bacterial cells (between 0.5 and

10 mm in size). The limit of detection of the FCMmethod in this

study is 5.0 � 103 cells mL�1 (unpublished work), suggesting

that either some bacteria were breaking through the

membrane or that there was possibly some form of

Table 1 e Average bacterial numbers (total and active) in the efbacteria (total and active) by each of the treatment processes f

Treatment process Average total numbersin effluent (cells mL�1)

Stream 1

conventional treatment

2.7 � 106

Stream 2

MIEX/conventional treatment

1.5 � 105

Stream 3

MIEX/conventional treatment/GAC

6.1 � 105

Stream 4

MF/NF

4.5 � 104

contamination or re-growth after the membrane during

sampling. The latter is possible since the sampling point for

the NF effluent is located on a stainless steel pipe approxi-

mately 2 m after the NF module.

Comparison of the bacterial diversity in the raw and

treated waters by DGGE (Fig. 6) showed that Streams 1, 2 and 3

had similar profiles to the raw water, with some minor band

differences between these samples and a dominant band

apparent in the treated samples and not detected in the raw

water. However, the profile from Stream 4 was noticeably

different to the raw water or the other treated waters, with

only a few bands in common (Fig. 6A), suggesting that this

community is different to the communities in the other

samples. This result suggests that either Stream 4 treatment

was allowing particular bacterial species to breakthrough

(that are not dominant in the rawwater and consequently not

detected), or that bacteria colonised the pipe post-NF and

these were being detected in the Stream 4 sample. Consid-

ering that therewere a few bands in common between Stream

4 and the raw water or other streams, a combination of some

breakthrough of bacteria from the rawwater and biofilm from

the post-NF pipe would also be consistent with this result.

fluent of the treatment processes and log removal values ofrom July 2010 to June 2011.

Average active numbersin effluent (cells mL�1)

Log removal(total)

Log removal(active)

1.6 � 106 1.0 � 0.3 0.9 � 0.3

7.7 � 104 2.3 � 0.4 2.3 � 0.3

5.1 � 105 1.5 � 0.2 1.3 � 0.2

2.5 � 104 2.7 � 0.4 2.7 � 0.3

Fig. 6 e Analysis of bacterial communities present in raw

and treated waters by denaturing gradient gel

electrophoresis of the V3 region of 16S rDNA. (A) Raw

results: ntc [ no template PCR control, showing

contribution of background bacterial DNA in reagents;

pos [ positive PCR control using genomic DNA from

Escherichia coli, Aeromonas hydrophila and Staphylococcus

epidermidis; Raw [ raw water sample; S1eS4 [ Streams

1e4. (B) Dendrogram showing similarity of the bacterial

communities inferred by analysis of the banding patterns

using Phoretix 1D software.

wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 23940

The relationships of the communities were determined

using Phoretix 1D software, which incorporates the presence/

absence of bands and also the relative band intensity to

calculate the relative similarity for each pair-wise combina-

tion of samples (where 1 indicates the samples are 100%

identical, 0.5 indicates 50% similarity, etc). Cluster analysis of

the sample similarity matrix resulted in the dendrogram

shown in Fig. 6B. The clustering pattern suggests that the

communities in Streams 2 and 3 were approximately 70%

similar to each other (with the same 4 dominant bands

present), with the Stream 1 community approximately 60%

similar to these (with the same 4 dominant bands as well as

some additional dominant bands present). The raw water

community was approximately 50% similar to the communi-

ties in Streams 1, 2 and 3, with most of the difference attrib-

utable to the presence/absence of minor bands. If the

dominant bands were considered in isolation, these samples

would be 70e80% similar. Stream 4 community was only 35%

similar to the rest of the samples, with only 2 of 6 dominant

bands in common with any of the other samples. These

results support the qualitative observation that Stream 4

microbial community is substantially different to the

communities in the raw water and other treatment streams.

Furthermore, the pattern of clustering of the treated water

communities correlated with the treatments. Both Streams 2

and 3 incorporated MIEX treatment (with the addition of GAC

for Stream 3) and these were the most similar communities.

Stream 1 only included conventional treatment, and

possessed a community that was the most similar to the

communities in Streams 2 and 3. The treatment for Stream 4

relied solely on membrane filtration (MF/NF), and this stream

had the lowest numbers of bacteria in the product water and

also the most different community as assessed by DGGE

analysis of the V3 region of 16S rDNA.

