Pem

ZDa

b

c

a

ARRAA

KAEKSTU

1

tahppmo

Uf

1h

Journal of Photochemistry and Photobiology A: Chemistry 240 (2012) 33– 41

Contents lists available at SciVerse ScienceDirect

Journal of Photochemistry and Photobiology A:Chemistry

journa l h o me pa g e: www .e lsev ier .com/ locate / jphotochem

hotocatalytic (UV-A/TiO2) degradation of 17�-ethynylestradiol innvironmental matrices: Experimental studies and artificial neural networkodeling

acharias Frontistisa, Vasileia M. Daskalakia, Evroula Hapeshib,c, Catherine Drosoua,espo Fatta-Kassinosb,c, Nikolaos P. Xekoukoulotakisa, Dionissios Mantzavinosa,c,∗

Department of Environmental Engineering, Technical University of Crete, GR-73100 Chania, GreeceDepartment of Civil and Environmental Engineering, University of Cyprus, 75 Kallipoleos St., 1678 Nicosia, CyprusNIREAS – International Water Research Center, University of Cyprus, Cyprus

r t i c l e i n f o

rticle history:eceived 26 September 2011eceived in revised form 4 May 2012ccepted 12 May 2012vailable online xxx

eywords:NNndocrine disrupting compoundsineticsynergyransformation productsltrasound

a b s t r a c t

The efficiency of heterogeneous photocatalysis to degrade 17�-ethynylestradiol (EE2), a synthetic estro-gen hormone, in environmentally relevant samples was investigated. In most cases, UV-A radiation at aphoton flux of 2.81 × 10−4 einstein/min was provided by a 9 W lamp and experiments were conductedat various concentrations of Aeroxide P25 TiO2 (50–1000 mg/L), EE2 concentrations (50–900 �g/L) andwater matrices (from ultrapure water to secondary treated wastewater). Some runs were performed atphoton fluxes between 6.4 × 10−7 and 3.7 × 10−4 einstein/min to study the effect of intensity on degra-dation. Changes in estrogen concentration were followed by high performance liquid chromatography.EE2 degradation, which follows first order kinetics, increases with (i) increasing catalyst loading up to athreshold value beyond which it remains unaffected; (ii) increasing photon flux and (iii) decreasing matrixcomplexity, i.e. the organic and inorganic constituents of wastewater retard degradation. This may beovercome coupling photocatalysis with ultrasound radiation at 80 kHz and 41 W/L power density; thecombined sonophotocatalytic process acts synergistically toward EE2 degradation. Several transforma-tion products were identified by means of UPLC–MS/MS and a reaction network for the photocatalytic

degradation of EE2 is suggested.

An artificial neural network comprising five input variables (reaction time, TiO2 and EE2 concentra-tion, organic content and conductivity of the water matrix), thirteen neurons and an output variable(EE2 conversion) was optimized, tested and validated for EE2 degradation. The network, based on tan-gent sigmoid and linear transfer functions for the hidden and input/output layers, respectively, and theLevenberg–Marquardt back propagation training algorithm, can successfully predict EE2 degradation.

. Introduction

Endocrine disrupting compounds (EDCs) constitute an impor-ant class of emerging environmental contaminants, which posen increasing threat to aquatic organisms, as well as to humanealth. EDCs, which include natural estrogens, synthetic estrogens,hyto-estrogens and various industrial chemicals (i.e. pesticides,

ersistent organochlorines, organohalogens, alkyl phenols, heavyetals), have the ability to interact with the endocrine system

f the organisms, thus leading to a variety of developmental

∗ Corresponding author at: Department of Environmental Engineering, Technicalniversity of Crete, GR-73100 Chania, Greece. Tel.: +30 2821037797;

ax: +30 2821037852.E-mail address: mantzavi@mred.tuc.gr (D. Mantzavinos).

010-6030/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.jphotochem.2012.05.007

© 2012 Elsevier B.V. All rights reserved.

and reproductive disorders, as well as feminizing effects [1,2].Of the various categories, natural and synthetic estrogens exhibitmuch stronger estrogenic activity than phyto- and xeno-estrogens[1].

EDCs are only partially removed in conventional wastewa-ter treatment plants (WWTPs) mainly through a combination ofbiodegradation processes and sorption onto microbial flocs andsuspended solids, although the relative contribution of each path-way is not fully understood [1]. Several monitoring campaigns[3–7] have confirmed the presence of estrogen hormones in WWTPdischarges worldwide, including the naturally occurring estrone(E1) and 17�-estradiol (E2), as well as 17�-ethynylestradiol (EE2), a

synthetic estrogen used in the oral contraceptive pill. These studies[1–7] and many more converge to the fact that WWTP dischargescontain residual estrogens at the ng/L level that constitute the maincontributors to the effluent’s estrogenic activity; in this respect,

3 and Photobiology A: Chemistry 240 (2012) 33– 41

ae

gcwretwIwisgwt[ttiaaheiTth

dscsttdoetorp

(sbneiwti

2

2

1SISnS

0.0

0.1

0.2

0.3

0.4

340320300280260240220200

Wavelength, nm

Ab

so

rba

nc

e,

au

HO

H H

CCH OH

CH

4 Z. Frontistis et al. / Journal of Photochemistry

dditional treatment may be needed to remove EDCs from theffluent.

In recent years, advanced oxidation processes (AOPs) haveained particular attention for the degradation of micro-ontaminants of emerging concern in various aqueous matricesith photochemical and photocatalytic processes playing a key

ole [8]. Semiconductor photocatalysis based on TiO2 has beenmployed to degrade a mixture of E1 and E2 in model aqueous solu-ions concerning the effect of operating conditions (i.e. radiationavelength, pH, catalyst and H2O2 concentration) on kinetics [9].

n further studies [10], a synthetic solution of E1, E2, EE2 and estriolas subjected to TiO2 photocatalysis under UV-C or UV-A radiation

n an annular photoreactor to model the radiation field and, sub-equently, develop kinetic expressions independent of the reactoreometry and field. EE2 in pure water or in water:methanol mixtureas photocatalytically oxidized to identify its major transforma-

ion intermediates and suggest possible degradation pathways11]. The effect of adding ethanol and/or urea in aqueous solu-ions of E2 and EE2 on the extent of estrogen adsorption ontoitania particles and subsequent photodegradation was evaluatedn other studies [12]. A pilot system comprising a slurry photocat-lytic reactor and a microfiltration membrane was tested to treat

mixture of 32 pharmaceuticals and EDCs (including five estrogenormones) spiked in drinking water; emphasis was given on theffective removal of target compounds and associated estrogenic-ty in relation to energy consumption of the UV lamps [13]. BesidesiO2 photocatalysis [9–13], the use of iron oxide-coated resins forhe heterogeneous photo-Fenton oxidation of E2 in distilled wateras been reported [14,15].

