Modeling Natural Attenuation with Source Control at a Chlorinated Solvents Dry Cleaner Site

Transcription

1 Modeling Natural Attenuation with Source Control at a Chlorinated Solvents Dry Cleaner Site by Meng Ling and Hanadi S. Rifai Abstract A ground water flow and solute transport model was developed for a site contaminated with chlorinated solvents to simulate the site remediation activities that included natural attenuation and source control. MODFLOW was used to model the flow 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 attenuation alone and source excavation followed by natural attenuation. Results demonstrated the feasibility of site remediation for both conditions with the latter one occurring in a shorter time frame. A detailed sensitivity analysis was performed to assess the uncertainty in model predictions. The most sensitive model parameters (i.e., initial contaminant concentrations, reaction rate constants, and source release rates) were perturbed and combined to form a number of parameter sets for use in the predictive simulations. The maximum plume length, the maximum plume concentration, and the persistence time for the noncompliance plume area were assessed to identify the possible failure scenarios and the most likely future plume configuration. Results indicated that further VC plume expansion or migration beyond site boundaries is unlikely. Introduction The use of natural attenuation for remediating contaminated ground water has gained greater acceptance by both industry and the regulatory community over the past decade (American Society for Testing and Materials 1998; National Research Council 000; U.S. EPA 001). Natural attenuation refers to naturally occurring physical, chemical, and biological processes in ground water such as biodegradation, dispersion, adsorption, and volatilization that cause a reduction in contaminant mass and concentration. Natural attenuation (i.e., monitored natural attenuation), applied alone or in combination with source control, is a viable treatment technology for some sites that has many advantages when compared to conventional engineered remediation technologies (Wiedemeier et al. 1999). Currently, natural attenuation is widely used in the treatment of ground water contaminated with organic solutes, primarily fuel hydrocarbons and chlorinated solvents. To evaluate the feasibility of using natural attenuation as a remedy and to demonstrate its effectiveness, ground water modeling is often used to understand the transport regime and the governing natural attenuation processes, and to predict plume evolvement and contaminant concentration levels over time. With the development of Copyright ª 007 The Author(s) Journal compilation ª 007 National Ground Water Association. mathematical and computer models that can be used to simulate the various natural attenuation processes in recent years (see, for example, Aziz et al. 1999; Bekins et al. 1993; Borden and Bedient 1986; Borden et al. 1997; Clement 1997; Kinzelbach et al. 1991; Newell et al. 1996; Rifai et al. 1997; Semprini and McCarty 1991, 199; Waddill and Widdowson 000), the modeling of natural attenuation at field sites has been greatly facilitated. Site modeling applications in the general literature include Brauner and Widdowson (001), Clement et al. (000, 00), Lu et al. (1999), Rifai et al. (000), and Schafer (001). Application of natural attenuation models to sites, however, has been somewhat limited, especially in the case of chlorinated solvents. Most of the modeling studies presented in the literature thus far have focused on natural attenuation alone and on demonstrating model development for simulating site data. While there are a number of natural attenuation modeling case study applications for fuel hydrocarbons, almost none have been published in the refereed literature for chlorinated solvents. In one of the very few such applications, Clement et al. (000) model the natural attenuation of chlorinated ethene compounds at a military site using RT3D and generate contaminant plumes that agree reasonably well with the observed data. They estimate field-scale degradation rates via model calibration. Their estimated rates are corroborated by laboratory and field-measured rates and close agreement between predicted and fieldestimated plume mass. Clement et al. (000) studied model 108 Ground Water Monitoring & Remediation 7, no. 1/ Winter 007/pages

2 sensitivity but did not undertake predictive simulations with their calibrated model for risk management purposes. Since natural attenuation may require relatively longer time frames for achieving site cleanup goals, it is important to use models to evaluate the efficacy of the technology over the long term. It is also necessary to understand the uncertainties associated with model predictions and the likelihood of failure when natural attenuation is used as a remedy. This paper addresses the aforementioned needs and demonstrates the use of natural attenuation models for predictive simulations. A dry cleaning facility, located in northeastern Texas and contaminated with chlorinated solvents, is modeled with MODFLOW and RT3D. This paper illustrates a modeling approach with uncertainty analysis for simulating natural attenuation as a sole remedy at the site, as well as natural attenuation with source control. The uncertainty analysis undertaken in this study is not a Monte Carlo-based uncertainty analysis but rather an approach aimed at characterizing the possibility and scenarios of failure. In this case, failure is defined as the case(s) when the plume boundary (delineated to the cleanup level) migrates outside the site boundaries or the plume concentrations exceed the cleanup goal. This paper is not intended to be a modeling guidance for developing and applying probabilistic natural attenuation models; rather it is intended to illustrate an approach for evaluating and modeling the risk involved with implementing natural attenuation remedies at dry cleaner sites. Unlike the site modeled by Clement et al. (000), this study site has limited data and a short case history, which is characteristic of many sites. Additionally, the site has undergone extensive remediation, including soil excavation and soil vapor extraction (SVE). While numerous model development papers have indicated that fate and transport models are most sensitive to source definition, biodegradation, and