Ecological Soil Screening Level
Guidance
DRAFT
U.S. Environmental Protection Agency
Office of Emergency and Remedial Response
1200 Pennsylvania Avenue, N.W.
Washington, DC 20460
July 10, 2000
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ACKNOWLEDGMENTS
The development of this guidance was a team effort led by the U.S. Environmental Protection Agency Office of
Emergency and Remedial Response (OERR). A Steering Committee coordinated the activities of four task groups.
Listed below are the members of the Steering Committee and each task group.
Steering Committee
Steve Ells, U.S. EPA OERR, Co-Chair
Ralph Stahl, DuPont, Co-chair
Randy Wenstel, U.S. EPA ORD, Co-chair
Bill Adams Kennecott Utah Copper
Doris Anders U. S. Air Force Center for Environmental Excellence (AFCEE)
John Bascietto U. S. Department of Energy (DOE)
David Charters U. S. EPA OERR
Ron Checkai U.S. Army Edgewood Contaminant Biological Center (ECBC)
Dale Hoff U. S. EPA, Region 8
Charlie Menzie Menzie-Cura & Associates
Brad Sample CffiMHill
Jason Speicher North Division of the Naval Facilities Engineering Command
Mike Swindoll Exxon Biomedical Sciences, Inc.
Task Group on Wildlife Toxicity Reference Values
Dale Hoff, U.S. EPA, Co-Chair
Doris Anders, U. S. Air Force, Co-Chair
Nelson Beyer U.S. Geological Survey
Janet Burris ISSI Consulting Group, Inc.
David Charters U. S. EPA OERR
David Cozzie U.S. EPA Office of Solid Waste
Dave Cragin Elf-Atochem North America
Steve Dole Exponent
Anne Fairbrother Parametrix
Bob Fares Environmental Standards, Inc.
Gary Friday Westinghouse Savannah River Co.
Kinzie Gordon Parsons Engineering Science, Inc.
Gerry Henningsen U.S. EPA, Region 8
Mark Johnson U.S. Army Center for Health Promotion and Preventative Medicine
Paul Kuhlmeier Dames & Moore
Jackie Little TN & Assoc.
Patricia Newell Environmental Health Associates, Inc.
Drew Rak U. S. Army Corps of Engineers, Baltimore District
Linda Schmeising Exponent
Lynn Woodbury ISSI Consulting Group, Inc.
Julie Yamamoto California EPA, Office of Environ. Health Hazard Assessment
Task Group on Soil Chemistry
Randy Wentsel, U.S. EPA, Co-Chair
Charlie Menzie, Menzie-Cura & Associates, Co-Chair
Bill Berti DuPont
Mary Goldade U. S. EPA, Region 8
Roman Lanno Oklahoma State University
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Charles R. Lee U.S. Army Corps of Engineers, Engineering Research and Development Center,
Waterways Experiment Station
Linda Lee Purdue University
Mike Ruby Exponent
John Samuelian OGDEN Environmental and Energy Services
Task Group on Soil Invertebrates and Plants
Ron Checkai, U.S. Army ECBC, Co-Chair
Mike Swindell, Exxon Biomedical Sciences, Inc., Co-Chair
David Barclift North Division of the Naval Facilities Engineering Command
Ron Checkai U. S. Army ECBC
William Desmond Tetra Tech EM, Inc.
Steve Ells US.EPA,OERR
Stiven Foster ISSI Consulting Group, Inc.
Dave Gannon Zeneca Corp.
Andrew Green International Lead Zinc Research Org. (ILZRO)
Larry Kapustka ecological, planning & toxicology, Inc.
Roman Kuperman U.S. Army ECBC
Daniel Mazur U. S. EPA, Region 5
Chris Russom U.S. EPA, ORD
Jason Speicher North Division of the Naval Facilities Engineering Command
Gladys Stephenson ESG International, Ltd. / University of Guelph
Task Group on Exposure Models for Wildlife Species
John Bascietto, U.S. Department of Energy (DOE), Co-Chair
Brad Sample, CffiMHill, Co-Chair
Amber Brenzikofer Parsons ES
Bridgette Deshields Harding Lawson Associates
Will Gala Chevron
Tracy Hammon ISSI Consulting Group, Inc.
Rob Pastorok Exponent
Phillip Rury Arthur D. Little, Inc.
Randy Ryti Neptune and Co.
William Schew Environmental Standards, Inc.
Ralph Stahl DuPont
Jeff Yurk U. S. EPA, Region 6
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TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLES
LIST OF APPENDICES
LIST OF EXHIBITS ON THE WEBSITE
LIST OF ACRONYMS AND ABBREVIATIONS
EXECUTIVE SUMMARY
1.0 INTRODUCTION 1-1
1.1 Scope of the Eco-SSLs 1-4
1.2 Peer Review Process 1-7
2.0 SOIL PROPERTIES 2-1
2.1 Introduction 2-1
2.2 Soil Properties Influencing Contaminant Bioavailability 2-1
2.3 Using Soil Properties to Guide Eco-SSL Derivation 2-7
3.0 DERIVATION OF PLANT AND SOIL INVERTEBRATE ECO-SSLs 3-1
3.1 Literature Search, Acquisition, and Acceptability 3-2
3.2 Literature Evaluation 3-5
3.3 Identification of Data for Derivation of Eco-SSLs 3-5
3.4 Quality Control Review 3-7
3.5 Calculation of the Plant and Soil Invertebrate Eco-SSLs 3-7
4.0 DERIVATION OF WILDLIFE ECO-SSLs 4-1
4.1 The Wildlife Risk Model for Eco-SSLs 4-1
4.2 Selection of Surrogate Wildlife Species 4-2
4.3 The Exposure Dose 4-4
4.4 Toxicity Reference Values (TRVs) 4-9
4.5 Calculation of Wildlife Eco-SSLs 4-18
5.0 ECO-SSL SUMMARIES 5-1
5.1 Antimony 5-1
5.2 Arsenic 5-3
5.3 Cadmium 5-7
5.4 Chromium 5-10
5.5 Cobalt 5-14
5.6 Copper 5-16
5.7 Dieldrin 5-19
5.8 RDX 5-21
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5.9 Zinc 5-23
5.10 Aluminum 5-26
6.0 USING ECO-SSLs TO SCREEN CONTAMINATED SOILS 6-1
6.1 Comparing the Site Conceptual-Model to the General Eco-SSL Model 6-1
6.2 Comparing Site Soil Concentrations to the Eco-SSLs 6-3
6.3 Consideration of Background Soil Concentrations 6-5
7.0 SITE-SPECIFIC CONSIDERATIONS FOR MODIFYING THE ECO-SSLS 7-1
7.1 Site-Specific Considerations for Wildlife 7-1
7.2 Site-Specific Considerations for Plants and Invertebrates 7-8
7.3 Site Specific Applications of Soil Chemistry Data 7-9
7.4 Soil Sampling Data Requirements 7-10
7.5 Soil Properties Suggested for Routine Measurement 7-10
7.6 Site-Specific Considerations for Wetlands 7-13
8.0 REFERENCES 8-1
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LIST OF FIGURES
Title Page
Figure 1.1 Eight Step Process Recommended in Ecological Risk Assessment Guidance for
Superfund (ERAGS) 1-2
Figure 1.2 Eco-SSL Contaminants 1-4
Figure 3.1 Literature Exclusion Categories 3-3
Figure 3.2 Summary of Literature Acceptance Criteria 3-4
Figure4.1 The Wildlife Risk Model for Eco-SSLs 4-2
Figure 4.2 Summary of Method Used for Estimation of Contaminant Concentrations
in Biota Types (B,) 4-7
Figure 4.3 Comparison of Mean Concentrations in Multiple Species near a Smelter 4-8
Figure 4.4 Wildlife TRY Derivation Process 4-9
Figure 4.5 Ten Attributes Scored as Part of the Wildlife Toxicological Data
Evaluation 4-12
Figure 4.6 Example of Mammalian TRV Derivation forDieldrin 4-16
Figure 4.7 TRV Derivation Process 4-17
Figure 7.1 Bioavailability Issues in Wildlife 7-6
Figure 7.2 Incorporating Bioavailability into Exposure Estimates 7-7
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LIST OF TABLES
Title Page
Table 2.1 General Contaminant Classification 2-2
Table 2.2 Log Kow Values for Organic Contaminants 2-5
Table 2.3 Qualitative Bioavailability of Metal Cations in Natural Soils 2-9
Table 2.4 Qualitative Bioavailability of Organic Contaminants in Natural Soils 2-9
Table 2.5 Qualitative Bioavailability of Anionic Species for Natural Soils 2-9
Table 3.1 Literature Evaluation Criteria for Plant and Soil Invertebrate Eco-SSLs 3-6
Table 3.2 Plant and Soil Invertebrate Eco-SSL Derivation Table 3-7
Table 3.3 Plant and Soil Invertebrate Eco-SSL Documents 3-8
Table 4.1 Parameterization of the Eco-SSL Wildlife Exposure Model 4-5
Table 4.2 Cases where the 90th Percentile of the BAF Distribution is Greater
or Less than One 4-9
Table 4.3 Results of the Wildlife Toxicological Literature Search and Review 4-11
Table 4.4 Example of Extracted and Scored Toxicity Data for Wildlife 4-14
Table 7.1 Use of Site-Specific Soil Toxicity Tests for Modifying Screening Levels for
Metal Cations Under Designated Soil Conditions 7-10
Table 7.2 Minimum Bulk Density Values for Which Plant Roots May
be Restricted for Various Soil Textures 7-13
Table 7.3 Recommended Application of Eco-SSLs and/or Sediment Benchmarks
toNWI Categories of Wetlands and Deepwater Habitats 7-16
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LIST OF APPENDICES
Appendix 3-1
Appendix 3-2
Appendix 3-3
Appendix 4-1
Appendix 4-2
Appendix 4-3
Appendix 4-4
Appendix 4-5
Appendix 4-6
Plant and Soil Invertebrate Standard Operating Procedure #3: Literature
Evaluation and Data Extraction
Plant and Soil Invertebrate Standard Operating Procedure #4: Eco-SSL
Derivation, Quality Assurance Review, and Technical Write-up
Completed Literature Evaluation Scoring Sheets for Studies Used to
Derive Plant and Soil Invertebrate Eco-SSLs
Exposure Factors and Bioaccumulation Models for Derivation of Wildlife
Eco-SSLs
Estimation of Exposure Doses and Soil Contaminant Concentrations
Associated with an HQ = 1
Wildlife TRY Standard Operating Procedure # 2: Literature Review, Data
Extraction and Coding
Wildlife TRV Standard Operating Procedure # 3: Data Evaluation
Wildlife TRV Standard Operating Procedure #4: TRV Derivation
Wildlife TRVs for Derivation of Eco-SSLs
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Exhibit 1-1
Exhibit 1-2
Exhibit 1-3
Exhibit 1-4
Exhibit 3-1
Exhibit 3-2
Exhibit 3-3
Exhibit 3-4
Exhibit 4-1
Exhibit 5-1
Exhibit 5-2
Exhibit 7-1
Exhibit 7-2
LIST OF EXHIBITS ON THE WEBSITE
http://www.epa.gov/oerrpage/superfund/programs/risk/
Review of Existing Soil Screening Guidelines
Discussion Concerning Soil Microbial Processes
Review of Dermal and Inhalation Exposure Pathway for Wildlife
Peer Review Process, Results, and Resolutions
Plant and Soil Invertebrate Standard Operating Procedure #1: Literature
Search and Acquisition
Plant and Soil Invertebrate Standard Operating Procedure #2: Literature
Review
Reference List of Papers Identified by Literature Searches for Plants and
Soil Invertebrates
Reference List of Acceptable Papers for Plants and Soil Invertebrates
Wildlife TRV Standard Operating Procedure # 1: Literature Search and
Retrieval
Review of Background Concentrations for Metals
Review of Aluminum Chemistry and Toxicity in Soil
Bioavailability Issues for Wildlife
Summary of Soil Toxicity Testing Methods
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LIST OF ACRONYMS AND ABBREVIATIONS
AUF
AFSJ
BO,
BAF
BMD
BTAG
BW
CCME
CEC
CERCLA
CLP
COPC
CSEM
CSM
DDT
DQO
DW
EA
Eco-SSL
EPA
EPC
ERA
ERAGS
FIR
HQ
HQ,
IRsoil
kg
LOEC
LOAEL
mg
NOAEL
NOEC
NPL
OERR
Area use factor
Absorbed fraction of contaminant (j) from biota type (i)
Absorbed fraction of contaminant (j) from soil (s)
Contaminant concentration in biota type (i)
Intercept from log-linear bioaccumulation model for contaminant (j) for
biota type (i)
Slope from log-linear bioaccumulation model for contaminant (j) for biota
type (i)
Bioaccumulation factor
Benchmark dose
Biological Technical Assistance Group
Body weight
Canadian Council of Ministers of the Environment
Cation Exchange Capacity
Comprehensive Environmental Response, Compensation, and Liability
Act
Contract Laboratory Program
Contaminant of Potential Concern
Conceptual Soil Exposure Model
Conceptual Site Model
1,1,1 -Trichloro-2,2-bis(p-chlorophenyl)ethane
Data Quality Objective
Dry weight
Exposure area
Ecological Soil Screening Level
U.S. Environmental Protection Agency
Exposure Point Concentration
Ecological Risk Assessment
Ecological Risk Assessment Guidance for Superfund
Food ingestion rate
Hazard Quotient
Hazard Quotient for contaminant (j)
Soil ingestion rate
kilograms
Lowest-Observed Effect Concentration
Lowest-Observed Adverse Effect Level
milligrams
No-Observed Adverse Effect Level
No-Observed Effect Concentration
National Priorities List
Office of Emergency and Remedial Response
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OM
ORNL
OSWER
Pi
Ps
PAH
PCB
QA/QC
RCRA
RDX
RI
RI/FS
RTVM
RME
ROD
SAB
SAP
SMDP
SOP
SSL
T,
TNT
TRY
VOC
Organic Matter
Oak Ridge National Laboratory
Office of Solid Waste and Emergency Response
Proportion of biota type (i) in diet
Soil ingestion as proportion of diet
Polycyclic aromatic hydrocarbon
Polychlorinated biphenyl
Quality Assurance/Quality Control
Resource Conservation and Recovery Act
Hexahy dro-1,3,5 -trinitro-1,3,5 -triazine
Remedial Investigation
Remedial Investigation/Feasibility Study
Dutch National Institute of Public Health and the Environment
Reasonable Maximum Exposure
Record of Decision
Science Advisory Board
Sampling and Analysis Plan
Scientific Management Decision Point
Standard operating procedure
Soil screening level
Soil-to-biota bioaccumulation factor for contaminant (j) for biota type (i)
Trinitrotoluene
Toxicity Reference Value
Volatile organic compound
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1.0 INTRODUCTION
This guidance provides a set of risk-based soil screening levels (Eco-SSLs) for many of the soil
contaminants that are frequently of ecological concern for terrestrial plants and animals at hazardous
waste sites. It also describes the process used to derive these levels and provides guidance for their
use. The Eco-SSL derivation process represents the collaborative effort of a multi-stakeholder
workgroup consisting of federal, state, consulting, industry and academic participants led by the U.S.
