A controlled field experiment on groundwater contamination by a multicomponent DNAPL: dissolved-plume retardation

Journal of Contaminant Hydrology 66 (2003) 117 – 146 www.elsevier.com/locate/jconhyd

A controlled field experiment on groundwater contamination by a multicomponent DNAPL: dissolved-plume retardation Michael O. Rivett a,*, Richelle M. Allen-King b a

Earth Sciences, School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK b Department of Geology, Washington State University, Pullman, WA 99164-2812, USA Received 13 April 2001; received in revised form 11 December 2002; accepted 13 December 2002

Abstract A natural gradient emplaced-source (ES) controlled field experiment was conducted at the Borden aquifer research site, Ontario, to study the transport of dissolved plumes emanating from residual dense nonaqueous-phase liquid (DNAPL) source zones. The specific objective of the work presented here is to determine the effects of solute and co-solute concentrations on sorption and retardation of dissolved chlorinated solvent-contaminant plumes. The ES field experiment comprised a controlled emplacement of a residual multicomponent DNAPL below the groundwater table and intensive monitoring of dissolved-phase plumes of trichloromethane (TCM), trichloroethylene (TCE), and perchloroethylene (PCE) plumes continuously generated in the aquifer down gradient from gradual source dissolution. Estimates of plume retardation (and dispersion) were obtained from 3-D numerical simulations that incorporated transient source input and flow regimes monitored during the test. PCE, the most retarded solute, surprisingly exhibited a retardation factor f 3 times lower than observed in a previous Borden tracer test by Mackay et al. [Water Resour. Res. 22 (1986) 2017] conducted f 150 m away. Also, an absence of temporal trend in PCE retardation contrasted with the previous Borden test. Supporting laboratory studies on ES site core indicated that sorption was nonlinear and competitive, i.e. reduced sorption of PCE was observed in the presence of TCE. Consideration of the effects of relatively high co-solute (TCE) concentration (competitive sorption) in addition to PCE concentration effects (nonlinear sorption) was necessary to yield laboratory-based PCE retardation estimates consistent with the field plume values. Concentration- and co-solute-based sorption and retardation analysis was also applied to the previous low-concentration pulse injection test of Mackay et al. [Water Resour. Res. 22 (1986) 2017] and was able to successfully predict the temporal field retardation trends observed in that test. While it is acknowledged that other ‘‘nonideal

* Corresponding author. Fax: +44-121-414-4942. E-mail addresses: [email protected] (M.O. Rivett), [email protected] (R.M. Allen-King). 0169-7722/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0169-7722(03)00006-8

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transport’’ effects may contribute, our analysis predicts differences in the PCE retardation magnitude and trend between the two experiments that are consistent with field observations based on the marked solute concentration differences that resulted from contrasting source conditions. Solute and co-solute concentration effects have heretofore received little attention, but may have wide significance in aquifers contaminated by point-source pollutants because many plumes contain mixed solutes over wide concentration ranges in strata that are likely subject to nonlinear sorption. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Sorption; Nonlinear sorption; Competitive sorption; Perchloroethylene, PCE; Trichloroethylene, TCE; Retardation

1. Introduction Dissolved chlorinated solvents, such as perchloroethylene and trichloroethylene (PCE and TCE), are among the most frequently detected groundwater contaminants (Bartow and Davenport, 1995; NRC, 1997) and thus pose an important threat to drinking water supply. Chlorinated solvents have historically been used and inadvertently released to the subsurface as dense nonaqueous-phase liquids (DNAPLs). Dissolved-phase plumes emanating from DNAPL sources may contain aqueous concentrations in excess of 1 g/l (e.g. TCE solubility is 1.4 g/l; Broholm and Feenstra, 1995), toward a million-fold greater concentration than some TCE drinking water standards, e.g. 1 Ag/l for Denmark and 5 Ag/l for USA (Danish Environmental Protection Agency, 1998; US EPA, 2000). Further, the majority of solvent plumes encountered contain multiple solutes because contaminant source areas often comprise mixtures of organic chemicals (Jackson and Dwarakanath, 1999), and/or dechlorination reactions occur within the groundwater (Wiedemeier et al., 1998). Clearly, for such chemicals, it is necessary to understand the potential effects of a very wide aqueous concentration range and the influences of co-occurring solutes on reaction processes to make accurate transport predictions. Although concentration-independent retardation is commonly presumed in practice, simulation studies have shown the potential importance of concentration-dependent retardation on dissolved-phase transport (e.g. Abulaban and Nieber, 2000; Young and Ball, 1999). Concentration-dependent retardation transport can arise from nonlinear sorption. Further, competition between similar solutes for sorption ‘‘sites’’ is predicted when isotherms exhibit a concave shape (often characterized by Freundlich isotherm slope less than unity). Concentration-dependent and competitive (co-solute concentrationdependent) chlorinated solvent retardation has been demonstrated at the bench scale by McGinley et al. (1996). The subsurface soil and solvent compounds used in the experiments exhibited nonlinear sorption isotherms with Freundlich exponent < 1 (McGinley et al., 1996). Important to the general case, nonlinear sorption isotherms have been documented for a number of other subsurface soils and aquifer materials (e.g. Allen-King et al., 1996; Xia and Ball, 1999; Kleineidam et al., 1999; Karapanagioti et al., 2000; Huang et al., 1997), hence concentration-dependent retardation should be anticipated for solvents in many groundwater systems. The overarching goal of the work presented here is to evaluate concentration and cosolute effects on dissolved solute transport processes at the field scale. The specific

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objective of this work is to determine whether solute and co-solute concentration effects can be significant in controlling sorption and retardation of chlorinated solvent contaminants. Our approach was to conduct a field experiment at the well-studied Canadian Forces Base (CFB) Borden field research site, Canada. For this (and other) purpose(s), an emplaced-source of multicomponent DNAPL (TCE, PCE, and TCM, trichloromethane, also known as chloroform) was intentionally installed below the water table at the Borden site (Rivett et al., 2001). The 475-day-long emplaced-source (ES) field experiment allowed the study of continuously generated dissolved plumes from a DNAPL source area under natural gradient conditions. DNAPL presence created solute plumes that were near the effective aqueous solubilities for the test chemicals, and concentrations of interest monitored within the plumes extended from 1 to greater than 600,000 Ag/l. Biodegradation or chemical reaction (transformation) of contaminants was not evident in the plumes, nor expected under the site conditions (Rivett et al., 2001; Butler and Barker, 1996), and hence permitted focused study of plume retardation (sorption) and dispersion behavior and estimation of associated transport parameters via 3-D numerical simulation. Laboratory batch studies were also undertaken to determine nonlinear and competitive sorption behavior for aquifer material from the ES test site with the chemicals used in the experiment. Our work builds on a plethora of research already conducted at the Borden site. Most importantly, elucidation of field-scale concentration effects may be made by comparing retardation observed in the current experiment to that documented in the previous, wellknown, ‘‘Stanford-Waterloo’’ (SW) low-concentration natural gradient field experiment conducted nearby (e.g. Mackay et al., 1986a; Freyberg, 1986; Roberts et al., 1986; Rajaram and Gelhar, 1991). Studies have shown that chlorinated solvent sorption by Borden aquifer solids is nonlinear (Freundlich exponent < 1; Curtis et al., 1986; Ball and Roberts, 1991a,b), hence concentration effects on reactive transport are anticipated. In this work, we apply competitive sorption theory to estimate plume retardation (R) from laboratory sorption data and known field plume concentrations for each of the ES and SW field experiments. We compare these concentration- and co-solute concentrationdependent R estimates to those obtained from the field experiments to test the role of nonlinear and competitive sorption processes.

