Ecotoxicology and Environmental Safety 110 (2014) 110–120

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Degradation of direct yellow 9 by electro-Fenton: Process study and optimization and, monitoring of treated water toxicity using catalase Sidali Kourdali a,b,c,n, Abdelmalek Badis a,b,c, Ahmed Boucherit a a

Department of Industrial Chemistry, Faculty of Technology, University of Saàd Dahlab at Blida, PO Box 270, 09000 Blida, Algeria Laboratory of Natural Substances Chemistry and Biomolecules, University of Saàd Dahlab at Blida, PO Box 270, 09000 Blida, Algeria c National Centre for Research and Development of Fisheries and Aquaculture (CNRDPA) 11, Bd Amirouche PO Box 67, Bousmail (W. Tipaza), Algeria b

art ic l e i nf o

a b s t r a c t

Article history: Received 11 January 2014 Received in revised form 18 August 2014 Accepted 19 August 2014

The present study was undertaken to investigate the degradation and removal of direct yellow 9 (DY9) by the electro-Fenton (EF) process in batch reactor using iron and stainless steel electrodes. DY9 removal decreased with the increase in pH (3 to 8) and increased with the increase in current intensity (0.05 to 0.2 A) and [H2O2] (0 to 0.5 g L
1, but not with high doses which led to low rates of DY9 removal and OH∙ uptake). The regression quadratic models describing DY9 degradation yield “R (percent)” and electrical energy consumption “EEC (kW h kg
1)” were validated by the analysis of variance (ANOVA) and were both noted to fit well with the experimental data. The R2 correlation coefficients (0.995, 0.978), those adjusted coefficients (0.986, 0.939), and F values (110.7, 24.9) obtained for the responses validated the efficiency of model. The results revealed that among several other parameters, EEC depended essentially on the degradation yield. The eco-toxicity tests showed a positive correlation between catalase activity and DY9 concentration, and catalase could be qualitatively identified to assess the effect of dye and its by-products generated during the EF process. & 2014 Elsevier Inc. All rights reserved.

Keywords: Electro-Fenton DY9 RSM Biomarker Catalase

1. Introduction Effluents containing high concentrations of toxic organic compounds (Z15 percent), particularly Azo dyes, are continuously discharged from various textile and paper industries in Algeria, as well as in several other countries throughout the world (Chafi et al., 2011; Daneshvar et al., 2006; Sengil and Özacar, 2009). When improperly treated and discharged into aquatic systems (water coastal, lakes and rivers), these effluents form a critical source of pollution and a real threat to aquatic life (e.g. various microbiological or marine animal species). In fact, they can interfere with the penetration of sunlight into waters and inhibit the growth aquatic micro and macro algae by disturbing the balance of gas solubility in the water (Golder et al., 2005; Sengil and Özacar, 2009; Willcock et al., 1992). These dyes and their byproducts have, therefore, often been reported to be toxic, carcinogenic, mutagenic and teratogenic (Willcock et al., 1992). This is, in n Corresponding author at: Department of Industrial Chemistry, Faculty of Technology, University of Saàd Dahlab at Blida, PO Box 270, 09000 Blida, Algeria. Fax: þ21325433631. E-mail address: [email protected] (S. Kourdali).

http://dx.doi.org/10.1016/j.ecoenv.2014.08.023 0147-6513/& 2014 Elsevier Inc. All rights reserved.

fact, particularly due to the properties and attributes of these dyes, including their color persistence, low biodegradability, and elevated pH. Their recalcitrance is also attributed to the high stability they confer to the dye molecules due to the presence of one or more azo chrophomores (N ¼N) or bonds between different aromatic rings (benzenic rings) and functional groups (–OH and –SO3H), which can supply color with absorption of radiant energy (Cruz-González et al., 2012; Dos Santos et al., 2007; Yousuf et al., 2010). The classical processes employed for azo dyes removal from wastewater effluents have often been reported to be limited in efficiency and to produce secondary pollution problems (Daneshvar et al., 2003). There has been, during the last few decades, an increasing interest in the search for efficient cost-effective and ecofriendly alternative strategies of azo dyes removal. The literature indicates that although adsorption and membrane techniques may yield into satisfactory results for the removal of dissolved and biodegradable resistant dyes, their application still remains relatively costly. In this context, various advanced electrochemical oxidation processes, including the electro-Fenton (EF) technique, have been proposed as promising substitutes. The latter allow for the in situ production of the hydroxyl radical OH∙ (in the presence of Fe2 þ and

S. Kourdali et al. / Ecotoxicology and Environmental Safety 110 (2014) 110–120

H2O2, see reaction (1)), a highly powerful oxidizing agent (Chemat et al., 2001; Feng et al., 2007; Ghosh et al., 2011; Kobya et al., 2011; Martinez et al., 2003; Nuñez et al., 2011). The EF technique is based on the electrochemical dissolution of a sacrificial iron anode (see reaction (2)) by the addition of Fe2 þ to the solution, using direct current or ferrous sulfate salt (in the latter case, another high O2 overvoltage anodes, such as borondoped diamond (BDD), platinum, or PbO2, is used (Chen and Chen, 2006; Panizza and Oturan, 2011), from which Fe2 þ generates the hydroxyl radical OH which will be used to attack organic compounds. H2O2 can also be electrochemically generated in situ by the reduction of dissolved oxygen (see reaction (3)) (Yavuz, 2007; Yavuz et al., 2010). Fe2 þ þ H2O2-Fe3 þ þOH∙ þOH


(1)

Fe-Fe2 þ þ2e


(2)

þ




O2 þ 2H þ 2e -H2O2

111

adequacy for application as bioindicators for mixed pollution (Dellali et al., 2001). The present work was undertaken to investigate the efficiency of the EF process in the treatment of an aqueous solution containing direct yellow 9 (DY9), a typical organic pollutant widely generated by the paper, tanning and textile industries. The study had three major objectives, namely: (1) the study of the effects of determining parameters ([H2O2], current intensity, initial pH, and nature of electrolytic support); (2) the optimization and the functional relationships between responses (removal efficiency and energy consumption) and the most significant independent variables (factors) by means of response surface methodology (RSM) using the DOEHLERT design experiment; and (3) the evaluation of the impacts brought by the dye and its by-products generated during EF treatment through measuring catalase activity in mussel gills as a biomarker of oxidative stress (Mytilus galloprovincialis).

