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).
-------
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).
-------
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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-------
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