Soil Specific Surface Area Research Articles

Long term forest conversion affected soil nanoscale pores in subtropical China

Long term land-use change, such as forest conversion, has been shown to strongly affect soil pore properties. The soil pores mediate soil processes and determine how soil functions. Despite their importance for understanding biological, water, and carbon stability, the properties of soil pores following long term forest conversion remain unclear in subtropical forests. We examined soil nanoscale pores in response to conversion from native forests to plantations and addressed how bacterial and fungal communities influenced soil pore properties. We found significant changes in soil pores 40 years after the conversion from native forests to Chinese fir forests or bamboo forests. The pores in mixed forest soils were similar to those in native forests, while Chinese fir forest soils contained the most pores, followed by bamboo forest soils. Chinese fir forests also had a higher surface area and volume of both total soil pores and macropores, whereas bamboo forests had a higher volume of mesopores and larger specific surface areas of the total, macro-, and mesopores. Both soil bacteria and fungi significantly influenced soil pore properties. The relative abundance of soil bacterial phyla (Planctomycetes and Verrucomicrobia), fungal phylum (Chytridiomycota), and bacterial and fungal diversity negatively affected average pore diameter. In addition, soil fungal phylum (Basidiomycota) and the total sequencing number positively affected soil mesopore volume and the specific surface areas of both total pores and mesopores. Our study demonstrated that forest conversion drove the changes in soil pores at the nanoscale. Changes in soil microbial abundance and diversity may be one of the factors that affect soil nanoscale pores, while soil fungal compositions influence soil mesopores and soil specific surface area.

Experimental investigation of dry density effects on dielectric properties of soil–water mixtures with different specific surface areas

The dielectric constant of soil is used to estimate its water content in a range of applications. Unlike the widely known effect of water content on the soil dielectric constant (consistent direct proportionality), only a limited number of studies have reported the effects of soil dry density, however, with equivocal results. This paper, therefore, investigates the effects of dry density or degree of compaction on the dielectric constant of five different soil types. The results of the experimental work for the soils ranging from sand to Bentonite clay with distinct specific surface areas were evaluated based on the use of two simple mixture models (De Loor and Birchak). The effects of dry density on the soil dielectric constant were found to be soil type dependent. This is demonstrated by the experimental data and further proven by the modified De Loor model. The behavior is shown to be defined by the changes in the free water, bound water, and solid particle volume fractions, ultimately controlled by the soil specific surface area. The dielectric constant changes from being directly proportional to dry density to inversely proportional at a threshold specific surface area of between 122 and 147 m2/g. Supported by the experimental observations, parametric analysis has revealed that the range for the dielectric constant of bound water was found to be 9–37, while the geometrical parameter α in the Birchak model was found to be 0.4–0.8.

Soil ozone deposition: Dependence of soil resistance to soil texture

Soil deposition is an essential pathway for tropospheric ozone (O3) removal, but its controlling factors remain unclear. Here, we explored the variability of soil O3 resistance in response to soil texture. To this aim, data of O3 deposition over bare soil obtained from micrometeorological measurements under contrasted meteorological conditions for five sites were used. The results obtained are twofold: (i) soil resistance (Rsoil) increased with soil surface relative humidity (RHsurf), but (ii) this relationship exhibited large site-by-site variability. Further analysis showed that the minimum soil resistance (corresponding to completely dry soil surface or RHsurf = 0%) and the increase of Rsoil with RHsurf are both linked to soil clay content. These results can be explained by (i) the soil surface available for O3 deposition at a microscopic scale which is a function of the soil specific surface area, and (ii) the capacity of a soil to adsorb water according to its clay content and therefore to reduce the surface active for O3 deposition. From these results, a new parameterization has been established to estimate Rsoil as a function of RHsurf and soil clay fraction.

Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks.

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55-63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE = 0.13) and PTF18 (RMSE = 0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE = 0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.

Soil Specific Surface Area Determination by Visible Near‐Infrared Spectroscopy

Core Ideas Feasibility of using vis–NIRS for SSA determination in a highly variable data set was investigated Acceptable predictive ability for SSA using PLS and iPLS was obtained. iPLS resulted in little improved accuracy for both SSA and clay iPLS indicated a higher specificity of SSA than of clay response The important wavelengths reflected the organo‐mineral composition and its interactions The soil specific surface area (SSA) affects soil physical and chemical properties. Numerous studies applied visible near‐infrared spectroscopy (Vis–NIRS) to estimate clay content (particles < 2 µm). Since SSA is better defined and more directly related to particle size distribution and mineralogy than clay content, predictions of SSA from Vis–NIRS are expected to be better than that for clay. Thus, the aims of this study were to (i) test the feasibility of using Vis–NIRS for SSA determination, (ii) compare the predictive ability of Partial Least Squares (PLS) model of SSA with that of clay, (iii) identify important wavelengths using interval Partial Least Squares (iPLS) regression, and to test if the application of iPLS improves the predictive ability of the models. A total of 550 soil samples with a wide range in SSA (3–437 m2 g–1) and clay content (1–83%) was divided into a calibration and a validation set. The PLS models had similar predictive ability for SSA (ratio of performance to interquartile range, RPIQ = 1.7) and clay content (RPIQ = 1.6). Utilizing iPLS led to only limited improvement in the prediction accuracy (RPIQ of 1.8 and 1.7 for SSA and clay content, respectively), yet decreased the number of relevant wavelengths and indicated a higher specificity of SSA over the broader spectral response of clay. The important wavelengths for SSA and clay predictions were indicative of the organo‐mineral content and its interactions, including spectral response from not only iron oxides and minerals but also organic matter due to masking effect of the non‐complexed organic carbon on the mineral phases of some of the soils.

Estimating base cation weathering rates in the United States: challenges of uncertain soil mineralogy and specific surface area with applications of the PROFILE model.

The weathering release rate of base cations (BCw) from soil minerals is fundamentally important for terrestrial ecosystem growth, function, and sensitivity to acid deposition. Understanding BCw is necessary to reduce or prevent damage to acid-sensitive natural systems, in that this information is needed to both evaluate the effectiveness of existing policies, and guide establishment of further policies in the event they are required. Yet BCw is challenging to estimate. In this study, major sources of uncertainty associated with a process-based model (PROFILE) commonly used to estimate weathering rates were quantified in the context of efforts to quantify BCw for upland forest sites across the continental U.S. These include uncertainty associated with parameterization of mineral content where horizon data are not available, stoichiometry of individual minerals, and specific surface area of soil and individual soil minerals. Mineral stoichiometry was not an important influence on BCw estimates (uncertainty < 1%). Characterizing B horizon mineralogy by averaging A and C horizons was found to be a minor (< 5%) contributor to uncertainty in some areas, but where mineralogy is known to vary with depth the uncertainty can be large. Estimating mineral-specific surface areas had a strong influence on estimated BCw, with rates increasing by as much as 250%. The greatest uncertainty in BCw estimates, however, was attributed to the particle size class-based method used to estimate the total specific surface area upon which weathering reactions can take place. The resulting uncertainty in BCw spanned multiple orders of magnitude at individual sites, highlighting this as the greatest challenge to ongoing efforts to produce robust BCw estimates across large spatial scales in the U.S. Recommendations for improving estimates of BCw to support robust decision making for protection against terrestrial acidification are provided.

Applicability of the Guggenheim–Anderson–Boer water vapour sorption model for estimation of soil specific surface area

SummarySoil specific surface area (SA) controls fundamental soil processes such as retention of water, ion exchange, and adsorption and release of plant nutrients and contaminants. Conventional methods for determining SA include adsorption of polar or non‐polar fluid molecules with associated advantages and limitations. The Guggenheim–Anderson–Boer (GAB) sorption model accurately characterizes soil water vapour sorption isotherms and is posited as an alternate approach for the determination of SA from water vapour sorption. The present study investigates the GAB model as an alternative to other water sorption‐based modelling approaches to determine SA. Measured water vapour adsorption and desorption isotherms for 321 soil samples were used to parameterize the GAB model, the Brunauer–Emmet–Teller (BET) equation and a film adsorption Tuller–Or (TO) model to estimate SA. For adsorption isotherms, the values of the GAB parameters varied depending on the water activity or relative humidity range of measured data (0.03–0.93 compared with 0.10–0.80), whereas the variation for desorption was minimal. For desorption isotherms, the average water activity value at which the GAB monolayer parameter was obtained was 0.24 for kaolinite‐rich samples, 0.31 for illite‐rich or mixed clay samples, 0.34 for smectitic samples and 0.30 for organic matter‐rich samples, respectively. The GAB model provided reasonable estimates of SA (root mean squared error from 11.6 to 36.4 m2 g−1), in particular for smectite‐rich soil samples, when compared with SA measured by the ethylene glycol monoethyl ether (EGME) method. For kaolinitic samples, however, the BET equation provided the best estimate of EGME‐SA. The SA estimates of the GAB model were comparable to those obtained by the TO adsorption model. Thus, the GAB model provides a good alternative to the TO model (applicable only to adsorption data) or the BET model, which fails when the fraction of swelling clay minerals increases.HighlightsGAB, BET and film adsorption models were parameterized with water vapour sorption data.Water activity at GAB monolayer coverage depended on clay mineralogy.Soil organic matter did not affect water activity at monolayer coverage.Estimates of SA from GAB were in accord with EGME‐measured SA.

Soil water retention, air flow and pore structure characteristics after corn cob biochar application to a tropical sandy loam

Soil structure is a key soil physical property that affects soil water balance, gas transport, plant growth and development, and ultimately plant yield. Biochar has received global recognition as a soil amendment with the potential to ameliorate the structure of degraded soils. We investigated how corn cob biochar contributed to changes in soil water retention, air flow by convection and diffusion, and derived soil structure indices in a tropical sandy loam. Intact soil cores were taken from a field experiment that had plots without biochar (CT), and plots each with 10tha−1 (BC-10), 20tha−1 without or with phosphate fertilizer (BC-20 and BC-20+P respectively). Soil water retention was measured within a pF range of 1 to 6.8. Gas transport parameters (air permeability, ka, and relative gas diffusivity, Dp/D0) were measured between pF 1.5 and 3.0. Application of 20tha−1 led to significant increase in soil water retention compared to the CT and BC-10 as a result of increased microporosity (pores <3μm) whereas for soil specific surface area, biochar had minimal impact. No significant influence of biochar was observed for ka and Dp/D0 for the BC treatments compared to the CT despite the larger values for the two properties in the 20tha−1 treatments. Although not significant, the diffusion percolation threshold reduced by 34% and 18% in the BC-20 and BC-20+P treatments, respectively, compared to the CT. Similarly, biochar application reduced the convection percolation threshold by 15 to 85% in the BC-amended soils. The moderate impact of corn cob biochar on soil water retention, and minimal improvements in convective and diffusive gas transport provides an avenue for an environmentally friendly disposal of crop residues, particularly for corn cobs, and structural improvement in tropical sandy loams.

Prediction of the soil water retention curve for structured soil from saturation to oven‐dryness

The soil water retention curve (SWRC) is the most fundamental soil hydraulic function required for modelling soil–plant–atmospheric water flow and transport processes. The SWRC is intimately linked to the distribution of the size of pores, the composition of the solid phase and the soil specific surface area. Detailed measurement of the SWRC is impractical in many cases because of the excessively long equilibration times inherent to most standard methods, especially for fine textured soil. Consequently, it is more efficient to predict the SWRC based on easy‐to‐measure basic soil properties. In this research we evaluated a new two‐stage approach developed recently to predict the SWRC based on measurements for disturbed repacked soil samples. Our study involved undisturbed structured soil and took into account the effects of bulk density, organic matter content and particle‐size distribution. Independently measured SWRCs for 171 undisturbed soil samples with organic matter contents that ranged from 3 to 14% were used for model validation. The results indicate that consideration of the silt and organic matter fractions, in addition to the clay fraction, improved predictions for the dry‐end SWRC. The dry‐end results revealed that the smallest matric potential at hypothetical ‘zero‐water‐content’ varies between −45 000 and −125 000 m (pF 6.65–7.1) even for soils with similar clay mineralogy. The sensitivity analysis of the two‐stage approach indicated that predicted SWRC results are more sensitive to bulk density than to organic matter content or soil texture. For the soils studied, the two‐stage approach showed reasonable agreement with measured data, with a root mean square error of 0.04 cm3 cm−3 for the matric potential range from pF 1 to pF 6.6.HighlightsIs a new approach to modelling the soil water retention (SWR) curve applicable to structured soil? The model accurately predicts the full SWR curve with an RMSE of 0.04 cm3 cm−3. Prediction accuracy of the wet part of the SWR curve was sensitive to variation in bulk density. The pF at zero water content, which affects the prediction of dry SWR, ranged from 6.65 to 7.10.

Stability of soil organic carbon and potential carbon sequestration at eroding and deposition sites

This study aims to explore the dynamics of the factors influencing soil organic carbon (SOC) sequestration and stability at erosion and deposition sites. Thermal properties and dissolved aromatic carbon concentration along with Al, Fe concentration and soil specific surface area (SSA) were studied to 1 meter depth at two contrasting sites. Fe, Al concentrations and SSA size increased with depth and were negatively correlated with SOC concentration at the erosion site (P 30 cm deep) had a large carbon sink potential.

Univariate and multivariate analysis to elucidate the soil properties governing americium sorption in soils

Americium (Am) solid–liquid distribution coefficients (Kd) and desorption percentages were quantified in 20 soils with contrasting edaphic properties by means of batch tests aimed at elucidating the mechanisms and soil properties governing Am sorption. The Kd (Am) values ranged from 103 to 105Lkg−1, whereas the desorption percentages were always <1.2%, thereby demonstrating a strong and irreversible sorption of Am. Univariate and multivariate data analyses were performed with the obtained sorption parameters to identify the soil properties controlling Am sorption behaviour and to propose Kd (Am) prediction models. Univariate Linear Regressions (ULR) and Multiple Linear Regressions (MLR) based on the use of original soil variables and a Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) based on latent variables revealed that Am sorption was controlled by organic matter, pH, the carbonate and amorphous iron content and the soil specific surface area. The MLR and PLS models explained up to 73% and 70% of the Kd (Am) variability, respectively; thus, they can be used to estimate Kd (Am) values directly from soil properties.

Advanced Geometric Mean Model for Predicting Thermal Conductivity of Unsaturated Soils

