Degradation of bisphenol A by persulfate coupled with dithionite: Optimization using response surface methodology and pathway

Science of the Total Environment 699 (2020) 134258

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Degradation of bisphenol A by persulfate coupled with dithionite: Optimization using response surface methodology and pathway Wei Song a, Ji Li a, Zhuoyue Wang a, Caixia Fu b,c, Xiaolei Zhang a,⁎,1, Jianpei Feng a, Zhiliang Xu a, Qi Song d a School of Civil and Environmental Engineering, Shenzhen Key Laboratory of Water Resource Application and Environmental Pollution Control, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, PR China b School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, PR China c Shenzhen Key Laboratory of Soil and Groundwater Pollution Control, Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control, Shenzhen, Guangdong 518055, PR China d China Meheco Topfond Pharmaceutical Co., Ltd, Zhumadian, Henan 463000, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• PS coupled with DTN was more efficient in BPA degradation than solo PS system. • The response surface model well fitted with the actual data and R2 was 0.9270. • SO·4 and ·OH were the dominant reactive species in the PS/DTN system. • BPA degradation pathway was predicted based on the identified intermediates.

a r t i c l e

i n f o

Article history: Received 8 June 2019 Received in revised form 23 August 2019 Accepted 2 September 2019 Available online 02 September 2019 Editor: Ching-Hua Huang Keywords: Bisphenol A Persulfate Dithionite Response surface methodology Central composite design Degradation pathway

a b s t r a c t The degradation efficiency of bisphenol A (BPA) was investigated in the process of persulfate (PS) coupled with dithionite (DTN) as a function of concentration of BPA, PS, DTN and solution pH. A simple response surface methodology (RSM) based on central composite design (CCD) was employed to determine the influence of individual and interaction of above variables and the optimum processing parameters. It is satisfactory of a quadratic model with low probabilities (b0.0001) at a confidence level of 95% to predict the BPA degradation efficiency. The model was well fitted to the actual data and the correlation coefficients of R2 and R2-adj were 0.9270 and 0.8885, respectively. In addition, the obtained optimum conditions for BPA degradation were 1.79 μM, 131.77 μM, 93.64 μM for BPA, PS, DTN and pH = 3.62, respectively. It achieved a degradation efficiency N90% within 150 min. Moreover, the trapping experiment of active species demonstrated that SO·4 and ·OH were the dominant species and natural water matrix showed an obvious inhibition effect on BPA degradation. The BPA degradation pathway was predicted based on GC-MS results in this study. © 2019 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Shenzhen Key Laboratory of Water Resource Application and Environmental Pollution Control, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China. E-mail address: [email protected] (X. Zhang). 1 Address: E202C HIT Campus of Xili University Town, Shenzhen, Guangdong, China (518055).

https://doi.org/10.1016/j.scitotenv.2019.134258 0048-9697/© 2019 Elsevier B.V. All rights reserved.

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W. Song et al. / Science of the Total Environment 699 (2020) 134258

