1. Characterization of microbial communities in water and biofilms along a
large scale SWRO desalination facility: Site-specific prerequisite for
biofouling treatments
Adi Levi a
, Edo Bar-Zeev a,b
, Hila Elifantz a
, Tom Berman c
, Ilana Berman-Frank a,
⁎
a
Bar Ilan University, Mina & Everard Goodman Faculty of Life Sciences, Ramat Gan, 5290002, Israel
b
Department of Environmental Hydrology & Microbiology, Zuckerberg Institute for Water Research (ZIWR), Ben-Gurion University of the Negev, Israel
c
Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research, P.O.B. 447, Migdal 14950, Israel
H I G H L I G H T S
• We monitored dynamics of microbial communities along a SWRO desalination facility.
• Microbial biofilm communities of RSF, CF, and RO differed from each other.
• Biofilms from treatment pathway (CF) provided inocula for biofouling on RO membrane.
• Conditions on RO restricted proliferation of RSF/CF biofilm's bacteria.
• Site-specific microbial community characterization is required for biofilm treatment.
a b s t r a c ta r t i c l e i n f o
Article history:
Received 12 March 2015
Received in revised form 21 September 2015
Accepted 23 September 2015
Available online xxxx
Keywords:
Desalination
Biofouling
Microbial-communities
Proteobacteria
Reverse-osmosis fouling
Desalination-pretreatment
Biofouling impacts seawater reverse osmosis (SWRO) desalination plants by hindering module performance, in-
creasing energetic demands, and incurring further costs. Here we investigated the spatial–temporal dynamics of
microbial communities along the feedwater, pretreatment, and reverse osmosis stages of a large-scale SWRO de-
salination facility. While the composition of water-based microbial communities varied seasonally, the composi-
tion of biofilm microbial communities clustered by locations. Proteobacteria dominated throughout the water
and biofilm communities while other dominant phyla varied seasonally and spatially. The microbial community
composition significantly differed along the pathway locations of feedwater, rapid sand filtration (RSF), cartridge
filters (CF), and the reverse osmosis (RO) membranes. Biofilms on the RSF and CF were composed of more diverse
microbial populations than RO biofilms as determined by the effective number of species. Biofilms that developed
along the treatment pathway (CF) served as inocula enhancing biofouling downstream on the RO membranes.
Subsequently, we believe that prior to the development of advanced antibiofouling treatments for the desalina-
tion industries, the site-specific microbial community of feedwater, pretreatment and RO biofouling should be
characterized. Site specific identification of these communities will enable optimization of pretreatment and
cleaning procedures and can ultimately reduce chemical usage and incurred costs.
© 2015 Elsevier B.V. All rights reserved.
1. Introduction
Potable water shortage and scarcity is a growing concern worldwide
with the expansion of global population and increasing water demand.
Concurrently, global climate change is predicted to expand drought affect-
ed areas and further exacerbate water shortages [1,2]. Sea water desalina-
tion is a promising, virtually steady, and unrestricted high quality water
source with large-scale facilities (N100 million m3
yr−1
) developing
worldwide [3,4]. The predominant technology applied in these facilities
is based on a separation process by reverse osmosis (RO) membranes.
RO technologies are characterized by lower energy consumption and re-
duced production costs compared with thermal desalination and thus,
the market share of large RO plants is projected to grow [4,5].
RO based desalination facilities must pretreat their feedwater to re-
duce membrane biofouling [3,6,7] causing subsequent reduction in RO
membrane performance [8,9]. To maintain the required volumes of de-
salinated water due to the biofilm layer, pressure on the RO membrane
must be increased with time. This increase in applied pressure results in
a significant rise in the overall energy cost of desalinated water [7,10].
Membrane biofouling is defined as complex sessile assemblage of mi-
crobial communities, embedded in a dense, self-produced gel-like
Desalination 378 (2016) 44–52
⁎ Corresponding author.
E-mail address: ilana.berman-frank@biu.ac.il (I. Berman-Frank).
http://dx.doi.org/10.1016/j.desal.2015.09.023
0011-9164/© 2015 Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
Desalination
journal homepage: www.elsevier.com/locate/desal

2. matrix of extracellular polymeric substances (EPS), which are primarily
composed of polysaccharides and proteins [10–14]. Once established,
biofilms are notoriously resistant to biocides and oxidizing agents due
to their multilayered EPS matrix protection, and therefore are very dif-
ficult to dislodge [10,13]. To reduce organic and inorganic fouling,
most large scale SWRO facilities base their pretreatment systems on
conventional coagulation/flocculation steps, followed by rapid sand fil-
tration (RSF) and cartridge filtration (CF) [6,7,12,15–17].
