Development of an Extended ASM3 Model for Predicting the Nitrous Oxide Emissions in a Full-Scale Wastewater Treatment Plant

Direct and indirect monitoring methods for nitrous oxide emissions in full-scale wastewater treatment plants: A critical review

Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns.

Limitations of a biokinetic model to predict the seasonal variations of nitrous oxide emissions from a full-scale wastewater treatment plant

A biokinetic model based on BioWin's Activated Sludge Digestion Model (ASDM) coupled with a nitrous oxide (N2O) model was setup and calibrated for a full-scale wastewater treatment plant (WWTP) Amsterdam West, in the Netherlands. The model was calibrated using one year of continuous data to predict the seasonal variations of N2O emissions in the gaseous phase. This, according to our best knowledge, is the most complete full-scale data set used to date for this purpose. The results obtained suggest that the currently available biokinetic model predicted the winter, summer, and autumn N2O emissions well but failed to satisfactorily simulate the spring peak. During the calibration process, it was found that the nitrifier denitrification pathway could explain the observed emissions during all seasons while a combination of the nitrifier denitrification and incomplete heterotrophic denitrification pathways seemed to be dominant during the emissions peak observed during the spring season. Specifically, kinetic parameters related to free nitrous acid (FNA) displayed significant sensitivity leading to increased N2O production. The obtained values of two kinetic parameters, i.e., the FNA half-saturation during ammonia oxidising bacteria (AOB) denitrification and the FNA inhibition concentration related to heterotrophic denitrification, suggested a strong influence of the FNA bulk concentration on the N2O emissions and the observed seasonal variations. Based on the suboptimal performance and limitations of the biokinetic model, further research is needed to better understand the biochemical processes behind the seasonal peak and the influence of FNA.

Seasonality of nitrous oxide emissions at six full-scale wastewater treatment plants

Nitrous oxide (N2O) is an ozone-depleting greenhouse gas that contributes significantly to the carbon footprint of a wastewater treatment plant (WWTP). Plant-specific measurement campaigns are required to reliably quantify the emission level that has been found to significantly vary between WWTPs. In this study, the N2O emissions were quantified from five full-scale WWTPs during 4-19-day measurement campaigns conducted under both cold period conditions (water temperature below 12 °C) and warm period conditions (water temperature from 12 to 20 °C). The measurement data were studied alongside long-term monitoring data from a sixth WWTP. The calculated emission factors (EFs) varied from near 0 to 1.8% relative to the influent total nitrogen load. The results confirmed a significant seasonality of N2O emissions as well as a notable variation between WWTPs in the emission level, which a single fixed EF cannot represent. Wastewater temperature was one explanatory factor for the emission seasonality. Both low and high emissions were measured from denitrifying-nitrifying activated sludge (AS) processes, while the emissions from only nitrifying AS processes were consistently high. Nitrite (NO2-) at the end of the aerobic zones of the AS process was linked to the variability in N2O emissions during the cold period.

Modelling greenhouse gas emissions from biological wastewater treatment by GPS-X: The full-scale case study of Corleone (Italy)

Greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs) can affect climate change and must be measured and reduced. Mathematical modelling is an attractive solution to get a tool for GHG mitigation. However, although many efforts have been made to create reliable tools that can simulate "sustainable" full-scale WWTP operation, these studies are not considered complete enough to include GHG emissions and energy consumption of biological processes under long-term dynamic conditions. In this study, activated sludge model no. 1 (ASM1) was modified to model nitrous oxide (N2O) emissions with a plant-wide modelling approach. The model is novel compared to the state of the art since it includes three steps denitrification, all N2O production pathways and its stripping in an ASM1. The model has been calibrated and validated through long-term water quality and short-term N2O emissions data collected from Corleone (Italy) WWTP. Different dissolved oxygen (DO) concentrations and return sludge (RAS) ratios were tested with dynamic simulations to optimise the full-scale WWTP. The scenarios have been compared synergistically with effluent quality, direct GHG emissions, and energy footprint by the water-energy‑carbon coupling index (WECCI). This modelling study is novel as it fully covers long-term calibration/validation of the model with N2O measurements and tests the dynamic optimisation. Decision-makers and operators can use this new model to optimise GHG emissions and treatment costs.