1. Introduction For decades, frequent anthropogenic activities have elevated the exposure risk of the various kinds of environmental pollutants, especially the endocrine disrupting compounds (EDCs) in the different environmental media (Kim et al., 2019; Luo et al., 2019; Song et al., 2018). Due to the environmental ubiquity and potential eco-toxicity, these chemical compounds have caused rising concern (Casati et al., 2015; Díaz-Cruz et al., 2019; Wang et al., 2019a, 2019b, 2019c). Several reports have confirmed the relationship between EDCs and myriad adverse health effect, such as obesity, diabetes mellitus and metabolic disease and hormone-sensitive cancers (Gore et al., 2015; Heindel et al., 2015; Messerlian et al., 2017). Bisphenol A (BPA) as one of the most utilized EDCs is extremely hard to be completely decomposed under natural conditions (Ma et al., 2019; Santhosh et al., 2018). Currently, efficient methods for BPA removal is highly demanded to be developed (Zhu et al., 2019). Several studies have shown that the traditional wastewater treatment is not effective in degradation of BPA due to its biologically toxic and refractory (Dong et al., 2019; Komesli et al., 2015). The physicchemical methods have shown the tremendous advantages and potential on degrading and mineralizing BPA (Liu et al., 2009). Studies have shown that the sulfate radical (SO·4 )-based advanced oxidation process (SR-AOP) exhibited great potential in terms of recalcitrant pollutants (Chen et al., 2018; Tang et al., 2018). In general, sulfate radical could be generated from activating persulfate (PS) and peroxymonosulfate (PMS) by input energy, homogeneous matters and heterogeneous materials. It was reported that ultrasonication (Ioan et al., 2007; Takdastan et al., 2018) and irradiation (Andrew Lin and Zhang, 2016; Sharma et al., 2015; Sharma et al., 2016) could significantly assist SRAOP. In addition, homogeneous methods including ferrous (Akbari et al., 2016), bisulfate (Qi et al., 2017), ozone (Yang et al., 2016), etc. are also gradually introduced to promote the generation of sulfate radicals in SR-AOP. Moreover, heterogeneous catalysts derived from transition metal (Lai et al., 2018; Oh et al., 2014) and metal-free materials (Lin and Zhang, 2017; Luo et al., 2018) for activating sulfate radical have appeared great potential to degrade BPA. In SR-AOP, sulfate radical (SO·4) served as the primary reactive species with the high redox potential (E0 = 2.5–3.1 V) and long half-life (t1/2 = 30–40 μs) (Song et al., 2019a, 2019b; Wang et al., 2019a, 2019b, 2019c). It suggests that more effective radical and longer exposure with pollutants occurs in the system. The SO·4 is a strong one-electron oxidant, but it could also be readily reacted due to addition of C_C bonds and H-abstraction (Lutze et al., 2015). These radicals usually can be generated by PS or PMS which is activated by heat, ultraviolet light (UV), ultrasound (US), microwave irradiation, and transition meta etc. (Dong et al., 2019; Tsitonaki et al., 2010). However, the activation methods based on energy may be limited by the huge energy consumption and specify of the environmental media (water, soil, and air). In fact, homogeneous agents (base (Furman et al., 2010), hydrogen oxyacid salts (Lou et al., 2014; Qi et al., 2017), phenol (Ahmad et al., 2013), quinones and quinone moieties (Fang et al., 2013; Zhou et al., 2015) etc.) also have the ability to activate PS or PMS to form radicals. Dithionite (DTN, S2O2− 4 ) contains a long and weak S\\S bond which could dissociate to form the highly reducing sulfur dioxide radicals (SO·2 ) as shown in Eq. (1) (Jung et al., 2016; Makarov and Silaghi Dumitrescu, 2013). It indicates that DTN can serve as the free radical initiator in the SR-AOP system with the standard reduction potential (E0 = 1.12 V). PS coupled with DTN to degrade organic pollutants is considered novel and significant mainly due to: When combining PS with DTN, several kinds of free radicals would be generated, which make a great contribute to BPA removal (Eqs. (1)– (4)) (Peebles, 1973; Sun et al., 2018; Tsuda, 1961); This original system can effectively degrade contaminants without any adjustment of the reaction conditions including the solution pH; Sulfate ion (SO2− 4 ), the single product of PS coupled with DTN, is usually not regarded as the

pollutants with the low concentration; In addition, it is promising to develop a practical REDOX coupled system which requires only to dose an initiator (the reduction of DTN and oxidation of PS). Therefore, it is significant to optimize the various operational parameters on the degradation of BPA by the PS/DTN.
− S2 O2− 4 →2SO2

ð1Þ

2− − 2−
−
− 2S2 O2− 4 þ 2S2 O8 þ H2 O→2HSO4 þ S2 O3 þ 2SO3 þ 2SO4

ð2Þ


− SO
− 3 þ O2 →SO5

ð3Þ

− SO
− 4 þ H2 O→HSO4 þ
OH

ð4Þ

In general, the concentration variation of liquid reactants is associated with the kinetic equations of homogeneous solution reactions (Corrsin, 2006; Yin et al., 1990). Therefore, the concentration of the reactants (BPA, PS, and DTN) were the significant factors in BPA degradation process. Moreover, the pH of the solution may affect the generation of free radicals and DTN decomposition. Currently, the employment of RSM has gained recognition as its competence in optimizing the process parameters with the reduction of workload as well as the production cost (Chehreghani et al., 2017). RSM can evaluate the interaction effect of multiple factors in different ranges with three-dimension figures (Gasemloo et al., 2019). In addition, CCD requires much fewer experiments than a full factorial and has been shown to be sufficient to describe the majority of steady-state process responses (Khataee et al., 2010). In this study, a SR-AOP system based on PS and DTN was explored to degrade BPA and several batch static experiments were conducted to optimize the operational parameters including the dosage of PS, DTN and BPA, initial pH using RSM. The optimization of BPA degradation depending on a response surface methodology (RSM) based on the central composite design (CCD) has been conducted in this study. Finally, the reliable degradation pathways of BPA degradation were recognized in PS/DTN system. 2. Materials and methods 2.1. Chemicals and materials Analytical grade of BPA was purchased from Sigma-Aldrich and used as such. The main characteristics of BPA were listed in Table S1 of the supplement materials. Potassium persulfate (K2S2O8, 99%), sodium dithionite (Na2S2O4, 90%) and humic acid (HA, 90%) were obtained from Aladdin Company. Acetic acid, methanol, and acetonitrile used in the detection process were from Merck. The solutions were prepared with deionized water (a resistance of 18.2 MΩ·cm from a Millipore system). 2.2. Experiment procedure BPA stock solution (50 mM) was prepared by dissolving a known amount of BPA in Mili-Q water without any organic co-solvent involvement. The stock solution was then used to prepare other samples through dilution. PS and DTN stock solutions (typically 20 mM) were freshly prepared before being used in each experiment. All experiments on the degradation of BPA were performed in a 100 mL brown glass flask at 30 ± 1 °C. The temperature was controlled by placing the flasks in a thermostatic water bath unit (THZS-01, China). All the reactions were initiated by mixing appropriate amounts of BPA, DTN, and PS in turn without continuous stirring. In particular, BPA concentration was increased by ten times to investigate the mineralization compared with other experiments. The pH was adjusted to near the demanding value using 0.1 M NaOH and 0.1 M H2SO4 without any other agents to – avoid the possible interference by the common pH buffer (CO2− 3 /HCO3

