United States
                       Environmental Protection
                       Agency
Office of
Research and
Development
  Office of Solid
  Waste
  and Emergency
  Response
EPA/540/4-91/003
March 1991
>EPA        Ground-Water  Issue
                       CHARACTERIZING SOILS FOR
                       HAZARDOUS WASTE SITE ASSESSMENTS

                       R. P. Breckenridge1, J. R. Williams2, and J. F. Keck1
                      INTRODUCTION

                      The Regional Superfund Ground Water
                      Forum is a group of ground-water scientists
                      representing EPA's Regional Offices, orga-
                      nized to exchange up-to-date information re-
                      lated to ground-water remediation at hazard-
                      ous waste sites.  Soil characterization at
                      hazardous waste sites is an issue identified by
                      the forum as a concern of CERCLA decision-
                      makers.

                      To address this issue, this paper was pre-
                      pared through  support from EMSL-LV and
                      RSKERL,  under the  direction of R. P.
                      Breckenridge,  with  the support of the
                      Superfund  Technical Support Project.  For
                      further information contact Ken Brown, EMSL-
                      LV Center Director, at FTS 545-2270 or R. P.
                      Breckenridge at FTS 583-0757.

                      Site investigation and remediation under the
                      Superfund  program is performed using the
                      CERCLA remedial investigation/feasibility
                      study (RI/FS) process. The goal of the RI/FS
                      process is to reach a Record of  Decision
                      (ROD)  in a timely manner. Soil characteriza-
             tion provides data types required for decision
             making in three distinct RI/FS tasks:

              1. Determination of the nature and extent of
                soil contamination.

              2. Risk assessment, and determination of
                risk-based soil clean-up levels.

              3. Determination of the potential effective-
                ness of soil remediation alternatives.

             Identification of datatypes required for the first
             task, determination of the nature and extent of
             contamination,  is relatively straightforward.
             The nature of contamination is related to the
             types of operations conducted  at the site.
             Existing records, if available, and interviews
             with personnel familiar with the site history are
             good sources of information to help determine
             the types of contaminants potentially present.
             This information may be used to shorten the
             list of target analytes from the several hundred
             contaminants of concern in the 40 CFR Part
             264 list (Date 7-1-89). Numerous guidance
             documents are available for planning  all
                       1 Idaho National Engineering Laboratory, Environmental Science and Technology Group, Idaho Falls, ID 83415.
                       2 Soil Scientist, U.S. EPA/R. S. Kerr Environmental Research Laboratory, Ada, OK 74820
                      Superfund Technology Support Center for Monitoring
                      and Site Characterization, Environmental Monitoring
                      Systems Laboratory Las Vegas, NV

                      Superfund Technology Support Center for
                      Ground-Water Fate and Transport, Robert S. Kerr
                      Environmental Research Laboratory Ada, OK
  . EPA,

Wafer W;
                            , D.G,

                         , Jr., Pri.0,, Director


                            Printed on Recycled Paper

-------
aspects of the subsequent sampling effort (US EPA,  1987a,
1988a, 1988b, and Jenkins et al., 1988).

The  extent of contamination is also related to the types of
operations conducted at the site. Existing records, if available,
and interviews with personnel familiar with the site history are
also good sources of information to help determine the extent of
contamination potentially present. The extent of contamination
is dependent on the nature of the contaminant source(s) and the
extent of contaminant migration from the source(s). Migration
routes may include air, via volatilization and fugitive dust emis-
sions; overland flow; direct discharge; leachate  migration to
ground water and surface runoff and erosion.  Preparation of a
preliminary site conceptual model is therefore an important step
in planning and directing the sampling effort.  The conceptual
model should identify the most likely locations of contaminants
in soil and the pathways through which they move.

The data type requirements for tasks 2 and 3 are frequently less
well understood. Tasks 2 and 3 require knowledge of both the
nature and extent of contamination, the environmental fate and
transport of the contaminants, and an appreciation of the need
for quality data to select a viable remedial treatment technique.

Contaminant fate and transport estimation is usually performed
by computer modeling. Site-specific information about the soils
in which contamination occurs,  migrates, and interacts  with, is
required as input to a model. The accuracy of the model output
is no better than the accuracy of the input information.

The purpose of this paper is to provide guidance to Remedial
Project Managers (RPM) and On-Scene Coordinators (OSC)
concerning  soil characterization data  types required for
decision-making in the CERCLA RI/FS process related to risk
assessment and remedial  alternative evaluation  for contami-
nated soils. Many of the problems that arise are due to a lack of
understanding the data types required for tasks 2 and 3 above.
This paper describes the soil characterization data types re-
quired to conduct model based  risk assessment for task 2 and
the selection of remedial design for task 3.   The information
presented in this paper is a compilation of current information
from the  literature and from experience combined to meet the
purpose of this paper.

EMSL-Las Vegas  and RSKERL-Ada convened  a technical
committee of experts to examine the issue and provide technical
guidance based on current scientific information.  Members of
the committee were Joe R. Williams, RSKERL-Ada; Robert G.
Baca, Robert P. Breckenridge,  Alan B. Crockett, and John F.
Keck from the Idaho National Engineering Laboratory, Idaho
Falls, ID; Gretchen L.  Rupp, PE, University of  Nevada-Las
Vegas; and Ken Brown, EMSL-LV.

This document was compiled by the authors and edited by the
members of the committee and a group of peer reviewers.