Environmental Protection Agency (EPA), Office of Emergency and Remedial Response (OERR). The
workgroup developed the following mission statement at the initiation of the Eco-SSL project:
Develop a set of generic, scientifically sound, ecologically based, soil screening levels that
are protective of the terrestrial environment for up to 24 contaminants of concern; and
methodologies and models that use site-specific exposure data to modify these screening
levels. The screening levels and methodologies should be sufficiently specific and
transparent to allow for consistent implementation by EPA and other Federal Agencies,
States, and private parties at all Superfund sites.
The Eco-SSLs are screening values that can be used routinely to identify those contaminants of
potential concern (COPCs) in soils requiring further evaluation in a baseline ecological risk assessment
(ERA) The Eco-SSLs are not designed to be used as cleanup levels and EPA emphasizes that
it would be inappropriate to adopt or modify these Eco-SSLs as national cleanup standards.
This document provides guidance and is designed to communicate national policy on identifying
contaminants in soil that may present an unacceptable ecological risk to terrestrial receptors. The
document does not, however, substitute for EPA's statutes or regulations, nor is it a regulation itself.
Thus, it does not impose legally-binding requirements on EPA, states, or the regulated community, and
may not apply to a particular situation based upon the circumstances of the site. EPA may change this
guidance in the future, as appropriate.
What are Eco-SSLs?
Eco-SSLs are concentrations of contaminants in soils that are protective of ecological receptors that
commonly come into contact with soil or ingest biota that live in or on soil. Eco-SSLs are derived
separately for four groups of ecological receptors, plants, soil invertebrates, birds and mammals. As
such, these values are presumed to provide adequate protection of terrestrial ecosystems.
These screening levels should be used in the ERA process to identify the COPCs that require further
evaluation in the site-specific baseline risk assessment. This Eco-SSL guidance is written with the
assumption that the reader is familiar with Superfund's guidance on performing ERAs (ERAGS, U.S.
EPA, 1997, Figure 1.1) and with the EPA risk assessment guidelines (U.S.EPA, 1998).
DRAFT 1 - 1 June 27, 2000
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Figure 1.1. Eight Step Process Recommended in Ecological Risk Assessment Guidance
for Superfund (ERAGS) (U.S. EPA, 1997)
o
o
U
STEP1: SCREENING LEVEL:
• Site Visit
• Problem Formulation
' Toxicity Evaluation
STEP 2: SCREENING LEVEL:
' Exposure Estimate
• Risk Calculation
STEP 3: PROBLEM FORMULATION
Toxicitv Evaluation
Assessment
Endpoints
Conceptual Model
Exposure Pathways
Questions/Hypotheses
STEP 4: STUDY DESIGN AND DQO
PROCESS
' Lines of Evidence
' Measurement Endpoints
Work Plan and Sampling and Analysis Plan
STEPS: VERIFICATION OF FIELD
SAMPLING DESIGN
STEP 6: SITE INVESTIGATION AND
DATA ANALYSIS
STEP 7: RISK CHARACTERIZATION
STEPS: RISK MANAGEMENT
Risk Assessor and
Risk Manager
Agreement
SMDP = Scientific Management Decision Point
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The Eco-SSLs presented here should be used during Step 2 of the Superfund ERA process, the
screening-level risk calculation. This step normally is completed at a time when limited soil
concentration data are available, and other site-specific data (e.g., contaminant bioavailability
information, area use factors) are not available. It is expected that the Eco-SSLs will be used to screen
the site soil data to identify those contaminants that are not of potential ecological concern and do not
need to be considered in the subsequent baseline ERA. The Eco-SSLs are intentionally conservative in
order to provide confidence that contaminants which could present an unacceptable risk are not
screened out early in the ERA process. EPA recognizes that for many soil types and conditions, the
Eco-SSLs may be conservative, but none the less, provide an appropriate balance of protectiveness
and reasonableness.
Why are Eco-SSLs Needed?
EPA derived the Eco-SSLs in order to conserve
resources by eliminating the need for EPA, state,
contractor, and other federal risk assessors to
perform repetitious toxicity-data literature
searches and toxicity data evaluations for the
same contaminants at every site. These
Eco-SSLs will also increase consistency among
screening risk analyses, decrease the possibility
that potential risks from soil contamination to
ecological receptors will be overlooked, and
allow risk assessors to focus their resources on
identifying key site studies needed for critical
decision-making.
In the process of deriving the Eco-SSLs, the
stakeholder workgroup examined currently
available soil screening guidelines (see text box)
for their use within the Superfund process.
Because these existing guidelines were
developed in response to country-specific
legislation and policies not totally consistent with
current EPA policies, EPA chose not to adopt
any established set of values. A summary and evaluation of the available guidelines is available from the
Eco-SSL Web Site [http://www.epa.gov/oerrpage/superfund/programs/risk/ecossl] as Exhibit 1-1.
Some Other Available Soil Screening Guidelines
Canadian Council of Ministers of the Environment
(CCME) Canadian Soil Quality Guidelines (SQGs).
The CCME guidelines are numerical limits for
contaminants intended to maintain, improve or protect
environmental quality and human health. They are
intended for use in the assessment and remediation of
contaminants at sites in Canada (CCME, 1997a).
The Dutch National Institute of Public Health and
the Environment (RIVM). Maximum permissible
concentrations (MFCs), maximum permissable additions
(MPAs) and negligible concentrations (NCs) were
developed in a series of reports for soils, sediments and
water for metals and pesticides (RIVM, 1997a and
1997b).
Oak Ridge National Laboratory (ORNL). A series of
reports have been issued from ORNL that provide
screening levels for plants (Efroymson et al., 1997a), soil
invertebrates and microbial processes (Efroymson et al.,
1997b), wildlife (Sample et al., 1996), and sediments
(Jones etal., 1997).
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How Were the Eco-SSLs Derived?
Eco-SSLs were derived by the work groups using standardized procedures for literature review,
toxicity data selection, and data evaluation. Where acceptable data were judged to be adequate, four
Eco-SSLs were derived for each contaminant, one each for plants, soil invertebrates, birds and
mammals.
Plant and soil invertebrate Eco-SSL values were derived directly from an evaluation of available plant
and soil invertebrate toxicity test data (measured toxicity related to soil contaminant concentrations), as
described in Chapter 3. The process for deriving mammalian and avian Eco-SSLs is described in
Chapter 4.0. The wildlife Eco-SSLs are the result of back-calculations from a Hazard Quotient (HQ)
of 1.0. The HQ is equal to the estimated exposure dose divided by a toxicity reference value (TRY).
An HQ of 1.0 is the condition where the exposure and the dose associated with no adverse effects are
equal, indicating adverse effects at this soil concentration are unlikely. A generic food-chain model was
used to estimate the relationship between the concentration of the contaminant in soil and the dose for
the receptor (mg per kg body weight per day). The TRV represents a receptor-class specific estimate
of a no-observed-adverse-effect level (NOAEL) (dose) for the respective contaminant.
1.1 Scope of the Eco-SSLs
Contaminants Considered
EPA prepared a list of twenty-four (24)
contaminants to be addressed initially by the
Eco-SSL guidance. This list was based on a
review of the contaminants of concern reported
to be the subject of soil remediation in recent
Record of Decisions (ROD) at Superfund
National Priority List sites. The Eco-SSL
contaminant list also includes contaminants
nominated by the EPA regional Biological
Technical Assistance Groups (BTAGs). The
list of 24 Eco-SSL contaminants contains 17
metals and seven organics (see Figure 1.2).
The omission of other contaminants, such as
phthalates and cyanides, does not imply that all
these contaminants can be excluded from the
ERA screening process for soil contamination,
only that these 24 contaminants have
historically been of greatest ecological concern
Figure 1.2. Eco-SSL Contaminants
Organics
• Dieldrin
• Total Poly chlorinated Biphenyls (PCBs)
• Hexahydro-l,3,5-trinitro-l,3,5-triazine (RDX)
• Trinitrotoluene (TNT)
• 1,1,1 -Trichloro-2,2-bis(p-chlorophenyl)ethane (DDT)
• Pentachlorophenol (PCP)
• Polycyclic Aromatic Hydrocarbons (PAHs)
Metals
Aluminum
Antimony
Arsenic
Barium
Beryllium
Cadmium
Chromium
Cobalt
Copper
Iron
Lead
Manganese
Nickel
Selenium
Silver
Vanadium
Zinc
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in soil The process and procedures established for the Eco-SSLs are intended to be
sufficiently transparent to derive Eco-SSL values for additional contaminants, as needed.
Ecological Receptors of Concern
The Eco-SSLs apply only to sites where terrestrial receptors may be exposed directly or indirectly to
contaminated soil. Seven groups of ecological receptors were initially considered in the development of
the Eco-SSLs. These included mammals, birds, reptiles, amphibians, soil invertebrates, terrestrial
plants and soil microbial processes. After investigation, the toxicity data for amphibians and reptiles
were deemed insufficient to derive Eco-SSLs. Eco-SSLs protective of soil microbial processes have
not been derived here either. Like amphibians and reptiles, the agency recognizes their importance
within terrestrial systems, but concurs with the workgroup recommendation that data are insufficient and
the interpretation too uncertain for establishing risk-based thresholds in a regulatory context. While
Eco-SSLs for microbial processes are not established at this time, they may be considered in the future
as the science develops and appropriate studies are completed. Exhibit 1-2 provides the discussion
concerning establishing Eco-SSLs for soil microbial processes.
Eco-SSLs were derived for four general groups of ecological receptors: mammals, birds, terrestrial
plants and soil invertebrates. By deriving conservative soil screening values protective of these groups,
it is assumed that the terrestrial ecosystem will be protected from possible adverse effects associated
with soil contamination. This is consistent with the use of "generic assessment endpoints" as discussed
in Section 1.2.5 of ERAGS.
Exposure Pathways for Ecological Receptors
A complete exposure pathway is defined in ERAGS as "one in which the contaminant can be traced or
expected to travel from the source to a receptor that can be affected by the contaminant". If any of
these conditions are missing, the pathway is considered to be incomplete. Exposure pathways can be
classified as incomplete, complete, or potentially complete. An exposure pathway is not considered
complete if habitat for ecological receptors is not present.
The Eco-SSLs for terrestrial plants consider direct contact of contaminants in soils under conditions of
high bioavailability. The Eco-SSLs for soil invertebrates consider ingestion of soil and direct contact
exposures also under conditions of high bioavailability.
