This research was to study the efficiency of the Fenton's treatment process for the removal of three herbicides, namely 2,4-dichlorophenoxy acetic acid (2,4-D), ametryn and dicamba from the sugarcane field runoff water. The treatment process was designed with the Taguchi approach by varying the four factors such as H2O2/COD (1–3.5), H2O2/Fe2+ (5–50), pH (2–5) and reaction time (30–240 min) as independent variables. Influence of these parameters on chemical oxygen demand (COD), ametryn, dicamba and 2,4-D removal efficiencies (dependent variables) were investigated by performing signal to noise ratio and other statistical analysis. The optimum conditions were found to be H2O2/COD: 2.125, H2O2/Fe2+: 27.5, pH: 3.5 and reaction time of 135 min for removal efficiencies of 100% for ametryn, 95.42% for dicamba, 88.2% for 2,4-D and with 75% of overall COD removal efficiencies. However, the percentage contribution of H2O2/COD ratio was observed to be significant among all four independent variables and were 44.16%, 67.57%, 51.85% and 50.66% for %COD, ametryn, dicamba and 2,4-D removal efficiencies, respectively. The maximum removal of herbicides was observed with the H2O2 dosage of 5.44 mM and Fe2+ dosage of 0.12 mM at pH 3.5.
INTRODUCTION
Herbicides are mainly used to kill unwanted plants (weed) from farm lands, industrial sites and forestry. During rainfall, immediately after application of herbicides leads to agricultural runoff and it moves towards downstream along with pollutants (natural and man-made) and thereby contributing the pollutant load on surface water body (Conte et al. 2016). The major source of water pollution includes overdose, improper application, air spraying, container washing and unintentional leakage from containers.
2,4-Dichlorophenoxy acetic acid (2,4-D) and dicamba are most commonly used herbicides around the world. These are inexpensive type of herbicides, used to control the Plantain plantago and White clover broad leaf types of weeds present in the field. It is a known fact that more than 1,500 pesticides contain 2,4-D as the main ingredient (Chu et al. 2004). When these herbicides are released to the water and soil environment, they undergo different biochemical processes and thereby form a variety of transformation products (2,4-dichloroaniline), which have a higher toxicity than the 2,4-D and dicamba (Farran & Ruiz 2004). Due to low soil sorption (kOC) and high water solubility (890 ppm for 2,4-D and 4,500 ppm for dicamba), the traces of these herbicides are detected in surface water body (Gouin et al. 2008). The 2,4-D and dicamba are well-known endocrine disrupting chemicals and exposure to these chemicals affects eyes, thyroid, liver, kidneys and nervous system of human beings (USEPA 2005). Also, these herbicides are having a significant effect on birds, beneficial species in soil and aquatic life. Therefore, the 2,4-D concentration in drinking water is recommended as 29 μg L−1 as its maximum permissible limit (WHO 2003). Ametryn is a triazine class herbicide used to control the Moneywort type of broad leaf weeds. Ametryn is also highly toxic to human beings and an extremely phytotoxic PSII type of herbicide (Jones & Kerswell 2003), low affinity towards soil (water solubility of 185 ppm) and high leachability, thereby creating a threat to the aquatic environment.
Nowaday's mixture of 2,4-D and dicamba, 2,4-D and ametryn, dicamba and ametryn formulations are more popular due to their synergic effect (Cserhati & Forgacs 1998) on variety of broad leaf weeds that are present in the field and these are economical, save time and destroy the 30–50% of other gross types of weeds (sedges) also. However, these herbicides do not ensure that they are safe for non-targeted plants, animals and useful microorganisms in soil. Therefore, the knowledge about residual concentration, degradation mechanism and the interaction between these herbicides with insecticides, fungicides and fertilizers in the farmland is more important than the ultimate receiving water body (Heppell & Chapman 2006).
The biodegradation studies were reported for the mixture herbicides such as 2,4-D, mecoprop and dicamba (Ghoshdastidar & Tong 2013), 2,4-D and ametryn (Sandoval-Carrasco et al. 2013), but herbicides containing stable carbon halogen bonds in their structures have been described to be much more resistant to microbial degradation. Therefore, many researchers successfully applied AOPs for the degradation of 2,4-D with photo-Fenton, (Conte et al. 2016), mixture of 2,4-D and dicamba with Zno-Fe2O3 catalyst (Maya-Treviño et al. 2014) and ametryn with UV/H2O2 (Gao et al. 2009). According to the best of the authors' knowledge, no research work has been reported on Fenton's treatment of mixture of 2,4-D, dicamba and ametryn in real agriculture runoff water. Therefore, in this research work, the Fenton's treatment was performed and the parameters were optimized with proper Design of Experiments (DOEs) tool.
In traditional optimization technique, changing one factor at a time and keeping other factors constant is impracticable and it does not give any interaction between different variables with responses. To overcome these limitations the different types of DOEs are proposed such as factorial design, response surface design, mixture of designs and Taguchi design. The Taguchi method involves the systematic way of designing the experiments and analysis of variance (ANOVA) is a tool for analysis of the results. The Taguchi design is cost effective, flexible, provides the high quality of the information at each point, and reduces the experiments than central composite design (CCD) (Ali et al. 2004), save the time and provides a global knowledge with help of standard statistical analysis (signal to noise, S/N ratio). In the present study, the objective is to treat the actual agricultural runoff water by Fenton's reagent and to optimize the variables involved in the process with the Taguchi method. This work also investigates the interactions between the independent factors (H2O2/COD (A), H2O2/ Fe2+ (B), pH (C) and reaction time (D)) and dependent factors (2,4-D, ametryn, dicamba and chemical oxygen demand (COD) removal).
MATERIALS AND METHODS
Chemicals
The 2,4-D, ametryn and dicamba were purchased from Sigma Aldrich. The physical and chemical properties of all these three herbicides are listed in Table 1. The reagents hydrogen peroxide (H2O2, 50%w/w), hydrochloric acid (HCl, 35%), sulfuric acid (H2SO4, 98%), iron (II) sulfate heptahydrate (FeSO4·7H2O), sodium hydroxide (NaOH, 98%), potassium iodide (KI), mercuric sulfate (HgSO4), potassium dichromate, silver sulfate (Ag2SO4), ferrous ammonium sulfate, ferroin indicator, starch, sodium thiosulfate (Na2S2O3) and ultra pure water were procured from Merck, manufactured in India.
Physical and chemical properties of dicamba, ametryn and 2,4-D
Properties . | Dicamba . | Ametryn . | 2,4 -D . |
---|---|---|---|
Structure | |||
Synonym | 3,6-dichloro-2-methoxybenzoic acid | (2-ethylamino)-4-(isopropylamino)-6-(methylthio)-s-triazine | 2,4-dichlorophenyloxy acetic acid |
Appearance | white crystalline solid | white crystalline solid | white to yellow powder |
M. W | 221 g/mol | 227.35 g/mol | 221 g/mol |
Chemical formula | C6H2 Cl2(OCH3)CO2H | C9H17N5S | C8H6Cl2O3 |
Water solubility (mg/L) | 4,500 at 25 °C | 209 at 25 °C | 890 at 20 °C |
M. P and B P | 115 °C and 200 °C | 84–85 °C and 337 °C | 140.5 °C and 160 °C |
Density (g/cc) | 1.57 | 1.18 | 1.416 |
Properties . | Dicamba . | Ametryn . | 2,4 -D . |
---|---|---|---|
Structure | |||