2. Solute transport and sorption theory The 3-D advection – dispersion equation can be written for a sorbing, but otherwise nonreactive, solute in a homogeneous medium under steady flow conditions as (Freeze and Cherry, 1979):

 B2 Ci B2 Ci B2 Ci BCi BCi BCi BCi Dx þ v¯ y þ v¯ z þ Dy þ Dz  v¯ x ¼ Ri Bx2 By2 Bz2 Bx By Bz Bt

ð1Þ

where vk and Dki are the average linear velocities and dispersion coefficients in the three principal directions (k = x, y, or z), Ci is the aqueous concentration of solute i, and Ri is the

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retardation factor for i assuming equilibrium sorption. The hydrodynamic dispersion coefficients can be expressed as (Freeze and Cherry, 1979): Dki ¼ ak vk þ Di*

ð2Þ

where the ak are the dispersivities and Di* = Dios, Dio is the free solution molecular diffusion coefficient and s is tortuosity. In the case that sorption can be described by a nonlinear Freundlich isotherm with 1/ ni < 1, the solution to the transport equation takes different forms for the advancing and trailing edges of a plume (Dagan and Cvetkovic, 1996). The retardation factor (Ri) for solute i obtained by inserting the derivative of the sorption isotherm into the transport equation (sorption is also assumed to be reversible and at local equilibrium) is (Fetter, 1999):    qb 1 ½ð1=n Þ1 Ri ¼ 1 þ ð3aÞ Kf i Ci i ni g while the behavior for the advancing front is consistent with the local equilibrium contaminant distribution (Crittenden et al., 1986):   qb ½ð1=n Þ1 Ri ¼ 1 þ ð3bÞ Kf i Ci i g where Kfi and 1/ni represent the magnitude of sorption and degree of isotherm nonlinearity (Freundlich coefficient and exponent, respectively) for solute i; and qb and g are the bulk density and porosity, respectively. Laboratory studies indicate that chlorinated solvent sorption on the Borden aquifer solids is nonlinear. When a wide range of concentrations was tested, Freundlich isotherm exponent values were less than unity (Curtis et al., 1986; Ball and Roberts, 1991a,b; AllenKing and Mackay, 2000). This condition suggests that bi-solute competition may occur when the concentration of at least one of the solutes is sufficient to result in relatively high sorbed concentrations (McGinley et al., 1996). In order to predict the effect of competition on transport, the sorption isotherms for each of the organic solutes present must be known. It is commonly accepted that linear sorption partition coefficients can be ‘‘scaled’’ between compounds based on solute hydrophobicity (solubility or octanol– water partition coefficient) (e.g. Schwarzenbach et al., 1993). Nonlinear Freundlich isotherms for multiple solutes can be scaled approximately by solute solubility (CiV= Ci/Si), as discussed by Allen-King et al. (2002). An appropriate scaling relationship will allow sorption and retardation predictions for multiple solvents based on measurements of a single compound isotherm. Using solubility to scale nonlinear sorption isotherms for hydrophobic organic compounds (HOCs) and soil or sediment samples was suggested by McGinley et al. (1996), and was demonstrated for pairs of compounds and a limited number of soil or subsurface sediment soil samples by AllenKing et al. (1996) and Carmo et al. (2000). Allen-King et al. (2002) provide a more comprehensive discussion of the theoretical basis of the observed isotherm scalability. They point out that both the absorption (or solvent partitioning) and adsorption (to

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hydrophobic surfaces) processes that contribute to nonlinear HOC sorption in the environment can be scaled or normalized based on aqueous solubility. In the process to create scaled isotherms, the appropriate normalizing factor for the sorbed concentration must also be considered. In particular, characteristic curves are commonly used to describe and scale HOC adsorption to granular activated carbon (GAC) for compounds that are liquids at environmental temperatures (Manes, 1998). Such characteristic curves rely on consistent volume adsorbed for a particular sorbent. Analogously, Xia and Ball (1999) successfully developed a volume-based characteristic curve that described adsorption of multiple compounds (liquids at environmental temperatures) for a soil sample. Allen-King et al. (2002) discuss broader applications of this concept and its limitations. For the chlorinated solvents used in this study, and many others, compound densities are relatively uniform (1.489 – 1.623 g/cm3). Therefore, we expect that similar curves or ‘‘scaled isotherms’’ for a suite of compounds should result from solubility-normalized sorption isotherms that use either mass- or volume-based sorbed concentrations. In this circumstance, the sorbing solutes will present a common line on a Freundlich plot (as shown by our experiment data in Results and discussion) with the scaled isotherm described by the following equation: 1=n qi ¼ K*ðC iVÞ f

ð4Þ

The ‘‘unscaled’’ individual solute sorption coefficients (Kfi) are related to the scaled Kf* according to the following equation: Kf i ¼

K*f 1=n

Si

ð5Þ

The ideal adsorbed solution theory (IAST) has been used in various chemical and environmental engineering applications to describe competitive sorption processes, and has more recently been applied to dissolved solvents in porous media by McGinley et al. (1996). Competitive sorption in a bi-solute system can be predicted using IAST as presented by McGinley et al. (1993) using the following:   1n i 0 nj ðqi Þm @ ðqi Þm þ ðqj Þm ni A ðCi Þm ¼ ð6Þ Kf i qT where the subscript m indicates the mixture, j is the competing sorbate, and qT ¼ ðqi Þm þ ðqj Þm

ð7Þ

Here, we extend the work of McGinley et al. (1993, 1996) by including isotherm scalability between similar solvent compounds in the prediction. Including the isotherm scalability both simplifies the prediction, and allows one to speculate as to the importance of solvent compounds for which site-specific data are unavailable. Incorporating the scaled isotherm (Eq. (4)) that occurs under the condition that ni = nj = n simplifies Eq. (6) to:   ðqi Þm qT n ð8Þ ðCi Þm ¼ Si qT K*f

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The magnitude of sorption for each of the two compounds in a bi-solute system is directly proportional to its own concentration, inversely proportional to the competing solute concentration, and scaled by the ratio of the individual solute aqueous solubilities, as follows:     qj Si Cj ¼ qi m Sj Ci m

ð9Þ

We make use of the concepts of scalability, competitive, and concentration-dependent sorption to interpret plume transport.

3. Materials and methods 3.1. Field site description The ES natural gradient tracer test was conducted at the Borden research site approximately 150 m from the SW natural gradient tracer test experiment site (Mackay et al., 1986a, among others). Fig. 1 depicts both sites showing plume outlines, monitoring locations, and sites of associated experiments (e.g. sites of core retrieval for laboratory experiments) relevant to this paper. The ES and SW experiments were conducted at similar elevations in the aquifer, although ground elevations are about 2 m lower in the vicinity of the SW site as the upper surface had been historically removed by quarrying. The unconfined Borden aquifer is comprised of beach and near-shore deposits from glacial Lake Algonquin (Fitzgerald, 1982; Burwasser and Cairns, 1974) and consists of clean, well sorted, fine- to medium-sand with occasional granule to pebble lenses (AllenKing et al., 1998). Horizontal stratification at the scale of millimeters to centimeters, where present, appears to extend horizontally over several meters. Hydraulic conductivity (K) for the ES site was measured using the method of Sudicky (1986) by falling head laboratory permeameter tests on 5-cm length core subsamples. Analysis of 764 subsamples from 16 cores taken within or immediately adjacent to the ES test tracer plumes gave a geometric mean K of 6.34  10 3 cm/s and a range of 1.62  10 5 to 3.12  10 2 cm/s (Rivett et al., 2001). Rivett et al. also indicate a mean ES site g = 0.33, mean qb = 1.75 g/cm3, and calculate a mean linear groundwater velocity of 8.5 cm/day (for the initial 322 days of the ES test) from the above mean data and weekly groundwater level monitoring data. Sediment from two 5-cm outer diameter cores, collected by the method of Starr and Ingleton (1992) and located approximately 25 m from the emplaced-source (Fig. 1, cores 62 and 63: located 3 m apart), was used for the sorption, grain size, inorganic (carbonate) and organic carbon content ( fic and foc, respectively) measurements. The core samples were split, geologically logged, and air-dried prior to use. A depth-integrated sample was created from these two cores with the material corresponding to the vertical elevation of the tracer plumes (94.1 –96.2 m relative to an arbitrary site datum, ground surface is f 100 m) to ensure sufficient material for laboratory testing and to obtain a representative

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Fig. 1. Plan location of the emplaced-source (ES) and Stanford-Waterloo (SW) tracer test sites at the Borden field site. The source zones and PCE plumes at late time are shown for each tracer test, as well as the location of sand samples taken for laboratory sorption studies and other small-scale field tests. Studies associated with the referenced locations at the SW site are as follows: sample A—Curtis et al. (1986), Ball et al., 1990, Ball and Roberts (1991a,b), Ptacek and Gillham (1992); core line B—Durant (1986); core C—Mackay et al. (1986b); sample D and field column D—Ptacek and Gillham (1992); forced gradient test E—Thorbjarnarson and Mackay (1994); sample F—Allen-King and Mackay (2000); core G for hydraulic conductivity analysis—Sudicky (1986); and core H—Allen-King et al. (1998).

sample. Material coarser than 2 mm (0.01% by mass) was removed from the depthintegrated sample to ease homogenization and replicate subsampling. Samples were divided with a riffle splitter to produce representative subsamples for analyses.

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The median grain size of the material was 0.15 mm. The foc was 0.0211% (r = 0.0009%, N = 4) and the fic was 1.65% (13.7% as CaCO3). The grain-size distribution was determined by dry sieving. Subsamples for foc, fic, and sorption determinations were pulverized in a shatter box with an alumina bowl (Ball et al., 1990). Inorganic carbon content was determined by acidification with 2N H2SO4 with coulometric quantitation of the CO2 evolved (using an instrument from UIC). Organic carbon content was determined by dry combustion at high temperature under pure oxygen following complete removal of carbonate by sulfurous acid (Allen-King et al., 1997a,b; after Heron et al., 1997). Following acidification, replicate subsamples were tested to ensure that the carbonate had been completely removed prior to combustion. 3.2. Field tracer test Relevant aspects of the Borden ES field tracer test are described below. Rivett et al. (2001) provide additional detail. The ES site was located about 150 m from the SW site used by Mackay et al. (1986a). Although it would have been interesting to have undertaken the ES experiment as the SW site (to more directly compare high- and lowconcentration effects), at the time of the ES experiment, the SW site was being used for other experiments and in any case a larger vadose zone thickness was sought for the soil – gas research aspect associated with the ES experiment than that encountered at the SW site. A block-shaped source zone measuring 1.5 m wide, 1 m high, and 0.5 m thick was purposely emplaced 1– 2 m below the seasonally fluctuating groundwater table. The emplaced-source contained native sand mixed with a multicomponent chlorinated solvent DNAPL (1.5 kg TCM, 8.9 kg TCE, and 12.6 kg PCE) at a residual saturation of 5% of the pore space and gypsum powder that provided dissolved sulfate as a conservative tracer. Sufficient solvent mass was present in the source for continuous generation of dissolved plumes over the 475-day natural gradient tracer test. Solubilities for these compounds are 8700, 1400, and 240 mg/l for TCM, TCE, and PCE respectively (Broholm and Feenstra, 1995). Based on the initial source mole fractions of 0.078 for TCM, 0.434 for TCE, and 0.488 for PCE, effective solubilities may be calculated via an analogue of Raoult’s Law: TCM—680 mg/l, TCE—610 mg/l, and PCE—120 mg/l. These values are used as representative (near maximum) source input concentrations (Co) and are used to normalize plume concentration results presented later. Although depletion of TCM ultimately occurs over about 600 days (Frind et al., 1999), the effective solubility temporal changes induced do not influence the analysis and determination of plume parameters undertaken in this manuscript. The dissolved plumes were monitored by a 3-D array of 173 multilevel samplers containing 2300 available sampling points (Fig. 1). Spatial snapshots were obtained at 56, 125, 194, 322, and 418 days after source emplacement. The initial four snapshots were fully 3-D, and the 418-day snapshot sampled three specific elevations in the leading portion of the plume. The temporal and spatial flux of dissolved solvent concentrations emitted by the source was monitored at the ‘‘1-m fence’’ (Fig. 1) that comprised a grid of over 100 point samplers spaced 0.2 m vertically and 0.5 m horizontally located 1 m down gradient of the source zone. The fence was monitored on 25 occasions during the ES

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natural gradient test. Further details on source concentration and DNAPL dissolution simulation are provided by Feenstra (1997) and Frind et al. (1999), respectively. Plan view and longitudinal plume profiles from the ES test 322-day data are shown in Fig. 2. Plumes were delineated over 4 orders of concentration magnitude (except sulfate), a concentration range significantly greater than previous field tracer test data (e.g. SW test,

Fig. 2. Emplaced-source (ES) tracer test 322-day field data—(a) plan views and (b) longitudinal profiles. Concentrations are vertically averaged over the field interval 93 – 97 m elevation (Cavg) and normalized to their respective Co values given in the text. The DNAPL emplaced-source (not shown) is located at the (0,0) coordinates in the plan views.

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Mackay et al., 1986a). Comparison of similar C/Co concentrations (contours) among the solutes in Fig. 2a provides an indication of the relative retardation of the plumes. It is evident from visual inspection that TCM was essentially nonretarded relative to the conservative sulfate profile and TCE only marginally retarded. Rivett et al. (2001) calculate RTCM = 1.03 (i.e. RTCM f 1.0) based on a comparison of the sulfate and TCM profiles. PCE was the most retarded solute. 3.3. Field tracer test—determination of plume transport parameters Numerical model simulation was used to determine plume transport parameters from the ES tracer test field data (moments analysis was not appropriate due to the continuous source input). The code FRAC3DVS (Therrien and Sudicky, 1996) was used to solve the 3-D transport equation (Eq. (1); Burnett and Frind, 1987) and estimate transport parameters by least-square residual fits of simulated to field plume data. The code was primarily selected for its effective handling of the transient boundary conditions and efficient transport solver. The aquifer was modeled as an anisotropic porous medium; fracture and variable saturation capabilities of the code were not implemented. A finite difference, 216,000 node, 3-D parallelepipedic domain was used (Fig. 3). Model coordinates were rotated from the field coordinates (Fig. 1) to allow the model X (Xmod) axis to be parallel to the predominant plume transport direction (later figures adopt this model-coordinate system). The transient groundwater flow regime monitored during the ES test has been shown to have an important influence on dissolved plume transport (Rivett et al., 2001) and was simulated via incorporation of a time-variant first-type boundary condition to the domain sides. Boundary heads (h) were calculated according to h ¼ a þ bXmod þ cYmod

