Evaluation of Nitrous Oxide Emission from Sulfide-and Sulfur-Based

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Evaluation of Nitrous Oxide Emission from Sulfide- and Sulfur-based Autotrophic Denitrification Processes Yiwen Liu, Lai Peng, Huu Hao Ngo, Wenshan Guo, Dongbo Wang, Yuting Pan, Jing Sun, and Bing-Jie Ni Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02202 • Publication Date (Web): 08 Aug 2016 Downloaded from http://pubs.acs.org on August 10, 2016

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Environmental Science & Technology

1

Evaluation of Nitrous Oxide Emission from Sulfide- and Sulfur-based

2

Autotrophic Denitrification Processes

3 4

Yiwen Liu1, Lai Peng2,3, Huu Hao Ngo1,*, Wenshan Guo1, Dongbo Wang4, Yuting

5

Pan5, Jing Sun2, Bing-Jie Ni2,*

6 7

1

Centre for Technology in Water and Wastewater, School of Civil and Environmental

8

Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia

9

2

State Key Laboratory of Pollution Control and Resources Reuse, College of

10

Environmental Science and Engineering, Tongji University, Shanghai 200092, PR

11

China 3

12

Laboratory of Microbial Ecology and Technology (LabMET), Ghent University,

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Coupure Links 653, 9000 Ghent, Belgium 4

14

College of Environmental Science and Engineering, Hunan University, Changsha

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410082, China; Key Laboratory of Environmental Biology and Pollution Control

16

(Hunan University), Ministry of Education, Changsha 410082, China

17 18

5

Department of Environmental Science and Engineering, School of Architecture and Environment, Sichuan University, Chengdu, Sichuan 610065, China

19 20

*Corresponding authors:

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Prof Dr. Bing-Jie Ni, Tel.: +86 21 65986849; Fax: +86 21 65983602; E-mail

22

[email protected]

23

Prof Dr. Huu Hao Ngo, Tel.: +61 2 9514 2745; Fax: +61 2 9514 2633; E-mail

24

[email protected]

25

1 Environment ACS Paragon Plus


26

TOC Art

27

28


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ABSTRACT

30

Recent studies have shown that sulfide- and sulfur-based autotrophic

31

denitrification (AD) process plays an important role in contributing to nitrous oxide

32

(N2O) production and emissions. However, N2O production is not recognized in the

33

current AD models, limiting their ability to predict N2O accumulation during AD. In

34

this work, a mathematical model is developed to describe N2O dynamics during

35

sulfide- and sulfur-based AD processes for the first time. The model is successfully

36

calibrated and validated using N2O data from two independent experimental systems

37

with sulfide or sulfur as electron donors for AD. The model satisfactorily describes

38

nitrogen reductions, sulfide/sulfur oxidation, and N2O accumulation in both systems.

39

Modeling results revealed substantial N2O accumulation due to the relatively low N2O

40

reduction rate during both sulfide- and sulfur-based AD processes. Application of the

41

model to simulate long-term operations of activated sludge systems performing

42

sulfide- and sulfur-based AD processes indicates longer sludge retention time reduced

43

N2O emission. For sulfide-based AD process, higher initial S/N ratio also decreased

44

N2O emission but with a higher operational cost. This model can be a useful tool to

45

support process operation optimization for N2O mitigation during AD with sulfide or

46

sulfur as electron donor.

47 48

INTRODUCTION

49

Groundwater is an important water source throughout the world. For example,

50

groundwater accounts more than 65% and 33% of the water used for drinking water

51

supply in Europe and the US, respectively 1, 2. With the increase of nitrogen input into

52

environment as a result of intensive use of nitrogen-based fertilizers in agricultural

53

activities and inappropriate discharge of wastewater and solid wastes


3-5

, nitrate


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contamination of groundwater has been recognized as a significant environmental

55

problem world widely 6. The elevated nitrate concentrations can cause human health

56

problems, i.e., increased risks of methaemoglobinaemia, non-Hodgkin’s lymphoma

57

and cancer 7, 8, as well as ecological disturbances such as the eutrophication of surface

58

water 9. For this reason, a maximum nitrate contaminant level of between 10 and 11.3

59

mg-N/L in potable water has been set by both US Environmental Protection Agency

60

(US EPA) and World Health Organization 6, 10.

61

Biological denitrification (i.e., heterotrophic and autotrophic denitrification (AD))

62

is recognized as one of the most promising and efficient processes for mass nitrate

63

removal

64

organic matter, resulting in large cost of heterotrophic denitrification process that

65

requires massive external organic carbon and subsequent treatment of the produced

66

excessive sludge 6. Alternatively, AD with reduced sulfur compounds (e.g., sulfide

67

and sulfur) as electron donors has attracted more attentions due to the lower sludge

68

production rate and the elimination of the need for exogenous carbon 11-15, which has

69

been demonstrated in aquifers where exists a sulfide or sulfur-rich zone in the nitrate-

70

contaminated groundwater

71

extensive studies have been carried out on the promising AD process, with the focus

72

on the reaction kinetics analysis, effects of S:N ratio, microbial community structure,

73

reactor types and other operation parameters 18-28.