Automatic control and optimal operation for greenhouse gas mitigation in sustainable wastewater treatment plants: A review

In order to promote low-carbon sustainable operational management of the wastewater treatment plants (WWTPs), automatic control and optimal operation technologies, which devote to improving effluent quality, operational costs and greenhouse gas (GHG) emissions, have flourished in recent years. There is no consensus on the design procedure for optimal control/operation of sustainable WWTPs. In this review, we summarize recent researches on developing control and optimization strategies for GHG mitigation in WWTPs. Faced with the fact that direct carbon dioxide (CO₂) emissions (considered biological origin) are generally not included in the carbon footprint of WWTPs, direct emissions (nitrous oxide (N₂O), methane (CH₄)) and indirect emissions are paid much attention. Firstly, the plant-wide models with GHG dynamic simulation, which are employed to design and evaluate the automatic control schemes as well as representative studies on identifying key factors affecting GHG emissions or comprehensive performance are outlined. Then, both traditional and advanced control methods commonly used in GHG mitigation are reviewed in detail, followed by the multi-objective optimization practices of control/operational parameters. Based on the mentioned control and (or) optimization strategies, a novel design framework for the optimal control/operation of sustainable WWTPs is proposed. The findings and design framework proposed in the paper will provide guidance for GHG mitigation and sustainable operation in WWTPs. It is foreseeable that more accurate and appropriate plant-wide models together with flexible control methods and intelligent optimization strategies will be developed to satisfy the upgrading requirements of WWTPs in the future.

Thermodynamic Analysis of Intermediary Metabolic Steps and Nitrous Oxide Production by Ammonium-Oxidizing Bacteria

Nitrous oxide (N₂O) is a greenhouse gas emitted from wastewater treatment, soils, and agriculture largely by ammonium-oxidizing bacteria (AOB). While AOB are characterized by being aerobes that oxidize ammonium (NH₄⁺) to nitrite (NO₂^-), fundamental studies in microbiology are revealing the importance of metabolic intermediates and reactions that can lead to the production of N₂O. These findings about the metabolic pathways for AOB were integrated with thermodynamic electron-equivalents modeling (TEEM) to estimate kinetic and stoichiometric parameters for each of the AOB's nitrogen (N)-oxidation and -reduction reactions. The TEEM analysis shows that hydroxylamine (NH₂OH) oxidation to nitroxyl (HNO) is the most energetically efficient means for the AOB to provide electrons for ammonium monooxygenation, while oxidations of HNO to nitric oxide (NO) and NO to NO₂^- are energetically favorable for respiration and biomass synthesis. The respiratory electron acceptor can be O₂ or NO, and both have similar energetics. The TEEM-predicted value for biomass yield, maximum-specific rate of NH₄⁺ utilization, and maximum specific growth rate are consistent with empirical observations. NO reduction to N₂O is thermodynamically favorable for respiration and biomass synthesis, but the need for O₂ as a reactant in ammonium monooxygenation likely precludes NO reduction to N₂O from becoming the major pathway for respiration.

Nitrous Oxide Emission from Full-Scale Anammox-Driven Wastewater Treatment Systems

Wastewater treatment plants (WWTPs) are important contributors to global greenhouse gas (GHG) emissions, partly due to their huge emission of nitrous oxide (N₂O), which has a global warming potential of 298 CO₂ equivalents. Anaerobic ammonium-oxidizing (anammox) bacteria provide a shortcut in the nitrogen removal pathway by directly transforming ammonium and nitrite to nitrogen gas (N₂). Due to its energy efficiency, the anammox-driven treatment has been applied worldwide for the removal of inorganic nitrogen from ammonium-rich wastewater. Although direct evidence of the metabolic production of N₂O by anammox bacteria is lacking, the microorganisms coexisting in anammox-driven WWTPs could produce a considerable amount of N₂O and hence affect the sustainability of wastewater treatment. Thus, N₂O emission is still one of the downsides of anammox-driven wastewater treatment, and efforts are required to understand the mechanisms of N₂O emission from anammox-driven WWTPs using different nitrogen removal strategies and develop effective mitigation strategies. Here, three main N₂O production processes, namely, hydroxylamine oxidation, nitrifier denitrification, and heterotrophic denitrification, and the unique N₂O consumption process termed nosZ-dominated N₂O degradation, occurring in anammox-driven wastewater treatment systems, are summarized and discussed. The key factors influencing N₂O emission and mitigation strategies are discussed in detail, and areas in which further research is urgently required are identified.

Hybrid differential equations: Integrating mechanistic and data-driven techniques for modelling of water systems

Mathematical modelling is increasingly used to improve the design, understanding, and operation of water systems. Two modelling paradigms, i.e., mechanistic and data-driven modelling, are dominant in the water sector, both with their advantages and drawbacks. Hybrid modelling aims to combine the strengths of both paradigms. Here, we introduce a novel framework that incorporates a data-driven component into an existing activated sludge model of a water resource recovery facility. In contrast to previous efforts, we tightly integrate both models by incorporating a neural differential equation into an existing mechanistic ODE model. This machine learning component fills in the knowledge gaps of the mechanistic model. We show that this approach improves the predictive capabilities of the mechanistic model and is able to extrapolate to unseen conditions, a problematic task for data-driven models. This approach holds tremendous potential for systems that are difficult to model using the mechanistic paradigm only.

Enrichment of a denitratating microbial community through kinetic limitation

Denitratation, or the intentionally engineered accumulation of nitrite (NO₂^-) from selective reduction of nitrate (NO₃^-), can be combined with downstream anammox to reduce chemical and energy use associated with conventional nitrification and denitrification. This study aimed to enrich a denitratating microbial community capable of significant NO₂^- accumulation by applying added kinetic limitation to an already stoichiometrically-limited, glycerol-driven denitratation process. Operation at solids residence time, SRT=3.0 d, resulted in optimal denitratation performance and a microbial community dominated by NO₃^--respirers, noted by one order of magnitude lower total copy numbers of nirS and nirK gene transcripts compared to longer SRTs. Selective NO₃^- reduction to NO₂^- was achieved at all SRTs although longer SRTs (less kinetic limitation) supported microbial communities more capable of full denitrification as described by a lower NO₂^- accumulation ratio (NAR=42±5%) and higher steady-state nitrous oxide (1.5 mg/L N₂O-N) accumulation. Shorter SRTs (more kinetic limitation) led to higher observed yields (Y=0.63 mg-COD/mg-COD) with more electrons dedicated for cell synthesis (f_s=0.56±0.10), which potentially contributed to the accumulation of NO₃^-. Enrichment of a denitratating-dominant microbial community by optimizing kinetic limitation operating parameters could support significant NO₂^- accumulation and reduce chemical and energy use for biological nitrogen removal when combined with downstream anammox.

An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants

Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (N₂O) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep learning techniques to predict N₂O emissions. The hybrid model was successfully implemented and validated with the N₂O emission data from a full-scale WWTP. This hybrid model is demonstrated to have higher accuracy for N₂O emission modeling in the WWTP than the mechanistic model or pure deep learning model. Equally important, the hybrid model is more applicable than the pure deep learning model due to the lower requirement of data and the pure mechanistic model due to the less calibration requirement. This superior performance was due to the hybrid nature of the proposed model. It integrated the essential wastewater treatment knowledge as the first principal component and the less understood N₂O production processes by the data-driven deep learning approach. The developed hybrid model was also successfully implemented under different circumstances for the prediction of N₂O flux, which showed the generalizability of the model. The hybrid model also showed great potential to be applied for the N₂O mitigation work. Nevertheless, the capability of the hybrid model in evaluating N₂O mitigation strategies still requires validation with experiments. Going beyond N₂O modeling in WWTP, the novel hybridization modeling concept can potentially be applied to other environmental systems.

