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在自然减弱的多重污染农药地点的地下水中,CAH和BTEX的生物降解:使用基于微生物组数据的机器学习方法识别关键生物指标的见解

Feiyang Xia¹, Tingting Fan¹, Mengjie Wang¹

¹State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China.

Ecotoxicology and environmental safety

|February 2, 2025

概括

机器学习识别了用于自然减弱地下水污染物的微生物指标,如CAH和BTEX. 这有助于评估受污染地点的生物降解过程.

科学领域:

环境微生物学环境微生物学
机器学习应用程序机器学习应用程序
地下水的整治,就是地下水的整治.

背景情况:

退役农药场所的地下水污染造成的风险是由于化亚利法碳化合物 (CAHs) 和,,乙烯和 (BTEX).
自然减弱 (NA) 依赖于微生物生物降解,但了解微生物在复杂,多重污染的地下水中的作用是具有挑战性的.

研究的目的:

应用机器学习 (ML) 来识别不同类型的地下水污染物 (CAH,BTEX和混合) 的关键微生物指标.
通过ML和并发网络分析,阐明多重污染地下水中的微生物降解途径和机制.

主要方法:

利用先进的机器学习算法,特别是支持矢量机 (SVM),分析污染地下水中的微生物组数据.
采用模型解释方法和并发网络分析来识别和理解关键微生物生物指标的作用.

主要成果:

使用SVM用于微生物指标识别,实现了高精度 (0.87) 和AUC (0.99).
确定了CAHs (氧化,共代谢),BTEX (无氧,减少硫酸盐) 和混合羽毛 (减少铁,减少脱) 的独特微生物群落和降解途径.

结论:

建立了一个结合ML和并发网络分析的框架,用于评估多重污染地下水中的自然衰减.
证明了ML在识别微生物生物指标方面的潜力,这对于理解复杂的污染物降解过程至关重要.

关键词:

地下水的地下水.机器学习机器学习微生物社区的微生物社区.多种溶剂污染多种溶剂污染自然减弱是一种自然减弱.

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