PORRO, JOSE, PORRO

Jose Porro is founder and CEO of Cobalt Water Global, an innovative ClimateTech company focused on decarbonization of the water industry. Prior to...

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About Porro, Jose

Jose Porro is founder and CEO of Cobalt Water Global, an innovative ClimateTech company focused on decarbonization of the water industry. Prior to founding the tech company in 2020 (company started as consultancy from 2014-2020), he had worked for over 20 years in the water industry as a consulting environmental engineer designing water (drinking water and wastewater) treatment facilities, modeling drinking water distribution system hydraulics, water quality, and energy consumption, and wastewater treatment process modeling across the US and internationally. Jose started water utility GHG accounting in 2008 working on projects with NYCDEP and NYSERDA, but eventually working as an international expert on GHG assessment, being recruited by the International Water Association (IWA) and the German Government to serve as the technical expert for the WaCCliM Project (2014-2019) helping to inventory and reduce GHG emissions from water utilities around the world. As part of the WaCCliM Project he also served as the technical lead for the development of the Energy and Carbon Assessment and Monitoring (ECAM) tool – the first web-based GHG assessment tool for the water industry. In 2010 Jose founded the IWA Task Group on GHG Modelling and has become the leading expert on wastewater N2O process emissions in practice. He has been recruited to participate in N2O applied research projects at the national level in the US, The Netherlands (for STOWA) and the UK (for UKWIR), and as one of the leading N2O experts in the world, was invited to give a keynote for the Melbourne Water Emission Impossible Workshop in Australia to talk about N2O. The N2ORisk DSS (AI/ML platform for WRRF N2O emissions accounting, reduction, and monitoring) is a product of his PhD research integrating AI and mathematical modelling for minimizing N2O emissions from WRRFs Jose was also co-editor and co-author of the recent IWA Publishing open access book, “Quantification and modeling of fugitive GHG emissions from urban water systems.” Jose is a licensed Professional Engineer in the State of New York, holds BS (Wilkes University) and ME (Manhattan College) degrees in Environmental Engineering, and is finishing a PhD in Bioscience Engineering from Ghent University.

Porro, Jose

Titles from this speaker

Accelerating N2O reduction from WRRFs in the Netherlands: A national effort

Abstract

Introduction Nitrous oxide (N2O) emissions from wastewater treatment have been increasingly the focus of decarbonization efforts in the water sector. This is because N2O can make up to 80 percent of a water resource recovery facility's (WRRF's) carbon footprint (Daelman et al., 2013) and most water utilities have no idea how much N2O they are emitting and most have no idea how to mitigate their N2O emissions. To tackle this problem a Community of Practice (CoP) was established in the Netherlands, which facilitated sharing knowledge and coordinating research on reducing nitrous oxide. But with the ambition of the Dutch wastewater utilities to be climate neutral by 2035, a coordinated national effort was needed. Therefore, the Association of Water Treatment Managers (VvZB) the Union of Water Authorities, Unie van Waterschappen (UvW), initiated a National N2O reduction acceleration program, consisting of three tracks: monitoring, mitigation, and an enabling ecosystem. The VvZB and UvW selected Witteveen + Bos and Cobalt Water Global for the first task of the acceleration program: analyze the N2O data collected from 21 water resource recovery facilities (WRRFs) among all of the water authorities to gain monitoring and mitigation insights. This paper summarizes the approach, results, and conclusions and lessons learned from the initial data analysis effort. Methods First the data was collected using a common data request template. Witteveen + Bos then used their data validation tool, Dataprophet, to assess data availability and measurement density (see Figure 1). Once the data was collected and initially assessed for completeness, the Dataprophet was also used to validate and clean the data (See Figure 2). Cobalt Water Global then used the validated data to calculate N2O emissions and emission factors (EFs) using their AI/ML N2O platform, the N2ORisk DSS. Machine learning (ML) was used to fill inherent gaps in the data and to gain more accurate emissions and emission factors (see Figure 3). Once emission factors were calculated, the WRRFs were grouped by predefined categories based on loading, whether anaerobic digestion was present, etc. to see if there were any trends based on WRRF type. The N2ORisk DSS was also used to gain knowledge-based AI insights on N2O formation risk per Porro et al. (2022). The data was also analyzed for identifying any possible trends with the calculated N2O emission factors and process parameters such as MLSS, dissolved oxygen, and TN removal efficiency. Results As anticipated, there was substantial variability in the N2O emission factors calculated for all of the sites based on % kg N2O/kg Influent TN (EF summary to be shared in final paper). There were no clear trends in N2O EF derived and WRRF groups. Figure 4 shows the results of the correlation analysis via liner regression. The TN removal efficiency had the highest correlation (negative) versus N2O EF. Figure 5 shows a strong correlation between removal efficiency, overall N2O formation risk (based on knowledge-based AI), and population equivalent versus N2O EF. By multiplying the removal efficiency (RE) by overall risk (OR) and by population equivalents (PE) we get the RExORxPE factor. The RE tells you how hard the WRRF is working, the OR tells you under what risk conditions the WRRF is working hard or not, and the PE indicates the amount of work done under those conditions. When analyzing the process conditions for individual WRRFs, the N2ORisk DSS consistently identified conditions in which N2O was forming under low DO risk conditions as well as High DO risk conditions and varied during different times of the year. This provide insight for mitigation because if it is clear that N2O is forming when risk is high, for example due to high DO, then the clear mitigation action will be to lower the DO. Conclusions The wastewater utilities in the Netherlands all joined forces to initiate an N2O reduction acceleration program. The first task was proved invaluable for deriving a country-specific EF, gaining insights on conditions resulting in higher N2O emissions, and potential actions for reducing N2O. The project has also been valuable from lessons learned in data collection, handling data issues, and the importance of proper sensor maintenance.

