Open Access

Kinetics of nitrous oxide (N2O) formation and reduction by Paracoccus pantotrophus

  • B. L. Read-Daily1,
  • F. Sabba2,
  • J. P. Pavissich3 and
  • R. Nerenberg2Email author
AMB Express20166:85

DOI: 10.1186/s13568-016-0258-0

Received: 27 July 2016

Accepted: 24 September 2016

Published: 3 October 2016

Abstract

Nitrous oxide (N2O) is a powerful greenhouse gas emitted from wastewater treatment, as well as natural systems, as a result of biological nitrification and denitrification. While denitrifying bacteria can be a significant source of N2O, they can also reduce N2O to N2. More information on the kinetics of N2O formation and reduction by denitrifying bacteria is needed to predict and quantify their impact on N2O emissions. In this study, kinetic parameters were determined for Paracoccus pantotrophus, a common denitrifying bacterium. Parameters included the maximum specific reduction rates, \(\hat{q}\), growth rates, \({\hat{\upmu }}\), and yields, Y, for reduction of NO3 (nitrate) to nitrite (NO2 ), NO2 to N2O, and N2O to N2, with acetate as the electron donor. The \(\hat{q}\) values were 2.9 gN gCOD−1 d−1 for NO3 to NO2 , 1.4 gN gCOD−1 d−1 for NO2 to N2O, and 5.3 gN gCOD−1 d−1 for N2O to N2. The \({\hat{\upmu }}\) values were 2.7, 0.93, and 1.5 d−1, respectively. When N2O and NO3 were added concurrently, the apparent (extant) kinetics, \(\hat{q}_{\text{app}}\), assuming reduction to N2, were 6.3 gCOD gCOD−1 d−1, compared to 5.4 gCOD gCOD−1 d−1 for NO3 as the sole added acceptor. The \({\hat{\upmu }}_{\text{app}}\) was 1.6 d−1, compared to 2.5 d−1 for NO3 alone. These results suggest that NO3 and N2O were reduced concurrently. Based on this research, denitrifying bacteria like P. pantotrophus may serve as a significant sink for N2O. With careful design and operation, treatment plants can use denitrifying bacteria to minimize N2O emissions.

Keywords

Paracoccus pantotrophus Nitrous oxide Denitrification Maximum specific reduction rates Kinetics

Introduction

Nitrous oxide (N2O) is a potent greenhouse gas with a global warming potential 300-fold greater than CO2 (IPCC 2006). It also is a major concern for ozone depletion in the stratosphere (Ravishankara et al. 2009). In recent years, wastewater treatment processes, especially those employing biological nutrient removal (BNR), have been found to be significant sources of N2O (Ni and Yuan 2015). The most common sources of N2O in BNR processes are ammonium-oxidizing bacteria (AOB) and heterotrophic denitrifying bacteria (DNB) (Law et al. 2012). AOB can form significant amounts of N2O, especially when the dissolved oxygen (DO) concentrations are low, or during transitions from anoxic to aerobic conditions (Chandran et al. 2011; Sabba et al. 2015). During denitrification, N2O can form when insufficient electron donor is available, when the pH is excessively high, when sufficient copper is lacking, or when inhibitors of the N2O reductase, such as DO, hydrogen sulfide, high nitrite (\({\text{NO}}_{2}^{ - }\)) or ammonia (NH3) concentrations, are present (Tallec et al. 2008; Bergaust et al. 2010; Lu and Chandran 2010; Pan et al. 2012, 2013a).

While DNB can be a source of N2O emissions, they also can scavenge N2O and reduce it to N2 (Zumft and Kroneck 2007). For example, N2O produced by nitrifying bacteria can be reduced by DNB in the anoxic zone of a suspended-growth process or in the deeper portions of a biofilm (Ikeda-Ohtsubo et al. 2013).

A better understanding, and quantification, of the kinetics of N2O reduction by DNB is critical to predicting N2O emissions from wastewater treatment processes and developing strategies for N2O mitigation. Since N2O reduction may take place in the presence of \({\text{NO}}_{3}^{ - }\), it also is important to explore the kinetics when both acceptors are present (Schreiber et al. 2012). These parameters are needed for more recent mathematical models that explicitly include N2O as a state variable, such as those developed by (Ni and Yu 2008; Hiatt and Grady 2008; Ni et al. 2011; Pan et al. 2013b).

