Estimation Of Cation Exchange Capacity Research Articles

Pedotransfer functions for cation exchange capacity estimation in highly weathered soils of the tropical highlands of NW Cameroon

Cation exchange capacity (CEC) is a very important soil property widely used in the assessment of soil quality. Unfortunately, its determination in the laboratory can be very tedious, especially when samples are many, necessitating high costs and time. To curb these challenges, pedotransfer functions (PTFs) can be used as indirect and reliable tools for CEC estimation using easy-to-measure soil properties such as pH, soil organic carbon (OC), and texture. Soil survey data from 98 soil profiles in the North West region of Cameroon, belonging to eleven Reference Soil Groups (RSGs) classified as Acrisols, Andosols, Cambisols, Ferralsols, Fluvisols, Histosols, Regosols, Nitisols, Alisols, Luvisols, and Chernozems were used to develop pedotransfer functions for estimating CEC in these soils. Pedotransfer functions were developed using linear and non-linear regressions, while their reliability was evaluated using the error ratio (ER), the geometric mean error ratio (GMER), and root mean square error (RMSE). Predicted and measured CECs were equally compared using non-parametric tests to assess the deviations observed. To enhance the quality of the PTFs, stratifications of the data were done at various levels; (i) pooled data for all soil samples, (ii) within each RSG and (iii) within pedogenic soil horizons. Results indicate that PTFs obtained within the various reference soil groups were better than those obtained from pooled data due to the reduced variability of soil properties. Best results were obtained when soils were stratified according to pedogenic soil horizons. The best PTFs were observed in the most highly weathered soils, the Ferralsols, probably associated with their existence in the near steady state with greater stability and equilibrium among various soil properties. In most of the cases, OC proved to be the dominant soil property contributing to CEC. Results of this study will enable estimation of CEC from available soil survey data in the study area and similar environments, as well as the need to consider stratifications in terms of soil types and pedogenic soil horizons as reported in the literature to improve the reliability of PTFs.

Comparison of Mehlich-3 and DTPA Soil Tests for Analysis of Micronutrients in Kansas Soils

Mehlich-3 (M3) was designed as a multi-nutrient soil test procedure and has become common at soil testing labs across the U.S. In Kansas, Mehlich-3 is predominately used as a soil test for phosphorus (P) and potassium (K), but recent studies have also investigated the use of M3 for the extraction of base cations and cation exchange capacity estimation. However, data relating M3 to traditional methods for soil micronutrient extraction remain scarce. The objective of this study was to investigate the relationship between M3 and diethylenetriamine pentaacetate (DTPA) extractable copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) across a wide range of Kansas soils. Strong positive correlations were observed between M3 and DTPA for each metal (Fe, r = .91; Zn, r = .98; Cu, r = .92). Correlations between M3 and DTPA were positive but weak for Mn (r = 0.17). Regression analyses suggest these relationships were not one-to-one and were dependent on soil pH. Results from this study show that conversion of M3 to a “DTPA equivalent” is possible but should take soil pH into consideration, especially for Fe and Mn.

Identification of Drilling Efficiency Using Log-Derived Cation Exchange Capacity in Shale Formation

Shale and shaly formations constitute about 70% to 80% of the total rock formations drilled worldwide, and the most of footage drilled in gas and oil wells is in shale and shaly rocks. Drilling in shale sections in many cases causes wellbore instability and slow drilling problems. In this study, cation exchange capacity of shale is estimated using a relatively simple petrophysical model. The validation of this model is achieved with experimental values of cation exchange capacity. The estimation of cation exchange capacity by this model and common logs data has exhibited potentiality for distinguishing effective/ineffective drilling in shale formations. Drilling and petrophysical data gathered at controlled condition is required in order to optimize the proposed technique. Have knowledge of properties and location of shales permits for remedial actions in future offset well or while drilling in case of logging while drilling (LWD) is used

New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity

Soil cation exchange capacity (CEC) strongly influences the chemical, physical, and biological properties of soil. As the direct measurement of the CEC is difficult, costly, and time-consuming, the indirect estimation of CEC from chemical and physical parameters has been considered as an alternative method by researchers. Accordingly, in this study, a new hybrid model using a support vector machine (SVM), coupling with particle swarm optimization (PSO), and integrated invasive weed optimization (IWO) algorithm is developed for estimating the soil CEC. The physical and chemical data (i.e., clay, organic matter (OM), and pH) from two field sites of Taybad and Semnan in Iran were used for validating the new proposed approach. The ability of the proposed model (SVM-PSOIWO) was compared with the individual model (SVM) and the hybrid model (SVM-PSO). The results of the SVM-PSOIWO model were also compared with those of existing studies. Different performance evaluation criteria such as RMSE, R2, MAE, RRMSE, and MAPE, Box plots, and scatter diagrams were used to test the ability of the proposed models for estimation of the CEC values. The results showed that the SVM-PSOIWO model with the RMSE (R2) of 0.229 Cmol + kg−1 (0.924) was better than those of the SVM and SVM-PSO models with the RMSE (R2) of 0.335 Cmol + kg−1 (0.843) and 0.279 Cmol + kg−1 (0.888), respectively. Furthermore, the ability of the SVM-PSOIWO model compared with existing studies, which used the genetic expression programming, artificial neural network, and multivariate adaptive regression splines models. The results indicated that the SVM-PSOIWO model estimates the CEC more accurately than existing studies.

Methylene Blue Spot Test for Cation Exchange Capacity (CEC) Estimation in Acid Soils of India

In order to establish a simple, rapid and cost effective method of estimating cation exchange capacity (CEC) of acidic soils, we tested the utility of methylene blue spot test (MBST) vis-à-vis commonly used neutral 1N ammonium acetate method (NAAM) in twenty acidic soils. The soils collected from ten different states of India varied widely in pH, organic carbon content and texture. Averaged across the soils, MBST-CEC was 37% lower than the NAAM-CEC, due possibly to the differences in pH at which the two methods operate. The CEC values estimated by MBST correlated strongly (R=0.89) with those obtained by NAAM, implying that MBST can be used satisfactorily to measure CEC of acidic soils. An equation for inter-conversion of CEC values obtained by the two methods was also worked out. Since NAAM is a time taking and tedious procedure, we recommend MBST method for routine estimation of CEC in acidic soils of India. The method is easily applicable with simple test equipments including filter papers, a glass rod or dropper and methylene blue dye. More than 60 samples a day can easily be analyzed for CEC using MBST method. Being a simple, rapid and cost effective method, the MBST can be included in rapid soil testing kits. Given the importance of CEC in soil fertility and plant nutrition, MBST-CEC may also be included in the soil health cards distributed to the farmers.

Evaluating models to estimate cation exchange capacity of calcareous soils

Cation exchange capacity (CEC) is one of the fundamental properties of soil that indicates soil’s ability to retain exchangeable cations. Several regression-based models exist to estimate CEC from widely available or easily measured basic soil properties. However, the majority of these models do not include CaCO3 as an input variable. Consequently, the applicability of these models for calcareous soils is largely unknown. This study evaluates the performance of selected CEC models for soils rich in calcium carbonate and assesses the influence of CaCO3 on CEC estimation performance. Thirty-eight soil samples that had a wide range in CEC (4 to 59 cmol(+) kg−1) and CaCO3 content (10 to 97%) were selected to evaluate seven published CEC models that had inputs such as clay, silt, sand, pH, organic carbon/organic matter, CaCO3, and hygroscopic water content. The models that comprised clay, silt, sand, and organic carbon overestimated CEC for samples with CEC > 40 cmol(+) kg−1 and vice versa (average RMSE = 13.7 ± 1.2 cmol(+) kg−1 and r2 = 0.41 ± 0.03). One model that included CaCO3, performed slightly better (RMSE = 11.7 cmol(+) kg−1). The model based on hygroscopic water content (28% relative humidity) gave the best estimates of CEC (RMSE = 6.2cmol(+) kg−1 and r2 = 0.88).Apart from the water content-based model, there was a consistent trend of increasing CEC estimation errors with increasing CaCO3 contents. A newly developed CEC model based on clay and CaCO3 contents explained the variation in CEC better than all the published models, except the water content model.

