Modelling greenhouse gas emissions from biological wastewater treatment by GPS-X: The full-scale case study of Corleone (Italy)

Effects of aeration control strategies on nitrous oxide emissions in alternating anoxic-oxic sequencing batch reactor systems

Reducing N2O emissions is key to controlling greenhouse gases (GHG) in wastewater treatment plants (WWTPs). Although studies have examined the effects of dissolved oxygen (DO) on N2O emissions during nitrogen removal, the precise effects of aeration rate remain unclear. This study aimed to fill this research gap by investigating the influence of dynamic aeration rates on N2O emissions in an alternating anoxic-oxic sequencing batch reactor system. The emergence of DO breakthrough points indicated that the conversion of ammonia nitrogen to nitrite and the release of N2O were nearly complete. Approximately 91.73 ± 3.35% of N2O was released between the start of aeration and the DO breakthrough point. Compared to a fixed aeration rate, dynamically adjusting the aeration rates could reduce N2O production by up to 48.6%. Structural equation modeling revealed that aeration rate and total nitrogen directly or indirectly had significant effects on the N2O production. A novel regression model was developed to estimate N2O production based on energy consumption (aeration flux), water quality (total nitrogen), and GHG emissions (N2O). This study emphasizes the potential of optimizing aeration strategies in WWTPs to significantly reduce GHG and improve environmental sustainability.

Assessment and optimization of wastewater treatment plant in terms of effluent quality, energy footprint, and greenhouse gas emissions: An integrated modeling approach

Wastewater treatment plants (WWTPs) can benefit from utilizing digital technologies to reduce greenhouse gas (GHG) emissions and to comply with effluent quality standards. In this study, the GHG emissions and electricity consumption of a WWTP were evaluated via computer simulation by varying the dissolved oxygen (DO), mixed liquor recirculation (MLR), and return activated sludge (RAS) parameters. Three different measures, namely, effluent water quality, GHG emissions, and energy consumption, were combined as water-energy-carbon coupling index (WECCI) to compare the effects of the parameters on WWTPs, and the optimal operating condition was determined. The initial conditions of the A2O process were set to 4.0 mg/L of DO, 100 % MLR, and 90.7 % RAS. Eighty scenarios with various DO, MLR, and RAS were simulated under steady-state condition to optimize the biological treatment process. The optimal operating conditions were found to be 1.5 mg/L of DO, 190 % MLR, and 90.9 % RAS, which had the highest WECCI of 2.40 when compared to the WECCI of the initial condition (1.07). This optimal condition simultaneously reduced GHG emissions by 1348 kg CO2-eq/d and energy consumption by 11.64 MWh/d. This implies that controlling DO, MLR, and RAS through sensors, valves, and pumps offers a promising approach to operating WWTPs with reduced electricity consumption and GHG emissions while attaining effluent quality standards. Additionally, the nitrous oxide stripping rate exhibited linear relationships with the effluent total ammonia and nitrite concentrations in the aerobic reactor, suggesting that monitoring dissolved nitrogen compounds in the effluent and reactor could be a viable strategy to control MLR and DO in the biological reactor. The digital-based assessment and optimization tools developed in this study are expected to hold promise for application in broader environmental management efforts.

Direct and indirect monitoring methods for nitrous oxide emissions in full-scale wastewater treatment plants: A critical review

Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns.