UCRL-AR-135949 Vol. 4 Ch. 8

Volume 4: Potential Ground and Surface

Water Impacts

Chapter 8: Screening Analysis of Potential

Groundwater Resource Impacts from Gasoline

Containing Ethanol or MTBE

Using an Empirically based, Probabilistic Screening Model

Author

Brendan P. Dooher*

Editors

D. Rice*

G. Cannon*

December 1999

*Lawrence Livermore National Laboratory, Livermore, CA 94550

University of California • Livermore, CA 94550

Lawrence Livermore National Laboratory

 Environmental Restoration Division
Work performed under the auspices of the U. S. Department of Energy by Lawrence Livermore National

Laboratory under Contract W-7405-Eng-48.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-iii

Table of Contents

8. Screening Analysis of Potential Groundwater Resource Impacts from Gasoline

Containing Ethanol or MTBE Using an Empirically based, Probabilistic

Screening Model...................................................................................... 8-1

8.1. Introduction .................................................................................. 8-1

8.1.1. Methodology ...................................................................... 8-2

8.2. Forecasting Benzene (Degrading) and MTBE (Nondegrading) Fuel

Hydrocarbon Plume Behavior ............................................................. 8-3

8.2.1. Introduction........................................................................ 8-3

8.2.2. Conceptual Model of a Plume................................................... 8-3

8.2.3. Monte Carlo Analysis............................................................ 8-4

8.2.4. Results of Plume Length Forecasts............................................. 8-5

8.3. Comparison of Potential Future Impacts Associated with Benzene Either

in the Presence of Ethanol or the Presence of MTBE ................................. 8-7

8.3.1. Location Relationship between LUFT Sites and Public Drinking

Water Wells........................................................................ 8-7

8.3.2. Screening Model Estimate of the Probability of Future Impacts to

Public Drinking Water Wells from LUFT Sites.............................. 8-7

8.4. Important Uncertainty Considerations...................................................8-11

8.4.1. Location Accuracy of Drinking Water Wells and LUFT Sites............8-11

8.4.2. Sampling Frequency and Impact Rates of Public Drinking Water

Wells and LUFT Sites...........................................................8-12

8.4.3. Comparisons of Well BTEX/MTBE Detection Levels, Well

Construction, Well Yield, and Groundwater Levels ........................8-13

8.4.4. Vulnerability of Private Drinking Water Wells..............................8-14

8.5. Conclusions .................................................................................8-15

8.6. Acknowledgements ........................................................................8-16

8.7 References...................................................................................8-17

List of Figures

Figure 8-1. Example of the derivation of the multiplier used in this analysis ..................8-23

Figure 8-2. Conceptualization of the three-dimensional plume ..................................8-23

Figure 8-3. Best Professional Judgement (BPJ) benzene plume length vs. maximum

benzene concentration....................................................................8-24

Figure 8-4. BPJ plume length vs. maximum concentrations (log-log scale)....................8-24

Figure 8-5. Comparison of 3-D Monte Carlo simulated plumes with non-parametric

and BPJ.....................................................................................8-25
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

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Figure 8-6. Cumulative distributions of benzene plume length over time ......................8-25

Figure 8-7. Plume length vs. time elapsed for various percentile intervals .....................8-26

Figure 8-8. Log scale plume length vs. time elapsed for various percentile intervals.........8-26

Figure 8-9. Plume length vs. time elapsed for randomly generated plumes ....................8-27

Figure 8-10. Time at which degrading plume reaches its maximum length......................8-27

Figure 8-11. Plume length distributions for time interval at which degrading plume

reaches its maximum length.............................................................8-28

Figure 8-12. Distribution of plume decay rate at various time intervals..........................8-28

Figure 8-13. Years required for plumes to reach zero plume length ..............................8-29

Figure 8-14. Maximum plume length distributions for time interval necessary for

plume length to reach zero...............................................................8-29

Figure 8-15. Plume length distributions for nondegrading plumes................................8-30

Figure 8-16. Plume length vs. time elapsed for various percentile intervals .....................8-30

Figure 8-17. Plume length vs. time elapsed for various percentile intervals .....................8-31

Figure 8-18. Log plume length vs. log time elapsed for various percentile intervals ...........8-31

Figure 8-19. Plume length vs. time elapsed for randomly generated plumes ....................8-32

Figure 8-20. Year at which simulated nondegrading plumes reach steady-state.................8-32

Figure 8-21. Growth rates of free-product and non-free-product plumes at 100 years .........8-33

Figure 8-22. Growth rates of free-product plumes over time.......................................8-33

Figure 8-23. Growth rates of non- free-product plumes over time ................................8-34

Figure 8-24. Distance of drinking water wells and LUFT sites from each other ................8-34

Figure 8-25. Distribution of the number of LUFT sites within x feet of a drinking

water well ..................................................................................8-35

Figure 8-26. Distribution of the number of drinking water wells within x feet of a

LUFT site ..................................................................................8-35

Figure 8-27. Distribution of the absolute probability of threat for LUFT sites with

benzene alone..............................................................................8-36

Figure 8-28. Distribution of the absolute probability of threat for LUFT sites with

benzene and ethanol ......................................................................8-36

Figure 8-29. Close-up view of the distribution of the absolute probability of threat for

LUFT sites with benzene alone .........................................................8-37

Figure 8-30. Close view of the distribution of the absolute probability of threat for

LUFT sites with benzene and ethanol..................................................8-37

Figure 8-31. Distribution of the absolute probability of threat for LUFT sites with

MTBE ......................................................................................8-38

Figure 8-32. Relative probability of threat for LUFT sites for benzene with and

without ethanol............................................................................8-38

Figure 8-33. Temporal relative change in probability of threat for LUFT sites for

benzene with and without ethanol ......................................................8-39
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

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Figure 8-34. Temporal change in absolute probability of threat for LUFT sites for

MTBE ......................................................................................8-39

Figure 8-35. Absolute probability of a site threatening any well using de Morgan’s

law, benzene and benzene with ethanol................................................8-40

Figure 8-36. Absolute probability of a site threatening any well using de Morgan’s

law, MTBE ................................................................................8-40

Figure 8-37. Relative probability of a site impacting any well using Morgan’s law............8-41

Figure 8-38. First and last sampling event dates for BTEX and MTBE in drinking

water wells.................................................................................8-41

Figure 8-39. Time between first and last sample event and the average time between

sample events..............................................................................8-42

Figure 8-40. Rate of detections per year of BTEX and MTBE in California public

drinking water sources ...................................................................8-42

Figure 8-41. Change in detection rate per year of BTEX and MTBE in California

public drinking water sources...........................................................8-43

Figure 8-42. Complementary cumulative distribution of BTEX and MTBE detections

at impacted drinking water wells in California .......................................8-43

Figure 8-43. Cumulative distribution of depth to top of screen for various public and

private drinking water wells in California.............................................8-44

Figure 8-44. Cumulative distribution of well yield for public and private drinking

water wells.................................................................................8-44

Figure 8-45. Correlation of top of screen interval to maximum groundwater depth for

private wells ...............................................................................8-45

Figure 8-46. Drinking water well density in California (private wells)...........................8-46

Figure 8-47. Specific vulnerability to private drinking water wells in California at the

block group level..........................................................................8-47

List of Tables

Table 8-1. Notation of parameters used in Cleary and Ungs (1978) equation ..................8-51

Table 8-2. Population input parameters for Monte Carlo forecasts...............................8-52

Table 8-3. Count of sampled public water sources, by year.......................................8-54

Table 8-4. Cumulative count of sampled public water sources, by year.........................8-55

Table 8-5. Change in count of sampled public water sources, by year...........................8-56

Table 8-6. Count of detections in sampled public water sources, by year.......................8-57

Table 8-7. Cumulative count of detections in sampled public water sources, by year.........8-58

Table 8-8. Change in count of detections in sampled public water sources, by year...........8-59

Table 8-9. Yearly public water source detection rate...............................................8-60

Table 8-10. Yearly public water source detection rate, based on cumulative count .............8-61

Table 8-11. Yearly public water source detection rate, based on count change ..................8-62
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

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Appendix

Appendix A. Cleary and Ungs (1978) 3-D Solution................................................ A-1

Appendix B. Results of Public Water Sources throughout California ............................B-1
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

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8. Screening Analysis of Potential Groundwater

Resource Impacts from Gasoline

Containing Ethanol or MTBE

Using an Empirically based, Probabilistic Screening Model

8.1. Introduction

Although California has implemented an Underground Storage Tank (UST) upgrade

program, releases from leaking underground fuel tanks (LUFTs) will continue to be an issue

(Giannopoulos, 1999). Moreover, various authors (Chapter 4, Vol. 4 of this report; Malcolm

Pirnie, Inc., 1998; Molson et al., 1999; Schirmer et al., 1999; Ulrich, 1999) report that ethanol

may lengthen benzene plume lengths due to preferential biodegradation of ethanol. Longer

plumes suggest that potentially more public and private drinking water wells in California may

be impacted by benzene released from LUFTs.

In this study, we utilized a systems-based approach to estimate the differences in groundwater

resource impacts from LUFT releases of benzene, benzene under the influence of ethanol, and

methyl tertiary butyl ether (MTBE). Benzene in association with ethanol is the primary focus of

this study, because it is a known human carcinogen, and because increased benzene groundwater

plumes are predicted. This report examines the potential for benzene to contaminate drinking

water sources from future releases of gasoline containing ethanol in contrast to the potential for

MTBE to contaminate drinking water sources. MTBE is a possible carcinogen, can affect water

quality, has a disagreeable taste and odor at low parts-per-billion (ppb) concentrations, and has

been shown as generally recalcitrant in groundwater under in situ conditions. This study does

not examine the potential for ethanol-only plumes to impact drinking water supplies because

ethanol degrades rapidly in groundwater and is expected to have very localized and minor

impacts relative to benzene or MTBE.

To estimate the potential future impact of gasoline containing either ethanol or MTBE, we

considered some important characteristics of drinking water wells and LUFT sites. These

characteristics include:

• The current distribution of the distance to the nearest well from a LUFT site.

• Densities of LUFTs and drinking water wells.

• Past impacts of benzene, toluene, ethyl benzene, and xylenes (BTEX) and MTBE on

drinking water wells.

The estimation makes use of probabilistic analytical screening models, using empirically

based compliance data collected throughout the state of California. Details of this collection are

found in Rempel et al. (1996), and the data are described in Dooher (1998).

For chemistry data, locations of wells, LUFT sites, and well yield and construction, we relied

on data collected from state and local agencies1 that has been reviewed and standardized for

 

1 The state agencies include information from the State Water Resources Control Board (SWRCB) Leaking

Underground Storage Tank Information System (LUSTIS) database (SWRCB, 1999); the California Department of
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

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import into the SWRCB Internet-accessible database, the Geographic Environmental Information

Management System (GEIMS)2.

8.1.1. Methodology

Our estimate of future impacts to public drinking water wells is based on a series of

assumptions regarding the spatial distribution of LUFTs that may act as release points, and their

proximity to drinking water wells. To compare the potential future impacts to drinking water

wells, we developed a baseline impact estimate for benzene without the presence of ethanol (or

MTBE). This baseline impact estimate provided a basis for comparing estimated future impacts

with MTBE and those associated with benzene under the influence of ethanol.

To estimate the baseline percentage of benzene LUFT-related releases that may impact

public drinking water wells over time, we developed a methodology to estimate benzene plume

behavior using a Monte Carlo simulation of a one-dimensional advection, three-dimensional

(3-D) dispersion equation developed by Cleary and Ungs (1978). The first step in this

methodology is to model expected populations of benzene plume lengths without ethanol. Next,

we compared the benzene-only plume-length results against measured, historical-case benzene

plume lengths to validate the model. Finally, we used the model to develop a population of

MTBE plume lengths to be compared against the baseline benzene impacts. Section 8.2. details

this methodology and the resulting benzene and MTBE plume length population distributions

that we used in this comparative analysis. The samples of benzene plume lengths with ethanol

present used for comparison to the baseline were derived from modeling results (McNab et al.,

1999b; Vol. 4, Chapter 4 of this report). Figure 8-1 is an example of how this was applied to two

sample populations of plume length distributions. We used the difference between benzene

plume lengths with and without ethanol as developed in McNab et al. (1999b; Vol. 4, Chapter 4

of this report) as a multiplier for the benzene plume lengths used in this study (Section 8.3.).

We used the three population distributions (benzene without ethanol, benzene with ethanol,

and MTBE) to estimate the absolute probability of threat to drinking water wells near LUFT

sites. The difference between the probabilities of impact was then used to develop the relative

impact threat between benzene with ethanol and MTBE compared against the baseline benzene

realizations. Finally, these results are discussed in context to known well BTEX and MTBE

detections as found in the CAL-DHS Water Quality Monitoring (WQM) database. Additionally,

we examined confounding factors and discussed their potential influences on public- and privatewell impacts.

 

Health Services (CAL-DHS) Water Quality Monitoring (WQM) and the Permitting, Inspection, Compliance,

Monitoring, and Evaluation (PICME) databases (CAL-DHS, 1999a,b); and the California Department of Water

Resources (CAL-DWR) EXTERRA database (CAL-DWR, 1997). Yield and well construction data was obtained

from local agencies including the Santa Clara Valley Water District (SCVWD, 1999) and Orange County Water

District (OCWD, 1999).

2 GEIMS is a water resources and contaminant database created for the SWRCB to manage LUFT sites. It is

accessible through an Internet Geographic Information System (GIS) interface, GeoTracker, at

< http://geotracker.llnl.gov >.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

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8.2. Forecasting Benzene (Degrading) and MTBE

(Nondegrading) Fuel Hydrocarbon Plume Behavior

8.2.1. Introduction

Dooher (1998) discussed the importance of developing models appropriate to the available

data when assessing situations of high uncertainty. Because the goal of any model is to predict

the future or to fill in the gaps where not enough information is available, a modeling approach

should attempt to develop confidence intervals for future predictions or fill in these gaps. These

predictions forecast at some point in space what may occur now, five, ten, or 100 years in the

future. In this screening model, these forecasts are used for developing “probability domains” to

describe the likely area of movement of fuel hydrocarbon (FHC) plumes in the subsurface. The

analysis of McNab et al. (1999b; Vol. 4, Chapter 4 of this report) applies a similar approach to

the assessment of benzene plumes affected by ethanol.

This section provides the details for three major methodologies used in forecasting resource

impacts associated with the use of ethanol as a fuel oxygenate:

• A comparison of forecast benzene plume lengths to plume lengths derived through

various methodologies (Dooher, 1998), in order to validate the general model.

• An examination of the time history of degrading chemicals (such as benzene), its plume

lengths, growth rates, and recedence rates.

• An examination of the time history of nondegrading chemicals, (such as MBTE), their

plume lengths, growth rates, and recedence rates.

These plume lengths derived here for a large population of sites are then used as the basis to

develop the probability of well impacts in California from benzene with and without ethanol and

from MTBE.

The following discussion addresses two important elements of the evaluation of plume

impacts to groundwater resources. The first examines the development of an underlying

conceptual model of a plume, and the second applies Monte Carlo analysis to the conceptual

model.

8.2.2. Conceptual Model of a Plume

A plume in its most abstract form moves downgradient, dispersing normally to the primary

gradient direction in two directions. Figure 8-2 shows the conceptualization of the threedimensional case. The greatest spread takes place horizontally in the primary direction of

groundwater flow, with a lesser lateral and vertical transverse spread due to dispersion and

smearing through zones of relatively higher conductivity (Gelhar et al., 1985).

For advection-dispersion and degradation in the subsurface, due to the heterogeneities

present, an “exact” solution for the transport of a contaminant is possible only when these

heterogeneities are accounted for—typically in some form of numerical modeling. This section

examines a probabilistic approach, wherein a mathematically exact or approximate analytical

solution is derived for three dimensions and used to produce a series of probabilistic realizations

by applying a Monte Carlo methodology. Although analytical solutions to the advectivedispersion solute transport equation lack the general flexibility offered by numerical methods,
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they may serve as probability distribution models for contaminant concentrations as a function of

space and time in many situations. Analytical approaches are particularly well suited for

application of Monte Carlo techniques because of the speed of solution.

Monte Carlo analyses allow uncertainties in governing parameters (for example, groundwater

velocity, contaminant degradation rate, source attenuation rate) to be addressed as assumed

probability distributions for input to the model. The analytical solutions typically assume a

homogeneous environment in which the groundwater flow is uniform in one direction.

Probability distribution functions for the various parameters are placed into the models, and

upper and lower confidence intervals are developed. When compared to actual environmental

measurements, this method leads to a greater understanding of the uncertainty of the

contaminant-plume movement relative to the variability of the subsurface.

The descriptive equation of transport for a uni-directional flow, three-dimensional plume

with sorption and decay is the advection-dispersion equation

 

R

C

t

Dx

2C

x

2

Dy

2C

y

2

Dz

2C

z

2

vx

C

x

RC (8-1)

There are multiple analytical solutions that can be used with Equation (8-1), both for

transient and nontransient situations. Many of these are discussed by Bear (1972, 1979), Wilson

and Miller (1978), Cleary and Ungs (1978), van Genuchten and Alves (1982), and Wexler

(1992). The 3-D solution to the Cleary and Ungs (1978) model of the form:

C = f(x, y,z,t,v, Dx, Dy, Dz , R, , ) (8-2)

is used to as the plume probability domain. The specifics of the solution are found in

Appendix A. Table 8-1 shows the variables used in the solution.

8.2.3. Monte Carlo Analysis

When used in conjunction with analytical models, Monte Carlo analyses of contaminant

transport involves running multiple simulations (realizations) in which input parameters are

allowed to vary in accordance with assumed probability distributions. The output of the Monte

Carlo analyses is forecast frequency distributions of variables of interest (that is, plume length,

plume growth, and recedence rate). Thus, the probability of a plume reaching a certain length or

achieving a certain growth rate may be quantified (along with associated uncertainties).

For the first analyses, we conducted 4000 Monte Carlo realizations on two different source

populations using the analytical solution (Equation A-4a) of Cleary and Ungs (1978). These

realizations were used to forecast the 1 part-per-billion (ppb) contour-interval hydrocarbonplume lengths and associated growth rates. Probability distributions of governing parameters

were generated using literature sources, best professional judgment (BPJ), and data developed in

Dooher (1998) (shown in Table 8-2). Plume-length distributions developed are then compared to

the resultant Monte Carlo forecasts.
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8.2.4. Results of Plume Length Forecasts

8.2.4.1. Degrading Plumes

8.2.4.1.1. Plume Lengths. Forecast cumulative distribution functions of benzene plume

lengths are generated, as defined by the distance from the source (x = 0) to the 1-ppb contour

interval along the plume longitudinal axis. These were created using the three-dimensional

model described earlier, applying two different input distributions to simulate free product (FP)

and non-free-product (NFP) sources. The only change in variables between them is the

concentration distribution. FP concentrations are based on observed distributions on sites that

have recorded free product, while the dissolved-concentration distribution is representative of

sites with no observed free product.

Staff of the State Water Resources Control Board (SWRCB) drew over 500 plumes (some

are plumes drawn at several periods on the same site) using BPJ. (BPJ is defined as manually

viewing the spatial distribution of concentrations and the groundwater gradient direction.)

Concentration contours are then drawn on a site map, and the plume length measured. The

measure of the plume length across its long axis is C to C, “contour to contour.” Max to C is the

measurement of the plume length across its long axis from the point of maximally found

concentration. Typically measured were the 10-ppb and 1-ppb contours for benzene. Contours

were drawn to be as conservative as possible, using historical data to fill in gaps of missing

groundwater chemistry concentrations. The length was estimated using the highest historical

groundwater concentrations. We used these lengths as a comparison in the following analyses.

Figure 8-3 shows BPJ plume lengths3 correlated against maximum site concentration.

It is immediately apparent that there is an effective “exclusion zone” in which, for a given

concentration, there are no plumes greater than a certain length, implying the existence of natural

constraints imposed by the physics of the subsurface transport phenomena. These constraints are

likely to be the maximum velocity and the minimum degradation rate. For higher maximum

benzene concentrations above 5000 ppb4, the plume lengths begin to fall out of the exclusion

zone. Figure 8-4 shows the same figure with log-transformed plume lengths, where the

“exclusion zone” still holds for larger plume lengths. McNab et al. (1999a) found a very similar

trend for chlorinated volatile organic compounds but with much exaggerated plume lengths.

Figure 8-5 compares this distribution with the distributions developed using BPJ and through

the nonparametric approach found in Dooher (1998). To create uniformity, all plume lengths

less than 25 ft were deleted because these small plumes would not be typically detected at the

sampling distances characterized by monitoring well spacing. Though there is a definite scatter

of the cumulative distributions, the same trends and ranges are very apparent. The Monte Carlogenerated plumes fall nicely into the center of the range and, more importantly, characterize what

would be the 95th-percentile confidence level associated with a plume forecast.

8.2.4.1.2. Plume-growth Rates. For the population evaluated, plumes were forecast

over time, starting at Year 1, and then at 5-year intervals starting at Year 5 after the release.

Figure 8-6 shows the cumulative distributions of plume lengths for Years 1, 5, 20, 30, 40, 50, 60,

 

3 As developed by Rick Rempel of the SWRCB (Dooher, 1998).

4 Dooher (1998) found that concentrations at these levels are highly correlated with “free product” or floating

nonaqueous phase liquid (NAPL) product.
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70, 80, 90, and 100. Some plumes literally explode into existence and can then rapidly decline,

whereas others grow gradually and degrade gradually. Because a large percentage of the plumes

expires over time, at Year 100 almost 70% of all plumes have decayed. Figure 8-7 shows the

percentile plume lengths over time. The 95th percentile results show a peak at ten years at

approximately 1600 ft, declining to 250 ft by Year 100. Figure 8-8 shows the same results with

the plume length on a log scale. Figure 8-9 shows a sampling of various generated degrading

plumes, from which the populations shown in Figures 8-6, 8-7, and 8-8 were developed.

Figure 8-10 shows the year interval at which the plume population reaches a peak. After

50 years, almost all plumes will have reached a maximum peak length. Figure 8-11 shows a box

distribution of the plume lengths at the time of maximum extent for the populations in

Figure 8-10. Median plume length ranges from 100 to 1000 ft with a slight trend upwards as

time progresses as expected. Plumes that are still growing after 50 years must have very small

degradation rates.

Figure 8-12 shows the cumulative distributions of plume-growth rates for various intervals of

time. The maximum growth rate typically takes place in the first year, diminishing over time to

a maximum of 0.1 ft/day for the 40- to 100-year range. For the plume-length decay rate, the

initial decay is almost at its peak, peaking in the five- to ten-year range and then dropping off.

The differences in the distributions are no more than a few tenths of a foot per day.

Figures 8-13 and 8-14 show the time required for plumes to extinguish. Initial surges of

plumes vanish entirely within one year (approximately 10%). These would be associated with

small releases. After this initial surge, there is an upward trend of time necessary to reach zero

length, peaking for both FP and NFP populations at around 50 years. Figure 8-14 shows the

maximum plume length associated with the time necessary to reach zero. The median for those

plumes taking less than 20 years to reach zero length is generally small—no more than 20 ft.

The median plume length tends to stabilize around 80 ft as longer times are necessary to reach

zero-plume length.

8.2.4.2. Nondegrading Plumes

8.2.4.2.1. Plume Lengths. Nondegrading plumes behave very differently from those

where source depletion and overall degradation occur. Figure 8-15 shows the cumulative

distributions of plume lengths for (nondegrading) plumes with a constant source. A small

percentage of simulations result in zero-length plumes; all are associated with low initial

concentrations and rapid velocities. As time progresses, however, the plume distribution

approaches an asymptote with the 95th percentile approaching approximately 15,000 ft (3 mi) in

length. In contrast to the degrading plume sites where, on a year-by-year basis, an increasing

number of sites reach a zero-plume length, no such effect occurs here. It is likely that sites exist

where groundwater has been impacted but due to local conditions (such as a low relative flux

rate from the vadose zone in combination with a high advective groundwater flow rate), the

effect is minimal.

8.2.4.2.2. Plume-growth Rates. Figures 8-16, 8-17, and 8-18 show the plume length

trends over time for various percentile intervals. Figures 8-16 and 8-17 show a close-up of the

lower percentile ranges as well as the total range. Very few of the percentiles have reached

steady state. Figure 8-18 shows this more explicitly. Most of the percentiles show a slowly

declining rate of growth trend on a log-log scale, with only the lowest percentiles showing any
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-7

deviation downward. These lengths are overly conservative because there is no source

attenuation, which would occur in reality. Figure 8-19 shows a sampling of the various plume

lengths from which the cumulative distributions are developed. Further work is being performed

to assess the general source attenuation rate for MTBE sites.