The Eco-SSLs for birds and mammals consider two potentially complete exposure pathways: 1)
incidental ingestion of soils during feeding, grooming and preening; and 2) ingestion of food
contaminated as a result of the uptake of soil contaminants. The exposure model for wildlife is fully
described in Chapter 4. Two potentially complete exposure pathways (dermal contact and inhalation)
were not considered in the derivation of wildlife Eco-SSLs for the 24 selected contaminants. The
rationale for this decision is summarized in the following bullets:
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Burrowing animals could be exposed to relatively high concentrations of volatile organic
compounds (VOCs) in their burrows via inhalation. With the exception of some of the PAHs,
none of the Eco-SSL contaminants are VOCs and this exposure pathway was not considered.
However, at sites with high VOC and/or certain PAH concentrations in soils with burrowing
mammals present, the inhalation exposure pathway may need to be considered in the baseline
ERA.
Soil particles containing non-VOC contaminants (by either adsorption or absorption) could also
be inhaled by wildlife. Respirable particles (>5 um) are, however, most likely ingested as a
result of mucocilliary clearance rather than being inhaled (Witschi and Last, 1996). As
discussed in Exhibit 1-3, at equal exposure concentrations inhalation of contaminants associated
with dust particles is expected to contribute less than 0.1% of total risk compared to oral
exposures.
Birds and mammals may also be exposed to contaminants in soils via dermal contact. Studies
investigating dermal exposures to birds resulting from the application of pesticides by spray to
tree branches have shown this exposure route to be significant relative to oral exposures for
some substances; e.g. organophosphate pesticides, (Abou-Donia and Graham 1978, Driver et
al. 1991, and Henderson et al. 1994). However, current information is insufficient to evaluate
dermal exposure for the 24 selected Eco-SSL contaminants in various soil matrices, or to
predict possible rates of absorption for many species. For most contaminants, the dermal
exposure is expected to contribute less than 1% to 11% of the total risk (Exhibit 1-3)
compared to oral exposures.
This approach is consistent with Section 9.2.4 of
ERAGS, which states that the ingestion route is most
important for terrestrial animals and that "although
other exposure routes can be important, more
assumptions are needed to estimate exposure levels
for these routes, and the results are less certain."
Exclusion of dermal and inhalation exposure routes for
these Eco-SSLs does not preclude their inclusion in
the site-specific baseline ERA. If it is expected that
receptors may be more exposed to some
contaminants via dermal and/or inhalation exposures
relative to oral exposures due to site-specific
conditions, these exposure routes should be evaluated
as part of the baseline ERA.
Exposure Pathways Considered
in Eco-SSLs
Birds and Mammals
Ingestion of soils during
grooming, feeding and preening
Ingestion of food contaminated
as a result of uptake of soil
contaminant
Plants
Direct contact
Soil Invertebrates
Direct contact
Soil ingestion
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Soil Types for Which Eco-SSLs are Applicable
Eco-SSLs are applicable to all sites where key soil parameters fall within a certain range of chemical
and physical parameters. The Eco-SSLs apply to soils where: the pH is greater than or equal to 4.0
and less than or equal to 8.5 and the organic matter content is less than or equal to 10%.
The Eco-SSLs are intended for use in upland soils. However, they may also be useful for screening
wetland soils. The wildlife Eco-SSLs are derived for several general receptor groups that are likely to
be representative of wildlife found in wetlands. A major caveat, however, is the omission of the
amphibians and reptiles from derivation of the wildlife Eco-SSLs. These groups could be especially
important in wetlands. The Eco-SSLs for plants and soil invertebrates are broadly applicable (i.e.,
conservative enough for most soils) as preference was given to studies with high bioavailability of the
chemicals in soils. For this reason, the Eco-SSLs for plants and soil invertebrates may be useful for
screening for contaminants in wetland soils. In general, wetland soils are expected to exhibit a lower
bioavailability (compared to those used to derive Eco-SSLs) as a result of the high organic content.
Site-specific considerations related to the presence of wetland soils and sediments are discussed in
Chapter 7.
Based on these stated parameters, it is expected that there are certain soils and situations to which
Eco-SSLs may not apply. These situations include (but may not be limited to):
Wetland soils that are regularly flooded, i.e., are sediments
• Sewage sludge amended soils where the % Organic Matter (OM) is > 10%
Waste types where the pH is < 4.0.
1.2 Peer Review Process
Two peer reviews were performed during the development of the Eco-SSLs. The first was a
consultation requested by EPA's Office of Solid Waste and Emergency Response of EPA's Science
Advisory Board (SAB). This consultation was held April 6, 1999, at which time members of the SAB
provided verbal comments to several members of the Eco-SSL Steering Committee. A peer review of
the draft guidance document was also performed. The peer review workshop was held on July 26 and
27, 2000 and was open to the public. The results of this peer review are summarized in
^ which is included as Exhibit 1-4.
DRAFT 1 - 7 June 27, 2000
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2.0 SOIL PROPERTIES
2.1 Introduction
Soil properties influence the exposure of invertebrates, plants, and wildlife to contaminants in
soils. Therefore, they are important to consider in the development of Eco-SSLs and to provide a
basis for guiding site-specific evaluations that may follow application of Eco-SSLs. This chapter
discusses the primary soil parameters that influence bioavailability of contaminants from soils.
The soil parameter information provides the rationale for defining a set of soil parameters used in
the selection of the most appropriate studies for deriving Eco-SSLs for plants and soil
invertebrates and specific recommendations for screening soils for aluminum and iron.
This chapter focuses primarily on the relationship between soil chemistry factors that influence
the exposure to and accumulation of contaminants in plants and soil invertebrates. The
absorption of contaminants bound to incidentally ingested soil particles in the animal gut, is
influenced by other parameters including residence time as well as toxicokinetic and
physiological factors that may affect the uptake of contaminants in wildlife.
2.2 Soil Properties Influencing Contaminant Bioavailability
Bioavailability is a measure of the potential for entry of the contaminant into ecological or
human receptors and is specific to the receptor, the route of entry, time of exposure, and the soil
matrix containing the contaminant (Anderson et al., 1999). In order to insure that Eco-SSLs are
adequately conservative for a broad range of soils, an effort was made to select studies that
favored the bioavailability of the selected contaminants. To accomplish this, it was first
necessary to develop a basic understanding of how various soil properties may influence
bioavailability. Several authors have stressed the importance of physical and soil properties on
the bioavailability of contaminants in soils and the influence they have on exposure (Linz and
Nakles, 1997; Alexander, 1995; Loehr and Webster, 1996; Allen et al., 1999). The behavior and
bioavailability of contaminants are greatly influenced by their interactions with soil constituents,
such that not all contaminants are equally available to biota. However, relating soil chemistry
parameters as important factors in estimating the availability of metals and organic contaminants
in soil to soil biota and plant toxicity is not a straightforward process.
The accessibility or availability of contaminants depends on specific physical and geochemical
binding mechanisms that vary among contaminants and soil types. Contaminants interact with
soil through interactions with the surface of particulate material in soils (adsorption), by
penetration through the particulate surfaces where the contaminant becomes associated with the
internal material (absorption or partitioning), and through specific contaminant reactions
sometimes referred to as chemisorption. Also some contaminants, in particular metals, can
associate with inorganic ligands and precipitate. The affinity of a contaminant to be associated
with soil particulates, thus removed from solution, irrespective of mechanism is generally
referred to as "sorption". The exception are precipitation reactions, which are often discussed
independently from generic sorption processes. Contaminants are generally considered to be
DRAFT 2 - 1 June 27, 2000
-------
bioavailable when they are released from interactions with the soil and soil constituents, thus
released into the pore-water. The exception to this rule is the direct ingestion by terrestrial
wildlife.
Identifying and quantifying soil factors that control the distribution of a contaminants in
soil/water systems at equilibrium is useful for exposure situations where time is sufficient for
equilibrium conditions to develop. For exposure situations that are dominated by discrete events
often of short duration (e.g., incidental ingestion of soil), the kinetics of contaminant release from
soils into another medium (i.e., the amount released per unit time) and residence time (i.e., time
allowed for transfer to occur) controls the fraction of a contaminant that would be labile to target
biota. Both adsorption and absorption partitioning processes are considered reversible, although
mass transfer from the particle to the pore-water can be constrained. In the case of interactions
within a particle, a contaminant can become sequestered or trapped through various physical and
contaminant alterations that occur over time, such that contaminant release is completely
constrained. The decline of the availability of many organic contaminants in soil over months or
years has been well-documented (Alexander, 1995; Loehr and Webster, 1996). For
chemisorption, the binding mechanism is considered irreversible under most environmental
conditions. For precipitation reactions, release to pore-water is controlled by the factors affecting
the stability or solubility of the contaminant precipitate. Overall, bioavailability of a contaminant
in soil strongly depends on its physical and chemical properties, the characteristics of the soil, the
interactions between the contaminant and the medium, including time of exposure, and the
physiological and biochemical conditions of the receptor.
Contaminant Characteristics Impacting Lability
The soil parameters important in affecting sorption and precipitation reactions and the extent of
their influence, thus contaminant bioavailability, are dependent on the intrinsic properties of the
contaminants. The 24 contaminants considered in this guidance include both metals and organic
contaminants. Metals can exist as either cations or anions in the soil environment, which
significantly affects their sorption, mobility, and solubility in soils. For example, soil is primarily
negatively charged, thus, metal cations have a higher propensity to be sorbed by soil particles
relative to metal anions. For organics, lipophilicity and persistence alter their availability, as well
as ionic potential in the case of organic contaminants with ionizable functional groups.
Collectively, the 24 contaminants may be classified into the following four groups (Table 2.1).
Table 2.1. General Contaminant Classification
Contaminant Class
Metal Cations
Metal Anions
Nonionic Organics
Ionizable Organics
EcoSSL Contaminant
aluminum, antimony, barium, beryllium, cadmium, cobalt, copper,
iron, lead, manganese, nickel, silver, and zinc
arsenic, chromium, selenium, and vanadium
DDT and metabolites, dieldrin, PCBs, PAHs, TNT, and RDX
PCP
DRAFT
2-2
June 27, 2000
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Metals. As identified in Table 2.1, most of the 24 contaminants considered in the Eco-SSLs are
metals that typically exist as cationic species (aluminum, antimony, barium, beryllium, cadmium,
cobalt, copper, iron, lead, manganese, nickel, silver, and zinc). These metals can complex with
inorganic soil constituents, e.g., carbonates, sulfates, hydroxides, sulfides, to form either
precipitates or positively charged complexes. Both complexation and precipitation reactions are
pH dependent. Therefore, although these metals can form complexes with a net negative charge,
under most environmentally relevant scenarios (pH = 4 to 8.5), these metals either precipitate or
exist as cationic species.
Arsenic, chromium, selenium, and vanadium complex with oxygen and typically exist as anionic
species under most environmentally relevant scenarios (Bohn et al., 1985; Lindsay, 1979). The
most common forms of arsenic are arsenate (arsenic V) and arsenite (arsenic in), which are
present in soil solution in the form of AsO43 and AsO2", respectively. The chemistry of arsenic
resembles that of phosphate (Barber, 1995; Bohn et al., 1985). Chromium can exist as chromate
(chromium VI or CrO42"), which is usually considered more soluble, mobile, and bioavailable
than the sparingly soluble chromite (Cr (HI)), which is normally present in soil as the precipitate
Cr(OH)3 (Barnhart, 1997; James et al., 1997). Similarly, selenium can be present as selenates
(SeO42") and selenites (SeO32"). For vanadium, vanadate (VO4 3~) is the most common form.
Metals in their various forms can exist in the pore-water as charged species, as soluble
complexes, or precipitate out of solution. Retention by soil is usually electrostatic with cationic
species and anionic species being associated with negatively and positively charged sites on the
soil, respectively. For most soils in the United States, negatively charged sites are more plentiful
with less than 5% of the total available charge on the soil surface being positively charged.
Therefore, metals existing as cationic species have a greater propensity to associate with the soil
and less bioavailable, whereas, distribution of anionic metals is generally more towards the
pore-water for most soil/water systems. The soil pH and availability of charged sites on soil
surfaces are the primary soil factors controlling their release to the pore-water, and subsequently,
its bioavailability.
Organic Contaminants. Of the seven organic contaminants identified in Table 2.1, DDT and
metabolites, dieldrin, and PCBs are very hydrophobic, highly lipophilic, and persistent nonionic
organic contaminants. These contaminants are highly sorbed to soil surfaces and organic matter
domains, thus persistent in soil, and tend to bioaccumulate and biomagnify in the food chain. The
structure and degree of chlorination of these contaminants and associated congeners for each
directly impacts their behavior, persistence, and bioavailability (e.g., see citations in Hansen et
al., 1999). Solubility decreases, sorption increases, and thus bioavailability generally decreases
with increasing chlorination. However, uptake, degradability, and toxicity are also impacted by
placement of the chlorines in the biphenyl structure. The remaining nonionic organic
contaminants, polyaromatic hydrocarbons (PAHs) and explosives (TNT and RDX) are generally
DRAFT 2 - 3 June 27, 2000
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considered less persistent and therefore, are more bioavailable than pesticides or PCBs under
identical soil conditions. PAHs are compounds with two or more aromatic rings in their
structure and consist of only C and H. PAHs can be highly retained by soil in a similar manner
as PCBs, but are considered less persistent due to their higher affinity to be degraded microbially.
TNT and RDX, a trinitro aromatic and trinitro nitrogen-heterocylcic respectively, are explosive
materials and are more polar than either PCBs or PAHs. The only ionizable organic contaminant
being considered at this time in the development of Eco-SSLs, is the organic acid
pentachlorophenol (PCP). Organic acids can exist as either a nonionic species or as an organic
anion, which is dependent on the acid dissociation constant (pKa) and pH. In the pH range
relevant to most environmental scenarios, PCP can exist as both a neutral species and as an
anionic species; however, the majority will exist as the organic anion (Lee et al, 1990).
For all nonionic organic compounds (NOC) and the neutral form of PCP, sorption by soil is
primarily related to their hydrophobicity and the amount of organic matter present in the soil
(Lagrega, 1994; Lee et al., 1990), with the exception of the more polar, nitro-substituted organic
contaminants (i.e., the explosives). Differences in the distribution of several NOCs in diverse
soil-water and sediment-water systems have been minimized by normalization to organic matter
or more specifically organic carbon (OC) with OC-normalized distribution coefficients, referred
to as Koc values (e.g., Lyman, 1990; Gertsl, 1990). The greater the affinity of a contaminant for
organic matter, the larger the Koc, and a soil with higher amounts of organic matter has a higher
propensity to sorb NOCs. The hydrophobicity of organic compounds, thus the Koc, increases
with the size of the compound and with increasing chlorine content, in the case of chlorinated
organics. Therefore, sorption by soils of PAHs increases with the number of aromatic rings. For
compounds like PCBs, sorption increases with increasing chlorination. Increasing compound
hydrophobicity also reflects increasing lipophilicity, which will result in a greater propensity to
bioaccumulate in the lipid fraction of biota. For PCP, an ionic contaminant, the anionic species
has a greater tendency relative to the neutral PCP to remain in the pore-water similar to metal
anions. Therefore, pH-dependent speciation drastically modifies the solubility, sorption,
transport, and bioavailability of PCP. Although organic matter is the primary sorption domain in
soils, all contaminants have some affinity to be associated with any surface through weak
physical forces (Schwarzenbach et al., 1993). In addition, the nitro-substituted NOCs have been
shown to have specific interactions with clay surfaces that are impacted by the inorganic cations
present and clay charge density, and less so by the amount of organic matter present (Weissmahr
et al., 1998; 1999).
A common contaminant index representing the degree of hydrophobicity and lipophilicity of an
organic contaminant is the octanol-water partition coefficient (Kow), which is the contaminant
distribution between octanol and water phases. Kow values are positively correlated to both Koc
values and bioconcentration factors (Lyman et al., 1990). For reference, log Kow values for
selected organic contaminants are summarized in Table 2.2.
DRAFT 2 - 4 June 27, 2000
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Table 2.2 Log K,,w Values for Organic Contaminants
Analyte
RDX
TNT
DDT
ODD
DDE
Dieldrin
Pentachlorophenol (PCP)
PCBs
CAS no.
121824
118967
50293
72548
72559
60571
87865
log Kow
0.87
1.6
6.53
6.1
6.76
5.37
5.09
4.5 (1 chlorine)
>8 (10 chlorines)
Source
SRC
SRC
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA(1996a)
Verschueren(1996)
Schwarzenbach(1993)
PAHs
Naphthalene (2 rings)
Acenaphthene (3 rings)
Phenanthrene (3 rings)
Anthracene (3 rings)
Chrysene (4 rings)
Benzo(a)anthracene (4 rings)
Benzo(a)pyrene (5 rings)
Dibenzo(ah)anthracene (5
Benzo(b)fluoranthene (5 rings)
Benzo(k)fluoranthene (5 rings)
Benzo(ghi)perylene (6 rings)
91203
83329
85018
120127
218019
56553
50328
53703
92240
207089
191242
3.36
3.92
4.55
4.55
5.7
5.7
6.11
6.69
6.2
6.2
6.7
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA (1995)
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA(1996a)
U.S. EPA (1995)
Key Soil Parameters Affecting Contaminant Bioavailability in Soils
From the preceding overview of how the contaminants interact with soil constituents, it is clear
that soil plays a very significant role in reducing the potential bioavailability of contaminants in
the environment. Given the types of contaminant-soil interactions presented, the primary soil
factors controlling the potential bioavailability of all contaminants are identified as soil pH,
available charged sites on soil surfaces, clay content, and soil organic matter. Below is a
discussion briefly detailing the key soil parameters affecting the various contaminants'
availability to the pore-water, thus bioavailability.
Soil pH. Soil pH is often termed the master soil variable because it controls virtually all aspects
of contaminant and biological processes in soil. These processes include solubility, precipitation,
speciation, and sorption processes as well as microbial activity. Soil pH controls the speciation
DRAFT
2-5
June 27, 2000
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of both ionizable organic contaminants such as PCP, and metals. For metals, the net charge of the
metal complexes and their precipitation/dissolution reactions are directly impacted by soil pH.
For organic acids such as PCP, the fraction of contaminant existing as an anion increases with
increasing pH. The anion has a lower affinity for the soil relative to the neutral species.
Increasing soil pH also results in an increase in the number of negatively charged soil sites with a
concomitant decrease in the positively charged sites. Therefore, increasing the soil pH directly
impacts the sorption and removal from the pore-water of metal or organic ions (Bohn et al.,
1985). The impact of pH on the behavior and bioavailability of nonionic organic contaminants is
less marked and is generally achieved through its influence on organic matter and on microbial
activity.
Cation and Anion Exchange Capacities. The available charges on soil surfaces are quantified
in the soil parameters known as cation exchange capacity (CEC) and anion exchange capacity
(AEC). CEC is a measure of the soil's ability to adsorb and release cations, which is directly
proportional to the number of available, negatively charged sites. Likewise, AEC is a measure of
the soil's ability to adsorb and release anions. As a result, the AEC is a measure of available
positively-charged surface sites. CEC is directly related to the clay mineral content and type,
organic matter and soil pH. CEC is greater for 2:1 clays such as montmorillonite (600 to 1,000
mmol/kg) compared to 1:1 clays such as kaolinite (20 to 160 mmol/kg). CEC in organic matter
ranges from 2,000 to 4,000 mmol/kg; however, the organic matter fraction of a soil is usually
much less than the clay fraction. CEC arising from pH-dependent charge, which includes
organic matter contributions to CEC, increases with increasing pH. CEC in soil ranges from
values as low as 10 mmol/kg for extremely coarse-textured soil to as much as 600 mmol/kg for
fine textured soil, containing large amounts of 2:1 clays and organic matter (Bohn et al., 1985).
AEC, which is primarily associated with amorphous oxides, decreases with increasing soil pH.
As previously mentioned, the number of positively charged sites (i.e., AEC) on the majority of
soil types is very small, and in environmentally-relevant pH ranges, is usually negligible.
Therefore, AEC is not generally considered an important parameter in assessing contaminant
availability at most sites in the United States.
Clay Minerals. Clays, by definition, are soil particles less than 2 microns in size (Miller and
Gardiner, 1998); therefore, high clay soils have higher surface areas relative to sandy soils (sand
particle sizeranges from: 20 microns to 2 mm). For nonionic organic contaminants, the primary
sorption domain is organic matter; however, soils with high surface area will result in enhanced
sorption of organic contaminants through weak physical interactions, as well. Much of the CEC
of a soil comes from the negatively charges sites on clay surfaces. Therefore, high clay soils will
have a higher affinity to sorb cationic species whether organic or inorganic due to CEC, and to
sorb nonionic organic contaminants due to high surface areas, thus making contaminants less
bioavailable relative to sandy soils. In addition to charged sites available in clays, siloxane
oxygens present in clays can interact specifically with contaminantsk such as the nitro-substituted
explosives. Metals can form precipitates with inorganic soil constituents, such as carbonate and
phosphate minerals under certain soil conditions. Carbonate- and phosphate-metal complexes
DRAFT 2 - 6 June 27, 2000
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have varying degrees of solubility and reactivity depending on the metal, its oxidation state, the
ligand to which it is bound, and pH. Precipitation removes a contaminant from the pore-water,
thus decreasing bioavailability.
Organic Matter (Organic Carbon) Content. Organic matter includes plant and animal
remains in various stages of decomposition, cells and tissues of soil organisms and substances
from plant roots and soil microbes (Sumner, 2000). Organic matter is primarily composed of
carbon, oxygen, and nitrogen. Organic matter is often reported or analytically determined on a
carbon basis. On average, approximately 58% of organic matter is organic carbon. Soils
encompass a range in organic matter from <1% for a sandy soil to almost 100% for a peat soil,
with most soils having organic matter contents <10% (Bohn et al., 1985). Also, organic matter
content is usually higher in surface soils or in the root zone and decreases with depth in the soil
profile.
Organic matter has a high affinity to bind organic compounds as well as some metals in soils
thereby, reducing their availability. Organic contaminants preferentially paritition to the organic
domain of organic matter relative to the polar aqueous phase, while the organic acid functional
groups typically present in organic matter have a high affinity to attract metal cations. For
nonpolar or neutral organic contaminants at equilibrium, sorption is positively correlated to the
amount of organic matter, usually reported as the fraction of organic carbon (foe), and inversely
proportional to aqueous solubility. Sorption of organic contaminants increases with increasing
amounts of soil organic matter. The greater the hydrophobicity or lipophilicity of an organic
contaminant, the greater potential it has to be sorbed onto organic matter. The latter has led to
the use of the organic carbon-normalized partition coefficients (Koc) for estimating contaminant
sorption with the soil-specific distribution coefficient estimated by Koc multiplied by foe.
Another indirect effect of soil organic matter is its role on limiting contaminant mass-transfer.
The rate of mass-transfer of an organic contaminant from soil particles to the surrounding
pore-water is inversely proportional to the contaminant's soil-water distribution coefficient
(Pignatello et al., 2000). Therefore, with increasing organic matter content, retention of an
organic contaminant increases and rates of release decrease, thereby, decreasing overall
contaminant bioavailability.
2.3 Using Soil Properties to Guide Eco-SSL Derivation
To simplify defining a set of soil parameters for use in selecting studies for deriving Eco-SSLs
for plants and soil invertebrates, four soil parameters were selected: soil pH, CEC, clay content,
and organic matter. However, when the plants and soil invertebrates work group evaluated the
current literature, they observed that CEC and clay content were not consistently reported. Thus,
these parameters were not used and matrices were constructed using only pH and organic matter
content as the primary soil parameters affecting bio-availability and toxicity. For these soil
parameters, ranges were established within what are typically found in soils. Soils with
DRAFT 2 - 7 June 27, 2000
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characteristics that fall outside the selected ranges were not initially considered. Although other
soil factors can be significant (discussed in Chapter 7), combinations of these two soil
parameters and their ranges are sufficient to be used in this screening process as a qualitative
guide in addressing how most soils from across the United States may influence bioavailability of
the various contaminants. Qualitative rankings of high, medium, and low availability are used to
categorize each combination of the soil parameters and their ranges. For Eco-SSL derivation,
information on bioavailability is used to help select and score studies to include in the derivation
of the Eco-SSL values. Greater weight is given to those studies that have higher bioavailability.
Using the selected soil parameters and defining ranges that correspond qualitatively to the soil's
affinity for the contaminant and thus for bioavailability, Tables 2.3, 2.4, and 2.5 were developed
for metal cations, nonionic organics, and anionic species, respectively. For each of the soil
parameters, the values typically found in soils were divided into three ranges. For example, most
environmentally relevant scenarios fall within pH values between 4.0 and 8.5. This pH range
was divided into the following sub-ranges: 4.0 to 5.5, 5.5 to 7.0, and 7.0 to 8.5. Qualitative
bioavailability indices of very high, high, medium, low, and very low were assigned for each
combination of soil parameters within each class of the contaminants (Tables 2.3, 2.4, and 2.5).
For example, a soil with a pH between 5.5 - 7.0, and organic matter content between 2 and 6%,
would bind metal cations to a moderate extent. Therefore, assigned an availability index of
'medium' for metal cations was assigned (see Table 2.3).
These tables simplify and facilitate the use of soil chemistry information in the derivation of soil
screening levels at Superfund sites for plants and soil invertebrates. The ranges given in these
tables were used in selecting the most appropriate plants and soil invertebrates toxicity data for
deriving Eco-SSLs (Chapter 3). To address data gaps for individual contaminants, experiments
are anticipated to be conducted, which meet a specific set of quality criteria (Chapter 3) and
using soils with characteristics for which the contaminants would more likely be bioavailable.
Recommended plant species and soil biota for testing purposes are put forward in Chapter 7.
The information presented in Tables 2.3 through 2.5 also provide insight into how Eco-SSLs may
be modified on a site-specific basis, as well as on the properties that may need to be considered if
a model of exposure is eventually developed. These topics are discussed in Chapter 7.
DRAFT 2 - 8 June 27, 2000
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Table 2.3. Qualitative Bioavailability of Metal Cations in Natural Soils
Soil Type
4 < SoilpH -5. 5
5.53.5)
Other Organics
(LogKow<3.5)
Pesticides /PCBs
(LogKow>3.5)
Other Organics
(LogKow<3.5)
Pesticides / PCBs
(LogKow>3.5)
Other Organics
(LogKow<3.5)
Organic Matter (%)
<2
High
V.High
Medium
High
Low
Medium
2-6
Medium
High
Low
Medium
Low
Low
6-10
Low
Medium
Low
Low
Low
Low
Table 2.5. Qualitative Bioavailability of Anionic Species for Natural Soils
Soil Type
4 < Soil pH • 5.5
5.5
-------
3.0 DERIVATION OF PLANT AND SOIL INVERTEBRATE ECO-SSLs
The development of Eco-SSLs for plants and soil invertebrates builds upon previous efforts (CCME,
1997; Efroymson et al., 1997 a,b) and establishes additional techniques to evaluate the literature and
select appropriate data from published studies. For this purpose, three sets of literature review criteria
were created and used to select studies with thorough experimental designs and quality control. The
selection process begins with a thorough literature and retrieval effort based on key words, and
"exclusion criteria". Retrieved papers are screened using ten "acceptance criteria" designed to identify
studies having appropriate information and sufficient detail to facilitate inter-study comparisons. To be
included in the data set for derivation of an Eco-SSL a study must meet all acceptance criteria.
Acceptable papers are then scored according to nine technical "evaluation criteria". Data sets with
total scores above a specific value are considered of sufficient quality to derive an Eco-SSL. Toxicity
data from these studies are then ranked by both treatment effects (e.g., reproduction, growth, etc.) and
toxicity parameter (e.g., NOEC, EC10, etc.), and assigned a preference level (A to D). The Eco-SSL
is then derived from this set of data based on the chronic effects values rated at the highest preference
level for which there is a sufficient number of data points. The process is completed with a quality
assurance review to ensure the appropriateness and accuracy of the contaminant-specific Eco-SSL
derivation.
The importance of physical and chemical soil parameters to contaminant bioavailability and ecotoxicity
for plants and soil invertebrates is well known (Linz and Nakles, 1997; Loehr, 1996). In order to
address contaminant bioavailability, the normalization of soil organism toxicity data using soil parameters
has been put forward by several authors (van Gestel, 1992; van Straalen, 1993). Typically these
techniques are contaminant-specific or have been shown to be appropriate for one group of organisms.
Alternatively, the Eco-SSL effort used qualitative bioavailability values as an initial step to relate
physical and chemical soil parameters to soil biota toxicity.
The Eco-SSL effort also examined soil invertebrate test methods for use when literature data gaps
exist, and there is a need for data sufficient to derive an Eco-SSL. A review of the available toxicity
test methods showed that several soil invertebrate toxicity tests, for which standardized protocols have
been developed, can effectively be used to establish ecotoxicity data from which Eco-SSLs may be
derived. The task group identified three such soil toxicity tests including: 1) a 21-day chronic
earthworm reproduction (cocoon production) toxicity test, 2) the enchytraeid reproduction test, and 3)
the collembolan reproduction test. These specific tests were selected on the basis of their ability to
measure chemical toxicity to ecologically relevant test species during chronic assays, and their inclusion
of at least one reproductive component among the measurement endpoints. The draft guidelines for
these methods are in the final stages of review or approval by one of several national and international
organizations, including the Organization for Economic Cooperation and Development (OECD), the
International Standards Organization (ISO), the American Society for Testing and Materials (ASTM),
the European Community (EC), and the Federal Biology Research Cooperative (FBRC). The
selection of these methods is not considered an absolute guarantee for protection of all soil biological
DRAFT 3 - 1 June 27, 2000
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resources, but rather an attempt to achieve a balance between the need to utilize different assays, each
addressing a specific aspect of the soil invertebrate toxicity, and practical considerations dictated by the
constraints of the ERA process. In the future, this test battery may include additional tests, as methods
are refined and protocols become standardized and accepted by international organizations.
The strengths of the plant and soil invertebrate Eco-SSL process include the transparency of the
methods used to review and select toxicity data, the use of ecologically-relevant endpoints, and the
incorporation of qualitative soil contaminant bioavailability values. The use of acceptance and
evaluation criteria minimizes variations due to individual expert judgement through clearly stated
evaluation parameters and a quality assurance review of the data selected for use in deriving Eco-SSLs.
The process used to derive Eco-SSLs for plants and soil invertebrates follows five steps:
1. Identify and retrieve literature studies and apply Literature Exclusion Criteria to either
the retrieved abstracts or study titles.
2. Identify acceptable data by applying Literature Acceptance Criteria to retrieved
studies.
3. Score the accepted studies according to the Literature Evaluation Process.
4. Perform a Quality Control Review of the scored and accepted studies.
5. Calculate soil invertebrate and plant Eco-SSLs using data from the most appropriate
studies.
These five steps were used to identify relevant published data of sufficient quality to be used to derive
Eco-SSLs and to remove from consideration the data that does not meet the prescribed criteria for
acceptance. Some studies reviewed may have been of high quality, yet were deemed not relevant or
appropriate for the intended purposes of deriving screening levels for plants and soil invertebrates and
therefore were excluded for use in deriving the Eco-SSL.
3.1 Literature Search. Acquisition and Acceptability
Literature Search and Acquisition (Step 1)
A literature search was conducted to identify all published studies that reported soil toxicity to terrestrial
plants or soil invertebrates for any of the 24 contaminants. The protocol for the literature search and
retrieval process, including the key words used for the search, is provided as Exhibit 3-1.
DRAFT 3 - 2 June 27, 2000
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The literature search included both
paper-based searches and online searches.
The paper-based literature search process
consisted of the manual review of
bibliographies, guidance documents, review
articles, and key journals held in the EPA
Office of Research and Development,
National Health and Ecological Effects
Research Laboratory, Mid-Continent
Ecology Division-Duluth (MED-Duluth)
library holdings. This search was not limited
by publication year. Online searches were
completed using electronic databases. The
search protocol included the use of
DIALOG, SilverPlatter and Ovid
commercial database vendors. The targeted
databases included AGRICOLA, BIOSIS
and Chem Abstracts. In addition, the
searches were supplemented with literature
abstracting databases including Toxline,
PolToxl, Toxnet, and Current Contents:
Agriculture, Biology & Environmental
Sciences. Online searches were limited to
studies published since 1988, except when
fewer than 20 publications were identified for
a contaminant-receptor pairing (e.g.,
cadmium-plants), then the online search was
expanded to include all publication years.
The online and paper-based literature
searches identified more than 7,200 papers.
These publications' abstracts and titles were
screened to determine if they were likely to
meet the Eco-SSL requirements. This
screening consisted of a review of titles and
abstracts which focused on whether or not
the publication addressed terrestrial plant
and soil invertebrate species and Eco-SSL
chemicals. A list of 23 Literature Exclusion
Criteria (see Figure 3.1) was then used to
screen out those studies not appropriate for
Figure 3.1. Literature Exclusion Criteria
Biological Product Studies of biological toxins
(venoms, etc.)
Chemical Methods Studies on methods for
determination of contaminants
Drug Testing for drug effects
Effluent Studies of effluent, sewage, polluted run-off
Contaminant Fate Studies of what happens to the contaminant
Human Health Studies with human or primate subjects
In Vitro In Vitro studies, including cell cultures and
excised tissues
Methods Studies reporting methods but no usable
specific toxicity tests
Mixture Studies of combinations of contaminants
Modeling Only modeling results reported
No Cone. No dose or concentration reported
No Duration No exposure duration reported
No Effect No effect reported for a biological test species
No Species No viable plant or organisms present or tested
No Toxicant No toxicant used
No Tox Data Toxicant used, but no results reported
Nutrient Nutrient studies
Oil Oil and petroleum products
Publ As
QSAR
Review
Author states information in report published
in another source
Data developed only from
Quantitative-Structure Activity Relationships
(QSAR)
Data reported are not primary data
Sediment Cone. Only exposure concentration of toxicant is
reported as sediment concentration
Survey
Assessment of toxicity in the field over a
period of time.
DRAFT
3-3
June 27, 2000
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use in deriving Eco-SSLs. These Exclusion Criteria were applied to retrieved abstracts, or to the
acquired literature if the needed information was not available in the abstract. Articles that appeared to
be relevant were ordered. This process resulted in the acquisition of over 4,800 papers.
Literature Acceptance Criteria (Step 2)
Acquired publications were screened using 10 Literature Acceptance Criteria (see Figure 3.2) for
potential acceptability. The purpose of applying the acceptance criteria was to assure relevancy of test
data for the Eco-SSL effort and to ensure that the test data were of sufficient quality to use in deriving
Eco-SSLs. Application of the acceptance
criteria ensured that the minimum data
requirements for derivation of Eco-SSLs were
included in each publication. The Standard
Operating Procedure (SOP) for using the
Literature Acceptance Criteria is presented as
part of Exhibit 3-1.
The acceptance criteria were applied to the
retrieved literature studies and an Acceptance
Criteria Checklist form (Exhibit 3-1) was
completed. Publications that did not meet all
10 acceptance criteria were excluded from
further consideration. Approximately 7% of the
retrieved papers met all ten acceptance criteria.
The completed checklists for all publications
(acceptable and excluded studies) are
maintained as part of the ECOTOX database.
Data from accepted studies were coded and
entered into the terrestrial component
(TERRETOX) of the ECOTOX database.
ECOTOX was developed at MED-Duluth and
is a comprehensive computer-based system that
provides chemical-specific toxicity information
for aquatic life, terrestrial plants, and terrestrial
wildlife. Complete details about the
TERRETOX coding process are provided in
Exhibit 3-2.
Figure 3.2. Summary of
Literature Acceptance Criteria
The document is a primary source of literature.
The adverse effects were caused by a single
chemical stressor (i.e., no mixture studies).
The contaminant form (i.e., metal salt used) and
concentration are reported by the author(s).
The test medium used in the study is a natural or
artificial soil.
The study reports the organic matter content and
it is < 10% of the composition of the soil.
With exception of studies on non- ionizing
substances, the study reports the pH of the soil,
and the soil pH is within the range of > 4.0 and
The study includes control treatment(s).
The duration of the exposure is reported, or a
standard study method is used with duration
referenced.
For studies conducted in a laboratory setting, at
least three treatment levels are used (i.e., control
+ two contaminant exposure).
Biological effects are reported for ecologically
relevant endpoints (ERE) (listed in Exhibit 3-2).
DRAFT
3-4
June 27, 2000
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3.2 Literature Evaluation (Step 3)
Each publication meeting all 10 acceptance criteria was reviewed and scored using the Literature
Evaluation procedure summarized in Table 3.1 and presented in Appendix 3-1. The Literature
Evaluation Procedure, which consisted of nine criteria, provided a standardized process for assessing
the applicability of each published study for deriving Eco-SSLs for soil invertebrates and terrestrial
plants. Scoring was completed for each of nine criteria using a three- point scale: 0, 1, or 2, with 2
indicating complete agreement with a criterion (Table 3.1).
For a given contaminant-receptor combination (e.g., copper-plants), those studies with a total
evaluation score > 10, out of a possible score of 18, were identified for further consideration for use in
deriving Eco-SSLs. In publications that reported results for more than one applicable study or
experiment, each study was scored separately. In cases where more than one toxicity value was
reported for a single study, only one value was selected for possible use in deriving the corresponding
Eco-SSL. Guidelines for the selection of data for possible use in deriving the Eco-SSL are provided in
Appendix 3-1.
Data from studies that scored >10 in the Literature Evaluation Process (Appendix 3-1) were grouped
according to bioavailability score and toxicity parameter (see Table 3.2). This grouping into "levels"
allowed for the preferential use of select data to derive Eco-SSLs ensuring that each Eco-SSL was
derived from the highest quality and most appropriate data available.
3.3 Identification of Data for Derivation of Eco-SSLs
Following the literature evaluation process (Step 3), studies were segregated based on their total
evaluation scores. Those studies that received a total score of 10 or less (out of the possible score of
18) were deemed of insufficient quality or otherwise inappropriate for use in deriving Eco-SSLs, while
studies with a review score >10 were identified for further consideration for Eco-SSL derivation.
