&EPA
                            United States
                            Environmental Protection
                            Agency
                         Robert S. Kerr Environmental
                         Research Laboratory
                         Ada, OK 74820
                            Research and Development
                                    EPA/600/S-93/006 August 1993
ENVIRONMENTAL
RESEARCH  BRIEF
               Spatial Heterogeneity of Geochemical and Hydrologic
               Parameters Affecting Metal Transport in Ground Water

                J.A. Davis1, C.C. Fuller1, J.A. Coston1, K.M. Hess2, and E. Dixon1
INTRODUCTION

Reliable assessment of the hazards or risks arising from ground-
water contamination and the design of effective rehabilitation
procedures require the capability to predict the movement and
fate of dissolved solutes in ground water. The modeling of metal
transport in ground water requires adsorption coefficients to
describe ion adsorption to soils, sediments, and rock surfaces.
The term adsorption coefficient, as used here, means a parameter
that quantifies the distribution of an ion between aqueous and
mineral phases, after accounting for speciation in the  aqueous
phase. Ideally, these adsorption coefficients would be determined
from surface compiexation models, which have been highly
successful in simulating adsorption data in laboratory studies of
reference mineral phases (see review by Davis and Kent, 1990).
However, there are difficulties in determining the appropriate type
and surface density of reactive functional groups for the mixtures
of mineral phases in  natural systems. Metal-ion sorption in
natural systems is commonly thought to be controlled by surface
reactions with iron (Fe) and aluminum (Al) oxyhydroxides and
organic coatings on particles. The importance of surface coatings
makes it difficult to relate the bulk mineralogical composition of a
sample to its adsorptive reactivity.
1    U.S. Geological Survey, MS 465, 345 Middlefield Ftd.
    Menlo Park, CA 94025

2    U. S. Geological Survey, 28 Lord Rd., Suite 280
    Marlborough, MA 01752
                     Application of hydrogeochemical transport models to real-world
                     problems involving toxic metals is limited by a lack of kinetic and
                     thermodynamic data for describing appropriate  reactions.
                     Adsorption-desorption reactions of metal ions with the porous
                     medium should be included within the transport model. Current
                     approaches to the problem usually involve  laboratory
                     measurements of adsorption coefficients in batch or column tests.
                     The relevance of adsorption coefficients determined in laboratory
                     tests to the field scale is not well known, and important questions
                     in this context concern the spatial variability  of adsorption
                     coefficients and the representativeness of small samples of
                     subsurface material used in laboratory tests.

                     Determining the predominant adsorbing surface in a mineral
                     assemblage can be a useful approach to modeling adsorption
                     with a surface compiexation model.  One objective of this study
                     was to search for a geochemical indicator of the mineral surface(s)
                     controlling lead (Pb) and zinc (Zn) sorption on the aquifer sand.
                     To determine the important mineral phases (or surfaces) for Pb2+
                     and Zn2* adsorption, batch adsorption experiments were done
                     with several grain-size and mineral fractions in which the surface
                     area to volume ratio was constant. A pure SiO2 sample was also
                     studied for comparison. The effect of removing surface coatings
                     on metal adsorption was investigated with commonly used selective
                     extraction techniques (Tessier et al., 1979; Chao, 1984).

                     The spatial variability of parameters used in models for solute
                     transport in ground water are of significance in the simulations of
                     solute movement. Variability of hydraulic conductivity has been
                     shown to control longitudinal macroscale dispersion in sand and
                     gravel aquifers (Garabedian etal., 1991; Hess eta!., 1992). The
                     random, but spatially correlated, variationsinhydraulicconductivity
                                                 U.S. Environnr
                                                      i on Recycled Paper

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cause small-scale variations in fluid velocity within an aquifer.  It
is commonly believed that the velocity variations cause the scale-
dependent observations of macrodispersion.

Like hydraulic conductivity, adsorption  coefficents  and other
geochemical properties of the porous medium are expected to
vary spatially, because of variations in surface area, grain coatings,
and sedimentation patterns. A number of theoretical studies have
shown the potential importance of correlations between hydraulic
conductivity and adsorption or ion exchange coefficients
(Garabedian, 1987; Valocchi, 1989; Chrysikopoulos et al., 1990;
Kabala and Sposito, 1991). In particular, it has been shown that
an adsorbing tracer will exhibit "enhanced mixing" relative to a
nonreactive tracer if the hydraulic conductivity and adsorption
coefficients are negatively correlated.  Robin et al. (1991) found
a weak, negative correlation between measured values of hydraulic
conductivity and distribution coefficients for strontium. No other
detailed measurements of the spatial variability  of adsorption
coefficients in aquifers have been reported, and the importance of
this type of variability to solute transport modeling is still unknown.

In this study, 14 continuous cores of subsurface  material were
collected from  a shallow, sand and gravel aquifer in Falmouth,
Massachusetts, where the spatial variability of hydraulic
conductivity has been previously studied (Hess et  al., 1992).
Eight of these cores were located along a transect (16 m in length)
oriented parallel to the direction of ground-water flow in order to
estimate correlation scales of physical and chemical properties.
Six other cores were collected in various locations in the field, with
distances on the order of several tens of meters separating each
core. Each core was sectioned into subsamples 10 cm in length,
yielding approximately 500 subsamples in total. For about 400 of
these subsamples, measurements were made of:  1) grain-size
distribution and 2)  lead (Pb2*) and zinc (Zn2+) adsorption for a
constant set of experimental conditions  (in an artificial ground-
water solution at pH 5.3). Grain-size measurements were used
to estimate hydraulic conductivity; it has been shown that these
two properties are highly correlated at this field site (Wolf et al.,
1991).

Partial chemical extractions of aquatic  sediments and aquifer
materials are commonly used to estimate the partitioning of trace
metals among secondary phases such as amorphous  iron and
manganese oxide (Tessier et al, 1979; Chao, 1984; Robinson,
1984/85; Tessier et al, 1989).  For a smaller set of subsamples,
we have used several chemical extraction methods to determine
if Pb2* and Zn2* adsorption correlate with the amount of iron (Fe),
aluminum (Al), and manganese (Mn) extracted from the aquifer
material.  If the variability in metal ion adsorption is caused by a
greater reactivity with the surfaces of a secondary mineral phase
or coating, the extraction results could reveal the cause of the
variability. If strong positive correlations exist, it may justify the
use of such  selective extraction techniques as geochemical
indicators of variability in the adsorptive  reactivity of subsurface
sediments.

Surface area  determined by gas adsorption can be used  to
estimate the total, non-selective adsorption capacity of mineral
surfaces (Davis and Kent, 1990). We measured the BET surface
area of a set of subsamples to examine the variability in reactive
surface area and the usefulness of this parameter as an indicator
of metal  ion  adsorption and  its variability.   A  strong positive
correlation between surface area and metal ion adsorption might
only  be expected  if  the sediment surfaces were relatively
homogeneous with respect to adsorptive reactivity. In this case,
the spatial variability in metal ion adsorption might only be related
to the variability in sediment surface area, which in turn might be
inversely correlated with the average grain-size.

FIELD SITE DESCRIPTION AND CORING
PROCEDURES

The study was conducted with sediments  collected at the U.S.
Geological Survey Toxic Substances Hydrology Research site in
Falmouth, Massachusetts (Fig.  1).  The aquifer at the site is
shallow, unconfined, and composed of glacial outwash sediments
(LeBlanc et al., 1991).  The unconsolidated sediments consist
predominantly of medium to coarse sand, with localized zones of
gravel, fine sand, and silt. The aquifer has an average porosity of
0.39 and an average ground-water velocity  of 0.4 meters/day.
The amount of sedimentary organic carbon in the sand and silt
fractions is between 0.01% and 0.05% of the dry weight (Barber
etal.  1992).

Ground water at the site is contaminated by the discharge of
treated sewage effluent onto rapid infiltration  sand beds, located
about 300  meters upgradient (LeBlanc,  1984). The effluent
percolates into ground water below the infiltration beds, creating
a plume of contaminated  water which  extends  over 4  km
downgradient.   The contaminant plume  contains elevated
concentrations of major cations (Ca, Mg, K, Na), anions (sulfate,
phosphate),  dissolved metals (e.g., Zn), and various detergent
compounds  (LeBlanc, 1984; Davis et al., 1991; Barber et al.,
1988).

Cores were collected using a wireline-piston  core barrel (Zapico
et al., 1987) and plastic core liners  5 cm in diameter and 1.5
meters in length and were frozen until used.  Three successive 1.5
meter sections were collected from each bore hole.  At  the
inception of the study, four cores were collected from the suboxic
zone of the aquifer (see Davis et al., 1991 for a description of
aquifer  chemistry) to provide material  for  a large sample of
homogenized aquifer sand.  This  sand  (see Composite Sand
below)  was used to measure  adsorptive  properties, to  test
experimental methods, and for detailed  characterization of bulk
geochemical properties of the sand.

