Mathematical Modeling of Nitrous Oxide (N2O) Emissions from Full

Jun 14, 2013 - Mathematical Modeling of Nitrous Oxide (N2O) Emissions from Full-Scale Wastewater Treatment Plants. Bing-Jie ... In this work, a mathem...
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Mathematical Modeling of Nitrous Oxide (N2O) Emissions from FullScale Wastewater Treatment Plants Bing-Jie Ni,† Liu Ye,†,‡ Yingyu Law,† Craig Byers,§ and Zhiguo Yuan*,† †

Advanced Water Management Centre, The University of Queensland, St. Lucia, Queensland 4072, Australia School of Chemical Engineering, The University of Queensland, Queensland, Australia § Water Corporation, Perth, Western Australia, Australia ‡

S Supporting Information *

ABSTRACT: Mathematical modeling of N2O emissions is of great importance toward understanding the whole environmental impact of wastewater treatment systems. However, information on modeling of N2O emissions from full-scale wastewater treatment plants (WWTP) is still sparse. In this work, a mathematical model based on currently known or hypothesized metabolic pathways for N2O productions by heterotrophic denitrifiers and ammonia-oxidizing bacteria (AOB) is developed and calibrated to describe the N2O emissions from fullscale WWTPs. The model described well the dynamic ammonium, nitrite, nitrate, dissolved oxygen (DO) and N2O data collected from both an open oxidation ditch (OD) system with surface aerators and a sequencing batch reactor (SBR) system with bubbling aeration. The obtained kinetic parameters for N2O production are found to be reasonable as the 95% confidence regions of the estimates are all small with mean values approximately at the center. The model is further validated with independent data sets collected from the same two WWTPs. This is the first time that mathematical modeling of N2O emissions is conducted successfully for full-scale WWTPs. While clearly showing that the NH2OH related pathways could well explain N2O production and emission in the two full-scale plants studied, the modeling results do not prove the dominance of the NH2OH pathways in these plants, nor rule out the possibility of AOB denitrification being a potentially dominating pathway in other WWTPs that are designed or operated differently.

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INTRODUCTION Nitrous oxide (N2O) can be produced and directly emitted from wastewater treatment plants (WWTPs).1,2 N2O not only is a greenhouse gas, with an approximately 300-fold warming effects compared to carbon dioxide,3 but also reacts with ozone in the stratosphere leading to ozone layer depletion.4 Recently, N2O emissions have been increasingly recognized by water utilities as a significant contributor to the carbon footprint of WWTPs. N2O is produced during biological nitrogen removal, typically attributed to autotrophic ammonia-oxidizing bacteria (AOB)5,6 and heterotrophic denitrifying organisms.6,7 According to the current understanding, N2O production by AOB can occur through two different routes: (i) N2O as the final product of AOB denitrification with NO2− as the terminal electron acceptor8,9 and (ii) N2O as a byproduct of incomplete oxidation of hydroxylamine (NH2OH) to NO2−.9−12 N2O is also a known intermediate in heterotrophic denitrification. Incomplete denitrification can lead to N2O emissions, which has been linked to several factors such as the chemical oxygen demand (COD) to N ratio, the substrate and biomass type, pH, temperature, among others.7,13 The N2O emission data collected from WWTPs to date show a huge variation in the emission factor, calculated as the fraction © XXXX American Chemical Society

of influent nitrogen load emitted as N2O. The emission factor has been found to vary in the range of 0.01−1.8%, and in some cases even higher than 10%.1,2,6,14 These variations in measured emissions strongly contrast with the fixed emission factors currently applied to estimate N2O emissions from WWTP.15,16 Mathematical modeling should be a more appropriate method for estimating site-specific emissions of N2O.17−20 Furthermore, the currently well-accepted floating chamber method to measure N2O emission from full-scale WWTPs is applicable in bubbling aeration systems, but not suitable for WWTPs with surface aerators (e.g., oxidation ditch system), which are widely used worldwide. It is expected that, in such plants, emission would mostly occur at the surface aerator, which cannot be properly captured with a gas hood. In this case, mathematical modeling could be applied to facilitate the estimation of N2O emission from the surface aerator zone. In addition, mathematical modeling provides a method for verifying hypotheses related to mechanisms for N2O emissions in Received: February 4, 2013 Revised: May 10, 2013 Accepted: June 14, 2013

