Special Section:
Reactive Transport Modeling

J.C. Seaman*
H. Chang
S. Goldberg
J. Šimůnek
The guest editors summarize
the eight studies in this issue’sSpecial Sec on: Reac ve Transport
Modeling. The studies represent thediverse applica on of reac ve transport modeling at a range of scales.
This modeling work addresses thefate and transport of both reacve solutes and colloidal material,with potential for synthesizing
diverse data, evalua ng compe ng
biogeochemical processes, identifying research gaps, and ul mately
improving our understanding of biogeochemical processes.

J.C. Seaman and H. Chang, Savannah River
Ecology Laboratory–The University ofGeorgia, Drawer E, Aiken, SC 29802; S.
Goldberg, U.S. Salinity Laboratory, 450 W.
Big Springs Road, Riverside, CA 92507; andJ. Šimůnek, Department of Environmental
Sciences, University of California Riverside,
Riverside, CA, 92521. *Corresponding author(seaman@srel.edu).

Vadose Zone J.
doi:10.2136/vzj2012.0066
Received 7 May 2012.
Open Access Ar cle© Soil Science Society of America
5585 Guilford Rd., Madison, WI 53711 USA.

All rights reserved. No part of this periodical may
be reproduced or transmi ed in any form or by anymeans, electronic or mechanical, including photocopying, recording, or any informa on storage
and retrieval system, without permission in wri ng
from the publisher.

Reac ve Transport ModelingThis special sec on in the Vadose Zone Journal focusing on reac ve transport modeling wasdeveloped from a special symposium jointly sponsored by the Soil Physics and Soil Chemistry Divisions of the Soil Science Society of America at the 2010 annual mee ngs held in Long
Beach, CA. It contains eight contribu ons reflec ng a diverse range of reac ve transportmodeling applica ons that address the fate and transport of both reac ve solutes and colloidal material (i.e., viruses). Further, the eight studies range in scale from laboratory batchand column experiments to the regional scale. Three of the submissions reflect eﬀorts tomodel experimental data that were already reported in the literature, providing addi onal
insight concerning underlying physical and chemical processes. In addi on to the obvious
predic ve applica ons, the eight contribu ons to this special sec on illustrate the addional advantages to including reac ve transport modeling as an ac ve component within
a diverse research agenda. In addi on to scenario tes ng, reac ve transport modelingprovides a framework for synthesizing diverse experimental data, evalua ng the rela ve
importance of various compe ng and interrelated biogeochemical processes, and iden fying important data gaps and research priori es under more natural condi ons at mul ple
temporal and spa al scales.
Abbrevia ons: AWI, air–water interfaces; CCM, constant capacitance model; CW, constructed wetlands;
MCL, maximum concentra on level; OM, organic ma er; SOTS, second-order two site [model]; SWI, solid–
water interfaces.

This special sec on focusing on reac ve transport modeling was developed from a special joint symposium of the Soil Physics and Soil Chemistry Divisions at
the 2010 annual Soil Science Society of America meetings held in Long Beach, CA. Thissection consists of eight studies illustrating a diverse range of reactive transport modelingapplications evaluating the interrelated nature of various biogeochemical processes fromthe laboratory to the regional scale. Initially, reactive solute transport modeling efforts
focused on improving our understanding of water movement in the subsurface environment, with less attention placed on the coupled biological and geochemical reactions
that impact solute behavior, where solute partitioning was generally treated in a simplistic manner that can be easily implemented within a hydrologic flow model. And, whileconsiderable progress has been made in recent years, addressing the scale-dependency ofreactive transport processes attributable to both physical and biogeochemical heterogeneities remains a challenge.
The most widely used approach for predicting reactive solute partitioning in advective
systems has been the linear distribution coefficient, Kd (Davis et al., 2004; Steefel et al.,2005; Tebes-Stevens et al., 2001). Although reasonable for certain applications, numerous
studies have demonstrated the inherent limitations in applying simplistic empirical expressions (e.g., Kd and linear kinetic terms) to describing contaminant migration at the field
scale (Davis et al., 2004; Steefel et al., 2005). For example, the Kd approach to predicting
solute partitioning neglects the impact of limited sorption sites and competitive effects
induced by the presence of other solute species. In part, the quest for more robust modelingapplications reflects the need to make accurate predictions of subsurface processes for theprotection of both human health and environmental quality.
From an experimental point of view, much of the recent environmental research has
focused on isolated processes at a micro-scale under well-defi ned conditions. Such an
approach eliminates issues associated with mass transfer or localized micro-environments,
factors that are often encountered as experimental scale increases. In contrast, reactive
transport modeling provides a framework for synthesizing experimental data (i.e., coupling
processes studied in isolation), evaluating the relative importance of various biogeochemical processes, and identifying important data gaps and research priorities under more natural conditions at multiple relevant temporal and spatial scales (Steefel et al., 2005). Beyond

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the obvious predictive applications, the eight contributions to thecurrent special section illustrate the diverse nature of reactive
transport modeling and its place within an active research agenda.

6 Summary of Contribu

ons

Bea et al. (2012) used reactive transport modeling to improve
our understanding of CO2 sequestration during the weatheringof ultramafic mine tailings initially containing minerals such asserpentine [Mg 3Si2O5(OH)4] and brucite [Mg(OH)2] at a sitenear Mount Keith, Australia. Th is work is based on the observation that hydrated magnesium carbonate minerals (e.g., hydromagnesite [Mg 5(CO3)4(OH)2⋅4H2O]) commonly form during theweathering of serpentine-rich mine wastes (Wilson et al., 2006,
2009; Wilson, 2009). Although hydromagnesite is consideredmeta-stable under exposed surficial conditions, it persists under
neutral to alkaline conditions that can serve as sink for capturing
atmospheric CO2 (Bea et al., 2012; Palandri and Kharaka, 2004).
Bea et al. (2012) use the MIN3P-D multicomponent reactive transport code of Henderson et al. (2009) to describe processes occurring within a mine tailing impoundment. The MIN3P-D code
was further enhanced to include energy balances and water vapor
transport, the application of which is documented in detail. Theseenhancements are essential to adequately describe evaporation at
the surface of the tailings impoundment. Additionally, reactive
transport modeling considers unsaturated flow, solute transport,
gas diff usion, and the requisite geochemical reactions under critical atmospheric boundary conditions associated with the arid minetailing site. The geochemical reactions include the dissolution ofserpentine, the dissolution and precipitation of brucite, and theprecipitation of primary carbonates and other associated minerals. The dissolution and precipitation of primary minerals aremodeled using kinetic rate expressions from the literature. Simulations were conducted for a 30-yr span to predict the level offuture carbon mineralization.
A unique aspect of the Bea et al. (2012) contribution is the limited role that water flow plays in the geochemical reactions underconsideration. Initially, the tailings are deposited in a saturated
state; however, arid conditions at the Mount Keith site result in
evaporation that causes an efflorescent salt crust to form at themine tailing surface. With time the evaporation front is driven
deeper, with the dissolution of serpentine and brucite serving as
the source of Mg and alkalinity to support hydromagnesite precipitation. The partial pressure of CO2 eventually declines with depth
due to hydromagnesite precipitation, with continued precipitation
limited by the downward diff usion of CO2 .
Model simulations were generally consistent with field observations for tailings that have been weathered for 10 yr. Laboratoryderived serpentine dissolution rates were decreased considerably to
be consistent with field observations, possibly due to mineral crust

