Yu Yadan, Zeng Hao, Wang Liyun, Wang Rui, Zhou Houzhen, Zhong Liang, Zeng Jun, Chen Yangwu, Tan Zhouliang
CAS Key Laboratory of Environmental and Applied Microbiology, Environmental Microbiology Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
CAS Key Laboratory of Environmental and Applied Microbiology, Environmental Microbiology Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China.
J Environ Manage. 2024 Mar;354:120256. doi: 10.1016/j.jenvman.2024.120256. Epub 2024 Feb 10.
Modeling the pollutant removal performance of wastewater treatment plants (WWTPs) plays a crucial role in regulating their operation, mitigating effluent anomalies and reducing operating costs. Pollutants removal in WWTPs is closely related to microbial activity. However, there is extremely limited knowledge on the models accurately characterizing pollutants removal performance by microbial activity indicators. This study proposed a novel specific oxygen uptake rate (SOUR) with adenosine triphosphate (ATP) as biomass. Firstly, it was found that SOUR and total nitrogen (TN) removal rate showed similar fluctuated trends, and their correlation was stronger than that of TN removal rate and common SOUR with mixed liquor suspended solids (MLSS) as biomass. Then, support vector regressor (SVR), K-nearest neighbor regressor (KNR), linear regressor (LR), and random forest (RF) models were developed to predict TN removal rate only with microbial activity as features. Models utilizing the novel SOUR resulted in better performance than those based on SOUR. A model fusion (MF) algorithm based on the above four models was proposed to enhance the accuracy with lower root mean square error (RMSE) of 2.25 mg/L/h and explained 75% of the variation in the test data with SOUR as features as opposed to other base learners. Furthermore, the interpretation of predictive results was explored through microbial community structure and metabolic pathway. Strong correlations were found between SOUR and the proportion of nitrifiers in aerobic pool, as well as between heterotrophic bacteria respiratory activity (SOUR) and the proportion of denitrifies in anoxic pool. SOUR also displayed consistent positive responses with most key enzymes in Embden-Meyerhof-Parnas pathway (EMP), tricarboxylic acid cycle (TCA) and oxidative phosphorylation cycle. In this study, SOUR provides a reliable indication of the composition and metabolic activity of nitrogen removal bacteria, revealing the potential reasons underlying the accurate predictive result of nitrogen removal rates based on novel microbial activity indicators. This study offers new insights for the prediction and further optimization operation of WWTPs from the perspective of microbial activity regulation.
模拟污水处理厂（WWTPs）的污染物去除性能对于规范其运行、减轻出水异常以及降低运营成本起着至关重要的作用。污水处理厂中的污染物去除与微生物活性密切相关。然而，关于通过微生物活性指标准确表征污染物去除性能的模型，相关知识极为有限。本研究提出了一种以三磷酸腺苷（ATP）作为生物质的新型比氧摄取率（SOUR）。首先，发现SOUR和总氮（TN）去除率呈现相似的波动趋势，并且它们之间的相关性强于以混合液悬浮固体（MLSS）作为生物质的TN去除率与普通SOUR之间的相关性。然后，仅以微生物活性为特征开发了支持向量回归器（SVR）、K近邻回归器（KNR）、线性回归器（LR）和随机森林（RF）模型来预测TN去除率。利用新型SOUR的模型比基于普通SOUR的模型表现更好。提出了一种基于上述四种模型的模型融合（MF）算法，以提高预测精度，其均方根误差（RMSE）更低，为2.25 mg/L/h，并且以SOUR为特征解释了测试数据中75%的变化，优于其他基础学习器。此外，通过微生物群落结构和代谢途径对预测结果进行了解释。发现SOUR与好氧池中硝化菌的比例之间存在强相关性，以及异养细菌呼吸活性（SOUR）与缺氧池中反硝化菌的比例之间存在强相关性。SOUR在糖酵解途径（EMP）、三羧酸循环（TCA）和氧化磷酸化循环中的大多数关键酶方面也表现出一致的正响应。在本研究中，SOUR为脱氮细菌的组成和代谢活性提供了可靠的指示，揭示了基于新型微生物活性指标的氮去除率准确预测结果背后的潜在原因。本研究从微生物活性调控的角度为污水处理厂的预测和进一步优化运行提供了新的见解。
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