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Jul 7, 2010 - organic compounds (HOCs) in marsh and mudflat sediments. This study assessed porewater movement in an intertidal mudflat...
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Environ. Sci. Technol. 2010, 44, 5842–5848

Assessment of Advective Porewater Movement Affecting Mass Transfer of Hydrophobic Organic Contaminants in Marine Intertidal Sediment YEO-MYOUNG CHO,† DAVID WERNER,‡ KEVAN B. MOFFETT,¶ AND R I C H A R D G . L U T H Y * ,† Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, School of Civil Engineering and Geoscience, Newcastle University, NE1 7RU, England, U.K., and Department of Environmental Earth System Science, Stanford University, Stanford, California 94305

Received November 25, 2009. Revised manuscript received June 23, 2010. Accepted June 25, 2010.

Advective porewater movement and molecular diffusion are important factors affecting the mass transfer of hydrophobic organic compounds (HOCs) in marsh and mudflat sediments. This study assessed porewater movement in an intertidal mudflat in South Basin adjacent to Hunters Point Shipyard, San Francisco, CA, where a pilot-scale test of sorbent amendment assessed the in situ stabilization of polychlorinated biphenyls (PCBs). To quantify advective porewater movement within the top 0-60 cm sediment layer, we used temperature as a tracer and conducted heat transport analysis using 14-day data from multidepth sediment temperature logging stations and onedimensional heat transport simulations. The best-fit conditions gave an average Darcy velocity of 3.8cm/d in the downward vertical direction for sorbent-amended sediment with a plausible range of 0cm/d to 8cm/d. In a limiting case with no net advection, the best-fit depth-averaged mechanical dispersion coefficient was 2.2 × 10-7m2/s with a range of 0.9 × 10-7m2/s to 5.6 × 10-7m2/s. The Pe´clet number for PCB mobilization showed that molecular diffusion would control PCB mass transfer from sediment to sorbent particles for the case of uniform distribution of sorbent. However, the advective flow and mechanical dispersion in the test site would significantly benefit the stabilization effect of heterogeneously distributed sorbent by acting to smooth out the heterogeneities and homogenizing pollutant concentrations across the entire bioactive zone. These measurements and modeling techniques on intertidal sediment porewater transport could be useful for the development of more reliable mass transfer models for the prediction of contaminant release within the sediment bed, the movement of HOCs in the intertidal aquatic environment, and in situ sequestration by sorbent addition.

* Corresponding author e-mail: [email protected]. † Stanford University, CEE. ‡ Newcastle University, CEG. ¶ Stanford University, EESS. 5842

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Introduction Sediments accumulate hydrophobic organic compounds (HOCs) such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and dichloro-diphenyltrichloroethane (DDT). Sediments thus act as reservoirs, exposing HOCs to benthic biota, releasing HOCs into porewater, and contributing HOCs to the aquatic food web. Recent research shows that certain sediment particle types, known as black carbon (BC), may have stronger sorption capacity than inorganic particles with coatings or inclusions of natural organic matter (1). Char, charcoal, soot, and their derivatives are such types with strong sorption capacity. Once the HOCs are sorbed into the BCs, they become much less available than HOCs sorbed on other mineral-based particles (1, 2). These findings motivated studies of a novel in situ sediment treatment strategy using activated carbon (AC) as a strong sorbent to sequester HOCs. By incorporating AC into HOC-contaminated sediment, HOCs would be redistributed, sorbed on to AC particles and become less available to porewater and biota. The proof of concept was demonstrated in a series of laboratory and field studies (3-11). For instance, introducing 3.4 dry wt% of AC into well-mixed sediment-water slurries in the laboratory yielded about 90% reductions of PCBs, PAHs, and DDTs in water phases and benthic organisms (3-9). Mixing about 2% AC into the top 30-cm sediment layer at a mudflat in San Francisco Bay, CA yielded 50-66% reduction in HOC concentrations in porewater, passive samplers and benthic test organisms (10, 11). Other than AC dosage, the difference in performance between laboratory and field trials may have been a result of HOC mass transfer in the field occurring without continuous and complete mixing of AC and sediment. In this case, diffusion between sediment and AC particles may be a limiting process for HOC mass transfer. To explain HOC mass transfer in a stagnant system, Werner et al. (12) built a mass transfer model of an unmixed sediment system that considered intra- and interparticle movement of HOCs by sorption-retarded molecular diffusion. The model predictions showed that the HOC mass transfer to AC in a quiescent system would be greatly retarded and the full effect of reduction in aqueous HOC concentration by AC would be delayed for a number of years (12). This model did not account for possible advective porewater movement in an intertidal mudflat (13-16), which could affect HOC mass transfer. Several mechanisms are believed to result in advective movement in intertidal or subtidal areas: bottom currents, propagating waves, subtidal pumping, bottom water density changes, resuspension of bed sediments, and bottom microtopography (13, 17, 18). The quantification of the porewater movement is challenging because the intertidal system undergoes rapid changes (13, 19). To assess advective porewater movement within the upper 0-60 cm sediment layer in a intertidal mudflat, we used “heat as a tracer” (20). The use of heat as a tracer of vertical groundwater flow has been a standard tool in hydrogeology (20), and remains a powerful tool in cases with predominantly vertical fluid and heat transport (20, 21). Such a case was the nearly horizontal intertidal mudflat of this study (slope approximately 0.25-0.5%, estimated from (22)). The low local relief and lack of significant inland water drainage imply very low horizontal hydrologic gradients at this site. The sediment surface temperature of intertidal areas fluctuates because of tidal action and solar radiation (23, 24). Heat 10.1021/es903583y

 2010 American Chemical Society

Published on Web 07/07/2010

transport by conduction and convection determines the temperature depth profiles in the sediment. We used the porewater movement determined by inverting a heat transport model to assess its relative importance in HOC mass transfer compared to diffusive mass transfer. An innovative aspect of this study was its combination of (a) the inverse approach of Silliman et al. (25) to quantify the vertical pore water flow beneath a water body of a known temperature with (b) the effects of ephemeral surface flooding on vertical fluid and heat flow (26) and (c) sediment temperature profile data from the challenging environment of an intertidal mudflat. This approach differs from previous descriptive studies of sediment temperature profiles in mudflats (23, 27) and studies that specified a priori a specific mechanism of porewater convection prior to calculating advective velocities (e.g., thermal density instability (13)). Another innovative aspect of this study was its use of heat as a tracer for understanding HOC transport in the environment. This work used field-collected temperature data and a heat transport model to estimate limiting values of vertical interstitial porewater flow velocities and mechanical dispersion coefficients. Then this information was applied to calculate a ratio of the rate of advection to the rate of molecular diffusion (a dimensionless Pe´clet number) as well as a ratio of the mechanical dispersion to the molecular diffusion. The study concludes with a discussion of the relative influences of advection, mechanical dispersion, and molecular diffusion processes on PCB mass transfer within the sediment bed.