Synonym | 3,6-dichloro-2-methoxybenzoic acid | (2-ethylamino)-4-(isopropylamino)-6-(methylthio)-s-triazine | 2,4-dichlorophenyloxy acetic acid |
Appearance | white crystalline solid | white crystalline solid | white to yellow powder |
M. W | 221 g/mol | 227.35 g/mol | 221 g/mol |
Chemical formula | C6H2 Cl2(OCH3)CO2H | C9H17N5S | C8H6Cl2O3 |
Water solubility (mg/L) | 4,500 at 25 °C | 209 at 25 °C | 890 at 20 °C |
M. P and B P | 115 °C and 200 °C | 84–85 °C and 337 °C | 140.5 °C and 160 °C |
Density (g/cc) | 1.57 | 1.18 | 1.416 |
Actual agricultural runoff water sampling
The agriculture runoff water was collected from Veerapur village, Belgaum district, Karnataka state, India (Latitude: 15°41′27.64″N; Longitude: 74°39′9.11″E). This district, produces more than 82 lakh tons per year of sugarcane in 2,000 ha area (80% of the total district) and the farmers are using the three herbicides 2,4-D, ametryn and dicamba with different formulations based on the type of weeds (broad leaf weeds) and the quantity of weeds that are present in the field. The usage of these herbicides increased 10 times from last four years and the farmers were spraying six to nine times in a year. Near that sampling site there is Malaprabha river (Latitude: 15°40′32.73″N; Longitude: 74°38′33.43″E) flowing and there are likely chances that the runoff water may reach the river and contaminate it. The runoff water was collected from 0.5 acres of land and water was preserved below 4 °C according to Standard Methods (APHA 2005) for further analysis.
Experimental methodology
The 1 mM of stock solution and the standard solutions of 0.13, 0.26, 0.39, 0.52, 0.65 mM were prepared in ultra pure water for all three compounds. The high performance liquid chromatography (HPLC) calibration curves were prepared by applying proper conditions listed in Table 2. Then, the runoff water was filtered with 0.2 μ filter paper and the herbicide concentrations were quantified with help of HPLC and they are 25.5 mg/L, 93.7 mg/L and 3.4 mg/L of 2,4-D, dicamba and ametryn, respectively.
HPLC conditions of dicamba, ametryn, and 2,4-D
Parameter . | Dicamba . | Ametryn . | 2,4-D . |
---|---|---|---|
Ratio of mobile phases | 50: 50 | 58: 42 | 80: 20 |
Temperature of the column | 35 °C | 25 °C | 30 °C |
Retention time (min) | 1.382 | 8.882 | 1.7 |
Wavelength λmax | 274 nm | 223 nm | 230 nm |
Flow rate | 0.75 mL/min | 1 mL/min | 0.5 mL/min |
Parameter . | Dicamba . | Ametryn . | 2,4-D . |
---|---|---|---|
Ratio of mobile phases | 50: 50 | 58: 42 | 80: 20 |
Temperature of the column | 35 °C | 25 °C | 30 °C |
Retention time (min) | 1.382 | 8.882 | 1.7 |
Wavelength λmax | 274 nm | 223 nm | 230 nm |
Flow rate | 0.75 mL/min | 1 mL/min | 0.5 mL/min |
Sample volume = 20 μL; total run time = 20 min; column name and size = RP-C18, 100*4.6 mm, 3.5 μ pore size; mobile phase = methanol: water.
The batch experiments were performed for 250 mL of actual samples in 500 mL capacity Erlenmeyer conical flasks at room temperatures (29–31 °C) and atmospheric pressure in a magnetic stirrer at a speed of 200 rpm. The pH of the actual sample was maintained between 2–5 with 0.1 N H2SO4. Each experimental run was conducted in triplicate by adding the suitable amount dosages of H2O2 (5.45mM–17.71 mM) and Fe2+ (0.11–3.54 mM) with a reaction time of 30–240 min and average or concordant values were finally considered. After each set of experiment the samples were filtered with 0.2 μ Sartorius filter paper and the final concentrations of all three herbicides were quantified with HPLC and results were analyzed with the help of Minitab software version 17.
