This study aims to optimize solar photocatalysis for textile wastewater using ZnO-ED NPs produced from Eleocharis dulcis (E. dulcis) by RSM. The maximum decolorization (87.34%) and COD removal (100%) were recorded at pH 7, time (60 min), ZnO-ED NPs dosage (2 g/L), and 10% of color concentrations with R2 coefficient of 0.78 at P < 0.05. FESEM analysis showed the presence of granules with smaller diameters than the diameter of the granules before SPD. EDX analysis revealed the presence of impurities like copper (Cu). XRD analysis indicated the purity of ZnO-ED NPs after SPD. The results of an AFM analysis presented that agglomerations of ZnO-ED NPs were somewhat homogeneous in size, nature, and dispersion. According to the FTIR study, OH, CH, C=O, C=C, and C-O-C appear to be the primary functional groups of ZnO NPs that contributed to SPD. ZnO-ED NPs' increased surface roughness was seen in their Raman spectra. Aromatic intermediates were produced, like aromatic amines or phenolic compounds, and led to the complete conversion of CO2 and H2O. These results indicated that ZnO-ED NPs play an important role in raw textile wastewater treatment and the possibility of reusability of ZnO-ED NPs over four different cycles.

  • Enriching research demand for raw textile wastewater.

  • Determine the finest operating variables for solar photocatalytic degradation (SPD) of raw textile wastewater colors utilizing green ZnO-ED NPs.

  • Modeling and optimization removal of color and COD onto ZnO-ED NPs using RSM.

  • Comparison and characterization of ZnO-ED NPs before and after SPD.

  • Phytotoxicity evaluation of textile wastewater before and after SPD.

Wastewater-containing dyes from the textile, paper, and paint industries have attracted great attention due to their harmful impacts on human well-being and the environment since 10–15% of the dyes are discharged into natural water resources (Maruthupandy et al. 2020). Treatment of dye wastewater by physical, chemical, and biological methods has many limitations, such as high costs and frequent ineffectiveness (Alharthi et al. 2021; Shindhal et al. 2021). There is much interest in advanced oxidation processes (AOPs), such as the photocatalytic destruction of dyes, because of its green, sustainable, low-cost, and high-efficiency technology that achieves almost total mineralization of organic pollutants such as dyes (Raizada et al. 2020). Using various catalysts (TiO2, MnO2, CuO, ZnO, etc.), external oxidants (H2O2, air, O3, persulfate, etc.), and additives (NaCl, surfactants, etc.) can significantly increase the efficiency of accelerating the degradation process when employing AOPs (Mohod et al. 2023). One of the key substances found in the raw textile wastewater matrix during AOPs is carbonates, which promote the breakdown of several newly emerging organic contaminants, such as dyes and drugs (Rayaroth et al. 2023). The high band gap of semiconductors makes them unsuitable for use as photocatalysts. Alternatively, ZIF-8/BiFeO3, BiOBr, and ZnO can be used as new photocatalysts to overcome these limitations. Instead, metal selenides such as polycationic selenides (PCS) can be employed as a photocatalyst for the degradation of dyes (Nawaz et al. 2023). Effective photocatalytic wastewater treatment can be achieved using BiOBr due to its suitable and narrow band structure (Saddique et al. 2023). The ZIF-8/BiFeO3 photocatalyst manufactured using ultrasonic conversion was reported to completely remove RhB dye in 50 min at pH 5 (Bethi et al. 2023), efficiently treating complex model wastewater. TiO2 was shown to be a financially viable option for use in TiO2/UV/H⁠2O⁠2 and TiO2/UV/O⁠3/H⁠2O⁠2 photocatalytic technologies for the effective and quick (100 min) breakdown of volatile organic compounds with notably low chemical concentrations (Fernandes et al. 2019).

ZnO photocatalysts are an excellent option for processing water dyes because they are non-toxic, economically viable, chemically stable, and highly efficient. They also have a broad absorption spectrum and are widely available (Wetchakun et al. 2019). ZnO photocatalysts used in wastewater treatment have disadvantages; the wide band gap and fast recombination of the photo-induced charge carrier restrict their usage (Hernández-Carrillo et al. 2020). These drawbacks create the urgent need to synthesize a new, improved ZnO photocatalyst by tailoring the particle size and modifying the surface area. ZnO nanoparticles manufactured by chemical and physical methods are associated with toxic residues and environmentally harmful compounds (Mohd Yusof et al. 2020). Meanwhile, the green synthesized ZnO nanoparticles have high purity, low cost, large surface area-to-volume ratios, and thermal, electrical, and other characteristics that encourage catalysis.

To enhance different initial operating parameters, including catalyst load, pH, temperature, wavelength, reaction time, and pollutant concentration, as well as effects on photocatalytic efficacy, response surface methodology (RSM) is a useful tool in experimental studies. RSM's central composite design (CCD) has demonstrated exceptional efficiency in determining a model's relationship coefficient (R2), lowering test errors, and guaranteeing data completeness for design, modeling, and process optimization (Garg et al. 2020; Tetteh et al. 2020). As the most popular approach, CCD is used to regulate the optimization parameters and the impact of each independent variable. This approach is considered the primary benefit of this statistical technique (Nasiri et al. 2021). Several studies have obtained the highest deterioration efficiencies using this approach. For example, Yashni et al. (2021) optimized the Congo red (CR) photodegradation using CCD-RSM. The maximum degradation occurred (96%) at CR concentrations of 5–20 mg/L, pH values of 3–11, and ZnO NPs of (0.05–0.2 g). Nur et al. (2019) recorded a fast degradation of methyl orange (MO) degradation efficiency of 86% by using CCD-RSM at (5–11) pH, (0.2–1.0 g) catalyst (CuO/ZnO) dose with (5–30 mg/L) dye. Kee & Wei (2017) recorded the best degradation of MO and methyl green (MG) and real textile wastewater by using ZnO PVP-12.5 at 93.83% of MO, 100% of MG and 94.14% of COD at 2.5 mg/L of MO and 5 mg/L of MG, pH of 6.5 and 240 min. Most previous studies focused on the degradation of textile dyes in aqueous solutions of single dyes. This does not show the actual situation because dyes with similar spectra may behave differently during photodegradation, restricting the possibility of the results being applied to raw samples. Consequently, there remains a need and a research demand for raw textile wastewater for study.

This study aims to determine the finest operating variables for solar photocatalytic degradation (SPD) of raw textile wastewater colors utilizing green ZnO-ED NPs prepared in our previous study (Quan et al. 2023), based on the independent variables such as ZnO-ED NPs (0.1–2 g), pH (4–9), contact time (60–200 min), and textile wastewater concentrations (10–100%) to optimize the SPD activity by the CCD-RSM, and this emphasizes the novelty of this study to fill the gap in research demand on raw textile wastewater and using sunlight available in Malaysia in large quantities throughout the year.

All chemicals employed were of analytical grade. Table 1 lists every substance used in this study.

Table 1

Chemicals used

ChemicalPurity (%)SupplierFunction
Sodium hydroxide solution (NaOH) 99.5 R & M Chemicals To adjust pH 
Hydrochloric acid solution (HCl) 99.5 R & M Chemicals To adjust pH 
Zn(CH3COO)2·2H2O powder – R & M Chemicals To prepare ZnO-ED NPs 
COD Reagent – Hash For COD analysis 
Raw textile wastewater – Maxim Technology Sdn Bhd factory, Johor, Malaysia For photocatalysis 
Ethanol (C2H5OH) 95 GENE Chemicals To prepare ZnO-ED NPs and for photocatalysis 
ChemicalPurity (%)SupplierFunction
Sodium hydroxide solution (NaOH) 99.5 R & M Chemicals To adjust pH 
Hydrochloric acid solution (HCl) 99.5 R & M Chemicals To adjust pH 
Zn(CH3COO)2·2H2O powder – R & M Chemicals To prepare ZnO-ED NPs 
COD Reagent – Hash For COD analysis 
Raw textile wastewater – Maxim Technology Sdn Bhd factory, Johor, Malaysia For photocatalysis 
Ethanol (C2H5OH) 95 GENE Chemicals To prepare ZnO-ED NPs and for photocatalysis 

Synthesis of ZnO-ED NPs and collection of raw textile wastewater samples

The synthesis of ZnO-ED NPs from E. dulcis extract and the collection of raw textile wastewater samples was conducted as described in Quan et al. (2023).

Characterization of ZnO-ED NPs after the photocatalysis process

ZnO-ED NPs solutions' optical characteristics were examined using a UV–Visible absorption spectrophotometer (HACH, DR6000) between 190 and 1,100 nm in the wavelength range (Sharma et al. 2018). The surface morphology and elemental composition of ZnO-ED NPs were determined using a field emission energy-dispersive X-ray scanning electron microscope (FESEM/EDX; Quanta 450, USA). ZnO-ED NPs were prepared by placing 100% ethanol on 1 g of ZnO NPs on silicon wafers, which were then dried in an oven at 60 °C overnight (Zarrabi et al. 2018). X-ray diffraction (XRD) XRD-7000 (Shimadzu, Japan) was utilized to decide the atomic or nuclear structure of the crystal ZnO NPs samples. ZnO NPs sample was inserted within a 2 × 5 cm measure of foil. Then, place the ZnO NPs foil sample into a purge test holder and squeeze it delicately with a glass slide (Behravan et al. 2019). Attenuated total reflection (ATR) method (Fahimmunisha et al. 2020), using Fourier transform infrared (FTIR) spectroscopy (Nicolet iS50 (Thermo Scientific, USA)). Five milligrams of sample were put on the sample holder and cleaned with acetone. After that, the sample was pressed using a metallic pressing device and scanned to get the IR spectrum. ZnO-ED NPs size distribution and surface morphology were done using non-contact AFM mode (Park System XE-100, Park System Inc, South Korea). The magnetic sample handle was placed over the sample. NX Chains SPM and Smart Scan TM Data Acquisition were used to analyze the collected data (Tan et al. 2017). The ordered and disordered crystal structure of ZnO-ED NPs, using Raman spectroscopy, deposited 1 g of ZnO NPs particles on the glass plate, investigated at 25 ± 2 °C (Bonon et al. 2016).

SPD activity of ZnO-ED NPs

A fixed volume (100 mL) of 10–100% of textile wastewater concentration was transferred into a glass beaker (250 mL, h = 70 mm, D = 95 mm) containing 0.1–2 g of ZnO-ED NPs and pH (4–9). The pH of solutions was adjusted using 0.1 N HCl or 0.1 M NaOH. The mixture was stirred using a magnetic stirrer in dark conditions (0 Klux using a Lux meter) at 400 rpm for 30 min to assess the adsorption–desorption balance. The solar photodegradation was conducted under direct sunlight irradiation at the same time and place (under natural conditions) on a clear day during the strongest light intensity at 12 pm to 3 pm (at 31–33 °C). The light intensity was maintained in the range of 60–75 Klux through measurement using a Klux meter for (60–200 min). The resulting liquid was separated using the centrifuge at 4,000 rpm for 20 min before measuring the absorbance using a UV–Vis spectrophotometer between 190 and 1,100 nm at its maximum wavelength of 645 nm. The photodegradation efficiency of the dye was calculated using the following equation (Joseph et al. 2020):
formula
(1)
where is the initial absorbance of raw textile wastewater concentration; and is the absorbance after solar photodegradation at time t.

Chemical oxygen demand (COD) analysis

During the solar photocatalytic reaction, 2 mL of dye samples were withdrawn and added into the COD high range (HR) bottle (20–1,500 mg/L). The COD HR reagents and COD blank (2 mL of distilled water) were then placed into the COD a Hach, DRB 200 COD reactor (Method 8000) to be digested at 150 °C for 2 h. When the COD samples were cooled to room temperature, their COD values were measured using a Hach DR6000 UV–Vis spectrophotometer. The COD values were calculated using the following equation:
formula
(2)
where C0 is the COD concentration (mg/L) in the raw textile wastewater, and Ci is the COD (mg/L) in the treated solution.

Experimental design

In this study, the photodegradation of color in the presence of direct solar radiation using ZnO-ED NPs synthesized in E. dulcis extract was optimized using CCD-RSM, Design Expert Software 11, to improve the best operating parameters for color and COD removal. CCD at 3 × 4 × 2 was carried out to test the independent variables that affect the photocatalytic degradation of color and COD. The data were analyzed using ANOVA, and the optimal value of color and COD degradation was represented using a three-dimensional (3D) response surface plot analysis of the variables. The experimental levels of independent variables for color removal are shown in Table 2. There are four independent variables in this study, including ZnO-ED NPs catalyst dosage (x1: 0.1–2 g), pH (x2: 4–9), contact time (x3: 60–200 min), and textile wastewater concentration (x4: 10–100%) with three points for each factor and two dependent variables, coded as low and high in the central cluster. In total, 30 runs were performed to study the effectiveness of the selected variables on color degradation and COD, as shown in Table 3.

Table 2

Experimental range and level of the independent test variables of optimization of solar photocatalytic degradation (SPD) efficiency of color

FactorNameUnitTypeMinimumMaximumCoded lowCoded highMeanStd. dev.
ZnO-ED NPs g/L Numeric −0.85 2.95 −1 ↔ 0.10 +1 ↔ 2.00 1.05 0.86 
Color concentration Numeric 10.00 145.00 −1 ↔ 10.00 +1 ↔ 100.00 58.00 37.25 
Contact time min Numeric 60.00 270.00 −1 ↔ 60.00 +1 ↔ 200.00 134.67 57.94 
pH – Numeric 4.00 11.50 −1 ↔ 4.00 +1 ↔ 9.00 6.67 2.07 
FactorNameUnitTypeMinimumMaximumCoded lowCoded highMeanStd. dev.
ZnO-ED NPs g/L Numeric −0.85 2.95 −1 ↔ 0.10 +1 ↔ 2.00 1.05 0.86 
Color concentration Numeric 10.00 145.00 −1 ↔ 10.00 +1 ↔ 100.00 58.00 37.25 
Contact time min Numeric 60.00 270.00 −1 ↔ 60.00 +1 ↔ 200.00 134.67 57.94 
pH – Numeric 4.00 11.50 −1 ↔ 4.00 +1 ↔ 9.00 6.67 2.07 
Table 3

Design layout of CCD for removal of color with experimental and predicted responses

StdGroupRunFactor 1Factor 2Factor 3Factor 4Response 1Response 2
a: ZnO-ED NPsB: Textile concentrationC: Contact timeD: pHColor removalCOD
g/L%min%%
20 −0.85 55 130 6.5 4.97 25 
16 100 200 17.63 100 
13 10 60 87.34 100 
12 100 200 27.12 100 
11 10 200 55.17 00 
14 100 60 8.96 11.9 
15 10 200 50.57 00a 
10 100 60 65.86 00a 
10 60 49.37 100 
18 10 1.05 55 130 6.5 36.20 00a 
19 11 1.05 55 130 6.5 42.53 70 
17 12 1.05 55 130 6.5 40.00 13.5 
13 0.1 10 60 70.00 00a 
14 0.1 100 200 22.03 80.77 
15 0.1 100 60 56.98 50 
16 0.1 100 60 62.60 00a 
17 0.1 10 60 8.51 25 
18 0.1 10 200 48.05 34 
19 0.1 100 200 26.1 100 
20 0.1 10 200 17.24 00a 
21 21 2.95 55 130 6.5 46.77 48.65 
29 22 1.05 55 130 6.5 38.00 
28 23 1.05 55 130 6.5 39.51 00a 
30 24 1.05 55 130 6.5 33.94 00a 
23 25 1.05 55 270 6.5 78.77 00a 
22 26 1.05 145 130 6.5 24.95 00a 
24 27 1.05 55 130 11.5 33.87 37.84 
26 28 1.05 55 130 6.5 41.4 59.46 
27 29 1.05 55 130 6.5 7.53 00a 
25 30 1.05 55 130 6.5 48.92 67.57 
StdGroupRunFactor 1Factor 2Factor 3Factor 4Response 1Response 2
a: ZnO-ED NPsB: Textile concentrationC: Contact timeD: pHColor removalCOD
g/L%min%%
20 −0.85 55 130 6.5 4.97 25 
16 100 200 17.63 100 
13 10 60 87.34 100 
12 100 200 27.12 100 
11 10 200 55.17 00 
14 100 60 8.96 11.9 
15 10 200 50.57 00a 
10 100 60 65.86 00a 
10 60 49.37 100 
18 10 1.05 55 130 6.5 36.20 00a 
19 11 1.05 55 130 6.5 42.53 70 
17 12 1.05 55 130 6.5 40.00 13.5 
13 0.1 10 60 70.00 00a 
14 0.1 100 200 22.03 80.77 
15 0.1 100 60 56.98 50 
16 0.1 100 60 62.60 00a 
17 0.1 10 60 8.51 25 
18 0.1 10 200 48.05 34 
19 0.1 100 200 26.1 100 
20 0.1 10 200 17.24 00a 
21 21 2.95 55 130 6.5 46.77 48.65 
29 22 1.05 55 130 6.5 38.00 
28 23 1.05 55 130 6.5 39.51 00a 
30 24 1.05 55 130 6.5 33.94 00a 
23 25 1.05 55 270 6.5 78.77 00a 
22 26 1.05 145 130 6.5 24.95 00a 
24 27 1.05 55 130 11.5 33.87 37.84 
26 28 1.05 55 130 6.5 41.4 59.46 
27 29 1.05 55 130 6.5 7.53 00a 
25 30 1.05 55 130 6.5 48.92 67.57 

aCOD = 00 because the COD value increases after treatment.

Statistical analysis

The study examined the linear effects and interactions of independent factors on photocatalysis efficiency with ZnO-ED NPs, using ANOVA and a 3D graphical presentation.

Optimization of degradation activity of color by analysis of RSM

Design Expert 11.1.2.0 version software was used for the optimization, response surface modeling, and statistical analysis. The effect of the four independent variables was investigated by the CCD of 30 sets of experiments, as in Table 3. Data were analyzed using analysis of variance (ANOVA) and the optimal value for color removal and COD were estimated using 3D response surface analysis variables. The highest color degradation efficiency rate at 2 g of ZnO-ED NPs, pH = 7, 10% of textile wastewater concentration, and a contact time of 60 min was 87.34% of color and 100% of COD.

The normal probability plot is crucial for determining if the residuals are normal (Khataee et al. 2011). The relationship between the normal graph, the predicted graph, and the actual residuals graph relative to the experimental data for color removal rate are observed in Figure 1 using ZnO-ED NPs. As observed in Figure 1(a) and 1(b), there is a semi-straight line for data dispersion, which indicates differences between them and suggests that they are suitable for understanding the operation under investigation. Furthermore, the residual schemes nearly form a straight line, suggesting that the division is normal. This shows that the test data depends on the expected response values. In addition, the plots of residues with analysis and residues with amounts predicted for color photocatalytic degradation rate are presented. The residual with a run scheme demonstrates near-zero random diffusion with a variation of ±4.0, as seen in Figure 1(c), confirming that the data are often dispersed throughout the model responses (Song et al. 2020). The lack of an individual form in the studentized residuals and expected is confirmed in Figure 1(d). Consequently, the obtained residual has normal diffusion and is consistent with the proposed model (Abolhasani et al. 2019).
Figure 1

(a) Normal probability plot for the residual, (b) plot of predicted actual amounts, (c) plot of residual for run number, and (d) residual for the predicted amount for the photocatalytic degradation of color.

Figure 1

(a) Normal probability plot for the residual, (b) plot of predicted actual amounts, (c) plot of residual for run number, and (d) residual for the predicted amount for the photocatalytic degradation of color.

Close modal
Figure 2(a) shows that lambda values in CCD plots for power transformations are close to 1, indicating that no transformation should be made to the photochromic degradation efficiency response. The absence of any effect of the model is confirmed by Cook's distance value of less than 0.50 (Figure 2(b); Igwegbe et al. 2019).
Figure 2

(a) CCD plots for power transformations, lambda values and (b) Cook's distance value for the photocatalytic degradation rate of color.

Figure 2

(a) CCD plots for power transformations, lambda values and (b) Cook's distance value for the photocatalytic degradation rate of color.

Close modal

Model selection

Linear, 2-factor (2F), quadratic, and cubic models were assessed for their ability to predict outcomes with sufficient power levels to choose models and regression equations for color light degradation performance. Concordance was observed for each model. Based on the results presented in Table 4, a quadratic model is proposed in this study as a suitable model for predicting the photodegradation performance of colors with an F-number of 4.22. This is higher than the tertiary model (1.83) and its 2FI model (2.59).

Table 4

Statistical parameters for sequential models

SourceSum of squaresdfMean squareF-valuep-value
Mean vs. Total 47,273.97 47,273.97    
Linear vs. Mean 1,883.23 470.81 1.06 0.3972  
2FI vs. Linear 5,003.49 833.91 2.59 0.0523  
Quadratic vs. 2FI 3,234.43 4 808.61 4.22 0.0174 Suggested 
Cubic vs. Quadratic 1,580.25 263.37 1.83 0.1984 Aliased 
Residual 1,292.51 143.61    
Total 60,267.87 30 2,008.93    
SourceSum of squaresdfMean squareF-valuep-value
Mean vs. Total 47,273.97 47,273.97    
Linear vs. Mean 1,883.23 470.81 1.06 0.3972  
2FI vs. Linear 5,003.49 833.91 2.59 0.0523  
Quadratic vs. 2FI 3,234.43 4 808.61 4.22 0.0174 Suggested 
Cubic vs. Quadratic 1,580.25 263.37 1.83 0.1984 Aliased 
Residual 1,292.51 143.61    
Total 60,267.87 30 2,008.93    

Model validation

Determination of R2 and adjusted R2 were used in this study to assess how robust the model is, with a difference of less than 0.2 (Tables 5 and 6). According to the fit statistics, the predicted R2 values for color and COD were −0.2376 and –NA had reasonably adjusted R2 values of 0.5726 and 0.1745. It was somewhat agreeable, with a difference of 0.3. R2 values for color (0.7789) and COD (0.7248). R2 becomes more significant when it is near 1 when considering the appropriate value of R2, which must be equal to or more than 0.8 (Chaker et al. 2021a). A negatively predicted R2 implies that the overall mean may better predict your response than the current model.

Table 5

Analysis of variance results for the quadratic response model for color

Std. dev. 13.84 R2 0.7789 
Mean 39.70 Adjusted R2 0.5726 
CV % 34.86 Predicted R2 −0.2376 
  Adeq precision 7.3967 
Std. dev. 13.84 R2 0.7789 
Mean 39.70 Adjusted R2 0.5726 
CV % 34.86 Predicted R2 −0.2376 
  Adeq precision 7.3967 
Table 6

Analysis of variance results for the quadratic response model for COD

Std. dev. 32.39 R2 0.7248 
Mean 54.14 Adjusted R2 0.1745 
CV % 59.83 Predicted R2 NA⁽¹⁾ 
  Adeq precision 3.2878 
Std. dev. 32.39 R2 0.7248 
Mean 54.14 Adjusted R2 0.1745 
CV % 59.83 Predicted R2 NA⁽¹⁾ 
  Adeq precision 3.2878 

Analysis of variance (ANOVA) and model fitting

Tables 5 and 6 show that R2, R2 adj, and R2 pred for the removal of color were 0.7789, 0.5726, and −0.2376, respectively. R2, R2 adj, and R2 pred for the removal of COD were 0.7248, 0.1745, and NA, respectively. The correlation coefficients were found to be close to each other. This suggests that the regression model explains the connection between the independent variables and the response very well. ANOVA confirmed this study on the quadratic model of color photodegradation, and a quadratic model was proposed.

After evaluation, ANOVA demonstrates the model's validity and statistical significance (p 0.05) (Tables 7 and 8). Since the models' p-values were less than 0.05, the F-values and Prob > F-values for the models and their independent parameters were deemed significant. The F-value of the pattern is 3.77, implying that the pattern makes sense. The probability of such a high F-value being caused by noise is merely 0.76%. Model terms with a p-value of less than 0.0500 are considered significant. C, aB, BD, and C2 are the significant model values in this instance. A value greater than 0.1000 indicates a lack of significance for the model terms. The nonconformity's F-value of 1.89 suggests that it is not very significant. Such a big nonconforming F-value has a 19.66% probability of being caused by noise. A negligible mismatch is a good thing. The F-value of the COD response of 1.32 means that the model has no statistical significance for noise. There is a 38.49% opportunity that such a large F-value will be caused by noise. Furthermore, the probability of noise causing such a high F-value is extremely low (0.01%). Terms with a p-value >0.05 (not significant) in the model were not taken into consideration because they are not required to uphold the model hierarchy (Tetteh et al. 2020).

Table 7

ANOVA results for quadratic models for SPD of color using ZnO-ED NPs

SourceSum of squaresdfMean squareF-valuep-value
Model 10,121.14 14 722.94 3.77 0.0076 Significant 
a – ZnO-ED NPs 749.40 749.40 3.91 0.0666  
B – Textile con. 550.09 550.09 2.87 0.1108  
C – Contact time 1,255.12 1,255.12 6.55 0.0218  
D – pH 213.25 213.25 1.11 0.3080  
aB 1,346.71 1,346.71 7.03 0.0181  
aC 34.90 34.90 0.1822 0.6755  
aD 835.64 835.64 4.36 0.0542  
BC 205.42 205.42 1.07 0.3168  
BD 2,543.94 2,543.94 13.28 0.0024  
CD 36.88 36.88 0.1925 0.6671  
a2 274.26 274.26 1.43 0.2500  
B2 13.30 13.30 0.0694 0.7957  
C2 2,938.44 2,938.44 15.34 0.0014  
D2 169.11 169.11 0.8830 0.3623  
Residual 2,872.76 15 191.52    
Lack of fit 1,788.69 255.53 1.89 0.1966 Not significant 
Pure error 1,084.07 135.51    
Cor total 12,993.90 29     
SourceSum of squaresdfMean squareF-valuep-value
Model 10,121.14 14 722.94 3.77 0.0076 Significant 
a – ZnO-ED NPs 749.40 749.40 3.91 0.0666  
B – Textile con. 550.09 550.09 2.87 0.1108  
C – Contact time 1,255.12 1,255.12 6.55 0.0218  
D – pH 213.25 213.25 1.11 0.3080  
aB 1,346.71 1,346.71 7.03 0.0181  
aC 34.90 34.90 0.1822 0.6755  
aD 835.64 835.64 4.36 0.0542  
BC 205.42 205.42 1.07 0.3168  
BD 2,543.94 2,543.94 13.28 0.0024  
CD 36.88 36.88 0.1925 0.6671  
a2 274.26 274.26 1.43 0.2500  
B2 13.30 13.30 0.0694 0.7957  
C2 2,938.44 2,938.44 15.34 0.0014  
D2 169.11 169.11 0.8830 0.3623  
Residual 2,872.76 15 191.52    
Lack of fit 1,788.69 255.53 1.89 0.1966 Not significant 
Pure error 1,084.07 135.51    
Cor total 12,993.90 29     
Table 8

ANOVA results for quadratic models for SPD of COD using ZnO-ED NPs

SourceSum of squaresdfMean squareF-valuep-value
Model 16,587.13 12 1,382.26 1.32 0.3849 
a – ZnO NPs 1,669.23 1,669.23 1.59 0.2541 
B – Textile con. 8.11 8.11 0.0077 0.9328 
C – Contact time 422.27 422.27 0.4024 0.5493 
D – pH 5.54 5.54 0.0053 0.9444 
aB 1,363.52 1,363.52 1.30 0.2978 
aC 342.80 342.80 0.3267 0.5884 
aD 5.54 5.54 0.0053 0.9444 
BC 480.59 480.59 0.4580 0.5238 
BD 166.05 166.05 0.1582 0.7046 
CD 0.0000    
a2 1.22 1.22 0.0012 0.9739 
B2 2,776.73 2,776.73 2.65 0.1549 
C2 0.0000    
D2 0.0265 0.0265 0.0000 0.9962 
Residual 6,296.51 1,049.42   
Lack of fit 830.26 830.26 0.7594 0.4234 
Pure error 5,466.25 1,093.25   
Cor total 22,883.64 18    
SourceSum of squaresdfMean squareF-valuep-value
Model 16,587.13 12 1,382.26 1.32 0.3849 
a – ZnO NPs 1,669.23 1,669.23 1.59 0.2541 
B – Textile con. 8.11 8.11 0.0077 0.9328 
C – Contact time 422.27 422.27 0.4024 0.5493 
D – pH 5.54 5.54 0.0053 0.9444 
aB 1,363.52 1,363.52 1.30 0.2978 
aC 342.80 342.80 0.3267 0.5884 
aD 5.54 5.54 0.0053 0.9444 
BC 480.59 480.59 0.4580 0.5238 
BD 166.05 166.05 0.1582 0.7046 
CD 0.0000    
a2 1.22 1.22 0.0012 0.9739 
B2 2,776.73 2,776.73 2.65 0.1549 
C2 0.0000    
D2 0.0265 0.0265 0.0000 0.9962 
Residual 6,296.51 1,049.42   
Lack of fit 830.26 830.26 0.7594 0.4234 
Pure error 5,466.25 1,093.25   
Cor total 22,883.64 18    

Effect of operating parameters on removal efficiency of color

The result of the interaction between the four parameters plus the 3D plots of the diagram is shown in Figure 3. The results of this work identified that the variables explored play an essential role in color and COD photocatalysis, and the effect of loading of ZnO NPs on color and COD degradation was statistically significant (p < 0.05) at 0.0076.
Figure 3

3D surface plots for the SPD efficiency of color and COD: (a,b) effect ZnO-ED NPs and textile wastewater concentration; (c,d) effect of ZnO-ED NPs and contact time; (e,f) effect of ZnO-ED NPs and pH; (g,h) effect of textile wastewater concentration and contact time; (i,j) effect of textile wastewater concentration and pH; (k,l) effect of pH and contact time.

Figure 3

3D surface plots for the SPD efficiency of color and COD: (a,b) effect ZnO-ED NPs and textile wastewater concentration; (c,d) effect of ZnO-ED NPs and contact time; (e,f) effect of ZnO-ED NPs and pH; (g,h) effect of textile wastewater concentration and contact time; (i,j) effect of textile wastewater concentration and pH; (k,l) effect of pH and contact time.

Close modal

Figure 3(a) and 3(b) shows the interaction between initial textile wastewater concentration and ZnO-ED NPs loading at constant pH and irradiation time. The effect of ZnO NPs loading on color and COD degradation was statistically significant (p < 0.05) at 0.0076. The highest color degradation efficiency was achieved at 78 and 100% of COD degradation when the initial color concentration was at its lowest of 10% and the ZnO-ED NPs concentration was at its highest of 2 g/L. The color degradation efficiency increased with increasing ZnO-ED NPs loading and decreasing textile wastewater concentration. The rate of degradation of color (78%) and COD (100%) increased as the amount of ZnO NPs increased to the optimum limit (2 g/L of ZnO-ED NPs). This is also due to the increased active sites as ZnO NPs increase (Eskizeybek et al. 2012). The interaction between textile wastewater and ZnO-ED NPs lowers the color and COD photodegradation efficiency caused by the increasing initial concentration of textile wastewater. The higher the concentration of decomposition in the dye textile wastewater, the higher the concentration of the intermediate, and the lower the photocatalytic rate due to the absorption of these materials on the surface of the photocatalyst. These intermediates adsorb on the catalyst surface and reduce the photocatalysis rate (Lam et al. 2012). In Figure 3(a), as the color concentration increased to 50%, the COD removal rate decreased to the minimum due to the increase in intermediates resulting from color degradation. The high COD removal rate at 100% color concentration is due to the low production of intermediates resulting from low color decomposition caused by the obstruction of light penetration and the blockage of the active sites for interaction with the catalyst.

Figure 3(c) and 3(d) shows the effect of initial ZnO-ED NPs loading and exposure time to sunlight. With an initial concentration of 2 g ZnO-ED NPs and an irradiation time of 60 min, the degradation rate reached 79% color and 100% COD. Figure 3(d) shows that the rate of color degradation gradually decreases with increasing duration of solar light irradiation, observed when color degradation progressively decreased as contact duration increased from 60 to 180 min due to the competition between the intermediates produced from decomposition and the reactants for the decomposition time (Dihom et al. 2022). The degradation gradually increased to 60% at 200 min with decreasing catalyst loading ZnO-ED NPs from 2 to 0.1 g/L. When the compound to be degraded is exposed to ultraviolet rays for a long time, the degradation rate will increase as the oxidation process in the existence of hydroxyl radical rises (Malakootian et al. 2019). The optimal ZnO-ED NP loading on the plot is the maximum at 0.1 g/L; the shorter the contact time, the bigger the color degradation efficiency. As mentioned earlier, the reduced color photodegradation of ZnO NPs under high loading occurred because overflow catalysts can prevent light transmission and cause light dispersion (AIK 2018). The COD removal increased with increasing ZnO-ED NPs from 0.1 to 2 g, with a decreasing contact time. Based on the findings of this study, COD and color removal improved by increasing ZnO-ED NPs from 0.1 to 2 g, with little effect on the color concentration.

Figure 3(e) and 3(f) shows 3D surface plots of the relationship between pH values, ZnO-ED NPs, and color and COD photodegradation efficiency rate at constant initial textile wastewater concentration and contact time. The photodegradation efficiency rate of the color increased with the increase in the pH of the solution and the increase in ZnO-ED NPs loadings. The photodegradation efficiency of the color increased with increasing ZnO-ED NPs dose but decreased when ZnO-ED NPs loading exceeded the optimal level because ZnO NPs partially absorb light at high ZnO NPs suspension loading (Dihom et al. 2022). In contrast, OMs photodegradation efficiency increased with decreasing pH, decreasing color contaminant concentration, and increasing amounts of photocatalyst (Abbas & Trari 2021; Chaker et al. 2021a; Rafiq et al. 2021). COD removal increased with increasing ZnO-ED NPs (0.1–2 g) at pH values (6–4). The highest COD photodegradation efficiency rate was achieved at 100%. Based on the findings of this study, COD and color removal improved by increasing ZnO-ED NPs from 0.1 to 2 g, with an increased pH value. This is because the dye has a positive charge at low pH as a result of the adsorption of hydrogen ions (H+) in a protonated form and the presence of a catalyst, where the photodegradation rate increases at pH 4–8 (Alsohaim & Aldawsari 2019).

