Pore-Network Modeling of Solute Transport and Biofilm Growth in Porous Media

Qin, Chao; Hassanizadeh, S. Majid

doi:10.1007/s11242-015-0546-1

Cited by 40 publications

(42 citation statements)

References 54 publications

(67 reference statements)

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“…Net microbial growth is therefore simulated as

\frac{\partial X_{h}}{∂t} = Y_{DOC} | R_{DOC} | - k_{h} X_{h},

\frac{\partial X_{a}}{∂t} = Y_{{NH}_{4}^{+}} | R_{{NH}_{4}^{+}} | - k_{a} X_{a},

where Y DOC and

Y_{{NH}_{4}^{+}}

are growth yields and k h and k a are biomass die‐off rates. This is a standard formulation for biomass growth in pore spaces that have been shown to adequately represent biomass in complex system geometries, such as reactor [ Rittmann and McCarty , ; Qin and Hassanizadeh , ; Peszynska et al , ]. Initial conditions are spatially homogeneous, with X h ,0 > X a ,0 (subscript 0 denotes t = 0) because autotrophic bacteria grow more slowly and are less numerous than the heterotrophic ones [ Oga et al , ; Liu et al , ; Ebeling et al , ].…”

Section: Hydraulic and Biogeochemical Modelmentioning

confidence: 99%

Biofilm‐induced bioclogging produces sharp interfaces in hyporheic flow, redox conditions, and microbial community structure

Caruso

Ridolfi

Chopp

et al. 2017

Geophysical Research Letters

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Riverbed sediments host important biogeochemical processes that play a key role in nutrient dynamics. Sedimentary nutrient transformations are mediated by bacteria in the form of attached biofilms. The influence of microbial metabolic activity on the hydrochemical conditions within the hyporheic zone is poorly understood. We present a hydrobiogeochemical model to assess how the growth of heterotrophic and autotrophic biomass affects the transport and transformation of dissolved nitrogen compounds in bed form‐induced hyporheic zones. Coupling between hyporheic exchange, nitrogen metabolism, and biomass growth leads to an equilibrium between permeability reduction and microbial metabolism that yields shallow hyporheic flows in a region with low permeability and high rates of microbial metabolism near the stream‐sediment interface. The results show that the bioclogging caused by microbial growth can constrain rates and patterns of hyporheic fluxes and microbial transformation rate in many streams.

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“…Net microbial growth is therefore simulated as

\frac{\partial X_{h}}{∂t} = Y_{DOC} | R_{DOC} | - k_{h} X_{h},

\frac{\partial X_{a}}{∂t} = Y_{{NH}_{4}^{+}} | R_{{NH}_{4}^{+}} | - k_{a} X_{a},

where Y DOC and

Y_{{NH}_{4}^{+}}

Section: Hydraulic and Biogeochemical Modelmentioning

confidence: 99%

Biofilm‐induced bioclogging produces sharp interfaces in hyporheic flow, redox conditions, and microbial community structure

Caruso

Ridolfi

Chopp

et al. 2017

Geophysical Research Letters

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“…The growth of biomass in the process of metabolism of DOC and DO is modeled with the well‐known growth‐death model (e.g., Baveye & Valocchi, ; Clement et al, ; Molz et al, ; Qin & Hassanizadeh, ; Thullner et al, ; Thullner et al, ):

\frac{\partial X_{AR}}{\partial t} = Y ((), 1 - \frac{X_{AR}}{ρ_{X_{AR}} ((), ε_{0} - θ_{r})}) V_{AR} X_{AR} \frac{C_{DOC}}{K_{DOC} + C_{DOC}} \frac{C_{O_{2}}}{K_{{normalO}_{2}} + C_{{normalO}_{2}}} - u_{dec} X_{AR},

where Y represents the yield coefficient (dimensionless), u dec (T −1 ) is the decay rate coefficient,

ρ_{X_{italicAR}}

(M/L 3 ) denotes the density of biomass (dry mass/wet volume) and ε 0 represents the initial porosity (dimensionless). The term

1 - \frac{X_{AR}}{ρ_{X_{AR}} ((), ε_{0} - θ_{r})}

in equation is introduced to account for the self‐limitation of biomass growth due to limited pore spaces (Brovelli et al, ; Samsó et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…The growth of biomass in the process of metabolism of DOC and DO is modeled with the well-known growth-death model (e.g., Baveye & Valocchi, 1989;Clement et al, 1998;Molz et al, 1986;Qin & Hassanizadeh, 2015;Thullner et al, 2004;Thullner et al, 2007):…”

Section: Simulation Of Microbial Growthmentioning

confidence: 99%

Reactive Transport of Nutrients and Bioclogging During Dynamic Disconnection Process of Stream and Groundwater

Xian

Jin

Zhan

et al. 2019

Water Resources Research

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Biofilm‐induced dynamic evolution of streambed permeability commonly concurs with the transition from connection to disconnection between surface water and groundwater in arid regions. However, in most previous studies, static streambeds were assumed to examine the evolution of disconnection, or despite dynamic streambeds being considered, the feedbacks between nutrients transport and microbial growth were ignored. In this study, we developed an innovative coupled variably saturated flow, microbial growth, biogeochemical reactions, and bioclogging model. We applied this model to investigate the feedbacks between nutrients transport and microbial growth and their controls on infiltration evolution. Our results showed that a new clogging layer can naturally develop due to these feedbacks and does not require prior clogging. The development of the new clogging layer promotes the occurrence of disconnection. These results illustrate that as bioclogging is a dominant process, previous static disconnection conditions cannot be used as criteria to predict whether disconnection can occur in a stream‐aquifer system. Furthermore, different from the previous assumption of constant specific microbial growth rates, biomass growth, and streambed permeability evolution are self‐limiting. Accordingly, due to initial low growth rate, infiltration increases when the water table declines, and it then decreases and reaches a minimum while a stable biofilm is developed. These trends coincide with the infiltration variations reported in previous field investigations. After reaching the minimum, infiltration increases again with decline of the water table until achieving a constant at the moment of disconnection. This stage was missing in previous studies because constant specific growth rates were assumed.

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“…may change with time owing to the increase in the thickness of the adsorbed layer. The modified form of R ij is described as :

true R_{ij} = \frac{8 μ_{l} l_{ij}}{π r_{ij}^{4} {(1 - ε_{ij})}^{2}}

…”

Section: Modeling Proceduresmentioning

confidence: 99%

Water Flux Reduction in Microfiltration Membranes: A Pore Network Study

et al. 2018

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A 3D pore network model was developed to simulate the removal of dextran from water. Advanced scanning electron microscopy combined with focused ion beam analysis was used to obtain the sizes of the different pore networks that represent the microscopic structure of a porous membrane. The required input transport parameters for modeling were obtained by performing dynamic experiments on dextran adsorption within the pores of a polysulfone membrane. The simulated flux changes demonstrated a good agreement with the experimental data showing that such a model can be used to study the effects of various parameters during the process. Specifically, the results showed an increase in the applied pressure, decreased membrane thickness, increased pore size, while small sizes of contaminant molecules lead to a rise of the flux passing through the membrane.

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Pore-Network Modeling of Solute Transport and Biofilm Growth in Porous Media

Cited by 40 publications

References 54 publications

Biofilm‐induced bioclogging produces sharp interfaces in hyporheic flow, redox conditions, and microbial community structure

Biofilm‐induced bioclogging produces sharp interfaces in hyporheic flow, redox conditions, and microbial community structure

Reactive Transport of Nutrients and Bioclogging During Dynamic Disconnection Process of Stream and Groundwater

Water Flux Reduction in Microfiltration Membranes: A Pore Network Study

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