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Funding Information: The authors acknowledge the support provided by InterPhD2 Program funded by European Union/European Social Fund (Project No. POWR.03.02.00-IP.08-00-DOK/16 ). Publisher Copyright: © 2022 The Authors Activated sludge models (ASMs), extended with an N2O emission module, are powerful tools to describe the operation of full-scale wastewater treatment plants (WWTPs). Specifically, such models can investigate the most contributive N2O production pathways and guide towards N2O and carbon footprint (CF) mitigation measures. A common practice is to develop and validate models using data from a single WWTP. In this study, a successfully validated model in one plant (Slupsk/Poland) was extrapolated to another full-scale WWTP (Viikinmäki/Finland). For this purpose, the previously developed ASM No. 2d with the N2O module was used. Moreover, the results of calibration and validation of that model were compared with those obtained on the basis of the ASM No. 3 with an N2O module. A novel, rigorous calibration protocol, based on the system engineering approach, was implemented to minimize the number of adjusted parameters without compromising the accuracy of model predictions. The validated model accurately predicted the behavior of the system in terms of the liquid N2O production in the bioreactor and gaseous N2O emissions. Model-based identification of N2O production pathways revealed the key role of heterotrophs duo to their high abundance in the microbial community. The N2O emission factor (EF) at the studied plant was found between 0.9 and 0.94% of the influent TN-load for the validation and calibration period, respectively. Based on the model predictions, it was estimated that the aerobic zones contributed to over 93% of the N2O emitted to the atmosphere, while the remaining portion (7%) resulted from the N2O liquid-gas transfer in the non-aerated zones. The difference between the predicted N2O EF and the empirical EF calculation would lead to almost 1000 tonnes of CO2 equivalent reduction of the annual CF of the plant, which highlights the importance of model applications in CF studies.

Funding Information: The authors acknowledge the support provided by InterPhD2 Program funded by European Union/European Social Fund (Project No. POWR.03.02.00-IP.08-00-DOK/16). The authors would like to further thank ?Finnish Water Utilities Development Fund? that provided funding for the project ?Carbon Footprint of Finnish Wastewater Treatment Plants?. Funding Information: The authors acknowledge the support provided by InterPhD2 Program funded by European Union / European Social Fund (Project No. POWR.03.02.00-IP.08-00-DOK/16 ). The authors would like to further thank “Finnish Water Utilities Development Fund” that provided funding for the project “Carbon Footprint of Finnish Wastewater Treatment Plants”. Publisher Copyright: © 2021 The Authors Finland and Poland share similar environmental interests with regard to their wastewater effluents eventually being discharged to the Baltic Sea. However, differences in the influent wastewater characteristics, treatment processes, operational conditions, and carbon intensities of energy mixes in both countries make these two countries interesting for carbon footprint (CF) comparison. This study aimed at proposing a functional unit (FU) which enables a comprehensive comparison of wastewater treatment plants (WWTPs) in terms of their CF. Direct emissions had the highest contribution (70%) to the total CF. Energy consumption dominated the total indirect emissions in both countries by over 30%. Polish WWTPs benefitted more from energy self-sufficiency than Finnish plants as a result of higher electricity emission factors in Poland. The main difference between indirect emissions of both countries were attributed to higher chemical consumption of the Finnish WWTPs. Total pollution equivalent removed (TPErem) FU proposed enabled a better comparison of WWTPs located in different countries in terms of their total CF. High correlations of TPErem with other FUs were found since TPErem could balance out the differences in the removal efficiencies of various pollutants. Offsetting CF was found a proper strategy for the studied WWTPs to move towards low-carbon operation. The studied WWTPs could reduce their CF from up to 27% by different practices, such as selling biofuel, electricity and fertilizers. These findings are applicable widely since the selected WWTPs represent the typical treatment solutions in Poland, Finland and in the Baltic Sea region.

Activated sludge models are widely used to simulate, optimize and control performance of wastewater treatment plants (WWTP). For simulation of nutrient removal and energy consumption, kinetic parameters would need to be estimated, which requires an extensive measurement campaign. In this study, a novel methodology is proposed for modeling the performance and energy consumption of a biological nutrient removal activated sludge system under sensitivity and uncertainty. The actual data from the wastewater treatment plant in Slupsk (northern Poland) were used for the analysis. Global sensitivity analysis methods accounting for interactions between kinetic parameters were compared with the local sensitivity approach. An extensive procedure for estimation of kinetic parameters allowed to reduce the computational effort in the uncertainty analysis and improve the reliability of the computational results. Due to high costs of measurement campaigns for model calibration, a modification of the Generalized Likelihood Uncertainty method was applied considering the location of measurement points. The inclusion of nutrient measurements in the aerobic compartment in the uncertainty analysis resulted in percentages of ammonium, nitrate, ortho-phosphate measurements of 81%, 90%, 78%, respectively, in the 95% confidence interval. The additional inclusion of measurements in the anaerobic compartment resulted in an increase in the percentage of ortho-phosphate measurements in the aerobic compartment by 5% in the confidence interval. The developed procedure reduces computational and measurement efforts, while maintaining a high compatibility of the observed data and model predictions. This enables to implement activated sludge models also for the facilities with a limited availability of data.