Outline of an Automated System for the Quasi-Continuous Measurement of Particle-size Distribution

Abstract

Soil texture is an important input parameter for many soil hydraulic pedotransfer functions (PTFs) of the day. Common soil particle-size classes are required to be able to uniformly determine the texture of soils. However, it is not always possible - due to different national classification systems - and much valuable information is disregarded while either deriving or applying PTFs. One way to get common particle-size class information is to interpolate the particle-size distribution (PSD) curve. Advanced interpolation solutions are becoming available, but there is always uncertainty associated with these techniques. Another possibility is to measure all PSD curves in such a way that it is compatible to the commonly used classification systems. A new automated measurement technique is introduced that can easily provide PSD data compatible to any (and all) of the existing national and international classification systems at the same time, without the burden of extra labour. A computerized measurement system has been developed to record density changes in a settling-tube system in any discretional (small) time steps, which in turn allows the derivation of a quasi-continuous PSD curve. The measurement is based on areometry (Stokes-law), thus the system is compatible to the most commonly applied settling-tube measurements. The new evaluation method of measured values takes into consideration the density changes along the areometer-body so it avoids the problem of reference point determination. The theory and setup of the system are explained and measurement examples are given. The presented comparative measurements show good correspondence with conventional settling-tube results, and the reproducibility of the measurement shows to be very high. This technique does not require more sample preparation than past methods. The automated reading requires less manpower to perform the measurement - which also reduces human error sources. However, it provides very detailed PSD data that has advantages, like revealing multi-modality in the particle-size distribution or providing data that complies with any of the classification systems.