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Dual-porosity models are another type of numerical model that is more computationally efficient because it uses a first order approximation to describe the mass transfer between the high K and low K zones. Dual-porosity models can be calibrated to match the effects of matrix diffusion on contaminant concentrations during early times.  However, the first order mass transfer coefficient is time-dependent<ref>Guan, J., Molz, F.J., Zhou, Q., Liu, H.H. and Zheng, C., 2008. Behavior of the mass transfer coefficient during the MADE‐2 experiment: New insights. Water Resources Research, 44(2). [https://doi.org/10.1029/2007WR006120 doi: 10.1029/2007WR006120]</ref>, so models calibrated to early time data will not accurately simulate concentrations at a later time.
 
Dual-porosity models are another type of numerical model that is more computationally efficient because it uses a first order approximation to describe the mass transfer between the high K and low K zones. Dual-porosity models can be calibrated to match the effects of matrix diffusion on contaminant concentrations during early times.  However, the first order mass transfer coefficient is time-dependent<ref>Guan, J., Molz, F.J., Zhou, Q., Liu, H.H. and Zheng, C., 2008. Behavior of the mass transfer coefficient during the MADE‐2 experiment: New insights. Water Resources Research, 44(2). [https://doi.org/10.1029/2007WR006120 doi: 10.1029/2007WR006120]</ref>, so models calibrated to early time data will not accurately simulate concentrations at a later time.
 
The REMChlor-MD model was developed under ESTCP project [https:\www.serdp-estcp.org\Program-Areas\Environmental-Restoration\Contaminated-Groundwater\Persistent-Contamination\ER-201426  ER-201426] to provide a practical alternative to traditional approaches for modeling matrix diffusion.  
 
The REMChlor-MD model was developed under ESTCP project [https:\www.serdp-estcp.org\Program-Areas\Environmental-Restoration\Contaminated-Groundwater\Persistent-Contamination\ER-201426  ER-201426] to provide a practical alternative to traditional approaches for modeling matrix diffusion.  
[[File:Falta1w2 Fig2.png|thumb|Figure 2.  A Typical Fine Grid Model Capable of Simulating Matrix Diffusion]]
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[[File:Falta1w2 Fig2.PNG|thumb|Figure 2.  A Typical Fine Grid Model Capable of Simulating Matrix Diffusion]]
  
 
==References==
 
==References==

Revision as of 19:15, 5 November 2019

REMChlor-MD is a free toolkit available for download and is capable of simulating matrix diffusion in groundwater contaminant plumes. It is a significant upgrade from REMChlor 1.0[1], which does not include matrix diffusion in the plume. REMChlor-MD is useful for planning-level approximations of contamination extent and duration at sites where matrix diffusion is important. REMChlor-MD employes a semi-analytical method for simulating mass transfer between high and low permeability zones that provides computationally accurate predictions of concentration distributions and mass discharge in the higher permeability portions of an aquifer. Model run times for REMChlor-MD are much shorter than traditional fine grid numerical models.

Related Article(s):

CONTRIBUTOR(S): Dr. Ron Falta and Kien Pham


Key Resource(s):


Matrix Diffusion of Dissolved Contaminants

Groundwater contamination with dissolved contaminants, such as chlorinated volatile organic compounds (CVOCs), is pervasive at many current and former industrial sites globally.

Plumes of CVOCs dissolved in groundwater often originate when the chemicals enter the subsurface as dense non-aqueous liquids (DNAPL). Being heavier than water and immiscible, DNAPls are particularly problematic because they can spread vertically into the aquifer, generating extensive dissolved groundwater plumes. In Figure 1, CVOCs in DNAPL form are shown in bold red, and the resulting dissolved groundwater plume is represented by the red halo emanating from the DNAPLs. One of the most significant lessons learned about chlorinated solvents is that their groundwater plumes can persist even when the source DNAPLs have been depleted. Plume persistence in the absence of source materials has been attributed to a phenomenon called matrix diffusion. Matrix diffusion can affect any type of dissolved contaminant, particularly when dissolved concentrations are high relative to regulatory standards. In addition to CVOCs, matrix diffusion can occur with other dissolved contaminants including radionuclides, per- and polyfluoroalkyl substances (PFAS), dissolved petroleum hydrocarbons such as benzene, toluene, ethylbenzene, and xylene (BTEX), and with solvent stabilizers such as 1,4-dioxane.

The life cycle of a matrix diffusion site is separated into two periods. In the loading period, the low-permeability (low K) zones act as storage areas for the contaminant mass. Following clean-up actions in the transmissive (high K) zones, the concentration gradient is reversed. The unloading period begins when the contaminant mass previously stored in the low-permeability zones diffuses back out into the transmissive zones. In Figure 1, the blue arrows represent the forward and backward diffusion gradients into the surrounding materials. Because the pace of back diffusion can be significantly slower than the loading of the contaminant, the plume resulting from the back diffusing mass can last for decades to centuries[2][3].

Given the prevalence and persistence of back diffusion plumes, modeling tools are needed that can accurately capture the dynamic progression of the contamination in both loading and unloading periods.

Figure 1. Spread of CVOCs in the Subsurface

Matrix Diffusion Modeling Options

There are limited options available for simulating matrix diffusion effects at groundwater contamination sites. Available analytical models require simplifications that may overlook the unique complexities at actual remediation sites. Numerical models are often used to simulate solute transport, but require fine discretization to capture matrix diffusion concentration gradients, which occur at millimeters to centimeters scale. This very fine discretization (fine model grid) greatly increases the number of model cells required to simulate transport at field sites, greatly increasing computer model run times. Figure 2 shows a typical fine grid model containing almost 3 million gridblocks (cells). Models with large numbers of cells greatly increases model run times.

Dual-porosity models are another type of numerical model that is more computationally efficient because it uses a first order approximation to describe the mass transfer between the high K and low K zones. Dual-porosity models can be calibrated to match the effects of matrix diffusion on contaminant concentrations during early times. However, the first order mass transfer coefficient is time-dependent[4], so models calibrated to early time data will not accurately simulate concentrations at a later time. The REMChlor-MD model was developed under ESTCP project [https:\www.serdp-estcp.org\Program-Areas\Environmental-Restoration\Contaminated-Groundwater\Persistent-Contamination\ER-201426 ER-201426] to provide a practical alternative to traditional approaches for modeling matrix diffusion.

Figure 2. A Typical Fine Grid Model Capable of Simulating Matrix Diffusion

References

  1. ^ Falta, R.W., 2008. Methodology for comparing source and plume remediation alternatives. Groundwater, 46(2), pp.272-285. doi: 10.1111/j.1745-6584.2007.00416.x
  2. ^ Chapman, S.W. and Parker, B.L., 2005. Plume persistence due to aquitard back diffusion following dense nonaqueous phase liquid source removal or isolation. Water Resources Research, 41(12). doi: 10.1029/2005WR004224
  3. ^ Parker, B.L., Chapman, S.W. and Guilbeault, M.A., 2008. Plume persistence caused by back diffusion from thin clay layers in a sand aquifer following TCE source-zone hydraulic isolation. Journal of Contaminant Hydrology, 102(1-2), pp.86-104. doi: 10.1016/j.jconhyd.2008.07.003
  4. ^ Guan, J., Molz, F.J., Zhou, Q., Liu, H.H. and Zheng, C., 2008. Behavior of the mass transfer coefficient during the MADE‐2 experiment: New insights. Water Resources Research, 44(2). doi: 10.1029/2007WR006120

See Also