Cation-Exchange Capacity Pedotransfer Functions for Danish Soils

Krogh, Lars Brian; Breuning‐Madsen, Henrik; Greve, Mogens Humlekrog

doi:10.1080/090647100750014358

Cited by 33 publications

(56 citation statements)

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“…This is in agreement with the high correlation between CEC and OM (see Fig. 2), result of the GEP model (see Table 3), and the literature (Sposito, 1989;Fooladmand, 2008;Krogh et al, 2000).…”

Section: Cec Estimates From the Mars Modelsupporting

confidence: 91%

“…The MAE and RMSE of the GEP model without OM are 31.4 % and 25.4% larger than those of the best GEP model (that uses pH, clay, and OM as input). This implies that OM has the most significant effect on CEC, which is in agreement with findings of Sposito (1989), Fooladmand (2008), and Krogh et al (2000) as well as high correlation between CEC and OM (see Fig. 2).…”

Section: The Last Step Contains Choosing a Function That Links The Susupporting

confidence: 91%

“…A positive correlation is found between CEC and pH ( 2 R = 0.28). Bell and Van Keulen (1995), Curtin and Ukrainetz (1997), Bortoluzzi et al (2006), and Krogh et al (2000) reported a positive correlation between CEC and pH as well. In contrast, a very weak correlation is obtained between CEC and silt ( 2 R = 0.018) or sand ( 2 R = 0.010).…”

Section: Resultsmentioning

confidence: 85%

See 2 more Smart Citations

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

Emamgolizadeh

Bateni

Shahsavani

et al. 2015

Journal of Hydrology

116

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Section: Cec Estimates From the Mars Modelsupporting

confidence: 91%

Section: The Last Step Contains Choosing a Function That Links The Susupporting

confidence: 91%

See 1 more Smart Citation

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

Emamgolizadeh

Bateni

Shahsavani

et al. 2015

Journal of Hydrology

116

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“…Soil pH and silt content had the least effect on the prediction of CEC. Krogh et al (2000) proposed an equation based on the same inputs (clay, silt, OC and pH) which explained 90% of soil CEC variation.…”

Section: T a B L E 2 Correlation Coefficients (R) Between Input And mentioning

confidence: 99%

Prediction of CEC Using Fractal Parameters by Artificial Neural Networks

Bayat¹,

Davatgar²,

Jalali³

2014

International Agrophysics

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A b s t r a c t. The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters.K e y w o r d s: cation exchange capacity, fractal theory, particle size distribution, pedotransfer functions

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“…4). However, the CEC pot of non-urban soil OM has been estimated to be around 300 cmol c kg −1 C by Parfitt et al (1995) for New Zealand soils and by Krogh et al (2000) for Danish soils. Higher CEC pot of up to 680 cmol c kg −1 C were observed in acidic sandy forest soils (Wilczynski et al, 1993).…”

Section: General and Surface Propertiesmentioning

confidence: 99%

Filter properties of seam material from paved urban soils

Nehls¹,

Józefaciuk

Sokołowska

et al. 2008

Hydrol. Earth Syst. Sci.

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Abstract.Depositions of all kinds of urban dirt and dust including anthropogenic organic substances like soot change the filter properties of the seam filling material of pervious pavements and lead to the formation of a new soil substrate called seam material.In this study, the impact of the particular urban form of organic matter (OM) on the seam materials CEC pot , the specific surface area (A s ), the surface charge density (SCD), the adsorption energies (E a ) and the adsorption of Cd and P b were assessed. The Cd and P b displacement through the pavement system has been simulated in order to assess the risk of soil and groundwater contamination from infiltration of rainwater in paved urban soils.A s , E a and SCD derived from water vapor adsorption isotherms, CEC pot , P b and Cd adsorption isotherms where analyzed from adsorption experiments. The seam material is characterized by a darker munsell-color and a higher C org (12 to 48 g kg −1 ) compared to the original seam filling. Although, the increased C org leads to higher A s (16 m 2 g −1 ) and higher CEC pot (0.7 to 4.8 cmol c kg −1 ), with 78 cmol c kg −1 C its specific CEC pot is low compared to OM of non-urban soils. This can be explained by a low SCD of 1.2×10 −6 mol c m −2 and a low fraction of high adsorption energy sites which is likely caused by the non-polar character of the accumulated urban OM in the seam material.The seam material shows stronger sorption of P b and Cd compared to the original construction sand. The retardation capacity of seam material for P b is similar, for Cd it is much smaller compared to natural sandy soils with similar C org concentrations. The simulated long term displacement scenarios for a street in Berlin do not indicate an acute contamination risk for P b. For Cd the infiltration from puddles can lead to a breakthrough of Cd through the pavement sysCorrespondence to: T. Nehls (thomas.nehls@tu-berlin.de) tem during only one decade. Although they contain contaminations itself, the accumulated forms of urban OM lead to improved filter properties of the seam material and may retard contaminations more effectively than the originally used construction sand.

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Cation-Exchange Capacity Pedotransfer Functions for Danish Soils

Cited by 33 publications

References 0 publications

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

Prediction of CEC Using Fractal Parameters by Artificial Neural Networks

Filter properties of seam material from paved urban soils

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