To evaluate feedwater fouling potential and its influence on the for-
mation of biofouling on the RO membranes studies have been conducted
in laboratory settings or at pilot scale systems. In these experiments, time-
dependent changes in microbial fouling of SWRO desalination mem-
branes were monitored [18–22]. Other studies compared the microbial
communities of cartridge filters and RO membranes [23], or reported
the effect of pretreatment methods on post-treatment permeate commu-
nities [24–26]. Here, we explored the spatial and temporal dynamics of
the planktonic and biofilm microbial communities in a large-scale
SWRO desalination facility. To do so, we monitored feedwater character-
istics and carried out seasonal sampling of water and biofilms along the
process stages to determine microbial composition.
To the best of our knowledge, this is the first study that comprehen-
sively follows the planktonic and biofouling community structure from
the feedwater along each stage of the process in a fully operational,
large scale SWRO desalination facility. Our results shed new light on the
complexity and stability of the biofilm communities formed prior to the
RO membranes, and their potential to serve as a microbial reservoir for
RO membrane biofilms. Our results also underscore the importance of
monitoring microbial communities and identifying their key species on-
site, prior to the development of advanced antibiofouling treatments.
2. Materials and methods
2.1. Sampling site and approaches
The bacterial community composition was followed by seasonal
sampling (February—winter, May—spring, September—summer, and
November—fall) at the ADOM desalination facility (Ashkelon, Israel) in
2011. We sampled nine locations along the desalination process
(Fig. 1). Water samples were collected from: intake-feedwater; pre-
RSF (overlying water immediately above the RSF following a ferric
sulfate coagulant addition); post-RSF (RSF filtrate); post-CF (cartridge
filter filtrate); and RO brine (Fig. 1). For microbial community composi-
tion, water samples (2 L) were filtered on 47 mm, 0.2 μm Supor®-200
filters (Pall, USA) which were then kept at −80 °C until further analyses.
Biofilm was sampled from the RSF at two locations; 0.5 m (anthracite
layer — RSF Ant.) and 1.5 m (sand layer — RSF Sand) 51–56 h after
backwash; CF (20 μm cut-off) and RO polyamide thin-film composite
membranes (FILMTEC™ SW30HRLE-400 reverse osmosis elements).
The RSF biofilm samples were collected into sterile 15 ml Falcon® poly-
propylene centrifuge tubes (Illinois, USA), via the filter bed medium and
interstitial water sampler device as described previously [27]. Freshly
used CFs and RO membranes were sectioned into coupons (1–
1.5 cm2
) and kept frozen at −80 °C until analyses (all RO samples
were taken from the center of the membrane sheet).
2.2. Feedwater characteristics
Feedwaters were sampled for turbidity (NTU) and temperature (°C)
by the ADOM desalination facility. Measurements for Chlorophyll a;
transparent exopolymeric particles (TEP) concentrations, and bacterial
production were taken to estimate the biological burden on the system.
Chl a concentrations were determined from 150 ml duplicates of
feedwater, vacuum-filtered on GF/F 25 mm filters (Whatman), extracted
in 90% acetone overnight at 4 °C in the dark, and determined according to
Holm-Hansen et al. [28] using a Luminescence spectrofluorometer
(Aminco Bowman® Spectronic Instruments, USA) (436 nm excitation/
680 nm emission filters). TEP was determined from quadruplets of
100 ml feedwater that were passed through 0.4 μm polycarbonate filters
under low vacuum (~100 to 150 mbar), processed, and analyzed accord-
ing to Passow and Alldredge [29] as μg Gum Xanthan [GX] equivalents l−1
(μg GX l−1
). Bacterial production (BP) rates were determined for 1.7 ml
feedwater triplicates with zero time controls using the 3
H-leucine
incorporation method [30,31] as modified by Smith and Azam [32]. Leu-
cine incorporation rates were calculated using a conversion factor of
3.1 kg C mol−1
with an isotope dilution factor of 2.0 [31].
2.3. DNA extraction
The CF and RO membrane coupons were transferred into 2 ml sterile
Eppendorf®, DNase RNase-Free, Microcentrifuge Tubes (Hamburg,
Germany) prior to DNA extraction. Subsamples (~0.6 ml) of RSF anthra-
cite and sand were also transferred into 2 ml sterile Eppendorf®. Pure
genomic DNA was obtained by extraction with High Pure PCR template
preparation kit (Roche, Germany), quality enhancement using a PCR
purification kit (Bioneer, Inc., USA) according to the manufacturer's pro-
tocols, and subsequent storage at −80 °C until sequencing.