formation (i.e., hydromangesite) that reduced the reactive surface
area. Long-term simulations indicate that 17 kg m−2 of CO2 istrapped in the tailings after 30 yr of impoundment, mostly in theform of hydromagnesite. However, field data reflecting the first 10
yr of weathering indicate that current model predictions underestimate hydromagnesite formation, suggesting more rapid CO2
diff usion than predicted. Such discrepancies may be attributed to
any combination of several factors, including gas advection due tobarometric pressure fluctuations, temperature-dependent diffusion
gradients not incorporated in the model, advective displacementof gas during infi ltration events, and wind effects (Bea et al., 2012).
Increasing the CO2 diff usion coefficient within the model as asurrogate for the processes noted above increased the amount of
CO2 trapped over 30 yr to 31 kg m−2 . Further, the limited degreeof serpentine consumption and the high pH maintained by continued mineral dissolution suggest the mine tailing may act as asignificant long-term sink for CO2 sequestration, with the ingressof atmospheric CO2 playing an important role in controlling therate of carbon fi xation.
The modeling efforts described in Chang et al. (2012) focus on
evaluating the potential efficacy of using Fe(II)-containing solutions (FeCl 2 and FeSO 4) as an in situ means of reducing toxic
hexavalent chromium, Cr(VI), to Cr(III), which subsequently
coprecipitates with Fe(III) to form a mixed Fe(III)/Cr(III) oxyhydroxide. The modeling efforts are based on a series of laboratory batch and column experiments conducted using Fe-oxide-richAtlantic Coastal Plain sediments that display a high capacity for
Cr(VI) sorption (Seaman et al., 1999). Initial laboratory column
experiments indicated that substantial Cr(VI) retardation would
make aquifer reclamation unfeasible using conventional pumpand-treat extraction, despite the aquifer sediments lack of inherent reduction capacity. Batch experiments using Fe(II) solutions
indicated that in situ reduction was a feasible alternative based on
the rapid loss of Cr(VI) using the diphenylcarbazide colorimetricanalysis method. However, Crtotal remained elevated in solution,
well above the USEPA maximum concentration level (MCL),
presumably due to the acidic conditions associated with Fe(II)oxidation and the subsequent hydrolysis of Fe(III). When acetate
buffer (pH 5.6) was added to the Fe(II) treatments, both Cr(VI)and Crtotal decreased below USEPA MCLs in batch experiments.
As observed in the batch experiments, effluent Crtotal levels [i.e.,
Cr(III)] generally remained elevated for unbuffered Fe(II) treatments due to the acidity associated with the oxidation and subsequent hydrolysis of applied Fe. Further, the FeSO4 treatment
increased the mobility of Cr(VI) when compared with FeCl 2 ,presumably due to SO4 competition for sorption sites, resulting
in two diverging plumes that limited the potential efficacy ofFe(II) additions. Buffered Fe(II) treatments, however, resulted
in an increase in Cr(VI) mobility regardless of the Fe(II) source(i.e., FeCl2 or FeSO4), a result that could not be anticipated basedon laboratory batch results (Seaman et al., 1999). Therefore, thewww.VadoseZoneJournal.org