Greenhouse gas emission estimation from municipal wastewater using a hybrid approach of generative adversarial network and data-driven modelling

Greenhouse gas (GHG) emissions including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) created via wastewater treatment processes are not easily modeled given the non-linearity and complexity of biological processes. These factors are also impacted by limited data availability making the development of artificial data generation algorithms, such as a generative adversarial network (GAN), useful for determination of GHG emission rate estimates (EREs). The main objective of this study was to develop a hybrid approach of using GAN and regression modelling to determine GHG EREs from a cold-region biological nutrient removal (BNR) municipal wastewater treatment plant (MWTP) in which the aerobic reactor has previously been established as the main GHG emission source. To our knowledge, this is the first application of GAN used for MWTP modelling purposes. The EREs were generated from laboratory-scale reactors used in conjunction with facility-monitored operating parameters to develop the GAN and regression models. Results showed that regression models provided reasonable EREs using parameters including hydraulic retention time (HRT), temperature, total organic carbon, and dissolved oxygen (DO) concentrations for CO₂ EREs; HRT, temperature, DO and phosphate (PO₄^3-) concentrations for CH₄ EREs; and temperature, DO, and nitrogen (nitrite, nitrate, and ammonium) concentrations for N₂O EREs. Additionally, the addition of 100 GAN-created virtual data points improved regression model metrics including increased correlation coefficient and index agreement values, and decreased root mean square error values. Clearly, virtual data augmentation using GAN is a valuable resource in supplementation of limited data for improved modelling outcomes. Genetic algorithm optimization was also used to determine operating parameter modifications resulting in potential for minimization (or maximization) of GHG emissions.

Insights into Nitrous Oxide Mitigation Strategies in Wastewater Treatment and Challenges for Wider Implementation

Nitrous oxide (N₂O) emissions account for the majority of the carbon footprint of wastewater treatment plants (WWTPs). Many N₂O mitigation strategies have since been developed while a holistic view is still missing. This article reviews the state-of-the-art of N₂O mitigation studies in wastewater treatment. Through analyzing existing studies, this article presents the essential knowledge to guide N₂O mitigations, and the logics behind mitigation strategies. In practice, mitigations are mainly carried out by aeration control, feed scheme optimization, and process optimization. Despite increasingly more studies, real implementation remains rare, which is a combined result of unclear climate change policies/incentives, as well as technical challenges. Five critical technical challenges, as well as opportunities, of N₂O mitigations were identified. It is proposed that (i) quantification methods for overall N₂O emissions and pathway contributions need improvement; (ii) a reliable while straightforward mathematical model is required to quantify benefits and compare mitigation strategies; (iii) tailored risk assessment needs to be conducted for WWTPs, in which more long-term full-scale trials of N₂O mitigation are urgently needed to enable robust assessments of the resulting operational costs and impact on nutrient removal performance; (iv) current mitigation strategies focus on centralized WWTPs, more investigations are warranted for decentralised systems, especially decentralized activated sludge WWTPs; and (v) N₂O may be mitigated by adopting novel strategies promoting N₂O reduction denitrification or microorganisms that emit less N₂O. Overall, we conclude N₂O mitigation research is reaching a maturity while challenges still exist for a wider implementation, especially in relation to the reliability of N₂O mitigation strategies and potential risks to nutrient removal performances of WWTPs.