This paper was presented at WEFTEC 2025, held September 27-October 1, 2025 in Chicago, Illinois.

Presentation time

15:30:00

15:40:00

Session time

15:30:00

17:00:00

SessionDecarbonizing Water: International Efforts to Measure and Reduce Process N2O Emissions

Session locationMcCormick Place, Chicago, Illinois, USA

TopicClimate Change Adaptation and Resilience

Author(s)

Porro, Jose, van Beek, Wesley, Qiu, Yuge, Dekker, Arjan, Deeke, Alexandra, Hanneman, Saskia, Dekkers, Hugo, van Nieuwenhuijzen, Arjen

Author(s)J. Porro1, W. van Beek2, Y. Qiu1, A. Dekker2, A. Deeke, S. Hanneman3, H. Dekkers4, A. van Nieuwenhuijzen2

Author affiliation(s)Cobalt Water Global1, Witteveen + Bos2, Waterschap Limburg3, Waterschap Aa en Maas4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Sep 2025

DOI10.2175/193864718825160133

Volume / Issue

Content sourceWEFTEC

Word count12

Description: Field Evaluation of Ammonia-Based Aeration Control and N₂O Response in a...

Field Evaluation of Ammonia-Based Aeration Control and N₂O Response in a Full-Scale BNR Reactor

Abstract

[b]Objectives and Status[/b] Wastewater treatment plants are under growing pressure to reduce aeration energy while also mitigating direct greenhouse gas emissions, particularly nitrous oxide (N₂O). Although ammonia-based aeration control (ABAC) is increasingly implemented to reduce energy demand, its combined impacts on aeration energy, nitrogen removal performance, and N₂O emissions have not been quantified comprehensively at full scale. Most previous studies have evaluated either energy savings or N₂O behavior in isolation, leaving their integrated effects under real operating conditions uncertain. This study presents a full-scale comparison of energy–N₂O–performance trade-offs under ABAC and conventional fixed dissolved oxygen (DO) control strategies in a mainstream biological nutrient removal (BNR) reactor. To the authors' knowledge, this is the first study to compare high-DO, reduced-DO, and ammonia-based aeration control within the same full-scale mainstream BNR reactor using continuous dissolved N₂O monitoring. [b]Methodology[/b] The investigation was conducted in a nine-cell BNR reactor serving approximately 1.37 million population equivalents in western Canada and treating 40–50 MLD. Three aeration strategies were implemented sequentially: (i) fixed high DO (1.5–3.0 mg L⁻¹), (ii) feedback-Feedforward ABAC using online ammonia measurements to dynamically adjust airflow to meet effluent ammonia targets, and (iii) fixed reduced DO (0.8–2.0 mg L⁻¹). Each strategy was operated for 3–4 weeks under steady-state conditions. Two online Clark-type dissolved N₂O sensors were installed along the reactor length to continuously measure dissolved N₂O, which was converted to gaseous emissions using established mass-transfer relationships. Blower airflow, bioreactor power, and specific aeration energy were extracted from the plant SCADA system, while routine grab samples and online sensors were used to monitor NH₄⁺–N, NO₂⁻–N, NO₃⁻–N, COD, DO, pH, temperature, and dissolved N₂O concentrations. [b]Findings[/b] All aeration strategies achieved stable organic matter removal, with COD removal ranging from 78–83% across all phases. Ammonia removal efficiencies were 98.2% under high DO, 96.9% under ABAC, and 89.0% under reduced DO, with ABAC consistently maintaining effluent ammonia within compliance limits despite operating at lower average DO. Relative to the high-DO baseline, ABAC reduced airflow by 16% and bioreactor power by 18%, lowering the specific aeration energy from 0.096 to 0.086 kWh m⁻³. Operation under reduced DO achieved larger energy savings, with airflow and power reductions of 24% and 26%, respectively, and specific energy decreasing to 0.074 kWh m⁻³. Despite these reductions in aeration demand, N₂O emission factors expressed as a percentage of total nitrogen removed remained statistically similar for high DO (0.50%) and ABAC (0.50%), demonstrating that ABAC delivered meaningful energy savings without increasing N₂O emissions. In contrast, reduced DO operation decreased the N₂O emission factor to 0.37% but was associated with reduced nitrification robustness, as reflected by lower ammonia removal efficiency. These results indicate that while more aggressive DO reduction can suppress N₂O formation, ABAC achieves a favorable balance by lowering energy consumption while preserving both treatment performance and N₂O stability. [b]Significance[/b] This full-scale dataset demonstrates that ammonia-based aeration control can achieve significant reductions in aeration energy while maintaining nitrogen removal performance and without increasing N₂O emissions relative to conventional high-DO operation. The findings provide rare full-scale evidence that dynamic ammonia-feedback-feedforward aeration represents an energy-efficiency strategy that is climate-neutral with respect to N₂O, whereas more aggressive DO minimization introduces trade-offs between energy savings, emissions, and process stability.

This paper was presented at WEFTEC 2026 in New Orleans, Louisiana.

SpeakerAlsaleh, Marwan

Presentation time

15:30:00

16:00:00

Session time

15:30:00

17:00:00

SessionDecarbonizing Water: Facility Optimization and N2O

Session locationErnest N. Morial Convention Center

TopicFacility Operations and Maintenance

Author(s)

Alsaleh, Marwan, Zaghloul, Mohamed Sherif, Alsayed, Ahmed, Elsayed, Ahmed, Al-Omari, Ahmed, Cavanaugh, Shannon, Kakar, Farokh Laqa, Da Silva, Allegra, Jayathilake, Nisa, Porro, Jose, Santoro, Domenico, Ismail, Amr, Elbeshbishy, Elsayed

Author(s)M. Alsaleh1, M. Zaghloul2, A. Alsayed3, A. Elsayed4, A. Al-Omari5, S. Cavanaugh5, F. Kakar5, A. Da Silva5, N. Jayathilake6, J. Porro7, D. Santoro8, A. Ismail4, E. Elbeshbishy4

Author affiliation(s)Toronto Metropolitan University, 1United Arab Emirates University, 2Western University, 3Toronto Metropolitan University, 4Brown and Caldwell, 5Brown and Caldwell, 5Brown and Caldwell, 5Brown and Caldwell, 5City of Calgary, 6Cobalt Water Global, 7USP Technologies, 8Toronto Metropolitan University, 4Toronto Metropolitan University, 4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Sep 2026

DOI10.2175/193864718825160536

Volume / Issue

Content sourceWEFTEC

Word count15

Description: Full-Scale Testing of Intentional Operational Changes to Influence Nitrous Oxide...

Full-Scale Testing of Intentional Operational Changes to Influence Nitrous Oxide Emissions

Abstract

Halton Region collects and treats wastewater from over 600,000 people in Ontario, Canada. In early 2024, the Region installed online dissolved nitrous oxide (N₂O) sensors at their two largest wastewater treatment plants (WWTPs), Skyway and Mid-Halton, and initiated a two-year project to measure and mitigate N₂O. N2O is a potent greenhouse gas (GHG) and the Region's largest emission from wastewater treatment. The objectives of the project include: (1) Measure N2O emission factors for Mid-Halton and Skyway; (2) Identify operating parameters influencing N2O, and (3) identifying low-cost operational optimization N2O mitigation techniques. Results of objectives 1 and 2 were presented at WEFTEC 2025. This presentation focuses on the full-scale mitigation testing (Objective 3) conducted at the Mid-Halton WWTP. Skyway WWTP is testing optimizations not directly related to N2O and therefore not reported here. Table 1 summarizes the operational optimization strategies tested at full-scale at Mid-Halton WWTP with potential to reduce N2O emissions. These strategies were developed and refined with the plant operations staff, as they considered current operational practices and limitations that exist both in operations (i.e. maintaining effluent quality) and physical equipment and control systems. The testing was constrained by the requirement to work within the plant's existing systems; there were no changes to SCADA programming and no capital improvements. As of writing, three of four tests are complete and the fourth will finish imminently (Feb. 2026). Tables 2, 3 and 4 summarize the process changes for each test and the schedule. Some tests included additional field sampling and data collection such as bioreactor dissolved oxygen (DO) profiling. The Region's Technical Services supported operations staff with customized field data collection sheets and standard procedures. Other than some additional grab sampling, the testing was constrained to using the existing data sources and frequency. For example, there was no online ammonia sensor available, only the typical composite sampling for ammonia available 2-3 times per week. Data and information sources included SCADA data for online instrumentation and flowmeters (including N₂O sensors), laboratory data from the Region's laboratory information management system, a plant operations spreadsheet with daily summary operating parameters maintained by plant staff, digital operator logbook, and N₂O sensor maintenance log. An important activity was weekly check-in meetings between plant operations, the Region's Technical Services, and consultant. These meetings provided immediate information about what was happening at the plant, allow fast decisions on adjusting test plans, and give plant staff an open forum for discussing how to run the plant and what changes would be acceptable within their experiences. Increasing the RAS rate from 45% to 75% during test periods 3 and 10 (Figures 1 to 3) saw slight N₂O reductions, however in test period 5 N₂O increased despite the higher RAS likely due to high DO risk. Running the secondary swing zones under anoxic conditions resulted in higher N₂O emissions, not lower (Figures 4 and 5). Although operations staff were able to step feed sufficient carbon to achieve low DO, there was little denitrification and higher nitrite leading to higher N2O. There were also limitations in airflow control – the law of unintended consequences found it was difficult to predict ahead of time all the follow-on effects of operational changes. Optimizing aeration is a common approach in the literature for mitigating N2O. The difficulty at Mid-Halton, however, was the constraints on both the physical airflow distribution (only one airflow controller for the whole train and limits on how the manual dropleg valves could be adjusted) and limits on how SCADA controls could be tweaked. Testing included using one DO probe for control (instead of normal averaging two probes) and adjustments to DO setpoints. Lowering the DO did not provide significant N₂O reductions (Figures 6 and 7). Because airflow cannot be controlled independently at the front and back of the tank, DO remained high in the back, leading to elevated N₂O. Slight reductions were observed when DO setpoints were lowered to 1.6–1.8 mg/L (from the normal 2.0–2.5 mg/L); however, the improvement was marginal, N₂O remained elevated due to high DO risk, and further lowering the setpoint was not recommended operationally. The higher MLSS / higher SRT testing will be finished imminently. Preliminary results (Figure 8) suggest that other factors, such as high DO, overshadowed SRT-driven changes in N₂O. Results of all tests will be fully described in the full paper. Halton Region's N2O project has demonstrated the need for considering site-specific operational practices and limitations. These results indicate it can be challenging to determine the exact cause of N₂O reductions during full-scale operations, as N₂O is highly influenced by multiple parameters. Halton Region's experience demonstrates, however, that to improve things sometimes we need to figure out how to make them worse. Understanding the conditions when N2O emissions have been higher has been a useful exercise. The Region plans to consider the outcomes of the intentional mitigation tests and how their typical operations may be modified for a more sustainable and optimized treatment process.

This paper was presented at WEFTEC 2026 in New Orleans, Louisiana.

Presentation time

16:00:00

16:30:00

Session time

15:30:00

17:00:00

SessionDecarbonizing Water: Facility Optimization and N2O

Session locationErnest N. Morial Convention Center

TopicFacility Operations and Maintenance

Author(s)

Kraemer, Jeremy, Pharand, Lizanne, Duong, John, sabzwari, Aby, Porro, Jose

Author(s)J. Kraemer1, L. Pharand2, J. Duong2, A. sabzwari1, J. Porro2

Author affiliation(s)GHD Group Pty Ltd., 1GHD Group Pty Ltd., 1Cobalt Water Global, 2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Sep 2026

DOI10.2175/193864718825160431

Volume / Issue

Content sourceWEFTEC

Word count12

Halton Region's Operations-Focused Nitrous Oxide Monitoring and Mitigation

Abstract

Halton Region in Ontario, Canada, collects and treats wastewater from over 600,000 people. The Region's WRRF operations staff have learned that N2O emissions can be an indicator of stress to the biology causing treatment inefficiencies. By adjusting process conditions to remove stressors, staff hope to both optimize treatment and prevent N2O emissions. The Region initiated a two-year project to pursue 'low hanging fruit' N2O mitigation strategies through operational optimization they believe are achievable based on current global experience. The objectives include: (1) Measuring WRRF N2O emission factors; (2) Identifying the operating parameters influencing N2O emissions and identifying low-cost operational optimization mitigation techniques; (3) Verifying the effectiveness of operational changes to reduce N2O emissions.

The Region installed online dissolved N2O sensors at two of its largest facilities. Figure 1 illustrates the locations of dissolved oxygen and N2O sensors. Once N2O emissions are measured within different areas of the plant, actions can be taken to reduce these emissions.

Table 1 summarizes the measured N2O emission factors from the first eight months of monitoring. Interestingly, the emission factors differ dramatically between Halton's two WRRFs and differ from the typical default which reaffirmed their decision to measure N2O.

Operations staff used a standard procedure and field data collection sheet at both plants to conduct quarterly bioreactor nitrogen profiling. The nitrogen concentration results for Mid-Halton WWTP in

#Figure 2 show the highest nitrification rates were at positions 4 and 5 which corresponded to the highest measured N2O concentrations.

#N2O concentrations varied continuously on an hourly, daily, and seasonal basis (Figure 3). High data sampling frequency from the N2O sensors, to capture short-term variation in emissions, presents data collection, handling and manipulation challenges. In addition, five different sources of data from each facility must be compiled together: SCADA data for online instrumentation and flowmeters including N2O sensors, laboratory data from the Region's laboratory information management system, a plant operations spreadsheet with daily summary operating data which is maintained manually by staff, the plant's digital operator logbook, and the N2O sensor maintenance log.

#Operations staff have observed the solids retention time can significantly influence N2O production. Figure 4 illustrates a period in March 2024 when the mixed liquor concentration at Mid-Halton was being increased in tank #12 in preparation to bring tank #11 back online which had been out of service for several months for repairs. During the initial rise from March 9 to 14 the N2O concentration decreased by about 50%. Then Tank #11 was brought online March 14 which divided Tank #12's mass inventory and the ammonia load was spread across two tanks resulting in lower mixed liquor concentrations and a further 50% decrease in N2O concentration. By the end of March, the N2O concentration was about 85% lower than at the beginning of the month, which clearly demonstrated how operational changes can influence N2O generation.

At Skyway, the Region dewaters biosolids using a centrifuge about 6 hours per day for 5 or 6 days per week. The centrate is stored and then returned to the headworks the same day typically over a four-hour period from 4 to 8pm. This slug load of ammonia reaches the aeration tanks in the late evening around midnight when influent loads are low. The impact of this time-varying ammonia loading is observable in the N2O concentrations of Figure 5. Each day when there is dewatering there is a corresponding spike in N2O generation around midnight, while on the weekend when there is no dewatering there is no midnight N2O spike.

Many of the Region's WRRFs experience seasonal alkalinity deficiency during warmer drier months of the year which results in low effluent pH. The Region observed an inverse relationship between N2O and pH at both plants: higher N2O is seen when effluent pH is lower (Figure 6). This relationship is stronger at Skyway which does not control pH in secondary treatment, while the relationship is not as strong at Mid-Halton where magnesium hydroxide is added as alkalinity supplement (although the dosing is limited to manage operating costs).

Dissolved oxygen (DO) that is too low or too high is the most common N2O risk and optimizing aeration is the most common mitigation strategy. Due to difficulty with aeration turndown and control, both Skyway and Mid-Halton have quite high DO concentrations, routinely >4 mg/L, whereas a DO of 1-2.2 mg/L would be recommended by the Cobalt Water Global N2ORisk AI/ML Decision Support System (Figure 7) being used on this project. The Region observed that changes in N2O concentrations are often aligned with DO changes (Figure 8).

Halton Region's operations team has found value from N2O measurement. The need to collect many data sources on regular basis and review changes in plant operation has been valuable for understanding how its facilities are performing. Operations staff have been keenly interested in the new data and have taken initiative to mitigate N2O emissions, for example at Skyway they are working to manage dewatering centrate returns over a longer duration. The project team is selecting several operational changes that will be field tested during 2025 to assess low-cost N2O mitigation.

This paper was presented at WEFTEC 2025, held September 27-October 1, 2025 in Chicago, Illinois.

Presentation time

13:30:00

13:45:00

Session time

13:30:00

15:00:00

SessionDecarbonizing Water: N2O Modeling - From Low DO to HPO

Session locationMcCormick Place, Chicago, Illinois, USA

TopicOptimization of Municipal Facility Operations

Author(s)

Kraemer, Jeremy, Pharand, Lizanne, Duong, John, Sabzwari, Aby, Beelen, Benjamin, Porro, Jose

Author(s)J. Kraemer1, L. Pharand2, J. Duong2, A. Sabzwari1, B. Beelen1, J. Porro4

Author affiliation(s)GHD Group Pty Ltd.1, Regional Municipality of Halton, Oakville2, Metro Water Recovery3, Cobalt Water Global4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Sep 2025

DOI10.2175/193864718825159974

Volume / Issue

Content sourceWEFTEC

Word count9

Monitoring and Mitigation of Nitrous Oxide Emissions from WRRFs

SpeakerPORRO, JOSE

Presentation time

13:30:00

15:00:00

Session time

13:30:00

15:00:00

TopicIntermediate Level, Facility Operations and Maintenance, Municipal Wastewater Treatment Design, Research and Innovation, Sustainability and Climate Change

Author(s)

PORRO, JOSE

Author(s)Jose Porro1; Amanda Lake2; Christopher Marks3; Colin Fitzgerald4; Nerea Uri5; Giulia Pizzagalli6

Author affiliation(s)Cobalt Water Global, USA1; Jacobs, UK2,City of Boulder Public Works, CO3; Jacobs, UK4; VandCenter Syd, Denmark; 6Anglian Water Services, UK5

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2022

DOI10.2175/193864718825158591

Volume / Issue

Content sourceWEFTEC

Word count10

Description: Operational Strategies and N2O Emission Hotspots: Integrating Monitoring Campaigns...

Operational Strategies and N2O Emission Hotspots: Integrating Monitoring Campaigns with Process Modelling

Abstract

Introduction and Objectives The wastewater sector is transforming as traditional wastewater treatment plants (WWTPs) evolve into water resource recovery facilities (WRRFs) to tackle resource scarcity. Simultaneously, the urgency of climate change calls for rapid progress toward net-zero greenhouse gas (GHG) emissions. However, achieving net-zero in the wastewater sector presents complex challenges. Scope 1 GHG emissions, especially methane (CHâ‚„) and nitrous oxide (N2O) from biological processes, are particularly significant as reliance on renewable energy reduces Scope 2 emissions. N2O is especially concerning, with a global warming potential (GWP) 265 times that of CO2 over 100 years, contributing up to 80% of a WRRF's carbon footprint. It is also the primary ozone-depleting substance of the 21st century [1]. Mitigating N2O emissions from WRRFs is challenging due to their high temporal and spatial variability, which complicates capturing actual emissions through short campaigns or simple methods. Factors like campaign duration, sampling frequency, and measurement techniques greatly influence results, limiting the effectiveness of monitoring efforts [2]. Additionally, N2O emissions depend heavily on process type, operational strategies, plant configuration, and environmental conditions, making standardized mitigation protocols difficult. Linking emissions to operational modes, control strategies, and environmental factors is crucial for effective mitigation [3]. This work presents a full-scale case study of N2O emissions as part of a project examining Canadian facilities with diverse treatment goals, operations, and designs. It underscores the significant impact of operational decisions on N2O production and emissions, challenging the assumption that plant design and process type are the primary determinants. By combining quantification, mathematical modeling, microbial analysis, and data analytics, this study demonstrates how a multi-faceted approach can overcome the limitations of monitoring campaigns and elucidate the complex relationships governing N2O dynamics. Methodology N2O emissions were monitored continuously using liquid sensors (Unisense A/S Denmark), and process data were collected from the biological treatment reactor in a local WRRF located in Canada, over 6 months period. During the campaign, different flow modes were testing including step-feed, semi-plug flow, and plug flow. Key parameters were measured including total ammonia nitrogen (NHx), nitrite (NO2-), nitrate (NO3-), soluble and total chemical oxygen demand (sCOD, TCOD), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), volatile suspended solids (VSS), dissolved oxygen (DO), pH, and temperature. A mathematical model representing two-AOB pathways and HB pathways was calibrated using effluent data for NHâ‚“, NO2â», NOâ‚ƒâ», and N2O sensor data. Findings This study examines how operational changes in a biological wastewater treatment reactor influence N2O emissions. The monitoring campaign tracked dynamic shifts across three operational configurations: step-feed, semi-plug flow, and plug flow. Figure 1 shows that plug flow mode resulted in the highest N2O emissions (average: 0.120 mg N-N2O/L), followed by semi-plug flow (0.0115 mg N-N2O/L) and step-feed mode (0.0095 mg N-N2O/L). While nitrogen removal during step-feed was minimal, transitioning to semi-plug flow (fewer feeding points) increased nitrification rates, elevating N2O concentrations. Ammonia removal efficiency rose from 22% (step-feed) to 60% (semi-plug flow) and 85% (plug flow), but this alone could not explain the observed emission patterns. Notably, N2O concentrations dropped significantly in late July despite unchanged operational modes, possibly due to ammonia-oxidizing bacteria (AOB) acclimation, reducing intermediates like NO2â» and NH2OH. Ongoing microbial analysis aims to confirm this. A mathematical model was calibrated using effluent nitrogen and N2O concentration data from step-feed and semi-plug flow operations. Figure 3 compares observed and simulated concentrations of nitrogen species (NHâ‚“, NO2â», NOâ‚ƒâ»), with the model qualitatively capturing nitrogen dynamics. Key parameters influencing the model were AOB kinetics (e.g., maximum specific growth rate and oxygen affinity constant) and NOB oxygen affinity, highlighting NO2â»'s role in N2O production via the nitrifier denitrification pathway. The model revealed emission hotspots across reactor segments (Pass 1/1 to Pass 4/3), confirming increased emissions following the transition to semi-plug flow (Figure 4). The study underscores limitations of sensor-based N2O monitoring without strategic sensor placement. Figure 5 shows aggregated N2O concentrations from model simulations along reactor segments, revealing discrepancies between actual sensor readings and emission hotspots. For instance, during the final 10 days of operation, sensors captured less than 20% of actual emissions, emphasizing the importance of mathematical modeling for effective monitoring and accurate quantification. In conclusion, this study highlights the significant influence of operational patterns on N2O emissions and the necessity of integrating mathematical models into monitoring and mitigation strategies. Future research will incorporate advanced analytics and AI-based methods, extending the work to multiple wastewater treatment facilities in Canada.

This paper was presented at WEFTEC 2025, held September 27-October 1, 2025 in Chicago, Illinois.

Presentation time

16:30:00

16:45:00

Session time

15:30:00

17:00:00

SessionDecarbonizing Water: Mathematical Modeling and Digital Twins to Reduce N2O Emissions from WWTP

Session locationMcCormick Place, Chicago, Illinois, USA

TopicResearch

Author(s)

Khalil, Mostafa, AlSaleh, Marwan, AlSayed, Ahmed, Elsayed, Ahmed, Zaghloul, Mohamed, Al-Omari, Ahmed, Kakar, Farokh Laqa, Bell, Katherine, Cavanaugh, Shannon, Porro, Jose, Elbeshbishy, Elsayed

Author(s)M. Khalil1, M. AlSaleh2, A. AlSayed3, A. Elsayed2, M. Zaghloul, A. Al-Omari4, F. Kakar4, K. Bell5, S. Cavanaugh4, J. Porro, E. Elbeshbishy2

Author affiliation(s)Stantec, Inc.1, Toronto Metropolitan University2, Northwestern University3, Brown and Caldwell4, Hazen and Sawyer5, Cobalt Water Global6

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Sep 2025

DOI10.2175/193864718825160129

Volume / Issue

Content sourceWEFTEC

Word count13

Description: Technical Brief 1: Pursuing Low-Cost Operational Changes to Mitigate Nitrous Oxide...

Technical Brief 1: Pursuing Low-Cost Operational Changes to Mitigate Nitrous Oxide at Two Halton Region WRRFs

Abstract

INTRODUCTION Nitrous oxide (N2O) is a potent greenhouse gas (GHG) with a global warming potential of nearly 300 times that of carbon dioxide. It is a major contributor to Scope 1 direct process emissions from wastewater treatment plants. In Ontario, Canada, the provincial electrical grid is already decarbonized, with a grid intensity <40 g-CO2-e/kWh, compared to the global average around 480 g-CO2-e/kWh (Ritchie and Rosado, 2020). Owing to low GHG emissions from electricity consumption, the Regional Municipality of Halton (Halton Region or Region) has estimated that N2O from treatment contributes a majority portion of Scope 1 GHG emissions at their wastewater treatment plants (WWTP). The Region's plant operations staff understand that N2O emissions may be an indicator of 'unhappiness' of the biomass and sub-optimal environmental conditions for treatment. Optimizing these conditions and removing stressors can reduce N2O emissions which is why operational optimization can be an effective mitigation approach. However, N2O generation mechanisms are complex due to multiple production and consumption pathways covering high dissolved oxygen (DO), low DO, and anoxic conditions (Figure 1; readers are referred to Ye et al. (2022) for a comprehensive explanation of N2O generation). Owing to variability of N2O production across space (i.e. different areas of a bioreactor) and time (daily, seasonally, yearly), Halton Region determined N2O needs to be measured to know what is happening. Therefore, the Region has undertaken a 2-year project with the goal of pursuing 'low hanging fruit' N2O mitigation strategies through operational optimization which is achievable based on current global experience. The objectives of the work include: (1) Measuring N2O emission factors (EFs) for Mid-Halton and Skyway WWTPs; (2) Identifying the operating parameters influencing N2O emissions & identifying low-cost operational optimization mitigation techniques; (3) Verifying the effectiveness of operational changes to reduce N2O emissions. METHODS In early 2024 Halton Region installed online dissolved nitrous oxide sensors (Unisense Environment, Aarhus, Denmark) at their two largest WWTPs, Skyway and Mid-Halton, with sensor data recorded starting early February 2024. The Skyway WWTP is rated 140 ML/d average day flow, with liquid treatment comprised of primary clarification, nitrifying activated sludge configured as long single pass tanks operated fully aerobic, dual-point ferric addition for phosphorus removal, deep bed tertiary sand filters, and sludge treatment comprised of thickening, anaerobic digestion, and dewatering. The Mid-Halton WWTP is rated 125 ML/d average day flow, with liquid treatment comprised of primary clarification, nitrifying activated sludge configured as 5-pass serpentine tanks operated with a year-round anoxic zone plus a seasonal anoxic swing zone, ferric addition for phosphorus removal, and sludge treatment comprised of thickening, anaerobic digestion, and dewatering. Figure 2 illustrates the bioreactors of both plants along with locations of dissolved oxygen and N2O sensors. Halton Region will conduct quarterly nitrogen profiling along the length of the bioreactors containing N2O sensors to understand where nitrogen conversion and N2O generation risk is highest. The Region created a standard operating procedure (SOP) and field data collection sheet for operations staff. The first bioreactor profiling was conducted at both plants on July 8, 2024 and the second one on October 28, 2024. Three sources of data are used: online SCADA data, lab data from the Region's laboratory information management system (LIMS), and a plant operations spreadsheet with daily summary data and operating information. A 'data lake' is generated by aggregating all data sources together through data merging and processing and calculating derived data (e.g. N2O mass emission rate, ammonia turn-over rate). Data analytics were performed including correlation 'heat maps' and other data synthesis. Artificial intelligence (AI) and machine learning (ML) methods are being applied through Cobalt Water Global's N2ORisk DSS which helps understand risk conditions for N2O, and is creating site- and location-specific ML models that will be used as soft-sensors later in the project when mitigations are tested. RESULTS Figures 3 and 4 illustrate the first 3 months of dissolved N2O concentrations measured at sensor location #1 at the Skyway and Mid-Halton WWTPs. As of writing this abstract, the second batch of data (months 4 through 8) are being analyzed. Four operational factors anticipated to relate to N2O generation are discussed. Dissolved Oxygen Both plants have high DO concentrations, routinely >4 mg/L, whereas the Cobalt Water N2O DSS recommends DO of 1-2.2 mg/L (Figure 5). There are periods of time when N2O generation is well aligned with DO changes (Figure 6). Optimizing aeration is the most common N2O mitigation technique owing to energy savings often being a co-benefit. Several European plants such as Eindhoven (Netherlands) have realized N2O reduction through DO adjustment (Porro et al., 2017) and this operational change is applicable to both Mid-Halton and Skyway WWTPs. Ammonia Turn-Over Rate Ammonia turn-over rate influences N2O generation at Mid-Halton and Skyway WWTPs. Figure 4 illustrates the Mid-Halton bioreactor MLSS compared to N2O concentration. The drop in both concentrations around March 14, 2024 was related to bringing the neighboring bioreactor online after a maintenance period. Operations staff built up the mass inventory prior to that time. With another bioreactor online the ammonia concentration and loading rate dropped resulting in less N2O. Bioreactor profiling was useful for understanding the nitrogen turnover rate along the length of the bioreactors, illustrated in Figure 7 for Mid-Halton and Figure 8 for Skyway. Although there is uncertainty in the nitrogen values at Mid-Halton (total nitrogen balance drops and then rises again), the highest ammonia turn-over rate was at positions 3, 4, and 5 at Mid-Halton (Figure 7) while it was positions 1 and 2 at Skyway (Figure 8). N2O production at a given sampling location was correlated with ammonia conversion in the upstream location (Figure 9). Skyway's profile was unexpected, and on investigation it was determined the ferric dosing prior to aerated grit tanks was resulting in extremely high primary clarifier performance with 80% TSS and 68% BOD removals. This extreme performance resulted in low organic loading to the Skyway bioreactors allowing immediate nitrification at the front of the tanks. These observations suggest a potential operational change to increase the MLSS so the biomass-specific ammonia turn-over rate will decrease. This mitigation was observed to reduce N2O emissions by 74% at the AvedÃ¸re WRRF (Andersen et al., 2022). Similarly, increasing the return activated sludge rate to dilute nitrogen concentrations and shorten hydraulic retention to push activity further down the train may also be useful. Diurnal Centrate Loadings At Skyway, the Region dewaters biosolids using a centrifuge about 6 hours per day for 5 or 6 days per week. The centrate is stored and then returned to the headworks over a four-hour period around 4-8pm. This slug load of ammonia reaches the aeration tanks in the late evening around midnight. The impact of this time-varying ammonia loading is observable in the N2O concentrations of Figure 10. Each day when there is dewatering there is a corresponding spike in N2O generation around midnight, while on the weekend when there is no dewatering there is no midnight N2O spike. At Skyway, extending the duration of centrate return would decrease the slug load ammonia. Rodriquez-Caballero et al. (2015) lowered N2O in a full-scale SBR with this technique. This approach is not applicable at Mid-Halton since dewatering centrate is already fully equalized and returned over a 24-hour period. pH There was also an observed correlation between N2O and pH at both plants (Table 1). This relationship could also be observed in the data (Figure 11). DISCUSSION/CONCLUSIONS Based on the first 3 months of data, Halton Region has observed N2O generation at the Skyway and Mid-Halton WWTPs is correlated as expected with DO levels, ammonia loadings, mass inventory levels, and pH. It is already clear there will be several operational changes that can be tested during year 2 to assess low-cost N2O mitigation. Interestingly, the N2O emission factors are quite different between the two WWTPs and differ from the IPCC default (Table 2). These results confirm the site-specific nature of N2O emissions and reaffirms the need for WRRFs to measure N2O to know their actual emission rate. At time of writing data from June to September is being analyzed. The conference presentation will include all data available and the Region will have decided which operational changes to test.

This paper was presented at the WEF Residuals & Biosolids and Innovations in Treatment Technology Joint Conference, May 6-9, 2025.

SpeakerKraemer, Jeremy

Presentation time

14:15:00

14:20:00

Session time

13:30:00

15:00:00

SessionHow DO Setpoints and Control Impacts Performance and Emissions

Session numberITT Session 04

Session locationBaltimore Convention Center, Baltimore, Maryland, USA

TopicAdvanced Biological Nutrient Removal (ABNR), Aeration Control, Full-scale Optimization, Greenhouse Gases, Microbial communities, Modeling, Nitrogen, Nitrous oxide, Simultaneous Nitrification-Denitrification (SND)

Author(s)

Kraemer, Jeremy, Duong, John, Baker, Chandra, Oberoi, Sanjeev, Pharand, Lizanne, Porro, Jose, Andersen, Mikkel, Ye, Liu, de Haas, David, Bhayani, Bhavin, Beelen, Benjamin, Sabzwari, Aby

Author(s)J. Kraemer1, J. Duong C. Baker2, S. Oberoi2, L. Pharand2, J. Porro3, M. Andersen L. Ye4, D. de Haas1, B. Bhayani1, B. Beelen1, A. Sabzwari1

Author affiliation(s)GHD, 1The Regional Municipality of Halton, 2Halton Regional Laboratory, 2Cobalt Water Global, Inc., 3

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date May 2025

DOI10.2175/193864718825159750

Volume / Issue

Content sourceResiduals and Biosolids Conference

Word count17

Base text size -

Porro, Jose

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