Chemosphere 112 (2014) 185–193

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Chemosphere

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Pollution-induced community tolerance to non-steroidalanti-inflammatory drugs (NSAIDs) in fluvial biofilmcommunities affected by WWTP effluents

http://dx.doi.org/10.1016/j.chemosphere.2014.03.1280045-6535/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +34 972183380.E-mail address: natalia.corcoll@gmail.com (N. Corcoll).

Natàlia Corcoll a,⇑, Vicenç Acuña a, Damià Barceló a,b, Maria Casellas a, Helena Guasch c, Belinda Huerta a,Mira Petrovic a,d, Lidia Ponsatí a, Sara Rodríguez-Mozaz a, Sergi Sabater a,c

a Catalan Institute for Water Research (ICRA), C/Emili Grahit 101, E-17003 Girona, Spainb Department of Environmental Chemistry (IIQAB – CSIC), C/Jordi Girona 18-26, E-08034 Barcelona, Spainc Institute of Aquatic Ecology, University of Girona (UdG), Campus de Montilivi, E-17071 Girona, Spaind Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, Spain

h i g h l i g h t s

� Biofilms of a WWTP-affected river acquired tolerance to ibuprofen + diclofenac.� Pharmaceuticals altered algal composition and the metabolic profile of microbial organisms.� PICT approach, a pertinent tool to assess in situ pharmaceutical effects on biofilms.

a r t i c l e i n f o

Article history:Received 7 November 2013Received in revised form 26 March 2014Accepted 28 March 2014Available online 13 May 2014

Handling Editor: Tamara S. Galloway

Keywords:PharmaceuticalsBiofilmsRiversNon-steroidal anti-inflammatory drugsPollution-induced community tolerance(PICT)

a b s t r a c t

We assessed the tolerance acquired by stream biofilms to two non-steroidal anti-inflammatory-drugs(NSAIDs), ibuprofen and diclofenac. Biofilms came from a stream system receiving the effluent of a waste-water treatment plant (WWTP). The response of biofilms from a non-polluted site (upstream the WWTP)was compared to that of others downstream with relevant and decreasing levels of NSAIDs. Experimentsperformed in the laboratory following the pollution-induced community tolerance (PICT) approachdetermined that both algae and microbial communities from biofilms of the sites exposed at the highestconcentrations of ibuprofen and diclofenac acquired tolerance to the mixture of these NSAIDs occurringat the sites. It was also observed that the chronic pollution by the WWTP effluent affected the microbialmetabolic profile, as well as the structure of the algal community. The low (at ng L�1 level) but chronicinputs of pharmaceuticals to the river ecosystem result in tolerant communities of lower diversity andaltered microbial metabolism.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Pharmaceutical compounds may reach the aquatic environmentvia human and animal excretion both as active metabolites and inunmetabolized form (Farré et al., 2008). They are considered‘‘emerging pollutants’’ since are not currently covered by existingwater-quality regulations, and their effects on the environmentor human health are still poorly understood. Wastewatertreatment plants (WWTP) are unable to effectively remove allpharmaceutical active compounds (Petrovic et al., 2005), and

WWTP effluents are an important and continuous source of entryof pharmaceutical active substances into aquatic ecosystemswhere may reach concentrations from ng L�1 to the low lg L�1

level. Even though these concentrations do not have acute toxiceffects on aquatic fauna and flora, can lead to long-term effectsby bioaccumulation, as well as by additive and synergistic effectsof mixing different pharmaceuticals (Farré et al., 2008). Under-standing the fate of mixtures of pharmaceutical compounds andtheir chronic effects on aquatic organisms is a research challengeas well as a subject of concern for water management agencies.

The non-steroidal anti-inflammatory drugs (NSAIDs) are one ofthe therapeutic families of pharmaceuticals most commonly foundin sewage impacted waters. The most widely used NSAIDs are

186 N. Corcoll et al. / Chemosphere 112 (2014) 185–193

ibuprofen and diclofenac in Europe, and ibuprofen and naproxen inthe U.S. (Khetan and Collins, 2007). These compounds are analge-sics (pain relievers) that work by blocking cyclooxygenase (COX)enzymes which catalyze the synthesis of different prostaglandinsfrom arachidonic acid (Vane and Botting, 1998). Several studieshave reported EC50 values for ibuprofen and diclofenac rangingfrom dozens of lg L�1 to mg L�1 depending on the species testedand/or endpoints used for toxicity evaluation, indicating a lowpotential toxicity under acute exposure on aquatic microorganisms(Cleuvers, 2003). The mode of action of these compounds onnon-target organisms (e.g. algae and bacteria) is non-specific andconsidered for narcosis (baseline toxicants), and their toxicity isreported to be concentration-additive in binary mixtures(Cleuvers, 2003).

Biofilms have been widely used as bioindicators of ecologicalstatus of rivers and streams due to their capacity to detect acuteand long-term effects produced by toxic substances (Sabateret al., 2007). Stream biofilms are complex biological communitiescomposed mainly of algae, cyanobacteria, bacteria, fungi andmicrofauna that live on submerged substrata (Lock, 1993). Biofilmsplay key roles in streams and rivers as primary producers and geo-chemical processers of nutrients and organic matter (Romaní andSabater, 2001). Algae and bacteria interact and develop interde-pendently in the biofilm (Barranguet et al., 2003), and assessingcommunity ecotoxicology has higher ecological relevance thansingle monocultures (Guasch et al., 2012). Effects of NSAIDs onbiofilms have been recently studied in laboratory conditions.Lawrence et al. (2005) observed that biofilms exposed for 8 weeksto ibuprofen at 10 lg L�1 showed a reduction of growth, whilealpha and beta proteobacteria growth rates increased. It has beenalso observed that algal and bacterial growth rate decreased inbiofilms exposed for a duration of 10 weeks to 100 lg L�1 of dic-lofenac (Paje et al., 2002).

