Desalination 378 (2016) 44–52

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Desalination

j ourna l homepage: www.e lsev ie r .com/ locate /desa l

Characterization of microbial communities in water and biofilms along alarge scale SWRO desalination facility: Site-specific prerequisite forbiofouling 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, Israelb Department of Environmental Hydrology & Microbiology, Zuckerberg Institute for Water Research (ZIWR), Ben-Gurion University of the Negev, Israelc 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.

⁎ Corresponding author.E-mail address: ilana.berman-frank@biu.ac.il (I. Berma

http://dx.doi.org/10.1016/j.desal.2015.09.0230011-9164/© 2015 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 12 March 2015Received in revised form 21 September 2015Accepted 23 September 2015Available online xxxx

Keywords:DesalinationBiofoulingMicrobial-communitiesProteobacteriaReverse-osmosis foulingDesalination-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 ofmicrobial communities along the feedwater, pretreatment, and reverse osmosis stages of a large-scale SWRO de-salination facility. While the composition of water-basedmicrobial communities varied seasonally, the composi-tion of biofilm microbial communities clustered by locations. Proteobacteria dominated throughout the waterand biofilm communities while other dominant phyla varied seasonally and spatially. The microbial communitycomposition significantly differed along the pathway locations of feedwater, rapid sand filtration (RSF), cartridgefilters (CF), and the reverse osmosis (RO)membranes. Biofilms on theRSF andCFwere composedofmorediversemicrobial populations than RObiofilms as determined by the effective number of species. Biofilms that developedalong 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 becharacterized. Site specific identification of these communities will enable optimization of pretreatment andcleaning 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 worldwidewith the expansion of global population and increasing water demand.Concurrently, global climate change is predicted to expanddrought affect-ed areas and further exacerbatewater shortages [1,2]. Seawater desalina-tion is a promising, virtually steady, and unrestricted high quality watersource with large-scale facilities (N100 million m3 yr−1) developingworldwide [3,4]. The predominant technology applied in these facilities

n-Frank).

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 ROmembrane performance [8,9]. To maintain the required volumes of de-salinated water due to the biofilm layer, pressure on the RO membranemust be increasedwith time. This increase in applied pressure results ina 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

45A. Levi et al. / Desalination 378 (2016) 44–52

matrix of extracellular polymeric substances (EPS), which are primarilycomposed of polysaccharides and proteins [10–14]. Once established,biofilms are notoriously resistant to biocides and oxidizing agents dueto 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 onconventional 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 ROmembranes studies have been conductedin 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 microbialcommunities of cartridge filters and RO membranes [23], or reportedthe effect of pretreatmentmethods on post-treatment permeate commu-nities [24–26]. Here, we explored the spatial and temporal dynamics ofthe planktonic and biofilm microbial communities in a large-scaleSWRO desalination facility. To do so, we monitored feedwater character-istics and carried out seasonal sampling of water and biofilms along theprocess 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 fromthe feedwater along each stage of the process in a fully operational,large scale SWRO desalination facility. Our results shed new light on thecomplexity and stability of the biofilm communities formed prior to theRO membranes, and their potential to serve as a microbial reservoir forRO membrane biofilms. Our results also underscore the importance ofmonitoring 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 seasonalsampling (February—winter, May—spring, September—summer, andNovember—fall) at the ADOM desalination facility (Ashkelon, Israel) in2011. 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 ferricsulfate coagulant addition); post-RSF (RSF filtrate); post-CF (cartridgefilter filtrate); and RO brine (Fig. 1). For microbial community composi-tion, water samples (2 L) were filtered on 47 mm, 0.2 μm Supor®-200filters (Pall, USA)whichwere then kept at−80 °Cuntil further analyses.Biofilm was sampled from the RSF at two locations; 0.5 m (anthracitelayer — RSF Ant.) and 1.5 m (sand layer — RSF Sand) 51–56 h after

Fig. 1. Schematic illustration of the ADOM (Ashkelon, Israel) SWRO desalination facility.Water sthe intake, pre-RSF, post-RSF, post-CF and brine. Biofilm samples (in red) were collected for mlayers), CF and RO membranes. (For interpretation of the references to color in this figure lege

backwash; CF (20 μm cut-off) and RO polyamide thin-film compositemembranes (FILMTEC™ SW30HRLE-400 reverse osmosis elements).The RSF biofilm samples were collected into sterile 15ml Falcon® poly-propylene centrifuge tubes (Illinois, USA), via the filter bedmediumandinterstitial water sampler device as described previously [27]. Freshlyused CFs and RO membranes were sectioned into coupons (1–1.5 cm2) and kept frozen at −80 °C until analyses (all RO sampleswere 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 bacterialproduction were taken to estimate the biological burden on the system.Chl a concentrations were determined from 150 ml duplicates offeedwater, vacuum-filtered on GF/F 25 mm filters (Whatman), extractedin 90% acetone overnight at 4 °C in the dark, and determined according toHolm-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 of100 ml feedwater that were passed through 0.4 μm polycarbonate filtersunder low vacuum (~100 to 150 mbar), processed, and analyzed accord-ing to PassowandAlldredge [29] as μgGumXanthan [GX] equivalents l−1

(μg GX l−1). Bacterial production (BP) rates were determined for 1.7 mlfeedwater triplicates with zero time controls using the 3H-leucineincorporation method [30,31] as modified by Smith and Azam [32]. Leu-cine incorporation rates were calculated using a conversion factor of3.1 kg C mol−1 with an isotope dilution factor of 2.0 [31].

2.3. DNA extraction

The CF and ROmembrane couponswere transferred into 2ml sterileEppendorf®, DNase RNase-Free, Microcentrifuge Tubes (Hamburg,Germany) prior to DNA extraction. Subsamples (~0.6ml) of RSF anthra-cite and sand were also transferred into 2 ml sterile Eppendorf®. Puregenomic DNAwas obtained by extraction with High Pure PCR templatepreparation kit (Roche, Germany), quality enhancement using a PCRpurification kit (Bioneer, Inc., USA) according to themanufacturer'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 wasperformed using 28F 5'GAGTTTGATCNTGGCTCAG and 519r5'GTNTTACNGCGGCKGCTG primers [33] at the Research and Testing

amples (in blue) were collected for analyses of the planktonicmicrobial composition fromicrobial community composition from the RSF (anthracite (RSF Ant.) and sand (RSF Sand)nd, the reader is referred to the online version of this chapter.)

46 A. Levi et al. / Desalination 378 (2016) 44–52

Laboratory (Lubbock, Texas, USA). PCR amplification and pyrosequenc-ing were performed as described in Dowd et al. [34]. The sequencingdata was analyzed using Mothur software v.1.30.2 [35]. After initialtrimming and clean-up of non-relevant sequences, pyrosequencinganalyses produced 90,044 16S rRNA gene sequences for the 36 sampledbacterial 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) wasplotted using Mothur software v.1.30.2 Jclass calculator [35] and editedwithMEGA software version 5.2. [36]. Shannon–Wiener diversity index[37] was calculated using Mothur v.1.30.2 [35] and then transformedvia exp(x) to effective number of species (a stable, easy to interpret sim-ilarity index). Effective number of species represents an equivalentcommunity composed of equally-common species [38,39].

Taxonomic identificationwas conducted through SINA v.1.2.11 [40],or by manual identification via the NCBI BLASTn database when SINAcould not be applied. Bacterial sequences were classified at suitable tax-onomic levels based on their sequence identity (I) (the percent of querysequence length that aligns with a specific database sequence or, a wellcharacterized 16S rRNA gene sequence). All sequences were attributedto 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 relativepercent of sequenceswithin individual sampleswas calculated based onthe relative numbers of reads. Significant differences between the bac-terial communities of the nine sampling points were detected usinganalysis ofmolecular variance (AMOVA),with alpha set to 0.05 (Mothursoftware 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 periodof study in Table 1. Chl a concentration, which serves as an indicator ofalgal biomass, peaked at the beginning of fall (Table 1) correspondingwith previous data from this site [44]. BP, indicating the total bacterialsecondary 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 andpeaked at the end of the spring, corresponding with previous reportsfrom the same site [41]. The observed seasonal changes in algal andTEP concentrations and bacterial productivity could have impactedpre-treatment efficiency [15]. High levels of organic matter can serveas nutrients for biofilmmicrobial populations, enhancing and accelerat-ing biofilm formation on the pretreatment filtersmedia (RSF andCF), onpipe surfaces, and downstream on the RO membranes [6,17,42].

Table 1Seasonalfluctuations in characteristics of the feedwater at theADOMdesalination plant during2transparent exopolymer particles (TEP) produced both abiotically and biotically; and the biologas averages (excluding turbidity) ± Standard deviation.

Winter

Chlorophyll α (μg l−1) 0.29 ± 0.011TEP (μg GX l−1) 465 ± 114Bacterial productivity ((μg C l−1 d−1) 4.3 ± 0.27Temperature (°C) 17.7 ± 0.34Turbidity (N TU) 0.35

3.2. Spatial variations of microbial communities

3.2.1. Major spatial trendsMicrobial 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 inthe dendrogram distinctly separated communities originating from theCF and RSF and those sampled from the water and RO membranes(Fig. 2). AMOVA analyses revealed significant differences between thebiofilm bacterial communities and bacteria found in the intake water(p-values are presented in supplementary, Table S1), as reported fromother desalination facilities [21,22,25]. RSF, CF and RO biofilm communi-ties were also distinctly different from each other, although no significantdifferences were found between the two RSF locations (RSF Ant. and RSFSand), (supplementary, Table S1). Also, no significant differences werefound between bacterial communities from the intake-water to thosedischarged in the brine (supplementary, Table S1). These findings indi-cate that the desalination process itself does not promote or stimulateproliferation of non-ambient or exotic populations that would then bedischarged to the sea. Nevertheless, the hypersaline discharge could stillimpact ambient microbial populations of coastal seawater communitiesthat are continuously exposed to it [46].

The changes between the various communities were also reflectedin 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 RSFAnt. and RSF Sand, respectively. CF communities were less diversewith the effective number of species ranging between 158 and 204. Incontrast, the microbial diversity on the RO membranes was generallyan order of magnitude lower, ranging from 10 to 50. The low microbialdiversity on the RO surface (Fig. 3) probably reflects the extreme hyper-saline (~80 ppt) and high operational pressures, ranging from 65 to80 bar [6,22]. These conditions restrict RO microbial diversity topressure-resistant, salt-tolerant species that can robustly attach tomembrane-surfaces.

3.2.2. Microbial diversity along the desalination facilityTo understand the underlying basis for the diversity within the dif-

ferent microbial communities, we further explored changes in taxafrom the phylum to the genus level. Taxonomic classification reveledthat Proteobacteriawas themost dominant phylum in allwater samplesranging from 48% to 94%, followed by Bacteroidetes (2% to 19%)(Table 2). The dominance of these two phyla is characteristic for theeastern Mediterranean Sea (EMS) surface waters [46–48]. In theProteobacteria, Alphaproteobacteria predominated throughout theyear 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 theEMS [47–49] and the Northwestern Mediterranean [22].

Further investigation of the Proteobacterial orders revealed thatthe dominant group of Proteobacteria within the water sampleswas the Alphaproteobacteria SAR11 cluster (mostly the Surface 1cluster), ranging from 21% to 81% (average of 54% ± 21%) (supple-mentary, Fig. S2). The SAR11 cluster comprises 33% of the global

011. Physical parameters includewater temperature and turbidity; chemical indicators areical parameters that include Chlorophyll a, and bacterial productivity. Values are presented

Spring Summer Fall

0.29 ± 0.001 0.23 ± 0.003 0.43 ± 0.011527 ± 63 300 ± 62 241 ± 3231.8 ± 4.31 51.5 ± 3.74 12.7 ± 5.322.5 ± 1.07 29.8 ± 0.18 21.3 ± 0.270.18 1.33 0.93

Fig. 2.Dendrogram delineating relationships between themicrobial communities charac-terized in this study. Water communities are represented by blue circles and biofilmcommunities 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-gramwas plotted usingMothur software v.1.30.2 [35] jclass calculator. (For interpretationof the references to color in thisfigure legend, the reader is referred to the online version ofthis chapter.)

Fig. 3. Effective number of species (representing an equivalent community composed ofequally-common species) [38], derived from Shannon–Wiener diversity index (exp(x)),calculated by Mothur software v.1.30.2 [35]. Water communities are represented byblue to gray scale circles and biofilm communities are represented by red to yellowscale inverted triangles. Black bars represent thehigh 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

ocean's surface water bacteria, and in some regions up to 50% of thetotal surface water microbial community [50]. Other dominant orderswere the Alphaproteobacterial Rhodobacterales ranging from 2% to26% (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 elevatedat the RSF filtrate (56% to 76%) compared with values from the intakewater (42% to 63%), and increased further at the CF permeate (60.2%to 81.5%) (Supplementary, Table S2). Manes et al. [22] also reported asimilar 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 typicalto 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 andparticle-attached bacteria [52]. By reducing the relative abundance ofthose bacteria, the relative abundance of the SAR11 cluster could in-crease (supplementary, Table S2) even though its absolute abundancein the water would be unchanged.

While Proteobacteria were dominant along the process, the pres-ence of Bacteroidetes generally decreased along pretreatment stagesfrom 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 ROfeedwater as was also reported elsewhere [25,26]. Both major classesof 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 reductionin Bacteroidetes.

In contrast to thewater communities, diversity indices and communi-ty composition suggested that the biofilm microbial communities weremore diverse. All biofilm samples were dominated by Proteobacteria(39% to 91%), followed by Bacteroidetes, Actinobacteria, Planctomycetesand Acidobacteria (Fig. 4). Proteobacterial abundance increased from39% to 65% of the community on the RSF and CF to 73% to 91% on theROmembrane (Fig. 4), similar to the relative abundance of Proteobacteriain the water samples.

The secondmost abundant phyla of the biofilms after Proteobacteriadepended on their location. RSF samples (RSF Ant. and RSF Sand) werelargely dominated by Bacteroidetes (11%–26% and 7%–18%, respective-ly), Acidobacteria (9%–14% and 8%–15%, respectively) andActinobacteria (7%–19% and 9%–15%, respectively). CF phyla distribu-tion differed from that of the RSF by the increased presence ofPlanctomycetes (Fig. 4). The ROmembranes biofilmswere 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 Chunet al. [23] in CF and RO membranes from the Gulf of Oman. Both theCF and RO in that study were dominated by Proteobacteria andcontained also Bacteroidetes, Actinobacteria and Planctomycetes.

The distribution of the major Proteobacterial classes in thebiofilms varied by location (Fig. 4). Both RSF samples did not fluctu-ate seasonally and the relative abundance of Alphaproteobacteriawas similar, 12%–24% (average 17.1% ± 3.5%) and 15%–21% (average18% ± 1.9%) in the RSF Ant. and within the RSF Sand, corresponding-ly. Gammaproteobacterial populations were very stable, composing16.9%–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

Table 2Temporal changes in water microbial communities collected from the ADOMdesalination facility during 2011. The average distribution of phyla (which represent N5% of sequences) andProteobacterial 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 ± 0.95Bacteroidetes 12 ± 5.6 12.4 ± 5.31 6.9 ± 2.86 3.7 ± 0.56 5.4 ± 2.52Chloroflexi 0.1 ± 0.14 0.7 ± 0.3 0.2 ± 0.12 0.1 ± 0.11 0.4 ± 0.57Cyanobacteria 1.3 ± 0.95 4.9 ± 3.87 0.6 ± 0.37 0.6 ± 0.12 0.8 ± 0.87Planctomycetes 0.5 ± 0.57 0.5 ± 0.53 0.1 ± 0.08 0.2 ± 0.18 0.1 ± 0.14Proteobacteria 81.5 ± 5.3 73.9 ± 5.9 89.4 ± 3.19 93.5 ± 0.27 90.2 ± 3.81Alphaproteobacteria 66.7 ± 6.83 57.1 ± 5.67 78.7 ± 4.08 85.5 ± 2.4 77.3 ± 9.31Betaproteobacteria 0.4 ± 0.44 0.8 ± 0.65 0.4 ± 0.31 0.1 ± 0.08 0.7 ± 1.08Deltaproteobacteria 0.7 ± 0.32 0.7 ± 0.43 1 ± 0.26 1.0 ± 0.12 1.3 ± 0.9Epsilonproteobacteria 0 ± 0.08 0.2 ± 0.26 0 ± 0.03 ND NDGammaproteobacteria 13.6 ± 2.68 18 ± 4.12 9.3 ± 1.15 6.8 ± 2.16 10.9 ± 4.54Other Bacteria 1.3 ± 0.89 1.6 ± 0.82 1.5 ± 0.47 1.2 ± 0.02 2.2 ± 0.91

48 A. Levi et al. / Desalination 378 (2016) 44–52

dominated by Alphaproteobacteria (31.6% ± 11.7%) with a noticeablyreduced Gammaproteobacterial abundance compared to the RSF sam-ples (12.9% ± 2.6%). Furthermore, Deltaproteobacterial abundancewas low and stable (4.9%± 2.3%)while Betaproteobacteria were scarce(Fig. 4), as reported elsewhere [23]. RO samples were dominated byAlphaproteobacteria in fall and winter, by Gammaproteobacteria inthe summer, and equally dominated by both in spring (Fig. 4).

Fig. 4. Temporal changes in biofilmmicrobial communities collected from the ADOMdesalinatsent N5% of sequences) and the Proteobacterial class (inset) distributions are presented as the

As described above, the Alphaproteobacteria SAR11 cluster was themost dominant group in the water samples, yet, its relative abundancevaried for the different biofilm samples (Fig. 5). While SAR11 was rareat the RSF (generally ≤1%), it was the major Proteobacterial order onthe CF biofilms (10–30%) and dominated the RO winter (64%) and fall(77%) communities (Fig. 5). SAR11 are known as free living ubiquitousmarine bacteria that are not adapted to a biofilm life style [50].

ion facility during the (A) winter, (B) spring, (C) summer and (D) fall. Phyla (which repre-percent of total communities for the RSF Ant., RSF Sand, CF and RO biofilms.

Fig. 5. The relative abundance (%) of Proteobacterial orders (which represent N3% of sequences) of the RSFAnt., RSF Sand, CF and RO biofilm communities at theADOMdesalination facilityin the (A) winter, (B) spring, (C) summer and (D) fall of 2011.

49A. Levi et al. / Desalination 378 (2016) 44–52

Nevertheless, SAR11 clusterwas reported as one of themajor colonizersof 10 and 120 days-old SWROmembraneswith 12% and 22% of total rel-ative abundance [22] andwas also found in a one-week old biofilm [49].The abundance of SAR11 within the CF biofilms and especially withinthe RO biofilms might be attributed to its relative enrichment in thewater that reach the CF and RO after pre-treatment (Supplementary,Table S2) and to randomattachment and accumulation due to the stickynature of mature biofilms, which can promote adhesion of planktonicbacteria to their surfaces [54]. Furthermore, the SAR11 cluster couldhave an active role in membrane biofouling as SAR11 rRNA was report-ed byManes et al. [22] as one of the dominant groups in a ROmembranebiofilm cDNA profile.

3.3. Temporal variations of microbial communities in biofilm

The biofilmmicrobial communities clustered mostly by surface type(media grains, filter or RO membrane) (Fig. 2). The seasonal stabilitydemonstrated by the RSF microbial populations (in contrast to the var-iations in the feedwater that reached the RSF) emphasizes its potentialto serve as a stable biological filter in addition to its particle and aggre-gate removal properties [15,55]. Seasonal differences observed in the CFbiofilm communities were influenced primarily by the seasonal

distribution of Planctomycetes whose relative abundance increasedfromwinter (10% of total phyla) to summer, (28%). In the fall, the abun-dance of Planctomycetes declined and Actinobacteria was the secondmost abundant phylum (26% of total phyla) (Fig. 4).

The biofilm communities on the RO membranes did show distinctseasonal differences (Fig. 4) when compared with the other biofilmcommunities along the process as reported elsewhere [22,23]. Weobserved a distinct decline in Proteobacterial abundance from winterto summer while the relative abundance of other phyla such asActinobacteria, Planctomycetes, and Chloroflexi increased. This trendwas reversed during the fall (Fig. 4). The Proteobacterial distribution re-vealed a clear seasonal trend with Alphaproteobacteria dominating thewinter and fall communities (with 74% and 83% respectively),Gammaproteobacteria dominating the summer community (53%) andboth 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 ROmicrobi-al communities shifted to Gammaproteobacterial dominance. The shiftwas caused by the increased abundance of the moderate halophilicgenus Kangiella [56] (Fig. 5). In spring and summer Kangiella comprised28.6% and 41.1% from the total microbial community, on the RO mem-branes and was also present, at lower abundance, on the RO during

50 A. Levi et al. / Desalination 378 (2016) 44–52

winter and fall, (4.8% and 0.7%, respectively) (Fig. 6). The summer pre-dominance of Kangiella could have been caused by the elevated intakewater temperatures or by increased summer salinities. The seasonalvariability 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 or0.14%) on the ROmembrane surface [6,58], comparedwith thewide sa-linity growth range of Kangiella [55] and therefore cannot explain itsseasonal 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 Kangiellaranging from 30 °C to 37 °C [56]. Although the average spring watertemperature (22.5 °C) was only slightly higher than the fall one(21.3 °C) (Table 1), Kangiella abundance on the RO was six fold higherat 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 Kangiellawas likely the elevation in feedwater temperatures rather than in-creased salinity.

Our detailed sampling along the desalination process also revealedpotential biofilm “dispersal”, the release of planktonic biofilms fromthe CF biofilm into the CF filtrate towards the RO membranes. Thiswas exemplified in the composition of the post-CF summer planktonicbacterial community (CF filtrate) which clustered within the CF biofilmcommunities and had an extremely high effective number of species(354) (Fig. 3). This was in contrast to the rest of CF filtrate planktoniccommunities that were well separated from the CF-RSF branch(Fig. 2). Enhanced biofilm dispersal, in which sessile biofilm pelliclesare 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 loadleading to the rapid clogging of the CFs which occurred during the sum-mer [63]. Nevertheless, the extreme physical conditions applied to theRO-membranes reduce the chance for successful proliferation of bacte-ria originating from the pretreatment (RSF/CF) biofilms on the ROsurface.

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 theeffectiveness of methods such as dispersal inducing signalingmolecules

Fig. 6. Seasonal fluctuations of the relative abundance of the SAR11 cluster and theGammaproteobacterial genus Kangiella observed in the RO membranes biofilms.

[64], the use of ultrasound waves to control biofouling [65,66] and theantimicrobial properties of hydrophobic polymers and super-hydrophobic nanoparticle surface modifications [67,68]. Our resultsdemonstrate the rare occurrence of typical biofilm-related bacterialgenera both in the water and within the biofilm communities along theprocess pathway. For example, the Gammaproteobacterial genera Pseu-domonas and Vibrio, are known contributors to biofilm formation andare frequently used in biofouling mechanistic studies [69–71] yet werepoorly 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 marinesurfaces and particles [24,72,73] and as a primary surface colonizer incoastal marine environments [26,74]. In our study, Alteromonas wasscarce in most of the described communities (Table 3).

Site-specific differences are also important factors determining thecomposition and dominance in both planktonic and biofilm microbialcommunities throughout the SWRO plants. Our results elucidating mi-crobial populations from the ADOM site on the EMS coastline of Israeldiffered significantly from populations described at a desalination sitealong the Red Sea coast in Oman [23]. Even small geographical changesper 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 andRoseobacter which predominated biofilms at the Palmachim desalina-tion plant ~48 km north of the ADOM plant [49]. In our study, boththese generawere scarce (b1.2%) or completely undetected inmicrobialcommunities along the process (Table 3).

The rarity of well-known biofilm-forming bacterial genera frombiofilms at the ADOMSWRO facility, and the reported variance betweenbiofilm communities from different desalination plants, emphasizes theneed for a site-specific approach. Moreover, we demonstrated thatbiofilms 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 toambient feedwater quality. Thus, on sitemonitoring of ROandupstreambiofilm bacterial communities (RSF, CF, UF, pipe surfaces, etc.) bycharacterizing the functional groups and identifying key species, shouldbe the first step en-route to developing advanced anti-biofoulingtreatments.

4. Conclusions

Large-scale desalination plant engineers have traditionallydisregarded the complexities of themicrobial populations along the de-salination process treatment stages, and have thus designed genericpretreatment systems. Our study, which followed the spatial and tem-poral composition of microbial communities along the completeprocess 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 differencesbetween populations from intake-waters and biofilm communitiesand between biofilms formed at the pretreatment stages and on theRO membranes. Our study emphasizes the importance of site-specificmonitoring in large-scale operational desalination plants, due to thevariation in community composition between sites. Our results clarifythe dynamic interactions between the ambient source planktonic bac-teria, pretreatment biofilms, and the subsequent biofouling of ROmem-branes.We suggest that themonitoring of feedwater, pretreatment andRO biofilm microbial communities, together with additional water bio-logical characteristics, should be taken into account as a prior stage inthe 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 adjustand fine tune pretreatment procedures and perhaps ultimately reducechemical usage and costs.

Table 3The 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 ± 0.44Pre-RSF 1.12 ± 0.9 0.02 ± 0.04 2.33 ± 4.5 0.1 ± 0.12 2.18 ± 2.17 1.2 ± 1.93Post-RSF ND 0.01 ± 0.02 0.03 ± 0.03 0.02 ± 0.05 0.1 ± 0.13 0.01 ± 0.03Post-CF ND 0.22 ± 0.44 0.04 ± 0.08 0.04 ± 0.04 ND 0.2 ± 0.32Brine 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 ± 0.31RSF Sand 0.1 ± 0.02 ND 0.01 ± 0.01 0.02 ± 0.04 0.02 ± 0.04 0.59 ± 0.2CF 0.13 ± 0.07 0.13 ± 0.22 0.03 ± 0.04 0.04 ± 0.08 0.11 ± 0.12 0.26 ± 0.51RO 0.02 ± 0.03 0.03 ± 0.06 0.01 ± 0.02 0.07 ± 0.12 0.02 ± 0.04 0.17 ± 0.15

51A. Levi et al. / Desalination 378 (2016) 44–52

Acknowledgments

This work was funded by an Israeli Water Authority (grant number4500445459) grant to IBF and TB (Reduction in Biofilm Formation atDesalination Facilities). We thank the ADOM management for accessto the Ashkelon desalination plant. This work is part of the Bar Ilan Uni-versity PhD requirements for AL. We thank Natalia Belkin and EyalRahav for sampling assistance and Tal Duvdevani Levi for the drawingof 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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