An advanced geometric mean model for predicting the effective thermal conductivity ( $$\lambda $$ ) of unsaturated soils has been developed and successfully verified against an experimental $$\lambda $$ database consisting of 40 Canadian soils, 15 American soils, 10 Chinese soils, four Japanese soils, three standard sands, and one pyroclastic soil (Pozzolana) from Italy (a total of 667 experimental $$\lambda $$ entries). Three soil structure-based parameters were used in the model, namely an inter-particle thermal contact resistance factor ( $$\alpha $$ ), the degree of saturation of a miniscule pore space $$(s_{\mathrm{r}})$$ , and the bulk thermal conductivity of soil solids $$(\lambda _{\mathrm{s}})$$ . The $$\alpha $$ factor strongly depended on the ratio of $$\lambda _{\mathrm{s}}$$ to $$\lambda _{\mathrm{f}}$$ (where $$\lambda _{\mathrm{f}}$$ is the thermal conductivity of interfacial fluid) and an inter-particle contact coefficient ( $$\varepsilon $$ ) whose value was obtained by reverse modeling of experimental $$\lambda $$ data of 40 Canadian soils; the average values of $$\varepsilon $$ varied between 0.988 and 0.994 for coarse and fine soils, respectively. In general, $$\varepsilon $$ depends on soil compaction, soil specific surface area, and grain size distribution. The use of $$\alpha $$ was essential for close $$\lambda $$ estimates of experimental data at a low range of degree of saturation $$(S_{\mathrm{r}})$$ . For $$\lambda _{\mathrm{s}}$$ estimates obtained from measured $$\lambda $$ at soil saturation or a complete soil mineral composition data or experimental quartz content, 69 % of $$\lambda $$ predictions were less than $$0.08\, \hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ , 15 % were between $$0.08\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ and $$0.13\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ , and 13 % were between $$0.13\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ and $$0.24\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ with respect to experimental data $$(\lambda _{\mathrm{exp}})$$ . The model gives close $$\lambda $$ estimates with an average root-mean-square error (RMSE) of $$0.051\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ for 22 Canadian fine soils and an average RMSE of $$0.092\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ for 18 Canadian coarse soils. In general, better $$\lambda $$ estimates were obtained for soils containing less content of quartz. Overall, the model estimates were good for all soils at dry state ( $$\hbox {RMSE} = 0.050\, \hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ ; 22 % of the average $$\lambda _{\mathrm{exp}}$$ ), saturated state ( $$\hbox {RMSE} = 0.090\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ ; 5 % of the average $$\lambda _{\mathrm{exp}}$$ ), soil field capacity ( $$\hbox {RMSE} = 0.105\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ ; 9 % of the average $$\lambda _{\mathrm{exp}}$$ ), and satisfactory near a critical degree of saturation, $$S_{\mathrm{r-cr}}$$ ( $$\hbox {RMSE} = 0.162\,\hbox {W} {\cdot } \hbox {m}^{-1} {\cdot } \hbox {K}^{-1}$$ ; 26 % of the average $$\lambda _{\mathrm{exp}}$$ ).

CHARACTERIZATION AND CLASSIFICATION OF FLOODPLAIN SOILS IN THE PORTO ALEGRE METROPOLITAN REGION, RS, BRAZIL

ABSTRACTIn the Porto Alegre metropolitan region (PAMR) there are a significant proportion of floodplain soils, mainly Planosols and Gleysols, in relation to upland soils. This study aimed to evaluate the morphological, chemical and physical characteristics, and to classify floodplain soils in the PAMR. Six soil profiles were evaluated under different sedimentary lithologies and drainage classes, and samples were collected for chemical and physical analyzes. Two orders of mineral soils (Planosols and Gleysols) and one order of organic soil (Organosols) were identified. The soils were moderately deep to deep and stratified. In mineral soils hue ranged between 7.5YR and 2.5Y, with the occurrence of Bg, Btg or Cg gley horizons, while in organic soil the colors were neutral. Sand and silt were the predominant particle sizes according to the origin sedimentary deposits. The organic carbon content was negatively related to soil density and positively related to soil specific surface area and with soil cation exchange capacity. Soil chemical characterization showed expressive variation in bases, aluminum and sodium saturation. Ki index and Fe(CBD)/Fe(H2SO4) ratio indicated a low soil weathering degree. The different sedimentary lithologies and the soil hydromorphism degree were the main factors related to differences in morphological, physical and chemical characteristics of soils in the PAMR.

Analyzing the effect of various soil properties on the estimation of soil specific surface area by different methods

Depending on the method used, measuring the specific surface area (SSA) can be expensive and time consuming and limited numbers of studies have been conducted to predict SSA from soil properties. In this study, 127 soil sample data were gathered from the available literature. The data set included SSA values and some of the soil physical and chemical index properties. At the first step, linear regression, non-linear regression, regression trees, artificial neural networks, and a multi-objective group method of data handling were used to develop seven pedotransfer functions (PTFs) for the purpose of finding the best method in predicting SSA. Results showed that the artificial neural networks performed better than the other methods used in the development and validation of PTFs. At the second step, to find the best set of SSA for predicting input variables and to investigate the importance of the input parameters, the artificial neural networks were further used and 25 models were developed. The results showed that the PTF, containing the input variables of sand%, clay%, plastic limit, liquid limit, and free swelling index performed better than the other PTFs. This can be attributed to the close relation between the free swelling index and Atterberg limits with the soil clay mineralogy, which is one of the most important factors controlling SSA. The sensitivity analysis showed that the greatest sensitivity coefficients were found for the cation exchange capacity, clay content, liquid limit, and plasticity index in different models. Overall, the artificial neural networks method was proper to predict SSA from soil variables.

Remediation of hydrocarbon polluted soils using 2.45 GHz frequency-heating: Influence of operating power and soil texture on soil temperature profiles and contaminant removal kinetics

2.45GHz frequency heating treatment of diesel-contaminated soils was applied to investigate the effects of operating power and soil texture on soil temperature profiles and contaminant removal kinetics. Four soil size aggregate fractions, corresponding to medium (200–350μm) and fine (75–200μm) sand, silt (10–75μm) and clay (<4μm) were artificially contaminated with diesel fuel and remediated with lab-scale apparatus for different lengths of time. Results suggest that soil texture significantly influences the contaminant removal kinetics and that thermodynamic and dielectric properties of soils have a major effect on the final temperature reachable during the heating processes. Minimal contaminant residual concentrations (<400mgkg−1) were achievable only for sandy soils, for which the highest contaminant removals (>90%) were reached. The power low kinetic model showed a quite good correlation (r2>0.976) with the experimental data for residual concentration at all operating powers and for all soils investigated. Contaminant rate decay (k) variation observed for the different soils indicates that contaminant removal processes strictly depend both on thermal properties and specific surface area of soil regulating the adsorption–desorption phenomena related to the decontamination treatment. Especially for sandy soils, microwave heat treatment could be a better choice for remediation of diesel polluted soils compared to several other biological, chemical–physical or conventional thermal treatments due to its removal efficiency.

Factors controlling labile soil organic matter vulnerability to loss following disturbance as assessed by measurement of soil‐respired δ13CO2

SummaryLabile soil organic matter (SOM) is often stabilized in soil, but is vulnerable to loss after soil disturbance. We used measurements of the 13carbon (13C) signature of soil‐respired CO2 (δ13CO2) immediately after the disturbance caused by coring and sampling the soil, to assess labile SOM availability and potential vulnerability to loss. We incubated a range of pasture soils over 300 minutes and periodically measured δ13CO2. Strong temporal trends in δ13CO2 suggest that labile SOM became vulnerable to loss within minutes of soil disturbance. Equilibrium soil‐respired δ13CO2 values were a function of the amount of labile SOM (measured as hot‐water extractable C, HWEC), total soil C and soil protection capacity (measured as specific soil surface area, SSA). An independent experimental approach that immobilized labile SOM on to allophane (a clay mineral with a large active surface area) was used to assess the effect of SSA on equilibrium soil‐respired δ13CO2. In this experiment δ13CO2 shifted progressively to more enriched values during the first six days of soil incubations after allophane addition, suggesting that labile SOM became less available after stabilization by allophane. At the same time there was a large reduction in HWEC when compared with the control treatments, also indicating limited availability of labile SOM. Further studies coupling the isotopic measurements with CO2 evolution rates are needed to test directly whether equilibrium δ13CO2 can reflect labile SOM vulnerability to loss.

Relating soil specific surface area, water film thickness, and water vapor adsorption

AbstractEstimation of soil specific surface area (SSA) and dry‐end water vapor adsorption are important for porous media characterization and for prediction of water and vapor fluxes in arid environments. The objective of the presented study was to model water adsorption, film thickness, and SSA based on the t‐curve theory originally developed for N2 adsorption. Data from 21 source soils with clay contents ranging from 0.6 to 52.2% were used to estimate specific surface area based on water retention, a t‐curve type method, the linear prediction method, and a simplified monolayer method. The water retention and the t‐curve methods were found to be mathematically analogous and were among the most accurate with regard to correlation coefficient (r = 0.97) and root‐mean‐square error (RMSE = 11.36 × 103 m2/kg) when compared to measurements obtained with the standard ethylene glycol monoethyl ether (EGME) method. The corrected t‐curve method significantly overestimated SSA when compared to EGME data. Comparison of all considered methods with N2‐BET (BET) measurements disclosed lower correlation coefficients. For soil studies, the vapor adsorption in conjunction with the t‐curve or water retention methods should be preferred for SSA estimation as they show much higher correlation with soil clay content and EGME measurements.

Prediction of the soil-water characteristic curve based on the specific surface area of fine-grained soils

This paper presents a new, mathematical expression for describing the soil-water characteristic curve (SWCC) over a range of water contents where fine-grained soils exhibit plastic properties. The finding that the relationship between the soil suction and the water content can be expressed in terms of the specific surface area of soils was based on experimentally determined relationships between the water content and the soil’s specific surface area, as well as between the thickness of the adsorbed water layer on the external surfaces of clay minerals and the quantity of free-pore water for the water content between the liquid and plastic limits. The double-porosity model for the pore-space geometry was considered, as well as that all the water in the clay-aggregates is adsorbed, and that the adsorption mechanism is dominated by the van der Waals forces. The validity and applicability of the proposed equation for the SWCC estimation was verified on three samples in which the SWCC was measured, as well as the specific surface area, the mineralogical and chemical compositions, the grain size distribution, and the Atterberg limits. Despite the fact that good correlations were found between the calculated parts of the SWCC and the measured, the practical applicability of the proposed equation remains problematic due to the values of Hamaker constant, which are not yet well defined for different minerals.

Modeling Soil Specific Surface Area with Artificial Neural Networks

Abstract This study presents artificial neural network (ANN) models to estimate the specific surface area of fine-grained soils as an alternative to sophisticated laboratory procedures. Geotechnical properties of 206 soils were measured experimentally based on ASTM standards. Soil input parameters used in database development were particle size at 10 %, 30 %, and 60 % finer, coefficient of curvature, coefficient of uniformity, percentage of silts and clays, percentage of soil passing sieve No. 200, fineness modulus, liquid limit, plastic limit, plasticity index, and activity. This data was used to train, test, and develop ANN models based on the backpropagation algorithm. Performance of ANN estimation was reliable when comparing the predictions with target outputs. Results indicated that the suggested ANN models exhibited excellent fit of the data as measured by the coefficient of determination and mean-square-error values. Thus, the developed ANN models could be used as a simple prediction tool to estimate soil-specific surface area reliably and efficiently as a rapid inexpensive substitute for cumbersome laboratory techniques.

Pore Size Distribution Patterns in Tropical Soils Obtained by Mercury Intrusion Porosimetry: The Multifractal Approach

Mercury intrusion porosimetry (MIP) is commonly used to determine soil pore size distributions (PSDs) in a wide, but incomplete, range of equivalent pore diameters. Mono‐ and multifractal analyses of soil PSDs are useful for analyzing the soil porous system. The main objective of this work was to characterize PSDs from tropical soils using the multifractal approach. Duplicate PSDs were measured by MIP for 54 horizons collected from 19 soil profiles in Minas Gerais, Brazil. Although 10 of the studied soils were Ferralsols (Oxisols or Latosols), other soil groups such as Nitisols, Acrisols, Alisols, Luvisols, Planosols, Cambisols, Andisols, and Leptosols also were sampled. Different patterns of PSDs were observed, depending on soil type, texture, and weathering intensity. Textural porosity (0.005–0.2 μm) was positively and significantly correlated to clay and Al2O3 contents and soil specific surface area. Comparison of multifractal analysis from Hg intrusion curves and N2 adsorption isotherms (NAIs) showed entropy dimension values and width of the singularity spectra smaller for the former than for the latter. Multifractal analysis of MIPs and NAIs allowed a thorough characterization of the complexity and heterogeneity of the soil pore space at different scales and was useful to provide new insights for portraying the soil pore system.