ð10Þ

The constants a, b, and c were obtained by fitting the planes to spatial groundwater level data sets obtained on 55 dates during the ES test (Farrell et al., 1994; Rivett et al., 2001). Temporal hydraulic head, gradient, and horizontal flow direction variations incorporated to the model are indicated in Fig. 3. Detailed temporal 1-m fence concentration data were used as a boundary condition to represent the DNAPL source input (Fig. 3). Feenstra (1997) indicates significant spatial and temporal variability of field concentrations at 1-m fence monitoring points that is a consequence of the transient flow regime causing significant lateral plume movement. Transient dissolved source behavior was represented by spatial interpolation of the 1-m fence field data to relevant nodes on the Xmod = 1 m model domain boundary at each sampling time. This method provided a temporally and spatially variant first-type transport boundary condition. Temporal concentration input from an example transport boundary node is indicated in Fig. 3. The K of the porous media domain was assumed homogeneous and weakly anisotropic throughout (Kxx = Kyy = 6.34  10 3 cm/s; Kzz = 3.57  10 3 cm/s). The anisotropy was intermediate between Nwankwor et al.’s (1992) field test value (located 70 m from the ES site) and previous values used in Borden modeling studies (Frind and Hokkanen, 1987). Other aquifer parameters were s = 0.67, g, and qb (values indicated above). The Do were

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Fig. 3. Schematic of FRAC3DVS numerical model domain and indication of boundary conditions for transient simulations. Transient simulations account for changing water table gradient and concentration input over time, as described in the text.

6.83  10 5, 6.39  10 5, and 5.88  10 5 m2/day for TCM, TCE, and PCE, respectively, and were calculated using the Wilke and Chang method described in Perry (1984). The modeling to determine Ri assumed simple linear equilibrium sorption (ni = 1, Eqs. (3a) and (3b)). This simple approach provides a fundamental quantitative description of overall plume retardation. Plume retardation values thus estimated could be directly compared to the Borden SW test retardation factors determined by moment analysis by Roberts et al. (1986). Our approach was to estimate plume dispersivities (longitudinal, al; transverse horizontal, ath; and vertical, atv) via least-squares model fit to the conservative TCM field-data profiles. The assumption that TCM was conservative (RTCM taken as unity) was supported by Rivett et al.’s (2001) relative analysis of TCM and conservative sulfate plume data depicted in Fig. 2b. Retardation factors, RTCE and RPCE, were

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determined via least-square residual fits of field plume longitudinal profiles to simulations that incorporated various combinations of Ri and al. The latter were based on the range of al values that fit the conservative TCM plume data. Sensitivity analyses are shown (ES test plume transport parameters) to indicate the relative certainty of transport parameters. Rix (1998) provided further details of the modeling methodology used and some of the simulation work undertaken. 3.4. Laboratory sorption measurements Equilibrium PCE and TCE sorption on ES site material were determined for a depthintegrated sample. Sorption competition was tested by measuring PCE sorption alone and in the presence of f 10 mg/l TCE. Rationale for the latter concentration was that much of the ES field test PCE plume core front existed at locations where TCE concentrations were 10 mg/l (f 0.02 C/Co) or greater. Equilibrium PCE and TCE sorption isotherms were measured using the batch technique established by Ball and Roberts (1991a) for Borden sediments. The PCE sorption batch systems for the depth-integrated samples consisted of 17.8 g of sediment, 6.8 ml of sterilesimulated Borden groundwater (solute composition provided by Ball and Roberts, 1991a,b), and 0.12 to 1500 Ag of PCE sealed into a 10 ml (nominal, true volume f 14.78 ml) flame-sealed ampoule (Wheaton). For the TCE sorption measurements, the sediment/water ratio was increased to 20.0 g to 5.5 ml to reduce sorbed concentration measurement error; the mass of TCE used ranged between 0.66 and 6800 Ag. The sediment was pulverized to speed sorption equilibrium for all systems. Ball and Roberts (1991a) have shown that for analytes such as PCE, pulverization of Borden sand does not affect the magnitude of equilibrium sorption, but greatly diminishes the time required to achieve equilibrium. The low-concentration batch systems were made using methanol stock solutions of the chlorinated solvents, and the highest concentrations were made by the addition of NAPL. Stock solutions were created that contained sufficient radiolabelled compound to maintain low analytical uncertainty, and sufficient additional unlabelled solute was added to achieve desired concentrations. The analytes were added as a NAPL phase to the batch systems that resulted in equilibrium PCE solution concentrations of approximately 32,000, 95,000, and 130,000 Ag/l and TCE concentrations of 580,000, 650,000, and 1,060,000 Ag/l. The analytes were added in methanolic stock solutions in all other cases, with no more than 10 4 mol fraction of methanol in the batch system to avoid an appreciable cosolvent effect (Munz and Roberts, 1986; Curtis et al., 1986). Samples with a nonaqueous-phase stock solution added were vortexed at high speed for 30 s following sealing to enhance the rate of solute dissolution. A time-to-equilibrium experiment was conducted for PCE batch systems with a nonaqueous-phase stock solution. As for the systems with methanolic stock solutions, sorption equilibrium occurred within 72 h. Therefore, all samples were equilibrated for 72 h at room temperature while being gently shaken (3 rpm). The sorbed mass of the analyte was calculated as the difference between the dissolved and total masses, with corrections for partitioning to headspace and sorption to glassware determined from sediment-free controls (Ball and Roberts, 1991a,b), and an unextractable radiolabeled contaminant (according to the method of Young and Ball, 1994). Both 14C-

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PCE and 14C-TCE (Sigma) were used as received and both contained radiolabeled contaminants that were not extractable in hexane and exhibited relatively low sorption (Young and Ball, 1994; Allen-King et al., 1995). The 14C-contaminant proportion of the total disintegrations per minute was 13.7% and 4.12% for the PCE and TCE, respectively. In the systems designed to test for competitive sorption, the total mass of PCE (Mj) was known and can be expressed as Mj ¼ ðCj Þm ðHj Va þ Vw Þ þ ðqj Þm ms

ð11Þ

where Hj is the dimensionless Henrys law constant for compound j, Va and Vw are the volumes of air and water in the vial, respectively, and ms is the mass of solids. The sorbed concentrations of PCE in the presence of f 10 mg/l TCE (75.0 Ag were added per vial; equilibrium TCE solution concentrations varied slightly depending upon the proportion of mass sorbed) were predicted by solving Eqs. (7) – (9) and (11) simultaneously using the solving routine in Quatro (Corel).

4. Results and discussion 4.1. ES test plume transport parameters Transverse vertical dispersivities (atv) of 1– 2 mm were determined by comparison of numerical simulations with 322- and 453-day vertical field profiles obtained from the 26m fence (Fig. 1 for location). Vertical spread (plume thickness) was reasonably matched for a concentration decline of 3 orders of magnitude from the peak concentration. The values accord with previous Borden plume transverse vertical dispersion estimates (SW test—Rajaram and Gelhar, 1991; Sudicky et al., 1983); their low magnitude confirming vertical dispersion at Borden is essentially diffusion-controlled. Transverse horizontal dispersivities (ath) were also evaluated by comparison of 26-m fence 322- and 453-day field and model TCM profiles (not shown). Small (millimeters to sub-millimeters) ath values were required to match simulated and field-measured plume widths. Simulated plume widths were relatively insensitive to the ath values used in the above range, suggesting that the majority of plume spreading was due to the transient flow field monitored at the site (and incorporated in the model). Although an ath of 1 mm could successfully match the plume core maximum and less-steep concentration gradient side of the TCM plume (see Fig. 2a for field data) over 4 orders of concentration change, the very steep concentration gradient on the alternate side of the plume was only well matched over the initial order of magnitude decline from its peak, with transverse spread of the lowest (Ag/l range) concentrations overpredicted by f 30%. The ath values cannot be directly compared to the SW test dispersion parameters (5 cm by Rajaram and Gelhar, 1991 and 3.9 cm by Freyberg, 1986), as those values include contributions of groundwater flow transience (explicitly accounted for in our analysis). Further analysis of the transverse horizontal dispersion behavior of the ES plume and influences of the transient flow regime are reported elsewhere (parts of the data set have contributed to research by Farrell et al., 1994; Schirmer et al., 2001).