A

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Figure 1. Layout of and sampling locations in the oxidation ditch in WWTP A. d1−d6 are the six sampling locations.

primary sedimentation tank followed by secondary treatment. The biological nutrient removal component of the plant comprises a circular tank that is evenly quartered into four basins. Each basin operates as a separate SBR. At the time of this study, the plant was operated on three-basin mode due to annual maintenance of the fourth basin. Each SBR cycle consisted of the following phases in sequence: 90 min continuous feeding and aeration, 35 min settling and 55 min decanting. The average exchange volume per cycle in each SBR was approximately 5 ML. Each SBR had a working volume of 28 ML, and hence the average HRT was 17 h. The total airflow to the three SBRs was fixed at 45000 m3 h−1 throughout the aeration phase with equal distribution among the three reactors. The SRT was maintained at 19 days. Data Collection and Analysis. WWTP A. A long-term sampling campaign was carried out for a period of one month (16 Oct−14 Nov, 2011). Total COD (TCOD) and total Kjeldahl nitrogen (TKN) in influent and effluent were monitored over the period through 24h composite sampling. TCOD and TKN in dewatered sludge were also determined for the same period. Other variables such as influent flow rate, aerator operational data (aerator working frequency), SRT, online DO data at d2 and d5 during this month were obtained from the plant operator. Two intensive sampling campaigns were conducted for three consecutive days (8 h) (18−20 Oct, 2011) and one day (13h) on 18th of April, 2012, respectively. The traditional floating chamber method for gas phase N2O samples is not suitable for WWTP A since N2O emission at the surface aerator cannot be properly captured with a gas hood due to the strong turbulence. Liquid phase N2O samples were collected hourly at all 6 sampling locations (d1−d6, Figure 1) into vacuum tubes and subsequently analyzed for dissolved N2O using gas chromatography (GC).23 Hourly liquid samples were taken manually during the daytime for ammonium, nitrite, nitrate, SCOD profiling for the influent and effluent, as well as along the OD (6 locations, d1−d6, Figure 1). The influent and effluent 24-h composite samples were also taken by autosamplers (hourly) for ammonium, nitrite, nitrate, and SCOD measurements. Other key process conditions in the OD, namely, temperature, pH, DO at each sampling location were also monitored. WWTP B. Long-term gas phase N2O monitoring was carried out on one of the three identically operated SBRs for a period of one month (13 April−13 May). The gas sample was captured using a sampling hood, with a sampling area of 0.04 m2, which was connected to a remote online infrared analyzer

WWTPs, and also acts as a tool to support the development of mitigation strategies.19 Even though mathematical modeling of N2O emissions is highly important, little effort has been dedicated to modeling N2O production and emissions from full-scale WWTPs. Yu et al.21 used a multiplicative Monod-model to link the specific metabolic activity of a Nitrosomonas europaea culture with N2O emission and found that its activity was related to the N2O profile. This approach was very successful, but its validity to describe N2O dynamics in mixed culture systems has not been tested. Hiatt and Grady22 incorporated four-step denitrification (sequential reduction of NO3− to N2 via NO2−, NO, and N2O) into a comprehensive activated sludge process model. However, calibration of their model parameters was not performed, and AOB mediated N2O production was ignored. Most recently, Ni et al.19 reviewed and examined four mathematical model structures describing different mechanisms of N2O production by AOB; none of these models have been applied to predicting N2O emission data from full-scale WWTPs. In this work, we aim to develop and calibrate a mathematical model based on the state of the knowledge regarding N2O production by both AOB and heterotrophic denitrifiers to describe N2O emissions from full-scale WWTPs for the first time. The validity and applicability of the established model is tested by comparing simulation results with extensive monitoring data of N2O and other water quality parameters from two different full-scale WWTPs in Australia: an open oxidation ditch system with surface aerators and a sequencing batch reactor system with bubbling aeration.

