H. R. Fooladmand, A. R. Sepaskhah, J. Niazi,
Volume 8, Issue 3 (10-2004)
Abstract
To obtain soil-moisture characteristic curve experimentally is time-consuming and usually subject to considerable errors. So, many investigators have tried to predict soil-moisture characteristic curve by different models. One of these models predicts soil moisture characteristic curve based on soil particle size distribution and bulk density. In this model, soil particle size distribution curve is divided into a number of segments, each with a specific particle radius and cumulative particle mass greater than that of the radius. Using these data, soil-moisture characteristic curve was estimated. In this model, a scale factor, α, is used which may be considered as a constant, or obtained by logistic or linear procedures. The average values of α for clay, silty clay, sandy loam, two loam soils, and two silty clay loam soils were 1.159, 1.229, 1.494, 1.391, 1.393, 1.253 and 1.254, respectively. For most conditions, soil particle size distribution curve is not available, but only the percentages of clay, silt, and sand could be obtained using soil textural data, which is not enough to draw a precise soil particle size distribution curve. In this situation, a precise soil particle size distribution curve must be initially developed on the basis of which the soil moisture characteristic curve can be predicted. In this study, using soil textural data of seven different soils, soil moisture characteristic curve of each was estimated. In these estimations, logistic and linear methods were used to obtain the α value. Then, the results were compared with those of measured soil moisture characteristic curve. For estimation of soil particle size distribution curve, two extreme values for soil particle radius, 125 and 999 m, were used. The results indicated that using particle radius of 999 µm is more appropriate. On the other hand, it was found that for clay, silty clay, and sitly clay loam texture, it is more appropriate to employ a linear equation to determine for estimating soil-moisture characteristic curve while the logistic equation can be more appropriately used for loam and sand loam textures.
H. R. Fooladmand,
Volume 11, Issue 41 (10-2007)
Abstract
Soil particle size distribution and bulk density are used for estimating soil-moisture characteristic curve. In this model, soil particle size distribution curve is divided into a number of segments, each with a specific particle radius and cumulative percentage of the particles greater than that radius. Using these data, soil-moisture characteristic curve is estimated. In the model a scale factor, a , is used which may be considered as a constant, or obtained by logistic or linear procedures. F or most conditions, soil particle size distribution curve is not available, but only the percentages of clay, silt and sand could be obtained using soil textural data. In this situation, at first a precise soil particle size distribution must be developed, based on which the soil-moisture characteristic curve can be predicted. According to the previous studies, using particle radius of 999 µ m is more appropriate than radius 125 µ m. Also, adjusted coefficients for estimating soil particle size distribution curve for radii 1 to 20 µ m was obtained. In this study, using the soil textural data of 19 different soils from UNSODA database, soil-moisture characteristic curve of each was estimated with logistic and linear methods based on initial and adjusted soil particle size distribution estimation. The estimated values were compared with the measured data. The results indicated that for most soils, using the combination of logistic and adjusted particle size distribution estimation procedures is more appropriate than the previous methods.
H. Beigi. Harchegani, G Banitalebi,
Volume 18, Issue 70 (3-2015)
Abstract
Texture fractal dimension is a physical index to describe soil particle size distribution having a variety of applications. Fractal dimension may be calculated from three relations of mass-time, mass-diameter and modified mass-diameter (Kravchenko-Zhang) with two linear and nonlinear options for fittings. The aim of the present study was to compare methods and select an appropriate one and fitting option for determining the fractal dimension using hydrometer data. Sixty soil samples were collected from four fields of Taqanak, near Shahrekord. After removal of organic matter and other initial treatments, hydrometer readings were obtained at 0.67, 1, 2, 5, 15, 30, 60, 120, 180, 1440 and 2880 minutes and were converted to mass-time or mass-diameter data. Nonlinear fitting of the Kravchenko-Zhang mass-diameter relation was selected as the most appropriate method of calculating the fractal dimension of solid particles, due to its highest coefficient of determination and smallest mean square error and lowest Akaike Information Criteria. Error analysis also confirmed this conclusion. There was a significant, though not very strong, relationship between the fractal dimension obtained by linear and nonlinear fitting of mass- diameter and Kravchenko-Zhang mass-diameter methods. These relationships can be used to correct the fractal dimension determined by other methods and fitting options.
M. Tayebi, M. Naderi, J. Mohammadi,
Volume 21, Issue 3 (11-2017)
Abstract
The aim of this work was to study distribution of some heavy metals in different soil particle-size fractions and to assess their spatial distribution. The study was carried out in Kafe Moor (Kerman, Iran) where the Gol-Gohar Iron Mine is located. One hundred twenty composite soil samples were randomly collected and transferred to the laboratory in bags. After air-drying, the samples were fractionated into six classes including 2- 0.5, 0.5-0.25, 0.25-0.125, 0.125- 0.075, 0.075-0.05 and <0.05 mm. Elemental concentrations (Fe, Mn, Cu, Zn, Pb and Ni) were determined using acid digestion method (HNO3, 4.0 N) and an atomic absorption spectrophotometer in each class. Ordinary Kriging technique was used for predicting spatial distribution of heavy metals. The results showed that content of metals in soil increased with decreasing particle size. The results also showed that the concentration of Fe, Mn, Cu, Zn, Pb and Ni in <0.05 mm size fraction were 2.13, 1.70, 4.79,2.43, 1.42, and 3.47 times higher than in 2-0.05 mm size fraction, respectively. In addition, mapping the concentrations of heavy metals with kiriging showed that metals pollution decreased with increasing distance from mines area.
