Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment - Shubo Zhang; Sai Gong; Ran Yin; Jinfeng Wang; Yanru Wang; Hongqiang Ren (Communications Earth & Environment)
Abstract Emerging contaminants (ECs) in wastewater pose significant threats to eco-systems, calling for upgrading of conventional wastewater treatment technologies to tackle this challenge. The in situ synthesis of functional microbiomes is a novel and promising method for sustainable control of ECs. However, challenges remain due to complexity of the microbiome. We herein developed a machine learning-assisted framework for in situ synthetic microbiome that encompasses multidimensional key species identification, community-level quantification and assessment, and determination of optimal microhabitat conditions. Altogether 1068 activated sludge samples from 177 biological wastewater treatment processes (BWWTPs) across China were collected to validate framework. The framework enabled in situ synthesis of microbiomes, significantly enhancing abundance of functional species capable of degrading sulfamethoxazole, a representative EC. The optimal microhabitat conditions for synthesis were also elucidated. The framework together with the protocol developed in this study can be applied in many other scenarios for in situ synthesis of functional microbiomes with specific degradation capabilities.