The University of Texas at Austin, Department of Civil, Architectural, and Environmental Engineering, 301 East Dean Keeton Street, Austin, TX 78712, United States; These authors contributed equally to this work..
The University of Waterloo, Department of Civil and Environmental Engineering, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada; These authors contributed equally to this work.
Curr Opin Biotechnol. 2019 Jun;57:197-204. doi: 10.1016/j.copbio.2019.05.009. Epub 2019 Jun 14.
Drinking water biofiltration processes have evolved over time, moving from unintentional to deliberate, with careful filter media selection, nutrient and trace metal supplementation, oxidant amendment, and bioaugmentation of key microorganisms, to achieve improvements in water quality. Biofiltration is on the precipice of a revolution that aims to customize the microbial community for targeted functional outcomes. These outcomes might be to enhance or introduce target functional activity for contaminant removal, to avoid hydraulic challenges, or to shape beneficially the downstream microbial community. Moving from the foundational molecular techniques that are commonly applied to biofiltration processes, such as amplicon sequencing and quantitative, real-time polymerase chain reaction, the biofiltration revolution will be facilitated by modern biotechnological tools, including metagenomics, metatranscriptomics, and metaproteomics. The application of such tools will provide a rich knowledge base of microbial community structure/function data under various water quality and operational conditions, where this information will be utilized to select biofilter conditions that promote the enrichment and maintenance of microorganisms with the desired functions.
饮用水生物滤过处理过程随着时间的推移不断发展，从无意识的处理发展为有目的的处理，通过仔细选择过滤介质、补充营养物质和痕量金属、添加氧化剂以及生物强化关键微生物，以改善水质。生物滤过正处于一场革命的边缘，旨在针对特定的功能目标定制微生物群落。这些目标可能是增强或引入针对污染物去除的目标功能活性，避免水力挑战，或有益地塑造下游微生物群落。从通常应用于生物滤过过程的基础分子技术（如扩增子测序和定量实时聚合酶链反应）开始，生物滤过革命将得到现代生物技术工具（包括宏基因组学、宏转录组学和宏蛋白质组学）的推动。这些工具的应用将为各种水质和运行条件下的微生物群落结构/功能数据提供丰富的知识库，利用这些信息可以选择促进具有所需功能的微生物富集和维持的生物滤过条件。