2.4. DNA pyrosequencing and data analysis
Amplification of the 16S rRNA gene variable regions V1-3 was
performed using 28F 5'GAGTTTGATCNTGGCTCAG and 519r
5'GTNTTACNGCGGCKGCTG primers [33] at the Research and Testing
Fig. 1. Schematic illustration of the ADOM (Ashkelon, Israel) SWRO desalination facility. Water samples (in blue) were collected for analyses of the planktonic microbial composition from
the intake, pre-RSF, post-RSF, post-CF and brine. Biofilm samples (in red) were collected for microbial community composition from the RSF (anthracite (RSF Ant.) and sand (RSF Sand)
layers), CF and RO membranes. (For interpretation of the references to color in this figure legend, the reader is referred to the online version of this chapter.)
45A. Levi et al. / Desalination 378 (2016) 44–52

3. Laboratory (Lubbock, Texas, USA). PCR amplification and pyrosequenc-
ing were performed as described in Dowd et al. [34]. The sequencing
data was analyzed using Mothur software v.1.30.2 [35]. After initial
trimming and clean-up of non-relevant sequences, pyrosequencing
analyses produced 90,044 16S rRNA gene sequences for the 36 sampled
bacterial communities, with an average of 2501 sequences per sample.
For further analyses, each of the samples was subsampled for 1375 se-
quences. A microbial community dendrogram (condensed tree) was
plotted using Mothur software v.1.30.2 Jclass calculator [35] and edited
with MEGA software version 5.2. [36]. Shannon–Wiener diversity index
[37] was calculated using Mothur v.1.30.2 [35] and then transformed
via exp(x)
to effective number of species (a stable, easy to interpret sim-
ilarity index). Effective number of species represents an equivalent
community composed of equally-common species [38,39].
Taxonomic identification was conducted through SINA v.1.2.11 [40],
or by manual identification via the NCBI BLASTn database when SINA
could not be applied. Bacterial sequences were classified at suitable tax-
onomic levels based on their sequence identity (I) (the percent of query
sequence length that aligns with a specific database sequence or, a well
characterized 16S rRNA gene sequence). All sequences were attributed
to taxonomic levels as following: species — I N 97%; genus —
95% b I ≤ 97%; family — 90% b I ≤ 95%; order — 85% b I ≤ 90%; class —
80% b I ≤ 85%; and phylum — 77% b I ≤ 80%. Subsequently, to provide rel-
ative abundance information within and between samples, the relative
percent of sequences within individual samples was calculated based on
the relative numbers of reads. Significant differences between the bac-
terial communities of the nine sampling points were detected using
analysis of molecular variance (AMOVA), with alpha set to 0.05 (Mothur
software v.1.30.2 [32].
3. Results and discussion
3.1. Feedwater characteristics
ADOM feedwater is drawn from the surface of the east Mediterra-
nean Israeli coastal waters and are characterized by seasonal physical–
chemical and biological fluctuations [15,41–43] described for the period
of study in Table 1. Chl a concentration, which serves as an indicator of
algal biomass, peaked at the beginning of fall (Table 1) corresponding
with previous data from this site [44]. BP, indicating the total bacterial
secondary productivity, peaked in the summer and was positively cor-
related with water temperature (R2
= 0.92, p-value b 0.04, n = 4).
TEP, which forms sticky substrates that enhance biofouling on mem-
branes [45], may have been formed by an earlier algal bloom and
peaked at the end of the spring, corresponding with previous reports
from the same site [41]. The observed seasonal changes in algal and
TEP concentrations and bacterial productivity could have impacted
pre-treatment efficiency [15]. High levels of organic matter can serve
as nutrients for biofilm microbial populations, enhancing and accelerat-
ing biofilm formation on the pretreatment filters media (RSF and CF), on
pipe surfaces, and downstream on the RO membranes [6,17,42].
3.2. Spatial variations of microbial communities
3.2.1. Major spatial trends
Microbial communities' composition of the various water types were
nearly completely separated from the composition of the microbial com-
munities of the biofilm (Fig. 2). The primary and preeminent branching in
the dendrogram distinctly separated communities originating from the
CF and RSF and those sampled from the water and RO membranes
(Fig. 2). AMOVA analyses revealed significant differences between the
biofilm bacterial communities and bacteria found in the intake water
(p-values are presented in supplementary, Table S1), as reported from
other desalination facilities [21,22,25]. RSF, CF and RO biofilm communi-
ties were also distinctly different from each other, although no significant
differences were found between the two RSF locations (RSF Ant. and RSF
Sand), (supplementary, Table S1). Also, no significant differences were
found between bacterial communities from the intake-water to those
discharged in the brine (supplementary, Table S1). These findings indi-
cate that the desalination process itself does not promote or stimulate
proliferation of non-ambient or exotic populations that would then be
discharged to the sea. Nevertheless, the hypersaline discharge could still
impact ambient microbial populations of coastal seawater communities
that are continuously exposed to it [46].
