Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment , Hohai University , Nanjing 210098 , P.R. China.
Basin Water Environmental Research Department , Changjiang River Scientific Research Institute , Wuhan 430010 , P.R. China.
Environ Sci Technol. 2019 Apr 16;53(8):4099-4108. doi: 10.1021/acs.est.8b07334. Epub 2019 Mar 27.
A growing awareness of the wider environmental significance of diffuse sediment pollution in interconnected river-lake systems has generated the need for reliable provenance information. Owing to their insufficient ability to distinguish between multiple sources, common sediment source apportionment methods would rarely be a practical solution. On the basis of the inseparable relationships between sediment and adsorbed microorganisms, community-based microbial source tracking may be a novel method of identifying dominant sediment sources in the era of high-throughput sequencing. Dongting Lake was selected as a study area as it receives considerable sediment import from its inflowing rivers during the flood season. This study was conducted to characterize the bacterial community composition of sediment samples from the inflow-river estuaries and quantify their sediment microbe contributions to the central lake. Metagenomic analysis revealed that the community compositions of source sediment samples were significantly different, allowing specific sources to be identified with the machine learning classification program SourceTracker. A modified analysis using SourceTracker found that the major contributors to three major lake districts were the Songzi, Zishui, and Xinqiang Rivers. The impacts of hydrodynamic conditions on source apportionment were further verified and suggested the practicability of this method to offer a systematic and comprehensive understanding of sediment sources, pathways, and transport dynamics. Finally, a novel framework for sediment source-tracking was established to develop effective sediment management and control strategies in river-lake systems.
人们越来越意识到连通的河湖系统中弥散沉积物污染的更广泛环境意义，这就产生了对可靠来源信息的需求。由于常见的沉积物源分配方法区分多个来源的能力不足，因此很少是一种实用的解决方案。基于沉积物和吸附微生物之间不可分割的关系，基于群落的微生物源追踪可能是高通量测序时代识别主要沉积物源的一种新方法。洞庭湖被选为研究区，因为它在洪水季节会从流入的河流中输入大量泥沙。本研究旨在描述来自入流河口的沉积物样本的细菌群落组成，并量化它们对中心湖泊的沉积物微生物的贡献。宏基因组分析表明，源沉积物样本的群落组成有显著差异，允许使用机器学习分类程序 SourceTracker 识别特定的来源。使用 SourceTracker 进行的改进分析发现，三个主要湖区的主要贡献者是松滋河、资水和新墙河。水动力条件对源分配的影响得到了进一步验证，表明该方法具有实用性，可以系统全面地了解沉积物的来源、路径和输运动态。最后，建立了一种新的沉积物源追踪框架，以制定河湖系统中有效的沉积物管理和控制策略。
