Reduction and prediction of N2O emission from an Anoxic/Oxic wastewater treatment plant upon DO control and model simulation

Complete long-term monitoring of greenhouse gas emissions from a full-scale industrial wastewater treatment plant with different cover configurations

The greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs), consisting mainly of methane (CH4) and nitrous oxide (N2O), have been constantly increasing and become a non-negligible contributor towards carbon neutrality. The precise evaluation of plant-specific GHG emissions, however, remains challenging. The current assessment approach is based on the product of influent load and emission factor (EF), of which the latter is quite often a single value with huge uncertainty. In particular, the latest default Tier 1 value of N2O EF, 0.016 ± 0.012 kgN2O-N kgTN-1, is estimated based on the measurement of 30 municipal WWTPs only, without involving any industrial wastewater. Therefore, to resolve the pattern of GHG emissions from industrial WWTPs, this work conducted a 14-month monitoring campaign covering all the process units at a full-scale industrial WWTP in Shanghai, China. The total CH4 and N2O emissions from the whole plant were, on average, 447.7 ± 224.5 kgCO2-eq d-1 and 1605.3 ± 2491.0 kgCO2-eq d-1, respectively, exhibiting a 5.2- or 3.9-times more significant deviation than the influent loads of chemical oxygen demand (COD) or total nitrogen (TN). The resulting EFs, 0.00072 kgCH4 kgCOD-1 and 0.00211 kgN2O-N kgTN-1, were just 0.36% of the IPCC recommended value for CH4, and 13.2% for N2O. Besides, the parallel anoxic-oxic (A/O) lines of this industrial WWTP were covered in two configurations, allowing the comparison of GHG emissions from different odor control setup. Unit-specific analysis showed that the replacement of enclosed A/open O with enclosed A/O reduced the CH4 EF by three times, from 0.00159 to 0.00051 kgCH4 kgCOD-1, and dramatically decreased the N2O EF by an order of magnitude, from 0.00376 to 0.00032 kgN2O-N kgTN-1, which was among the lowest of all full-scale WWTPs.

Enhanced nitrogen removal via partial nitrification/denitrification coupled Anammox using three stage anoxic/oxic biofilm process with intermittent aeration

Pre-capturing organics in municipal wastewater for biogas production, combined with Anammox-based nitrogen removal process, improves the sustainability of sewage treatment. Thus, enhancing nitrogen removal via Anammox in mainstream wastewater treatment becomes very crucial. In present study, a three-stage anoxic/oxic (AO) biofilm process with intermittent aeration was designed to strengthen partial nitrification/denitrification coupling Anammox (PNA/PDA) in treatment of low C/N wastewater, which contained chemical oxygen demand (COD) of 79.8 mg/L and total inorganic nitrogen (TIN) of 58.9 mg/L. With a hydraulic retention time of 8.0 h, the process successfully reduced TIN to 10.6 mg/L, achieving a nitrogen removal efficiency of 83.3 %. The 1st anoxic zone accounted for 32.0 % TIN removal, with 10.3 % by denitrification and 21.7 % by PDA, meanwhile, the 2nd and 3rd anoxic zones contributed 19.4 % and 4.5 % of TIN removal, primarily achieved through PDA (including endogenous PD coupling Anammox). The 1st and 2nd intermittent zones accounted for 27.2 % and 17.0 % of TIN removal, respectively, with 13.7 %-21.3 % by PNA and 3.2 %-5.3 % by PDA. Although this process did not pursue nitrite accumulation in any zone (< 1.5 mg-N/L), PNA and PDA accounted for 35.1 % and 52.1 % of TIN removal, respectively. Only 0.21 % of removed TIN was released as nitrous oxide. The AnAOB of Candidatus Brocadia was enriched in each zone, with a relative abundance of 0.66 %-2.29 %. In intermittent zones, NOB had been partially suppressed (AOB/NOB = 0.73-0.88), mainly due to intermittent aeration and effective nitrite utilization by AnAOB since its population size was much greater than NOB. Present study indicated that the three-stage AO biofilm process with intermittent aeration could enhance nitrogen removal via PNA and PDA with a low N2O emission factor.