6, 8

. Generally, groundwater contaminated with nitrate is insufficient of

16

or using sulfur-packed bed bioreactors

17

. Therefore,

74

Recently, increasing evidences have revealed that nitrous oxide (N2O) can be

75

produced and accumulated as a significant intermediate product during AD process

76

with sulfide or sulfur as electron donors

77

owing to its potent greenhouse gas effect and its ability to deplete stratospheric ozone

78

33-35

29-32

, which has raised increasing concerns

. It has been reported that the amount of N2O emission in this process ranged from


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0.5% to 0.9% of the nitrogen load 30, 31. It should be noted that an emission factor of 1%

80

would increase the carbon footprint by about 30% due to the high global warming

81

potential of N2O

82

sulfide- or sulfur-based AD is of great significance to the application of such system.

83

36

. Therefore, understanding and reducing N2O production during

Mathematical modeling is particularly important toward a full understanding of 33, 37

84

mechanisms involved in biological denitrification systems

85

applied to describe sulfide- or sulfur-based AD process for sulfide or nitrate

86

attenuation purpose

87

N2O dynamics during this process despite of considerable N2O production. To date,

88

current existing models only describe sulfide- or sulfur-based AD as a one-step

89

denitrification (NO3- to N2) or two-step denitrification (NO3- to NO2-, and NO2- to N2),

90

without consideration of N2O accumulation (NO2- to N2O, and N2O to N2).

, which has been

38-44

. However, little effort has been dedicated to modeling the

91

This study aims to develop a new model for the prediction of N2O production

92

during sulfide- or sulfur-based AD. The typical experimental N2O data sets from two

93

independent study reports with highly different experimental conditions in different

94

sulfide- and sulfur-based AD process (e.g., different mass transfer processes and

95

different initial electron acceptors conditions) were used to test the validity of the

96

model and the obtained parameters reliably. The model is also applied to investigate

97

the optimal conditions for achieving high level of nitrate removal with relative low

98

N2O emission in AD.

99 100

MATERIALS AND METHODS

101

Model development. Autotrophic denitrifiers use sulfide (S2-) or sulfur (S0) as

102

electron donors to reduce nitrate to nitrogen gas, for their growth and maintenance.

103

The model developed in this work considered the three-step denitrification (NO3- to



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N2 via NO2- and N2O) 45 and two-step sulfide oxidation (S2- to S0 and S0 to SO42-) 40 to

105

describe all potential N2O accumulation steps (Figure 1). Nitric oxide (NO) is not

106

taken into account in our model since the NO reduction related parameters are beyond

107

the ability of measurement. Indeed, NO reduction is usually prioritized by bacteria to

108

avoid its toxicity and thus ensure no accumulation of NO as intermediate

109

thiosulfate and sulfite are not considered in the biological model since they are the

110

intermediate products of chemical oxidation rather than biological oxidation 31, 40.

33

. Also,

111

The developed model describes the relationships among seven compounds

112

involved in autotrophic denitrifiers (XSOB), namely NO3- (SNO3), NO2- (SNO2), N2O

113

(SN2O), N2 (SN2), S2- (SS2), S0 (SS0) and SO42- (SSO4). The units are g-N m-3 for all

114

nitrogenous species and g-S m-3 for non-nitrogen compounds (Table S1 in Supporting

115

Information). Two groups of biological processes (Table S2 and S3 in Supporting

116

Information) were considered, namely, sulfide-based denitrification processes

117

(Process 1 – 3) and sulfur-based denitrification processes (Process 4 – 6), each

118

modeled as three sequential denitrification processes from NO3- to N2 via NO2- and

119

N2O with individual reaction-specific kinetics (i.e., double Monod-type kinetic

120

equations). In addition, biomass decay (Process 7) was also included. Table S4 in the

121

Supporting Information lists the definitions, values, units, and sources of all

122

parameters used in the developed model.

123

Experimental data for model evaluation. Sulfide-based autotrophic denitrifying 31

124

culture: Experimental data previously reported by Yang et al.

125

and validate the sulfide-based AD process. A sulfide-based autotrophic denitrifying

126

sludge (seeded as flocculent sludge from a local wastewater treatment plant in

127

Guangzhou, China) was enriched in a continuous-flow granular sludge reactor with a

128

working volume of 30 L for four months

31

are used to calibrate

. The reactor was fed with synthetic


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wastewater mainly consisting of KNO3 and Na2S•9H2O (detailed composition

130

described in Yang et al. 31), leading to the influent nitrate concentration at 70 mg-N/L

131

and the sulfide concentration at 100 – 145 mg-S/L, respectively. The hydraulic

132

retention time (HRT) varied from 5 – 20 h. The dissolved oxygen (DO) concentration

133

in the feed was always maintained below 0.5 mg-O2/L. After 120 days, the average

134

particle size of the sulfide-based autotrophic denitrifying sludge reached 700 µm.

135

More details of the reactor operation and performance can be found in Yang et al. 31.

136

Batch tests were then carried out with sulfide-based autotrophic denitrifying sludge on

137

day 120 in a 1.2-L batch reactor equipped with a N2O microsensor (N2O-100,

138

Unisense A/S, Aarhus, Denmark) for real-time monitoring of dissolved N2O in mixed

139

liquid. There was no headspace during the batch tests. Two types of batch tests were

140

conducted:

141

In Batch test I, N2O accumulation under two different granule sizes (i.e., 700±12

142

and 1350±20 µm, screened out by different screen meshes) of sulfide-based

143

autotrophic denitrifying sludge was investigated. For each test, approximately 4.2 g of

144

granular sludge was screened from the parent reactor above and added to the batch

145

reactor. A certain amount of sulfide stock and nitrate stock solution were added to

146

batch reactor, resulting in an initial total dissolved sulfide (TDS) concentration of 150

147

mg-S/L and nitrate concentration of 30 mg-N/L, in order to ensure the presence of

148

adequate electron donors (sulfide or sulfur) for AD during the batch test as the

149

required sulfide for the complete nitrate (30 mg-N/L) reduction to nitrogen gas is

150

about 43 mg-S/L according to the stoichiometric equation: 5 HS-+8 NO3-+3H+ = 5

151

SO42-+4 N2+4 H2O.

152

In Batch test II, N2O reduction under different initial S/N ratios with 700-µm

153

granular sludge was carried out. For each test, approximately 4.2 g of granular sludge



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was screened from the parent reactor above and added to the batch reactor. Oxygen-

155

free Milli-Q water was flushed with 99.9% N2O gas for 30 min to achieve N2O

156

saturation with a maximum dissolved N2O concentration of 700 mg-N/L (N2O stock

157

solution). The N2O concentration in the stock solution was determined using gas

158

chromatograph

159

conductivity detector). Approximately 20 mL of the N2O stock solution (i.e., 700 mg-

160

N/L) was added to the batch reactor to achieve an initial N2O concentration of 12 mg-

161

N/L. A certain portion of a concentrated sulfide solution was spiked into the batch

162

reactor to achieve initial TDS concentrations of 10, 30, and 60 mg-S/L, corresponding

163

to respective S/N mass ratios of 0.8, 2.5 and 5.0.

(GC)

(GC2014C

Shimadzu,

RTX-502.2

column,

electrical

164

In both batch tests, the dissolved N2O concentration was monitored continuously

165

using the real-time microsensor. Mixed liquor samples were taken periodically to

166

determine the nitrate, nitrite, TDS, thiosulfate, sulfite and sulfate concentrations,

167

using an ion chromatograph equipped with a conductivity detector and an IC-AS23 or

168

IC-CS12A analytical column (DIONEX ICS-900). More detailed batch experimental

169

setup and analysis methods can be found in Yang et al. 31.

170

Sulfur-based autotrophic denitrifying culture: Experimental data previously

171

reported by Zhang et al. 29 and Zhang et al. 30 are used to calibrate and validate sulfur-

172

based AD process. An enriched autotrophic denitrifying culture was employed as the

173

inoculum and further developed in a 2.3-L lab-scale continuous-flow anaerobic

174

fluidized bed membrane bioreactor. Initially, 200 g of sulfur was added in the reactor

175

as electron donor for sulfur-based AD, and additional 50 – 100 g of sulfur was

176

supplemented every 2-month, to ensure a stable sulfur concentration. The reactor was

177

fed with N2-sparged synthetic wastewater mainly consisting of KNO3 (detailed

178

composition described in Zhang et al. 30), resulting in an influent nitrate concentration


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from 25 to 80 mg-N/L according to the different operational stages (i.e., HRT varied

180

from 0.5 – 5 h). More details of the reactor operation and performance can be found in

181

Zhang et al. 30.

182

Batch experiments with different initial

15

N-NO3- concentrations (30 – 50 mg-

183

N/L) as a sole nitrogen substrate, were conducted with this culture in a 0.5-L glass

184

serum flask supplemented with 0.2 L medium free of NH4+. The experiments were

185

incubated with denitrifying culture and elemental sulfur from the bioreactor above.

186

The headspace was flushed with helium to exclude oxygen and background nitrogen.

187

Mixed liquor and gas samples were taken periodically for NO3−, NO2−, N2O, N2 and

188

sulfate analysis, respectively. Nitrate, nitrite, and sulfate were determined by ion

189

chromatography (Dionex ICS 2000, USA). 15N-labeled N2O and N2 were determined

190

by a Delta V Advantage Isotope Ratio Mass Spectrometer (IRMS, Thermo Fisher

191

Scientific Inc., USA). More detailed batch experimental setup and analysis methods

192

can be found in Zhang et al. 30.

193

Model calibration and validation. The developed model includes 21

194

stoichiometric and kinetic parameters as summarized in Table S4 (Supporting

195

Information). Most of these model parameter values (e.g., 17) are well established in

196

previous studies. Thus, literature values were directly adopted for these parameters.

197

The remaining four parameters,

198

(maximum reaction rate of Process 3), , (maximum reaction rate of Process 5) and

199

,

200

the key parameters relating to the N2O dynamics during AD process, are then

201

calibrated using experimental data.

,

(maximum reaction rate of Process 2), ,

(maximum reaction rate of Process 6), which are unique to this model and are

202

Parameter values were estimated by minimizing the sum of squares of the

203

deviations between the measured data and the model predictions in all cases, using the



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46

. Experimental data set (NO3-, NO2-,

204

secant method embedded in AQUASIM 2.1d

205

N2O, TDS, S0 and SO42-) from Batch test I with an average granule size of 700 µm of

206

the sulfide-based autotrophic denitrifying culture were used to calibrate the model.

207

Model validation was then carried out with the calibrated model parameters using the

208

other two sets, i.e., one also from Batch test I but with an average granule size of 1350

209

µm and the other N2O dynamics data under different initial S/N ratios with 700-µm

210

granular sludge from Batch test II. A one-dimension granule-based model was utilized

211

to simulate the granular sulfide-based autotrophic denitrifying culture used in Yang et

212

al.

213

components involved in the biological reactions, the first step is their diffusion into

214

the granule where the reactions take place. Discretization in time of the partial-

215

differential equation was used to describe the reaction-diffusion kinetics in a spherical

216

particle

217

possible difference of N2O production resulting from diffusional limitation in

218

different granule sizes would be properly modeled with the same biological model

219

presented in Table S2 of Supporting Information. A granule diameter (i.e., 700 or

220

1350 µm) was used, depending on the types of batch experiments. The number of

221

granules was calculated according to Gonzalez‐Gil et al. 49.

31

, according to the method previously described in Ni et al.

47

. For the soluble

48

. Biomass in the granules is fixed without migration. In this way, any

222

To further verify the validity and applicability of the model, we also applied the

223

model to evaluate the batch experimental data sets of NO3-, NO2-, N2O and SO42- from

224

the sulfur-based autotrophic denitrifying culture. For this culture, Processes 1 – 3

225

were not considered in the model due to the absence of sulfide in batch tests (Figure

226

1). Therefore, only two model parameters , (maximum reaction rate of Process 5)

227

and , (maximum reaction rate of Process 6) were calibrated for this culture using

228

the batch experimental data at initial nitrate concentrations of 50 mg-N/L. The


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obtained parameter values and the model were then validated using the dada sets at

230

different initial nitrate concentrations (i.e., 30 and 40 mg-N/L).

231 232

RESULTS

233

N2O production in sulfide-based AD. The calibration of the new model

234

involved optimizing four key parameter values (Table 1) by fitting simulation results

235

to the experimental data from Batch test I of sulfide-based autotrophic denitrifying

236

sludge with an average granule size of 700 µm. The predicted NO3-, NO2-, N2O, TDS,

237

S0 and SO42- profiles with the established model are shown in Figure 2a and 2b, along

238

with the experimental results. Sulfide was used up via anoxic autotrophic sulfide

239

oxidation within the first 15 min, along with accumulation of sulfur, nitrite and N2O.

240

During this period, anoxic autotrophic sulfur oxidation occurred simultaneously, as

241

indicated by sulfate production. Afterwards, anoxic sulfur oxidation continued, but

242

accompanied with a much lower nitrate consumption rate as compared to the previous

243

step. Accordingly, N2O concentrations gradually decreased to undetectable ranges in

244

the next 1 h. The developed model captured all these trends well. The good agreement

245

between these simulated and measured data supported that the developed model

246

properly captures the relationships among N2O dynamics, nitrogen reduction and

247

sulfide oxidation during sulfide-driven AD process.

248

The calibrated parameter values giving the optimum model fittings with the

249

experimental data are listed in Table 1. Maximum reaction rates of sulfide-driven AD

250

(i.e., ,

, 2 and , for Processes 1, 2, and 3, respectively) are about one order ,2

251

magnitude higher than those of sulfur-driven AD (i.e.,

252

Processes 4, 5, and 6, respectively), in agreement with the fact that anoxic autotrophic

253

sulfide oxidation is a faster reaction than that of sulfur oxidation


,

, 0 ,2

50

and

,

for

. The calibrated


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254

,

255

Process 5) values are 0.317 and 0.021 h-1, which are in the same order of magnitude

256

with the literature reported values of 0.135 – 0.218 and 0.083 – 0.093 h-1, respectively

257

41, 43

258

than those of

259

reaction rate of Process 2), thus leading to a substantial N2O accumulation under the

260

experimental conditions of S:N ratio of 5 during sulfide-driven denitrification process;

261

while no N2O accumulation was observed during sulfur-driven denitrification process

262

due to the comparable values of ,

, 0 and , (maximum reaction rates for ,2

263

Processes 4, 5, and 6, respectively).

(maximum reaction rate of Process 2) and

,

(maximum reaction rate of

. The estimated , (maximum reaction rate of Process 3) value is much lower

,


,

(maximum

264

The developed model and the calibrated parameter set (Table 1) were then further

265

tested for their ability to predict N2O dynamics in another data set from Batch test I of

266

sulfide-based autotrophic denitrifying sludge but with an average granule size of 1350

267

µm. The model predictions and the experimental results are shown in Figure 2c and

268

2d. Overall, a slightly lower nitrate consumption as well as nitrite and N2O

269

accumulation rates were observed, due to the impacts of granule size on mass transfer

270

51

271

measured data of concentrations in the validation experiment, which supports the

272

validity of the developed model.

. The validation results showed that the model predictions well matched the

273

The experimental results (i.e., N2O reduction under different initial S/N ratios)

274

obtained from Batch test II of sulfide-based autotrophic denitrifying sludge were also

275

used to evaluate the developed model with the calibrated parameter set (Table 1) and

276

the same parameters in Table S4 in terms of N2O dynamics (Figure 3). The required

277

sulfide for the complete N2O (12 mg-N/L) reduction to nitrogen gas is about 3.43 mg-

278

S/L with sulfide being oxidized to sulfate according to the stoichiometric equation:


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HS-+4 N2O+7H+ = SO42-+4 N2+4 H2O, corresponding to a S:N ratio of 0.29. S:N

280

ratios of 0.8, 2.5 and 5.0 used in batch tests would ensure the complete N2O reduction

281

during AD. Rather than suppression, higher S:N ratio (i.e., 5.0 g-S/g-N) promoted

282

N2O reduction and thus enhanced sulfide-based AD process as compared to lower S:N

283

ratios (i.e., 0.8 and 2.5 g-S/g-N). Overall, the model predictions matched the

284

experimental results in Figure 3, again supporting the validity of the developed model.

285

N2O production in sulfur-based AD. Due to the absence of sulfide, only two

286

parameter values, i.e.,

287

(maximum reaction rate of Process 6) (Table 1) were calibrated for sulfur-based

288

denitrification process, by comparing simulation results to the batch experimental data

289

from Zhang et al. 29 with an initial nitrate of 50 mg-N/L (Figure 4). At the beginning 7

290

h of the batch test for this culture, both nitrite and N2O gradually accumulated along

291

with the reduction of nitrate. After nitrate depletion, nitrite was consumed up within

292

ca. 10 h. Afterwards, N2O was the only electron acceptor for sulfur oxidation and

293

used up within ca. 24 h. Accordingly, higher sulfate production rates were observed in

294

the first 10 h while lower rates afterwards, coincident with the lower sulfur conversion

295

rate by N2O as compared to nitrate and nitrite. The model captured these trends

296

reasonably well. The long sampling interval of nitrite and N2O (ca. 2 hours per

297

sample) and N2O sampling method (manually rather than using N2O microsensor)

298

during sulfur-based AD might decrease the data resolution and lead to slight

299

mismatches between model prediction and experimental data. However, the model

300

has captured the overall trends of both nitrite and N2O reasonably well. In addition,

301

the accuracy of prediction of nitrite accumulation is highly important for the

302

simulation of N2O emission in the developed 3-step AD model due to the fact the N2O

303

production kinetics would be directly regulated by the availability of nitrite in the

,



,


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304

system as indicated in Tables S2 and S3 of Supporting Information, which has been

305

also confirmed by our modeling results in this work.

306

The obtained , (maximum reaction rate of Process 5) and , (maximum

307

reaction rate of Process 6) for sulfur-based denitrifying culture were 0.035 and 0.010

308

h-1, comparable to those of 0.021 and 0.017 h-1 for sulfide-based denitrifying culture

309

respectively (Table 1), again supporting the validity of the developed model for

310

different autotrophic denitrifying cultures. The relative low standard deviations of the

311

estimated , and , for sulfur-based denitrifying culture (Table 1) also indicates

312

the reliability of the obtained parameter values. However, a larger difference between

313

,

314

this case.

and , (i.e., 3.5 times) led to a much higher and longer N2O accumulation in

315

The developed model was then validated with experimental data of NO3-, NO2-

316

and N2O from batch tests of sulfur-based denitrifying culture with an initial nitrate

317

concentration of 30 and 40 mg-N/L, respectively (Figure 5). As shown in Figure 5,

318

substrate dynamics were similar to those at the initial nitrate concentration of 50 mg-

319

N/L, except for the higher N-species consumption rates (Figure 5) due to lower initial

320

nitrate concentrations. The good agreement between simulations and measured results

321

further demonstrated the validity of the developed model.

322 323 324

DISCUSSION

Sulfide- or sulfur-based AD process is a promising and efficient process for 6, 12, 13

325

nitrate removal from contaminated groundwater insufficient of organic matter

326

However, recent studies have revealed substantial N2O accumulation during this

327

process

328

and predicting N2O emission

29-32

.

. Modeling of N2O production is of great importance for understanding 36, 37

, therefore being a powerful tool to support


Page 15 of 34


329

operation optimization for mitigation purpose during AD process with sulfide or

330

sulfur as electron donors. However, the previously developed AD models have not

331

recognized N2O formation and thus are not capable to describe N2O dynamics during

332

this process 39-41, 43.

333

In this work, a new mathematical model is developed to predict N2O production

334

in sulfide- and sulfur-based AD process for the first time. The model validity was

335

confirmed by several independent data sets from two different cultures

336

different operating conditions. The set of best-fit parameter values are shown in Table

337

1, which are consistently within a relatively narrow range. The parameter values

338

obtained were robust in their ability to predict nitrate, nitrite, N2O, and S-species

339

dynamics under different operational conditions, indicating that the developed model

340

is applicable for different AD systems. It should be note that the parameter value of

341

yield coefficient for sulfide-based autotrophic denitrifiers (YSOB) was directly adapted

342

from that of sulfide-based autotrophic denitrifiers to avoid over-parameterisation, in

343

order to construct a practically applicable N2O model that is able to predict N2O

344

emissions from sulfide- and sulfur-based AD processes. This is acceptable as the yield

345

coefficient for SOB is not significantly thermodynamically variable during sulfide-

346

and sulfur-based AD processes due to the similar microbial community groups, which

347

has been confirmed in previous studies 8, 43. In addition, the sensitive analysis on YSOB

348

revealed that the parameter value of YSOB is not sensitive in terms of N2O production

349

in the system.

29-31

with

350

Modeling results demonstrated that a substantial amount of N2O could

351

accumulate during the initial stage of sulfide-based AD process due to the relatively

352

lower autotrophic N2O reduction rate when utilizing sulfide. Correspondingly, the

353

estimated , (maximum reaction rate of Process 3) value is much lower than those



Page 16 of 34

354

of ,

(maximum reaction rate of Process 1) and , (maximum reaction rate of

355

Process 2) (Table 1). Therefore, great care should be taken to avoid turbulence

356

induced N2O emission during the fast autotrophic nitrogen reduction with sulfide as

357

electron donor. It has been reported that sulfide inhibition on N2O reduction could be

358

due to sulfide precipitation of copper needed for nitrous oxide reductase activity

359

However, sulfide-quinone reductase is a membrane-bound enzyme 53, and the sulfide

360

oxidase produced by sulfide-driven autotrophic denitrifiers can instantly oxidize

361

sulfide into sulfur and store the sulfur globules in the periplasmic space, leading to

