School of Geography Science, Nanjing Normal University, Nanjing, 210023, China; School of Environment and Surveying Engineering, Suzhou University, Anhui, 234000, China; National Engineering Research Center of Coal Mine Water Hazard Controlling, Anhui, 234000, China.
School of Environment, Nanjing Normal University, Nanjing, 210023, China.
J Environ Manage. 2018 Oct 15;224:147-155. doi: 10.1016/j.jenvman.2018.07.017. Epub 2018 Jul 21.
The multiple proxies involving elemental and stable isotope ratios (C/N, δN and δC) and biomarkers are powerful tools for estimating sedimentary organic matter (SOM) sources. However, the systematic and reasonable evaluation of organic matter sources existing with serious spatial heterogeneity in large, shallow and eutrophic lakes is still far from clear. Samples of sediments, aquatic plants and particulate organic matter (POM) collected from different ecotype regions of Taihu Lake, China, including algae-type lakeshore, grass-type lakeshore, algae-grass-type lakeshore, inflow rivers and estuary, groove reed zone, offshore and central regions, were analyzed for their SOM sources via elemental and stable isotope ratios (C/N, δN and δC), n-alkanes and fatty acids (FA). More depleted δC values (-26.3‰ to -25.4‰) and higher relative percentages of odd n-alkanes (C to C) and long-chain FA (C to C) clarified the influence of inflow rivers carrying terrestrial inputs on SOM. The higher relative percentages of n-alkanes from C to C, FA (C), and polyunsaturated FA (C and C) in the reed belt of the groove demonstrated that some special terrain was important for the accumulation of algae-derived OM in sediments. Short-chain and middle-chain biomarker compounds revealed a large contribution from macrophytes in the grass-type region and an obvious algae-derived organic matter accumulation in the algae-type region, respectively. However, some overlapping ranges of C/N, δN and δC among aquatic plants, the ubiquity of lipid biomarkers compounds, anthropogenic influences, meteorological factors and lake topography caused some biased identification results for partial samples using different indicators. These biased identifications were mainly embodied in the source category and contribution difference based on principal component analysis and an end-member mixing model. Therefore, the estimation of SOM sources by multiple proxies cannot be uniformly applied in large freshwater lakes. The systematic investigation and comprehensive understanding of the different ecotypes and their surrounding environments are the important links in the identification of SOM sources via multiple indicators.
涉及元素和稳定同位素比值（C/N、δN 和 δC）和生物标志物的多种示踪剂是估计沉积物有机质（SOM）来源的有力工具。然而，对于大型、浅水和富营养化湖泊中存在严重空间异质性的有机质来源的系统和合理评估仍然远远不够清楚。本研究从中国太湖不同生态型区域（藻类湖滨、草型湖滨、藻草型湖滨、入湖河流和河口、槽型芦苇带、近岸和湖心区）采集沉积物、水生植物和颗粒有机质（POM）样品，通过元素和稳定同位素比值（C/N、δN 和 δC）、正烷烃和脂肪酸（FA）分析其 SOM 来源。更贫化的 δC 值（-26.3‰ 至-25.4‰）和更高的奇数正烷烃（C 至 C）和长链 FA（C 至 C）相对百分比表明，携带陆地输入的入湖河流对 SOM 有影响。槽型芦苇带中更高的 C 至 C 正烷烃、FA（C）和多不饱和 FA（C 和 C）相对百分比表明，一些特殊地形对藻类衍生 OM 在沉积物中的积累很重要。短链和中链生物标志物化合物表明，草型区域中大型植物的贡献较大，藻类型区域中藻类衍生有机质的积累明显。然而，水生植物之间 C/N、δN 和 δC 的一些重叠范围、脂质生物标志物化合物的普遍存在、人为影响、气象因素和湖泊地形导致使用不同指标对部分样品的偏置识别结果。这些有偏差的识别主要体现在基于主成分分析和端元混合模型的源类别和贡献差异上。因此，在大型淡水湖中，不能统一应用多种示踪剂来估计 SOM 来源。对不同生态型及其周围环境的系统调查和综合了解是通过多种指标识别 SOM 来源的重要环节。