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.

Nitrogen and nitrous oxides emission characteristics of anoxic/oxic wastewater treatment process under different oxygen regulation strategies

Nitrous oxide (N2O) and nitrogen oxides (NOx) (i.e., nitric oxide (NO) and nitrogen dioxide (NO2)), which could be produced in wastewater treatment process and result in greenhouse effect and atmospheric pollution, respectively, have been studied limitedly in their emission characteristics and transformation mechanisms. In this study, intelligent oxygen regulation was applied in anoxic/oxic wastewater treatment process (I-A/O), and its effects on regulating NOx and N2O transformations were extensively explored by comparing it with conventional A/O process (C-A/O). Results showed that the average emission amounts of N2O and NOx in I-A/O were 7.45 ± 0.66 mg and 1.88 ± 0.10 mg, respectively. Satisfactory reduction of N2O by 29.28 %-45.08 % was achieved in I-A/O compared to that of C-A/O, but together with increased NOx emission by 83.19 %-120.57 %. Pearson correlation and transcriptional analysis suggested that NO2--N reduction in the anoxic phase dominated N2O production, while no significant N2O production in the oxic phase was found. Hence, the reduced N2O production in I-A/O was mainly attributed to its efficient denitrification process. On the other hand, both the anoxic and oxic phases played important roles in NO production. More importantly, sufficient oxygen in I-A/O promoted the ammonia oxidation process, resulting in higher NO emission in I-A/O in the oxic phase. The imbalance in NO and N2O emissions was then amplified by the NOR enzyme, which mediates the conversion of NO to N2O in both the anoxic and oxic phases. Besides, carbon emission reduction by 31.32 %-36.50 % was obtained in I-A/O due to aeration consumption savings and greenhouse gas emissions reduction compared to C-A/O. Overall, intelligent oxygen regulation optimized the nitrogen transformation and achieved carbon emission reduction in A/O process, but special attention should be paid to the associated risk caused by increased NO emissions.

A novel carbon emission evaluation model for anaerobic-anoxic-oxic urban sewage treatment

The proposal of the dual carbon goal and the blue economy in China has sparked a keen interest in carbon emissions reduction from sewage treatment. Carbon accounting in urban sewage plants serves as the foundation for carbon emission reduction in sewage treatment. This paper re-evaluated carbon accounting in the operational processes for urban sewage treatment plants to develop a novel carbon emission evaluation model for anaerobic-anoxic-oxic treatment plants. The results show that the carbon emissions generated by non-carbon dioxide gases far exceed the carbon emissions from carbon dioxide alone. Moreover, the recycling of sewage leads to carbon emissions reduction that offsets the carbon emissions generated during the operation of the sewage plant. Also, the carbon emissions generated by sewage treatment plants are lower than those generated by untreated sewage. The findings and insights provided in this paper provide valuable references for carbon accounting and the implementation of low-carbon practices in urban sewage treatment plants.

The trade-off between N2O emission and energy saving through aeration control based on dynamic simulation of full-scale WWTP

This study investigated the trade-off between energy saving and N₂O emission reduction of WWTP under the precise control of dissolved oxygen (DO) concentration through model simulation. A long-term dynamic model for full-scale WWTP GHG emissions was established and calibrated with monitored year-round hourly water quality data to quantify the annual GHG emissions from WWTP. Results showed that N₂O dominated the direct emission, up to 76.1%, and the variability of N₂O generation could better be revealed by dynamic simulation. Furthermore, GHG emissions of the WWTP were mainly contributed by electric energy, among which the blower consumes the most electricity. To reduce the electricity consumption of blowers, improve mechanical efficiency and reduce DO concentration should be considered. DO setting played a significant role in the N₂O and CH₄ emission, electricity consumption and effluent quality, which was challenging to balance. The ultralow-oxygen (0-1/0.2-1 mg/L) and low oxygen (1-2 mg/L) control strategies were proposed, and their effects on total GHG emissions and effluent water quality were discussed. If the anaerobic environment (DO<0.2 mg/L)could be avoided, the control frequency (high and low) of the DO set-point did not have a significant effect on the emissions of N₂O and CH₄ and the effluent quality. The ultralow-oxygen strategy (0.2-1 mg/L) with a high-frequency control strategy achieved the lowest GHG emissions under the current energy mix. However, by 2050, as the energy supply gets cleaner, the total GHG emissions of WWTPs with ultralow-oxygen aeration (0.2-1 mg/L) will exceed low-oxygen aeration by 3.6%-4.2%, as N₂O dominates 61.6%. Therefore, considering the trade-off between N₂O emission and energy saving in WWTP, ultralow-oxygen aeration is a transition scheme to cleaner energy.