The use of the pollution-induced community tolerance (PICT)approach has been used to assess the effects of chronic exposureof contaminants (mainly to metals and herbicides) on biofilms, bycomparing their physiological response to a sudden exposure(short-term toxicity tests) to high doses of toxicant (Blanck, 2002).The PICT concept develops on the induced inter-and intraspecificselection of the most tolerant organisms to a toxicant, as well asto the establishment of mechanisms for detoxification. The entirecommunity may be restructured, present physiological alterations,and finally display an overall increase in tolerance to the toxicantcompared to a reference community (Tlili and Montuelle, 2011).Defining a PICT therefore allows approaching the cause-effect rela-tionships between toxicant exposure and community response. Inthis context, we hypothesized that (i) biofilms could develop toler-ance to NSAIDs if toxicant levels and/or exposure time is enough toexert a selection pressure on microorganisms living in biofilms; andalso that (ii) the effect of NSAIDs on biofilm should be lower to thosecaused by other environmental factors, such as light availability ornutrient concentrations, because low toxicity has been reportedfor NSAIDs on algae and bacteria organisms.

We aimed to evaluate the acquisition of tolerance to twoNSAIDs, ibuprofen and diclofenac in biofilms from several riversites exposed to different NSAIDS concentrations. Our main objec-tives were: (i) to assess the effect of ibuprofen, diclofenac and theirbinary mixture on the tolerance of biofilms in a gradient ofpharmaceuticals pollution, (ii) to determine the relevance ofenvironmental factors (light availability, discharge, nutrients andpharmaceuticals) on the biofilm functioning along the selectedriver segment, and iii) to compare the specific sensitivity of algaeand bacteria to pollution by pharmaceuticals. Our main hypothesiswas that different sensitivities to the considered NSAIDs would beobserved in the different river segments because of the differencesin the long-term exposure.

2. Materials and methods

2.1. Study site

The study was conducted in the Segre River, a tributary to theEbro River in the Oriental Pyrenees (NE Iberian Peninsula). Thestream system selected for this study was 5 km long with a drain-age area of 287 km2, with a rain-snow fed flow regime. It receivedthe effluent from the WWTP of Puigcerdà (UTM X: 411856 andUTM Y: 4698346, 31N/ETRS 89) with �30000 population equiva-lents. The study site is located at an elevation of 1108 m a.s.l,and included riffles and pools through sedimentary and silicatesubstrata. The average annual precipitations in the region rangebetween 700 and 1000 mm and the average monthly air tempera-tures range from �3 �C to 18 �C. The riparian vegetation of the riversegment was well developed and was mainly composed by decid-uous (Alnus glutinosa, Fraxinus excelsior and Salix alba). The streamwatershed comprised of 85% natural vegetation, 11% agriculture,and 3% urban development by land cover.

2.2. Experimental design

Five sites were selected along the studied stream system,including a control site (C), located 500 m upstream of the WWTPdischarge and four impact sites moving downstream at distancesof 1500 m (Impact 1; I1), 2500 m (Impact 2; I2), 3500 m (Impact3; I3) and 4500 m (Impact 4; I4). It was predicted that there wouldbe a decreasing gradient of nutrient and pharmaceuticals pollutionmoving downstream from impact site I1 to I4.

Artificial substrata for biofilm colonization were deployed ineach site (C, I1, I2, I3 and I4), and biofilms were allowed to growon site for six weeks (30th August to 10th October 2012). Three arti-ficial substrata made up of flat bricks (20 � 20 cm) with attachedceramic tiles (110 units of 1.25 cm � 1.25 cm) were placed in eachsite parallel to water flow at a depth of �30 cm, closer to the bankand spaced apart 1 m to each other. Six weeks old biofilm sampleswere collected in each sampling site. Separate ceramic tiles fromeach of the three artificial substrata were sampled for chloro-phyll-a (Chl-a), photosynthetic capacity, algal composition, densityof live bacteria, b-glucosidase activity and microbial communitylevel physiological profile (CLPP). For community composition bio-films were scraped, submerged in 5 mL of stream water and fixedwith formaldehyde 4%. For CLPP and live bacteria analyses ceramicstiles were placed in sterile vials containing 30 mL of Ringer solution(Sigma, Aldrich) and transported to the laboratory. For the rest ofbiofilm analyses, a ceramic tile was placed in individual glass vials,filled with 10 mL of stream water of each site and transported to thelaboratory. In the field and during transport, all samples were keptdark and cool. Once at the laboratory, within 6–9 h of sampling, themeasurements that required in vivo analysis were performedimmediately, while the rest of samples were frozen (�20 �C) untilanalysis. A separate series of ceramic tiles were used to performPICT short-term dose–response tests at the laboratory.

2.3. Environmental measurements

Radiation reaching the streambed of each site was estimated byfiltering the data series of global radiation by the light interceptioncoefficients calculated by the Hemiview Canopy Analysis Software(version 2.1, Delta-T Devices Ltd). Global radiation was convertedto photosynthetically active radiation (PAR) according to McCree(1972).

River discharge (Q) was measured in each site following Goreand Hamilton (1996) and using an acoustic Doppler velocity meter(FlowTracker Handheld-ADVr, SonTek, San Diego, CA, U.S.A).

N. Corcoll et al. / Chemosphere 112 (2014) 185–193 187

Dissolved oxygen concentration (DO), temperature, pH and spe-cific conductivity were measured in situ at every sampling siteusing WTW (Weilheim, Germany) hand-held probes. Water sam-ples for nutrient and pharmaceutical analyses were collectedsimultaneously to biofilm sampling. Water samples for nutrientsanalyses were filtered by 0.7 lm and those for pharmaceuticalanalyses by 0.45 lm (Whatman International Ltd., Maidstone, Eng-land), transported to the laboratory and kept at �20 �C until anal-ysis. The concentration of phosphate (P–PO4

3�) was determinedcolorimetrically using an Alliance-AMS Smartchem 140 (AMSFrance, Frepillon, France). The concentration of dissolved organiccarbon (DOC) was measured on a Shimadzu TOC-V CSH coupledto a TNM-1 module (Shimadzu Corporation, Kyoto, Japan). Theconcentrations of nitrite (N–NO2