The changes between the various communities were also reflected
in the diversity indices (Fig. 3). RSF communities had the highest effec-
tive numbers of species, hence the highest species diversity. The effec-
tive number of species ranged between 237–276 and 276–384, for RSF
Ant. and RSF Sand, respectively. CF communities were less diverse
with the effective number of species ranging between 158 and 204. In
contrast, the microbial diversity on the RO membranes was generally
an order of magnitude lower, ranging from 10 to 50. The low microbial
diversity on the RO surface (Fig. 3) probably reflects the extreme hyper-
saline (~80 ppt) and high operational pressures, ranging from 65 to
80 bar [6,22]. These conditions restrict RO microbial diversity to
pressure-resistant, salt-tolerant species that can robustly attach to
membrane-surfaces.
3.2.2. Microbial diversity along the desalination facility
To understand the underlying basis for the diversity within the dif-
ferent microbial communities, we further explored changes in taxa
from the phylum to the genus level. Taxonomic classification reveled
that Proteobacteria was the most dominant phylum in all water samples
ranging from 48% to 94%, followed by Bacteroidetes (2% to 19%)
(Table 2). The dominance of these two phyla is characteristic for the
eastern Mediterranean Sea (EMS) surface waters [46–48]. In the
Proteobacteria, Alphaproteobacteria predominated throughout the
year ranging from 47% to 87%, (excluding summer post-CF anomaly)
(Table 2). Gammaproteobacteria comprised 6–23% of the water com-
munities, while Deltaproteobacteria and Betaproteobacteria were near-
ly absent (Table 2), corresponding with surface-water results from the
EMS [47–49] and the Northwestern Mediterranean [22].
Further investigation of the Proteobacterial orders revealed that
the dominant group of Proteobacteria within the water samples
was the Alphaproteobacteria SAR11 cluster (mostly the Surface 1
cluster), ranging from 21% to 81% (average of 54% ± 21%) (supple-
mentary, Fig. S2). The SAR11 cluster comprises 33% of the global
Table 1
Seasonal fluctuations in characteristics of the feedwater at the ADOM desalination plant during 2011. Physical parameters include water temperature and turbidity; chemical indicators are
transparent exopolymer particles (TEP) produced both abiotically and biotically; and the biological parameters that include Chlorophyll a, and bacterial productivity. Values are presented
as averages (excluding turbidity) ± Standard deviation.
Winter Spring Summer Fall
Chlorophyll α (μg l−1
) 0.29 ± 0.011 0.29 ± 0.001 0.23 ± 0.003 0.43 ± 0.011
TEP (μg GX l−1
) 465 ± 114 527 ± 63 300 ± 62 241 ± 32
Bacterial productivity ((μg C l−1
d−1
) 4.3 ± 0.27 31.8 ± 4.31 51.5 ± 3.74 12.7 ± 5.3
Temperature (°C) 17.7 ± 0.34 22.5 ± 1.07 29.8 ± 0.18 21.3 ± 0.27
Turbidity (N TU) 0.35 0.18 1.33 0.93
46 A. Levi et al. / Desalination 378 (2016) 44–52

4. ocean's surface water bacteria, and in some regions up to 50% of the
total surface water microbial community [50]. Other dominant orders
were the Alphaproteobacterial Rhodobacterales ranging from 2% to
26% (10% ± 7%) and the Gammaproteobacterial Oceanospirillales rang-
ing between 2% and 11% (6% ± 3%) (Supplementary, Fig. S2).
The relative abundance of the SAR11 cluster was distinctly elevated
at the RSF filtrate (56% to 76%) compared with values from the intake
water (42% to 63%), and increased further at the CF permeate (60.2%
to 81.5%) (Supplementary, Table S2). Manes et al. [22] also reported a
similar increase in SAR11 cluster relative abundance from 27% at the in-
take water to 47% of the community that reaches the RO membrane.
This phenomenon can be caused due to the extremely small cells typical
to the SAR11 cluster with an average diameter of 0.12–0.20 μm
(0.37–0.89 μm in length) [51]. The fractionation created by RSF (remov-
al of large aggregates) and CF filtration (removal of N20 μm particles),
could eliminate floating biofilms, aggregate-inhabiting bacteria and
particle-attached bacteria [52]. By reducing the relative abundance of
those bacteria, the relative abundance of the SAR11 cluster could in-
crease (supplementary, Table S2) even though its absolute abundance
in the water would be unchanged.
While Proteobacteria were dominant along the process, the pres-
ence of Bacteroidetes generally decreased along pretreatment stages
from 12% ± 5.6% at the intake to 3.7% ± 0.56% at the post-CF water
(Table 2). The decline of Bacteroidetes along the pretreatment stages in-
dicates that pretreatment efficiently removes this phylum from RO
feedwater as was also reported elsewhere [25,26]. Both major classes
of Bacteroidetes, Cytophaga and Flavobacteria, include important inhab-
itants of marine aggregates and conglomerations of organic detritus
[53]. Thus the removal of such aggregates from the RO feedwater, a pri-
mary objective of pretreatment [6], can explain the observed reduction
in Bacteroidetes.
In contrast to the water communities, diversity indices and communi-
ty composition suggested that the biofilm microbial communities were
more diverse. All biofilm samples were dominated by Proteobacteria
(39% to 91%), followed by Bacteroidetes, Actinobacteria, Planctomycetes
and Acidobacteria (Fig. 4). Proteobacterial abundance increased from
39% to 65% of the community on the RSF and CF to 73% to 91% on the
RO membrane (Fig. 4), similar to the relative abundance of Proteobacteria
in the water samples.
The second most abundant phyla of the biofilms after Proteobacteria
depended on their location. RSF samples (RSF Ant. and RSF Sand) were
largely dominated by Bacteroidetes (11%–26% and 7%–18%, respective-
ly), Acidobacteria (9%–14% and 8%–15%, respectively) and
Actinobacteria (7%–19% and 9%–15%, respectively). CF phyla distribu-
tion differed from that of the RSF by the increased presence of
Planctomycetes (Fig. 4). The RO membranes biofilms were largely dom-
inated by Bacteroidetes (with 11%, 15%, and 4% in the winter, spring,
and fall correspondingly) and Actinobacteria (with 12% in the summer)
(Fig. 4). Similar community composition was observed by Khan et al.
[25] in RO membranes using Red Sea surface feedwater and by Chun
et al. [23] in CF and RO membranes from the Gulf of Oman. Both the
CF and RO in that study were dominated by Proteobacteria and
contained also Bacteroidetes, Actinobacteria and Planctomycetes.
The distribution of the major Proteobacterial classes in the
biofilms varied by location (Fig. 4). Both RSF samples did not fluctu-
ate seasonally and the relative abundance of Alphaproteobacteria
was similar, 12%–24% (average 17.1% ± 3.5%) and 15%–21% (average
18% ± 1.9%) in the RSF Ant. and within the RSF Sand, corresponding-
ly. Gammaproteobacterial populations were very stable, composing
16.9%–20.3% and 15.1%–20.6% of the RSF Ant. and RSF Sand communi-
ties, respectively. In contrast to their scarcity within the water samples,
Deltaproteobacteria and Betaproteobacteria comprised 8.9% (±1.6%)
and 2.2% (±1.1%) within both RSF sites (Fig. 4). CF samples were
Fig. 2. Dendrogram delineating relationships between the microbial communities charac-
terized in this study. Water communities are represented by blue circles and biofilm
communities are represented by red inverted triangles (W — winter, Sp — spring, Su —
summer and F — fall). The black numbers indicate the branch support values. The dendro-
gram was plotted using Mothur software v.1.30.2 [35] jclass calculator. (For interpretation
of the references to color in this figure legend, the reader is referred to the online version of
this chapter.)
Fig. 3. Effective number of species (representing an equivalent community composed of
equally-common species) [38], derived from Shannon–Wiener diversity index (exp(x)
),
calculated by Mothur software v.1.30.2 [35]. Water communities are represented by
blue to gray scale circles and biofilm communities are represented by red to yellow
scale inverted triangles. Black bars represent the high and low coefficient intervals. (For in-
terpretation of the references to color in this figure legend, the reader is referred to the on-
line version of this chapter.)
47A. Levi et al. / Desalination 378 (2016) 44–52

5. dominated by Alphaproteobacteria (31.6% ± 11.7%) with a noticeably
reduced Gammaproteobacterial abundance compared to the RSF sam-
ples (12.9% ± 2.6%). Furthermore, Deltaproteobacterial abundance
was low and stable (4.9% ± 2.3%) while Betaproteobacteria were scarce
(Fig. 4), as reported elsewhere [23]. RO samples were dominated by
Alphaproteobacteria in fall and winter, by Gammaproteobacteria in
the summer, and equally dominated by both in spring (Fig. 4).
As described above, the Alphaproteobacteria SAR11 cluster was the
most dominant group in the water samples, yet, its relative abundance
varied for the different biofilm samples (Fig. 5). While SAR11 was rare
at the RSF (generally ≤1%), it was the major Proteobacterial order on
the CF biofilms (10–30%) and dominated the RO winter (64%) and fall
(77%) communities (Fig. 5). SAR11 are known as free living ubiquitous
marine bacteria that are not adapted to a biofilm life style [50].
Table 2
Temporal changes in water microbial communities collected from the ADOM desalination facility during 2011. The average distribution of phyla (which represent N5% of sequences) and
Proteobacterial classes are presented as the percent of total community for the intake, pre-RSF, post-RSF, post-CF and brine (n = 4, excluding post-CF (n = 3), ±Standard deviation, ND =
not detected).
Taxa Intake Pre-RSF Post-RSF Post-CF (excluding summer) Brine
Actinobacteria 3.3 ± 0.81 5.9 ± 7 1.3 ± 0.87 0.7 ± 0.38 0.9 ± 0.95
Bacteroidetes 12 ± 5.6 12.4 ± 5.31 6.9 ± 2.86 3.7 ± 0.56 5.4 ± 2.52
Chloroflexi 0.1 ± 0.14 0.7 ± 0.3 0.2 ± 0.12 0.1 ± 0.11 0.4 ± 0.57
Cyanobacteria 1.3 ± 0.95 4.9 ± 3.87 0.6 ± 0.37 0.6 ± 0.12 0.8 ± 0.87
Planctomycetes 0.5 ± 0.57 0.5 ± 0.53 0.1 ± 0.08 0.2 ± 0.18 0.1 ± 0.14
Proteobacteria 81.5 ± 5.3 73.9 ± 5.9 89.4 ± 3.19 93.5 ± 0.27 90.2 ± 3.81
Alphaproteobacteria 66.7 ± 6.83 57.1 ± 5.67 78.7 ± 4.08 85.5 ± 2.4 77.3 ± 9.31
Betaproteobacteria 0.4 ± 0.44 0.8 ± 0.65 0.4 ± 0.31 0.1 ± 0.08 0.7 ± 1.08
Deltaproteobacteria 0.7 ± 0.32 0.7 ± 0.43 1 ± 0.26 1.0 ± 0.12 1.3 ± 0.9
Epsilonproteobacteria 0 ± 0.08 0.2 ± 0.26 0 ± 0.03 ND ND
Gammaproteobacteria 13.6 ± 2.68 18 ± 4.12 9.3 ± 1.15 6.8 ± 2.16 10.9 ± 4.54
Other Bacteria 1.3 ± 0.89 1.6 ± 0.82 1.5 ± 0.47 1.2 ± 0.02 2.2 ± 0.91
Fig. 4. Temporal changes in biofilm microbial communities collected from the ADOM desalination facility during the (A) winter, (B) spring, (C) summer and (D) fall. Phyla (which repre-
sent N5% of sequences) and the Proteobacterial class (inset) distributions are presented as the percent of total communities for the RSF Ant., RSF Sand, CF and RO biofilms.
48 A. Levi et al. / Desalination 378 (2016) 44–52

6. Nevertheless, SAR11 cluster was reported as one of the major colonizers
of 10 and 120 days-old SWRO membranes with 12% and 22% of total rel-
ative abundance [22] and was also found in a one-week old biofilm [49].
The abundance of SAR11 within the CF biofilms and especially within
the RO biofilms might be attributed to its relative enrichment in the
water that reach the CF and RO after pre-treatment (Supplementary,
Table S2) and to random attachment and accumulation due to the sticky
nature of mature biofilms, which can promote adhesion of planktonic
bacteria to their surfaces [54]. Furthermore, the SAR11 cluster could
have an active role in membrane biofouling as SAR11 rRNA was report-
ed by Manes et al. [22] as one of the dominant groups in a RO membrane
biofilm cDNA profile.
3.3. Temporal variations of microbial communities in biofilm
The biofilm microbial communities clustered mostly by surface type
(media grains, filter or RO membrane) (Fig. 2). The seasonal stability
demonstrated by the RSF microbial populations (in contrast to the var-
iations in the feedwater that reached the RSF) emphasizes its potential
to serve as a stable biological filter in addition to its particle and aggre-
gate removal properties [15,55]. Seasonal differences observed in the CF
biofilm communities were influenced primarily by the seasonal
distribution of Planctomycetes whose relative abundance increased
from winter (10% of total phyla) to summer, (28%). In the fall, the abun-
dance of Planctomycetes declined and Actinobacteria was the second
most abundant phylum (26% of total phyla) (Fig. 4).
The biofilm communities on the RO membranes did show distinct
seasonal differences (Fig. 4) when compared with the other biofilm
communities along the process as reported elsewhere [22,23]. We
observed a distinct decline in Proteobacterial abundance from winter
to summer while the relative abundance of other phyla such as
Actinobacteria, Planctomycetes, and Chloroflexi increased. This trend
was reversed during the fall (Fig. 4). The Proteobacterial distribution re-
vealed a clear seasonal trend with Alphaproteobacteria dominating the
winter and fall communities (with 74% and 83% respectively),
Gammaproteobacteria dominating the summer community (53%) and
both classes co-dominating in the spring (Fig. 4).
While in winter and fall SAR11 was a dominant member of the RO-
membrane community, during the spring and summer, the RO microbi-
al communities shifted to Gammaproteobacterial dominance. The shift
was caused by the increased abundance of the moderate halophilic
genus Kangiella [56] (Fig. 5). In spring and summer Kangiella comprised
28.6% and 41.1% from the total microbial community, on the RO mem-
branes and was also present, at lower abundance, on the RO during
Fig. 5. The relative abundance (%) of Proteobacterial orders (which represent N3% of sequences) of the RSF Ant., RSF Sand, CF and RO biofilm communities at the ADOM desalination facility
in the (A) winter, (B) spring, (C) summer and (D) fall of 2011.
49A. Levi et al. / Desalination 378 (2016) 44–52

7. winter and fall, (4.8% and 0.7%, respectively) (Fig. 6). The summer pre-
dominance of Kangiella could have been caused by the elevated intake
water temperatures or by increased summer salinities. The seasonal
variability in EMS surface water salinity ranges from 39.1 to 39.8 [57].
This change (0.7) is nearly negligible, even when doubled (1.4 or
0.14%) on the RO membrane surface [6,58], compared with the wide sa-
linity growth range of Kangiella [55] and therefore cannot explain its
seasonal abundance in the RO hypersaline biofilm environment. Alter-
natively, elevated temperatures from spring to summer may have pro-
moted Kangiella growth with summer average temperatures (29.8 °C)
(Table 1) consistent with the optimal growth temperature for Kangiella
ranging from 30 °C to 37 °C [56]. Although the average spring water
temperature (22.5 °C) was only slightly higher than the fall one
(21.3 °C) (Table 1), Kangiella abundance on the RO was six fold higher
at the spring (Fig. 6). This observation might indicate a minimum tem-
perature threshold limiting Kangiella growth during autumn. Therefore,
the main factor leading to the summertime predominance of Kangiella
was likely the elevation in feedwater temperatures rather than in-
creased salinity.
Our detailed sampling along the desalination process also revealed
potential biofilm “dispersal”, the release of planktonic biofilms from
the CF biofilm into the CF filtrate towards the RO membranes. This
was exemplified in the composition of the post-CF summer planktonic
bacterial community (CF filtrate) which clustered within the CF biofilm
communities and had an extremely high effective number of species
(354) (Fig. 3). This was in contrast to the rest of CF filtrate planktonic
communities that were well separated from the CF-RSF branch
(Fig. 2). Enhanced biofilm dispersal, in which sessile biofilm pellicles
are released as planktonic biofilms [59,60] is related to increased nutri-
ent levels [59,61,62], a situation likely to occur due to the increased load
leading to the rapid clogging of the CFs which occurred during the sum-
mer [63]. Nevertheless, the extreme physical conditions applied to the
RO-membranes reduce the chance for successful proliferation of bacte-
ria originating from the pretreatment (RSF/CF) biofilms on the RO
surface.
3.4. The importance of monitoring biofilm communities on-site
The identification of key species is important in the search for effec-
tive biofilm removal techniques, as differences in sensitivity and resis-
tance of individual microbial groups or morphologies can impact the
effectiveness of methods such as dispersal inducing signaling molecules
[64], the use of ultrasound waves to control biofouling [65,66] and the
antimicrobial properties of hydrophobic polymers and super-
hydrophobic nanoparticle surface modifications [67,68]. Our results
demonstrate the rare occurrence of typical biofilm-related bacterial
genera both in the water and within the biofilm communities along the
process pathway. For example, the Gammaproteobacterial genera Pseu-
domonas and Vibrio, are known contributors to biofilm formation and
are frequently used in biofouling mechanistic studies [69–71] yet were
poorly represented in this study (less than 1% throughout all biofilm com-
munities) (Table 3). Another biofilm related Gammaproteobacterial gen-
era Alteromonas, is known as a biofilm-forming bacteria on marine
surfaces and particles [24,72,73] and as a primary surface colonizer in
coastal marine environments [26,74]. In our study, Alteromonas was
scarce in most of the described communities (Table 3).
Site-specific differences are also important factors determining the
composition and dominance in both planktonic and biofilm microbial
communities throughout the SWRO plants. Our results elucidating mi-
crobial populations from the ADOM site on the EMS coastline of Israel
differed significantly from populations described at a desalination site
along the Red Sea coast in Oman [23]. Even small geographical changes
per site can contribute to large differences in key biofilm producing spe-
cies and their relative dominance. This was exemplified in the low rep-
resentation at ADOM of the Rhodobacterales genera Ruegeria and
Roseobacter which predominated biofilms at the Palmachim desalina-
tion plant ~48 km north of the ADOM plant [49]. In our study, both
these genera were scarce (b1.2%) or completely undetected in microbial
communities along the process (Table 3).
