Numerical modeling of arsenic mobility - In-Situ Remediation of Arsenic-Contaminated Sites - page 47

where r pyr is the specific oxidation rate for pyrite, C O 2 , C NO 3 , and C H are the oxygen, nitrate
and proton concentrations in groundwater, A pyr / V is the ratio of mineral surface area to solution
volume and ( C / C 0 ) is a factor that accounts for changes in A pyr as pyrite is progressively depleted
by the reaction. Aqua regia extraction of sediment cores found As, Co, Ni and Zn to be associated
with pyrite with a most probable stoichiometry of Fe 0 . 98 Co 0 . 0037 Ni 0 . 01 Zn 0 . 01 S 2 As 0 . 0053 at the site.
Arsenic release from pyrite was simulated by kinetic dissolution of an As-bearing pyrite component
that was stoichiometrically linked to the pyrite oxidation rate (at a molar ratio of 0.0053).
Postma et al. (2007) used an alternative approach to quantify the mobilization of arsenic in a
shallow Holocene aquifer on the Red River flood plain near Hanoi, Vietnam. In their case organic
carbon decomposition in the anoxic aquifer induced distinct redox zones that were dominated by
the reduction of Fe-oxides and methanogenesis. In their 1D PHREEQC model As(V) was incor-
porated as a minor constituent at an As/Fe molar ratio of 0.0025 within the Fe-oxides originally
present in the aquifer. Consequently the progressive reductive dissolution of Fe-oxide triggered
As(V) release into the aqueous phase in the model.

2.4 SUMMARY AND OUTLOOK

In this chapter a general overview on numerical modeling approaches, common tools and some
model applications was provided. From this overview it is evident that enormous progress has
been made over the last decade, and both understanding of processes as well as modeling tools
have now matured to the degree that geochemical and reactive transport modeling can routinely
be part of studies that assess and quantify the fate of arsenic in groundwater. To date, most
model applications have been focused on using modeling as a tool to analyze existing laboratory
and field data whereby the calibration of models to measured data was generally performed via
trial and error. Over the next decade further enhanced computational resources such as high-
performance computing clusters and cloud computing will see, similar to trends in groundwater
flow modeling, a shift to an increased use of automatic parameter estimation tools that allow
a more rigorous estimation of optimal model parameters and evaluation of their sensitivities.
Within such frameworks model predictions that forecast the long-term fate of arsenic will be
accompanied by advanced assessments of model uncertainty.

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