Those studies with total evaluation scores >10 were organized into four groups based on their
respective toxicity parameters and bioavailability scores. The four groups or levels (identified in Table
3.2 as Level A, B, C or D) are prioritized from highest (Level A) to lowest (Level D) for preferential
use in calculating Eco-SSLs.
DRAFT 3 - 5 June 27, 2000
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Table 3.1 Summary of Literature Evaluation Process for Plant and Soil Invertebrate Eco-SSLs
Criteria
Rationale
Scoring
#1: Testing was Done Under
Conditions of High
Unavailability.
Unavailability of metals and polar organic compounds is
influenced by pH and soil organic matter, cationic
exchange capacity, and clay content. The scoring is
intended to favor relatively high bioavailability.
Scores based on the bioavailability matrix
(see Appendix 3-1). Score 2 if
bioavailability of natural soil is high or
very high. Score 1 for natural soil with
medium bioavailability or standard
artificial soil. Score 0 for natural soil with
low and very low bioavailability.
#2A (laboratory) and 2B
(field): Experimental Designs
for Studies are Documented and
Appropriate.
Experimental design can significantly influence the
quality of a study. Higher quality studies will use an
experimental design sufficiently robust to allow analysis
of the test variables and discriminate non-treatment
effects.
Score 2 if in complete agreement with
criterion. Score 1 if some but not all of
the conditions for the criterion are met.
Score 0 if it fails to meet the criterion.
#3: Concentration of Test
Substance in Soil is Reported.
The concentration of the contaminant tested must be
reported unambiguously.
Score 2 if measured concentrations were
reported. Score 1 if nominal
concentrations were reported. Score 0 in
all other cases.
#4: Control Responses are
Acceptable.
Negative controls are critical to distinguish treatment
effects from non-treatment effects.
Score 2 if in complete agreement with
criterion. Score 1 if control results were
not reported or ambiguous. Score 0 if it
fails to meet the criterion.
#5: Chronic or Life Cycle Test
was Used.
Chronic toxicity tests assessing long-term adverse sub-
lethal impacts on the life-cycle phases of an organism
are considered superior to acute toxicity tests.
Score 2 if chronic exposures were used.
Score 1 if acute tests were used. Score 0 if
very short term exposures were used.
#6: Contaminant Dosing
Procedure is Reported and
Appropriate for Contaminant and
Test.
Contaminant dosing procedure may affect the outcome
of a test. Dosing procedure should include: (A) The form
of the contaminant; (H) The carrier or vehicle (e.g.,
solvent, water, etc.); (C) How the carrier was dealt with
following dosing (i.e., allowed to volatilize, controls,
etc.); (D) procedure for mixing of soil with contaminant
(homogeneity).
Score 2 if in complete agreement with
criterion. Score 1 if some, but not al of
the conditions for the criterion were met.
Score 0 if it fails to meet the criterion.
#7: A Dose-Response
Relationship is Reported or can
be Established from Reported
Data.
Two methodologies that can be used to identify this
benchmark concentration. The first method generates a
no observed effect concentration (NOEC) and a lowest
observed effect concentration (LOEC). The second
method uses a statistical model to calculate a dose
response curve and estimate an effect concentration for
some percentage of the population (ECxx), usually
between an EQ and an EC50.
Score 2 if in complete agreement with
criterion. Score 1 if some, but not all of
the conditions were met. Score 0 if it fails
to meet the criterion.
#8: The Statistical Tests used to
Calculate the Benchmark and the
Level of Significance were
Described.
Statistical tests and results reported in the study should be
sufficient to determine the significance of the results.
Score 2 if in complete agreement with the
criterion. Score 1 if some, but not all of
the conditions for the criterion were met.
Score 0 if it fails to meet the criterion.
#9: The Origin of the Test
Organisms is Described.
The results of a toxicity test can be influenced by the
condition of the test organisms. Culture conditions
should be maintained such that the organisms are healthy
and have had no exposure above background to
contamination prior to testing (inverts) or detailed
information is provided about the seed stock (plants).
Score 2 if in complete agreement with the
criterion. Score 1 if some, but not all of
the conditions for the criterion were met.
Score 0 if it fails to meet the criterion.
DRAFT
3-6
June 27, 2000
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Table 3.2 Plant and Soil Invertebrate Eco-SSL Derivation Table
Level
A
B
C
D*
Toxicity Endpoint
EC20, EC10, MATC
EC20,EC10,MATC
EC20, EC10, MATC
EC20, EC10, MATC, EC50
Bio availability Score
2
Ior2
0,1, or 2
0,1, or 2
ECxx = Effect Concentration for defined percentages of the population (i.e., 20%, 10-19%, 21-50%),
MATC = Maximum Acceptable Threshold Concentration or the geometric mean of the No Observed Effect Concentration
(NOEC) and Lowest Observed Effect Concentration (LOEC).
* Data which are used to derive Eco-SSLs at the D level were adjusted with the appropriate application factor.
If the EC50 > MATC then the values was divided by 5.
If the EC50 < MATC then the value was divided by 2.
If there were only EC50 values then the value was divided by 5.
3.4 Quality Control Review (Step 4)
Once the literature evaluation process was completed and the selected studies grouped into levels
according to bioavailability and toxicity endpoints, a quality control review was conducted by task
group members of those data identified for consideration in deriving an Eco-SSL. A description of the
Quality Control Review is included in Appendix 3-2. The objectives of the Quality Control Review
included: confirming that the appropriate data were selected and documented by the reviewer; resolving
any comments or concerns; and, reaching consensus on which data would be used to derive an Eco-
SSL. For example, for a study that reported data for multiple test species and for several endpoints,
the quality control process provided a forum for review of the identified data to ensure that the most
appropriate information was used to derive the Eco-SSL.
3.5 Calculation of the Plant and Soil Invertebrate Eco-SSLs (Step 5)
Following the Quality Control Review (Step 4), an Eco-SSL for a contaminant-receptor pairing (e.g.,
lead-invertebrates) was calculated. The Eco-SSL was calculated as the geometric mean of all toxicity
values from the highest preference "level" (see Table 3-2) that had a sufficient number of data. Three
toxicity data values were the minimum required to calculate an Eco-SSL. If a sufficient number of data
(N=3) were available at the highest level (Level A), then the Eco-SSL was calculated using only Level
A data. If Level A contained less than three values, then additional data was added from subsequent
levels (B, C and D) until the minimum of three data values was obtained. For example, if a specific
contaminant-receptor pairing has only two toxicity values at Level A, there would not be sufficient data
to generate an Eco-SSL using only Level A data. However, if in this case there were toxicity values
(one or more) at Level B, in addition to the two values at Level A, these combined values (three or
more) would be used to derive an Eco-SSL as the geometric mean of the combined data set. In this
DRAFT 3 - 7 June 27, 2000
-------
example, Level C data would only be used if there were less than three values from the combined A
and B levels.
The Eco-SSL derivation process was completed separately for plants and soil invertebrates for each
contaminant. Once an Eco-SSL was calculated, a technical discussion was prepared that provided
additional information concerning the derivation of each Eco-SSL value. Technical discussions and the
calculated Eco-SSLs for each contaminant-receptor are presented in Chapter 5. The process for
derivation of plant and soil invertebrate Eco-SSLs is provided as Appendix 3-2. The completed
scoring sheets and Eco-SSL derivation for each contaminant for plants and soil invertebrates are
reported in Appendix 3-3 . The documents pertaining to derivation of Eco-SSLs for plants and soil
invertebrates are listed in Table 3.3.
Table 3.3 Plant and Soil Invertebrate Eco-SSL Documents
Document
Plant and Soil Invertebrate Standard Operating Procedure #1 : Literature
Search and Retrieval
Plant and Soil Invertebrate Standard Operating Procedure #2: Literature
Review
Plant and Soil Invertebrate Standard Operating Procedure #3: Literature
Evaluation and Data Extraction
Plant and Soil Invertebrate Standard Operating Procedure #4: Eco-SSL
Derivation, Quality Assurance Review,
And Technical Write-up
Reference List of Papers Identified by Literature Searches
Reference List of Acceptable Papers
Literature Evaluation Scoring Sheets for Studies Used to Derive Plant
and Soil Invertebrate Eco-SSLs
Location
Exhibit 3-1
Exhibit 3-2
Appendix 3-1
Appendix 3-2
Exhibit 3-3 (to be posted)
Exhibit 3-4 (to be posted)
Appendix 3-3
DRAFT
3-
June 27, 2000
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4.0 DERIVATION OF WILDLIFE ECO-SSLs
Eco-SSLs for wildlife were derived using a five step process that includes: selecting the wildlife risk
model, selecting the surrogate species, parameterizing the exposure dose model, deriving wildlife
toxicity reference values (TRVs), and calculating the Eco-SSLs. Wildlife Eco-SSLs were derived for
two groups of wildlife receptors: mammals and birds. Eco-SSLs were not derived for amphibians or
reptiles at this time due to lack of adequate toxicity and exposure data.
4.1 The Wildlife Risk Model for Eco-SSLs
The basic equation used for estimating potential risks to wildlife is as follows:
Hazard Quotient (HQ) =
Exposure Dose (mg / kgBW / day)
Effect Dose (mg / kgBW / day)
Contaminant exposure for terrestrial wildlife is
expressed as an Exposure Dose in milligram
(mg) contaminant per kilogram (kg) body
weight (BW) per day or mg/kg BW/day, and
the Effect Dose is represented by a toxicity
reference value (TRY) expressed in the same
units.
The Eco-SSL is the soil concentration that
results in an HQ=1, that is, when the Effect
Dose (TRV) and the Exposure Dose are equal.
The Exposure Dose for wildlife is equal to the
amount of contaminant in the diet that is taken
up or transferred from the soil. Therefore, it is
necessary to model the soil concentration that
would result in dietary concentrations equal to
the Exposure Dose that is equal to the TRV.
Estimation of the Exposure Dose is described in
Section 4.3. Derivation of the Effect Dose or
TRV is described in Section 4.4. Calculation
of the Eco-SSLs to solve for an HQ =1 is
described in Section 4.5. The full HQ equation
is provided in Figure 4.1.
Steps for Establishing a Wildlife Eco-SSL
1. Identify the Risk Wildlife Model - Equation
relates the contaminant soil concentration to an
acceptable threshold based on a food-chain exposure
model.
2. Select Surrogate Wildlife Species - Specific
indicator species were identified for parameterization
of the exposure model.
3. Estimate Exposure Dose - Parameterization of the
exposure dose model for the estimation of exposure
doses for each contaminant.
4. Derive the Effects Dose or TRV- Identification of
an acceptable dose.
5. Calculate the Eco-SSL Calculation of the Eco-
SSLs by solving equation for an HQ =1.
DRAFT
4-1
June 27, 2000
-------
Figure 4.1. The Wildlife Risk Model for Eco-SSLs (Equation 4-1)
ilj • Ps • FIR • AFjs]' [. Bi • Pi • FIR • AFtj\ I •
'
where:
HQj = Hazard quotient for contaminant (j) (unitless),
Soilj = Contaminant concentration for contaminant (j) in soil (mg/kg dry weight),
N = Number of different biota types in diet,
Bj = Contaminant concentration in biota type (i) (mg/kg dry weight),
PI = Proportion of biota type (i) in diet,
FIR = Food ingestion rate (kg food [dry weight]/ kg BW [wet weight] / d),
AFy = Absorbed fraction of contaminant (j) from biota type (i),
AFSJ = Absorbed fraction of contaminant (j) from soil (s),
TRVj = The no adverse effect dose (mg/kg BW/day) (Section 4.4),
P, = Soil ingestion as proportion of diet,
AUF = Area use factor.
4.2 Selection of Surrogate Wildlife Species
It is neither feasible nor necessary to derive an Eco-SSL for each and every wildlife species potentially
present at a hazardous waste site; therefore, surrogate species were used to derive wildlife Eco-SSLs.
In this approach, specific species were selected as "representatives" for other species within the same
class (mammalian or avian) with similar diets. The advantages of focusing Eco-SSLs on generic trophic
groups as opposed to specific species include, but are not limited to, the following:
This approach provides generic screening values that can be applied to any site,
regardless of the presence or absence of a particular species. The trophic groups
selected are expected to be present or potentially present at all sites across the nation.
This approach provides results that can be used to examine comparative risks
associated with different exposure routes (e.g., ingestion of food versus soil ingestion)
representing different contaminant transport pathways (e.g., soil to herbivore, soil to
ground insectivore, soil to soil invertebrate, and soil to plant) versus direct soil ingestion.