Aquifer material from 14 cores was used to evaluate the spatial
variability of metal adsorption. Eight closely-spaced cores (1 to 4
meters apart) were collected in a transect parallel to the ground-
water flow direction (see map, Fig. 1).  An additional six cores
used to assess geochemical and hydrologic variability were
collected at more widely-spaced intervals  (approximately 30
meters  apart on average). In the vertical direction, the transect
and widely-spaced cores spanned from the pristine, recharge
zone near the water table  down into the suboxic,  sewage-
contaminated zone of the aquifer (see Davis et al., 1991).

EXPERIMENTAL MATERIALS

The Composite Sand.  Subsections  of the four cores collected
from the suboxic zone were air-dried  and sieved to remove
material greater than 1000 jim diameter (>1000 n.m  = 20.7% by
weight of the total). The >1000 n.m fraction was removed because
it was difficult to sample representatively in small samples (10 g)
used for batch adsorption experiments.  The remaining sand,
called the <1000 jam or composite sand,  was mixed together and

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                                      73° 00'
                                     r
                               42° 30'-/
                                                                         70° 00'
                TRANSECT CORES
                    F415C38
                   F415C40,
           SF415C39

F415C41 _JJ

   F415C42*
                      F415C43

                      F415C53*


                       F415C66 *
                             30 FEET
                             10 METERS
                                                                                  0     100 FEET


                                                                                  0     30 METERS
                                                F415C54
                                                                EXPLANATION
                                                          LOCATION OF CORES AND IDENTIFIER
Figure 1.  Map showing the U. S. Geological Survey Toxic Substances Hydrology Research site in Falmouth, Massachusetts (USA) and the
          locations of cores taken in the study.

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TABLE 1. DISTRIBUTION OF GRAIN-SIZE FRACTIONS AND MINERALOGY FOR THE CAPE COD COMPOSITE SAND
                                           Size Fractions, j

Weight %        48 hours.  The
composition of the artifical ground-water solution (Table 2) was
designed to closely  match that of the sewage-contaminated
ground water in the suboxic zone of the aquifer (Davis et al.,
1991). The suboxic zone of the aquifer, however,  contains highly
variable concentrations of bicarbonate  and nitrate anions, and
these ions were not included in the composition of the artifical
solution.

Mineral abundances in the composite sand were determined by
density separations using bromoform diluted with  acetone (Table
1).  Diamagnetic sand fractions of the composite sand were
prepared using a Frantz electromagnetic barrier  separator.

The composite sand was predominately quartz  (90-95% by
weight) with 5-10% heavy minerals, feldspars, and lithic fragments.
The abundance of heavy  minerals as well as  the amount of
magnetic/paramagnetic  minerals increased  as grain-size
decreased (Barber et  al., 1992). The 250-500 |im fraction makes
up approximately 50% (by weight) of the <1000 u.m fraction and
accounts for 27% of the BET surface area (Table 1). The 500-
1000 and 64-250 |o.m fractions comprise 36% and  16% by weight,
respectively (Coston  et al., 1993). The fine fraction material
makes up <1 % by weight of the composite, which is typical for this
aquifer (Barber et al.,  1992). However, the specific surface area
of the <64 |im fraction is an order of magnitude greater than other
size fractions; therefore, the fine fraction accounts for nearly 10%
of the composite sand surface area.

The grain surfaces typically had a heavily weathered appearance,
and well-rounded quartz grains were common.  Orange-red
coatings of variable thickness covered portions of grain surfaces.
Magnetite, hematite, and glauconite were identified in the heavy
mineral fraction.  No carbonate minerals have been reported in
this aquifer (Barber, 1990).

Transect and widely-spaced core samples. Cores were subdivided
into subsamples using vertical intervals approximately 10 cm in
length and dried in a laminar flow hood, yielding 476 subsamples
in total. After drying, each subsample was sieved to remove
grains greater than 1000 urn, and the weights were recorded. The
<1000 urn fraction was then divided into two parts with a riffle
splitter, taking care to minimize the loss of fines. One part of each
sample was used  for  batch  adsorption experiments and
geochemical characterization methods; the other part was used
for  grain-size analysis of the <1000  u/n fraction.  Grain-size
distributions were determined on each subsample  using a dry
sieve analysis (ASTM, 1986) in the USGS Sediment Laboratory
in Harrisburg, Pennsylvania. Grain-size sieves of 710,500, 355,
250, 180, 125,  90, and 63 microns were  employed for each
sample.

Pure quartz analog.  Min-U-Sil. A cleaned and sized fraction of
Min-U-Sil 30 (Pennsylvania Glass and  Sand Co., Pittsburgh,
Pennsylvania) was used to simulate the reactivity of Pb2+ and Zn2+
with a purified quartz surface. Min-U-Sil is a crushed and sieved
quartz powder sized for commercial applications. The powder

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TABLE 2.  ARTIFICIAL GROUND-WATER SOLUTION USED IN
          ADSORPTION MEASUREMENTS
Ion

Na
Ca
Mg
K
Cl
SO,
Actual aquifer
concentrations, jjN
1350
274
180
130
1380
305
Artificial solution
I concentrations, nM
1300
250
150
100
1400
400
was treated to remove organic material, leach out contaminant
metal oxides, and dissolve amorphous silica from the surface (D.
Kent, personal communication, U.S. Geological Survey, Menlo
Park, California, 1991).  The grain-size distribution after the
treatment was 8-25 urn and the BET surface area was 0.32 m2/g.
                                                 EXTRACTION METHODS

                                                 Hydroxylamine  hydrochloride sand extraction.  Sand samples
                                                 were extracted with 0.25M NH2OH-HCI in 0.25M HCI at 50°C for
                                                 0.5, 72, or 96 hours (see Table 3).  Hydroxylamine-HCI (HA)
                                                 extracts iron by  reductive dissolution at low pH (Chao and Zhou,
                                                 1983). The HA extraction is not selective for Fe; for example, Al,
                                                 P, Ca, and Mn were also extracted from the sand. Al dissolved by
                                                 HA may be produced by partial dissolution of feldspars and other
                                                 aluminosilicate phases as well as aluminum oxide coatings on
                                                 grain surfaces. The HA extraction for 0.5 hours was used because
                                                 it has been reported to be the most effective method for selective
                                                 dissolution of amorphous iron oxyhydroxides with minimal
                                                 dissolution of crystalline iron oxyhydroxides (Chao and Zhou,
                                                 1983). Dissolved Fe increased with time in HA extracts of the 250-
                                                 500 urn diamagnetic fraction of the sand and reached a constant
                                                 concentration after 48 hours of extraction.  The 96-hour HA
                                                 extractions were used to examine the effects of partial dissolution
                                                 of crystalline iron oxyhydroxide coatings on Pb2* and Zn2*
                                                 adsorption by diamagnetic fractions of the composite sand. The
                                                 72-hour HA extractions were used to study the spatial variability
                                                 of Fe and Al dissolution from a set of subsamples taken from
                                                 transect cores.
TABLE 3.
YIELDS OF DISSOLVED Fe AND Al FOR SEVERAL EXTRACTION TECHNIQUES APPLIED TO FRACTIONS OF THE CAPE
COD SAND.
            Fe
            Al
            Mn
            P
            Fe
            Al
            Mn
            P
                        <1000
              0.87
               1.8
              0.02
              0.29
               17
               20
              0.18
               nm
                         500-
                         1000
0.41
0.90
0.01
 nm
                 Size Fractions, tim
          250-500    250-500    250-500    64-250    64-250
                  diamagnetic  Quartz            diamagnetic
  Hydroxylamine-HCI, 0.5 hours
0.62       0.45       0.35
 1.3       0.86       0.82
0.01       0.00       0.00
 nm       0.19       0.12

  Hydroxylamine-HCI, 96 hours
 —       2.7        —
 —        3         —
 —       0.04        —
 —       nm        —
 1.1
 2.5
0.02
 nm
                                                                                                <64
 23
 36
0.31
 nm
            Fe
            Al
            Mn
            P
            Fe
            Al
            Mn
            P
                                         Dithionite-Citrate
              9.5        5.8          8.1       4.0
              1.7        0.99         1.3       0.83
              0.04        0.04        0.03       0.02
              nm        0.6          0.8       0.5

                                          HCI, 8 hours
                                                        8.1
                                                         17
                                                        0.1
                                                        nm
                                          20
                                          2.4
                                         0.08
                                          1.5
                                          124
                                          69
                                         0.82
                                          nm
                                        5.9
                                        1.4
                                        0.03
                                        nm
                                         12
                                         31
                                        0.09
                                        nm
                     22
                     8.3
                    0.19
                     5.0
                                 nm =  not measured
                                                            — = not extracted

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Dithionite-citrate (DC)  sand extraction.  Sand samples were
extracted at 25°C for 24 hours with 0.08M Na2S2O4 in 0.2M
ammonium citrate solution buffered at pH 8.5. The low-temperature
DC extraction at pH 8.5 was used to extract diamagnetic sand
because it is effective in dissolving crystalline iron coatings with
minimal dissolution of aluminum (Table 3) (S. Short, ANSTO,
Australia, personal communication).

