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«Proceedings of the American Society of Civil Engineers Conference on Non-aqueous Phase Liquids in the Subsurface Environment: Assessment and ...»

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Application of the Hydrocarbon Spill Screening Model to Field Sites

James W. Weaver

Proceedings of the American Society of Civil Engineers

Conference on Non-aqueous Phase Liquids in the Subsurface Environment:

Assessment and Remediation

(To appear)

November 12-14, 1996

Washington, D.C.

1 Weaver

Application of the Hydrocarbon Spill Screening Model

to Field Sites

James W. Weaver1


The Hydrocarbon Spill Screening Model, HSSM, was developed for estimating the impacts of petroleum hydrocarbon contamination on subsurface water resources.

The model simulates the release of the hydrocarbon at the ground surface, formation of lens in the capillary fringe, dissolution of constituents of the gasoline, and transport to a receptor in the aquifer. Field data from two case histories were used to develop input parameter sets for HSSM. In one case there were aqueous concentration data from an extensive monitoring network. In the second case the monitoring network was small, but the date and volume of the release could be estimated. Both of these cases have features that are well suited for testing of the model. In both cases the model was able to reproduce the trends in the data set and the concentrations to within an order of magnitude.

Introduction The Hydrocarbon Spill Screening Model (HSSM) was intended as a simplified model for estimating the impacts of petroleum hydrocarbons on subsurface water resources (Weaver et al., 1994 and Charbeneau et al., 1995). In this paper the model was applied to two sites with releases from leaking underground storage tanks. The data were drawn from State Agency case files and were not intended for research purposes. The objectives of the work were to determine if HSSM could reproduce the observed contaminant distributions and to demonstrate the effect of data gaps on model results. These applications demonstrate the effects of parameter uncertainty on model results, because in each case some information for running the model was not available.

National Risk Management Research Laboratory, United States Environmental Protection Agency, Ada, Oklahoma 74820 1 Weaver Field Data Sets Contamination from underground storage tank releases is usually characterized by contaminant concentrations in the ground water and soils. Normally, data are collected for benzene, toluene, ethylbenzene and the xylenes (BTEX), and total petroleum hydrocarbons (TPH). Water samples give concentrations that in effect are

related through time and space by the transport equation:

(c 5  D 5 c q  5c  Rc + J (t) R = (1) (t where is the porosity, R is the retardation factor, c is the contaminant concentration in the ground water, q is darcy velocity, D is the dispersion constant,  is a first order decay constant, J (t) is the amount of mass per unit volume of aquifer added per unit time. Each term on the right hand side of equation 1 can cause the concentration to change. Apparent dilution along the length of contaminant plumes is characterized by the dispersion constant (term 1: 5  D 5 c), which is assumed to depend upon the seepage velocity and inherent dispersivity of the aquifer. Water flowing along divergent streamlines can cause reduction in concentration (term 2:

q 5c). In equation 1 biodegradation is assumed to follow first order decay (term 3:

 Rc). The last term, J (t), is the source/sink term which can include losses due to hydrolysis, extraction or volatilization, for example. It may also include gains due to loading from the contaminant source.

For leaks from underground storage tanks the source of contamination is the released hydrocarbon liquid. Normally the volume and timing of the release is unknown. The magnitude of the source term in equation 1, however, depends directly upon the volume, rate and timing of the release. Typical data from a site that reect the hydrocarbon liquid are free product levels observed in wells, and concentrations from soil samples. Free product levels can not provide a reliable estimate of the release volume, because there is not a clear relationship between the free product levels in wells and the amount of free product in the formation (Kemblowski and Chiang, 1990). Free product recovery data can obviously give a lower bound on the release volume. Core extracts could provide more reliable estimates of the product volume, but they are only taken upon installation of wells and may not be analyzed over their full length. Hydrogeologic data normally consists of information on regional hydrology and geology from published sources, well logs, and measured or estimated hydraulic conductivities. Well logs define the vertical and spatial stratigraphy.

The Hydrocarbon Spill Screening Model The Hydrocarbon Spill Screening Model (HSSM) was intended as a screening model for estimating the impacts of hydrocarbon (or light nonaqueous phase liquid, LNAPL) releases to the subsurface (Weaver et al., 1994, and Charbeneau et al., 2 Weaver 1995). The model consists of three modules that treat transport in the vadose zone, the formation and decay of an oil lens in the capillary fringe, and transport of soluble constituents of the LNAPL in the aquifer to receptor locations. The model uses semi-analytical solutions of the transport equations which include many of the important physical and chemical processes. It does not include all processes which may be important, and because of the usage of semi-analytical solutions, it does not account directly for heterogeneity.

There are 36 physical and chemical parameters required to run HSSM. Each of these has an impact on the model results. Depending on the specific model scenario, some of the parameters are far more important than others. Of the entire suite of parameter values, only a small number of the most important parameters were varied to achieve the fits to the data described below (Table 2).

Features of the data sets

Both of the data sets simulated below were accepted by the State Agency that was responsible for managing the site and thus met the regulatory requirements for assessment of contamination. In neither of these cases was modeling of the spill considered an essential or integral assessment activity. Table 1 lists some features of each spill. The data used in this paper came from excerpts of state agency case files (Weaver et al., 1996, and State of Utah, 1996). Each data set has unique features that led to its inclusion in this study. Hagerman Avenue has a large number of monitoring wells and Mountain Fuels has estimates of the date and volume of release. For each case model results for benzene are discussed below. These results are intended to illustrate certain features of the cases and the ability of the model to duplicate them.

Mountain Fuels, Salt Lake City, Utah

At the Mountain Fuels site in Salt Lake City, Utah, approximately 7500 gallons of gasoline leaked from a tank that was punctured in 1979 (State of Utah, 1996).

Four monitoring wells were sampled from June 1991 to December 1994 to characterize the resulting aquifer contamination (Figure 1). Thus these data date from 12 to 15 years after the release. Concentrations in two of the four monitoring wells were always below the detection limits. One of the remaining wells is clearly directly in the path of the contaminant plume (MW-2). The fourth well (MW-1) appears to be on the edge of the plume, both because of its geographic location and its concentration history.

Parameter values given in Table 2 were used in the simulation. The hydraulic conductivity and gradient were estimated from the field data. The other parameters listed in the table were estimates that resulted in order of magnitude matches to the observed data. At the furthest down gradient well, MW-2, the concentrations

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decline steadily. This implies that for all the simulations and the field data that the peak concentration has already passed this receptor. Table 3 lists the simulated peak concentrations, maximum mass fluxes to the aquifer, and times of their occurrence.

With increasing conductivity, the benzene is released sooner at a higher maximum rate. The peak concentrations in the aquifer occur sooner, but with higher velocities the peak concentrations may decline due to the effect of increasing dispersion (which is proportional to the velocity).

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uniformly. The concentrations are seen to be dependent upon the hydraulic conductivity because of their dependence upon the release rate of benzene from the gasoline and advective-dispersive transport in the aquifer. The value of concentration can also be adjusted by changing the degradation rate constant as noted in the figure. Both the simulation with a Ks = 1.5 m/d and half life of 30 days and that with Ks = 0.75 m/d and half life of 69.3 days (loss rate of 0.01% per day) give a similar match to the field data. The breaks in the concentration curves for Ks = 1.5 m/d and Ks = 3.0 m/d occurring at about January 1, 1990 and January 1, 1996, respectively, are caused by the ending criterion used in HSSM (Weaver et al., 1994).

Observation well MW-1 was located within the oil lens generated by HSSM.

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0.75 968 0.1281 731 0.0066 1.5 839 4.7276 502 0.0262 3.0 390 2.9253 306 0.0742 Table 3: Mountain Fuels Simulated Peak Concentrations, MW-2

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Therefore no contaminant concentrations were calculated for this location by the aquifer module. The concentrations in the ground water below the lens, however, give an indication of concentrations that would be observed in MW-1. Figure 3 shows a comparison of the model and the data for this well. Three values of hydraulic conductivity were used in the simulations: the reported average of 1.5 m/d, and half and twice this value. As the hydraulic conductivity increased, the rate of release of benzene to the aquifer increased. Thus the benzene concentrations for the higher conductivity simulations decrease more rapidly than for the low conductivity cases.

The case with the lowest estimate of conductivity (0.75 m/d) falls through the scatter of the field data, but the variation in concentration observed in the monitor well cannot be matched by the model. Both MW-1 and MW-2 are best fit by the simulation with conductivity of 0.75 m/d and half life of 69.3 days.

Hagerman Avenue, East Patchogue, New York

The gasoline spill at East Patchogue, New York is described in detail in another paper in this proceedings (Weaver et al, 1996). In that paper, the extensive data set was used to estimate the ground water flow velocity, the volume of gasoline released and the mass of BTEX and methyl tert-butyl ether, MTBE, released to the aquifer.

The data from the site suggest a release volume of at least 13,200 gallons, which contains 420 kg of benzene (Weaver et al., 1996). The release likely occurred as a 6 Weaver Figure 3: Model results and Field data for MW-1 Mountain Fuels, Salt Lake City, Utah series of continuing leaks over several years. The tanks at the service station were removed in 1988, so any releases ended that year. Since some fraction of the gasoline contained MTBE, that gasoline was released after 1979 when MTBE was approved for use as an octane enhancer. The MTBE in the aquifer traveled from the source zone to its center of mass in 1994 and 1995 in 16 years or less. Using the centersof-mass calculated by Weaver et al. (1996), the MTBE plume traveled at the rates listed in Table 4. The rates show remarkable consistency suggesting that the average transport time, averaged over the duration of the contamination event, is nearly constant for distances between 1387 m and 1583 m from the suspect source. The rate would have been 0.65 m/d if the entire release occurred on December 31, 1988 and the 0.25 m/d if the release began on January 1, 1979.

The ground water flow velocity influences advective transport in the aquifer and the rate of release of mass from the oil lens. These two processes must both be consistent with the data from the field for the simulation to be appropriate. Parameter values for the simulation are listed in Table 2. In separate simulations the hydraulic conductivity was taken as the average and the average plus or minus one standard deviation of values determined from slug tests performed at the site. The water table fluctuation was determined from wells near in the source zone. The other parameters listed in Table 2 were taken as reasonable estimates that resulted in order of magnitude matches to the observed data.

The critical parameters for simulating the Hagerman Avenue site were the rate

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and duration of the release, ground water flow velocity, degradation rate constant, and water table fluctuation. The ground water velocity was selected to be 0.40 m/d which is within the range given in Table 4. The release was assumed to occur continuously from January 1, 1979 to December 31, 1988 at a rate that resulted in 50 m3 (13200 gallons) of gasoline in the aquifer. This release scenario was used because the true rates and timings of releases are unknown and because it was found necessary to generate relatively low concentrations in the ground water to match the monitor well data. From sample round one and two water level data near the source, the water table fluctuation was approximately 0.33 ft (0.10 m). Figure 4 shows a comparison of the vertically averaged measured concentration in MW-13, MW-12 and MW-1,4 with the HSSM model results (see Weaver et al., 1996, Figure 1).

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