A field experiment and numerical modeling of a tracer at a gravel ...

Hydrogeology Journal (2014) 22: 1795–1805 DOI 10.1007/s10040-014-1184-3

A field experiment and numerical modeling of a tracer at a gravel beach in Prince William Sound, Alaska Qiaona Guo & Hailong Li & Michel C. Boufadel & Jin Liu Abstract Oil from the 1989 Exxon Valdez oil spill persists in many gravel beaches in Prince William Sound (Alaska, USA), despite great remedial efforts. A tracer study using lithium at a gravel beach on Knight Island, Prince William Sound, during the summer of 2008 is reported. The tracer injection and transport along a transect were simulated using the two-dimensional numerical model MARUN. Model results successfully reproduced the tracer concentrations observed at wells along the transect. A sensitivity analysis revealed that the estimated parameters are well determined. The simulated spatial distribution of tracer indicated that nutrients applied along the transect for bioremediation purposes would be washed to the sea very quickly (within a semidiurnal tidal cycle) by virtue of the combination of the two-layered beach structure, the tidal fluctuation and the freshwater flow from inland. Thus, pore-water samples in the transect were found to be clean due to factors other than bioremediation. This may explain why the oil did not persist within the transect. Keywords Gravel beach . Tide . Tracer tests . Numerical modeling . Alaska (USA)

Received: 25 September 2013 / Accepted: 7 August 2014 Published online: 30 August 2014 * Springer-Verlag Berlin Heidelberg 2014 Q. Guo ()) : J. Liu School of Earth Sciences and Engineering, Hohai University, No. 1 Xikang Road, Nanjing, 210098, People’s Republic of China e-mail: [email protected] H. Li State Key Laboratory of Biogeology and Environmental Geology and School of Water Resources and Environmental Science, China University of Geosciences, Beijing, 100083, China H. Li MOE Key Laboratory of Groundwater Circulation & Environment Evolution, China University of Geosciences, Beijing, 100083, China M. C. Boufadel Department of Civil and Environmental Engineering, Center for Natural Resources Development and Protection, New Jersey Institute of Technology, Newark, NJ 07102, USA

Introduction Oil spills are potentially the most destructive type of pollution to impact gravel beaches (Bodin 1988; Owens et al. 2008; Hayes et al. 2009). In March 1989, the Exxon Valdez tanker spilled about 41 million liters of Alaskan North Slope crude oil in Prince William Sound (PWS), Alaska. The spill polluted around 800 km of rocky intertidal shorelines within PWS (Bragg et al. 1994; Neff et al. 1995). Subsurface oil residues persist in many polluted beaches in PWS, although there have been great efforts with respect to remediation (Guo et al. 2010; Hayes and Michel 1999; Li and Boufadel 2010, 2011; Owens et al. 2008; Short et al. 2004, 2006, 2007; Taylor and Reimer 2008; Xia et al. 2010; Xia and Boufadel 2011). Recently, Li and Boufadel (2010) demonstrated the hydrogeological mechanism contributing to the persistence of subsurface Exxon Valdez oil in a two-layered beach on Eleanor Island, PWS. They found that the water table dropped into the lower layer during low tide, and subsequently, oil floating on it entered the lower layer and remained entrapped there due to capillary forces. They further conducted a tracer study along two transects on this beach, and explained the oil persistence within one of the transects using tracer methods (Li and Boufadel 2011). Xia et al. (2010) reported the hydrodynamic factors affecting the persistence of the Exxon Valdez oil at a shallow bedrock beach on Knight Island, PWS. They found that nutrient amendment to this beach would enhance oil biodegradation if nutrients were the limiting factor. It is necessary to have a thorough understanding of the beach hydrogeological characteristics and groundwater dynamics in order to restore the oiled beaches (Owens et al. 2008; Li and Boufadel 2010). In a study conducted on a gravel beach on Knight Island (KN-114A, see Fig. 1a) located at 147°47′24.34″W and 60°29′5.56″N, Guo et al. (2010) found a two-layered beach structure which is characterized by a high-permeability upper layer underlain by a low-permeability lower layer. Their findings corroborated the general structure of beaches in mid- and high-latitude regions (Davies 1980; Hayes 1967; Owens et al. 2008). They also found that the water table of the beach is higher than the interface between the upper and lower layers, which prevented Exxon Valdez oil from penetrating into the lower layer in 1989.