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Transient model simulations to determine al, and subsequently RTCE and RPCE, assume ath = atv = 1 mm. Longitudinal dispersivities (al) were estimated from (the conservative) longitudinal TCM plume profiles, e.g. the 322-day TCM data (Fig. 4a). A least-squared fit to the log-transformed data indicated that the al = 0.7 m profile provided the best fit (such

Fig. 4. Longitudinal 322-day plume profiles used to estimate dispersivities and retardation factors—comparison of ES plume data and transient model results: (a) TCM (nonreactive solute); (b) TCE; and (c) PCE (ath = atv = 1 mm for all cases shown).

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fits are sensitive to the low-concentration leading front). Fits to the normal-scaled data (not shown) indicate that an al = 0.5 m best fits the overall profile and al = 0.3 m best fits the profile section at f 20 m from the source where the main steep concentration decline is present. Thus a decision on which al is deemed the ‘‘best’’ is dependent upon the relative importance attached to the fitting of the various parts of the profile—a single dispersivity value could not adequately match the entire field data profile that ranged over 4 orders of magnitude. Overall, al’s ranging from about 0.1 to 0.9 m encompass the entire range of plume concentrations monitored at 125, 194, 322, and 418 days. Low al’s of f 0.3 m reasonably fit the 125- and 194-day profiles throughout. Higher al’s within the above range were required to fit the lower concentration fronts of the later-time plume data. The above estimates are reasonably consistent with estimates from the SW test. Freyberg (1986) obtained a longitudinal macrodispersivity of 0.36 m and predicted an asymptotic value of 0.49 m from the SW test plume; Rajaram and Gelhar (1991) reinterpret that data to give a dispersivity of 0.5 m (linear trendline to 259– 1038 day second-moment data). Simulations to determine RTCE and RPCE were undertaken using al in the range determined for the conservative TCM profiles. Profiles with al = 0.3 m and al = 0.7 m are presented (Fig. 4). The 322-day TCE longitudinal plume profile was best fit by the al = 0.7 m and RTCE = 1.2 simulation results (Fig. 4b). Similar to TCM, the highconcentration TCE plume front section at 20- to 33-m distance is best fit with a lower al (al = 0.3 m rather than 0.7 m) with RTCE = 1.1. However, this lower al poorly matches the rest of the profile. Simulations of all ‘‘snapshot’’ data confirm that TCE was minimally retarded with RTCE = 1.1 – 1.2. Fig. 4c compares the 322-day PCE longitudinal plume profile with a range of model simulations. The best fit was the al = 0.7 m and RPCE = 1.6 simulation result. It provides a good mean fit of the 0 –20 m profile section (Fig. 4c) and excellent fit over the leading lower-concentration plume front. Simulations with lower dispersivity values (e.g. al = 0.3 m with R = 1.6 shown (Fig. 4c) and other R’s (not shown)) have very poor fits throughout both the log- and normal-scaled data, in contrast to the findings of the conservative TCM data analysis. The better fit of the higher al for the more strongly sorbing PCE plume is likely symptomatic of the increased importance of nonideal sorption processes (e.g. nonlinearity, competition, nonequilibrium, and/or heterogeneity) that enhance the longitudinal spreading of the plume, but are not explicitly modeled here. The finding of enhanced dispersion of PCE relative to conservative TCM is consistent with the previous SW test. In that test, longitudinal PCE plume spreading was about three times larger than that of the nonsorbing tracers and was ascribed to nonideal transport of the sorbing tracer (Brusseau and Srivastava, 1997). Fig. 5 shows the temporal development of longitudinal PCE plume profiles between 56 and 418 days. Simulation profiles with al = 0.7 m provided reasonable fits to all of the PCE profiles (except 56 days) and are depicted. The earliest (56-day) profile, a very irregular profile, is approximated by a simulation with RPCE = 1.4. Other field profiles are best simulated by RPCE values within a range of just 1.6 to 1.8, i.e. RPCE appeared essentially invariant with time. Application of lower al values, particularly to the earlier-time plume, may be anticipated to be appropriate; however, such simulations did not improve upon the fits depicted. In fact, a simulation that simply assumes RPCE = 1.6 and al = 0.7 m

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Fig. 5. PCE longitudinal plume profiles used to estimate PCE retardation factors for each sampling time— comparison of ES plume data (symbols) and transient model results (lines) (al = 0.7 m, ath = atv = 1 mm). The 418day data are based on maximum concentrations because insufficient field data were available to justify Cavg calculation.

(ath = atv = 1 mm) throughout provided a good representation of all field observations over the wide concentration ranges monitored. 4.2. Laboratory sorption measurements and Ri estimates 4.2.1. Sorption parameters Single solute sorption isotherms for PCE and TCE indicate enhanced sorption of PCE compared to TCE (Kf = 0.41 compared to 0.10) anticipated from their relative hydrophobicity. The solutes exhibit similar nonlinearity (1/n f 0.91, Table 1). When TCE and PCE sorption measurements are scaled by compound solubility, the resulting isotherms are not significantly different from each other (Fig. 6, Table 1), thus supporting the concept of nonlinear isotherm scaling for these chlorinated solvents, as proposed earlier (Eqs. (4) and (5)). A common best-fit scaled isotherm was determined (Fig. 6) and used to estimate the TCM sorption parameters in this work (Eq. (5)). The sorption isotherm was not measured for TCM because the low amount of sorption (predicted based on the solubility) would have resulted in unacceptably high relative error.

Table 1 Laboratory-determined solvent sorption parameters for the ES site Compound/ material

Kfi [(Ag/kg)/ (Ag/l)1/n]

Kfi 95% CIa

Kf*b (mg/kg)

1/n

1/n 95% CIa

N

Kfi/foc [(Ag/kgoc)/ (Ag/l)1/n]

PCE TCE All data

0.41 0.10

0.36 – 0.46 0.06 – 0.17

37 34 37

0.92 0.90 0.91

0.90 – 0.94 0.85 – 0.96 0.88 – 0.94

25 24 49

1900 480

a 95% CI are the confidence intervals for the sorption isotherm estimated using standard statistical methods for an unbalanced data set (Sokal and Rohlf, 1981). b Conversion to reduced concentration units facilitates comparison between Kf* determined for individual solutes to that derived from the entire data set and shown in Fig. 6.

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Fig. 6. Sorption isotherms for PCE and TCE scaled by compound solubility. The coefficients for the composite isotherm are Kf*= 37 (30 – 45) (mg/kg) and 1/n = 0.91 (0.88 – 0.94). The best fit individual PCE and TCE isotherms (not shown) are nearly indistinguishable from the best fit of all data (Table 1).