Materials and Methods Site Description. The test site is a shallow tidal mudflat in South Basin adjacent to Hunters Point Shipyard, San Francisco, CA (Supporting Information (SI) Figure S1) with water depth ranging from 1.8 m to less than 0.6 m (28). This site is rarely exposed at low tide, emerging only during the lowest stages of the lower low water events of this mixed semidiurnal tidal system. Historical site activities at the shipyard resulted in the release of chemicals to the environment, including offshore sediments (29). There exist no visible surface water channels on the test sites. A sheet pile wall and rip-rap exist along the shoreline to prevent possible contamination from the upland. The combined results of Sedflume experiments (30) with site sediment and comprehensive hydrodynamic modeling studies (28) indicate that the South Basin area is a net depositional zone. The site comprises cohesive sediments not subject to exceeding sediment critical shear stress in most storm events (30). The top 10 cm of the sediment in the demonstration area comprise small gravel, shell, and clay particles. Underneath this top layer is a more homogeneous layer of clay, characteristic of bay mud. The sediment has a midrange PCB concentration of 1-10 ppm (29). Because PCBs tend to adsorb to finegrained sediment particles and organic matter, sediment resuspension and deposition are major contaminant transport pathways in South Basin. The net sediment deposition rate is about 1 cm per year or less (30). We utilized two of four test plots that were assessed in the previous field-scale AC application project (11), identified as Plots D and E (SI, Figure S1(C)). The two plots were separated by a distance of 4 m and were located approximately 25 m from the shoreline, within the tidal mudflat region. In Plot D, about 2 wt % of activated carbon(AC) was mechanically mixed down to a nominal 30 cm depth in January 2006, whereas Plot E served as an unmixed reference plot. Detailed descriptions of the test site, test plots, and AC introduction are given by Cho et al. (11). Vertical Sediment Temperature Profile. Vertical sediment temperature profiles were measured for 14 days in mid-

August 2007 and early March 2008. The sampling method was similar to the method described by Cho et al. (23). At each sampling time, two temperature logging stations were installed at the center of each plot, separated by approximately 0.6 m. After 14 days of data logging, the stations were retrieved and data downloaded. Each temperature logging station had eight temperature loggers (iBCod Type 22 L, Alpha Mach Inc.). These were situated at two levels above (15 cm, 2.5 cm) and at six levels below (2.5 cm, 5 cm, 15 cm, 30 cm, 45 cm, and 60 cm) the sediment surface. The temperature loggers had a resolution of 0.0625 °C with a precision of 0.5 °C (manufacturer-provided specifications (31)). The method is described more in detail in the SI. During temperature sampling periods, incoming shortwave solar radiation was measured by a solar radiation sensor (Solar Radiation Sensor and Micro Station Data Logger, Onset Computer Corporation) that was installed at the shore. We used the tide predictions by the National Oceanic and Atmospheric Administration (NOAA) as sea level data, which was corrected to use the surface elevation of the test plots (0.5 m above mean sea level) as the new datum (32). Heat Transport Model. We developed a heat transport model to assess the extent of heat transport by advective porewater movement in the saturated mudflat. The derivation and implementation of the heat transport equations followed the method developed for the commercial numerical simulation program SHEMAT for reactive groundwater flow (33). Assuming no heat sinks or sources in the sediment layer, the change in volumetric heat storage is equal to the sum of heat transport by diffusion and advection. For one-dimensional heat flow in the vertical direction (z), ∂(FbcbT) ∂T ∂ k ) - FwcwTv ∂t ∂z b ∂z

(

)

(1)

where Fb is the bulk density of the system (kgm-3), cb is the bulk specific heat capacity (Jkg-1K-1), T is the temperature (K), t is the time (s), kb is the bulk thermal conductivity (Wm-1K-1), Fw is the water density (kgm-3), cw is the water specific heat capacity (Jkg-1K-1), and v is the Darcy velocity (ms-1). We assumed a constant Darcy velocity independent of depth during model simulation time periods. We used linear combinations of solid and water contributions weighted by the porosity φ to estimate the bulk heat capacity (33). For estimation of bulk sediment thermal conductivity, the geometric mean model was used (34, 35). Assuming constant material properties during the time interval and spatial homogeneity, (φFwcw + (1 - φ)Fscs)

2 ∂T ∂T φ (1-φ) ∂ T ks ) 2 - Fwcwv ) (kw ∂t ∂z ∂z

(2) where Fs is the solid density (kgm-3), cs is the solid specific heat capacity (Jkg-1K-1), ks is the solid thermal conductivity (Wm-1K-1), and kw is the water thermal conductivity (Wm-1K-1). Additionally, the consideration of mechanical dispersion will modify eq 2 as, ∂T ) ∂t 2 2 ∂T ∂T φ (1-φ) ∂ T ks ) 2 + φFwcwDdisp 2 - Fwcwv (kw ∂z ∂z ∂z (φFwcw + (1 - φ)Fscs)