Tracing N2O formation in full-scale wastewater treatment with natural abundance isotopes indicates control by organic substrate and process settings

Nitrous oxide (N₂O) dominates greenhouse gas emissions in wastewater treatment plants (WWTPs). Formation of N₂O occurs during biological nitrogen removal, involves multiple microbial pathways, and is typically very dynamic. Consequently, N₂O mitigation strategies require an improved understanding of nitrogen transformation pathways and their modulating controls. Analyses of the nitrogen (N) and oxygen (O) isotopic composition of N₂O and its substrates at natural abundance have been shown to provide valuable information on formation and reduction pathways in laboratory settings, but have rarely been applied to full-scale WWTPs. Here we show that N-species isotope ratio measurements at natural abundance level, combined with long-term N₂O monitoring, allow identification of the N₂O production pathways in a full-scale plug-flow WWTP (Hofen, Switzerland). Heterotrophic denitrification appears as the main N₂O production pathway under all tested process conditions (0-2 mgO₂/l, high and low loading conditions), while nitrifier denitrification was less important, and more variable. N₂O production by hydroxylamine oxidation was not observed. Fractional N₂O elimination by reduction to dinitrogen (N₂) during anoxic conditions was clearly indicated by a concomitant increase in site preference, δ¹⁸O(N₂O) and δ¹⁵N(N₂O). N₂O reduction increased with decreasing availability of dissolved inorganic N and organic substrates, which represents the link between diurnal N₂O emission dynamics and organic substrate fluctuations. Consequently, dosing ammonium-rich reject water under low-organic-substrate conditions is unfavorable, as it is very likely to cause high net N₂O emissions. Our results demonstrate that monitoring of the N₂O isotopic composition holds a high potential to disentangle N₂O formation mechanisms in engineered systems, such as full-scale WWTP. Our study serves as a starting point for advanced campaigns in the future combining isotopic technologies in WWTP with complementary approaches, such as mathematical modeling of N₂O formation or microbial assays to develop efficient N₂O mitigation strategies.

Dynamic modelling of N2 O emissions from a full-scale granular sludge partial nitritation-anammox reactor

Partial nitration-anammox is a resource-efficient pathway for nitrogen removal from wastewater. However, the advantages of this nitrogen removal technology may be counter-acted by the emission of N₂ O, a potent greenhouse gas. In this study, mathematical modelling was applied to analyse N₂ O formation and emission dynamics and to develop N₂ O mitigation strategies for a one-stage partial nitritation-anammox granular sludge reactor. Dynamic model calibration for such a full-scale reactor was performed, applying a 1-dimensional biofilm model and including several N₂ O formation pathways. Simultaneous calibration of liquid phase concentrations and N₂ O emissions leads to improved model fit compared to their consecutive calibration. The model could quantitatively predict the average N₂ O emissions and qualitatively characterize the N₂ O dynamics, adjusting only seven parameter values. The model was validated with N₂ O data from an independent data set at different aeration conditions. Nitrifier nitrification was identified as the dominating N₂ O formation pathway. Off-gas recirculation as a potential N₂ O emission reduction strategy was tested by simulation and showed indeed some improvement, be it at the cost of higher aeration energy consumption. This article is protected by copyright. All rights reserved.

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.

