Their global warming potential (GWP) is 298 times higher than that of carbon dioxide, CO2) (IPCC, 2014). N2O emitted from wastewater treatment plants (WWTPs) is mainly produced during the biological conversion of nitrogen into nitrogen gas (N2) through the nitrification and denitrification processes (Kampschreur et al., 2009). During nitrification, ammonium (NH4+) is oxidised to nitrite (NO2-) by ammonia oxidizing bacteria (AOB) and then to nitrate (NO3-) by nitrite oxidizing bacteria (NOB) under aerobic conditions. Though N2O is not an intermediate of this process, it can be produced as an end product by AOB via two metabolic pathways: (i) hydroxylamine oxidation (Law et al., 2012) and (ii) nitrifier denitrification (Wunderlin et al., 2012). The activation of these pathways by AOB is influenced by several factors such as NH-4 dissolved oxygen (DO) concentration, NO2- accumulation, and transient conditions from low activity to high activity (Law et al., 2012, Yu et al., 2010). During denitrification, heterotrophic bacteria reduce NO3- to nitrogen gas (N2) under anoxic conditions with NO2-, nitric oxide (NO), and N2O as intermediates. There are several factors affecting the accumulation of N2O during denitrification such as the limiting organic matter present in the wastewater, and the presence of DO and free nitrous acid (FNA, the protonated species of NO2-) (Law et al., 2012). The different N2O formation pathways are shown in the following figure.
Related documentation:
Methane (CH4) has a GWP 21 times higher than that of CO2 (IPCC, 2014) and is the second most important GHG after CO2. WWTPs emit methane. CH4 can be present in the influent of a WWTP in a dissolved form after forming under the anaerobic environments present in the sewer network (Gutierrez et al., 2014). In addition, significant dissolved CH4 concentrations are found in the reject wastewater stream originating from the anaerobic digesters which is normally recirculated to the inlet of the WWTP (Rodriguez-Caballero et al., 2014). Part of this CH4 can be biologically oxidised in the bioreactor but some is stripped to the atmosphere in the aerated compartments. Despite this, only a few studies have quantified CH4 emission dynamics in domestic wastewater treatment systems (Daelman et al., 2013, 2012; Rodriguez-Caballero et al., 2014).
Detailed quantification of GHG emissions from my system.
Monitoring
The first comprehensive and practical guide on quantifying N2O and CH4 emissions of WWTPs will be launched at the end of 2019 by the IWA Task Group on GHG in their Scientific and Technical Report titled: “Quantification and Modelling of Fugitive GHG Emissions from Urban Water Systems: A report from the IWA Task Group on GHG.” Please refer to this report for details regarding the different methods. Here in the REaCH guideline we provide guidance on which methods are used for different purposes. Therefore, first question to answer when considering detailed quantification of the N2O and CH4 emissions from wastewater treatment plants is… Why do I need to measure direct emissions of CH4 and N2O? Here are possible objectives for embarking on a field measurement campaign of GHG emissions from WWTPs:
The matrix below lists the possible methods for each of the above reasons for needing to measure direct emissions of N2O and CH4.
| Measurement objective | Chandran et al., 2009 | Alternative gas flow rate determination | MMicrosensor | Bag/GC method | Tube/GC method |
|---|---|---|---|---|---|
| Monitoring, reporting, and verification of reductions | R | O | O | NR | NR |
| Calibrating/validating models | O | O | R | NR | NR |
| Researching/investigating emissions | O | O | O | O | O |
In addition to measuring N2O and CH4, collection of additional data is recommended depending on what the objectives are. For example, nitrogen species, COD, and other process data may be required to properly characterize the conditions leading to the N2O and/or CH4 emissions. Details on what additional data is needed based on the objectives will be provided in the Task Group GHG report.
