Directed site exploration for permeable reactive barrier design
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Journal of Hazardous Materials 162 (2009) 222229
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Journal of Hazardous Materials
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Directe e b
Jejung Le rd Wa Department ob Department o AL 35c 5306 W.57thd U.S. Geologica MI 48
a r t i c l
Article history:Received 26 DReceived in reAccepted 6 MaAvailable onlin
Keywords:Permeable reaSite explorationUncertainty analysis
beingnts. Sn appee spavariaampess o
the Kansas City Plant, Kansas City, MO, a case study where a PRB was installed and failed. It is shownthat additional quantitatively directed site exploration during the design phase could have prevented theremedial failure that was caused by missing a geologic body having high hydraulic conductivity at thesouth end of the barrier. The most contributing input parameter approach using head uncertainty clearlyindicatedwhere the next sampling should bemade toward the high hydraulic conductivity zone. This casestudy demonstrates the need to include the specic design as well as site characterization uncertainty
Permeabin situ treaPRB is desigmoving undinto an envtaminant ledown graditive mediamost commWhen, for ethey are potcompoundsdehalogenaplume of dethe residuaand may co. Althoug
0304-3894/$ doi:10.1016/j.jwhen choosing the sampling locations. 2008 Elsevier B.V. All rights reserved.
le reactive barriers (PRBs) are being employed as antment technology for groundwater contamination. Aned to intercept a plume of contaminated groundwaterer the natural gradient and transform the contaminantironmentally acceptable form. The goal is to have con-vels below target concentration at compliance pointsent of the barrier. The barrier is made out of a reac-that is selected based on the type of contaminant. Theon form of reactive media is zero-valent iron (Fe0).xample, chlorinated organics come in contact with Fe0,entially degraded into nontoxic dehalogenated organicand inorganic chloride . The ability of a PRB to
te a compound is a signicant benet, especially for anse non-aqueous phase liquid (DNAPL). This is becausels of a DNAPL contamination cannot be easily locatedntinue to generate a plume of dissolved hydrocarbonsh the pump and treat (PT) technique can control such
ding author. Tel.: +1 816 235 6495; fax: +1 816 235 5535.resses: email@example.com (J. Lee), firstname.lastname@example.org (A.J. Graettinger),sbcglobal.net (J. Moylan), email@example.com (H.W. Reeves).
a DNAPL plume, PT requires extensive long-term maintenance andcontinual energy input. In contrast, a DNAPL plume can be con-trolledwithaPRBwithminimalmaintenancebecausePRBsoperatein a totally passive manner. Other advantages of PRBs are: (1) con-taminants are treated away from the surface, which minimizesworker and public exposure to toxic contaminants and (2) wasteconstituents are concentrated into a relatively small volumewithinthe treatment zone, which is the barrier itself.
There are several disadvantages of using PRBs though. First,long-term performance of PRBs may generate preferential owsthrough the barrier with pore clogging, thus the PRB itself becomesheterogeneous and ineffective for treatment . Second, hydraulicheterogeneity of the subsurface around the barrier can cause somuch uncertainty in performance that use of a passive PRB is pre-cluded . Schipper et al.  investigated the effect of hydraulicconductivity change during the construction of denitrication walland found that the performance of permeable wall approacheswas highly dependent on the aquifer material. Gupta and Fox implemented groundwater ow modeling and particle trackingtechniques to nd how aquifer heterogeneity affects the perfor-mance of PRB for different design parameters such as the capturezone width, residence time, ow velocity, and discharge.
US Environmental Protection Agency (EPA)  reported the useof PRBs for contaminated groundwater treatment at 47 sites in
see front matter 2008 Elsevier B.V. All rights reserved.hazmat.2008.05.026d site exploration for permeable reactiv
ea,, Andrew J. Graettingerb, John Moylanc, Howaf Geosciences, University of Missouri-Kansas City, Kansas City, MO 64110, United Statesf Civil and Environmental Engineering, University of Alabama, Box 870205 Tuscaloosa,Street, Shawnee Mission, KS 66205, United Statesl Survey, USGS Michigan Water Science Center, 6250 Mercantile Way, Suite 5, Lansing,
e i n f o
ecember 2007vised form 4 May 2008y 2008e 13 May 2008
a b s t r a c t
Permeable reactive barriers (PRBs) aretypically owing under natural gradiea PRB. A design-specic site exploratiosented. TheQDEapproach employs thrsensitivity of model outputs, and (3) coto explore based on a specic design. Sproduces a higher probability of succ/ locate / jhazmat
487, United States
911, United States
employed for in situ site remediation of groundwater that isite characterization is of critical importance to the success ofroach called quantitatively directed exploration (QDE) is pre-tially relatedmatrices: (1) covariance of input parameters, (2)nce of model outputs to identify the most important locationling at the location that most reduces overall site uncertaintyf a particular design. The QDE approach is demonstrated on
J. Lee et al. / Journal of Hazardous Materials 162 (2009) 222229 223
the United States, Canada, and selected locations abroad. One ofthe lessons learned from this study was the importance of exten-sive site characterization during the design phase, which is theperiod whethe best rein the EPACanyon casization affecontinuousdichloroethduring opepassing arowhich wasThe Fry Cauranium cofunnelsnoincompleteconning ugroundwatethe PRB insan obliquesite-specicgeneity.
It is sugemployed tis then follosite explorais chosen. Chad been pof remedialthat the adthat the addparameters
Four aspbefore implloading, (3)characterizawater owpInaccurate the barriernecessary toasmaximumparameterscharacterizaPRB systemtation buildThe microbthe interactreactive bar
It is welcharacterizarelatively feMost PRB steters suchof the barrassist in thand Folkesresearch haassociated whydrogeolobeensecondusing a Mogeologic unfor uncertadirected sit
tions prove a more economical and conservative approach ratherthan over-designing caused by a lack of data.
Unlike current published literature, thepresent study focuses onnal sesennd dverstioncalibcalid methotionsenprednd ved ahethingn ofitionr thed expstudsingB pet effing mntribPRBtionhich
o incf thealuah re-vari2000wasns wputed dethocertaicted.he ststratf suco shoay af
QDEes: (outplemea sited fnce,il inn the remedial design parameters are determined andmedial option is selected. Two cases were presentedreport, The Kansas City Plant (KCP) case and the Frye, which demonstrate how insufcient site character-cted the performance of the PRBs. At the KCP site atrench PRB was designed and installed to treat 1,2-ylene (1,2-DCE) and vinyl chloride (VC). It was foundration that a high concentration of contaminants wasund the endof the barrier due to a high conductive zone,not detected during the design site exploration phase.nyon site adopted a funnel-and-gate design to treatntamination in groundwater. Although the design hadow barriers to block the bypass of contaminants, thecontact between the uneven surface of the underlyingnit and the gates provided pathways for contaminatedr. In addition, a large bedrock nosewas detected duringtallation and resulted in re-orientation of the PRB withangle into the funnels. Therefore, the design should bewith a thorough understanding of subsurface hetero-
gested that a design phase of site exploration beo select an appropriate remediation technique, whichwed by a design-specic site exploration. Additionaltion is usually not considered after the remedial designase studies indicate that if additional site explorationerformed with a specic design in mind, many casesfailure could have been avoided. It should be noted
ditional site exploration should be design-specic soitional information can be used to optimize the designto avoid potential failure.ects of site characterization that should be evaluatedementing a PRB are: (1) hydrogeology, (2) contaminantgeochemistry, and (4) microbiology . Hydrogeologiction must be done accurately as it determines ground-atterns and the distribution of the contaminant plume.ow patterns may cause bypass of contaminant aroundand lead to a potential failure of remediation. It is alsohave an adequate contaminant characterization (suchconcentration) and totalmass of contaminant as thesedetermine the dimensions of the PRB. Geochemicaltion is needed for estimating the expected life of abecause, for example, the potential effect of precipi-up on the medium may cause bypassing of the barrier.ial aspects of the PRB should be known as well sinceions of nativemicrobial populations, contaminants, andrier materials are quite complex.l documented in the literature that hydrogeologic sitetion is critical to the success of a PRB, yet there arew studies emphasizing site characterization for PRBs.udies focus on the determination of PRB design param-as length, thickness, angle, or hydraulic conductivityier and simply employ a groundwater ow model toe design parameter determination (for example, Scott). Although not the primary focus of a paper, somes been published on the uncertainty of site conditionsith PRBs design parameters . Heterogeneity of the
gic settings and its effect on theperformanceof PRBshasarily considered [11,12]. Typically a stochastic approachnte Carlo simulation is employed to propagate hydro-certainty through a model to the result. Accountinginty is critical to the success of a PRB, but additionale exploration to reduce the uncertainty in site condi-
additioThe prtions amay aexploramodelters aredata antion. Mexploradictionmodel, aemploymine wby tracpositioan addsion fodirecteto thepling uthe PRgreatesarrangthat coa givenexploraputs, wThis Qter remlikely tment oand evthrougoutputFLOWmodellocatioall outincreasa PT mthe unof predchange
In tdemonbility ostudy tthat m
Allmatricmodeldata ewithinidenticovariain detaite exploration after a specic design has been selected.t approach may reduce the uncertainty in site condi-nd potentially hidden hydrogeologic structures thately affect the performance of a PRB. Published sitestudies for hydrologic conditions generally focus onration . The unknown or inaccurate parame-brated byminimizing the differences betweenobservedodel outputstypically head elevation and concentra-ds exist to improve model calibration through directedthat employs a number of techniques including: pre-sitivity and kriged error variance [17,18], minimizingiction uncertainty , composite scaled sensitivitiesalue of improved information . Cirpka et al. rst-order second moment (FOSM) method to deter-er the funnel-and-gate system would succeed or failvariance reduction in the streamline functions. Themost data worth for the variance reduction becomesal sampling location, so that the reliability of deci-design success or failure increases. The quantitativelyloration (QDE) approach presented herein is very closey of Cirpka et al.  in a sense of directed sam-uncertainty. The difference is that instead of usingrformance, we identify which input data have theect on model results given a specic design by re-atrix calculation in FOSM and nd the data locationutes the most to output variances across a site fordesign. The original concept for QDE was to directto the location of greatest variance in model out-is where model results are most uncertain .
pproach has been successfully applied to groundwa-tion to nd a next sampling location that is mostrease remedial design reliability . Further develop-QDE approach by Graettinger et al.  investigated
ted seven different techniques to direct explorationanalysis and comparison of the input variance matrix,ance matrix, and model sensitivity matrix using MOD-. This work determined that the piezometric headmost improved by obtaining additional samples athere input information contributed the most to over-variance. Lee  showed how directed explorationesign reliability as compared to random sampling ford. Additional QDE sampling of transmissivity reducedinty of predicted concentration while the sensitivityconcentration to transmissivity did not signicantly
udy presented herein, we adopted the QDE approach toe how additional exploration produces a higher proba-cess of a particular PRB design. We used the KCP casew howQDE is able to detect unrevealed site conditionsfect the performance of PRBs.
approaches in Graettinger et al.  are based on three1) covariance of input parameters, (2) sensitivity ofuts, and (3) covariance of model outputs. Each of thents in these matrices is related to a specic locatione. Therefore a precise parameter and location can beor additional sampling. Each of these matrices, inputmodel sensitivity, and output covariance, are describedthis section.
224 J. Lee et al. / Journal of Hazardous Materials 162 (2009) 222229
2.1. Input parameter estimation and covariance
TheQDE approach uses amultivariate normally distributed con-ditional proparametersdatavaluesance) that ivalue of thethe KCP castain spatialhydraulic csampled lois producedductivity atmatrix, Covthat represhydraulic cces, the prioE(U|V) andthe followin
E(U|V) = E(
E(U|V) isknown hydV) is a subsance betweknown hydagain a subthe covariais a subset omates at thematrix, andcovariancethehydraulconditionalerated outpand contam
The sen[22,26]. In tcoded direcously produDDC methodifferentiathead and/orthederivatiADIFOR 2.0gram. In ouindependention were pby the derivmanually. D
Withinmodel sensexpansion tple, calcula
lan vimple2000t ends
67.1 42.7 8.282.3 79.3 3.4
lic conductivities is shown by Eq. (3) .
)Cov[Ki, Kj] (3)
ov[hl, hk] is the covariance of computed head (h) betweenand node k. (hk/Ki) is the sensitivity of head at node k to ain the hydraulic conductivity (K) at node i. Cov[Ki, Kj] is thence between hydraulic conductivities at nodes i and j.g the input parameter and covariance les produced by Eqs.(2), and the sensitivitymatrix fromthedirectderivative cod-C) calculation, covariances in predicted piezometric head
ncentration are calculated for the entire model using Eq. (3).
rameters used to model the KCP site
ic parametersudinal dispersivity (aL) 0.6merse dispersivity (aT) 0.3mion coefcient 4.7104 m2/dayivity 0.001ty 0.33
al parametersize 121.9m91.4mnt size (triangular) 3m3mumber of nodes 1271umber of elements 1200time step 10 daysimulation time 100 daysbability calculation to interpolate and extrapolate input. This calculation generates a regularized grid of inputandassociatedspatiallydistributeduncertainty (covari-s assumed to be normally distributed about the meaninput data at each nite diffe...