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MATERIALS AND METHODS Full-Scale Wastewater Treatment Plants. WWTP A, located in Perth, Western Australia, is an Oxidation Ditch (OD) system with surface aerators, which receives domestic wastewater at approximately 4 mega liter (ML) per day (see Figure 1 for configuration). The plant consists of primary clarifier and an activated sludge system. After primary sedimentation, wastewater is introduced into the activated sludge unit, which is an oxidation ditch with a working volume of 8750 m3. The average hydraulic retention time (HRT) in the oxidation ditch is approximately 48 h. The mixed liquor from the oxidation ditch flows into a secondary settler. The solids retention time (SRT) is approximately 10 d. WWTP B, also located in Perth, Western Australia, is a sequencing batch reactor (SBR) system. The average daily flow of the plant is 120 ML. The plant is commissioned with a B

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generation by AOB.9,11,12 Thus, N2O production from AOB denitrification is not considered in this model. The metabolism for NOB in the reaction model is as described by Hao et al.,25 which considered both the growth and decay processes (processes 6−7 in SI Tables S1−S2). The commonly used “death−regeneration” approach was used to model heterotrophic growth utilizing decay-produced products. With this approach, biomass decay is assumed to produce slowly biodegradable substances (Xs), which is then hydrolyzed (process 8) to readily biodegradable substances (Ss) that feed heterotrophic growth. Besides aerobic heterotrophic growth (Process 9), the model considers denitrification as a four-step process consisting of the sequential reduction of nitrate, nitrite, NO, and N2O (processes 10−13), with the kinetics of each step modeled independently and as a function of the activity of the respective enzyme and the substrates required (electron donor and acceptor), following the activated sludge model for nitrogen (ASMN) proposed by Hiatt and Grady.22 Model Calibration, Uncertainty Analysis, and Model Validation. The model was calibrated using the extensive monitoring data from the three-day sampling campaigns at WWTP A (18−20 Oct, 2011). The three key parameters for AOB the N2O production processes by AOB (μHAO AOB , KS2,O2, and ηAOB, see SI Table S3) were estimated by using the secant method embedded in AQUASIM.26 All other stoichiometric and kinetic parameters, given in SI Table S3, were taken or adapted directly from the literature. The oxygen volumetric transfer coefficients at different locations along the OD were experimentally determined (details can be found elsewhere, Ye et al.27). The vertical stratification profiles (every 50 cm below the surface along the depth) of dissolved oxygen at different locations along the OD were also measured experimentally, which were applied for model simulations of the OD system. The parameter estimation and parameter uncertainty evaluation were done according to Batstone et al.,28 with a 95% confidence level for significance testing and parameter uncertainty analysis. The standard errors and 95% confidence intervals of individual parameter estimates were calculated from the mean square fitting errors and the sensitivity of the model to the parameters. The determined F-values were used for HAO , KSAOB , and ηAOB) and parameter combinations (i.e.,μAOB 2,O2 degrees of freedom in all cases. This finds a true multidimensional parameter surface by surface searching (to a critical objective function value), rather than parameter sampling. Residual sum of squares (RSS) between the objective data and model was used as the objective function. A modified version of AQUASIM 2.1d was used to determine the parameter surfaces.29 The validation step was then carried out with the calibrated model parameters by the other set of the monitoring data from sampling campaign at WWTP A on 18 April, 2012, with different inflow conditions which has not been used to estimate the parameters. To further verify the validity and applicability of the model, we also applied the model to evaluate the monitoring data collected from WWTP B during the two-day intensive sampling campaign on 5 and 6 May, 2011. At the first place, the values AOB for parameters μHAO AOB , KS2,O2, and ηAOB were adjusted until the predicted results match those measured data on fifth May 2011. As a validation, the resulting parameter values were used to generate additional sets of simulation data for comparison with

(N-Tox, Water Innovate). The analyzer has an internal sampling pump with a flow rate of 1L/min. N2O concentration was automatically recorded every minute. As the SBR has a variable volume, a hardware system was created that allowed the hood to move up and down with the liquid level. The final setup included floating polystyrene balls attached to the hoods to provide increased stability against water turbulence and fluctuating water levels. Intensive liquid phase sampling was conducted for two consecutive days from fifth- sixth May, 2011. Two refrigerated autosamplers were used to collect influent and effluent samples of the basin hourly for ammonium, nitrite, nitrate, solids, and SCOD analysis. In the basin, liquid grab samples were collected at a location close to the gas hood location at an interval of 0.5 to 1 h for an 8 h period during daytime. The pH, temperature, and DO readings were automatically logged every 5 min. A mixed liquor sample was collected daily for suspended solids analysis. Physical data, including the plant configuration, wastewater and aeration flows, SRT were obtained from the plant operator. COD, TKN, mixed liquor suspended solids (MLSS), and its volatile components (MLVSS) were analyzed according to Standard Methods.24 The ammonium (NH4+), nitrate (NO3−) and nitrite (NO2−) concentrations were analyzed using Lachat QuickChem8000 Flow Injection Analyzer (Lachat Instrument, Milwaukee, USA). Mathematical Model for N2O Production. The kinetics and stoichiometry of the N2O model used are summarized in Tables S1−S2 in Supporting Information (SI). The reaction model describes the relationships among the five particulate species, namely AOB (with concentration denoted as XAOB), NOB (XNOB), heterotrophs (XH), slowly biodegradable COD (XS) and residual inert biomass (XI), nine soluble species, namely, ammonium (SNH4), NH2OH (SNH2OH), nitrite (SNO2), nitrate (SNO3), NO (SNO), N2O (SN2O), N2 (SN2), and readily biodegradable COD (SS) and DO (SO2). Table S3 in SI shows the definitions, values, and units of all parameters used in the developed model. The model for N2O production by AOB is developed based on the assumption that N2O is formed through the biological reduction of NO that is formed as an intermediate of NH2OH oxidation, that is, an NH2OH-related pathway.11,20 In this mechanistic model for AOB (processes 1−4 in SI Tables S1− S2), ammonium is first oxidized to NH2OH (process 1), and then NH2OH is further oxidized to NO (process 2) and nitrite (process 3) sequentially, with NO as an intermediate. N2O is produced by AOB during biological NO reduction, with NH2OH as the electron donor (Process 4). Here, NO reduction is catalyzed by nitric oxide reductase (Nor).11 It is assumed that DO has no inhibitory effect on NO reduction in this model.21 To describe a lower specific reaction rate of AOB when using NO as electron acceptor (in comparison with oxygen), the model employs an anoxic correction factor (ηAOB) in the Monod-expression describing NO reduction (rate expression of process 4). Although the actual mechanisms are still under debate, N2O production by the AOB denitrification pathway appears to occur at low DO concentrations (generally lower than 1.0 mg O2 L−1).6 Since most of the N2O emissions (over 90% of the emissions) were observed under relatively high DO conditions (higher than 2 mg O2 L−1) at both WWTPs in this work, it is assumed that the NH2OH-related pathway rather than AOB denitrification could be the dominant contributor to N2O C

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Figure 2. Model calibration results based on the three-day N2O production data from WWTP A collected on 18−20 October 2011 (real data, symbols; model predictions, lines): ammonium, nitrite, nitrate, liquid phase N2O, and DO profiles at the sampling location d4 (A−C), d5 (D−F), and d2 (G−I).

