pH control and microbial community analysis with HCl or CO2 addition in H2-based autotrophic denitrification

Journal Pre-proof pH control and microbial community analysis with HCl or CO2 addition in H2-based autotrophic denitrification Wei Xing, Yan Wang, Tianyu Hao, Zhenglan He, Fangxu Jia, Hong Yao PII:

S0043-1354(19)30974-1

DOI:

https://doi.org/10.1016/j.watres.2019.115200

Reference:

WR 115200

To appear in:

Water Research

Received Date: 2 April 2019 Revised Date:

13 October 2019

Accepted Date: 15 October 2019

Please cite this article as: Xing, W., Wang, Y., Hao, T., He, Z., Jia, F., Yao, H., pH control and microbial community analysis with HCl or CO2 addition in H2-based autotrophic denitrification, Water Research (2019), doi: https://doi.org/10.1016/j.watres.2019.115200. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

1

pH Control and Microbial Community Analysis with HCl or

2

CO2 Addition in H2-based Autotrophic Denitrification

3

Wei Xing a, Yan Wang a, Tianyu Hao a, Zhenglan He a, Fangxu Jia a, Hong Yao a,*

4

a Department of Civil and Environmental Engineering, Beijing Key Laboratory of

5

Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Civil

6

Engineering, Beijing Jiaotong University, Beijing 100044, China

7

8

Frist author: Wei Xing. School of Civil Engineering, Beijing Jiaotong University, No.3

9

Shangyuancun, Haidian District, Beijing 100044, PR China.

10

E-mail: [email protected]; Tel / Fax: 86-10-5168-5917

11 12

Corresponding author: Hong Yao. School of Civil Engineering, Beijing Jiaotong

13

University, No.3 Shangyuancun, Haidian District, Beijing 100044, PR China.

14

E-mail: [email protected]; Tel / Fax: 86-10-5168-2157.

15

1

16

ABSTRACT

17

H2-based autotrophic denitrification is promising to remove nitrate from water or

18

wastewater lacking organic carbon sources, and pH is one of its most important process

19

parameters. HCl and CO2 addition are known as adequate pH control methods for

20

practical purposes. However, because of H2, added CO2 may participate in microbial

21

metabolisms and affect denitrification mechanisms. Here, a combined micro-electrolysis

22

and autotrophic denitrification (CEAD) reactor, in which H2 is generated based on

23

galvanic-cell reactions between zero-valent iron and carbon, was optimized and

24

continuously operated for 233 days by adding HCl or CO2 to control pH in the range of

25

7.2-8.2. Microbial communities were compared between the two pH-control methods

26

through high-throughput sequencing of 16S rRNA, nirS, and nirK genes. Under a low

27

COD/N ratio of 0.5 in the influent (with ~36 mgNO3-–N/L), when adding HCl, the total

28

nitrogen (TN) removal efficiency reached 91.4% ± 0.9% with a 28-h hydraulic retention

29

time (HRT). When adding CO2, the TN removal efficiency was improved to 96.5% ±1.7%

30

with 24-h HRT. Significant differences of 16S rRNA and nirS genes between the two pH-

31

control stages indicated the variation of microbial communities and nirS-type denitrifiers.

32

With

33

Limnobacter, and Thermomonas, which were reported previously as likely autotrophic or

34

heterotrophic denitrifiers, were most dominant in the biofilms. With CO2 addition, the

35

biofilms became dominated by Anaerolineaceae and Methylocystaceae (related to

36

organic carbon metabolism), Denitratisoma (likely heterotrophic denitrifier), and

37

uncultured bacteria TK10 and AKYG587. The results suggest that the added CO2 not

38

only contributed to pH control but also participated in microbial metabolisms. This study

HCl

addition,

Thiobacillus,

unclassified

2

Comamonadaceae,

Arenimonas,

39

provides useful insights into microbial mechanisms and further optimization of H2-based

40

autotrophic denitrification in water and wastewater treatment.

41

KEYWORDS

42

Autotrophic

43

sequencing; microbial community.

denitrification;

zero-valent

iron;

carbon

dioxide;

high-throughput

44

45

1. Introduction

46

In a water environment, nitrogen pollutants can cause eutrophication and potentially

47

harm human health (Rivett et al., 2008). Biological heterotrophic denitrification, in which

48

microorganisms usually rely on organic carbon to obtain electron donors, is widely used

49

for removing oxidized inorganic nitrogen in water and wastewater treatment processes

50

(Gan et al., 2019). In some cases, polluted water (such as groundwater contaminated by

51

nitrate or secondary effluent requiring advanced nitrogen removal) is characterized by a

52

low COD/N ratio and therefore lacks organic carbon sources for heterotrophic

53

denitrification (Rivett et al., 2008; Pelaz et al., 2018). Accordingly, dosing with

54

exogenous organic carbon (such as methanol and acetate) is necessary to ensure the

55

effectiveness of denitrification. However, this method is unsustainable, costly, and may

56

lead to the secondary pollution of organics or nitrite accumulation when the added

57

organic carbon is excessive or insufficient (Park and Yoo, 2009; Alzate Marin et al.,

58

2016).

59

Autotrophic denitrification, in which inorganic electron donors and carbon sources 3

60

are utilized to reduce nitrate, was recently reported to be attractive for water and

61

wastewater treatment under low COD/N conditions (Park and Yoo, 2009; Di Capua et al.,

62

2015).

63

denitrification (Karanasios et al., 2010), zero-valent iron (ZVI)-supported biological

64

denitrification (Xu et al., 2017b), and bio-electrochemical denitrification (Ghafari et al.,

65

2008), has attracted widespread attention. In our previous studies, we developed an

66

innovative process of combined micro-electrolysis and autotrophic denitrification

67

(CEAD) based on nitrate removal mechanisms similar to ZVI-supported biological

68

denitrification (Xing et al., 2016). In CEAD, iron-carbon micro-electrolysis carriers

69

(MECs) filled in the reactor provide H2 based on numerous galvanic-cell reactions

70

between ZVI and carbon, promoting the growth of autotrophic denitrifying bacteria.

H2-based

autotrophic

denitrification,

which

includes

hydrogenotrophic

71

H2-based autotrophic denitrification has been proven to be an effective process for

72

nitrate removal (Sunger and Bose, 2009; Karanasios et al., 2010; Mousavi et al., 2012).

73

However, pH is one of the most important parameters affecting its performance (Ghafari

74

et al., 2010b; Mousavi et al., 2012). Many researchers have pointed out that the optimum

75

pH value for hydrogenotrophic denitrification, which related to the different

76

hydrogenotrophic cultures used, is in the range of 7.6–8.6, with higher pH values

77

resulting in lowered nitrate removal (Lee and Rittmann, 2003; Karanasios et al., 2010).

78

Till et al. (1998) reported that hydrogenotrophic denitrification was inhibited at pH of 10

79

or greater. On the other hand, in many studies on H2-based autotrophic denitrification, the

80

significant pH increases to 9.5 or even greater were reported without adequate pH control

81

(Karanasios et al., 2010; Xing et al., 2016). Therefore, implementing pH control for

82

optimized bacterial growth could benefit H2-based autotrophic denitrification.

4

83

Particularly, in ZVI-supported biological denitrification, abiotic reduction of nitrate to

84

ammonium occurred at the same time, and inoculating hydrogenotrophic denitrifiers was

85

effective at decreasing the generation of ammonium (An et al., 2009; Di Capua et al.,

86

2019). Considering the completion of abiotic reduction and biological denitrification,

87

providing the optimal pH required to enhance biological denitrification could help further

88

reduce undesired ammonium in effluents.

89

Some researchers controlled the pH in H2-based autotrophic denitrification using

90

phosphate buffers (Lee and Rittmann, 2003; Zhang et al., 2019). However, phosphate

91

buffers are only suitable for studies on a lab-scale. For practical purposes, continuous

92

acid supplements such as HCl and CO2 have been investigated for pH control in H2-based

93

autotrophic denitrification (Sakakibara and Nakayama, 2001; Ghafari et al., 2010a; Xia et

94

al., 2015). Ghafari et al. (2009) found that continuous sparging of CO2 gas was difficult

95

to control, causing the pH to decrease dramatically to the range of 5.5–6, thereby

96

inhibiting denitrification. However, Xia et al. (2015) proved that the sparging of CO2

97

through membranes was suitable for accurate pH control and stable operation.

98

Regarding pH control for H2-based autotrophic denitrification, previous studies

99

mainly focused on process performances and kinetic parameters (Ghafari et al., 2010b;

100

Tang et al., 2011; Xia et al., 2015). Because of the existence of H2 in the autotrophic

101

denitrification system, the added CO2 may result in acetogenesis from its reaction with

102

H2. Marshall et al. (2013) established a microbial electrosynthesis system for consuming

103

CO2 and generating acetate and H2, and they found that the acetogen (Acetobacterium

104

spp.) dominated the active microbial population on the cathodes. Usher et al. (2015)

105

reported that CO2 was reduced and fixed as acetate on the corrosion of steel via H2 5

106

production. It is hypothesized that the added CO2 may not only affect the pH value in H2-

107

based autotrophic denitrification but also participate in carbon and nitrogen metabolism.

108

To our knowledge, relevant studies on microbial communities and mechanisms in H2-

109

based autotrophic denitrification with CO2 addition are rare. Only Xia et al. (2016)

110

reported on microbial communities in a hydrogenotrophic denitrification reactor under

111

CO2 addition. However, they mainly focused on the performance and model, without

112

discussing if the CO2 addition affected the microbial communities. A comparison of

113

microbial communities in H2-based autotrophic denitrification with and without CO2 is

114

worth further study, and the knowledge gap regarding the effect of CO2 on microbial

115

mechanisms should be considered further.

116

In the present study, two pH-control strategies, namely HCl addition and CO2

117

addition, were implemented in the 5.73-L H2-based CEAD reactor we developed

118

previously (Xing et al., 2016). Using synthetic water under a low COD/N ratio of 0.5

119

(with NO3-–N = ~36 mgNO3-–N/L) as the influent, the reactor was operated continuously

120

for 233 days under pH control. Under two strategies, the pH ranges in stable stages were

121

maintained between 7.2-8.2. Microbial communities in the two pH-control strategies

122

were investigated through high-throughput sequencing of 16S rRNA, nirS, and nirK

123

genes. The purpose of the study was to investigate both the reactor performance and

124

microbial communities in H2-based autotrophic denitrification with pH control, and

125

specifically determine whether CO2 addition affects the microbial communities and

126

microbial metabolisms. The findings could help to improve the process performance by

127

pH control and provide useful insights into microbial mechanisms of H2-based

128

autotrophic denitrification in water and wastewater treatments.

6

129

2. Materials and Methods

130

2.1 Reactor structure

131

The reactor was constructed with a plexiglass cylinder 120 cm in height and 9 cm in

132

inner diameter, as illustrated in Fig. 1. The influent was pumped in from the bottom with

133

a water pump (AKS603NHP0800, Seko Co., Ltd., Italy) and the effluent was discharged

134

from the top. A water bath jacket with a thermometer was installed to control the

135

temperature in the reactor. The MECs developed in our laboratory were filled in the

136

reactor to a height of 90 cm, resulting in an effective volume of 5.73 L and a filling water

137

volume of 3.62 L. As previously reported (Deng et al., 2016; Xing et al., 2018), the

138

MECs were produced with powdered iron (17.5% volume), scrap iron (25.0% volume),

139

powdered activated carbon (35.0% volume, passed through a 200-mesh screen), three

140

types of catalysts (each of 2.5% volume), adhesive X (10.0% volume), and foaming agent

141

Y (5.0% volume), and were roasted in an oxygen-free atmosphere at 900–1000°C for 3 h.

142

The MECs possessed a diameter of 0.5–1.0 cm, a specific surface area of 3.3×104–

143

4.2×104 m2/kg, and a porosity of 50%. Based on galvanic cell reactions, which occurred

144

between anodes (ZVI) and cathodes (activated carbon) in MECs, H2 was generated, thus

145

supporting the autotrophic denitrifiers. The detailed mechanisms of this process are

146

described in the Supporting Information (Fig. S1).

147

In this study, to further enhance biological denitrification and improve the total

148

nitrogen (TN) removal, the previously reported reactor was optimized as follows. First, a

149

pH-control module providing either HCl or CO2 was installed. When adjusting the pH

150

with diluted HCl, as shown in the red box in Fig. 1, the acid storage tank was filled with 7

151

1:100 diluted HCl, and the acid peristaltic pump was started by a timer for 1 min every 2

152

h to inject HCl into the reactor. Each time, 10 mL of diluted HCl was added, and the pH

153

in the reactor changed periodically during the HCl addition cycles, as shown in Fig.

154

S2(a). When adjusting the pH with CO2, it was continuously sprayed into the reactor

155

using a module of polypropylene hollow-fiber membranes, which was submerged in

156

water at the top of the reactor, as shown in the green box in Fig. 1. Thus, pH was

157

maintained by adjusting the CO2 flow rate, and the pH value in the reactor could be more

158

stable than with HCl addition, as illustrated in Fig. S2(b). Second, to ensure good mixing

159

and flush generated precipitation on carriers, internal circulation was added. The water

160

was pumped from the top to the bottom by a circulation pump (MP-20RZ, Xinxishan Co.,

161

Ltd., Shanghai, China) with a circulation ratio (i.e., circulation pump flow / influent flow)

162

of 10:1.

163

2.2 Experimental operation of the reactor

164

The reactor was inoculated with activated sludge from a municipal wastewater

165

treatment plant in Beijing that was applied to an anaerobic–anoxic–oxic process. Before

166

this study began, it continuously run for approximately 8 months without pH control,

167

resulting in a pH of greater than 10 in effluents for a long time, and TN removal

168

efficiencies lower than 40% (the representative results were shown in Fig. S3). The first

169

day of this study was defined as the day on which pH control was started, and the reactor

170

operation was divided into two stages: Stage A (days 1-95), in which pH was controlled

171

by diluted HCl, and Stage B (days 118-233), in which pH was controlled by CO2

172

addition. Between days 96 and 117, the reactor was maintained at the conditions of Stage

173

A but was not monitored. Nine specific stages were defined with different pH control 8

174

methods and HRTs, as listed in Table 1.

175

Considering nitrogen contaminated natural water is usually organic-limited rather

176

than organic-free, synthetic influent containing ~36 mg/L NO3−-N with a low COD/N

177

ratio of 0.5 was prepared with tap water for simulating the actual water quality with

178

organic-limited conditions. COD was prepared with CH3COONa, which could

179

theoretically remove ~ 6.3 mg/L NO3−-N through heterotrophic denitrification according

180

to the stoichiometric equation S5 in the Supporting Information. The influent also

181

contained 280 mg/L NaHCO3 and a 1 mL/L trace element solution (Till et al., 1998).

182

Although the CO2 added in Stage B could also supply inorganic carbon for autotrophic

183

denitrification, thereby replacing NaHCO3, the same concentration of NaHCO3 was used

184

in all influents for the entire 233-day operation period for the purpose of comparison. The

185

initial pH of the influent was ~8.0. The temperature of the reactor was maintained at 27 ±

186

1 °C. No oxygen was aerated into the reactor, and the dissolved oxygen was maintained

187

at 0.1-0.3 mg/L because a small amount of oxygen dissolved in the influent.

188

2.3 Monitoring methods

189

The concentrations of NH4+–N, NO3-–N, and NO2-–N were determined according to

190

standard methods (APHA, 2005) by using a UV spectrophotometer (2102C, UNICO

191

Company, USA). The TN was determined from the sum of NH4+–N, NO3-–N, and NO2-–

192

N, organic nitrogen was not involved. The pH and DO values were determined using a

193

digital multi-parameter meter (Multi 3430, WTW, Germany). At Stage A, the daily pH of

194

the effluent was determined before periodic HCl addition at the same time every day.

195

2.4 Microbial analysis 9

196

2.4.1 Sludge sample collection

197

Eight sludge samples were collected from the reactor for microbial community

198

analysis. Four samples were collected at Stage A (day 50, with HCl addition): three

199

biofilm samples attached to the MECs (1T from the top area, 1M from the middle sludge-

200

sampling outlet, and 1B from the bottom sludge-sampling outlet) and one suspended

201

sludge sample (1S from the bottom sludge-sampling outlet). The other four samples were

202

collected at Stage B (day 233, with CO2 addition): three biofilm samples attached to the

203

MECs and one suspended sludge sample (2T, 2M, 2B, and 2S, which were collected from

204

the same positions as 1T, 1M, 1B, and 1S, respectively).

205

2.4.2 DNA extraction and PCR

206

For each sludge sample, triplicate genomic DNA samples were extracted using a

207

FastDNA Spin Kit for Soil (MP Biomedicals, Irvine, CA, USA). The samples were

208

pooled together to ensure replication. 16S rRNA genes were amplified using barcode-

209

containing universal primers 515F/806R targeting both bacteria and archaea (Bates et al.,

210

2010). The nirS and nirK genes, which encode nitrite reductase, were amplified using

211

barcode-containing primers nirS4F/nirS6R (Liu et al., 2014) and nirK1aCuF/nirKR3CuR

212

(Zhou et al., 2016), respectively. The amplification conditions can be found in the above

213

references. Triplicate amplicons were combined and then purified for high-throughput

214

sequencing.

215

2.4.3 High-throughput sequencing

216

High-throughput sequencing was performed on the Illumina MiSeq platform

217

(Majorbio Bio-pharm Technology Co. Ltd., Shanghai, China). After pre-processing, 10

218

31,268–65,095 effective 16S rRNA gene sequences were obtained for the eight samples.

219

For nirS and nirK, 28,958–63,216 and 30,657–60,076 effective sequences were obtained,

220

respectively. Subsequently, sequences in all samples were subsampled randomly

221

according to the minimum sequence number.

222

The sequences were divided and clustered into operational taxonomic units (OTUs)

223

with 97% similarity. Taxonomic assignment of the sequences was performed using the

224

SILVA 16S rRNA database (https://www.arb-silva.de/) and the functional gene database

225

(http://fungene.cme.msu.edu/). Heat maps were acquired using HemI (version 1.0.3), and

226

principal component analysis (PCA) was conducted using Canoco5. The Bray–Curtis

227

dissimilarity and Euclidean distance were employed for cluster analysis. Representative

228

sequences were analysed using BLAST (https://www.ncbi.nlm.nih.gov/BLAST/).

229

Phylogenetic trees were generated with the neighbour-joining algorithm by using

230

Molecular Evolutionary Genetics Analysis (MEGA 6.0).

231

2.4.4 Data deposition

232

Sequences obtained through high-throughput sequencing of the eight samples were

233

deposited in the NCBI short-read archive under accession numbers SRR8617516–

234

SRR8617523 (16S rRNA), SRR8661666–SRR8661673 (nirS), and SRR8661744–

235

SRR8661751 (nirK).

236

3. Results

237

3.1 Nitrogen removal performance with HCl addition

238

The performance of the reactor in continuous operation for 95 days at Stage A is 11

239

shown in Fig. 2. In prior of Stage A-1, without pH control, the pH values in the effluents

240

were observed to be higher than 10 for a long time. Then, pH control strategy was

241

implemented, and the HCl dosage was adjusted frequently at Stage A-1 to determine the

242

optimal dosage, resulting in pH fluctuation in the effluent (7.6 ± 0.5) in this period.

243

During the first 10 days of Stage A-1, the nitrate removal efficiency reached 88.7± 4.1%,

244

but the TN removal efficiency was only 35.5± 3.0% because of significant ammonium

245

and nitrite accumulation.

246

From the end of Stage A-1, the HCl dosage was maintained at 10 mL per 2 h,

247

whereby the daily effluent pH stabilized at 7.9 ± 0.2 afterwards. At the end of Stage A-1,

248

with an HRT of 32 h, the nitrate removal efficiency was observed at 94.5± 1.7%, and the

249

TN removal efficiency increased significantly to 85.9± 4.9%, indicating that TN removal

250

increased significantly during this stage. At Stage A-2, when the HRT was shortened to

251

28 h, the TN removal efficiency dropped at the beginning, then recovered to 91.4± 0.9%.

252

From the 51st day, the sludge sampling resulted in a decrease of TN removal efficiency,

253

and the residual TN was mainly attributed to the unreacted nitrate and generated

254

ammonium. Then, TN removal efficiency increased again to 88.4± 3.3% at the end of

255

Stage A-2. At Stage A-3, when the HRT was further shortened to 20 h, the nitrate and TN

256

removal efficiencies were 94.8±1.6% and 82.7± 4.1%, respectively. These results indicate

257

that TN removal efficiency is more significantly affected than nitrate removal efficiency.

258

In general, HCl pH control resulted in good TN removal performance under weak

259

alkaline conditions in the reactor. Under a low COD/N ratio of 0.5, with an HRT of 28 h,

260

the TN concentration in effluents was observed at 3.18 ± 0.34 mg/L, and the nitrate and

261

TN removal rates were calculated as 29.7 ± 1.0 mgN/(L·d) and 28.9 ± 0.9 mgN/(L·d) , 12

262

respectively. At Stage A-3, the highest nitrate and TN removal rates reached 41.5 ± 0.9

263

mgN/(L·d) and 36.2 ± 1.8 mgN/(L·d), respectively.

264

3.2 Nitrogen removal performance with CO2 addition

265

The pH-control strategy was changed to CO2 addition during days 118 to 233 (Stage

266

B). The reactor performance at Stage B is shown in Fig. 3. At the beginning of Stage B-1,

267

we also adjusted the CO2 flow rate to test the optimal dosage, which led to pH instability

268

in the effluent (7.6±0.4). From the end of Stage B-1, the CO2 flow rate was fixed at 2

269

mL/min. The TN removal efficiency increased to 93.8±1.6% with an HRT of 24 h at the

270

end of Stage B-1, and 95.6±1.7% with an HRT of 18 h in Stage B-2. However, when the

271

HRT was shortened to 12 h, the TN removal efficiency gradually decreased to 63.3±4.0%

272

at the end of Stage B-3. At the same time, the pH in the effluent increased from 7.5±0.1

273

(in Stage B-2) to 7.9±0.1 (in Stage B-3) with the same CO2 dosage. To avoid the potential

274

effects caused by pH variation, the CO2 flow rate at Stage B-4 was increased to 3

275

mL/min. This operation recovered pH in the effluent to 7.4±0.1 in Stage B-4, but the TN

276

removal efficiency was still at 62.2±4.3%. This may indicate that the reactor performance

277

is not sensitive to pH values in the range of 7.5 to 8.0. Therefore, we increased the HRT

278

to 24 h again and found that the TN removal efficiency returned to 96.5±1.7%.

279

In the entire CO2 addition period, the average nitrate removal efficiency stably

280

reached 97.8±1.9%. The TN concentration in effluents was maintained at 1.72 ± 0.90

281

mg/L with an HRT of 24 h. The nitrate and TN removal rates were calculated as 37.0 ±

282

0.3 mgN/(L·d) and 36.0 ± 0.6 mgN/(L·d), respectively, in the end of Stage B-6 with the

283

highest removal efficiency. The highest nitrate and TN removal rates reached 72.3 ± 0.9

13

284

mgN/(L·d) and 50.9 ± 2.9 mgN/(L·d), respectively, in the end of Stage B-3 with an HRT

285

of 12 h. It is worth noting that residual TN in the effluent at Stage B was dominated by

286

nitrite, rather than ammonium, which was dominant in Stage A.

287

3.3 Comparison of microbial communities between HCl and CO2 addition

288

3.3.1 High-throughput sequencing of 16S rRNA

289

The compositions of microbial communities in the eight samples were analysed by

290

high-throughput sequencing. Rarefaction curves and rank-abundance curves of the 16S

291

rRNA gene are shown in Fig. S4, indicating that high-throughput sequencing data

292

effectively represented the microbial communities. The community richness and diversity

293

indices based on the 16S rRNA gene are shown in Table S1 in the Supporting

294

Information. The microbial communities at class, family, and genus levels are shown in

295

Fig. S5. A heat map and PCA are shown in Fig. 4. At Stage A, the microbial communities

296

in 1T, 1M, and 1B were clustered together, but those in the suspended sludge sample 1S

297

were different from those in the three biofilm samples attached to the MECs. At Stage B,

298

the microbial communities of 2T, 2M, 2B, and 2S were clustered together but differed

299

significantly from those at Stage A.

300

In 1T, 1M, and 1B, the genus Thiobacillus (affiliated with the family

301

Hydrogenophilaceae) was predominant, accounting for 5.0%, 14.2%, and 13.6%,

302

respectively. Other dominant microbes in these three samples included genera

303

Arenimonas, Limnobacter, and Thermomonas, as well as unclassified bacteria in the

304

family Comamonadaceae. It was verified in our previous work that these microorganisms

305

represent the dominant autotrophic or heterotrophic denitrifying bacteria in the system 14

306

(Xing et al., 2016; Xing et al., 2018). In 1S, the proportions of Thiobacillus and

307

Thermomonas were significantly lower than in 1T, 1M, and 1B, while the proportions of

308

the genus Arenimonas and family Comamonadaceae were similar. Additionally, the

309

proportion of the family Anaerolineaceae was significantly higher in 1S.

310

In the four samples at Stage B, the family Anaerolineaceae was predominant, with a

311

proportion of 16.3 ± 3.2%. Moreover, uncultured bacteria TK10, unclassified bacteria in

312

the family Methylocystaceae, genus Denitratisoma, and uncultured bacteria AKYG587,

313

which had proportions of 0.7 ± 0.4%, 0.3 ± 0.0%, 0.2 ± 0.0%, and <<0.1% at Stage A,

314

increased to 9.5 ± 0.5%, 7.4± 1.3%, 5.4 ± 2.0%, and 5.3 ± 1.1% at Stage B, respectively.

315

The proportions of the family Comamonadaceae at Stage A (5.9 ± 2.1%) and Stage B

316

(3.1 ± 0.3%) showed an insignificant difference.

317

3.3.2 High-throughput sequencing of nirS and nirK

318

The results of high-throughput sequencing based on nirS and nirK were analysed to

319

investigate denitrifiers in the reactor. Rarefaction curves and rank-abundance curves are

320

shown in Fig. S6 and S7. The richness and diversity indices based on nirS and nirK gene

321

are shown in Table S2 and S3. The analysis indicated that most OTUs could not be

322

classified into subdivision levels; these OTUs were classified as Proteobacteria at the

323

phylum level or no-rank bacteria. Here, we discuss the results at the OTU level. The heat

324

map and PCA are shown in Fig. 5 (nirS) and S8 (nirK), respectively. Dominant OTUs

325

with proportions higher than 2% were selected to generate phylogenetic trees, as shown

326

in Fig. 6 (nirS) and S9 (nirK).

327

Similar to the results with 16S rRNA, the nirS genes from the two stages were

15

328

significantly different. For example, the nirS OTU 380, which was clustered with

329

uncultured bacteria and Thiobacillus sp., accounted for 10.7 ± 4.1% in 1T, 1M, and 1B

330

from Stage A, but reduced significantly in Stage B. The nirS OTUs 24, 66, 49, and 44,

331

which were almost zero at Stage A, increased significantly to 17.7 ± 1.0%, 11.7 ± 0.7%,

332

9.6 ± 0.7%, and 8.4 ± 1.2%, respectively, in 2T, 2M, and 2B. These abundant nirS OTUs

333

were clustered with uncultured bacteria and Rhodocyclaceae / Rhodocyclales bacteria.

334

Although many nirS OTUs were only clustered with uncultured bacteria, these results

335

indicate that the nirS-type denitrifiers were significantly different between the two stages.

336

According to the sequencing analysis of nirK, more than 60% of the total sequences

337

could be classified as the nirK OTU 158, indicating that this OTU was the major nirK-

338

type denitrifier in the reactor. Other OTUs also showed significant differences between

339

the two stages. For example, the nirK OTUs 155 and 75 were dominant in 1T, 1M, and

340

1B; the nirK OTUs 2 and 21 increased significantly to 19.2 ± 2.1% and 15.0 ± 0.1%,

341

respectively, in 2T, 2M, and 2B. However, the phylogenetic analysis revealed that most

342

nirK OTUs were only clustered with uncultured bacteria.

343

4. Discussion

344

4.1 Effects of different pH-control strategies on nitrogen removal

345

In the current study, the results indicate that good performances were achieved when

346

both of the pH-control strategies were applied in the H2-based autotrophic denitrification

347

system. In stable stages, the TN removal efficiency was 91.4% ± 0.9% with HCl addition

348

at an HRT of 28 h, and it reached 96.5% ±1.7% with CO2 addition at an HRT of 24 h.

16

349

With HCl or CO2 addition, the highest nitrate removal rates were 41.5 ± 0.9 mgN/(L·d)

350

and 72.3 ± 0.9 mgN/(L·d), respectively.

351

The nitrate removal mechanisms in the current study were similar to those of ZVI-

352

supported biological denitrification, as shown in the Supporting Information; i.e., the

353

major reactions in the reactor are hydrogenotrophic denitrification (biological reaction)

354

and ammonium generation from ZVI (abiotic reaction) (Till et al., 1998). In studies on

355

ZVI-supported biological denitrification, Sunger and Bose (2009) applied an influent

356

nitrate loading of 0.029 mgN/(L·d) and achieved a nitrate removal efficiency of 95% with

357

a long HRT of 15.6 days. Till et al. (1998) fed 50 mg/L NO3--N into the influent and

358

achieved a stable nitrate removal efficiency of 61% under an HRT of 2.33 days; however,

359

50% of the removed nitrate was converted to ammonium and the nitrate removal rate was

360

calculated as 0.012 mgN/(L·d). The removal rates were low and limited by hydrogen

361

generation through ZVI corrosion (Biswas and Bose, 2005). By using a high biomass of

362

3930±100 mg MLSS/L in an SBR, Wang et al. (2012) obtained a nitrate removal rate as

363

high as 52.32 mgN/(L·d) with nitrate removal efficiency of 79.8%. In a recent literature

364

(Zhang et al., 2019), the effect of initial nitrate concentration, pH, and ZVI dosage on the

365

nitrate removal rate were investigated, and the results showed that three days were

366

required to achieve complete nitrate removal under the optimal conditions (≤ 25 mg

367

NO3−-N/L in influents).

368

Ammonium accumulation, resulting in low TN removal, was often reported in ZVI-

369

supported biological denitrification (Di Capua et al., 2015; Xie et al., 2017). The

370

treatment target of this current study is to achieve high and stable TN removal

371

efficiencies, and the results were satisfactory. In addition, the nitrate removal rates were 17

372

better than most results obtained in comparable studies on ZVI-supported biological

373

denitrification. As shown in the Supporting Information, the required H2 in the reactor

374

was produced by galvanic cell reactions in MECs (Eq. S2). Thus, nitrate removal rate

375

could be improved by promoting electron transfer between anodes (ZVI) and cathodes

376

(carbon) (Xing et al., 2016). In a comparison with those reported in hydrogenotrophic

377

denitrification, for example a maximum of 770 mgN/(L·d) (Ergas and Reuss, 2001), the

378

nitrate removal rates in this study as well as the other studies on ZVI-supported biological

379

denitrification were much lower. This is because the H2 in the reactor was generated in-

380

situ from ZVI rather than a directly supply, which reduces risks associated with H2

381

storage and explosions. Therefore, although biological denitrification based on ZVI has

382

the drawback of low removal rate, ZVI was considered environmentally friendly, non-

383

toxic, abundant, with versatile functions as a reductant, sorbent and coagulant (Fu et al.,

384

2014; Sun et al., 2016), and the ZVI-supported biological denitrification was easy to

385

handle and especially suitable for in situ remediation of contaminated groundwater or

386

surface water (Di Capua et al., 2019).

387

Additionally, compared with the results in the beginning of Stage A-1, controlling

388

pH stably in neutral or weakly alkaline ranges (7.5-8.0) achieved good performance with

389

much less ammonium or nitrite accumulation. Karanasios et al. (2010) have reported that

390

an increase of the pH value above 8.6 could significantly decrease the nitrate removal

391

rate in hydrogenotrophic denitrification, but low pH values of 7 or below could also

392

inhibit biological reactions. In abiotic reduction of nitrates using ZVI, it was reported that

393

decreasing pH values in the acidic range increased nitrate reduction rates; however, lower

394

pH accelerated the formation of undesired ammonium rather than nitrogen gas (Hu et al.,

18

395

2001; Alowitz and Scherer, 2002). For biological denitrification based on ZVI, the factors

396

for promoting biological reaction, rapidly generating of H2 through abiotic reaction, and

397

decreasing of ammonium should be considered as a whole. Therefore, pH control in a

398

precise and quantitative method is crucial and warrants further study.

399

4.2 Effects of different pH-control strategies on microbial communities

400

The microbial communities and nirS / nirK genes presented significant differences

401

between Stage A and Stage B. Since the carriers were direct sources of H2 produced in

402

this reactor, the autotrophic denitrification cloud mainly occurs in the biofilms attached to

403

the carriers. Additionally, owing to the internal water circulation from the top to the

404

bottom, the microbial communities in the three biofilm samples were similar for both

405

stages. At Stage A, genera Thiobacillus, Arenimonas, and Thermomonas, as well as

406

unclassified bacteria in the family Comamonadaceae, were the dominant bacteria in 1T,

407

1M, and 1B. Bacteria in these genera and family are often detected in denitrification

408

systems for wastewater treatment (Adav et al., 2010; Liu et al., 2015). In our previous

409

studies with DNA-based stable-isotope probing (DNA-SIP), Thiobacillus-like and

410

Thermomonas-like bacteria have been identified as the typical autotrophic denitrifying

411

bacteria in similar systems (Xing et al., 2017; Xing et al., 2018). It is reasonable that the

412

proportion of autotrophic denitrifying bacteria reduced significantly in the suspended

413

sludge sample 1S. Comamonas-like (genus of the family Comamonadaceae) and

414

Arenimonas-like bacteria were identified as heterotrophic and mixotrophic with DNA-

415

SIP, respectively (Xing et al., 2018). The proportions of unclassified Comamonadaceae

416

and Arenimonas did not change significantly in 1S. Heterotrophic denitrifying bacteria

417

existed in the reactor because of the low concentration of acetate added in the influent 19

418

and the organics decomposed from sludge, which could function as an organic carbon

419

source.

420

At Stage B, it was surprising that the proportions of genera Thiobacillus,

421

Thermomonas, and Arenimonas decreased significantly in all samples (2T, 2M, 2B, and

422

2S). This result indicated that the typical autotrophic or mixotrophic denitrifiers in the

423

reactor declined after CO2 addition, although the TN removal performance was still

424

satisfactory. The family Comamonadaceae, likely containing heterotrophic denitrifying

425

bacteria (Wang and Chu, 2016), showed similar proportions in the two stages. However,

426

at Stage B, the genus Denitratisoma increased to 6.5 ± 0.8% in three biofilm samples (2T,

427

2M, and 2B), and 2.0 % in the suspend sludge sample 2T. The 16S rRNA gene of the

428

representative OTU showed 97% similarity with Denitratisoma oestradiolicum, which

429

was reported to be a heterotrophic denitrifier isolated from activated sludge in a

430

municipal wastewater treatment plant (Fahrbach et al., 2006). Species in this genus have

431

been proven to be dominant succinate-assimilating denitrifiers by DNA-SIP (Saito et al.,

432

2008)

433

wastewater treatment (Ma et al., 2015; Xu et al., 2017a). Overall, based on currently

434

known denitrifying bacteria, it showed that autotrophic denitrifiers were weakened and

435

heterotrophic denitrifiers were strengthened after CO2 addition.

and have been detected commonly as heterotrophic denitrifiers in domestic

436

Although the 16S rRNA gene based approach is the most widely used technique for

437

community analysis in wastewater treatment systems (Sanz and Köchling, 2007), simply

438

studying the 16S rRNA gene is not sufficient to reveal the difference in denitrifiers. In

439

this instance, high-throughput sequencing of functional genes nirS and nirK were also

440

carried out in this study. Tables S1-S3 showed that the richness and diversity of the four 20

441

samples in Stage B were lower than those in Stage A. This trend is the same for nirS and

442

nirK genes as well as the 16S rRNA gene, indicating that CO2 addition is a selection

443

factor for both microbial communities and denitrifiers. Comparing the heat maps in Figs.

444

4 and 5, it can been seen that both microbial community and nirS-type denitrifiers varied

445

significantly after CO2 addition, and the results of 16S rRNA and nirS genes showed

446

similar trends. The phylogenetic tree in Fig. 6 showed that the nirS OTUs that were

447

clustered with Thiobacillus sp. decreased, but those in cluster ( that were affiliated with

448

Rhodocyclaceae / Rhodocyclales bacteria increased significantly in Stage B. As genus

449

Denitratisoma belongs to class Rhodocyclaceae and order Rhodocyclales, this implies

450

that the results of nirS and 16S rRNA genes were also consistent in this aspect. However,

451

studies on nirS and nirK genes have some limitations due to the lack of registered

452

sequence data in the database, and most of the registered nirS and nirK sequences are

453

retrieved from uncultured clones (Osaka et al., 2006). In this study, most of the nirS and

454

the nirK genes were affiliated with uncultured bacteria, rather than sequences from

455

known bacteria. Therefore, further studies are required and technological advances are

456

expected in the future.

457

In a comparison of the microbial communities in two stages, besides the proportion

458

of denitrifiers, the abundance of microbes related to carbon metabolism also changed

459

significantly. Firstly, the family Anaerolineaceae presented low proportions in 1T, 1M,

460

and 1B but high proportions in 1S and the four Stage-B samples. Most species of

461

Anaerolineaceae have been reported to have a fermentative metabolism, degrading

462

carbohydrates and proteinaceous carbon sources under anaerobic conditions (Liang et al.,

463

2015; McIlroy et al., 2017). In the suspended sludge samples 1S and 2S,

21

464

Anaerolineaceae-like bacteria may participate in sludge decay. For biofilm samples

465

attached to the carriers, the proportion of Anaerolineaceae increased significantly after

466

CO2 addition, indicating that the organic carbon metabolism was enhanced. Because of

467

the low COD concentration supplied in the influent, the biomass is unlikely to increase

468

significantly to become the primary organic carbon source, which implies that CO2

469

probably participated in the generation of organic carbon in the reactor. In addition, the

470

abundance of Methylocystaceae increased to 7.4± 1.3% in Stage B. The 16S rRNA gene

471

of the presentative OTU showed 99% similarity with Methylocystis hirsuta, which has

472

been reported to be a type-II methane-oxidizing bacteria (Lindner et al., 2007). The

473

growth of methane-oxidizing bacteria usually relies on methane. Recently, it has been

474

reported that M. hirsute can also use volatile fatty acids (including acetic, propionic, and

475

butyric acids) as the sole carbon and energy source for growth (López et al., 2018). In the

476

current study, it seems that CO2 addition was responsible for the growth of Methylocystis-

477

like bacteria in the H2-based autotrophic denitrification reactor. However, the detailed

478

metabolism to support the growth of Methylocystis-like bacteria needs to be further

479

studied. However, in the dominated OTUs, none was identified as being highly similar to

480

the acknowledged acetogens. Most known acetogens have been reported within the

481

genera Clostridium or Acetobacterium (Drake et al., 2008; Sewell et al., 2017), which

482

were detected but not abundant in the reactor. Although it remains unclear if acetogenesis

483

took place in the reactor, CO2 addition did induce the changes of microbes related to

484

organic carbon metabolism, especially those of the families Anaerolineaceae and

485

Methylocystaceae. Determining how the CO2 affect the organic carbon metabolism in the

486

H2-based autotrophic denitrification system is a potential topic for further studies.

22

487

4.3 Implications and perspectives

488

The results revealed that both the HCl addition and the CO2 addition contributed to

489

pH control in the H2-based autotrophic denitrification systems and resulted in good

490

nitrogen removal performance. In particular, CO2 addition changed the microbial

491

communities and participated in microbial metabolisms. Supplying CO2 in H2-based

492

autotrophic denitrification enhanced organic carbon metabolism in the system, thus

493

promoting heterotrophic denitrification. Since heterotrophic denitrifiers normally grow

494

faster than autotrophic denitrifiers, the combined heterotrophic and autotrophic

495

denitrification could achieve higher nitrate removal rates. However, rapid growth of

496

heterotrophic denitrifiers may imply more sludge production, which mitigates the

497

superiority of autotrophic denitrification in sludge management. Therefore, the two

498

strategies for pH control both have advantages and disadvantages.

499

It is worth noting that the electrons provided by H2 can be utilized either in

500

autotrophic denitrification or to participate in carbon metabolism together with CO2 for

501

heterotrophic denitrification. From this point of view, similar amount of electrons are

502

ultimately required in both cases for similar nitrate removal. However, electricity

503

utilization efficiencies and rates in autotrophic denitrification and heterotrophic

504

denitrification are worth studying further. Moreover, economic and environmental issues

505

are crucial for prospective large-scale application, and CO2 addition can benefit

506

greenhouse gas reduction and lower costs by using CO2 originated from industrial

507

exhaust gas rather than pure product. The emission of N2O should also be monitored to

508

quantify the global warming potentials. In addition, the accuracy pH control with precise

509

quantification of HCl and CO2 addition is important to quantitatively verify their effects 23

510

in H2-based autotrophic denitrification in further studies. Last but not least,

511

comprehensive investigation of the biological reaction mechanisms is required by

512

applying other advanced methods and techniques.

513

5. Conclusions

514

In this study, two pH-control strategies, namely HCl addition and CO2 addition,

515

were implemented in a H2-based autotrophic denitrification reactor. The nitrogen removal

516

performance and microbial communities were compared between the two strategies. The

517

main conclusions are as follows.

518

(1) Good performances were achieved when applying both of the pH-control

519

strategies in the CEAD (a modified ZVI-supported biological denitrification) reactor,

520

with low accumulation of ammonium and nitrite. In stable stages, the highest TN removal

521

efficiency was 91.4% ± 0.9% for HCl addition at an HRT of 28 h; and the TN removal

522

efficiency reached 96.5% ±1.7% for CO2 addition at an HRT of 24 h.

523

(2) High-throughput sequencing revealed similar results in the three biofilm

524

samples for each stage. However, the microbial communities and nirS-type denitrifiers

525

were significantly affected by CO2 addition; i.e., the abundance of Thiobacillus,

526

Arenimonas, Limnobacter, and Thermomonas decreased, and the abundance of

527

Anaerolineaceae, Methylocystaceae, and Denitratisoma increased significantly with CO2

528

addition. These results indicated that the typical autotrophic denitrifiers declined, but

529

carbon metabolism and heterotrophic denitrification were strengthened with CO2

530

addition.

24

531

This study showed for the first time that CO2 addition in H2-based autotrophic

532

denitrification essentially affects the microbial communities and denitrification

533

mechanisms. The findings could help to improve optimization of H2-based autotrophic

534

denitrification (especially ZVI-supported autotrophic denitrification) in water and

535

wastewater treatment.

536

Acknowledgement

537

This study was supported by the National Natural Science Foundation of China (no.

538

51408028 and 51678185), the Beijing Municipal Natural Science Foundation (no.

539

8182047), and the Fundamental Research Funds for the Central Universities (no.

540

2018JBM039). We would like to express our sincere gratitude to Prof. Ye Deng at the

541

Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, for

542

discussing the data processing of high-throughput sequencing.

543

Appendix A. Supplementary data

544

Supplementary data to this article can be found online. The data include: detailed

545

principles of the CEAD process; variation of pH values with two pH-control strategies;

546

reactor performance without pH control; rarefaction curves and rank-abundance curves

547

according to high-throughput sequencing; community richness and diversity indices;

548

relative abundance of preponderant populations at class, family, and genus levels; heat

549

maps and PCAs according to nirK genes; and the phylogenetic tree based on the deduced

550

nirK amino acid sequences.

25

551

References

552

Adav, S.S., Lee, D.-J. and Lai, J.Y., 2010. Microbial community of acetate utilizing

553

denitrifiers in aerobic granules. Applied Microbiology and Biotechnology 85(3),

554

753-762.

555

Alowitz, M.J. and Scherer, M.M., 2002. Kinetics of Nitrate, Nitrite, and Cr(VI)

556

Reduction by Iron Metal. Environmental Science & Technology 36(3), 299-306.

557

Alzate Marin, J.C., Caravelli, A.H. and Zaritzky, N.E., 2016. Nitrification and aerobic

558

denitrification in anoxic-aerobic sequencing batch reactor. Bioresource Technology

559

200, 380-387.

560

An, Y., Li, T., Jin, Z., Dong, M., Li, Q. and Wang, S., 2009. Decreasing ammonium

561

generation using hydrogenotrophic bacteria in the process of nitrate reduction by

562

nanoscale zero-valent iron. Science of The Total Environment 407(21), 5465-5470.

563 564

APHA, 2005. Standard methods for the examination of water and wastewater. American Public Health Association (APHA): Washington, DC, USA.

565

Bates, S.T., Berg-Lyons, D., Caporaso, J.G., Walters, W.A., Knight, R. and Fierer, N.,

566

2010. Examining the global distribution of dominant archaeal populations in soil.

567

The Isme Journal 5, 908-917.

568 569

Biswas, S. and Bose, P., 2005. Zero-Valent Iron-Assisted Autotrophic Denitrification. Journal of Environmental Engineering 131(8), 1212-1220.

570

Deng, S., Li, D., Yang, X., Zhu, S. and Xing, W., 2016. Advanced low carbon-to-nitrogen

571

ratio wastewater treatment by electrochemical and biological coupling process.

572

Environmental Science and Pollution Research 23(6), 5361-5373.

573

Di Capua, F., Papirio, S., Lens, P.N.L. and Esposito, G., 2015. Chemolithotrophic

574

denitrification in biofilm reactors. Chemical Engineering Journal 280, 643-657.

575

Di Capua, F., Pirozzi, F., Lens, P.N.L. and Esposito, G., 2019. Electron donors for

576 577

autotrophic denitrification. Chemical Engineering Journal 362, 922-937. Drake, H.L., Gößner, A.S. and Daniel, S.L., 2008. Old Acetogens, New Light. Annals of 26

578

the New York Academy of Sciences 1125(1), 100-128.

579

Ergas, S.J. and Reuss, A.F., 2001. Hydrogenotrophic denitrification of drinking water

580

using a hollow fibre membrane bioreactor. Journal of Water Supply Research and

581

Technology-Aqua 50(3), 161-171.

582

Fahrbach, M., Kuever, J., Meinke, R., Kampfer, P. and Hollender, J., 2006. Denitratisoma

583

oestradiolicum gen. nov., sp. nov., a 17beta-oestradiol-degrading, denitrifying

584

betaproteobacterium. International Journal of Systematic and Evolutionary

585

Microbiology 56(7), 1547-1552.

586

Fu, F., Dionysiou, D.D. and Liu, H., 2014. The use of zero-valent iron for groundwater

587

remediation and wastewater treatment: A review. Journal of Hazardous Materials

588

267, 194– 205.

589

Gan, Y., Zhao, Q. and Ye, Z., 2019. Denitrification performance and microbial diversity

590

of immobilized bacterial consortium treating nitrate micro-polluted water.

591

Bioresource Technology 281, 351-358.

592

Ghafari, S., Aroua, M.K. and Hasan, M., 2010a. Control of pH during water

593

denitrification in an upflow bio-electrochemical reactor (UBER) using a

594

pumparound system. Separation and Purification Technology 72(3), 401-405.

595 596

Ghafari, S., Hasan, M. and Aroua, M.K., 2008. Bio-electrochemical removal of nitrate from water and wastewater - A review. Bioresource Technology 99(10), 3965-3974.

597

Ghafari, S., Hasan, M. and Aroua, M.K., 2009. Effect of carbon dioxide and bicarbonate

598

as inorganic carbon sources on growth and adaptation of autohydrogenotrophic

599

denitrifying bacteria. Journal of Hazardous Materials 162(2), 1507-1513.

600

Ghafari, S., Hasan, M. and Aroua, M.K., 2010b. A kinetic study of autohydrogenotrophic

601

denitrification at the optimum pH and sodium bicarbonate dose. Bioresource

602

Technology 101(7), 2236-2242.

603 604 605

Hu, H.-Y., Goto, N. and Fujie, K., 2001. Effect of ph on the reduction of nitrite in water by metallic iron. Water Research 35(11), 2789-2793. Karanasios,

K.A.,

Vasiliadou,

I.A.,

Pavlou, 27

S.

and

Vayenas,

D.V.,

2010.

606

Hydrogenotrophic denitrification of potable water: A review. Journal of Hazardous

607

Materials 180(1-3), 20-37.

608

López, J.C., Arnáiz, E., Merchán, L., Lebrero, R. and Muñoz, R., 2018. Biogas-based

609

polyhydroxyalkanoates production by Methylocystis hirsuta: A step further in

610

anaerobic digestion biorefineries. Chemical Engineering Journal 333, 529-536.

611

Lee, K.-C. and Rittmann, B.E., 2003. Effects of pH and precipitation on

612

autohydrogenotrophic denitrification using the hollow-fiber membrane-biofilm

613

reactor. Water Research 37(7), 1551-1556.

614

Liang, B., Wang, L.-Y., Mbadinga, S.M., Liu, J.-F., Yang, S.-Z., Gu, J.-D. and Mu, B.-Z.,

615

2015.

Anaerolineaceae

and

Methanosaeta

turned

to

be

the

dominant

616

microorganisms in alkanes-dependent methanogenic culture after long-term of

617

incubation. AMB Express 5(1), 37.

618

Lindner, A.S., Pacheco, A., Aldrich, H.C., Costello Staniec, A., Uz, I. and Hodson, D.J.,

619

2007. Methylocystis hirsuta sp. nov., a novel methanotroph isolated from a

620

groundwater aquifer. International Journal of Systematic and Evolutionary

621

Microbiology 57(8), 1891-1900.

622

Liu, C., Zhao, C., Wang, A., Guo, Y. and Lee, D.-J., 2015. Denitrifying sulfide removal

623

process on high-salinity wastewaters. Applied Microbiology and Biotechnology

624

99(15), 6463-6469.

625

Liu, L., Shen, G., Sun, M., Cao, X., Shang, G. and Chen, P., 2014. Effect of biochar on

626

nitrous oxide emission and its potential mechanisms. Journal of the Air & Waste

627

Management Association 64(8), 894-902.

628

Ma, J., Wang, Z., He, D., Li, Y. and Wu, Z., 2015. Long-term investigation of a novel

629

electrochemical membrane bioreactor for low-strength municipal wastewater

630

treatment. Water Research 78, 98-110.

631

Marshall, C.W., Ross, D.E., Fichot, E.B., Norman, R.S. and May, H.D., 2013. Long-term

632

Operation of Microbial Electrosynthesis Systems Improves Acetate Production by

633

Autotrophic Microbiomes. Environmental Science & Technology 47(11), 6023-

634

6029. 28

635

McIlroy, S.J., Kirkegaard, R.H., Dueholm, M.S., Fernando, E., Karst, S.M., Albertsen, M.

636

and

Nielsen,

P.H.,

2017.

Culture-Independent

Analyses

Reveal

Novel

637

Anaerolineaceae as Abundant Primary Fermenters in Anaerobic Digesters Treating

638

Waste Activated Sludge. Frontiers in Microbiology 8, 1134.

639

Mousavi, S., Ibrahim, S., Aroua, M.K. and Ghafari, S., 2012. Development of nitrate

640

elimination by autohydrogenotrophic bacteria in bio-electrochemical reactors – A

641

review. Biochemical Engineering Journal 67, 251-264.

642

Osaka, T., Yoshie, S., Tsuneda, S., Hirata, A., Iwami, N. and Inamori, Y., 2006.

643

Identification of Acetate- or Methanol-Assimilating Bacteria under Nitrate-Reducing

644

Conditions by Stable-Isotope Probing. Microbial Ecology 52(2), 253-266.

645

Park, J.Y. and Yoo, Y.J., 2009. Biological nitrate removal in industrial wastewater

646

treatment: which electron donor we can choose. Applied Microbiology and

647

Biotechnology 82(3), 415-429.

648

Pelaz, L., Gómez, A., Letona, A., Garralón, G. and Fdz-Polanco, M., 2018. Nitrogen

649

removal in domestic wastewater. Effect of nitrate recycling and COD/N ratio.

650

Chemosphere 212, 8-14.

651

Rivett, M.O., Buss, S.R., Morgan, P., Smith, J.W.N. and Bemment, C.D., 2008. Nitrate

652

attenuation in groundwater: A review of biogeochemical controlling processes.

653

Water Research 42(16), 4215-4232.

654

Saito, T., Ishii, S., Otsuka, S., Nishiyama, M. and Senoo, K., 2008. Identification of

655

Novel Betaproteobacteria in a Succinate-Assimilating Population in Denitrifying

656

Rice Paddy Soil by Using Stable Isotope Probing. Microbes and Environments

657

23(3), 192-200.

658

Sakakibara, Y. and Nakayama, T., 2001. A novel multi-electrode system for electrolytic

659

and biological water treatments: electric charge transfer and application to

660

denitrification. Water Research 35(3), 768-778.

661 662 663

Sanz, J.L. and Köchling, T., 2007. Molecular biology techniques used in wastewater treatment: An overview. Process Biochemistry 42(2), 119-133. Sewell, H.L., Kaster, A.K. and Spormann, A.M., 2017. Homoacetogenesis in Deep-Sea 29

664

Chloroflexi, as Inferred by Single-Cell Genomics, Provides a Link to Reductive

665

Dehalogenation in Terrestrial Dehalococcoidetes. MBio 8(6), e02022-02017.

666

Sun, Y., Li, J., Huang, T. and Guan, X., 2016. The influences of iron characteristics,

667

operating conditions and solution chemistry on contaminants removal by zero-valent

668

iron: A review. Water Research 100, 277-295.

669 670

Sunger, N. and Bose, P., 2009. Autotrophic denitrification using hydrogen generated from metallic iron corrosion. Bioresource Technology 100(18), 4077-4082.

671

Tang, Y., Zhou, C., Ziv-El, M. and Rittmann, B.E., 2011. A pH-control model for

672

heterotrophic and hydrogen-based autotrophic denitrification. Water Research 45(1),

673

232-240.

674 675

Till, B.A., Weathers, L.J. and Alvarez, P.J.J., 1998. Fe(0)-Supported Autotrophic Denitrification. Environmental Science & Technology 32(5), 634-639.

676

Usher, K.M., Kaksonen, A.H., Bouquet, D., Cheng, K.Y., Geste, Y., Chapman, P.G. and

677

Johnston, C.D., 2015. The role of bacterial communities and carbon dioxide on the

678

corrosion of steel. Corrosion Science 98, 354-365.

679

Wang, J. and Chu, L., 2016. Biological nitrate removal from water and wastewater by

680

solid-phase denitrification process. Biotechnology Advances 34(6), 1103-1112.

681

Wang, Z., Wang, H. and Ma, L., 2012. Iron shavings supported biological denitrification

682

in sequencing batch reactor. Desalination and Water Treatment 49(1-3), 95-105.

683

Xia, S., Wang, C., Xu, X., Tang, Y., Wang, Z., Gu, Z. and Zhou, Y., 2015. Bioreduction of

684

nitrate in a hydrogen-based membrane biofilm reactor using CO2 for pH control and

685

as carbon source. Chemical Engineering Journal 276, 59-64.

686

Xia, S., Xu, X., Zhou, C., Wang, C., Zhou, L. and Rittmann, B.E., 2016. Direct delivery

687

of CO2 into a hydrogen-based membrane biofilm reactor and model development.

688

Chemical Engineering Journal 290, 154-160.

689

Xie, Y., Dong, H., Zeng, G., Tang, L., Jiang, Z., Zhang, C., Deng, J., Zhang, L. and

690

Zhang, Y., 2017. The interactions between nanoscale zero-valent iron and microbes

691

in the subsurface environment: A review. Journal of Hazardous Materials 321, 39030

692

407.

693

Xing, W., Li, D., Li, J., Hu, Q. and Deng, S., 2016. Nitrate removal and microbial

694

analysis by combined micro-electrolysis and autotrophic denitrification. Bioresource

695

Technology 211, 240-247.

696

Xing, W., Li, J., Cong, Y., Gao, W., Jia, Z. and Li, D., 2017. Identification of the

697

autotrophic denitrifying community in nitrate removal reactors by DNA-stable

698

isotope probing. Bioresource Technology 229, 134-142.

699

Xing, W., Li, J., Li, D., Hu, J., Deng, S., Cui, Y. and Yao, H., 2018. Stable-Isotope

700

Probing Reveals the Activity and Function of Autotrophic and Heterotrophic

701

Denitrifiers in Nitrate Removal from Organic-Limited Wastewater. Environmental

702

Science & Technology 52(14), 7867-7875.

703

Xu, D., Liu, S., Chen, Q. and Ni, J., 2017a. Microbial community compositions in

704

different functional zones of Carrousel oxidation ditch system for domestic

705

wastewater treatment. AMB Express 7(1), 40.

706

Xu, Y., Wang, C., Hou, J., Wang, P., You, G., Miao, L., Lv, B., Yang, Y. and Zhang, F.,

707

2017b. Application of zero valent iron coupling with biological process for

708

wastewater treatment: a review. Reviews in Environmental Science and

709

Bio/Technology 16(4), 667-693.

710

Zhang, Y., Douglas, G.B., Kaksonen, A.H., Cui, L. and Ye, Z., 2019. Microbial reduction

711

of nitrate in the presence of zero-valent iron. Science of The Total Environment 646,

712

1195-1203.

713

Zhou, S., Huang, T., Zhang, C., Fang, K., Xia, C., Bai, S., Zeng, M. and Qiu, X., 2016.

714

Illumina MiSeq sequencing reveals the community composition of NirS-Type and

715

NirK-Type denitrifiers in Zhoucun reservoir – a large shallow eutrophic reservoir in

716

northern China. RSC Advances 6(94), 91517-91528.

717

31

Table 1. Operation conditions of the reactor at different defined stages.

1

Stage

Operation time (day)

Stage

1:100 HCl addition

1-20

Stage A-1

32

Trial and error test

21-70

Stage A-2

28

71-95

Stage A-3

20

10 mL once at intervals of 2 h

118-137

Stage B-1

24

Trial and error test

138-144

Stage B-2

18

2

145-186

Stage B-3

12

187-210

Stage B-4

12

3

211-215

Stage B-5

18

3

216-233

Stage B-6

24

2

A: HCl control

B: CO2 control

CO2 flow

HRT (h)

2

1

/

rate （ml/min） ）

/

2

1 2

Fig. 1. Structure diagram and photographs of the reactor. a) Structure diagram. b) Photograph

3

of the membrane module for sparging of CO2. c) Photograph of the reactor. 1. Iron–carbon

4

micro-electrolysis carriers; 2. influent water tank; 3. water pump; 4. water inlet; 5. water outlet; 6.

5

water bath jacket; 7. water heater; 8. water bath pump; 9. circulation water pump; 10. sludge sampling

6

outlets, middle (a) and bottom (b); 11. water sampling outlets, middle (a), and bottom (b); 12.

7

thermometer; 13. diluted HCl storage tank; 14. peristaltic pump for HCl addition; 15. CO2 gas

8

cylinder; 16. gas flowmeter; 17. membrane module for sparging of CO2 (13 and 14 were installed in

9

Stage A; 15-17 were installed in Stage B). 1

a)

b)

StageA-2 HRT=28h

StageA-1 HRT=32h

StageA-3 HRT=20h

60

100 90

Nitrogen removal rate (mgN/L·d)

50

Removal efficiency (%)

80 70

40

60 30

50 40

20 30 20

10

10 0 0

10

20

30

40

50

60

70

80

90

Time (d) -

NO3 -N removal efficiency

TN removal efficiency

NRR-TN

NRR-NO3

-

1

Fig. 2. Reactor performance with HCl addition (Stage A). (a) Nitrogen concentration and pH

2

values in the influent and effluent. (b) Nitrogen removal efficiencies and rates in the reactor. NRR-TN

3

and NRR-NO3- represent nitrogen removal rate (NRR) of TN and nitrate, respectively. (Four sludge

4

samples 1T, 1M, 1B, and 1S were collected on the 50th day.) 1

a)

b)

StageB-1 StageB-2 HRT=24h HRT=18h

StageB-4 StageB-5 StageB-6 HRT=12h HRT=18h HRT=24h

StageB-3 HRT=12h

100 80 70

Removal efficiency (%)

80 70

60

60

50

50

40

40 30 30 20

20

10

10 0

Nitrogen removal rate (mgN/L·d)

90

0 120

130 -

140

150

NO3 -N removal efficiency

160

170

180

190

200

210

220

230

Time(d) TN removal efficiency

NRR-TN

NRR-NO3 -N

1

Fig. 3. Reactor performance with CO2 addition (Stage B). (a) Nitrogen concentration and pH

2

values in the influent and effluent. (b) Nitrogen removal efficiencies and rates in the reactor. NRR-TN

3

and NRR-NO3- represent nitrogen removal rate (NRR) of TN and nitrate, respectively. (Four sludge

4

samples 2T, 2M, 2B, and 2S were collected on the 233rd day.) 1

a)

b)

1T, 1B, 1M — three biofilm samples attached to the carriers with HCl addition; 1S — the suspended sludge sample with HCl addition. 2T, 2B, 2M — three biofilm samples attached to the carriers with CO2 addition; 2S — the suspended sludge sample with CO2 addition. 1

2

Fig. 4. Heat map and principal component analysis (PCA) of samples from the HCl and CO2

3

addition stages according to the high-throughput sequencing of 16S rRNA at the genus level. (a)

4

Heat map of the top 35 predominant genera or unclassified bacteria, with relative abundance indicated

5

by color intensity. (b) Distribution of the eight samples according to PCA. OTUs were clustered with

6

97% similarity.

1

a)

b)

1T, 1B, 1M — three biofilm samples attached to the carriers with HCl addition. 1S — the suspended sludge sample with HCl addition. 2T, 2B, 2M — three biofilm samples attached to the carriers with CO2 addition. 2S — the suspended sludge sample with CO2 addition.

1

Fig. 5. Heat map and principal component analysis (PCA) of samples from the HCl and CO2

2

addition stages, according to the high-throughput sequencing of nirS gene at the OTU level. a)

3

Heat map of the top 30 predominant OTUs, with relative abundance indicated by color intensity. b)

4

Distribution of the eight samples according to PCA. OTUs were clustered with 97% similarity.

1

1 2

Fig. 6. Phylogenetic tree based on the deduced nirS amino-acid sequences drawn using the

3

neighbour-joining method. OTUs with proportions higher than 2% were selected and are shown in

4

red (dominant in stage A) or blue (dominant in stage B). The “Stage_B_2nd” represents the second

5

most dominant OTU in stage B. The “uncultured_b” represents uncultured bacteria. Bootstrap values

6

(%) were generated from 1000 replicates, and only values >70% are shown.

1

Highlights: • • • • •

HCl or CO2 were added in a modified ZVI-supported denitrification for pH control TN removal efficiencies were > 90% in both stages with little ammonium accumulation Great effect of CO2 on microbial communities and denitrifiers was firstly revealed Typical autotrophic denitrifiers declined with CO2 addition Carbon metabolism and heterotrophic denitrification were strengthened with CO2

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: