A Comparison Study to Determine the Mean of Particle Size Distribution for Truthful Characterization of Environmental Data, Part (1) (original) (raw)
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Mathematical description of most classical particle size distribution (PSD) data is often used for estimating soil hydraulic properties. Fast laser diffraction (LD) techniques now provide more detailed PSDs, but deriving a function to characterize the entire range of sizes is a major challenge. The aim of this study was to compare the fi tting performance of seven PSD functions with one to four parameters on sieve-pipette and LD data sets of fi ne-textured soils. The fi ts were evaluated by the adjusted R 2 , MSE, and Akaike's information criterion. The fractal and exponential functions performed poorly while the performance of the Gompertz model increased with clay content for the LD data sets. The Fredlund function provided very good fi ts with sieve-pipette PSDs but not the corresponding LD data sets, probably due to underestimation of the clay fraction in the latter. The two-parameter lognormal function showed better overall performance and provided very good fi ts with both sieve-pipette and LD data sets.
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Models for Estimating Soil Particle-Size Distributions
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Since particle size distribution (PSD) is a fundamental soil physical property, so determination of its accurate and continuous curve is important. Many models have been introduced to describe PSD curve, but their fitting capability in different textural groups have been rarely investigated. The aim of this study was to evaluate the fitting ability of 15 models on 2653 soil samples from 13 province of Iran, and to determine the best model among them for the PSD of all soil samples as well as for each soil textural group based on evaluation criteria. Results showed that the Weibull model was the most accurate model for all soil samples as well as for the clayey and loamy groups. After the Weibull, Fredlund, Rosin-Rammler and van Genuchten were the most accurate models. However, their differences were not significant (p B 0.05). Also, for the coarse texture group the S-shape model showed the better fit than the others. These results showed the performance of a particular model varies with the soil textural characteristics.
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Canadian Journal of Soil Science, 2013
A modified model for estimating the full description of soil particle size distribution. Can. J. Soil Sci. 93: 65Á72. A full description of the particle size distribution (PSD) curve is widely used as a basis for estimating soil hydraulic properties. However, the incomplete experimental data of PSD sets (i.e., percent clay, silt, and sand) often limit its uses. In this study, an empirical model modified from the Weibull model was used to estimate the complete description PSD from clay, silt and sand fractions. Results show that the estimated distribution agrees reasonably well with the actual distribution for 168 soils samples studied within six different textures. The values of coefficients of determination, the average absolute deviation and the maximum absolute deviation were 98.6%, 3.3% and 12.6%, respectively. The modified model performed best for silt loam samples containing about 70% silt content. The estimation accuracy varied with radii interval. For the estimated PSD curve, a 0.05Á0.1 mm size interval was most poorly estimated and had the maximum deviation (28.2%), while the 0.5Á1 mm interval was estimated well by the modified model. The model is recommended to estimate complete soil PSD using only clay, silt and sand fractions, which can be well fitted by the Weibull model. Further studies are suggested to validate the PSD estimation by this model when applied to soils with greater variation in texture.
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International Journal of Computational Methods and Experimental Measurements, 2021
Particle size distribution is one of the most important physical properties of a particulate sample. Traditional particle-sizing methods to estimate a geometrical particle size distribution employ a sieve analysis (or gradation test), which entails filtering the particles through a series of sieves and measuring the weight remaining on each sieve to estimate the number-weighted particle size distribution. However, these two quantities have the same value only if particles are perfectly spherical and round. On the other hand, a particle sizer such as the Malvern particle size analyzer, which uses laser diagnostics to measure the particle sizes, can be a hefty investment. Alternatively, imaging techniques can be applied to estimate the size of these particles by scaling a reference dimension to the pixel size, which in turn is used to estimate the size of the visible particles. The focus of this work is to present a simple methodology using a DSLR camera and an illuminated LED panel to generate enough contrast. Using the camera and lens properties, the scale, or size, of any image can be obtained based on the mounting distance of the camera with respect to the target. An analysis tool was developed in MATLAB where the images are processed automatically based on the prescribed camera and lens properties embedded within the same image file and requiring the user to only input the mounting distance of the camera. So far, results show a positive agreement when comparing to measurements using ImageJ imaging tools and a sieve analysis. Future tests will analyze different particle sizes and types, as well as using a Malvern particle size analyzer to corroborate the results.
Knowledge on particle size distribution of soils is the basis for construction activities (green field investments, houses, roads etc.), land use, soil management, soil protection, soil fertilizing etc. It plays an important role in the everyday life of people. Numerous methods exist for measuring particle size distribution. Older ones are used just as well as new technologies. A continually increasing need for precisely measured soil parameters is obvious and there is also a big need for easier methods and for exclusion of as much influencing circumstances as possible. The present research focuses on the comparison of different methods used in Hungarian institutions. Eight soils were analysed in four institutions with three methods. Different analytical methods produced different results for particle size distribution. χ 2 analyses revealed significant differences with different p values. There was a big influence detected by laboratory personnel (the analyses were repeated and one method had less than 5% error, while another had more than 20%). It appears that not only do the well known differences between methods, sample preparation and physical background matter but also there are other factors (e.g. routine vs closely checked analyses) which may influence the results.