Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling
Abstract Biogeochemical soil processes are closely linked to the structure of soil. In particular, nutrient transport depends on diffusivity and permeability within the soil’s pore network. A deeper understanding of the relationship between microscopic soil structure and such effective macroscopic p...
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Nature Portfolio
2025-06-01
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| Online Access: | https://doi.org/10.1038/s41598-025-05825-0 |
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| author | Benedikt Prifling Matthias Weber Maximilian Rötzer Nadja Ray Alexander Prechtel Maxime Phalempin Steffen Schlüter Doris Vetterlein Volker Schmidt |
| author_facet | Benedikt Prifling Matthias Weber Maximilian Rötzer Nadja Ray Alexander Prechtel Maxime Phalempin Steffen Schlüter Doris Vetterlein Volker Schmidt |
| author_sort | Benedikt Prifling |
| collection | DOAJ |
| description | Abstract Biogeochemical soil processes are closely linked to the structure of soil. In particular, nutrient transport depends on diffusivity and permeability within the soil’s pore network. A deeper understanding of the relationship between microscopic soil structure and such effective macroscopic properties can be obtained by tomographic imaging combined with a quantitative analysis of soil morphology and numerical simulations of effective macroscopic properties. In a previous work it has been shown that different parametric regression formulas can be used to predict these relations for finely sieved soils of loam and sand. In the present paper, we validate these formulas and further extend their applicability to structured soils. In particular, 3D CT data of a total of six samples, consisting of three loam and three sand samples, are used as the basis for an extensive structural analysis. As expected, the performance of most regression formulas can be improved by specifically adjusting their parameters for the considered soil structures. However, it turns out that some regression formulas based on, e.g., tortuosity which were fitted for finely sieved soils still reliably predict diffusion for structured soils without adjusting their parameters. For additional validation and improvement of the considered regression formulas, artificially generated soil structures can be utilized. Therefore, a method for the generation of such structures via samples drawn from a parametric stochastic 3D microstructure model is outlined which may serve as a basis for further work. |
| format | Article |
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| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-6a279f434fda4e09875b3f46c89acd8e2025-08-20T02:36:50ZengNature PortfolioScientific Reports2045-23222025-06-0115111210.1038/s41598-025-05825-0Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modelingBenedikt Prifling0Matthias Weber1Maximilian Rötzer2Nadja Ray3Alexander Prechtel4Maxime Phalempin5Steffen Schlüter6Doris Vetterlein7Volker Schmidt8Institute of Stochastics, Ulm UniversityInstitute of Stochastics, Ulm UniversityDepartment of Mathematics, Friedrich-Alexander University of Erlangen-NürnbergMathematical Institute for Machine Learning and Data Science, Catholic University of Eichstätt-IngolstadtDepartment of Mathematics, Friedrich-Alexander University of Erlangen-NürnbergDepartment of Soil System Science, Helmholtz-Centre for Environmental Research-UFZDepartment of Soil System Science, Helmholtz-Centre for Environmental Research-UFZDepartment of Soil System Science, Helmholtz-Centre for Environmental Research-UFZInstitute of Stochastics, Ulm UniversityAbstract Biogeochemical soil processes are closely linked to the structure of soil. In particular, nutrient transport depends on diffusivity and permeability within the soil’s pore network. A deeper understanding of the relationship between microscopic soil structure and such effective macroscopic properties can be obtained by tomographic imaging combined with a quantitative analysis of soil morphology and numerical simulations of effective macroscopic properties. In a previous work it has been shown that different parametric regression formulas can be used to predict these relations for finely sieved soils of loam and sand. In the present paper, we validate these formulas and further extend their applicability to structured soils. In particular, 3D CT data of a total of six samples, consisting of three loam and three sand samples, are used as the basis for an extensive structural analysis. As expected, the performance of most regression formulas can be improved by specifically adjusting their parameters for the considered soil structures. However, it turns out that some regression formulas based on, e.g., tortuosity which were fitted for finely sieved soils still reliably predict diffusion for structured soils without adjusting their parameters. For additional validation and improvement of the considered regression formulas, artificially generated soil structures can be utilized. Therefore, a method for the generation of such structures via samples drawn from a parametric stochastic 3D microstructure model is outlined which may serve as a basis for further work.https://doi.org/10.1038/s41598-025-05825-0Structure–property relationshipSoil gas diffusion3D CT dataStatistical image analysisStochastic 3D modeling |
| spellingShingle | Benedikt Prifling Matthias Weber Maximilian Rötzer Nadja Ray Alexander Prechtel Maxime Phalempin Steffen Schlüter Doris Vetterlein Volker Schmidt Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling Scientific Reports Structure–property relationship Soil gas diffusion 3D CT data Statistical image analysis Stochastic 3D modeling |
| title | Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling |
| title_full | Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling |
| title_fullStr | Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling |
| title_full_unstemmed | Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling |
| title_short | Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling |
| title_sort | correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand including stochastic 3d microstructure modeling |
| topic | Structure–property relationship Soil gas diffusion 3D CT data Statistical image analysis Stochastic 3D modeling |
| url | https://doi.org/10.1038/s41598-025-05825-0 |
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