(3)

The literature presents several studies wherein the EF process has been successfully applied for the degradation of various organic pollutants, including drugs (Daniel et al., 2012; Skoumal et al., 2008, 2009), dyes (Kayan et al., 2010; Ruiz et al., 2011), pesticides (Diagne et al., 2007), petroleum, and hydrocarbons (Yavuz et al., 2010). This line of research indicates that living organisms can be used as pollution bio-indicators to predict the pollution state of ecosystems and to monitor the impacts of the by-products generated during EF treatment prior to their ultimate discharge into the aquatic environment (Ferrat et al., 2003). Bivalves are, for instance, commonly used as bio-indicators for the assessment and control of anthropic activities on biocenotic systems because their tissues can be used to investigate histological changes and trace elements of accumulated pollutants (Ferrat et al., 2003; Rainbow and Phillips, 1993). Several biomarkers have been proposed to detect and assess the exposure of bivalves to different levels of contamination (Bonacci et al., 2007, 2008; De la Torre et al., 2007) For example, oxidative stress is one of the most significant effects caused by xenobiotics in receiving media and can be estimated by measuring the concentration of biomarkers of antioxidant activity (Livingstone, 2001). Biotransformation in organisms involves the activity of several oxygenases that oxidize xenobiotics to produce more soluble and extractable metabolites and reactive oxygen species (ROS). The latter can oxidize large amounts of key bio-molecules or be intercepted by antioxidant defenses such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) (Jebali et al., 2007). CAT is present in almost all animal and plant tissues and is commonly used as a marker of oxidative stress caused by certain pollutants in the environment, including pesticides, HAPs, PCBs, and dissolved metals (Lagadic et al., 1997, 1998). Several recent works have shown the increase of catalase activity in fish and bivalves exposed to organic pollutants. Several other works on biomarkers of oxidative stress have also been carried out in laboratory (especially in-situ), and the results have shown the non-specific nature of their responses, which demonstrates their

2. Material and methods 2.1. Chemical and reagents The commercial azo dye, furnished by the Boufarik textile plant (Algeria), was used without further purification. The chemical structure and major characteristics of this compound are presented in Table 1. Azo dye stock solutions were prepared in distillated water and diluted according to the desired concentration. Sulfuric acid (Sigma-Aldrich, 98.9 percent) and sodium hydroxide (Cheminova, 98 percent) were used for pH adjustment, sodium sulfate (Panreac, 99.5 percent) as electrolytic support, and H2O2 (Merck, 30 percent) as an OH radical source.

2.2. Electro-Fenton (EF) system An electrochemical glass reactor (140 mm  40 mm  2 mm, 1 L capacity, open and undivided at room temperature) with two electrodes (stainless steel as cathode and iron as anode) was employed. Precisely, a 40 cm2 electrode plate was submerged and placed vertically with an inter-electrode distance of 4 cm. The homogeneity of the solution in the reactor was assured using a magnetic agitator system (model Fisher Scientific FB 15010). The electrodes were connected to a DC power supply (ELEKTROLYSER, ELYN1 brand), an ammeter (PHYWE), and a voltmeter (DIGITAL VOLTMETER (G-1002-500).

2.3. Treatment procedure The experiments were performed using 800 mL of aqueous dye solution (50 mg L
1 of DY9), and the pH was adjusted to 3 in the presence of a known amount of electrolytic support. H2O2 was added at the concentrations recommended for dye solution. All experiments were carried out under agitation (200 rpm). Sampling (5 mL of DY9 treated solution) was performed at regular time intervals and filtered (0.45 mm) before each analysis.

2.4. Analytical methods 2.4.1. DY9 dye concentration A double-beam UV–vis spectrophotometer (Shimadzu UV-1700, Japan) equipped with a 10 mm quartz cell was used to measure [DY9 dye] by determining absorbance at Ymax (Ymax ¼403 nm). The degradation efficiency was calculated as

Table 1 Characteristics of treated azo dye (DY9). Chemical structure

Molecular formula

Color index name

Chemical name

C28H19N5Na2O6S4 Direct yellow 2,20 -(1-Triazene-1,3-diyldi-4,1-phenylene)bis(6-methyl-79 (DY9) benzothiazolsulfonic acid) disodium salt

λ max (nm)

Weight molecular M/ g mol
1

403

695

112

S. Kourdali et al. / Ecotoxicology and Environmental Safety 110 (2014) 110–120 concentration and electrolysis time). In this case, mussels were exposed for 3 days (72 h) in separate tanks as follows:

follows (Eq. (4)): ðC 0
C t Þ  100 R ð%Þ ¼ c0

ð4Þ

where C0 and Ct refer to initial concentration and concentration at time t, respectively. DY9 decay was monitored by HPLC using UV–visible detector at 254 nm, 125  4.6 mm RP-C18 column and isocratic mobile phase of acetonitrile (Panreac, 99.99 percent): deionized water 0.3:0.7 (v/v) at a flow rate of 0.6 mL min
1. Data were analysed using class VP Shimadzu software, and samples were filtered (0.45 mm) before injection.