Impact of light on anoxic/oxic reactors: performance, quorum sensing, and metagenomic characteristics

The effect of light has raised attention on wastewater treatment. However, little research has concentrated on the influences of light on activated sludge. In this study, the influences of light on the performance, quorum sensing (QS) and metagenomic characteristics of anoxic/oxic reactors were investigated. The reactor without light (AO1) showed higher total nitrogen (TN) removal (79.15 ± 1.69%) than the reactor with light (AO2) (74.54 ± 1.30%), and significant differences were observed. It was observed that light facilitated the production of protein-like and tryptophan-like substances by employing parallel factor analysis for extracellular polymeric substance (EPS), resulting in more EPS production in AO2, indicating light was beneficial to EPS production. The concentrations of N-acyl-homoserine lactones (AHLs) were various in the two reactors, so the AHLs-mediated QS behaviors in both reactors were also different. These results revealed that light significantly influenced nitrogen removal, EPS, and QS. Metagenomic analysis based on Tax4Fun demonstrated that light reduced the denitrification, stimulated the polysaccharide and protein biosynthesis pathways and down-regulated the AHLs synthesis pathway, resulting in lower TN removal, more EPS production, and lower AHLs concentrations. Based on the above, the likely mechanism was proposed for the influences of light on the reactor.

Greenhouse gases emission control in WWTS via potential operational strategies: A critical review

Greenhouse gases (GHGs; particularly, CO₂, CH₄, and N₂O) emission from wastewater treatment systems (WWTS) is one of the inevitable concerns for sustainable development. This indicator is directly linked with the carbon footprint and potential impacts of WWTS on climate change. In this view, various modeling, design, and operational tools have been introduced to mitigate the WWTS associated GHGs, at regional and global scales. In this study, authors have critically reviewed the selected potential operational control strategies for GHGs emission, particularly emitted from the operational stages of biological WWTS. The investigated operational control strategies and/or treatment configurations included intermittent aeration, varying dissolved oxygen, enhanced sludge retention time, coupled aerobic-anoxic nitrous decomposition operation, and microalgae integrated treatment process. Based on this analysis and considering the trade-off between treatment performance of WWTS and GHGs control, an integrated framework is also proposed for existing and upcoming WWTS. The findings of this study and proposed framework will play an instrumental role in mitigating the GHGs at various operational stages of WWTS. Future research works in this direction can lead to a better understanding of investigated operational GHGs emission control strategies in WWTS.

Complete nitrogen removal via simultaneous nitrification and denitrification by a novel phosphate accumulating Thauera sp. strain SND5

Bacteria capable of simultaneous nitrification and denitrification (SND) and phosphate removal could eliminate the need for separate reactors to remove nutrients from wastewater and alleviate competition for carbon sources between different heterotrophs in wastewater treatment plants (WWTPs). Here we report a newly isolated Thauera sp. strain SND5, that removes nitrogen and phosphorus from wastewater via SND and denitrifying-phosphate accumulation, respectively, without accumulation of metabolic intermediates. Strain SND5 simultaneously removes ammonium, nitrite, and nitrate at an average rate of 2.85, 1.98, and 2.42 mg-N/L/h, respectively. Batch testing, detection of functional genes, nitrogenous gas detection and thermodynamic analysis suggested that nitrogen gas, with hydroxylamine produced as an intermediate, was the most likely end products of heterotrophic ammonium oxidation by strain SND5. The generated end products and intermediates suggest a novel nitrogen removal mechanism for heterotrophic ammonium oxidation in strain SND5 (NH₄⁺→NH₂OH→N₂). Strain SND5 was also found to be a denitrifying phosphate-accumulating organism, capable of accumulating phosphate, producing and storing polyhydroxybutyrate (PHB) as an intracellular source of carbon while using nitrate/nitrite or oxygen as an electron acceptor during PHB catabolism. This study identifies a novel pathway by which simultaneous nitrogen and phosphorus removal occurs in WWTPs via a single microbe.