Nonlinear sorption of hydrophobic compounds has been previously observed for Borden aquifer sediments (e.g. Ball and Roberts, 1991a,b; Curtis et al., 1986). Recently, the carbonaceous matter (noncarbonate, C-containing matter) within a bulk sample of Borden aquifer sediment has been extracted, and its chemical composition and sorption properties were characterized (Huang, personal communication, 2000; Grathwohl, personal communication, 2001). These researchers identified the presence of a condensed (likely thermally altered by geologic processes or as a consequence of combustion or pyrolysis) carbon phase. Such phases have been associated with adsorption of hydrophobic pollutants (Allen-King et al., 2002 and references therein) that is particularly apparent at lower aqueous concentrations (compared to solubility). Allen-King and Mackay (2000) recently modeled nonlinear PCE sorption in 20 samples taken from different depths in the Borden aquifer using a combined adsorption/partitioning model (as described in Allen-King et al., 2002). They found that adsorption was dominant in all of the tested samples at aqueous concentrations below approximately 10 mg/l PCE or CPCE/ SPCE f 4% and that a linear partitioning mechanism was dominant at higher concentrations. In particular relevance to the present study, Allen-King et al. (2002) point out that sorption estimates based on simple Koc foc calculations are more accurate at higher P concentrations. Overall, the above implies that when m C i¼1 i =Si zc4% in Borden aquifer sediments, sorption and retardation are expected to be partition-dominated and to approach the value obtained by a simple Koc foc estimate. Competitive sorption of PCE in the presence of a relatively high TCE concentration (f 10 mg/l = 0.007STCE) was observed in the laboratory batch system (Fig. 7). Compar-

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Fig. 7. Sorption competition. Sorption of PCE in the presence of TCE (f 10 mg/l) is diminished compared to the sorption observed without TCE. The dashed line is the competitive sorption predicted by the IAST theory. The solid line shows the scaled isotherm (Fig. 6) adjusted for PCE using Eq. (5).

ison of the PCE only and PCE with TCE isotherms indicates decreased PCE sorption in the latter over the entire range of PCE concentrations tested. Competition was more pronounced when the relative TCE concentration (CTCE/STCE) was much greater than the relative PCE concentration (CPCE/SPCE). The relative PCE concentration range observed in the competition experiment was approximately 0.00004– 0.008SPCE (Fig. 7). The independent prediction of competitive sorption is shown as a dashed line in Fig. 7 and was generated using the IAST theory (Eqs. (7) – (9) and (11)) and the scaled isotherm parameters (Fig. 6, Table 1). Given the simplistic approach taken, the fit is relatively good and evaluation of its utility in a transport analysis is warranted. 4.2.2. Comparison of empirical, laboratory-based, and field-derived estimates of Ri Estimates of Ri for each solute in the ES experiment may be obtained using the wellknown empirical relationship, Kd = Kocfoc (e.g. Schwarzenbach et al., 1993), and Eqs. (3a) and (3b) assuming ni = 1 (linear sorption). Using qb, g, and foc values given earlier and literature geometric mean Koc (carbon-normalized linear sorption coefficient) of 53, 94, and 265 ml/g for TCM, TCE, and PCE, respectively (US EPA, 1996), the empirical estimates are RTCM = 1.1 (1.06 calculated), RTCE = 1.1, and RPCE = 1.3. These empirical estimates are similar to the ES field plume R values for TCM and TCE. However, the PCE retardation is underpredicted by this method. Laboratory batch sorption experiment results (Table 1) were also used in Eq. (3b) to estimate the effect of concentration range (and nonlinear sorption) on Ri for each solute in the ES experiment (advancing solute front). The RTCE estimated for aqueous concentrations from 1 to 610,000 Ag/l (CTCE/STCE = 7  10 7 to 0.44) were 1.1 – 1.4, and RTCM corresponding to aqueous concentrations from 1 to 680,000 Ag/l (CTCM/STCM = 1  10 7 to 0.08) were 1.02– 1.07. The lower Ri corresponds to the higher concentration. For comparison, the numerical estimates from the field plumes were RTCE = 1.1 –1.2 and

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RTCM f 1.0. Competitive effects, if taken into account, would further reduce the single solute laboratory-based Ri predictions, moving them closer to the lower end of the predicted range (1.1 for TCE and 1.02 for TCM). Because retardation is minimal for these two compounds, quantitative assessment of competitive effects is not pursued for them. For TCM and TCE, there is a good agreement between empirical, field, and laboratory-based estimates of Ri without consideration of competitive effects. In order to estimate the potential effects of competitive sorption on PCE transport, the concentrations of PCE and important potential competing solutes at points within the plume must be known. Fig. 8 shows the TCE/PCE concentration ratios as a function of PCE concentration for samples collected at 322 days along a vertical planar section through the approximate centerline of the plume. In the near-field samples ( < 20 m distance from the source), the highest CPCE samples correspond to CTCE/CPCE similar to the source (e.g. 1 < Co,TCE/Co,PCE < f 5– 10). These samples are within the plume core. Lower CPCE samples collected at greater distances from the source (Fig. 8, solid symbols) show the more extreme (higher) concentration ratios that correspond to the low-concentration PCE plume front invading the high-concentration TCE plume core. It is apparent that some samples had low CTCE/CPCE ratios (e.g. < 1). This was ascribed to a variety of reasons: rapid depletion of TCE relative to PCE in certain parts of the source; retarded transverse lateral motion of the PCE plume relative to the TCE plume causing some lateral separation of the plume cores from each other; and inadvertent minor spillage of source material during installation that partially volatilized and produced an enriched PCE subplume in the uppermost elevations monitored (Rivett, 1995).

Fig. 8. The ES plume TCE/PCE concentration ratio observed at 322 days shown as a function of the PCE concentration. Data points are based on individual sample point data collected from multilevel samplers located along the longitudinal plume centerline. The dashed line shows the PCE/TCE effective solubility ratio (Co,TCE/ Co,PCE) predicted from the initial source composition and Raoult’s law. Filled symbols are used for samples located >20 m from the source that show concentration ratios at the PCE plume front.

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The predicted effects of concentration and co-solute competition on RPCE were made for ES plume concentrations observed along the 322 days plume centerline and are shown in Fig. 9a. Predictions were made for particular PCE and TCE concentrations using Eqs. (3b), (5), and (7) –(9) and the scaled isotherm parameters derived from the observed laboratory data (Table 1)). If PCE had been present as a single solute in the ES plume at the observed concentrations, the RPCE estimated would span a range from f 1.9 near the source area to f 3.5 at the plume front (Table 2). This prediction (Fig. 9a, dotted line) incorporates the effects of single solute concentration and sorption isotherm nonlinearity on retardation, but does not consider competition from the co-solutes present. The symbol-marked solid lines in Fig. 9a show the effect of TCE sorption competition on RPCE for three representative CTCE/CPCE. Because TCM sorption is estimated to be much weaker than that of either TCE or PCE, and because plume core CTCM/STCM < CTCE/ STCE or CPCE/SPCE, our calculations suggest that TCM sorption competition had little effect on PCE mobility. Within the high-concentration core of the plume, the CTCE was as much as 10-fold greater than the CPCE (Fig. 8) and caused a modest reduction in RPCE compared to the prediction for PCE alone (Fig. 9a, solid line with + compared to dotted line). Near the front of the plume (CPCE f 1 –500 Ag/l), lower concentrations of PCE invaded the core of the TCE plume and much greater CTCE/CPCE were observed (Fig. 8). The  - and o-marked solid lines delineate the CPCE ranges for which these predictions apply. Co-solute competition is predicted to reduce the point-wise retardation estimates at the PCE plume front to the greatest extent (shown schematically as gray shaded region in Fig. 9a), where CTCE/STCEHCPCE/SPCE. Overall, the ‘‘best’’ RPCE for the length of the plume along the centerline is shown schematically by the gray region (for low concentrations) and line with + symbols for higher concentrations. Considering the effects of relatively high co-solute concentrations in addition to PCE concentration effects improves the laboratory-based point-wise RPCE estimate range to be consistent with that determined for the field plume (Table 2) and closer to estimates based on a partitioning-dominated (e.g. Kocfoc) mechanism. 4.2.3. Comparison of PCE solute retardation between ES and SW experiments Transport comparisons between the ES and SW experiments will focus on the only common solute, PCE, with relevant sorption data and retardation estimates for both experiments summarised in Table 2. Although the two experiments were conducted less

Fig. 9. Theoretical prediction of concentration and competition effects on RPCE for observed plume aqueous concentrations using laboratory sorption isotherm parameters as described in the text for (a) ES experiment at 322 days and (b) SW experiment over time. Dotted and dashed lines are predictions for PCE alone over the concentration range reported for the field plumes. Dotted lines represent predictions for the advancing contaminant front estimated with Eq. (3b) while dashed lines result from Eq. (3a). The other lines in (a) and (b) show predictions for RPCE in the presence of relevant co-solute concentrations (TCE in the ES experiment and DCB in the SW experiment). The shaded gray region in (a) and (c) (including line with + in (a) and the similar solid line in (c)) outlines ‘‘best’’ RPCE for the length of the plume centerline of the 322-day ES plume. The shaded gray arrow on (b) and (c) shows the ‘‘best’’ estimate of the RPCE trend with transport (dispersion, transformation) over the duration of the SW experiment. Part (c) compares the single solute and multisolute ‘‘best’’ estimates for the two experiments (transcribed from (a) and (b) transformed to a common scale). Assumptions for these determinations are described in the text.

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Table 2 Comparison of PCE retardation estimates from comparable field and laboratory methods for the ES and SW tests ES test

SW testa

Dissolved concentration Initial source zone input (mg/l)

up to f 120

0.03

Organic carbon content (95% confidence intervals) foc (%), depth-integrated sample

0.0211 (0.001)

0.021 (0.006)b

Laboratory sorption coefficients (95% confidence intervals) Kf

Kf /foc

1/n

Kf

1/n

0.41 0.92 1.3 0.81 (0.36 – 0.46)c (0.90 – 0.94)c (0.72 – 2.3)d (0.79 – 0.83)d 1900 6200

Retardation factor, R, estimates (95% confidence intervals) Laboratory-derivede Single solute sorption Low-concentration range, 0.0005 – 0.03 mg/l PCE 2.8 – 3.6 (2.5 – 4.3)f High-concentration range, 0.001 – 120 mg/l PCE 1.9 – 3.5 (1.7 – 4.1)f Multisolute sorption PCE with co-solutes f 1.8 – 2.6h Field-derived f 1.4 – 1.8

4.0 – 9.1g 1.6 – 8.1g f 2.7 – 7.5i 2.7 – 5.9 j

Coefficients for retardation estimates from the laboratory batch data are listed below or described within the text. a Mackay et al. (1986a). b Ball and Roberts (1991a). c PCE sorption concentration range, f 0.002 – 130 mg/l. d Ci f 0.0005 to 40 mg/l (Ball and Roberts, 1991a). e Laboratory-derived R values were estimated for both sites using the Borden particle density of 2.71 g/ml (Ball et al., 1990) which gave a bulk density of 1.81 g/ml for the ES site (g = 0.33). f The 95% CI (in parenthesis) were estimated as Ri = 1+[(qb+g)( qi, CI/Ci)], where qi, CI is the upper or lower 95% CI sorbed concentration corresponding to solution concentration Ci. g Using sorption isotherm parameters from Ball and Roberts (1991a). h 0.001 – 120 mg/l PCE with TCE as shown in Fig. 9a and described in text. i Fig. 9b and associated discussion. j Estimates were made at 56, 125, 194, 322, and 418 days for the ES test and 15, 30, 85, 250, 400, and 650 days for the SW test (Roberts et al., 1986).

than 150 m apart, analysis of the field-plume data indicates that PCE retardation in the ES plume (ES test plume transport parameters) was remarkably ( f 2 to 3 times) lower than observed for the SW plume with no obvious temporal variation (Fig. 10). In contrast, the SW plume exhibited an increase in RPCE from 2.7 to 5.9 with travel time and distance (Fig. 10). Such differences in retardation behavior might be considered surprising because the hydrologic and geologic properties were very comparable between the sites (i.e. groundwater velocity, hydraulic gradient, porosity, hydraulic conductivity (mean and variance), background hydrochemistry, foc, and dispersivities; values herein and Rivett et al., 2001). However, this logic does not consider the important source condition differences between the two experiments, or the resultant combined impacts of nonlinear sorption and solute/cosolute concentrations on PCE retardation.

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Fig. 10. Temporal variability in PCE retardation (RPCE) for both the ES and SW natural gradient field experiments. Data for SW test are from Roberts et al. (1986). Data for the ES test are based on Fig. 5.

In the SW field experiment, five initial organic solutes were injected in a single dissolved pulse at concentrations ranging from 20 to 332 Ag/l. The initial PCE concentration (30 Ag/l) was almost 4000 times lower in the SW than in the ES experiment (Table 2), with accompanying co-solutes at similarly lower concentrations. Solute and co-solute concentrations in the SW experiment declined with travel distance due to dispersion (and transformation for some compounds), and the reactive solute plumes tended to separate with transport. Calculations to qualitatively determine the combined concentration and competition effects on sorption and retardation for the SW plume scenario used the measured PCE sorption isotherm for the SW site (Table 2) and the same equations (and simplifying assumptions) described for the ES scenario. Eq. (3a) is also used to bracket the transport conditions (concentration gradient) affecting RPCE. The behavior of the co-solutes present in the SW experiment (and for which no complete sorption isotherms are available) is estimated by scaling (Eqs. (4) and (5)) from the PCE isotherm parameters reported by Ball and Roberts (1991a). The concentration-dependent single-solute RPCE prediction for the SW experiment over the relevant concentration range (1 –30 Ag/l = 0.001 – 0.030 mg/l) is 4.0 –9.1 (Fig. 9b, dashed and dotted lines). However, the co-solutes are predicted to compete against PCE for more favorable adsorption sites, particularly during the early times in the experiment when the co-solute concentrations were greatest. The highest concentration solute injected in the SW test was 1,2-dichlorobenzene (DCB) at 332 Ag/l (Mackay et al., 1986a), approximately 11-fold greater than the Co,PCE. The effect of the greater DCB concentration on the predicted RPCE is to diminish it by more than one retardation unit at the initial PCE concentration (Fig. 9b, lines marked with + and ). The majority of the injected DCB mass persisted for f 63 to 85 days of transport (and was presumably transformed after that time). At later times, the DCB had transformed and solute plumes partially separated in space, hence removing the competitive solute sorption effect. Maximum plume PCE concentrations were also reduced due to dispersion. Reduction