Although the aforementioned studies [9–15] are importantescribing mostly experimental work in the area, they have exclu-ively been carried out in model aqueous solutions and/or atonditions that are far from those typically met in environmentalamples (e.g. presence of organic solvents, estrogen concentra-ions at the mg/L level, highly acidic or alkaline conditions, etc.). Inhis perspective, the aim of our work was to study the UV-A/TiO2egradation of EE2 in environmentally relevant samples (i.e. sec-ndary treated effluents spiked with EE2 at the �g/L level) withmphasis on the effect of water matrix, catalyst type and concentra-ion, estrogen concentration, photon flux and ultrasound radiationn degradation kinetics. An attempt was also made to elucidateeaction pathways and mechanisms through the identification ofrimary transformation products (TPs).

In addition to experimentation, artificial neural networksANNs) were employed to simulate the process and define theignificance of the various operating variables. ANN are computer-ased systems designed to simulate the learning process ofeurons in the human brain [16]. In recent years, they have beenmployed in many areas of science and engineering to model var-ous complex processes including biological and physicochemical

ater/wastewater treatments [17]. Notably, the number of publica-ions dealing with ANN modeling in heterogeneous photocatalysiss rather limited as pointed out in a recent review article [16].

. Experimental and analytical

.1. Materials

The majority of the experiments were performed with7�-ethynylestradiol (C20H24O2), which was purchased fromigma–Aldrich (CAS number: 57-63-6) and used as received.

ts structural formula and absorbance spectrum are shown inchematic 1. Some runs were done with other EDCs, namely bisphe-ol A (BPA) and 17�-estradiol, which were also purchased fromigma–Aldrich and used as received.

Scheme 1. The structural formula and absorbance spectrum of EE2.

Three commercially available titania samples were tested,namely: (i) Aeroxide P25 (formerly known as Degussa P25, 75:25anatase:rutile, 50 m2/g BET area, 21 nm particle size, supplied byEvonik Industries); (ii) Hombikat UV100 (>99% anatase, >250 m2/gBET area, 5 nm particle size, supplied by Sachtleben Chemie); (iii)Tronox AK1 (100% anatase, 90 m2/g BET area, 20 nm particle size,supplied by Tronox Inc.).

Stock estrogen solutions were prepared at 1 mg/L concentra-tion and the appropriate volume was spiked to the water matrixto obtain the desired estrogen concentration; four matrices wereemployed, namely: (i) wastewater (WW) collected from the out-let of the secondary treatment of a municipal WWTP. The matrixwas characterized by standard methods [18] as follows: the chem-ical oxygen demand and dissolved organic carbon (DOC) was 24and 8.4 mg/L, respectively, while its pH was about 8 and theconductivity 820 �S/cm. Moreover, it contained 172 mg/L chlo-rides, 194 mg/L bicarbonates, 54 mg/L sulfates, 37 mg/L nitrates and37 mg/L nitrites; (ii) ultrapure water (UPW) taken from a waterpurification system (EASYpureRF – Barnstead/Thermolyne, USA)with 5.5 �S/cm conductivity and pH 6.1; (iii) a 50:50 mixture ofWW and UPW; (iv) drinking water (DW) at pH 7.9, 308 �S/cmconductivity and 152 mg/L bicarbonates.

2.2. Photocatalytic experiments

Unless otherwise stated, UV-A radiation at2.81 × 10−4 einstein/min was provided by a 9 W lamp (RadiumRalutec, 9W/78) emitting predominantly at 350–400 nm. Thephoton flux was determined actinometrically using the potassiumferrioxalate method. Experiments were conducted in an immersionwell, batch type, laboratory scale photoreactor, purchased fromAce Glass (Vineland, NJ, USA) and described in detail elsewhere[19]. To study the effect of radiation intensity on degradation,experiments were also performed at various fluxes in the rangeof 6.4 × 10−7–3.7 × 10−4 einstein/min; this was done either usinga similar lamp of lower (i.e. 7 W) nominal intensity or partiallycovering the 9 W lamp with aluminum foil.

In a typical run, 0.3 L of the water matrix containing EE2 inthe range of 50–900 �g/L were introduced in the reaction vesselmade of borosilicate glass and then added titania in the range of50–1000 mg/L. The suspension was magnetically stirred for 30 minin the dark and then the lamp was turned on, while air wascontinuously sparged in the reaction mixture under stirring. Allexperiments were performed at inherent pH which, although it

was left uncontrolled during the reaction, remained practicallyunchanged. The temperature was maintained constant at 25 ± 2 ◦Cwith a temperature control unit.

and Photobiology A: Chemistry 240 (2012) 33– 41 35

2

ies

ultttuoS

2

Wg2b3wtcwtE

mpwa(saatoUflg5

ue

3

isabphetpdb

T

Table 1Range and relative significance of the ANN input variables used in this work.

Input variable Range Significance (%)

Reaction time 0–60 min 28TiO2 concentration 50–1000 mg/L 21.5EE2 concentration 50–900 �g/L 13.8Water matrix DOC 0–8.4 mg/L 12.3

inantly due to the interaction between photonic energy and thecatalyst surface and only partially due to photooxidation inducedby the continuous oxygen supply in the liquid phase. At the con-ditions in question, direct photolysis is unlikely to occur since EE2

0

0.2

0.4

0.6

0.8

1

302520151050

C/C

o

Tronox AK1

Hombikat

P25

UV-A only

Z. Frontistis et al. / Journal of Photochemistry

.3. Sonophotocatalytic experiments

An Ultrason 250 (LabPlant, Huddersfield, UK) horn-type son-cator operating at 80 kHz was employed for sonophotocatalyticxperiments, i.e. involving the simultaneous application of ultra-ound and light radiation.

