Modeling Experimental Design for Photo-Fenton Degradation of Methomyl

doi:10.4236/ojapps.2012.24032

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Open Journal of Applied Sciences, 2012, 2, 216-223

doi:10.4236/ojapps.2012.24032 Published Online December 2012 (http://www.SciRP.org/journal/ojapps)

Modeling Experimental Design for Photo-Fenton

Degradation of Methomyl

Abdelhadi Abaamrane1,2*, Samir Qourzal2, Saïd Mançour Billah1, Ali Assabbane2, Yhya Ait-Ichou2

1Laboratoire de Génie des Procédés, Département de Chimie, Faculté des Sciences,

Université Ibn Zohr, Agadir, Morocco

2Equipe de Matériaux, Photocatalyse et Environnement, Département de Chimie, Faculté des Sciences,

Université Ibn Zohr, Agadir, Morocco

Email: *[email protected]

Received August 27, 2012; revised September 30, 2012; accepted October 10, 2012

ABSTRACT

Modeling experimental design was used to study the main effects and the interaction effects between operational pa-

rameters in the photocatalytic degradation of pesticide methomyl. The important parameters which affect the removal

efficiency of methomyl such as concentration of Fe(NO3)3, concentration of H2O2, initial concentration of the pesticide

and pH. The parameters were coded as x1, x2, x3 and x4, consecutively, and were investigated at two levels (–1 and +1).

The effects of individual variables and their interaction effects for dependent variables, namely, photocatalytic degrada-

tion efficiency (%) were determined. From the statistical analysis, the most effective parameters in the photocatalytic

degradation efficiency were initial concentrations of the methomyl and Fe(NO3)3. The interaction between initial con-

centration of the pesticide and Fe(NO3)3 was the most influencing interaction. The optimum conditions that were ob-

tained for the photocatalytic degradation of methomyl were: minimum quantity of contaminant: 6 × 10–5 mol·L–1,

maximum quantity of Fe(NO3)3: 5 × 10–4 mol·L–1, initial pH of the solution: 3 and maximum quantity H2O2: 10–2

mol· L –1.

Keywords: Methomyl; Photocatalytic Degradation; Response Surface Methodology (RSM); Full Factorial Design

1. Introduction

Pesticides are commonly used worldwide to face the

need for increasing and improving agricultural produc-

tion [1,2]. The detection of pesticides in storm and waste-

water effluent is reported to be a major obstacle as re-

gards wide ranging acceptance of water recycling. Fur-

thermore, their variety, toxicity and persistence can di-

rectly impact the ecosystem and threaten humans through

contamination of drinking water supplies (surface and

ground water).

Methomyl is an oxime carbamate insecticide (Figure

1). It is produced by reacting S-methyl-N-hydroxylthio

acetamidate (MHTA) in methylene chloride with gaseous

methyl isocyanate at 30˚C - 35˚C. Methomyl is highly

soluble in water (57.9 g·L−1) [3]. It has a low sorption

affinity to soil and can therefore easily cause groundwa-

ter contamination in agricultural areas.

Methomyl is effective in two ways: 1) as a “contact

insecticide” because it kills target insects upon direct

contact; and 2) as a “systemic insecticide” because of its

capability to cause overall “systemic” poisoning in target

insects, after it is absorbed and transported throughout

the pests that feed on treated plants. This insecticide is

mainly used in Morocco on a wide range of tomato crops.

However, it has been classified as a very toxic and ha-

zardous pesticide [4].

Recently, chemical treatment methods, based on the

generation of hydroxyl radicals (OH•), known as Ad-

vanced Oxidation Processes (AOPs), due to their effi-

ciency in oxidizing a great variety of organic contami-

nants [5-9].

The Fenton treatments are the requirement of H2O2,

Fe2+ salts and pH adjustment (mostly acidic). With the

additions of H2O2 and Fe2+ salts, highly reactive and un-

selective oxidants are produced as shown in Equation (1)

that leads to the formation of less powerful hydroperoxyl

Figure 1. Chemical structure of methomyl.

