Dynamically predicting nitrous oxide emissions in a full-scale industrial activated sludge reactor under multiple aeration patterns and COD/N ratios.

The use of digital tools has become essential for quantifying and predicting greenhouse gas (GHG) emissions in urban wastewater treatment plants (WWTPs), enabling the development of operational regimes with a high probability of achieving net-zero targets. However, comprehensive studies documenting validation of model predictions-such as effluent quality, process economics, and emission factors-remain scarce within full-scale industrial settings. This paper aims to develop a decision support tool (DST) for (dynamically) predicting nitrous oxide (NO) emissions in full-scale industrial activated sludge reactors (ASRs) and suggesting mitigation strategies.