velocity, little information is available on how to deal with these variables in the published literature. In particular, little information is found on modeling source remediation via excavation or other technologies. Hence, the issues of source decay and changing sources over time, as well as how to assess risk reduction with source cleanup, are of interest to many researchers and practitioners undertaking site remediation. This paper presents an approach to address these issues that can be applied to other field sites with limited data. Methodology Site Description The site plan is illustrated in Figure 1. The dry cleaning facility is located in the northeast corner of the site and used perchloroethene (PCE) as the dry cleaning agent from 198 to the mid-1990s. In a site investigation conducted in 199, PCE and its degradation products were found in soil samples and the shallow perched ground water near the facility. Neither PCE nor its degradation products were detected in three monitoring wells that were installed in a lower confined aquifer. No known ground water receptors exist in the vicinity of the site, except for two supply wells that are screened in the deeper aquifer and located Figure 1. Site plan and monitoring locations, and the area excavated in 001. M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

3 0 0 approximately feet downgradient of the site. In August 1996, 30 cubic yards of affected soil were excavated immediately downgradient of the dry cleaning structure. SVE was undertaken between July 1997 and June 1998 to remediate the affected soils. Also in June 1998, 13 cubic yards of affected soil were excavated from the interior of the dry cleaner building, and seven monitoring wells (MW-4 to MW-10) were installed in the perched ground water zone. Between December 1998 and July 1999, 0 additional monitoring wells (MW-11 to MW-30) were installed in the perched ground water zone to fully delineate the contaminant plume. The measured contaminants include PCE, trichloroethene (TCE), three isomers of dichloroethene (DCE), and vinyl chloride (VC). Monitoring data indicated that cis-1,-dce is the dominant DCE isomer, accounting for more than 90% of the DCE concentration. Dense nonaqueous phase liquid (DNAPL) has not been observed at the site, nor do ground water concentrations indicate the presence of DNAPL. Contaminant concentration contours for PCE, TCE, DCE (sum of all isomers), and VC from the November 1999 data are shown in Figure. The PCE, TCE, and VC plumes were delineated to their respective maximum contaminant level (MCL), i.e.,,, and ppb. The DCE plume was delineated to 0 ppb. The highest concentration observed in November 1999 was 84 ppb for PCE, 00 ppb for TCE, 1768 ppb for DCE, and 670 ppb for VC. The plume lengths were approximately 60,, 180, and 190 feet, for PCE, TCE, DCE, and VC, respectively. The cleanup standard was determined in previous site studies to be times the MCL, i.e., 0 ppb for PCE, 0 ppb for TCE, 7000 ppb for cis-1,-dce, 10,000 ppb for trans-1,-dce, 700 ppb for 1,1-DCE, and 00 ppb for VC. In order to meet these goals in a shorter time frame, more than 600 cubic yards of affected soils around the source area as shown in Figure 1 were excavated (to a depth of approximately 10 feet below ground surface [bgs]) between July to October 001 and in December 001. Wells MW-, MW-10, and MW-1 were sealed and abandoned during the excavation and were replaced with MW-31, MW-3, and MW-33, respectively. Postexcavation monitoring was performed 1 year later to evaluate whether cleanup goals had been met. Two relatively complete monitoring data sets, from November 1999 and from March 00, were used in model calibration and testing. The ground surface elevation varies from 606 feet above mean seal level (msl) at the northwest boundary to 60 feet above msl at the southeastern boundary. Most of the surface in the study area is covered with 6 inches of concrete pavement or asphalt. The general geology of the site is sandy clay from 6 inches to approximately 6 feet bgs, silty clay from feet to approximately 1 feet bgs, and an underlying layer of clay with a relatively low permeability at approximately 1 feet bgs. The shallow aquifer, composed of sandy/silty clay, varies in thickness between 6 and 1 feet throughout the site. The underlying clay layer is relatively impermeable and serves as a confining unit separating the perched ground water from the deeper aquifer. A PCE TCE DCE VC Figure. Observed contaminant plumes in November 1999 (contours in ppb drawn with inverse distance interpolation). The general ground water flow direction is indicated by the gray arrow. 110 M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

4 94 soil bulk density of 1.83 kg/l and an effective porosity of 0.34 were determined from soil testing. Ground water elevations range from 601 feet above msl at the northwest boundary to 94 feet above msl at the southeastern boundary (Figure 3). It was determined that the ground water flow field is relatively stable based on an analysis of the ground water level data. Because of the shallow depth of the perched ground water and the presence of relatively large sewers underneath the site, the ground water gradient varies between 0.01 to 0.11 ft/ft. It should be noted that the large sewers underneath the site are areas of high conductivity backfill and likely act as a source of exfiltrating water to the ground water. The hydraulic conductivity, calculated by others, ranged from to 0.3 ft/d and well yields ranged from 1. to 118 gal/d. The average annual precipitation for the study area is 3 inches. Although no geochemical parameters were collected during site investigations, the presence of PCE degradation products, i.e., TCE and high levels of cis-1,-dce and VC, indicates that PCE is being reductively dechlorinated. Model Development Based on the site hydrogeology and the saturated thickness of the perched ground water zone (3 to 6 feet), the site was modeled as a shallow unconfined aquifer (i.e., a single saturated layer). Borehole and ground water elevation data from the 7 monitoring wells (MW-4 to MW-30) were analyzed using inverse distance weighted interpolation to define the aquifer thickness. A steady-state flow field was assumed in this study because (1) observed ground water elevation contours indicate that the flow system is relatively stable and () there are no conditions within the model domain that have influenced or are influencing the ground water flow, causing transient conditions (e.g., pumping wells). Hypothetical injection wells were used to simulate the historical release of PCE. Injected concentrations were varied with time to simulate the effects of the remedial activities (e.g., SVE) on source concentrations. A release date of approximately halfway through the operating life of the dry cleaning facility was assumed. Since the facility began operation in 198 and closed in the mid-1990s, the assumption means the contaminant released came into contact with the ground water in This amounted to approximately 10 years for the plume to develop into the distributions observed on November 1999 (Figure ). Retardation caused by sorption was modeled using linear sorption isotherms. However, since the level of organic carbon in the saturated zone was unknown, sorption coefficients were estimated through model calibration. Due to the lack of information about the released mass of PCE and biodegradation parameters such as reaction rate constants, these variables were also treated as calibration parameters in the transport simulations. In this study, MODFLOW (Harbaugh and McDonald 1996) was used to generate the flow field, and RT3D (Clement 1997) was used to model the fate and transport of the chlorinated ethenes. RT3D is a modular simulator for predicting multispecies bioreactive transport that is based on the EPA model MT3D (Zheng 1990). Reductive dechlorination of the cholinated ethenes (see, for example, Bouwer 199; Bradley and Chapelle 1996; Davis and Carpenter 1990; Vogel and McCarty 198, 1987; Wiedemeier et al. 1999) was modeled using first-order kinetics. The first-order kinetic model was selected as it is widely used in natural attenuation modeling applications and is easy to simulate and interpret. In addition, the large amount of literature data for laboratory and field-scale first-order rate constants (e.g., Aronson and Howard 1997; Suarez and Rifai 1999) facilitates the estimation of decay rates. Model Domain and Boundary Conditions The model domain encompasses the entire site as shown in Figure 4. The northwest boundary corresponds to the residential area and the southeast boundary corresponds Figure 3. Observed ground water elevations (feet) from November 1999 data. M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

5 Reaction Zone II INJ #3 INJ #4 INJ # Reaction Zone I INJ #1 Figure 4. Model grid. The ground water flow direction is indicated by the gray arrow. to a highway. A single-layer uniform grid of 4 rows and 38 columns was superimposed over the model domain. The size of each grid cell was 1 by 1 feet. The northwest boundary of the model domain (i.e., the right-hand border of the model grid) was defined as a constant head boundary with heads ranging from to 98. feet, while those at the southeast boundary (i.e., the left-hand border of the model grid) were set to a constant level of 91. feet. The northeast (top border) and southwest (bottom border) boundaries of the model grid were treated as no-flow boundaries. For the transport model, the upgradient boundary, and the upper and the lower boundaries of the model grid were defined as no mass flux boundaries. Contaminants, however, were allowed to move freely out of the site through the downgradient boundary of the model grid. Initial Conditions, Sources, and Reaction Zones It was assumed that no PCE or other chlorinated ethenes were present in the aquifer prior to Since the exact sources of PCE leakage were not known, the potential source locations were represented as follows. First, because the dry cleaning operations caused the release, the location of the dry cleaning facility (the area close to wells MW-4 and MW-) was considered within the source zone. This assumption was supported by the presence of PCE and its degradation products in samples taken beneath and around the dry cleaning facility in 199. Second, the highconcentration areas within the PCE and TCE plumes, i.e., the areas near wells MW-, MW-8, and MW-1, were also considered to be part of the source zone. Third, the area near MW-10 was considered to be within the source zone because of the high DCE and VC concentrations (more than 0 ppb for DCE and more than 00 ppb for VC) observed in this area. Even though these assumed sources are not in proximity to the dry cleaning facility, they could have been formed by the migration of PCE along the leaking sewers beneath the facility. Physically, these sources may be in the form of DNAPL initially and/or residuals adsorbed onto the soil matrix. As mentioned previously, the PCE releases were modeled using hypothetical injection wells placed within the source zone locations discussed previously (see INJ #1, INJ #, INJ #3, and INJ #4 shown in Figure 4). The flow rates for the source injection wells were maintained at very low levels (e.g., 1 to gal/d) to minimize their influence on the flow field. The fact that PCE concentrations were much lower than those for DCE and VC (Figure ) indicated that PCE mass release rates from source areas were higher initially and then decreased over time. To model this timevariable source, multiple stress periods were used during which the PCE mass release rates were varied by changing the release concentrations at the injection wells. The release concentrations for different stress periods were determined through model calibration. One pattern can be observed by studying the plumes shown in Figure : the reaction rates for PCE and TCE appear to be faster than those for DCE and VC as the DCE and VC plumes are much larger in extent than those for PCE and DCE. Also, the observed concentrations of DCE and VC were much higher than those for PCE and TCE. In order to simulate these variable reaction rates, the model domain was divided into two reaction zones (zone I and zone II as shown in Figure 4). Zone I is approximately enclosed by the 00 ppb DCE contour (Figure ), and zone II contains all other areas. Direct evidence for the above zonation could not be obtained due to the lack of data. However, justifications can still be made from existing data and current understanding of related mechanisms. For example, the level of electron acceptors such as dissolved oxygen is generally higher where plume concentrations are lower; thus, DCE and VC could have a higher reaction rates since they can be degraded aerobically. This is consistent with a well-recognized mechanism that biodegradation 11 M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

6 rates differ significantly between highly contaminated areas and plume edges where concentrations are low (Wiedemeier et al. 1999). By using two reaction zones, it was possible to generate the observed high concentrations in the center of the DCE and VC plumes while simulating lower concentrations elsewhere. Since only VC and DCE might have concentrations that remain above the cleanup goals of 00 and 7000 ppb, respectively, the ability to replicate observed high concentrations in the model was key to the natural attenuation assessment. Model Calibration and Prediction The objective of the flow model calibration was to reproduce the direction and rate of ground water flow observed in November 1999 (Figure 3). The calibration process was performed by trial and error after an initial estimation using the automated parameter estimation code PEST (Doherty 000). Recharge rates and hydraulic conductivities were varied throughout the model grid according to surface and subsurface features to match the observed ground water elevation contours. The flow calibration error was calculated using the mean absolute error (MAE): MAE 1 n X n i1 jh c h o j i (1) where n is the number of observations, h c is the computed value, and h o is the observed value. The MAE between the computed and the observed heads should be less than 10% of the overall head change across the site. In addition, the flow balance of the calibrated model was examined to assess the validity of the flow model. The transport part of the model was calibrated after the flow field was reasonably reproduced. The contaminant concentrations observed in November 1999 were used to calibrate the transport model. Assuming the leak began in November 1989, three stress periods were used to represent the 0-year period from 1989 to 009 (Figure ). The first stress period (46 d) models the time period from the beginning of the leak to July 1996, just before the beginning of the first remedial action. The second stress period is 670 d long, from August 1996 to June 1998, and reflects a period during which two remedial actions were taken. The third stress period starts from July 1998 after 13 cubic yards of soil were excavated from the dry cleaning facility and continues for another 416 d into the future until the year 009. The plumes generated at 36 d (or 10 years) were used for comparison with the observed data in November The transport calibration was conducted via a trial-and-error process after selecting a set of initial parameters from literature data. The calibrated model was then used to predict plume status (1) under solely natural attenuation conditions (assuming that the excavation in the second half of 001 was not conducted) and () by natural attenuation after source excavation. The first predictive simulation was used as a worst-case scenario to determine if natural attenuation alone would be adequate to confine the plume and reduce contaminant concentrations to below the cleanup levels. The second predictive simulation evaluated the effectiveness and strength of the actual remedies conducted at the site. To assess the influence of limited supporting data on the developed model, an uncertainty analysis was finally conducted to identify conditions that would cause noncompliance (plume expansion or migration beyond site boundary). The detailed setup and results of the calibrated model, the two predictive simulations, and the uncertainty analysis are discussed in the following section. Model Details and Results Calibrated Model Because most of the site surface is covered with concrete or asphalt, a recharge rate of ft/d (% of the average annual precipitation) was applied over most of the flow field. A higher recharge rate of ft/d (equivalent to % of the average annual precipitation) was assigned to the northwest corner of the model grid (Figure 6) to simulate a ground water mound located near one of the residential areas. Such a high recharge rate was likely due to the percolation of irrigation water applied to this area and Natural Attenuation Only Nov Aug Jul (30 cu. yd soil excavated) (13 cu. yd soil excavated) Stress period 1 (46 days) Stress period [Soil vapor extraction] (670 days) Stress period 3 (416 days) Nov. 009 Sewers and trenches Recharge = t/day Release Date Nov (36 days from start) Natural Attenuation with Source Control One stress period Oct. 001 Mar. 004 Jul. to Oct. 001 (900 days) Soil excavation (,600 cu yd) Post-excavation simulation started Mar. 00 Predictive Model calibration simulation (180 days from start) Figure. Timeline for model development. Recharge = t/day Recharge = ft/day Figure 6. Locations of recharge zones and sewers and trenches. M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

7 should not be construed as being equivalent to % of the precipitation becoming recharge. Additionally, a recharge rate of ft/d (0% of the average annual precipitation) was assigned around the dry cleaning facility (MW- and MW-7) to create the ground water mound observed in that area (Figure 3). The hydraulic conductivity ranged from to 0.3 ft/d in most of the model area and from 0.1 to 1. ft/d in areas where major sewers and trenches are located (Figure 6). The injection rates for the four injection wells were fixed at 1.0, 1.0, 1.0, and 0. gal/d for INJ #1, INJ #, INJ #3, and INJ #4, respectively. The generated ground water contours are presented in Figure 7. Although the generated contours do not fully replicate the observed ones, they mimic the major features of the flow domain. The difference in the downgradient part of the model grid can be attributed to the lack of observation points in the area. The flow calibration error (MAE) is 0.41 feet, which is about % of the overall head change (8 feet) across the site. Since the simulated ground water flow field mimics the observed flow field and the K values are in general within the measured range, the ground water velocities are considered reasonably represented. A flow balance indicated the total flux into the model is approximately 38 ft 3 /d, of which 9% comes from the constant head boundaries and 90% comes from recharge (1% from the injection wells). That the majority of the inflow comes from recharge is consistent with the perched