objective of Chang et al. (2012) was to determine if laboratory
batch and column results could have been predicted based onreactive transport modeling that accounted for the temporal andspatial dynamics of competitive Cr(VI) sorption and the shifts inpH that accompanied Fe(II) oxidation and hydrolysis.
Using the PHREEQC-2 geochemistry modeling code (Parkhurst
and Appelo,1999), Chang et al. (2012) described Cr partitioning
behavior on the basis of two major mechanisms: (i) competitive
Cr(VI) sorption to weak binding sites on Fe oxides using thediff use double layer model (DDLM) based on surface complexation constants derived from the published literature and (ii) the
precipitation of a mixed Cr(III)/Fe(III) hydroxide. Chang et al.
(2012) used only two optimized parameters in model development,
apparent sorption site density and the solubility of the Cr(III)/
Fe(III) precipitate. The reactive transport modeling was successful in qualitatively simulating the dynamic column experiments,
including the enhanced transport of Cr(VI) in the presence of SO4
that limited interaction with Fe(II), and the enhanced solubility
and continued migration of Cr(III) due to the acidity generated
in unbuffered Fe(II) treatments. However, the reactive transportmodeling overpredicted shifts in effluent pH that could be attributed to Cr(VI) sorption, which Chang et al. (2012) attributed tothe inherent soil buffering capacity that wasn’t fully realized in themodel system. Furthermore, column results indicated that acetate
was successfully competitive for Cr(VI) sorption sites, which can
reduce the efficacy of Fe(II) treatments by limiting Cr(VI) interaction with Fe(II) in advective systems. The appropriate surface complexation constants for including such competition in the current
model were not available in the published literature. Additional
discrepancies between experimental results and model simulations
were largely attributed to the kinetics associated with Cr(VI) sorption reactions and redox processes.
Metal retention at high pH in calcareous soils has been attributed
to the formation of carbonate salts with ion exchange becoming
more important as carbonates dissolve and pH decreases (Elbana
and Selim, 2012; Elzahabi and Yong, 2001). In previous experiments, Selim and Ma (2001) characterized Cu retention in soils
over a wide range of conditions, describing the nonlinear kinetics of sorption and desorption using the second-order two site
(SOTS) model within the multireaction model code developed
by Selim and Amacher (1997). The SOTS model assumes that alimited number of sites are available for reactive solute retention,a portion of which are rate limited with the remaining fraction
reacting instantaneously with the aqueous phase. The full versionof the SOTS model includes six fitting parameters, four kinetic
terms, a partitioning factor, and the sorption maximum (Selimand Amacher, 1997).
Elbana and Selim (2012) used batch and column experiments tocharacterize Cu fate and transport in calcareous soils, with greater
retention observed with increasing CaCO3 content, presumably ina nonexchangeable form. Batch equilibrium results were describedusing the Freundlich and Langmuir isotherms to depict the nonlinear partitioning behavior and differences in sorption affi nity
for the test soil materials. For describing Cu transport in a column
system, a linear equilibrium partitioning model within the advection–dispersion equation, including a fi rst-order decay term tooperationally account for irreversible retention, failed to describe
Cu breakthrough or extensive tailing in the current study. In
contrast, the SOTS model that accounts for both reversible andirreversible (both concurrent and consecutive) solute partitioning effectively described Cu transport in column experiments,
including the diffuse initial Cu arrival and the limited, slow releaseduring extensive leaching. Copper retention also coincided withrelease of Ca into solution (Elbana and Selim, 2012).
Langergraber and Šimůnek (2012) illustrated the use of reactive
transport modeling with the publically available wetland module
within the HYDRUS soft ware code as a tool for evaluating complex physical and chemical processes occurring within wetland
systems constructed for the treatment of various contaminants.
As they noted, the complex reactions occurring within constructedwetlands (CW) have largely been treated empirically. In subsurface
flow wetlands, the water level remains below the soil surface, withhorizontal flow generally restricted to the “saturated zone” to simplify modeling. For vertical flow CW systems experiencing intermittent loading, the ability to mimic dynamic flow under variably
saturated conditions is essential (Langergraber, 2011).
To fully describe CW systems, the reactive transport modelingmust describe water flow and reactive solute transport, including solute partitioning reactions, biochemical transformations
and degradation processes, plant processes (i.e., growth, decay,
nutrient uptake, oxygen release), and matrix clogging processes(Langergraber et al., 2009). Version 2 of the HYDRUS wetland
module, the focus of Langergraber and Šimůnek (2012), includes
two biokinetic models for simulating biochemical transformations
and degradation processes, CW2D and CWM1 (Langergraber
et al., 2009; Langergraber and Šimůnek, 2005). Model CW2D,
developed mainly for vertical flow wetlands, includes both the aerobic and anoxic transformation and degradation of organic matter(OM), while accounting for nitrogen and phosphorus. However,model CW2D does not consider anaerobic processes, making it
more appropriate for use in describing vertical flow wetlands under
low to moderate nutrient loading rates. Model CWM1, developedfor both horizontal and vertical flow wetlands, accounts for aerobic, anaerobic, and anoxic processes related to OM transformations,while also considering the fate of nitrogen and sulfur.
In addition to the two biokinetic models, HYDRUS considers many critical processes occurring within a CW, with the
exception of porous media clogging and certain plant responses(Langergraber and Šimůnek, 2011). HYDRUS is unable to simulate the growth and decomposition of wetland plants, and the

