A reactive transport modeling approach to simulate biogeochemical processes in pore structures with pore-scale heterogeneities

Gharasoo, Mehdi; Centler, Florian; Regnier, Pierre; Harms, Hauke; Thullner, Martin

doi:10.1016/j.envsoft.2011.10.010

Cited by 59 publications

(70 citation statements)

References 89 publications

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“…The transport of a non-sorbing soluble substrate and its utilization by bacteria could be simulated simultaneously in relatively sophisticated representations of meso-and macropore space, as compared to the simplistic media found in other microbial dynamics models integrating spatial heterogeneity like [25][26][27][28][29][30].…”

Section: Discussionmentioning

confidence: 99%

“…An increasing number of modeling studies have begun to account for pore-scale spatial heterogeneity when simulating biodegradation kinetics (e.g. [25][26][27][28][29][30]) but they have been restricted to relatively simple artificial media. Few attempts to model biological activity using 3D tomographic images of soil have pointed out that the combination of different soil pore space geometries and hydration status can affect organic matter decomposition [31] and/or the growth and colonization of soil by fungi [32].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Modeling the effect of soil meso- and macropores topology on the biodegradation of a soluble carbon substrate

Vogel

Makowski

Garnier

et al. 2015

Advances in Water Resources

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International audienceSoil structure and interactions between biotic and abiotic processes are increasingly recognized as important for explaining the large uncertainties in the outputs of macroscopic SOM decomposition models. We present a numerical analysis to assess the role of meso- and macropore topology on the biodegradation of a soluble carbon substrate in variably water saturated and pure diffusion conditions . Our analysis was built as a complete factorial design and used a new 3D pore-scale model, LBioS, that couples a diffusion lattice-Boltzmann model and a compartmental biodegradation model. The scenarios combined contrasted modalities of four factors: meso- and macropore space geometry, water saturation, bacterial distribution and physiology. A global sensitivity analysis of these factors highlighted the role of physical factors in the biodegradation kinetics of our scenarios. Bacteria location explained 28% of the total variance in substrate concentration in all scenarios, while the interactions among location, saturation and geometry explained up to 51% of it

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Section: Discussionmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Modeling the effect of soil meso- and macropores topology on the biodegradation of a soluble carbon substrate

Vogel

Makowski

Garnier

et al. 2015

Advances in Water Resources

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“…Such pore-scale information would be valuable for understanding macro scale solute dispersion in variably saturated porous media. As a surrogate to laboratory experiments, one may use pore network modeling to obtain such pore-scale information [Varloteaux et al, 2012;Köhne et al, 2011;Gharasoo et al, 2011;Nsir and Sch€ afer, 2010;Bodla et al, 2010]. An early example of pore-network modeling of solutes is the work of Mohanty [1982], who studied dispersivity under two-phase flow conditions.…”

Section: Experimental Work and Modeling Studiesmentioning

confidence: 99%

Saturation‐dependent solute dispersivity in porous media: Pore‐scale processes

Raoof

Hassanizadeh

2013

Water Resources Research

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[1] It is known that in variably saturated porous media, dispersion coefficient depends on Darcy velocity and water saturation. In one-dimensional flow, it is commonly assumed that the dispersion coefficient is a linear function of velocity. The coefficient of proportionality, called the dispersivity, is considered to depend on saturation. However, there is not much known about its dependence on saturation. In this study, we investigate, using a pore network model, how the longitudinal dispersivity varies nonlinearly with saturation. We schematize the porous medium as a network of pore bodies and pore throats with finite volumes. The pore space is modeled using the multidirectional pore-network concept, which allows for a distribution of pore coordination numbers. This topological property together with the distribution of pore sizes are used to mimic the microstructure of real porous media. The dispersivity is calculated by solving the mass balance equations for solute concentration in all network elements and averaging the concentrations over a large number of pores. We have introduced a new formulation of solute transport within pore space, where we account for different compartments of residual water within drained pores. This formulation makes it possible to capture the effect of limited mixing due to partial filling of the pores under variably saturated conditions. We found that dispersivity increases with the decrease in saturation, it reaches a maximum value, and then decreases with further decrease in saturation. To show the capability of our formulation to properly capture the effect of saturation on solute dispersion, we applied it to model the results of a reported experimental study.Citation: Raoof, A., and S. M. Hassanizadeh (2013), Saturation-dependent solute dispersivity in porous media: Pore-scale processes,

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“…Other spatial soil or pore space models were directed at studying basic processes in microbial ecology (Dechesne et al 2010;Gharasoo et al 2012;Resat et al 2012;Wang and Or 2013;Wang and Or 2014). These models gave important insights into the microbial life in soils, for example showing how multiple strategies of resource utilization may reduce stochasticity in biofilm dynamics (Resat et al 2012), or how spatial segregation of cells is achieved either by temporal habitat fragmentation by varying hydration conditions (Wang and Or 2013) or simply by limits in chemotaxis that define a trophic interaction distance (Wang and Or 2014).…”

Section: Agent-based Models (Abms) and Adaptionsmentioning

confidence: 99%

Modeling microbial growth and dynamics

Esser

Leveau

Meyer

2015

Appl Microbiol Biotechnol

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Modeling has become an important tool for widening our understanding of microbial growth in the context of applied microbiology and related to such processes as safe food production, wastewater treatment, bioremediation, or microbe-mediated mining. Various modeling techniques, such as primary, secondary and tertiary mathematical models, phenomenological models, mechanistic or kinetic models, reactive transport models, Bayesian network models, artificial neural networks, as well as agent-, individual-, and particle-based models have been applied to model microbial growth and activity in many applied fields. In this mini-review, we summarize the basic concepts of these models using examples and applications from food safety and wastewater treatment systems. We further review recent developments in other applied fields focusing on models that explicitly include spatial relationships. Using these examples, we point out the conceptual similarities across fields of application and encourage the combined use of different modeling techniques in hybrid models as well as their cross-disciplinary exchange. For instance, pattern-oriented modeling has its origin in ecology but may be employed to parameterize microbial growth models when experimental data are scarce. Models could also be used as virtual laboratories to optimize experimental design analogous to the virtual ecologist approach. Future microbial growth models will likely become more complex to benefit from the rich toolbox that is now available to microbial growth modelers.

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A reactive transport modeling approach to simulate biogeochemical processes in pore structures with pore-scale heterogeneities

Cited by 59 publications

References 89 publications

Modeling the effect of soil meso- and macropores topology on the biodegradation of a soluble carbon substrate

Modeling the effect of soil meso- and macropores topology on the biodegradation of a soluble carbon substrate

Saturation‐dependent solute dispersivity in porous media: Pore‐scale processes

Modeling microbial growth and dynamics

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