As shown in Figure 3(g) and 3(h), at an initial concentration of 10% solution color and an irradiation time of 60 min, the photodegradation rate reached 78%, with the COD removal percentage increasing as the color concentration decreased and contact time increased. The removal rate of color has dropped significantly with the increase in irradiation period and textile wastewater concentration. The decrease in interaction performance with contact time is due to the consumption of the photocatalyst as a result of competition for degradation between intermediates and starting materials. In addition, the interaction of short chains of aliphatic molecules with OH groups is responsible for the dye's gradual degradation over time (Salama et al. 2021). In contrast, high textile wastewater concentration (100%) and high reaction time (200 min) reduced the degradation rate of color (20%). This could be due to the bulk of dye molecules obstructing light transmission, the sluggish and low formation of active species, and the short interaction period between active species and the molecules in textile wastewater. Consistent with Aik (2018), the degradation of dye percentage achieved 50% of the 10 ppm SSY concentration at 180 min. Furthermore, with an increase in the initial concentration of SSY, the photodegradation rate of SSY decreased significantly, regardless of whether the irradiation time was 60 or 180 min.

The reaction between textile wastewater concentration and pH values is observed in Figure 3(i) and 3(j) for a constant catalyst dose and contact time. The degradation efficiency of the color reached its highest value at 78% when the solution concentration decreased and the pH increased. However, color degradation significantly reduced at low initial concentrations as the pH increased toward a higher pH. This result was studied in experiments with only one parameter at a time, and the maximum photodegradation performance was at pH 4 due to the electrostatic gravitation between the positively charged ZnO-ED NPs at low pH 4–8 and the anionic dyes, which accelerated the photodegradation of the color due to the adsorption some of the hydrogen ions (Alsohaim & Aldawsari 2019). The presence of ions such as carbonate anions in the raw textile effluent matrix promotes the decomposition of several newly emerging organic contaminants, such as phenol derivatives, aniline, bisphenol A, Rhodamine B, acid orange 7, and naphthalene (Rayaroth et al. 2023). However, it was shown that the high initial effluent concentration of the fabric and the low pH reduced the dye degradation efficiency. The COD photodegradation rate increased with an increased initial solution concentration and decreased pH. Similarly, the photocatalysis rate reduced with increasing pH 3–11 from 98.67 to 83.67% at pH 11 (150 mg of ZnO NPs and 5 mg/L of BR51) because ZnO NPs having a positive charge due to their adsorption of hydrogen ions (H+). ZnO NPs have a positive charge since they adsorb hydrogen (H+) (Gopalakrishnan et al. 2021).

In Figure 3(k) and 3(l), the percentage of color removal was approximately 78% at pH 9 and 60 min contact time. The efficiency of color photolysis increases when pH is increased and contact time decreases. The efficiency of color photodegradation at pH 4 increased with increasing contact time. This may be explained by the anionic dispersion of dyes and the electrostatic gravity of the positively charged ZnO-ED NPs. The longer time results in persistent electrostatic attraction, and the generated active species could decompose color molecules on the surface of ZnO-ED NPs (AIK 2018). In contrast, at high solution pH, the electrostatic repulsion of the deprotonated ZnO-ED NPs and the anionic arrangement of the color hinder the color degradation rate (Vidya et al. 2016; Dihom et al. 2022). The repulsive force makes it difficult for the color to adhere to the surface of the ZnO-ED NPs, regardless of whether the reaction time is long or short. Therefore, the color separation efficiency does not change significantly from 60 to 200 min. Hence, the degradation efficiency of the COD increased to 100%, both at low and high pH values. At the same time, the effect of contact time on color photodegradation performance shows a similar trend regardless of whether the solution pH is low or high.

Additionally, other researchers noticed similarities in the present study's results: as pH decreased, the concentration of dye pollutants decreased, and the dosage of photocatalyst increased, the photodegradation efficiency of color and COD in textile wastewater increased (Abbas & Trari 2021; Chaker et al. 2021a; Rafiq et al. 2021). Similarly, the effectiveness of electro-oxidation (EO) treatment for real textile wastewater employing a Ti electrode coated with RuO2 (Ti/RuO2) was investigated; actual COD and color removal values were discovered to be 97.25 and 80.0%, respectively (Kaur et al. 2017). The optimization using RSM for textile wastewater treatment resulted in 82.8, 96.2, and 75.6% efficiencies for COD, color, and TOC removal using the Fenton process (Ilhan et al. 2019).

Determination of optimal conditions for operational variables for SPD of color and COD

The optimization of SPD efficiency of color and COD was done using CCD-RSM, Design Expert 11.1.2.0 software. The desired objective of the RSM model was set for maximum color and COD degradation efficiency. Table 9 and Figure 4 provide the ideal values for each distinct parameter and desirability function. With desirability of 0.883 overall and a maximum photodegradation rate of 87.34% for color, the results were obtained at a pH of 6–7, 2 g/L of ZnO-ED NPs, 10% concentration of textile wastewater, and 60 min of contact time. The RSM model is a useful tool for designing the ideal conditions, as demonstrated by the desirability function of 0.883, which showed suitable settings for the photodegradation efficiency of color by ZnO-ED NPs as photocatalysts. Furthermore, it strongly correlated with the expected outcome, indicating the model's suitability and validity. Table 10 displays the outcomes of RSM optimization for the color solar photodegradation efficiency. According to the RSM results, the settings of 2 g of ZnO-ED NPs, a 10% textile wastewater concentration, 60.085 min of contact time, and pH = 6.987 result in the maximum color degradation performance (72.399%) and COD degradation performance (95.337%).
Table 9

Variables and their desired goal for optimizing color and COD photodegradation

NameGoalLower limitUpper limitLower weightUpper weightImportance
a: ZnO NPs  in range 0.1 
B: Textile concentration  in range 10 100 
C: Contact time in range 60 200 
D: pH in range 
Color removal Maximize 4.97 87.34 
COD removal Maximize 100 
NameGoalLower limitUpper limitLower weightUpper weightImportance
a: ZnO NPs  in range 0.1 
B: Textile concentration  in range 10 100 
C: Contact time in range 60 200 
D: pH in range 
Color removal Maximize 4.97 87.34 
COD removal Maximize 100 
Table 10

