Environ Sci Pollut Res DOI 10.1007/s11356-014-2790-2 ELECTROCHEMICAL ADVANCED OXIDATION PROCESSES FOR REMOVAL OF TOXIC/PERSISTENT ORGANIC POLLUTANTS FROM WATER

Factorial design analysis for COD removal from landfill leachate by photoassisted Fered-Fenton process Xiaogang Wu & Hui Zhang & Yanli Li & Daobin Zhang & Xianwang Li

Received: 19 February 2014 / Accepted: 10 March 2014 # Springer-Verlag Berlin Heidelberg 2014

Abstract The Fered-Fenton process has been shown to be an effective method for leachate treatment, but it still faces problems of inadequate regeneration of ferrous ion. However, the use of the photoassisted Fered-Fenton process could overcome this difficulty and improve the efficiency of chemical oxygen demand (COD) removal since photoassisted FeredFenton process induces the production of hydroxyl radicals from the regeneration of ferrous ions and the reaction of hydrogen peroxide with UV light. As there are so many operating parameters in photoassisted Fered-Fenton process, it is necessary to develop a mathematical model in order to produce the most economical process. In the present study, a factorial design was carried out to evaluate leachate treatment by photoassisted Fered-Fenton process. The influence of the following variables: H2O2 concentration, Fe2+ concentration, current density, and initial pH in the photoassisted FeredFenton process was investigated by measuring COD removal efficiencies after 60-min reaction. The relationship between COD removal and the most significant independent variables was established by means of an experimental design. The H2O2 concentration, Fe2+ concentration, initial pH, and the interaction effect between current density and initial pH were all significant factors. The factorial design models were derived based on the COD removal efficiency results and the models fit the data well.

Responsible editor: Philippe Garrigues X. Wu : H. Zhang (*) : Y. Li : D. Zhang : X. Li Department of Environmental Engineering, Hubei Biomass-Resource Chemistry and Environmental Biotechnology Key Laboratory, Wuhan University, 430079 Wuhan, China e-mail: [email protected] X. Wu School of Urban Construction, Yangtze University, 434023 Jingzhou, China

Keywords COD removal . Electrochemical advanced oxidation processes, experimental design . Factorial design . Landfill leachate . Photoassisted Fered-Fenton process

Introduction Landfill is the most commonly used method for the management of municipal solid waste (MSW) due to technological maturity and economic merits. Leachate generated from landfills through physiochemical and biological decomposition of solid wastes is a mixture of highly concentrated organics which may exhibit acute and chronic toxicity (Renou et al. 2008). It is difficult for conventional biological or physicochemical treatment to deal with landfill leachate before discharge into the environment because of its refractory contaminants and large variations in the type and amounts of pollutants present (Kurniawan et al. 2006; Deng and Englehardt 2006). Advanced oxidation processes (AOPs) are one of the most promising alternative methods which are capable of degrading a wide variety of refractory compounds from stabilized landfill leachate (Kurniawan et al. 2006; Deng and Englehardt 2006; Ammar et al. 2012; Umar et al. 2010). The Fenton process is a commonly used AOP with low cost and simple operability as well as efficient performance for landfill leachate treatment (Deng and Englehardt 2006; Umar et al. 2010; Deng 2007; Mohajeri et al. 2010a; Zhang et al. 2005a, 2009; Lak et al. 2012; Lopez et al. 2004). The Fenton process is based on the electron transfer between hydrogen peroxide and ferrous ion, which acts as a homogenous catalyst, yielding hydroxyl radicals that can degrade organic compounds (Deng and Englehardt 2006; Umar et al. 2010). Since the regeneration of ferrous ion is much slower in Fenton chain reactions, the combination of an electrochemical method along with the Fenton process, such as Fered-Fenton

Environ Sci Pollut Res

method and electrochemical peroxidation (ECP) process, has been proposed for the treatment of landfill leachate (Atmaca 2009; Mohajeri et al. 2010b; Zhang et al. 2006, 2012a, b). For example, Mohajeri et al. (2010b) used Fered-Fenton method where both hydrogen peroxide and ferrous ion were externally applied. Under optimum conditions, 94.07 % of chemical oxygen demand (COD) and 95.83 % of color were removed from the leachate. Atmaca (2009) reported 72 % COD removal, 90 % color removal, 87 % PO4-P removal, and 28 % NH4-N removal were achieved by ECP process in which hydrogen peroxide was applied from outside and a sacrificial iron anode was used as ferrous ion source. In Fered-Fenton process, the propagation of Fenton’s reaction is enhanced by electrochemical regeneration of ferrous ion from ferric ion at the cathode when electrical current is applied (Brillas et al. 2009). Fe3þ þ e− → Fe2þ

