Combined effect of a pretreatment and optical settings on the laser diffraction particle size distribution of soils and sediments

作者
Fruzsina Gresina,Zoltán Szalai,Dóra Zacháry,Klaudia Kiss,Balázs Madarász,Zsuzsanna Angyal,Gergely Jakab,Gabriella Darabos,György Varga
出处
期刊:Journal of Soils and Sediments [Springer Science+Business Media]
卷期号:25 (1): 160-178 被引量:6
标识
DOI:10.1007/s11368-024-03933-4
摘要

Abstract Purpose The two main challenges in providing good quality granulometric data are the limitations of laser diffraction method and the insufficient chemical pretreatments. The present study aims to determine the combined effect of these two sources of error and to find optimized procedures to fit the physical and chemical parameters of the soil and sediment samples. Methods Five soil and sediment samples with diverse characteristics were analysed. Three commonly used pretreatment procedures (FAO, USDA, Wageningen Agricultural University) were applied and compared to the “untreated” Hungarian standard. The particle size distributions (PSD) were determined by HORIBA LA-950V2 laser diffraction device with 25 optical setting combinations. Results The effectiveness of pretreatment protocols was related to the particle size, SOM content, size of the aggregates, clay ratio and the order of reagents. Descriptive statistics showed which preparation processes were effective for the dispersion of different sample types. Samples containing a relatively high amount of clay and/or SOM are sensitive to disaggregation procedures, affecting their texture. The refractive index (RI) 1.40 and absorption coefficient (AC) 0.00 and 0.01 provided the highest degree of disaggregation. By the changes in the mean and median values, we could determine which optical settings gave similar results that could be characterized by the same texture classifications. Conclusions None of the procedures was generally considered to be the best procedure. The disaggregation efficiency of the three sample preparation methods differs for different materials. These uncertainties, combined with inadequate optical settings, make it difficult to detect poor quality granulometric data. Graphical Abstract

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