Modeling Natural Attenuation with Source Control at a …andy/g727/GWMR_RT3D model.pdf · Modeling Natural Attenuation with Source Control at a Chlorinated Solvents Dry Cleaner Site

Modeling Natural Attenuation with Source Controlat a Chlorinated Solvents Dry Cleaner Site

by Meng Ling and Hanadi S. Rifai

AbstractA ground water flow and solute transport model was developed for a site contaminated with chlorinated solvents to sim-

ulate the site remediation activities that included natural attenuation and source control. MODFLOW was used to model theflow field, and RT3D was employed to simulate the sequential decay reactions involving perchloroethene, trichloroethene,dichloroethene, and vinyl chloride (VC). Predictive simulations were completed for two conditions: natural attenuationalone and source excavation followed by natural attenuation. Results demonstrated the feasibility of site remediation forboth conditions with the latter one occurring in a shorter time frame. A detailed sensitivity analysis was performed to assessthe uncertainty in model predictions. The most sensitive model parameters (i.e., initial contaminant concentrations, reactionrate constants, and source release rates) were perturbed and combined to form a number of parameter sets for use in thepredictive simulations. The maximum plume length, the maximum plume concentration, and the persistence time for thenoncompliance plume area were assessed to identify the possible failure scenarios and the most likely future plume configu-ration. Results indicated that further VC plume expansion or migration beyond site boundaries is unlikely.

IntroductionThe use of natural attenuation for remediating conta-

minated ground water has gained greater acceptance byboth industry and the regulatory community over the pastdecade (American Society for Testing and Materials 1998;National Research Council 2000; U.S. EPA 2001). Naturalattenuation refers to naturally occurring physical, chemical,and biological processes in ground water such as bio-degradation, dispersion, adsorption, and volatilization thatcause a reduction in contaminant mass and concentration.Natural attenuation (i.e., monitored natural attenuation),applied alone or in combination with source control, isa viable treatment technology for some sites that has manyadvantages when compared to conventional engineeredremediation technologies (Wiedemeier et al. 1999). Cur-rently, natural attenuation is widely used in the treatment ofground water contaminated with organic solutes, primarilyfuel hydrocarbons and chlorinated solvents.

To evaluate the feasibility of using natural attenuationas a remedy and to demonstrate its effectiveness, groundwater modeling is often used to understand the transportregime and the governing natural attenuation processes,and to predict plume evolvement and contaminant con-centration levels over time. With the development of

mathematical and computer models that can be used tosimulate the various natural attenuation processes in recentyears (see, for example, Aziz et al. 1999; Bekins et al. 1993;Borden and Bedient 1986; Borden et al. 1997; Clement1997; Kinzelbach et al. 1991; Newell et al. 1996; Rifaiet al. 1997; Semprini and McCarty 1991, 1992; Waddilland Widdowson 2000), the modeling of natural attenuationat field sites has been greatly facilitated. Site modelingapplications in the general literature include Brauner andWiddowson (2001), Clement et al. (2000, 2002), Lu et al.(1999), Rifai et al. (2000), and Schafer (2001). Applicationof natural attenuation models to sites, however, has beensomewhat limited, especially in the case of chlorinated sol-vents. Most of the modeling studies presented in the litera-ture thus far have focused on natural attenuation alone andon demonstrating model development for simulating sitedata. While there are a number of natural attenuation mod-eling case study applications for fuel hydrocarbons, almostnone have been published in the refereed literature for chlo-rinated solvents. In one of the very few such applications,Clement et al. (2000) model the natural attenuation ofchlorinated ethene compounds at a military site usingRT3D and generate contaminant plumes that agree reason-ably well with the observed data. They estimate field-scaledegradation rates via model calibration. Their estimatedrates are corroborated by laboratory and field-measuredrates and close agreement between predicted and field-estimated plume mass. Clement et al. (2000) studied model

Copyright ª 2007 The Author(s)Journal compilationª 2007National GroundWater Association.

108 Ground Water Monitoring & Remediation 27, no. 1/ Winter 2007/pages 108–121

sensitivity but did not undertake predictive simulationswith their calibrated model for risk management purposes.

Since natural attenuation may require relatively longertime frames for achieving site cleanup goals, it is impor-tant to use models to evaluate the efficacy of the technol-ogy over the long term. It is also necessary to understandthe uncertainties associated with model predictions and thelikelihood of failure when natural attenuation is used asa remedy.

This paper addresses the aforementioned needs anddemonstrates the use of natural attenuation models forpredictive simulations. A dry cleaning facility, located innortheastern Texas and contaminated with chlorinated sol-vents, is modeled with MODFLOW and RT3D. This paperillustrates a modeling approach with uncertainty analysisfor simulating natural attenuation as a sole remedy at thesite, as well as natural attenuation with source control. Theuncertainty analysis undertaken in this study is not a MonteCarlo-based uncertainty analysis but rather an approachaimed at characterizing the possibility and scenarios of fail-ure. In this case, failure is defined as the case(s) when theplume boundary (delineated to the cleanup level) migratesoutside the site boundaries or the plume concentrationsexceed the cleanup goal. This paper is not intended to bea modeling guidance for developing and applying probabi-listic natural attenuation models; rather it is intended toillustrate an approach for evaluating and modeling the riskinvolved with implementing natural attenuation remediesat dry cleaner sites.

Unlike the site modeled by Clement et al. (2000), thisstudy site has limited data and a short case history, which is

characteristic of many sites. Additionally, the site has unde-rgone extensive remediation, including soil excavation andsoil vapor extraction (SVE). While numerous model devel-opment papers have indicated that fate and transport modelsare most sensitive to source definition, biodegradation, andvelocity, little information is available on how to deal withthese variables in the published literature. In particular, lit-tle information is found on modeling source remediationvia excavation or other technologies. Hence, the issues ofsource decay and changing sources over time, as well ashow to assess risk reduction with source cleanup, are ofinterest to many researchers and practitioners undertakingsite remediation. This paper presents an approach to addressthese issues that can be applied to other field sites withlimited data.

Methodology

Site DescriptionThe site plan is illustrated in Figure 1. The dry clean-

ing facility is located in the northeast corner of the site andused perchloroethene (PCE) as the dry cleaning agent from1982 to the mid-1990s. In a site investigation conducted in1995, PCE and its degradation products were found in soilsamples and the shallow perched ground water near thefacility. Neither PCE nor its degradation products weredetected in three monitoring wells that were installed ina lower confined aquifer. No known ground water receptorsexist in the vicinity of the site, except for two supply wellsthat are screened in the deeper aquifer and located

Figure 1. Site plan and monitoring locations, and the area excavated in 2001.

M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 27, no. 1: 108–121 109

approximately 1200 feet downgradient of the site. InAugust 1996, 30 cubic yards of affected soil were exca-vated immediately downgradient of the dry cleaning struc-ture. SVE was undertaken between July 1997 and June1998 to remediate the affected soils. Also in June 1998, 13cubic yards of affected soil were excavated from the inte-rior of the dry cleaner building, and seven monitoring wells(MW-4 to MW-10) were installed in the perched groundwater zone. Between December 1998 and July 1999, 20additional monitoring wells (MW-11 to MW-30) wereinstalled in the perched ground water zone to fully delin-eate the contaminant plume. The measured contaminantsinclude PCE, trichloroethene (TCE), three isomers of di-chloroethene (DCE), and vinyl chloride (VC). Monitoringdata indicated that cis-1,2-DCE is the dominant DCE iso-mer, accounting for more than 90% of the DCE concentra-tion. Dense nonaqueous phase liquid (DNAPL) has notbeen observed at the site, nor do ground water con-centrations indicate the presence of DNAPL. Contaminantconcentration contours for PCE, TCE, DCE (sum of all iso-mers), and VC from the November 1999 data are shown inFigure 2. The PCE, TCE, and VC plumes were delineatedto their respective maximum contaminant level (MCL), i.e.,5, 5, and 2 ppb. The DCE plume was delineated to 20 ppb.The highest concentration observed in November 1999 was84 ppb for PCE, 200 ppb for TCE, 1768 ppb for DCE, and670 ppb for VC. The plume lengths were approximately60, 100, 180, and 190 feet, for PCE, TCE, DCE, and VC,respectively.

The cleanup standard was determined in previous sitestudies to be 100 times the MCL, i.e., 500 ppb for PCE,500 ppb for TCE, 7000 ppb for cis-1,2-DCE, 10,000 ppbfor trans-1,2-DCE, 700 ppb for 1,1-DCE, and 200 ppb forVC. In order to meet these goals in a shorter time frame,more than 5600 cubic yards of affected soils around thesource area as shown in Figure 1 were excavated (toa depth of approximately 10 feet below ground surface[bgs]) between July to October 2001 and in December2001. Wells MW-5, MW-10, and MW-12 were sealed andabandoned during the excavation and were replaced withMW-31, MW-32, and MW-33, respectively. Postexcavationmonitoring was performed 1 year later to evaluate whethercleanup goals had been met. Two relatively complete mon-itoring data sets, from November 1999 and from March2002, were used in model calibration and testing.

The ground surface elevation varies from 606 feetabove mean seal level (msl) at the northwest boundary to602 feet above msl at the southeastern boundary. Most ofthe surface in the study area is covered with 6 inches ofconcrete pavement or asphalt. The general geology of thesite is sandy clay from 6 inches to approximately 6 feetbgs, silty clay from 5 feet to approximately 12 feet bgs,and an underlying layer of clay with a relatively low per-meability at approximately 12 feet bgs. The shallow aquifer,composed of sandy/silty clay, varies in thickness between 6and 12 feet throughout the site. The underlying clay layer isrelatively impermeable and serves as a confining unit sepa-rating the perched ground water from the deeper aquifer. A

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Figure 2. Observed contaminant plumes in November 1999 (contours in ppb drawn with inverse distance interpolation). The generalground water flow direction is indicated by the gray arrow.

M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 27, no. 1: 108–121110

soil bulk density of 1.83 kg/L and an effective porosity of0.342 were determined from soil testing.

Ground water elevations range from 601 feet abovemsl at the northwest boundary to 594 feet above msl at thesoutheastern boundary (Figure 3). It was determined thatthe ground water flow field is relatively stable based on ananalysis of the ground water level data. Because of theshallow depth of the perched ground water and the pres-ence of relatively large sewers underneath the site, theground water gradient varies between 0.015 to 0.11 ft/ft. Itshould be noted that the large sewers underneath the siteare areas of high conductivity backfill and likely act asa source of exfiltrating water to the ground water. Thehydraulic conductivity, calculated by others, ranged from0.0015 to 0.3 ft/d and well yields ranged from 1.25 to 118gal/d. The average annual precipitation for the study area is32 inches.

Although no geochemical parameters were collectedduring site investigations, the presence of PCE degradationproducts, i.e., TCE and high levels of cis-1,2-DCE andVC, indicates that PCE is being reductively dechlorinated.

Model DevelopmentBased on the site hydrogeology and the saturated thick-

ness of the perched ground water zone (3 to 6 feet), thesite was modeled as a shallow unconfined aquifer (i.e.,a single saturated layer). Borehole and ground water eleva-tion data from the 27 monitoring wells (MW-4 to MW-30)were analyzed using inverse distance weighted interpola-tion to define the aquifer thickness. A steady-state flowfield was assumed in this study because (1) observedground water elevation contours indicate that the flow sys-tem is relatively stable and (2) there are no conditionswithin the model domain that have influenced or are influ-encing the ground water flow, causing transient conditions(e.g., pumping wells).

Hypothetical injection wells were used to simulate thehistorical release of PCE. Injected concentrations were

varied with time to simulate the effects of the remedialactivities (e.g., SVE) on source concentrations. A releasedate of approximately halfway through the operating lifeof the dry cleaning facility was assumed. Since the facilitybegan operation in 1982 and closed in the mid-1990s, theassumption means the contaminant released came intocontact with the ground water in 1989. This amounted toapproximately 10 years for the plume to develop into thedistributions observed on November 1999 (Figure 2). Retar-dation caused by sorption was modeled using linear sorp-tion isotherms. However, since the level of organic carbonin the saturated zone was unknown, sorption coefficientswere estimated through model calibration. Due to the lackof information about the released mass of PCE and bio-degradation parameters such as reaction rate constants,these variables were also treated as calibration parametersin the transport simulations.

In this study, MODFLOW (Harbaugh and McDonald1996) was used to generate the flow field, and RT3D(Clement 1997) was used to model the fate and transport ofthe chlorinated ethenes. RT3D is a modular simulator forpredicting multispecies bioreactive transport that is basedon the EPA model MT3D (Zheng 1990). Reductive de-chlorination of the cholinated ethenes (see, for example,Bouwer 1992; Bradley and Chapelle 1996; Davis andCarpenter 1990; Vogel and McCarty 1985, 1987; Wiedemeieret al. 1999) was modeled using first-order kinetics. Thefirst-order kinetic model was selected as it is widely usedin natural attenuation modeling applications and is easy tosimulate and interpret. In addition, the large amount ofliterature data for laboratory and field-scale first-order rateconstants (e.g., Aronson and Howard 1997; Suarez andRifai 1999) facilitates the estimation of decay rates.

Model Domain and Boundary ConditionsThe model domain encompasses the entire site as

shown in Figure 4. The northwest boundary corresponds tothe residential area and the southeast boundary corresponds

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Figure 3. Observed ground water elevations (feet) from November 1999 data.


to a highway. A single-layer uniform grid of 24 rows and38 columns was superimposed over the model domain. Thesize of each grid cell was 15 by 15 feet. The northwestboundary of the model domain (i.e., the right-hand borderof the model grid) was defined as a constant head boundarywith heads ranging from 601.0 to 598.5 feet, while those atthe southeast boundary (i.e., the left-hand border of themodel grid) were set to a constant level of 591.5 feet. Thenortheast (top border) and southwest (bottom border)boundaries of the model grid were treated as no-flowboundaries.

For the transport model, the upgradient boundary, andthe upper and the lower boundaries of the model grid weredefined as no mass flux boundaries. Contaminants, how-ever, were allowed to move freely out of the site throughthe downgradient boundary of the model grid.

Initial Conditions, Sources, and Reaction ZonesIt was assumed that no PCE or other chlorinated

ethenes were present in the aquifer prior to 1989. Since theexact sources of PCE leakage were not known, the poten-tial source locations were represented as follows. First,because the dry cleaning operations caused the release, thelocation of the dry cleaning facility (the area close to wellsMW-4 and MW-5) was considered within the source zone.This assumption was supported by the presence of PCEand its degradation products in samples taken beneath andaround the dry cleaning facility in 1995. Second, the high-concentration areas within the PCE and TCE plumes, i.e.,the areas near wells MW-5, MW-8, and MW-12, were alsoconsidered to be part of the source zone. Third, the areanear MW-10 was considered to be within the source zonebecause of the high DCE and VC concentrations (morethan 1000 ppb for DCE and more than 200 ppb for VC)observed in this area. Even though these assumed sourcesare not in proximity to the dry cleaning facility, they couldhave been formed by the migration of PCE along the

leaking sewers beneath the facility. Physically, these sour-ces may be in the form of DNAPL initially and/or resid-uals adsorbed onto the soil matrix.

As mentioned previously, the PCE releases weremodeled using hypothetical injection wells placed withinthe source zone locations discussed previously (see INJ #1,INJ #2, INJ #3, and INJ #4 shown in Figure 4). The flowrates for the source injection wells were maintained at verylow levels (e.g., 1 to 2 gal/d) to minimize their influence onthe flow field. The fact that PCE concentrations were muchlower than those for DCE and VC (Figure 2) indicated thatPCE mass release rates from source areas were higherinitially and then decreased over time. To model this time-variable source, multiple stress periods were used duringwhich the PCE mass release rates were varied by chang-ing the release concentrations at the injection wells. Therelease concentrations for different stress periods weredetermined through model calibration.

One pattern can be observed by studying the plumesshown in Figure 2: the reaction rates for PCE and TCEappear to be faster than those for DCE and VC as the DCEand VC plumes are much larger in extent than those forPCE and DCE. Also, the observed concentrations of DCEand VC were much higher than those for PCE and TCE. Inorder to simulate these variable reaction rates, the modeldomain was divided into two reaction zones (zone I andzone II as shown in Figure 4). Zone I is approximately en-closed by the 200 ppb DCE contour (Figure 2), and zone IIcontains all other areas. Direct evidence for the abovezonation could not be obtained due to the lack of data.However, justifications can still be made from existing dataand current understanding of related mechanisms. Forexample, the level of electron acceptors such as dissolvedoxygen is generally higher where plume concentrations arelower; thus, DCE and VC could have a higher reactionrates since they can be degraded aerobically. This is consis-tent with a well-recognized mechanism that biodegradation

Reaction Zone I

Reaction Zone II

INJ #1

INJ #2 INJ #3

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Figure 4. Model grid. The ground water flow direction is indicated by the gray arrow.


rates differ significantly between highly contaminatedareas and plume edges where concentrations are low (Wie-demeier et al. 1999). By using two reaction zones, it waspossible to generate the observed high concentrations in thecenter of the DCE and VC plumes while simulating lowerconcentrations elsewhere. Since only VC and DCE mighthave concentrations that remain above the cleanup goals of200 and 7000 ppb, respectively, the ability to replicateobserved high concentrations in the model was key to thenatural attenuation assessment.

Model Calibration and PredictionThe objective of the flow model calibration was to

reproduce the direction and rate of ground water flowobserved in November 1999 (Figure 3). The calibration pro-cess was performed by trial and error after an initial estima-tion using the automated parameter estimation code PEST(Doherty 2000). Recharge rates and hydraulic conductivi-ties were varied throughout the model grid according tosurface and subsurface features to match the observedground water elevation contours. The flow calibrationerror was calculated using the mean absolute error(MAE):

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where n is the number of observations, hc is the computedvalue, and ho is the observed value. The MAE between thecomputed and the observed heads should be less than 10%of the overall head change across the site. In addition, theflow balance of the calibrated model was examined toassess the validity of the flow model.

The transport part of the model was calibrated after theflow field was reasonably reproduced. The contaminantconcentrations observed in November 1999 were used tocalibrate the transport model. Assuming the leak began inNovember 1989, three stress periods were used to repre-sent the 20-year period from 1989 to 2009 (Figure 5). Thefirst stress period (2465 d) models the time period from thebeginning of the leak to July 1996, just before the

beginning of the first remedial action. The second stressperiod is 670 d long, from August 1996 to June 1998, andreflects a period during which two remedial actions weretaken. The third stress period starts from July 1998 after 13cubic yards of soil were excavated from the dry cleaningfacility and continues for another 4165 d into the futureuntil the year 2009. The plumes generated at 3650 d (or 10years) were used for comparison with the observed data inNovember 1999. The transport calibration was conductedvia a trial-and-error process after selecting a set of initialparameters from literature data.

The calibrated model was then used to predict plumestatus (1) under solely natural attenuation conditions(assuming that the excavation in the second half of 2001was not conducted) and (2) by natural attenuation aftersource excavation. The first predictive simulation was usedas a worst-case scenario to determine if natural attenuationalone would be adequate to confine the plume and reducecontaminant concentrations to below the cleanup levels.The second predictive simulation evaluated the effective-ness and strength of the actual remedies conducted at thesite. To assess the influence of limited supporting data onthe developed model, an uncertainty analysis was finallyconducted to identify conditions that would cause noncom-pliance (plume expansion or migration beyond site bound-ary). The detailed setup and results of the calibratedmodel, the two predictive simulations, and the uncertaintyanalysis are discussed in the following section.

Model Details and Results

Calibrated ModelBecause most of the site surface is covered with con-

crete or asphalt, a recharge rate of 0.00015 ft/d (2% of theaverage annual precipitation) was applied over most of theflow field. A higher recharge rate of 0.0073 ft/d (equivalentto 100% of the average annual precipitation) was assignedto the northwest corner of the model grid (Figure 6) tosimulate a ground water mound located near one of the res-idential areas. Such a high recharge rate was likely due tothe percolation of irrigation water applied to this area and

Oct. 2001 Mar. 2004One stress period(900 days)

Natural Attenuation with Source Control

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Soil excavation(5,600 cu yd)

Post-excavationsimulation started

Mar. 2002Model calibration(180 days from start)

Predictivesimulation

Aug. 1996 (30 cu. yd soil excavated)

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ReleaseDate

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Stress period 3(4165 days)

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(670 days)

Nov. 1999(3650 days from start)

Natural Attenuation Only

Figure 5. Timeline for model development.

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Figure 6. Locations of recharge zones and sewers andtrenches.


should not be construed as being equivalent to 100% of theprecipitation becoming recharge.

Additionally, a recharge rate of 0.0014 ft/d (20% of theaverage annual precipitation) was assigned around the drycleaning facility (MW-5 and MW-7) to create the groundwater mound observed in that area (Figure 3). The hydrau-lic conductivity ranged from 0.004 to 0.3 ft/d in most of themodel area and from 0.1 to 1.5 ft/d in areas where majorsewers and trenches are located (Figure 6). The injectionrates for the four injection wells were fixed at 1.0, 1.0, 1.0,and 0.5 gal/d for INJ #1, INJ #2, INJ #3, and INJ #4,respectively.

The generated ground water contours are presented inFigure 7. Although the generated contours do not fully rep-licate the observed ones, they mimic the major features ofthe flow domain. The difference in the downgradient partof the model grid can be attributed to the lack of observa-tion points in the area. The flow calibration error (MAE) is0.41 feet, which is about 5% of the overall head change (8feet) across the site. Since the simulated ground water flowfield mimics the observed flow field and the K values are ingeneral within the measured range, the ground water veloc-ities are considered reasonably represented. A flow balanceindicated the total flux into the model is approximately 38ft3/d, of which 9% comes from the constant head bound-aries and 90% comes from recharge (1% from the injectionwells). That the majority of the inflow comes fromrecharge is consistent with the perched condition of thiswater bearing zone. Therefore, the flow calibration resultswere considered acceptable.

The model transport parameters are listed in Table 1.The resultant grid Peclet number is 1.25 (Pe ¼ �x/aL ¼15/12 ¼ 1.25), which is less than 2, indicating that the gridspacing is small enough to eliminate artificial oscillation inthe finite-difference method. The retardation coefficientscorresponding to the sorption coefficients are 1.30, 1.19,1.16, and 1.11 for PCE, TCE, DCE, and VC, respectively,indicating low sorption effects. The reaction rate constantswere bounded by the literature values given in Suarez andRifai (1999). The major difference in the reaction rate con-stants for the two reaction zones was the increased reactionrates for DCE and VC outside reaction zone I. The cali-brated PCE release concentrations at the four injection

wells for the three stress periods are given in Table 2. It canbe seen from Table 2 that high concentrations were appliedto create high mass release rates at the beginning of thesimulation, while lower concentrations were used later toreflect the reduction in source strength due to active reme-diation and source depletion. The calibrated plumes areshown in Figure 8. The elevated concentrations observed inthe center of the DCE and VC plumes were reproduced,and the modeled plumes agree with the observed plumesreasonably well.

A sensitivity analysis was performed to investigatethe influence of key model parameters on the calibratedmodel. Three key model parameters, reaction rates, sorp-tion coefficients, and source release rates, were examined.Each parameter was perturbed 50% above and below thecalibrated values (i.e., the baseline), and the resultingplumes at 3650 d (November 1999) were compared to thecalibrated plumes. For reaction rates, the calibrated rateconstants for all contaminants (Table 1) were perturbedsimultaneously at every model cell throughout the modeldomain. For source strength, the calibrated source releaseconcentrations (Table 2) were perturbed simultaneously forall injection wells and for all stress periods. The results areshown in Figures 9A and 9B, illustrated using only the VCplume since VC is the constituent with the highest associ-ated risk at the site. Figure 9A indicates that the most influ-ential model parameter, in terms of the extent of the VCplume represented by the 2 ppb contour, is reaction rate.However, when results are evaluated in terms of the high-concentration area represented by the 100 ppb contourshown in Figture 9B, source strength appears to be moreimportant. Sorption coefficients had little effect on modelresults in both cases.

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Figure 7. Modeled flow field (contours in feet) calibrated toNovember 1999 data.

Table 1Transport Model Parameters for Simulating

Natural Attenuation Alone

Parameter Value

Longitudinal dispersivity (aL) 12 feet (calibrated)Ratio of transverse tolongitudinal dispersivity

0.3 (calibrated)

Porosity 0.342 (measured)Soil bulk density 1.83 kg/L (measured)Kd for PCE 0.056 L/kg (calibrated)Kd for TCE 0.036 L/kg (calibrated)Kd for DCE 0.030 L/kg (calibrated)Kd for VC 0.020 L/kg (calibrated)YTCE/PCE (yield coefficient) 0.792 (stoichiometric)YDCE/TCE 0.738 (stoichiometric)YVC/DCE 0.644 (stoichiometric)KPCE (zone I) 0.007/d (calibrated)KTCE (zone I) 0.009/d (calibrated)KDCE (zone I) 0.0003/d (calibrated)KVC (zone I) 0.0005/d (calibrated)KPCE (zone II) 0.005/d (calibrated)KTCE (zone II) 0.006/d (calibrated)KDCE (zone II) 0.0012/d (calibrated)KVC (zone II) 0.006/d (calibrated)


No validation/verification of the calibrated model waspossible due to the lack of data in the 2 years prior to thelarge-scale excavation. Although there may be nonunique-ness problems in the aforementioned model calibration,the previous results nonetheless simulated a plausible sce-nario for the site. Potential nonuniqueness problems wereone of the driving forces for the assessment of uncertaintyin model predictions discussed later in this paper.

Predictive Simulation with Natural Attenuation AloneThe calibrated model was used to predict plume status

into 2009 or 10 years into the future. This was accom-plished mostly to evaluate plume behavior if natural atten-uation were the sole remedy (it also assumes that theexcavation in the second half of 2001 was not conducted).

The resulting predictive simulation was used as a worst-case scenario to determine if natural attenuation alonewould be adequate to confine the plume and reduce con-taminant concentrations to below the cleanup levels.

Results indicated that the contaminant plumes stabi-lized around day 5475 (or November 2004). The predictedplumes are shown in Figure 10. The VC plume (delineatedto its MCL or 2ppb) extends to well MW-27, well withinthe site boundary. Also, the VC concentrations are predi-cated to fall below 200 ppb at every location across the site(VC is the only contaminant with concentrations above itscleanup level). However, the model indicates that prior toNovember 2004, an area with VC concentrations higherthan 200 ppb was present around MW-5 and MW-9. Thisresult shows that natural attenuation alone could be used to

Table 2Stress Periods and Source Release Rates for Simulating Natural Attenuation Alone

Injection Welland Injection Rate(gal/d)

Source PCE Release Concentration (ppb)

Stress Period 1(0–2465 d,

November 1989 to July 1996)


August 1996 to June 1998)


July 1998 to October 2009)

INJ #1 (1.0) 50,000 (0.2 g/d) 6000 (0.02 g/d) 3000 (0.01 g/d)INJ #2 (1.0) 30,000 (0.1 g/d) 15,000 (0.06 g/d) 7500 (0.03 g/d)INJ #3 (1.0) 8000 (0.03 g/d) 6000 (0.02 g/d) 6000 (0.02 g/d)INJ #4 (0.5) 100,0001 (0.4 g/d) 100,0001 (0.4 g/d) 2000 (0.008 g/d)

1This high concentration is used with a low injection rate to produce the desired mass release rate.