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Fig. 1 a Location of the studied beach (KN-114A) on Knight Island, Prince William Sound, Alaska; b Cross-sectional view of the transect, as the simulation domain when the aquifer has a uniform thickness of 3 m. The filled circle represents the tracer injection, and the rectangle represents the port A of each well

However, it was still not known why the bioremediation conducted on the beach did not successfully prevent the oil from persisting there. A tracer study was conducted using lithium as a conservative tracer on beach KN114A in order to analyze the hydrodynamic factors associated with the transport and residence time of nutrients applied for oil bioremediation in the beach, which are closely related to oil-persistence in beaches. The water table and the pore-water concentrations of salinity and lithium in the variably saturated beach aquifer were observed during the tracer study. The results of the observations and two-dimensional (2D) numerical simulations of the water table and pore-water salinity have been reported in Guo et al. (2010). Here, the results of the tracer study are reported and analyzed. The present investigation has two goals, the first being to better understand the hydrodynamics within the beach and how it affects the persistence of oil in the beach, while the second is to discuss the implications of the Hydrogeology Journal (2014) 22: 1795–1805

tracer study with regard to the effects of bioremediation on the oil degradation.

Field experiment In order to quantify the beach groundwater hydrodynamics, a tracer study was conducted on beach KN-114A, Knight Island (147° 47′ 24.34″ W and 60° 29′ 5.56″ N; Fig. 1a), during the period 20–29 June 2008. The selected transect has an along-shore width of only 70 m with an average intertidal beach slope of 10 %. The surface material of the beach includes boulders, cobbles, and pebbles. Beneath the upper layer is a lower layer that comprises compacted coarse sediments and fine-grained sediments. The lower layer is much less permeable than the upper layer, due to the compactness and fine-grained sediments. The maximum tidal range during the study was 4.8 m, which represents a spring tide. Beach KN-114A DOI 10.1007/s10040-014-1184-3

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was contaminated by the 1989 Exxon Valdez oil spill (Neff et al. 1995); however, no oil residues were found in the transect that was established in 2008. Lithium in a technical grade anhydrous LiNO3 (Cyprus Foote Mineral, Kings Mountain, North Carolina, USA) was used as the conservative tracer, which has been applied successfully in previous beach tracer studies (Wrenn et al. 1997a, b). Tracer studies were conducted during the falling tide using seawater solutions of lithium nitrate from two 200L tanks in sequence. The first tank contained a concentration of 3,290 mg/L of lithium nitrate and was used in the first 55 min, and the second tank contained a concentration of 4,350 mg/L of lithium nitrate and was used in the following 50 min. The corresponding lithium concentrations of tracer solution were 329 and 435 mg/L, respectively. The difference in concentration was because it was not feasible to accurately measure the water volume in the tanks because they got deformed upon placement on uneven pavements. The tracer solution was applied to the beach surface through small holes (diameter 2 mm) uniformly distributed along the whole length of a manifold located between the wells W3 and W4 (Fig. 1b). The application occurred during the falling tide, started at 8:30 AM, 21 June 2008 (t=19.91 h) and lasted for 1 h and 45 min. The observation time during the field study started from 20 June 12:35 AM, 2008, which is taken as initial time (t=0) throughout this report. The flow rate per unit length of the 5-m long manifold was 86±9.1 L/ h/m. During the tracer injection, no ponding was observed on the beach surface. The measurements of tracer concentration began immediately at the end of the tracer injection. Along the transect, five pits were hand dug with depths of 0.53–1.02 m (Table 1). A whole-length-slotted PVC pipe and a multiport sampling well were installed vertically within each pit. There is vertical flow within the PVC pipe since it was screened for the whole depth. A self-logging pressure transducer (Mini-Diver, data logger) was placed at the bottom of the PVC pipe to record the water pressure every 10 min. The readings of the pressure transducer were calibrated against the barometric pressure monitored by an air-pressure sensor (BaroLogger, DL500, Schlumberger) during the same period. Rainfall occurred between 0:00 AM and 8:00 AM 23 June 2008, and the cumulative amount was measured at 1.5 cm, using a bucket rain gauge.