ð1Þ

Ferrous ion regeneration by electrochemical method alone is, however, limited in the electrochemical efficiency (Ting et al. 2008). The photoassisted Fered-Fenton process appears to be the most promising alternative (Zhang et al. 2012c), as it has the several major advantages. Firstly, the photolysis of [Fe(OH)]2+, the predominant Fe3+ complex in acid medium, produces a higher amount of •OH (Eq. 2) (Pignatello et al. 2006). ½FeðOHފ2þ → Fe2þ þ •OH hv

ð2Þ

Additionally, hydroxyl radicals may also be generated by photolysis of hydrogen peroxide (Kochany and Bolton 1992). hv

H2 O2 → 2 • OH

ð3Þ

Consequently, the use of the photoassisted Fered-Fenton process in the treatment of leachate has become of considerable interest lately due to the high efficiency of this technique. To the best of our knowledge, there is only one report on leachate treatment by photoassisted electrochemical peroxidation process (ECP) (Altin 2008) and the traditional one-factorat-a-time approach was used to investigate the operating conditions on the efficiency of photoassisted ECP (Altin 2008). In this case, the effect of a single variable was measured over its range while the other variables were held constant. However, this technique fails to consider any possible interaction between the factors and thus ignores any influence of the other factors at different levels (Zhang et al. 2009; Mohajeri et al. 2010b). As there are many possible combinations of variables in the photoassisted ECP as well as photoassisted Fered-Fenton

process, interaction between factors are very common, and if they occur, the one-factor-at-a-time strategy will usually produce poor results and is always less efficient than other methods based on a statistical approach to design. In general, factorial designs are the most effective for this type of experiment since all possible combinations of the levels of the factors can be investigated. Factorial designs have several advantages in that they are more efficient than one-factor-ata-time experiments and they provide a systematic way of working that allows conclusions to be drawn about the variables (or combinations of them) that are most influential in the response factor while carrying out the minimum possible number of experiments. Furthermore, when interactions are present, the technique avoids misleading conclusions being drawn. Finally, a factorial design allows the effects of a factor to be estimated at several levels of the other factors, yielding conclusions that are valid over a range of experimental conditions (Montgomery 2001). Several papers have been published about the application of factorial design to the determination of important variables in Fenton-related advanced oxidation processes as well as to the influence of these variables on the degradation process (Mosteo et al. 2006; Martins et al. 2010; Pérez-Moya et al. 2007; Galvao et al. 2006; Ormad et al. 2006; Torrades et al. 2003; Ramirez et al. 2005; Lim et al. 2009; Molina et al. 2006). To the best of our knowledge, there appears to be no reports of the application of factorial design to the photoassisted Fered-Fenton process for treatment of landfill leachate. Therefore, factorial design was used in this study to verify the most influential factors as well as to investigate the effects of four variables on COD removal during the photoassisted Fered-Fenton oxidation of landfill leachate. The four variables investigated were ferrous ion dosage, hydrogen peroxide concentration, initial pH, and current density.

Materials and methods Design of experiments Factorial designs were employed to investigate the effects of the four independent variables on the response function. The independent variables considered in this study were the following: hydrogen peroxide concentration (A), ferrous ion dosage (B), current density (C), and initial pH (D). The low and high levels of each variable are designated as −1 and +1, respectively, as illustrated in Table 1. The 16 (24) runs of the two-level factorial design experiment for four parameters were performed randomly (runs 1–16 in Table 2). This design is considered to be the most suitable in order to obtain knowledge of the influence of the variables on the process being studied (Ormad et al. 2006).

Environ Sci Pollut Res Table 1 Experimental range and levels of the independent variables Variables

−1

1

H2O2 (mol/L), A Fe2+ (g/L), B Current density (mA/cm2), C pH, D

0.15 4.587 11.1 3

0.30 9.592 18.5 6

Table 3 Properties of the leachate used

Parameter

Value

COD (mg/L) NH4+ (mg/L) TN (mg/L) NO3− (mg/L) NO2− (mg/L)

2,416 3,152 2,694 5.32 0.14

Cl− (mg/L) BOD5 (mg/L) pH

3,052 361 8.5

Experimental methods Leachate samples were acquired from a landfill site at Wuhan, China using polyethylene bottles. Samples were refrigerated at 4 °C in accordance with the American Public Health Association Standard Methods (APHA 1998). Table 3 shows the main characteristics of the leachates used in the experiments. Batch experiments were performed in a rectangular electrolytic reactor (Plexiglas) which has been used in our previous study (Zhang et al. 2012c). The reactor was immersed in a tap water bath to keep at room temperature. Electrolyses were operated at constant current using a DC power supply with one 20 cm ×15 cm plate anode (Ti / RuO2 –IrO2) and one plate cathode (stainless steel) of the same dimensions arranged parallel to each other at a distance of 3.4 cm. Two 15-W UV lamps were used to irradiate the solution with light of λmax =254 nm. A magnetic stirrer was used to mix the solution in the rector. In each run, leachate (1 L) was transferred to the electrolytic reactor and a selected amount of ferrous sulfate heptahydrate (see Table 1) was dissolved in the leachate. The initial pH value was adjusted

Table 2 Design matrix in coded and natural/uncoded units and the experimental responses

to 3 using concentrated sulfuric acid, and the experiments were initiated after hydrogen peroxide (Table 1) was added to the reactor and the DC power and UV lamps were turned on. After 60 min of treatment time, an aliquot (20 mL) was transferred into tubes containing sodium hydroxide solution to quench the reaction by increasing the pH to around 8.0. This was allowed to settle for 2 h and then the supernatant was withdrawn to measure COD. The solution pH was measured with an Orion 420Aplus pH meter, and chemical oxygen demand (COD) was determined using a closed reflux titrimetric method based on the American Public Health Association Standard Methods (APHA 1998). Five-day biological oxygen demand (BOD5) was measured by incubation for 5 days at 20 °C using an OxiTop BOD analyzer (WTW). The analyses of total nitrogen, ammonianitrogen, nitrate-nitrogen, nitrite-nitrogen, and chloride were performed according to the standard methods.

Run No.

H2O2 (mol/L), A

Fe2+ (g/L), B

Current density (mA/cm2), C

Initial pH, D

COD removal efficiency (%)

1 2 3 4 5 6 7 8

0.15 (−1) 0.30 (+1) 0.15 (−1) 0.30 (+1) 0.15 (−1) 0.30 (+1) 0.15 (−1) 0.30 (+1)

4.587 (−1) 4.587 (−1) 9.592 (+1) 9.592 (+1) 4.587 (−1) 4.587 (−1) 9.592 (+1) 9.592 (+1)

11.1 (−1) 11.1 (−1) 11.1 (−1) 11.1 (−1) 18.5 (+1) 18.5 (+1) 18.5 (+1) 18.5 (+1)

3 (−1) 3 (−1) 3 (−1) 3 (−1) 3 (−1) 3 (−1) 3 (−1) 3 (−1)

64.2 82.3 63.8 93.0 79.2 87.3 89.6 94.5

9 10 11 12 13 14 15 16

0.15 (−1) 0.30 (+1) 0.15 (−1) 0.30 (+1) 0.15 (−1) 0.30 (+1) 0.15 (−1) 0.30 (+1)

4.587 (−1) 4.587 (−1) 9.592 (+1) 9.592 (+1) 4.587 (−1) 4.587 (−1) 9.592 (+1) 9.592 (+1)

11.1 (−1) 11.1 (−1) 11.1 (−1) 11.1 (−1) 18.5 (+1) 18.5 (+1) 18.5 (+1) 18.5 (+1)

6 (+1) 6 (+1) 6 (+1) 6 (+1) 6 (+1) 6 (+1) 6 (+1) 6 (+1)

61.3 64.8 71.7 82.1 54.4 62.5 67.3 73.2

Environ Sci Pollut Res

Results and discussion Analysis of experimental data The design matrix of the two-level factorial design experiment and the response results shown by the removal efficiencies of COD in the photoassisted Fered-Fenton process are presented in Table 2. The removals of COD from the two-level factorial design experiment were between 54.4 and 94.5 %. Based on Table 2, the main effects plot and the interaction plots for COD removal efficiencies were developed. The main effects plots are illustrated in Fig. 1 and show the effect of each factor on COD removal. This type of representation shows the contribution to the response factor of changing one of the variables selected for the photoassisted Fered-Fenton process. It can be seen that the effects of A (H2O2), B (Fe2+), and C (current density) on COD removal efficiencies at 60-min reaction time are positive, i.e., greater removal of COD could be achieved at the high level (+1) of each factor compared to the low level (−1). On the other hand, the effect of D (initial pH) on COD removal at 60-min reaction time was negative. In Fig. 1, the slope of the plot is indicative of the importance of the variable on the response factor (Mosteo et al. 2006; Torrades et al. 2003), and it can be seen that the effect of hydrogen peroxide, ferrous ion dosage, and initial pH on COD removal is a little more important than that of current density. It should be noted that Fig. 1 just depicts the main effects plot for the COD removal efficiency and the dependence between the response variable (COD removal efficiency) and all the design variables are not necessarily as linear as depicted in Fig. 1. This will be revealed from the quadratic model discussed later. Figure 2 indicates interaction plots showing the existence or otherwise of interaction among the factors. It may be assumed that there is an interaction between variables when the change in COD removal efficiency from the low level to the high level of one variable is not the same as the change in response at the same two levels of a second variable (Ormad et al. 2006). In other words, the effect of one variable is dependent upon a second variable (Ormad et al. 2006).

C-

B+

B+

B-

BA+

A-1

A+

A+

AD

1

-1

C

A1

80

70

Factorial design provides systematic ways to analyze the most influential factors in the response result, and then a halfnormal plot is drawn to screen several key factors from those insignificant in the experiment. Figure 3 shows the normal probability of the absolute values of various effects, which is used for isolating the main effects, and Pi is calculated using (Poblete et al. 2011) Pi ð%Þ ¼ 100½ð j−0:5Þ=15Š

Fig. 1 Main effects plot for COD removal efficiency

ð5Þ

where Pi is the normal probability and j is the serial number of the absolute values of various effects from the least level to the maximum level.

99

50 -1 C 1

1

Analysis of factorial design of experiments

1

-1 B 1

B

Thus, it is observed that the interaction effect between current density and initial pH is significant since the plots cross as shown in Fig. 2.

60

-1 A 1

-1

Fig. 2 Interaction plots for COD removal efficiency

95 90 80 70 50 30 20 10 5

90

COD removal (%)

C+

90 80 70 60 50 90 80 70 60 50 90 80 70 60 50

-1 D 1

Fig. 3 The normal probability of the effect

Environ Sci Pollut Res D

5

t-value

4

A B CD

3 t-value limit 2.201 2 1 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Rank

Fig. 4 The Pareto chart of the effects of the variables

The factors and interactions that lie along the line are negligible, while the rest of the factors or their crossinteractions are significant and are used to estimate the experimental error (Poblete et al. 2011; Anglada et al. 2011a). On the basis of the calculated effects, it can be seen that hydrogen peroxide concentration (A), ferrous ion dosage (B), initial pH (D), and the interaction of current density and initial pH (C×D) have significant effects on COD removal. A Pareto graphic (Fig. 4) is used to draw conclusions about which of these variables and interactions are most significant and shows both the magnitude and the importance of these effects. There is a reference line (the horizontal plot) on the Pareto chart and any effect that extends above this line is potentially important (Mosteo et al. 2006; Torrades et al. 2003). The variables and interactions which can be considered as especially important are also the effect of hydrogen peroxide concentration (A), ferrous ion dosage (B), initial pH (D), and the interaction of current density and initial pH (C×D). Based on the variables and interactions which are statistically significant, hydrogen peroxide concentration (A), ferrous ion dosage (B), initial pH (D), and the interaction of current density and initial pH (C×D), a reduced model describing the experimental response was constructed. The accuracy of the models was justified through analysis of variance (ANOVA) where experimental data (the selected significant effects) was dealt with using multiple regression analysis

Table 4 ANOVA for the response surface quadratic model

and the total variation was subdivided into variation due to main factors, variation due to interacting factors, and variation due to error. Statistical tests, like the F test, are used to study statistically significant control factors (CFs) and interacting factors, which help in screening many factors to discover those that are important and how they interact. The coefficients of the reduced model in the polynomial expression are calculated by multiple regression analysis and represent the weight of each variable. For the current study, statistically significant CFs and interacting factors shown in Table 4 are determined using ANOVA. The computed values of the F ratio are lower than that of the tabular F0.05(4,4) value of 6.39. P values of less than 0.05 indicate that coefficients of the model equation are significant. The statistical significance of the model was also confirmed by the coefficients of determination of the models, and the fit of the model was checked by the determination coefficient (R2), which quantitatively evaluates the correlation between the experimental data and the predicted responses. The values of the determination coefficient (R2 =0.857) indicate that 14.3 % of the variability in the response were not explained by the model. The model of the second-order polynomial response equation was used to correlate the dependent and independent variables. Accordingly, an empirical relationship between the response and independent variables was attained and expressed by the following second-order polynomial equation to calculate COD removal efficiencies at 60 min: Y ¼ 74:45 þ 5:52A þ 4:96B − 7:29D − 4:37CD

