Predicting the Gas Diffusion Coefficient in Undisturbed Soil from Soil Water Characteristics

The gas diffusion coefficient in soil (D P), and its dependency on soil physical characteristics, governs the diffusive transport of oxygen, greenhouse gases, fumigants, and volatile organic pollutants in agricultural, forest, and urban soils. Accurate models for predicting D P as a function of air-filled porosity (ϵ) in natural, undisturbed soil are needed for realistic gas transport and fate simulations. Using data from 126 undisturbed soil layers, we obtained a high correlation (r ² = 0.97) for a simple, nonlinear expression describing D P at −100 cm H₂O of soil water potential (D P,₁₀₀) as a function of the corresponding air-filled porosity (ϵ₁₀₀), equal to the volume of soil pores with an equivalent pore diameter >30 μm. A new D P(ϵ) model was developed by combining the D P,₁₀₀(ϵ₁₀₀) expression with the Burdine relative hydraulic conductivity model, the latter modified to predict relative gas diffusivity in unsaturated soil. The D P,₁₀₀ and Burdine terms in the D P(ϵ) model are both related to the soil water characteristic (SWC) curve and, thus, the actual pore-size distribution within the water content range considered. The D P(ϵ) model requires knowledge of the soil's air-filled and total porosities and a minimum of two points on the SWC curve, including a measurement at −100 cm H₂O. When tested against independent gas diffusivity data for 21 differently textured and undisturbed soils, the SWC-dependent D P(ϵ) model accurately predicted measured data and gave a reduction in root mean square error of prediction between 58 and 83% compared to the classical, soil type-independent Penman and Millington-Quirk models. To further test the new D P(ϵ) model, gas diffusivity and SWC measurements on undisturbed soil cores from three 0.4-m soil horizons (sandy clay loam, sandy loam, and loamy sand) within the 4 to 7 m depth below an industrially polluted soil site were carried out. For these deep subsurface soils the SWC-dependent model best predicted the measured gas diffusivities.