The stainless-steel multiport sampling wells had three sampling ports, labeled A, B and C from the bottom up. They were covered with fine stainless-steel screen to prevent blockage by fine sediments. The distance between any two adjacent ports was 23 cm. The depths of the deepest ports (A) for all wells are listed in Table 1. Each port was connected to the bottom of a stainless-steel tube whose top was connected to a Luer lock three-way valve at the top of the well through Tygon tubing. Water samples (approximately 100 ml) were collected using 60-ml Luer lock syringes from the multiport sampling wells and placed in 120-ml polyethylene bottles (Fischer Scientific, Fairlawn, NJ, USA). The samples were shipped to the laboratory at Temple University in Philadelphia, for chemical analysis of chlorine concentration and lithium concentration. The chlorine concentration of each sample was transformed into salinity using the chlorine–salinity ratio of 19.4:35 (Duxbury and Duxbury 2001). Based on this method, salinity data were obtained for a total of 76 samples. Lithium concentration was measured by atomic absorption spectroscopy with an airacetylene flame at 670.8 nm (210VGP Atomic Absorption Spectrophotometer, Buck Scientific, Inc). Lithium standards were prepared with ACS (American Chemicals Standard) grade Li2CO3 (Wrenn et al. 1997b). The average salinity in the samples collected from the seawater adjacent to the beach was 24.2 g/L, which is close to values reported by Coyle and Pinchuk (2005), where they reported that the salinity of shallow seawater varies seasonally and is 28.8 ± 0.8 g/L during the beginning of July in Prince William Sound. More details about field measurements can be found in Guo et al. (2010).

Numerical simulations Numerical model

Numerical simulations were conducted using the 2D finite element model MARUN (MARine UNsaturated model), taking into account the effects of salt concentration on fluid density and viscosity (Boufadel et al. 1999a). MARUN is able to simulate two-component groundwater flow (one component is salt and another could be a tracer

Table 1 Surface elevation and thickness of the highly permeable upper layer at different locations in the transect (after Guo et al. (2010)) Location

Distance from W1, x (m)

Surface elevation, z (m)

Thickness of upper layer (m)

Depth of deepest port (m)

Depth of pit (m)

W1 P1 W2 W3 W4 W5 P2 P3

0 5.28 10.56 15.2 19.66 24.39 28.47 70.0

4.95 4.28 3.61 3.12 2.51 1.93 1.68 −0.86

0.8 0.4 0.4 0.25 0.1 0.15 0.2 0.2

0.83 NA 0.62 0.57 0.69 0.34 NA NA

1.02 NA 0.81 0.76 0.88 0.53 NA NA

Points P1, P2, and P3 are reported because they represent a change in the surface geometry and/or the interface between layers. NA not applicable Hydrogeology Journal (2014) 22: 1795–1805

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or nutrients) in variably saturated porous media. The variable pore-water saturation, the relative permeability and the capillary pressure were described by the van Genuchten (1980) model. The governing equations for the groundwater flow, and the solute and tracer transports are given in details in Guo et al. (2010). The MARUN code was verified by reproducing previous well-known numerical results such as the Henry’s problem of seawater intrusion (Frind 1982; Croucher and O’ Sullivan 1995; Boufadel et al. 1999a) and the Elder problem (Elder 1967; Boufadel et al. 1999b). It was also validated extensively in the literature (Boufadel 2000; Naba et al. 2002; Li et al. 2007, 2008; Guo et al. 2010) using laboratory and field experiment data.

