Climate action: Transforming research into climate solutions
Making waves in wastewater: Charting a more sustainable future
Intersection
Making waves in wastewater: Charting a more sustainable future
Photo credit: Elsayed Elbeshbishy
Water Resource Recovery Facilities (WRRF) treat wastewater and recover valuable resources, such as energy, clean water and nutrients. According to Environment Canada, approximately six billion cubic meters of municipal wastewater is generated and treated by WRRFs annually. While crucial to safeguard water bodies, WRRFs produce large amounts of greenhouse gases (GHGs) that contribute significantly to global warming. Due to several factors, gathering and measuring reliable and standardized GHG emissions data from WRRFs presents many challenges. But, without it, climate action plans are greatly hindered.
Toronto Metropolitan University civil engineering professor Elsayed Elbeshbishy’s research outlines the critical need for accurate and consistent WRRF GHG emissions data. He recommends emerging technologies capable of delivering more precise and consistent data that overcome the limitations of traditional methods for measuring and reducing these emissions.
Why accurate emissions data is crucial
Accurately quantifying GHG emissions from WRRFs is critical for several reasons. First, WRRFs are considered the third-largest source of nitrous oxide emissions. Nitrous oxide is a potent greenhouse gas with a 273 times higher global warming potential than carbon dioxide, the main GHG emitted through human activities. Next, precise emissions measurement enables facilities to effectively manage and reduce emissions, ensuring their operations meet environmental compliance and sustainability goals.
Additionally, accurate data is essential for improving operational efficiency and selecting optimal treatment processes. Furthermore, it provides essential information for climate action inventories. “Inaccuracies in GHG emission data could result in misguided environmental policies and regulations and missed opportunities for meaningful reductions,” said professor Elbeshbishy.
TMU professor Elsayed Elbeshbishy, his research team and industry partners at the site of the first digital twin pilot sewer in Canada, located at the Komoka Wastewater Treatment Plant in Delaware, ON.
Photo credit: Elsayed Elbeshbishy
Challenges with conventional emissions tracking
Conventional methods for measuring GHG emissions from WRRFs rely on top-down approaches. They quantify emissions using standardized emission factors and simplified activity data, such as energy consumed and wastewater treated. There is a high degree of uncertainty with the current methods. This uncertainty is illustrated, for example, in the Environment Protection Agency’s 2023 report, which shows a fourfold increase in estimated nitrous oxide emissions compared to five years ago. This increase is due primarily to non-standardized measurement protocols, insufficient consideration of WRRF dynamics and, more importantly, the lack of tools to capture these dynamics.
“This variability hampers the ability to derive universal emission factors or predictive models that can be generalized across facilities, impacting the accuracy of national and global GHG emissions,” said professor Elbeshbishy.
The role of advanced technologies
Professor Elbeshbishy recommends advanced GHG emissions modelling technologies that enhance data accuracy and standardization and lead to operational efficiencies. These technologies include multi-level monitoring, data-driven models using machine learning, hybrid models (mechanistic and machine learning) and remote sensing.
He notes these emerging models can reflect process dynamics more effectively and allow researchers to better predict emissions patterns. These approaches provide advanced capabilities for handling complex datasets and can adapt to diverse treatment scenarios and site-specific conditions where traditional models might fall short. “Data-driven and hybrid models can allow for real-time adjustments and informed decision-making regarding operational strategies,” he said.
In his research, professor Elbeshbishy explains that improving WRRFs’ operational efficiencies using advanced technologies can substantially lower GHG emissions by fine-tuning treatment processes. He notes that advanced operational methods can capture and repurpose methane emissions as renewable energy. These process improvements not only enhance sustainability but also directly contribute to lowering WRRF’s carbon footprint.
His research into WRRF GHG emissions modelling lays the foundation for more sustainable wastewater management practices. “Enhanced emissions quantification and mitigation frameworks allow facilities to better manage their environmental impacts and shift towards sustainable resource recovery processes,” he said.
By providing reliable emissions data and actionable insights for reduction, these modelling advancements support a circular economy and contribute to broader climate action strategies.
Inaccuracies in GHG emission data could result in misguided environmental policies and regulations and missed opportunities for meaningful reductions.
The research described in this article was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).