condition of this water bearing zone. Therefore, the flow calibration results were considered acceptable. The model transport parameters are listed in Table 1. The resultant grid Peclet number is 1. (Pe ¼ x/a L ¼ 1/1 ¼ 1.), which is less than, indicating that the grid spacing is small enough to eliminate artificial oscillation in the finite-difference method. The retardation coefficients corresponding 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 constants were bounded by the literature values given in Suarez and Rifai (1999). The major difference in the reaction rate constants for the two reaction zones was the increased reaction rates for DCE and VC outside reaction zone I. The calibrated PCE release concentrations at the four injection Figure 7. Modeled flow field (contours in feet) calibrated to November 1999 data Table 1 Transport Model Parameters for Simulating Natural Attenuation Alone Parameter Longitudinal dispersivity (a L ) Ratio of transverse to longitudinal dispersivity Porosity Soil bulk density K d for PCE K d for TCE K d for DCE K d for VC Y TCE/PCE (yield coefficient) Y DCE/TCE Y VC/DCE K PCE (zone I) K TCE (zone I) K DCE (zone I) K VC (zone I) K PCE (zone II) K TCE (zone II) K DCE (zone II) K VC (zone II) Value 1 feet (calibrated) 0.3 (calibrated) 0.34 (measured) 1.83 kg/l (measured) 0.06 L/kg (calibrated) L/kg (calibrated) L/kg (calibrated) 0.00 L/kg (calibrated) 0.79 (stoichiometric) (stoichiometric) (stoichiometric) 0.007/d (calibrated) 0.009/d (calibrated) /d (calibrated) 0.000/d (calibrated) 0.00/d (calibrated) 0.006/d (calibrated) 0.001/d (calibrated) 0.006/d (calibrated) wells for the three stress periods are given in Table. It can be seen from Table that high concentrations were applied to create high mass release rates at the beginning of the simulation, while lower concentrations were used later to reflect the reduction in source strength due to active remediation and source depletion. The calibrated plumes are shown in Figure 8. The elevated concentrations observed in the center of the DCE and VC plumes were reproduced, and the modeled plumes agree with the observed plumes reasonably well. A sensitivity analysis was performed to investigate the influence of key model parameters on the calibrated model. Three key model parameters, reaction rates, sorption coefficients, and source release rates, were examined. Each parameter was perturbed % above and below the calibrated values (i.e., the baseline), and the resulting plumes at 36 d (November 1999) were compared to the calibrated plumes. For reaction rates, the calibrated rate constants for all contaminants (Table 1) were perturbed simultaneously at every model cell throughout the model domain. For source strength, the calibrated source release concentrations (Table ) were perturbed simultaneously for all injection wells and for all stress periods. The results are shown in Figures 9A and 9B, illustrated using only the VC plume since VC is the constituent with the highest associated risk at the site. Figure 9A indicates that the most influential model parameter, in terms of the extent of the VC plume represented by the ppb contour, is reaction rate. However, when results are evaluated in terms of the highconcentration area represented by the ppb contour shown in Figture 9B, source strength appears to be more important. Sorption coefficients had little effect on model results in both cases. 114 M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

8 0 Table Stress Periods and Source Release Rates for Simulating Natural Attenuation Alone Injection Well and Injection Rate (gal/d) Stress Period 1 (0 46 d, November 1989 to July 1996) Source PCE Release Concentration (ppb) Stress Period ( d, August 1996 to June 1998) Stress Period 3 ( d, July 1998 to October 009) INJ #1 (1.0),000 (0. g/d) 6000 (0.0 g/d) 3000 (0.01 g/d) INJ # (1.0) 30,000 (0.1 g/d) 1,000 (0.06 g/d) 70 (0.03 g/d) INJ #3 (1.0) 8000 (0.03 g/d) 6000 (0.0 g/d) 6000 (0.0 g/d) INJ #4 (0.),000 1 (0.4 g/d),000 1 (0.4 g/d) 000 (0.008 g/d) 1 This high concentration is used with a low injection rate to produce the desired mass release rate. No validation/verification of the calibrated model was possible due to the lack of data in the years prior to the large-scale excavation. Although there may be nonuniqueness problems in the aforementioned model calibration, the previous results nonetheless simulated a plausible scenario for the site. Potential nonuniqueness problems were one of the driving forces for the assessment of uncertainty in model predictions discussed later in this paper. Predictive Simulation with Natural Attenuation Alone The calibrated model was used to predict plume status into 009 or 10 years into the future. This was accomplished mostly to evaluate plume behavior if natural attenuation were the sole remedy (it also assumes that the excavation in the second half of 001 was not conducted). The resulting predictive simulation was used as a worstcase scenario to determine if natural attenuation alone would be adequate to confine the plume and reduce contaminant concentrations to below the cleanup levels. Results indicated that the contaminant plumes stabilized around day 47 (or November 004). The predicted plumes are shown in Figure 10. The VC plume (delineated to its MCL or ppb) extends to well MW-7, well within the site boundary. Also, the VC concentrations are predicated to fall below 00 ppb at every location across the site (VC is the only contaminant with concentrations above its cleanup level). However, the model indicates that prior to November 004, an area with VC concentrations higher than 00 ppb was present around MW- and MW-9. This result shows that natural attenuation alone could be used to PCE TCE DCE VC Figure 8. Modeled contaminant plumes (contours in ppb) calibrated to November 1999 data. M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

9 A Reaction Rates B Reaction Rates Baseline baseline 1 baseline Baseline 1 baseline baseline Sorption Coefficients Sorption Coefficients 1 baseline Baseline baseline 1 baseline Baseline baseline Source Strength Source Strength Baseline baseline Baseline baseline 1 baseline 1% baseline Figure 9. (A) Results of sensitivity analysis for the calibrated model (illustrated using the ppb VC contour); (B) results of sensitivity analysis for the calibrated model (illustrated using the ppb VC contour). achieve cleanup but would require at least years to meet the remediation goals. Source removal such as the 001 excavation would be required to achieve compliance in a shorter time frame. Postexcavation Predictive Simulations As mentioned previously, during the period from July to December 001, more than 600 cubic yards of affected soils around the source area shown in Figure 1 were excavated. Such a large-scale (relative to the scale of the site) excavation helped reduce the contaminant concentrations to below the cleanup levels almost immediately. To simulate the effects of this remediation on the fate and transport of the contaminants and predict the future plume behavior, a simulation that accounted for the excavation was undertaken. A 900-day stress period was selected, starting around October 001 (the excavation was assumed to have been completed by September 001) and continuing for. years into the future until about March 004 (Figure ). The 900-day postexcavation simulation uses downwardly adjusted concentrations from the preexcavation simulations as initial concentrations. The starting model concentrations throughout the plume were reduced to % of the generated concentrations obtained by running the previously calibrated model to October 001. In addition, initial concentrations at some model cells were manually adjusted so that the resulting predicted concentrations in March 00 agreed with their observed counterparts. To model the source reduction caused by the excavation, two scenarios (scenario I and scenario II) were considered. In scenario I, the source injection concentrations were set to zero for all injection wells; i.e., the source was completely turned off. In scenario II, the source injection concentrations were set to 0% of their respective preexcavation injection concentrations (Table, stress period 3) to simulate an 80% reduction in source strength. The reaction rate constants for zone I (the source zone beneath the dry cleaner) were also reduced for both scenarios: K PCE 0.00/d, 116 M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

10 PCE TCE DCE VC Figure 10. Contaminant plumes (contours in ppb) predicted for November 004 under natural attenuation only conditions. K TCE 0.00/d, K DCE 0.000/d, and K VC /d. This adjustment can be explained by the loss of soil microbes, introduction of oxygen, and reduction in source-derived carbon as a result of the excavation. These rate constants are still within the range of reported values in Suarez and Rifai (1999) but are closer to the lower end of the range. In both scenarios, reaction rate constants for zone II and the remaining transport parameters were unchanged. Although the resulting modeled plumes in March 00 were larger in extent and higher in concentration than the observed ones, they provided a conservative representation of the postexcavation plume condition. The model established previously was used in the predictive simulation for both scenarios to confirm the attainment of cleanup goals. Results for both scenarios indicated that the cleanup goals would be achieved quickly after the excavation and the plume will not migrate further downgradient. The generated plumes for scenario II (the 80% source reduction case) on March 00 are shown in Figure 11. Generated plumes at later points in time also indicated that there would be no increase in the concentration levels of the plumes. In scenario I, the plumes will eventually diminish to below detection levels as no further PCE release is assumed. In scenario II, the plumes will eventually reach steady state and exhibit lower concentrations due to the assumed continuous PCE release rates. Evaluation of Uncertainty in Model Predictions As 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-calibrated model with a different set of variables. This nonuniqueness, coupled with the data deficiencies described throughout the paper and emphasized in this section, has a potential impact on the simulated rates of attenuation. For example, the actual contaminant release history is unclear and the total amount of PCE discharged into the subsurface is unknown. Also unknown is the mass of the remaining chemicals in the source zone. Additionally, there are no field or lab-scale studies to provide corroborating information regarding site-specific reaction rate constants or the sustainability of those rates into the future. The flow field in Figure 3 may reflect potential artifacts of the interpolation scheme used to contour the data. Some of the isolated peaks and valleys along the perimeter of the domain may actually represent broader regional trends in the piezometric surface rather than the interpretation used in this research. These data deficiencies, while typical for many sites, lead to uncertainty in model predictions. Furthermore, monitoring results from September 00 at the study site showed that at one location, MW-3, the VC concentration was higher than 00 ppb in contrast with previous sampling results that were below 00 ppb. Of concern, then, is the issue of continued compliance and determining the set of conditions that would trigger plume expansion and/or noncompliance. An uncertainty analysis of the model predictions would help identify conditions that would cause noncompliance. A commonly used method for studying uncertainty and its impacts on model results is Monte Carlo analysis (e.g., McNab and Dooher 1998; Meyer et al. 1994). In Monte Carlo analysis, empirical probability distributions M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