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achieve cleanup but would require at least 5 years to meetthe remediation goals. Source removal such as the 2001excavation would be required to achieve compliance ina shorter time frame.

Postexcavation Predictive SimulationsAs mentioned previously, during the period from July

to December 2001, more than 5600 cubic yards of affectedsoils around the source area shown in Figure 1 were exca-vated. Such a large-scale (relative to the scale of the site)excavation helped reduce the contaminant concentrationsto below the cleanup levels almost immediately. To simu-late the effects of this remediation on the fate and transportof the contaminants and predict the future plume behavior,a simulation that accounted for the excavation was under-taken. A 900-day stress period was selected, startingaround October 2001 (the excavation was assumed to havebeen completed by September 2001) and continuing for2.5 years into the future until about March 2004 (Figure 5).

The 900-day postexcavation simulation uses downwardlyadjusted concentrations from the preexcavation simulationsas initial concentrations. The starting model concentrationsthroughout the plume were reduced to 50% of the gener-ated concentrations obtained by running the previouslycalibrated model to October 2001. In addition, initial con-centrations at some model cells were manually adjusted sothat the resulting predicted concentrations in March 2002agreed with their observed counterparts.

To model the source reduction caused by the excava-tion, two scenarios (scenario I and scenario II) were con-sidered. In scenario I, the source injection concentrationswere set to zero for all injection wells; i.e., the source wascompletely turned off. In scenario II, the source injectionconcentrations were set to 20% of their respective preexca-vation injection concentrations (Table 2, stress period 3) tosimulate an 80% reduction in source strength. The reactionrate constants for zone I (the source zone beneath the drycleaner) were also reduced for both scenarios: KPCE 0.002/d,

50 baseline

150 baseline

50 baseline

150 baseline

50 baseline

150 baseline

2

2

2

2

2

2

2

22

2

2

2

2

22

2

22

2

2

2

222

2

222

Reaction Rates

Sorption Coefficients

Source Strength

Baseline

Baseline

Baseline

100100

100 100

100

100

100

100

100

100100

100

100

Reaction Rates

Sorption Coefficients

Source Strength

Baseline

Baseline

Baseline

50 baseline

150% baseline

50 baseline

150 baseline

150 baseline

50 baseline

A B

Figure 9. (A) Results of sensitivity analysis for the calibrated model (illustrated using the 2 ppb VC contour); (B) results of sensi-tivity analysis for the calibrated model (illustrated using the 100 ppb VC contour).


KTCE 0.002/d, KDCE 0.00025/d, and KVC 0.0004/d. Thisadjustment can be explained by the loss of soil microbes,introduction of oxygen, and reduction in source-derivedcarbon as a result of the excavation. These rate constantsare still within the range of reported values in Suarez andRifai (1999) but are closer to the lower end of the range. Inboth scenarios, reaction rate constants for zone II and theremaining transport parameters were unchanged. Althoughthe resulting modeled plumes in March 2002 were larger inextent and higher in concentration than the observed ones,they provided a conservative representation of the post-excavation plume condition.

The model established previously was used in the pre-dictive simulation for both scenarios to confirm the attain-ment of cleanup goals. Results for both scenarios indicatedthat the cleanup goals would be achieved quickly after theexcavation and the plume will not migrate further down-gradient. The generated plumes for scenario II (the 80%source reduction case) on March 2002 are shown inFigure 11. Generated plumes at later points in time alsoindicated that there would be no increase in the concentra-tion levels of the plumes. In scenario I, the plumes willeventually diminish to below detection levels as no furtherPCE release is assumed. In scenario II, the plumes willeventually reach steady state and exhibit lower concen-trations due to the assumed continuous PCE release rates.

Evaluation of Uncertainty in Model PredictionsAs discussed in the previous sections, a number of

model parameters were estimated through trial and error.The calibrated variables are by no means a unique

solution. It is possible to construct an equally well-cali-brated model with a different set of variables. This non-uniqueness, coupled with the data deficiencies describedthroughout the paper and emphasized in this section, hasa potential impact on the simulated rates of attenuation.For example, the actual contaminant release history isunclear and the total amount of PCE discharged into thesubsurface is unknown. Also unknown is the mass of theremaining chemicals in the source zone. Additionally,there are no field or lab-scale studies to provide corrobo-rating information regarding site-specific reaction rate con-stants or the sustainability of those rates into the future.The flow field in Figure 3 may reflect potential artifacts ofthe interpolation scheme used to contour the data. Some ofthe isolated peaks and valleys along the perimeter of thedomain may actually represent broader regional trends inthe piezometric surface rather than the interpretation usedin this research. These data deficiencies, while typical formany sites, lead to uncertainty in model predictions. Fur-thermore, monitoring results from September 2002 at thestudy site showed that at one location, MW-32, the VCconcentration was higher than 200 ppb in contrast with pre-vious sampling results that were below 200 ppb. Of con-cern, then, is the issue of continued compliance anddetermining the set of conditions that would trigger plumeexpansion and/or noncompliance.

An uncertainty analysis of the model predictions wouldhelp identify conditions that would cause noncompliance.A commonly used method for studying uncertainty andits impacts on model results is Monte Carlo analysis(e.g., McNab and Dooher 1998; Meyer et al. 1994). InMonte Carlo analysis, empirical probability distributions

PCE TCE

DCE VC

Figure 10. Contaminant plumes (contours in ppb) predicted for November 2004 under natural attenuation only conditions.


for governing parameters (e.g., hydraulic conductivities)are provided as input to the model. The uncertainty orprobabilistic distribution of the model predictions can thenbe quantified. However, as is typical of small sites withlimited data and site characterization, probabilistic uncer-tainty analysis is not feasible. A deterministic sensitivityanalysis is used instead to evaluate how applicable and reli-able the model predictions are with respect to plumeexpansion or migration.

A preliminary sensitivity analysis of the postexcavationmodel indicated that the initial concentrations, reactionrate constants, hydraulic conductivities, and source releaserates (determined through source release concentrations)were the most sensitive parameters affecting model results.The first two parameters were perturbed based on theirbaseline values (i.e., the calibrated values in the postexca-vation model). For source release rates, the baseline valueswere taken from scenario II of the postexcavation model.Although hydraulic conductivity is a sensitive factor thataffects model predictions, it was not used in the analysisfor two reasons. First, changes of hydraulic conductivitieswithin a small range (less than or equal to two times) didnot affect the size of the plume sufficiently so that a failurecase would occur. Second, for significant variations in con-ductivity values (orders of magnitude), the scenario wasconsidered unreasonable based on site data. Not incorpo-rating hydraulic parameters in the uncertainty analysis alsoreduced the number of model runs significantly so resultscan be processed with less effort.

When perturbing initial concentrations and reactionrates, the values for these parameters were either increasedor decreased by a certain percentage at every model cellthroughout the model domain. For initial concentrations,the baseline values, 300%, and 500% of the baselinevalues were used. No reduced initial concentrations wereconsidered because they are unlikely to create elevatedconcentrations. For reaction rate constants, a total of sevenscenarios (baseline and cases 1 through 6) were considered(Table 3). In cases 1 to 3, KVC was reduced (or increased)when the other rate constants were increased (or reduced).In cases 4 to 6, all the rate constants were reduced (orincreased) using the same percentage. The resulting scenar-ios were used to assess the VC plume behavior under twodifferent sets of conditions. For the source release rates,

zero release rate (i.e., zero release concentration), the base-line values, 300%, and 500% of the baseline values wereconsidered. Complete combinations of these perturbedparameter values resulted in a total of 112 parameter sets(see Table 3). Therefore, 112 simulations were conducted.Only the VC plume was assessed, as it poses the highestrisk and was the only species exceeding the cleanup levelsby then.

The maximum plume length, the maximum concentra-tion within plume, and the persistence time for the morethan 200 ppb plume area were assessed to describe thepostexcavation plume behavior (Table 4). Since the param-eter sets were not generated based on the inferred probabil-ity density function of the parameter, the model predictionsare not equally likely, and therefore the outcomes can onlybe summarized in terms of percentage rather than probabil-ity. Results show that only 17% of the plume realizationsmigrate past the site boundary, i.e., reaching or crossingMW-26. All plumes shrink after reaching their maximumlength, stabilize (with nonzero source contaminant load), ordisappear (with zero source contaminant load) afterward.For the 93 plumes (83% of all plumes) that remain insidethe site boundary, 28 plumes have a maximum concentra-tion that exceeded 200 ppb, but this maximum concentra-tion gradually decreased to less than 200 ppb. Therefore,65 plumes (58% of all) remain in compliance (within siteboundary and less than 200 ppb). The 17% of the plumesthat migrate beyond the site boundary are generally associ-ated with high initial concentrations (300% or 500% ofbaseline values) and low reaction rate for KVC (cases 2, 3,and 4). An increase in source release rates will also lead toan increase in plume length (and plume concentrationlevel), but the increase is slight when compared to a changein the other two parameters. Since the 17% failure ratewas largely caused by extreme parameter values that areunlikely under existing site conditions (i.e., small probabil-ity events), the probability of real failure would be muchlower than 17%, indicating that the plume will likelyremain inside the site boundary.

Approximately 36% of the plumes have a maximumVC concentration greater than 200 ppb, among which 24plumes (22% of all) persist for more than 8 years (mostlylong plumes) and 16 plumes (14% of all) persist for 2 to 6years (mostly short plumes). These 36% of plumes are

Table 3Perturbed Parameters Used in the Sensitivity Analysis

Initial Concentrations(four scenarios)

Reaction Rate Constants (seven scenarios) Source ReleaseRates (four scenarios)Scenario KPCE KTCE KDCE KVC

0.5X Baseline Baseline Baseline Baseline Baseline 0Baseline Case 1 0.5X 0.5X 0.5X 2X Baseline3X Case 2 2X 2X 2X 0.5X 3X5X Case 3 4X 4X 4X 0.25X 5X

Case 4 0.5X 0.5X 0.5X 0.5XCase 5 2X 2X 2X 2XCase 6 4X 4X 4X 4X


always associated with high initial concentrations (300%or 500% of baseline values). When the reaction rate forKVC is low (e.g., cases 2 and 3), the plume is longer and itsnoncompliance area persists for a longer time. It is the lon-ger plumes (22% of all) that are likely to cause non-compliance problems due to their persistence. When thereaction rate for KVC is high (cases 5 and 6), the plume isshorter and its noncompliance area persists for onlya shorter time. Again, since most of the plumes exceedingthe cleanup goal are the extreme cases, the probability ofreal exceedance would be much less than 36%. Therefore,under normal site conditions, the plume will remain below

200 ppb or decrease to below the 200 ppb level relativelyquickly.

