School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China.
School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, PR China.
Sci Total Environ. 2024 Jan 10;907:167730. doi: 10.1016/j.scitotenv.2023.167730. Epub 2023 Oct 16.
The effectiveness of wastewater treatment plants (WWTPs) is largely determined by the microbial community structure in their activated sludge (AS). Interactions among microbial communities in AS systems and their indirect effects on water quality changes are crucial for WWTP performance. However, there is currently no quantitative method to evaluate the contribution of microorganisms to the operating efficiency of WWTPs. Traditional assessments of WWTP performance are limited by experimental conditions, methods, and other factors, resulting in increased costs and experimental pollutants. Therefore, an effective method is needed to predict WWTP efficiency based on AS community structure and quantitatively evaluate the contribution of microorganisms in the AS system. This study evaluated and compared microbial communities and water quality changes from WWTPs worldwide by meta-analysis of published high-throughput sequencing data. Six machine learning (ML) models were utilized to predict the efficiency of phosphorus and nitrogen removal in WWTPs; among them, XGBoost showed the highest prediction accuracy. Cross-entropy was used to screen the crucial microorganisms related to phosphorus and nitrogen removal efficiency, and the modeling confirmed the reasonableness of the results. Thirteen genera with nitrogen and phosphorus cycling pathways obtained from the screening were considered highly appropriate for the simultaneous removal of phosphorus and nitrogen. The results showed that the microbes Haliangium, Vicinamibacteraceae, Tolumonas, and SWB02 are potentially crucial for phosphorus and nitrogen removal, as they may be involved in the process of phosphorus and nitrogen removal in sewage treatment plants. Overall, these findings have deepened our understanding of the relationship between microbial community structure and performance of WWTPs, indicating that microbial data should play a critical role in the future design of sewage treatment plants. The ML model of this study can efficiently screen crucial microbes associated with WWTP system performance, and it is promising for the discovery of potential microbial metabolic pathways.
污水处理厂 (WWTP) 的效率在很大程度上取决于其活性污泥 (AS) 中的微生物群落结构。AS 系统中微生物群落之间的相互作用及其对水质变化的间接影响对 WWTP 的性能至关重要。然而，目前还没有一种定量的方法来评估微生物对 WWTP 运行效率的贡献。传统的 WWTP 性能评估受到实验条件、方法和其他因素的限制，导致成本增加和实验污染物的产生。因此，需要一种有效的方法来根据 AS 群落结构预测 WWTP 效率，并定量评估 AS 系统中微生物的贡献。本研究通过对已发表高通量测序数据的荟萃分析，评估和比较了来自世界各地 WWTP 的微生物群落和水质变化。利用六种机器学习 (ML) 模型预测 WWTP 中磷和氮的去除效率；其中，XGBoost 表现出最高的预测精度。交叉熵用于筛选与磷和氮去除效率相关的关键微生物，建模结果证实了结果的合理性。从筛选中获得的与氮磷循环途径相关的 13 个属被认为非常适合同时去除磷和氮。结果表明，具有氮磷循环途径的微生物 Haliangium、Vicinamibacteraceae、Tolumonas 和 SWB02 可能对磷和氮的去除至关重要，因为它们可能参与污水处理厂的磷和氮去除过程。总的来说，这些发现加深了我们对微生物群落结构与 WWTP 性能之间关系的理解，表明微生物数据应在未来污水处理厂的设计中发挥关键作用。本研究中的 ML 模型可以有效地筛选与 WWTP 系统性能相关的关键微生物，并且有望发现潜在的微生物代谢途径。