In this research, we determined denitrification kinetics of a pure culture of Paracoccus pantotrophus (formerly Thiosphaera pantotropha), a versatile denitrifying bacterium isolated from denitrifying wastewater treatment processes (Robertson and Kuenen 1983). We used a multistep model including the reduction of \({\text{NO}}_{3}^{ - }\) to \({\text{NO}}_{2}^{ - }\), \({\text{NO}}_{2}^{ - }\) to N2O, and N2O to N2, and determined the biomass yield (Y), \(\hat{q}\), and maximum growth rate (\({\hat{\upmu }}\)) for each step. We also determined the apparent \(\hat{q}\) and \({\hat{\upmu }}\), based solely on donor oxidation and biomass formation, for the reduction of \({\text{NO}}_{3}^{ - }\) to N2 and concurrent reduction of \({\text{NO}}_{3}^{ - }\) and N2O. Our objective was to gain a better understanding of the mechanisms of N2O formation and reduction by DNB.

Materials and methods

Bacterial strain and growth medium

We used a pure culture of P. pantotrophus (ATCC 35512) in this study. A minimal growth medium was used, consisting of 1.386 g Na2HPO4, 0.849 g KH2PO4, 0.02 g MgSO4·7H2O, and 0.1 g (NH4)2SO4, 0.1 mL Ca–Fe solution, and 0.1 mL trace mineral solution (Nerenberg et al. 2002). The medium also included a trace amount of Luria–Bertani (LB) broth, at 1 % of the usual concentration, to minimize microbial aggregation during growth. All chemicals were analytical grade. Nitrogen gas was UHP grade and \({\text{NO}}_{3}^{ - }\) was added as needed to obtain the desired initial concentrations. N2O gas was 99.5 % purity and was added into the headspace.

Batch studies

Batch tests were carried out in 1-L glass bottles with 200 mL of minimal medium. Bottles were capped with a cored rubber stopper containing a sectioned Balch tube with a butyl rubber stopper and aluminum crimp seal, allowing for sample collection. Bottles were successively vacuum-degassed to −1.7 atm and pressurized with either N2 or N2O at 1.3 atm, three times. The final headspace contained either N2 or N2O at 1.3 atm. Batch tests were carried out at least in triplicate.

Bottles were inoculated with 100 µL of P. pantotrophus culture with an optical density at 600 nm (OD600) of 0.6. Bottles were shaken on their sides at 150 rpm at room temperature (22 °C). The medium was amended with acetate as an electron donor and carbon source, with an initial concentration of 650 mgCOD L−1 (600 mg/L as acetate). When \({\text{NO}}_{3}^{ - }\) was used, its initial concentration was 50 mgN L−1.

Analytical methods

Acetate, \({\text{NO}}_{3}^{ - }\), and \({\text{NO}}_{2}^{ - }\) were analyzed using a Dionex ICS2500 ion chromatograph (IC, Dionex Corporation, Sunnyvale, CA) with a 4-mm Dionex AS-11 column, an AG-11 guard column, and a conductivity detector. The program consisted of a 5-min equilibration with 4 mM sodium hydroxide eluent, injection of the sample, a 9-min isocratic run at 4 mM, and a linear gradient from 4 to 50 mM sodium hydroxide over 2 min. A Dionex ASRS suppressor was used in internal recycle mode. Injection was performed with a Dionex AS40 automated sampler. The injection volume was 200 μL. The detection limit for acetate, \({\text{NO}}_{3}^{ - }\), and \({\text{NO}}_{2}^{ - }\) was approximately 0.1 mgN L−1. The biomass concentration was assessed with a spectrophotometer via the OD600 (UV10, Thermo, Rochester, NY) and converted to dry weight (DW) using a conversion factor. A conversion factor of 385 mgDW L−1 per OD unit was determined following (Nerenberg et al. 2006).

Determination of parameters

The maximum specific growth rates, \({\hat{\upmu }}\) (d−1), maximum specific substrate utilization rates, \(\hat{q}\) (gCOD gCOD−1 d−1 or gN gCOD−1 d−1), and yields, Y (gCOD gCOD−1 or gCOD gN−1), were determined by parameter fitting (Reichert et al. 1995; Wild et al. 1995). A three-step model was used, including (1) \({\text{NO}}_{3}^{ - }\) reduction to \({\text{NO}}_{2}^{ - }\), (2) \({\text{NO}}_{2}^{ - }\) reduction to N2O, and (3) N2O reduction to N2. The model lumped NO reduction together with \({\text{NO}}_{2}^{ - }\) reduction, as NO reduction to N2O is very fast and NO accumulation during denitrification is minimal (Schreiber et al. 2012).

The process matrix is shown in Table 1 while the model components and the kinetic and stoichiometric parameters are shown in Additional file 1: Tables S1 and S2. Since the \({\text{NO}}_{3}^{ - }\), N2O, and acetate concentrations were well above their expected half-saturation constants for essentially the entire duration of the tests, the half saturation constants Ks for \({\text{NO}}_{3}^{ - }\), \({\text{NO}}_{2}^{ - }\), N2O, and acetate were not determined experimentally. Values were taken from (Ni et al. 2011). The specific rate of decay coefficient, b, also was considered insignificant compared to the maximum growth rates and therefore not independently determined. The value for b was taken as 0.15 d−1 (Rittmann and McCarty 2001).
Table 1

Process matrix for denitrification model

Components reactions

SNO3-N mgN L−1

SNO2-N mgN L−1

SN2O-N mgN L−1

S mgCOD L−1

X mgCOD L−1

Rate expression

Nitrate reduction (NAR, NAP)

\(- \frac{{1 - Y_{{NO_{3}^{ - } }} }}{{1.14Y_{{NO_{3}^{ - } }} }}\)

\(\frac{{1 - Y_{{NO_{3}^{ - } }} }}{{1.14Y_{{NO_{3}^{ - } }} }}\)

 

\(\frac{ - 1}{{Y_{{NO_{3}^{ - } }} }}\)

1

\(\hat{q}_{{NO_{3}^{ - } }} \, \times \,Y_{{NO_{3}^{ - } }} \, \times \,\frac{{S_{{NO_{3}^{ - } }} }}{{K_{{NO_{3}^{ - } }} + S_{{NO_{3}^{ - } }} }}\, \times \,\frac{{S_{S} }}{{K_{S} + S_{S} }}\, \times \,X_{H}\)

Nitrite reduction (NIR)

 

\(- \frac{{1 - Y_{{NO_{2}^{ - } }} }}{{1.14Y_{{NO_{2}^{ - } }} }}\)

\(\frac{{1 - Y_{{NO_{2}^{ - } }} }}{{1.14Y_{{NO_{2}^{ - } }} }}\)

\(\frac{ - 1}{{Y_{{NO_{2}^{ - } }} }}\)

1

\(\hat{q}_{{NO_{2}^{ - } }} \, \times \,Y_{{NO_{2}^{ - } }} \, \times \,\frac{{S_{{NO_{2}^{ - } }} }}{{K_{{NO_{2}^{ - } }} + S_{{NO_{2}^{ - } }} }}\, \times \,\frac{{S_{S} }}{{K_{S} + S_{S} }}\, \times \,X_{H}\)

Nitrous oxide reduction (N2OR)

  

\(- \frac{{1 - Y_{{NO_{2}^{ - } }} }}{{0.57Y_{{NO_{2}^{ - } }} }}\)

\(\frac{ - 1}{{Y_{{NO_{2}^{ - } }} }}\)

1

\(\hat{q}_{{NO_{2}^{ - } }} \, \times \,Y_{{NO_{2}^{ - } }} \, \times \,\frac{{S_{{NO_{2}^{ - } }} }}{{K_{{NO_{2}^{ - } }} + S_{{NO_{2}^{ - } }} }}\, \times \,\frac{{S_{S} }}{{K_{S} + S_{S} }}\, \times \,X_{H}\)

Cell decay

    

−1

\(- b_{H} \, \times \,X_{H}\)

The experimental strategy consisted of (1) determining the \(\hat{q}\), Y, and \(\hat{\mu }\) for N2O using batch tests with N2O as the sole added acceptor; (2) after incorporating the parameters for N2O into the denitrification model (Table 1), determining the \(\hat{q}\), Y, and \(\hat{\mu }\) for reduction of \({\text{NO}}_{3}^{ - }\) to \({\text{NO}}_{2}^{ - }\), as well as the \(\hat{q}\) for reduction of \({\text{NO}}_{2}^{ - }\) to N2O, from batch tests with \({\text{NO}}_{3}^{ - }\) as the sole added acceptor. When \({\text{NO}}_{3}^{ - }\) was added, accumulation of \({\text{NO}}_{2}^{ - }\) occurred at values greatly exceeded the reported Ks for \({\text{NO}}_{2}^{ - }\), which typically are below 1 mgN L−1. This accumulation allowed the \(\hat{q}\) value for \({\text{NO}}_{2}^{ - }\) reduction to be determined from the \({\text{NO}}_{3}^{ - }\) reduction test. The Y for reduction of \({\text{NO}}_{2}^{ - }\) to N2O, in gCOD/gCOD, was assumed to be the same as the Y for reduction of N2O to N2 (Hiatt and Grady 2008; Ni et al. 2011).