results suggest that AOB likely played an important role in the N2O production. Indeed various studies have shown that AOB are key contributors to N2O in WWTPs.1,31 The increasing N2O production corresponded with an increase in ammonium concentrations without concurrent increase in nitrite concentration, indicative of an ammonia oxidation related pathway by AOB (i.e., NH 2 OH pathways) instead of the AOB denitrification pathway (low DO and high nitrite conditions required) in WWTP A. AOB is also likely responsible for the relatively high N2O emissions for WWTP B (Figure 4). Because of the three SBR basin operation (instead of four basins), the SBR was clearly overloaded. The N profiles for the monitored basin shows an accumulation of ammonium in the reactor. The constant availability of high ammonium supply resulted in higher biomass specific ammonia oxidation activity. This promotes the production of byproducts such as NH2OH from AOB, which may result in increased N2O production. The N2O emission factor for WWTP B was determined to vary between 1.0% and 1.5% of the nitrogen load to the treatment plant. The N2O emission of the plant is expected to decrease when the four-basin operation is restored. Model Calibration. Since AOB may play a major role in the N2O production observed, model calibration of this work involved adjusting key parameter values for the N2O production processes by AOB so that the ammonium, nitrite, nitrate, liquid phase N2O, and DO profiles produced by the

the measured data on 6 May 2011 at this WWTP. These extensive evaluations could strongly support the validity and applicability of the developed N2O model in this work.

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RESULTS N2O Emissions from WWTPs. Figure 2 shows the ammonium, nitrite, nitrate, liquid-phase N2O, and DO profiles at the sampling location d4 (A−C), d5 (D−F), and d2 (G−I) in the oxidation ditch of WWTP A collected on 18−20 October 2011. The three-day 8-h intensive sampling results clearly showed that liquid phase N2O accumulated with increased ammonium concentrations at d4 and d5, where oxygen was present. No obvious N2O accumulation was observed at d1−d3 in anoxic zones. In addition, increased N2O emission at the different locations was observed to coincide with the increase in the DO concentrations (Figures 2 and 3). These results suggest that N2O production in this oxidation ditch system mainly occurred in aerobic zones and could be correlated to ammonium and DO concentrations. These observations were also consistent with the monitoring results from WWTP B (Figure 4), in which N2O emissions also occurred mainly during aerated periods with high ammonium concentrations. However, this is contrasting with several other studies that have reported increased N 2 O production at decreased DO concentration.5,30 Although it is not possible to determine the exact mechanism responsible for the observed N2O production trend, these D

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Figure 3. Model validation results using the one-day data from WWTP A collected on 18th April 2012 (real data, symbols; model predictions, lines): ammonium, nitrite, nitrate, liquid-phase N2O, and DO profiles at the sampling location d4 (A−C); d5 (D−F), and d2 (G−I).

systems obtained in this study are also comparable to those reported by Ni et al.19 Model Parameter Uncertainty Analysis. Parameter uncertainty analysis of a model structure is important as it informs which parameter combinations can be estimated under given measurement accuracy and quantity. In the uncertainty analysis, parameter surface of the objective function for the degrees of correlation between the two parameters (μHAO AOB vs HAO KAOB S2,O2 and μAOB vs ηAOB) were evaluated. The parameter surfaces in Figure S1 (in SI) were calculated around the optimum for different combinations of parameters. SI Figure S1 shows that both parameter surfaces that bound the 95% confidence regions for the two parameter combinations (μHAO AOB HAO vs KAOB S2,O2 and μAOB vs ηAOB). The maximum HAO reaction rate −1 of AOB (μHAO AOB ) was generally between 0.04 and 0.14 h , with −1 narrow confidence regions (0.06−0.11 h ). The oxygen affinity constant for NH2OH oxidation (KAOB S2,O2) varied in the range 0.04−0.12 g DO m−3, with relative small confidence intervals (0.05−0.09 g DO m−3) (SI Figure S1A). In contrast, the anoxic reduction factor for NO reduction by AOB (ηAOB) was between 0.2 and 0.4, with confidence intervals of 0.2−0.35 (SI Figure S1B). Overall, the 95% confidence regions for the AOB parameters μHAO AOB , KS2,O2, and ηAOB were bounded by small ellipsoids having mean value for the parameter estimates approximately at the center, indicating a good identifiability of these three estimated parameters. In addition to the analysis of the parameter surfaces, two other aspects of our experimental design support the identifiability of the obtained parameter values. First, we used 5 different experimental parameters (ammonium, nitrite, nitrate, N2O, and DO) at different sampling locations, which

model closely agreed with the measured data at the different sampling locations of WWTP A. The three key parameters AOB (i.e.,μHAO AOB , KS2,O2, and ηAOB), the values of which govern the N2O production by AOB in this model, were estimated by fitting simulation results to the monitoring data. The good agreement between model simulations and measured ammonium, nitrite, nitrate, liquid phase N2O, and DO data (Figure 2) supported that the developed model properly captures the relationships among ammonium utilization, N2O production, and DO consumption. In this work, the experimental data used for model calibration did not contain NH2OH and NO data, which are difficult to measure but expected to be very low. Hence, NH2OH and NO formation were modeled with parameter values taken from literature, resulting in trace concentrations of NH2OH and NO throughout our simulations. The NH2OH and NO dynamics, if can be measured in future studies, would provide further support to the model. HAO , KSAOB , and ηAOB) giving the Parameter values (μAOB 2,O 2 optimum model fit with the experimental data are listed in SI Table S3, together with their standard errors. The retrieved parameter values appear realistic. μHAO AOB is of the same order of magnitude and commensurate with μAMO AOB . The calibrated ηAOB value of 0.285 indicates much lower AOB activity for NO reduction, consistent with Yu et al.21 The KAOB S2,O2 (0.073 g DO m−3) is much smaller than KAOB (0.4 g DO m−3). This S1,O2 difference reflects the different oxygen affinity of ammonium oxidation vs NH2OH oxidation. O2 is incorporated as a substrate in the formation of NH2OH during ammonium oxidation, while NH2OH oxidation uses O2 as terminal electron acceptor. The model parameter values for full scale WWTP E

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Figure 4. Model evaluation results of the two-day N2O production data from WWTP B collected on 5 and 6 May, 2011 (real data, symbols; model predictions, lines): ammonium, nitrite, nitrate, N2O emission rate, and DO profiles: (A−C) data from 5 May, 2011, and (D−F) data from 6 May, 2011.

validity of the developed N2O model for full scale WWTP modeling. To further validate the N2O model for full scale WWTP modeling, experimental results of N2O production data from an independent WWTP (WWTP B) collected on 5 and 6 May 2011 were also used to evaluate the developed model. The measured effluent ammonium, nitrite, nitrate concentrations, N2O emission rate and DO profiles of the SBR were used for model evaluation in this work. The experimental and simulated results of the profiles are shown in Figure 4 (dot and line). The AOB values for parametersμHAO AOB , KS2,O2, and ηAOB were first calibrated using the monitoring data on 5 May 2011 (Figure 4A−C). The AOB obtained parameter values of μHAO AOB , KS2,O2, and ηAOB giving the optimum model calibration with the measured data from WWTP B are 0.091 h−1, 0.058 g DO m−3, and 0.337, respectively. These values are comparable with those for the WWTP A. As can be seen in Figure 4A−C, agreement between simulated and measured results was good for all fitted variables. N2O production rate increased with the increase of DO concentration during the three SBR cycles, indicating that DO is an important factor causing N2O production in the SBR

reflect different aspects of the kinetics of the N2O production in full scale WWTP. Second, we carried out independent experiments to validate the estimated parameters (see following section). In particular, the good correspondence for independent experimental data supports the validity of the developed model and the identifiability of the parameter estimations. Model Validation. Model and parameter validation was based on the comparison between the model predictions and the monitoring data collected during a different measurement campaign on WWTP A and from WWTP B. The model and its parameters (the same N2O production parameters shown in SI Table S3) were first evaluated with the one-day data of WWTP A collected on 18 April 2012. The validation data had different input conditions from the calibration data: different inflow rate and wastewater composition. The validation results in Figure 3 show that the model predictions match the measured data in terms of ammonium, nitrite, nitrate, liquid phase N2O, and DO profiles at the three sampling locations for the validation experiments. Furthermore, the model shows no systematic deviations. The good correspondence for independent data set supports the F

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complicate model calibration and validation, likely resulting in much higher uncertainties with parameter estimates. Eventually, a truly unified N2O model considering both the nitrifier denitrification and NH2OH pathways, and indeed any new pathways as they are revealed, could and should be established to predict N2O production under varying operational conditions, with their relative contributions regulated by environmental conditions and also by intracellular electron transfer processes. Intracellular electron carriers could be included into the model to describe the kinetic competition between the two pathways, with NH2OH oxidation donating electrons to these carriers and NH3 oxidation (to NH2OH) and oxygen and nitrogen oxides reduction consuming electrons from these carriers. Such a mechanistic model, when integrated with a set of other powerful tools such as molecular approaches,21 isotopic signature studies32 and measurements of reaction intermediates will help reveal the detailed mechanisms of N2O production. Implications to Quantifying N2O Emissions from WWTPs. The possibility of modeling N2O emissions from full-scale WWTPs is an important step toward understanding the full environmental impact of wastewater treatment systems. The ability to predict N2O production by modeling provides an opportunity to include N2O production as an important consideration in the design, operation and optimization of biological nitrogen removal processes. As demonstrated in this work, the developed N2O model could serve as a quantitative predictor of N2O emissions in the two full-scale biological nitrogen removal WWTPs investigated under the present operational conditions. This model is attractive for the complex N2O production processes in full-scale WWTPs because its parameters can be identified relatively easily with small uncertainties (only 3 N2O-related parameters have to be calibrated). Furthermore, its continued testing and verification against experimental data will serve to confirm the dominating mechanism of N2O emission across full-scale WWTPs. It should be noted that the proposed model of this study may not necessarily be able to describe all experimental data in all WWTPs. As discussed above, the model has been proposed based on the NH2OH pathway, while the N2O production mechanisms by AOB are complex and likely involve both AOB denitrification and NH2OH pathways operational under different conditions. 9 As demonstrated in previous work,6,8,9,18,19,21 DO, ammonium and nitrite concentrations could influence N2O production and the production pathways. It is possible that the high NH4+ and DO concentrations with low NO2− accumulation (see Figures 2−4) for both WWTPs studied in this work could have stimulated the NH2OH pathway. The applicability of this model to other WWTPs operated under different conditions (e.g., low NH4+ and DO with high NO2− accumulation) remains to be investigated. It is possible that a different model, for example one based on the nitrifier denitrification pathway, may be more suitable for other plants. Nevertheless, this work represents a significant step forward toward the model-based prediction of N2O production and emission in full-scale WWTPs. The current online N2O emission measurements in WWTPs mainly rely on the floating chamber method,1 which has been well applied in bubbling aeration systems with less turbulence. However, this method is not valid for WWTPs with surface aerators (e.g., the OD system at WWTP A) as N2O emission would mostly occur at the surface aerator, which cannot be properly captured with a gas hood (because of the strong

system (Figure 4B and C). Our model captures all these trends and the effluent ammonium, nitrite, nitrate dynamics (Figure 4A), suggesting that this model is able to describe the presented N2O production data from WWTP B of this work. The validation of the resulting parameter values were further performed through comparison between model predictions with the measured data on 6 May 2011 at WWTP B (Figure 4D−F), again showing good correspondence to the monitoring data.