All The experiments were conducted three times and 7 standard deviations are reported.

2.4.2. Energy consumption Energy consumption was calculated as follows (Eq. (5)): EEC ðkW h kg


1

Þ¼

UIt V  R  ½DY90

– Tanks 1–6 containing DY9 solutions (DY9 þ seawater) before EF treatment at 0, 250, 500, 1000, 2000, and 4000 mg L
1, respectively. – DY9 solution after EF treatment at ○ 1st dilution (06 percent) in seawater: 0, 20, 40, 60, and 80 min, corresponding to tanks 7–11, respectively. ○ 2nd dilution (04 percent) in seawater: 0, 20, 40, 60, and 80 min, corresponding to tanks 12–16, respectively.

ð5Þ

where U refers to the cell potential (V), I to current intensity (A), t to the time of electrolysis (h), V to the volume of solution treated (L), and R to the ratio of degradation. 2.4.3. pH measurements The pH of the solutions before and after treatment was measured using a EUTECH-pH meter (resolution and accuracy: 70.01). 2.5. Experimental design Response surface methodology (RSM) is a collection of statistical and mathematical techniques useful for developing, improving and optimizing processes. It is commonly used for the modeling and analysis of problems in which a response of interest is influenced by several operating variables. This technique allows obtaining maximum data needed to optimize this response, including the effects of multiple parameters and their interactions, using a reduced number of experimental trials (Gengec et al., 2012; Khataee et al., 2010; Korbahti et al., 2007). The DOEHLERT design is an equally popular multivariate second order design frequently used in experimental set-ups of process optimization studies. In this study, RSM was adopted with the DOEHLERT design to optimize and examine the effects of three important operating variables, namely initial pH (X1), [H2O2] (X2), and operating time (X3) on DY9 degradation (Y1) and energy consumption (Y2). The DOEHLERT design was used to generate higher polynomial equations, such as that of the quadratic model, that describe the interactions (Eq. (6)). Y ¼ β0 þ β1 X 1 þ β2 X 2 þ β3 X 3 þ β11 X 21 þ β22 X 22 þ β33 X 23 þ β12 X 1 X 2 þ β13 X 1 X 3 þ β23 X 2 X 3 ð6Þ The predicted response (Y) is, therefore, correlated to the set of regression coefficients (β): the constant (β0), linear effect (β1, β2, β3), interaction effect between factors X1, X2 and X3 (β12, β13, β23) and quadratic effects of factors X1, X2 and X3 (β11, β22, β33). DY9 degradation (percent, Y1) and energy consumption (kW h kg
1 of dye, Y2) were selected as the response for each test. In brief, the high, middle and low factors were represented by þ1(X1 ¼ 5, X2 ¼ 1000 mg L
1 and X3 ¼ 60 min), 0 (X1 ¼4, X2 ¼ 625 mg L
1 and X3 ¼ 35 min) and
1(X1 ¼ 3, X2 ¼250 mg L
1 and X3 ¼ 10 min), respectively. The range of these experimental values was determined from prior tests. A total of 15 experiments (each experiment was performed in triplicates) were performed and employed for each optimization study, using the Matlab 7 software for the statistical design of experiments. In order to evaluate the goodness of fit of the polynomial model, R2, R2adjusted, F-test and lack of fit were checked. Model terms were evaluated by the P value with 95 percent confidence level. 2.6. Eco-toxicity assay 2.6.1. Sampling and preparation of mussels Standard shell size (40–50 mm) samples of M. galloprovincialis were collected in November 2011 at the reference site (Figuier, Boumerdes, Algeria). This site is located in a remote region, far from industrial activity, and has high mussel densities that allow for repeated sampling. The samples were transported to the laboratory, scrubbed and cleaned for further use. A sufficient amount of natural seawater (35.9‰, 16–16.5 1C) was collected for mussel acclimation and toxicity tests (all experimental with mussels were conducted in accordance with national and institutional guidelines for the protection of human subjects and animal welfare). The mussels were acclimated for 10 days during which seawater was regularly aerated and renewed. Toxicity tests were carried out in the laboratory using 25-L tanks (seawater) containing 10 acclimated mussels. Before any treatment, mussels were checked for health conditions with 30-min recordings of pre-exposure control. After EF treatment, the DY9 solution was added to the seawater in each tank (at the desired

2.6.2. Preparation of samples At the end of each exposition cycle, the mussels (n ¼10) were immediately dissected, and soft tissues (gills and adductor muscle) were removed and stored at
20 1C. All preparation phases were carried out at þ 4 1C. Tissues were homogenized in tris–HCl buffer (20 mM; pH 7.8) (Biochem, Chemopharma, 99 percent) at ratio of 1/10 (w/v). The homogenates were centrifuged (10,000g for 30 min), and the supernatant (especially S9 fraction) was collected and used for the dosing of proteins and catalase.

2.6.3. Biochemical analyses Total protein and catalase activities were measured according to Lowry et al. (1951) and Clairbone (Romeo et al., 2003), respectively. Kinetic mod was applied for catalase estimation as follows: absorbance variations at 240 nm caused by the dismutation of H2O2 (100 mL, 30 percent, ε ¼40 M
1 cm
1) prepared in buffer phosphate (2.4 mL, 75 mM and pH 7) was measured by the addition 20 mL of S9 for 1 min. The results were expressed as micromoles of substrate (H2O2) dismutated per minute per milligram of total homogenate protein (mmol min
1 mg prot
1).

3. Results and discussion 3.1. Effect of H2O2 concentration Fig. 1a shows that the increase in the concentration of H2O2 induced an increase in the degradation of DY9. Without the addition of H2O2, the ratio of DY9 degradation was slightly enhanced with time, up to 20 min. The degradation ratio was noted to increase significantly depending on [H2O2] (28.82 percent after 60 min at 0.25 g L
1 and reached 80 percent at 0.5 g L
1). This could be attributed to the additional formation of hydroxyl radicals (OH∙) in the presence of a catalyst (Fe2 þ ) in which OH∙ attacked the pollutant in a preferential way. These results are in agreement with the findings previously reported in the literature (Gulkaya et al., 2006; Liu et al., 2007; Wang et al., 2000). However, when [H2O2] exceeded 0.5 g L
1, the degradation ratio was noted to undergo a progressive decrease (58.47 percent at 0.8 g L
1 and less of 50 percent at 1 or 2 g L
1 for 60 min). This result indicated that higher doses of H2O2 could not provide satisfactory yields. In this case, there was a scavenging reaction between H2O2 and OH∙, as shown in reaction (7), wherein they were used less effectively; i.e., H2O2 was not fully employed to attack DY9 (Ghosh et al., 2011; Gulkaya et al., 2006). Several studies have previously reported on the effect of H2O2 and showed that Fenton reaction treatment represent a more efficient source for free OH∙. OH∙ þH2O2-HO∙2 þH2O

(7)

Bautista et al. (2007) showed that the effect of H2O2 is limited by a concentration equivalent to an optimum degradation value. They noted that the percentage of humic acid destruction increases with increasing [H2O2] up to 0.01 percent and that it starts to decrease beyond this concentration. Contrariwise, Gulkaya et al. (2006) proved that [H2O2] has a considerable effect on the decomposition of dyes in synthetic wastewater and that the elimination of COD (Chemical Oxygen Demand) is directly proportional to the initial [H2O2].

S. Kourdali et al. / Ecotoxicology and Environmental Safety 110 (2014) 110–120

113

degradation ratio (80 percent for 60 min electrolysis) and the rapid kinetics of degradation were then determined. For the same period of electrolysis time and at pH 2.5 and 4, the degradation ratio did not exceed 60 and 32 percent, respectively. Slower degradation rates reaching less than 15 percent were also observed for 60 min of electrolysis at pH 7 and 8. As often reported in the literature, pH is one of the significant operating factors that influence the electrochemical performance process (Kobya et al., 2006a; Savas et al., 2008). It is described to determine iron speciation in the solution, and its optimum value for the treatment of dye solutions by Fenton and EF processes is determined at around 3. At low pH, iron, such as ferric ions (Fe2 þ ), is soluble in water and plays a catalytic role to generate OH∙. In traditional Fenton process, iron species begin to precipitate as ferric hydroxides at higher pH values. On the other hand, iron species form stable complexes with H2O2 at lower pH values, leading to deactivation of catalysts. The latter results can, therefore, be attributed to the predominance of ferrous species (Fe2 þ ) at pH 3 and their lower levels at high pH values (Pignatello and Baehr, 1994; Zhao et al., 2012). In addition, at pH 3 iron species (Fe2 þ ) and hydrogen peroxide would remain steady and keep its forms. Consequently, the Fenton reaction can strongly take place under these conditions. Furthermore, in a highly acidic medium (pH o3), H2O2 was previously shown to become very unstable and to have a tendency to solvate a proton to give H3O2þ (reaction (8)). This form strongly reduces its reactivity with the catalyst (Gulkaya et al., 2006; Zhao et al., 2012). In low acidic media (3 opH o 6) on the other hand, the dominant forms of iron in the solution are the Fe2 þ or Fe3 þ -hydroxy complexes such as Fe(OH)2 þ and Fe(OH)2þ which can less catalyse the decomposition of H2O2 according to Gallard et al. (1999). Also at pH 6, the ferrous and ferric ions are found in precipitate forms (iron hydroxides (reaction (9))), such as Fe(OH)3(s) and Fe(OH)2(s).the reactivity of all of these iron forms with H2O2 are very low and, hence, less levels of OH∙ production were observed. In low acidic media (pH43) on the other hand, the ferrous and ferric ions precipitate in iron hydroxides forms (reaction (9)), such as Fe (OH)3(s) and Fe(OH)2(s)), whose reactivities with H2O2 are very low and, hence, less levels of OH∙ production were observed. Chu et al. (2004) showed that at pH45, H2O2 becomes unstable and breaks up into O2 and H2O, thus losing its oxidation capacities. H2O2 þH3O þ -H3O2þ þ H2O

(8)

Fe3 þ þ3H2O-Fe(OH)3(s) þ3H þ

(9)

The values calculated for energy consumption based on different initial pH are given in Fig. 2b. The results revealed that energy consumption decreased with the increase of pH, with the minimum value being recorded at pH 3 after 60 min of electrolysis. 3.3. Effect of current intensity on DY9 degradation Fig. 1. Effect of the operating parameters on the degradation of DY9 dye as a function of electrolysis time (pHi ¼ 3, I¼ 0.2 A, C0 ¼ 50 mg L
1, d¼ 4 cm, [Na2SO4] ¼ 1.5 g L
1, [H2O2] ¼0.5 g L
1,): (a) H2O2 concentration; (b) Initial pH; (c) Current intensity; (d) effect of nature electrolytic support.

