A comprehensive literature mining and analysis of nitrous oxide emissions from different innovative mainstream anammox-based biological nitrogen removal processes

Shuang Liu; Fan Wu; Mingzhu Guo; Ming Zeng; Wei Liu; Zhiqiang Wang; Nan Wu; Jingguo Cao

doi:10.1016/j.scitotenv.2023.166295

A comprehensive literature mining and analysis of nitrous oxide emissions from different innovative mainstream anammox-based biological nitrogen removal processes

Sci Total Environ. 2023 Dec 15:904:166295. doi: 10.1016/j.scitotenv.2023.166295. Epub 2023 Aug 14.

Authors

Shuang Liu¹, Fan Wu¹, Mingzhu Guo¹, Ming Zeng², Wei Liu³, Zhiqiang Wang⁴, Nan Wu⁴, Jingguo Cao⁵

Affiliations

¹ College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457 Tianjin, China.
² College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457 Tianjin, China. Electronic address: ming.zeng@tust.edu.cn.
³ Department F.A. Forel for Environmental and Aquatic Sciences, Faculty of Sciences, University of Geneva, Carl-Vogt 66, CH-1211 Geneva, Switzerland. Electronic address: Wei.liu@unige.ch.
⁴ College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China.
⁵ College of Chemical Engineering and Materials Science, Tianjin University of Science & Technology, 300457 Tianjin, China.

PMID: 37586540
DOI: 10.1016/j.scitotenv.2023.166295

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.

Keywords: Anammox; Biological nitrogen removal; Machine learning; Nitrogen; Nitrous oxide.

MeSH terms

Anaerobic Ammonia Oxidation
Bioreactors
Data Mining
Denitrification
Nitrogen* / metabolism
Nitrous Oxide* / analysis
Oxidation-Reduction
Sewage

Substances

Nitrous Oxide
Nitrogen
Sewage