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DISCUSSION N2O Models with Different Metabolic Pathways. Chandran et al.9 summarized that both the AOB denitrification and NH2OH pathways could be involved in N2O production by AOB. The two pathways are likely differently affected by DO concentrations. It is however unclear under what conditions each of the pathways will become the dominating pathway. Based on the two pathways, several mechanistic models have been proposed for N2O production by AOB. Ni et al.18 developed a N2O model based on the AOB denitrification pathway (called the AOB denitrification model here), in which oxygen is assumed to inhibit nitrite reduction by AOB to ensure an increase in N2O production under decreased DO conditions. The N2O model proposed by Law et al.12 assumes that N2O production is due to the chemical decomposition of the unstable NOH, an intermediate of NH2OH oxidation (called the NH2OH/NOH model here). In contrast, the model established in this work assumes biological reduction of NO, generated from the NH2OH oxidation, produces N2O (called the NH2OH/NO model here). Both NH2OH/NOH model and NH2OH/NO model could describe the increased N2O production by AOB under increased DO conditions.19 We performed additional simulation studies using the other two N2O models (the AOB denitrification model and the NH2OH/NOH model) to evaluate the experimentally observed N2O data from the two full-scale WWTPs. The results indicated that the AOB denitrification model could not predict the N2O data from either WWTP. Indeed, the AOB denitrification model predicted a trend of the N2O production dependency on DO that is opposite to that experimentally observed at the two full-scale WWTPs. The NH2OH/NOH model, on the contrary, obtained very similar fit between the model-predicted and experimentally observed N2O data. Therefore, for modeling the NH2OH oxidation pathway, it is yet to be fully clarified if NO or NOH are the direct source of N2O. Indeed, it cannot be excluded that N2O is formed from all these compounds. We also performed further simulations using the heterotrophic denitrification pathway solely (by switching off the N2O production kinetics from NH2OH pathway in our model) to evaluate the experimentally observed N2O data. The results showed that the heterotrophic denitrification pathway alone could not reproduce the N2O data from the WWTP. In this work, we applied the NH2OH related pathway to describe N2O production in full-scale WWTPs based on the monitoring results. However, the choice does not exclude N2O production by AOB denitrification as a potential pathway in full-scale WWTPs, and ongoing investigations may reveal conditions under which this alternative pathway becomes important. Although Ni et al.19 suggested to include both the AOB denitrification and NH2OH oxidation pathways in an N2O model, the simple combination of these pathways would yield a model that is complex and potentially overparameterized with strong parameter correlations. This would significantly G

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turbulence). Therefore, the overall N2O emission factor would be underestimated significantly if the emission factor is determined solely based on N2O captured by the gas hood. In this case, model predictions could be used as an important complementary tool for the floating chamber measurements to facilitate the calculation of the overall N2O emissions from the surface aerator zone. As a demonstration, Figure S2 in SI shows the model predicted N2O production data in WWTP A from 18 October to 13 November 2011 at the sampling location d4 (the surface aerator point) and d5. On the basis of these predictions, the determined N2O emission factor at WWTP A over a full month would be 0.52 ± 0.16% of the nitrogen load to the treatment plant. The detail calculation methodology for the N2O emission factor by using the model prediction data can be found elsewhere.27 It is much higher (over 10 times) than the value reported by the U.S. study1 for similar plant determined solely based on floating chamber measurement. It should be noted that other factors such as different wastewater compositions and plant operational conditions could have also contributed to the difference. In fact, more than 90% of the N2O emission occurred at the surface aerator zone. This suggests that the developed N2O model could be used as quantitative predictor leading to estimate of N2O emissions. In summary, a mathematical model based on the NH2OHrelated pathway for N2O production by AOB and the four-step heterotrophic denitrification is developed to predict N2O emissions from two full-scale WWTPs for the first time in this work. The validity and applicability of the model is confirmed by independent N2O emission data from two different full-scale WWTPs. The results demonstrated that model prediction of N2O emissions from full-scale WWTPs is possible and the developed N2O model could be a useful tool for quantifying N2O emissions. The NH2OH related pathways could play important roles on N2O production under fully aerobic conditions in full-scale WWTPs. In addition, the model allows for successful simulation of N2O emissions from the surface aerator zone in OD WWTP, which could correct the underestimation of the N2O emissions in the full-scale WWTPs where floating chamber method is not valid.

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ASSOCIATED CONTENT

S Supporting Information *

Additional tables and figures are shown in Supporting Information. This information is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone + 61 7 3365 4374; fax +61 7 3365 4726; e-mail [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was funded by the Australian Research Council, Western Australia Water Corporation and Melbourne Water Corporation through project LP0991765 and DP0987204. B.J.N. acknowledges the supports of Australian Research Council Discovery Early Career Researcher Award (DE130100451) and The University of Queensland (UQ) Postdoctoral Fellowship. H

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