Energy consumption is inversely proportional to [H2O2] (Fig. 2a). A minimum energy was consumed (less of 100 kW h kg
1 of dye) with highest [H2O2] (0.5, 0.8, 1 and 2 g L
1) then the ratio of degradation of DY9 was more important. 3.2. Effect of initial pH The effect of initial pH on DY9 dye degradation is illustrated in Fig. 1b. The optimal value was obtained at pH 3 with a maximum

Fig. 1c. shows that increasing current intensity between 0.05 and 0.2 A caused an increase in DY9 degradation (39.45 to 80.11 percent during 60 min). The enhancement effect was due to the electro-generation/availability of ferrous ions (Fe2 þ ) at the iron anode which was, in turn, caused by the increased current. This led to the generation of a high amount of hydroxyl radicals by the Fenton reaction. At 0.3 A, however, the degradation ratio did not exceed 30 percent after 60 min. This was presumably due that the rapid increase recorded for the pH solution (final pH ¼5.9) which was unfavourable for Fenton oxidation. In fact, similar results were previously reported by Ghosh et al. (2011) for COD reduction from industry wastewater. Furthermore, at higher current, an increase was observed for the competitive electrodes reactions: hydrogen by cathode and oxygen by anode (reactions (10) and (11)).

114

S. Kourdali et al. / Ecotoxicology and Environmental Safety 110 (2014) 110–120

Fig. 2. Effect of the operating parameters on the energy consumption and degradation of DY9 dye (pHi ¼ 3, I¼ 0.2 A, C0 ¼ 50 mg L
1, d¼ 4 cm, [Na2SO4]¼ 1.5 g L
1, [H2O2]¼ 0.5 g L
1,): (a) H2O2 concentration; (b) initial pH; (c) current intensity.

H2O-1/2O2 þ2H þ þ2e


(10)

2H þ þ2e
-H2

(11)

Fig. 2c shows a slight augmentation in energy consumption with increasing current intensity, presumably because the applied potential was improved with current intensity. The degradation ratios recorded at 0.05, 0.1, and 0.2 A during 60 min of electrolysis were 38, 62 and 80 percent, corresponding to 12.19, 21.19, and 47.48 kW h kg
1, respectively. However, at 0.3 A and during the same period of time, the system was noted to consume a high

amount of energy (255 kW h kg
1). Therefore, it appears that treatment with 0.2 A was the appropriate value for attaining high degradation with minimum amounts of energy consumption. Several studies (Ozcan et al., 2009; Panizza and Oturan, 2011) have previously concluded that current intensity has a determining impact on the amount of Fenton reagents (Fe2 þ and H2O2). In fact, the sufficient amount of OH∙ was related to the concentration of the Fenton reagent applied to oxidize organic pollutants. Moreover, the bubble generation rate was noted to increase with current intensity, which was beneficial for homogenizing the solution and for increasing the possibilities of contact

S. Kourdali et al. / Ecotoxicology and Environmental Safety 110 (2014) 110–120

between the existing molecules and OH∙ (Kobya et al., 2006b; Secula et al., 2011). 3.4. Effect of the nature of electrolytic support Fig. 1d shows that degradation rate was higher when NaCl and KCl were used as electrolytic supports as compared to other salts, with degradation rates of 91.53 and 87.24 percent being obtained after 60 min of electrolysis, respectively. Under the same conditions, however, the degradations rates obtained with Na2SO4 and Na2CO3 were 80.35 and 67.29 percent, respectively. The improvement in degradation obtained with NaCl and KCl could be attributed to the strong mobility of the ions of these electrolytes by ensuring the good passage of the current and decreasing the losses of energy in heat (Joule phenomenon). As well as the probability of release of the secondary reactions will be minimal on the one hand. On the other hand, to the oxidation capacity of the hypochlorous acid (HOCl) on the organic compounds, whose formation can occur, to a lesser extent, by the oxidation of chlorine ions Cl
according to reactions (12) and (13). The presence of chlorine ions (Cl
) and free radicals (OH∙) simultaneous (synergic) ameliorate and enhance the degradation of DY9. In fact, similar conclusions were reported by Ghoneim et al. (2011). 2Cl
-Cl2g þ2e


(12)


Cl2g þH2O-HOCl þCl þH

þ

(13)




Besides, Cl anions can hinder the formed passivation layer on the electrode and enhance the anodic dissolution of the metal (Chou et al., 2009; Lee and Pyun, 1999) which leads to the generatation of more catalysts and, hence, higher levels of OH∙ production. The use of this type of electrolytes remains, however, very limited by the formation of carcinogenic compounds (THM). Furthermore, Fan et al. (2010) have previously shown that the rate of rodamine B (RhB) degradation by EF reached 93.8 percent when Na2SO4 was used. It was, however, reported to be very weak (20.6 percent) with the use of sodium bicarbonate. This could be attributed to the reaction of OH∙ by CO23
which gave an electron to the hydroxyl radical, thus rendering it an anion hydroxyl (OH∙
). Indeed, the presence of CO23
in solution can significantly reduce the effectiveness of EF. The nature of salt plays an important role in the dyeing steps (diffusion, adsorption and fixation) and is considered one of more important factors contributing to the efficiency of electrochemical systems. 3.5. Kinetics of DY9 decay The kinetics of DY9 reaction with OH∙ were investigated to check the pseudo-order of the oxidation reaction at different current intensities and different H2O2 concentrations. DY9 degradation related to the simple reaction at operating conditions (pH¼ 3, C0 ¼50 mg L
1) and followed (Eq. (14)):
Ln C 0 =C t ¼ k1  t ð14Þ where k1 refers to the apparent first order rate constant, and C0 and Ct to the concentration of DY9 at initial and electrolysis time t, respectively. This equation was obtained from equation (Eq. (15)):
dC=dt ¼ k  C ð15Þ The k1 value and the corresponding correlation coefficient calculated from these analyses at different current intensities and H2O2 concentrations are given in Table 2. The results revealed that the concentration decay during EF followed a pseudo first-order and that the plot of LnC0/C according to the electrolysis time presented good linearity with all correlation coefficients r2 40.93. It was also observed that DY9