4M HCI sand extraction.  The 64-250 u,m diamagnetic sand
fraction was extracted with 4M HCI at 100°C for 1 hour in Teflon
centrifuge tubes, as a preparative procedure for batch adsorption
experiments. This sand fraction and a density-separated quartz
fraction (250-500 urn) were also extracted for 8 hours under the
same conditions to estimate the total amount of Fe and Al present
on the surfaces of grains (Table 3).

SURFACE SENSITIVE SPECTROSCOPIC TECHNIQUES

Microscale analysis of  the surface coatings was accomplished
using three techniques:

1)  scanning  electron   microscopy-energy   dispersive
    spectroscopy (SEM-EDS),

2)  time of flight-secondary ion massspectroscopy(TOF-SIMS),
    and

3)  auger electron spectroscopy (AES).

Handpicked sand  grains from the 250-500 H.ITI fraction of the
composite sand were examined before and after treatment with
the extractants discussed above.  Compositional data for the
surface coatings were collected by TOF-SIMS and AES on grains
mounted in conducting foils. The sensitivity of these two techniques
to the outer few monolayers of the surface is orders of magnitude
betterthan SEM-EDS, which collects compositional dataaveraged
over depths of 1 micron thickness.

ADSORPTION EXPERIMENTS

Kinetics of adsorption.  The rates of Pb2* and Zn2+ adsorption by
the composite sand were determined in experiments conducted
with a constant partial pressure of CO2 (1%), which resulted in a
constant  pH of 5.3. These experiments demonstrated that Pb2>
and Zn2+ adsorption appeared to  reach adsorptive equilibrium
within 48 hours (Costonetal., 1993). The approach to equilibrium
was probably limited by diffusion to adsorption sites located within
intragranular pores (Wood etal., 1990). In accordance with these
results, all  batch experiments were conducted for a 48-hour
reaction period.

Batch adsorption experiments with composite sand. All adsorption
experiments were conducted in the AGW solution (Table 2).  The
dried sand was rinsed  with AGW before initiating an adsorption
experiment. Adsorption experiments with different size or mineral
fractions  used a constant surface area to water volume ratio, the
same value as used for experiments with the entire <1000 \im
fraction. The surface area to volume ratio used for Zn2+ experiments
was 176  m2/L; for Pb2* experiments the ratio was 22 m2/L The
total concentration of  Pb2+ or Zn2+ was  10 nM-  To begin an
adsorption experiment, the sand-AGW slurry was spiked with an
acidified  metal stock solution, and  then acid or base (HNO^
NaOH) was added such that the desired pH was reached at the
end of the reaction period  (48 hours, see below).   For Zn2+
experiments, 0.73 milliequivalents (mequiv) of acid per liter of
artificial ground-water solution were added with the Zn2+ stock
solution; for Pb2+ experiments, 0.66 mequiv per liter of solution
were added.  Subsequent experiments (Coston et al.,  1993)
showed that the Zn2+ adsorption results were slightly affected by
the amount of initial acid added to the batch experiments (possibly
by dissolution and reprecipitation of aluminum), but that Pb2*
adsorption was not affected. Metal adsorption by the sand was
calculated from the  difference between the amount of metal
added to experiment blanks (AGW and metal solution only) and
the amount remaining in solution at the end of the adsorption
experiment.  In some cases, dissolved Zn concentrations were
estimated  from the activity of a  65Zn  radiotracer.  Otherwise,
experimental solutions were analyzed by inductively-coupled
plasma atomic emission spectrometry (ICP-AES).

Batch experiments to determine  the spatial variability  of metal
adsorption. For each of 374 subsamples (all transect samples
plus half of the widely-spaced core samples), Pb2* and Zn2+
adsorption was measured in three batch experiments, using the
same method outlined above, but using a sand to water ratio of 50
g/L.  Pb2+ and Zn2* were added simultaneously from a combined
stock solution  to yield total concentrations of 20 and 5 nM,
respectively.   The metal stock solutions had a lower acid
concentration than that used in the composite  sand batch
experiments; 0.065 mequiv of acid were added per liter of artificial
ground-water solution. Each of the three batch experiments for a
subsample was designed to attain a different pH value after 48
hours of reaction by addition of predetermined aliquots of 0.04M
NaOH  or  0.04M HNO3.   The target  pH values  of the  three
experiments were 5.1,  5.3, and 5.5.  The metal adsorption
measurements for each subsample were then interpolated to a
pH value of 5.3, as described in the Results section, and averaged.
In cases where the agreement among the three experiments was
poor, or all three pH values were greater or less than 5.3, the
experiments were rerun.

RESULTS AND DISCUSSION

The Composite Sand

Metal adsorption by size and mineral fractions. Metal adsorption
experiments were conducted on four size fractions (<64,64-250,
250-500,  500-1000 jim) of the composite  sand and  on  a
diamagnetic fraction of the 250-500 urn size fraction.  Pb2+ was
preferentially adsorbed by the <64 p.m and 250-500 ^m fractions
(Fig. 2); both fractions were more reactive at a given pH than the
<1000 urn (the composite) sand on a surface area per volume
basis.  The diamagnetic sand  from the 250-500 JJ.ITI fraction
adsorbed less Pb2+ than the entire 250-500 urn fraction (Fig. 3).
The decreased  Pb2+ adsorption by the diamagnetic fraction
suggests that iron oxide minerals and/or mineral grains with
coatings removed by magnetic  separation had more reactive
sorption sites. The  greater reactivity  of Pb2+ with the <64 urn
fraction (Fig. 2) could be due in partto the greater amount of heavy
minerals in that size fraction (Table 1). Zn2+ adsorption varied less
among the different  size fractions (Fig. 4).  In contrast to Pb2*,
removing magnetic material had noeffecton Zn2+ adsorption (Fig.
5).

The composite sand is dominated by quartz in all the size fractions
examined (Table 1).  Density separations were used to determine
the  distribution  of adsorbed Pb2+ and  Zn2+ among  mineral
components of the  sand.   Although  the  heavy minerals and

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Figure 3.  Pb2* adsorption by diamagnetic and extracted diamagnetic sand from the 250-500 urn fraction. The surface area of
          sand used in each experiment was held constant at 22 m2/L; total Pb2* concentration was 10 micromolar in each experiment. Pb2*
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-------
 feldspars were more reactive than quartz grains on a weight-
 normalized basis, greater than 80% of Zn2* was adsorbed by the
 quartz fraction and more than 90% of Pb2* by the quartz and
 feldspar fractions (Coston et al., 1993). The quartz accounts for
 such a large weight percentage of the sand that it dilutes the effect
 of preferential adsorption by the heavy minerals.

 Metal adsorption after extraction of the composite sand. The
 diamagnetic fraction of the composite sand had a much greater
 average reactivity than that observed for the pure quartz surface
 (Min-U-Sil)(Figs. 3 and  5). Even  though quartz represents a
 large fraction by weight of the sand, adsorption  on a clean
 quartz surface (i.e., complexation with surface silanol groups)
 of equal surface area to  water volume does not account for the
 observed adsorption of either metal ion.   A coating must be
 associated with the quartz grain surfaces that enhances metal
 adsorption.  Chemical extractions (hydroxylamine-HCI (HA),
 dithionite-citrate (DC), and HCI) were used to: 1) dissolve and
 quantify the extent of grain coatings, and 2) prepare diamagnetic
 sand fractions for further metal adsorption experiments (Coston
 et al., 1993).  The visible orange-red hue of the sand was
 lightened or completely removed  by each  of these extraction
 procedures. Amounts of Al and Fe removed by each extraction
 technique are shown in Table 3. In addition, sand grains were
 examined by surface analytical techniques to determine the
 composition and distribution  of surface coatings  before and
 after extraction.

 A 0.5 hr HA extraction, intended to dissolve noncrystalline hydrous
 iron oxides (Chao and Zhou, 1983), did not affect Pb2+ or Zn2+
 adsorption  (e.g., Fig. 3).  Pb2+  adsorption was reduced after HA
 extraction of Fe from the 250-500 urn diamagnetic fraction was
 complete (96 hours, Fig. 3).  This  extraction,  however, had no
 significant effect on Zn2* adsorption (Fig. 5). Neither the 0.5 or 96
 hr HA extractions affected the specific surface area measured for
 these samples.  The 96  hr HA extraction, although  intended to
 selectively  dissolve crystalline iron oxides, also dissolves  a
 significant amount of aluminum. The experimental and modeling
 results (Coston etal., 1993; Davis etal., 1993) suggest that Pb2+
 adsorption  is influenced more by crystalline iron oxides in the
 sand, while Zn2+ adsorption is likely influenced more by aluminum-
 bearing coatings or minerals.

 SEM-EDS  backscatter images showed  Fe enrichment  at
 irregularities in the surface topography such as cracks, cleavage
 planes, and pits.  The coatings appear to fill in irregularities on the
 grain surfaces. After extraction by HA for 96 hours, detectable Fe
 remained along the base of cleavage fractures on feldspars and
 in conchoidal fractures on quartz grains.  However, the surfaces
 of extracted  grains examined by the TOF-SIMS technique
 appeared to retain  coatings rich in Fe and Al at least several
 monolayers thick. The differences between the SEM-EDS and
 TOF-SIMS  results can be attributed to the different depths over
 which the surface composition is analyzed.

 For all size fractions, the ratio of Fe to Al dissolved was much
 greater in the DC extraction than for HA extractions (Table 3).
 Therefore, the DC-extracted surface was enriched in Al and
 depleted in Fe relative to the HA-extracted sand surface.  There
 was 10 to 30 times more Al than Fe detectable by AES at the
 surface (10-100 A deep) of unextracted quartz grains. Fe was not
detected by AES on the surface of either the HA or DC extracted
quartz grains. However, significant Al remained on the surface of
all extracted quartz grains examined.
 DC extraction of the 250-500 (xm diamagnetic fraction dissolved
 more Fe than the 96-hour HA extraction (Table 3), had a small
 effect on Pb2* adsorption (Fig. 3), and did not affect the specific
 surface area measured for the sample.  The Pb2+  adsorption
 results suggest the surface coating structure may be  more open
 after the extraction, and that  aluminol surface  sites  may be
 important after  Fe is dissolved. The DC extraction appeared to
 cause a slight increase in Zn2+ adsorption (Fig. 5), suggesting that
 the Al-enriched surface is more reactive with Zn2*.  It was also
 found that excess acid added with metal stock solutions caused
 an increase in Zn2+ adsorption in batch adsorption experiments (where
 pH is allowed to drift), as compared to experiments exposed  to a
 constant partial pressure of CO2.  Pb2* adsorption, however, was not
 affected. It was hypothesized that the addition of excess acid caused
 a dissolution and reprecipitation of Al at the surface,  resulting in
 increased Zn2+  adsorption (Coston et al., 1993).

 Adsorption of Zn2+ by 64-250 |im diamagnetic sand was decreased
 by a 4N HCI extraction (1-hour) at 100°C (Fig. 6).  This harsh
 extraction nearly doubled the BET surface area (from 0.25 to 0.48
 m2/g) of the sample. Al was easily detected on the surface of the
 HCI-extracted  sand by  TOF-SIMS; the amount of  Fe at the
 surface, however, was reduced to just above the limit of detection
 under the same beam conditions. These results, the apparent
 redistribution of Al on the sand surface when excess acid was
 added to batch experiments, and the effects of the DC treatment
 on  the diamagnetic sand each suggest  that aluminol (AIOH)
 groups may  be important in complexing Zn2+ at the sand surface
 (Coston etal., 1993).

 SPATIAL VARIABILITY OF SUBSURFACE  SEDIMENT
 PROPERTIES

 Spatial variability of metal adsorption. Adsorption results for each
 of the three batch experiments with each vertical subsample were
 interpolated  to  pH  5.3  using  the Kurbatov equation with  a
 macroscopic proton coefficient of one (Kurbatov et al.,  1951;
 Honeyman and Leckie, 1986).  Figure 7 shows the variability of
 Pb2+ and Zn2* adsorption data for core F415-C38 (core 38), a
 transect core (see Fig. 1). The results from this core were typical
 of those from other cores in the transect. In most cores, Pb2*
 adsorption varied by a factor of two over the length of the core.
 Zn2+ adsorption was less than  that observed for Pb2+, but the
 variability of Zn2* adsorption was generally greater  (usually about
 a factor of three or four) than that observed for Pb2* adsorption.

 Table 4 summarizes the results of Pb2* and Zn2* adsorption for all
 cores. The mean values of metal adsorption for the transect cores
 were not significantly different from the mean values observed for
 widely-spaced cores, but a larger range of vertical variability in
 Pb2+ adsorption  was observed in some widely-spaced  cores. The
 range of Pb2+ adsorption  observed for all cores was 0.091-0.371
 nmoles/g sand;  the Zn2+  range was 0.00-0.028 ixmoles/g. Pb2+
 and Zn2* adsorption were significantly correlated (p<0.01; r=0.53,
 n=374). In this report, a linear correlation between two variables
 is considered significant if the absolute value of the correlation
coefficient (r) is greater than the critical value of r for the 1 % level
of significance (p<0.01) for n-2  degrees of freedom (Sokal and
 Rohlf, 1973).

The greatest adsorption measurements reported here do  not
represent adsorption maxima for those subsamples; they  are
simply the greatest measurements observed under the constant
conditions imposed in the batch experiments (pH 5.3;  total Zn of

-------




0)

1
to
c
N
^





IUU
90
80
70

60
50

40
30

20

10
n
A x
— A /A
X
&
-
-
A X
A

* * x X
_
A
Ax A
- A 64-250 H.ITI fraction
A diamagnetics
X 1 hr HCI extraction
i i i i i
                                                                                       0.057
                                                                                       0.038
                                                                                       0.019
                                                                                                N

                                                                                                 CO
                                                                                                jj>

                                                                                                 O
                 4
                                                         7
                                                                                       0.000
                                              Final pH
Figure 6.   Effect of 4N HCI-extraction (100°C) on Zn2* adsorption by the 64-250 \nn diamagnetic fraction. The surface area of
          sand used in each experiment was held constant at 176 m2/L; total Zn2* concentration was 10 micromolar in each experiment
TABLE 4.  RANGE, MEAN, AND STANDARD DEVIATION OF Pb2* AND Zn2* ADSORPTION MEASUREMENTS (nmoles/g) AT pH 5.3 IN

         TRANSECT AND WIDELY-SPACED CORES
Core


F415C38
F415C39
F415C40
F415C41
F415C42
F415C43
F415 C53
F415C66
All transect cores
F415C44
F415C51
F415C54
F509C2
F510C3
F511 C2
All wide-spaced cores
Average Pb
adsorbed
(Std. Dev)
0.22 (0.02)
0.22 (0.02)
0.23 (0.02)
0.22 (0.03)
0.23 (0.02)
0.23 (0.02)
0.24 (0.02)
0.22 (0.02)
0.23 (0.02)
0.22 (0.02)
0.22 (0.05)
0.23 (0.06)
0.22 (0.06)
0.22 (0.02)
0.23 (0.03)
0.22 (0.04)
RANGE
High

0.28
0.27
0.28
0.34
0.26
0.29
0.27
0.27
0.34
0.26
0.32
0.37
0.28
0.27
0.29
0.37
Low

0.18
0.19
0.19
0.20
0.18
0.18
0.20
0.20
0.18
0.19
0.14
0.15
0.09
0.19
0.19
0.09
Average Zn
adsorbed
(Std. Dev.)
0.012 (0.005)
0.010(0.005)
0.014 (0.002)
0.012 (0.004)
0.012(0.003)
0.01 1 (0.003)
0.014 (0.003)
0.011 (0.002)
0.012 (0.004)
0.008 (0.005)
0.01 1 (0.003)
0.014 (0.004)
0.007 (0.005)
0.012 (0.001)
0.01 1 (0.004)
0.010(0.005)
RANGE
High

0.028
0.022
0.019
0.026
0.018
0.018
0.022
0.015
0.028
0.017
0.018
0.021
0.014
0.015
0.021
0.021
Low

0.003
0.000
0.011
0.006
0.006
0.006
0.006
0.004
0.000
0.003
0.006
0.008
0.000
0.010
0.004
0.000
          All cores
0.22 (0.03)
                                              0.37
                           0.09
0.012 (0.004)
                                                                                        0.028
                                                                     0.000
                                                      10

-------
                 13
                 12
              (1)

              §  11
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1 10
<
           % Pb Adsorbed at pH 5.3

      0      20     40     60     80     100
                                                                          % Zn Adsorbed at pH 5.3

                                                                    0      10     20     30     40     50
                     i i  i i   i i i  i   i i i  i  i
                                        I i  i i  i I
                                       ®
                                                  A:
      _   Odata
          0 interpolated
                                                                  13
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                                                                                        © interpolated  .

                                                                                      i , I . ,  , , I  , , .
                   0       0.1      0.2      03      0.4

                    Pb Adsorbed at pH 5.3 (u.moles/g)
                                                      0     0.01   0.02   0.03   004    0.05

                                                       Zn Adsorbed at pH 5.3 (umoles/g)
Figure 7.   Pb2* and Zn2* adsorption measured in batch experiments with artificial ground water and subsamples of subsurface
          material (Core 38). Diamonds show actual measurements made near pH 5.3. Circles are interpolated to pH 5.3 using data of 3 batch
          experiments in the pH range 5.1-5.5. Horizontal error bars represent the standard deviation of the measurement. MSL = mean
          sea level.
5 uM; total Pb of 20 ^M; 50 grams sand per liter of AGW solution).
An adsorption maxima of 1.7 u.moles/g can be estimated from the
surface area (Table 1) of the composite sand (<1000 u.m) and the
recommended adsorption site density of 3.84 nmoles/m2, from
Davis and Kent (1990).

Spatial variability of grain-size distribution and  estimates of
hydraulic conductivity. K. The range of grain-size distributions in
the 376 subsamples is shown in Figure 8. The results were similar
to those  observed by Wolf et al. (1991), for cores collected
approximately 6 meters west of our transect cores (Hess et al.,
1992). Table 5 shows the maximum, minimum, average, and
standard deviation values of d10 for subsamples in the transect
and widely-spaced cores. d1p is the grain diameter at which 10%
of thesubsample mass was of smaller size. Hydraulic conductivity
was estimated for each subsample from the empirical relation of
Hazen(1893):
                 K=A-(d10)2
                           (D
where A is 1.157 for Kin cm/sec (at 10°C) and d,0 is in mm.

Figure 9 illustrates the variability of /(estimates for subsamples of
core 38.  The range of values for K for all samples (Table 5) is
similar to that reported by Wolf et al. (1991) for Kestimated from
grain-size distributions and permeameter measurements.  The
average Kestimated from a tracer test (LeBlanc et al., 1991) and
from borehole  flowmeter measurements (Hess et al., 1992) is
larger by  about 30 percent.  The small-scale variability in K
estimated from the flowmeter method is similar to that estimated
from permeameter measurements (Hess et al., 1992) and from
grain-size distributions (Wolf et al., 1991, and this study).

Variability of surface area and sand extraction yields. The BET
surface area of subsamples from core 38 varied by a factor of two,
with no apparent trend with depth (Figure 10). The d10 values in
this core ranged from 0.174 to 0.362 mm. An inverse relationship
between surface area and d, 0 for core 38 subsamples is observed
(Fig. 11).   Excluding  the obvious outlier, a linear regression
through the data gave a significant correlation (r= -0.62). The
relationship does not appear to be linear, however; and a parabolic
function would probably yield a better fit.

The range in extracted Fe, Al and Mn from this core is shown in
Table 6. No apparent trend with depth was observed for any of the
extracted metals.  In units of u.moles/g, Al was  significantly
correlated with Fe in the three extractions, despite the greater
selectivity of DC for Fe over Al. The dissolution rates of Fe and
Al in the extracting solutions is likely proportional to the exposed
surface areas of minerals containing these elements. The fact
that Fe and Al  were highly correlated in all three extractions
suggests that the source is largely from the dissolution of mixed
oxide coatings on the grain surfaces (Coston et al., 1993). If the
source was from  dissolution of separate minerals, such as a
mixture of magnetite and feldspar, it is unlikely that the abundances
of these minerals would covary throughout the core. Extracted
Mn was strongly correlated with Fe in both the 0.5 and 72-hour HA
extractions, but not in the DC extraction. Extracted Fe, Al, or Mn
by the three methods did not correlate with BET surface area. The
lack of correlation may be due to two factors:
                                                          11

-------
                   100 I   i  i  i  i
                                               Particle-Size Diameter (mm)

Figure 8.   Range of grain-size distributions measured for the 376 subsamples from transect and widely-spaced cores. The  right curve
          shows the grain-size distribution for the subsample with the largest value of d10. The left curve shows the grain-size distribution
          for the subsample with the smallest value of d10. The average value for d,0 (and one standard deviation) are shown; d10 is the
          grain diameter at which 10% of the sample (by weight) is of smaller size.

                                       1 w i i  I i i r      i   i   i I T~l i f i       i    (~"~1
                                       12
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TABLE 5.  RANGE, MEAN, AND STANDARD DEVIATION OF d,0 FOR TRANSECT AND WIDELY-SPACED CORES
                                       Transect
   Widely Spaced
                  Mean, mm             0.252
                  (Standard deviation)      0.046
                  Maximum, mm          0.475
                  Minimum, mm           0.076
                       K
                  Mean, cm/s             0.076
                  (Standard deviation)       0.029
                  Maximum, cm/s          0.261
                  Minimum, cm/s           0.007
       0.264
       0.046
       0.387
       0.170
       0.083
       0.029
       0.173
       0.033
All Cores
  0.255
  0.046
  0.475
  0.076
  0.078
  0.029
  0.261
  0.007
 1)   the BET surface area is indicative of the entire surface area
     of samples, including surfaces other than those containing
     the extracted elements, and

 2)   variable thickness of coatings on grain surfaces could break
     down any expected relationship between elements dissolved
     and surface area.

 Characterization  techniques as  geochernjcal indicators  of
 adsorption pptentiql.  Linear regressions  of the Pb2+ adsorption
 data in units of jimoles per gram of sand did not yield significant
 correlations with grain-size (d10) or  surface area.   This result
 suggests that Pb2* adsorption occurs at specific areas on the sand
 surface that are more reactive, whereas the BET surface area is a
 measurement of the entire surface area. Pb2* adsorption was also not
 correlated well with 0.5 hour HA extractable Fe, Al, or the  sum of
 extractable iron and aluminum (Fe+AI) in units of nmoles/g. These
 elements are expected to dissolve from the the reactive portion of the
 surface area (Davis and Kent, 1990). However, significant correlations
 were observed for linear regressions  of  Pb2* adsorption with 72-hour
 HA Al and Fe+AI and with the DC Fe, Al, and Fe+AI (table 7). Because
 the molar ratio of Pb2* adsorbed to Mn extracted was greater than one
 for all extractions, Mn was not used as a variable for correlation analysis
 with  Pb2*.   No significant correlations were found between Zn2+
 adsorption (in units of nmoles/g) and BET surface area, grain-size, or
    any of the dissolved elements determined  in the three extraction
    methods.

    Because the batch adsorption experiments (to determine spatial
    variability) were conducted using conditions of equal sand mass
    per volume of AGW, the adsorption and extraction  data were
    normalized per unit surface area to eliminate surface area as a
    variable. Significant, positive correlations between Pb2+ and Zn2*
    adsorption and 0.5 hour HA, 72-hour HA, and DC-extracted Fe,
    Al, Fe+AI, Fe+AI+Mn were obtained after normalizing to surface
    area (for example, see Figure 12);  the correlation coefficients
    were greater than those found without normalization to surface
    area.  Both Pb2+ and Zn2+ adsorption were more highly correlated
    with the 72-hour HA- and DC-extracted Fe and Al than with the
    0.5-hour HA extraction (Table 7). Because Fe and Al are highly
    correlated with each other in all extractions, Pb2t or Zn2+ adsorption
    each correlate well with extractable Fe, Al, or the sum of Fe and
    Al, when normalized to surface area. Thus, the extraction data
    does not allow one to distinguish between metal adsorption onto
    Al or Fe phases.

    The significant correlation of Zn2+ adsorption with Pb2+ adsorption
    suggests that  the abundance of adsorption sites for these two
    metals covaries within a core, or that Pb2+ and Zn2+ adsorb to the
    same type of mineral surface sites.  Assuming that the 0.5 hr HA
 TABLE 6.  RANGE OF EXTRACTED IRON, ALUMINUM, AND MANGANESE FOR SUBSURFACE SAMPLES OF CORE 38

                                                              Extraction
         Element                0.5-hour HA
                         nmoles/g    \imoles/m2
     72-hour HA
\imoles/g   \imoles/m2
             DC
   nmo/es/g    (imotes/m2
Fe
Al
Mn
2.4 - 0.4
2.4 - 0.7
0.16-0.02
10-1.2
9.9-2.6
0.79 - 0.05
19- 12
1 -11
0.20-0.11
86-33
81 -33
1.5-0.3
11-5.6
1.9-1.0
0.12-0.03
52-22
7.9 - 3.7
0.60-0.12
                                                            14

-------
TABLE 7.  SUMMARY OF REGRESSION ANALYSIS FOR PB*' ADSORPTION DATA AT pH 5.3 FOR SUBSURFACE SAMPLES OF CORE 38
            Dependent
             variable
Independent
  variable
n-2
                                 slope  intercept
        nmoles/g Pb, Fe, Al

                Pb
                Pb
                Pb
                Pb
                Pb
               Znc

        (imoles/m2 Pb, Fe, Al
  72 hr Ala
72 hr Fe+Ala
  DCFe"
   DCAI"
 DC Fe+AI"
    Pbc
 19
 19
 21
 21
 21
266
0.60
0.52
0.63
0.62
0.64
0.58
0.55
0.55
0.53
0.53
0.53
0.16
0.006
0.003
0.012
0.082
0.011
0.099
0.144
0.145
0.121
0.112
0.115
0.011
Pb
Pb
Pb
Pb
Pb
Pb
Pb
Pb
Pb
0.5 hr Fed
0.5 hr Ald
0.5 hr Fe+Ald
72 hr Fea
72 hr Ala
72 hr Fe+Ala
DCFe»
DCAI"
DC Fe+Alb
24
24
24
19
19
19
21
21
21
0.60
0.76
0.73
0.86
0.88
0.89
0.80
0.87
0.82
0.50
0.50
0.50
0.55
0.55
0.55
0.53
0.53
0.53
0.045
0.060
0.030
0.009
0.010
0.005
0.017
0.111
0.015
0.718
0.533
0.595
0.365
0.336
0.326
0.308
0.279
0.293
        • 72 hour hydroxylamine hydrochloride extraction at 50°C
        b 24 hour dithionite-ammonium citrate extraction
        c All transect cores
        d 0.5 hour hydroxylamine hydrochloride extraction at 50°C
        8 Critical value of r for 1% level of significance
            

             "o
             E

             JD
             CL
                                     20
                      40              60

                      Fe  (jimoles/m2)
                                  80
                                  100
Figure 12.  Pb2* adsorption (in units of micromoles per square meter of surface) as a function of iron extracted from sand grain coatings by
          hydroxylamine hydrochloride (micromoles per square meter) for subsamples of subsurface material (Core 38).
                                                           15

-------
extraction accurately estimated the ferrihydrite content (Chao
and Zhou, 1983), the adsorptive reactivity of the composite sand
for Zn2+ was calculated using a two-site, diffuse double layer
surface complexation model (Dzombak and Morel, 1990).  The
model  simulations (Davis et al., 1993) suggest that insufficient
ferrihydrite was present to appreciably affect Zn2+ adsorption by
the sand, which is consistent with the experimental results (Fig.
5). The experiments showed that a HA extraction (0.5 hr) did not
significantly change Pb2+ adsorption either (Fig. 3).

The results forZn2+ and Pb2+ adsorption by diamagnetic fractions
of the sand showed that very strong extractions were required to
alter the adsorptive interactions,  i.e., extractions capable of
dissolving crystalline iron oxides, such as goethite or hematite.
Using literature data, we estimated apparent stability constants
for the adsorption reactions with goethite and hematite, and the
surface site density of the crystalline iron oxides on the sand was
estimated from the DC extractions. The modeling (Davis et al.,
1993) suggests that goethite surfaces are not present in sufficient
abundance to explain the reactivity of  the sand.  A similar
conclusion was reached assuming that all  iron dissolved was
present as hematite.

As mentioned above, normalization of the extracted Fe and Al to
surface area significantly improved the  correlation with metal
adsorption. Thesignificantcorrelationsbetween metal adsorption
and the extracted Fe and Al (DC or HA-72 hr) suggest that these
extractions, combined with surface area measurements, could be
used as an effective geochemical indicator of the potential variability
of metal adsorption, at least in this aquifer. Because of the many
potential  artifacts  and time-intensive  nature  of adsorption
experiments conducted  with natural materials (Davis and Kent,
                                                1990; Coston  et al., 1993), simple laboratory characterization
                                                methods are needed to provide  a first approximation of the
                                                adsorptive reactivity of site-specific materials at contaminated
                                                field sites.  Such measurements will obviously never provide a
                                                more accurate measurement than carefully-controlled adsorption
                                                measurements, but the measurement of simple geochemical
                                                indicators may be preferable  in site assessment or other non-
                                                research applications. However, the usefulness  of the indicator
                                                discovered in this study needs to be tested at several other sites
                                                before it can be considered an effective characterization tool.

                                                Correlation between  metal  ion  adsorption  and hydraulic
                                                conductivity.   When all the data are considered, a statistically
                                                significant linear correlation between K and Pb2+ adsorption is
                                                found (r=-0.29,n=374). In contrast, Zn2+adsorption was statistically
                                                independent of K(r=-0.089, n=374). Figure 13 shows the scatter
                                                diagram for K and Pb2* and Zn2*  adsorption  (data from the
                                                transect cores only). The transect data exhibited larger correlation
                                                coefficients for both Pb2+ and Zn2+ adsorption with /(than did the
                                                data from the widely-spaced cores. For example, the correlation
                                                of Pb2+ adsorption  with K (transect data only)  had a greater
                                                absolute value for the correlation coefficient (r=-0.41, n=268).

                                                The overall correlations can be compared to that determined for
                                                Sr sorption by a calcareous, sandy aquifer material at the Borden
                                                experimental site (Robin et al., 1991). These authors also found
                                                a very weak, negative correlation between the natural log (In) of
                                                the distribution coefficient for Sr (Kd) and In K. The r of this
                                                correlation  was  -0.13,  thus having an absolute value for the
                                                correlation  coefficient larger than found in this study for Zn but
                                                much weaker than that found  for Pb.
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                                                                                        0.02
         o
         CO
        T3
         CO

0.01    ^
                                                                                        0
                  0          0.05         0.10         0.15        0.20         0.25         0.30

                                                      K (cm/s)

 Figure 13.  Pb2* and Zn2* adsorption as a function of hydraulic conductivity (estimated from grain-size distribution) for the transect cores.
                                                            16

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STATISTICAL EVALUATION OF PARAMETER
VARIABILITY

Hydraulic-conductivity  data sets typically exhibit a skewed
statistical distribution; Kcan vary by several orders of magnitude,
but all values are positive  (Freeze et al., 1990).  Most workers
have found that a log normal distribution provides a good fit to the
data (e.g., Woodbury and Sudicky, 1991). Thus if,

                      Y, = In K,                   (2)

the parameter Y is normally distributed. However, the variability
of /(within the aquifer is not completely random; commonly, the
observed values of Y, are spatially correlated with one another.
Values  separated by  short distances  are  generally  highly
correlated, and those separated by long distances may be only
weakly correlated or not correlated at all.  The function that
displays the drop in correlation with  distance is called an
autocorrelation function (Freeze et al., 1990).

The autocorrelation function may take a number of forms; one of
the most commonly used functions is the exponential model,
            pY(x) = exp [-|
(3)
where x is the separation distance, py(x) is the autocorrelation
function, and Xy is an exponential decay parameter, known as the
correlation length or scale. The latter parameter is a measure of
the distance over which the Y-value is correlated; specifically, it is
the distance over which py(x) decays to a value  of e~1.  It is
reasonable  to  anticipate a direct  relationship between  the
magnitude of the correlation length of the hydraulic conductivity
and  the average dimension of bedding  structures within a
sedimentary deposit (Freeze et al., 1990).

Var'iQgram analysis.  A spatial autocorrelation function requires
continuous data. In  practice, we typically have discrete samples
that are continuous in the vertical direction and discontinuous in
horizontal directions. A geostatistical analysis allows quantification
of the spatial structure of discrete data (Journel and Huijbregts,
1978).  The basic tool of the  analysis is the experimental
semivariogram  (referred to below as the variogram) which is
calculated as the mean-squared differences between sample
values at specified separation distances,



where y is the variogram statistic, h is the  separation distance
between observations, n(h) is the number of data pairs separated
by h, and the summation is made from i=1 to \=n(h). In practice
the variogram is constucted so  that h is  the  mean distance
between points within a range of distances known as a lag class
(Hess et al., 1992).  Data exhibiting spatial correlation will give
variogram statistics that are small at small separation distances
and that increase to the data set variance at distances beyond the
correlation scale.  Isotropic and directional analyses  can be
performed.

To characterize the spatial correlation structure of the hydraulic-
conductivity  and adsorption data sets,  directional variogram
analyses were conducted on a subset of each data set.  Data from
the 8 cores located in the transect (Fig. 1) were included in the
analyses.  This transect is in the vicinity of and is approximately
parallel to the transect used by Hess et al. (1992) to characterize
the correlation scales of the variability in hydraulic conductivity in
this aquifer. The transect is also approximately parallel to the
mean direction of ground-water transport at the site and to the
hypothesized mean direction of the glacial outflow which deposited
the sediments at the site (Hess et al., 1992).

The  hydraulic-conductivity and adsorption data sets display
truncated  and skewed  natures which suggest lognormal
distributions.  The lognormal nature of the adsorption data is
speculated here; however, Robin et al. (1991) found little evidence
that the log transformation affected  the spatial behavior of the
variables, only the magnitude of the spectral estimates. Because
of the correlated nature  of  our data sets, a rigorous test of
normality is difficult.  The variogram analyses were conducted on
In-transformed data sets. The In-transformed data sets—hydraulic
conductivity (In K), lead adsorption (In Pb ads), and zinc adsorption
(In Zn ads)— contained 268 values from the 8 boreholes.

In the variogram analyses, the lag classes were based on the
sample spacing.  In the vertical, samples were on average 0.1 m
in length, and for the most part, sampling was continuous within
boreholes. Therefore, the minumum lag in the vertical was set to
0.1 m, which represented the average vertical spacing between
the centroids of adjacent samples.  Each lag class calculation
included at least  100 data pair comparisons in the vertical.

In the horizontal direction, sampling was not continuous; samples
were limited to discrete boreholes. The minimum spacing between
boreholes was approximately 1 m; a mean distance of 1.4 m was
used for the smallest lag in the horizontal variograms to ensure a
minimum of 50 data pairs in each lag class. Partly because of the
continuous nature of the vertical sampling and the discontinuous
nature of the horizontal sampling, vertical variograms were better
defined than were horizontal variograms; this is the case with the
variograms reported below.

Negative-exponential models were  chosen to represent  the
variogram trends:
                                                                              y(h) = s2[1 -
                                                              (5)
                                                              where y(/i) is the variogram  statistic  (Equation 4),  s2 is the
                                                              variogram sill, h is the average separation distance between data
                                                              pairs compared in the lag class, and A. is the correlation length. To
                                                              fit the model to the experimental variograms, both the sill and the
                                                              correlation length can be varied. The sill sets the value which the
                                                              model approaches asymptotically as the  separation distance
                                                              increases.   The sill is typically set to (or near to) the sample
                                                              variance, as was done here. The correlation length is, therefore,
                                                              the primary model parameter that is  fit to the data; this length
                                                              controls the rate at which the model approaches the asymptote
                                                              and quantifies the spatial correlation within  the data.

                                                              Correlation scales for In /Cand In (metal  adsorbed). The vertical
                                                              variograms (Fig. 14) show greater correlations (smaller variogram
                                                              statistics) at smaller separation distances than at larger distances.
                                                              As  the separation distance increases,  the variogram statistic
                                                              approaches the sample variance. The vertical correlation scales
                                                              identified by the fitted models were 0.1,0.15, and 0.26 m for In K,
                                                              In Pb ads, and In Zn ads, respectively. These values are similar
                                                              to the vertical correlation scale for In K(0.18-0.38 m) identified by
                                                              Hessetal. (1992). The fact that the vertical correlation scales for
                                                              In K, In Pb ads, and In Zn ads is of similar  magnitude is interesting
                                                              and warrants further  investigation; however, the similarity in
                                                          17

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                          0.015
                           0.01
                          0.005
                           0.2
                          0.15
                           0.1
                          0.05
                                                0.5                 1
                                                 Separation Distance, in meters
                                                                                    1.5
Figure 14. Vertical variograms of A) In (lead adsorption), B) In (zinc adsorption), and C) In (K) (hydraulic conductivity). Exponential model of
          the type y =  s2[1 - e<'xrt>] is fit to the data, where 7 is the variogram statistic, s* is the variogram sill, x is the average separation
          distance between data pairs compared in the lag class, and K is a characteristic length (correlation scale).
                                                                18

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correlation scales  does not imply causality.   The  negative
exponential model  is only one of several models (Journel and
Huijbregts, 1978) that might adequately fit these variograms.
However, the alternative correlation scales are not expected to
vary in their orders  of magnitude from those presented here.

The horizontal spacing between the eight boreholes along the
transect ranged between 1  and 16 meters. At the maximum
spacing of 16m, data pairs from only two boreholes are available
and the number of pairs is less than 50.  Therefore, the resulting
variogram statistics at the largest separation distances are not
reported.  The resulting horizontal variograms (Fig. 15) provide
correlation estimates between separation distances of 1.4 and 12
m; the variograms show a lack of correlation at these horizontal
separation distances.  The horizontal sampling density at small
separation distances was not adequate to resolve a small-scale
correlation structure.  Therefore, no models were fit to  these
horizontal variograms. Additional data would be necessary to
resolve the horizontal correlation structures of these variables.


CONCLUSIONS

1.    Adsorption of Pb2+ and Zn2* on the highly-weathered, coarse
     aquifer sand was dominated by quartz grains (95% by
     weight) in the  sand.  Heavy minerals and feldspars were
     more reactive  than quartz  grains on a weight-normalized
     basis, but greater than  80% of Zn2+ was adsorbed by the
     quartz fraction  and more than 90% of Pb2+ by the quartz and
     feldspar fractions because of the greater abundance of this
     mineral fraction.  The reactivity of the metal ions with the
     quartz grains in  the sand was much greaterthan that observed
     for purified, commerically-prepared quartz grains. The greater
     reactivity of the natural quartz surfaces can be attributed to
     thick (100-300 nm) coatings on the sand grains. The coatings
     are composed of surface  precipitates of complex mixed
     oxides and silicates, containing high concentrations  of Fe
     and  Al. The coatings are derived from the weathering  of
     feldspars and other accessory minerals in the aquifer.

2.    The  spatial variability of Pb2+ and Zn2+ adsorption under
     constant chemical conditions was moderate in this aquifer at
     a scale of 100  meters. Pb2+ adsorption ranged from 0.091-
     0.371 nmoles/g sand; Zn2+  adsorption ranged from  0.00-
     0.028 nmoles/g sand.   The mean values of metal-ion
     adsorption for  all samples (n=268) of a 16 meter transect
     were not significantly different from the mean values observed
     for samples (n=106) taken from more widely-spaced cores.

3.    The variability in Pb2+ and Zn2+ adsorption was attributed to
     both variability in  surface area and in the composition  of
     surface coatings of grains, as indicated by extracted Fe and
    Al. Strong correlations between metal-ion adsorption and Fe
     and Al extraction yields were observed when both quantities
    were normalized per unit surface area. A non-linear, inverse
     relationship was observed between surface area and grain-
     size distribution.

4.    Pb2* adsorption showed a  significant, but weak, negative
    correlation with hydraulic conductivity (K), estimated from
    the grain-size  distribution;  Zn2+ adsorption  exhibited no
    correlation with K. The correlation between Pb2+ adsorption
    and K were stronger when only the  data from the transect
    cores were considered.
5.  Vertical correlation scales identified by a simple exponential
    model were 0.1, 0.15, and 0.26 m for In K, In Pbads, and In
    Zn ads, respectively. These values are similar to the vertical
    correlation scale identified by Hess et al. (1992) for In  K.
    Variograms for the three parameters  showed a lack  of
    correlation at the horizontal separation distances examined
    (between 1.4 and 12m).
ACKNOWLEDGEMENTS

This work was funded by the  U.S. Environmental Protection
Agency under Interagency Agreement DW14934639 with the
Robert  S.  Kerr Environmental Research Laboratory, Ada,
Oklahoma.  It has been subjected to Agency review and approved
for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
Technical advice from R. Pulsof the R. S. Kerr Lab throughout the
study is gratefully acknowledged.  B. Rea performed the Pb2*
adsorption experiments with different grain-size fractions of the
composite sand.  D.  Kent (USGS-Menlo Park,  California), D.
LeBlanc, C. Mclntosh, and R. Quadri  (USGS, Marlborough,
Massachusetts) assisted in obtaining cores of subsurface material
from the field site. K. Bussey (USGS, Marlborough, Massachusetts)
assisted in the preparation of Figure 1.
QUALITY ASSURANCE STATEMENT

All research  projects making conclusions or recommendations
based on environmentally related measurements and funded by
the Environmental Protection Agency are required to participate
in the Agency Quality Assurance Program.  This project was
conducted under the approved Quality Assurance Program Plan.
The procedures specified in the plan were used without exception.
Information on the plan and documentation of the quality assurance
activities and results are available from the Principal Investigator.
                                                         19

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U.UI3
0.01
0.005
0
0.25
0.2
.a
Crt
•a
n
on
a 0.15
U
i o.i
'i
0.05
0
0.2
0.15
0.1
0.05
n
T" 	 1 	 1 	 -i 	 1 	 1
A
-
•

•
* «


~
Bfi
~



C
-
• •
-
                                                    468
                                                Separation Distance, in meters
                                                                                       10
12
Figure 15.  Horizontal variograms of A) In (lead adsorption), B) In (zinc adsorption), and C) In (K) (hydraulic conductivity).
                                                               20

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REFERENCES

ASTM (1986) Annual Book of ASTM Standards: Soil, Rock and
Building  Stones. American Society of Testing Materials,
Philadelphia, Pennsylvania.

Barber, L. B. II (1990) Geochemical heterogeneity in glacial
outwash  aquifer:  Effect of Particle Size and Mineralogy on
Sorption  of  Nonionic Organic Solutes,  Ph.D. Dissertation.
University of Colorado, Boulder, CO.

Barber, L. B. II, Thurman, E.  M,  and Runnells, D. D. (1992)
Geochemical heterogeneity in a sand and gravel aquifer: Effect of
sediment mineralogy and  particle size  on  the sorption  of
chlorobenzenes, Journal of Contaminant Hydrology 9. 35-54.

Barber, L B. II,Thurman, E. M, and Schroeder, M.P. (1988) Long-
term fate of organic  micropollutants in sewage-contaminated
groundwater, Environ. Sci. Technol.. 22, 205-211.

Chao, T. T. (1984) Use  of partial dissolution techniques in
geochemical exploration, Journal of Geochemical Exploration.
20, 101-135.

Chao, T. T. and Zhou, L. (1983) Extraction techniques for selective
dissolution of amorphous iron oxides from soils and sediments.
Soil. Sci.  Soc. Am. J. 47, 225-232.

Chrysikopoulos, C. V., Kitanidis, P. K., and Roberts, P. (1990)
Analysis  of one-dimensional solute transport through porous
media with spatially variable retardation factor. Water Resources
Research. 26, 437-446.

Coston, J. A., Fuller, C. C., and Davis, J. A. (1993) Importance of
aluminum- and iron-bearing  surface coatings on Pb2* and Zn2+
adsorption by an aquifer sand. 1. Experimental studies. Geochimica
et Cosmochimica Acta. submitted.

Davis, J. A. and Kent, D. B. (1990) Surface complexation modeling
in aqueous geochemistry  In:  Mineral-Water  Interface
Geochemistry. Hochella. M. F., and White A. F. (Eds) Mineralogical
Society of America, Washington, DC, 177-260.

Davis, J. A., Coston, J. A., and Fuller, C. C. (1993) Importance of
aluminum- and iron-bearing  surface coatings on Pb2+ and Zn2*
adsorption by an aquifer sand. 2. Surface complexation modeling,
Geochimica et  Cosmochimica Acta. submitted.

Davis, J.  A., Kent, D. B.,  Rea, B. A., Garabedian, S. P., and
Anderson, L. D. (1991) Effect of the geochemical environment on
heavy-metal transport in ground  water,  In:  U.S. Geological
Survey Toxic Substances Hydrology Program-Proceedings.
G. E. Mallard and D. A. Aronson (Eds.) WRIR  91-4034, 53-62.

Dzombak, D. A. and Morel, F. M. M. (1990) Surface Complexation
Modeling: Hydrous Ferric Oxide. John Wiley, New York.

Freeze, R. A, Massmann, J., Smith, L., Sperling, T., James, B.
(1990) Hydrogeological decision analysis; 1. A framework, Ground
Water 28, 739-776.

Garabedian, S. P. (1987)  Large-Scale Dispersive Transport in
Aquifers:  Field Experiments and  Reactive Transport  Theory,
Ph.D. Thesis, Dept. Civil Eng., Mass. Inst of Tech. Cambridge, MA.
Garabedian, S. P., LeBlanc, D. R., Gelhar, L W., and Celia, M. A.
(1991)  Large-scale natural gradient tracer test in sand  and
gravel, Cape Cod, Massachusetts. 2. Analysis of spatial moments
for a nonreactive tracer, Water Resources Research. 27(5), 911-
924.

Hazen, A. (1893) Some physical properties of sands and gravels,
Mass. State Board of Health. 24th Annual Report.

Hess, K. M., Wolf, S. H., and Celia, M. A. (1992) Large-scale
natural gradient  tracer  test in sand and  gravel, Cape Cod,
Massachusetts. 3. Hydraulic conductivity variability and calculated
macro-dispersivities, Water Resources Research. 28(8), 2011-
2027.

Honeyman.B.D., and Leckie.J.O. (1986) Macroscopic partitioning
coefficients for metal ion adsorption: Proton  stoichiometry at
variable pH and adsorption density, in Geochemical Processes at
Mineral  Surfaces: J. A. Davis and  K.  F. Hayes,  Eds. ACS
Symposium Ser. 323, American Chemical Society, Washington
DC, p. 162-190.

Journel, A. G. and Huijbregts, C. J. (1978) Mining Geostatistics.
Academic Press, San Diego, CA.

Kabala, Z. J., and  Sposito, G. (1991)  A stochastic model of
reactive solute transport with a  time-varying velocity in a hetero-
geneous aquifer, Water  Resources Research. 27, 341-350.

Kurbatov,  M. H., Wood,  G. B.,  and Kurbatov,  J.  D. (1951)
Isothermal adsorption of cobalt from dilute solutions, Journal
Phys.Chem. 55,1170-1182.

LeBlanc, D.  R. (1984) Sewage Plume in a Sand and Gravel
Aquifer, Cape Cod, Massachusetts,  U.  S. Geological Survey
Water-Supply Paper 2218, 28 p.

LeBlanc, D. R., Garabedian, S. P., Hess, K. M., Gelhar, L. W.,
Quadri,R.D.,Stollenwerk,K.G.,andWood,W.W. (1991) Large-
scale natural gradient tracer test in sand and gravel, Cape Cod,
Massachusetts.  1. Experimental design and observed tracer
movement, Water Resources Research. 27, 895-910.

Rea, B. A., Kent, D. B.,  LeBlanc, D. R., and Davis, J. A. (1991)
Mobility of zinc in a sewage-contaminated aquifer, Cape Cod,
Massachusetts, In:  U.S. Geological  Survey Toxic Substances
Hydrology Program-Proceedings. G. E. Mallard  and  D. A.
Aronson (Eds.) WRIR 91-4034, 88-95.

Robin, J. J. L., Sudicky, E. A., Gillham, R. W., and Kachanoski, R.
G. (1991) Spatial variability of strontium distribution coefficients
and their correlation with hydraulic conductivity in the Canadian
Forces Base Borden Aquifer, Water Resources Research. 27,
2619-2632.

Robinson, G.  D. (1984/85) Sequential chemical extractions and
metal partitioning in hydrous  Mn-Fe-oxide coatings: reagent
choice and substrate composition affect results, Chemical Geology
47,97-112.

Sokal, R. R., and Rohlf, F. J. (1973) Introduction to Biostatistics.
W.  H. Freeman and Co., San Francisco, CA. 368 p.
                                                         21

-------
Tessier, A., Campbell, P. G. C., and Bisson, M. (1979) Sequential
extraction procedure for the speciation of particulate trace metals,
Analytical Chemistry. 51, 844-851.

Tessier, A., Carignan, R., Dubreuil, B., and Rapin, F. (1989)
Partitioning of zinc between the water column and the oxic
sediments  in lakes.  Geochimica et Cosmochimica Acta. 53,
1511-1522.

Valocchi, A. J. (1989) Spatial moment analysis of the transport of
kinetically absorbing solutes through stratified aquifers, Water
Resources Research. 25, 273-279.

Wolf, S. H., Celia, M. A., and Hess, K. M. (1991) Evaluation of
hydraulic conductivities calculated from multiport-permeameter
measurements, Ground Water. 29, 516-525.

Wood, W.  W.,  Kraemer, T.  F., and Hearn, P. P., Jr. (1990)
Intragranular diffusion, an important mechanism influencing solute
transport in clastic aquifers? Science. 247,1569-1572.

Woodbury, A. D., and Sudicky, E. A. (1991) The geostatistical
characteristics of the Borden aquifer, Water Resources Research.
27, 533-546.

Zapico, M. M., Vales, S., and Cherry, J. A. (1987) A wireline piston
core barrel for sampling cohesionless sand and gravel below the
water table. Ground Water Monit. Rev. 74-82.
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