Characterization of a hazardous waste site should be done
using an integrated investigative approach to determine quickly
and cost effectively the potential health effects and appropriate
response measures at a site.  An integrated approach involves
consideration of the different types and sources  of contami-
nants, their fate as they are transported through and are parti-
tioned, and their impact on different parts of the environment.
CONCERNS

This paper addresses two concerns related to soil characteriza-
tion for CERCLA remedial response.  The first concern is thi
applicability of traditional soil classification methods to CERCLA
soil characterization. The second is  the identification of soil
characterization data types required for CERCLA risk assess-
ment and analysis of remedial alternatives. These concerns are
related,  in that the Data Quality Objective  (DQO)  process
addresses both. The DQO process was developed, in part, to
assist CERCLA decision-makers in identifying the data types,
data quality, and data quantity required to support decisions that
must be made during the RI/FS process. Data Quality Objec-
tives for Remedial Response Activities: Development Process
(US EPA, 1987b) is a guidebook on developing DQOs.  This
process as it relates to CERCLA soil characterization is dis-
cussed in the Data  Quality Objective section of this  paper.

Datatypes required for soil characterization must be determined
early in the RI/FS process, using the DQO process.  Often, the
first soil data types related to risk assessment and remedial
alternative selection available during a CERCLA site investiga-
tion are soil textural descriptions from the borehole logs pre-
pared by a geologist during investigations  of the nature and
extent of contamination.  These boreholes might include instal-
lation of ground-water monitoring wells, or soil boreholes. Typi-
cally, borehole logs contain soil lithology and textural  descrip-
tions, based on visual analysis of drill cuttings.

Preliminary site data are potentially valuable, and can provide
modelers and engineers with data to begin preparation of the
conceptual model and perform scoping calculations. Soil tex-
ture affects movement of air and water in soil, infiltration rate,
porosity, water holding capacity, and other parameters.
Changes in lithology identify heterogeneities in the subsurface
(i.e., low permeability layers, etc.). Soil textural classification is
therefore important to contaminant fate and transport modeling,
and to screening and analysis of remedial  alternatives. How-
ever, unless collected properly, soil textural descriptions are of
limited value for the following reasons:

 1.  There are several different systems for classification of soil
    particles with respect to size. To address this problem it is
    important to identify which system  has been or will be used
    to classify a soil so that data can be properly compared.
    Figure 1 can be used to compare the different systems (Gee
    and Bauder, 1986).  Keys to Soil Taxonomy (Soil Survey
    Staff, 1990) provides details to one of the  more useful
    systems that should be consulted prior to classifying a site's
    soils.

 2.  The accuracy of the field classification is dependent on the
    skill of the observer. To overcome this concern  RPMs and
    OSCs should collect soil textural data that are quantitative
    rather than qualitative. Soil texture can be determined from
    a soil sample by sieve analysis or hydrometer. These data
    types are superior to qualitative description based on visual
    analysis and are more likely to meet DQOs.

 3.  Even if the field person accurately classifies a soil (e.g., as
    a silty sand or a sandy loam), textural descriptions do not
    afford accurate estimations of actual physical  properties
    required for modeling and remedial alternative evaluation,

-------
    such as hydraulic conductivity. For example, the hydraulic
    conductivity of silty-sand can range from 105 to 10'1 cm/sec
    (four orders of magnitude).

 These ranges of values may be used for bounding calculations,
 or to assist in preparation of the preliminary conceptual model.
 These data may therefore meet DQOs for initial screening of
 remedial alternatives, for example, but will likely not meet DQOs
 or detailed analysis of alternatives.
DATA QUALITY OBJECTIVES

EPA has developed the Data Quality Objective (DQO) process
to guide CERCLA site characterization. The relationship be-
tween CERCLA RI/FS activities and the DQO process is shown
in Figure 2 (US EPA, 1988c, 1987a). The DQO process occurs
in three stages:

•  Stage 1. Identify Decision Types.  In this stage the types of
   decisions that must be made during the RI/FS are identified.
                     PARTICLE SIZE LIMIT CLASSIFICATION
                 USDA
                           CSSC
                                     ISSS
                                             ASTM (unified)
0.0002-
0.001
0.002 -
0.003
0.004
0.008
0.01
0.02 -
0.03
0.04 |
~ 0.06 -
E 0.08 .
£ 01 -
35 0.2
"J 0.3 "
0 0.4 .
t 0.6
S ?:§ -
2.0
3.0
4.0 .
6.0
8.0
10

20
30
40
60
80
- uj
BER OR SIZ
i/lnch)
if
c
111 fl>
IUO
5>~
300
- 270
. 140

-40
-20

- 4
- 1/2 In
~ 3/4 In
- 3 In.


CLAY

SILT

VERY FINE

RNE
SAND
MEDIUM
SAND
COARSE
SAND
VERY COARSE
SAND
RNE
GRAVEL

COARSE
GRAVEL
COBBLES
FINE CLAY
COARSE
CLAY
FINE
SILT
MEDIUM
SILT
COARSE
SILT
VERY FINE

FINE
SAND
MEDIUM
SAND
COARSE
SAND
VERY COARSE
SAND

GRAVEL


COBBLES

COARSE
CLAY

SILT

FINE



COARSE
SAND




GRAVEL



FINES
(SILT AND
CLAY)



FINE
SAND

MEDIUM
SAND
COARSE
SAND
FINE
GRAVEL

COARSE
GRAVEL
COBBLES
USDA - US. DEPARTMENT OF AGRICULTURE, (SOIL SURVEY STAFF, 1975)
CCS - CANADA SOIL SURVEY COMMITTEE (McKEAGUE, 1978)
ISSS - INTERNATIONAL SOIL SCI. SOC. (YONG AND WARKENTIN, 1966)
ASTM -AMERICAN SOCIETY FOR TESTING & MATERIALS (ASTM, D-2487,1985a)


Figure 1. Particle-size limits according to several current
          classification schemes (Gee and Bauder, 1986).
  The types of decisions vary throughout the RI/FS process, but
  in general they become increasingly quantitative as the pro-
  cess proceeds. During this stage it is important to identify and
  involve the data users (e.g. modelers, engineers, and scien-
  tists), evaluate  available data,  develop a conceptual site
  model, and specify objectives and decisions.

  Stage 2. Identify Data Uses/Needs.  In this stage data uses
  are defined. This includes identification of  the required data
  types, data quality and data quantity  required to make deci-
  sions on how to:

  - Perform risk assessment

  - Perform contaminant fate and transport modeling

  - Identify and screen remedial alternatives
• Stages. Design Data Collection Program. After Stage 1 and
  2 activities have been defined and reviewed, a data collection
  program addressing the data types, data quantity (number of
  samples) and data quality required to make these decisions
  needs to be developed as part of a sampling and analysis
  plan.

Although this paper focuses on datatypes required for decision-
making in  the CERCLA RI/FS process related to soil contami-
nation, references are provided to address data quantity quality
issues.

Data Types

The OSC  or RPM must determine which soil parameters are
needed  to make various RI/FS decisions. The types of deci-
sions to be made therefore drive selection of data types. Data
types required for RI/FS activities including risk assessment,
contaminant fate and transport modeling and remedial alter-
native selection are discussed in Soil characteristics Data Types
Required for Modeling Section, and the Soil Characterization
Data Type Required for Remedial Alternative Selection Section.

Data Quality

The RPM or OSC must decide "How good does the data need
to be in order for me to  make a given decision?".  EPA has
assigned quality levels to different RI/FS activities as a guide-
line. Data Quality Objectives for Remedial Response Activities
(US EPA,  1987a) offers guidance on this subject and contains
many useful references.

Data Quantity

The RPM or OSC must decide "How many  samples do I need to
determine the mean and  standard deviation of a given param-
eter at a given site?", or "How does a given parameter vary
spatially across the site?".   Decisions of this type must be
addressed by statistical design of the sampling effort. The So/7
Sampling Quality Assurance Gu/cte(Barth et al., 1989)and Data
Quality Objectives for Remedial Response (US EPA, 1987a)
offer guidance on this subject and contain many  useful refer-
ences.

-------
                                                                                         Record
                                                                                       of Decision
                                                                    Remedial
                                                                  Investigation
                                                                     Report
                                                                                Feasibility
                                                                                  Study
                                                                                  Report
  DQO
Additional
  Data
 Needed
Initiation
of RI/FS
  RI/FS
SCOPING
                                              DQO
                                            Additional
                                              Data
                                             Needed
                                              DQO
                                           Additional
                                              Data
                                             Needed
1
YES
^
r
ADDITIONAL
RI/FS AND
DQO PHASES

DQO
Stage
ll/lll

Figure 2. Phased RI/FS approach and the DQO process (EPA, 1987a).

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 IMPORTANT SOIL CHARACTERISTICS IN SITE
 EVALUATION
   in the vadose zone, and of transformation and degradation
   processes.
 Tables 1 and 2 identify methods for collecting and determining
 data types for soil characteristics either in the field, laboratory,
 or by calculation. Soil characteristics in Table 1 are considered
 the primary indicators that are needed to complete Phase I of the
 RI/FS process. This is a short, but concise list of soil data types
 that are needed to  make CERCLA decisions and should be
 planned for and collected early in the sampling effort.  These
 primary  data types  should allow for the initial screening  of
 remedial treatment alternatives and preliminary modeling of the
 site for risk assessment. Many of these characteristics can be
 obtained relatively inexpensively during periods of early field
 work when the necessary drilling and sampling equipment are
 already on site. Investigators should plan to collect data for all
 the soil characteristics at the same locations and times soil
 boring is done to install monitoring wells. Geophysical logging of
 the well should also be considered as a cost effective method for
 collecting lithologic information prior to casing the well.  Data
 quality and quantity must also be considered before beginning
 collection of the appropriate data types.

 The soil characteristics in Table 2 are considered ancillary only
 because they are needed in the later stages and tasks of the
 DQO process and the RI/FS process. If the site budget allows,
 collection of these data types during early periods of field work
 will improve  the database available  to  make decisions  on
 remedial treatment  selection and  model-based risk assess-
 ments. Advanced planning and knowledge of the need for the
 ancillary soil characteristics should be factored into early site
 work to reduce overall costs and the time required to reach a
 ROD.  A small additional investment to collect ancillary data
 during early site visits is almost always more cost effective than
 having to send crews back to the field to conduct additional soil
 sampling.

 Further detailed descriptions of the soil characteristics in Tables
 1 and 2 can be found in Fundamentals of Soil Physics and Ap-
 plications of Soil Physics (Hillel, 1980) and in a series of articles
 by Dragun (1988, 1988a, 1988b).  These references provide
 excellent discussions of these characteristics and their influ-
 ence on water movement in soils as well as contaminant fate and
 transport.

 SOIL CHARACTERISTICS DATA TYPES REQUIRED
 FOR MODELING

 The information presented here is not intended as a review of all
 data types required for all models, instead it presents a sampling
 of the more appropriate models used in risk assessment and
 remedial design.