�), nitrate (N–NO3�) and ammo-

nium (N–NH4+) were determined on a Dionex ICS-5000 ion chro-

matograph (Dionex Corporation, Sunnyvale, U.S.A.).Analysis of pharmaceuticals (91 compounds) from several ther-

apeutical classes (NSAIDs, lipid regulators, diuretic, antihyperten-sive, psychiatric drugs, b-blockers and antibiotics) was performedaccording to the method developed by Gros et al. (2012). Briefly,100 mL of water were pre-concentrated on Oasis HLB cartridges(3 cm3, 60 mg; Waters Corporation, Milford, MA) in a Baker vac-uum system (J.T. Baker, Deventer, The Netherlands) after additionof 3 mL of 1 M EDTA (4%, v/v). Cartridges were further rinsed with5 mL of high performance liquid chromatography (HPLC)-gradewater, dried under vacuum for 30 min, and eluted with 2 � 3 mLof methanol. Extracts were evaporated to dryness, reconstitutedwith 1 mL of methanol–water mixture (10:90, v/v), and fortifiedwith 10 lL of a mixture of internal standards. Extracts wereanalyzed by a ultra HPLC (UHPLC) using Waters Acquity Ultra-Performance™ (Milford, MA, USA) in tandem with a 5500 QTRAPmass spectrometer equipped with a turbo ion spray source(Applied Biosystems–Sciex, Foster City, CA, USA). Chromatographicseparations for the compounds analyzed under positive electro-spray ionization were achieved with an Acquity UPLC HSS T3column (50 mm � 2.1 mm i.d., particle size 1.8 lm) with methanoland HPLC-water with 10 mM ammonium formiate/formic acid(pH = 3.2) as mobile phase. For the analysis in negative ionizationmode an Acquity UPLC BEH C18 column (50 mm � 2.1 mm i.d.,particle size 1.7 lm) was used, with acetonitrile and 5 mM ammo-nium acetate/ammonia (pH = 8) as the mobile phase. The targetcompounds were analyzed in multiple reaction monitoring(MRM) mode, monitoring two transitions between the precursorion and the most abundant fragment ions for each compound.

2.4. Biofilm analyses

Chlorophyll-a concentration was used as a surrogate of algalbiomass. Chlorophyll-a was extracted on the ceramic tiles in 90%acetone for 12 h in the dark at 4 �C, and determined spectrophoto-metrically using a Lambda UV/VIS spectrophotometer (U-2000

Table 1General physicochemical parameters and nutrient concentrations in each sampling site. E

C I1 I2

Discharge (m3 s�1) 0.305 0.620 0.659Light (lmol phot. m�2 s�1) 201.1 ± 123.3 420.7 ± 254.2* 527.1 ±T (�C) 13.2 ± 1.7 13.1 ± 1 13.3 ±Conductivity (lS cm�1) 183.7 ± 1.1 211.3 ± 5.5* 207.7 ±pH 7.5 ± 0.1 8.6 ± 0.1* 8.6 ±P–PO4

3� (lg L�1) 40.1 ± 2.3 174.6 ± 48.8* 172.9 ±N–NO3

� (lg L�1) 583.6 ± 27.1 1006.6 ± 21.6* 1230.4 ±N–NO2

� (lg L�1) 5.4 ± 0.43 64.2 ± 8.7* 69.8 ±N–NH4

+ (lg L�1) 14.6 ± 2.8 902.9 ± 389.4* 476.9 ±DOC (mg L�1) 2.4 ± 0.02 3.4 ± 0.5 3.02 ±

* Indicates significant difference (p < 0.05) in comparison to C site.

Spectrophotometer; Hitachi, Tokyo, Japan) following Jeffrey andHumphrey (1975). Algal community composition was identifiedby microscopic examinations, performed at 600X (Nikon CS1,Tokyo, Japan). Semiquantitative abundances of algae and cyano-bacteria were recorded by ranking them on a scale from 1 to 5: 1(very rare, 0–5%), 2 (rare, 5–20%), 3 (common, 20–40%), 4 (abun-dant, 40–70%) and 5 (very abundant, 71–100%). The photosyntheticcapacity of autotrophs was determined in vivo by measuring chlo-rophyll fluorescence of PSII with a pulse amplitude modulated(PAM) fluorometer (Phyto-PAM_EDF; Walz, Effeltrich, Germany)according to Corcoll et al. (2011). The density of live bacteria wasdetermined by counting live and dead bacteria using the Live/DeadBaclight Bacterial Viability kit (Carlsbad, California, U.S.A.) follow-ing Freese et al. (2006). The microbial CLPP in biofilms wasassessed using Biolog Ecoplates (Biolog, Inc., Hayward, U.S.A.). Thistechnique was used to determine the ‘‘metabolic fingerprint’’ ofmicrobial microorganisms present in biofilms based on the carbonsource they utilize. Biofilm was aseptically scraped of ceramic,diluted 5 times with Ringer solution (total volume = 50 mL) and130 lL of the suspended cells were used to inoculate the 96 wellsof Biolog microplates. Color development lasted for 5 days at 19 �Cin dark, and the appearance of purple coloration were dailymeasured by microplate spectrophotometer reader, with a590 nm filter (Epoch Microplate Spectrophotometer, BioTek Instru-ments, Winooski, U.S.A.). Substrate utilization efficiency wasgrouped in five functional classes: amines, amino acids, carbohy-drates, carboxylic acids and polymers, to highlight general pattersof substrate utilization (Tiquia, 2010). The extracellular enzymeactivity b-glucosidase was used as a surrogate of microbial biofilmdegradation capacity of autochthonous carbon. It was measuredspectrofluorometrically using fluorescent-linked substrate [4-methylumbelliferyl-b-D glucopyranoside (MUF)] (Sigma–Aldrich)as the respective substrate (Romaní et al., 2004).

