Gaseous diffusion in porous media Part 1. - A non-steady state method

Currie, J. A.

doi:10.1088/0508-3443/11/8/302

Cited by 206 publications

(136 citation statements)

References 7 publications

Supporting

Mentioning

133

Contrasting

Unclassified

Order By: Relevance

“…The soil-gas diŠusion coe‹cients (Dp) were measured by the method of Currie (1960) as recommended by Rolston and Moldrup (2002). The apparatus uses a diŠusion chamber with oxygen as the experimental gas at 209 C (see Fig.…”

Section: Measurement Methodsmentioning

confidence: 99%

Linear Model to Predict Soil-Gas Diffusivity from Two Soil-Water Retention Points in Unsaturated Volcanic Ash Soils

Resurreccion

Komatsu

Kawamoto

et al. 2008

Soils and Foundations

View full text Add to dashboard Cite

Section: Measurement Methodsmentioning

confidence: 99%

Linear Model to Predict Soil-Gas Diffusivity from Two Soil-Water Retention Points in Unsaturated Volcanic Ash Soils

Resurreccion

Komatsu

Kawamoto

et al. 2008

Soils and Foundations

View full text Add to dashboard Cite

“…B is the relative soil gaseous diffusion coefficient, which was determined from air-filled porosity (e) of soil using their power relationship (B = 0.82e 2.03 , r 2 = 0.94) (Currie, 1960). The relationship was obtained with undisturbed soil cores by the diffusion chamber method (Currie, 1960). e was determined as residual of the volume fractions of solid and water (θ).…”

Section: Soil Co 2 Gradient Systemmentioning

confidence: 99%

Soil CO<sub>2</sub> efflux of a larch forest in northern Japan

et al. 2010

View full text Add to dashboard Cite

Abstract. We had continuously measured soil CO 2 efflux (R s ) in a larch forest in northern Japan at hourly intervals for the snow-free period in 2003 with an automated chamber system and partitioned R s into heterotrophic respiration (R h ) and autotrophic respiration (R r ) by using the trench method. In addition, we applied the soil CO 2 concentration gradients method to continuously measure soil CO 2 profiles under snowpack in the snowy period and to partition R s into topsoil (O a and A horizons) CO 2 efflux (F t ) with a depth of 0.13 m and sub-soil (C horizon) CO 2 efflux (F c ). We found that soil CO 2 effluxes were strongly affected by the seasonal variation of soil temperature but weakly correlated with soil moisture, probably because the volumetric soil moisture (30-40% at 95% confidence interval) was within a plateau region for root and microbial activities. The soil CO 2 effluxes changed seasonally in parallel with soil temperature in topsoil with the peak in late summer. On the other hand, the contribution of R r to R s was the largest at about 50% in early summer, when canopy photosynthesis and plant growth were more active. The temperature sensitivity (Q 10 ) of R r peaked in June. Under snowpack, R s was stable until mid-March and then gradually increased with snow melting. R s summed up to 79 gC m −2 during the snowy season for 4 months. The annual R s was determined at 934 gC m −2 y −1 in 2003, which accounted for 63% of ecosystem respiration. The annual contributions of R h and R r to R s were 57% and 43%, respectively. Based on the gradient approach, R s was partitioned vertically into litter (O i and O e horizons) with a depth of 0.01-0.02 m, topsoil and sub-soil respirations with proportions of 6, 72 and 22%, respectively, on an annual basis. The vertical distribution of CO 2 efflux was consistent with those of soil carbon and root biomass.

show abstract

“…The effective diffusion coefficients predicted by the SP and PS models for square arrays can be obtained from table 1 The model of Wakao and Smith [9] underpredicts the diffusivities obtained from the experimental data whilst both RUC models are in good agreement with the experimental data as well as all the other analytical models. The experimental data of Currie [12] and Hoogschagen [13] were obtained from beds of spheres. The RUC model involving the tortuosity proves to be more accurate at porosities greater than 0.8 than the RUC model based on the geometric factor.…”

Section: Fully Staggered Array Of Squaresmentioning

confidence: 99%

Modelling of diffusion in porous structures

Plessis

Woudberg

2009

WIT Transactions on Engineering Sciences

View full text Add to dashboard Cite

An existing pore-scale model is used to predict the effective diffusivity of staggered two-dimensional rectangular unconsolidated arrays through the use of a Representative Unit Cell concept. A tri-diagonal matrix algorithm is used to solve the diffusive flux field and to compute the effective diffusion coefficient for concentration gradients of staggered arrays. The numerical results and analytical model are compared critically with theoretical and numerical studies, as well as experimental data reported in literature. The good correlations obtained for the effective diffusivity provide confidence in both the computational and analytical work.

show abstract

Gaseous diffusion in porous media Part 1. - A non-steady state method

Cited by 206 publications

References 7 publications

Linear Model to Predict Soil-Gas Diffusivity from Two Soil-Water Retention Points in Unsaturated Volcanic Ash Soils

Linear Model to Predict Soil-Gas Diffusivity from Two Soil-Water Retention Points in Unsaturated Volcanic Ash Soils

Soil CO<sub>2</sub> efflux of a larch forest in northern Japan

Modelling of diffusion in porous structures

Contact Info

Product

Resources

About

Gaseous diffusion in porous media Part 1. - A non-steady state method

Cited by 206 publications

References 7 publications

Linear Model to Predict Soil-Gas Diffusivity from Two Soil-Water Retention Points in Unsaturated Volcanic Ash Soils

Linear Model to Predict Soil-Gas Diffusivity from Two Soil-Water Retention Points in Unsaturated Volcanic Ash Soils

Soil CO&lt;sub&gt;2&lt;/sub&gt; efflux of a larch forest in northern Japan

Modelling of diffusion in porous structures

Contact Info

Product

Resources

About

Soil CO<sub>2</sub> efflux of a larch forest in northern Japan