Probabilistic estimation of specific surface area and cation exchange capacity: a global multivariate distribution

Specific surface area (SSA) and cation exchange capacity (CEC) are two fundamental clay properties. However, the determination of CEC and SSA is challenging due to inherent uncertainties and difficulty in experimental measurement. A popular approach is to employ transformation models for their estimation. However, most of the existing models were developed on limited sample sizes and quantification of uncertainty associated with the estimate is not possible. Therefore, this study proposes a multivariate probabilistic approach for estimation of CEC and SSA. First, a five-dimensional database (278 × 5) for the parameters liquid limit (LL), plasticity index (PI), clay fraction (CF), CEC, and SSA (labelled as CLAY/C-S/5/278) is developed. Thereafter, multivariate distribution for the five parameters in the database is constructed using the vine copula approach. Implementation of the proposed approach is demonstrated by updating the prior–unconditional probability density functions (PDFs) of CEC and SSA given single or multiple clay parameters using Bayes’ rule. The posterior or conditional PDFs of CEC and SSA are also summarized as practitioner-friendly analytical expressions. Two geotechnical application examples are shown as well. In the proposed approach, CEC and SSA are characterized by their complete joint distribution and therefore this approach is superior to the popular deterministic transformation approach in literature.

Model Development to Predict CEC Using the Intelligence Data Mining Approaches

ABSTRACTUsing easily measurable soil properties and pedotransfer functions (PTFs) is a time-saving, non-destructive and cost-saving way in the prediction of the cation exchange capacity (CEC). The purpose of this study was to compare and evaluate the regression tree (RT), multiple linear regression (MLR) and Mamdani fuzzy inference system (MFIS) in estimating CEC. For this work, 100 soil samples from unsaturated soil hydraulic database (UNSODA) data-set were used. %Organic matter (OM), bulk density (BD), the geometric mean particle diameter (dg) and fractal dimension of particle size (D) were applied as the input predictive variables. First, the type of relationship between easily measurable soil properties and CEC was investigated and, then used for the development of PTFs and fuzzy membership functions. The results showed that MLR method was developed only based on %OM (r = 0.68, p < .01) and D (r = 0.68, p < .01). While in the RT method, all of the predictive variables were appeared in the tree-like based on their correlation coefficient with CEC. The D and %OM also were considered as input variables in developing fuzzy membership functions. Results also revealed that RT method had a higher performance than MLR and MFIS in the estimation of CEC with the highest coefficient of determination (R2 = 0.77), smallest root-mean-square error (RMSE = 5.14 meq/100gsoil), normalized root-mean-square error (NRMSE = 0.25 meq/100gsoil) and mean error (ME = −1.80 meq/100gsoil). In addition, the MFIS had a higher efficiency than the MLR in the CEC estimation.

A new experimental multi-physics method for quantifying cation exchange capacity of shaly-sand formations

In this paper, a new method is proposed to quantify cation exchange capacity (CEC) in rocks with complex mineralogy and in the presence of multiple types of clay minerals by integrating nitrogen adsorption-desorption, nuclear magnetic resonance (NMR), and X-ray diffraction (XRD) measurements (including quantitative XRD). In the new method, a successive controlled nitrogen adsorption-desorption measurements as well as NMR measurements were performed on multiple samples from the same rock type. Then, an inversion algorithm was introduced to estimate surface area and volume of hydration water of each mineral, which were used as inputs to our recently-developed novel physics-based CEC estimation workflow. The CEC estimates obtained from the new method were compared against those from the ammonium acetate method and inductively coupled plasma mass spectrometry (ICP-MS) measurements in synthetic and actual powdered shaly sand rock samples. The results showed that the CEC estimates from the two methods were in good agreement, with less than 4 meq/100 g difference (i.e., average relative error of less than 23%). The experimental results demonstrated reliability of the proposed method for CEC quantification in clay-rich formations with complex lithology in the presence of multiple types of clay minerals.