 Uses of Vadose Zone Models for Cercla Remedial
 Response Activities

 Models are used in  the CERCLA RI/FS process to estimate
contaminant fate and transport. These estimates of contami-
 nant behavior in the environment are subsequently used for:

• Risk assessment. Risk assessment includes contaminant
  release assessment, exposure assessment, and determining
  risk-based clean-up levels. Each of these activities requires
  estimation of the rates and extents of contaminant movement
 •  Effectiveness assessment of remedial alternatives.  This
   task may also require determination of the rates and extents
   of contaminant movement in the vadose zone, and of rates
   and extents of transformation and degradation  processes.
   Technology-specific data requirements are cited in the Soil
   Characterization Data Type Required for  Remedial Alterna-
   tive Selection Section.

 The types, quantities, and quality of site characterization  data
 required for modeling should be carefully considered during Rl/
 FS scoping. Several currently available vadose zone fate and
 transport models are listed in Table 3. Soil characterization data
 types required for each model are included in the table.  Model
 documentation should be consulted for specific questions con-
 cerning uses and applications.

 The Superfund Exposure Assessment Manual discusses  vari-
 ous vadose zone models (US  EPA, 1988e).  This document
 should be consulted to select codes that are EPA-approved.

 Data Types Required for Modeling

 Soil characterization data types required for modeling are in-
 cluded in Tables 1 and 2.  Most of the models are one-  or
 two-dimensional solutions  to the advection-dispersion equa-
 tion, applied to unsaturated flow. Each is different in the extent
 to which transformation and degradation processes may be
 simulated; various contaminant release scenarios are accom-
 modated; heterogeneous soils and other site-specific charac-
 teristics are accounted for.  Each, therefore, has different data
 type input requirements.

 All models require physicochemical data for the contaminants of
 concern. These data are available in the literature, and from
 EPA databases (US EPA, 1988c,d).  The amount  of physico-
 chemical data required is generally related to the complexity of
 the model.  The models that account for  biodegradation  of
 organics,  vapor phase diffusion and other processes require
 more input data than the relatively simpler transport models.

 Data Quality and Quantity Required for Modeling

 DQOs for the modeling task should be defined during RI/FS
 scoping.  The output of any computer model is only as valid as
 the quality of the input data and code itself. Variance may result
 from the data collection methodology or analytical process, or as
 a  result of spatial  variability in the soil characteristic being
 measured.

 In general, the physical and chemical properties of soils vary
 spatially. This variation rarely follows well defined trends; rather
 it exhibits a stochastic (i.e.,  random) character. However, the
 stochastic character of many  soil  properties tends to follow
 classic statistical distributions. For example, properties such as
 bulk density and effective porosity of soils tend to be  normally
distributed (Campbell, 1985). Saturated hydraulic conductivity,
 in  contrast, is often found to follow a  log-normal distribution.
 Characterization of a site, therefore, should be performed  in
 such a manner as to permit the determination of the statistical
characteristics (i.e., mean  and variance)  and their spatial
correlations.
                                   (Continued on page  8)

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                               TABLE 1. MEASUREMENT METHODS FOR PRIMARY SOIL CHARACTERISTICS
                                       NEEDED TO SUPPORT CERCLA DECISION-MAKING PROCESS
                                                        Measurement Technique/Method (w/Reference)
Soil Characteristic*  Field
                                       Laboratory
                                        Calculation or Lookup Method
Bulk density



Soil pH



Texture
Depth to
ground water
Horizons or
stratigraphy
Hydraulic
conductivity
(saturated)
Water retention
(soil water
characteristic
curves)
Air permeability
and water content
relationships
Porosity (pore
volume)
Climate
Neutron probe (ASTM, 1985),
Gamma radiation (Blake and Hartage,
1986, Blake, 1965).

Measured in field in same manner as
in laboratory.
Collect composite sample for each soil
type. No field methods are available,
except through considerable
experience of "feeling" the soil for an
estimation of % sand, silt, and clay.

Ground-water monitoring wells or
piezometers using EPA approved
methods (EPA 1985a).

Soil pits dug with backhoe are best. If
safety and cost are a concern, soil
bores can be collected with either a
thin wall sample driver and veilmayer
tube (Brown etal., 1990).

Auger-hole and piezometer methods
(Amoozeger and Warrick, 1986) and
Guelph permeameter (Reynolds &
Elrick, 1985; Reynolds & Elrick, 1986).
Field methods require a considerable
amount of time, effort, and equipment.
For a good discussion of these methods
refer to Bruce and Luxmoore (1986).
None
Coring or excavation for lab analysis
(Blake and Hartage, 1986).
Using a glass electrode in an aqueous
slurry (ref. EPRIEN-6637) Analytical
Method - Method 9045, SW-846, EPA.

ASTM D 522-63 Method for Particle
Analysis of Soils. Sieve analysis better at
hazardous waste sites because organics
can effect hydrometer analysis
(Kluate, 1986).

Not applicable.
Not applicable.
Constant head and falling head methods
(Amoozeger and Warrick, 1986).
Obtained through wetting or drainage of
core samples through a series of known
pressure heads from low to high or high
to low, respectively (Klute, 1986).
Several methods have been used,
however, all use disturbed soil samples.
For field applications the structure of
soils are very important, For more
information refer to Corey (1986).
                                       Gas pycnometer (Danielson and
                                       Sutherland, 1986).
Precipitation measured using either
Sacramento gauge for accumulated value
or weighing gauge or tipping bucket gauge
for continuous measurement (Finkelstein
et al., 1983; Kite, 1979). Soil temperature
measured using thermocouple.
Not applicable.
Not applicable.