Analytical methods
Initial characteristics of agricultural runoff water
Parameter . | Value . | Unit . |
---|---|---|
Nitrate nitrogen as NO3-N | 57 ± 3 | mg/L |
pH | 5.9 ± 1 | – |
Chlorides as Cl− | 88 ± 2 | mg/L |
Conductivity | 0.8 ± 0.01 | mS/cm |
Turbidity | 52 ± 2 | NTU |
Iron as Fe3+ | 1.6 ± 0.01 | mg/L |
COD | 185 ± 4 | mg/L |
Ametryn | 3.4 | mg/L |
2,4-D | 25.5 | mg/L |
Dicamba | 93.7 | mg/L |
Sulfates as SO42− | 78 ± 2 | mg/L |
Parameter . | Value . | Unit . |
---|---|---|
Nitrate nitrogen as NO3-N | 57 ± 3 | mg/L |
pH | 5.9 ± 1 | – |
Chlorides as Cl− | 88 ± 2 | mg/L |
Conductivity | 0.8 ± 0.01 | mS/cm |
Turbidity | 52 ± 2 | NTU |
Iron as Fe3+ | 1.6 ± 0.01 | mg/L |
COD | 185 ± 4 | mg/L |
Ametryn | 3.4 | mg/L |
2,4-D | 25.5 | mg/L |
Dicamba | 93.7 | mg/L |
Sulfates as SO42− | 78 ± 2 | mg/L |
where CODi is the initial COD (mg/L) of all three herbicides and CODf (mg/L) is its final COD after reaction time.
Taguchi experimental design
In Fenton's process, the selection of H2O2 dose is very important and there should have some basis like COD or total organic carbon value of herbicides. Therefore, in this research work, the initial COD value of herbicide was taken as reference. The ratio of H2O2/COD was selected as 2.125 (center value), which is the theoretical relation between the COD and H2O2, in which the maximum number of •OH radicals are produced (Kim et al. 1997). The minimum and maximum values were selected as 1 and 3.25, respectively, and these values were near to 2.15–2.4 (Kavitha & Palanivelu 2004). The selection of H2O2/Fe2+ ratio is typical and in many literature sources it was reported as 9.5 (Torrades et al. 2011), 50 (Martins et al. 2010) and 165 (Manu & Mahamood 2011). It was also said that this ratio was case specific and depends on the type of the compounds (Mater et al. 2007). Therefore, with extensive literature survey the ratios of H2O2/Fe2+are selected as 5, 27.5 and 50. Here, the ratio of H2O2/ Fe2+ is based on mass (molar basis) and it was reported by many of the researchers (Bach et al. 2010; Hasan et al. 2012). In the Fenton's process, the pH has been considered an important factor than other treatment technologies, because it was reported that the process works in the acidic range from 2–5 (KiriMart et al. 2010). Based on this, the pH values were selected as 2, 3.5 and 5. The range of reaction times was selected from the evidence given by the literature, such as 30–240 min (Hasan et al. 2012), 60–240 min (Martins et al. 2010) and 120–360 min (Li et al. 2010). Therefore, the reaction time was selected as 30, 135 and 240 min. All the four factors with three levels are listed in Table 4 and the Fenton's dosage along with four responses are shown in Table 5.
Factors and levels of orthogonal array
Parameter . | Level 1 . | Level 2 . | Level 3 . |
---|---|---|---|
A (H2O2/COD) | 1 | 2.125 | 3.25 |
B (H2O2/Fe2+) | 5 | 27.5 | 50 |
C (pH) | 2 | 3.5 | 5 |
D (reaction time in min) | 30 | 130 | 240 |
Parameter . | Level 1 . | Level 2 . | Level 3 . |
---|---|---|---|
A (H2O2/COD) | 1 | 2.125 | 3.25 |
B (H2O2/Fe2+) | 5 | 27.5 | 50 |
C (pH) | 2 | 3.5 | 5 |
D (reaction time in min) | 30 | 130 | 240 |
Taguchi design matrix
Independent variables . | Dosage of H2O2 and Fe2+ (mM) . | Dependent variables (%) . | S/N ratio . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Run no. . | A . | B . | C . | D . | H2O2 . | Fe2+ . | COD R . | AR . | D R . | 2,4-D R . | COD R . | AR . | D R . | 2,4-D R . |
1 | 1 | 5 | 2 | 30 | 5.44 | 0.67 | 37 | 85 | 45.35 | 64.94 | 31.36 | 38.59 | 33.13 | 36.25 |
2 | 1 | 27.5 | 3.5 | 135 | 5.44 | 0.12 | 75 | 100 | 95.42 | 88.02 | 37.50 | 40.00 | 39.59 | 38.89 |
3 | 1 | 50 | 5 | 240 | 5.44 | 0.07 | 50 | 75 | 81.19 | 68.09 | 33.98 | 37.50 | 38.19 | 36.66 |
4 | 2.125 | 5 | 3.5 | 240 | 11.56 | 1.42 | 58 | 80 | 83.47 | 77.81 | 35.27 | 38.06 | 38.43 | 37.82 |
5 | 2.125 | 27.5 | 5 | 30 | 11.56 | 0.26 | 71.3 | 95.93 | 86.27 | 82.4 | 37.06 | 39.64 | 38.72 | 38.32 |
6 | 2.125 | 50 | 2 | 135 | 11.56 | 0.14 | 64 | 95.43 | 78.49 | 80.19 | 36.12 | 39.59 | 37.90 | 38.08 |
7 | 3.25 | 5 | 5 | 135 | 17.68 | 2.16 | 45 | 62 | 53.71 | 61.74 | 33.06 | 35.85 | 34.60 | 35.81 |
8 | 3.25 | 27.5 | 2 | 240 | 17.68 | 0.39 | 47.6 | 65 | 58.31 | 60.93 | 33.55 | 36.26 | 35.31 | 35.70 |
9 | 3.25 | 50 | 3.5 | 30 | 17.68 | 0.22 | 46 | 70 | 47 | 70 | 33.25 | 36.90 | 33.44 | 36.90 |
Independent variables . | Dosage of H2O2 and Fe2+ (mM) . | Dependent variables (%) . | S/N ratio . | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Run no. . | A . | B . | C . | D . | H2O2 . | Fe2+ . | COD R . | AR . | D R . | 2,4-D R . | COD R . | AR . | D R . | 2,4-D R . |
1 | 1 | 5 | 2 | 30 | 5.44 | 0.67 | 37 | 85 | 45.35 | 64.94 | 31.36 | 38.59 | 33.13 | 36.25 |
2 | 1 | 27.5 | 3.5 | 135 | 5.44 | 0.12 | 75 | 100 | 95.42 | 88.02 | 37.50 | 40.00 | 39.59 | 38.89 |
3 | 1 | 50 | 5 | 240 | 5.44 | 0.07 | 50 | 75 | 81.19 | 68.09 | 33.98 | 37.50 | 38.19 | 36.66 |
4 | 2.125 | 5 | 3.5 | 240 | 11.56 | 1.42 | 58 | 80 | 83.47 | 77.81 | 35.27 | 38.06 | 38.43 | 37.82 |
5 | 2.125 | 27.5 | 5 | 30 | 11.56 | 0.26 | 71.3 | 95.93 | 86.27 | 82.4 | 37.06 | 39.64 | 38.72 | 38.32 |
6 | 2.125 | 50 | 2 | 135 | 11.56 | 0.14 | 64 | 95.43 | 78.49 | 80.19 | 36.12 | 39.59 | 37.90 | 38.08 |
7 | 3.25 | 5 | 5 | 135 | 17.68 | 2.16 | 45 | 62 | 53.71 | 61.74 | 33.06 | 35.85 | 34.60 | 35.81 |
8 | 3.25 | 27.5 | 2 | 240 | 17.68 | 0.39 | 47.6 | 65 | 58.31 | 60.93 | 33.55 | 36.26 | 35.31 | 35.70 |
9 | 3.25 | 50 | 3.5 | 30 | 17.68 | 0.22 | 46 | 70 | 47 | 70 | 33.25 | 36.90 | 33.44 | 36.90 |
RESULTS AND DISCUSSION
Interactions between independent factors (A, B, C and D) and % COD removal
Taguchi analysis of % COD removal (%COD R) versus A, B, C and D as S/N ratio
Level . | A (H2O2/COD) . | B (H2O2/Fe2+) . | C (pH) . | D (reaction time) . |
---|---|---|---|---|
1 | 34.28 | 33.23 | 33.68 | 33.02 |
2 | 36.15 | 36.04 | 34.47 | 35.56 |
3 | 32.42 | 33.58 | 34.70 | 34.27 |
Delta | 3.74 | 2.81 | 1.02 | 2.54 |
Rank | 1 | 2 | 4 | 3 |
Level . | A (H2O2/COD) . | B (H2O2/Fe2+) . | C (pH) . | D (reaction time) . |
---|---|---|---|---|
1 | 34.28 | 33.23 | 33.68 | 33.02 |
2 | 36.15 | 36.04 | 34.47 | 35.56 |
3 | 32.42 | 33.58 | 34.70 | 34.27 |
Delta | 3.74 | 2.81 | 1.02 | 2.54 |
Rank | 1 | 2 | 4 | 3 |
ANOVA analysis of % COD removal (% COD R) versus A, B, C and D
Source . | DFa . | Adj SSb . | Adj MSc . | F-valued . | PCe . |
---|---|---|---|---|---|
A | 2 | 742.4 | 371.2 | 2.37 | 44.16 |
B | 2 | 564.0 | 282.0 | 1.51 | 33.54 |
C | 2 | 72.48 | 36.24 | 0.14 | 4.31 |
D | 2 | 302.5 | 151.2 | 0.66 | 17.99 |
Source . | DFa . | Adj SSb . | Adj MSc . | F-valued . | PCe . |
---|---|---|---|---|---|
A | 2 | 742.4 | 371.2 | 2.37 | 44.16 |
B | 2 | 564.0 | 282.0 | 1.51 | 33.54 |
C | 2 | 72.48 | 36.24 | 0.14 | 4.31 |
D | 2 | 302.5 | 151.2 | 0.66 | 17.99 |
aDF = degrees of freedom.
bAdj SS = adjacent sum square.
cAdj MS = adjacent mean square.
dF-value.
ePC = percent contribution.
The dosage of H2O2 was varied from 5.44–17.68 mM and these values were calculated from H2O2/COD (A) and H2O2/Fe2+ (B). Usually, by increasing the H2O2 concentration, increases the COD removal by producing the more •OH radicals (Pignatello 1992). However, from Table 5, it is seen that the dosage of (17.68 mM) H2O2 was able to yield lesser % COD removal (34–45%). This is due to the fact that by adding the excess amount of H2O2, the Fenton's process was inhibited by decreasing the •OH radical production and increasing the O2 production (Masomboon et al. 2009). When the dosage of H2O2 was decreased from 17.68–11.5 mM, 71.3% COD removal was achieved. Decreasing the H2O2 dosage from 11.5–5.44 mM, maximum removal of 75% was achieved with 0.12 mM of Fe2+ in 135 min. The removal was faster at 30 min and then it was slowly increased from 71.3–75% irrespective of pH 5 (Run 5). After that, no COD removal was observed. Hence, the pH has less contribution in the Fenton's process. This is probably due to the fact that, initially, there was a reaction between ferrous ion (Fe2+) and H2O2, after that there is a reaction between ferric (Fe3+) and H2O2 (Masomboon et al. 2009). Therefore, the ratio of H2O2/Fe2+ helps in higher COD removal efficiency.
Interactions between independent factors (A, B, C and D) and % ametryn removal (%AR)
Taguchi analysis of % ametryn removal (%AR) versus A, B, C and D as S/N ratio
Level . | A (H2O2/COD) . | B (H2O2/Fe2+) . | C (pH) . | D (reaction time) . |
---|---|---|---|---|
1 | 38.70 | 37.50 | 38.15 | 38.38 |
2 | 39.10 | 38.63 | 38.32 | 38.48 |
3 | 36.34 | 38.00 | 37.66 | 37.27 |
Delta | 2.76 | 1.13 | 0.66 | 1.21 |
Rank | 1 | 3 | 4 | 2 |
Level . | A (H2O2/COD) . | B (H2O2/Fe2+) . | C (pH) . | D (reaction time) . |
---|---|---|---|---|
1 | 38.70 | 37.50 | 38.15 | 38.38 |
2 | 39.10 | 38.63 | 38.32 | 38.48 |
3 | 36.34 | 38.00 | 37.66 | 37.27 |
Delta | 2.76 | 1.13 | 0.66 | 1.21 |
Rank | 1 | 3 | 4 | 2 |
ANOVA analysis of % ametryn removal versus A, B, C and D
Source . | DF . | Adj SS . | Adj MS . | F-value . | PC . |
---|---|---|---|---|---|
A | 2 | 1,069.7 | 534.86 | 6.25 | 67.57 |
B | 2 | 194.7 | 97.33 | 0.42 | 12.30 |
C | 2 | 52.06 | 26.03 | 0.10 | 3.29 |
D | 2 | 266.7 | 133.3 | 0.61 | 16.84 |
Source . | DF . | Adj SS . | Adj MS . | F-value . | PC . |
---|---|---|---|---|---|
A | 2 | 1,069.7 | 534.86 | 6.25 | 67.57 |
B | 2 | 194.7 | 97.33 | 0.42 | 12.30 |
C | 2 | 52.06 | 26.03 | 0.10 | 3.29 |
D | 2 | 266.7 | 133.3 | 0.61 | 16.84 |
Interactions between independent factors (A, B, C and D) and % dicamba removal
Taguchi analysis of % dicamba removal (% D R) versus A, B, C and D as S/N ratio
Level . | A (H2O2/COD) . | B (H2O2/Fe2+) . | C (pH) . | D (reaction time) . |
---|---|---|---|---|
1 | 36.97 | 35.39 | 35.45 | 34.56 |
2 | 38.35 | 37.87 | 36.61 | 37.36 |
3 | 33.91 | 35.97 | 37.17 | 37.31 |
Delta | 4.44 | 2.49 | 1.72 | 2.81 |
Rank | 1 | 3 | 4 | 2 |
Level . | A (H2O2/COD) . | B (H2O2/Fe2+) . | C (pH) . | D (reaction time) . |
---|---|---|---|---|
1 | 36.97 | 35.39 | 35.45 | 34.56 |
2 | 38.35 | 37.87 | 36.61 | 37.36 |
3 | 33.91 | 35.97 | 37.17 | 37.31 |
Delta | 4.44 | 2.49 | 1.72 | 2.81 |
Rank | 1 | 3 | 4 | 2 |
ANOVA analysis of % dicamba removal versus A, B, C and D
Source . | DF . | Adj SS . | Adj MS . | F-value . | PC . |
---|---|---|---|---|---|
A | 2 | 1,686 | 843.1 | 3.23 | 51.85 |
B | 2 | 585.7 | 292.9 | 0.66 | 18.01 |
C | 2 | 312.2 | 156.1 | 0.32 | 9.60 |
D | 2 | 668.1 | 334.1 | 0.78 | 20.54 |
Source . | DF . | Adj SS . | Adj MS . | F-value . | PC . |
---|---|---|---|---|---|
A | 2 | 1,686 | 843.1 | 3.23 | 51.85 |
B | 2 | 585.7 | 292.9 | 0.66 | 18.01 |
C | 2 | 312.2 | 156.1 | 0.32 | 9.60 |
D | 2 | 668.1 | 334.1 | 0.78 | 20.54 |
Interactions between independent factors (A, B, C and D) and % 2,4-D removal
Taguchi analysis of % 2,4-D removal (% 2,4-D R) versus A, B, C and D as S/N ratio
Level . | A (H2O2/COD) . | B (H2O2/Fe2+) . | C (pH) . | D (reaction time) . |
---|---|---|---|---|
1 | 37.27 | 36.63 | 36.68 | 37.16 |
2 | 38.07 | 37.64 | 37.87 | 37.60 |
3 | 36.14 | 37.22 | 36.93 | 36.73 |
Delta | 1.94 | 1.01 | 1.20 | 0.87 |
Rank | 1 | 3 | 2 | 4 |
Level . | A (H2O2/COD) . | B (H2O2/Fe2+) . | C (pH) . | D (reaction time) . |
---|---|---|---|---|
1 | 37.27 | 36.63 | 36.68 | 37.16 |
2 | 38.07 | 37.64 | 37.87 | 37.60 |
3 | 36.14 | 37.22 | 36.93 | 36.73 |
Delta | 1.94 | 1.01 | 1.20 | 0.87 |
Rank | 1 | 3 | 2 | 4 |
ANOVA analysis of % 2,4-D removal versus A, B, C and D
Source . | DF . | Adj SS . | Adj MS . | F-value . | PC . |
---|---|---|---|---|---|
A | 2 | 384.2 | 192.11 | 3.08 | 50.66 |
B | 2 | 120.3 | 60.14 | 0.57 | 15.86 |
C | 2 | 164.6 | 82.29 | 0.83 | 21.70 |
D | 2 | 89.33 | 44.67 | 0.40 | 11.78 |
Source . | DF . | Adj SS . | Adj MS . | F-value . | PC . |
---|---|---|---|---|---|
A | 2 | 384.2 | 192.11 | 3.08 | 50.66 |
B | 2 | 120.3 | 60.14 | 0.57 | 15.86 |
C | 2 | 164.6 | 82.29 | 0.83 | 21.70 |
D | 2 | 89.33 | 44.67 | 0.40 | 11.78 |
It was also said that at a low pH of 2, the less soluble species of Fe3+ are available to enhance the •OH radical production and at higher pH(>3.5) the formation of iron hydroxides were observed, which helps in suppressing the Fe2+ species regeneration and thereby reducing efficiency of treatment process (Wang 2008). In this Fenton's process with similar optimum conditions of 5.44 mM (H2O2), 0.12 mM (Fe2+) and 3.5 (pH), 40–50% of sulfates, nitrates and chlorides were removed along with dicamba, ametryn and 2,4-D. Therefore, finally the optimum values of A, B, C and D were selected as 2.125, 27.5, 3.5 and 135 min, respectively.
Normal plot of residuals: (a) % COD R, (b) % AR, (c) % D R, (d) % 2,4-D R.
Residuals vs. observation order: (a) % COD R, (b) % AR, (c) % D R, (d) % 2,4-D R.
CONCLUSIONS
Agricultural runoff water containing three herbicides (ametryn, dicamba and 2,4-D) was successfully treated with Fenton's reagent and Taguchi method was applied to study the interactive effects between the variables involved in the treatment process. The obtained results are summarized as follows:
The optimum values were found to be 2.125, 27.5, 3.5 and 135 min for A, B, C and D, respectively, for all four responses.
From the ANOVA results, it was found that higher F-value was observed for H2O2/COD ratio, which indicates that percent contribution is (PC) more in all four responses and they are 44.16%, 67.57%, 51.85% and 50.66% for %COD, ametryn, dicamba and 2,4-D removal efficiencies, respectively.
In addition, H2O2/Fe2+, reaction time and pH are ranked as 2 for all four responses. The optimal COD, ametryn, dicamba and 2,4-D removal was observed at reaction time of 135 min and they are 75%, 100%, 95% and 88%, respectively.
The maximum removal efficiency was achieved with H2O2 dosage of 5.44 mM and Fe2+ dosage of 0.12 mM at pH 3.5 for all three herbicides. Therefore, with these results, Taguchi approach can help to identify the most influencing factor in Fenton's treatment process for the removal herbicides from actual agricultural runoff water.
ACKNOWLEDGEMENTS
The authors are grateful to MHRD Government of India for an institute fellowship to carry out this research.