A new method for quantifying cation exchange capacity in clay minerals

Abstract In this paper, a new method is proposed to theoretically quantify cation exchange capacity (CEC) of pure clay minerals, by use of nuclear magnetic resonance (NMR), X-ray diffraction (XRD), and nitrogen adsorption-desorption isotherm analysis. A theoretical model is introduced to estimate CEC, which is derived based on energy balance between chemical potential and electric potential energy. The change in chemical potential is a function of relative humidity and volume of hydration water. The change in electric potential energy is a function of the CEC, basal spacing, and surface area of clay minerals. XRD measurements were performed on clay minerals before and after exposure to 95% relative humidity. These measurements were used to estimate the basal spacing before and after hydration as well as the number of interlayers in each particle of clay minerals. NMR laboratory measurements were performed on the clay mineral samples before and after hydration to obtain the volume of hydration water. The nitrogen adsorption-desorption isotherms provided the surface area of clay minerals. The estimates of CEC from the new method were cross-validated against the directly-measured CEC, obtained from ammonium acetate method by use of inductively coupled plasma mass spectrometry (ICP-MS) measurements. The introduced method was successfully tested on six types of clay minerals (i.e., illite, kaolinite, chlorite, and three types of montmorillonite). The calculated CEC results from the new method were in agreement with those from the wet chemistry method, with less than 7 meq/100 g difference for all types of clay minerals. The experimental results on the performance of the new method demonstrated reliability of this method for quantification of CEC. The outcomes of this paper can be potentially applied to variety of formations with measurable concentrations of clay minerals such as shaly sands and organic-rich mudrocks for reliable assessment of CEC.

Rapid estimation of cation exchange capacity from soil water content

SummaryEstimates of cation exchange capacity (CEC) for soil or earth materials are vital for several applications in agriculture and geotechnical engineering. The time‐consuming and laborious nature of standard methods of CEC determination prompted the development of regression‐based techniques and more sophisticated methodologies, such as artificial neural networks (ANNs), to estimate CEC from other soil properties (e.g. clay, organic matter and pH). The present study proposes regression relations for estimating CEC from soil water content at different relative humidity (RH) values that range from 10 to 90% and considering sorption hysteresis. For model development, water adsorption and desorption isotherms were measured for 203 soil samples from different parts of the world with a vapour sorption analyser within an RH range from 3 to 93%. Regression models were developed for both adsorption and desorption for nine RH values (10, 20, 30, 40, 50, 60, 70, 80 and 90%). For 35 independent soil samples, the models predicted CEC accurately (root mean squared error (RMSE): adsorption = 2.47 (0.04) cmol(+) kg−1; desorption = 2.52 (0.09) cmol(+) kg−1) with no clear effect of RH value or sorption direction on the accuracy of prediction. The new models were superior to three models from the literature based on clay, organic matter and pH; the smallest RMSE from these models was 4.13 cmol(+) kg−1 for the same 35 soil samples. Furthermore, the water sorption‐based models performed better than literature models based on data mining tools (ANNs, support vector machines) and visible near infrared spectroscopy when standardized RMSE values were compared. Thus, a simple measure of soil water content at a known RH can serve as an accurate substitute for measuring soil CEC.Highlights A simple method to estimate cation exchange capacity (CEC) accurately is needed. Regression models for estimating CEC from soil water contents were developed and tested. Models predicted CEC accurately with no effect of relative humidity or sorption direction. Proposed models performed better than existing models based on the validation dataset.