(3)

where Ddisp is the mechanical dispersion coefficient (m2s-1). Equation 3 was discretized forward in time and centrally in space for numerical simulation. The discretization methods and equations are shown in the SI. Model parameters were obtained experimentally or from the literature (SI Table S1). VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Temporal variations in the sediment temperature profile (colored lines) and the sea level (black solid line) at AC-mixed Plot D-1, and the solar radiation (black dotted line) for 14 days in August 2007. Solid thermal conductivity was determined by ex-situ measurements (2.27 ( 0.27 Wm-1K-1 (36)). The initial temperature conditions for each simulation were from the measured data. The upper-boundary condition for simulations was the measured temperature at 2.5 cm depth. As the lower boundary condition at 60 cm, we assumed a constant temperature. For model calibration, two simulation scenarios were considered and the best-fit parameter was determined by the least root mean squared error (RMSE) between the simulation results and field measurements. First, we tested the limiting case that heat transport could be explained by heat diffusion and advection without mechanical dispersion. By neglecting the mechanical dispersion term we tried to maximize the directional advective flow. The equation for this scenario was, (φFwcw + (1 - φ)Fscs)

The second limiting-case scenario assumed no advection but nonzero mechanical dispersion. This assumption was intended to consider a plausible case of a mud flat with very small net advection but with considerable mechanical dispersion. Although advective flow and mechanical dispersion occur concurrently in real systems, these two hypothetical model scenarios bracket the limits of advection and dispersion rates, consistent with our primary objective to explore the significance of fluid advection and dispersion in the sediments of the field site. We therefore assumed that the contribution of the dispersion term is negligible when fitting the maximum plausible vertical advective flow velocity in the first scenario and vice versa in the second scenario: 2 ∂T ∂2T φ (1-φ) ∂ T ks ) 2 + φFwcwDdisp 2 ) (kw ∂t ∂z ∂z (5)

There is a limit to the minimum Darcy velocity and mechanical dispersion coefficient that can be reliably derived from temperature measurements and heat transport modeling. The minimum extractable parameter can be defined by the ratio of the relative weight of each process with a certain tolerance level. In this study, we selected a relative tolerance of 1%, which was large enough to give an apparent change 5844

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Ddisp,min )

φ (1-φ) (kw ks ) × 0.01 ) 4.6 × 10-9m2 /s φFwcw

(6)

The detection limit of Darcy velocity was indirectly calculated using the minimum detectable dispersion coefficient. As dispersivity in a system is generally assumed as 10% of the length of the system (i.e., the measured temperature profile, 60 cm) (37), the minimum detectable Darcy velocity would be 0.7 cm/d. These detection limits were also useful to determine minimum simulation steps of mechanical dispersion coefficient and Darcy velocity, where 1 × 10-8 m2/s and 1 cm/d were selected respectively.

Results and Discussion

2 ∂T ∂T φ (1-φ) ∂ T ks ) 2 - Fwcwv ) (kw ∂t ∂z ∂z

(4)

(φFwcw + (1 - φ)Fscs)

in model simulation data plots. Therefore, the detection limit for the dispersion coefficient would be,

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Temporal Variation in the Sediment Temperature Profiles. Figure 1 and Figure 2 show temporal variations of sediment temperature at various depths with solar radiation and tides for 14 days in August 2007 and March 2008 for station D-1 (figures for other stations are shown in the SI). Each temperature data set showed clear diurnal patterns with variations depending on tidal cycle and solar radiation. These diurnal patterns were previously observed in a tidal mudflat by Cho et al. (23). Temperature fluctuations were greater at shallower depths in the mud. Sediment surface temperature (2.5 cm depth) was highest at mid-day, when the solar radiation was highest, and lowest just before sunrise. Seasonal differences between the two sampling periods were evident with temperatures at the lowest sediment layer (60 cm) 5 °C higher in August 2007 than in March 2008. This difference was probably due to the higher temperature of overlying seawater during the summer. We noted that there was a change of the temperature profiles, which correlated well with the apparent change of tidal pattern during the two weeks (08/19/07 and 03/11/08). Anomalously large temperature variability was observed at depth (g30 cm) in part of the control plot during both sampling periods (station E-2 August 2007; station E-1 March 2008). Scatter plots and coefficients of determination confirmed the anomaly (SI Figure S9, Tables S2 and S3). Among the four August 2007 data sets, the data set for logging station E-2 showed weak correlation with the other three temperature data sets (SI Table S2). The difference between E-2 and the other stations increased with depth. Similarly, in March 2008,