While Lovins et al. (2002) demonstrated excellent rejection

of organisms (including bacteria) using three different NF

membranes (withmolecular weight cut-off values from 100 to

300 Da), the authors still found that organisms did pass

through the membranes, supporting the contention of bacte-

rial breakthrough. Furthermore, Liikanen et al. (2003) and Park

and Hu (2010) observed growth of bacteria in NF and reverse

osmosis (RO) permeates, with bacterial numbers of between

1.2 � 103 and 2.1 � 105 cells mL�1 detected, similar to the

numbers in our study. This is thought to be due to the RO

permeate creating more conducive conditions for bacterial

growth, where more assimilable low molecular weight

organics would pass through the membrane (Drewes et al.,

2003; Park and Hu, 2010). It is worth bearing in mind that

these are not sterile closed systems, so even in the absence of

bacteria breaking through the membrane, any bacteria

present in the post treatment pipes could colonise the system

provided sufficient nutrients were present. The relatively

higher numbers of culturable bacteria (as determined by HPC,

see Fig. 4) in the NF permeate supports this contention;

a consequence of the lower community diversity in the NF

permeate (Park and Hu, 2010).

Stream 3 was designed to be the second most effective

advanced multi-stage process, based on the addition of a GAC

filter. However, this did not translate to the second best

treatment option in terms of the bacterial removal where an

average number of 6.1 � 105 cells mL�1 was detected in the

effluent, approximately 4 times higher than Stream 2. This

strongly suggests that the GAC filter contributed to the higher

numbers. Stewart et al. (1990) documented that carbon parti-

cles (fines) could be detected in the effluent of GAC filters and

that these fines were colonised with large numbers of bacte-

rial cells (several thousand), lending support to this conten-

tion. Similarly, Velten et al. (2011) documented detachment of

high numbers of bacteria from a GAC filter with numbers of

w2.5 � 105 cells mL�1 detected in the effluent, the same order

ofmagnitude as in our study. The authors determined that the

bacteria in the effluent represented 84% of the total bacteria

colonised in the GAC filter during steady state. This is not

surprising as GAC biofilms are considered nutrient poor

environments (Velten et al., 2011) and such conditions have

been documented to decrease bacterial adhesion to porous

media due to the greater production of extracellular polymeric

substances (EPS) which causes the cells to be more hydro-

phobic (Haznedaroglu et al., 2008). Interestingly, the commu-

nity in Stream 3 was very similar to the community in Stream

2, suggesting that any bacteria detaching from the GAC must

be similar to the bacteria in the raw water, or the bacteria

detaching are not very diverse because there are few unique

bands present in the DGGE analysis that are only associated

with Stream 3.

The difference between Stream 2 and Stream 1 was the

addition of MIEX pre-treatment prior to coagulation in Stream

Table 2 e The initial floc aggregation (IFA), relativesettling factor (RSF) and variance values derived from thephotometric dispersion analyser (PDA) from laboratorycoagulation experiments simulating Streams 1 and 2.

Treatmentprocess

IFA RSF Variance

Stream 1

conventional

treatment

0.28 � 0.07 0.58 � 0.07 0.002 � 0.001

Stream 2

MIEX/conventional

treatment

0.37 � 0.07 0.71 � 0.06 0.021 � 0.007

wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 2 3941

2. The addition of MIEX treatment not only enhances removal

of organics (see colour removal in Fig. 2 and also previous

studies by Singer and Bilyk (2002), Jarvis et al. (2008) andDrikas

et al. (2011)), but it also appeared to achieve better removal of

bacteria, where the log removal values for Stream 2weremore

than double than that of Stream 1 (see Fig. 5 and Table 1). The

mechanisms of organics removal by MIEX treatment are well

documented by the aforementioned studies; however, its

ability to enhance removal of bacteria has yet to be established

and/or published.

It is hypothesised that MIEX treatment in Stream 2 may

have resulted in more efficient coagulation through more

compact flocs which were able to entrap bacteria within their

structure, resulting inmore efficient removals of bacteria than

the processes employed in Stream 1. In order to validate this

hypothesis, additional experiments were conducted using

a PDA instrument to characterise the flocs generated through

laboratory simulation of Streams 1 and 2.

Three key parameters were derived from these experi-

ments, the IFA, RSF and variance. The IFA has been used to

describe the growth rate of the flocs; the RSF has been used to

represent the settling of the flocs; while the variance has been

used to assess floc structural differences (both size and

distribution) (Hopkins and Ducosto, 2003; Staaks et al., 2011).