Not applicable.



Not applicable.
Not applicable.
May be possible to obtain information
from SCS soil survey for the site.
Although there are tables available that
list the values for the saturated
hydraulic conductivity, it should be
understood that the values are given for
specific soil textures that may not be the
same as those on the site.

Some look-up and estimation methods
are available, however, due to high
spatial variabiltiy in this characteristic
they are not generally recommended
unless their use is justified.

Estimation methods for air permeability
exist that closely resemble the estimation
methods for unsaturated hydraulic
conductivity.  Example models those
developed by Brooks and Corey (1964)
and van Genuchten (1980).

Calculated from particle and bulk
densities (Danielson and Sutherland,
1986).

Data are provided in the Climatic Atlas of
the United States or are available from
the National Climatic Data Center,
Asheville, NC Telephone (704) 259-0682.
    Soil characteristics are discussed in general except where specific cases relate to different waste types (i.e., metals, hydrophobia organics or polar organics).

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                                 TABLE 2. MEASUREMENT METHODS FOR ANCILLARY SOIL PARAMETERS
                                        NEEDED TO SUPPORT CERCLA DECISION-MAKING PROCESS
Soil Characteristic*  Field
                                                       Measurement Technique/Method (w/Reference)
                                       Laboratory
                                        Calculation or Lookup Method
Organic carbon      Not applicable.
Capacity Exchange   See Rhoades for field methods.
Capacity (CEC)

Erodibility
Water erosion
Universal Soil Loss
Equation (USLE)
or Revised USLE
(RUSLE)

Wind erosion
Vegetative cover
Soil structure
Organic carbon
partition
cooefficient (KJ

Redox couple ratios
of waste/soil system
Measurement/survey of slope (in ft
rise/ft run or %), length of field,
vegetative cover.
                                      High temperature combustion (either
                                      wet or dry) and oxidation techniques
                                      (Powell et al., 1989) (Powell, 1990).

                                      (Rhoades, 1982).
                                        Not applicable.
Not applicable.
Air monitoring for mass of containment.    Not applicable.
Field length along prevailing wind
direction.
Visual observation and documented
using map. USDA can aid in identification
of unknown vegetation.

Classified into 10 standard kinds - see
local SCS office for assistance (Soil
Survey Staff, 1990) or Taylor and
Ashcroft(1972),p.310.

In situ tracer tests (Freeze and Cherry,
1979).
Platium electrode used on lysimeter
sample (ASTM, 1987).
Not applicable.
Not applicable.
(ASTM £1195-87,1988)
Same as field.
Estimated using standard equations and
graphs (Israelsen et al., 1980) field data
for slope, field length, and cover type
required as input. Soils data can be
obtained from the local Soil Conservation
Service (SCS) office.

A modified universal soil loss equation
(USLE) (Williams, 1975) presented in
Mills et al., (1982) and US EPA (1988d)
source for equations.
                                        The SCS wind loss equation (Israelsen
                                        et al., 1980) must be adjusted (reduced)
                                        to account for suspended particles of
                                        diameter <1 Ou.m Cowherd et al., (1985)
                                        for a rapid evaluation (<24  hr) of particle
                                        emission fro  a Superfund site.
See local soil suivey for the site.
Calculated from K , water solubility
(Mills etal., 1985; s"imsetal., 1986).
Can be calculated from concentrations of
redox pairs or 02 (Stumm and Morgan, 1981).
Liner soil/water
partition coefficient
Soil oxygen
content (aeration)
In situ tracer tests (Freeze and Cherry,
1979)
02 by membrane electrode 02 diffusion
rate by R microelectrode (Phene, 1986).
0, by field GC (Smith, 1983).
Batch experiment (Ash et al., 1973);
column tests (van Genuchten and
Wierenga, 1986).

Same as field.
Mills etal., 1985.
Calculated from pE (Stumm and Morgan,
1981) or from 02 and soil-gas diffusion
rate.
                                                                                                                         (Continued)

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                                                   TABLE 2. (CONTINUED)
                                                   Measurement Technique/Method (w/Reference)
Soil Characteristic* Field
                                    Laboratory
                                    Calculation or Lookup Method
Soil temperature (as  Thermotery (Taylor and Jackson, 1986).
 it affects volatilization)

Clay mineralogy     Parent material analysis.
                                   Same as field.
                                     Brown and Associates (1980).
Unsaturated
hydraulic
conductivity
Moisture content
Soil biota
Unsteady dranage-flux (or instantaneous
profile) method and simplified unsteady
drainage flux method (Green et al.,
1986).The instantaneous profile method
was initially developed as a laboratory
method (Watson, 1966), however it was
adapted to the field (Hillel et al., 1972).
Constant-head borehole infiltration
(Amoozegar and Warrick, 1986).

Two types of techniques - indirect and
direct. Direct menthods, (i.e., gravimetric
sampling), considered the most accurate,
with no calibration required. However,
methods are destructive to field systems.
Methods involve collecting samples,
weighing, drying and re-weighing to
determine field moisture. Indirect methods
rely on calibration (Klute, 1986).

No standard method exists (see model or
remedial technology for input or remedial
evaluation procedures).
X-ray diffraction {Whittig and Allardice, 1986).

Not usually done; results very difficult to
obtain.
A number of estimation methods exists,
each with their own set of assumptions
and requiremnts. Reviews have been
presented by Mualem (1986), and
van Gehuchten (in press).
No standard method exists; can use agar
plate count using MOSA method 99-3
p. 1462 (Klute, 1986).
 * Soil characteristics are discussed in general except where specific cases relate to different waste types (i.e., metals, hydrophobic organics or polar organics).
Significant advances have been made in understanding and
describing the spatial variability of soil properties (Neilsen and
Bouma, 1985). Geostatistical methods and techniques (Clark,
1982; Davis, 1986) are available for statistically characterizing
soil properties important to contaminant migration. Information
gained from a geostatistical analysis of data can be used  for
three major purposes:

•  Determining the heterogeneity and complexity of the site;

•  Guiding the data collection and interpretation effort and thus
   identifying areas where additional sampling may be needed
   (to reduce uncertainty by estimating error); and

•  Providing data for a stochastic model of fluid flow and con-
   taminant migration.

One of the geostatistical tools useful to help in the interpolation
or mapping  of a site is referred to as kriging (Davis, 1986).
General kriging computer codes are presently available. Ap-
plication of this type of tool, however, requires an adequate
                                             sample size. As a rule of thumb, 50 or more data points are
                                             needed to construct the semivariogram  required  for use  in
                                             kriging. The benefit of using kriging in site characterization is
                                             that it allows one to take point measurements and estimate soil
                                             characteristics at any point within the domain of interest, such as
                                             grid points, for a computer model. Geostatistical packages are
                                             available from the US EPA, Geo-EAS and GEOPACK (Englund
                                             and Sparks, 1988 and Yates and Yates, 1990).

                                             The use  of stochastic models in hydrogeology has increased
                                             significantly in recent years. Two stochastic approaches that
                                             have been widely used are the first order uncertainty method
                                             (Dettingerand Wilson, 1981) and Monte Carlo methods (Clifton
                                             et al.,  1985; Sagar  et al., 1986; Eslinger and Sagar, 1988).
                                             Andersson and Shapiro  (1983) have compared these two ap-
                                             proaches for the case of steady-state unsaturated flow. The
                                             Monte Carlo methods are more general and easierto implement
                                             than the first order uncertainty methods.  However, the Monte
                                             Carlo method is more computationally intensive, particularly for
                                             multidimensional problems.


                                                                                  (Continued on page 10)

-------
                                TABLE 3.  SOIL CHARACTERISTICS REQUIRED FOR VADOSE ZONE MODELS
Model Name
[Reference(s)]
Properties and Parameters
Soil bulk density
Soil pH
Soil texture
Depth to ground water
Horizons (soil layering)
Saturated hydraulic conductivity
Water retention
Air permeability
Climate (precipitation)
Soil porosity
Soil organic content
Cation Exchange Capacity (CEC)
Degradation parameters
Soil grain size distribution
Soil redox potential
Soil/water partition coefficients
Soil oxygen content
Soil temperature
Soil mineralogy
Unsaturated hydraulic conductivity
Saturated soil moisture content
Microorganism population
Soil respiration
Evaporation
Air/water contaminant densities
Air/water contaminant viscosities
Help
(A,B)
O
O
•
O
•
•
•
O
•
•
0
0
•
O
O
O
O
O
O
•
•
O
O
•
O
O
Sesoil
(C,D)
•
•
O
•
•
•
•
•
•
•
•
•
•
O
0
•
O
•
•
•
•
O
O
•
O
O
Creams
(E,F)
•
O
•
O
•
•
•
O
•
•
•
O
•
0
O
•
O
O
O
•
•
O
O
•
O
O
PRZM
(G,H,I)
•
O
•
O
•
•
•
O
•
•
•
0
•
O
0
•
O
•
O
•
•
O
O
•
O
O
Vadoft
(H,J)
•
0
•
•
•
•
•
0
O
•
•
0
•
O
O
•
O
•
O
•
•
O
0
O
O
0
Minteq
(J)
O
•
0
O
O
0
O
0
O
O
•
•
O
O
•
•
O
•
0
O
O
O
O
O
O
O
Fowl™
w
•
•
0
O
O
•
•
O
•
0
O
0
O
O
0
•
O
O
O
•
•
O
O
O
•
O
Rite
(L)
•
0
•
O
O
•
O
0
•
•
•
O
•
O
O
•
O
•
O
O
•
O
O
•
•
O
Vip
(M)
•
O
•
O
O
•
O
•
•
•
•
0
•
0
O
•
•
•
O
O
•
0
O
•
•
O
Chemflo
(N)
•
O
O
O
O
•
•
O
•
O
O
O
•
O
O
•
O
O
O
•
•
O
O
•
O
O
REFRENCES
A. Schroeder, etal.,1984.         F.
B. Schroeder, etal.,1984a.        G.
C. Bonazountas and Wagner, 1984. M
D. Chen, Wollman, and Liu, 1987.    I.
E. Leonard and Ferreira, 1984.     J.
Devaurs and Springer, 1988.
Carsel etal.,1984.
Dean etal., 1989.
Deanetal., 1989a.
Brown and Allison, 1987.
K. Hosteller, Erickson, and Rai, 1988.
L. Nofziger and Willaims, 1988.
M. Stevens etal.,1989.
N. Nofziger etal., 1989.
I Required   O Not required  O Used indirectly*
 Used in (her estimation of other required
 characteristics or the intrpretation of the models,
 out not directly entered as input to models.

-------
Application of stochastic models to hazardous waste sites has
two main advantages. First, this approach provides a rigorous
way to assess the uncertainty associated with the spatial vari-
ability of soil properties. Second, the approach produces model
predictions in terms of the likelihood of outcomes, i.e., probabil-
ity of exceeding water quality standards. The use of models at
hazardous waste sites  leads to a thoughtful  and  objective
treatment of compliance issues and concerns.

