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A model for delineating the relative importance of particle size, particle shape, and porosity, (and their interactions), in explaining the variability of hydraulic conductivity of a granular porous medium is developed and tested. Three types of porous media are considered in this work: spherical glass beads; granular sand; and irregularly shaped, shredded glass particles. A reliable method for quantifying the three‐dimensional shape and packing of large samples of irregular particles based on their angle of repose is presented. The results of column experiments indicate that in the size range examined (i.e., 149 μm to 2380 μm), the single most important predictor of hydraulic conductivity is seen to be particle size, explaining 69% of the variability. Porous media comprising irregular particles exhibit lower hydraulic conductivity only for the larger (707 to 841 μm) particles. For the smaller (149 to 177 μm) particles, particle shape has no observable influence on hydraulic conductivity. The results of the regression analysis reveal the importance of the interaction between particle size and porosity, indicating that similar pore configurations for a given type of particle are not achieved at different sizes. This empirical model seems to provide better estimates of the hydraulic conductivity of granular porous media comprising irregular particles than selected models based solely on grain size, including Hazen, Kozeny‐Carman, and more recently Alyamani and Sen.

Abstract. Unique laboratory test chambers with attendant procedures are described, and the results of a comprehensive test protocol are discussed in terms of the mechanical, chemical and biological factors contributing to NO flux from agricultural soil to the lower levels of the troposphere. Soil moisture content, pH, and temperature are investigated to determine the effects of these important variables on NO flux. The flux is seen to increase with temperature and is greatest at pH <5 and pH >8 for the ranges studied. Further, NO flux is seen to decrease as soil moisture content is <1% or >45% water filled pore space. Mechanical, chemical, and biological factors in the soil which contribute to these observed fluxes are addressed. IntroductionSevere air pollution problems result whenever and wherever NO is available to serve as a precursor to O3 formation in the lower levels of the troposphere.

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This study evaluates commonly used geostatistical methods to assess reproduction of hydraulic conductivity (K) structure and sensitivity under limiting amounts of data. Extensive conductivity measurements from the Cape Cod sand and gravel aquifer are used to evaluate two geostatistical estimation methods, conditional mean as an estimate and ordinary kriging, and two stochastic simulation methods, simulated annealing and sequential Gaussian simulation. Our results indicate that for relatively homogeneous sand and gravel aquifers such as the Cape Cod aquifer, neither estimation methods nor stochastic simulation methods give highly accurate point predictions of hydraulic conductivity despite the high density of collected data. Although the stochastic simulation methods yielded higher errors than the estimation methods, the stochastic simulation methods yielded better reproduction of the measured ln (K) distribution and better reproduction of local contrasts in ln (K). The inability of kriging to reproduce high ln (K) values, as reaffirmed by this study, provides a strong instigation for choosing stochastic simulation methods to generate conductivity fields when performing fine‐scale contaminant transport modeling. Results also indicate that estimation error is relatively insensitive to the number of hydraulic conductivity measurements so long as more than a threshold number of data are used to condition the realizations. This threshold occurs for the Cape Cod site when there are approximately three conductivity measurements per integral volume. The lack of improvement with additional data suggests that although fine‐scale hydraulic conductivity structure is evident in the variogram, it is not accurately reproduced by geostatistical estimation methods. If the Cape Cod aquifer spatial conductivity characteristics are indicative of other sand and gravel deposits, then the results on predictive error versus data collection obtained here have significant practical consequences for site characterization. Heavily sampled sand and gravel aquifers, such as Cape Cod and Borden, may have large amounts of redundant data, while in more common real world settings, our results suggest that denser data collection will likely improve understanding of permeability structure.