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transport and deposition of suspended particulates that may
adversely impact hydraulic conductivity, factors that can impact
the long-term efficacy of CW systems. HYDRUS describes water
movement under variably saturated conditions using Richards’
equation, and heat and solute migration by the convection–dispersion equation, with a sink term to account for water uptake
by plants (Šimůnek et al., 2011).
Langergraber and Šimůnek (2012) verified the two biokinetic
models using a simplified example, i.e., the hydrolysis and mineralization of OM by heterotrophic bacteria, as well as cell lysis withno flow into or out of the model domain. Such a simplified example
is required for comparison because inherent differences in the specific processes within the two models prohibit more complex comparisons. Langergraber and Šimůnek (2012) provided a more complex comparison of CWM1 implemented within HYDRUS andwithin the two-dimensional, finite element RETRSO code under
initial conditions originally defi ned by Llorens et al. (2011a,b).
Since the RETRSO requires introduction of bacteria as soluble
species at unrealistically high inlet concentrations, implementation of CWM1 within the HYDRUS model resulted in more
realistic, complex bacteria distribution profi les despite the use of
the same biokinetic model (i.e., CWM1) parameters. As a result
the concentration profi les for various solute compounds were very
different as well.
In the last reactive transport modeling scenario, Langergraber andŠimůnek (2012) considered the impact of plants for the two biokinetic models under conditions similar to those described above,
assuming that roots are largely restricted to the upper layers of
the CW and decrease with depth. Such an assumption restricts
direct water uptake to the upper portions of the CW. Water uptake
is assumed constant, and oxygen release from the roots is basedon reported values (5 g m−2 d−1) for common reed (Kadlec andWallace, 2009). The presence of plants provides a constant releaseof oxygen that facilitates the persistence of aerobic microorganisms, resulting in a different bacterial distribution profi le. Further,the HYDRUS model can be expanded to include plant uptake ofnutrients in addition to water.
CWM1 generally produces better results than CW2D becauseof its ability to consider anaerobic degradation processes. Thesemodeling efforts also demonstrated the importance of considering
the influence plants can have in controlling wetland processes, suchas the subsurface release of oxygen from plant roots.
Lichtner and Hammond (2012) used PFLOTRAN to evaluategeochemical processes controlling the aqueous phase distribution
and solid phase speciation of the hexavalent uranium [i.e., U(VI)]plume underlying the Hanford 300 Area bordering the ColumbiaRiver, Hanford, WA. The PFLOTRAN reactive transport code
was executed on Oak Ridge National Laboratory’s Jaguar XT5
Cray supercomputer. The goal of the present study is to estimate

the flux of U(VI) to the Columbia River. Much of the U(VI)
underlying the Hanford 300 Area is thought to reside within thevadose zone, where fluctuations in the water table can result in thecontinued release of U(VI) to groundwater.
The current modeling effort extended Hammond and Lichtner(2010) to predict cumulative U(VI) flux for the next 100 yr in thepresence and absence of both labile and nonlabile U(VI) phases.
The aqueous phase U(VI) speciation has been updated to takeadvantage of more recent estimates that are consistent with field
conditions (Dong and Brooks, 2006). Simulations designed toestimate the initial plume distribution were extended.
In the current context, Lichtner and Hammond (2012) defined
sorption as surface complexation reactions that can be describedusing simple partitioning isotherms, excluding U(VI) bearing precipitates. The previous study of Hammond and Lichtner (2010)
indicated that such sorption reactions had a relatively minor
impact on U(VI) flux into the Columbia River, with equilibrium
and multirate approaches to describing sorption yielding resultsthat were almost identical to the no sorption model. Model results
also agreed with field estimates for both U and water flux into the
Columbia River (Peterson et al., 2009).
Solid-phase U(VI) was considered in two forms—labile U(VI)subject to surface complexation reactions and a nonlabile U(VI)form for which metatorbernite, a uranium bearing phosphate mineral [Cu(UO2)2(PO4)2⋅8H2O], was used as a surrogate—as theexact solid-phase U speciation is still in dispute. Initial model conditions were based on current plume distributions due to the lackof historical data related to the initial generation and discharge of
the waste stream. Therefore, the initial condition was the same asin Hammond and Lichtner (2010), reflecting present-day conditions. The cumulative flux of U(VI) to the Columbia River was
evaluated for model simulations considering equilibrium and multirate controlled sorption, as well as no sorption, in the presence
and absence of the nonlabile U(VI), i.e., metatorbernite. Model
simulation indicated that the presence of nonlabile U(VI) was
critical in explaining the longevity of the U(VI) plume.
The Lichtner and Hammond model includes aqueous phase complexation/speciation, nonelectrostatic surface complexation (Bondet al., 2008), the multirate model of Liu et al. (2008), and reactions
with metatorbernite and calcite included as solid phases. Oxidation reduction reactions were not considered, based on observed
field data. Despite the obvious complexity, the reactive transportmodeling included only two parameters that are not based on fieldor laboratory estimates: the conductance coefficient for the riverwater interface representing the hyporheic zone and the effective
rate constant for the nonlabile source term. As expected the U
is rapidly flushed from the system in the no sorption case. Similar results were observed for the equilibrium and multirate sorption cases, with a much larger inherent U inventory observed for