Simulation domains The simulated domain has a uniform thickness of 3 m and a horizontal extent of 70 m (Fig. 1b). Field investigation, water-table observation and numerical simulations indicated that the beach sediments can be conceptually modeled as two piecewise homogeneous layers: a permeable upper layer of coarse, loose material and a much less permeable lower layer with compacted fine material (Guo et al. 2010). The thickness of the upper layer at each location is listed in Table 1. The two-layered structure model was supported by qualitative descriptions of the beaches in mid- and high-latitude regions including Prince William Sound (Hayes and Michel 1999; Owens et al. 2008). The two-layered beach structure was also observed in gravel beaches in Prince William Sound investigated by Li and Boufadel (2010), and Xia et al. (2010). During installation of the observation wells in each pit, the original compacted/consolidated material of the lower layer was replaced by loose, mixed materials from the surface and lower layers. This significantly enhanced the permeability of the materials of the lower layer in the pit (referred to as pit effect hereafter), which should be considered in modeling the field observations. In model calibration, the pit was regarded as a zone with the same permeability as the upper layer. Each pit was 0.4–0.6 m in diameter at the top narrowing down to 0.2–0.3 m at the bottom. The bottom of the pit was 0.19 m lower than the deepest sampling port (i.e., port A, Table 1).

(10−9 m2/s) based on chloride diffusion in pure water (Schulz 2000); seawater salinity (24.2 g); and seawater– freshwater density ratio (1.02) which was calculated by the method of Fofonoff and Millard (1983) based on the in-situ average temperature (12 °C) and barometric pressure (1,037 kPa).

Boundary and initial conditions

The tracer injection was simulated by specified water flow rate and the concentrations of salt and tracer during the injection at the boundary node on the beach surface closest to the manifold. At the saturated part of the landward boundary of the domain, the observed water level in well W1 and the observed salinity (~0.0 g/L) were used as the boundary conditions for the water table and salinity, respectively (Guo et al. 2010). No-flow and nomass transport boundary conditions were assigned to the domain bottom and the boundaries of the unsaturated zone, which include the upper part of the landward boundary and the beach surface higher than the sea level. On the submerged beach surface and sea-floor, the water pressure was equal to the tidal seawater column above the beach surface, and the salinity was flow-direction dependent, with constant seawater concentration where flow was inward, but zero concentration where flow was outward. At each time step, the portion of the beach surface submerged by tide was updated by comparing the tidal level and beach surface elevation. No seepage face was observed during the study (Guo et al. 2010). The simulation started from a hydrostatic initial condition at low tide with a sharp freshwater-seawater interface approximated by the Ghyben-Herzberg approximation (Michael et al. 2005), and was run for ten springneap-tidal cycles to obtain the quasi-steady state numerical solution. Then the tracer injection was simulated. The convergence criterion of the Picard iteration for solving the nonlinear groundwater flow equation was 10−5 m. In order to eliminate numerical oscillations, the variable time step was used and controlled by the grid Courant number which was set to be less than 0.95 (The courant number is defined as Cr= vΔt/Δl, where v is the Darcy velocity, Δl is the maximum of Δx and Δz of the mesh).

Results Model parameters Model parameter values used in the simulation are summarized in Table 2. In order to approximate the negligible capillary effects of the upper layer, relatively great values were chosen for the van Genuchten parameters (α =40 m−1 and n=7). Although values of α > 40 m−1 and n >7 can describe the coarse material in the upper layer more accurately, they will enhance the nonlinearity of the flow equation and increase dramatically the computation time and, therefore, they were not used. Other fixed model parameter values used are: saturated water content (0.3 m3/m3); residual water content (0.01 m3/m3); molecular diffusion coefficient Hydrogeology Journal (2014) 22: 1795–1805

Simulation with pits The numerical simulations of density-coupled groundwater flow and the solute transport model reproduced the observed water table, salinity, and lithium concentration at wells W1–W5 in the transect. The results of water-table monitoring and salinity have been shown and discussed by Guo et al. (2010). Here, the focus is on the lithium concentration. Figure 2 shows the observed and simulated lithium concentrations at different ports and the tidal level variations. One can see that the match between the simulation and observation is very good at ports B and C in W4. The root mean square error (RMSE) was used to DOI 10.1007/s10040-014-1184-3

1799 Table 2 Model parameters and their values used in the numerical simulations (after Guo et al. 2010) Parameter α n K φ Sr Ss β sea αL αT τDm

Definition Capillary fringe parameter of the van Genuchten (1980) model Sand grain-size distribution parameter of the van Genuchten (1980) model Hydraulic conductivity for the medium saturated with freshwater Saturated water content Residual water content Specific storativity Density ratio of the seawater to freshwater Longitudinal dispersivity Transverse dispersivity Molecular diffusion coefficient in porous media

Unit m –

−1

m/s m3/m3 m3/m3 m – m m m2/s

Value 40.0 7.0 10−3 and 10−2 (upper layer), 10−5 (lower layer) 0.3 0.01 10−5 1.02 0.1 0.005 10−9

Fig. 2 Observed (symbols) and simulated (solid lines) lithium (Li) concentration of the pore water at the ports of wells: a W3, b–d W4 and e–f W5. The tidal seawater level variation with time is also shown on the right vertical axis. The initial time (t=0) is 12:35 AM, 20 June 2008. The 1.75-h-long seawater-lithium-tracer injection started at t=19.91 h. In each panel, observed and simulated results only from one port for W4 and W5 are shown. The tidal level, the elevations of the beach surface, of the interface between the surface and lower layers, of port A at these observation wells are also shown Hydrogeology Journal (2014) 22: 1795–1805

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evaluate the model’s performance; RMSE evaluates the residual between observed and predicted data by the models (Anderson and Woessner 1992). The lower the RMSE, the more accurate the prediction is. The RMSE for port B and port C in W4 are 9.0 and 5.6, respectively (Table 3). However, the relatively small magnitude of RMSE values in the model indicates that the predictions of the numerical model are reasonable. Figure 2 shows that the match between the simulation and observation became worse at port A in W4 and ports A and B in W5, albeit more or less acceptable. The error may be caused by modeling the three-dimensional (3D) pits by a 2D trench. Despite the errors between the simulation and observation results, the simulation results captured the main features of the observations of the groundwater flow and tracer transport. There are no observed data at wells W1 and W2, which is not surprising considering their distance from the manifold. Both the observed and simulated lithium concentrations at W3 are very small. The maximum of the observed lithium concentration at port A and port C is around 0.3 and 0.7 mg/L, and the simulated ones are 1.7 and 4.4 mg/L, respectively. This is because the tracer was applied during the falling tide (started at t=19.91 h), and the tracer would not arrive at W3 until W3 was submerged by the next high tide (at the time around 28 h), and most lithium had flowed into the low platforms or into the sea before the surface of W3 was submerged. The variations of the lithium concentrations with time at W4 and W5 are similar, which are different from those at well W3. The lithium concentration at W4 and W5 increased continuously before reaching its maximum at the low tide after application, and then decreased with time during the rising tide. The observed lithium concentration approached zero within 24 h. The simulated arriving time of lithium at W4 is well matched with the observed one. The simulated arriving time at port B of W5 shows a small delay compared with the observed one. In spite of this small discrepancy, the arriving time of lithium at W4 and W5 indicates that the estimated hydraulic conductivities for the two layers are reasonable. Figure 3 reports the simulated contours of the lithium concentration and groundwater flow velocity at various times after the tracer injection. Figure 3a shows that the plume moves mainly downward below the water table when tide falls due to the pit effect. Figure 3b shows that the plume moves seaward and downward under the force of freshwater flow from inland at the first low tide after the tracer injection. Figure 3c shows that the plume moves mainly seaward affected by freshwater flow from inland and tide forcing during tide rising until the lithium

solution mixes with the seawater. When the rising tide reached the lithium plume, the tide carried the lithium solution moving landward and the lithium plume covered the submerged beach areas where the lithium solution mixes with the seawater (Fig. 3d). Figure 3e shows that the lithium solution at t=33.0 h (11.3 h after injection was finished) was distributed within the upper layer, and its concentration was reduced to about 10 % of the total concentration for injection. In this case, the simulated tracer spatial distribution in Fig. 3 indicates that the tracer applied along the transect would be washed to the sea very quickly (within a semi-diurnal tidal cycle) by the combination of the two-layered beach structure, the tidal fluctuation and the freshwater flow from inland.

Sensitivity analysis Different scenarios were investigated to assess the sensitivity of the beach hydrodynamics to the conceptualization of the model and the parameters’ values. The above-discussed simulation is referred to as the “base case”. In each simulation, only the value of the model parameter for sensitivity analysis was changed and all the other parameters were fixed. Effect of dispersivity Dispersivity is an important parameter for solute transport. Sensitivity analysis with respect to the dispersivity was conducted for the lithium concentration. In each simulation, the value of longitudinal dispersivity αL or the ratio of transverse dispersivity to longitudinal dispersivity αT/ αL was changed from the base case (αL = 0.1 m, αT/αL =0.05). Only the cases with fixed ratio αT/αL of 0.05 are reported here. Figure 4a,b shows that increasing or decreasing αL values decreased the maximum lithium concentration at ports B and C in W4, whereas Fig. 4c shows that, with the base case, the difference between the simulated results when increasing αL to 0.5 m or decreasing it to 0.05 m (while keeping the dispersivity ratio constant) is essentially equal. The maximum lithium concentration at port B in W5 decreased with increasing αL value, while it increased when decreasing the αL value. Changing the dispersivity had no effect on the arrival time of the peak at any location, and its effects were only limited to the magnitude of the concentration. Both increasing and decreasing the dispersivity had no effect on the lithium concentration at W3, for which data are not shown here. In addition, from Table 3 one can see that the RMSE values in both models of increasing and decreasing αL are higher than those of base case at all the ports,

Table 3 The root mean square error (RMSE) of fitting at different ports for different models Port

Base case

Increasing αL

Decreasing αL

Increasing K

Decreasing K

W4B W4C W5B

9.0 5.6 16.9

9.8 17.5 36.7

21.1 10.9 47.5

7.6 12.9 32.9

40.5 20.6 53.3

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Fig. 3 Simulated spatial lithium concentration distributions and water table (black dashed lines) of the transect at different times. The pit effect was considered. a At 40 min after the injection was started (t=20.6 h), b at the first low tide after the tracer injection (t=21.7 h), c at rising tide after the tracer injection (t=23.2 h), d at high tide after the tracer injection (t=28.2 h), and e at second low tide after the tracer injection (t=33.7 h). The initial time (t=0) is 12:35 AM, 20 June 2008. The 1.75-h long lithium tracer injection started at t=19.91 h. The five lithium concentration contours represent 50, 100, 150, 200 and 250 mg/L of the concentration of tracer solution, respectively

which indicates that both numerical predictions overpredict the lithium concentration as shown in Fig. 4. Effect of hydraulic conductivity The saturated hydraulic conductivity of the layers was determined mainly based on model calibration and laboratory measurement. The beach was treated as consisting of two layers whose properties were obtained. For the surface layer, the hydraulic conductivity was found to be around 10−3 m/s for 0 m