An Improvement in Cation Exchange Capacity Estimation and Water Saturation Calculation in Shaly Layers for One of Iranian Oil Fields

Water saturation and cation exchange capacity are the most significant parameters used to calculate a hydrocarbon zone potential. In clean formations, by applying the famous Archie model, which assumes that in the formation the only electric conductor is the formation water, the water saturation can be calculated. Additionally, in shaly sand formations this assumption may not be true as the ions are associated with clay minerals and transport electricity. We attempt to utilize some logs, as well as experimental data, to improve the water saturation calculations and the cation exchange capacity within shaly sand layers for an Iranian oil field. Therefore, several shaly sand models were considered for the calculations of these values for three wells of the Ahvaz oil field. The validity of the different models was controlled through the measured values of cation exchange capacity and water saturation on core samples. The comparative results show that cation exchange capacity and water saturation calculated using Ipek-Bassiouni model are more indicative of zone hydrocarbon potential with correlation coefficients of 0.91 and 0.95 respectively. The results of this study show the requirement for the I-B model in studied reservoir and its superiority compared to other models because it is on the basis of principles reflecting the formation water and clay counter ions conductive behavior. Concerning to this model, two cement exponents are incorporated to illustrate the electric current path tortuosity in clay bound water and free water. In the current work, cementation exponent in free water (mf) and clay bound water (mc) were estimated from pure shale and clean sand respectively. The results of this study are promising and can be simply extended in other similar neighboring shaly sand reservoirs.

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

The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg−1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg−1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

Estimation of cation exchange capacity and cation saturation of Luvisols developed from loess

The cation exchange capacity and saturation of the sorptive complex with base cations are very important indicators for quality assessment of soils, because they define soil fertility and resistance to chemical degradation. The objective of the following study was to estimate the cation exchange capacity and saturation with exchangeable cations of Luvisols developed from loess, under agricultural use. The study comprised 12 pedons under winter wheat culture, classified into various complexes of soil suitability. Exchangeable cations of alkaline character were assayed with the Pallmann method in 1 mol NH4Cl dm-3 extract with pH 8.2. The content of cations Ca2+, K+ and Na+ was determined with a flame photometer, while that of Mg2+ was measured with the AAS method. In the Ap horizons of the analyzed soils, the quantitative sequence of base cations was most frequently Ca2+>Mg2+>K+>Na+. The total exchange bases (TEB) was favourable in most of the soils (3.8-16.9 cmol(+) kg-1), and the total acidity (TA) was varied (0.53.7 cmol(+) kg-1) but generally low. The cation exchange capacity (CEC) in the Ap, Et, EB, Bt and BC horizons of the soils was assessed as medium or high (5.2-17.9 cmol(+) kg-1). Only the C horizon, containing calcium carbonate, was characterised by very high CEC (76.2 cmol(+) kg-1). The base saturation (BS) was highly favourable in most of the soils (62.9-99.4%) and increased deeper into the pedon. The soils were characterised by generally favourable shares of particular cations: the percentage of Ca2+ was 51.5-95.1%, most often higher than the optimum, while the shares of Mg2+ and K+ were slightly below the optimum. The calculated quantitative ratios between the cations showed very strong variation of values among particular pedons. The content of Mg2+ cations and the values of TEB and CEC in the Ap horizons of soils from the very good wheat complex were lower than in the Ap horizons of soils from the good wheat complex and the deficient wheat complex, whereas the levels of Ca2+, K+, Na+ and H+ as well as BS displayed only minor differences.

Influence of ion exchange processes on salt transport and distribution in historic sandstone buildings

Crystallisation of salts in the pores of stone is a major concern in the preservation of heritage buildings, monuments and sculptures, but the mechanism of transport and distribution of salts is still not properly understood. The fractionation and distribution of salts in the porous matrix has, in building material research, conventionally been attributed to the solubility and concentration of salts present in the groundwater. We propose another mechanism contributing to the control of the salt distribution based on the interaction of ions in the salt solution with the charged mineral phases within the stone. The transport of mixed salt solutions was studied in laboratory simulated flow-through experiments on two fluvial sandstones – a Permo-Triassic red bed sandstone and a Carboniferous sandstone, both from the UK. The experiments were carried out under non-evaporative conditions, eliminating the possibility of solubility-dependent crystallisation. The results indicate that the process of ion exchange significantly controls the transport of ions in the pores and leads to fractionation of solutes in the course of transport even in the absence of evaporation and crystallisation. The sandstones behave like a chromatographic column and retention of various ionic species is significantly controlled by ion exchange processes. A quantitative estimation of cation exchange capacity (CEC) indicates that sandstones with higher CEC have greater influence on retention and fractionation of salts in the course of capillary transport than those with lower CEC. Simple scoping calculations using a geochemical modelling code and the ion exchange properties based on those determined in the laboratory experiments, demonstrate that ion exchange can have a significant effect on mineral precipitation during evaporation.

Characterization of Egyptian Smectitic Clay Deposits by Methylene Blue Adsorption

Problem statement: The characterization of smectitic clays has been traditionally made using physical and chemical methods which are time-consuming and require costly equipment. Adsorption of methylene blue by these clays has been introduced as a quick and cheap method for the estimation of cation exchange capacity and surface area. Therefore, the aim of the present study was to test the applicability of the methylene blue method for the determination of cation exchange capacity and specific surface area of Egyptian smectitic clay deposits and hence to estimate their content of expansive clays. Approach: The distribution of sand, silt and clay fractions was determined in the studied samples by grain size analysis. The methylene blue test was applied using the halo method for the determination of the optimum adsorption point. Results: A positive correlation was found between the cation exchange capacity of the claystone as determined by the methylene blue value and its clay fraction and between the specific surface area and the cation exchange capacity. Conclusion/Recommendations: It has been concluded that the methylene blue test can be used as a reliable and rapid method for the characterization of smectitic clay deposits and soils containing smectite. These properties are important for engineering and environmental geological assessments of sites for different construction purposes.

Artificial Neural Network Approach for Predicting Cation Exchange Capacity in Soil Based on Physico-Chemical Properties

Cation exchange capacity (CEC) of soil is an important characteristic that determines the soil buffering capacity to maintain cationic nutrients and pollutants against leaching to the subsurface layers. Radial basis function neural networks (RBFN) were applied into the estimation of cation exchange capacity (CEC) from soil physicochemical properties. A total of 457 soil samples collected from surface to deep underground horizons under various climatic and geographic conditions were successfully compiled in the comprehensive modelling research. Soil horizon; pH; organic carbon content; and clay, silt, and sand contents were taken as the input variables. Four RBFN models applied in CEC estimates for A, B, C horizons as well as the aggregate soils from all horizons were developed, with high correlation coefficients of 0.9397, 0.8368, 0.8014, and 0.8335 between the predicted and measured values of the external test samples, respectively, after optimization of the network architectures. Furthermore, the influence of each input variable on the soil CEC was particularly evaluated and sequenced by sensitivity analysis, confirming that clay content, organic carbon content, and soil horizons are the most important determinants of CEC estimate. Due to the existing nonlinear and uncertain characteristics of natural soil properties demonstrated in this paper, the optimized RBFN models showed high superiority in large-scale data simulation of complicated soil samples compared with multiple linear regressions (MLR). All the results confirm that an artificial neural network is a precise, sensitive, and robust method in the quantitative study of CEC in widely distributed soil samples in the complex ecosystem.

