The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Inma Lebron - One of the best experts on this subject based on the ideXlab platform.
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using microscope observations of thin sections to estimate Soil Permeability with the kozeny carman equation
Journal of Hydrology, 2001Co-Authors: Marcel G. Schaap, Inma LebronAbstract:Abstract In this study we used the Kozeny–Carman (K–C) equation as a semi-physical model for estimating the Soil Permeability using data derived from microscope observations. Specific surface areas and porosities were obtained from two-point correlation functions derived from scanning electron microscope images of thin sections using a magnification of 50 and a resolution of 1.88 μm pixel−1. Permeabilities were predicted using two published (‘Ahuja’ and ‘Berryman’) and one generalized variant of the K–C equation. The latter model was similar to the Berryman variant, but used a free parameter C rather than a porosity dependent formation factor. All K–C model variants were optimized on measured permeabilities. The Ahuja and Berryman K–C models performed relatively poorly with R2 values of 0.36 and 0.57, respectively, while the generalized model attained R2 values of 0.91. The parameter C was strongly related to texture and, to a lesser extent, particle density. The general model still required measured surface area and porosity. However, we showed that it was possible to estimate these parameters from texture resulting in an R2 of 0.87. A fully empirical model that did not assume K–C concepts performed slightly worse (R2=0.84). The results indicate that after developing the model using microscope information, only macroscopic data are necessary to predict Permeability of Soils in a semi-physical manner with the K–C equation.
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Using microscope observations of thin sections to estimate Soil Permeability with the Kozeny–Carman equation
Journal of Hydrology, 2001Co-Authors: Marcel G. Schaap, Inma LebronAbstract:Abstract In this study we used the Kozeny–Carman (K–C) equation as a semi-physical model for estimating the Soil Permeability using data derived from microscope observations. Specific surface areas and porosities were obtained from two-point correlation functions derived from scanning electron microscope images of thin sections using a magnification of 50 and a resolution of 1.88 μm pixel−1. Permeabilities were predicted using two published (‘Ahuja’ and ‘Berryman’) and one generalized variant of the K–C equation. The latter model was similar to the Berryman variant, but used a free parameter C rather than a porosity dependent formation factor. All K–C model variants were optimized on measured permeabilities. The Ahuja and Berryman K–C models performed relatively poorly with R2 values of 0.36 and 0.57, respectively, while the generalized model attained R2 values of 0.91. The parameter C was strongly related to texture and, to a lesser extent, particle density. The general model still required measured surface area and porosity. However, we showed that it was possible to estimate these parameters from texture resulting in an R2 of 0.87. A fully empirical model that did not assume K–C concepts performed slightly worse (R2=0.84). The results indicate that after developing the model using microscope information, only macroscopic data are necessary to predict Permeability of Soils in a semi-physical manner with the K–C equation.
Noam Weisbrod - One of the best experts on this subject based on the ideXlab platform.
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Impact of wind speed and Soil Permeability on aeration time in the upper vadose zone
Agricultural and Forest Meteorology, 2019Co-Authors: Elad Levintal, Maria I. Dragila, Noam WeisbrodAbstract:Abstract In high Permeability Soils, gas flux through the Earth–atmosphere interface can be significantly greater than expected from only diffusion. One mechanism that can contribute to the overall flux increase is wind-induced transport (WIT). Here, we explored the magnitude of WIT as a function of the averaged wind speed and Soil Permeability. Five columns, each filled with homogeneous dry Soil or Soil-aggregates with different Permeability were installed in a bare field. The permeabilities in the columns ranged from 3.87 × 10−10 m2 (sand) up to 2.67 × 10−6 m2 (large aggregates collected from a nearby agricultural field). CO2-enriched air was used to quantify air transport in each Soil column. Measurements were carried out under natural wind conditions. Data collected included atmospheric (wind speed, air temperature, barometric pressure, etc.) and Soil parameters inside the columns (temperatures and CO2 concentration at −0.2 m). Data of changing CO2 concentration over time were compared to (1) an analytical diffusion transport solution, and (2) a numerical advection–dispersion solution. Results show that for sand, air transport was governed by diffusion with a very small additional WIT effect, increasing total air transport by up to ∼25% for cases of high wind speed (>5 m/s). From the Permeability of small gravel and above (≥1.02 × 10−8 m2), WIT dominated air transport and the effect of WIT was clear even under low wind speeds. The increase in total air transport in the large aggregates for high wind speed was up to one order of magnitude greater than pure diffusive transport.
Marcel G. Schaap - One of the best experts on this subject based on the ideXlab platform.
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using microscope observations of thin sections to estimate Soil Permeability with the kozeny carman equation
Journal of Hydrology, 2001Co-Authors: Marcel G. Schaap, Inma LebronAbstract:Abstract In this study we used the Kozeny–Carman (K–C) equation as a semi-physical model for estimating the Soil Permeability using data derived from microscope observations. Specific surface areas and porosities were obtained from two-point correlation functions derived from scanning electron microscope images of thin sections using a magnification of 50 and a resolution of 1.88 μm pixel−1. Permeabilities were predicted using two published (‘Ahuja’ and ‘Berryman’) and one generalized variant of the K–C equation. The latter model was similar to the Berryman variant, but used a free parameter C rather than a porosity dependent formation factor. All K–C model variants were optimized on measured permeabilities. The Ahuja and Berryman K–C models performed relatively poorly with R2 values of 0.36 and 0.57, respectively, while the generalized model attained R2 values of 0.91. The parameter C was strongly related to texture and, to a lesser extent, particle density. The general model still required measured surface area and porosity. However, we showed that it was possible to estimate these parameters from texture resulting in an R2 of 0.87. A fully empirical model that did not assume K–C concepts performed slightly worse (R2=0.84). The results indicate that after developing the model using microscope information, only macroscopic data are necessary to predict Permeability of Soils in a semi-physical manner with the K–C equation.
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Using microscope observations of thin sections to estimate Soil Permeability with the Kozeny–Carman equation
Journal of Hydrology, 2001Co-Authors: Marcel G. Schaap, Inma LebronAbstract:Abstract In this study we used the Kozeny–Carman (K–C) equation as a semi-physical model for estimating the Soil Permeability using data derived from microscope observations. Specific surface areas and porosities were obtained from two-point correlation functions derived from scanning electron microscope images of thin sections using a magnification of 50 and a resolution of 1.88 μm pixel−1. Permeabilities were predicted using two published (‘Ahuja’ and ‘Berryman’) and one generalized variant of the K–C equation. The latter model was similar to the Berryman variant, but used a free parameter C rather than a porosity dependent formation factor. All K–C model variants were optimized on measured permeabilities. The Ahuja and Berryman K–C models performed relatively poorly with R2 values of 0.36 and 0.57, respectively, while the generalized model attained R2 values of 0.91. The parameter C was strongly related to texture and, to a lesser extent, particle density. The general model still required measured surface area and porosity. However, we showed that it was possible to estimate these parameters from texture resulting in an R2 of 0.87. A fully empirical model that did not assume K–C concepts performed slightly worse (R2=0.84). The results indicate that after developing the model using microscope information, only macroscopic data are necessary to predict Permeability of Soils in a semi-physical manner with the K–C equation.
K L Ford - One of the best experts on this subject based on the ideXlab platform.
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a study on the correlation between Soil radon potential and average indoor radon potential in canadian cities
Journal of Environmental Radioactivity, 2017Co-Authors: J. Chen, K L FordAbstract:Exposure to indoor radon is identified as the main source of natural radiation exposure to the population. Since radon in homes originates mainly from Soil gas radon, it is of public interest to study the correlation between radon in Soil and radon indoors in different geographic locations. From 2007 to 2010, a total of 1070 sites were surveyed for Soil gas radon and Soil Permeability. Among the sites surveyed, 430 sites were in 14 cities where indoor radon information is available from residential radon and thoron surveys conducted in recent years. It is observed that indoor radon potential (percentage of homes above 200 Bq m-3; range from 1.5% to 42%) correlates reasonably well with Soil radon potential (SRP: an index proportional to Soil gas radon concentration and Soil Permeability; average SRP ranged from 8 to 26). In five cities where in-situ Soil Permeability was measured at more than 20 sites, a strong correlation (R2 = 0.68 for linear regression and R2 = 0.81 for non-linear regression) was observed between indoor radon potential and Soil radon potential. This summary report shows that Soil gas radon measurement is a practical and useful predictor of indoor radon potential in a geographic area, and may be useful for making decisions around prioritizing activities to manage population exposure and future land-use planning.
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Preliminary Findings of Radon Potential Indexes in Five Canadian Cities
Environment and Natural Resources Research, 2012Co-Authors: J. Chen, Deborah Moir, K. Maclellan, E. Leigh, D. Nunez, S. Murphy, K L FordAbstract:Radon has been identified as the second leading cause of lung cancer after tobacco smoking. Since radon in Soil is believed to be the main source of radon in Canadian homes, a radon potential index determined from Soil radon concentration and Soil Permeability can be used to describe the indoor radon potential resulting from radon in Soil gas. The index increases with increasing radon concentration in Soil gas and Soil Permeability. This study reports detailed measurements of Soil gas radon concentrations and Soil Permeability in a total of 254 sites in five cities, Montreal, Gatineau, Ottawa, Kingston and Toronto. Average radon potential indexes were determined for each individual site of five measurement locations. The results provided additional data for the mapping of radon potentials in Canada.
Elad Levintal - One of the best experts on this subject based on the ideXlab platform.
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Impact of wind speed and Soil Permeability on aeration time in the upper vadose zone
Agricultural and Forest Meteorology, 2019Co-Authors: Elad Levintal, Maria I. Dragila, Noam WeisbrodAbstract:Abstract In high Permeability Soils, gas flux through the Earth–atmosphere interface can be significantly greater than expected from only diffusion. One mechanism that can contribute to the overall flux increase is wind-induced transport (WIT). Here, we explored the magnitude of WIT as a function of the averaged wind speed and Soil Permeability. Five columns, each filled with homogeneous dry Soil or Soil-aggregates with different Permeability were installed in a bare field. The permeabilities in the columns ranged from 3.87 × 10−10 m2 (sand) up to 2.67 × 10−6 m2 (large aggregates collected from a nearby agricultural field). CO2-enriched air was used to quantify air transport in each Soil column. Measurements were carried out under natural wind conditions. Data collected included atmospheric (wind speed, air temperature, barometric pressure, etc.) and Soil parameters inside the columns (temperatures and CO2 concentration at −0.2 m). Data of changing CO2 concentration over time were compared to (1) an analytical diffusion transport solution, and (2) a numerical advection–dispersion solution. Results show that for sand, air transport was governed by diffusion with a very small additional WIT effect, increasing total air transport by up to ∼25% for cases of high wind speed (>5 m/s). From the Permeability of small gravel and above (≥1.02 × 10−8 m2), WIT dominated air transport and the effect of WIT was clear even under low wind speeds. The increase in total air transport in the large aggregates for high wind speed was up to one order of magnitude greater than pure diffusive transport.