The rarity of well-known biofilm-forming bacterial genera from
biofilms at the ADOM SWRO facility, and the reported variance between
biofilm communities from different desalination plants, emphasizes the
need for a site-specific approach. Moreover, we demonstrated that
biofilms that develop along the treatment pathway (CF) can serve as in-
ocula enhancing biofouling further downstream on the RO membranes
[75]. Therefore, biofilm accumulation cannot be entirely attributed to
ambient feedwater quality. Thus, on site monitoring of RO and upstream
biofilm bacterial communities (RSF, CF, UF, pipe surfaces, etc.) by
characterizing the functional groups and identifying key species, should
be the first step en-route to developing advanced anti-biofouling
treatments.
4. Conclusions
Large-scale desalination plant engineers have traditionally
disregarded the complexities of the microbial populations along the de-
salination process treatment stages, and have thus designed generic
pretreatment systems. Our study, which followed the spatial and tem-
poral composition of microbial communities along the complete
process pathway of a large scale SWRO desalination plant, clearly illus-
trates these complex dynamics within intake and pretreatment micro-
bial communities. We also elucidate dramatic spatial differences
between populations from intake-waters and biofilm communities
and between biofilms formed at the pretreatment stages and on the
RO membranes. Our study emphasizes the importance of site-specific
monitoring in large-scale operational desalination plants, due to the
variation in community composition between sites. Our results clarify
the dynamic interactions between the ambient source planktonic bac-
teria, pretreatment biofilms, and the subsequent biofouling of RO mem-
branes. We suggest that the monitoring of feedwater, pretreatment and
RO biofilm microbial communities, together with additional water bio-
logical characteristics, should be taken into account as a prior stage in
the development of advanced antibiofouling treatments for the desali-
nation industries. Such a site specific approach can reveal the key bacte-
rial species and their functional classifications, help to properly adjust
and fine tune pretreatment procedures and perhaps ultimately reduce
chemical usage and costs.
Fig. 6. Seasonal fluctuations of the relative abundance of the SAR11 cluster and the
Gammaproteobacterial genus Kangiella observed in the RO membranes biofilms.
50 A. Levi et al. / Desalination 378 (2016) 44–52

8. Acknowledgments
This work was funded by an Israeli Water Authority (grant number
4500445459) grant to IBF and TB (Reduction in Biofilm Formation at
Desalination Facilities). We thank the ADOM management for access
to the Ashkelon desalination plant. This work is part of the Bar Ilan Uni-
versity PhD requirements for AL. We thank Natalia Belkin and Eyal
Rahav for sampling assistance and Tal Duvdevani Levi for the drawing
of the ADOM sampling locations schematic (Fig. 1). This study is dedi-
cated to the memory of Prof. Tom Berman who died unexpectedly dur-
ing the study.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.desal.2015.09.023.
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Table 3
The average relative abundance of biofilm-forming bacterial genera, as percent (%) of total abundance. n = 4, excluding post-CF (n = 3), ±Standard deviation, ND = not detected.
Sampling location Ruegeria Roseobacter Alteromonas Pseudoalteromonas Vibrio Pseudomonas
Water Intake 0.43 ± 0.65 0.04 ± 0.09 0.87 ± 1.38 0.1 ± 0.2 0.25 ± 0.1 0.26 ± 0.44
Pre-RSF 1.12 ± 0.9 0.02 ± 0.04 2.33 ± 4.5 0.1 ± 0.12 2.18 ± 2.17 1.2 ± 1.93
Post-RSF ND 0.01 ± 0.02 0.03 ± 0.03 0.02 ± 0.05 0.1 ± 0.13 0.01 ± 0.03
Post-CF ND 0.22 ± 0.44 0.04 ± 0.08 0.04 ± 0.04 ND 0.2 ± 0.32
Brine ND ND ND 0.03 ± 0.05 0.12 ± 0.08 0.09 ± 0.17
Biofilm RSF Ant. 0.02 ± 0.04 ND ND ND 0.05 ± 0.03 0.75 ± 0.31
RSF Sand 0.1 ± 0.02 ND 0.01 ± 0.01 0.02 ± 0.04 0.02 ± 0.04 0.59 ± 0.2
CF 0.13 ± 0.07 0.13 ± 0.22 0.03 ± 0.04 0.04 ± 0.08 0.11 ± 0.12 0.26 ± 0.51
RO 0.02 ± 0.03 0.03 ± 0.06 0.01 ± 0.02 0.07 ± 0.12 0.02 ± 0.04 0.17 ± 0.15
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