Prediction of cation exchange capacity from soil index properties

AbstractIn many areas of geotechnical engineering it is necessary to have an estimate of the cation exchange capacity (CEC) of a soil in order to allow preliminary design estimates. Standard methods of CEC determination are time-consuming and involve several steps (e.g. displacement of the saturating cation requires several washings with alcohol). Therefore, a rapid method of CEC estimation would be very useful. During preliminary site investigations, the soil engineering parameters can be estimated from the considerable number of correlations available in the literature. In this study, relationships between CEC and various other soil engineering properties have been investigated, resulting in a quick method for estimating CEC.Simple correlations were developed between CEC and specific surface area (SSA), soil organic matter (OM), clay fraction (CF), activity (A), Atterberg limits (liquid (LL), plastic (PL), and shrinkage (SL)), and modified free swell index (MFSI) of the soils. Strong correlations are observed between the CEC values and those for ethylene glycol monoethyl ether (EGME) uptake and methylene blue (MB) titration. However, no significant correlation was found between CEC and N2_SSA. No unique relationship was seen between CEC and CF (r2 &lt;0.5). No relationship was observed between CEC and OM in this study. The best correlation coefficient between the CEC and Atterberg limits exists between CEC and LL (r2 = 0.61). No significant relationship was seen between CEC and PL or SL. The correlation coefficient between CEC and MFSI was 0.65. Multiple linear regression analyses were developed to investigate the contributions of different soil parameters to CEC. These analyses show that EGME_SSA, in combination with LL, accounted for 91% of the variation in CEC. Correlations between CEC and EGME_SSA, MB_SSA and LL appear to be sufficiently good to enable an indication of CEC to be estimated from these parameters.

Large-scale modeling of reactive solute transport in fracture zones of granitic bedrocks

Final disposal of high-level radioactive waste in deep repositories located in fractured granite formations is being considered by several countries. The assessment of the safety of such repositories requires using numerical models of groundwater flow, solute transport and chemical processes. These models are being developed from data and knowledge gained from in situ experiments such as the Redox Zone Experiment carried out at the underground laboratory of Äspö in Sweden. This experiment aimed at evaluating the effects of the construction of the access tunnel on the hydrogeological and hydrochemical conditions of a fracture zone intersected by the tunnel. Most chemical species showed dilution trends except for bicarbonate and sulphate which unexpectedly increased with time. Molinero and Samper [Molinero, J. and Samper, J. Groundwater flow and solute transport in fracture zones: an improved model for a large-scale field experiment at Äspö (Sweden). J. Hydraul. Res., 42, Extra Issue, 157–172] presented a two-dimensional water flow and solute transport finite element model which reproduced measured drawdowns and dilution curves of conservative species. Here we extend their model by using a reactive transport which accounts for aqueous complexation, acid–base, redox processes, dissolution–precipitation of calcite, quartz, hematite and pyrite, and cation exchange between Na + and Ca 2+. The model provides field-scale estimates of cation exchange capacity of the fracture zone and redox potential of groundwater recharge. It serves also to identify the mineral phases controlling the solubility of iron. In addition, the model is useful to test the relevance of several geochemical processes. Model results rule out calcite dissolution as the process causing the increase in bicarbonate concentration and reject the following possible sources of sulphate: (1) pyrite dissolution, (2) leaching of alkaline sulphate-rich waters from a nearby rock landfill and (3) dissolution of iron monosulphides contained in Baltic seafloor sediments. Based on these results, microbially mediated processes are postulated as the most likely hypothesis to explain the measured increase of dissolved bicarbonates and sulphates after tunnel construction.

Estimation of Cation Exchange Capacity of Montmorillonite by Cationic Surfactant Adsorption

Cation exchange capacity (CEC) is one of the fundamental properties of clays and provides a means for estimating the general chemical properties of clays. The ammonium acetate saturation and sodium saturation methods are commonly used for measuring CEC. However, both methods are experimentally quite difficult and time consuming. This study investigates the feasibility of using cationic surfactant adsorption to determine the CEC of, an expandable clay, montmorillonite. Two laboratory grade montmorillonite samples with CEC of 71 (montmorillonite A) and 22 (montmorillonite B) mmol/100 g respectively were used in this study. Experimental results indicate that benzyltriethyl ammonium (BTEA) ion with an effective number of CH2 groups in hydrocarbon chain of 5–7 should be used to provide reasonable estimations for monolayer capacity and CEC. The method has advantages compared to traditional methods in that it is simple and can easily be applied without the requirement for specialized equipment.