11 for governing parameters (e.g., hydraulic conductivities) are provided as input to the model. The uncertainty or probabilistic distribution of the model predictions can then be quantified. However, as is typical of small sites with limited data and site characterization, probabilistic uncertainty analysis is not feasible. A deterministic sensitivity analysis is used instead to evaluate how applicable and reliable the model predictions are with respect to plume expansion or migration. A preliminary sensitivity analysis of the postexcavation model indicated that the initial concentrations, reaction rate constants, hydraulic conductivities, and source release rates (determined through source release concentrations) were the most sensitive parameters affecting model results. The first two parameters were perturbed based on their baseline values (i.e., the calibrated values in the postexcavation model). For source release rates, the baseline values were taken from scenario II of the postexcavation model. Although hydraulic conductivity is a sensitive factor that affects model predictions, it was not used in the analysis for two reasons. First, changes of hydraulic conductivities within a small range (less than or equal to two times) did not affect the size of the plume sufficiently so that a failure case would occur. Second, for significant variations in conductivity values (orders of magnitude), the scenario was considered unreasonable based on site data. Not incorporating hydraulic parameters in the uncertainty analysis also reduced the number of model runs significantly so results can be processed with less effort. When perturbing initial concentrations and reaction rates, the values for these parameters were either increased or decreased by a certain percentage at every model cell throughout the model domain. For initial concentrations, the baseline values, 300%, and 0% of the baseline values were used. No reduced initial concentrations were considered because they are unlikely to create elevated concentrations. For reaction rate constants, a total of seven scenarios (baseline and cases 1 through 6) were considered (Table 3). In cases 1 to 3, K VC was reduced (or increased) when the other rate constants were increased (or reduced). In cases 4 to 6, all the rate constants were reduced (or increased) using the same percentage. The resulting scenarios were used to assess the VC plume behavior under two different sets of conditions. For the source release rates, zero release rate (i.e., zero release concentration), the baseline values, 300%, and 0% of the baseline values were considered. Complete combinations of these perturbed parameter values resulted in a total of 11 parameter sets (see Table 3). Therefore, 11 simulations were conducted. Only the VC plume was assessed, as it poses the highest risk and was the only species exceeding the cleanup levels by then. The maximum plume length, the maximum concentration within plume, and the persistence time for the more than 00 ppb plume area were assessed to describe the postexcavation plume behavior (Table 4). Since the parameter sets were not generated based on the inferred probability density function of the parameter, the model predictions are not equally likely, and therefore the outcomes can only be summarized in terms of percentage rather than probability. Results show that only 17% of the plume realizations migrate past the site boundary, i.e., reaching or crossing MW-6. All plumes shrink after reaching their maximum length, stabilize (with nonzero source contaminant load), or disappear (with zero source contaminant load) afterward. For the 93 plumes (83% of all plumes) that remain inside the site boundary, 8 plumes have a maximum concentration that exceeded 00 ppb, but this maximum concentration gradually decreased to less than 00 ppb. Therefore, 6 plumes (8% of all) remain in compliance (within site boundary and less than 00 ppb). The 17% of the plumes that migrate beyond the site boundary are generally associated with high initial concentrations (300% or 0% of baseline values) and low reaction rate for K VC (cases, 3, and 4). An increase in source release rates will also lead to an increase in plume length (and plume concentration level), but the increase is slight when compared to a change in the other two parameters. Since the 17% failure rate was largely caused by extreme parameter values that are unlikely under existing site conditions (i.e., small probability events), the probability of real failure would be much lower than 17%, indicating that the plume will likely remain inside the site boundary. Approximately 36% of the plumes have a maximum VC concentration greater than 00 ppb, among which 4 plumes (% of all) persist for more than 8 years (mostly long plumes) and 16 plumes (14% of all) persist for to 6 years (mostly short plumes). These 36% of plumes are Initial Concentrations (four scenarios) Table 3 Perturbed Parameters Used in the Sensitivity Analysis Reaction Rate Constants (seven scenarios) Scenario K PCE K TCE K DCE K VC Source Release Rates (four scenarios) 0.X Baseline Baseline Baseline Baseline Baseline 0 Baseline Case 1 0.X 0.X 0.X X Baseline 3X Case X X X 0.X 3X X Case 3 4X 4X 4X 0.X X Case 4 0.X 0.X 0.X 0.X Case X X X X Case 6 4X 4X 4X 4X 118 M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

12 Table 4 Summary of Sensitivity Analysis Results Category Number of Plumes Percentage Maximum plume length (based on ppb contour) Reach or cross MW Between MW-6 and MW Between MW-7 and MW Between MW-1 and MW Highest plume concentration >00 ppb > and <00 ppb 4 1 < ppb Persistence time for more than 00 ppb plume area >8 years years Plumes with 3X and X initial concentrations: (1) reach or cross MW-6 for reaction rate cases and 3; () between MW-1 and MW-7 for reaction rate cases and 6. (1) Plumes with reaction rate case 1; () plumes with reaction rate cases 4 and, and baseline and 0.X initial concentrations. 3 (1) Plumes with X initial concentrations except reaction rate case 1; () plumes with 3X initial concentrations, and reaction rate cases and 3. 4 Plumes with 3X and X initial concentrations and reaction rate cases and 6. always associated with high initial concentrations (300% or 0% of baseline values). When the reaction rate for K VC is low (e.g., cases and 3), the plume is longer and its noncompliance area persists for a longer time. It is the longer plumes (% of all) that are likely to cause noncompliance problems due to their persistence. When the reaction rate for K VC is high (cases and 6), the plume is shorter and its noncompliance area persists for only a shorter time. Again, since most of the plumes exceeding the cleanup goal are the extreme cases, the probability of real exceedance would be much less than 36%. Therefore, under normal site conditions, the plume will remain below 00 ppb or decrease to below the 00 ppb level relatively quickly. Summary and Discussion This paper describes a modeling study to simulate the natural attenuation of chlorinated solvents with source control at a PCE dry cleaning site. The modeling results indicated that natural attenuation alone would achieve the cleanup goal given sufficient time. The large-scale source excavation followed by natural attenuation was confirmed to be more effective in achieving the cleanup goal in PCE TCE DCE VC Figure 11. Modeled contaminant plumes (contours in ppb) in March 00 after the excavation. M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1:

13 a shorter time frame. The limited supporting data in model development, typical of many practical field problems, required additional effort to assess the uncertainty in model predictions and assess the potential for failure. The uncertainty in key model parameters was evaluated, and the results indicated that under existing site conditions, the plume is not likely to migrate off-site and its concentrations will likely remain below cleanup levels. Due to data deficiency, the developed model and its parameters are by no means a unique solution, and the confidence in the model is therefore an issue of concern. Some direct evidence supporting the fate and transport model assumptions was lacking, including geochemical indicators of redox conditions, the presence of responsible microorganisms, and the presence of a carbon source. Additional site investigations could have been conducted to measure these parameters and improve model development. However, the available data provided sufficient justification for the model assumptions. One important supporting piece of evidence was the pervasive presence of daughter products, including TCE, cis-1,-dce, and VC. It is well known that reductive dechlorination is the most effective mechanism for the degradation of chlorinated ethenes. Aerobic and/or abiotic degradation is also possible under favorable conditions. Modeling the biodegradation as reductive dechlorination allows using existing code to facilitate the study. It would be more appropriate to consider the modeling method as incorporating more than one degradation mechanisms into a sequential first-order decay scenario. This is easy to simulate as well as realistic when data collection is seriously constrained by limited resources, a typical scenario for many sites. One key objective of this modeling study was to assess the consequences of these uncertainties through varying related model parameters such as source strength and degradation rates. Source uncertainty is an issue of concern for many practical field problems. In this study, the reduction in source release rates over time was primarily determined from the observed concentration patterns and the potential effects of various remedial activities. However, there could be interpretations other than decreasing source release rates from the observed concentration patterns. For example, it could be that biodegradation at the site had already lowered the PCE concentration to a much lower level than it did for DCE and VC. A more convincing method would be mass balance calculations of all chlorinated ethenes at different times. But, for the study site, the plumes were not delineated until July Before June 1998, there were only three monitoring wells that had been consistently sampled. In contrast, the model was started since 1989 and had to account for the remedial activities conducted before Once again, data deficiency made it impossible for thorough mass balance analyses. The source release rates were thus more of calibration parameters than convincible estimations. Source uncertainty is unavoidable in practical field problems and needs to be thoroughly assessed whenever possible. Another concern is the sustainability of natural attenuation as a remedial strategy. Using natural attenuation means that the longevity of the carbon source needs to exceed that of the chlorinated solvents in order to sustain the necessary subsurface conditions conducive to natural attenuation over the lifetime of the contaminant source and plume. The existence of major sewers and trenches beneath the site (Figure 6) provides this needed carbon source. The flow model can also be improved if more borehole data, soil properties, and water budget data can be collected. The model grid can then be refined, and the uncertainty in hydraulic parameters and water balance can be reduced. A postaudit of the model analyses would be possible if sufficient postexcavation monitoring data were available. Nonetheless, this study illustrates an approach that allows evaluating and modeling the risk involved with implementing natural attenuation remedies, especially for sites with very limited supporting data. In cases where additional data can be collected, this approach can be further validated and corroborated. Acknowledgment We thank the reviewers for their valuable inputs in revising the original manuscript. References American Society for Testing and Materials (ASTM) Standard Guide for Remediation of Ground Water by Natural Attenuation at Petroleum Release Sites. Philadelphia, Pennsylvania: ASTM. Aronson, D., and P.H. Howard Anaerobic biodegradation of organic chemicals in groundwater: A summary of field and laboratory studies. SRC TR-97-03F. Washington, D.C.: American Petroleum Institute. Aziz, C.E., C.J. Newell, J.R. Gonzales, P. Haas, T.P. Clement, and Y. Sun BIOCHLOR Natural Attenuation Decision Support System User s Manual. San Antonio, Texas: Air Force Center for Environmental Excellence, Brooks AFB. Bekins, B.A., E.M. Godsy, and D.F. Goerlitz Modeling steady-state methanogenic degradation of phenols in groundwater. Journal of Contaminant Hydrology 14, no. 3 4: Borden, R.C., and P.B. Bedient Transport of dissolved hydrocarbons influenced by oxygen limited biodegradation Theoretical development. Water Resources Research, no. 13: Borden, R.C., R.A. Daniel, L.E. LeBrun IV, and C.W. Davis Intrinsic biodegradation of MTBE and BTEX in a gasoline-contaminated aquifer. Water Resources Research 33, no. : Bouwer, E.J Bioremediation of subsurface contaminants. In Environmental Microbiology, ed. R. Mitchell, New York: Wiley-Liss. Bradley, P.M., and F.H. Chapelle Anaerobic mineralization of vinyl chloride in Fe(III)-reducing, aquifer sediments. Environmental Science and Technology 30, no. 6: Brauner, J.S., and M.A. Widdowson Numerical simulation of a natural attenuation experiment with a petroleum hydrocarbon NAPL source. Ground Water 39, no. 6: Clement, T.P RT3D A modular computer code for simulating reactive multispecies transport in 3-dimensional groundwater aquifers. PNNL-SA Richland, Washington: Pacific Northwest National Laboratory. 10 M. Ling and H.S. Rifai/ Ground Water Monitoring & Remediation 7, no. 1: