Calculation of nitrous oxide emissions of municipal wastewater treatment plants under dynamic loads with simulation models

Abstract

In recent years, the urgent need to address environmental pollution has highlighted the importance of effective wastewater treatment processes. Municipal wastewater treatment plants (WWTPs) are complex systems where numerous biochemical processes are employed to remove pollutants from wastewater before it is released into natural bodies of water. The increasing global focus on environmental sustainability mandates these facilities to operate under stringent regulatory frameworks to minimize their ecological footprint, particularly concerning greenhouse gas (GHG) emissions. Among the various GHGs, nitrous oxide (N2O) is of significant concern due to its potent global warming potential, which surpasses that of carbon dioxide by nearly 300 times. Traditional methods of wastewater treatment are often not equipped to handle the dynamic nature of influent streams, which can vary dramatically in composition due to a variety of factors including seasonal variations, industrial discharges, and climatic conditions. This variability can hinder the efficiency of biological processes like nitrification and denitrification, which are crucial for the removal of nitrogenous compounds, consequently increasing the emissions of N2O. This thesis introduces an approach using simulation models to predict and mitigate N2O emissions in municipal WWTPs under dynamic loading conditions. The study utilizes the Sumo software to simulate the operations of a WWTP based in the Marmara Region, Turkey. By incorporating real-time data and simulating various operational scenarios, this research aims to understand the impacts of fluctuating influent characteristics on the efficacy of N2O emission reduction. The objectives of this thesis are to calibrate and validate a simulation model that can accurately forecast the behavior of N2O emissions under variable loading conditions; and to propose operational adjustments and system optimizations that can significantly reduce the emission of N2O. Modeling and simulation results successfully demonstrated the dynamic behavior of N2O emissions in response to fluctuating wastewater characteristics, emphasizing that dynamic loading conditions can significantly influence N2O emissions. Peak emissions were closely associated with transient peaks in influent loads. It was crucial for understanding how different operational conditions and varying influent characteristics impact GHG emissions from wastewater treatment processes. The study observed that OD-1, primarily operating under anoxic conditions, demonstrated a lower N2O emission rate compared to the others. This is attributed to its limited oxygen availability, which is less conducive to the formation of N2O. In contrast, OD-2, which alternates between anoxic and aerobic conditions via controlled aeration, showed a slightly higher rate of N2O emissions. This reactor's operational flexibility helps mitigate emissions but still presents challenges in maintaining low emission levels during transitions between anoxic and aerobic conditions. OD-3 and OD-4, consistently operated under aerobic conditions, recorded the highest N2O emissions among the four reactors. The consistent aeration in these reactors supports aerobic processes that are necessary for efficient breakdown of organic matter; however, it also facilitates conditions that favor N2O production. The study points out that the operational design of OD-3 and OD-4, while effective for organic removal, does not optimize the control of N2O emissions. The findings indicate a clear relationship between the operational mode of each reactor and its N2O emissions, underscoring the importance of reactor configuration and management in controlling greenhouse gas emissions from WWTPs. Considering the information obtained from the simulation studies, it can be said that it is necessary of optimizing operational parameters like aeration rates and internal recycling flows to mitigate conditions conducive to N2O production. The study suggests that flexible operational strategies that can be adjusted in real-time based on the loading conditions are essential for reducing N2O emissions effectively. Furthermore, the thesis recommends the exploration and adoption of advanced nitrogen removal technologies, such as Anammox, which could potentially reduce N2O emissions further. This comprehensive analysis not only addresses the challenges of managing N2O emissions but also promotes the critical role of innovative operational strategies and ongoing research in advancing environmental management practices.