Results of RSM optimization for optimal operational variables for SPD of color

NumberZnO NPsTextile concentrationContact timepHColor removalCODDesirability
1 1.999 10.000 60.000 7.000 72.399 95.337 0.883 Selected 
2.000 10.001 60.000 6.851 71.846 94.999 0.878  
2.000 10.000 60.002 6.699 71.253 94.643 0.873  
2.000 10.082 64.453 7.000 70.411 95.643 0.872  
1.932 10.245 60.231 7.000 71.819 93.060 0.869  
1.998 10.000 61.081 6.673 70.652 94.644 0.869  
2.000 10.031 60.000 6.523 70.531 94.155 0.866  
2.000 10.003 60.099 6.443 70.158 94.047 0.863  
1.887 10.000 60.000 6.925 71.388 92.216 0.862  
10 1.998 10.000 68.954 7.000 68.548 96.263 0.862  
11 2.000 10.000 60.004 6.251 69.367 93.590 0.855  
12 1.827 10.000 60.000 7.000 71.277 90.833 0.855  
13 1.926 10.002 60.000 6.544 70.071 92.335 0.854  
14 1.998 10.000 60.000 6.150 68.898 93.300 0.851  
15 1.801 10.000 60.005 7.000 71.088 90.157 0.851  
16 1.974 10.000 60.000 6.231 69.059 92.840 0.850  
17 2.000 10.468 200.000 7.000 64.428 109.600 0.850  
18 1.993 10.530 200.000 6.989 64.308 109.151 0.849  
19 2.000 10.000 199.985 6.815 64.154 109.894 0.848  
20 1.997 10.058 198.735 7.000 64.146 109.974 0.848  
21 2.000 10.035 200.000 6.804 64.116 109.816 0.847  
22 1.997 10.000 200.000 6.713 63.858 109.536 0.846  
23 2.000 10.001 200.000 6.616 63.614 109.428 0.844  
24 2.000 10.000 198.966 6.553 63.076 109.170 0.840  
25 2.000 13.655 60.000 7.000 71.424 87.340 0.839  
26 1.954 10.000 200.000 6.538 62.905 107.233 0.839  
27 2.000 10.125 200.000 6.367 62.826 108.650 0.838  
28 1.989 10.890 200.000 6.451 62.689 107.205 0.837  
29 1.934 10.000 200.000 6.522 62.639 106.305 0.837  
30 2.000 14.728 200.000 7.000 62.608 103.268 0.837  
31 2.000 10.000 200.000 6.148 62.163 108.325 0.833  
32 1.772 10.129 198.640 6.985 61.843 100.000 0.831  
33 2.000 10.000 192.611 6.824 61.786 109.126 0.831  
34 1.953 10.000 62.866 6.010 66.573 92.049 0.830  
35 2.000 10.000 199.799 6.000 61.586 107.956 0.829  
36 2.000 11.576 199.994 6.000 61.159 105.623 0.826  
37 1.827 10.000 200.000 6.343 60.785 101.129 0.823  
38 1.701 10.000 200.000 6.940 61.418 97.044 0.815  
39 1.996 10.000 181.656 7.000 59.248 108.206 0.812  
40 2.000 10.000 74.967 6.013 62.237 94.629 0.811  
41 2.000 13.301 60.003 6.003 67.716 85.924 0.809  
42 1.996 10.000 103.616 7.000 57.963 99.895 0.802  
43 1.960 19.222 199.916 7.000 60.351 95.583 0.802  
44 2.000 10.001 117.387 7.000 55.693 101.487 0.785  
45 1.326 10.000 60.000 6.009 60.675 75.168 0.713  
46 0.100 100.000 60.000 6.155 57.921 54.305 0.591  
47 0.100 100.000 60.000 6.273 57.943 54.128 0.590  
48 0.100 100.000 60.003 6.459 57.945 53.852 0.589  
49 0.100 99.596 60.000 6.001 57.800 53.932 0.588  
50 0.100 100.000 60.000 6.570 57.932 53.686 0.588  
51 0.100 100.000 60.210 6.845 57.712 53.337 0.584  
52 0.100 100.000 60.000 6.962 57.772 53.098 0.583  
53 0.939 99.690 199.975 6.000 33.595 95.532 0.576  
54 1.333 99.968 200.000 6.000 33.047 95.696 0.571  
55 1.092 100.000 199.933 6.122 32.990 95.682 0.571  
56 0.665 99.950 200.000 6.156 32.873 96.004 0.570  
57 1.370 100.000 199.974 6.000 32.930 95.707 0.570  
58 0.100 97.572 60.001 6.096 57.513 50.856 0.570  
59 1.305 100.000 200.000 6.049 32.891 95.669 0.569  
60 2.000 66.688 200.000 6.000 42.431 71.213 0.569  
61 0.699 100.000 197.362 6.000 32.769 95.329 0.567  
62 1.257 100.000 200.000 6.162 32.506 95.480 0.565  
63 0.891 100.000 200.000 6.342 32.379 95.503 0.564  
64 0.630 100.000 199.992 6.483 31.908 95.570 0.559  
65 0.922 99.979 200.000 6.512 31.722 95.114 0.556  
66 0.619 100.000 200.000 6.679 31.311 95.247 0.552  
67 0.517 100.000 200.000 6.730 31.112 95.309 0.550  
68 0.100 100.000 199.931 6.351 30.709 96.525 0.549  
69 0.585 100.000 199.962 6.939 30.476 94.835 0.542  
70 0.103 99.998 83.787 7.000 45.105 60.217 0.542  
71 0.100 100.000 107.481 6.000 36.251 68.960 0.512  
72 0.106 100.000 119.347 6.000 32.879 72.500 0.496  
73 0.767 100.000 81.816 7.000 44.616 48.545 0.483  
74 0.363 100.000 133.708 6.000 30.735 74.364 0.482  
NumberZnO NPsTextile concentrationContact timepHColor removalCODDesirability
1 1.999 10.000 60.000 7.000 72.399 95.337 0.883 Selected 
2.000 10.001 60.000 6.851 71.846 94.999 0.878  
2.000 10.000 60.002 6.699 71.253 94.643 0.873  
2.000 10.082 64.453 7.000 70.411 95.643 0.872  
1.932 10.245 60.231 7.000 71.819 93.060 0.869  
1.998 10.000 61.081 6.673 70.652 94.644 0.869  
2.000 10.031 60.000 6.523 70.531 94.155 0.866  
2.000 10.003 60.099 6.443 70.158 94.047 0.863  
1.887 10.000 60.000 6.925 71.388 92.216 0.862  
10 1.998 10.000 68.954 7.000 68.548 96.263 0.862  
11 2.000 10.000 60.004 6.251 69.367 93.590 0.855  
12 1.827 10.000 60.000 7.000 71.277 90.833 0.855  
13 1.926 10.002 60.000 6.544 70.071 92.335 0.854  
14 1.998 10.000 60.000 6.150 68.898 93.300 0.851  
15 1.801 10.000 60.005 7.000 71.088 90.157 0.851  
16 1.974 10.000 60.000 6.231 69.059 92.840 0.850  
17 2.000 10.468 200.000 7.000 64.428 109.600 0.850  
18 1.993 10.530 200.000 6.989 64.308 109.151 0.849  
19 2.000 10.000 199.985 6.815 64.154 109.894 0.848  
20 1.997 10.058 198.735 7.000 64.146 109.974 0.848  
21 2.000 10.035 200.000 6.804 64.116 109.816 0.847  
22 1.997 10.000 200.000 6.713 63.858 109.536 0.846  
23 2.000 10.001 200.000 6.616 63.614 109.428 0.844  
24 2.000 10.000 198.966 6.553 63.076 109.170 0.840  
25 2.000 13.655 60.000 7.000 71.424 87.340 0.839  
26 1.954 10.000 200.000 6.538 62.905 107.233 0.839  
27 2.000 10.125 200.000 6.367 62.826 108.650 0.838  
28 1.989 10.890 200.000 6.451 62.689 107.205 0.837  
29 1.934 10.000 200.000 6.522 62.639 106.305 0.837  
30 2.000 14.728 200.000 7.000 62.608 103.268 0.837  
31 2.000 10.000 200.000 6.148 62.163 108.325 0.833  
32 1.772 10.129 198.640 6.985 61.843 100.000 0.831  
33 2.000 10.000 192.611 6.824 61.786 109.126 0.831  
34 1.953 10.000 62.866 6.010 66.573 92.049 0.830  
35 2.000 10.000 199.799 6.000 61.586 107.956 0.829  
36 2.000 11.576 199.994 6.000 61.159 105.623 0.826  
37 1.827 10.000 200.000 6.343 60.785 101.129 0.823  
38 1.701 10.000 200.000 6.940 61.418 97.044 0.815  
39 1.996 10.000 181.656 7.000 59.248 108.206 0.812  
40 2.000 10.000 74.967 6.013 62.237 94.629 0.811  
41 2.000 13.301 60.003 6.003 67.716 85.924 0.809  
42 1.996 10.000 103.616 7.000 57.963 99.895 0.802  
43 1.960 19.222 199.916 7.000 60.351 95.583 0.802  
44 2.000 10.001 117.387 7.000 55.693 101.487 0.785  
45 1.326 10.000 60.000 6.009 60.675 75.168 0.713  
46 0.100 100.000 60.000 6.155 57.921 54.305 0.591  
47 0.100 100.000 60.000 6.273 57.943 54.128 0.590  
48 0.100 100.000 60.003 6.459 57.945 53.852 0.589  
49 0.100 99.596 60.000 6.001 57.800 53.932 0.588  
50 0.100 100.000 60.000 6.570 57.932 53.686 0.588  
51 0.100 100.000 60.210 6.845 57.712 53.337 0.584  
52 0.100 100.000 60.000 6.962 57.772 53.098 0.583  
53 0.939 99.690 199.975 6.000 33.595 95.532 0.576  
54 1.333 99.968 200.000 6.000 33.047 95.696 0.571  
55 1.092 100.000 199.933 6.122 32.990 95.682 0.571  
56 0.665 99.950 200.000 6.156 32.873 96.004 0.570  
57 1.370 100.000 199.974 6.000 32.930 95.707 0.570  
58 0.100 97.572 60.001 6.096 57.513 50.856 0.570  
59 1.305 100.000 200.000 6.049 32.891 95.669 0.569  
60 2.000 66.688 200.000 6.000 42.431 71.213 0.569  
61 0.699 100.000 197.362 6.000 32.769 95.329 0.567  
62 1.257 100.000 200.000 6.162 32.506 95.480 0.565  
63 0.891 100.000 200.000 6.342 32.379 95.503 0.564  
64 0.630 100.000 199.992 6.483 31.908 95.570 0.559  
65 0.922 99.979 200.000 6.512 31.722 95.114 0.556  
66 0.619 100.000 200.000 6.679 31.311 95.247 0.552  
67 0.517 100.000 200.000 6.730 31.112 95.309 0.550  
68 0.100 100.000 199.931 6.351 30.709 96.525 0.549  
69 0.585 100.000 199.962 6.939 30.476 94.835 0.542  
70 0.103 99.998 83.787 7.000 45.105 60.217 0.542  
71 0.100 100.000 107.481 6.000 36.251 68.960 0.512  
72 0.106 100.000 119.347 6.000 32.879 72.500 0.496  
73 0.767 100.000 81.816 7.000 44.616 48.545 0.483  
74 0.363 100.000 133.708 6.000 30.735 74.364 0.482  
Figure 4

The desirability in optimal conditions (pH: 7, ZnO-ED NPs: 2 g, textile wastewater concentration: 10%, and contact time: 60 min).

Figure 4

The desirability in optimal conditions (pH: 7, ZnO-ED NPs: 2 g, textile wastewater concentration: 10%, and contact time: 60 min).

Close modal

Moreover, the optimum conditions obtained for color degradation were confirmed in Table 10 and verified by closer laboratory experiments (70.48%), and 98% of COD had a deviation of less than 2.7. This is consistent with previous research showing that RSM makes economic sense for experimental optimization due to its predictive ability and accuracy (Garg et al. 2020; Chaker et al. 2021b).

Characterization of ZnO-ED NPs after photocatalytic degradation of raw textile wastewater

The external morphologies, functional groups, and thermal decomposition of ZnO-ED NPs photocatalysts after tissue wastewater SPD were studied by FESEM/EDX, AFM, FTIR, and Raman spectroscopy analyses.

FESEM/EDX analysis

FESEM images of ZnO-ED NPs are shown in Figure 5(a1) and 5(a2). The external formation of ZnO-ED NPs after SPD, where some random gatherings of ZnO-ED NPs granules were observed, had a non-uniform distribution, similar to the external morphology of ZnO-ED NPs before SPD. Similarly, Faisal et al. (2021) revealed that ZnO NPs particles are well-shaped and spherical in size. However, it was an individual and a collection of particles. According to the findings, the conglomerate is caused by the particles' high surface energy. In Figure 5(b1) and 5(b2), the appearance of granules with smaller diameters than the diameter of the ZnO-ED NPs granules before SPD. The FESEM image revealed granules with average diameters ranging from 18 to 90 nm; this may mean that some particles of textile wastewater components are adsorbent due to the ZnO-ED NPs particles' high surface energy (Vidya et al. 2016).
Figure 5

FESEM images of ZnO-ED NPs: (a1, a2) The external formation and crystalline size of ZnO-ED NPs before SPD; (b1, b2) The external formation and crystalline size of ZnO-ED NPs after SPD.

Figure 5

FESEM images of ZnO-ED NPs: (a1, a2) The external formation and crystalline size of ZnO-ED NPs before SPD; (b1, b2) The external formation and crystalline size of ZnO-ED NPs after SPD.

Close modal
Figure 6 shows the results of EDX analysis of ZnO-ED NPs after SPD, where the composition of that image confirmed the reveal of chemical elements for ZnO-ED NPs (Zn = 54.96%, O = 15.47%, Cu = 1.17%, and C = 15.30%), and the atomic weights were Zn = 26.55% and O = 30.54%, Cu = 0.58%, and C = 40.23%. The gold (Au) signal in the EDX spectrum is due to the coating process of the ZnO-ED NPs samples during FESEM analysis. Other impurities like copper (Cu) and carbon (C) have been revealed in ZnO-ED NPs compared with the EDX image of before SPD due to trace amounts of compounds in the fiber effluent that may be adsorbed on the ZnO-ED NPs' surface. This is a result of the numerous organic chemicals in textile dyes present in textile wastewater (Etchepare & van der Hoek 2015).
Figure 6

EDX spectra of ZnO-ED NPs before (a) and after SPD (b).

Figure 6

EDX spectra of ZnO-ED NPs before (a) and after SPD (b).

Close modal

XRD analysis

The XRD analysis of ZnO-ED NPs after SPD is depicted in Figure 7. All the diffraction peaks were labeled with 2θ values of ZnO-ED NPs comparing to the crystal plane levels (h, l, k) of (010), (002), (011), (012), (110), (013), (020), (112), (021), (004), (022), (014), and (023), respectively, as they were all quite similar to the XRD values before SPD. Thus, that denotes the purity of ZnO-ED NPs photocatalysts. The sharp and limited crests demonstrated that the biosynthesized ZnO NPs were still exceedingly crystallized (Hamouda et al. 2023).
Figure 7

XRD spectra of ZnO-ED NPs before (a) and after SPD (b).

Figure 7

XRD spectra of ZnO-ED NPs before (a) and after SPD (b).

Close modal

AFM analysis

Figure 8 displays the findings of the AFM examination of ZnO NPs' exterior morphology following SPD, where the presence of agglomerations of ZnO-ED NPs was observed, in contrast to somewhat homogeneous size, nature, and dispersion of ZnO-ED NPs particles observes before SPD, which were in the form of closely packed sheets with needle-like ends. These clusters of ZnO-ED NPs indicate the bonding of molecules by strong bonding forces, especially H bonds between OH groups and other radicals in the structure of phenols present in intermediates produced during the photolysis of tissue wastewater (Bibi et al. 2019). In conclusion, there is a significant effect on the size, distribution, and bonding of ZnO-ED NPs particles after SPD in textile wastewater due to the adsorption of some particles on them.
Figure 8

AFM analysis of ZnO-ED NPs before (a) and after SPD (b).

Figure 8

AFM analysis of ZnO-ED NPs before (a) and after SPD (b).

Close modal

FTIR analysis

Figure 9 shows the FTIR analysis of ZnO-ED NPs following SPD. The peak at 919 cm−1, consigned to C = C, = C–H, and –C–O–C for protein compounds. The peaks at 2,343 cm−1 were due to stretching vibrations of the C–H band and axial distortion of the carbonyl group (Momeni et al. 2016). The bands at 3,421 cm−1 are due to a symmetric stretching of the OH hydrogen-bonded alcohol (Khan et al. 2019). Consequently, there is a minimal variation in the location and intensity of ZnO-ED NPs when compared to their pre-SPD counterparts. Changes at the stretching frequency where OH, CH, C = O, C = C, and C–O–C appear could cause this: alcohol substitution, out-of-plane helix in C–O–H, and CH = CH (Bozetine et al. 2016).
Figure 9

FTIR results of ZnO-ED NPs before (a) and after SPD (b).

Figure 9

FTIR results of ZnO-ED NPs before (a) and after SPD (b).

Close modal

Raman spectroscopy

Figure 10 shows the Raman spectra of ZnO-ED NPs after SPD. The Raman spectrum consists of several peaks at 50.34, 330.30, 539.16, 1101.35, and 1650.60. Compared to ZnO-ED NPs before SPD, changes in the Raman shifts of ZnO-ED NPs were observed due to increased surface roughness after photocatalytic degradation (Velanganni et al. 2018). However, the ZnO-ED NPs are almost the same size as his ZnO-ED NPs before SPD, suggesting that the ZnO-ED NPs partly retain their structure even after the solar photocatalytic process (Tahir et al. 2016).
Figure 10

Raman analysis of ZnO-ED NPs before (a) and after SPD (b).

Figure 10

Raman analysis of ZnO-ED NPs before (a) and after SPD (b).

Close modal

Phytotoxicity analysis of textile wastewater before and after SPD

The photodegradation process is promising for the degradation of dyes and the improvement of water quality. However, it may produce more toxicity than the parent dyes. Hence, biological evaluation of the toxicity of textile wastewater dyes is required before discharge into water bodies. Tables 11 and 12 show the lengths of shoots and roots (cm) after 5 days for Raphanus sativus (radish) seedlings after exposure to different raw and treated textile wastewater. Compared to untreated samples, most treated samples exhibited a significant increase in germination percentage, average root length, and shoot length. The germination rate of R. sativus seed-treated samples increased from 52.94 to 80.43%, 65.71 to 71.18%, and 62.50 to 72.22% for textile wastewater (10%), textile wastewater (55%), and textile wastewater (100%), respectively. Because the products were created after degradation, poisonous dyes, and other pollutants in untreated wastewater resulted in stronger germination inhibition than in treated wastewater. Aromatic amine degradation is most likely the cause of non-toxic breakdown (Jaison & Sebastian 2023). In conclusion, Tables 11 and 12 show that the roots and shoot lengths of R. sativus seeds in raw and treated textile wastewater do not differ significantly from their germination in water. This ensures less deathly degradation of metabolites in textile wastewater.

Table 11

Phytotoxicity of different textile wastewater on Raphanus sativus (Radish) seeds after 5 days of exposure

Type of textile wastewaterLength of shoot and root (cm) after 5 days
Germination (%)Shoot length (cm)Root length (cm)
Textile wastewater (10%) 52.94 6.27 6.86 
Textile wastewater (55%) 65.71 5.08 5.39 
Textile wastewater (100%) 62.50 4.48 4.68 
Control (distilled water) 72.22 6.04 8.96 
Type of textile wastewaterLength of shoot and root (cm) after 5 days
Germination (%)Shoot length (cm)Root length (cm)
Textile wastewater (10%) 52.94 6.27 6.86 
Textile wastewater (55%) 65.71 5.08 5.39 
Textile wastewater (100%) 62.50 4.48 4.68 
Control (distilled water) 72.22 6.04 8.96 
Table 12

Phytotoxicity of different treated textile wastewater on Raphanus sativus (Radish) seeds after 5 days of exposure

Type of textile wastewaterLength of shoot and root (cm) after 5 days
Germination (%)Shoot length (cm)Root length (cm)
Textile wastewater (10%) 80.43 6.12 8.38 
Textile wastewater (55%) 71.18 6.15 5.35 
Textile wastewater (100%) 72.22 5.85 4.58 
Control (distilled water) 72.22 6.04 8.96 
Type of textile wastewaterLength of shoot and root (cm) after 5 days
Germination (%)Shoot length (cm)Root length (cm)
Textile wastewater (10%) 80.43 6.12 8.38 
Textile wastewater (55%) 71.18 6.15 5.35 
Textile wastewater (100%) 72.22 5.85 4.58 
Control (distilled water) 72.22 6.04 8.96 

Reusability of ZnO-ED NPs

To examine the reusability of the biosynthesized ZnO-ED NPs photocatalyst after use to check the stability of its solar photocatalytic efficiency. It was reused four times for SPD under improved conditions (2 g of ZnO-ED NPs, pH 7, and 10% of textile wastewater concentration for 60 min). The photodegradation activity of color in textile wastewater is depicted in Figure 11. At the first cycle, second cycle, third cycle, and fourth test run, it was 70.48 ± 1.26%, 68.19 ± 2.67%, 59.32 ± 2.67%, 56.88 ± 1.4%, and 40.20 ± 2.4%, respectively, noticed a decrease in the color SPD efficiency in the textile wastewater by about 20% after the first three cycles. The reduced photocatalytic efficiency of ZnO-ED NPs results from a photo-erosion effect that occurs upon exposure to UV light (Zarrabi et al. 2018). This indicates that ZnO-ED NPs can be reused multiple times as effective photocatalysts and photocorrosion is simple. Many researchers have observed a reduction in photocatalytic efficiency after catalyst reuse. Asiedu (2018) observed the recycling efficiency of ZnO NPs in the SPD of RhB and MB over four different cycles, with degradation efficiency decreasing with each cycle. Degradation of the dye occurred in decreasing order in all four cycles due to photocorrosion of the ZnO nanoparticles.
Figure 11

Reusability of the ZnO-ED NPs within four successive experimental runs.

Figure 11

Reusability of the ZnO-ED NPs within four successive experimental runs.

Close modal

Phytotoxicity analysis of ZnO-ED NPs

Due to the expected negative impact of ZnO NPs on the environment, their negative effect on plants is being studied because they are the largest interface between the environment and living organisms and play an important role in transporting particles in the environment (Yang et al. 2023). The seedling germination results indicate that ZnO-ED NPs had harmless effects on radish seed germination after 5 days of incubation. Figure 12 concludes that ZnO-ED NPs synthesized from E. dulcis extract helped in the growth of Raphanus sativus (Radish) roots and stems for all concentrations (50, 100, 200, and 500 mg/L) compared with water (control) because zinc ions are considered an essential nutrient for plant growth (Singh et al. 2019). However, the root growth of Raphanus sativus (Radish) seeds was greater than that of the stems, which may be attributed to the roots being in direct contact with ZnO-ED NPs. The seed germination rate decreased with increasing ZnO-ED NPs concentration from 90, 80, 80, 76, and 78.1% to 50, 100, 200, and 500 mg/L compared with the control water (distilled water), respectively (Table 13). This is because very small ZnO-ED NPs molecules in high concentrations easily adhere to the biological components of the seed and are harmful to the germination stage.
Table 13

Phytotoxicity of different concentrations of ZnO-ED NPs (mg/L) on R. sativus seeds after 5 days of exposure

ZnO-ED NPs concentrationsLength of shoot and root (cm) after 5 days
Germination (%)Shoot length (cm)Root length (cm)
Control (water) 90 3.3 4.1 
50 mg/L 80 3.1 4.5 
100 mg/L 80 3.3 5.6 
200 mg/L 76.7 2.8 3.5 
500 mg/L 83.3 3.7 
ZnO-ED NPs concentrationsLength of shoot and root (cm) after 5 days
Germination (%)Shoot length (cm)Root length (cm)
Control (water) 90 3.3 4.1 
50 mg/L 80 3.1 4.5 
100 mg/L 80 3.3 5.6 
200 mg/L 76.7 2.8 3.5 
500 mg/L 83.3 3.7 
Figure 12

Phytotoxicity of different concentrations of ZnO-ED NPs (mg/L) on Raphanus sativus (Radish) seeds after 5 days of exposure.

Figure 12

Phytotoxicity of different concentrations of ZnO-ED NPs (mg/L) on Raphanus sativus (Radish) seeds after 5 days of exposure.

Close modal

The proposed mechanism and pathway for photodegradation of raw textile wastewater dyes

The various organics, such as dyes, coupling agents, polishing and coating agents, fabric softeners, anti-wrinkle agents, and cleaning fluids, contribute to the TDS of textile wastewater. Talouizte et al. (2020) showed very high values for the main pollution parameters in Fez, Morocco, and revealed the presence of organic compounds were alkanes, aromatic amines, aromatic carboxylic acids, phthalates, aliphatic carboxylic acids, and linear aliphatic alcohols. GC-MS analysis showed the presence of 3,5-bis (ethoxycarbonyl) benzoic acid (a coloring component of basic dye) and tetracosamethyl-cyclododecasiloxane (a component of fluorescent dye) in raw textile wastewater (Kaur et al. 2017).

Photocatalytic color removal depends on chemicals used in dyeing, with oxidation rates influenced by water matrix elements like salts, metals, and ions. Fe-TNT photocatalyst shows low photodegradation of CR dye in polluted water due to the interference of dissolved organic matter and metals, and it was much higher in deionized water. The decolorization effect was minimal, with iron, aluminum chloride, magnesium sulfate, and sodium carbonate inhibiting decolorization (Zafar et al. 2021).

When ZnO nanoparticles absorb UV radiation, electrons (obtained in the conduction band) and holes (obtained in the valence band) are formed on the ZnO surface. The electrons gained at the conduction band level are then recovered by molecular oxygen absorption, while water molecules capture the resulting holes to produce hydroxyl radicals (OH). Free radicals oxidize the dye to CO2 and H2O. Hydrogen peroxide produces many hydroxyl radicals when exposed to UV light, which increases dye degradation. Additionally, hydrogen peroxide can capture electrons gained in the conduction band. Therefore, it reduces the hole/electron recombination rate and increases dye degradation (Abdelrahman et al. 2019; Hegazey et al. 2020).
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The degradation of textile dyes begins via the breaking of the C–S+ bond by the aggression of OH radicals between the aromatic ring and sulfonate members, also ─N = N─, C─N, or C─C bonds, as well as the decarboxylation. Aromatic intermediates are produced for many textile dyes like aromatic amines or phenolic compounds and then lead to the complete conversion of nitrogen, carbon, and sulfur heteroatoms NH+4, , , and mineralization ions. Lastly, CO2 and H2O are produced from the mineralization of aliphatic products (Bhoi et al. 2020). For instance, the CR degradation pathway suggests that the generated azo radical replaced 4,4-dihydroxy biphenyl and 4-amino naphthalene sulfonic acid. Subsequently, the naphthalene alternative was further broken down to replace the dihydroxy with an analog of benzene. By removing the ─NH2 group as NH2OH, which may be formed as a result of the synthesis of phenol, the ensuing amino naphthol generates diol. The loss of as sulfuric acid and the vanishing of the azo group as and ions result in the creation of 1,4-naphthalenediol. The oxidation of dihydroxy-substituted naphthalene, followed by the beating of an ethylene molecule, maybe what forms naphthoquinone. The complete mineralization of the dye is ensured by the production of CO2 (Yashni et al. 2021).

This study investigated the solar photodegradation performance of raw textile wastewater from aqueous solutions using green ZnO-ED NPs as photocatalysts under sunlight irradiation. The CCD-RSM method was used to study the interaction effects between different variables. Based on the results of the ANOVA analysis, achieved a maximum photodegradation efficiency of 87.34% for color and 95.34% for COD with an overall chance of 0.883 under the conditions: pH of 7, 2 g/L of ZnO-ED NPs, and 10% of color concentration at 60 min. The researchers found that the effects of pH value and color concentration on the effectiveness of pollutant removal were plausible, and the suggested models were suitable based on the statistical metrics, such as the R2 coefficient (0.78, p < 0.05), coefficient of determination, adjusted R2, anticipated R2, and sufficient accuracy. The results suggest that the synthesized ZnO-ED NPs can be crucial in raw textile wastewater treatment. They also found that the possibility of reusability of ZnO-ED NPs in the photocatalytic degradation of raw textile wastewater decreased with each cycle in decreasing order. The results demonstrated the detoxification of dyes and their by-products following treatment using phytotoxic assays for evaluating toxicity. The optimal conditions were validated and tested in the laboratory and had less than a 2.7% deviation. This confirms the usefulness of using CCD-RSM to optimize and model photocatalytic degradation processes in textile wastewater treatment.

This research was supported by the Ministry of Higher Education (MOHE) through the Fundamental Research Grant Scheme (FRGS/1/2023/WAB02/UTHM/03/2).

The study's design, proposal development, findings interpretation, statistical analysis, article drafting, and revision before submission, and final manuscript reading and approval were all done by the two authors.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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