Tests were also carried out with \({\text{NO}}_{3}^{ - }\) plus N2O as concurrently added acceptors. For these tests, as well as for the previous tests with \({\text{NO}}_{3}^{ - }\) as the sole added acceptor, we determined apparent (extant) parameters \(\hat{q}_{app}\), \(Y_{app}\) and \(\hat{\mu }_{app}\). These were determined solely from acetate oxidation and biomass growth data, without considering acceptor utilization. Thus, these parameters reflect the concurrent use of multiple acceptors. The model was adapted from Ni et al. (2011) implemented using AQUASIM (Reichert et al. 1995; Wild et al. 1995). Parameters were determined using AQUASIM’s parameter estimation function. Each batch test was carried out at least in triplicate. The reported values are the average and standard deviation.

Results

Parameters for partial reduction steps

Typical plots for the batch tests are shown in Fig. 1. The tests with N2O as the sole electron acceptor showed vigorous growth. Since one atmosphere of pure N2O gas was supplied in the headspace, and the bottles were vigorously shaken, the theoretical value of N2O in the aqueous phase was 905 mg L−1 and therefore non-rate-limiting. This was confirmed by the exponential growth observed throughout the tests with N2O as the sole acceptor. Because N2O was in excess, acetate was fully consumed during the experiment. In contrast, the tests with \({\text{NO}}_{3}^{ - }\) as the sole added electron acceptor had an initial \({\text{NO}}_{3}^{ - }\) concentration of only 50 mgN L−1. In these tests, acetate was only partially consumed and the final biomass concentration was much lower.
Fig. 1

Typical batch and modeling (data fitting) results for a N2O as sole electron acceptor, b \({\text{NO}}_{3}^{ - }\) as sole added electron acceptor; model sCOD (dotted line), model biomass ( ), model \({\text{NO}}_{3}^{ - }\) ( ), model \({\text{NO}}_{2}^{ - }\) ( ), experimental sCOD (square), experimental biomass (diamond), experimental \({\text{NO}}_{3}^{ - }\) (circle), experimental \({\text{NO}}_{2}^{ - }\) (triangle)

Data fitting was used to determine kinetic parameters from the experimental data. Parameters included the \(\hat{\mu }\), \(\hat{q}\), and Y for reduction of \({\text{NO}}_{3}^{ - }\) to \({\text{NO}}_{2}^{ - }\), \({\text{NO}}_{2}^{ - }\) to N2O, and N2O to N2. Results are summarized in Table 2. The \(\hat{\mu }\) for \({\text{NO}}_{3}^{ - }\) reduction to \({\text{NO}}_{3}^{ - }\) was highest (2.7 d−1), and that for NO2 reduction to N2O was the lowest (0.93 d−1). The \(\hat{\mu }\) for N2O reduction (1.7 d−1) was lower than for \({\text{NO}}_{3}^{ - }\), but around double that for \({\text{NO}}_{3}^{ - }\). Note that these rates are for individual denitrification steps. The observed growth rates on \({\text{NO}}_{3}^{ - }\) or \({\text{NO}}_{3}^{ - }\), where the reduction products are utilized concurrently, would probably be higher.
Table 2

Summary of kinetic and stoichiometric parameters

Reactions

\({\hat{\upmu }}\)

\(\hat{q}\)

Y

d−1

gCOD gCOD−1 d−1

gN gCOD−1 d−1

gCOD gCOD−1d−1

gCOD gN−1

\({\text{NO}}_{3}^{ - }\)\({\text{NO}}_{2}^{ - }\)

2.7

6.0 ± 1.5

2.9 ± 0.72

0.45 ± 1.5

0.93 ± 0.72

\({\text{NO}}_{2}^{ - }\) → N2O

0.93

2.6 ± 0.44

1.4 ± 0.25

0.36a

0.65

N2O → N2

1.7

4.8 ± 0.48

5.3 ± 0.27

0.36 ± 0.02

0.32 ± 0.27

a \({\text{NO}}_{2}^{ - }\) yields were assumed to be the same as N2O

The \(\hat{q}\) can be expressed in terms of the acceptor (gN gCOD d−1) or in terms of the donor (gCOD gCOD−1 d−1). The first is useful for identifying kinetic bottlenecks during sequential reduction of nitrogen oxides, as the downstream rate must be equal or higher than the upstream to avoid significant intermediate accumulation. The second is useful when assessing donor demand resulting from different combinations of acceptors. The two forms are related by stoichiometry.

