Editorial Feature

How Chemical Pre-Treatments in Particle Size Analysis Impact Wind Erosion Modeling

Extreme climate conditions and poor land-use management blight the semi-arid steppe regions of Asia, where some of the world’s largest grain exporters are located in the region. Kazakhstan, the largest country in Central Asia, is predicted to be a heat stress hotspot, and as a consequence concern grows about the negative impact on soil productivity that enhanced water and wind erosion will have on it. Here, soil erosion risk estimation in Kazakhstan is explored and presented via the research being headed up by M. Koza et al. at the Institute of Geosciences and Geography, Martin Luther University in Germany.

wind erosion, desert

Image Credit: LutsenkoLarissa/Shutterstock.com

Soil Texture

Sustainable agricultural practices are reliant on data sets where a key component is soil texture. Soil texture is reliant on particle size distribution (PSD).

PSD is a mathematical function defining the quantity (usually by mass) of particles contained within a sample according to size, creating a value profile. It is critical in predicting soil’s susceptibility to water and wind erosion from the effects of management practices and crop rotations for any individual field.

Assigning texture classes via PSD requires soil aggregates to be dispersed which, in turn, necessitates the removal of binding agents such as organic matter, carbonates, and metal oxides.

Although different pre-treatments expedite removal, there has never been any assessment into how effective these are nor the impact this processing has on wind erosion modeling. The collaboration between the Institute of Geosciences and Geography in Germany and the Institute of Geography at Altai State University in Russia together with the Department of Soil and Crop Management in Kazakhstan has changed this.

Sampling and Pre-Treating Kazakhstan Soils

Taking topsoil samples from 90 test sites around the dry steppe biome of Kazakhstan, the team experimented with Chernozem and Kastanozem soils. The former (meaning “black soil”) is very fertile, containing high levels of ammonia and phosphorus-related compounds to store moisture, resulting in high crop yields. Kastanozem soil means “chestnut soil” characterized by a meter-deep brown surface layer rich in humus.

The researchers ensured soil samples covered all typical land-use types and farming methods which generally means the inclusion of calcium carbonate and organic carbon content.

Uncertainty has previously surrounded the consequences of soil pre-treatments in the context of analysis and modeling, so the team compared PSD data from non-pre-treated soil, soil after two different hydrochloric acid (HCl) pre-treatments, after hydrogen peroxide (H2O2) pre-treatment, and post-sequential H2O2 and HCl pre-treatments.

The gathered samples were subjected to 30% H2O2 to oxidize organic binding material while 10% HCl was utilized to dissolve carbonates. Complete sample dispersion was possible by removing organic matter with H2O2 but the HCl pre-treatment resulted in partial dispersion and in some cases even aggregation due to calcium ions being released by the carbonate dissolution.

Particle Size Analysis by Laser Diffraction

The team used laser diffraction to determine the PSD of complete sample dispersions. This technique is an industry-standard, processing data quickly due to automation advances.

As the laser beam passes through a dispersion of soil particles, the angular variation in the intensity of light scattered is measured. As particle size decreases, the angle of diffraction increases, scattering light at large angles. Intensity data from angular scatterings are then analyzed to ascertain the size of the particles responsible for the resulting patterns. Equating particle size as a sphere diameter is typical.

Calculation of Texture-Based Properties

The harvested PSD data is used to calculate texture-based properties as a pedotransfer function (PTF). This is a predictive function that takes basic information usually obtained from soil surveys, field morphology, soil structure, and pH, and translates it into pertinent quality assessment estimates that would be enormously laborious to gather otherwise.

The PSD uses data mining and regression analysis to deliver content from different textures required by a process-based computer model that simulates wind erosion for a single-day storm event. It is called Single–Event Wind Erosion Evaluation Program (SWEEP) and in the team’s work, it estimates potential soil losses by wind erosion for an arable field in Kazakhstan’s dry steppe.

In addition to texture and texture-based properties, another input called the Geometric Mean Diameter (GMD, central tendency of particle size composition) is incorporated into the model which also utilizes a sub-model called the Wind Erosion Prediction System (WEPS). This decision-support system predicts wind erosion worldwide and estimates soil losses for a single-day storm event under the influence of site-specific input data.

Information on soil texture and organic matter content help to derive the soil erodibility factor allowing the estimation of soil loss via water erosion. This is determined by the Revised Universal Soil Loss Equation (RUSL) which considers slop and vegetative cover. The Revised Wind Erosion Equation (RWEQ) independently calculates the erodible fraction under field conditions. Components of interest include clay content and the percentages of sand and slit contained in the soils.

Under the influence of different pre-treatments and calculations based on PSD, SWEEP simulations flagged substantial variations. The extent of these has made the team conclude that further investigation into dry steppe soils is needed but not without advancing knowledge.

In terms of pre-treatment efficiencies in removing binding agents, the Chernozem and Kastanozem soils (via the particle size analysis results) suggest the avoidance of HCl because of the resulting problematic dispersions leading to misleading results while efficient organic binding material removal was found with pre-treatments involving H2O2.

Deriving additional texture-based parameters via pedotransfer functions is very significant for wind erosion because silt-dominated Chernozems and Kastanozems are not necessary as soil loss modeling comparisons with either calculated or independently measured GMDs indicates the consequences are minimal. The next step needs to be the conducting of in-situ experiments under field conditions to make soil loss validations.

References and Further Reading

Koza, M. et al. (2021) Consequences of chemical pretreatments in particle size analysis for modelling wind erosion. Geoderma. https://doi.org/10.1016/j.geoderma.2021.115073

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John McAleese

Written by

John McAleese

Combining a scientific pedigree that includes a PhD and a six-year Research Fellowship at Imperial College, London, with a passion for writing, John recently refocused his consultancy exclusively on knowledge transfer, exploiting the full richness of a career that has spanned both the private and public sectors; academia, industry, business support, consultancy, and personal development training. Front and center is science outreach, this year the muse has approved of his dedication with “ Machine Learning in Forensic Fire Debris Analysis” and “Understanding Water Resources in Latin America and the Caribbean via Isotopic Tracers ” among a broad range of diverse topics ready for circulation.


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