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in the concentration of PCE and co-solute DCB produces an increase in the predicted proportion of PCE sorbed and corresponding laboratory-based predictions for RPCE increase to f 7.5 for a low-concentration range of single solute PCE (CPCE = 1 Ag/l) (Fig. 9b, low-concentration portion of dotted and dashed lines). Near the end of the monitoring period (f 630 days), the front of the PCE plume overlapped the back of the carbon tetrachloride (CTET) plume (Roberts et al., 1986) as the plumes separated in space. Assuming CTET concentrations approximately 10-fold greater than PCE concentration to represent the effect of competition in this region, the predicted RPCE is reduced only f 0.5 units compared to the single solute (Fig. 9b, arrowhead region). At this time in the experiment, co-solute sorption competition in the front but not the back portion of the PCE plume could have affected plume spreading causing the appearance of enhanced dispersion. However, the effect is expected to be minimal compared to competitive effects on retardation at the initiation of the experiment. The effects of diminishing solute and co-solute concentration with travel time/distance for the SW plume result in a trend in the laboratory-based RPCE predictions shown schematically by the shaded arrow in Fig. 9b. The predicted trend is qualitatively consistent with the observed field behavior (Table 2). Fig. 9c compares single and ‘‘best’’ multisolute RPCE estimates for the two experiments over a very wide aqueous concentration range as developed in Fig. 9a and b and presented on a common scale. The single-solute estimates show that measured sorption isotherm parameters (Table 2) produce essentially coincident RPCE estimates at high aqueous concentration (CPCE> f 5 mg/l) (we note here that these predictions are not appropriate for the SW experiment because of the low source concentration). In fact, sufficiently high solute or co-solute (competitive) concentrations reduce the predicted RPCE to be approximately equivalent to that estimated by the Koc foc-based empirical method. Differences between the measured sorption isotherms result in significant differences between RPCE predictions at low concentrations only. The differences between the estimates arise from relatively modest differences in the sorption parameters measured for the two experiment sites (Table 2) compared to much greater variability (f 50-fold) reported for samples taken from narrower vertical intervals (Durrant, 1986; Kwan, 1991; Allen-King et al., 1998; Divine et al., 2001; Divine, 2002). Differences between sorption parameters for depth-integrated samples from each of the sites appear to be within the range reported previously for this aquifer (Durrant, 1986), albeit limited data availability prohibits more rigorous statistical evaluation of this point. The differences between the laboratory-based RPCE predictions for the two experiment plumes are consistent with field plume behavior (Table 2, Fig. 10). Importantly, Fig. 9c shows that the very low RPCE predicted for the ES site does not result from the relatively modest differences in the measured sorption parameters (Kf,PCE, 1/nPCE) between the two sites, but instead is an important consequence of source concentration and nonlinear/competitive sorption effects on retardation. We conclude that the significant contrast in source conditions for the two experiments combined with the observed nonlinear sorption behavior could be the cause of the observed differences in PCE transport behavior (magnitude and trend of RPCE). There are numerous works that seek to describe the fundamental causes of nonideal behavior for the SW plume. The various mechanisms tested or described include

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physical/chemical nonequilibrium (including intragranular diffusion), physical/chemical heterogeneity, and sorption isotherm nonlinearity (Ball and Roberts, 1991b; Young and Ball, 1994; Mackay et al., 1986b; Goltz and Roberts, 1988; Harmon and Roberts, 1992; Ptacek and Gillham, 1992; Quinodoz and Valocchi, 1993; Burr et al., 1994; Thorbjarnarson and Mackay, 1994; Allen-King et al., 1998; Cushey and Rubin, 1997; Brusseau and Srivastava, 1997) (the reader is referred to Brusseau and Srivastava for a discussion of the preceding works). Most of these works provide convincing evidence that the mechanism studied could have contributed substantively to the observed plume nonideal transport behavior, and suggest that nonideal transport behavior cannot be satisfactorily ascribed to only one of the above mechanisms. To the list of processes to which nonideal behavior can be attributed, we add another previously not well-considered process— competitive sorption. We also note that some works rely on assumptions that remain unproven for the Borden aquifer. In particular, our group (Divine et al., 2001) recently reported that the correlation structure between aquifer permeability and PCE sorption coefficient is different to that used in several previous studies (e.g. Burr et al., 1994; Brusseau and Srivastava, 1997) and that no spatial trend in sorption coefficient was identifiable. Detailed assessment of the influence of all of these combined properties/ processes (time-dependent sorption, physical/chemical property heterogeneity and correlation, source function, and nonlinear/competitive sorption) on transport of solutes in either the SW or ES experiments is yet to be undertaken. The fact that models incorporating subsets of the processes known to exist within the aquifer can fit the plume behavior for each experiment separately suggests that a more sophisticated analysis that combines these processes to examine transport in both experiments is warranted.

5. Summary and concluding remarks To our knowledge, the ES field experiment provides the first quantitative field investigation of transport in a plume dissolving from a DNAPL chlorinated solvent source located in a natural aquifer setting in which the source history, plume development, and hydrogeological conditions are well documented. Longitudinal and transverse vertical dispersion parameters, determined in the course of the study, are consistent with those estimated from the previous Borden SW test. Modeling of the ES test data indicates that only PCE was retarded to any significant degree with TCM behavior essentially conservative and TCE near conservative. The apparent near-ideal behavior of PCE in the ES test provides a sharp contrast to the nonideal behavior of the same compound in the SW test. Retardation of PCE was f 3-fold lower in the ES (RPCE f 1.6) compared to the SW test (RPCE as large as f 5.7), and there was no significant temporal trend in RPCE of the ES plume. Although excellent PCE fits were achieved with transport simulations that assumed simple linear equilibrium sorption, larger longitudinal dispersivity values that better fit this solute (relative to TCM) are likely symptomatic of the occurrence of nonideal effects not explicitly modeled. For the most part, the high solute concentrations present in this test caused the plumes to behave in a nearly ideal fashion, although nonideal conditions

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(nonlinear sorption, physically heterogeneous materials, and potentially grain-scale nonequilibrium and chemical heterogeneity) are known or suspected based on complementary observations. Sorption data obtained from the laboratory studies predict that had both experiments been conducted at high concentrations, little difference in retardation between the two test sites would have been observed since differences in the strength of sorption were observed primarily at low concentrations. The concentration- and co-solute-based sorption and retardation analysis, albeit simplistic, produces trends that match those of the field plumes for both the ES and SW experiments. Importantly, the analysis predicts substantive differences in RPCE magnitude and trend between the two experiments based on consistent underlying assumptions and quantified experiment differences (e.g. different source conditions). While other ‘‘nonideal transport’’ effects may contribute, we suggest that solute and co-solute concentrations, parameters known to differ between the two experiments, may be important determinants in the differences in solute transport behavior between the two plumes that have heretofore received little attention. It is concluded that more advanced modeling approaches for both plumes are needed that incorporate a combination of transport nonidealities and hence provide a more rigorous analysis of the important contributions of the multiple potential underlying mechanisms. Substantive differing mobility for a common compound (PCE) between two tracer test experiments situated a short distance apart in a common aquifer was a surprising and interesting result that serves to underline the difficulty of making accurate predictions of contaminant migration at real sites. Many sites may have differences in source terms, solute plume compositions, and geological/physiochemical heterogeneity that far exceed the differences between the two Borden sites discussed—much larger differences in transport behavior may hence result within an individual site or plume. Our work specifically indicates that in circumstances where sorption is nonlinear and multiple solutes exist (both likely common), consideration of sorption properties and single and cosolute concentrations throughout the plume extent may be necessary to adequately predict solute transport.

Acknowledgements We wish to thank J. Rix for his assistance with numerical model simulations, H. Groenevelt and H. Mosaad for their assistance with laboratory sorption studies, Dr S. Feenstra for his fieldwork support and scientific discussions, Prof. J. Cherry for his original insight into the emplaced-source experiment concept and logistical support, and R. McLaren and Prof. E. Sudicky for assistance with initialization of the modeling work. The helpful comments provided by two anonymous reviewers are also acknowledged. The work was supported by the University of Waterloo-based University Consortium Solventsin-Groundwater Research Program which at the time of the ES field test was sponsored by Boeing, Ciba Geigy, Eastman Kodak, General Electric, Laidlaw Environmental Services, the Ontario Research Incentive Fund, and the Natural Sciences and Engineering Research Council of Canada. Partial financial support was also supplied by NSF through grant no. EAR-9804980.

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