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wat e r r e s e a r c h 4 6 ( 2 0 1 2 ) 3 9 3 4e3 9 4 2
Available online at w
journal homepage: www.elsevier .com/locate /watres
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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