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both cases, when compared to the no sorption case (232 vs. 14 kg).
Adding the nonlabile phase results in an extended linear flux of U
reaching the river, with “no sorption” producing the least U flux;
followed by the multirate condition; and, finally the equilibrium
condition that provides the highest flux to the river.
After accounting for the impact of river stage fluctuations, themajor conclusion from Lichtner and Hammond (2012) is that,
including labile U (i.e., sorbed) alone is insufficient to explain thelongevity of the plume, regardless of whether sorption in assumed
to be in equilibrium or kinetically controlled. When sorption
alone is considered, the aqueous phase U falls below the MCL
within a relatively short time period, well before all of the U(VI)has been removed. When a nonlabile phase is present, the aqueous
phase concentration of U(VI) is maintained at a much higher levelthat remains above the EPA MCL. Similar to Chang et al. (2012),
Lichtner and Hammond (2012) further asserted that the slow
release slow of U from the nonlabile phase, in this case a U-bearing
precipitate, is the primary cause of the failure of pump-and-treat
remediation strategies.
Reading et al. (2012a) used reactive transport modeling to address
issues associated with irrigation and management of sodic soils,
where chemical ameliorants are often used to improve infi ltrationand hydraulic conductivity and enhance the leaching of excess salts
from the root zone. Describing such a system requires a modelthat accommodates solute transport and the requisite chemical
reactions (i.e., cation exchange and mineral dissolution/precipitation). As Reading et al. (2012a) noted, however, the model mustalso describe physical changes in soil properties that accompany
changes in soil chemistry; more specifically, changes in hydraulic conductivity that occur when sodium dominates the cationexchange complex. While several available models can be used todescribe such basic chemical reactions, only UNSATCHEM andHYDRUS-1D can address changes in hydraulic conductivity associated with shifts in soil chemistry (Šimůnek et al., 1996, 2008).
However, the hydraulic conductivity reduction function (Kred)
included within Richards’ equation for the two models is basedon a limited range of soils (Suarez et al., 1984; McNeal, 1968) andmay not be generally applicable to all soil types due to differencesin texture, clay mineralogy, and bulk density.
In their study, Reading et al. (2012a) compared simulations using
HYDRUS-1D (ver. 4.14) to a series of recent laboratory experiments reported in Reading et al. (2012b) that evaluated the useof gypsum to ameliorate a nonsaline sodic clay soil from North
Queensland, Australia. The HYDRUS model including the “major
ion chemistry” option was used to simulate cation exchange, carbonate chemistry, and carbon dioxide transport (Šimůnek et al.,2008), with cation exchange capacity and initial composition of
the exchange complex based on soil characterization data (Reading et al., 2012b). For comparison, HYDRUS simulations were
run with and without the Kred factor, to evaluate how failure toconsider changes in hydraulic conductivity can impact the duration of soil amelioration.
Initial column leachates displayed electrical conductivity values
that were much higher than the inlet solution. Th is was attributed to dissolution of salt that had precipitated within the soil
profi le. The initial salt concentration within the model profi le wasincreased to mimic this observation. The HYDRUS model simulated general experimental trends, including the order in which
exchangeable cations were leached from the column (i.e., Na andthen Mg and Ca). Including the Kred function improved cationleaching simulation results; without including the Kred factor, thedecrease in exchangeable Na occurred more rapidly at the higher
initial sodicity levels.
Cation exchange reactions depended on the soil bulk density,
indicating the importance of pore structure and exchange site
accessibility in limiting observed kinetics of cation exchange, afactor not considered by the model. Model-predicted changes inhydraulic conductivity generally followed measured trends, butreached a simulated maximum more rapidly than observed forthe experimental columns, resulting in more rapid Na leachingin the simulated columns. Reading et al. (2012a) attributed thisdiscrepancy to the fact that Kred reached a maximum when thesodium adsorption ratio falls below 10, while the observed hydraulic conductivity of the experimental columns continued to change
as additional Na and Mg were leached from the column. Reading
et al. (2012a) further asserted that enabling the user to modify the
Kred function for a specific soil could greatly enhance the model’s
predictive capabilities, and they noted that additional model flexibility with respect to transport boundary conditions, more specifically adding an upflow condition often required for leachingfine-textured materials, could improve transport simulations.
Understanding the reactions controlling the fate and transportof boron in soils is critical to the appropriate management of
B-containing waste waters in arid systems (Suarez et al., 2012).
Boron partitioning in the soil environment depends on bothsoil and pore-water properties in a complex manner that limits
the validity of using empirical sorption isotherms for describing the pH dependence of partitioning (Goldberg and Glaubig,
1986; Goldberg et al., 2000). Version 3.1 of the UNSATCHEM
code used the constant capacitance model (CCM) to describe
B sorption behavior with model constants estimated based on
average soil parameters and the specific surface area for the soil
in question (Suarez and Šimůnek, 1996, 1997). Version 4.1 of
USATCHEM, however, incorporates the equations of Goldberg
et al. (2000) for estimating soil specific CCM parameters basedon readily available soil properties (i.e., organic and inorganiccarbon contents and extractable Al) and surface area, with surface area determined experimentally or estimated based on clay
content and mineralogy.

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Suarez et al. (2012) evaluated B transport in column experiments
using three arid-land soils from southern California as a means ofdetermining the ability of UNSATCHEM to predict B adsorption and desorption using both averaged and soil specific partitioning model constants. The B transport experiments were conducted at two pH conditions (pH 6.0 and 9.0). Boron transport
as impacted by pH was generally well described using the B subroutine in UNSATCHEM with CCM constants based on specificsoil properties. Significant model discrepancies were attributed
to the inability to simulate initial pH conditions resulting fromapplication of the high pH treatment solution. Using the averaged
soil CCM partitioning constants combined with soil surface area
yielded somewhat less satisfactory descriptions of experimental
results, but indicated that reasonable predictions could be madebased on average soil properties, without the need to extensively
characterize parameters for each specific soil.
Studies evaluating virus transport have generally focused on steadystate flow at a uniform degree of saturation, failing to account forthe dramatic remobilization of retained virues often observed
under transient flow conditions. Zhang et al. (2012) used reactive
transport modeling to describe virus transport through porous
media under variably saturated conditions, where fluctuating
moisture regimes can impact both virus attachment and detachment, focusing on the influence of imbibition and drainage on theremobilization of viruses attached to both solid–water interfaces(SWI) and air–water interfaces (AWI). Further, virus transportmechanisms are also relevant to other contaminants that can bemobilized in colloidal form.
Zhang et al. (2012) used three modeling approaches to simulate aseries of virus transport experiments reported in Torkzaban et al.
(2006a,b) that included imbibition and drainage events. Torkzaban et al. (2006a,b) evaluated the fate and transport of viruses
under steady-state flow conditions, the results of which theymodeled using HYDRUS1D with attachment and detachment
coefficients derived for different steady-state flow conditions; however, some of their initial virus transport studies were followed bytransient flow conditions initiated by decreasing inlet flow andallowing the column to drain, or increasing flow to full saturation
before allowing the column to drain. Both types of transient events
resulted in a major increase in virus mobilization.
The first modeling approach used by Zhang et al. (2012) assumed
an increased, yet constant, detachment coefficient for the duration of the transient flow event (e.g., drainage or imbibition). The
authors referred to this as the “constant-detachment model.” Theremaining two modeling approaches reflected modifications of
the model developed by Cheng and Saiers (2009), assuming virus
detachment to be proportional to the rate of change in watercontent. The model of Cheng and Saiers (2009) was modified toinclude separate terms for attachment/detachment on the SWI
and AWI (Zhang et al., 2012). The second modeling approachaccounted for virus attachment and detachment at the AWI, a
process not specifically considered by Cheng and Saiers (2009),
with desorption kinetics described as a function of changes in air
or water content without explicitly accounting for the area of the
AWI. This approach is called the “variable-coefficient model.” Thefi nal modeling approach incorporated variations in the AWI as
controlled by water content while assuming attachment–detachment to be an equilibrium process referred to as the “apparent
linear partitioning model.”The best simulations of virus mobilization during both drainage and imbibition events reported in Torkzaban et al. (2006a,b)
were achieved with the simpler, third modeling approach, which
accounts for equilibrium based virus sorption on the available
AWI. Zhang et al. (2012) noted that virus transport processes
observed at the column scale may provide only limited information
concerning the actual virus attachment–detachment mechanisms.
The authors further admit that alternate virus partitioning expressions may be required for structured, finer textured soils.

6 Conclusions
This special section consists of eight studies illustrating a diverse
range of reactive transport modeling applications evaluating theinterrelated nature of various biogeochemical processes from thelaboratory to the regional scale. In recent years, greater focus has
been placed on improving our understanding of the coupled biological and geochemical reactions that impact solute behavior.
Addressing the scale-dependency of reactive transport processes
attributable to both physical and biogeochemical heterogeneities
remains a challenge. While the contribution to the remediationand management of subsurface contamination is obvious, reactive
transport models can also improve our understanding of fundamental biogeological processes, such as chemical weathering andnutrient and elemental cycling.

Acknowledgments

The authors want to acknowledge the thoughtful comments of Dr. M. Young on an
early version of this manuscript. Drs. Seaman and Chang’s efforts were supportedby the Savannah River Ecology Laboratory through Financial Assistance Award DEFC09-07SR22506 from the Department of Energy to the University of Georgia Research Foundation.

References
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Reac ve transport modeling of natural carbon sequestra on in ultramafic
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