在三个Anammox系统中对微生物群落和去除率预测的全面分析
Xuan Zhang1, Tao Ya1, Lu Han1
1Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Microorganisms
|December 31, 2025
概括
亚纳莫克斯去除的过程显示了不同的微生物群落和环境因素在不同系统中的影响. 机器学习准确地预测了去除率,突出了特定系统的优化策略.
科学领域:
- 环境微生物学环境微生物学
- 废水处理技术 废水处理技术
- 生物技术是生物技术.
背景情况:
- 氧化 (Anaerobic Ammonium Oxidation) 对于生物去除至关重要,但微生物群落动态和不同系统中的环境反应尚未完全理解.
- 在上游无氧污泥毯 (UASB),集成固膜激活污泥部分化/亚纳摩克斯 (IFAS-PN/A) 和集成固膜激活污泥同时化,亚纳摩克斯和脱化 (IFAS-SNAD) 系统中微生物结构和功能的变化需要调查.
研究的目的:
- 阐明微生物社区结构及其与环境因素的关系,在三个不同的anammox系统中.
- 使用机器学习开发精确的去除率 (NRR) 预测模型.
- 确定影响NRR的关键环境因素及其系统特定意义.
主要方法:
- 使用16S rRNA测序对206个微生物样本和2126个环境数据点进行分析.
- 生物信息分析和机器学习 (ML) 技术,包括堆叠随机森林 (ST-RF) 和夏普利添加式扩展 (SHAP).
- 同时发生的网络分析,以评估微生物相互作用.
主要成果:
- 观察到微生物组成的显著差异:富含IFAS-PN/A的*Candidatus_Brocadia*,IFAS-SNAD显示出高多样性,UASB则以低多样性的*Candidatus_Kuenenia*为主.
- 同时发生的网络揭示了IFAS-SNAD系统中更复杂,更紧密的微生物相互作用.
- 在预测NRR方面,ST-RF模型实现了高准确性 (R2>0.987),SHAP分析表明环境因素对NRR的系统依赖影响.
结论:
- 微生物社区结构在不同类型的反应器中显著影响 anammox 过程性能.
- 机器学习为预测NRR和理解环境因素和微生物群落之间的复杂相互作用提供了强大的框架.
- 优化去除需要量身定制的策略,考虑每个anammox系统的特定微生物生态和环境敏感性.
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