Summary and DiscussionThis paper describes a modeling study to simulate the

natural attenuation of chlorinated solvents with source con-trol at a PCE dry cleaning site. The modeling results indi-cated that natural attenuation alone would achieve thecleanup goal given sufficient time. The large-scale sourceexcavation followed by natural attenuation was confirmedto be more effective in achieving the cleanup goal in

Table 4Summary of Sensitivity Analysis Results

Category Number of Plumes Percentage

Maximum plume length (based on 2 ppb contour) Reach or cross MW-26 19 17Between MW-26 and MW-27 35 31Between MW-27 and MW-21 41 37Between MW-21 and MW-19 17 15

Highest plume concentration >200 ppb1 40 36>100 and <200 ppb 24 21<100 ppb2 48 43

Persistence time for more than 200 ppb plume area >8 years3 24 602–6 years4 16 40

1Plumes with 3X and 5X initial concentrations: (1) reach or cross MW-26 for reaction rate cases 2 and 3; (2) between MW-21 and MW-27 for reaction rate cases 5 and 6.2(1) Plumes with reaction rate case 1; (2) plumes with reaction rate cases 4 and 5, and baseline and 0.5X initial concentrations.3(1) Plumes with 5X initial concentrations except reaction rate case 1; (2) plumes with 3X initial concentrations, and reaction rate cases 2 and 3.4Plumes with 3X and 5X initial concentrations and reaction rate cases 5 and 6.

PCE TCE

DCE VC

Figure 11. Modeled contaminant plumes (contours in ppb) in March 2002 after the excavation.


a shorter time frame. The limited supporting data in modeldevelopment, typical of many practical field problems,required additional effort to assess the uncertainty inmodel predictions and assess the potential for failure. Theuncertainty in key model parameters was evaluated, andthe results indicated that under existing site conditions, theplume is not likely to migrate off-site and its concentra-tions will likely remain below cleanup levels.

Due to data deficiency, the developed model and itsparameters are by no means a unique solution, and theconfidence in the model is therefore an issue of concern.Some direct evidence supporting the fate and transportmodel assumptions was lacking, including geochemical in-dicators of redox conditions, the presence of responsiblemicroorganisms, and the presence of a carbon source.Additional site investigations could have been conductedto measure these parameters and improve model devel-opment. However, the available data provided sufficientjustification for the model assumptions. One important sup-porting piece of evidence was the pervasive presence ofdaughter products, including TCE, cis-1,2-DCE, and VC.It is well known that reductive dechlorination is the mosteffective mechanism for the degradation of chlorinatedethenes. Aerobic and/or abiotic degradation is also possibleunder favorable conditions. Modeling the biodegradationas reductive dechlorination allows using existing code tofacilitate the study. It would be more appropriate to con-sider the modeling method as incorporating more than onedegradation mechanisms into a sequential first-order decayscenario. This is easy to simulate as well as realistic whendata collection is seriously constrained by limited re-sources, a typical scenario for many sites. One key objec-tive of this modeling study was to assess the consequencesof these uncertainties through varying related model pa-rameters such as source strength and degradation rates.

Source uncertainty is an issue of concern for manypractical field problems. In this study, the reduction insource release rates over time was primarily determinedfrom the observed concentration patterns and the potentialeffects of various remedial activities. However, there couldbe interpretations other than decreasing source releaserates from the observed concentration patterns. For exam-ple, it could be that biodegradation at the site had alreadylowered the PCE concentration to a much lower level thanit did for DCE and VC. A more convincing method wouldbe mass balance calculations of all chlorinated ethenes atdifferent times. But, for the study site, the plumes were notdelineated until July 1999. Before June 1998, there wereonly three monitoring wells that had been consistentlysampled. In contrast, the model was started since 1989 andhad to account for the remedial activities conducted before1999. Once again, data deficiency made it impossible forthorough mass balance analyses. The source release rateswere thus more of calibration parameters than convincibleestimations. Source uncertainty is unavoidable in practicalfield problems and needs to be thoroughly assessed when-ever possible.

Another concern is the sustainability of natural attenu-ation as a remedial strategy. Using natural attenuationmeans that the longevity of the carbon source needs to

exceed that of the chlorinated solvents in order to sustainthe necessary subsurface conditions conducive to naturalattenuation over the lifetime of the contaminant source andplume. The existence of major sewers and trenchesbeneath the site (Figure 6) provides this needed carbonsource. The flow model can also be improved if more bore-hole data, soil properties, and water budget data can becollected. The model grid can then be refined, and theuncertainty in hydraulic parameters and water balance canbe reduced. A postaudit of the model analyses would bepossible if sufficient postexcavation monitoring data wereavailable.

Nonetheless, this study illustrates an approach thatallows evaluating and modeling the risk involved withimplementing natural attenuation remedies, especially forsites with very limited supporting data. In cases where addi-tional data can be collected, this approach can be furthervalidated and corroborated.

AcknowledgmentWe thank the reviewers for their valuable inputs in

revising the original manuscript.

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Biographical SketchesMeng Ling Ph.D., P.E., is a senior project engineer at

Acton Mickelson Environmental Inc. He can be reached at 1200Smith Street, 16th Floor, Houston, TX 77002; (713) 353-8772;[email protected] S. Rifai, Ph.D., P.E., corresponding author, is an asso-

ciate professor in the Civil and Environmental EngineeringDepartment, University of Houston. She can be reached at 4800Calhoun Road, N107D, Houston, TX 77204-4003; (713) 353-8772; [email protected].


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