School of Environmental Studies and State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, PR China.
School of Environmental Studies and State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, PR China.
Environ Pollut. 2022 Aug 1;306:119463. doi: 10.1016/j.envpol.2022.119463. Epub 2022 May 12.
The occurrence of excessive ammonium in groundwater threatens human and aquatic ecosystem health across many places worldwide. As the fate of ammonium in groundwater systems is often affected by a complex mixture of transport and biogeochemical transformation processes, identifying the sources of groundwater ammonium is an important prerequisite for planning effective mitigation strategies. Elevated ammonium was found in both a shallow and an underlying deep groundwater system in an alluvial aquifer system beneath an agricultural area in the central Yangtze River Basin, China. In this study we develop and apply a novel, indirect approach, which couples the random forest classification (RFC) of machine learning method and fluorescence excitation-emission matrices with parallel factor analysis (EEM-PARAFAC), to distinguish multiple sources of ammonium in a multi-layer aquifer. EEM-PARAFAC was applied to provide insights into potential ammonium sources as well as the carbon and nitrogen cycling processes affecting ammonium fate. Specifically, RFC was used to unravel the different key factors controlling the high levels of ammonium prevailing in the shallow and deep aquifer sections, respectively. Our results reveal that high concentrations of ammonium in the shallow groundwater system primarily originate from anthropogenic sources, before being modulated by intensive microbially mediated nitrogen transformation processes such as nitrification, denitrification and dissimilatory nitrate reduction to ammonium (DNRA). By contrast, the linkage between high concentrations of ammonium and decomposition of soil organic matter, which ubiquitously contained nitrogen, suggested that mineralization of soil organic nitrogen compounds is the primary mechanism for the enrichment of ammonium in deeper groundwaters.
地下水中超标氨的出现威胁着全球许多地方的人类和水生生态系统健康。由于地下水系统中氨的命运通常受到多种输运和生物地球化学转化过程的复杂混合影响，因此确定地下水氨的来源是规划有效缓解策略的重要前提。在中国长江流域中部一个农业区下的冲积含水层系统中，浅层和深层地下水中都发现了氨含量升高。在这项研究中，我们开发并应用了一种新颖的间接方法，该方法将机器学习方法的随机森林分类（RFC）与荧光激发-发射矩阵和并行因子分析（EEM-PARAFAC）相结合，以区分多层含水层中的多种氨源。EEM-PARAFAC 被用于深入了解潜在的氨源以及影响氨命运的碳氮循环过程。具体来说，RFC 被用于揭示分别控制浅层和深层含水层中氨含量高的不同关键因素。研究结果表明，浅层地下水中高浓度的氨主要来自人为源，然后受到硝化、反硝化和异化硝酸盐还原为氨（DNRA）等密集微生物介导的氮转化过程的调节。相比之下，高浓度氨与土壤有机质分解之间的联系，而土壤有机质普遍含有氮，这表明土壤有机氮化合物的矿化是深层地下水中氨富集的主要机制。
