Granulometric Classification of Soils in Geotechnics, Agronomy, Geology, Hydrogeology and Atmosphere

The study of the particle size distribution of solid particles in soils, water, sediments and atmosphere is of great importance. Granulometric classification varies across different fields depending on the investigation’s objectives. In geotechnics, particle size characteristics determine the physical, mechanical, and hydraulic properties of materials [1]. In agronomy, they influence water retention characteristics, the characteristic curve, capillary height and adsorption capacity, etc., [2]. In geology, these characteristics help determine the formation conditions of sedimentary rocks and sediments [3]. In hydrogeology, determining the effective porosity and water storage capacity of an aquifer is crucial for understanding groundwater flow and storage dynamics [4]. In air pollution studies, particles smaller than 10 micronscoarse PM₁₀ (<10μm), fine PM_2.5 (<2.5μm), and ultrafine PM₁ (<1μm)-are of particular interest due to their ability to be transported by air and water [5] and have an effect on the health of humans, animals and plants. Hence, the study of fine particles is essential in environmental research to assess the flux and transport of pollutants in water, soil, sediment, atmosphere, and associated ecosystems.

Figure 1a illustrates the particle size distribution (PSD), categorizing particles into gravel, sand, silt, and clay based on their sizes. Figure 1b presents fifteen internationally recognized methodologies for classifying soils, sediments, tailings, and wastes according to particle size. In some cases, classification methodologies vary within a single country. For example, in the USA, four different criteria are used: (1) the ASTM for geotechnical purposes, (2) the American Association of State Highway and Transportation Officials [6], (3) the Department of Agriculture [2], and the Massachusetts Institute of Technology [7] in Casagrande [7]. There is a general consensus on the existence of four main particle size classes: gravel, sand, silt, and clay, as agreed upon by 13 out of 15 methodologies. However, the issue of grading intervals presents a different challenge. The European Union has established classification ranges through ISO 14688 [8] as a uniform criterion for all member countries. Among the fifteen standards compared, there are four sieve size ranges for gravel, eleven for sand, seven for silt, nine for clay, and one for colloids. In hydrological and hydrogeological studies, particles smaller than 0.45μm are considered colloids. Notably, the USCS-ASTM [9] standards do not separate fines into clay and silt. Internationally, fines are typically considered as the fraction below 75μm, while in the European Union, this threshold is set at 63.5μm. From the literature consulted, the most commonly used standards are USCS-ASTM [9], USDA [2], and ISO [8].

Figure 1:Classification of loose granular materials (soils, tailings, etc.): a) Representation of the particle size distribution (PSD) of mining waste on a logarithmic scale. b) Comparison of different classifications used worldwide. VC: very coarse, C: coarse, M: medium, F: fine, VF: very fine [7].

The study utilized 244 mine tailings samples from 27 tailings storage facilities, TSFs (unless stated otherwise), representing anthropogenic sedimentary catchments. In TSFs, physical, chemical, and biological processes similar to those in natural soils and sediments occur due to interactions with the atmosphere, climate, geological, hydrogeological, and biological environments.

Geotechnical engineering classification

In civil engineering, the results of PSD expressed as percentage passing are plotted using a logarithmic graph (Figure 1a). The size at which i% of the particles are smaller is denoted by D_i. The size D₁₀ is defined as the effective size, as it can be used to estimate the permeability, capillary height, water retention curve of soil, solid wastes, mine tailings, minerals, and more. The general slope of the distribution curve is described by the coefficient of uniformity C_u, defined as C_u= D₆₀/D₁₀. Coefficient of curvature C_c is used to measure the shape of the distribution curve, computed as Cc = D₂ 30 ⁄ (D₆₀∙D_10D). For a single-sized soil, both coefficients are equal to 1. C_u > 5 indicates a well-graded soil while C_u < 3 indicates a uniform soil. C_c between 0.5 and 2.0 suggests a well-graded soil whereas C_c < 0.1 indicates a possible gap-graded soil.

The European Standard EN ISO 14688-1 [10] defines the basic soil types, with composite soil types based on these classifications. Table 1 provides a simplified classification of the basic soil types, assuming a laser diffraction tool range of 0.02-2000μm. As a general guideline, a soil material is classified as sand, silt, or clay if the largest mass fraction corresponds to that size. The main fraction, which determines the geotechnical properties of the soil, is denoted with a capitalized symbol. Secondary fractions are considered adjectives of the main fraction and are symbolized with lowercase letters. For example, ‘siFSa’ represents silty Fine Sand. Figure 2a-f shows an example of mine tailings obtained from 19 borehole from in 6 tailings storage facilities. The PSDs indicate variability in soil composition, with some samples containing more fine particles than others. Moreover, the plasticity chart is useful for classification of fine-grained soils and fine-grained fraction of coarse-grained soils. An example of plasticity chart is shown in Figure 2f. For the plasticity chart according to the Unified Soil Classification System (USCS), it typically involves plotting the liquid limit and plasticity index of the soil samples. The chart helps classify fine-grained soils into categories such as low plasticity (CL), high plasticity (CH), and others based on their Atterberg limits.

Table 1:Particle size classes according to the European Standard EN ISO 14688-1 [8].

Figure 2:(a-f) 169 PSDs of mine tailings obtained from 19 borehole from in 6 TSFs. The first number refers to the storage facility, and the number after the dot refers to the borehole (e.g., 5.1 means storage facility 5, borehole 1) and (e) plasticity chart according to USCS (TD refers to tailings storage facility).

Agronomic classification/

The results of the PSDs are represented in the USDA textural triangle (Figure 3). Based on the sand-silt-clay ratios, there are twelve distinct textural classes. The agronomic triangle is important for several reasons: (a) is crucial for understanding soil properties and behavior, (b) it helps determine which crops are best suited for a particular soil type, optimizing crop yields and quality, (c) it helps in soil management, (d) it helps predict how well a soil will retain water and how quickly it will drain, which is vital for preventing waterlogging and ensuring adequate moisture, (e) it helps in assessing nutrient availability and planning appropriate fertilization strategies. Figure 3b illustrates the results of mine tailings material. Only two samples are classified as clay particles. From an agronomic perspective, mine waste material with higher clay content drains less efficiently, increasing the risk of waterlogging and prejudicing TSF (Tailings Storage Facility) stability.

Figure 3:a) Textural triangle according to ratio sand-silt-clay particle in USDA classification and b) representation of different mine tailings according to the ore extracted.

Geologic classification

The ratio of sand, silt, and clay particles, considering geological criteria, was represented using triangular diagrams developed by Flemming [3] as shown in Figure 4. Flemming’s [3] scheme distinguishes 25 textural sediment classes, each defined by a generic name and a letter-number code. This scheme incorporates a genetic element by differentiating between various hydrodynamic regimes. Sandier and more silty sediments, which reflect deposition under higher energy conditions, are progressively segregated from muddier and more clayey sediments, which reflect deposition under lower energy conditions.

Figure 4:a) Triangular diagram developed by Flemming [3] and b) representation of different mine tailings according to the ore extracted.

The advantage of this scheme over previous ones is its better spatial resolution of textural provinces and a sharper delimitation of the sand and mud end members by restricting reciprocal contamination to less than 5% in each case. This restriction is necessary to better differentiate sedimentary environments consisting of pure sands, silts, or clays. In this classification, the term “muds” represents a mixture of water and any combination of soil, silt, and clay.

Figure 4b shows the results of mine tailings material. The studied tailings originate from various mineral deposits. None of the samples contain more than 50% clay-sized particles. These results indicate different hydrodynamic regimes of deposition in mine tailings. The PSD curves best-fit in sixteen textural classes, dominated by classes A-I, B-I, B-II, C-I, C-II, and D-I, with B-I (very silty sand) being the most prevalent.

Hydrogeological classification

In hydrogeology, granulometric classification is crucial for determining effective porosity, with the primary goal of assessing water storage capacity (Figure 5). The heterogeneity and stratification of grain sizes significantly impact the in situ hydrogeological behavior of soils, sediments, alluvial materials, and tailings, which are strongly influenced by particle size distribution.

Figure 5:a) Soil-classification triangle showing relation between particle size and specific yield [4] and b) representation of different mine tailings according to the ore extracted.

Specific yield, or effective porosity (S_y), is defined as the ratio of the volume of water that a saturated rock or soil will yield by gravity to the total volume of the rock or soil. Specific yield is typically expressed as a percentage. This value is not definitive, as the quantity of water that will drain by gravity depends on variables such as drainage duration, temperature, and the mineral composition of the water, which affect its surface tension, viscosity, and specific gravity, as well as various physical characteristics of the rock or soil. Despite these variables, specific yield values provide a convenient means for hydrologists to estimate the water-yielding capacities of earth materials, making them very useful in hydrologic studies.

Johnson [4] proposed a correlation between particle size composition (in percent) and specific yield. These relationships are depicted by lines of equal specific yield on a triangular graph of textural classification (Figure 5). The textural classification of tailings shows alternating layers with significantly different specific yields (very low, medium, and very high) both in profile and longitudinally. Differences of one order of magnitude in Sy are observed within a single profile. These properties limit the drainage and consolidation of tailings. Additionally, variations of up to four orders of magnitude in saturated hydraulic conductivity are observed in vertical profiles and two orders in longitudinal profiles [11-20].

This mini review highlights the critical role of granulometric classification in diverse scientific disciplines. The study of particle size distribution provides valuable insights into soil behavior, hydrodynamic conditions, and environmental impacts. The use of textural triangles, plasticity charts, and various classification systems demonstrates the complexity of granulometric classification and the necessity for comprehensive methodologies tailored to specific scientific needs. This study reveals significant variability in particle size distributions within mine tailings, impacting their behavior within TSF. Findings from the analysis of mine tailings samples indicate that a clear understanding of particle size distributions can enhance material management, optimize water retention, and improve environmental assessments. Future research should focus on refining classification methodologies and exploring novel technologies to achieve more accurate and representative particle size analyses across different fields.

This research was funded by the Ministry for Ecological Transition and Demographic Challenges (MITECO), Directorate General for Biodiversity; TD by PRTR Measure C04.I03 belonging to the ‘Advice on restoration actions in mining areas around the Mar Menor’ Project (J. Butlanska) and “Study of critical and strategic raw materials for the ecological transition and the supply of the main industrial value chains in Spain” Project (A.O. Oliva-González). This work was also supported by grant PID2022-138197OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by “ERDF/E” (R. Rodríguez-Pacheco).

The authors have no competing interests.

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