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

Sediment fingerprinting experiments have been used to demonstrate the sensitivity of numerical mixing model outputs different particle size distributions in source materials and experimental sediment mixtures. The study aims examine further grain effects distribution geochemical elements by soils through a laboratory experiment simulating sorting processes, investigate if fractions are influencing analyses unmixing results. Multiple analysed understand relationship between signal elemental signatures. FingerPro was applied unmix six mixtures with known percentages contribution from three sources. design comprised four setups specific fraction for sources (S) (M). Setups A (S <63 M μm) B <20 relies upon comparable mixtures, while C PSC) D <20) address impacts fine enrichment, without single correction factor, respectively. Tracers were extracted after applying two statistical tests, range test (RT) combination RT, Kruskal-Wallis (KW) DFA tests thus obtaining set optimum tracers each mixture. Our findings indicate that apportionment results sensitive tracer selection size. most accurate achieved when comparing μm grain-size (setup A) using KW (mean RMSE: 2%, AE: 2%). Larger errors obtained progressively B, better more number RT 7, 10, 13%, 8, 11, 15%, respectively). main strength on reducing uncertainty outputs, one limitations fingerprint studies. impact SSA concentration is difficult predict because positive linearity them does not apply equally all this assumption needs be constantly examined considered Otherwise, use factor could negatively affect outcomes research will help develop appropriate strategies fingerprinting, contributing our knowledge processes affecting geochemistry transport across sizes.