A continuous flow MFC-CW coupled with a biofilm electrode reactor to simultaneously attenuate sulfamethoxazole and its corresponding resistance genes

Science of the Total Environment 637–638 (2018) 295–305

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

A continuous flow MFC-CW coupled with a biofilm electrode reactor to simultaneously attenuate sulfamethoxazole and its corresponding resistance genes Hua Li a,1, Hai-Liang Song b,1, Xiao-Li Yang c,⁎, Shuai Zhang a, Yu-Li Yang b,c,d, Li-Min Zhang b,⁎⁎, Han Xu a, Ya-Wen Wang b a

School of Energy and Environment, Southeast University, Nanjing 210096, China School of Environment, Nanjing Normal University, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Jiangsu Engineering Lab of Water and Soil Eco-remediation, Wenyuan Road 1, Nanjing 210023, China c School of Civil Engineering, Southeast University, Nanjing 210096, China d Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA b

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

• Stacked MFC-CWs powered BER sufficiently to remove SMX. • MFC-CW lowered the ARGs abundance and SMX concentration in BER effluent. • SMX and HRT significantly impacted the abundances of sul and 16S rRNA genes in system. • Bio-electricity may reduce ARGs abundances and microbial community diversity in BER.

a r t i c l e

i n f o

Article history: Received 24 December 2017 Received in revised form 12 April 2018 Accepted 26 April 2018 Available online xxxx Editor: Paola Verlicchi Keywords: Sulfamethoxazole Antibiotic resistance genes Biofilm electrode reactor Microbial fuel cell Constructed wetland

a b s t r a c t A continuous flow microbial fuel cell constructed wetland (MFC-CW) coupled with a biofilm electrode reactor (BER) system was constructed to remove sulfamethoxazole (SMX). The BER unit powered by the stacked MFCCWs was used as a pretreatment unit, and effluent flowed into the MFC-CW for further degradation. The experimental results indicated that the removal rate of 2 or 4 mg/L SMX in a BER unit was nearly 90%, and the total removal rate in the coupled system was over 99%. As the hydraulic retention time (HRT) was reduced from 16 h to 4 h, the SMX removal rate in the BER decreased from 75% to 48%. However, the total removal rate in the coupled system was still over 97%. The maximum SMX removal rate in the MFC-CW, which accounted for 42%–55% of the total removal, was obtained in the anode layer. In addition, the relative abundances of sul genes detected in the systems were in the order of sulI N sulII N sulIII, and significant positive correlations of sul gene copy numbers versus SMX concentration and 16S rRNA gene copy numbers were observed. Furthermore, significant negative correlations were identified between sul genes, 16S rRNA gene copy numbers, and HRT. The abundances of the sul genes in the effluent of the MFC-CW were lower than the abundances observed in the BER effluent. Highthroughput sequencing revealed that the microbial community diversity of the BER was affected by running

⁎ Correspondence to: X.-L. Yang, School of Civil Engineering, Southeast University, Dongnandaxue Road 2, Jiangning District, Nanjing 211189, China. ⁎⁎ Correspondence to: L.-M. Zhang, School of Environment, Nanjing Normal University, Wenyuan Road 1, Qixia District, Nanjing 210023, China. E-mail addresses: [email protected], (H.-L. Song), [email protected], (X.-L. Yang), [email protected] (L.-M. Zhang). 1 These authors contributed equally to this work and should be considered co-first authors.

https://doi.org/10.1016/j.scitotenv.2018.04.359 0048-9697/© 2018 Elsevier B.V. All rights reserved.

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time, power supply forms and HRT. Bio-electricity from the MFC-CW may reduce microbial community diversity and contribute to reduction of the antibiotic resistance gene (ARG) abundance in the BER. Taken together, the BER-MFC-CW coupled system is a potential tool to treat wastewater containing SMX and attenuate corresponding ARG abundance. © 2018 Elsevier B.V. All rights reserved.

1. Introduction Antibiotic residues present in aquatic environments have led to the occurrence of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) (Bergeron et al., 2015). Moreover, there is the risk that pathogenic bacteria could obtain ARGs from different species through horizontal gene transfer, thereby enriching ARGs and threatening human health (Stokes and Gillings, 2011). The traditional treatment processes of wastewater treatment plants (WWTPs) were not designed to remove antibiotics and ARGs. In fact, it has been reported that biological treatment methods in WWTPs may accelerate the development of ARGs (Rizzo et al., 2013). Therefore, given the harm that antibiotics and ARGs cause to the environment, there is an urgent need to develop effective, inexpensive treatment technologies to eliminate antibiotic residues and ARGs in wastewater. Recent developments in biofilm electrode reactors (BERs) that combine the advantages of both biological and electrochemical treatment technologies to degrade various high toxicity antibiotics have attracted a great deal of attention (Liang et al., 2013; Kong et al., 2014). BERs enhance the degradation rate and produce less sludge than conventional anaerobic methods and activated sludge processes (Shuang et al., 2013). Key metabolic enzymes for refractory organics have been shown to be activated in BERs (Feng et al., 2015). Zhang et al. (2016b) reported a removal rate of 200 μg/L for sulfamethoxazole (SMX) in a three-dimensional BER. Moreover, their system reached 88.9%–93.5% removal with a hydraulic retention time (HRT) of 40 h. Although BERs have been shown to be effective as a potential tool for antibiotic degradation, research into the occurrence and abundance of ARGs in BER is still in the exploratory stage. For example, a recent study showed that a BER system could change the microbial community, and thus eliminate ARGs (Cheng et al., 2016). Therefore, it is essential that the fate of ARB and ARGs in BER receive attention. Energy consumption in a BER caused by the application of a power source is unavoidable, which poses one of the greatest obstacles to BER applications. Therefore, new methods for powering BERs are required. Constructed wetlands (CWs) are considered a reasonable option for wastewater treatment because of their low cost and reduced maintenance requirements. Microbial fuel cells (MFC) coupled CWs are new technologies that embed MFC into a CW. The MFC use microorganisms as catalysts to drive the reduction or oxidation reactions on electrodes to produce bioelectricity. Previous studies have demonstrated that MFC-CWs can be used to degrade dyes, nitrates, and organics in wastewater (Doherty et al., 2015; Fang et al., 2015). However, less attention has been given to the potential for the MFC-CWs to remove trace antibiotics and ARGs. CWs have been found to be effective at reducing several antibiotics and corresponding ARGs (Chen et al., 2016). However, it is not clear if MFC-CWs have similar antibiotic and ARG treatment effects as CWs. Furthermore, the collection and utilization of electrical energy generated from MFC-CWs remains a problem. Notably, recent research into the construction of a microbial electrolytic cell (MEC)-MFC coupled system to apply the electrical energy from an MFC to an MEC has attracted our attention (Zhao et al., 2012; Zhang et al., 2013). The MEC-MFC coupled system is a good example of an in situ approach to the utilization of power from an MFC. In this study, we investigated the possibility that BERs could be operated on a long-term basis powered by stacked MFC-CWs. Furthermore, the MFC-CW could be used as an advanced treatment unit in coupled systems to further

reduce antibiotics and ARGs. These possibilities have not previously been investigated, and it is not clear what the fate of the antibiotic residues and their corresponding ARGs would be in such a coupled system. The effects of bio-electricity from MFC-CWs on the abundance of ARGs and microbial community changes in BERs are also unknown. SMX is a broad-spectrum antibiotic that is widely used because it is inexpensive and effective (Wang et al., 2016). However, SMX has ecological toxicity and is often detected in the effluent following wastewater treatment (Batt et al., 2006). In this study, stacked MFC-CWs were used to power a BER forming a BER-MFC-CW coupled system. Our hypothesis was that use of the BER as a preprocessing unit will reduce the concentration of SMX, after which the effluent of the BER would flow into the advanced treatment unit MFC-CW for further SMX degradation, thereby improving the degradation rate of the entire system. In addition, the corresponding ARG abundance may decline because of MFC-CW treatment. A key breakthrough was the effective utilization of the low-power output generated by the MFC-CW, so that the BER no longer required external power. To validate this hypothesis, a coupled system was constructed by connecting MFC-CWs in series to power the BER. The removal rate of SMX in both the BER and the MFC-CW units of the coupled system were then evaluated under different influent SMX concentrations and HRTs. The dynamic fate of the representatives of SMX ARGs (sulI, sulII and sulIII) and the 16S rRNA gene in the effluent of each unit of the system were also studied, and the electricity production performance of the stacked MFC-CW for different HRTs in the coupled systems was evaluated. Finally, the effects of bio-electricity and HRT on the microbial community of the BER were assessed.

2. Materials and methods 2.1. Reactor configuration The main structure of the MFC-CW was made of a polyacrylic plastic chamber (Fig. 1). The internal diameter and height of the device was 19 cm and 32 cm, respectively, and the total effective volume was 4 L. Along the water flow direction, four layers were constructed corresponding to the four sampling ports. Specifically, there was a 14 cm deep bottom layer and an 8 cm middle layer that was both filled with gravel (5–7 mm in diameter). Additionally, anode (8 cm deep) and air cathode (3 cm deep) layers were constructed using granular activated carbon (GAC, specific surface area 500–900 m2/g and diameter 3– 5 mm, Jiangsu Zhuxi Activated Carbon Co., Ltd., Jiangsu, China) and stainless-steel mesh (Nanjing Zhongdong Chemical Glass Instrument Co., Ltd., Nanjing, China). A 1 mm titanium wire was used to connect the anode, cathode, and the external resistance of 1000 Ω. A polycarbonate plastic cylinder with a diameter of 15 cm and height of 30 cm was used to make the BER reactor. The reactor has an effective depth of 25 cm and effective volume of 4 L. Stainless steel rings lined with graphite felt were used as the cathode. A graphite rod 24 cm in length and 2 cm in diameter was fixed in the center of the reactor as the anode and the cathode was placed around it. A vertical upward flow was adopted in the BER with the water inlet placed at the bottom and a water outlet placed at the top. All reactors in this experiment were wrapped in black fabric to prevent light from entering the system so that photolysis of SMX would not occur (Willach et al., 2018).

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Fig. 1. Schematics of continuous flow BER-MFC-CW coupled system (1 water tank; 2 water inlet; 3 anode of MFC-CW; 4 cathode of MFC-CW; 5 wetland plants; 6 middle layer; 7 water outlet; 8 data acquisition module).

2.2. Inoculation and operation of the systems The inoculum was concentrated anaerobic sludge obtained from the Municipal Wastewater Treatment plant of Nan Jing. GAC mixed with anaerobic sludge was introduced into the anode of MFC-CW. Next, four strains of Oenanthe javanica were planted in the cathode layer of a MFC-CW after they had been cultivated for more than one month at room temperature. Active carbon fiber pretreated by mixing with fresh sludge was introduced into BER, after which the inoculated active carbon fiber was stabilized for 3 h in the reactor.

Following inoculation, simulated wastewater was pumped into each reactor. The composition of simulated wastewater was as follows: glucose, 200 mg/L; NH4Cl, 25 mg/L; KH2PO4, 5 mg/L; FeCl3·6H2O, 15 mg/L; CuSO4·5H2O, 0.3 mg/L; MnCl2·4H2O, 1.2 mg/L; ZnSO4·7H2O, 1.2 mg/L; CoCl2·6H2O, 1.5 mg/L (Zhang et al., 2016b). The stimulated wastewater in the reactor was renewed every 2 days. After about 20 days, a gray color biofilm was observed on the cathode surface, indicating that the inoculation of BER was accomplished. Upon stable electricity production of MFC-CW, the wastewater was pumped continuously by peristaltic pumps (BT100-1L, Baoding Longer Precision

Fig. 2. The specific influent mode and circuit connection way to form the BER-MFC-CW system in two experiment stages. (A) Four BERs and four MFC-CWs were constructed to form four BER-MFC-CW systems in first stage; (B) three BERs and six MFC-CWs were constructed to form three BER-MFC-CW coupled systems in second stage.

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Pump Co., Ltd., China) into each reactor from the bottom. The process was run for two weeks to achieve system stability. The experiment included two stages. In the first stage (May 15 to August 15, 2017), four BERs and four MFC-CWs that had achieved stability were constructed to form four BER-MFC-CW systems (Fig. 2A). It is important to highlight that BER 2 and 4 were both powered by bioelectricity supplied by two MFC-CWs in series, and the system was called the coupled system (Fig. 2A). As shown in Fig. 1, the cathode of a series of two MFC-CWs was connected to the anode of the BER unit, while the cathode of the BER unit was connected to the anode of the MFC-CWs, so the coupled system was formed. A data acquisition module (DAM-3057, Art Technology Co. Ltd., China) was applied to monitor the voltage data of the two MFC-CWs in series. Another two BERs (BER1, BER3) powered by direct current (DC) were constructed as a control group, and the corresponding system was called the composite system. The voltage provided by DC in the control group was set at 1 V. All systems were continuously fed simulated wastewater for about one month from May 15, after which the simulated wastewater supplemented with SMX was pumped into the BER-MFC-CW systems on June 15. The SMX concentration in the influent of BER 1 and 2 was 2000 μg/L, and the concentration in BER 3 and 4 was 4000 μg/L. The effluent of the BER flowed into the corresponding MFC-CW in each system and the HRT of each reactor was 2.5 days. The influence of different HRTs on coupled system performance in the second stage was investigated from August 16 to October 15. Three BERs and six MFC-CWs were constructed to form three coupled systems (Fig. 2B). Each BER was powered by two MFC-CWs in series. The SMX concentration was 4000 μg/L in the influent of the BER units of all coupled systems. The effluent of BER 1 entered MFC-CW 1 and 2. The effluent of BER 2 entered MFC-CW 3 and 4, and the effluent of BER 3 entered MFC-CW 5 and 6. It should be noted that the effluent of the BER further flowed into the two MFC-CWs. The HRT of each BER was set at 16 h, 8 h, and 4 h, so the HRT of the corresponding MFCCW was halved. The removal rate of SMX in the aqueous phase of each unit during the two stages was measured. The microbial biomass was expressed as 16S rRNA gene copy number and the absolute and relative abundances of the corresponding ARGs (sulI, sulII, sulIII) in each unit of the systems was determined.

2.3. Quantification of antibiotic concentrations Triplicate samples of effluent (100 mL) were collected from each unit of the systems at three sampling times during the two stages. The sampling time was July 15, August 15 and October 15. Water samples were filtered through a 0.22 μm filter. Next, Qasis HLB (6 mL, Waters, USA) was applied to enrich the water samples (Wu et al., 2015). The SDZ concentrations were quantified using a 1200 binary liquid chromatography (LC) system coupled with a G6410B LC/MS equipped with an electrospray ion source (ESI) (Agilent, USA) using multiple reaction monitoring (MRM) modes. The ESI-MS/MS parameters for the detection of SMX were based on the method described by Wu et al. (2015). Separation of the samples was conducted using an Eclipse plus C18 column (2.1 mm × 150 mm; 3.5 μm; Agilent, USA) at a flow rate of 0.2 mL/min. The mobile phase was composed of a 0.1% formic acid solution (90%) with acetonitrile (10%). External calibration curves were used to determine the SMX concentration and the correlation coefficient was N0.996. The SMX concentration in the influent of each unit was C1, and the corresponding concentration in the effluent of each unit was C2. The removal rate (η) of SMX was calculated using Eq. (1).