Table 2 shows results from the simulation of Streams 1 and 2.

Stream 2 had higher values for each of the parameters

compared with Stream 1. The higher IFA for Stream 2 indi-

cates that the rate of floc formation is greater than for Stream

Fig. 7 e Numbers of bacteria (total and active, as

determined by flow cytometry) sampled after laboratory

simulation of Streams 1 and 2 (using photometric

dispersion analyser).

1. Furthermore, the higher RSF in Stream 2 is indicative of

better floc settling performance. Finally, and perhaps most

relevant is the variance, where a higher value indicates not

only larger flocs (with a larger range of floc sizes) but also

a stronger more compact floc (Hopkins and Ducosto, 2003;

Staaks et al., 2011). These results corroborate the previous

contention of more efficient coagulation and enhanced

bacterial removal with the incorporation of MIEX treatment.

Additional supporting evidence is displayed in Fig. 7, where

samples taken after the laboratory simulation of Streams 1

and 2 show lower bacterial numbers (total and active) after

Stream 2 treatment.

Each of the treatment processes generally removed active

bacterial cells equally to that of the total bacterial cells with

log removals ranging from 0.9 � 0.3 (Stream 1) to 2.7 � 0.4

(Stream 4) (Table 1). Interestingly, the percentage of the active

bacterial numbers was between 50 and 60% of the total

bacterial numbers in the effluents of the treatment processes,

except for Stream 3 where the percentage was considerably

higher at 84%. This supports the previous contention of

bacterial detachment from the GAC filter and that a majority

were active due to their ability to produce EPS, a physiological

mechanism thought to resist stressful (oligotrophic) condi-

tions (Haznedaroglu et al., 2008). Such EPS-producing bacteria

could potentially result in greater biofilm formation in

downstream distribution systems.

4. Summary and conclusions

Despite significant water quality challenges, the four pilot-

scale treatment process streams employed were able to

effectively reduce colour and turbidity to below ADWG levels,

with the exception of Stream 1which periodically struggled to

comply with the turbidity target; a consequence of the reac-

tive nature of coagulation control where the decline of treated

water quality dictated the operational changes.

In terms of the bacterial enumeration, FCM was shown to

be a bettermonitoring tool thanHPCs, which allowed formore

definitive comparisons to be made between each of the

treatment streams. This suggests that FCM can be used to

monitor water quality during treatment and distribution and

could be useful in facilitating the design and optimisation of

specific treatment processes.

Based on removal of total and active bacteria, the treat-

ment process streamswere ranked in the order: Stream 4 (MF/

NF) > Stream 2 (MIEX/Conventional treatment) > Stream 3

(MIEX/Conventional treatment/GAC) > Stream 1 (Conven-

tional treatment). Some of the interesting observations

included:

� detection of bacteria in NF effluent with an average number

of 4.5 � 104 cells mL�1;

� demonstration that the bacterial community in the NF

effluent (Stream 4) was very different to the communities

present in the other treated water streams;

� detachment of bacteria from GAC with an average total

number of 6.1 � 105 cells mL�1 detected in the filter effluent,

of which 84% were shown to be active (in comparison with

the other processes which ranged between 50 and 60%);

wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 23942

� DGGE analysis identified only a few novel amplicons (rep-

resenting 1 or 2 bacterial species) associated with the GAC

treated stream;

� overall, the community analysis suggested that the treat-

ments affected the bacteria present, with the communities

in streams incorporating conventional treatment clustering

with each other, and the streams with MIEX being the most

similar of the communities compared;

� verification that MIEX treatment enhanced removal of

bacteria through more efficient coagulation by the novel

application of the PDA (eg. greater rate of floc formation,

better floc settling performance and larger, more compact

and stronger flocs).

Negligible differences were observed between the removal

of active bacteria cells compared with total bacteria cells by

the treatment processes with log removals ranging from

0.9 � 0.3 to 2.7 � 0.4.

Acknowledgements

This project was supported by Water Quality Research

Australia, South Australian Water Corporation, United Water

International, Grampians Wimmera Mallee Water, Water

Corporation, Delft University of Technology, DCM Process

Control and Orica Watercare. The assistance of Jasper Ver-

berk, Paul Colby, Renae Phillips and Nic Reid are duly

acknowledged.

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