The methods to quantify GHG emissions have evolved from analysing grab samples to continuous online gas monitoring using commercially available portable gas analysers (GWRC, 2011). Gas collection hoods are located on the bioreactor surface, capturing the gas emitted and allowing a more reliable quantification of the emission dynamics and their diurnal variability (Pan et al., 2016). This quantification is particularly challenging in plug-flow bioreactors because wastewater flows through the bioreactor without horizontal mixing, originating different gradients in DO and nitrogen concentrations that can be found along the bioreactor path.
With the aim of obtaining more reliable emission patterns, Pan et al. (2016) used multiple gas collection hoods to simultaneously measure N2O emissions along a plug-flow bioreactor. N2O fluxes showed strong spatial variations along the bioreactor path, demonstrating that it is crucial to consider spatial variations when quantifying emissions in plug-flow bioreactors.
The monitoring involves the following fundamentals:
Rough quantification of GHG emissions from my system.
If you want to understand how/where/why your emissions are generated, embarking into a modelling exercise exercise can provide invaluable insight that may not be easily obtained from measurements.
Mechanistic models have been widely applied in WWTPs for the prediction of organic matter, nitrogen and phosphorus concentrations, and are gaining attention for the prediction of N2O accumulation and emissions. Such models provide an opportunity to include N2O production as an important consideration in the design, operation and optimization of biological nitrogen removal processes. Of special interest is the usage of models to evaluate N2O mitigation strategies.
N2O modelling has evolved rapidly in the past few years:
Adapted from Ni and Yuan (2015)
| N2O models | Reference | Model components | Stoichiometric | Kinetic | |
|---|---|---|---|---|---|
| Single-pathway models by AOB | Model A - AOB denitrification | Ni et al. (2011) | SNH4, SNO2; with SNH2OH | 2-step NH4+ oxidation; 2 step NO2- reduction; cell growth during NH2OH oxidation | 2 different oxygen affinity constants; oxygen inhibition on NO2- and NO reductions; anoxic reduction factor |
| Model A1 - AOB denitrification | Pocquet et al. (2013) | SNH3, SHNO2; with SNH2OH | = model A | 2 different oxygen affinity constants; without oxygen inhibition; NH3 inhibition on NH3 oxidation; anoxic reduction factor | |
| Model B - AOB denitrification | Mampaey et al. (2013) | SNH3, SHNO2; without SNH2OH | 1-step NH4+ oxidation; 2 sep No2- reduction; cell growth during all 3 processes | 1 oxygen affinity constant; without oxygen inhibition; anoxic reduction factor | |
| Model B1 - AOB denitrification | Guo and Vanrolleghem (2014) | = Model B | = Model B | 1 oxygen affinity constant; NH3 and HNO2 inhibitions on NH3 oxidation; haldane function for oxygen limitation; anoxic reduction factor. | |
| Model C - NH2OH pathway (via NOH) | Law et al. (2012) | SNH4, SNO2; with SNOH | 3-step NH4+ oxidation via NOH; cell growth during NH2OH oxidation | 2 different oxygen affinity constants; NOH breakdown to produce N2O | |
| Model D - NH2OH pathway (via NO) | Ni et al. (2013) | SNH4, SNO2; with SNO | 3-step NH4+ oxidation via NO; cell growth during NH2OH oxidation | 2 different oxygen affinity constants; NO reduction to produce N2O; without oxygen inhibition | |
| Two-pathway models by AOB | Model E | Ni et al. (2014) | SNH3, SNO2; with electron carriers | 3-step NH3 oxidation; 1-step NO2- reduction; without cell growth | Applying electron competition concept; without oxygen inhibition; without anoxic reduction factor |
| Model F | Peng et al. (2015) | = model E; with SCO2; with energy carriers | = model E; with energy carriers involved; with cell growth considered | = model E; with energy carriers involved; with effect of inorganic carbon considered | |
| Model I | Pocquet et al. (2016) | SNH3; NH2OH, NO; NO2- | 3-step NH4+ oxidation via NH2OH; NH2OH oxidation pathway; nitrifier denitrification pathway | No AOB inhibition by NH3 or HNO2; DO inhibition for nitrifier denitrification pathway. | |
| Model J | Domingo-Félez and Smets (2016) | NH3, NH2OH, HNO2, NO, NO2- | 2 processes: to NO and to HNO2; HNO2 production requires O2, NO does not require O2; NH2OH substrate for HNO2 production; denitrifying NO production | ||
| N2O models by heterotrophs | Model G | Hiatt and Grady (2008) | Without electron carriers | Coupling carbon oxidation and nitrogen reduction (4 processes) | Without electron competition concept |
| Model H | Pan et al. (2013) | With electron carriers | Decoupling carbon oxidation and nitrogen reduction (5 processes) | With electron competition concept | |
| Biologically-driven abiotic N2O production | Model K | Domingo-Félez and Smets (2016) | NH2OH, HNO2 | NH2OH → N2O; NH2OH + HNO2 → N2O |
Reaction rates are modelled with pH dependent second order kinetics> |
A combination of two or more of the independent models.
| Independent models integrated | Reference | Comments |
|---|---|---|
| BSM implementation (model G) | Flores-Alsina et al. (2011) | |
| Model A + model G | Ni et al. (2011) | |
| Model A1 + model G | Pocquet et al. (2013) | |
| Model D + model G | Ni et al. (2013)* | |
| Model B + model G | Guo and Vanrolleghem (2013) | |
| Model B1 + model G | Guo and Vanrolleghem (2014)* | |
| Model B + model G | Flores-Alsina et al. (2014) | This implementation was inspired after the implementation of Guo and Vanrolleghem (2013) |
| Model A1 + model G; model B1 + model G; model D + model G | Spérandio et al. (2014)* | |
| Model E + model G | Ni et al. (2015)* | |
| BSM implementation (Model B + model G) | Lindblom et al. (2016)*, Santín et al. (2017). | |
| (Model J + model G + model K) | Domingo-Félez and Smets (2016) | Adds the denitrification contribution to the NO pool that could not be considered in current models |
| ASM2d-N2O (model I + model G) | Massara et al. (2017) | |
| ASM2d-N2O (model I + model G) | Solís et al. (under preparation)* | Within the frame of the REaCH project the ASM2d-N2O model has been calibrated for the full-scale WWTP of Girona |
| ASM2d-N2O | Wisniewski et al. (2018) | Denitrification by OHOs is considered as a three-step process, i.e. the sequential reduction of nitrate (SNO3) to SN2O via nitrite (SNO2) and ultimately to nitrogen gas (SN2). NO not considered; PAO contribution to N2O production is neglected |
| ASM3-N2O (model A + model D + model G) | Blomberg et al. (2018)* | 2-step NH4+ oxidation; Two separate NO state variables (NOAOB and NODEN) |
* calibrated/validated with full-scale WWTP data
Mathematical modeling of N2O production provides an opportunity to fully understand the environmental effect of WWTPs and to optimize the design and operation of biological nitrogen removal processes with N2O production as an important consideration. The two-pathway model, incorporating both the AOB denitrification and NH2OH oxidation pathways, has the potential to describe all the N2O data from WWTPs with different operational conditions, but may require more efforts for model calibration. In contrast, the two single-pathway models would be useful for prediction of N2O emission from full-scale WWTPs under specific conditions (as demonstrated in this work) due to their less structural complexities (only one pathway included) with fewer parameters to be calibrated.
Figure X. Summary of applicable regions for the AOB denitrification model, the NH2OH oxidation model and the two-pathway model under various DO and NO2 concentrations. Source: Peng et al. (2015).
These are the recommendations (extracted from Peng et al., 2015):
- Sewex
The N2O risk model by Porro et al. (2014) incorporates artificial intelligence to predict the risk of producing and emitting N2O from WWTPs. This model does not require calibration and can provide key insights on why N2O is being produced and how to mitigate emissions. It provides a dynamic risk score a given set of conditions based on historical online data or mathematical output data of different scenarios. Implementing control strategies identified by the N2O risk model in a full-scale WWTP in the Netherlands results in a 40% reduction in the combined electric CO2 and N2O emissions (Porro et al., 2017).