W. Song et al. / Science of the Total Environment 699 (2020) 134258

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Table 1 Experimental factors and levels of parameters in CCD. Factors

Symbols

BPA concentration (μM) PS concentration (μM) DTN concentration (μM) Initial solution pH

Levels

X1 X2 X3 X4

Low (−1)

Central (0)

High (+1)

-α

+α

1 55 55 5

1.5 105 105 7

2 155 155 9

0.5 5 5 3

2.5 205 205 11

2− − and PO3− 4 /HPO4 /H2PO4 ) (Jiang et al., 2017). When conducting the scavenge tests, various scavenger reagents were added to identify the primary reactive species under the optimal value achieving from the response surface optimization. After 150 min of reaction, the samples with the constant volume (1 mL) were filtered through a 0.45 μm syringe filter and injected into the glass injection bottles for the analysis of residual concentration with the special volume methanol (10 μL) to scavenge of SO·4 . Samples were taken and stored at 4 °C to reduce the volatilization.

2.3. Experimental design A central composite design (CCD) based on response surface methodology was employed to estimate the main and interactive effects of parameters on response (Zhang and Zhao, 2018). CCD is the most effective and commonly used design method for the creation of second order response surface model in environment processes and it can yield useful prediction of the interactive effects with considerably less experimental runs (Karimifard and Alavi Moghaddam, 2018). In this study, the RSM optimization was conducted with Design Expert 8.0.6 software (Stat-Ease, registered). CCD was based on a fourfactors five-levels design including BPA concentration (X1), PS concentration (X2), DTN concentration (X3), and initial solution pH (X4). The ranges of independent variables and the coded levels were shown in Table 1 which were set based on the results of preliminary experiments (Fig. S1 in supplementary material) and the results in the literatures. The dependent variable R was the BPA degradation efficiency (DE) after 150 min of reaction.

DE (%)

R ¼ β0 þ

k X

βi X i þ

i¼1

0.4

k X

βii X 2i þ

i¼1

k X k X

βij X i X j þ ε

ð6Þ

i¼1 j¼1

where, R represents the predicted response by the model (DE); β0 is the constant factor; βi, βj and βij refers to the coefficient of linear, square and interactions effects, respectively; ε refers to the random error (Gasemloo et al., 2019). 2.4. Analysis methods BPA concentrations were determined by HPLC (Waters e2695, USA) with a Waters 717 auto-sampler and a UV dual detector (Waters 2487). A Waters Symmetry C18 column (4.6 mm × 100 mm, 5 mm particle

0.4

b BPA+DTN BPA+PS BPA+PS+DTN

0.3

0.6

ð5Þ

where k is the number of factors, 2k is the cubic runs, 2 k is the axial runs and 6 is the center point's runs. The experimental repeatability was controlled by the replication of the center point. In addition, the replication also could control the model fitting adequacy and assess the pure error (Agarwal et al., 2016). All the runs were randomly conducted to minimize systematic errors. The least-squares regression method was used to analyze the results to predict the process response (Eslami et al., 2016) and the system dynamics could be explained using quadratic equation (Eq. (6)), served as the common model for the pollutant degradation (Asfaram et al., 2018).

TOC DE(%)

0.8

Total Number of Experiments ¼ 2k þ 2 k þ 6

a

BPA BPA+DTN BPA+PS BPA+PS+DTN BPA+PS+S2O32-

1.0

The total number of the experiments was determined by Eq. (5) (Gou et al., 2017; Zhang and Zhao, 2018):

0.2

0.1 0.2

0.0

0.0 0

20

40

60

Time (min)

80

100

120

0

30

60

90

120

150

Time (min)

Fig. 1. The accelerated degradation of BPA (a) and the TOC removal (b) of BPA degradation in PS/DTN system. Conditions: T = 30 °C, pH = 7.00, and (a) [BPA]0 = 1 μM, [PS]0 = 0.5 mM, [DTN]0 = 0.25 mM; (b) [BPA]0 = 10 μM, [PS]0 = 5 mM, [DTN]0 = 2.5 mM.

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W. Song et al. / Science of the Total Environment 699 (2020) 134258

with the rotatory evaporator at 40 °C. Then, the analysis was conducted with GC–MS in this study which was equipped with HP-5 capillary column (30 m × 0.25 mm, 0.25 μm film thickness). The carrier gas was helium at a flow rate of 1 mL·min−1. One mL aliquot sample was injected at 220 °C in splitless mode. The initial oven temperature was 40 °C holding for 2 min, then elevated to 200 °C at a 10 °C·min−1 rate holding for 2 min, then ramped to 210 °C at a 1 °C·min−1 rate, finally increased to 280 °C at a 10 °C·min−1 rate holding for 3 min. The MS analysis was adopted the electron impact ionization mode with the MS scan

size) was utilized for the separation. The detection wavelength used was 275 nm. The mixture of 0.5‰ acetic acid and methane (1:1, v/v) were employed as the mobile phase at a flow rate of 1 mL·min−1. The column temperature was set at 35 °C. The injection volume was 50 μL. The samples to determine the by-products during BPA degradation were subjected for extraction prior to the GC–MS analysis. Dichloromethanes (10 mL) was used in the extraction by mixing with 50 mL reaction mixture, and the extraction was performed for five times. The extracted solution was concentrated to approximately 1 mL

Table 2 Central composite design and responses for BPA degradation based on experimental results. Run

Coded levels of variables

Actual levels of variables X3

X2

DE

SD

X1

X2

X4

X1

X3

X4

−1.00

−1.00

1.00

−1.00

1.00

55.00

155.00

3.00

0.0337

0.0036

1.00

−1.00

1.00

−1.00

2.00

55.00

155.00

3.00

0.0799

0.0172

−1.00

1.00

−1.00

−1.00

1.00

155.00

55.00

3.00

0.6854

0.0210

0.00

0.00

0.00

0.00

1.50

105.00

105.00

5.00

0.8152

0.0318

−2.00

0.00

0.00

0.00

0.50

105.00

105.00

5.00

0.5728

0.0303

1.00

−1.00

−1.00

−1.00

2.00

55.00

55.00

3.00

0.6385

0.0738

1.00

1.00

−1.00

1.00

2.00

155.00

55.00

7.00

0.2213

0.0680

0.00

0.00

0.00

0.00

1.50

105.00

105.00

5.00

0.8003

0.0331

0.00

0.00

0.00

2.00

1.50

105.00

105.00

9.00

0.2092

0.0743

0.00

0.00

0.00

0.00

1.50

105.00

105.00

5.00

0.8255

0.0415

−1.00

−1.00

−1.00

1.00

1.00

55.00

55.00

7.00

0.1214

0.0013

0.00

0.00

2.00

0.00

1.50

105.00

205.00

5.00

0.1379

0.0587

0.00

2.00

0.00

0.00

1.50

205.00

105.00

5.00

0.8795

0.0175

1.00

1.00

1.00

−1.00

2.00

155.00

155.00

3.00

0.8756

0.0138

0.00

−2.00

0.00

0.00

1.50

5.00

105.00

5.00

0.0004

0.0114

0.00

0.00

0.00

0.00

1.50

105.00

105.00

5.00

0.8105

0.0185

−1.00

1.00

1.00

−1.00

1.00

155.00

155.00

3.00

0.1951

0.0215

−1.00

−1.00

−1.00

−1.00

1.00

55.00

55.00

3.00

0.5675

0.0513

−1.00

−1.00

1.00

1.00

1.00

55.00

155.00

7.00

0.6116

0.0438

1.00

−1.00

1.00

1.00

2.00

55.00

155.00

7.00

0.2968

0.0678

1.00

−1.00

−1.00

1.00

2.00

55.00

55.00

7.00

0.0065

0.0038

0.00

0.00

0.00

0.00

1.50

105.00

105.00

5.00

0.8173

0.0308

2.00

0.00

0.00

0.00

2.50

105.00

105.00

5.00

0.9105

0.0538

−1.00

1.00

1.00

1.00

1.00

155.00

155.00

7.00

0.2917

0.0078

0.00

0.00

0.00

0.00

1.50

105.00

105.00

5.00

0.7813

0.0674

−1.00

1.00

−1.00

1.00

1.00

155.00

55.00

7.00

0.0042

0.0372

1.00

1.00

−1.00

−1.00

2.00

155.00

55.00

3.00

0.9602

0.0085

0.00

0.00

0.00

−2.00

1.50

105.00

105.00

1.00

0.4444

0.0225

0.00

0.00

−2.00

0.00

1.50

105.00

5.00

5.00

0.0019

0.0370

1.00

1.00

1.00

1.00

2.00

155.00

155.00

7.00

0.9345

0.0176

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

W. Song et al. / Science of the Total Environment 699 (2020) 134258

from 40 to 500 m/z. The pH values were measured by a basic pH/ORP meter (HANA H1211). Total organic carbon (TOC) of samples was analyzed by TOC analyzer (Shimazu, Japan). 3. Results and discussion 3.1. Preliminary experiments The preliminary experiments were carried out to compare the degradation efficiencies of BPA in the different systems under the similar experimental conditions. The BPA degradation by 0.5 mM PS, 0.25 mM DTN, or PS/DTN was shown in Fig. 1a at pH = 7.0 after 120 min. It was observed that there was no obvious decrease of BPA in the system of DTN. In addition, only b20% of BPA was removed in the solo PS system. It indicates the weak degradation ability of PS to BPA. It could 2− be attributed to the oxidative potential of PS itself (E0(S2O2− 8 /SO4 ) = +1.96 V) (Lee et al., 2016). The result was similar with the study by Kang (Kang et al., 2018) in which the degradation efficiency of BPA was approximate 17.9% with PS alone. In fact, it is doubtable that S2O2− served as the decomposition product of DTN could activate PS 3 to generate sulfate radicals. To verify, the control experiment was conducted and the effect of S2O2− could be excluded obviously (Fig. 1a). 3 Moreover, toxicity assessment of BPA degradation process has been performed based on the inhibition effect on activated sludge (in Text S1 of the supplementary material). In addition, the mineralization of the PS/DTN system and TOC concentration (Fig. 1b) in BPA degradation process were investigated. It had nearly 35% of TOC removal within 150 min in PS/DTN system. According to our preliminary study, the slight degradation may be attributed to the oxidation of sulfur-containing reactive species (e.g. SO·3, SO·5 etc.) due to the properties of DTN (Song et al., 2019a, 2019b). However, the same initial concentration of BPA was completely degraded by PS/DTN over the similar period. The remarkable distinction confirms the availability of PS coupled with DTN for the BPA removal. Hence, it is significant to explore the optimal reaction parameters of BPA/PS/DTN system. 3.2. RSM analysis CCD was employed to design the experimental runs to study the designated independent variables in BPA removal. The proposed tests and results of R(DE) and corresponding standard deviation (SD) were presented in Table 2. The BPA residual concentration was expressed

5

by the second-order fitting equation model without the insignificant model term and was computed as the sum of a constant, four firstorder influences, three interaction influences and three second-order influences based on the coded values and presented as Eq. (7). RðDEÞ ¼ 0:79 þ 0:091  1 þ 0:15  2 þ 0:016  þ 0:13  1  2− 0:061  2  4 þ 0:22   2 2 –0:18  3 2 –0:12  4 2

 4  4 –0:088

3− 0:084 3

ð7Þ

The positive and negative coefficients indicated that their corresponding terms were positively and negatively affected the response (Nasab et al., 2019). CCD is considered as a reliable method to conduct the analysis of diagnostic plots, such as the normal probability plot of residuals and to predict versus actual values, to validate the adequacy of the model (Moghaddam et al., 2019). The normal probability plot of the studentized residuals is a good graphical representation for the diagnosis of data normality (Fig. 2a). The data were well fitted with the line. In addition, the residuals followed a random distribution around zero with a variation of ±3.0 (Fig. 2b). The result indicates that the data were normally distributed in the model responses (Agarwal et al., 2016; Zhang and Zhao, 2018). In addition, the corresponding relationship between the residual and the predicted value of the equation also could reflect the reliability of the model. When the points distributed discretely in the figure, it could represent a higher reliability of the model. Obviously, distribution rules of residuals versus predicted values (Fig. S2 in the supplement materials) could demonstrate the accuracy and reliability of the model. Therefore, it is quite satisfactory to predict the BPA degradation by the second-order polynomial model (Shokoohi et al., 2019). In addition, the relevance of the suggested model and its significance were verified by ANOVA analysis which was based on the Fisher's test (F-test) and lack of fit (Abd Manan et al., 2019; Mahdavi and Ashraf Talesh, 2019). The analysis of variance (ANOVA) of quadratic models above was displayed in Table 3. As shown in Table 3, Fisher's F-value of 24.12 represents the suitable efficiency of the presented model for data analysis and is confirmed by the very low probability value (ProbNF b 0.0001) at significant level of 0.05. In general, coefficients of determination (R2) could be used to evaluate the suitability and validity of the model (Wang et al., 2019a, 2019b, 2019c; Zhang et al., 2014). The R2 and the adjusted R2 (R2 adj) of the quadratic polynomial model for the response surface regression were

Fig. 2. Design expert plot: (a) normal probability plot of the internally studentized residuals for BPA degradation; (b) the run number versus residual data for BPA removal by CCD.

6

W. Song et al. / Science of the Total Environment 699 (2020) 134258

Table 3 ANOVA results for the response surface quadratic model on the BPA degradation. Source

Sum of squares

Degrees of freedom

Mean square

F value

p-value Prob N F

Quadratic Model X1-BPA X2-PS X3-DTN X4-pH X1 X2 X2 X4 X3 X4 X22 X23 X24 Residual Pure Error Cor Total

3.24 0.20 0.53 6.209E-003 0.17 0.28 0.060 0.74 0.22 0.91 0.38 0.26 1.226E-003 3.49

10 1 1 1 1 1 1 1 1 1 1 19 5 29

0.32 0.20 0.53 6.209E-003 0.17 0.28 0.060 0.74 0.22 0.91 0.38 0.013 2.453E-004

24.12 14.72 39.56 0.46 12.64 21.07 4.48 55.37 16.09 67.84 28.12

b0.0001 0.0007 b0.0001 0.4661 0.0014 0.0001 0.0343 b0.0001 0.0005 b0.0001 b0.0001

R2 = 0.9270. Adjusted R2 = 0.8885. Adequate precision = 16.667.

0.9270 and 0.8885, respectively, which admit the accuracy and fit of the model. Besides, R2 = 0.9270 indicated that about 92.70% of the variations occur in BPA degradation efficiency explained by the independent factors and their interactions. In addition, the two values of R2 and R2 adj

were close, which indicates the unnecessary variables were not included in the model (Salarian et al., 2016). Furthermore, the adequate precision ratio of 16.667 indicates an adequate signal for the quadratic model, which can measure the signal to noise ratio and a ratio N4 is desirable. This model can be used to navigate the design space. Overall, the ANOVA analysis was reliable to optimize and determine the level of each factor for BPA degradation in PS/DTN system. 3.3. Three-dimension response surface and two-dimension contour plots The graphical tool, including three-dimension (3D) response surface and two-dimension (2D) contour plots, could exhibit the interactive effects of operational parameters on degradation efficiency of BPA (Guo et al., 2011). The fitted 3D response surface plots and 2D contour plots were shown in Figs. 3 and S3, respectively, which depict the interactions between two variables by keeping the other variables constant at a central levels codified as value of 0 (Daneshgar et al., 2019; Fu et al., 2007). The shape of 2D contour plots, including circle and ellipse, indicates whether there are the mutual interactions between variables. In general, the elliptical contour plots suggests that the interactions between variables were significant (Muralidhar et al., 2001). As shown in Figs. 3a and S2a, the degradation efficiency was enhanced by increasing BPA concentration to 2.0 μM with PS concentration beyond 71.08 μM. In addition, BPA degradation efficiency exhibited obsolete change with the

Fig. 3. The response surface plots showing the effects of concentration of (a) PS and BPA, (b) PS and initial pH value, (c) DTN and initial pH value on DE, with the temperature of 30 °C.

W. Song et al. / Science of the Total Environment 699 (2020) 134258

increasing of BPA concentration when PS concentration was b71.08 μM. Therefore, the BPA concentration played a key role in degradation process with excess PS presence. The BPA degradation efficiency had the positive correlation when initial pH lower than 5.0 and PS concentration N155 μM (Fig. 3b). With increase of PS concentration, more sulfate radicals were generated in the solution and resulted in a corresponding increase in BPA degradation. Besides, BPA degradation efficiency increased when the concentration of DTN b 105 μM (Fig. 3c). However, further increase in DTN concentration had a negative effect on process performance and subsequently decreased the BPA degradation. It could be due to that the higher DTN concentration caused the consumption on the generated radicals. Similar result has been observed by other researchers (Sun et al., 2019). Additionally, as depicted in Fig. 3b~c, the initial pH value had significant influence on BPA degradation efficiency because of the effect on the reaction between PS and DTN. It was easy to find that lower pH value had the beneficial effect on the generation of sulfate radical compared to the higher ones. Increasing DTN concentration to 80 μM resulted in the higher BPA degradation; however, by further increasing the DTN concentration, BPA degradation efficiency decreased. The positive relationship between initial pH value and DTN concentration was observed in Fig. 3c. Namely, lower DTN concentration could also achieve the higher BPA degradation in the lower initial pH value. The higher initial pH could also effectively enhance the removal of BPA with the higher concentration of DTN. It could be attributed to the decomposing of DTN as shown in Eqs. (8)–(9) (Burlamacchi et al., 1969; Lister and

7

Garvie, 1959; Veguta et al., 2017). 2− þ 2S2 O2− 4 þ 2H →S2 O3 þ 2SO2 þ H2 O

ð8Þ

− 2− 2− 2S2 O2− 4 þ 2OH →S2 O3 þ 2SO3 þ H2 O

ð9Þ

3.4. Validation of the models The optimization of operating conditions was conducted to determine the optimum values of these parameters required to achieve the highest BPA degradation efficiency. Optimization was performed by numerical technique built in the Design Expert 8.0.6 software. The desired goal for the variables was chosen as “in range”, while DE of BPA was chosen as “maximize”. According to the output results, the degradation efficiency of BPA could reach maximum value of 98.52% with the BPA concentration of 1.79 μM, PS concentration of 131.77 μM, DTN concentration of 93.64 μM and the initial pH value of 3.62. In this study, the additional experiment was conducted to valid the model prediction (the optimal conditions). It was found that the results were greatly agreed with the predicted value through the quadratic model (Fig. S4-a). Moreover, Fig. S4-b had depicted the relationship between the predicted and the actual value. The data were roughly distributed on a straight line, indicating the accurate prediction ability of the model (Navamani Kartic et al., 2018; Teixeira et al., 2019).

Fig. 4. Proposed transformation pathways of BPA in PS/DTN system.

8

W. Song et al. / Science of the Total Environment 699 (2020) 134258

3.5. Identification of reactive radicals

different processes as following (Eqs. (2), (4), and (10)).

It was normal that the reactive radicals (SO·4 , ·OH, etc.) generated would participate in oxidation processes related to PS, which was regarded as the dominant oxidizing species (Miklos et al., 2018; Song et al., 2019a, 2019b). The scavenger test had been conducted to valid the dominating reactive species in the process of BPA degradation by PS/DTN based on the common radical scavengers (Xu et al., 2018). In general, tert-butyl alcohol (TBA) is more selective toward ·OH than others, with a rate constant of 3.8–7.6 × 10 8 M −1 s −1 . The rate constants is approximately 418–1900 times higher than that of the sulfate radical (4.0–9.1 × 105 M−1 s−1 ) (Neta et al., 1988). The methanol was supposed to quench efficiently both SO ∙4 and ∙OH, with rate constants of 9.7 × 10 8 and 2.5 × 10 7 M −1 s −1 , respectively (Buxton et al., 1988). As shown in Fig. S5, around 91.75% and 9.62% of BPA degradation were inhibited in the presence of methanol and TBA, respectively. It indicates that SO·4 was the dominating reactive species during BPA degradation in PS/DTN system and ·OH also had involved in this process. Based on the scavenge tests, the BPA degradation was simultaneously accomplished by these radicals, which were generated through the

SO
− 4 þ
OH þ BPA→BPA radicals→Intermediate Products

0.4

HCO3-=0 mM

0.8

HCO3-=0.5 mM

0.6

HCO3-=2.0 mM

0.4

HCO3-=4.0 mM

0.2

0.0

0.0 -20

0

20

40

60

80

100

120

140

b

1.0

0.2

160

HCO3-=0.25 mM HCO3-=1.0 mM HCO3-=3.0 mM HCO3-=5.0 mM

-20

0

20

40

60

Time (min)

80

100

120

140

160

Time (min)

c

Cl-=0 mM Cl-=1 mM Cl-=2 mM Cl-=4 mM Cl-=6 mM Cl-=8 mM Cl-=10 mM

1.0

0.8

0.6

DE (%)

DE (%)

0.6

To understand the BPA degradation in PS/DTN system, the generated intermediates were identified by GC-MS analysis. Tentative pathways for the transformation of BPA in PS/DTN system were proposed in Fig. 4 based on identified oxidation products and several previous reports (Choi et al., 2018; Lai et al., 2018; Wang et al., 2018; Zhang et al., 2018a, 2018b). The detected intermediate products have been presented in Table S2 of the Supplementary materials. Firstly, the dominant radicals attacked the aromatic ring and produced two different resonance forms of BPA radicals (R1 and R2) (Qi et al., 2017; Zhang et al., 2018a, 2018b). Secondly, highly oxidized phenoxyl BPA radical (R1) could further form p-isopropyl phenol radical (R3) and phenoxyl radical (R4) through β-scission (C\\C) (Choi et al., 2018; Jiang et al., 2019). Moreover, R2 also could undergo β-scission and release phenol radical (R5) and cationic isopropyl phenol (R6) (Jiang et al., 2014; Wang et al., 2018). Thirdly, R3 could be oxidized into P1 and continued to be

DE (%)

0.8

3.6. Proposed degradation pathway of BPA

a

NOM=0mg/L NOM=0.5mg/L NOM=1mg/L NOM=5mg/L NOM=10mg/L NOM=20mg/L NOM=30mg/L

1.0

ð10Þ

0.4

0.2

0.0 -20

0

20

40

60

80

100

120

140

160

Time (min) − Fig. 5. Effect of (a) NOM, (b)HCO− 3 , (c)Cl on BPA degradation in PS/DTN system. Conditions: T = 30 °C, pH = 3.62, [BPA] = 1.79 μM, [PS] = 131.77 μM, [DTN] = 93.64 μM.

W. Song et al. / Science of the Total Environment 699 (2020) 134258

oxidized into P6 through dehydrogenation and carbonylation, respectively (Choi et al., 2018). Then, P1 would be oxidized into P4 and be subsequently transformed into P2 (Zhang et al., 2018a, 2018b). In addition, phenol radical (R5) could be subjected to a coupling reaction with the generation of P3 or be further oxidized into hydroquinone (P4) and phenol (P2) successively; furthermore, the released R6 could be transformed into P5 through the substitution of a proton of water afterward (Wang et al., 2018). Furthermore, it is deduced that several ring opening intermediates may be generated but fail to be detected because of the negligible formation amount and might be further mineralized into CO2 and H2O. 3.7. Effect of natural water matrix

−
− SO
− 4 þ HCO3 →H2 O þ CO3

ð11Þ


−
OH þ HCO− 3 →H2 O þ CO3

ð12Þ

−

optimum strategy. In addition, it was identified that SO·4 as the primary reactive species in PS/DTN system and ·OH had been involved in BPA degradation. In this study, the BPA degradation pathway involved βscission, dehydrogenation and carbonylation, coupling reaction, hydroxylation, and ring opening was proposed based on GC-MS results. − Moreover, it is found that natural water matrix (NOM, HCO− 3 , Cl ) presented the obvious inhibition effect on BPA degradation. Declaration of competing interest None. Acknowledgments

Several studies have revealed that various natural water matrixes had the significant influence on contaminations degradation in SRAOPs (Song et al., 2019a, 2019b; Zhang et al., 2019). Natural water constituents, such as natural organic matter (NOM), bicarbonate and chloride ion, widely exist in the environment (Gao et al., 2019). In this study, the effect of HA (to simulate NOM), HCO–3 and Cl− on BPA degradation in PS/DTN system under the optimal conditions was investigated and the results were shown in Fig. 5. As depicted in Fig. 5a, the presence of HA had the obvious influence on BPA degradation. When dosing 0.5 mg/L of HA, BPA degradation was reduced half due to the scavenging effect and competition of reactive radicals with BPA. In addition, the inhibition effect became more and more obvious with the increase of HA concentration and this phenomena was consistent with other reports (Gao et al., 2019; Kim and Yu, 2005). Moreover, HCO–3 also had shown inhibitory effect on BPA degradation and the results were presented in Fig. 5b. With the addition of 0.25 mM HCO− 3 , BPA degradation efficiency reduced by 30.9% in 150 min and there was almost no degradation with the increasing of HCO–3 addition beyond 0.5 mM. It could be attribute to the quench of reactive radicals by HCO–3 according to Eqs. (11)–(12) (Buxton et al., 1988; Ji et al., 2018). The presence of Cl− slightly inhibits BPA degradation when Cl− concentration is lower than 6 mM (Fig. 5c). The BPA degradation efficiency decreased 48.7% and 25.8% at Cl− concentration of 8 mM and 10 mM, respectively. Similar results had been observed in several SR-AOPs system studied by others (Gu et al., 2019; Tan et al., 2014). We could ascribe the effect of Cl· generated from the reaction between SO·4 and low concentration of Cl− (Eq. (13)), which has a high redox potential of 2.4 V and might degrade organic compounds in a similar way as SO·4 (Deng and Ezyske, 2011). Nevertheless, the existed SO·4 and Cl· could be scavenged by the high Cl− concentration, resulting in the reduction of the overall reaction efficiency.

2− SO
− 4 þ Cl →SO4 þ Cl

9

ð13Þ

4. Conclusion The center composite design based on response surface methodology was successfully applied to model and predict the effects of BPA, PS, DTN and initial solution pH on response factor. It was reliable of the proposed quadratic model with high correlation coefficients (R2 = 0.9270) to predict the optimal operating parameters in the process of BPA degradation. The proposed model equation illustrates the interactions among these variables. The predicted BPA degradation efficiency well matched with the experimental result under the optimized experimental variables (1.79 μM, 131.77 μM, 93.64 μM for BPA, PS, DTN and pH = 3.62, respectively), which verified the practicability of this

Sincerely thanks are due to the Major Project of National Water Pollution Control and Governance of Science and Technology (2017ZX07401001), Shenzhen Demonstration Project (KJYY20171012140149523). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.134258.

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