Experiments were performed in an apparatus identical to thatsed in Section 2.2 but slightly modified to house both the UV-A

amp and the ultrasound probe. A glass tube at an angled posi-ion was connected to the external cylindrical reaction vessel nearhe surface of the liquid, thus permitting the introduction of theitanium-made probe inside the reaction mixture. The emittedltrasound power was determined calorimetrically at 41 W/L. Allther experimental procedures were similar to those described inection 2.2.

.4. Analytical protocols

High performance liquid chromatography (HPLC: Alliance 2690,aters) was employed to monitor the concentration of estro-

ens. Separation was achieved on a Luna C-18(2) column (5 �m,50 mm × 4.6 mm) and a security guard column (4 mm × 3 mm),oth purchased from Phenomenex. The mobile phase consisting of5:65 UPW:acetonitrile eluted isocratically at 1 mL/min and 30 ◦C,hile the injection volume was 100 �L. Detection was achieved

hrough a fluorescence detector (Waters 474 Scanning Fluores-ence Detector) in which the excitation and emission wavelengthsere set at 280 nm and 305 nm, respectively. At these conditions,

he limits of detection and quantitation were 0.63 and 2.11 �g/L forE2, 0.68 and 2.32 �g/L for BPA and 0.59 and 1.95 �g/L for E2.

TPs of EE2 photodegradation were identified by liquid chro-atography coupled with mass spectrometry. Analysis was

erformed on an ACQUITY TQD UPLC–MS/MS system equippedith a sample manager, sample organizer, binary solvent man-

ger and column manager. A triple quadrupole mass spectrometerserial number QBA012) coupled with electrospray ionization (ESI)ource running in positive mode was used for sample analysis. Datacquisition was performed with MassLynxTM software. In order tochieve sensitive analysis for the identification of TPs, data acquisi-ion was performed with ESI in full scan mode. Separation was donen a BEH Shield RP18T column with the mobile phase consisting ofPW + 0.1% formic acid as eluent A and methanol as eluent B. Theow rate was 0.3 mL/min and the temperature 40 ◦C. The elutionradient was: 0 min 5% B, 1.5 min 5% B, 2 min 30% B, 3 min 50% B,

min 70% B, 6 min 90% B, 7 min 90% B, 7.1 min 5% B, 9 min 5% B.The yeast estrogen screening (YES) test was employed to eval-

ate overall estrogenicity according to the procedures describedlsewhere [20].

. Artificial neural networks (ANN)

A neural network consists of artificial neurons that are groupednto layers and interconnected in a variety of structures. Thetrength of these interconnections is determined by the weightssociated with the neurons [16,17]. In this work, a three-layeredack propagation ANN was chosen comprising an input layer (inde-endent variables), an output layer (dependent variable) and aidden layer. A tangent sigmoid (tansig) transfer function wasmployed to activate the hidden layer, while a linear (purelin) func-ion for the input/output layers. The Levenberg–Marquardt backropagation algorithm was chosen for training purposes; more

etails regarding transfer functions and the training algorithm cane found elsewhere [16].

The input layer includes five variables which are shown inable 1 alongside the respective range of values, while EE2

Water matrix conductivity 5.5–820 �S/cm 24.4

Total 100

conversion is the dependent variable of the output layer. A set of222 experimental data was divided into training (70%, 156 data),validation (15%, 33 data) and test (15%, 33 data) subsets. TheNeural Network Toolbox of Matlab R2011 mathematical softwarewas employed for EE2 removal prediction.

4. Results and discussion

4.1. Preliminary experiments

Preliminary runs were performed to screen three titania sam-ples for EE2 degradation in WW; as seen in Fig. 1, photocatalyticactivity decreases in the order Aeroxide P25 (23.1) > HombikatUV100 (11) > Tronox AK1 (6.5) with the numbers in bracketsshowing initial (i.e. after 1 min of reaction) degradation rates in�g/(L min). Although the surface area of Aeroxide P25 is lowerthan the other two, its superiority is attributed to the slowerelectron/hole recombination taking place on the catalyst surfacecompared to other TiO2 photocatalysts [21]. Another explanationascribes the higher activity of Aeroxide P25 to its structure which isa mixture of anatase and rutile; this mixture is more active than theindividual pure crystalline phases [22]. According to the above find-ings, all subsequent photocatalytic experiments were performedwith Aeroxide P25 TiO2.

The degree of EE2 adsorption onto the catalyst surface in thedark was also monitored and it never exceeded 10% of its initial con-centration irrespective of the conditions in question (i.e. type andconcentration of titania, EE2 concentration and the water matrix).

As also seen in Fig. 1, irradiation in the absence of catalystyields just 25% conversion after 30 min which increases to 55% after90 min (not shown); this confirms that EE2 degradation is predom-

Time, min

Fig. 1. Photodegradation of 100 �g/L EE2 in WW with or without 750 mg/L of vari-ous TiO2 samples.

36 Z. Frontistis et al. / Journal of Photochemistry and Photobiology A: Chemistry 240 (2012) 33– 41

FAa

dc

4

m(iaFaEaUirSt(ohsw[

fidoAanig

4

tA2ies

0

10

20

30

40

50

60

70

10008006004002000

TiO2 concentration, mg/L

r o, μ

g/(

L.m

in)

UPW

WW

Fig. 4a shows the effect of EE2 concentration on its photodegra-dation in WW at 750 mg/L Aeroxide P25 TiO2; regardless the initial

0

100

200

300

400

500

600

700

800

900

20151050

Time, min

Co

ncen

trati

on

, μ

g/L

50μg/L

100μg/L

270μg/L

620μg/L

900μg/L

(a)

(b)

0.2

0.25

0.3

0.35

0.4

9007506004503001500

Concentration, μg/L

kP

C, m

in-1

0

50

100

150

200

ro , μ

g/(L

.min

)

ig. 2. Effect of water matrix on 100 �g/L EE2 photodegradation with 750 mg/Leroxide P25 TiO2. Inset graph shows initial photodegradation rates (i.e. calculatedfter 1 min of reaction).

oes not absorb over 300 nm (as clearly seen in Schematic 1), whichoincides with the transmittance threshold of borosilicate.

.2. The role of water matrix

The organic and inorganic species typically found in environ-entally relevant matrices may interfere with the oxidizing agents

e.g. photogenerated holes, as well as reactive oxygen speciesncluding predominantly, but not exclusively, hydroxyl radicals)nd affect the rate of substrate elimination. This is evident inig. 2 showing that the matrix has an adverse effect on conversionnd/or reaction rate; for instance, the time needed for completeE2 degradation in WW is three times longer than that in UPWnd twice longer that that in WW diluted with an equal volume ofPW. This can be explained taking into account that (i) the oxidiz-

ng agents are competitively consumed in reactions involving theesidual organic fraction present in treated WW but not in UPW.ince this is known to be refractory to biological or chemical oxida-ion [23] and constitutes most of the matrix’s total organic contenti.e. 94–99% depending on EE2 initial concentration), non-selectivexidizing agents will partly be wasted attacking this fraction; (ii)ydroxyl radicals may be scavenged by bicarbonates, chlorides andulfates present in WW and/or DW to form the respective radicals,hose oxidation potential is lower than that of hydroxyl radicals

23].The fact that the various matrices have pH values between ∼6

or UPW and ∼8 for WW and DW is not expected to alter the ion-zation state of either EE2 or the catalyst surface and, consequently,etermine their interaction; this is so because EE2 with a pKa valuef 10.2 [23] will exist in its molecular form in either matrix, whileeroxide P25 TiO2 with a zero point charge of 6.2 [24] will be neg-tively charged. The effect of changing matrix pH on kinetics wasot investigated in this work because this would not be practical

n environmentally relevant samples such as WWTP discharges orroundwater destined for the production of drinking water.

.3. The effect of TiO2 concentration

Fig. 3 shows the effect of increasing titania concentration onhe initial photodegradation rate, ro, of EE2 in UPW and WW.s seen, the rate increases with increasing concentration up to

50–500 mg/L in UPW and 500–750 mg/L in WW beyond which

t practically levels off. This threshold concentration (which isxpectedly greater in more complex water matrices for the rea-ons discussed in Section 4.2) is characteristic of a heterogeneous

Fig. 3. Effect of Aeroxide P25 TiO2 concentration on the initial photodegradationrate (i.e. calculated after 1 min of reaction) of 100 �g/L EE2.

catalytic regime since the fraction of incident light absorbed by thesemiconductor progressively increases in suspensions containinghigher amounts of TiO2 up to the point where all particles are fullyilluminated [25].

4.4. The effect of EE2 concentration

Fig. 4. Effect of initial EE2 concentration on (a) concentration–time profiles; (b) rateconstants and initial rates (calculated after 1 min of reaction) during photodegrada-tion in WW with 750 mg/L Aeroxide P25 TiO2.

and Photobiology A: Chemistry 240 (2012) 33– 41 37

e1ettct

−

wculdFaaiooi(

ritttctoFt

eIEeWtd

4

6Uadslera

4

Watfiaro

tion. On the other hand, sonophotocatalysis leads to 90% conversionin just 4 min, twice as fast as photocatalysis.

If the data of Fig. 7 up to 90% conversion are fitted to Eq.(1), one can compute the rate constants of sonophotocatalysis

0

0.2

0.4

0.6

0.8

1

C/C

o

BPA

E2

EE2

Z. Frontistis et al. / Journal of Photochemistry

strogen concentration, ≥95% conversion is always achieved after0 min of reaction. Given that (i) the range of EE2 concentrationsmployed in this work is relatively low; (ii) the formation rate ofhe various oxidizing agents should be constant at fixed experimen-al conditions (i.e. photon flux, catalyst concentration, matrix), oneould safely assume first order kinetics regarding EE2 concentra-ion, i.e.:

dC

dt= kPCC ⇔ ln

Co

C= kPCt (1)

here kPC is an apparent rate constant incorporating the (nearly)onstant concentration of oxidizing agents. If the results of Fig. 4ap to 90% conversion are plotted in the form of Eq. (1), straight

ines passing through the origin (not shown) fit the experimentalata well with the coefficient of linear regression, R2, being ≥99%.rom the slopes of the resulting lines, kPC values can be computednd these are shown in Fig. 4b. For concentrations between 50nd 620 �g/L, kPC is equal to 32.7 ± 0.6 × 10−2 min−1, thus denot-ng true first order kinetics since the rate constant is independentf the estrogen concentration. Only at the higher concentrationf 900 �g/L, does kPC slightly decrease to 28 × 10−2 min−1, whichmplies deviation from first order kinetics (although fitting to Eq.1) is still excellent).

This deviation can better be seen comparing reaction ratesather than rate constants. An increase in EE2 concentrationncreases the chance of oxidizing agents to attack the substrate,hus increasing the reaction rate; this is particularly true duringhe early stages when the concentration of competing transforma-ion by-products is low. As seen in Fig. 4b, the relationship betweenoncentration and ro is indeed linear in the range of 50–620 �g/L. Ifhis were the case at 900 �g/L too, one could predict an initial ratef 225 �g/(L min) extrapolating the respective line (marked –x– inig. 4b); nonetheless, the actual value is 12% lower indicating aransition from first to lower order kinetics.

Previous studies dealing with the photocatalytic degradation ofstrogen hormones have also reported first order kinetics [9,10,13].n particular, Zhang et al. [9], who studied the degradation of E1 and2 in the range of 100–1000 ng/L at 253 nm, reported rate constantsqual to 2.42 ± 0.08 and 2.29 ± 0.02 h−1 for E1 and E2, respectively.hen the experiments were repeated with a light source emit-

ing in the range of 238–579 nm, the corresponding rate constantsecreased by about 65%.

.5. The effect of radiation intensity

Fig. 5 shows the effect of altering photon flux in the range.4 × 10−7–3.7 × 10−4 einstein/min on 100 �g/L EE2 degradation inPW. Complete EE2 removal occurs rapidly (i.e. within 1–3 min)t photon fluxes in the order of 10−4 einstein/min; nonetheless, aecrease in photon flux by 2–3 orders of magnitude results in aignificant drop of photocatalytic performance, thus verifying theight-induced nature of the activation of the catalytic process. Forxample, the initial degradation rate suffers a 2.5-fold and 18-foldeduction, when the flux decreases from 3.7 × 10−4 to 4.6 × 10−6

nd 6.4 × 10−7 einstein/min, respectively.

.6. Reactivity of EDCs

To assess the ability of photocatalysis to degrade other EDCs inW, experiments were carried out with BPA and E2 and the results

re shown in Fig. 6. The initial degradation rate is common for allhree estrogens at 20 ± 3 �g/(L min), while 15 min of reaction suf-

ce to remove 95% of BPA and E2. These results are consistent with

recent work of our group [26] reporting comparable degradationates for EE2 and BPA subject to solar light-induced photocatalysisver various titania samples in WW. Likewise, Li Puma et al. [10]

Fig. 5. Effect of photon flux on 100 �g/L EE2 photodegradation in UPW with250 mg/L Aeroxide P25 TiO2. Inset graph shows initial photodegradation rates (i.e.calculated after 1 min of reaction).

reported similar rate constants for the degradation of E2 and EE2under UV-A or UV-C radiation over Aeroxide P25 TiO2.

4.7. Enhancement of photocatalytic performance by ultrasoundradiation

Coupling photocatalysis to other AOPs is a way to improveprocess efficiency due to cumulative or synergistic effects. Theapplication of ultrasound (US) in an aqueous medium leads tothe cyclic formation, growth and adiabatic collapse of cavities thatbehave as “hot spot” micro-reactors. There are two distinct mecha-nisms for organic matter decomposition involving (i) pyrolysis nearor inside the cavity; (ii) reactions mediated by hydroxyl radicalswhose formation is due to water sonolysis [27]. Recent studies havedemonstrated the ability of US radiation to degrade various estro-gen hormones [23,28,29], while others [27,30] have reported thebeneficial effect of coupling US and photocatalysis for the mineral-ization of BPA.

In this perspective, we decided to test EE2 degradation by USradiation, photocatalysis and their combination, i.e. sonophoto-catalysis. As seen in Fig. 7, US alone results in partial EE2 elimination(e.g. 20% after 20 min) which is comparable to that of photooxida-

20151050

Time, min

Fig. 6. Reactivity of 100 �g/L EE2, E2 and BPA during photodegradation in WW with750 mg/L Aeroxide P25 TiO2.

38 Z. Frontistis et al. / Journal of Photochemistry and Photobiology A: Chemistry 240 (2012) 33– 41

0

0.2

0.4

0.6

0.8

1

20151050

Time, min

C/C

o

0

20

40

60

80

100

UV-AUSUV-A/USPhotocatalysisSonophotocatalysisYES

Estro

gen

icity

, μg

/L

Fte

(ad

%

dbcpniltobt

cActpgcaodette(ttt

4

ttpa

0

20

40

60

80

100

109876543210

Time, min

Rela

tive a

bu

nd

an

ce, %

(a)

Peak 1

0

20

40

60

80

100

55050045040035030025020015010050

m/z

Rela

tive a

bu

nd

an

ce, %

(b) 329.3

327.3

300.7

282.5

251.2

279.5229.4

207.3

Fig. 8. (a) TIC of mass spectra for EE2 photodegradation; (b) average mass spectra

ig. 7. Degradation of 100 �g/L EE2 in WW by 80 kHz ultrasound radiation, pho-ocatalysis (750 mg/L Aeroxide P25 TiO2) and sonophotocatalysis. Changes instrogenicity during sonophotocatalysis are shown in secondary axis.

kUSPC = 60 × 10−2 min−1), photocatalysis (kPC = 32.3 × 10−2 min−1)nd US radiation (kUS = 1.1 × 10−2 min−1) and then assess theegree of synergy, S, as follows:

S = kUSPC − (kPC + kUS)kUSPC

× 100 (2)

In this case, S = 44.3% indicating that the individual processeso not simply add up (i.e. S = 0) but there is a two-way synergyetween them. On one hand, application of an ultrasound fieldan result in (i) enhanced mass transfer of EE2 between the liquidhase and the titania surface; (ii) catalyst excitation by sonolumi-escence which has a wide wavelength range below 375 nm; (iii)

ncreased catalytic activity due to ultrasound de-aggregating cata-yst particles, thus increasing surface area [27]. On the other hand,he sonochemical activity itself may be facilitated by the presencef titania particles acting as nucleation sites (this said, particles maye responsible for sound attenuation leading to reduced degrada-ion).

Such synergistic effects could conceptually be crucial for the suc-essful implementation of AOPs in water/wastewater treatment.

proper strategy is the simultaneous application of AOPs thatould complement each other in a synergistic rather than addi-ive way. Since the organic and inorganic, non-target constituentsresent in complex water matrices behave antagonistically to tar-et species consuming a fraction of non-selective radicals, thisould be compensated by the increased generation of radicalsrising from process integration. This is clear from the profile ofverall estrogenicity also shown in Fig. 7 and the way it changesuring sonophotocatalysis. The WW sample contains 103 �g/L ofquivalent estrogenicity, 22 �g/L of which are due to EE2 andhe rest to matrix constituents. It is notable that the quanti-ative EE2 removal after 5 min of reaction coincides with 27%strogenicity reduction, which remains nearly constant thereafteri.e. at 30% after 20–30 min). This reduction is marginally greaterhan the contribution of EE2 to initial estrogenicity, implyinghat other resistant matrix species retain their estrogenic proper-ies.

.8. Identification of TPs and proposed pathways

Several samples were analyzed by UPLC–MS/MS in an attempt

o determine major, early-stage EE2 TPs. Fig. 8a shows a typicalotal ion chromatogram (TIC) of the mass spectra obtained fromhotocatalytically treated EE2 samples in UPW, where a major peakt 5 min (referred to as peak 1) can clearly be seen. Three main

corresponding to peak 1 in TIC.

product ions (whose relative intensity (RI) is greater than 50%) atm/z 329, m/z 282, m/z 251, and four other important product ions(whose RI is between 20 and 50%) at m/z 301, m/z 279, m/z 229, m/z207 are observed in the average mass spectra of peak 1 (Fig. 8b).

Overall, as many as ten compounds were tentatively identi-fied with their peaks being greater than 20% of the highest peakintensity, thus allowing the precise identification of TPs; these areshown in Schematic 2 (TP1–TP10), alongside the proposed reac-tion pathways for EE2 photocatalytic degradation in UPW. Basedon the results obtained herein and on previous studies regard-ing EE2 photooxidation [31], TPs correspond to quinone methideand 1,2-quinone derivatives, ring-monohydroxylation photoprod-ucts and photoproducts arising from the hydroxylation onto thesaturated ring linked to the aromatic one. TP1(A) with 329 m/z(RI = 80–100%) is one of the major products, which may be formedafter di-hydroxylation of the aromatic ring. The attack of hydroxylradicals on the aromatic ring can lead to the formation of dihydroxyphotoproducts such as 2-HO-EE2 [32]. In addition, the formation ofTP1(B) may be due to the direct attack of OOH radicals [33,34]. AsPeiro et al. reported [35], with the rearrangement of the structure atthe phenol moiety, the resonance structure can be formed, where asite between aromatic and cyclohexane ring is active by the attackof OOH radical. TP2 (m/z 327, RI = 70–100%) can be formed fromTP1(B) with the abstraction of two hydrogen atoms. EE2 dealky-lation (i.e. loss of C2H2 moiety) at position C17 could lead to the

formation of its analogue, TP3, (m/z 272, RI = 20–70%), whose fur-ther reduction would yield TP4 (RI = 20–40%). Another analogue,TP5 (RI = 30–45%), may be formed following EE2 demethylationat position C13. Cleavage of the aromatic ring of EE2 also occurs

Z. Frontistis et al. / Journal of Photochemistry and Photobiology A: Chemistry 240 (2012) 33– 41 39

ork fo

rswi(c(a

ftw

EE2 conversion in any of the analyzed samples did not exceed60%. Nonetheless, compounds with molecular mass <100 Da, whichmay be attributed to carboxylic acids and other ring-cleavage com-pounds, were also observed.

1

Reaction

Scheme 2. The proposed reaction netw

esulting in the formation of TP6 (m/z 251, RI = 40–70%), whoseubsequent decarboxylation gives TP7 (m/z 207, RI = 20–35%). TP8ith m/z 279 (RI = 20–40%) may be formed from EE2 after los-

ng a hydroxyl moiety from position 20, while TP9 with m/z 313RI = 25–50%) may be generated by direct attack of hydroxyl radi-al at position C10. Finally, TP10 with m/z 300 has been identifiedRI = 40–60%) possibly coming from the addition of four hydrogentoms to EE2.

It should be noted here that the analytical efforts deliberatelyocused on the determination of early-stage intermediates ratherhan deep oxidation TPs since the former could give a better insightith respect to reaction mechanisms and pathways; in this respect,

Fig. 9. Optimization of number of neurons in relation to MSE.

r the photodegradation of EE2 in UPW.

2

3

4

12

13

EE2

conversion

Bias

time

Matrix

DOC

TiO2concentration

EE2

concentration

Matrix

conductivity

Bias

Fig. 10. Structure of the optimized ANN used in this work.

40 Z. Frontistis et al. / Journal of Photochemistry and Photobiology A: Chemistry 240 (2012) 33– 41

Table 2Weight matrix of the 5-13-1 ANN.

Neuron Input Output

Reaction time TiO2 concentration EE2 concentration Matrix DOC Matrix conductivity EE2 conversion

1 1.53764 0.982186 0.994701374 −0.44103 −0.90914 −2.401242 −3.64354 1.11884 −0.541472 −2.71862 −3.0725 −1.475313 −2.02585 −0.34478 1.420114092 0.988853 −1.50342 −1.363524 −0.39224 −0.03654 1.548429525 −0.42222 1.353924 3.6345755 −1.90047 −1.02472 1.576929355 0.419648 −2.72433 −3.004076 3.048945 −1.41193 1.081351343 −1.89837 −3.21408 4.0397887 −4.38209 0.551387 −0.11770629 −1.21532 −2.94864 2.1127938 0.088965 1.89757 −1.45296734 0.09734 1.465299 1.2035729 0.442358 −3.13383 −0.23744591 −1.08274 1.540996 −0.89676

10 −0.18396 −0.55574 2.248250784 −1.91393 −0.83784 1.392484334 −0.37601 0.759601 2.58932702 0.033718 0.367257 −7.46583608 0.924036 −0.95619 0.902249

4

snwtof

M

woAa

vmelcthit[u

Fv

0

0.2

0.4

0.6

0.8

1

1086420

Time, min

C/C

o

WW

WW:UPW 50:50

DW

11 −0.94887 −1.50986 0.85542612 12.09814 −0.40191 0.06882413 2.950806 −0.59749 −0.97153

.9. Modeling

To determine the optimum number of neurons needed to con-truct the ANN, a series of topologies were employed in which theumber of neurons was varied between 1 and 15. Each topologyas repeated 5 times at least to avoid random correlation due to

he random initialization of the weights. Optimization was basedn minimizing the mean square error, MSE, which is defined asollows:

SE =∑i=N

i=1 (yi,pred − yi,exp)2

N(3)

here yi,pred and yi,exp are the predicted and experimental valuesf the dependent variable, respectively and N is the number of data.s seen in Fig. 9, MSE is minimized when 13 neurons are employednd the resulting ANN is schematically illustrated in Fig. 10.

Fig. 11 shows a comparison between the measured EE2 con-ersion and the predicted values to test the precision of the ANNodel; there are two lines corresponding to the perfect fit, y = x (i.e.

xperimental and predicted values would coincide), and the bestinear fit, y = 0.996x + 0.0026, obtained with regression analysis. Theoefficient of regression, R2, is 99.4% and the MSE is 7.77 × 10−4,hus implying a very good fit; this is verified in Fig. 12 which showsow well the model can simulate EE2 concentration–time profiles

n representative experiments. Previous studies have also reportedhe application of ANN models to simulate the degradation of EDTA36], nitrilotriacetic acid [37], azodye [38] and nitrogen oxides [39]nder UV radiation and Aeroxide P25 TiO2.

0

0.2

0.4

0.6

0.8

1

10.80.60.40.20

Measured EE2 conversion

Pre

dic

ted

EE

2 c

on

vers

ion

Data points

Best linear fit (y=0.996x+0.0026)

y=x

ig. 11. Comparison between measured and predicted values of the dependentariable.

Fig. 12. Experimental data (symbols) and ANN modeling (dashed lines) for repre-sentative experiments of 100 �g/L EE2 photodegradation with 750 mg/L AeroxideP25 TiO2.

Finally, the relative significance of the input variables was eval-uated using the neural weight matrix and the following equationproposed by Garson [40]:

Ij =∑m=Nh

m=1

((|wih

jm|/

∑Nik=1|wih

km|)

× |whomn|

)

∑k=Nik=1

{∑m=Nhm=1

(|wih

km|/∑Ni

k=1|wihkm

|)

× |whomn|

} (4)

where Ij is the relative significance of the jth input variable on theoutput variable, Ni and Nh are the numbers of input and hidden neu-rons, respectively and w is the connection weight. The subscriptsk, m and n refer to input, hidden and output neurons, respectively,while the superscripts i, h and o refer to input, hidden and outputlayers, respectively. The weight matrix of the ANN model in ques-tion is given in Table 2, while the relative significance of the inputvariables derived from Eq. (4) is given in Table 1. Reaction time,catalyst concentration and matrix conductivity are nearly equallyimportant for EE2 degradation followed by the other two variables.

5. Conclusions

The conclusions drawn from this study are summarized as fol-lows:

(1) TiO2 photocatalysis is an effective technology for the elimi-nation of estrogen hormones in environmental samples, i.e.complex matrices containing organic and inorganic species(at the mg/L level) that compete with micro-contaminants (at

and P

(

(

(

A

aUem(

R

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

Z. Frontistis et al. / Journal of Photochemistry

the ng �g/L level) for the non-selective hydroxyl radicals andother oxidizing agents. Although working with model solutionsmay still provide some useful information, actual treatmentconditions can realistically be represented employing environ-mentally relevant matrices.

2) EE2 degradation can be approached by a first order rate expres-sion, which shifts to lower order kinetics at higher estrogenconcentrations. A complex reaction network occurs involvingthe formation of several transformation products.

3) Process integration is conceptually advantageous in enhancingthe efficiency of photocatalysis (or, indeed, any other advancedoxidation process). In this respect, the simultaneous applicationof photocatalysis and ultrasound radiation promotes degrada-tion in a synergistic rather than cumulative way.

4) Artificial neural networks can be a useful tool for the develop-ment of semiempirical models in wastewater treatment. In thislight, a 5-13-1 network was optimized, tested and validated forthe photocatalytic degradation of estrogen hormone. The rela-tive significance of the five independent variables decreases inthe order: reaction time ≈ matrix conductivity > TiO2 concen-tration > estrogen concentration ≈ matrix DOC.

cknowledgments

This work was partly co-funded by the Republic of Cyprusnd the European Regional Development Fund through GrantsPGRADING/DURABLE/0308/07 (Project title: IX-Aqua – Fate,ffect and removal potential of xenobiotics present in aqueousatrices) and “NIREAS – International Water Research Center”

NEA IPODOMI/STRATH II/0308/09).

eferences

[1] M. Auriol, Y. Filali-Meknassi, R.D. Tyagi, C.D. Adams, R.Y. Surampalli, Endocrinedisrupting compounds removal from wastewater: a new challenge, ProcessBiochemistry 41 (2006) 525–539.

[2] M. Kuster, D.A. Azevedo, M.J. Lopez de Alda, F.R. Aquino Neto, D. Barcelo, Anal-ysis of phytoestrogens, progestogens and estrogens in environmental watersfrom Rio de Janeiro (Brazil), Environment International 35 (2009) 997–1003.

[3] M.R. Servos, D.T. Bennie, B.K. Burnison, A. Jurkovic, R. McInnis, T. Neheli, A.Schnell, P. Seto, S.A. Smyth, T.A. Ternes, Distribution of estrogens, 17�-estradioland estrone, in Canadian municipal wastewater treatment plants, Science of theTotal Environment 336 (2005) 155–170.

[4] C. Desbrow, E.J. Routledge, G.C. Brighty, J.P. Sumpter, M. Waldock, Identifica-tion of estrogenic chemicals in STW effluent. 1: chemical fractionation andin vitro biological screening, Environmental Science and Technology 32 (1998)1549–1558.

[5] T.A. Ternes, M. Stumpf, J. Mueller, K. Haberer, R.D. Wilken, M. Servos, Behaviorand occurrence of estrogens in municipal sewage treatment plants. I: investi-gations in Germany, Canada and Brazil, Science of the Total Environment 225(1999) 81–90.

[6] C. Baronti, R. Curini, G. D’Ascenzo, A. Di Corcia, A. Gentili, R. Samperi, Monitoringnatural and synthetic estrogens at activated sludge sewage treatment plantsand in a receiving river water, Environmental Science and Technology 34 (2000)5059–5066.

[7] S. Matsui, H. Takigami, T. Matsuda, N. Taniguchi, J. Adachi, H. Kawami, Y.Shimizu, Estrogen and estrogen mimics contamination in water and the role ofsewage treatment, Water Science and Technology 42 (2000) 173–179.

[8] M. Klavarioti, D. Mantzavinos, D. Kassinos, Removal of residual pharmaceu-ticals from aqueous systems by advanced oxidation processes, EnvironmentInternational 35 (2009) 402–417.

[9] Y. Zhang, J.L. Zhou, B. Ning, Photodegradation of estrone and 17b-estradiol inwater, Water Research 41 (2007) 19–26.

10] G. Li Puma, V. Puddu, H.K. Tsang, A. Gora, B. Toepfer, Photocatalytic oxidationof multicomponent mixtures of estrogens (estrone (E1), 17�-estradiol (E2),17�-ethynylestradiol (EE2) and estriol (E3)) under UVA and UVC radiation:photon absorption, quantum yields and rate constants independent of photonabsorption, Applied Catalysis B: Environmental 99 (2010) 388–397.

11] W. Sun, S. Li, J. Mai, J. Ni, Initial photocatalytic degradation inter-mediates/pathways of 17�-ethynylestradiol: effect of pH and methanol,Chemosphere 81 (2010) 92–99.

12] T. Karpova, S. Preis, J. Kallas, Selective photocatalytic oxidation of steroid estro-gens in water treatment: urea as co-pollutant, Journal of Hazardous Materials146 (2007) 465–471.

13] M.J. Benotti, B.D. Stanford, E.C. Wert, S.A. Snyder, Evaluation of a photocat-alytic reactor membrane pilot system for the removal of pharmaceuticals

[

hotobiology A: Chemistry 240 (2012) 33– 41 41

and endocrine disrupting compounds from water, Water Research 43 (2009)1513–1522.

14] Y. Zhao, J. Hu, Photo-Fenton degradation of 17�-estradiol in presenceof �-FeOOHR and H2O2, Applied Catalysis B: Environmental 78 (2008)250–258.

15] Y. Zhao, J. Hu, H. Chen, Elimination of estrogen and its estrogenicity by hetero-geneous photo-Fenton catalyst �-FeOOH/resin, Journal of Photochemistry andPhotobiology A: Chemistry 212 (2010) 94–100.

16] A.R. Khataee, M.B. Kasiri, Artificial neural networks modeling of contaminatedwater treatment processes by homogeneous and heterogeneous nanocatalysis,Journal of Molecular Catalysis A: Chemical 331 (2010) 86–100.

17] E.S. Elmolla, M. Chaudhuri, M.M. Eltoukhy, The use of artificial neural network(ANN) for modeling of COD removal from antibiotic aqueous solution by theFenton process, Journal of Hazardous Materials 179 (2010) 127–134.

18] L.S. Clesceri, A.E. Greenberg, A.D. Eaton, Standard Methods for the Examinationof Water and Wastewater, 20th ed., APHA, 1999.

19] C. Fotiadis, N.P. Xekoukoulotakis, D. Mantzavinos, Photocatalytic treatment ofwastewater from cottonseed processing: effect of operating conditions, aerobicbiodegradability and ecotoxicity, Catalysis Today 124 (2007) 247–253.

20] E.J. Routledge, J.P. Sumpter, Estrogenic activity of surfactants and some of theirdegradation products assessed using a recombinant yeast screen, Environmen-tal Toxicology and Chemistry 15 (1996) 241–248.

21] S.T. Martin, H. Herrmann, W. Choi, M.R. Hoffmann, Time-resolved microwaveconductivity. Part 1: TiO2 photoreactivity and size quantization, Journal of theChemical Society, Faraday Transactions 21 (1994) 3315–3322.

22] R.I. Bickley, T. Gonzalez-Carreno, J.S. Lees, L. Palmisano, R.J.D. Tilley, A struc-tural investigation of titanium dioxide photocatalysts, Journal of Solid StateChemistry 92 (1991) 178–190.

23] Z. Frontistis, D. Mantzavinos, Sonodegradation of 17�-ethynylestradiol in envi-ronmentally relevant matrices: Laboratory-scale kinetic studies, UltrasonicsSonochemistry 19 (2012) 77–84.

24] M. Saquib, M. Muneer, Titanium dioxide mediated photocatalyzed degradationof a textile dye derivative, acid orange 8, in aqueous suspensions, Desalination155 (2003) 255–263.

25] M.N. Abellan, J. Gimenez, S. Esplugas, Photocatalytic degradation of antibiotics:the case of sulfamethoxazole and trimethoprim, Catalysis Today 144 (2009)131–136.

26] H. Dimitroula, V.M. Daskalaki, Z. Frontistis, D.I. Kondarides, P. Panagiotopoulou,N.P. Xekoukoulotakis, D. Mantzavinos, Solar photocatalysis for the abatementof emerging micro-contaminants in wastewater: synthesis, characterizationand testing of various TiO2 samples, Applied Catalysis B: Environmental117–118 (2012) 283–291.

27] R.A. Torres-Palma, J.I. Nieto, E. Combet, C. Petrier, C. Pulgarin, An innovativeultrasound, Fe2+ and TiO2 photoassisted process for bisphenol A mineralization,Water Research 44 (2010) 2245–2252.

28] H. Fu, R.P.S. Suri, R.F. Chimchirian, E. Helmig, R. Constable, Ultrasound-induceddestruction of low levels of estrogen hormones in aqueous solutions, Environ-mental Science and Technology 41 (2007) 5869–5874.

29] R.P.S. Suri, T.S. Singh, S. Abburi, Influence of alkalinity and salinity on thesonochemical degradation of estrogen hormones in aqueous solution, Envi-ronmental Science and Technology 44 (2010) 1373–1379.

30] R.A. Torres, J.I. Nieto, E. Combet, C. Petrier, C. Pulgarin, Influence of TiO2 con-centration on the synergistic effect between photocatalysis and high-frequencyultrasound for organic pollutant mineralization in water, Applied Catalysis B:Environmental 80 (2008) 168–175.

31] P. Mazelier, L. Méité, J. De Laat, Photodegradation of the steroid hormones17b-estradiol (E2) and 17a-ethinylestradiol (EE2) in dilute aqueous solution,Chemosphere 73 (2008) 1216–1223.

32] Y. Zhao, J. Hu, W. Jin, Transformation of oxidation products and reduction ofestrogenic activity of 17�-estradiol by heterogeneous photo-Fenton reaction,Environmental Science and Technology 42 (2008) 5277–5284.

33] J. Mai, W. Sun, L. Xiong, J. Ni, Titanium dioxide mediated photocatalytic degra-dation of 17�-estradiol in aqueous solution, Chemosphere 73 (2008) 600–606.

34] B.E. Segmuller, B.L. Armstrong, R. Dunphy, A.R. Oyler, Identification of autoxida-tion and photodegradation products of ethynylestradiol by on-line HPLC–NMRand HPLC–MS, Journal of Pharmaceutical and Biomedical Analysis 23 (2000)927–937.

35] A.M. Peiro, J.A. Ayllon, J. Peral, X. Domenech, TiO2-photocatalyzed degrada-tion of phenol and ortho-substituted phenolic compounds, Applied CatalysisB: Environmental 30 (2001) 359–373.

36] C.A. Emilio, M.I. Litter, J.F. Magallanes, Semiempirical modeling with appli-cation of artificial neural networks for the photocatalytic reaction ofethylenediaminetetraacetic acid (EDTA) over titanium oxide (TiO2), HelveticaChimica Acta 84 (2001) 799–813.

37] C.A. Emilio, J.F. Magallanes, M.I. Litter, Chemometric study on the TiO2-photocatalytic degradation of nitrilotriacetic acid, Analytica Chimica Acta 595(2007) 89–97.

38] A.R. Khataee, Photocatalytic removal of C.I. basic red 46 on immobilized TiO2

nanoparticles: artificial neural network modelling, Environmental Technology30 (2009) 1155–1168.

39] F.L. Toma, S. Guessasma, D. Klein, G. Montavon, G. Bertrand, C. Coddet, Neu-

ral computation to predict TiO2 photocatalytic efficiency for nitrogen oxidesremoval, Journal of Photochemistry and Photobiology A: Chemistry 165 (2004)91–96.

40] G.D. Garson, Interpreting neural-network connection weights, AI Expert 6(1991) 46–51.