115

degradation was strongly accelerated and improved with the current intensity due to the free radicals (OH∙) produced in significant amounts. Consequently, a significant enhancement was recorded for k1, and the pseudo-first rate constant was 1noted to shift from 0.0057 for 0.05 A to 0.0271 min
1 for 0.2 A. The degradation rates were, however, noted to undergo a deceleration when the concentration of H2O2 increased from 0.5 to 2 g L
1, which were attributed to the second reactions of hydroxyl radicals with H2O2 and other elements present in the solution. This could, therefore, confirm that, after 60 min of electrolysis, the first order constant k1 decreased from 0.0271 min
1 at 0.5 g L
1 to 0.0113 at 2 g L
1. 3.6. Optimization of the EF treatment of DY9 by RSM 3.6.1. Effect of factors on DY9 degradation and energy consumption The DOEHLERT design was applied for the optimization of EF treatment using three factors in three levels and three additional experimental trials as replicates of the central point. The results are presented in Table 3. The effects of X1, X2 and X3 on the degradation ratio (R) and energy consumption (EEC) were examined. The quadratic model (Eq. (6)) fitted very well with the experimental data and estimated coefficients presented in Table 4. A positive sign designated a synergetic factor effect and indicated that the response was improved when the factor increased, whereas a negative sign designated an antagonistic factor effect and indicated that the response was not enhanced with the factor level. It could be concluded from the model applied for the degradation of DY9 in the present study that: – [H2O2], electrolysis time (individual factor), interaction between [H2O2] and electrolysis time, and quadratic pH (quadratic factors effects) represented positive effects, – pH (individual factor), interaction between pH and [H2O2], and interaction between pH and electrolysis time represented negative effects. In fact, pH, electrolysis time, and the interactions between [H2O2] and electrolysis time were noted to exert relatively more effects on DY9 degradation and energy consumption. These results are congruent with the findings previously obtained in the first part of this work. 3.6.2. Evaluation and validation of models The adequacy of the models for DY9 degradation and energy consumption were justified through analysis of variance (ANOVA) and the results are given in Table 5. The residual dispersion was very small for the studied cases. The results showed that the values of the predicted and measured responses were very close. The R2 coefficient presented the total variation in the response variables that accounted for the predictors included in the model. The R2 values of 0.995 and Table 2 Effect of operating parameters on the pseudo first-order rate constant under conditions given in figs.

[H2O2] (g L
1)

I (A)

0.25 0.5 1 2 0.005 0.1 0.15 0.2

Pseudo-first rate constant Kap (min
1)

Correlation coefficient (r2)

0.0057 0.0271 0.0131 0.0113 0.0057 0.0084 0.0164 0.0271

0.9974 0.9998 0.8741 0.9264 0.9919 0.9369 0.9623 0.9998

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Table 3 Experimental results of DOEHLERT design experiments for EF treatment. Run no.

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

Real levels

Coded levels

Response

X1 (pH)

X2 [H2O2] (mg L
1)

X3 Electrolysis time (min)

X1 pH

X2 [H2O2] (mg L
1)

X3 electrolysis time (min)

R (%)

EEC (kW h kg
1)

4 5 4.5 3.5 3 3.5 4.5 4.5 3.5 4 4.5 3.5 4 4 4

625 625 949.75 949.75 625 300.25 300.25 733.375 733.375 408.625 516.625 516.625 841.375 625 625

35 35 35 35 35 35 35 55.4 55.4 55.4 14.6 14.6 14.6 35 35

0 þ1 0.5
0.5
1
0.5 þ 0.5 þ 0.5
0.5 0 þ 0.5
0.5 0 0 0

0 0 þ0.866 þ0.866 0
0.866
0.866 þ0.289 þ0.289
0.577
0.289
0.289 þ0.577 0 0

0 0 0 0 0 0 0 þ 0.816 þ 0.816 þ 0.816
0.816
0.816
0.816 0 0

24.30 11.42 15.20 43.60 61.20 41.02 16.23 24.18 55.60 22.60 16.15 44.01 16.23 24.30 25.01

98.42 209.43 157.35 54.85 39.08 58.30 147.36 156.56 68.09 167.51 61.78 22.67 61.47 98.42 95.63

Table 4 Estimated values coefficients of response functions. Regression coefficient

Responses EEC(kW h kg
1)

Degradation R (%) β0 β1 β2 β3 β12 β13 β23 β11 β22 β33

24.5367
26.5037 0.8321 5.3081
2.0843
1.4432 18.5894 11.7733 2.0435 4.4311

( 71.0697) ( 70.9264) ( 70.9264) ( 70.9270) ( 72.1395) ( 72.3937) ( 72.3937) ( 71.6914) ( 71.6915) ( 71.6065)

97.4867 82.4800
3.7717 50.2957 7.7540 27.5050
61.9989 26.7583 0.3817
18.5022

( 7 7.8723) ( 7 6.8176) ( 7 6.8178) ( 7 6.8218) ( 7 15.7451) ( 7 17.6158) ( 7 17.6123) ( 7 12.4472) ( 7 12.4480) ( 7 11.8229)

Table 5 Analysis of variance (ANOVA) results for the treatment of azo dye (DY9) by EF. R (%)

Residual dispersion Total dispersion Predicted dispersion Residual variance total variance Predicted variance Pure error Lack of fit R2 R2 Adj Value of Ficher

17.16 3440.0 3422.8

Degree of freedom

EEC (kW h kg
1)

5

929.60

5

14 9

42719 41789

14 9

3.43

185.92

245.71 380.31

3051.3 4643.2

0.252 16.89 0.9950 0.9860 110.7

Degree of freedom

1 4

3.92 925.45 0.9782 0.9391 24.97

1 4

0.978 recorded for the model of degradation and for the models of energy consumption, respectively, were noted to be relatively high. There was also a desirable and reasonable agreement between the predicted and experimental uptake values from these models. The adjusted determination coefficient R2adjust showed that 98.6 and 93.9 percent of the variability observed could be explained by the models, respectively. The lack of fit also showed that the models could be used to predict the degradation ratio and energy consumption from the initial experimental conditions used and to investigate the surface response for the optimization of the process.

3.6.3. Response surface and contour plotting The 3D surface and 2D contour plots (isoresponse curves) generated for the degradation ratio of DY9 and energy consumed by the EF process and to visualize the interaction effects of the factors involved are shown in Fig. 3 and Fig. 4, respectively. Fig. 3a shows that the degradation R of DY9 increased with the decrease in pH when the electrolysis time was kept at 60 min. A concurrent slight improvement in degradation was observed when [H2O2] increased from a low (
1) to a high (þ1) level. In fact, the interaction between these factors is known to have less effects on this particular response. It was, therefore, possible to conclude from this case that the optimal zone for better DY9 degradation rates of more than 70 percent was between two levels of pH (of
1 and
0.75) and two levels of H2O2 concentration (0.5 and 1). The second case is illustrated in Fig. 3b which shows the effect of the interaction between pH and electrolysis time on DY9 degradation for 650 mg L
1 of H2O2. The data indicated that this response underwent a slight increase as a function of electrolysis time and a considerable decrease with pH from its low to high levels. The contour surface also revealed the presence of a small zone where the degradation ratio was maximum. This zone was limited by two pH levels ranging between
0.80 and
1, which corresponded to 3.5 and 3, and to two electrolysis times ranging from 0.6 to 1, which corresponded to 45 and 60 min, respectively. Fig. 3c shows that the best degradation ratio of DY9 was obtained with increasing the electrolysis time by keeping pH constant at 3. The H2O2concentration was noted to bring a positive effect on the response between high and middle levels (1 and 0, corresponding to 625 and 1000 mg L
1, respectively). Beyond this level (0), the degradation of DY9 underwent a slight decrease with all electrolysis times. Both factors were also noted to have a significant interactive effect on this response. These results could be accounted for based on the same arguments presented in the previous part of the study. The findings revealed that maximum degradation rates could be obtained under the following conditions: – Electrolysis time ranging between 0.5 and 1, corresponding to 47.5 and 60 min, respectively. – H2O2 concentrations ranging between 0.5 and 1, corresponding to 812.5 and 1000 mg L
1 respectively. Fig. 4 shows that the most significant effects of both pH and [H2O2] on the energy consumed by the EF process during the treatment varied depending on their levels, with electrolysis time

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117

Fig. 3. Three dimensional response surface plot and isoresponse curves for percent DY9 dye degradation using EF process as a function of: (a) H2O2 concentration and pH, (b) electrolysis time and pH, (c) electrolysis time and H2O2 concentration.

fixed at 60 min. The minimum value recorded for EEC was attained by decreasing the pH value, which was limited between
1 and
0.7 (3 and 3.3) (Fig. 4a). Similar results could be observed from Fig. 3 in relation to the effect of H2O2 concentrations on EEC, suggesting that the EF process could consume minimum energy when the levels of H2O2 shifted from 1 (1000 mg L
1) to 0 (625 mg L
1). Fig. 4b and c, on the other hand, showed that electrolysis time exerted fewer effects on EEC than the other factors under investigation. The interactions between electrolysis time and pH or H2O2 concentrations were also noted to induce slight positive effects on this response. Taken together the results presented above had strong effects on the rates of DY9 degradation. The rates of DY9 degradation and of the EEC required for the EF process were optimized according to the study performed by RSM. The curves of isoresponses were projected in the same plans of the factors under study as follow: pH and H2O2 concentration, pH and electrolysis time, and finally H2O2 concentration and electrolysis time. The 2-D contour (isoresponses) plots are presented in Fig. 4d–f. The results showed the dependence of EEC on the effectiveness of the process employed for the treatment of the dye under investigation. Nevertheless, and as illustrated in Fig. 4d, the optimal conditions were limited to pH values ranging between 1 and
0.6 and to H2O2 concentrations ranging between 0.4 and 1. These conditions ensured maximum efficiency and minimum EEC (65– 87 percent, less 50 kW h kg
1). Moreover, and as can be seen from Fig. 4e, a small optimized zone (more 56 percent of degradation and less 55 kW h kg
1 of energy) was obtained when the H2O2 concentration was kept constant (at 650 mg L
1) with increasing the electrolysis time from
1 to 1 (corresponding to et) and using pH values ranging from
08 to
1. In the third case (Fig. 4f), and with a pH fixed at 3, the optimum rates of efficiency were obtained with electrolysis times ranging between 0, 5 and 1 and with H2O2 concentrations ranging between 0.7 and 1.

In order to further optimize the experimental conditions of DY9 dye treatment by EF, the RSM was applied in combination with the DOEHLERT design.

3.7. Eco-toxicity tests of DY9 This part of the study focused particularly on catalase activity as a biomarker of oxidative stress in M. galloprovincialis exposed to DY9-colored water (before and after EF treatment). This step allows for the control of the treated DY9-colored water and related by-products that cause oxidative stress in mussels. By-products could accelerate pro-oxidants by oxy-radical formations, and their excess can increase lipid peroxidation (loss of membrane integrity) (Ferrat et al., 2003; Rijstenbil et al., 1994a).

3.7.1. Physicochemical parameters Temperature variations were almost identical and stable throughout the period of study (0.133–0.433 1C) for all series of experiments. Salinity variation was also noted to be quasi-null (0.033–0.100 percent), in the interval of optimal salinity for mussel growth. No difference was, therefore, observed between the reference and contaminated groups, and the bio-tests revealed that those two physicochemical parameters did not affect the metabolic activities of the indicator species used. In fact, the importance of salinity has often been highlighted in recent research, and its influence as an abiotic factor on the metabolic activity and particularly on enzymatic responses is also wellestablished in the literature (Fernández et al., 2010; Paital and Chainy, 2010). Several recent studies have also reported on the effects of temperature and salinity on pollution accumulations, metabolic activity, and biomarker responses in Mediterranean mussels (Kopecka et al., 2006).

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Fig. 4. Response surface plot, isoresponse curves for energy consumption and Combined isoresponse curves for percent degradation and energy consumption in DY9 degradation using EF treatment as a function of: (a) and (d) H2O2 concentration and pH, (b) and (e) electrolysis time and pH, (c) and (f) electrolysis time and H2O2 concentration.

The nitrate and phosphorus contents in the experiments (seawater) were measured (data not shown). Very weak fluctuations in the variation (seemed negligible) of nitrate and phosphorus contents were observed for all experiments. This led to the conclusion that exposure to the pollutant exerted no effects on the metabolic activity of the specimen under investigation.

3.7.2. Biochemical measurements 3.7.2.1. Proteinic reserves. The effects of DY9 dye on the energy reserves in the mussels were investigated (data not shown). Exposures to various DY9 dye concentrations (from 0 to 4 mg L
1) were noted to induce significant reductions in the proteinic reserves as compared to the control. The colored water treated by EF did not have any significant effect on the proteinic reserves. Infact, Amiard and Amiard-Triquet (2008) have previously concluded that, following laboratory or in-situ

exposure to various contaminants, the response tends towards a reduction in protein reserves. According to Mosleh et al. (2007), protein depletion represents an early defense response front to chemical stress. 3.7.2.2. Catalase activity as biomarker of oxidative stress. Fig. 5a shows catalase activity dosed in gills exposed to different DY9 concentrations as compared to the control tank. It can be noted that the toxicity tests carried out in the laboratory on the specimen revealed significant effects exerted on the gills. A positive correlation was, for instance, noted between the catalase response and DY9 concentrations. In fact, the highest catalase activity (65 U mg
1) was obtained after the mussels were exposed to the highest DY9 concentration (4 mg L
1). This increase in enzymatic activity followed the increase in the rate of ROS caused by the DY9 dye and resulted in the significant increase in CAT values as compared to that of the control.

S. Kourdali et al. / Ecotoxicology and Environmental Safety 110 (2014) 110–120

119

that the colored water treated by EF does not cause oxidative stress nor other dangerous effects on the mussels.

4. Conclusion The findings of the present study demonstrated that the application of an EF process using iron metal as anode and stainless steel as cathode is appropriate for the attainment of effective results in terms of DY9 dye degradation in an aqueous solution. A number of parameters, including [H2O2], pH, current intensity, and electrolytic support type and concentration, were noted to have significant effects on the process. RSM was used to optimize those parameters and conditions required for maximum DY9 degradation with minimum energy consumption during EF. The DOHLERT design was also applied to produce two quadratic models for two responses. The values generated by the models fitted well with the experimental values and the R2 coefficients confirmed their adequacy. The 3D-plot and isoresponse curves allowed for the determination of optimal condition zones. The results showed a positive correlation between catalase activity and DY9 concentration in water. Catalase activity was also noted to decrease processing time, indicating the absence of provocative species of oxidative stress. Further studies involving the dosage of other biomarkers (lipid peroxidation estimated by malondialdehyde, superoxide dismutase and glutathione peroxidase) are needed to further confirm and advance the results presented in this work, which might open new promising opportunities for the monitoring of water quality treated by the electrochemical processes prior to discharge in aquatic systems.

Acknowledgments

Fig. 5. Evolution of CAT (as a biomarker of oxidative stress) in mussels “Mytilus galloprovincialis”: (a) effect of DY9 dye concentration; (b) exposition to DY9 dye solution treated by EF as a function of time (Dilution (06%)); (c) exposition to DY9 dye solution treated by EF as a function of time (Dilution (06%)).

Organic compounds and metals are known to be involved in ROS generation through Fenton reactions, inactivation of antioxidant defenses, and inhibition of electron transport chains, thus allowing for the transference of electrons directly to oxygen (Ochi et al., 1987; Tatrai et al., 2001). The results obtained for the mussels’ exposure to the colored solutions treated by EF are presented in Fig. 5b and c. CAT activity was noted to undergo a considerable decrease with electrolysis time applied to the water containing DY9 and treated by EF in both dilutions. The maximum value recorded for CAT activity (65 U mg
1) was recorded at an electrolysis time of 0 min, and was then noted to decrease to 30 U mg
1 and to less than 20 U mg
1 at 40 and 80 min, respectively for the first dilution. The reduction in CAT activity with electrolysis time provided evidence for the decrease of the dye concentration following EF treatment, and, consequently, the effect exerted by this substance on the mussels started to disappear. The cases involving by-products generated in the solution during the EF process revealed that those by-products did not induce oxidative stress in the mussels. This could be attributed to their reactivities or weak tenors in the solution. Overall, the findings from the preliminary tests presented in this work provide strong evidence

This work was funded by “l’Observatoire National de l’Environnement et de Développement Durable (ONEDD Project code 35/changements climatiques) and le Ministère de l’enseignement supérieur et la recherche scientifique (Projet CNEPRU code J0100420120004)”. The authors would like to express their gratitude to Mr Anouar Smaoui from FSS for his valuable proofreading and language polishing services.

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