College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457 Tianjin, China.
College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457 Tianjin, China.
Sci Total Environ. 2023 Dec 15;904:166295. doi: 10.1016/j.scitotenv.2023.166295. Epub 2023 Aug 14.
The biological nitrogen removal (BNR) process in wastewater treatment plants generates a substantial volume of nitrous oxide (NO), which possesses a potent greenhouse gas effect. A limited number of studies have systematically investigated the NO 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 NO emission factors when compared to traditional BNR processes. The median values for NO 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 NO emission factors, the NO emissions from PNA, SNAD and PDA processes were calculated to be 0.016 g·d, 0.037 g·d and 0.008 g·d, respectively. Furthermore, the machine learning models (SVM and ANN) exhibited excellent predictive performance for NO emissions in the BNR processes. However, after removing environmental factors, the R 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 NO emissions and operational factors as well as microbial communities. The results demonstrated a negative correlation between HRT, temperature and C/N with NO emissions. Moreover, strong associations were observed between Nitrosomonas, Nitrospira, Denitratisoma, Thauera species and NO emissions. The contribution of NO 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 NO emissions.
污水处理厂中的生物脱氮（BNR）工艺会产生大量的氧化亚氮（NO），它具有很强的温室气体效应。只有少数研究系统地调查了主流条件下不同 BNR 工艺的基于厌氧氨氧化的系统的 NO 排放。基于广泛的大数据统计分析，已经揭示出同步硝化反硝化、部分亚硝化-厌氧氨氧化（PNA）和部分反硝化-厌氧氨氧化（PDA）与传统 BNR 工艺相比，具有显著更低的 NO 排放因子。SNAD、PNA 和 PDA 的 NO 排放因子的中位数分别确定为 1.01%、1.15%和 1.43%。基于氮去除数据和 NO 排放因子，计算出 PNA、SNAD 和 PDA 工艺的 NO 排放量分别为 0.016 g·d、0.037 g·d 和 0.008 g·d。此外，机器学习模型（SVM 和 ANN）对 BNR 工艺中的 NO 排放具有出色的预测性能。然而，去除环境因素后，SVM 模型的 R 值急剧下降。SHAP 特征分析表明，环境因素对机器学习模型预测性能的准确性有重大影响。Spearman 相关性分析用于研究 NO 排放与操作因素和微生物群落之间的关系。结果表明，HRT、温度和 C/N 与 NO 排放呈负相关。此外，还观察到氨氧化菌（AOB）途径的 NO 生成与 Nitrosomonas、Nitrospira、Denitratisoma 和 Thauera 等物种以及 NO 排放之间存在很强的关联。定量计算表明，PNA、SNAD 和 PDA 工艺中 AOB 途径的 NO 生成贡献分别为 93%、80%和 48%。这些发现突显了这些创新的 BNR 工艺在减少 NO 排放方面的潜力。