In terms of N, the \(\hat{q}\) for reduction of \({\text{NO}}_{3}^{ - }\) to \({\text{NO}}_{3}^{ - }\) was 2.9 gN gCOD d−1, and for reduction of \({\text{NO}}_{3}^{ - }\) to N2O was 1.4 gN g CODd−1 (Table 2). The \(\hat{q}\) for reduction of N2O was highest at 5.3 gN gCOD d−1. When examining the COD oxidation results, the highest \(\hat{q}\) was obtained for \({\text{NO}}_{3}^{ - }\) reduction to \({\text{NO}}_{3}^{ - }\), at 6.0 gCOD gCOD−1 d−1, consistent with its high growth rate. The \(\hat{q}\) for \({\text{NO}}_{3}^{ - }\) reduction to N2O was only 2.6 gCOD gCOD−1 d−1, while N2O was 4.8 gCOD gCOD−1 d−1.

Batch tests with concurrent addition of \({\text{NO}}_{3}^{ - }\) and N2O

Batch tests were used to compare the reduction rates of \({\text{NO}}_{3}^{ - }\), as the sole added acceptor, with rates of concurrently added \({\text{NO}}_{3}^{ - }\) and N2O. In order to explore the aggregate specific rates of growth and donor oxidation, the batch tests were fitted to determine the “apparent” or extant specific growth rates and donor utilization rates. Figure 2 shows the resulting plots and Table 3 summarizes the parameters. The combined addition of N2O and \({\text{NO}}_{3}^{ - }\) slowed the apparent \(\hat{\mu }\) from 2.5 to 1.6 d−1. However, the apparent \(\hat{q}\) increased from 5.4 to 6.3 gCOD gCOD−1 d−1.
Fig. 2

Typical batch tests for the determination of apparent rates for a \({\text{NO}}_{3}^{ - }\) and b \({\text{NO}}_{3}^{ - }\) plus N2O. Model sCOD (dotted line), model biomass (dashed line), experimental sCOD (square), experimental biomass (diamond)

Table 3

Summary of apparent parameters

Reactions

\({\hat{\upmu }}_{app}\)

\(\hat{q}_{app}\)

Yapp

d−1

gCOD gCOD−1 d−1

gN gCOD−1 d−1

gCOD gCOD−1 d−1

gN gCOD−1 d−1

\({\text{NO}}_{3}^{ - }\) → N2

2.5 ± 0.96

5.4 ± 0.48

0.99 ± 0.09a

0.48 ± 0.09

2.6 ± 0.09a

\({\text{NO}}_{3}^{ - }\) + N2O → N2

1.6 ± 0.11

6.3 ± 1.3

1.7 ± 0.34a

0.25 ± 0.03

0.95 ± 0.03a

aCalculated from donor utilization data, considering \({\text{NO}}_{3}^{ - }\) reduction to N2

Discussion

Kinetic parameters for the denitrification pathway for P. pantotrophus were determined. The growth rates on N2O are high, suggesting that DNB can thrive when N2O is the sole electron acceptor. When \({\text{NO}}_{3}^{ - }\) and N2O are supplied together, the growth rates are higher than with N2O alone, but lower than with \({\text{NO}}_{3}^{ - }\) alone.

The lower \(\hat{q}\) value for \({\text{NO}}_{2}^{ - }\) indicates a bottleneck on the denitrification pathway, i.e., when \({\text{NO}}_{3}^{ - }\) is present at non-rate-limiting concentrations, \({\text{NO}}_{2}^{ - }\) necessarily accumulates, and the observed rate of N2O reduction is limited to the maximum rate of N2O formation from \({\text{NO}}_{2}^{ - }\). Since the \(\hat{q}\) for N2O, expressed as N, is around triple that of \({\text{NO}}_{2}^{ - }\) and almost double that of \({\text{NO}}_{3}^{ - }\), there appears to be significant capacity for N2O reduction concurrently with \({\text{NO}}_{3}^{ - }\) or \({\text{NO}}_{2}^{ - }\). In fact, our research shows that P. pantotrophus can concurrently utilize \({\text{NO}}_{3}^{ - }\) and N2O. Thus, DNB should be able to reduce externally supplied N2O concurrently with \({\text{NO}}_{3}^{ - }\) or \({\text{NO}}_{2}^{ - }\).

Few sets of kinetic data for the individual reduction steps have been previously reported. While some values have been reported for mixed culture (Additional file 1: Tables S3–S5), very few studies have assessed pure culture kinetics values. While environmental systems typically are based on mixed cultures, such mixed cultures are not reproducible and may give false indications of the mechanisms and regulation of denitrification. For example, for a given inoculum, a reduction test for N2O typically will be different from the community for a \({\text{NO}}_{3}^{ - }\) reduction test (Shade et al. 2013). The latter could select for bacteria that reduce \({\text{NO}}_{3}^{ - }\) to \({\text{NO}}_{2}^{ - }\) over denitrifiers, so \({\text{NO}}_{2}^{ - }\) accumulation would be due to microbial selection, not the intrinsic kinetics of a denitrifying system.