In order to obtain accurate results with models, quality data
types must be used.  The issue of quality and confidence in data
can be partially addressed by obtaining as representative data
as possible. Good quality assurance and quality control plans
must be in place for not only the acquisition of samples, but also
for the application of the models (van der Heijde, et al., 1989).

Specific soil characteristics vary both laterally and vertically in
an undisturbed soil profile.  Different soil characteristics  have
different variances. As an example, the sample size required to
have 95 percent probability of detecting a change of 20 percent
in the mean bulk density at a specific site was 6; however, for
saturated hydraulic conductivity the sample size would need to
be 502 (Jury, 1986). A good understanding of site soil charac-
teristics can help the investigators understand these variations.
This is especially true for most hazardous waste sites  because
the soils  have often been disturbed, which may cause  even
greater variability.

An important aspect of site characterization data and models is
that the modeling process is dynamic, i.e., as an increasing
number of "simplifying" assumptions are needed, thecomplexity
of the models  must  increase to adequately simulate the addi-
tional processes that must be included.  Such simplifying as-
sumptions might include an isotropic homogeneous medium or
the presence of only one mobile phase (Weaver, et al., 1989).
In order to decrease  the number of assumptions required, there
is usually a need to increase the number of site-specific soil
characteristic data types in a model (see Table 2); thus providing
greater confidence in the values produced. For complex sites,
an  iterative process of  initial data collection and evaluation
leading to more data collection and evaluation until an accept-
able level of confidence in the evaluation can be reached can be
used.

Table 3 identifies selected unsaturated zone models and their
soil characteristic needs. For specific questions regarding use
and application of the model, the reader should refer to the
associated manuals. Some of these models are also reviewed
by Donigan and Rao (1986) and van der Heijde et al.  (1988).
SOIL CHARACTERISTICS DATA TYPES REQUIRED
FOR REMEDIAL ALTERNATIVE SELECTION

Remedial Alternative Selection Procedure

The CERCLA process involves the identification, screening and
analysis  of remedial alternatives at  uncontrolled  hazardous
waste sites (US EPA, 1988c). During screening and analysis,
decision  values for process-limiting characteristics for a given
remedial alternative  are compared to site-specific values of
those characteristics.  If site-specific values are outside the
range required for effective use of a particular alternative, that
alternative is less likely to be selected. Site soil conditions are
critical process-limiting characteristics.
Process-Limiting Characteristics

Process-limiting  characteristics  are site- and waste-specific
data types that are critical to the effectiveness and ability to
implement remedial processes. Often, process-limiting charac-
teristics are descriptors of rate-limiting  steps in the overall
remedial process.   In  some cases, limitations imposed by
process-limiting characteristics can be overcome by adjustment
of soil characteristics such as pH, soil moisture content, tem-
perature and others. In other cases, the level of effort required
to overcome these limitations will preclude use of a remedial
process.

Decision values for process limiting characteristics are increas-
ingly available in the literature, and may be calculated for
processes where design equations are known. Process limiting
characteristics are identified and decision values are given for
several  vadose zone remedial alternatives in Table 4.  For
waste/site characterization,  process-limiting  characteristics
may be  broadly grouped in four categories:

 1.  Mass transport characteristics
 2.  Soil reaction characteristics
 3.  Contaminant properties
 4.  Engineering characteristics

Thorough soil characterization is required to determine site-
specific values for process-limiting characteristics. Most reme-
dial alternatives  will have process-limiting  characteristics in
more than one category.

Mass Transport Characteristics

Mass transport is the bulk flow, or advection of fluids through
soil.  Mass transport characteristics  are  used to calculate
potential rates of movement of liquids or gases through soil and
include:

     Soil texture
     Unsaturated hydraulic conductivity
     Dispersivity
     Moisture content vs. soil moisture tension
     Bulk density
     Porosity
     Permeability
     Infiltration rate, stratigraphy and others.

Mass transport processes are often  process-limiting for both in
situ and extract-and-treat vadose zone remedial alternatives
(Table 4).  In situ alternatives frequently use a  gas  or liquid
mobile phase to move reactants or  nutrients through contami-
nated soil. Alternatively, extract-and-treat processes such as
soil vapor extraction (SVE) or soil flushing use a gas or liquid
mobile phase to move contaminants to a surface treatment site.
For either type of process to be effective,  mass transport rates
must be large enough to clean up a site within a reasonable time.

Soil Reaction Characteristics

Soil reaction characteristics describe contaminant-soil interac-
tions. Soil reactions include bio- and  physicochemical reactions
that occur between the contaminants and the site soil.  Rates of
reactions such as biodegradation, hydrolysis, sorption/desorp-
tion, precipitation/dissolution,  redox reactions, acid-base
reactions, and others are process-limiting characteristics for

                                   (Continued on page 12)
                                                        10

-------
TABLE 4. SOIL CHARACTERIZATION CHARACTERISTICS REQUIRED FOR REMEDIAL TECHNOLOGY EVALUATION ,
              (US EPA, 1988e,f; 1989a,b; 1990; Sims etal., 1986; Sims, 1990; Towers et al., 1989)
Technology
Pretreatment/
materials handling
Soil vapor
extraction
In situ enhanced
bioremediation
Thermal treatment
Process
Limiting Characteristics
Large particles interfere
Clayey soils or hardpan
difficult to handle
Wet soils difficult
to handle
Applicable only to volatile
organics w/significant vapor
pressure >1 mm Hg
Low soil permeability inhibits
air movement
Soil hydraulic conductivity
>1E-8 cm/sec required
Depth to ground water
>20 ft recommended
High moisture content
inhibits air movement
High organic matter
content inhibits
contaminant removal
Applicable only to
specific organics
Hydraulic conductivity
>1 E-4 cm/sec preferred
to transport nutrients
Stratification should be
minimal
Lower permeability layers
difficult to remediate
Temperature 15-45°C
required
Moisture content 40-80%
of that at -1/3 bars tension
preferred
pH 4.5-3.5 required
Presence of microbes
required
Minimum 10% air-filled
porosity required for
aeration
Applicable only to organics
Soil moisture content
affects handling and
Site Data
Required
Particle size
distribution
Soil moisture content
Contaminants
present
Soil permeability
Hydraulic
conductivity
Depth to ground water
Soil moisture content
Organic matter content
Contaminants present
Hydraulic conductivity
Soil stratigraphy
Soil stratigraphy
Soil temperature
Soil moisture
characteristic curves
Soil pH
Plate count
Porosity and soil
moisture content
Contaminants present
Soil moisture content
Technology
Thermal treatment
(continued)
Solidification/
stabilization
Chemical
extraction
(slurry reactors)
Soil washing
Soil flushing
Glycolate
dechlorination
Chemical oxidation/
reduction (slurry
reactor)
In situ
vitrification
Process
Limiting Characteristics
Particle size affects
feeding and residuals
pH <5 and >1 1 causes
corrosion
Not equally effective for
all contaminants
Fine particles < No. 200
mesh may interfere
Oil and grease >10%
may interfere
Not equally effective
for all contaminants
Particle size <0.25 in.
pH<10
Not equally effective
for all contaminants
Silt and clay difficult
to remove from wash
fluid
Not equally effective
for all contaminants
Required number of
pore volumes
Not equally effective
for all contaminants
Moisture content <20%
Low organic matter
content required
Not equally effective
for all contaminants
Oxidizable organics
interfere
pH <2 interferes
Maximum moisture
content of 25% by weight
Particle size <4 inches
Requires soil hydraulic
conductivity <1 E-5 cm/sec
Site Data
Required
Particle size
distribution
PH
Contaminants
present
Particle size
distribution
Oil and grease
Contaminants
present
Particle size
distribution
PH
Contaminants
present
Particle
size distribution
Contaminants
present
Infiltration rate
and porosity
Contaminants
present
Moisture content
Organic carbon
Contaminants
present
Organic carbon
pH
Moisture
content
Particle size
distribution
Hydraulic conductivity

                                           11

-------
many remedial alternatives (Table 4). Soil reaction character-
istics include:

      Kd, specific to the site soils and contaminants
      Cation exchange capacity (CEC)
      Eh
      pH
      Soil biota
      Soil nutrient content
      Contaminant abiotic/biological degradation rates
      Soil mineralogy
      Contaminant properties, described  below, and others.

Soil reaction  characteristics  determine the  effectiveness  of
many remedial alternatives. For example, the ability of a soil to
attenuate metals (typically described by Kd) may determine the
effectiveness of an alternative that relies on capping
and natural attenuation to immobilize contaminants.

Soil Contaminant Properties

Contaminant properties are critical to contaminant-soil interac-
tions, contaminant mobility,  and to the ability of treatment
technologies to remove,  destroy or  immobilize contaminants.
Important contaminant properties include:

      Water solubility
      Dielectric constant
      Diffusion coefficient
     KH
     Molecular weight
     Vapor pressure
     Density
     Aqueous solution chemistry, and others.

Soil contaminant properties will determine the effectiveness of
many treatment techniques. For example, the aqueous solution
chemistry of metal contaminants often dictates the  potential
effectiveness of stabilization/solidification alternatives.

Soil Engineering Characteristics and Properties

Engineering characteristics and properties of the soil relate both
to implementability and effectiveness of the remedial action.
Examples include the ability of the treatment method to remove,
destroy or immobilize contaminants; the costs and difficulties in
installing slurry walls and other containment options at depths
greater than 60 feet; the ability of the site to withstand vehicle
traffic (trafficability); costs and difficulties in deep excavation of
contaminated soil; the ability of soil to be worked for implemen-
tation  of in situ treatment technologies (tilth); and others.
Knowledge of site-specific engineering  characteristics and
properties is therefore required for analysis of effectiveness and
implementability of remedial alternatives. Engineering charac-
teristics and properties include, but are not limited to:

     Trafficability
     Erodability
     Tilth
     Depth to groundwater
     Thickness of saturated zone
     Depth and total volume of contaminated soil
     Bearing capacity, and others.
SUMMARY AND CONCLUSIONS

The goal of the CERCLA RI/FS process is to reach a ROD in a
timely manner. Soil characterization is critical to this goal. Soil
characterization provides data for RI/FS tasks including deter-
mination of the nature and extent of contamination,  risk as-
sessment, and selection  of remedial techniques.

This paper is intended to inform investigators of the data types
required for  RI/FS tasks, so that data may be collected as
quickly, efficiently, and  cost  effectively  as possible.  This
knowledge should improve the consistency of site evaluations,
improve the  ability of OSCs and RPMs to communicate data
needs to site contractors, and aid in the overall goal of reaching
a ROD in a timely manner.
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ASTM, 1987.  American Society for Testing and Materials,
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ASTM, 1987.  American Society for Testing and Materials.
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Andersson, J. and A. M. Shapiro, 1983. "Stochastic Analysis of
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                                                       12

-------
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