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

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», NO₃â», 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», NO₃â»), 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.