Figure 8-20 shows the year at which the simulated plumes reach steady state for

nondegrading plumes. By Year 100, 46% of the simulations have not reached steady state.

Figure 8-21 shows the growth rates for FP and NFP plumes after 100 years. The 95th percentile

is still approximately 60 ft/y. Figures 8-22 and 8-23 show growth rates at various time intervals

for FP and NFP. Initial growth rates are much higher than those found in degrading plumes and

stay high. This is in contrast to the growth rates of degrading sites that at Year 100 are an order

of magnitude less in size than nondegrading plumes.

8.3. Comparison of Potential Future Impacts Associated

with Benzene Either in the Presence of Ethanol or

in the Presence of MTBE

8.3.1. Location Relationship between LUFT Sites and Public

Drinking Water Wells

The potential threat from a LUFT site release to a well depends on the relative locations of

nearby drinking water wells and the density (or count) of LUFT sites near the well. Figure 8-24

shows the minimum separation distance of LUFT sites from public drinking water wells and

vice-versa5. Because there are many more LUFT sites than drinking water wells (approximately

35,000 LUFTs versus approximately 15,000 wells), 32% of LUFT sites are estimated to be

within 2000 ft of a drinking water well, while 38% of well locations are estimated to be within

2000 ft of a LUFT site.

Figures 8-25 and 8-26 show density distributions as a percentage of LUFT sites and public

drinking water wells within a range of distances. These graphs show the percentage of wells (or

LUFT sites) and a count of the number of LUFT sites (or wells) within a given distance. For

example, in Figure 8-25, approximately 45% of wells have at least one LUFT site within 2500 ft,

and 31% of wells have at least two LUFT sites within 2500 ft.

8.3.2. Screening Model Estimate of the Probability of Future Impacts

to Public Drinking Water Wells from LUFT Sites

8.3.2.1. Probability of a LUFT Site Impacting the Closest Well

To assess the potential impact of LUFT releases of gasoline containing either ethanol or

MTBE, we considered the distribution of the separation distances between known LUFT sites

and drinking water wells (Figure 8-24). The probability distributions of plume lengths for

MTBE and benzene (Figures 8-6 and 8-15) and for the increased lengths for benzene with

ethanol may be compared against distances between LUFT sites and drinking water wells as a

function of time since plume release. The distance between the LUFT site (the plume source)

 

5

 The determination of the locations of LUFT sites and public drinking water wells are discussed in Dooher and

Happel (1999) and were originally found in SWRCB (1999) and CAL-DHS (1999b).
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and the nearest drinking water well is related to the probability that a plume may migrate that

distance. The probability of impact from benzene, benzene under the influence of ethanol, or

MTBE for each LUFT/well distance may then be determined. It is then possible to compare the

absolute and relative probabilities6 of increased threat to the closest well adjacent to a given

LUFT.

In developing these probabilities of impact, we assumed that the plume is heading

downgradient toward the drinking water well. This conservative assumption is made because the

direction of groundwater flow for these sites are unknown. Typically, LUFT sites are randomly

situated in relation to drinking water wells; it is likely that the actual impacts may be much less.

These impact probabilities for MTBE are conservative in that they assume no source or dissolved

plume attenuation; however, they do allow the determination of relative changes in the

probability over time. Section 8.5. discusses further how various uncertainties can affect these

estimates.

Figures 8-27 and 8-28 show absolute probability of threat for benzene, with and without

ethanol. Intervals as shown are for 1-, 5-, 10-, 15-, 50-, and 100-year cumulative probabilities of

threat. The absolute probability of threat for benzene under the influence of ethanol, as seen in

Figure 8-28, increases substantially. For the one-year interval for at least 10% of sites, the

absolute probability of threat more than doubles. Similarly, the five-year interval, which shows

the maximal predicted threat probability, also increase by a factor of two, to approximately 28%.

Figure 8-29 and 8-30 show the absolute probabilities of threat in detail. Twenty-five years

after the benzene release, in both cases, the plume has returned to approximately the same

probability distribution as Year 1. Maximal probabilities of threat occur in the five- to ten-year

range. The decline of benzene over this timeframe (as seen in Section 8.2.) is due primarily to

the attenuation of benzene sources and the degradation of the plume. This attenuation and

degradation cause the benzene plume to slowly recede over time. Source attenuation rates are

based on the results of field compliance data as stated in Dooher (1998). Degradation rates are

based on results obtained from the same source as well as published data. In the case of MTBE,

Figure 8-31 shows the total range of the LUFT-site populations and probabilities, as do

Figures 8-27 and 8-28. In contrast, MTBE continues to have an increasing absolute probability

of impacting wells for as long as 100 years although at this point it is reaching an asymptotic

limit.

Of greater interest is Figure 8-32, which shows the relative change in probability between the

absolute probabilities of Figures 8-27 and 8-28. The differences in these two distributions were

used. We assumed that the benzene probabilities are the baseline case because benzene has been

a ubiquitous contaminant at LUFT sites for many years. The important factor, therefore, is the

change in the probability of impact to a drinking water well. The results show an initial increase

in impact probability of 18% by the fifth year for a small percentage of sites (less than 3%). The

average impact is an increase of approximately 10%. Examining the temporal change in relative

probability (Figure 8-33), the greatest increase is in the first five years, dropping to a negative

 

6 The meaning of “absolute probability” is different from a statement that this is the actual probability that the well

will be impacted, as there are many additional confounding factors that are not taken into account in this simple twodimensional exercise. It is the relative probability that is of the greatest importance here. See Section 8.4. for a

discussion of these.
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rate change thereafter (as can be seen from the overall trends). The average change is

approximately 2.5% in the first five years.

Figure 8-34 shows the temporal trend of the change in absolute probability for MTBE. The

first five years show the greatest growth patterns, reaching a maximum of approximately 24%.

This growth continues in the five- to ten-year range, where the relative change is still 10%,

dropping to 5% for the five- to ten-year range, and to 2.5% for the 20- to 25-year range. This

indicates that unmanaged MTBE sources can continue to act as a hazard for long periods into the

future. Current work is attempting to ascertain rates of source attenuation that can be applied to

MTBE; earlier work (reported in Happel et al., 1999) shows a marked difference when a sourceattenuation rate similar to benzene is used.

8.3.2.2. Probability of a LUFT Site Impacting Many Surrounding Wells

Basic probability calculus (de Morgan’s rule) (Ang and Tang, 1975) can be used to estimate

an absolute and relative probability of threat to drinking water wells. This approach assumes

independence of the well locations. For multiple wells in a given LUFT-site area, the probability

of impacting any well in the area is given by de Morgan’s rule:

 

P E1 UE2UKUEn 1 P E1UE2 UKU En

1 P E 1E 2KE n

(8-3)

where Ei is the event probability, and 

 

E i is the complementary form of the event probability, or

 

E i 1 Ei. An assumption of independence for the surrounding wells is not exactly correct, as

their spatial position is approximately known. Known locations turns the problem into one

where there is dependence (that is, if the plume moves in a direction where three wells are

located, the probability of impact of a further well necessitates the impact of the closer well). A

sensitivity study of this dependence is under development.

As an example, we assumed that the probability of a plume reaching a well 1000 ft distant

would be 0.25, and for a well 2000 ft distant, it would be 0.10. Assuming a well at 1000 ft and

one at 2000 ft, the total probability of impact threat to either well is

{ } ( )( )

0.325

1 1 0.25 1 0.10 1 2 =P E U E = − − −

(8-4)

Taking the example further, using this screening approach the probability of threat to any of six

wells, all at a distance of 1000 ft, would be 82.2%.

Using information in the GEIMS database, the above approach to estimating the probability

of impact from a single LUFT was applied to all known open LUFT sites and active drinking

water wells in California. Drinking water wells that exceeded a distance of 30,000 ft from a

given LUFT were excluded in this analysis7. In all, we evaluated 15,525 open-case LUFT sites

with geocodable addresses.

 

7 This is due to the almost nonexistent probability of a plume reaching this distance (based on the modeling).
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Figure 8-35 shows the modeled absolute probability over a 100-year time span for the 30%

of LUFT sites in California where benzene may threaten to impact a well. Potential impacts for

benzene with and without ethanol are shown. For example, the addition of ethanol increases the

initial impact threat difference by over 20% for up to 10% of LUFT sites. Thereafter, this change

in total probability decreases. For at least 20% of LUFT sites, the increased difference in

probability is at least approximately 14%. For 30% or more of sites, the change in total threat

probability is at least 7%. All these maxima occur in the five- to ten-year timeframe, much as we

saw with the LUFT site and the single well. The decrease is due to benzene-source attenuation

and overall degradation.

The relative change, as stated above, is of primary import. Given the addition of ethanol, a

majority of LUFT sites will show relatively little additional chance of impacting nearby wells.

Those sites that are already at a high absolute probability of threat will increase dramatically up

to 20%, or approximately 1550 wells.

Figure 8-36 shows the estimated absolute probability of a given percentage of LUFT sites in

California where MTBE may threaten to impact a well across a 100-year time span. For

example, 10% of the sites have a 30% probability of threat in the first five years, increasing

again by an additional 15% during Years 5 to 10. Because the modeled MTBE plumes are

assumed to be nondegrading, they continue to expand and represent increasing threats with time.

This expansion and increasing threat are in contrast to the benzene sites that have peak potential

impact threat approximately seven years after release, after which point they begin to decrease in

threat (Figure 8-35).

Figure 8-37 shows the relative probability increase between sites impacted by benzene alone

and those sites where benzene is under the influence of ethanol, for approximately 5% of sites

(the 95th percentile as shown on the figure). Benzene is again stated to be the baseline for

comparison. The result of this comparative estimate is that there is an approximately 20%

maximum predicted increase in public drinking water well impacts for benzene with ethanol in

the five- to ten-year frame. This relative rate of impact begins to decrease thereafter.

An examination of the relative change in probability of MTBE impact reveals a significantly

increased chance of impacting a well. At about seven years, the probability of impact for MTBE

and benzene under the influence of ethanol is about the same. The major difference between

MTBE and benzene under the influence of ethanol is that the probability of MTBE impact

continues to increase with time as MTBE plumes continue to grow. Figure 8-37 shows the

maximum expected change in well impacts, using benzene without ethanol as a baseline. By

Year 10, an MTBE release has an increased probability of impact of 45% over the baseline

benzene impacts, or more than twice the threat that benzene in the presence of ethanol

represents.

It is important to note that all quantitative findings, such as the estimated relative probability

of impacts, reflect probabilistic modeling results and are highly sensitive to the underlying

probability distributions and simplifying assumptions for the various modeling inputs. This

comparative analysis is intended as a screening tool and is not intended to provide formal

quantitative predictive values that could be used for detailed resource planning purposes. Further

work is being performed to assess the absolute and relative probabilities with known maximum

concentrations of benzene and MTBE. These results should be significantly less than those

shown here and will be of significantly greater use.
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8.4. Important Uncertainty Considerations

To estimate the impact to public drinking water wells, we assumed that the modeled increase

in probability due to a change in gasoline composition could be based on what is current

knowledge of benzene impacts. However, some important uncertainties are inherent in the use

of the known benzene impacts:

• Many wells had not been tested as of January 1995 (approximately 50% of those wells

had been sampled more than once). That five-year period may have resulted in a low

concentration impact that, upon mixing with other water sources, would not be noticed.

The same holds true for MTBE.

• The statistical population used as a baseline for MTBE impacts is those wells that are

required by CAL-DHS8 to sample for nonregulated chemicals or secondary maximum

contaminant levels (MCLs) (such as MTBE). This decrease in sampling frequency may

increase the possibility that benzene or MTBE could reach a drinking water well

undetected.

• Although most LUFT data collected are two-dimensional in nature, the problem is in

reality three-dimensional. Wells are screened at different depth intervals across the state,

depending on the type of water usage and the local groundwater depth. Factors that

introduce uncertainty include vertical flows that occur from well use (which could

increase the vertical transport downward), artesian aquifers (that cause upward gradients),

and confining layers of low hydraulic conductivity that act to prevent downward flow,

upwards or downwards. Although hydrocarbons, which are light nonaqueous phase

liquids (LNAPLs), are referred to as “neutrally buoyant” in the dissolved phase, rainfall

can also act to drive the plume downward.

8.4.1. Location Accuracy of Drinking Water Wells and LUFT Sites

An important uncertainty in this analysis and in any future assessment of potential impacts is

the accuracy of locations of public drinking water wells in the CAL-DHS (1999b) PICME

database. Dooher and Happel (1999) demonstrated a technique to improve significantly the

location accuracy of public drinking water wells. The improved location data are used in this

analysis. CAL-DHS, in cooperation with the SWRCB, plans to expedite finding accurate

locations of wells in close proximity to LUFTs by November 2000 in order to comply with

Senate Bill 989 (1999).

Ascertaining the locations of LUFT sites and operating UST sites within the pilot areas and

within the state is much simpler than locating wells accurately. Because LUFTs and USTs are

specific, large facilities, their locations can be determined with moderate to high accuracy based

on addresses of commercial sites. LUFT sites used in this study are based on ETAK9 geocoding,

which returns an approximate location and associated accuracy based on street addresses.

Geocoding finds latitude and longitude of street addresses using an electronic database of streets

 

8 Excluded from this population are 3859 public water systems that are considered to be transient noncommunity

(TNC) systems (e.g., restaurants, campgrounds). These TNC systems are not required to monitor for unregulated

chemicals or chemicals with secondary MCLs.

9 Available at < http://www.etak.com/ >.
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and returns a qualifier stating the source of the position. Dooher and Happel (1999) examine the

accuracy of geocoding.

Potential problems in geocoding are based more on poor LUFT site and UST facility address

data. The SWRCB, in cooperation with Regional Water Quality Control Boards and local

permitting agencies, is currently addressing this problem by reconciling local and state databases

within the GEIMS database.

8.4.2. Sampling Frequency and Impact Rates of Public Drinking

Water Wells and LUFT Sites

Because our estimate indicates that the use of ethanol as a fuel oxygenate may be expected to

increase drinking water well impacts as much as 20%, routine monitoring of public and private

drinking water wells would be an important step to identifying actual impacts. Figure 8-38

shows the first and most recent sampling-date distributions for 10,972 drinking water sources

that have been tested for benzene 10 (CAL-DHS, 1999b). Only those wells that have had multiple

sampling are included in this graph. The reported, first-time sampling rate of approximately

1800 sources/year decreased substantially in 1987 to a reported rate of approximately

450 sources/year. This rate is likely due to new wells annually created around the state and

reflects initial sampling. In 1999, the sampling-event rate for many of these wells substantially

increased. However, a large percentage (40%) of wells has not been sampled or reported as

sampled in eight years. It is, thus, not known if there have been any well impacts for this

percentage of the population. For MTBE, most sampling has taken place since January 1996,

approximately the same time as the first reported impact to Santa Monica’s well fields.

In estimating the impact to public drinking water wells, we assumed that the modeled

increase in probability due to a change in gasoline composition can be based on the current

detection rate. Variations in that detection rate may have a direct impact on the estimate of

future impacts.

Figure 8-39 shows the cumulative distribution of time between samples and the average time

between samples. The average time between samples is conservative, because “two samples

over 10 days” would result in an artificially high sampling rate. In the past year, 10% more

wells—a large increase—have been sampled for BTEX. Only 50% of the wells sampled have

been sampled more than once. The above results point to an uncertainty in the baseline

“detection rate” of BTEX and MTBE in wells around California; therefore, there is inherent

uncertainty in the exact estimate of impacted wells.

Appendix B summarizes the results of the routine testing of public water sources throughout

California. Tables 8-3 to 8-5 show the yearly count of tested public water sources throughout

California. Tables 8-6 to 8-8 show the same counts for sources reporting BTEX and MTBE

detections. Overall, the average yearly benzene source detection rate is under 0.35%, as seen in

Table 8-9 (also seen as Figure 8-40). Both toluene and total xylenes have a higher rate, 0.53%

 

10 It should be noted that these are the numbers reported to CAL-DHS and may not accurately reflect the true

sampling frequencies. Almost half (2285) of the 4681 public water systems are small systems and nontransient

noncommunity systems regulated by local primary agencies (LPAs), designations given to 34 of California’s 58

counties. Data from LPA-regulated systems are not required to be submitted to CAL-DHS. In July 1998, CAL-DHS

requested LPAs to provide their MTBE detection data. These data are included as they are received from the LPAs.
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and 0.36% respectively. MTBE shows a 1.17% yearly detection rate, much higher than any of

the BTEX constituents alone, although similar to the combined BTEX detection rate (1.15%).

BTEX and MTBE have a combined water source detection rate of 1.75%. The total detection

rate for BTEX and MTBE, as of 1999 (Table 8-10) shows a 0.68% detection for benzene alone,

1.64% for toluene, 1.0% for the xylenes, and 0.62 for ethylbenzene. MTBE, which has not had

as many sources tested (approximately 42.3%) and which has only been tested for (and used)

recently, has already almost equaled the cumulative yearly detections of toluene, at 1.55%. The

combined cumulative detection rate of BTEX is 2.85% and BTEX with MTBE is 3.35%.

Finally, the change in detections are seen in Table 8-11. Figure 8-41 shows these trends in a

better format, but it can be seen that the average change in detection for MTBE is higher, by

25%, then the change in benzene detections. The overall change in detection trends for BTEX is

6.6%, and with MTBE included, 8.3%.

8.4.3. Comparisons of Well BTEX/MTBE Detection Levels, Well

Construction, Well Yield, and Groundwater Levels

Figure 8-42 shows a cumulative distribution of maximum concentrations as found in public

drinking water wells. These concentrations are orders of magnitudes less than what is observed

typically at LUFT sites. If ethanol is added to gasoline, it will likely have a similar effect on the

entire range of BTEX components. The levels observed may rise as a result of ethanol’s addition

to unknown but higher levels.

Figure 8-43 shows the depth to the top of the screen interval for various well types found

throughout California. Municipal (public) wells are generally deeper than domestic wells. The

median depth for domestic wells, based on selected CAL-DWR records and a more complete

database obtained (Dooher and Happel, 1999) from the Santa Clara Valley Water District

(SCVWD, 1999) shows a range of 75 to 100 ft. The median for SCVWD and from data

collected from the public wells of the Orange County Water District (OCWD, 1999) shows a

median that is more wide ranging—from 175 to 350 ft. This range is probably characteristic of

depth to top of screen interval for California groundwater, the depth of which is in general

deeper in southern California. The CAL-DWR and SCVWD data are from northern California.

Figure 8-43 also shows the cumulative distribution of the top of the screen depth for MTBEimpacted wells across California. It is similar to the SCVWD public wells and deeper than the

private well distribution, implying that the depth of private wells may not be sufficient protection

against impact.

Of importance in determining concentration levels in wells are both the well yield and the

plume concentration that the well takes in. As an example, the Santa Monica Arcadia wells were

found to have maximum MTBE levels of 19.6 ppb and 86.5 ppb for average monthly well yields

of 7.1 and 13 million gal, respectively (CAL-DHS, 1999a). These levels represent the 85th and

95th percentiles of the distribution of known MTBE impacts to public wells. The result of

performing a simple mass balance gives the actual MTBE levels (on a monthly basis) of 0.14 and

1.13 gal, respectively. Figure 8-44 shows the well yields as collected from SCVWD (1999).

The Arcadia wells represent the 84rd and 89th percentiles of the well yields as found in that

database. Private well yields are much less; these are also shown in Figure 8-44.

Figure 8-45 shows the correlation of the depth to top of well screen and the maximum depth

to groundwater, based on CAL-DWR records, for the set of private wells found in Figure 8-43.
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Where the points lie above the line, the groundwater level is below the top of the well screen,

leaving the well more vulnerable to contaminants that come from the surface. Twenty percent of

the wells have top of screen intervals that are greater than the groundwater depth. An additional

20% of the maximum groundwater level in the wells are within 20 ft of the top of the screened

interval. Various studies have shown the intrinsic vulnerability of domestic wells to surface

contamination (specific vulnerability). Details of these studies are found in Dooher (1999).

It is apparent that in public wells, any combination of concentration and yield is possible

(that is, low concentrations, low yields; high concentrations, high yields). The Santa Monica

Arcadia wells represent the near maximum yield and impacted concentrations seen in California

and are, therefore, near the upper limit of what is known to be possible. Little is known

concerning the levels of concentrations as seen in private wells. It is known that a private well in

Glennville, California, reported MTBE concentrations at the 20,000-ppb level in a fractured rock

environment. There were other concerns with this, however, including impacts of coliform and

nitrates, as well as E. coli in other nearby private wells. Little is known concerning actual

impacts to private wells, and more work must be performed to assess that population.

8.4.4. Vulnerability of Private Drinking Water Wells

What is not reflected in the screening analysis of potential impacts to public wells are the

approximately 1.3 million individuals in California who rely on private drinking water wells as

their primary source of water. According to 1990 United State census data, 464,621 California

households use some kind of private well US Census, 1992a). Although the locations of these

are not exactly known, U.S. census data do provide the information to determine the densities of

private wells (see Figure 8-46) from which the relative specific vulnerability of these private

wells may be determined11, as seen in Figure 8-47. Some local agencies possess databases or

listings of private wells; due to privacy considerations, this information is not generally

available.

Few compendiums have been issued that look in detail at LUFT sites that have impacted

wells, nor the reason for those impacts. In 1995, 55 water-supply wells in the jurisdiction of the

Central Valley Regional Water Quality Control Board (CVRWQCB) were reportedly affected by

BTEX. The SWRCB reviewed 36 LUFT case files (Rempel, 1995) on those wells and found that

unreasonable impairment of actual beneficial uses by benzene from LUFT sites is rare. Of the

55 wells reviewed, only 25 could be reasonably attributed to 19 LUFT sites out of the 5871 sites

then reported in the LUSTIS database. Seven of these sites were located in fractured rock and

impacted nine water wells, with six of these being onsite wells. The remaining 12 LUFT sites

were located in alluvial soils and affected 16 supply wells, five of which were onsite wells. In all

cases, the affected wells were less than 200 ft away from the LUFT. Twenty-four of the

25 affected wells were private, domestic wells. The review also found that 11 of the 24 affected

private, domestic water-supply wells were shallow, onsite wells at the LUFT site itself. Many of

them were described as “hand-dug” and probably had not been completed to current drinking

water well specifications. The review concluded that the LUFT potential impacts to groundwater

resources should be evaluated based on the proximity and the construction of water-supply wells

within a few hundred feet of a LUFT site.

 

11 The methodology used to determine this is described in Dooher (1998).
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This evaluation was performed prior to the use of MTBE in gasoline, and there are now

several hundred well-affected LUFT sites reported in LUSTIS. These sites should be

re-examined for detection of MTBE. Additionally, examination of MTBE-impacted public wells

may uncover trends or weaknesses in current regulatory oversight that require correction.

8.5. Conclusions

In conclusion, we used a distance approach combined with known impact probabilities and

estimated plume lengths to compare the probabilities of threat from LUFT benzene releases

(with or without ethanol), or MTBE, impacting drinking water wells in California. This method

provides an important advantage because it provides comparative estimates of potential future

impacts between MTBE and benzene in the presence of ethanol. The methodology described

herein can act as a screening-level approach to identify highly vulnerable areas. The

identification of such areas would then be based not only on a single site, but also on multiple

sites. The methodology also provides a probability factor for assessing the degradation of the

resource in a specific area using a summation of sites.

This method is based on the assumption that only one major variable, plume length, will

change as a result of the addition of ethanol to gasoline. All other variables, such as well depth,

yield, and locations will stay the same. Therefore, we can use the benzene without ethanol as a

baseline on which to assess the changes in detections.

Based on the results of our analysis, an approximate 20% peak relative difference in public

drinking water wells impacted by benzene was estimated if MTBE is replaced by ethanol, when

compared to benzene alone. This relative increase in public drinking water well impacts is

estimated to decline from this peak by ten years after the initial release. However, the estimated

potential future increase in public wells impacted by MTBE is significantly higher if MTBE

remains the primary fuel oxygenate. By the conclusion of the first ten-year period, estimated

MTBE well impacts increase by as much as a 45% difference form the baseline, and continue to

increase thereafter. This analysis is very conservative, especially with MTBE. Known

concentration levels at LUFT sites were not used in the analysis because only a limited number

of LUST sites have a known concentration associated with them. A comparative analysis is

underway to evaluate the difference between the above analysis and that of the more limited

population.

This approach may be subject to misinterpretations. Because we are examining first an

absolute probability, there is a concern that the absolute probability can be taken out of context.

It is important that the relative probabilities be used, using the benzene-alone distributions as the

baseline.

The evaluation of known detection levels in public water sources as discussed in Section

8.4.2, is to provided some perspective to the magnitude of benzene or MTBE impacts in the past.

Overall, the average yearly public drinking water source benzene detection rate is under 0.35%.

Both toluene and total xylenes have a higher rate, 0.53% and 0.36%, respectively. MTBE shows

a 1.17% yearly detection rate—much higher than any of the BTEX constituents alone—although

similar to the combined BTEX detection rate (1.15%). Currently, benzene shows a fairly

constant detection rate over time, with a slight downward trend when yearly rates are compared.

The use of ethanol as a fuel oxygenate would likely increase this detection rate which may

stabilize at some higher level. MTBE shows definite upward detection trends; its continued use
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could, based on the trend results, result in more detections in water sources than benzene within

the foreseeable future.

Based on the results of this preliminary evaluation, the following are recommended:

• Modeling based on detailed site-specific information is needed. Because the results of

these estimates are largely dependent on the input parameter probability distributions,

historical-case data that better constrain the uncertainty in these probability distributions

will improve the predictive capability of any future modeling. The uncertainty inherent in

using the complete distribution of benzene concentrations and velocities increases

substantially the increase in expected probability for well impacts. Site-specific,

maximum concentrations should be used, as opposed to a generic distribution that

assumes no knowledge of the LUFT site.

• Groundwater capture zones should be included in the analysis. This model approach

assumes that all plumes move towards a nearby well but with no influence from the well

itself. Further probabilistic modeling should be performed to ascertain the sensitivity of

this approach to well-capture zones and known groundwater-flow directions.

• More knowledge is required concerning the subsurface environment in California. One

of the major unknowns in any hydrogeological investigation is the lack of knowledge of

subsurface geology. A great deal can be learned even from the moderate to poor quality

of data available in CAL-DWR well logs. This data should be transcribed into electronic

format for use by researchers attempting to draw conclusions on the subsurface.

• A drinking water well sampling frequency policy that is based on proximity to LUFT

sites may be more protective of public water supplies. Public water agencies should

consider higher sampling rates for wells that are situated near known LUFT releases, or

any other known release, in order to protect public health12.

• Further comparative analysis of impacted public drinking water wells to gasoline

continuing ethanol or MTBE and well-impacted LUFT sites is needed. Rempel’s 1995

report should be updated to help to understand what factors result in well impacts

throughout California.

• A voluntary sampling program for private wells should be established by the state of

California. In order to protect a portion of the public that currently has no regulatory

oversight to protect against well contamination, California should institute a program

designed to identify areas where sampling of private wells may be required and offer

screening samples to that population.

• Data already collected by various state organizations should be systematically organized

for use in decision analysis.

8.6 Acknowledgments

I would like to thank all those who gave me constructive criticism and support in the

development of this work. These individuals include Walt McNab, Dave Rice, Anne Happel,

Dorothy Bishop, and Judy Steenhoven of Lawrence Livermore National Laboratory, and

 

12 This was a recommendation of the MTBE Blue Ribbon Panel (BRP, 1999) as well.
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Paulo Ricci of the University of San Francisco. Others who have given me feedback on this

research effort in the past and whose advice I have valued include James Giannopoulos,

Rick Rempel, and Heidi Temko of the State Water Resources Control Board.

8.7. References

Ang, A.H-S., and W.H. Tang (1975). Probability Concepts in Engineering Planning and

Design, Vol. 1. John Wiley and Sons, New York, NY.

Bear, Jacob (1972). Dynamics of Fluids in Porous Media. Elsevier, New York, NY.

Bear, Jacob (1979). Hydraulics of Ground Water. McGraw-Hill, New York, NY.

BRP (Blue Ribbon Panel) (1999). Achieving Clean Air and Clean Water; The Report of the Blue

Ribbon Panel in Oxygenates in Gasoline. Unit States Environmental Protection Agency,

Washington, DC. September.

California State Senate (1999a). Senate Bill 529 (Bowen) Motor Vehicles Fuels. Available:

< http://www.sen.ca.gov/ >.

CAL-DHS (California Department of Health Services) (1999a). Water Quality Monitoring

(WQM) Database. California Department of Health Services, Sacramento, CA. Accessed:

October 1999.

CAL-DHS (California Department of Health Services) (1999b). Permits, Inspection,

Compliance, Monitoring, and Evaluation (PICME) Database. California Department of

Health Services, Sacramento, CA. Accessed: October 1999.

CAL-DWR (California Department of Water Resources) (1997). EXTERRA Database.

California Department of Water Resources, Sacramento, CA.

Cleary, R.W., and M.J. Ungs (1978). Analytical Models for Groundwater Pollution and

Hydrology. Princeton University, Princeton, NJ. Water Resources Program Report 78-WR15.

Dooher, B.P. (1998). Making Risk-Based Management Decisions at Fuel Hydrocarbon

Impacted Sites Under Sparse Data Conditions. Ph.D. Dissertation, University of California at

Los Angeles, Los Angeles, CA.

Dooher, B.P., and A.M. Happel (1999). Evaluating the Feasibility of a Statewide Geographic

Information System—Design and Testing of an Internet Accessible Geographical

Environmental Information Management System. Lawrence Livermore National Laboratory,

Livermore, CA. UCRL-AR-134611.

Giannopoulos, J. (1999). Public Hearing of the State Water Resources Control Board;

Testimony on the University of California at Davis MTBE Report. State Water Resources

Control Board, Sacramento, CA, February 19.

Gelhar, L.W., A. Mantoglou, C. Welty, and K.R. Rehfeldt (1985). A Review of Field Scale

Subsurface Solute Transport Processes under Saturated and Unsaturated Conditions (Draft

Report). Electric Power Research Institute, Groundwater Studies, Palo Alto, CA. EPRI EACCCC.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-18

Guven, O., F.J. Molz, and J.G. Melville (1984). An analysis of dispersion in a stratified aquifer.

Water Resour. Res. 20: 1337-1354.

Happel, A.M., E.H. Beckenbach, and R.U. Halden (1998). An Evaluation of MTBE Impacts to

California Resources. Lawrence Livermore National Laboratory, Livermore, CA. UCRLAR-130897

Jury, W.A. (1985). Spatial Variability of Soil Physical Parameters in Solute Migration: A

Critical Literature Review. Electric Power Research Institute, Palo Alto, CA, EPRI EA-4228.

MacKay, D.M., P.V. Roberts, and J.A. Cherry (1985). Transport of organic contaminants in

groundwater. Environ. Sci. Technol. 19: 384-392.

McNab, W.W., D.W. Rice, J. Bear, R. Ragaini, C. Tuckfield, and C. Oldenburg (1999a).

Historical Case Analyses of Chlorinated Volatile Organic Plumes. Lawrence Livermore

National Laboratory, Livermore, CA. UCRL-AR-133361.

McNab, W., S.E. Heermann, and B. Dooher (1999b). Screening model evaluation of the effects

of ethanol on benzene plume lengths. In: Health and Environmental Assessment of the Use of

Ethanol as a Fuel Oxygenate—Report to the California Environmental Policy Council in

Response to Executive Order D-5-99. Vol. 4, Potential Ground and Surface Water Impacts,

D.W. Rice and G. Cannon (Eds.). Lawrence Livermore National Laboratory, Livermore, CA.

UCRL-AR-135949 Vol. 4 Ch. 4.

Malcolm Pirnie, Inc. (1998). Evaluation of the Fate and Transport of Ethanol in the

Environment. Report prepared for American Methanol Institute, Washington, DC. Malcolm

Pirnie, Inc., Oakland, CA, November. Report No. 3522-001.

Molson, J.W., J.F. Barker, and M. Schirmer (1999). Modeling the Impact of Ethanol on the

Persistence of BTEX Compounds in Gasoline Contaminated Groundwater. California

MTBE Research Partnership, Waterloo, Canada. NWRI-99-DRAFT. November.

(OCWD) Orange County Water District. (1999). Well Description Database. Orange County

Water District, CA. Accessed:

Rempel, R. (1995). Review of available data from sites reported in LUSTIS database as having

affected a water supply well. State Water Resources Control Board Memorandum. August.

Rempel, R., H. Temko, and K. McGee (1996). California Leaking Underground Fuel Tank

(LUFT) Historical Case Analyses: Data Collection, Data Input, and Data Quality Control.

Contaminated Soils and Groundwater Conference, Association for the Environmental Health

of Soils, Newport Beach, CA, March 11–14.

Schirmer, M, J.W. Molson, and J.F. Barker (1999). The potential impact of alcohol as a gasoline

oxygenate on BTEX degradation at spill sites. Proceedings of the Petroleum Hydrocarbons

and Organic Chemicals in Ground Water. National Ground Water Association, Houston,

TX, Nov. 17–19, 1999. National Ground Water Association, Westerville, Ohio.

SCVWD (Santa Clara Valley Water District) (1999). FELIX and Water Resources Management

Databases. Santa Clara Valley Water District, Santa Clara, CA. Accessed:

Senate Bill 989 (1999).
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-19

SWRCB (State Water Resources Control Board). (1989). Leaking Underground Fuel Tank Field

Manual: Guidelines for Site Assessment, Cleanup, and Underground Storage Tank Closure.

State Water Resources Control Board, Sacramento, CA.

SWRCB (State Water Resources Control Board). (1999). Leaking Underground Fuel Tank

Information System (LUSTIS) Database. State Water Resources Control Board, Sacramento,

CA. 3rd Quarter.

Ulrich, G. (1999). The Fate and Transport of Ethanol-blended Gasoline in the Environment.

Governors’ Ethanol Coalition, Lincoln, NE. October.

US Census (United States Bureau of the Census) (1992a). Census of Population and Housing,

1990: Summary Tape File 3 on CD-ROM (California) (machine-readable data files).

Prepared, produced, and distributed by the U.S. Bureau of the Census, Washington, DC.

US Census (United States Bureau of the Census) (1992b). Census of Population and Housing,

1990: Summary Tape File 3 on CD-ROM Technical Documentation. Prepared by the U.S.

Bureau of the Census, Washington, DC.

van Genuchten, M.T., and W.J. Alves (1982). Analytical Solutions of the One-dimensional

Convective-dispersive Solute Transport Equation. United States Department of Agriculture,

Washington, DC. Technical Bulletin 1661.

Wexler, E.J. (1992). Analytical Solutions for One-, Two-, and Three-dimensional Solute

Transport in Groundwater Systems with Uniform Flow. U.S. Geological Survey,

Washington, DC. USGS TWRI 3-B7.

Wilson, J.L., and P.J. Miller (1978). Two-dimensional plume in uniform groundwater flow.

ASCE Journal of the Hydraulic Division 104: 503–514.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-20
12-99/Ethanol Ch. 8 8-21

UCRL-AR-135949 Vol. 4 Ch. 8

Figures
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-22
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-23

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000 10000

Plume length, feet

Difference between estimated

 benzene plume lengths 

with and without ethanol

Estimated benzene 

plume lengths 

without ethanol

Estimated benzene 

plume lengths 

with ethanol

Figure 8-1. Example of the derivation of the multiplier used in this analysis.

Monte Carlo analysis with Cleary and Ungs (1978)

model

Source Concentration (C0 )

Groundwater velocity

Degradation Rate (λ )

Plume length

Source Attenuation (γ )

Source width

and height

Figure 8-2. Conceptualization of the three-dimensional plume.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-24

1

10

100

1000

10000

100000

0 100 200 300 400 500 600 700 800

Plume Length, 1 ppb Contour (Feet)

Figure 8-3. Best Professional Judgement benzene plume length vs. maximum benzene

concentration.

1

10

100

1000

10000

100000

10 100 1000

Plume Length, 1 ppb Contour (Feet)

Figure 8-4. Best Professional Judgement plume length vs. maximum concentrations (log-log

scale).
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-25

Comparison of BPJ Plumes, Non-Parametric Plumes, and 3-D MC Plume Distributions

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 100 200 300 400 500 600 700 800 900 1000

Plume Length, feet

MC

BPJ - CtoC

BPJ - MaxtoC

Non Parametric

Figure 8-5. Comparison of 3-D Monte Carlo simulated plumes with nonparametric and Best

Professional Judgement derived plumes.

Plume Length Distributions Over Time For Benzene

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000 10000

Plume Length, feet

Year 1

Year 5

Year 20

Year 100

Year 90

Year 80

Year 70

Year 50

Year 60

Year 40

Year 30

Figure 8-6. Cumulative distributions of benzene plume length over time.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-26

Plume Growth and Decay of Degrading Species Over Time

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80 90 100

Time, years

95th Percentile

90th Percentile

85th Percentile

80th Percentile

Trend moves at 5 

percentile intervals

Figure 8-7. Plume length vs. time elapsed for various percentile intervals.

Plume Growth and Decay of Degrading Species Over Time

1

10

100

1000

10000

0 10 20 30 40 50 60 70 80 90 100

Time, years

95th Percentile

90th Percentile

85th Percentile

75th Percentile

70th Percentile

60th

Percentile

50th

Percentile

25th

Percentile

40th

Percentile

10th

Percentile

Figure 8-8. Log scale plume length vs. time elapsed for various percentile intervals.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-27

Various Degrading Plumes and Their Evolution Over Time

0

50

100

150

200

250

300

350

400

450

500

0 10 20 30 40 50 60 70 80 90 100

Time, years

Figure 8-9. Plume length vs. time elapsed for randomly generated plumes.

Year at Which Simulation Reaches Maximum Plume Length

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

<1 1 5 10 15 20 25 30 35 40 45 50

Year

All

FP

Non-FP

Figure 8-10. Time at which degrading plume reaches its maximum length.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-28

Plume Length Distribution at Peak Extent

1

10

100

1000

10000

0 5 10 15 20 25 30 35 40 45 50

Time, years

1st Percentile

50th Percentile

99th Percentile

Figure 8-11. Plume length distributions for time interval at which degrading plume reaches its

maximum length.

Plume Length Decay Rate Distributions

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.00001 0.0001 0.001 0.01 0.1 1

Decay Rate - Feet/day

1-5 years

5-10 years

95-100 years

45-50 years

Figure 8-12. Distribution of plume decay rate at various time intervals.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-29

Years Needed to Reach Zero Plume Length

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Years to Zero

All Simulations

FP

Non-FP

Note: 33% of simulations fail to reach 

zero plume length after 100 years. 44% 

of these are FP and 22% are non-FP.

Figure 8-13. Years required for plumes to reach zero plume length.

Distribution of Maximum Plume Lengths and Time to Reach Zero Plume Length

0.01

0.1

1

10

100

1000

10000

0 10 20 30 40 50 60 70 80 90 100

Years From Max to Zero

0.01

0.50

0.99

75th Percentile

25th Percentile

Figure 8-14. Maximum plume length distributions for time interval necessary for plume length to

reach zero.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-30

Cumulative Distribution of PLume Lengths for Non-Degrading Chemicals

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000 10000 100000

Plume Length, feet

Year 1

Year 5

Year 10

Year 20

Year 50

Year 75

Year 100

Figure 8-15. Plume length distributions for nondegrading plumes.

Plume Growth of Non-Degrading Chemical Species Over Time

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

0 10 20 30 40 50 60 70 80 90 100

Time, Years

75th percentile

90th percentile

85th percentile

95th percentile

Figure 8-16. Plume length vs. time elapsed for various percentile intervals.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-31

Plume Growth of Non-Degrading Chemical Species Over Time

0

250

500

750

1000

1250

1500

1750

2000

0 10 20 30 40 50 60 70 80 90 100

Time, Years

10th percentile

15th percentile

20th percentile

25th percentile

30th percentile

40th percentile

50th percentile

60th percentile

35th percentile

45th percentile

55th percentile

Figure 8-17. Plume length vs. time elapsed for various percentile intervals.

Plume Growth of Non-Degrading Chemical Species Over Time

1

10

100

1000

10000

100000

1 10 100

Time, Years

5th percentile

10th percentile

95th percentile

Figure 8-18. Log plume length vs. log time elapsed for various percentile intervals.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-32

Various Plume Evolutions Over Time of Non-Degrading Chemical Species

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 10 20 30 40 50 60 70 80 90 100

Time, Years

Figure 8-19. Plume length vs. time elapsed for randomly generated plumes.

Year at Which Simulated Non-Degrading Plume Reaches Steady-State (No Growth)

0%

2%

4%

6%

8%

10%

12%

14%

1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Time - Years

All

FP

Non-FP

Note: At Year 100, 46% of all 

simulations have not reached SteadyState, 57% of Which are FP source and 

34% are Non-FP Source.

Figure 8-20. Year at which simulated nondegrading plumes reach steady-state.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-33

Growth Rates of FP and Non-FP Non-Degrading Plumes at 100 Years

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.001 0.01 0.1 1

Growth Rate - feet/year

FP Source

Non-FP Source

Figure 8-21. Growth rates of free-product and non-free-product plumes at 100 years.

Plume Growth Rate for Non-Degrading Constituents

Free Product Source

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.001 0.01 0.1 1 10

Growth Rate - feet/day

Year 0-1

Year 1-5

Year 5-10

Year 95-100

Figure 8-22. Growth rates of free-product plumes over time.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-34

Plume Growth Rate for Non-Degrading Constituents

Non-Free Product Source

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0001 0.001 0.01 0.1 1 10

Growth Rate - feet/day

Year 0-1

Year 1-5

Year 5-10

Year 95-100

Figure 8-23. Growth rates of non-free-product pumes over time.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 1,000 10,000 100,000

Minimum separation distance, feet

Distance of LUFT site 

to nearest public 

drinking water well

Distance of public 

drinking water well 

to nearest LUFT 

Figure 8-24. Distance of drinking water wells and LUFT sites from each other.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-35

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000

Number of LUFT sites within x feet of a drinking water well

500 ft

1500 ft

2500 ft

5000 ft

1000 ft

7500 ft

10000 ft

20000 ft

30000 ft

50000 ft

Figure 8-25. Distribution of the number of LUFT sites within x feet of a drinking water well.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000

Number of drinking water wells within x feet of a LUFT site

500 ft

1500 ft

2500 ft

5000 ft

1000 ft

7500 ft

10000 ft

20000 ft

30000 ft

50000 ft

Figure 8-26. Distribution of the number of drinking water wells within x feet of a LUFT site.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-36

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of sites, benzene alone

100 years

50 years 1 year

10 years

15 years

5 years

Figure 8-27. Distribution of the absolute probability of threat for LUFT sites with benzene alone.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of sites, benzene with ethanol

100 years 50 years

25 years

1 year

10 years

15 years

5 years

Figure 8-28. Distribution of the absolute probability of threat for LUFT sites with benzene and

ethanol.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-37

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%

Percent of sites, benzene alone

100 years

50 years

25 years

1 year

10 years

15 years

5 years

Figure 8-29. Close view of the distribution of the absolute probability of threat for LUFT sites with

benzene alone.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%

Percent of sites, benzene with ethanol

100 years

50 years

25 years

1 year

10 years

15 years

5 years

Figure 8-30. Close view of the distribution of the absolute probability of threat for LUFT sites with

benzene and ethanol.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-38

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of sites, MTBE

100 years

50 years

1 year

5 years

10 years

15 years

25 years

Figure 8-31. Distribution of the absolute probability of threat for LUFT sites with MTBE.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0% 5% 10% 15% 20% 25% 30% 35% 40%

Percent of sites, benzene with and without ethanol

100 years

50 years

25 years

1 year

10 years

15 years

5 years

Figure 8-32. Relative probability of threat for LUFT sites for benzene with and without ethanol.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-39

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Percent of sites, benzene with and without ethanol

1-5 years

5-10 years

20-25 years

10-15 years

95-100 years

Figure 8-33. Temporal relative change in probability of threat for LUFT sites for benzene with and

without ethanol.

0.00

0.05

0.10

0.15

0.20

0.25

0% 5% 10% 15% 20% 25% 30% 35% 40%

Percent of sites, MTBE

1-5 years

5-10 years

10-15 years

20-25 years

45-50 years

95-100 years

Figure 8-34. Temporal change in absolute probability of threat for LUFT sites for MTBE.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-40

0%

10%

20%

30%

40%

50%

60%

70%

0 10 20 30 40 50 60 70 80 90 100

Time, years

1% of sites

5% of sites

10% of sites

20% of sites

30% of sites

Benzene alone

Benzene with ethanol

Figure 8-35. Absolute probability of a site threatening any well using de Morgan’s law, benzene

and benzene with ethanol.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90 100

Time, years

40% of sites

1% of sites

5% of sites

10% of sites

20% of sites

30% of sites

50% of sites

60% of sites

Figure 8-36. Absolute probability of a site threatening any well using de Morgan’s law, MTBE.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-41

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 15 30 45 60 75

Time, years

Benzene, 95th percentile -Base Case

Benzene with ethanol,

 95th percentile

Difference between 

MTBE and Base Case

Difference between

Benzene with ethanol 

and Base Case

MTBE,

 95th percentile

Figure 8-37. Relative probability of a site impacting any well using de Morgan’s law.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00

Sampling date

First BTEX 

Sampling Event

as of Oct, 1999

First BTEX 

Sampling Event

as of Dec, 1998

Last BTEX 

Sampling Event

as of Dec, 1998

Last BTEX 

Sampling Event

as of Oct, 1999

First MTBE 

Sampling Event

as of Oct, 1999 Last MTBE 

Sampling Event

as of Oct, 1999

Figure 8-38. First and last sampling event dates for BTEX and MTBE in drinking water wells.
UCRL-AR-135949 Vol. 4 Ch. 8 DR Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-42

30%

40%

50%

60%

70%

80%

90%

100%

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500

Time, days

Time between first and 

last Sampling Event

Average time between 

sampling event

BTEX, Dec 1998

BTEX, Oct 1999

MTBE, Oct 1999

BTEX, Dec 1998

BTEX, Oct 1999

MTBE, Oct 1999

Figure 8-39. Time between first and last sample event and the average time

between sample events.

0.000

0.005

0.010

0.015

0.020

0.025

1985 1987 1989 1991 1993 1995 1997 1999

Time, years

B

T

E

X

MTBE

BTEX

BTEX/MTBE

Figure 8-40. Rate of detetections per year of BTEX and MTBE in California public drinking water

sources.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-43

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

1993 1994 1995 1996 1997 1998 1999

Time, years

B

T

E

X

MTBE

BTEX

BTEX/MTBE

BTEX/MTBE Trend

BTEX Trend

MTBE Trend

X Trend

T Trend

B Trend

E Trend

Figure 8-41. Change in detetection rate per year of BTEX and MTBE in California public drinking

water sources.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.1 1 10 100 1000

Concentration, ppb

MTBE

Toluene

Ethylbenzene

Benzene

Xylenes

Figure 8-42. Complementary cumulative distribution of BTEX and MTBE detections

at impacted drinking water wells in California.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-44

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 50 100 150 200 250 300 350 400 450 500

Depth to Top of Screen, ft

SCVWD

Domestic

DWR

Domestic

SCVWD

Municipal

OCWD

Municipal

MTBE Statewide

Impacted

Municipal

Figure 8-43. Cumulative distribution of depth to top of screen for various public and private

drinking water wells in California.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.01 0.1 1 10 100 1000 10000

Acre-feet/year

SCVWD 

Domestic

SCVWD 

Municipal

Figure 8-44. Cumulative distribution of well yield for public and private drinking water wells.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-45

1

10

100

1000

1 10 100 1000

Depth to Top of Well Screen, ft

Maximum depth to groundwater, ft

Figure 8-45. Correlation of top of screen interval to maximum groundwater depth for private

wells.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-46

Wells per Acre

0

0.00 - 0.01

0.01 - 0.03

0.03 - 0.08

0.08 - 4.50

Private well density based

1990 Census Block Group

D(~450,000 Private Wells)

Figure 8-46. Drinking water well density in California (private wells). Darker areas indicate higher

densities.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 8-47

Specific Vulnerability

Very Low

Low

Moderate

High

Very High

Figure 8-47. Specific vulnerability to private drinking water wells in California at the block group

level. Darker areas indicate higher densities of LUFT sites and private wells.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-48
11-99/Ethanol Ch. 8 8-49

UCRL-AR-135949 Vol. 4 Ch. 8

Tables
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-50
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-51

Table 8-1. Notation of Parameters Used in Cleary and Ungs (1978) Equation.

Symbol Notation Description

C Concentration

CP Maximum or Peak Concentration of Gaussian Source

Dx Dispersion coefficient in the x (longitudinal) direction

Dy Dispersion coefficient in the y (horizontal) direction

Dz Dispersion coefficient in the z (vertical) direction

f

oc Organic carbon content of soil

koc Partition coefficient of chemical

R Retardation

t Time

vx velocity component, x direction

vy velocity component, y direction

vz velocity component, z direction

x Distance in the x direction

y Distance in the y direction

Y0 Center location of Gaussian source in the y direction

z Distance in the z direction

Z0 Center location of Gaussian source in the z direction

L Dispersivity coefficient in the x (longitudinal) direction

H Dispersivity coefficient in the y (horizontal) direction

V Dispersivity coefficient in the z (vertical) direction

Dummy integration variable

First order decay/growth coefficient for an exponential boundary source

Porosity

First order decay constant for the plume

b Bulk density of soil

Correlation coefficient of the source

y Standard deviation of the Gaussian Source in the y direction

z Standard deviation of the Gaussian Source in the z direction
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-52

Table 8-2. Population Input Parameters for Monte Carlo Forecasts.

Parameter Description Remarks Prescribed distribution

t Elapsed time

since source

initiation

Captures a range of typical ages

encountered at LUFT sites.

Lognormal distribution.

5th percentile = 730 days (2 years)

mean = 3260 days (8.9 years)

95th percentile = 14,600 days (40 years)

y Source width Captures a range of possible release

types, from small LUFT tank and

pipeline leaks to releases from large

industrial facilities. Mean is based on

typical 10,000-gal tank length.

Assumed to correlate with elapsed

time, R = +0.75.

Lognormal distribution.

5th percentile = 1.9 m

mean = 10.7 m

95th percentile = 29.6 m

Source

depletion rate

(first-order)

Pseudo-first-order decay coefficient

which accounts for volatilization,

engineered source removal, and other

mechanisms of source depletion

(Dooher, 1998).

Lognormal distribution.

5th percentile = 7.35 × 10-5 day-1 (t1/2 =

9,420 days or 25.8 years)

mean = 2.78 × 10-4 day-1 (t1/2 = 2500 days or

6.8 years)

95th percentile = 6.67 × 10-4 day-1 (t1/2 =

1040 days or 2.8 years)

C0FP Maximum

concentration

associated

with free

product sites

(38.2% of

sites)

Results of MC forecasts are raised to

the power of 10 and divided by 1000

(Dooher, 1998).

 

C0FP =

10 (20.05,6.84,5.55 )

1000

Beta Distribution.

a = 20.05

b = 6.84

scale = 5.55

C0L Maximum

concentration

associated

with sites

where FP is

detected

(61.8% of

sites)

Results of MC forecasts are raised to

the power of 10 and divided by 1000

(Dooher, 1998).

 

C0L =

10T(−0.48,3.93,5.09)

1000

Triangular Distribution

Minimum = -0.48

Likeliest = 3.93

Maximum = 5.09

K Hydraulic

conductivity

Values typifying California

hydrogeology (Dooher, 1998). Values

supported by by Mackay et al. (1985)

and Guven et al. (1984).

Lognormal distribution.

5

th percentile = 3.82 × 10-6 m/s (0.33 m/day)

mean = 1.25 × 10-4 m/s (10.8 m/day)

95th percentile = 4.75 × 10-4 m/s (41 m/day)

h Hydraulic

gradient

Values typifying California

hydrogeology (Dooher, 1998). Values

supported by by Mackay et al. (1985)

and Guven et al. (1984).

Lognormal distribution.

5

th percentile = 1.42 × 10-3

mean = 1.66 × 10-2

9

th percentile = 5.58 × 10-2
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-53

Table 8-2. Population Input Parameters for Monte Carlo Forecasts (continued).

Parameter Description Remarks Prescribed distribution

Porosity Based on 252 samples taken at the

Lawrence Livermore National

Laboratory Superfund Site

(Dooher, 1998) and Jury (1985).

Normal distribution.

mean = 0.42

standard deviation = 0.07

x:L ratio Ratio of

longitudinal

dispersivity to

plume length

Based on results presented in

Gelhar et al. (1992) fitted to the

equation

 

x = 1.022(logL )

3.117910N(0,0.74

Normal distribution.

mean = 0.0

standard deviation = 0.74

y:L ratio Ratio of

horizontal

transverse

dispersivity to

plume length

Based on results presented in

Gelhar et al. (1992) fitted to the

equation

 

y = 0.017L0.7526910N(0,0.8)

Normal distribution.

mean = 0.0

standard deviation = 0.80

First-order

degradation

rate

Median value may be fairly typical as

a macroscopic average for many sites

(e.g., Buscheck et al., 1996).

Lognormal distribution.

5th percentile = 4.62 × 10-4 day-1 (t1/2 =

1500 days or 4.1 years)

mean = 9.47 × 10-3 day-1 (t1/2 = 73.2 days or

0.20 year)

95th percentile = 3.47 × 10-2 day-1 (t1/2 =

20 days or 0.055 year)

foc Fractional

organic carbon

content

Based on 278 samples taken at the

Lawrence Livermore National

Laboratory Superfund Site

(Dooher, 1998).

Lognormal distribution.

5th percentile = 1.37 × 10-4

mean = 1.4 × 10-3

95th percentile = 4.6 × 10-3

b Bulk density Based on 595 samples taken at the

Lawrence Livermore National

Laboratory Superfund Site

(Dooher, 1998).

Normal distribution.

mean = 1.75 g/cm3

standard deviation = 0.22 g/cm3
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-54

Table 8-3. Count of sampled public water sources, by year.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980 1 1 1 1 0 1 1

1981 1 1 1 0 0 1 1

1982 2 2 2 0 0 2 2

1983 0 0 0 0 0 0 0

1984 1,196 1,188 1,188 1,169 0 1,196 1,196

1985 1,580 1,573 1,571 1,486 0 1,580 1,580

1986 2,957 2,950 2,941 2,828 0 2,957 2,957

1987 2,951 2,960 2,949 2,848 0 2,960 2,960

1988 2,375 2,375 2,357 2,334 0 2,375 2,375

1989 5,708 5,710 5,709 5,687 0 5,710 5,710

1990 3,563 3,547 3,561 3,470 3 3,565 3,568

1991 2,619 2,609 2,618 2,421 0 2,619 2,619

1992 3,676 3,635 3,672 3,452 9 3,672 3,681

1993 3,182 3,143 3,188 3,164 0 3,188 3,188

1994 3,141 3,143 3,141 3,160 1 3,160 3,160

1995 3,294 3,292 3,291 3,292 129 3,294 3,326

1996 3,338 3,338 3,337 3,319 2,279 3,338 3,589

1997 3,149 3,147 3,144 3,198 2,888 3,198 3,493

1998 3,285 3,279 3,281 3,266 3,635 3,285 3,982

1999 2,491 2,483 2,488 2,474 2,310 2,491 2,707
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-55

Table 8-4. Cumulative count of sampled public water sources, by year.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980 5 3 4 3 0 5 5

1981 6 4 5 3 0 6 6

1982 8 6 7 3 0 8 8

1983 8 6 7 3 0 8 8

1984 1,202 1,192 1,193 1,172 0 1,202 1,202

1985 2,614 2,601 2,601 2,499 0 2,614 2,614

1986 4,920 4,907 4,904 4,718 0 4,920 4,920

1987 6,857 6,850 6,845 6,687 0 6,857 6,857

1988 8,212 8,201 8,191 8,112 0 8,212 8,212

1989 10,971 10,971 10,962 10,896 0 10,971 10,971

1990 11,533 11,529 11,522 11,459 3 11,533 11,536

1991 11,924 11,917 11,914 11,839 3 11,924 11,927

1992 12,331 12,327 12,324 12,243 12 12,331 12,343

1993 12,706 12,696 12,700 12,635 12 12,706 12,718

1994 12,995 12,980 12,990 12,937 13 12,995 13,007

1995 13,263 13,247 13,258 13,197 142 13,263 13,288

1996 13,567 13,554 13,563 13,486 2,301 13,567 13,609

1997 13,825 13,812 13,820 13,749 3,853 13,825 13,874

1998 14,066 14,051 14,060 14,004 5,418 14,066 14,144

1999 14,190 14,179 14,183 14,134 6,081 14,190 14,274
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-56

Table 8-5. Change in count of sampled public water sources, by year.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980

1981 1 1 1 0 0 1 1

1982 2 2 2 0 0 2 2

1983 0 0 0 0 0 0 0

1984 1,194 1,186 1,186 1,169 0 1,194 1,194

1985 1,412 1,409 1,408 1,327 0 1,412 1,412

1986 2,306 2,306 2,303 2,219 0 2,306 2,306

1987 1,937 1,943 1,941 1,969 0 1,937 1,937

1988 1,355 1,351 1,346 1,425 0 1,355 1,355

1989 2,759 2,770 2,771 2,784 0 2,759 2,759

1990 562 558 560 563 3 562 565

1991 391 388 392 380 0 391 391

1992 407 410 410 404 9 407 416

1993 375 369 376 392 0 375 375

1994 289 284 290 302 1 289 289

1995 268 267 268 260 129 268 281

1996 304 307 305 289 2,159 304 321

1997 258 258 257 263 1,552 258 265

1998 241 239 240 255 1,565 241 270

1999 124 128 123 130 663 124 130
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-57

Table 8-6. Count of detections in sampled public water sources, by year.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980 0 0 0 0 0 0 0

1981 0 0 0 0 0 0 0

1982 0 0 0 0 0 0 0

1983 0 0 0 0 0 0 0

1984 0 0 0 1 0 1 1

1985 5 4 1 6 0 15 15

1986 2 11 3 9 0 15 15

1987 5 11 4 10 0 21 21

1988 13 24 6 12 0 35 35

1989 21 36 10 14 0 61 61

1990 8 15 5 9 3 30 33

1991 12 14 7 10 0 29 29

1992 15 17 9 10 0 36 36

1993 9 12 10 11 0 31 31

1994 18 29 16 8 0 48 48

1995 10 15 5 9 4 34 38

1996 14 27 8 22 25 49 66

1997 9 19 3 9 31 35 66

1998 13 7 5 11 45 31 74

1999 10 14 5 11 29 30 54
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-58

Table 8-7. Cumulative count of detections in sampled public water sources, by year.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980 0 0 0 0 0

1981 0 0 0 0 0

1982 0 0 0 0 0

1983 0 0 0 0 0

1984 0 0 0 1 0 1 1

1985 5 4 1 6 0 15 15

1986 7 15 4 15 0 30 30

1987 12 26 8 25 0 51 51

1988 25 50 14 37 0 85 85

1989 44 84 24 49 0 140 140

1990 48 99 28 57 3 166 169

1991 54 110 34 65 3 187 190

1992 65 125 42 73 3 218 221

1993 69 136 51 83 3 242 245

1994 78 163 66 90 3 279 282

1995 80 175 71 98 7 302 309

1996 87 199 78 115 29 339 363

1997 89 217 80 122 50 364 403

1998 96 222 84 132 81 387 452

1999 97 233 88 141 94 405 478
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-59

Table 8-8. Change in count of detections in sampled public water sources, by year.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980

1981 0 0 0 0 0 0 0

1982 0 0 0 0 0 0 0

1983 0 0 0 0 0 0 0

1984 0 0 0 1 0 1 1

1985 5 4 1 5 0 14 14

1986 2 11 3 9 0 15 15

1987 5 11 4 10 0 21 21

1988 13 24 6 12 0 34 34

1989 19 34 10 12 0 55 55

1990 4 15 4 8 3 26 29

1991 6 11 6 8 0 21 21

1992 11 15 8 8 0 31 31

1993 4 11 9 10 0 24 24

1994 9 27 15 7 0 37 37

1995 2 12 5 8 4 23 27

1996 7 24 7 17 22 37 54

1997 2 18 2 7 21 25 40

1998 7 5 4 10 31 23 49

1999 1 11 4 9 13 18 26
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-60

Table 8-9. Yearly public water source detection rate.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980

1981

1982

1983

1984 0.00086 0.00084 0.00084

1985 0.00316 0.00254 0.00064 0.00404 0.00949 0.00949

1986 0.00068 0.00373 0.00102 0.00318 0.00507 0.00507

1987 0.00169 0.00372 0.00136 0.00351 0.00709 0.00709

1988 0.00547 0.01011 0.00255 0.00514 0.01474 0.01474

1989 0.00368 0.00630 0.00175 0.00246 0.01068 0.01068

1990 0.00225 0.00423 0.00140 0.00259 0.00842 0.00925

1991 0.00458 0.00537 0.00267 0.00413 0.01107 0.01107

1992 0.00408 0.00468 0.00245 0.00290 0.00980 0.00978

1993 0.00283 0.00382 0.00314 0.00348 0.00972 0.00972

1994 0.00573 0.00923 0.00509 0.00253 0.01519 0.01519

1995 0.00304 0.00456 0.00152 0.00273 0.03101 0.01032 0.01143

1996 0.00419 0.00809 0.00240 0.00663 0.01097 0.01468 0.01839

1997 0.00286 0.00604 0.00095 0.00281 0.01073 0.01094 0.01889

1998 0.00396 0.00213 0.00152 0.00337 0.01238 0.00944 0.01858

1999 0.00401 0.00564 0.00201 0.00445 0.01255 0.01204 0.01995

Average 0.00348 0.00534 0.00203 0.00360 0.01553 0.01149 0.01745
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-61

Table 8-10. Yearly public water source detection rate, based on cumulative count.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980

1981

1982

1983

1984 0.00085 0.00083 0.00083

1985 0.00191 0.00154 0.00038 0.00240 0.00574 0.00574

1986 0.00142 0.00306 0.00082 0.00318 0.00610 0.00610

1987 0.00175 0.00380 0.00117 0.00374 0.00744 0.00744

1988 0.00304 0.00610 0.00171 0.00456 0.01035 0.01035

1989 0.00401 0.00766 0.00219 0.00450 0.01276 0.01276

1990 0.00416 0.00859 0.00243 0.00497 0.01439 0.01465

1991 0.00453 0.00923 0.00285 0.00549 0.01568 0.01593

1992 0.00527 0.01014 0.00341 0.00596 0.01768 0.01790

1993 0.00543 0.01071 0.00402 0.00657 0.01905 0.01926

1994 0.00600 0.01256 0.00508 0.00696 0.02147 0.02168

1995 0.00603 0.01321 0.00536 0.00743 0.04930 0.02277 0.02325

1996 0.00641 0.01468 0.00575 0.00853 0.01260 0.02499 0.02667

1997 0.00644 0.01571 0.00579 0.00887 0.01298 0.02633 0.02905

1998 0.00682 0.01580 0.00597 0.00943 0.01495 0.02751 0.03196

1999 0.00684 0.01643 0.00620 0.00998 0.01546 0.02854 0.03349

Average 0.00467 0.00995 0.00354 0.00617 0.02106 0.02603 0.02888
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 8-62

Table 8-11. Yearly public water source detection rate, based on count change.

YEAR B T E X MTBE BTEX BTEX/MTBE

1980

1981

1982

1983

1984 0.00000 0.00000 0.00000 0.00086 0.00084 0.00084

1985 0.00354 0.00284 0.00071 0.00377 0.00992 0.00992

1986 0.00087 0.00477 0.00130 0.00406 0.00650 0.00650

1987 0.00258 0.00566 0.00206 0.00508 0.01084 0.01084

1988 0.00959 0.01776 0.00446 0.00842 0.02509 0.02509

1989 0.00689 0.01227 0.00361 0.00431 0.01993 0.01993

1990 0.00712 0.02688 0.00714 0.01421 0.04626 0.05133

1991 0.01535 0.02835 0.01531 0.02105 0.05371 0.05371

1992 0.02703 0.03659 0.01951 0.01980 0.07617 0.07452

1993 0.01067 0.02981 0.02394 0.02551 0.06400 0.06400

1994 0.03114 0.09507 0.05172 0.02318 0.12803 0.12803

1995 0.00746 0.04494 0.01866 0.03077 0.03101 0.08582 0.09609

1996 0.02303 0.07818 0.02295 0.05882 0.01019 0.12171 0.16822

1997 0.00775 0.06977 0.00778 0.02662 0.01353 0.09690 0.15094

1998 0.02905 0.02092 0.01667 0.03922 0.01981 0.09544 0.18148

1999 0.00806 0.08594 0.03252 0.06923 0.01961 0.14516 0.20000

Average 0.01267 0.03732 0.01522 0.02360 0.01883 0.06570 0.08271
11-99/Ethanol Ch. 8 A-1

UCRL-AR-135949 Vol. 4 Ch. 8

Appendix A

Cleary and Ungs (1978) 3-D Solution
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 A-2
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 A-3

Appendix A

Cleary and Ungs (1978) 3-D Solution

A-1. Exact Analytical Solutions to the Advection-Dispersion

Equation

Exact analytical solutions are available for some simple geometries. These solutions are

useful in developing the model of the plume probability domain, as they give approximations

that range from extremely conservative and simple (one-dimensional) to much more realistic

(three-dimensional). In the following solutions, the initial condition is one in which there is a

rate constant , which can apply either to a source growth or attenuation rate.

In the following equations, retardation may be added by dividing the velocity or dispersivity

coefficients by R, which is

 

R =1+

kocfoc b

(A-1)

where koc is the partition coefficient for the chemical species in question. (Table 8-1 has the

symbols for the following equations.)

For a three-dimensional bivariate gaussian distributed boundary source (represented spatially in

Figures A-1 and A-2), the change in concentration over time,

 

C

t

=Dx

2C

x

2

+ Dy

2C

y

2

+ Dz

2C

z

2

−vx

C

x

−vy

C

y

− vz

C

z

−λ C (A-2)

subject to the boundary and initial conditions,

 

C = Coexp −γt −

1

2 1 −ρ

2

( )

(y −Y0 )

2

2 y

2

−

2 (y − Y0)(z −Z0)

y z

+

(y −Y0 )

2

2 y

2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

C

x

0 x +∞

C

y

0 y ±∞

C

z

0 z ±∞

C = 0 t = 0

(A-3)

the equivalent analytical solution is
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 A-4

 

C(x,y,z,t)=

CPx y z 1 −

2

( )

4 Dx

exp

vxx

2Dx

−γt +

vz (z − Z0)

2Dz

+

x

2 1 −ρ2

( )

2

vy

2Dy

+

y −Y0

x

2

1 −ρ

2

( )

 

 

 

 

 

 

 

2  

 

 

 

−

(y −Y0)

2

2 x

2

1 −

2

( )

 

 

 

 

×

4

3

exp −η

4

−

x

2

4Dx

4

−

(z − Z0)

2

4Dz

4

+

2

4

2

 

 

 

 

 

 

 

 

η=0  

t

4

× z

2

1 −

2

( )+ 2Dz

 4

 

 

 

 

 2 y

2

1−ρ

2

( )+ 4Dy

 4

 

 

 

 

 −8DyDz

2 8

2

 

 

 

 

 

 

−1

2

×exp −

y

4

1 −ρ

2

( )

2

4Dy

4 +2 y

2 1 −2

( )

vy

2Dy

+

y −Y0

x

2 1 −ρ2

( )

 

 

 

 

 

 

 

2  

 

 

 

 

 

 

 

 

 

 

 

 

d

(A-4a)

 

=

1

2 z

2

1−ρ

2

( )

+

4Dy

4

−

2Dy

2 4

ψσ z

2

1 −

2

( ) 4Dy

4 +2 y2

1 −

2

( ( ))

(A-4b)

 

β=

vz

2Dz

−

(z −Z0)

2Dz

4

−

(y −Y0)

y z 1 −ρ

2

( )

+

4Dy yρη

4

ψσ z 4Dy 4 +2 y

2

1 −

2

( ( ))

vy

2Dy

+

y −Y0

x

2

1−ρ

2

( )

 

 

 

 

 

 

 

(A-4c)

 

= − +

vx

2

4Dx

+

v y

2

4Dy

+

v z

2

4Dz

(A-4d)

When there is uni-directional flow, the above becomes

 

C(x,y,z,t)=

CPx y z 1 −

2

( )

4 Dx

exp

vxx

2Dx

−γt

 

 

 

 

 

 

4

3

exp −η

4 −

x

2

4D x

4

−

(z −Z0)

2

4Dz

4

+

2

4

2

 

 

 

 

 

 

 

 

η=0  

t

4

× z

2

1 −

2

( )+ 2Dz

 4

 

 

 

 

 2 y

2

1−ρ

2

( )+ 4Dy

 4

 

 

 

 

 − 8DyDz

2 8

2

 

 

 

 

 

 

−1

2

×exp −

(y −Y0 )

2

4Dy

4 +2 y

2

1 −

2

( )

 

 

 

 

 

 

 

 

 

 

 

 

 

d

(A-5a)
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 A-5

 

=

1

2 z

2

1−ρ

2

( )

+

4DH

4

−

2DH

2 4

ψσ z

2

1−ρ

2

( ) 4DH

4

ψ+ 2 y

2

1 −

2

( ( ))

(A-5b)

 

β=

4DHρη

4

(y −Y0)

ψσ y z4DH

4 +2 y2

1−

2

( ( )) 1−ρ

2

( )

−

(z −Z0)

2D V

4

−

(y −Y0)

y z

1 −ρ

2

( )

(A-5c)

 

= − +

v x

2

4DL

(A-5d)

Where , , and are temporary variables.

A-2. References

Cleary, R.W., and M.J. Ungs (1978). Analytical Models for Groundwater Pollution and

Hydrology. Princeton University, Princeton, NJ. Water Resources Program Report 78-WR15.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 A-6

+

8

8

+

8

8

+8

0

Z

0

Y0

z

x

y

Figure A-1: Bivariate Gaussian source of infinite areal extent for three-dimensional

flow.

Y0

Z0

y

z

C

Figure A-2. Bivariate Gaussian source of infinite areal extent for threedimensional flow – concentration distribution.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

11-99/Ethanol Ch. 8 A-7
2-99/Ethanol Ch. 8 B-1

UCRL-AR-135949 Vol. 4 Ch. 8

Appendix B

Results of Public Water Sources

throughout California
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 B-2
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 B-3

Appendix B

Results of Public Water Sources

throughout California

Tables 8-3 to 8-5 show the yearly count of tested public water sources throughout California.

Table 8-3 shows the count of tested water sources by year. Public water sources include public

drinking water wells and also surface water sources. Public drinking water wells are the vast

majority of sources represented. The use of the results of this testing as part of an evaluation of

potential groundwater impacts will yield conservative results because groundwater impacts will

be overestimated somewhat.

The BTEX column is the combined count of wells tested for BTEX alone; the BTEX/MTBE

is the combined count for BTEX and MTBE. There are a small number of water sources that

have not been tested for BTEX, but that have been tested for MTBE. This likely reflects small

systems that have been asked to report MTBE testing, but are not required to report BTEX

sampling unless there is a detection. Table 8-4 is the year cumulative total of the sampled water

sources, and Table 8-5 shows the yearly change in the number of water sources sampled. Tables

8-6 to 8-8 show the same counts for sources reporting BTEX and MTBE detections.

Tables 8-9 to 8-11 combine the above six tables. Table 8-9, also seen as Figure 8-40, shows

the yearly detection rate for benzene, toluene, ethylbenzene, total xylenes, MTBE, and the

combined count. Figure 8-40 shows a fairly constant pattern of BTEX detections, with a slight

upward trend in MTBE. Figure 8-41 looks at the change in these trends on a yearly basis. There

is a large upward trend in total xylenes and MTBE, and a small upward trend in toluene

detections. Both benzene and ethylbenzene exhibit negative detection trends over time. The

upward trend in BTEX detections is driven by the large total xylenes detection trend. The cyclic

trend can be partially explained through a cyclic source sampling trend, but the cycling needs to

be examined more closely.
UCRL-AR-135949 Vol. 4 Ch. 8 Potential Ground and Surface Water Impacts December 1999

12-99/Ethanol Ch. 8 B-4