FIGURE 2. Temporal variations in the sediment temperature profile (colored lines) and the sea level (black solid line) at AC-mixed Plot D-1, and the solar radiation (black dotted line) for 14 days in March 2008.

TABLE 1. Heat Transport Model Simulation Summary Considering Diffusion Only or Addition of an Advection or Dispersion Terma D-1 August 2007 first wk

second wk

scenario

D-2 RMSE

first wk

second wk

RMSE

E-2 RMSE

RMSE

heat diffusion only with advection v (cm/d) with dispersion Ddisp (m2/s)

6 1.3 × 10-7

0.093 0.056 0.062

4 3.9 × 10-7

0.166 0.160 0.107

14 1.6 × 10-7

0.203 0.140 0.189

17 3.0 × 10-7

0.402 0.334 0.375

heat diffusion only with advection v (cm/d) with dispersion Ddisp (m2/s)

2 0.9 × 10-7

0.093 0.079 0.076

0b 5.6 × 10-7

0.258 0.258 0.162

5 1.3 × 10-7

0.123 0.096 0.099

18 5.9 × 10-7

0.641 0.522 0.556

D-1 March 2008

E-1

scenario

D-2 RMSE

E-1 RMSE

E-2 RMSE

RMSE

heat diffusion only with advection v (cm/d) with dispersion Ddisp (m2/s)

8 2.4 × 10-7

0.124 0.044 0.082

5 1.1 × 10-7

0.100 0.063 0.074

74 3.51 × 10-6

0.511 0.456 0.285

7 2.1 × 10-7

0.108 0.045 0.056

heat diffusion only with advection v (cm/d) with dispersion Ddisp (m2/s)

3 1.8 × 10-7

0.119 0.075 0.066

2 0.9 × 10-7

0.114 0.072 0.097

2 1.84 × 10-6

0.588 0.585 0.309

4 2.4 × 10-7

0.172 0.073 0.101

a Best-fit models are presented for Darcy velocity (v), the dispersion coefficient (Ddisp), and root mean squared error (RMSE). b Below tolerance criterion (0.7 cm/d).

the data from logging station E-1 showed anomalous behavior compared to the other three temperature data sets (SI Table S3, Figure S9). The shape of these temperature profiles suggests a degree of thermal homogenization by macropore mixing around the loggers. It is possible that the seal between the instruments and surrounding sediments was compromised, permitting tidal flow and convective mixing within the profiles, but the confinement of this phenomenon to the undisturbed control plot suggests that the macropores may be of natural origin due to bioturbation or buried debris. Therefore, in the following section, we conservatively withheld a conclusion from the model simulation results for Plot E, only showing them for reference in Table 1. Heat Transport Model. The sediment temperature profiles were simulated with either nonzero Darcy velocities (scenario one) or nonzero mechanical dispersion coefficients (scenario two) during four days (day 2 to day 6) of the first week of sampling and four days (day 8 to day 12) of the second week of sampling in August 2007 and March 2008 (Table 1). The best-fit parameters were separately assessed