2.5. PICT short-term dose–response tests

Short-term dose–response tests (48 h) were performed in labo-ratory conditions with biofilm from the control and impact sites.Biofilms were exposed to increasing concentrations of ibuprofen,diclofenac and their binary mixture to determine EC50 values. Dif-ferences in EC50 among sites were used as an indicator of biofilmtolerance resulting from chronic exposure to different NSAIDs con-centration levels. These tests (ibuprofen, diclofenac and mixturedose–response tests) will be hereafter referred as the PICT short-term dose–response tests. Two different functional descriptorswere used to assess biofilm tolerance: photosynthetic efficiencyto target autotrophic organisms, and b-glucosidase activity to tar-get heterotrophic organisms. Tests were performed in glass vials(20 mL) containing a single colonized substratum. Ibuprofensodium salt (CAS number 31121-93-4) and diclofenac sodium salt(CAS number 15307-79-6) supplied by Sigma–Aldrich were used

ach value corresponds to AVG ± SD; n = 3, except for discharge where n = 1.

I3 I4 F p-Value

0.800 0.995318.5* 642.5 ± 388.2* 624.3 ± 377.2* 1500 <0.0011.4 13.4 ± 1.6 13.1 ± 1.3 0.01 0.921.5* 170.7 ± 1.5* 202.3 ± 1.5* 68.00 <0.0010.6* 8.8 ± 0.1* 7.3 ± 0.1 1.37 0.3113.1* 168.9 ± 1.5* 177.8 ± 1.9* 16.25 <0.00144.5* 1329.6 ± 62.9* 1409.6 ± 110.6* 21.23 <0.0018.5* 76.5 ± 13.6* 54.8 ± 5* 15.32 <0.00197.6* 393.7 ± 64* 116.2 ± 31.9* 12.04 <0.0010.3 2.8 ± 0.03 3.0 ± 0.4 0.45 0.77

5

10

15

20

25

30

0.2

0.4

0.6

0.8

1.0A Photosynthetic capacity (r.u

Chl

-a µ

g cm

-2

* * *

188 N. Corcoll et al. / Chemosphere 112 (2014) 185–193

for toxicity tests. Stock solutions for ibuprofen (88 mM) and dic-lofenac (63 mM) were prepared in 0.2 lm filtered water from con-trol site (C) and stored at 4 �C before dilution in the test vessels.Geometric series of increasing concentrations were freshly pre-pared by serial dilution of the stock solutions in 0.2 lm filteredwater from C site (2 blanks and 6 concentrations). Three replicateswere used per concentrations and blanks. Final test concentrationin the vessels ranged: for ibuprofen dose–response tests from2.2 � 10�3 to 4.4 � 101 mM, for diclofenac dose–response testsfrom 1.6 � 10�3 to 3.1 mM and for mixture dose–response testsfrom 1.8 � 10�2 to 1.85 mM. Previous studies reported that theeffect of the binary mixture of ibuprofen and diclofenac followsthe concept of concentration addition (Cleuvers, 2003). Accord-ingly, the ratio of ibuprofen: diclofenac concentrations in the mix-ture tests was set as 3.5:1 in order that each substance of themixture had the same contribution to the total toxicity. This ratiowas based on EC50 values obtained in photosynthetic efficiencytests with biofilms grown at control site. During the tests, sampleswithin an incubator (Radiber AGP 570) remained agitated usingsingle-speed orbital mixers at 19 �C and a photon flux of�120 lmol photons/m2 s for a 12:12 h photoperiod. Biofilm sam-ples were taken at the end of the exposure (48 h) to determinephotosynthetic efficiency by measuring chlorophyll fluorescencewith a PhytoPAM fluorometer (Corcoll et al., 2011) and for b-gluco-sidase activity analyses.

C I1 I2 I3 I40

20

60

80

DiatomsCyanobacteria Chlorophyta Rhodophyta

C I1 I2 I3 I40 0.0

.)

)

C

B

Alga

l gro

up a

bund

ance

s (%

) *

Transform: Square rootResemblance: S17 Bray Curtis similarity

* * * *

*

*

2.6. Data analyses

The EC50 of the respective parameters was estimated from PICTshort-term dose–response tests, using two control and six NSAIDconcentrations. Data were transformed to natural logarithms.EC50 values were determined using a four parameter sigmoiddose–response curve (standard Hill-slope model). Afterwards, lin-ear regression analyses were applied to test the relationshipbetween EC50 values and ibuprofen and diclofenac concentrationsin river. Both analyses were performed using SigmaPlot v11.0 soft-ware (Systat Software Inc., San Jose, U.S.A.).

Differences in general physicochemical parameters (water tem-perature, DO, light intensity, conductivity and pH), nutrients con-centration (P–PO4

3�, N–NO3�, N–NO2

�, NH4+ and DOC), biofilm

metrics (Chl-a, photosynthetic capacity, algal group’s abundances,density of live bacteria, and b-glucosidase activity) and EC50 values(calculated on photosynthetic efficiency and b-glucosidase activityendpoints) among sites, were analyzed by means of generalizedleast-squares (GLS) models that incorporate spatial structure

C I1 I2 I3 I40

200

400

600

800

1000

1200NSAIDs Lipid regulators Diuretic Antihypertensive Psychiatric drugs ß-blockers Antibiotics

ng L

-1

Fig. 1. Concentrations of pharmaceutical compounds in each sample site. Note thatpharmaceuticals compounds are grouped for therapeutic families (more details inSuppl. Table A1). NSAIDs, non-steroidal anti-inflammatory drugs.

directly into model residuals. Sampling sites were relatively closebetween 1 and 2 km to each other. The GLS is therefore appropriatesince it allows including spatial autocorrelation analyses in themodel (Crawley, 2007). Univariate GLS models were also used toanalyze the influence of environmental variables (general physico-chemical parameters, nutrient concentrations and pharmaceuticalsconcentrations) on the observed biofilm metrics. The model perfor-mance criteria were based on the Akaike Information Criterion(AIC). Generally, lower AIC correspond to closer approximationsto reality, and therefore the best model is the one with the lowestAIC. Differences in AIC values P3 are also generally considered todenote a ‘‘remarkable’’ difference between models (Zhao et al.,2009). GLS models were computed with R software version

Sim

ilarit

y (A

lgal

com

posi

tion

C_c

C_a

C_b

I1_c

I1_a

I1_b

I2_a

I2_b

I2_c

I4_c

I4_a

I4_b

I3_a

I3_b

I3_c

100

95

90

85

Fig. 2. Characterization of algal compartment. (A) Chl-a concentration and photo-synthetic capacity of biofilm growth in each sampling site. (B) Abundances of eachalgal group in biofilm growth in each sampling site. Each value corresponds toAVG ± SD; n = 3. (*) symbol indicates significant difference (p < 0.05) in comparisonto C site; ANOVA test of GLS models. (C) Cluster of similarity distances betweenbiofilm growth in C, I1, I2, I3 and I4 sites based on taxonomic analyses of algalcommunity (Suppl. Table A2). Black lines denote significantly different groups andred lines indicate no significantly different groups. Labels a, b and c indicatesamples taken from three different replicates belonging the same sampling site.(For interpretation of the references to color in this figure legend, the reader isreferred to the web version of this article.)

