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

Abstract

The gas diffusion coefficient in soil (DP), 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 DP 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 (r2 = 0.97) for a simple, nonlinear expression describing DP at −100 cm H2O of soil water potential (DP,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 DP(ϵ) model was developed by combining the DP,100(ϵ100) expression with the Burdine relative hydraulic conductivity model, the latter modified to predict relative gas diffusivity in unsaturated soil. The DP,100 and Burdine terms in the DP(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 DP(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 DP(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 DP(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.