Particle Size Effect on Geochemical Composition of Experimental Soil Mixtures Relevant for Unmixing Modeling

L. Gaspar, W. Blake, I. Lizaga, B. Latorre, A. Navas
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Abstract

Sediment fingerprinting experiments have been used to demonstrate the sensitivity of numerical mixing model outputs to different particle size distributions in source materials and experimental sediment mixtures. The study aims to examine further grain size effects in the distribution of geochemical elements by soils through a laboratory experiment simulating mixing and sorting processes, to investigate if different size fractions are influencing fingerprinting analyses and unmixing model results. Multiple particle size fractions are analysed to understand the relationship between particle size and source signal through elemental signatures. FingerPro model was applied to unmix six experimental mixtures with known percentages contribution from three experimental sources. The experimental design comprised four different setups with a specific range fraction for sources and mixtures. Setups A (63-63 μm) and B (20-20 μm) relies upon a comparable particle size fraction for sources and mixtures, while C (63-20 psc) and D (63-20) address particle size impacts simulating fine enrichment, with and without a single particle size correction factor, respectively. Tracers were extracted after applying two statistical tests, the range test (RT) and a combination of RT, Kruskal-Wallis (KW) and DFA tests thus obtaining the set of optimum tracers for each mixture. Our findings indicate that source apportionment results are sensitive to tracer selection and particle size. The most accurate source apportionment results were achieved when comparing sources and mixtures with the <63 μm grain-size fraction (setup A) by using the set of tracers extracted after RT, KW and DFA tests, (mean RMSE: 2%, AE: 2%). Larger errors were obtained progressively for setups B, C and D with better results when using more number of tracers from RT (mean RMSE: 7, 10, 13%, AE: 8, 11, 15%, respectively). The impact of SSA on the elemental content is difficult to predict because the positive linearity between them does not apply equally to all elements and this assumption needs to be constantly examined and considered for fingerprinting studies. Otherwise, the use of a single particle size correction factors could negatively affect unmixing results. The outcomes of this research will help to develop appropriate strategies for sediment fingerprinting, contributing to our knowledge of processes affecting sediment geochemistry and transport across different particle sizes.
粒径对分离模型相关试验土混合料地球化学组成的影响
泥沙指纹实验证明了数值混合模型的输出对源物质和实验泥沙混合物中不同粒度分布的敏感性。本研究旨在通过模拟混合和分选过程的室内实验,进一步研究粒度对土壤地球化学元素分布的影响,探讨不同粒度组分是否影响指纹分析和解混模型结果。通过元素特征分析了多个粒度分数,以了解粒度与源信号之间的关系。应用FingerPro模型对3个实验源贡献百分比已知的6种实验混合物进行解混。实验设计包括四种不同的装置,对源和混合物具有特定的范围分数。设置A (63-63 μm)和B (20-20 μm)依赖于源和混合物的可比较粒度分数,而C (63-20 psc)和D(63-20)分别解决了模拟细粒富集的粒度影响,分别有和没有单个粒度校正因子。采用极差检验(RT)和RT、Kruskal-Wallis (KW)和DFA联合检验两种统计检验提取示踪剂,从而获得每种混合物的最佳示踪剂集。我们的研究结果表明,源分配结果对示踪剂选择和颗粒大小敏感。当使用经过RT、KW和DFA测试提取的一组示踪剂对<63 μm粒度组分(设置A)的源和混合物进行比较时,获得了最准确的源分配结果(平均RMSE: 2%, AE: 2%)。设置B、C和D的误差逐渐增大,当使用更多的RT示踪剂时,结果更好(平均RMSE: 7、10、13%,AE: 8、11、15%)。SSA对元素含量的影响很难预测,因为它们之间的正线性关系并不适用于所有元素,这一假设需要在指纹图谱研究中不断检验和考虑。否则,使用单个粒度校正因子可能会对解混结果产生负面影响。这项研究的结果将有助于制定沉积物指纹识别的适当策略,有助于我们了解影响沉积物地球化学和不同粒径的迁移过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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