η¼

C1−C2  100% C1

ð1Þ

2.4. Quantification of ARGs Effluents from the MFC-CWs and BERs were collected in triplicate at the three sampling times mentioned above. Each water sample was filtered through a sterile membrane filter (0.22 μm pore diameter) with a vacuum filtration apparatus. A power soil DNA isolation kit (MoBio, Carlsbad, CA, USA) was used to extract bacterial genomic DNA following the manufacturer's instructions. The concentration and quality of the DNA was determined by spectrophotometry (UV-9100, Lab Tech Ltd., Beijing, China) and gel electrophoresis, respectively. The PCR primer sequences used to amplify the target genes are shown in Table S1. The qPCR conditions are shown in Table S2 and the protocols were based on previously reported methods (Rodriguezmozaz et al., 2015). The standards were prepared by 10-fold dilution of the plasmid carrying the corresponding ARGs obtained from Sangon Biotech Co. Ltd. (Shanghai, China) and the standard curves were created (Table S3). Three sul genes (sulI, sulII, sulIII) were quantified using the CFX Connect RealTime PCR System (Bio-Rad, Shanghai, China). High determination coefficients (R2 N 0.998) and amplification efficiencies (96.7%–99.8%) were obtained. The corresponding gene copies of the tested samples were determined according to the standard curves. The Ct values of the samples were used to calculate the copy number of each gene normalized to 1 mL effluent and the copy numbers were further log-transformed for analysis. To minimize the variance in the total bacterial population size and reduce the difference in DNA extraction efficiency, the relative abundance of sul genes was calculated according to their absolute copy numbers divided by the 16S rRNA gene copies. 2.5. DNA sequencing Samples were collected from BER3 and BER4 during the first experimental period, and the samples of BERs operated at different HRTs were collected during second stage. The DNA was extracted and was measured according to the method described in Section 2.4. The V4 and V5 regions of the bacterial 16S rRNA gene were amplified utilizing PCR and using the primers 515F 5′ -GTGCCAGCMGCCGCGGTAA-3′ and 907R 5′-CCGTCAATTCMTTTRAGTTT-3′ (Bokulich et al., 2013). Each qualified DNA segment was then used to construct a library. Bioinformatics analysis was conducted with the Illumina platform (MAGIGENE Co., Ltd., Guangzhou, China). After sequencing analysis, the raw singleend reads were optimized through removal of low quality reads to give high-quality sequences, which were subsequently clustered into operation taxonomy units (OTUs) using UCLUST with a cutoff of 97% similarity (Cui et al., 2016). Alpha diversity indexes including (PD whole tree, Chao1, and Shannon's indices) were calculated using OTU number by QIIME. To normalize the data, the relative abundance of each OUT was calculated. A 2-D graph was then constructed by principal component analysis (PCA) to display the differences in OTU composition among samples (Yang et al., 2018). 2.6. Statistical analysis Data analyses were conducted using Microsoft Excel 2010 and the figures were plotted using Origin 2016. Treatment means among groups were compared by one-way ANOVA (p b 0.05) using Microsoft Excel 2010. A Pearson bivariate correlation analysis was performed using Excel 2010. 3. Results and discussion 3.1. Removal rate of SMX The target SMX concentration in the effluent of all BERs and MFCCWs consisting of coupled or composite systems at the two sampling times in the first stage are shown in Table 1. It should be noted that the SMX removal rates in all of the BER-MFC-CWs systems were N99%.

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Table 1 Concentrations of SMX (mean ± SD, n = 3) in the BER and MFC-CW effluent of BER-MFC-CW system. System

BER-MFC-CW 1 BER-MFC-CW 2 BER-MFC-CW 3 BER-MFC-CW 4

BER-MFC-CW 1 BER-MFC-CW 2 BER-MFC-CW 3

Unit

Influent SMX concentration (μg/L)

Effluent SMX concentration (μg/L)

Removal rate (%)

Jul. 15th

Aug. 15th

Jul. 15th

Aug. 15th

Jul. 15th

Aug.15th

Jul. 15th

Aug. 15th

BER1 MFC-CW1 BER2 MFC-CW2 BER3 MFC-CW3 BER4 MFC-CW4

2000 285.4 ± 15.6 2000 207.9 ± 20.79 4000 423.3 ± 28.22 4000 382.4 ± 23.9

2000 263.4 ± 15.5 2000 194.8 ± 16.2 4000 434.6 ± 17.38 4000 362.7 ± 24.18

285.4 ± 15.6 14.2 ± 1.5 207.9 ± 20.79 12.1 ± 0.9 423.3 ± 28.22 23.1 ± 1.5 382.4 ± 23.9 25.8 ± 1.2

263.4 ± 15.5 10.4 ± 0.6 194.8 ± 16.2 12.5 ± 10.9 434.6 ± 17.38 25.6 ± 1.3 362.7 ± 24.18 19.4 ± 0.97

85.73 95.02 89.61 94.1 89.42 94.54 90.44 93.25

86.83 96.05 90.26 93.58 89.14 94.11 90.93 94.65

99.29

99.48

99.39

99.38

99.42

99.36

99.35

99.51

BER1 MFC-CW1, 2 BER2 MFC-CW3, 4 BER3 MFC-CW5, 6

Oct.15th 4000 954.3 ± 53.0 4000 1507.7 ± 75.4 4000 2140.5 ± 97.30

Oct. 15th 954.3 ± 53.0 90.3 ± 5.6 1507.7 ± 75.4 94.7 ± 7.9 2140.5 ± 97.3 98.2 ± 5.3

These results suggested that it was practical to power the BER by using two MFC-CWs in series. In the first stage, the average effluent concentration in BERs 1 and 3 powered by DC at the two sampling times was 274.4 μg/L and 429.0 μg/ L, respectively, with corresponding removal rates of 86.28% and 89.28%. It is important to note that a higher removal rate (89.9% and 90.7%) was obtained in the BER powered alone by two MFC-CWs in series. The corresponding average effluent concentration was 201.4 and 372.6 μg/L. Another interesting finding was that the voltages of the stacked MFCCWs were all in the range of 1.09–1.26 V when the SMX concentration was 2 mg/L and 4 mg/L, indicating that the bioelectricity production did not decrease under the high SMX concentration. BER unit functioned as an electrical appliance in a coupled system, and the electrical energy from the stacked MFC-CWs was provided to the BER unit; therefore, the electrons could be transported from the anode of the MFC-CW to the BER unit though wires. The degradation of SMX in the BER system required electrons. With the elimination of SMX and glucose, an effective circuit in the coupled system was formed and electron transfer from the MFC-CW to the BER accelerated. A previous study demonstrated that the removal efficiency of chemical oxygen demand (COD) and azo dye was correlated with the applied voltage in the BER (Liu et al., 2015). Similarly, Yang et al. (2018) reported that sulfadiazine removal efficiency in BER was enhanced with increased voltage. Electron flow could increase the activity of microorganisms and enhance the removal rate of SMX (Zhang et al., 2016b). Therefore, the removal rate of SMX was higher in BER powered by two MFC-CWs in series due to the higher voltage supply. Effluent from the BER unit flowed into the MFC-CW unit for further degradation, and the average SMX concentration in the final effluent of the MFC-CW in coupled systems was 12.3 and 22.6 μg/L, respectively. In the second stage, the removal rate of SMX in the coupled systems under the three investigated HRT was analyzed (Table 1). A strong impact of HRT on SMX removal in the BER was observed, and the SMX removal rate reached a maximum (75%) in the BER when the HRT was 16 h, then decreased with the shorter HRT, eventually reaching only 48% when the HRT was 4 h. The SMX concentration in the effluent of the BER was 954.3, 1507.7, and 2140.5 μg/L at the three HRT, respectively. The removal of refractory organics in the BER occurs via a process of biodegradation, bio-sorption and electro-sorption (Franks et al., 2010). The mechanism of SMX elimination in the BER may be driven simultaneously by the above behaviors. Use of BER as a pretreatment unit in a coupled system has shown to be promising and very useful to maintenance of the stability of the entire system. Indeed, it has been reported that SMX and its degradation products may possess potential antibacterial activity (Wang et al., 2016). Additionally, a previous study has shown that high concentrations of antibiotic in a MFC-CW impeded

Oct. 15th 75.00 90.54 62.50 93.72 48.00 95.33

Total removal rate (%)

Oct. 15th 97.74 97.63 97.54

the activity of electrochemical active bacteria and led to lower power output (Zhang et al., 2016a). Moreover, recent studies have shown that the BER could increase antibiotic removal efficiency by 60% when compared with the common anaerobic reactor (Cheng et al., 2016; Harnisch et al., 2013). About 90% of SMX was eliminated in the BER unit in the first stage, which further reduced the toxicity to electrochemically active bacteria (EAB) in the MFC-CW. Therefore, the BER unit is very important to maintaining the stability of the entire system. In contrast, the HRT had only a slight influence on the SMX removal rate in the MFC-CW. Interestingly, as the HRT decreased from 32 h to 8 h in the MFC-CW, the SMX removal efficiency was well over 90%. The SMX concentration in the effluent of the MFC-CWs was between 90 and 100 μg/L, which showed that the MFC-CW had a high resistance to shock loads. The effluent of the BER passed into the MFC-CW for further degradation to ensure the low SMX concentration in the final effluent. A series of biological and physicochemical processes, including microbial degradation, sorption of the substrate layer, and hydrolysis accounted for the high removal rate in the MFC-CW (Huang et al., 2015; Zhang et al., 2016a). The removal rate of the three coupled systems was N97% in our study. To clearly reveal the specific removal of SMX in the MFC-CW, an investigation of the effect of the different layers on SMX removal in the MFC-CW was conducted. The results showed that the maximum removal rate was obtained in the anode layer, which accounted for 42%– 55% of the total removal. The corresponding removal rate in the bottom layer was approximately 20%; however, the SMX removal rates in the middle layer and cathode layer were b1%. 3.2. The dynamics of typical sul genes and the 16S rRNA gene in the coupled system Sulfonamides competitively inhibit dihydropteroate synthase enzymes via their structural analogy with p-aminobenzoic acid substrate (Sköld, 2000). In the present study, three sul genes and the 16S rRNA gene were detected in the systems by q-PCR. Based on comparison of the gene abundances at the two different sampling times in the first stage, the level of target sul genes and the 16S rRNA gene all showed a decreasing trend with time in each reactor (Fig. 3). Furthermore, the trend of relative abundances of the three sul genes was in the order of sulI N sulII N sulIII (Fig. 4). These findings are consistent with observations from a previous study that showed the sulI gene was the most prevalent among sul genes in a horizontal subsurface flow constructed wetland (Nõlvak et al., 2013). Significant correlations were observed between the SMX concentrations and the corresponding three sul genes in the effluent of each unit with the correlation coefficients of 0.91, 0.90, and 0.91, respectively. These findings imply that the target

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Fig. 3. Thermodynamic diagram for distributions of 16S rRNA and sul genes in effluent. (A) Distributions of log (copy numbers of sul genes and 16S rRNA gene) in each reactor under 2 mg/L or 4 mg/LSMX in influent; (B) distributions of log (copy numbers of sul genes and 16S rRNA gene) in each unit of the system under different HRT of influent.

genes abundance in the reactors was mainly affected by the SMX concentration. Furthermore, a clear increasing trend in gene copies and relative abundances of sul genes in each unit was observed as the influent SMX concentration increased from 2 mg/L to 4 mg/L (Figs. 3 and 4). These results are consistent with those of previous studies that showed high antibiotic concentrations selected for antibiotic resistant plasmidbearing cells and increased ARGs copy numbers (Rysz et al., 2013). It is important to note that the copies of sul gene abundances in the effluent of the BER were an order of magnitude higher than those found in the MFC-CW effluent, which implied that the abundance of ARGs in the system was mainly driven by SMX. In contrast, sul genes exhibited a low relative abundance and number of copies in the effluent of the MFC-CW, implying that the MFCCW performed well at inhibiting ARGs. The total copies of the 16S rRNA gene of general bacteria in the effluent of the MFC-CW were far less than in the BER effluent, indicating that the MFC-CW also controlled the overall amount of bacteria well. Previous studies have shown that CWs had high ARB and ARGs removal efficiency, which was attributed to the sorption of ARB onto the media (Chen and Zhang, 2013; Chen et al., 2015). A 96.8–99.7% reduction of bacteria in CWs was observed by determining the number of colony-forming units, and the capacity of CWs to filter out bacteria contributed to the reduction of ARGs (Vacca et al., 2005). Moreover, a 10–13% reduction of total bacteria was observed in our study based on quantification of the 16S rRNA gene, and there was a significant correlation between sul genes and the 16S rRNA gene in our study (correlation coefficients of the three sul genes versus 16S rRNA gene = 0.937, 0.945, and 0.917, respectively). Therefore, the reduction of total bacteria in the MFC-CW may be the most efficient way to inhibit ARGs in MFC-CWs. As shown in Figs. 3B and 5, the HRT had a significant influence on the removal of sul genes. A greater abundance of ARGs was detected with shorter HRTs, while statistically significant negative correlations were observed between the three sul genes and the 16S rRNA gene

abundances versus the HRTs, with corresponding values were −0.87, −0.87, −0.84, and −0.86, respectively. It is well known that shorter HRTs lead to higher organic loading rates (OLR) (Huang et al., 2011), and that the OLR may affect ARG abundance. These results are consistent with those of previous studies that showed that OLR had a positive impact on tetracycline resistance propagation in the domestic wastewater of the activated sludge process (Kim et al., 2007). In addition, a lower ARG loss and metabolic burden of plasmid reproduction was observed when the OLR was high (Rysz et al., 2013). 3.3. Effects of HRT on electricity generation To explore the effects of HRT on electricity production of the stacked MFC-CWs in a coupled system, the voltages of three coupled systems in the second stage were collected. The coupled system (HRT = 32 h) was run for 7 days before stabilization, and the output voltage stabilized at average approximately 1.29 V. The total voltage of the coupled systems (HRT = 16 h and 8 h) were stable at average 1.24 V and 1.04 V, respectively. In this study, the biodegradable co-substrate glucose and bio-refractory compound SMX contributed to the COD in the synthetic wastewater. Most of the COD was glucose. When the HRT was low, there were a large number of carbon sources left in the effluent of the BER, which may not have influenced the organic matter supply to the anode of the MFC-CW. Excessive consumption of glucose may occur in the BER when HRTs are too long, which may further influence the electricity production of the MFC-CW. It should be noted that the total voltage decreased when the HRT of MFC-CW decreased significantly from 16 h to 8 h, which was likely because that the excess SMX in the influent of the MFC-CW impeded the activity of EAB (Zhang et al., 2016a). Although ARGs may spread because of effluent in the BER, we determined that the BER-MFC-CW coupled system can significantly remove SMX and attenuate its ARGs because the CW inhibited ARGs better than other currently available methods (Huang et al., 2015). However,

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Fig. 4. Quantities of ARGs (sulI, sulII, sulIII) normalized to 16S rRNA gene in four BERs and four MFC-CWs in the system under 2 mg/L or 4 mg/L SMX in influent (A: relative abundance of sulI gene in effluent of BERs; B: relative abundance of sulII gene in the effluent of BERs; C: relative abundance of sulIII gene in the effluent of BERs; D: relative abundance of sulI gene in effluent of MFC-CWs; E: relative abundance of sulII gene in the effluent of MFC-CWs; F: relative abundance of sulIII gene in the effluent of MFC-CWs).

additional studies should be conducted to improve this coupled system. The BER and the MFC-CW had a mutual influence in the coupled system; therefore, related factors may change the stability of the entire system when even a slight change occurs in one unit. One of the great challenges is to maintain the long-term stability of the BER-MFC-CW coupled system. For example, it is essential to prevent the tendency of stacked CW-MFCs to produce a voltage reversal (Cao et al., 2017). Fortunately, no voltage reversal occurred in our study. Nevertheless, it is necessary to develop a more efficient anode of the MFC-CW to enhance the power supply to the BER.

3.4. Sequencing cluster analysis of the BER during the electrical stimulation process Few studies have explored the change in bacterial communities because of the electrical stimulation process in the BER. The effects of electrical stimulation from DC and bioelectricity on microbial community structure are unknown. In our study, variations in microbial communities in the BER induced by electrical stimulation from DC and bioelectricity were assessed. The microbial diversity indices are shown in Table 2. Sample A1.1 was collected from the inoculated sludge while

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Fig. 5. Quantities of ARGs (sulI, sulII, sulIII) normalized to 16S rRNA gene in each unit of the system under different HRT of influent (A: BER system; B: MFC-CW system).

samples B1.1 B1.2, B1.3 and B1.4 were collected from BER3 (30 d), BER4 (30 d), BER3 (60 d) and BER4 (60 d), respectively. Table 2 shows the OTU numbers and microbial diversity indices of the BER samples, as Table 2 Microbial diversity indices of samples in BER systems. Sample

No. of OTUs

PD whole tree

Chao1

Shannon

B1.1 B1.2 B1.3 B1.4 B1.5 B1.6 B1.7

1996 1745 839 480 1460 1273 1005

100 86 71 43 84 72 53

1784 1504 1249 479 1463 1236 920

8.06 7.68 7.49 6.24 7.33 6.26 5.38

well as the richness (Chao) and diversity (Simpson and Shannon) indexes. The sequences were divided into many OTUs based on the similarity of the sequences. Moreover, lower OUT, PD, Chao 1, Simpson and Shannon index values were obtained as the running time increased from 30 days to 60 days, indicating that community diversity decreased significantly. Furthermore, lower microbial diversity indices of B1.4 were obtained, indicating that the bacterial diversity decreased when the BER was powered by bioelectricity. This may be attributed to the fact that the stacked MFC-CWs can provide a relatively higher electricity supply to BER than a direct source of electricity (1.0 V). It has been reported that appropriate electrical stimulation resulted in the changes in bacterial communities (Liu et al., 2015; Zhang et al., 2016b). Furthermore, a previous study showed that the OTU composition in the BER reactors was influenced by voltage, as was the bacterial community (Yang

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Fig. 6. Principal component analysis based on OTU abundance of samples in BER during the two stages (A: first stage; B: second stage).

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et al., 2018). Integrons have also been shown to have the ability to capture and spread ARG cassettes containing ARGs, and ARGs carried by the integrons may be lost due to the higher external electric field (Berglund et al., 2014; Cheng et al., 2016). Thus, some bacteria may not survive due to the loss of ARGs. The bacterial diversity was diminished, which caused all DNA sequence types to be reduced (Cheng et al., 2016). The ARGs were also controlled at a low level when the microbial diversity was weak. Therefore, the relative abundance of the sul gene was lower in the BER unit of the coupled system than in the composite system in our study. The exact reason for the diminished microbial diversity and further influence of the gene abundances in the BER system powered by bioelectricity remains to be determined, which reflected our limited understanding of the effects of electrical stimulation on bacterial communities and sul gene maintenance and reproduction. Principal component analysis (PCA) was used to construct a 2-D graph to summarize the similarity between samples. The five samples were grouped into four categories based on PCA (Fig. 6A). In addition, the effects of bio-electricity led to a clear distinction in the bacterial communities of samples B1.3 and B1.4. The samples collected from BER (16 h), BER (8 h), and BER (4 h) (denoted B5, B6, and B7, respectively) revealed similarities and differences in the bacteria at the phylum level. Specifically, the three samples were grouped into three categories based on PCA (Fig. 6B). As the HRT decreased, the richness and microbial diversity indices decreased significantly, indicating that low HRT may contribute to decrease the community diversity (Table 2). These findings may be attributed to

the fact that high loads of SMX are harmful to some bacteria, and that bacteria without SMX resistance cannot survive in such a system. As shown in Fig. 7, there were 12 phyla were identified in our study, with Euryarchaeota, Proteobacteria, Chloroflexi, Bacteroidetes, OD1 (Parcubacteria), and Firmicutes being the most dominant. The relative abundance of Euryarchaeota, Chloroflexi and OD1 decreased, while that of Firmicutes, Bacteroidetes and Proteobacteria increased. It has been reported that Firmicutes and Proteobacteria could carry and disseminate ARGs (Jiang et al., 2017; Huerta et al., 2013). Therefore, the increase in relative abundance of these bacteria may be one reason to explain the higher abundance of ARGs at lower HRT that was observed in the present study. 4. Conclusions This study examined the use of a BER-MFC-CW coupled system to effectively remove SMX. The fate of the corresponding ARGs in this system was also studied. The results showed that the investigated system could supply adequate and stable electricity for the BER. The level of the sul genes in the effluent of the MFC-CW unit exhibited a lower abundance than the effluent of the BER unit. The relative abundances of sul genes were significantly affected by influent SMX concentration and HRT, and a greater abundance of ARGs was detected with shorter HRTs and higher SMX concentrations in the coupled system. Highthroughput sequencing revealed that the microbial diversity of the BER was affected by running time and HRT, with significantly lower

Fig. 7. The effects of three investigated HRT on the relative abundance of bacteria classified to phyla level in BER.

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microbial diversity being associated with shorter HRT. Furthermore, the decrease of ARGs in the BER powered by higher output bio-electricity may be related to the lower microbial diversity obtained. The degradation rate of SMX was high, and the corresponding ARGs abundances may have declined because the developed system was coupled. Notes The authors declare no competing financial interest. Acknowledgements This work was supported by National Natural Science Foundation of China (41571476), National Science and Technology Major Project of China (2017ZX07202004), Provincial Key Technologies R&D Program of Jiangsu, China (BE2015358), the Fundamental Research Funds for the Central Universities of SEU (2242017K41048). Hai-Liang Song would like to acknowledge the Qing Lan Project of Jiangsu Province and Startup Fund for Talented Scholars of Nanjing Normal University. Hua Li would like to acknowledge Postgraduate Research Practice Innovation Program of Jiangsu Province. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.04.359. References Batt, A.L., Bruce, I.B., Aga, D.S., 2006. Evaluating the vulnerability of surface waters to antibiotic contamination from varying wastewater treatment plant discharges. Environ. Pollut. 142, 295–302. Bergeron, S., Boopathy, R., Nathaniel, R., Corbin, A., Lafleur, G., 2015. Presence of antibiotic resistant bacteria and antibiotic resistance genes in raw source water and treated drinking water. Int. Biodeterior. Biodegrad. 102, 370–374. Berglund, B., Khan, G.A., Lindberg, R., Fick, J., Lindgren, P.E., 2014. Abundance and dynamics of antibiotic resistance genes and integrons in lake sediment microcosms. PLoS One 9, e108151. Bokulich, N.A., Subramanian, S., Faith, J.J., Gevers, D., Gordon, J.I., Knight, R., Mills, D.A., Caporaso, J.G., 2013. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59. Cao, X., Wang, H., Li, X.Q., Fang, Z., Li, X.N., 2017. Enhanced degradation of azo dye by a stacked microbial fuel cell-biofilm electrode reactor coupled system. Bioresour. Technol. 227, 273–278. Chen, H., Zhang, M., 2013. Effects of advanced treatment systems on the removal of antibiotic resistance genes in wastewater treatment plants from Hangzhou, China. Environ. Sci. Technol. 47, 8157–8163. Chen, J., Liu, Y.S., Su, H.C., Ying, G.G., Liu, F., Liu, S.S., He, L.Y., Chen, Z.F., Yang, Y.Q., Chen, F. R., 2015. Removal of antibiotics and antibiotic resistance genes in rural wastewater by an integrated constructed wetland. Environ. Sci. Pollut. R 22, 1794–1803. Chen, J., Wei, X.D., Liu, Y.S., Ying, G.G., Liu, S.S., He, L.Y., Yang, Y.Q., 2016. Removal of antibiotics and antibiotic resistance genes from domestic sewage by constructed wetlands: optimization of wetland substrates and hydraulic loading. Sci. Total Environ. 565, 240–248. Cheng, Z., Hu, X., Sun, Z., 2016. Microbial community distribution and dominant bacterial species analysis in the bio-electrochemical system treating low concentration cefuroxime. Chem. Eng. J. 303, 137–144. Cui, E., Wu, Y., Zuo, Y., Chen, H., 2016. Effect of different biochars on antibiotic resistance genes and bacterial community during chicken manure composting. Bioresour. Technol. 203, 11–17. Doherty, L., Zhao, Y., Zhao, X., Wang, W., 2015. Nutrient and organics removal from swine slurry with simultaneous electricity generation in an alum sludge-based constructed wetland incorporating microbial fuel cell technology. Chem. Eng. J. 266, 74–81. Fang, Z., Song, H.L., Cang, N., Li, X.N., 2015. Electricity production from Azo dye wastewater using a microbial fuel cell coupled constructed wetland operating under different operating conditions. Biosens. Bioelectron. 68, 135–141. Feng, H., Zhang, X., Guo, K., Vaiopoulou, E., Shen, D., Long, Y., Yin, J., Wang, M., 2015. Electrical stimulation improves microbial salinity resistance and organofluorine removal in bioelectrochemical systems. Appl. Environ. Microbiol. 81, 3737–3744. Franks, A.E., Nevin, K.P., Glaven, R.H., Lovley, D.R., 2010. Microtoming coupled to microarray analysis to evaluate the spatial metabolic status of Geobacter sulfurreducens biofilms. ISME. J. 4, 509–519.

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