N. Corcoll et al. / Chemosphere 112 (2014) 185–193 189

2.15.0 (R Foundation for Statistical Computing, Vienna, Austria)and the nlme library (Pinheiro et al., 2013).

A principal component analyses (PCA) of the main microbialfunctional groups detected by the BiologEcoplates (amines, carbo-hydrates, amino acids, carboxylic acids and polymers) was per-formed as ordination analysis of sites based on their microbialCLPPs. Similarities of algal community composition and microbialCLPP among sites were analyzed using cluster analyses (usingthe average linkage method and Bray-Curtis dissimilarity coeffi-cient). Thereafter, both communities were compared. PCA andcluster analyses were computed by PRIMER-6 software (PRIMERLtd. Plymouth, UK). The Simprof test was used to test significantdifferences between cluster groups. For all analyses, data weresquare root or log (x + 1) transformed when necessary to achievenormality. The significance level of all analyses was set at p 6 0.05.

3. Results

3.1. Physicochemical conditions

Higher river discharge occurred in the impact sites than in thecontrol site (Table 1). Water temperature was around 13 �C in allsites during the sampling. Light intensity reaching the streambedincreased from upstream to downstream according to differencesin the riparian canopy (Table 1). Conductivity was slightly lowerin C than in I1, I2 and I4 sites (Table 1). Dissolved oxygen in waterwas lower in I1 and I2 (7.1 and 7.8 mg L�1, respectively), than inthe control (8.7 mg L�1) site (F = 11.72, p < 0.001). Higher pH valueswere detected in sites I1, I2 and I3 than in the C and I4 sites(Table 1).

Nutrients (N–NH4+, N–NO3

� and P–PO43�) increased considerably

at the impact sites, whereas a relatively small increase of N–NO2�

occurred (Table 1). The highest N–NH4+ concentration occurred at

I1 and decreased gradually downstream reaching comparable val-ues in I4 than in the C site. The concentration of NO3

� at I1 insteadincreased downstream and reached maximal concentrations at I4.Dissolved organic carbon concentration in C was �2.41 mg L�1 andin impacted sites ranged from 2.85 to 3.4 mg L�1, but differencesamong sites were not significant (Table 1).

3.2. Pharmaceuticals occurrence

Twenty-two different compounds from seven therapeuticalgroups were detected in surface water out of the 91 pharmaceuticals

Table 2Summary of GLS models of the effects of environmental variables (explanatory variables)values (p < 0.05) for some one of the explanatory variables tested are shown n = 15.

Response variable Explanatory variables

Light Discharge

Chl-a Coefficient �0.85 �2.25AIC �8.74 �9.67

Photosynthetic capacity Coefficient �0.11 �0.31AIC �71.64 �75.56

Cyanobacteria Coefficient ns nsAIC

Diatoms Coefficient �0.88 nsAIC 6.49

Amines_microbial functional group Coefficient �0.10 �0.35AIC �64.76 �68.43

Carbohydrates_microbial functional group Coefficient 0.22 0.57AIC �46.16 �44.58

Carboxylic acids_microbial functional group Coefficient ns nsAIC

Polymers_microbial functional group Coefficient ns 0.64AIC �64.51

AIC, Akaike Information Criterion; ns, no significant.

and metabolites analyzed (Suppl. Table A.1. and Fig. 1). Pharmaceu-tical concentrations were higher in the impact sites, and the maxi-mum concentration (considering the sum of all therapeuticfamilies) occurred in I1 (values up to �2300 ng L�1) (Suppl.Table A.1 and Fig. 1). All the identified therapeutic groups of phar-maceuticals (NSAIDs, lipid regulators, diuretics, antihypertensive,psychiatric drugs and b-blockers), except antibiotics, showed thesame pattern of longitudinal decrease of concentrations from I1 toI4 (Suppl. Table A1, Fig. 1). This pattern was more marked in the caseof the NSAIDs. Among the 7 therapeutic groups of pharmaceuticalsdetected, concentrations of the NSAIDs group were the highest inall sampling sites; being up to 3 times higher in the most pollutedsites (I1 and I2) than other groups (Fig. 1). High concentrations ofNSAIDs at I1 (�1120 ng L�1) decreased gradually in downstreamsites reaching up at I4 �290 ng L�1. Ibuprofen and diclofenacoccurred in the impact sites but not in the control site, and alsoshowed a decreasing gradient of pollution from I1 to I4. Moreover,ibuprofen metabolites occurred at higher concentrations in theimpact sites than ibuprofen itself. Up to 192.6, 572.6 and 87.7 ng L�1

were detected in I1 for Ibuprofen, 1-hydroxy-ibuprofen and1-hydroxy-ibuprofen, respectively. The levels of the metabolitesalso decreased from I1 to I4 (Suppl. Table A.1).

