Abstract

The biological nitrogen removal (BNR) process in wastewater treatment plants generates a substantial volume of nitrous oxide (N2O), which possesses a potent greenhouse gas effect. A limited number of studies have systematically investigated the N2O emissions of anammox-based systems with different BNR processes under mainstream conditions. Based on extensive big data statistical analysis, it had been revealed that simultaneous nitritation, anammox and denitrification (SNAD), partial nitritation anammox (PNA) and partial denitrification anammox (PDA), exhibit significantly lower N2O emission factors when compared to traditional BNR processes. The median values for N2O emission factors were determined to be 1.01 %, 1.15 % and 1.43 % for SNAD, PNA and PDA, respectively. Based on nitrogen removal data and N2O emission factors, the N2O emissions from PNA, SNAD and PDA processes were calculated to be 0.016 g·d−1, 0.037 g·d−1 and 0.008 g·d−1, respectively. Furthermore, the machine learning models (SVM and ANN) exhibited excellent predictive performance for N2O emissions in the BNR processes. However, after removing environmental factors, the R2 value of the SVM model sharply decreased. The SHAP feature analysis demonstrated the significant impact of environmental factors on the accuracy of predictive performance in machine learning models. Spearman correlation analysis was employed to investigate the relationship between N2O emissions and operational factors as well as microbial communities. The results demonstrated a negative correlation between HRT, temperature and C/N with N2O emissions. Moreover, strong associations were observed between Nitrosomonas, Nitrospira, Denitratisoma, Thauera species and N2O emissions. The contribution of N2O production via AOB pathways played a key role that was quantitatively calculated to be 93 %, 80 % and 48 % in the PNA, SNAD and PDA processes, respectively. These findings highlight the potential of these innovative BNR processes in mitigating N2O emissions.

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