Values for \(\hat{q}\) were reported by several researchers (von Schulthess et al. 1994; Wild et al. 1994; von Schulthess et al. 1995; Wild et al. 1995; Wicht 1996) (Additional file 1: Tables S3–S5). However, these values vary widely from 0.88 to 11.1 gN gCOD d−1 for a mixed culture grown on N2O (Additional file 1: Table S5). In other studies, \(\hat{\mu }\) values were reported for growth on pure cultures of denitrifying bacteria using N2O as an acceptor, but not for \({\text{NO}}_{3}^{ - }\) to \({\text{NO}}_{2}^{ - }\) or \({\text{NO}}_{2}^{ - }\) to N2O (Strohm et al. 2007). The \(\hat{\mu }\) for N2O in this study was 1.7 d−1, falling in the range that was previously reported for P. denitrificans (Koike and Hattori 1975), 1.37–2.57 d−1. The \(\hat{q}\) values fall within the range of values previously reported for mixed cultures of denitrifying bacteria when N2O is reduced to N2. The yields on N2O presented in this paper are consistent with previous studies on the closely related DNB species P. denitrificans and Pseudomonas stutzeri, using acetate as an electron donor.

When examining the batch tests where N2O an \({\text{NO}}_{3}^{ - }\) were both supplied as electron acceptors, the results suggest that N2O was being reduced concurrently with \({\text{NO}}_{3}^{ - }\), leading to higher specific rates of donor utilization. The addition of N2O may have diverted electron equivalents from \({\text{NO}}_{3}^{ - }\) to N2O, which has a lower specific growth rate. This could lead to the lower overall apparent specific growth rate. Competition for electron carriers in DNB has been proposed by some researchers, who incorporated it in a metabolic model (Pan et al. 2013b, 2015). This approach has much greater complexity than conventional models, but may be warranted in cases where the donor oxidation rate is limiting (Pocquet et al. 2016).

The results from this study provide important insights into the mechanisms of N2O formation and consumption by denitrifying microorganisms. In particular, the parameters may be important for assessing the role of DNB in scavenging N2O produced by nitrifiers or due to incomplete denitrification (Sabba et al. 2015). N2O may be produced at a given time or location within a process, but could potentially be consumed at a different time or location by N2O-reducing microorganisms such as P. pantotrophus.

The role of DNB in producing and consuming N2O is illustrated schematically in Fig. 3. In Fig. 3a, a biofilm is supplied with ammonium, DO, and COD. N2O is formed by AOB, especially as the DO decreases, and some also is produced by the DNB. However, DNB provide a sink for N2O in the anoxic zone, so only a fraction of the produced N2O escapes to the bulk liquid (Sabba et al., submitted). If COD does not reach the base of the biofilm, little or no N2O will be reduced. Thus, all formed N2O will be released to the bulk (Fig. 3b). Another example is a denitrifying filter (Fig. 3c). If an influent containing COD and \({\text{NO}}_{3}^{ - }\) enters the top, \({\text{NO}}_{3}^{ - }\) is reduced first, with some \({\text{NO}}_{2}^{ - }\) and N2O accumulation. Then \({\text{NO}}_{2}^{ - }\) is reduced, and finally N2O is fully reduced towards the bottom. Again, if COD is limiting (Fig. 3d), N2O can break through the filter and be emitted to the environment. This breakthrough of N2O was recently demonstrated in a full-scale denitrifying filter (Bollon et al. 2016).
Fig. 3

Top panels theoretical behavior of denitrifying bacteria in biofilms under (a) excess or (b) limiting electron donor conditions. Lower panels theoretical nitrogen profiles in a denitrifying filter in presence of (c) excess or (d) limiting electron donor

Our research suggests that, while DNB be a source of N2O, proper management of treatment conditions can allow DNB to scavenge N2O previously produced by AOB or DNB. This is especially true for biofilm systems or denitrifying filters, where zones of N2O formation may be adjacent to, or precede, zones where DNB can scavenge N2O. Providing anoxic conditions and sufficient electron donor is a key for effective N2O scavenging.

Abbreviations

AOB: 

ammonium oxidizing bacteria

BNR: 

biological nitrogen removal

CO2

carbon dioxide

COD: 

chemical oxygen demand

DNB: 

denitrifying bacteria

DW: 

dry weight

DO: 

dissolved oxygen

IC: 

ion chromatography

H2O: 

water

LB: 

Luria–Bertani

N2O: 

nitrous oxide

NH3

ammonia

NO: 

nitric oxide

\({\text{NO}}_{2}^{ - }\)

nitrite

OD: 

optical density

O2

oxygen

Declarations

Authors’ contributions

BLRD conducted the batch study experiments, determined the kinetic parameters using the model, and analyzed the experimental data. FS, JPP, and RN helped interpret data. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Compliance with ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Funding

B. Read-Daily was funded by NSF project CBET0954918 (Nerenberg CAREER award) and the University of Notre Dame Center for Environmental Science and Technology Bayer Fellowship. F. Sabba was funded by WERF grant U2R10 and the University of Notre Dame Center for Environmental Science and Technology Bayer Fellowship.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Engineering and Physics, Elizabethtown College
(2)
Department of Civil Engineering and Environmental Engineering and Earth Sciences, University of Notre Dame
(3)
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez

References

  1. Bergaust L, Mao Y, Bakken LR, Frostegard A (2010) Denitrification response patterns during the transition to anoxic respiration and post-transcriptional effects of suboptimal pH on nitrogen oxide reductase in Paracoccus denitrificans. Appl Environ Microbiol 76:6387–6396View ArticlePubMedPubMed CentralGoogle Scholar
  2. Bollon J, Filali A, Fayolle Y, Guerin S, Rocher V, Gillot S (2016) Full-scale post denitrifying biofilters: sinks of dissolved N2O? Sci Total Environ 563–564:320–328View ArticlePubMedGoogle Scholar
  3. Chandran K, Stein LY, Klotz MG, van Loosdrecht MCM (2011) Nitrous oxide production by lithotrophic ammonia-oxidizing bacteria and implications for engineered nitrogen-removal systems. Biochem Soc Trans 39:1832–1837View ArticlePubMedGoogle Scholar
  4. Hiatt WC, Grady CPL Jr (2008) An updated process model for carbon oxidation, nitrification, and denitrification. Water Environ Res 80:2145–2156View ArticlePubMedGoogle Scholar
  5. Ikeda-Ohtsubo W, Miyahara M, Kim S, Yamada T, Matsuoka M, Watanabe A, Fushinobu S, Wakagi T, Shoun H, Miyauchi K, Endo G (2013) Bioaugmentation of a wastewater bioreactor system with the nitrous oxide-reducing denitrifier Pseudomonas stutzeri strain TR2. J Biosci Bioeng 115:37–42View ArticlePubMedGoogle Scholar
  6. IPCC 2 (2006) Wastewater treatment and discharge; 2006 IPCC Guidelines for National Greenhouse Gas Inventories, vol. 5; Japan 2006Google Scholar
  7. Koike I, Hattori A (1975) Energy yield of denitrification—Estimate from growth yield in continuous cultures of Pseudomonas denitrificans under nitrate-limited, nitrite-limited and nitrous oxide-limited conditions. J Gen Microbiol 88:11–19View ArticlePubMedGoogle Scholar
  8. Law Y, Ye L, Pan Y, Yuan Z (2012) Nitrous oxide emissions from wastewater treatment processes. Philos Trans R Soc B Biol Sci 367:1265–1277View ArticleGoogle Scholar
  9. Lu H, Chandran K (2010) Factors promoting emissions of nitrous oxide and nitric oxide from denitrifying sequencing batch reactors operated with methanol and ethanol as electron donors. Biotechnol Bioeng 106:390–398PubMedGoogle Scholar
  10. Nerenberg R, Kawagoshi Y, Rittmann BE (2006) Kinetics of a hydrogen-oxidizing, perchlorate-reducing bacterium. Water Res 40:3290–3296View ArticlePubMedGoogle Scholar
  11. Nerenberg R, Rittmann B, Najm I (2002) Perchlorate reduction in a hydrogen-based membrane-biofilm reactor. J Am Water Works Assoc 94:103–114Google Scholar
  12. Ni B, Yu H (2008) An approach for modeling two-step denitrification in activated sludge systems. Chem Eng Sci 63:1449–1459View ArticleGoogle Scholar
  13. Ni B, Ruscalleda M, Pellicer-Nacher C, Smets BF (2011) Modeling nitrous oxide production during biological nitrogen removal via nitrification and denitrification: extensions to the general ASM models. Environ Sci Technol 45:7768–7776View ArticlePubMedGoogle Scholar
