Estación Experimental de Aula-Dei (EEAD-CSIC), Spanish National Research Council, Zaragoza, Spain; Avenida Montañana, 1005, 50059 Zaragoza, (Spain).
Estación Experimental de Aula-Dei (EEAD-CSIC), Spanish National Research Council, Zaragoza, Spain; Avenida Montañana, 1005, 50059 Zaragoza, (Spain).
Sci Total Environ. 2021 Oct 1;789:147804. doi: 10.1016/j.scitotenv.2021.147804. Epub 2021 May 17.
Fingerprinting technique is a widely used tool to assess the sources of sediments and particle bound chemicals within a watershed, and the results obtained from unmixing models are becoming valuable data to support soil and water resources monitoring and conservation. Nowadays, numerous studies have used fingerprinting techniques to examine specific catchment management problems. Despite its shortcomings and the lack of standardization, the technique continues on an upward trend globally. This paper takes a new look at the utility of the mostly used tracer selection methods and their influence when using fingerprinting models. Furthermore, the increase in the analysis capabilities and the use of more tracers than n-1 tracers (where n is the number of sources) for unmixing leads to the possibility of mathematical inconsistency and the existence of multiple solutions in the analysis of a particular mixture, which is a possible source of errors that remains unexplored nowadays. Within the framework of these criteria, we have i) inspected if both types of models, Frequentist and Bayesian, are sensitive to tracers with erroneous information; ii) examined the most commonly used tracer selection methods; iii) tested the consistency and the existence of multiple solutions in over-determined systems and iv) devised a Consistent Tracer Selection (CTS) method to extract the solutions present in the dataset. The strength of this novel study lies in the valuable and useful tracer selection method that has been presented. Frequentist model such as FingerPro and a Bayesian model, MixSIAR, are implemented to test the method. Both models agreed on their solutions when selecting the tracers based on the new method, while both disagreed when selecting the tracers following previous methods. The new CTS method's ability to extract the multiple discriminant and consistent solutions inside fingerprinting datasets has no precedent in the literature.
指纹技术是评估流域内沉积物和颗粒结合化学物质来源的广泛应用工具，从混合模型中获得的结果正在成为支持土壤和水资源监测和保护的有价值数据。如今，许多研究已经使用指纹技术来研究特定的流域管理问题。尽管存在缺点和缺乏标准化，该技术在全球范围内仍呈上升趋势。本文重新审视了最常用示踪剂选择方法的效用及其在使用指纹模型时的影响。此外，分析能力的提高以及在混合分析中使用比 n-1 个示踪剂（其中 n 是源的数量）更多的示踪剂，导致了分析特定混合物时可能出现数学不一致和存在多个解的可能性，这是一个尚未探索的可能的误差源。在这些标准的框架内，我们：i）检查了频繁论和贝叶斯两种模型是否对具有错误信息的示踪剂敏感；ii）检查了最常用的示踪剂选择方法；iii）测试了过度约束系统中的一致性和多个解的存在；iv）设计了一种一致示踪剂选择（CTS）方法，以提取数据集存在的解。这项新研究的优势在于提出了一种有价值和有用的示踪剂选择方法。实施了频繁论模型 FingerPro 和贝叶斯模型 MixSIAR 来测试该方法。当根据新方法选择示踪剂时，两种模型在其解决方案上达成一致，而当按照以前的方法选择示踪剂时，两种模型则不一致。新的 CTS 方法能够从指纹数据集中提取多个判别和一致的解，这在文献中是前所未有的。