in each week to consider apparent changes in temperature profile pattern and the tidal pattern during the sampling period (Figure 1 and Figure 2). Simulated temperature profiles with heat diffusion as the only transport process yielded RMSEs between the field data and the simulations much less than the precision of the loggers (0.5 °C) and close to the resolution (0.0625 °C) for most cases. For example, the simulation results for the first week of the March 2008 period at D-1 show that heat diffusion alone could explain the temperature fluctuation throughout the sediment profile with an RMSE value of 0.124 (Figure 3A). Although heat transport by diffusion alone could largely account for the measured temperature fluctuations, still discrepancies remained. In most cases, the addition of an advection term enhanced the model fit. For the simulation results of week 1 of March 2008 at D-1, the least RMSE (0.044) was obtained by including a downward 8 cm/d Darcy velocity, resulting in less than one-half of the RMSE without the advection term (Figure 3B). For sorbent amended sediment VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Simulation results for day 2 to day 6 (week 1) in March 2008 at Plot D-1. A) Heat diffusion only. (B) Diffusion and advection. Measurements (dotted line) and simulation results (solid line). The x-axes and y-axes represent day and temperature (°C), respectively. (Plot D), the best fit Darcy velocity ranged from 0 to 8 cm/d, and the reduction in RMSE compared to the diffusion-only simulations ranged from 0% to 65%. Possible mechanisms for the downward movement are density-driven convection (13) or a repetitive pressure head fluctuation coupled with pressure relief at a location not affected by tides (38). It is also plausible that the saturated mudflat sediment may have no net fluid movement in the vertical direction. Instead, the sediments may experience oscillating flow due to tidal pumping (38), so a mechanical dispersion term was examined to assess whether the model fit could be enhanced by consideration of nondirectional advective movement. As shown in Table 1, the use of mechanical dispersion in a limiting case with zero advection also enhanced the model fit compared to simulations with diffusion only, with a plausible range of dispersion coefficients from 0.9 × 10-7 m2/s to 5.6 × 10-7 m2/s for sorbent-amended plot D. The enhancement in RMSEs compared to the diffusion-only cases ranged from 15% to 46%. An average dispersion coefficient for the mixed sediment plot D was 2.2 × 10-7 m2/s. In summary, heat transport in the intertidal mudflat was explained largely by heat diffusion, while advective porewater movement or mechanical dispersion further improved the match of predicted and measured temperature profiles. The best-fit Darcy velocity and mechanical dispersion coefficient for the AC mixed plot were 3.8 ( 2.5 cm/d and 2.2 × 10-7 ( 1.7 × 10-7 m2/s, respectively (mean ( one standard deviation). Relative Importance of Advection and Mechanical Dispersion for PCB Mass Transfer. To compare the relative contribution of advection and molecular diffusion to PCB mass transfer within the sediment layer, the dimensionless Pe´clet number (Pe ) (Lu)/(D)) was evaluated, where L is the characteristic length (m), u is the interstitial velocity ((Darcy velocity)/(porosity), m/s), and D is a nominal diffusion coefficient for PCBs (5 × 10-10 m2/s). Although PCB mass transfer would be retarded in the presence of sorbents by 5846

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sorption, it would not affect the ratio because both transfer processes would be slowed equally. An average Darcy velocity of v ) 3.8 cm/d and a porosity of 0.57 gave an average interstitial velocity of u ) 6.7 cm/d. The calculation of a Pe´clet number is sensitive to the characteristic length estimate. In this study, the characteristic length was defined as the interparticle distance between sediment particles and closest activated carbon (AC) particle considering the process of PCB stabilization by AC. Then, the characteristic length would greatly depend on the AC distribution with a fixed AC dose. If the AC distribution is not homogeneous, then the characteristic length for mass transfer tends to increase. First, we assessed an ideal case: a uniform distribution of AC particles. Assuming 3 wt % of AC addition, the characteristic length was estimated as L ) 240 µm (see SI). The resulting Pe´clet number is Pe ) 0.37, indicating that the PCB diffusion process contributes more than directional advective PCB transport to the sequestration of PCBs by AC, although interstitial flow should not be ignored. The opposite extreme case would be an absence of AC-sediment mixing, where AC particles are placed on the sediment surface just like a thin cap. Then the characteristic length would be the entire target sediment layer thickness or the bioactive zone thickness (10-15 cm), which would give a Pe´clet number larger than 100. In an actual AC amendment application, the characteristic length would be in between these two extreme cases (probably a few mm to a few cm) indicating a significant contribution by advective flow (Pe ) 1-10). PCB mass transport may also be partially controlled by mechanical dispersion, which is driven by differential advective porewater movement. In this study, the average dispersion:diffusion coefficient ratio was on the order of 400:1, which suggests that mechanical dispersion may accelerate PCB mass transport within the sediment bed. In a homogeneously distributed AC-sediment system, the role of mechanical dispersion would not be important because

the characteristic length is at the pore size scale. However, in the case of heterogeneous AC distribution, mechanical dispersion can smooth out the heterogeneity and benefit the treatment by AC within the sediment layer by homogenizing pollutant concentrations across the entire bioactive zone. Our heat transport simulation results showed that advective flow as well as mechanical dispersion may vary temporally and spatially. The plausible interstitial flow rate was up to u ) 14 cm/d (for Plot D, where Darcy velocity is 8 cm/d), implying that PCB transport by advective porewater flow could be more substantial at certain field conditions. The dispersion/diffusion ratio might also range from several hundreds to thousands. In brief, the advective flow and mechanical dispersion quantified in our test site would benefit the heterogeneous AC-sediment system by promoting PCB mass transfer from remote sediment particles to AC particles. Significance. In this study, we investigated the relative significance of molecular diffusion, directional advective flow, and mechanical dispersion for mass transfer of a PCB, a typical example of hydrophobic organic compounds (HOCs) in sediment. While the information may be useful to assess general HOC transport and fate, it is especially useful to predict the long-term benefit of in situ treatment processes such as sequestration by activated carbon (AC) sorbent within the sediment bed. The existence of advective flow and mechanical dispersion would accelerate the remedial action by AC, shorten the monitoring period, and further increase public acceptance of the remediation option. Heat transport modeling is a convenient and applicable indirect method to examine such flow and dispersion for shallow mudflat or marsh-like sediments. It should be noted that this method has certain detection limits for Darcy velocities and dispersion coefficients. Heat diffusion is faster than molecular diffusion for HOCs in many cases, so the model may be unable to extract values from a system with small, but still-significant, advective flow or mechanical dispersion properties under the detection limits. However, these caveats did not severely affect the utility of the method in this study, since the simulation results showed significantly higher values of Darcy velocity (3.8 cm/d) and mechanical dispersion (2.2 × 10-7 m2/s) than the estimated detection limits (0.7 cm/d and 4.6 × 10-9 m2/s). We expect this modeling framework and simulation results could enable more reliable assessments of the fate and movement of HOCs within the sediment bed and thus benefit the prediction of the performance of in situ sediment amendments.

Acknowledgments This project was supported by the Department of Defense Environmental Security Technology Certification Program (ESTCP), project ER-0510, and the Strategic Environmental Research and Development Program (SERDP), project ER1552. Y.-M.C. was supported by a Stanford Graduate Fellowship. K.M. was supported by National Science Foundation grant EAR-0634709. Collaboration with Newcastle University was facilitated by the Leverhulme Trust, grant FOO 125/AA. We thank Keith Forman (U.S. Navy, NAVFAC Southwest Division, San Diego, CA) and Dane Jensen (U.S. Navy, NAVFAC Southwest Division, San Diego, CA, Remedial Project Manager for Hunters Point Parcel F), and Leslie Lundgren (Tetra Tech EM, Inc., San Francisco, CA) for their assistance and support in the study.

Supporting Information Available Model discretization of the heat transport model, model parameters, temperature measurement method, additional temperature profiles, coefficients of determination among

the four temperature profiles, and estimation of characteristic length. This material is available free of charge via the Internet at http://pubs.acs.org. Also, raw sediment temperature profile data, tide prediction data, and solar radiation data are freely available from the authors.

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