ð6Þ

where Y is the response variable of removal efficiency and A, B, C, and D are the coded experimental levels of the variables. The examination of residuals was used to investigate the model adequacy, and the normality of the data can be checked by constructing a normal probability plot of the residuals (Fig. 5). Normal plots presented are normally distributed and resemble a straight line assembly and residuals contain no obvious patterns and are structureless (95 % confidence interval), so it can be seen that the normality assumption was confirmed and the models are adequate (Anglada et al. 2011b).

Source of variation

Sum of squares

Degree of freedom

Mean square

F value

P value

Model A B D CD Residual Cor total

2,036.5 487.5 393.4 850.5 305.0 340.2 2,376.8

4 1 1 1 1 11 15

509.1 487.5 393.4 850.5 305.0 30.9

16.4 15.7 12.7 27.5 9.8

0.0001 0.0022 0.0044 0.0003 0.0094

Environ Sci Pollut Res 81

0.8

Fe2+

Normal probability (%)

0.4

78 75

0.0 69

72

-0.4 -0.8

66

-0.8

-0.4

0.0

0.4

0.8

H2O2

0.8

66

Internally studentized residuals

69

Fig. 5 The normal probability of the residuals pH

0.4

100

Predicted value

80 60

0.0 75

-0.4 -0.8

81

78

-0.8

84

-0.4

0.0

0.4

0.8

Current density

Fig. 7 Contour lines of response surface showing COD removal efficiency as a function of variables (the coded values of other variables are zero)

contrast, an elliptical or saddle nature of the contour plots indicates that the interaction between the corresponding variables is significant. As can be seen in Fig. 7, COD removal efficiencies decreased sharply with the pH increment at higher current density applied, while COD removal increased gradually with current density increased at lower pH. The effect of current density had a slight effect on COD removal, and consequently,

100

COD removal efficiency (%)

The experimental results and the predicted values obtained from the model (Eq. 6) were compared. In an ideal case where R2 value is 1, all points on the predicted versus the actual graph would lie on the line y = x. The reduced model, expressed as Eq. (6) in terms of the coded factors, could be used to calculate COD removal efficiency. As can be seen from Fig. 6, the statistical significance of the model was further evident from the fact that the values calculated with the predictive equation were very close to the experimental values. A graphical interpretation of the model is presented in Fig. 7. The contour plots of the model-predicted responses, with two variables being kept constant and varying the others within the experimental ranges, were utilized to assess the interactive relationships between the process variables and the response treatment outputs for COD removal. The contours of the plots help in identification of the type of interactions between these variables. Each contour curve represents an infinite number of combinations of two tested variables with the other two maintained at their respective zero level. A circular contour of response surfaces indicates that the interaction between the corresponding variables is negligible. In

72

Photoassisted Fered-Fenton process Fered-Fenton

80

60

40

20

40

0

20 0

0

20

40 60 Actual value

80

100

Fig. 6 Predicted versus actual values plot for COD removal efficiency

20

40

60

80 100 Time (min)

120

140

160

Fig. 8 COD removal from the leachate using the photoassisted FeredFenton process and the Fered-Fenton process (pH0 3.0, i=14.8 mA/cm2, de =3.4 cm, [Fe2+]=2.43 g/L, [H2O2]=0.22 mol/L; Fenton’s reagent added in a single step)

Environ Sci Pollut Res

it is a minor factor. The possible reason is analyzed as follows: the regeneration of ferrous ion could induce a Fenton chain reaction efficiently in addition to the electro-regeneration of ferrous ion from ferric ion at the cathode (Eq. 1). There is another way to regenerate ferrous ion, the photolysis of [Fe(OH)]2+ which is the predominant Fe3+ complex in acid medium (Eq. 2). When the current density becomes small, the reaction shown in Eq. (1) slowed down, but the reactions given in Eq. (2) could regenerate ferrous ion and produce additional hydroxyl radicals via photolysis in the photoassisted Fered-Fenton processes, indicating that current density is less important in the photoassisted Fered-Fenton processes. While the H2O2 concentration, Fe2+ concentration, and initial pH all play a major role in the photoassisted FeredFenton processes.

Conclusions

Comparison between the photoassisted Fered-Fenton process and the Fered-Fenton process

Acknowledgments This study was supported Human Settlements and Environment Commission of Shenzhen Municipality, Wuhan Science and Technology Bureau through “The Gongguan Project”, China (Grant No.201060723313) and the National High-Tech R&D Program (863 Program) of China (Grant No. 2008AA06Z332). We appreciate the valuable comments and suggestions from the reviewers. The generous help of Professor David H. Bremner in revising this manuscript is also greatly appreciated.

In order to compare leachate treatment carried out using the photoassisted Fered-Fenton process and the Fered-Fenton process, the initial pH of the leachate was adjusted to 3.0, current density (i) was set at 14.8 mA/cm2, the distance between the electrodes (de) was 3.4 cm, and Fenton’s reagent was added in a single step with the [Fe2+] at 2.43 g/L and [H2O2] being 0.22 mol/L. As can be seen in Fig. 8, organic materials were rapidly degraded by the photoassisted Fered-Fenton processes and the Fered-Fenton processes and most of the removal of the organics occurred during the first 60 min and consequently all later experiments were terminated at 60 min. COD removal efficiency was observed to be 60 % after 60 min using the Fered-Fenton process, whereas after the same time, the photoassisted Fered-Fenton process reached 71 %. These results indicate that the photoassisted Fered-Fenton process can more effectively accelerate the COD removal compared to the Fered-Fenton process. Altin (2008) also observed a 10 % difference in the COD removal efficiency between photoassisted ECP process and ECP process alone. Moreover, both processes could achieve higher COD removal efficiencies than UV/H2O2 and electro-coagulation processes. Besides organic contaminants, ammonia nitrogen is another principal component in the leachate (Deng and Englehardt 2007; Zhang et al. 2010). Although electro-oxidation are capable of removing ammonia nitrogen from the leachate via the mechanism similar to “breakpoint reactions” (Anglada et al. 2011a; Zhang et al. 2010, 2011; Cabeza et al. 2007; Panizza and Martinez-Huitle 2013), Fenton-related processes are ineffective for ammonia removal (Cabeza et al. 2007; Zhang et al. 2005b). The electro-oxidation could be integrated with Fered-Fenton or photoassisted Fered-Fenton process to efficiently remove organic contaminants as well as ammonia nitrogen.

The results of factorial design analysis showed that the H2O2 concentration, Fe2+ concentration, initial pH, and the interaction effect between current density and initial pH were all significant factors. The COD removal increases with increasing H2O2 concentration and Fe2+ concentration, stays nearly constant with increasing current density, but increasing pH hinders COD removal. Current density is less important due to regeneration of ferrous ion through the photolysis-assisted oxidation in the photoassisted Fered-Fenton processes. The response models were derived based on COD removal efficiency results and the contour plots of the response surface were developed accordingly.

References Altin A (2008) An alternative type of photoelectro-Fenton process for the treatment of landfill leachate. Sep Purif Technol 61:391–397. doi: 10.1016/j.seppur.2007.12.004 Ammar S, Asma M, Oturan N (2012) Electrochemical degradation of anthraquinone dye alizarin red: role of the electrode material. Curr Org Chem 16:1978–1985. doi:10.2174/138527212803251613 Anglada A, Urtiaga A, Ortiz I, Mantzavinos D, Diamadopoulos E (2011a) Boron-doped diamond anodic treatment of landfill leachate: evaluation of operating variables and formation of oxidation byproducts. Water Res 45:828–838. doi:10.1016/j.watres.2010.09.017 Anglada A, Urtiaga A, Ortiz I, Mantzavinos D, Diamadopoulos E (2011b) Treatment of municipal landfill leachate by catalytic wet air oxidation: assessment of the role of operating parameters by factorial design. Waste Manag 31:1833–1840. doi:10.1016/j. wasman.2011.03.023 APHA, AWWA, WPCF (1998) Standard methods for the examination of water and wastewater, 20th edn. American Public Health Association, American Water Works Association, Water Pollution Control Federation, Washington Atmaca E (2009) Treatment of landfill leachate by using electro-Fenton method. J Hazard Mater 163:109–114. doi:10.1016/j.jhazmat.2008. 06.067 Brillas E, Sires I, Oturan MA (2009) Electro-Fenton process and related electrochemical technologies based on Fenton’s reaction chemistry. Chem Rev 109:6570–6631. doi:10.1021/cr900136g Cabeza A, Urtiaga A, Rivero MJ, Ortiz I (2007) Ammonium removal from landfill leachate by anodic oxidation. J Hazard Mater 144:715– 719. doi:10.1016/j.jhazmat.2007.01.106 Deng Y (2007) Physical and oxidative removal of organics during Fenton treatment of mature municipal landfill leachate. J Hazard Mater 146: 334–340. doi:10.1016/j.jhazmat.2006.12.026

Environ Sci Pollut Res Deng Y, Englehardt JD (2006) Treatment of landfill leachate by the Fenton process. Water Res 40:3683–3694. doi:10.1016/j.watres. 2006.08.009 Deng Y, Englehardt JD (2007) Electrochemical oxidation for landfill leachate treatment. Waste Manag 27:380–388. doi:10.1016/j. wasman.2006.02.004 Galvao SA, Mota AL, Silva DN, Moraes JE, Nascimento CA, ChiavoneFilho O (2006) Application of the photo-Fenton process to the treatment of wastewaters contaminated with diesel. Sci Total Environ 367:42–49. doi:10.1016/j.scitotenv.2006.01.014 Kochany J, Bolton JR (1992) Mechanism of photodegradation of aqueous organic pollutants. 2. Measurement of the primary rate constants for reaction of hydroxyl radicals with benzene and some halobenzenes using an EPR spin-trapping method following the photolysis of hydrogen peroxide. Environ Sci Technol 26:262– 265. doi:10.1021/es00026a004 Kurniawan TA, Lo WH, Chan GYS (2006) Radicals-catalyzed oxidation reactions for degradation of recalcitrant compounds from landfill leachate. Chem Eng J 125:35–57. doi:10.1016/j.cej.2006.07.006 Lak MG, Sabour MR, Amiri A, Rabbani O (2012) Application of quadratic regression model for Fenton treatment of municipal landfill leachate. Waste Manag 32:1895–1902. doi:10.1016/j.wasman. 2012.05.020 Lim CL, Morad N, Teng TT, Ismail N (2009) Treatment of Terasil Red R dye wastewater using H2O2/pyridine/Cu(II) system. J Hazard Mater 168:383–389. doi:10.1016/j.jhazmat.2009.02.061 Lopez A, Pagano M, Volpe A, Di Pinto AC (2004) Fenton’s pre-treatment of mature landfill leachate. Chemosphere 54:1005–1010. doi:10. 1016/j.chemosphere.2003.09.015 Martins RC, Rossi AF, Quinta-Ferreira RM (2010) Fenton’s oxidation process for phenolic wastewater remediation and biodegradability enhancement. J Hazard Mater 180:716–721. doi:10.1016/j.jhazmat. 2010.04.098 Mohajeri S, Aziz HA, Isa MH, Bashir MJK, Mohajeri L, Adlan MN (2010a) Influence of Fenton reagent oxidation on mineralization and decolorization of municipal landfill leachate. J Environ Sci Health Part A Toxic/Hazard Subst Environ Eng 45:692–698. doi:10.1080/ 10934521003648883 Mohajeri S, Aziz HA, Isa MH, Zahed MA, Adlan MN (2010b) Statistical optimization of process parameters for landfill leachate treatment using electro-Fenton technique. J Hazard Mater 176:749–758. doi: 10.1016/j.jhazmat.2009.11.099 Molina R, Martinez F, Melero JA, Bremner DH, Chakinala AG (2006) Mineralization of phenol by a heterogeneous ultrasound/Fe-SBA15/H2O2 process: multivariate study by factorial design of experiments. Appl Catal B: Environ 66:198–207. doi:10.1016/j.apcatb. 2006.03.015 Montgomery DC (2001) Design and analysis of experiments, 5th edn. Wiley, New York Mosteo R, Ormad P, Mozas E, Sarasa J, Ovelleiro JL (2006) Factorial experimental design of winery wastewaters treatment by heterogeneous photo-Fenton process. Water Res 40:1561–1568. doi:10. 1016/j.watres.2006.02.008 Ormad MP, Mosteo R, Ibarz C, Ovelleiro JL (2006) Multivariate approach to the photo-Fenton process applied to the degradation of winery wastewaters. Appl Catal B: Environ 66:58–63. doi:10.1016/ j.apcatb.2006.02.014 Panizza M, Martinez-Huitle CA (2013) Role of electrode materials for the anodic oxidation of a real landfill leachate—comparison between Ti– Ru–Sn ternary oxide, PbO2 and boron-doped diamond anode. Chemosphere 90:1455–1460. doi:10.1016/j.chemosphere.2012.09.006

Pérez-Moya M, Graells M, del Valle LJ, Centelles E, Mansilla HD (2007) Fenton and photo-Fenton degradation of 2-chlorophenol: multivariate analysis and toxicity monitoring. Catal Today 124:163–171. doi:10.1016/j.cattod.2007.03.034 Pignatello JJ, Oliveros E, MacKay A (2006) Advanced oxidation processes for organic contaminant destruction based on the Fenton reaction and related chemistry. Crit Rev Environ Sci Technol 36: 1–84. doi:10.1080/10643380500326564 Poblete R, Otal E, Vilches LF, Vale J, Fernandez-Pereira C (2011) Photocatalytic degradation of humic acids and landfill leachate using a solid industrial by-product containing TiO2 and Fe. Appl Catal B: Environ 102:172–179. doi:10.1016/j.apcatb.2010.11.039 Ramirez JH, Costa CA, Madeira LM (2005) Experimental design to optimize the degradation of the synthetic dye Orange II using Fenton’s reagent. Catal Today 107–108:68–76. doi:10.1016/j. cattod.2005.07.060 Renou S, Givaudan JG, Poulain S, Dirassouyan F, Moulin P (2008) Landfill leachate treatment: review and opportunity. J Hazard Mater 150:468–493. doi:10.1016/j.jhazmat.2007.09.077 Ting WP, Lu MC, Huang YH (2008) The reactor design and comparison of Fenton, electro-Fenton and photoelectro-Fenton processes for mineralization of benzene sulfonic acid (BSA). J Hazard Mater 156:421–427. doi:10.1016/j.jhazmat.2007.12.031 Torrades F, Pérez M, Mansilla HD, Peral J (2003) Experimental design of Fenton and photo-Fenton reactions for the treatment of cellulose bleaching effluents. Chemosphere 53:1211–1220. doi:10.1016/ S0045-6535(03)00579-4 Umar M, Aziz HA, Yusoff MS (2010) Trends in the use of Fenton, electro-Fenton and photo-Fenton for the treatment of landfill leachate. Waste Manag 30:2113–2121. doi:10.1016/j.wasman.2010.07. 003 Zhang H, Choi HJ, Huang CP (2005a) Optimization of Fenton process for the treatment of landfill leachate. J Hazard Mater 125:166–174. doi: 10.1016/j.jhazmat.2005.05.025 Zhang H, Choi HJ, Huang C-P (2005b) Landfill leachate treatment by Fenton’s reagent: the variation of leachate characteristics. Fresenius Environ Bull 14:1178–1183 Zhang H, Zhang DB, Zhou JY (2006) Removal of COD from landfill leachate by electro-Fenton method. J Hazard Mater 135:106–111. doi:10.1016/j.jhazmat.2005.11.025 Zhang H, Choi HJ, Canazo P, Huang CP (2009) Multivariate approach to the Fenton process for the treatment of landfill leachate. J Hazard Mater 161:1306–1312. doi:10.1016/j.jhazmat.2008.04.126 Zhang H, Li Y, Wu XG, Zhang YJ, Zhang DB (2010) Application of response surface methodology to the treatment landfill leachate in a three-dimensional electrochemical reactor. Waste Manag 30:2096– 2102. doi:10.1016/j.wasman.2010.04.029 Zhang H, Ran XR, Wu XG, Zhang DB (2011) Evaluation of electrooxidation of biologically treated landfill leachate using response surface methodology. J Hazard Mater 188:261–268. doi:10.1016/j. jhazmat.2011.01.097 Zhang H, Ran XN, Wu XG (2012a) Electro-Fenton treatment of mature landfill leachate in a continuous flow reactor. J Hazard Mater 241: 259–266. doi:10.1016/j.jhazmat.2012.09.040 Zhang H, Wu XG, Li XW (2012b) Oxidation and coagulation removal of COD from landfill leachate by Fered-Fenton process. Chem Eng J 210:188–194. doi:10.1016/j.cej.2012.08.094 Zhang H, Li YL, Wu XG (2012c) Statistical experiment design approach for the treatment of landfill leachate by photoelectro-Fenton process. J Environ Eng ASCE 138:278–285. doi:10.1061/(ASCE)EE.19437870.0000448