  14. Ni B, Yuan Z (2015) Recent advances in mathematical modeling of nitrous oxides emissions from wastewater treatment processes. Water Res 87:336–346View ArticlePubMedGoogle Scholar
  15. Pan Y, Ye L, Yuan Z (2013a) Effect of H2S on N2O reduction and accumulation during denitrification by methanol utilizing denitrifiers. Environ Sci Technol 47:8408–8415PubMedGoogle Scholar
  16. Pan Y, Ni BJ, Lu H, Chandran K, Richardson D, Yuan Z (2015) Evaluating two concepts for the modelling of intermediates accumulation during biological denitrification in wastewater treatment. Water Res 71:21–31View ArticlePubMedGoogle Scholar
  17. Pan Y, Ni B, Yuan Z (2013b) Modeling electron competition among nitrogen oxides reduction and N2O accumulation in denitrification. Environ Sci Technol 47:11083–11091View ArticlePubMedGoogle Scholar
  18. Pan Y, Ye L, Ni B, Yuan Z (2012) Effect of pH on N2O reduction and accumulation during denitrification by methanol utilizing denitrifiers. Water Res 46:4832–4840View ArticlePubMedGoogle Scholar
  19. Pocquet M, Wu Z, Queinnec I, Sperandio M, Spérandio M (2016) A two pathway model for N2O emissions by ammonium oxidizing bacteria supported by the NO/N2O variation. Water Res 88:948–959View ArticlePubMedGoogle Scholar
  20. Ravishankara AR, Daniel JS, Portmann RW (2009) Nitrous oxide (N2O): the dominant ozone-depleting substance emitted in the 21st century. Science 326:123–125View ArticlePubMedGoogle Scholar
  21. Reichert P, von Schulthess R, Wild D (1995) The use of Aquasim for estimating parameters of activated sludge models. Water Sci Technol 31:135–147View ArticleGoogle Scholar
  22. Rittmann BE, McCarty PL (2001) Environmental biotechnology: principles and applications. McGraw-Hill Book Co, New YorkGoogle Scholar
  23. Robertson LA, Kuenen JG (1983) Thiosphaera pantotropha gen-nov pp-nov, a facultatively anaerobic, facultatively autotrophic sulfur bacterium. J Gen Microbiol 129:2847–2855Google Scholar
  24. Sabba F, Picioreanu C, Perez J, Nerenberg R (2015) Hydroxylamine diffusion can enhance N2O emissions in nitrifying biofilms: a modeling study. Environ Sci Technol 49:1486–1494View ArticlePubMedGoogle Scholar
  25. Schreiber F, Wunderlin P, Udert KM, Wells GF (2012) Nitric oxide and nitrous oxide turnover in natural and engineered microbial communities: biological pathways, chemical reactions, and novel technologies. Front Microbiol 3:372View ArticlePubMedPubMed CentralGoogle Scholar
  26. Shade A, Caporaso JG, Handelsman J, Knight R, Fierer N (2013) A meta-analysis of changes in bacterial and archaeal communities with time. ISME J 7:1493–1506View ArticlePubMedPubMed CentralGoogle Scholar
  27. Strohm TO, Griffin B, Zumft WG, Schink B (2007) Growth yields in bacterial denitrification and nitrate ammonification. Appl Environ Microbiol 73:1420–1424View ArticlePubMedPubMed CentralGoogle Scholar
  28. Tallec G, Garnier J, Billen G, Gousailles M (2008) Nitrous oxide emissions from denitrifying activated sludge of urban wastewater treatment plants, under anoxia and low oxygenation. Bioresour Technol 99:2200–2209View ArticlePubMedGoogle Scholar
  29. von Schulthess R, Wild D, Gujer W (1994) Nitric and nitrous oxides from denitrifying activated sludge at low-oxygen concentration. Water Sci Technol 30:123–132Google Scholar
  30. von Schulthess R, Kuhni M, Gujer R (1995) Release of nitric and nitrous oxides from denitrifying activated-sludge. Water Res 29:215–226View ArticleGoogle Scholar
  31. Wicht H (1996) A model for predicting nitrous oxide production during denitrification in activated sludge. Water Sci Technol 34:99–106View ArticleGoogle Scholar
  32. Wild D, von Schulthess R, Gujer W (1995) Structured modeling of denitrification intermediates. Water Sci Technol 31:45–54View ArticleGoogle Scholar
  33. Wild D, von Schulthess R, Gujer W (1994) Synthesis of denitrification enzymes in activated-sludge—modeling with structured biomass. Water Sci Technol 30:113–122Google Scholar
  34. Zumft WG, Kroneck PMH (2007) Respiratory transformation of nitrous oxide (N2O) to dinitrogen by bacteria and archaea. Adv Microb Physiol 52:107View ArticlePubMedGoogle Scholar

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