*Corresponding author.

A. ABAAMRANE ET AL. 217

radical as per Equation (2) [10,11].



•



•

Fe2+ + H2O2 → Fe3+ + OH• + OH– (1)

Fe3+ + H2O2 → Fe2+ + + H+ (2)

In order to overcome the sludge problem and enhance

the Fenton treatment, the photo-Fenton process was de-

veloped by introducing a UV light to the Fenton process.

In the presence of UV-irradiation, the Fe3+ complex

formed in Equation (1) can be photo reduced to Fe2+.

This could facilitate the reaction of photo reduced Fe2+

with more H2O2 molecules, which produce new OH•

(Equation (3)) and form a Fenton reaction cycle [12].

This cycle is useful for the progress of the Fenton treat-

ment with no continuous addition of Fe2+ and devoid of

ferric hydroxide sludge. Moreover, two OH• can be pro-

duced from hydrogen peroxide under the UV-irradiation

as shown in Equation (4) [13,14].

FeOH2+ + hν → Fe2+ + OH• (3)

H2O2 + hν → 2OH• (4)

In conventional AOPs methods, the experiments are

usually conducted by varying some studied parameters

while keeping others constant. To avoid repeating this pro-

cess for all influential parameters, factorial design [15,16]

is an experimental strategy that allows the simultaneous

manipulation of many factors and possible synergistic

and antagonistic interactions between them that are de-

termined. In addition, system optimization can be attained

performing a smaller number of experiments than that

needed for univariate techniques resulting in lower reagent

consumption and considerably less laboratory work.

The Response Surface Methodology (RSM) also is an

efficient experimental strategy for determining the opti-

mal conditions. This method is a collection of statistical

and mathematical techniques used for development, im-

provement, and optimization of certain processes in

which a response of interest is affected by several pro-

cess variables and the objective is to optimize this re-

sponse. RSM was applied to AOPs to design and formu-

late new processes and products. The results have been

satisfactory in studies that involve the application of fac-

torial design in the photocatalytic degradation of organic

compounds [17-20].

The objective of this study is to determine the optimal

experimental conditions for methomyl degradation in

photo-Fenton treatment combining H2O2/Fe2+/UV using

RSM, and to examine both single and combined effects

among independent variables of Fe(NO3)3, H2O2, metho-

myl concentration and pH.

2. Experimental

2.1. Reagents

De-ionized water is used throughout this study. Metho-

myl (C5H10N2O2S) is purchased from Merck Chemical

Company at highest purity (99.9%). Ferric nitrate nona-

hydrate (Fe(NO3)3‧9H2O) is provided by Prolabo. Hy-

drogen peroxide solution (35%, v/v) in stable form is

provided by Panreac. The pH of the pesticide solution is

adjusted by using H2SO4 or NaOH (Merck).

2.2. Experimental Apparatus

All photo-Fenton experiments were performed in a well

stirred, batch, cylindrical photoreactor with a total vo-

lume of 500 mL (Figure 2). The reactor is made of glass

and does not contain any metal parts. At the top, the re-

actor has inlets for feeding reactants, and ports for meas-

uring temperature and withdrawing samples. The reactor

was exposed to a luminous source composed of a me-

dium pressure mercury-lamp (Philips HPK, 125 W)

which emitted a maximum radiation at 365 nm, placed in

axial position inside a quartz sleeve. The agitation was

assured by means of a magnetic stirrer placed at the re-

actor base.

2.3. Procedures and Analysis

Preliminary experiments were carried out to screen the

appropriate parameters and to determine the experimen-

tal domain. From these experiments, concentration of

Fe(NO3)3, concentration of H2O2, initial concentration of

the pesticide and pH were the factors involved in this

study. Levels of the factors studied are shown in Table 1.

A two-level-four-factor (24) full factorial experiment

was designed to observe the effect of the parameters in-

fluencing photocatalytic degradation of pesticide me-

thomyl.

Figure 2. Schematic diagram of the photocatalytic reactor.

A. ABAAMRANE ET AL.

218

Table 1. Factors and levels used in the 24 factorial design

study.

Parameter name Code Low (–1) High (+1)

Fe(NO3)3 concentration

(mol·L–1) x1 10–4 5 × 10–4

H2O2 concentration

(mol·L–1) x2 10–3 10–2

Methomyl concentration

(mol·L–1) x3 6 × 10–5 1.23 × 10–4

Initial pH value x4 3 5.4

The photocatalytic efficiency was determined by using

the following equation:



Y

1

(5)

where Y is the photocatalytic efficiency (%), A0 and Ar

both in (mol·L–1) are, respectively, the initial and residual

concentrations of methomyl in solution.

In this research, a 24 full factorial design was em-

ployed to fit a second-order polynomial model. The ge-

neral equation of the second degree polynomial is stated

as follows:

0iiiji j

xxxYa aaε 

 (6)

where Y is the dependent variable (response variable) to

be modelled, xi and xj are the independent variables (fac-

tors), a0, ai, aij are regression coefficients and is the

error.

The analysis of results was performed with statistical

and graphical analysis software (Design-Expert 8.0.1, by

Stat-Ease Inc., USA). This software was used for regres-

sion analysis of the data obtained and to estimate the

coefficient of regression equation. ANOVA (analysis of

variance) which is statistical testing of the model in the

form of linear term, squared term and interaction term

was also utilized to test the signiﬁcance of each term in

the equation and goodness of ﬁt of the regression model

obtained [21]. This response surface model was also used

to predict the result by isoresponse contour plots and

three dimensional surface plots. Contour plot is the pro-

jection of the response surface as a two dimensional

plane where as 3D surface plots is the projection of the

response surface in a three dimensional plane [22].

3. Results and Discussions

3.1. Experimental Results and Its Evaluation

In the development of a degradation process, the number

of the necessary empirical experiments to determine the

process conditions can be reduced by using a practical

approach that exploits the known effects of system pa-

rameters on the degradation behaviour of methomyl. To

analyze the effect of changes in parameters involved and

to model the dimensional accuracy for various degrada-

tion conditions and to better evaluate the interaction be-

tween the parameters, the full factorial experimental de-

sign and response surface methodology was implemented.

The adequacy of the model is also tested by the method of

analysis of variance (ANOVA) and additional degradation

experiments [23]. This factorial design results in sixteen

tests with all possible combinations of x1, x2, x3 and x4. The

methomyl removal efficiency (Y) after 15 min light irra-

diation as shown in Table 2.

A first-order model with all possible interactions was

chosen to fit the experimental:

0112233 44

12 121313232314 14

24243434123 123124124

134 1342342341234 1234

Y aaxaxaxax

ax ax axax

axaxa xa x

axaxa x



 



(7)

The coefficients of the first-order polynomial equation

corresponding to each dependent variable were deve-

loped by multiple regression analysis using the Design-

Expert 8.0.1 statistical software.

Data analysis using the Design-Expert 8.0.1 statistical

software at 95% of confidence level permitted to obtain a

semi-empirical expression which consists of 12 statisti-

cally significant coefficients having absolute value greater

than zero, with a probability of 95% (p < 0.05):

Table 2. Experimental results of 24 factorial design for the

photo-Fenton degradation of methomyl.

Experiment x1x2x3x4 Y

(Experimental)

(Predicted)

1 –1–1–1–1 98.94 98.99

2 +1–1–1–1 100 99.95

3 –1+1–1–1 99.37 99.32

4 +1+1–1–1 100 100.05

5 –1–1+1–1 66.62 66.67

6 +1–1+1–1 96.96 96.91

7 –1+1+1–1 73.68 73.63

8 +1+1+1–1 97.99 98.04

9 –1–1–1+1 96.68 96.63

10 +1–1–1+1 100 100.05

11 –1+1–1+1 98.27 98.32

12 +1+1–1+1 100 99.95

13 –1–1+1+1 58.33 58.28

14 +1–1+1+1 94.94 94.99

15 –1+1+1+1 63.10 63.15

16 +1+1+1+1 95.60 95.55

A. ABAAMRANE ET AL. 219

1234

1213231434

123 134

90.03 8.16 0.97 9.131.66

0.76 7.31 0.72 1.111.24

0.51 0.69

Yxxxx

xxx

  

 



x (8)

The fit of the model was further checked by the coef-

ficient of determination R2. The R2 value is always be-

tween 0 and 1. The closer the R2 value is to 1, the better the

model predicts the response [24]. The statistically sig-

nificant variables at 95% level of confidence were tested

using analysis of variance (ANOVA) and were: R2 =

0.9995, x2 = 183.07 and F = 11.103 × 10–6; where R2 is the

correlation coefficient, x 2 the sum of quadratic residuals

and F is the F-value. The coefficients of multiple deter-

minations, R2, representing the fit of the models to the

experimental data was 0.9995 indicating that 99.95% of

the variability in the response could be explained by the

model.

Figure 4. The residual value plot of photo-Fenton degrada-

tion of methomyl.

A graph of the actual response values versus the pre-

dicted response values are shown in Figure 3. Actual

values are the experimental response data for a particular

run, and the predicted values were evaluated from the

model and generated by using the approximating function.

As can be seen in the Figure 3, the experimental results

are in good agreement with the values calculated by the

first-order polynomial equation.

Figure 4 shows the residual value and the order of the

corresponding observations. This plot can be helpful to a

designed experiment in which the runs are not randomized.

For residual activity data, the residuals appear to be ran-

domly scattered about zero. No evidence exists that the

regression terms are correlated with one another.

3.2. Analysis of RSM

variable. The response surface analyzes the geometric

nature of the surface, the maxima and minima of the re-

sponse and the significance of the coefficients of the ca-

nonical equation. The polynomial response surface model

obtained may be maximized or minimized to obtain the

optimum points. Whereas a contour plot is a graphical

technique for representing a three dimensional surface by

plotting constant z-slices called contours, on a two di-

mensional format. That is, given a value for z, lines are

drawn for connecting the (x,y) coordinates where that z

value occurs [25].

To investigate the individual and interactive effect of

these four factors on the methomyl removal efficiency,

three dimensional and contour plots were drawn with the

help of Design-Expert 8.0.1 statistical software and the

inferences thus obtained are discussed below.

The response surface graphs of methomyl removal are

shown in Figures 5-8. It can be shown from Figures 5-8

that a strong interaction exists among Fe(NO3)3 concen-

tration, initial methomyl concentration, H2O2 concentra-

tion and pH.

The 3D response surface, which is a three dimensional

graphic representation was used to determine the indi-

vidual and cumulative effect of the variable and the mu-

tual interaction between the variable and the dependent

Figure 5 shows the effect of Fe(NO3)3 concentration

and H2O2 concentration on methomyl removal efficiency.

The response surface of methomyl removal efficiency

gradually increased with increasing H2O2 concentration

from 10–3 mol·L–1 to 10–2 mol·L–1. The maximum value

of photo-Fenton degradation determined was 98% at

Fe(NO3)3 5 × 10–4 mol·L–1 and 10–2 mol·L–1 H2O2.

This can be explained by the effect of the additionally

produced hydroxyl radicals. This may be due to recom-

bination of hydroxyl radicals and also hydroxyl radicals

reaction with H2O2, contributing to the OH• scavenging

capacity (Equations (9)-(11)) [4].

H2O2 + OH• → H2O + (9)

HO 



+ OH• → H2O+ O2 (10)

OH• + OH• → H2O2 (11)

Figure 6 shows the effect of Fe(NO3)3 concentration

and initial methomyl concentration on methomyl removal

Figure 3. Experimental and calculated values for methomyl

removal.

A. ABAAMRANE ET AL.

220

Figure 5. The response surface and contour plot as a function of Fe(NO3)3 dosage rate and H2O2 dosage of methomyl removal

at 15 min.

Figure 6. The response surface and contour plot as a function of Fe(NO3)3 dosage rate and initial methomyl concentration of

methomyl removal at 15 min.

Figure 7. The response surface and contour plot as a function of Fe(NO3)3 dosage rate and pH of methomyl removal at 15

min.

A. ABAAMRANE ET AL. 221

Figure 8. The response surface and contour plot as a function of initial methomyl concentration and pH of methomyl rem oval

at 15 min.

efficiency. The response surface of methomyl removal

efficiency gradually increased with increasing Fe(NO3)3

concentration from 10–4 mol·L–1 to 5 × 10–4 mol·L–1. The

maximum value of photo-Fenton degradation determined

was 100% at Fe(NO3)3 5 × 10–4 mol·L–1 and 6 × 10–5

mol· L –1 methomyl concentration.

The reason is when Fe2+ concentration increased, the

catalytic effect also accordingly increased. When the

concentration of Fe2+ was higher, a great amount of Fe3+

from the process of H2O2 decomposition by Fe2+ was

easy to exit in the form of Fe(OH)2+ in acidic environ-

ment.

Figure 7 shows the effect of Fe(NO3)3 concentration

and pH on methomyl removal efficiency. The response

surface of methomyl removal efficiency gradually in-

creased with decreasing initial pH value from 5.4 to 3.

The maximum value of photo-Fenton degradation deter-

mined was 98% at Fe(NO3)3 5 × 10–4 mol·L–1 and 3 initial

pH value.

The degradation decreased at pH values higher than

5.4, because iron precipitated as hydroxide, which re-

sulted in a reduction in the transmission of the radiation

[26]. Additionally, the oxidation potential of hydroxyl

radical was known to decrease with increasing pH [2-7].

Another reason for the inefﬁcient degradation at pH > 3

is due to the dissociation and auto-decomposition of

H2O2 [1]. For pH values below 3, there action of hydro-

gen peroxide with Fe2+ is seriously affected causing the

reduction in hydroxyl radical production [27].

In Figure 8 the effect of initial methomyl concentra-

tion and pH on methomyl removal efficiency. The re-

sponse surface of methomyl removal efficiency gradually

decreased with increasing initial methomyl concentration

from 6 × 10–5 mol·L–1 to 1.23 × 10–4 mol·L–1. The

maximum value of photo-Fenton degradation determined

was 99% at 6 × 10–5 mol·L–1 methomyl concentration and

3 initial pH value.

The maximum photo-Fenton degradation efficiency of

methomyl obtained in this study was found to be 100%,

corresponding to the operating conditions of 5 × 10–4

mol· L –1, 10–2 mol·L–1, 6 × 10–5 mol·L–1 and 3, respec-

tively, for the Fe(NO3)3 concentration, H2O2 dosage, ini-

tial methomyl concentration and pH.

The interaction between Fe(NO3)3 concentration and

initial methomyl concentration was the most important

interaction. However, the interaction between Fe(NO3)3

concentration, H2O2 concentration and pH was least in-

fluencing because photo-Fenton degradation efficiency

does not change significantly.

4. Conclusion

The work presented here provides support for the degra-

dation of methomyl as a model for environmental con-

taminant. The experimental design used allows rigorous

analysis of the factors influencing methomyl degradation

in a photo-Fenton process. The optimum process condi-

tions obtained through a statistical method full factorial

experimental design was successfully determined to

maximize the methomyl degradation. The model pre-

dicted that initial methomyl concentration and Fe(NO3)3

has significant effects on photocatalytic methomyl deg-

radation. Maximal methomyl removal efficiency of

100% was obtained at the optimum conditions as follows:

Fe(NO3)3 concentration (5 × 10–4 mol·L–1), H2O2 con-

centration (10–2 mol·L–1), methomyl concentration (6 ×

10–5 mol·L–1) and initial pH value (3). The high correla-

tion of the model with the experimental results indicates

that RSM analytical procedure could be a general method

to describe the similar photo-Fenton system and to pre-

dict its behavior. Good agreement between predicted and

A. ABAAMRANE ET AL.

222

experimental data at the optimum conditions confirms

the usefulness of the model.

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