Beyond Site-Specificity: Machine Learning Uncovers N2O Emission Patterns across Biological Nitrogen Removal Processes

Abstract

Wastewater biological nitrogen removal (BNR) systems are energy-intensive and a significant source of N2O emissions. Due to diversity processes and system variations, facility- and technology-specific studies provide useful but fragmented insights on emissions, which may obscure universal emission drivers. To address this gap, we develop a cross-process machine-learning framework by assembling a meta-data set of N2O emissions from conventional nitrification/denitrification-based BNR systems and applying a tiered classification model to establish a generalized emission-risk screening framework. Four statistically distinct emission tiers were identified: high (17.39 ± 19%, n = 106), medium-high (1.30 ± 0.9%, n = 288), medium-low (0.10 ± 0.07%, n = 359), and low (0.0025 ± 0.003%, n = 106). The optimized gradient boosting (GB) classifier reached tier-specific AUCs of 0.60–0.71 despite the heterogeneity of literature-derived data. Feature-importance analysis identified nitrogen loading rate (NLR), hydraulic retention time (HRT), and effluent ammonium as the most influential predictor within the model. Complementary Bayesian Network analysis further uncovered cross-system interdependencies among loading, retention, and effluent nitrogen conditions, suggesting that system context can shape emission-risk patterns beyond individual-site observations. This open framework provides a basis for cross-system risk contextualization, hypothesis generation, and prioritization of potential future monitoring, while highlighting the need for standardized, time-resolved N2O data sets to support more precise prediction and more reliable mitigation strategies.