362

continuous electron transport that is mediated by membrane bound electron transport

363

54

364

dissolved sulfide in the cell periplasm and thus likely not affecting the N2O reduction

365

activity in sulfide-based AD processes

366

reaction rate of Process 6) value is lower than those of ,

(maximum reaction rate

367

of Process 4) and , (maximum reaction rate of Process 5) (Table 1), thus resulting

368

in substantial N2O accumulation during sulfur-based AD process. Such difference was

369

not observed in sulfide-based denitrifying culture ( ,

, 0 and , values are ,2

370

comparable, Table 1), likely due to the differences in autotrophic denitrifying

371

community under different feeding conditions (i.e., sulfide or sulfur). For sulfur-based

372

AD, Thiobacillus and Sulfurimonas formed the dominant sulfur-oxidizing autotrophic

373

denitrifiers according to Zhang et al.

374

Arcobacter sp., and Thiobacillus were the dominant autotrophic denitrifiers

375

Another study reported the dominant autotrophic denitrifiers were Thiobacillus,

376

Azoarcus, and Sulfurovum

377

structure likely determines the difference in terms of their kinetic parameters between

378

sulfide- and sulfur-based AD. In addition, the surface area, morphology, particle size

52

.

. Hence, the copper co-factor in N2O reductase might not be precipitated by

31

. Similarly, the obtained

,

(maximum

30

. For sulfide-based AD, Thiomicrospira, 55

.

56

. Therefore, the difference in microbial community


Page 17 of 34


379

and source (in-situ generation or external addition) of sulfur might also affect sulfur

380

mass transfer and thus influence the reaction rates of sulfur oxidation during both

381

sulfide- and sulfur-based AD 12. For example, AD rates were shown to increase with

382

surface area when considering a range of coarse sulfur particles from 2.8 to 16 mm in

383

diameter 39. Also, the dependence of AD rates on sulfur concentration occurred up to

384

a relatively high concentration of sulfur with coarse particles, but such dependence

385

was only evident up to much lower sulfur concentrations with fine particles

386

AD rates were also reported to depend on the particle size, i.e., increasing with the

387

decrease of sulfur granule size 58. In addition, in-situ sulfur generated during sulfide-

388

driven AD can be instantly stored in the periplasmic space of the cell and lead to

389

better electron transport, and thus likely exhibiting higher AD rates than that of

390

externally dosed sulfur

391

an appropriate reaction time is required to maintain in order to achieve complete

392

denitrification for N2O mitigation purpose. In addition,

393

(0.010 – 0.017 h-1) for autotrophic N2O reduction are smaller than widely used anoxic

394

growth rate on N2O by heterotrophic denitrifiers, e.g., 0.134 h-1 33, likely due to the

395

fact that heterotrophic and autotrophic denitrifying cultures utilize completely

396

different substrates as electron donors (carbon and sulfide/sulphur, respectively) for

397

growth, thus resulting in distinct denitrifying microbial community with different

398

growth kinetics on nitrogen. This again suggests the important role of sulfide- and

399

sulfur-based AD process in N2O production and emission.

57

. The

53, 54

. Overall, for both sulfide- and sulfur-based AD process,

,

(0.076 h-1) and

,

400

The developed model in this work is useful to design and optimize sulfide- or

401

sulfur-based AD process in terms of N2O emission. In particular, sludge retention

402

time (SRT) is an important process parameter determining the nitrate removal

403

efficiency and N2O emission of the AD process. To reveal the detailed role of SRT,



Page 18 of 34

404

model simulations were conducted to study the effect of SRT on N2O emission in

405

both continuous-flow sulfide-based and sulfur-based AD systems through varying the

406

ratio between the return and waste sludge. The simulation system was designed

407

according to that used in Liu et al.

408

which is a typical concentration in contaminated groundwater 19. The influent sulfide

409

concentration was 175 mg-S/L for sulfide-based system, while the influent sulfur was

410

set at 1000 mg-S/L for sulfur-based system as sulfur is not a limiting factor in such

411

system operation. Figure 6 shows N2O emission factors in both systems with >95%

412

nitrate removal at a HRT of 2 h and SRTs ranging from 25 to 400 d, similar to actual

413

groundwater AD field applications

414

investigated in order to provide an overall insight into the SRT impact, which is

415

valuable as there is an increasing trend applying prolonged SRT reactors, e.g.,

416

membrane bioreactors, for nitrogen removal from groundwater and wastewater

417

59, 60

418

optimum nitrate removal efficiency (i.e., >95%) along with the enhanced nitrate

419

removal efficiency due to the increase of activated sludge concentration (data not

420

shown), while achieving a significant N2O mitigation, i.e., 2% to 0.2% for sulfide-

421

based AD process, and 18% to 2% for sulfur-based AD process. However, such an

422

extended SRT might induce a lower active biomass fraction in the sludge and a higher

423

organic substance in the effluent due to the increasing production of soluble microbial

424

products 61.

33

. The influent nitrate was fixed at 35 mg-N/L,

20, 30

. Such a broad SRT ranges have been

18, 30,

. With the increase of SRT from 25 to 250 d, both systems could retain the

425

For sulfide-based AD process, initial S/N ratio also plays an important role in

426

regulating N2O emission, as indicated by model simulation results from this

427

continuous-flow system with varying sulfide concentrations at an influent nitrate of

428

35 mg-N/L, a SRT of 250 d and a HRT of 2 h while achieving >95% nitrate removal


Page 19 of 34


429

(Figure 7). As expected, increasing S/N (e.g., 2.5 to 6 g-S/g-N) ratio led to a

430

substantial decrease in N2O emission (e.g., 1.9% to 0.1%), due to the relative fast

431

autotrophic N2O reduction rate by sulfide. However, increasing S/N ratio also resulted

432

in the more extent of incomplete sulfide oxidation, i.e., more percentage of sulfur in

433

the effluent. Therefore, considerations should be taken on the optimum S/N ratio, in

434

order to simultaneously minimize operation cost and N2O emission. Alternatively, an

435

effective way to recover extra sulfur during this process should be adopted 62.

436

In addition to SRT and S:N ratio, pH may also influence the AD system

437

performance as lower pH could result in more free nitrous acid and thus possibly have

438

an inhibition on AD process, likely leading to higher nitrite accumulation and thus

439

more N2O emissions 31, 32, 63.

440

Both sulfide- and sulfur-based AD processes produce a large amount of sulfate.

441

The US EPA allowable sulfate limit in drinking water is 250 mg/L 19. Theoretically,

442

about 58 mg-N/L and 33 mg-N/L nitrate in groundwater can be denitrified with

443

sulfide and sulfur, respectively, without exceeding the above sulfate limit if

444

groundwater does not contain background sulfate. Therefore, for high-concentration

445

nitrate removal from groundwater, carbon source is often supplied to achieve

446

simultaneous heterotrophic and AD process in order to control sulfate formation 19, 29.

447

Under such circumstances, the competition for nitrogen compound between

448

heterotrophic and autotrophic could induce a different scenario on N2O emission,

449

which is not accounted for in current study. However, the developed model is based

450

on the activated sludge model (ASM) and thus can be readily integrated with the

451

ASM-based heterotrophic denitrifying N2O models

452

of overall N2O dynamics if simultaneous heterotrophic and AD processes are applied

453

for high-concentration nitrate removal.

33, 37

for achieving the prediction



454

In summary, a new mathematical model is proposed to describe N2O production

455

in sulfide- and sulfur-based AD process for the first time. The developed model was

456

successfully applied to reproduce experimental data obtained from two AD systems

457

with different conditions, and clearly showed the potential wide applicability of the

458

developed model. Modeling results demonstrated substantial N2O accumulation due

459

to the relatively low N2O reduction rate during sulfide- and sulfur-based AD

460

processes. The increasing SRT would substantially reduce N2O emission in both

461

systems. The increasing S/N ratio would also lead to a substantial decrease in N2O

462

emission from the sulfide-based AD process.

463 464

ACKNOWLEDGEMENTS

465

This work was partially supported by the Recruitment Program of Global Experts

466

and the Natural Science Foundation of China (No. 51578391). Dr. Yiwen

467

Liu acknowledges the support from the UTS Chancellor's Postdoctoral Research

468

Fellowship. The authors are grateful to the research collaboration among University

469

of Technology Sydney, Tongji University, Ghent University, Hunan University and

470

Sichuan University.

471 472

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473 474 475 476 477 478 479 480 481 482

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Page 24 of 34

Page 25 of 34


670

Table Caption

671

Table 1. Best-Fit Parameters with 95% Confidence Intervals (h-1).

672 673

Figure Captions

674

Figure 1. Schematic representation of the proposed N2O model concept in AD

675

processes.

676 677

Figure 2. Model calibration and validation with experimental data from Batch test I

678

of the sulfide-based autotrophic denitrifying culture: (a) NO3-, NO2- and N2O profiles

679

with 700-µm granular sludge; (b) TDS, S0 and SO42- dynamics with 700-µm granular

680

sludge; (c) NO3-, NO2- and N2O profiles with 1350-µm granular sludge; and (d) TDS,

681

S0 and SO42- dynamics with 1350-µm granular sludge.

682 683

Figure 3. Model evaluation with experimental data obtained from Batch test II of

684

sulfide-based autotrophic denitrifying sludge on N2O reduction under different initial

685

S/N ratios.

686 687

Figure 4. Model calibration with experimental data on (a) NO3-, NO2-, N2O and (b)

688

SO42- from batch test of sulfur-based denitrifying culture with an initial nitrate

689

concentration of 50 mg-N/L.

690 691

Figure 5. Model validation with experimental data on NO3-, NO2- and N2O from

692

batch tests of sulfur-based denitrifying culture with an initial nitrate concentration of

693

(a) 30 mg-N/L; and (b) 40 mg-N/L.

694 695

Figure 6. Model simulation of the effect of SRT (25 – 400 d) on N2O accumulation

696

while achieving >95% nitrate removal during continuous-flow (a) sulfide-based and



697

(b) sulfur-based AD processes at steady state. The simulation conditions for both

698

systems are: HRT = 2 h and influent nitrate = 35 mg-N/L. The influent sulfide

699

concentration is 175 mg-S/L for (a) sulfide-based system, while the influent sulfur is

700

set at 1000 mg-S/L for (b) sulfur-based system as sulfur is not a limiting factor in such

701

system operation.

702 703

Figure 7. Model simulation of the steady-state N2O accumulation and residual

704

effluent sulfur percentage while achieving >95% nitrate removal in a continuous-flow

705

sulfide-based autotrophic denitrifying system as a function of S/N ratio. The

706

simulation conditions are: HRT = 2 h, SRT = 250 d and influent nitrate = 35 mg-N/L.

707

Such simulation is not performed for sulfur-based autotrophic denitrifying system

708

since it is not feasible to control S/N ratio with sulfur as electron donor.


Page 26 of 34

Page 27 of 34


709

710

Table 1. Best-Fit Parameters with 95% Confidence Intervals (h-1).

d

Process

Parameter

1 2 3 4 5 6

,

, , ,

, ,

Sulfide-based autotrophic denitrifying culture 0.245 d 0.317±0.024 0.076±0.002 0.02 d 0.021±0.002 0.017±0.001

Sulfur-based autotrophic denitrifying culture ------0.02 d 0.035±0.002 0.010±0.001

default value adapted from literature.

711



712 713

Figure 1. Schematic representation of the proposed N2O model concept in AD

714

processes.

715


Page 28 of 34


Concentration (mg-N/L)

40

a

300

measured N2O modelled N2O

30

measured NO3modelled NO3measured NO2-

20

modelled NO2

10 0 0.0

0.5

1.0

1.5

2.0

-

2.5

Concentration (mg-S/L)

Page 29 of 34

200 150 100 50 0 0.0

3.0

modelled TDS measured TDS modelled S0 measured S0 modelled SO42measured SO42-

b

250

0.5

1.0

Time (h)

30

716

measured NO3modelled NO3measured NO2-

20

modelled NO2-

10 0 0.0

300


c

0.5

1.0

1.5

2.0

2.5

3.0

Concentration (mg-S/L)


40

1.5

2.0

2.5

3.0

Time (h)

d

modelled TDS measured TDS modelled S0 measured S0 modelled SO42measured SO42-

250 200 150 100 50 0 0.0

0.5

Time (h)

1.0

1.5

2.0

2.5

3.0

Time (h)

717

Figure 2. Model calibration and validation with experimental data from Batch test I

718

of the sulfide-based autotrophic denitrifying culture: (a) NO3-, NO2- and N2O profiles

719

with 700-µm granular sludge; (b) TDS, S0 and SO42- dynamics with 700-µm granular

720

sludge; (c) NO3-, NO2- and N2O profiles with 1350-µm granular sludge; and (d) TDS,

721

S0 and SO42- dynamics with 1350-µm granular sludge.

722



Page 30 of 34

30 measured at 0.8 g-S/g-N modelled at 0.8 g-S/g-N measured at 2.5 g-S/g-N modelled at 2.5 g-S/g-N measured at 5.0 g-S/g-N modelled at 5.0 g-S/g-N

N2O (mg-N/L)

25 20 15 10 5 0

0

1

2

3

4

5

6

7

8

Time (h)

723 724

Figure 3. Model evaluation with experimental data obtained from Batch test II of

725

sulfide-based autotrophic denitrifying sludge on N2O reduction under different initial

726

S/N ratios.




60 50


40

measured NO3-

30

modelled NO3measured NO2-

20

modelled NO2-

10 0

727

a

0

5

10

15

20

25

Sulfate concentration (mg/L)

Page 31 of 34

1000

b

modelled SO42measured SO42-

800 600 400 200 0

0

5

Time (d)

10

15

20

25

Time (d)

728

Figure 4. Model calibration with experimental data on (a) NO3-, NO2-, N2O and (b)

729

SO42- from batch test of sulfur-based denitrifying culture with an initial nitrate

730

concentration of 50 mg-N/L.



60

50


40

measured NO3-

30


20

modelled NO2-

10 0

731

a

0

5

10

15

20

25



60

Page 32 of 34

b

50


40

measured NO3-

30


20

modelled NO2-

10 0

0

5

Time (h)

10

15

20

25

Time (h)

732

Figure 5. Model validation with experimental data on NO3-, NO2- and N2O from

733

batch tests of sulfur-based denitrifying culture with an initial nitrate concentration of

734

(a) 30 mg-N/L; and (b) 40 mg-N/L.


5

100

a

80

3

60 N2O emission factor

2

Effluent S0

1 0

0

40 20

0 50 100 150 200 250 300 350 400

Effluent S0 ratio (%)

4

N2O emission per N load (%)



Page 33 of 34

20

b

15 10 5 0

0

50 100 150 200 250 300 350 400

SRT (d)

735

SRT (d)

736

Figure 6. Model simulation of the effect of SRT (25 – 400 d) on N2O accumulation

737

while achieving >95% nitrate removal during continuous-flow (a) sulfide-based and

738

(b) sulfur-based AD processes at steady state. The simulation conditions for both

739

systems are: HRT = 2 h and influent nitrate = 35 mg-N/L. The influent sulfide

740

concentration is 175 mg-S/L for (a) sulfide-based system, while the influent sulfur is

741

set at 1000 mg-S/L for (b) sulfur-based system as sulfur is not a limiting factor in such

742

system operation.


743

Page 34 of 34

5

100

4

80

3

60

2

N2O emission factor Effluent S0

1 0 2.5

3.0

3.5

4.0

4.5

5.0

40 20

5.5

Effluent S0 ratio (%)



0 6.0

S:N

744

Figure 7. Model simulation of the steady-state N2O accumulation and residual

745

effluent sulfur percentage while achieving >95% nitrate removal in a continuous-flow

746

sulfide-based autotrophic denitrifying system as a function of S/N ratio. The

747

simulation conditions are: HRT = 2 h, SRT = 250 d and influent nitrate = 35 mg-N/L.

748

Such simulation is not performed for sulfur-based autotrophic denitrifying system

749

since it is not feasible to control S/N ratio with sulfur as electron donor.


Evaluation of Nitrous Oxide Emission from Sulfide-and Sulfur-Based

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