Particle size effect on geochemical composition of experimental soil mixtures relevant for unmixing modelling

14 Pags.- 7 Figs.- 6 Tabls. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.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 size fraction for sources (S) and mixtures (M). Setups A (S <63 and M <63 μm) and B (S <20 and M <20 μm) relies upon a comparable particle size fraction for sources and mixtures, while C (S <63 and M <20 with PSC) and D (S <63 and M <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 main strength of using experimental mixtures with a known contribution of the sources relies on reducing the uncertainty of the unmixing model outputs, one of the main limitations in fingerprint studies. The impact of SSA on the elemental concentration 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 factor 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 sediment transport across different particle sizes.This research has been supported by projects PID2019-103946RJ-I00 and PID2019-104857RB-I00 funded by the Spanish Ministry of Science and Innovation (State Research Agency).Peer reviewe

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

Abstract: Purpose: This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. Methods: Web of Science and Google Scholar were used to review published papers spanning the period 2013–2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018–2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. Scope: Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. Conclusions: The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach