Experimental investigation of solute transport across transition interface of porous media under reversible flow directions

Zhou, C.; Ma, Xiang; Zhan, Hongbin; Dou, Zhi; Wang, Jinguo; Zhou, Zhen; Peng, Chuanlu

doi:10.1016/j.ecoenv.2022.113566

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…(2021); Z. Chen et al. (2022) have also reported a larger time scale for unloading versus loading. Based on the the former studies which reported concentration‐dependent diffusion coefficient (Carey et al., 1995; Dunlop & Stokes, 1951; Harned & Nuttall, 1949; Matuura & Koga, 1959; McCall & Douglass, 1965), we have proposed an empirical function for the diffusion coefficient as follows:

{D}_{m}={D}_{m}^{0}\left(1+\beta {C}^{\gamma }\right)

…”

Section: Modeling‐assisted Interpretation Of Resultsmentioning

confidence: 98%

Experimental Analysis of Mass Exchange Across a Heterogeneity Interface: Role of Counter‐Current Transport and Non‐Linear Diffusion

Walczak

Erfani

Karadimitriou

et al. 2022

Water Resources Research

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Solute transport in heterogeneous and fractured systems is a complex process given the permeability contrasts and the time scales discrepancies of transport in high‐permeability versus low‐permeability regions. We studied this phenomenon by injecting a solute (dyed water) in a micromodel comprising a single channel in contact with a porous medium and evaluated the mass exchange across the interface between the channel and porous medium (resembling the interface between free flow and porous media regions). Two sets of transport experiments were performed at three injection rates of 0.01, 0.1, and 1 ml/hr. Injection of dyed water into a clean‐water‐filled micromodel (referred to as the loading process hereafter) and injection of clean water into a dyed‐water‐filled micromodel (referred to as the unloading process hereafter). The dynamics of solute transport was recorded using time‐lapse optical imaging. Our experimental results demonstrated the change of the mass exchange rate coefficient with time and a much smaller transfer rate coefficient during the unloading compared to the loading process. It is proposed that concentration‐dependent counter‐current advection‐diffusion cause slow‐down and further delay in the transport. These results may provide further explanation for the observed slow release of contamination in aquifers.

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{D}_{m}={D}_{m}^{0}\left(1+\beta {C}^{\gamma }\right)

…”

Section: Modeling‐assisted Interpretation Of Resultsmentioning

confidence: 98%

Experimental Analysis of Mass Exchange Across a Heterogeneity Interface: Role of Counter‐Current Transport and Non‐Linear Diffusion

Walczak

Erfani

Karadimitriou

et al. 2022

Water Resources Research

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“…The Convection-Dispersion model is recovered when the immobile fraction of the soil solution is equal to zero. The MIM model is generally more powerful and versatile, and could better explain the ow of water and solutes in porous, structured media under laboratory-controlled conditions (Brusseau and Rao, 1990; Goncalves et al, 2001;Gri oen et al, 1998;Haggerty et al, 2004, Chen et al, 2022.…”

Section: Introductionmentioning

confidence: 99%

Transport of Bromide Under Five Steady State Water Flow Rates and Three Soil Depth: Field Experiment in Molisol Soil

Aparicio

Costa

Giménez

2022

Preprint

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The simulation of solute transport with models provides a cost-effective and rapid assessment tool. However, both model selection and its parameterization using data obtained from field-scale experiments, it is essential to obtain useful and accurate results. In our work under field conditions, we obtained bromide concentration data of soil water samples from three depths and five water flows to adjust 180 breakthrough curves for estimating solute transport parameters in soil: V, D and dispersivity (λ) and we checked the effect of three previous N treatments in these solute transport parameters. The velocity and dispersion parameters were fitted using the CXTFIT 2.1 model. After selecting the best model using Akaike criteria, we analyzed the distribution of transport parameters estimated using a normality test. The transport parameters were analyzed considering each water flow as a locality, prior fertilization treatments (0N, 100N and 200N) and depth (100, 150 and 190cm respectively) as a repeated measure in space using SAS 9.2. The MIM was the best model to fit the data of relative concentration of Br integrating the soil profile compared to CD and CLT models. In general, transport parameters obtained from adjustment breakthrough curves 180 have a normal distribution. In the analysis of the transport parameters, the velocity was seen to be statistically different between water flows, while the dispersion varied both between flows and between sampling depths and prior fertility treatment. The overall average of the mobile water fraction β was 0.35, indicating preferential flow. The soil structure and the physical non-steady state had a greater impact on the movement of solutes at depth than the steady state. The logarithm of the dispersivity differed statistically by flow and by depth.

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Detection of Mobile and Immobile Solute Transport and Their Residence Time in Saturated Low and High Permeability Soil Layers

Zaheer,

Haq,

Huang

et al. 2024

Water Air Soil Pollut

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Experimental investigation of solute transport across transition interface of porous media under reversible flow directions

Cited by 8 publications

References 26 publications

Experimental Analysis of Mass Exchange Across a Heterogeneity Interface: Role of Counter‐Current Transport and Non‐Linear Diffusion

Experimental Analysis of Mass Exchange Across a Heterogeneity Interface: Role of Counter‐Current Transport and Non‐Linear Diffusion

Transport of Bromide Under Five Steady State Water Flow Rates and Three Soil Depth: Field Experiment in Molisol Soil

Detection of Mobile and Immobile Solute Transport and Their Residence Time in Saturated Low and High Permeability Soil Layers

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