Unwanted mainstream nitritation-denitritation causing massive N2O emissions in a continuous activated sludge process

Nitrous oxide emissions can contribute significantly to the carbon footprint of municipal wastewater treatment plants even though emissions from conventional nitrogen removal processes are assumed to be moderate. An increased risk for high emissions can occur in connection with process disturbances and nitrite (NO₂^-) accumulation. This work describes the findings at a large municipal wastewater treatment plant where the levels of NO₂^- in the activated sludge process effluent were spontaneously and strongly increased on several activated sludge lines which was suspected to be due to shortcut nitrogen removal that stabilized for several months. The high NO₂^- levels were linked to a dramatic increase in nitrous oxide (N₂O) emissions. As much as over 20% of the daily influent nitrogen load was emitted as N₂O. These observations indicate that highly increased NO₂^- levels can occur in conventional activated sludge processes and result in high nitrous oxide emissions. They also raise questions concerning the risk of increased greenhouse gas (GHG) emissions of the nitritation-denitritation processes - although the uncontrolled nature of the event described here must be taken into consideration - and underline the importance of continuous monitoring and control of N₂O emissions.

Comparison of methods for nitrous oxide emission estimation in full-scale activated sludge

Nitrous oxide (N₂O) gas transfer was studied in a full-scale process to correlate liquid phase N₂O concentrations with gas phase N₂O emissions and compare methods of determining the volumetric mass transfer coefficient, K_La. Off-gas and liquid phase monitoring were conducted at the Viikinmäki wastewater treatment plant (WWTP) over a two-week period using a novel method for simultaneous measurement of dissolved and off-gas N₂O and O₂ from the same location. K_La was calculated with three methods: empirically, based on aeration superficial velocity, from experimentally determined O₂ K_La, and using a static value of best fit. The findings of this study indicated trends in local emitted N₂O consistently matched trends in local dissolved N₂O, but the magnitude of N₂O emissions could not be accurately estimated without correction. After applying a static correction factor, the O₂ method, using experimentally determined O₂ K_La, provided the best N₂O emission estimation over the data collection period. N₂O emissions estimated using the O₂ method had a root mean square error (RMSE) of 70.5 compared against measured concentrations ranging from 3 to 1,913 ppm and a maximum 28% error. The K_La value, and therefore the method of K_La determination, had a significant impact on estimated emissions.

Evaluating the roles of coexistence of sludge flocs on nitrogen removal and nitrous oxide production in a granule-based autotrophic nitrogen removal system

Certain levels of sludge flocs would always coexist in granule-based reactors due to the biomass detachment from granules. Such inevitable coexistence could affect both total nitrogen (TN) removal and nitrous oxide (N₂O) production in autotrophic nitrogen removal systems. This work utilized a mathematical approach to systematically study the influence of the coexisting sludge flocs on TN removal and N₂O production in a granular nitritation-anaerobic ammonium oxidation (Anammox) process for the first time, based on a 2-pathway N₂O production model concept. The modelling results reveal that the highest TN removal efficiency decreases from ca. 87-88% to ca. 41-49% as the fraction of sludge flocs in the system increases from 10% to 40%, while the N₂O production rate gradually increases with such increase. Meanwhile, both bulk dissolved oxygen (DO, 0.05-0.3 mg/L) and the size of granule (200-400 μm) could also influence the TN removal efficiency and N₂O production. As the fraction of sludge flocs increases from 10% to 40%, the contribution of granular biomass to total N₂O production is reduced due to increase of N₂O-producing ammonia-oxidizing bacteria (AOB) in the sludge flocs, and the increase of granule size could intensify such decrease. In addition, the hydroxylamine oxidation pathway dominates the nitrifier denitrification pathway in both granules and sludge flocs under various testing conditions, whereas the increasing contribution of the latter would occur at a certain DO range, higher fraction of sludge flocs and smaller granule size. These results disclose an important influence of the coexisting sludge flocs on the performance of granular nitritation-Anammox systems.

Energy neutrality versus carbon footprint minimization in municipal wastewater treatment plants

This work aimed to compare the carbon footprint (CF) of six full-scale wastewater treatment plants (WWTPs). The CF was estimated in the range of 23-100 kg CO_2e per population equivalent. In the total CF, the direct emissions held the highest share (62-74%) for the plants with energy recovery from biogas. In the plants depending entirely on the power grid, the indirect emissions due to energy consumption dominated the total CF (69-72%). The estimated CF was found highly sensitive towards the choice of N₂O emission factors. A dual effect of external substrates co-digestion on the CF has been presented. After co-digestion, the overall CF decreased by 7% while increasing the biogas production by 17%. While applying the empirical model, the level of energy neutrality was strongly related to the ratio of the indirect to direct emissions.