3.3. Biofilm characterization

Biofilm from the C site had higher concentration of Chl-a(F = 8.95, p = 0.002), higher photosynthetic capacity (F = 21.36,p < 0.001), higher diatoms abundance (F = 4.88, p = 0.019), andlower abundance of cyanobacteria (F = 16.64, p < 0.001) and rod-ophyta (F = 4.43, p = 0.025) (Fig. 2). Cluster analyses based on algalcomposition showed that C was significantly different than impactsites (Fig. 2C and Suppl. Table A2), at �85% of similarity. Withindiatom taxa, higher abundances of Fragilaria and Cocconeis weredetected in C site than in impact sites, which presented higherabundances of Nitzschia and Gomphonema (Suppl. Table A2). Oscill-atoria and Chroococcales species (taxa from cyanobacteria group)were more abundant in impact sites than in C sites (Suppl.Table A2). Within the impact sites, chlorophyll-a concentrationwas higher in I1 than in I2 (Fig. 2A), and the cluster analysesshowed that the lowest similarity in algal composition wasbetween I1 and the rest of impact sites (I2, I3 and I4) (Fig. 2C). Cya-nobacteria decreased from I1 to I4 (Fig. 2B). Results of GLS modelsshowed that nutrients (PO4

3�, NO32� and NH4

+) and general physico-chemical parameters (light and discharge) have a negative influ-ence on Chl-a concentration (Table 2). PO4

3� and NO32� have the

on biofilm metrics (response variables). Note that only those models with significant

P–PO43� N–NO3

2� N–NO2� N–NH4

+ DOC Pharmaceuticals

�5.64 �2.58 ns �1.31 ns ns�14.71 �13.24 �7.79�0.57 �0.28 ns ns ns �0.02�64.69 �71.29 �60.17ns ns ns 2.87 ns 0.42

15.23 16.78�7.89 �14.24 �1.74 ns ns �0.313.28 0.45 5.50 2.16ns ns ns ns ns �0.03

�61.711.78 0.62 3.66 0.34 �0.85 0.06�51.44 �47.70 �61.86 �43.84 �56.54 �50.21ns ns 2.44 ns ns 0.06

�55.17 �47.251.32 ns 1.32 ns ns 0.02�66.40 �66.40 �59.90

β-glucosidaseactivity

(nmols M

UF cm

-2 h-1)Ba

cter

ia li

ve c

ells

cm

-2

Sim

ilarit

y (B

acte

rial C

LPP)

C_c

C_a

C_b

I4_b

I4_a

I4-c

I2_a

I2_b

I1_a

I1_c

I2_c

I1_b

I3-c

I3_a

I3_b

100

99

98

97

96

95

Transform: Square rootResemblance: S17 Bray Curtis similarity

A

B

C

C I1 I2 I3 I40

1e+7

2e+7

3e+7

4e+7

5e+7

6e+7

0

10

20

30

40

50Bacteria live cellsß-glucosidase

-1.1 1.2

Amines

Amino acids

Carbohydrates

Carboxylic acids

Polymers

C_aC_b

C_c

I1_a

I1_b

I1_cI2_a

I2_bI2_c

I3_a

I3_b

I3-c

I4_aI4_b

I4-c

PC1 56.4%

PC2

34.

5%

-1.0

1.0

Fig. 3. Characterization of bacterial compartment. (A) Live cells of bacteria and b-glucosidase activity in biofilm growth in each sampling site. (B) PCA of bacterialfunctional groups (amines, amino acids, carbohydrates, carboxylic acids andpolymers) in biofilm growth in each sampling site. (C) Cluster of similaritydistances between biofilm growth in C, I1, I2, I3 and I4 sites based on bacterial CLPP.Black lines denote significantly different groups and red lines indicate no signif-icantly different groups. Labels a, b and c indicate samples taken from threedifferent replicates belonging the same sampling site. (For interpretation of thereferences to color in this figure legend, the reader is referred to the web version ofthis article.)

190 N. Corcoll et al. / Chemosphere 112 (2014) 185–193

highest influence (AIC = �14.71 and AIC = �13.24, respectively).According to GLS models, photosynthetic capacity was influencednegatively by general physicochemical parameters (light and dis-charge) and nutrient concentrations in water (PO4

3� and NO32�),

and in lower magnitude by pharmaceuticals occurrence (Table 2).The abundance of cyanobacteria was influenced positively bynutrients (NH4

+) and pharmaceuticals concentrations (Table 2).The abundance of diatoms was influenced negatively by lightintensity, nutrients (PO4

3-, NO32- and NO2

�) and pharmaceuticals con-centrations (Table 2), conditions that instead favored cyanobacte-ria abundance.

Live bacteria had similar densities among sites (Fig. 3A). Similarb-glucosidase activity was also observed among sites (Fig. 3A).However, the CLPP showed that the microbial community fromthe C site was significantly different from those of the rest (at�96% similarity) and had a higher capacity to degrades aminesand a lower capacity to degrades carbohydrates, polymers and car-boxylic acids than those in the impact sites (Fig. 3B and C, and Sup-pl. Table A3). Microbial communities from the I4 sites presented alower capacity to degrade amines, carbohydrates and polymers incomparison to those from I1, I2 and I3 sites (Fig. 3B). The GLS mod-els showed that amines microbial functional group was influencednegatively by light and discharge and in a lower magnitude bypharmaceuticals occurrence (Table 2). The carbohydrates microbialfunctional group was influenced negatively by DOC and positivelyby nutrients (PO4

3� and NO2�) and pharmaceuticals (Table 2). The

carboxylic acids microbial functional group was influencedpositively by NO2

� and pharmaceuticals concentrations in water(Table 2). Polymers occurrence was influenced positively bydischarge, nutrients (PO4

3� and NO2�) and in lower magnitude by

pharmaceuticals (Table 2).

3.4. Sensitivity of algae and microbial communities to NSAIDs

The EC50 values to diclofenac based on photosynthetic efficiencyin the control site was 263 ± 37 mg L�1, and based on b-glucosidaseactivity was 167 ± 35 mg L�1. These were �3.5 and �5 times,respectively, lower than those observed with ibuprofen(886 ± 92 mg and 815 ± 129 mg L�1, respectively). These resultsindicated that diclofenac was more toxic than ibuprofen for thebiofilms in the control site. The two pharmaceutical mixture EC50

based on photosynthetic efficiency and b-glucosidase activitywas 2.5 ± 0.2 mM and 1.8 ± 0.04 mM, respectively. These resultssuggest that the microbial compartment was more sensitive todiclofenac and to the mixture with ibuprofen than the algalcompartment of the biofilm.

3.5. Biofilm tolerance to NSAIDs

Biofilm from the impact sites I1and I2 presented a higheribuprofen EC50 value (based on photosynthetic efficiency) thanthose from the C site (F = 19.85, p < 0.001; Fig. 4). The EC50 values(based on photosynthetic efficiency) for the ibuprofen + diclofenacmixture were higher in biofilms from the impact sites I1, I2 and I3than in those from C site (F = 10.04, p = 0.002; Fig. 4). The tolerance(measured as EC50 values) developed by the algal compartmentsignificantly correlated to the concentrations of ibuprofen(R2 = 0.49; p = 0.003) and ibuprofen + diclofenac (R2 = 0.66;p = 0.0003) in the river (Fig. 5). However, the diclofenac EC50 valuesfor photosynthetic efficiency did not show tolerance (F = 0.32,p = 0.857; Fig. 4). Based on EC50 values for b-glucosidase activity,a higher tolerance to ibuprofen (F = 6.64, p = 0.007), diclofenac(F = 7.02, p = 0.006) and to ibuprofen + diclofenac (F = 2.72,p = 0.049) was in biofilms from I1 and I2 sites than in those fromC (Fig. 4). Tolerance developed by microbial communities ofbiofilm was positively correlated to concentrations of ibuprofen

(R2 = 0.46; p = 0.0003), diclofenac (R2 = 0.62; p = 0.0005) and alsoibuprofen + diclofenac (R2 = 0.76; p = 0.0001) concentrations inthe river (Fig. 5).

4. Discussion

Twenty-two pharmaceutical compounds were detected down-stream of Puigcerdà’s WWTP effluents, and the NSAIDs reachedconcentrations of up to 1200 ng L�1, at comparable levels to those

C I1 I2 I3 I4

0

3

6

9

12

C I1 I2 I3 I40.0

0.3

0.6

0.9

1.2

C I1 I2 I3 I40

1

2

3

4

5

C I1 I2 I3 I4

0

3

6

9

12

C I1 I2 I3 I40.0

0.3

0.6

0.9

1.2

C I1 I2 I3 I40

1

2

3

4

5

Phot

osyn

thet

ic e

ffici

ency

EC

50 (m

M)

Ibuprofen short-term toxicity tests

Β-G

luco

sida

se a

ctiv

ity

EC50

(mM

)

Diclofenac short-term toxicity tests

Mixture short-term toxicity tests

*

*

* *

* *

* *

* *

Fig. 4. EC50 values for ibuprofen, diclofenac and the mixture (ibuprofen + diclofenac) in biofilm of C, I1, I2, I3 and I4 sites based on photosynthetic efficiency (solid columns)and b-Glucosidase activity (dashed columns) endpoints. Each column represents the AVG ± SE; n = 3. Note that (*) symbol indicates significant difference (p < 0.05) incomparison to C site.

Ibuprofen+Diclofenac in river (mM)

Phot

osyn

thet

ic e

ffici

ency

EC

50 (m

M)

β-gl

ucos

idas

eac

tivity

EC50

(mM

)

Ibuprofen short-term toxicity tests

Diclofenac short-term toxicity tests

Mixture (Ibu+Dcf) short-term toxicity tests

Ibuprofen in river (mM) Diclofenac in river (mM)

0 2e-7 4e-7 6e-7 8e-7 1e-6-4-202468

101214

0 2e-7 4e-7 6e-7 8e-7 1e-6-4-202468

101214

0 2e-8 4e-8 6e-8 8e-8 1e-70.2

0.4

0.6

0.8

1.0

1.2

0 2e-8 4e-8 6e-8 8e-8 1e-70.2

0.4

0.6

0.8

1.0

1.20 2e-7 4e-7 6e-7 8e-7 1e-6

1.01.52.02.53.03.54.04.55.0

0 2e-7 4e-7 6e-7 8e-7 1e-61.01.52.02.53.03.54.04.55.0

R2=0.49; p=0.003 R2=0.66; p=0.0003

R2=0.46; p=0.005 R2=0.62; p=0.0005 R2=0.76; p=0.0001

Fig. 5. Linear regression between EC50 values for ibuprofen, diclofenac and the mixture (ibuprofen + diclofenac) and NASAIDs concentrations in sampling sites. Dashed linesmeans the confidence interval of 95% n = 15 for each test.

N. Corcoll et al. / Chemosphere 112 (2014) 185–193 191

from other sewage-affected rivers (Petrovic et al., 2005). Amongthe identified compounds, none is currently included in the listof priority pollutants by the EU, though diclofenac is in the list ofsubstances subject to review for possible identification as priorityor priority hazardous substance by the European Water Frame-work Directive (Directive 2008/105/EC). While concentrations ofpharmaceutical products in the water may fluctuate as result ofdaily consumption, discharge inputs, rain and/or snow melting(Osorio et al., 2012), the biota, and in particular biofilms, integratethe effects on their community composition and functioning(Sabater et al., 2007).

Our study shows that algal and microbial communities ofbiofilms acquire tolerance to ibuprofen + diclofenac when they

are exposed at environmental concentrations, and that the expo-sure to the toxicants causes structural changes in the community.Sensitive biofilm species inhabiting waters receiving the WWTPeffluents have been replaced by others tolerant to the new condi-tions, and pharmaceutical pollution is (at least partly) responsiblefor these changes. Pharmaceuticals are not the exclusive factor inmodeling the response of biofilm communities, since nutrientsand light or discharge may also account for changes in the struc-ture of the community. Nutrients and pharmaceuticals co-variedwith the variations in the algal community composition (Table 1and Fig. 1). The two type of stressors could contribute to the shifttowards more tolerant algal communities, since algal sensitivityto nutrients (Douterelo, 2004; Veraart et al., 2008) and toxicants

192 N. Corcoll et al. / Chemosphere 112 (2014) 185–193

has been reported at length (Sabater, 2000; Schmitt-Jansen andAltenburger, 2005; Corcoll et al., 2012). However, it cannot bedecided whether nutrients only influence competitive abilities ofdifferent algae and microbial organisms, or also potentially ‘mask’toxicant effects through antagonistic interactions on certainresponses, as it has been shown for macroinvertebrates(Vinebrooke et al., 2004; Alexander et al., 2013). Pharmaceuticals,jointly with nutrients and other sources of pollution, exert a posi-tive selection pressure on cyanobacteria as the algal group mosttolerant in the Llobregat River (Proia et al., 2013). Is also true thatibuprofen and diclofenac co-exist with other types of pharmaceu-ticals (including more NSAIDs and/or their metabolites), and bio-films might therefore develop tolerance to other contaminantswithin the same family of products that presents similar mode ofaction (Knauer et al., 2010). However, the observed PICT responseof biofilms to the mixture of ibuprofen + diclofenac, as well as thepositive relation between the tolerance acquisition and the con-centrations of NSAIDs at which biofilms were exposed in the river(Fig. 5), support the relevance of the chronic exposure of thesepharmaceuticals in the selection of tolerant algae and microbialcommunities. Our observations cannot define the concentrationthresholds or the time needed to induce biofilm tolerance. How-ever, other analyses have revealed very high NSAIDs concentra-tions in the sites (up to 896 ng L�1 for ibuprofen and 78 ng L�1

for diclofenac; Acuña et al. submitted).Pharmaceuticals exerted specific effects on algae and microbial

communities. Both the tolerance acquisition (PICT) and the struc-tural and functional attributes of algae and microbial communitiesstress this specificity. The algal community acquired tolerance toibuprofen and to the ibuprofen + diclofenac mixture in the mostpolluted sites (Figs. 4 and 5). The observed PICT of algal communityto ibuprofen and to ibuprofen + diclofenac may be explained by theselection pressure of ibuprofen (alone and in mixtures) on the algalspecies. In fact, a reduction of diatoms abundance (i.e. Fragilariaand Cocconeis) in favor of cyanobacteria abundance (i.e. Oscillatoriaand Chroococcales) occurred and was related to pharmaceuticalspollution (Table 2). The exposure at ng L�1 of ibuprofen and dic-lofenac indeed shifted the benthic algal community composition.A stimulatory effect of ibuprofen on cyanobacteria growth rate(Pomati et al., 2004) has been also observed in laboratory condi-tions, and ibuprofen jointly with other NSAIDs drove the observedincrease of cyanobacteria abundance within river biofilms (Proiaet al., 2013). On the other side, the microbial community acquiredtolerance to ibuprofen, diclofenac and ibuprofen + diclofenac mix-ture (Figs. 3 and 4), showing its higher sensitivity to diclofenacthan that of the algal community. In line with this reasoning, thediclofenac EC50 based on b-glucosidase activity of biofilms from Csites was 1.6 times lower than that based on photosynthetic effi-ciency. Overall, the diclofenac toxicity for algae and microbialorganisms is higher than the ibuprofen toxicity. Monoculture stud-ies have also shown a higher sensitivity of bacteria to diclofenac,and higher toxicity of diclofenac vs ibuprofen in the short term(Cleuvers, 2003; Ferrari et al., 2004). Ibuprofen and diclofenacpresent baseline toxicity (or narcosis) (Escher et al., 2005), whichconstitute the minimum toxicity exhibited by any componentdue to accumulation in biological membranes (van Wezel andOpperhuizen, 1995; Di Toro and McGrath, 2000). Many lipids andproteins that are essential for cellular functioning are located inthe membrane, and narcosis toxicity is the result of accumulatingnarcotic chemicals on the cell membrane, leading to disorder andpoorer functioning of lipids and proteins (van Wezel andOpperhuizen, 1995). Accumulation is controlled by several factors:such as pH that controls the dissociation of non-polar compounds(hence their bioavailability), the hydrophobicity, and the log Kow(n-octanol water partitioning coefficient) that influence theirbioconcentration (Verhaar et al., 1992). While the log Kow of

diclofenac is 4.51, for ibuprofen is 3.97 (Escher et al., 2005), thiscontributing to the observed higher toxicity of the former. Biofilmsdownstream the WWTP accumulated pharmaceutical compounds,and diclofenac was the NSAIDs showing the highest accumulationfactor (up to 102.2 ng g�1 dry weight) (Huerta et al. in prep).

Pharmaceuticals pollution also led to the development ofmicrobial communities with different CLPP among sites, and inthe polluted sites the microbial communities had a reduced capac-ity to degrade amines in favor to an increase to degrade carbohy-drates, carboxylic acids and polymers. These results confirm thatmicrobial communities may acquire a certain tolerance to NSAIDsbecause species selection, acclimation (phenotypic plasticity) orother adaptation mechanisms have occurred (Tlili and Montuelle,2011). Microbial may show different CLPP according to differentlevels of pollution (Tiquia, 2010; Tlili et al., 2011), reflecting onmicrobial community structure and microbial respiration becauseof exposure to pharmaceuticals (Rosi-Marshall et al., 2013). Collec-tively, these findings indicate that changes in biofilms may occurunder a selection pressure caused by a low, but chronic, exposureto pharmaceuticals. Alterations in algal structure and microbialfunctioning may have potential adverse effects on river ecosystems(Romaní and Sabater, 2001; Battin et al., 2003), where biofilmsplay an important role in primary production and in the processingof organic matter.

5. Conclusions

Biofilms exposed to environmental concentrations of ibupro-fen + diclofenac acquired tolerance to these NSAIDs as measuredby photosynthetic efficiency and b-glucosidase activity EC50 val-ues. These pharmaceutical concentrations, combined withincreased nutrient loading and other environmental factors (lightand discharge), contribute to alter the algal community as wellas the microbial metabolic profile, with potential adverse effectson river ecosystems. The PICT approach was revealed as a pertinentcomplementary tool to assess pharmaceutical pollution, allowingestablishing links between pharmaceutical exposure and toleranceinduction.

Acknowledgements

This research was financed by the Spanish Ministry of Economyand Competitiveness through the Project SCARCE (Consolider-Ingenio 2010 CSD2009-00065) and by the European Union throughthe European Regional Development Fund (FEDER), and by a MarieCurie European Reintegration Grant (PERG07-GA-2010-259219)(V. Acuña). This work was partly supported by the Generalitat deCatalunya (Consolidated Research Group: Water and Soil QualityUnit 2009-SGR-965). Prof. Barceló acknowledges King SaudUniversity for his visiting professorship.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.chemosphere.2014.03.128.

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