Recent advances in nitrous oxide production and mitigation in wastewater treatment

Nitrous oxide (N₂O) emitted from wastewater treatment plants has caused widespread concern. Over the past decade, people have made tremendous efforts to discover the microorganisms responsible for N₂O production, elucidate metabolic pathways, establish production models and formulate mitigation strategies. The ultimate goal of all these efforts is to shed new light on how N₂O is produced and how to reduce it, and one of the best ways is to find key opportunities by integrating the information obtained. This review article critically evaluates the knowledge gained in the field within a decade, especially in N₂O production microbiology, biochemistry, models and mitigation strategies, with a focus on denitrification. Previous research has greatly deepened the understanding of the N₂O generation mechanism, but further efforts are still needed due to the lack of standardized methodology for establishing N₂O mitigation strategies in full-scale systems. One of the challenges seems to be to convert the denitrification process from a net N₂O source into an effective sink, which is recommended as a key opportunity to reduce N₂O production in this review.

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.

Tackling environmental challenges in pollution controls using artificial intelligence: A review

This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.

Characteristics of N2O generation within the internal micro-environment of activated sludge flocs under different dissolved oxygen concentrations

Nitrous oxide (N₂O) is a strong greenhouse gas that is produced in significant quantities through biological nitrogen removal processes in wastewater treatment plants; however, N₂O generation within the internal micro-environment of activated sludge flocs (ASFs) is poorly understood. In this study, microelectrodes and molecular techniques were employed to investigate the concentrations of N₂O and other chemicals and the composition and distribution of microbes within ASFs, respectively. The results showed that N₂O generation was correlated with the ASF micro-environment, and was significantly influenced by the dissolved oxygen (DO) concentration of the bulk wastewater. Equal N₂O, DO, NH₄⁺-N, and NO₃^--N concentrations were found in small flocs (<100 μm). By contrast, higher N₂O generation rates and lower DO, NH₄⁺-N, and NO₃^--N concentrations were detected in the center of large flocs (>200 μm) compared with those at their surfaces. Microbial structures of varying particle sizes were distinct and depended on the micro-environmental characteristics.

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.

Engineering feasibility, economic viability and environmental sustainability of energy recovery from nitrous oxide in biological wastewater treatment plant

Currently, the biological wastewater treatment has been challenged by their high energy consumption. An increasing effort has been devoted to exploring energy recovery from nitrous oxide (N₂O) as a powerful fuel additive rather than as an unwanted byproduct during biological nitrogen removal. This review aims to offer a holistic and critical analysis of the ideas for N₂O production and energy recovery in terms of engineering feasibility, economic viability and environmental sustainability. It turns out that the recoverable energy from N₂O produced in municipal wastewater is below 0.03 kWh/m³, which is insignificant compared with the in-plant energy consumption, while complicated process configuration and high cost associated with harvesting and post-purification of N₂O will be incurred. An environmental risk related to global climate change due to the emission of residual dissolved N₂O is also concerned. Further effort on N₂O production and recovery technologies is indeed required to improve the overall energy balance.

The effect of temperature shifts on N2O and NO emissions from a partial nitritation reactor treating reject wastewater

Temperature has a known effect on ammonia oxidizing bacteria (AOB) activities, reducing its ammonia oxidizing rate (AOR) when temperature is lowered. However, little is known concerning its effect on N₂O and NO emissions which are produced during ammonia oxidation having a greenhouse effect. To study this, an AOB enriched partial nitrification sequencing batch reactor (PN-SBR) was operated within a two step-wise feed under 5 different temperatures (30-25-20-15-10 °C). A decrease on the specific AOR (sAOR) was detected when decreasing the temperature. N₂O emissions were also affected by the temperature but only the ones produced during the first aeration of the cycle, when AOBs shifted from a period of low activity to a period of high activity. N₂O emission factors (%) detected during the second aerobic phase were similar among all temperatures tested and lower than the emissions detected during the first aerated phase. The average N₂O emission factor was in the range of 0.15-0.70% N₂O-N/NH₄⁺-N oxidized in the first aeration phase and 0.14-0.15% N₂O-N/NH₄⁺-N-oxidized in the second aeration phase at 10 to 30 °C, respectively. On the other hand, NO emissions were very similar under all temperatures resulting in 0.03-0.06% of NH₄⁺-N oxidized.