Strategies for mitigating nitrous oxide production and decreasing the carbon footprint of a full-scale combined nitrogen and phosphorus removal activated sludge system

Nitrous oxide (N₂O) emitted from biological nutrient removal activated sludge systems contributes significantly to the total carbon footprint of modern wastewater treatment plants. In the present study, N₂O production and emissions were experimentally determined in a large-scale plant (220,000 PE) employing combined nitrogen (N) and phosphorus (P) removal. As a modelling tool, the Activated Sludge Model 2d (ASM2d) was extended with modules describing multiple N₂O production pathways and N₂O liquid-gas transfers. The new model was calibrated and validated using the results of laboratory experiments and full-scale measurements. Different operational strategies were evaluated following the proposed model-based procedure. Heterotrophic denitrification was found to be the predominant pathway of N₂O production under both anoxic and aerobic conditions. This behaviour could primarily be attributed to the predominant abundance of heterotrophic denitrifiers over nitrifiers. Simulations revealed that the optimal solution for minimizing liquid N₂O production is to set the dissolved oxygen concentration in the aerobic zone from 1 to 2 mg O₂/L and to enhance the mixed liquor recirculation rate (MLR) (>500% of the influent flowrate) while not compromising effluent standards. Regarding the actual conditions, the potential reduction in the carbon footprint was estimated to be 10% by applying the proposed operational strategy. The results suggest that considerable improvements can be achieved without substantial upgrades and increased costs.

A decade of nitrous oxide (N2O) monitoring in full-scale wastewater treatment processes: A critical review

Direct nitrous oxide (N₂O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. Recent onsite measurement of N₂O emissions at WWTPs have been used as an alternative to the controversial theoretical methods for the N₂O calculation. The full-scale N₂O monitoring campaigns help to expand our knowledge on the N₂O production pathways and the triggering operational conditions of processes. The accurate N₂O monitoring could help to find better process control solutions to mitigate N₂O emissions of wastewater treatment systems. However, quantifying the emissions and understanding the long-term behaviour of N₂O fluxes in WWTPs remains challenging and costly. A review of the recent full-scale N₂O monitoring campaigns is conducted. The analysis covers the quantification and mitigation of emissions for different process groups, focusing on techniques that have been applied for the identification of dominant N₂O pathways and triggering operational conditions, techniques using operational data and N₂O data to identify mitigation measures and mechanistic modelling. The analysis of various studies showed that there are still difficulties in the comparison of N₂O emissions and the development of emission factor (EF) databases; the N₂O fluxes reported in literature vary significantly even among groups of similar processes. The results indicated that the duration of the monitoring campaigns can impact the EF range. Most N₂O monitoring campaigns lasting less than one month, have reported N₂O EFs less than 0.3% of the N-load, whereas studies lasting over a year have a median EF equal to 1.7% of the N-load. The findings of the current study indicate that complex feature extraction and multivariate data mining methods can efficiently convert wastewater operational and N₂O data into information, determine complex relationships within the available datasets and boost the long-term understanding of the N₂O fluxes behaviour. The acquisition of reliable full-scale N₂O monitoring data is significant for the calibration and validation of the mechanistic models -describing the N₂O emission generation in WWTPs. They can be combined with the multivariate tools to further enhance the interpretation of the complicated full-scale N₂O emission patterns. Finally, a gap between the identification of effective N₂O mitigation strategies and their actual implementation within the monitoring and control of WWTPs has been identified. This study concludes that there is a further need for i) long-term N₂O monitoring studies, ii) development of data-driven methodological approaches for the analysis of WWTP operational and N₂O data, and iii) better understanding of the trade-offs among N₂O emissions, energy consumption and system performance to support the optimization of the WWTPs operation.