## 3 Results and Discussion
### Taguchi Analysis  
Taguchi method tests the mean response for each experimental run in the inner array and variation by evaluating the signal-to-noise ratio (SN). The signal is considered as a response or experimental yield and noise as an unavoidable loss [33]. Usually, in data evaluation, the standard deviation directly depends on the mean value of response data. Thus, the error cannot be minimised and it gets difficult to adjust the mean to the target value. Therefore, SN ratios were measured as a replacement of the standard deviation to obtain a robust optimum reaction condition for the Fenton oxidation. The Taguchi method performs a two-stage optimisation at the parameter level combination, with minimum standard deviation and keeping the mean on target [28]. Thereby, the oxidation performance was adjusted with control factors (affect process variability as measured by SN ratios). In the real experiment condition, the targets mean value can change during the process development, therefore SN ratios calculate quadratic loss function and have three approaches: Smaller is better; nominal is better and larger is better. For this study, smaller is better was chosen to obtain the minimum DOCf/i factor value thereby maximising the DOC removal. The SN ratios for smaller is better was evaluated using Equation (21), which is shown in Section 2.3.  
The SN ratios of each experiment run are shown in Table 5. The residuals for each treatment model are given in Supplementary Table S1. The responses in Table 6 are the averages of each DOCf/i response characteristics for each level of control factors. The tables also include ranks based on delta statistics, which compares the relative magnitude of effects [34]. The delta statistic is the difference between the highest and lowest average for each control factor. The ranks are allotted based on each control factor contribution in maximising the experimental results. The number 1 to \(n\) (equals to total control factors) are allotted based on the impact of each control factor over the treatment performance. Rank 1 means the given control factor has a high impact and any level changes would hugely affect the final process yield. Likewise, the ascending numbering hierarchy is allotted. From the allotted ranks, FeSO4 Fenton oxidation efficiency depends upon the initial contaminant concentration. The solution initial pH also retains the second responsible factor to determine the process yield. Whereas the Fe(0) efficiency was least affected by the initial contaminant concentration, rather it is highly dependent on  
Figure 2: Normal probability plot of the DOCf/i factor with 95% Cis in the (**a**) FeSO4 treatment and (**b**) Fe(0) treatment. DOCCf/i_2: Dissolved organic carbon removal factor Replicate 1; DOCCf/i_2: Dissolved organic carbon removal factor Replicate 2; \(n\): Number of samples; (i) AD: [PERSON]; (ii) RJ: [PERSON]; (iii) KS: [PERSON].  
the initial pH at which the reaction initiates. The FeSO\({}_{4}\) achieved the best removal when the water was initially contaminated with 0.005 mg/L MPs whereas, Fe(0) can be used for contaminated water of around 0.5 mg/L MPs or probably more. The performance of each control factors was given by the main effects plot in Figure 3, which predicts the optimal conditions. Thus, for given conditions used in the experimental design, the FeSO\({}_{4}\) and Fe(0) was predicted to perform its best at 60 min reaction time and pH 3. Besides, the Fe(0) catalytic Fenton oxidation requires less dose of H\({}_{2}\)O\({}_{2}\); a molar ratio of H\({}_{2}\)O\({}_{2}\):Fe(0), 0.7:1, was enough to obtain efficient MPs removal. Whereas, 2.7:1 (H\({}_{2}\)O\({}_{2}\):FeSO\({}_{4}\)) was acquired an optimum condition for the FeSO\({}_{4}\) Fenton oxidation.