A knowledge discovery framework to predict the N₂O emissions in the wastewater sector

Area of Science:

• Environmental Engineering
• Wastewater Treatment
• Data Analytics
• Greenhouse Gas Emissions

Background:

• Nitrous oxide (N2O) emissions from wastewater treatment plants contribute significantly to the operational carbon footprint.
• N2O emissions can reach 7.6% of the ammonia-nitrogen load and up to 97% of the carbon footprint in specific biological phosphorus removal processes.
• Dissolved N2O concentration in sidestream sequence batch reactors (SBRs) can vary significantly despite similar operational conditions.

Purpose of the Study:

• To predict dissolved nitrous oxide (N2O) concentration in a full-scale sidestream sequence batch reactor (SBR).
• To identify operational conditions that mitigate N2O emissions.
• To develop a data-driven approach for predicting N2O emissions using low-cost sensors.

Main Methods:

• Deployment of data analytics, including density-based clustering, support vector machine (SVM), and support vector regression (SVR) models.
• Correlation analysis between dissolved N2O concentration and operational parameters like dissolved oxygen (DO), conductivity, and pH.
• Construction of SVM classifiers to predict N2O consumption and SVR models to predict N2O concentration during different SBR phases.

Main Results:

• Aerobic dissolved N2O concentration is correlated with the drop in aerobic conductivity rate (Spearman correlation coefficient = 0.7) and DO levels (Spearman correlation coefficient = -0.7).
• Operational strategies such as step-feeding, controlling initial ammonium concentrations, and adjusting aeration duration were identified to reduce N2O peaks (<0.6 mg/L).
• N2O is consumed after nitrite depletion during the denitritation phase (post-'nitrite knee').

Conclusions:

• The proposed data analytics approach accurately predicts N2O emissions using readily available sensor data.
• Predictive models can estimate N2O concentration and behavior within the SBR system.
• Mitigation strategies can effectively control N2O emissions, reducing the carbon footprint of wastewater treatment processes.