Project title: Resilience of urban wastewater systems to emerging challenges: from fundamental understanding to improved management practices.
Acronym: REACH
Reference: CTM2015-66892-R
Project duration: 36 MESES
Funding organism: MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD
Program: RETOS DE LA SOCIEDAD, MODALIDAD 1, PROYECTOS I+D+i Bases
Reguladoras: Orden ECC/1780/2013, de 30 de septiembre (publicado en BOE 236 el miércoles, 02 de octubre de 2013
Project leaders:
Urban wastewater systems (UWS) have the functions of collecting, transport (sewer systems), store (storm water tanks) and treat (wastewater treatment plants, WWTP) the urban wastewater and the stormwater before discharging to the receiving water bodies (river, lake, sea). UWS have been traditionally managed independently and without proper understanding the interdependencies amongst their elements, leading to suboptimal performance. The integrated management of UWS presents big challenges, but also great opportunities to decrease total management costs and environmental impacts and increase overall system performance.
Within this context, the main objective of the REaCH project is to increase the knowledge about greenhouse gas emissions in sewer systems, wastewater treatment plants and rivers in order to identify best management strategies. More specifically, this project aims at monitoring N2O and CH4 emissions in the entire system (also including the emissions from rivers).
The project methodology involves experimental work in the lab under controlled conditions (using a singular pilot plant mimicking a sewer, a WWTP and a river) to assess the generation of N2O, CH4 emissions, and experimental work at full-scale to verify the finding from the lab experiments. The resilience of the pilot plant to two environmental stressors (seasonal variability and storms) will be investigated by changing the temperature and manipulating the hydraulic retention time of the system combined with simulated mix of raw wastewater with rainwater. State-of-the art models will be used to support the findings from the lab experiments. Moreover, Life Cycle Assessment (LCA) will be applied to the evaluated strategies in order to identify the trade-offs between direct emissions to water, soil and atmosphere and indirect emissions from the production of energy and chemicals used in the process.
REaCH will contribute to better understanding the relationships between the UWS elements. In addition, the project will enhance the development of current UWS mathematical models and will propose solutions that facilitate the application of LCA in UWS. REaCH will also create two simple Decision Support Systems: First, a framework based on the principles of Life Cycle Assessment using a standard platform (Excel) and combined with advanced programming. Second, a web-based Guide to integrated management of UWS GHG emissions (REaCHGuide). REaCHGuide will be a user-friendly web-based tool for use by decision makers and technical personnel in the public and private sectors, to learn and get trained about GHG monitoring and mitigation. Examples include protocols for monitoring N2O and CH4 emissions, for the selection of best integrated management practices. The project will be undertaken by a research team with proven experience on the monitoring, control and optimization of the different parts of UWS and with a strong track record delivering high quality research, with state of the art infrastructure support from the host organisation.
Dr. Lluís Corominas is a research scientist responsible of a research group at ICRA on Integrated Management of Urban Water Systems and Sustainability Assessment. It involves the development of new monitoring tools, models for water quality prediction and methods for incorporating sustainability into decision-making. He has experience in leading national and international EU projects that involve fundamental and applied research, with more than 50 peer-reviewed journal papers. Participant in 3 IWA Task Groups (GHG on greenhouse gases emissions, GMP on good modelling practices, BSM on benchmarking wastewater treatment), and editor of the journal Water Practice and Technology from IWA Publishing since 2014. Principal inventor of a patent and enterpreneur, co-creator of InnoWatt spin-off. Dr. Corominas chairs the IWA Working Group on Life Cycle Assessment of Water and Wastewater Treatment, and is leading the initiative of delivering a roadmap for LCA studies applied to wastewater treatment. ORCID: https://orcid.org/0000-0002-5050-2389.
Maite received her PhD in Environmental Engineering at the University Autonomous of Barcelona in 2004.
After its completion she moved to the University of Queensland (Australia) to take a postdoctoral research position at the Advanced Water Management Centre (AWMC).
During her postdoctoral career she focused her research in aerobic granulation for the treatment of wastewater and nutrient removal optimization in conventional systems.
In 2010, she joined ICRA with a Ramon y Cajal Research fellowship.
PhD in Civil and Environmental Engineering from the University of Girona.
Leads the research line in sewer systems at the Catalan Institute for Water Research.
Adjunct Research Fellow of the Advanced Water Management Centre (AWMC), the University of Queensland (Australia). 2005 joined the AWMC as Research Fellow in the frame of the Australian Research Council project “Understanding the Biotransformation Processes in a Sewer System to Achieve Optimal Management (LP0454182)”. January 2008 became Subproject 6 Leader for the SCORe (Sewer Corrosion & Odour Research) ARC LP0454182
Principal Investigator of the Marie Curie project SGHGEMS “Sulfide and Greenhouse Gas emissions from Mediterranean sewers” (PIRG08-GA-2010-277050) funded by the EU.
Prof. Manel Poch graduated in Environmental Chemistry at the Universitat Autònoma de Barcelona (UAB) in 1979 and got his PhD in sciences at the Universitat Autònoma de Barcelona (UAB) in 1983 under the title “Modelització de la qualitat de l’aigua del riu Llobregat”. He is professor at the Laboratory of Chemical and Environmental Engineering from Universitat de Girona (Spain). He was awarded with the 6th so-called “sexenio” (the maximum number to be attained) recognising his research efforts over his professional career as a university professor. Prof. Poch research has evolved from the mathematical modelling of biotechnological and environmental processes, to the development of environmental decision support systems (EDSS), particularly those related to the urban water cycle and evaluated from an integrated point of view (sewer system, waste-water treatment plant and river). He is also active in projects related to the selection and maintenance of new waste-water treatment systems. He has published about 100 publications in internationals journals with peer review, H-index=26 and supervised 25 PhD. Prof Poch was founding partner in Sisltech, a spin-off which markets a supervision system for wastewater treatment plants, that implemented its software ATL at different locations throughout Europe and America. He is co-author of a divulgation book on application of decision support systems to water management that can be found at the web of his research group http://lequia.udg.es/. In the period 2009-2012 he joined Catalan Institute for Water Research Water (ICRA team) as senior researcher and group leader of Technologies and Evaluation Area.
Titular de la Cátedra de investigación del Canadá en Modelización de la Calidad del Agua (modelEAU) y Profesor del Departamento de Ingenieria Civil y de Ingenieria de las Aguas, Université Laval, Quebec, Canadá (CA)
Jose Porro is founder of Cobalt Water LLC and Cobalt Water EU BVBA , and brings more than 18 years' experience working with drinking water and wastewater utilities for planning, modeling, design, and applied research. He has worked on projects in the U.S., Latin America, Europe, Middle East, and Asia.
Dr. Juan A. Baeza is Associate Professor at Universitat Autònoma de Barcelona since December 2004. He is a researcher in Environmental Engineering since 1994. Dr. Baeza is in charge of the research line on nutrient removal from urban wastewater in the "GENOCOV group on biological treatment of liquid and gas effluents" of the Universitat Autònoma de Barcelona. This group has 25 members and it is recognized as Quality Research Group by the Catalan Government. He’s currently teaching courses at UAB in Chemical Engineering: Simulation and optimization of chemical processes, Control and instrumentation for chemical processes, Advanced control, Applied chemical kinetics, Engineering of biotechnological processes, Computer applications for chemical engineering.
Albert Guisasola and Canudas (Mataro, 1978) is a Chemical Engineer (UAB, 2001) and PhD in Environmental Sciences (UAB, 2005) with the thesis entitled "Modelling biological organic matter and nutrient removal from wastewater using Respirometric and titrimetric techniques" which receivedUAB PhD honors. After a postdoctoral stage at University of Queensland (Australia, 2006/2007) and University of Girona (2007), he is nowadays a lecturer in the Departament d'Enginyeria Quimica at UAB since 2007.