• This approach is consistent with ERAGS which states: "for the screening-level
ecological assessment, assessment endpoints are any adverse effects on ecological
receptors, where receptors are plant and animal populations and communities,
habitats, and sensitive environments. " (p. 1-7)
DRAFT 4-2 June 27,2000
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Criteria for Selection of Surrogate Taxa
Three general trophic groups (e.g., herbivore, ground insectivore, and carnivore) for both mammals and
birds were selected for the Eco-SSL wildlife exposure model. Within each of these trophic groups, a
specific species was identified as a "surrogate" species.
Selection of specific species was necessary for parameterization of the Eco-SSL wildlife model, which
requires estimates of body weights, food ingestion rates, and soil ingestion rates. The following criteria
were used to guide the selection of surrogate species for each trophic group:
1) Exposure pathway link to soil. Each surrogate species has a clear direct or indirect
exposure pathway link to soil. Direct exposure pathways to soil include ingestion of soil
dwelling biota (e.g., plants or soil invertebrates) and incidental ingestion of soil as a result of
foraging at the soil surface (as opposed to from plants). Species with direct exposure pathways
to soil are assumed to be the most highly exposed species to soil contamination with the
exception of contaminants that biomagnify. Indirect exposure includes ingestion by carnivores
of prey that have direct contact with soil.
2) Diet Composition. The selected, surrogate species forage in terrestrial, upland habitats. This
criteria ensures that only potential exposures related to soil contamination are considered and
consumption of aquatic prey items (exposures to the aquatic environment) are not considered.
3) Diet composition can be
simplistically classified. The dietary
composition of each surrogate species
can be easily classified into one of the
three selected trophic groups
(herbivore, ground insectivore,
carnivore). Clear classification of diet
serves to simplify the exposure
assumptions related to dietary
composition into three classes: plants,
invertebrates and animals. This
simplification permits examination of
the potential extremes in exposure by
dietary type (What are the risks if an
animal consumes earthworms,
exclusively? Or plants?), avoiding the
alternative use of variable dietary
compositions and associated
uncertainties. For this reason,
What Wildlife Groups were not Considered
Appropriate for Eco-SSLs?
Some specific wildlife groups were not considered suitable
for deriving of wildlife Eco-SSLs. These groups include:
• Generalist species (e.g., raccoons, jays) were
excluded due to difficulty in defining diet and,
therefore, exposure. These species forage
opportunistically and are likely to consume different
foods in different parts of their range.
• Piscivores (e.g., herons, otter) were excluded due to
the lack of a direct exposure pathway to soil.
• Aerial Insectivores (e.g., swallows) and Arboreal
Insectivores (e.g., warblers) were excluded as they do
not forage primarily from terrestrial environments.
DRAFT
4-3
June 27, 2000
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omnivorous wildlife were excluded as potential receptors.
Further, selection of species for which diet composition may be realistically assumed to consist
100% of a single food type allows for the evaluation of the potential maximum exposure and
risk from that dietary pathway. Evaluation of the maximum risk that may be presented by a
given pathway (i.e., plants, invertebrates, or vertebrates) produces results that are protective of
species with more varied diets. Omnivorous species will likely consume foods with differing
contaminant concentrations. As a result, their total exposure will be less than that by species
whose diets consist of the single most contaminated food type. By selecting surrogate species
that would forage exclusively on plants, invertebrates, or vertebrates, regardless of through
which pathway maximal risks are expressed for any given chemical, protectiveness of all other
species is ensured.
4) Mammalian and avian species identified. Because toxic responses for the same
contaminant can differ among wildlife taxa, surrogate species are selected for both mammalian
and avian classes. Based upon the above factors, six mammalian and avian species (listed in
Table 4.1) were selected to represent some of the most highly exposed species. It is assumed
that use of these species also protects other herbivores, ground insectivores, and carnivores.
Surrogate species were selected to provide a conservative representation of their respective trophic
guilds. Selected species are generally small in size relative to other species within their respective
trophic groups (e.g., weasels and voles vs foxes and coyotes or rabbits and deer). Because small size is
associated with higher metabolic rates (Nagy et al, 1999) and smaller home ranges (McNab, 1963 ),
exposure and risk for small receptors is maximized. Eco-SSLs based on these species are therefore
likely to be protective of other, larger species in their trophic guild.
4.3 The Exposure Dose
Estimation of the exposure dose associated with contaminant concentrations in soil requires
parameterization of the general model provided as Equation 4-1.
Wildlife Risk Model
The Eco-SSLs are intended to be conservative screening values that are used to eliminate contaminants
clearly not associated with unacceptable risks. Therefore, several simplifying, conservative assumptions
were made in the parameterization of the general wildlife Eco-SSL risk model. These assumptions
include:
• Surrogate species are assumed to reside and forage exclusively on and within the
contaminated site. Therefore, the area use factor (AUF) is set equal to 1.
DRAFT 4-4 June 27,2000
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Bioavailability of the contaminant in both soil and food is assumed to be comparable to
the bioavailability of the contaminant in the laboratory studies used to establish the
TRVs. Therefore, the absorbed fraction from soil (AFsj) and absorbed fraction from
biota type i (AFy) are both equal to 1.
• The surrogate species' diet consists of 100% of one food type. Therefore, the
proportion of biota type in the diet (P;) is equal to 100% and the number of biota types
(N) in diet is equal to 1.
Parameterizing the Model for Estimating Exposure Dose
Parameterization of the model includes exposure factors related to the surrogate species (see Table
4.1) and estimation of the contaminant concentrations in biota items (B;) consumed in the diet. The
identification and derivation of surrogate species-specific exposure factors for the Wildlife Eco-SSLs
are described in Appendix 4-1. The food and soil ingestion rates used in the exposure model are
represented by the 90th percentiles from their respective distributions. Use of exposure parameter
values from the upper tails of the distributions ensures the protectiveness of the Eco-SSLs for other
wildlife species.
Table 4. 1. Parameterization of the Eco-SSL Wildlife Exposure Model
Receptor Group
(Surrogate Species)
Mammalian Herbivore
(Meadow Vole)
Avian Grainivore
(Mourning dove)
Mammalian Ground
Insectivore
(Short-tailed shrew)
Mammalian Carnivore
(Long-tailed weasel)
Avian Ground Insectivore
(American woodcock)
Avian Carnivore
(Red-tailed hawk)
Body
Weight
(kg)1
0.039
0.115
0.018
0.202
0.159
1.076
Food Ingestion Rate
(kg dw/kg BW day) 2
0.58
0.23
0.20
0.10
0.17
0.12
Soil
Ingestion
(Ps)
0.029
0.16
0.03
0.04
0.12
0.05
Assumed Diet
100% foliage
100% seed
100% earthworms
100% small mammals
100% earthworm
100% small mammals
DRAFT
4-5
June 27, 2000
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Table 4.1. Parameterization of the Eco-SSL Wildlife Exposure Model
Receptor Group
(Surrogate Species)
Body
Weight
(kg)1
Food Ingestion Rate
(kg dw/kg BW day) 2
Soil
Ingestion
(Ps)
Assumed Diet
Parameterization Details Provided in Appendix 4-1.
1 Mean value for both males and females. Derivation of mean presented in Appendix 4-1
2 Mean value is presented but the full distribution of body weights (not a conservatively skewed value) was used to derive
the food ingestion distributions.
Estimating Contaminant Concentrations in Biota
The contaminant concentrations in biota types (B;) composing the wildlife diets were estimated by
assuming that the concentration of the contaminant in the food type can be predicted from the
concentration of the contaminant in the soil (Csoil) by using a Bioaccumulation Factor (BAF). The
function that typically relates B; to Csoil is a constant, which is referred to as the Bioaccumulation Factor
(BAF):
However, the concentration of the contaminant in the food item may be better described by linear or
nonlinear functions that predict bioaccumulation, such as:
B, = a * Csoll + b (linear)
ln(B;)= a * ln(Csoil) + b (logarithmic)
B;= a + b * (1 - exp(-c * Csoil )) (exponential)
where a, b, and c are the parameters of the best-fit equation through the paired data (soil versus soil
organism or plant). These are referred to as regression models.
A hierarchy was established for decision-making concerning the use of available data to estimate
contaminant concentrations in biota types (B;). The following values were used in order of preference:
1) Existing Regression Models. If regression models were currently available and the r-
square values are > 0.2, then these were preferentially used. The primary sources of existing
regression models are: Sample et al. (1999) for earthworms; Sample et al. (1998) for small
mammals; and Bechtel -Jacobs (1998) for plants.
2) New Regressions . If paired data (contaminant concentrations in soil organism or plant versus
soil) were sufficient to establish regression models and these models were significant with r-
square values > 0.2, then these regression models were developed and used.
DRAFT 4-6 June 27, 2000
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Figure 4.2 Summary of Method Used for Estimation of
Contaminant Concentrations in Biota Types (B , )
COC Soil to
Plant
Antimony R
Arsenic BAF
Barium BAF
Beryllium BAF
Cadmium R
Chromium BAF
Cobalt BAF
Copper R
Lead R
Manganese BAF
Nickel R
Selenium R
Silver BAF
Vanadium A
Zinc R
Dieldrin R
DDT BAF
ODD BAF
DDE BAF
PCP BAF
PAHs
TNT M
RDX M
Soil to Diet to Soil to
Earthworm Mammal
BAF BAF
R
BAF BAF
BAF BAF
R
BAF
BAF
BAF
R
R
BAF
R
BAF
A A
R
M BAF
M BAF
M BAF
M BAF
M R
Chemical Specific
M A
M A
Mammal
NA
R
NA
NA
R
R
R
R
R
R
R
R
BAF
—
R
—
—
--
—
—
--
—
M = Estim ated based on equation relating physical-chem ical factor to
bioaccumulationl (model).
R = Log-linear regression uptake model (Appendix 4-1)
BAF = Bioaccumulation Factor (Appendix 4-1)
A = Assumption
NA = Not available
3) Ratios (BAFs). BAFs (or ratios of
the contaminant in soil to the
contaminant in the food item) were
identified based on existing BAFs
reported in the scientific literature. If
reported ratios were not identified, then
paired data (contaminant in soil versus
contaminant in food item) were
collected from the literature to derive
these ratios.
4) Models Estimating BAFs or B; If
BAFs were not available in the
literature or the paired data were not
available to derive the BAF, then
models were used. Existing models
associating contaminant parameters of
the contaminant with the potential for
accumulation in biota or plant tissue
were available and were used to
estimate B;. These existing estimation
models were evaluated and reviewed in
Appendix 4-1.
5) Assumptions. In instances where
data was not available to complete any
of the previously listed options in the
hierarchy (1 to 4) then it was necessary
to make assumptions concerning the
bioaccumulation of contaminants for
soil into B;. These assumptions are
discussed in Appendix 4-1.
How Contaminant Concentrations Are Determined for Plants and Soil Invertebrates (B)
The specific information concerning how contaminant concentrations were estimated for the plant and
soil invertebrate components (B;) of the diets of the surrogate species is provided as Appendix 4-1.
This appendix includes descriptions of the use of any existing models. Figure 4.2 provides a summary
of the type of data (from the hierarchy) used to estimate the contaminant concentrations. Some
specific discussions concerning the bioaccumulation of dieldrin, DDT (and metabolites), and PAHs
DRAFT
4-7
June 27, 2000
-------
from soil into plant tissue are provided in the following subsections.
How Contaminant Concentrations Were Determined for Mammals and Birds (Bj
Empirical soil-whole body loglinear regression models and BAFs are available from Sample et al.
(1998a) for 11 of the 24 contaminants. For the remaining organic contaminants for which empirical
regression models or BAFs were not available, diet-to-tissue BAFs were estimated using the methods
presented in Appendix 4-2.
Although many species of predatory wildlife consume both birds and mammals as prey, few data are
available to estimate bioaccumulation of contaminants into birds. As a consequence, the
bioaccumulation models for mammals are assumed to produce estimates that adequately represent
concentrations in birds. The validity of this assumption is supported by data presented in Beyer et al.
(1985). Birds (representing multiple species), white-footed mice, and short-tailed shrews were
collected from two locations in the vicinity of a zinc smelter in Pennsylvania. Analyses are available for
carcasses (tissue remaining after removal of the GI tract, skin, feet, and beaks) for lead, zinc, cadmium,
and copper. Mean analyte concentrations (and 95% confidence limits) in birds and mammals from
both locations are presented in Figure 4.3. Based on these data, concentrations in birds appear to be
approximately equivalent to or less than those found in omnivorous or insectivorous small mammals.
£•
•o
0) 100 -
t :
tfl
a
ii
8
c
i
loncentrat
0
Lead
i
}
]
i
f
[ '
100 •
Zinc
•
,
{ i i 1
_ Birdsl Birds2 Micel Mice2 Shrewl Shrew2 Birdsl Birds2 Micel Mice2 Shrewl Shrew2
rcass (mg/kg drj
o
8
•-
o
£
at 1 .
g
/?
Cadmium
i
,
1 I
1 ^
I
4
1
•L
12 -
10 •
8 -
6 •
Copper
i •
}j
1
T
I
Birdsl Birds2 Micel Mice2 Shrewl Shrew2
Birdsl Birds2 Micel Mice2 Shrewl Shrew2
Figure 4.3. Comparison of mean concentrations in multiple species near a smelter.
DRAFT
June 27, 2000
-------
What if Data were not Available to Estimate Bf!
For some contaminants and biota types (e.g., earthworms and small mammals for antimony, plants and
small mammals for beryllium, and earthworms for chromium), data were not available to derive BAFs
(as described in Appendix 4-2). For these contaminants, default BAFs of 1 were used. This
assumption is supported by analyses of BAFs for plants, earthworms, and small mammals from Bechtel
Jacobs (1998), Sample et al. (1998b), and Sample et al. (1998a), respectively (refer to Table 4.2).
Table 4.2. Cases where the 90th Percentile of the BAF Distribution
is Greater or Less than One
Plants
Earthworms
Small Mammals
Total Number of
Contaminants
21
31
24
BAFs < 1
12
14
16
BAFs > 1
9
17
8
4.4 Toxicity Reference Values
(TRVs)
As presented in Figure 4.4, a four-step
process was used to select TRVs
appropriate for calculation of wildlife Eco-
SSLs. The four steps included: 1) a
literature search, 2) literature review and
data extraction, 3) literature data
evaluation, and 4) TRY derivation. The
TRY is defined as:
Doses above which ecologically relevant
effects might occur to wildlife species
following chronic dietary exposure and
below which it is reasonably expected
that such effects will not occur.
Literature Search and Retrieval
A literature search was first completed for
each of the Eco-SSL contaminants to
identify lexicological studies for retrieval
and review. The search procedure is
Figure 4.4. Wildlife TRV Derivation Process
The wildlife TRV derivation process is composed of four
general steps:
• Literature Search and Retrieval
Wildlife TRV SOP 1: Literature Search and Retrieval
(Exhibit 4-1)
A literature search identifies dose-response literature for
retrieval.
• Literature Review and Data Extraction
Wildlife TRV SOP 2: Literature Review, Data Extraction
and Coding (Appendix 4-3).
The retrieved literature studies are reviewed and data are
extracted according to an established coding system. Data
are entered into an electronic data base
• Data Evaluation
Wildlife TRV SOP 3: Data Evaluation (Appendix 4-4).
Each of the results identified in the reviewed literature is
scored for quality and applicability for TRV derivation.
• TRV Derivation
Wildlife TRV SOP 4: TRV Derivation (Appendix 4-5).
This procedure plots the collective dose-response
information and establishes the process for estimating the
TRV.
DRAFT
June 27, 2000
-------
described in detail as Exhibit 4-1 and can be used by others to identify relevant data for other
contaminants. The literature search process has been completed for eleven of the Eco-SSL
contaminants including aluminum, antimony, cadmium, chromium, cobalt, copper, DDT, Dieldrin, lead,
PAHs and RDX. Literature searches for the remaining Eco-SSL contaminants are currently in
progress.
Literature Review and Data Extraction
Dose-response studies from retrieved literature were reviewed. Literature exclusion criteria (similar to
those discussed in Chapter 3 for plants and soil invertebrates) were applied to the retrieved wildlife
literature. Additional literature exclusion criteria for wildlife lexicological studies include:
Genotoxicity and mutagenicity studies
• Carcinogenicity studies
Physiology studies
• Acute studies
Non-oral routes of exposure (inhalation, injection, dermal, etc.)
• Studies unrelated to the contaminant
and receptor groups of interest
Where possible, the exclusion criteria were applied to identified titles and abstracts prior to retrieval of
the paper. For retrieved studies that passed the exclusion criteria, the relevant lexicological data were
extracted and entered into an electronic database according to established extraction and coding
procedures detailed as Appendix 4-3.
The primary purpose of the data extraction process was to identify two values associated with each
study result:
A no observed adverse effect level (NOAEL), which is the highest dose that does not cause a
statistically significant adverse effect; and
A lowest observed adverse effect level (LOAEL), which is the lowest dose that caused a
statistically significant adverse effect.
In theory, the threshold for the particular adverse effect lies between the NOAEL and the LOAEL.
DRAFT 4-10 June 27,2000
-------
Table 4.3 Results of the Wildlife Toxicological Literature Search and Review
Contaminant
Aluminum
Antimony
Cadmium
Chromium
Cobalt
CoDDer
DDT
Dieldrin
Lead
RDX
Selenium
Studies
Identified
from
Search
210
46
544
113
115
382
565
276
463
30
471
Studies
Rejected
49
34
228
63
71
53
331
151
48
11
140
Studies
with Data
Extracted
0
10
7
27
30
5
85
101
1
16
58
Studies
not
retrieved
86
2
150
22
2
143
120
24
70
3
155
Studies
Pending
Review
75
0
159
0
0
181
29
0
344
0
121
Data Evaluation
Each test result extracted during the literature review process was scored for quality and applicability
for TRY derivation. The data evaluation process is provided as Appendix 4-4. In instances where
more than one "experiment" (i.e., different combinations of receptor, dose, exposure route, exposure
duration, and endpoint) was reported in a study, the individual "experiments" were scored separately.
In cases of more than one experiment, the scoring system was applied independently to each
experimental result.
The scoring system is based on evaluation often attributes of the lexicological study (Figure 4.5)
assigning a score for each attribute, ranging from zero (no merit in setting a TRV) to 10 (extremely
valuable and relevant to setting a TRV). Note that a low score does not necessarily imply the study
itself is poor, only that the study design is not optimal for the narrow goal of deriving an oral TRV. The
total score was calculated by adding the results of the evaluation of each attribute.
DRAFT
4-11
June 27, 2000
-------
The total score is interpreted as follows:
80 to 100 High confidence
71 to 79 Medium confidence
66 to 70 Low confidence
0 to 65 NotusedinEco-SSL
derivation
The results of the scoring process were used to
evaluate and weight the lexicological study results
used in the derivation of TRVs according to
procedures specified in Appendix 4-5.
A web-based data entry system and database was
created as a tool to facilitate efficient and accurate
data extraction from individual reviewed
toxicological studies as well as data evaluation.
Extraction of the data directly into an electronic
database facilitates necessary sorting, searching and
presentation of the data for the purposes of TRV
derivation. The TRV database is focused on
extracting the no observed adverse effect level
(NOAEL) and lowest observed adverse effect level
(LOAEL) doses from each of the toxicological
studies.
TR V Derivation
The dose-response information for mammals and
birds was plotted separately, and a TRV was
identified for each class using an established
procedure. The process is fully described in
Appendix 4-5. The following general steps were
completed to derive the TRVs:
Dose-Response Data Sorted The
toxicity data were downloaded from the
database into spreadsheet files for each
contaminant using a consistent tabular
format. One table was constructed for avian
data and a second for mammalian data. The
Figure 4.5. Ten Attributes Scored as Part of
the Wildlife Toxicological Data Evaluation
1. Data Source
Primary sources only considered
2. Dose Route
Dietary studies scored higher than gavage,
capsule and liquid. Non oral exposures are
excluded.
3. Test Substance Concentrations
Studies with measured exposures scored higher
than nominal exposures.
4. Contaminant Form
Contaminant forms similar to soil forms scored
higher compared to dissimilar forms.
5. Dose Quantification
Exposures reported as doses scored higher than
those reported as concentrations.
6. Endpoint
Reproductive effects scored higher than
lethality and growth. Sublethal changes are
scored lower and biomarkers scored lowest.
7. Dose Range
Studies with both NOAEL and LOAEL values
scored higher than studies which report only
one value. Narrower ranges between NOAEL
and LOAEL scored higher.
8. Statistical Power
The statistical power of a NOAEL is scored.
9. Exposure Duration
Exposure durations encompassing multiple
generations and critical lifestages scored higher
than chronic, subchronic, and acute.
10. Test Conditions
Studies that report standard exposure
conditions scored higher then those that report
fewer or none.
DRAFT
4-12
June 27, 2000
-------
tables provide the essential information concerning each of the toxicity testing results. Table
4.4 provides an example using the results for mammals and cobalt. The results were numbered
sequentially and sorted by general effect group, then by effect measure.
Dose-Response Data Plotted. The data were downloaded from the database and were
used to produce summary plots depicting the NOAELs and LOAELs for each contaminant.
Summary plots were constructed for each mammalian and avian data set for each contaminant.
The data plots were organized by General Effect Group in order from left to right as:
Biochemical (BIO)
Behavioral (BEH)
Physiological (PHY)
Pathology (PTH)
Reproduction (REP)
Growth (GRO)
Morality (MOR)
Figure 4.6 provides an example plot showing the mammalian data for cobalt.
Exclusion of Data with Limited Utility in Establishing an Eco-SSL. Each NOAEL and
LOAEL result was evaluated according to the Data Evaluation process (Appendix 4-4) and
scored within a range of 0 to 100 (worst to best) for usefulness in establishing an oral TRY.
Data with limited utility were defined as study endpoints receiving a Total Data Evaluation
Score of 65 or less. These data points were excluded from the plots. The purpose of the
exclusion was to ensure that the TRY derivation used the most suitable data.
Within each lexicological study there may be several effect measures reported that have the
same NOAEL and/or LOAEL values. Inclusion of the NOAEL and LOAEL values for all
endpoint measures would result in repetitive values on the plots. To avoid the inclusion of
repetitive and duplicative data, the results for only one Effect Measure per Effect Type were
recorded on the plots.
TRV Selected. The general steps and conditional statements of the derivation process are
outlined in Figure 4.7. These steps are an a priori framework for selection of the TRV value
based on the results of the lexicological plots. The flow chart was used with the toxicity data
plots to derive the TRV according to the described steps. If there were enough data, the TRV
was equal to the geometric mean of the NOAEL values for growth (GRO) and reproductive
(REP) effects adjusted and weighted by the Data Evaluation Score. In cases where the
geometric mean NOAEL was higher than the lowest reported LOAEL for mortality (MOR),
the TRV was equal to the highest NOAEL below the lowest LOAEL for mortality effects. An
example is provided with the mammalian cobalt plot depicted on Figure 4.6.
DRAFT 4-13 June 27,2000
-------
Table 4.4
Example of Extracted and Scored Toxicity Data for Wildlife
TEST INFORMATION
*
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
Q
H
1 146-Dld-Walke-ML-OR-2-BIO-3
1 146-Dld-Walke-ML-OR-2-BIO- 1
1 146-Dld-Walke-ML-OR-2-BIO-2
1 146-Dld-Walke-ML-OR-2-BIO-4
1 122-Dld-Steve-ML-FD-l-BIO-6
1 139-Dld-van R-ML-FD-1-BIO-l
1056-Dld-Murph-ML-FD-l-BIO-7
1026-Dld-Kramp-ML-GV-l-BIO-3
1 146-Dld-Walke-ML-FD-l-BIO-5
1 146-Dld-Walke-ML-FD-l-BIO-6
998-Dld-Hurka-ML-GV-l-BIO-4
961 -Dld-Foste-ML-FD-1 -BIO- 1
1026-Dld-Kramp-ML-GV-l-BIO-4
1141-Dld-Virgo-ML-FD-l-BIO-2
1040-Dld-Mehro-ML-FD-l-BIO-5
999-Dld-Hurka-ML-GV-l-BIO-4
999-Dld-Hurka-ML-GV-l-BIO-5
998-Dld-Hurka-ML-GV-l-BIO-3
1 163-Dld-Zemai-ML-FD-l-BIO-l
911 -Dld-Bandy-ML-GV-1 -BIO-5
911 -Dld-Bandy-ML-GV-1 -BIO-4
1056-Dld-Murph-ML-FD-l-BEH-l
1 146-Dld-Walke-ML-FD-l -BEH-3
988-Dld-Harr -ML-FD-l-BEH-3
1 023-Dld-Kolaj-ML-FD-l -BEH-3
1023-Dld-Kolaj-ML-FD-2-BEH-l
918-Dld-Bilds-ML-FD-l-BEH-2
1 14 1-Dld-Virgo-ML-FD-l -BEH-3
1020-Dld-Kimbr-ML-FD-l-BEH-3
1040-Dld-Mehro-ML-FD-l-BEH-3
1056-Dld-Murph-ML-FD-l-PHY-10
1 146-Dld-Walke-ML-OR-2-PTH-8
1026-Dld-Kramp-ML-GV-l-PTH-l
1 146-Dld-Walke-ML-OR-2-PTH-6
1 146-Dld-Walke-ML-FD-l-PTH-l
1 122-Dld-Steve-ML-FD-l -PTH-4
1023-Dld-Kolaj-ML-FD-l-PTH-l
1056-Dld-Murph-ML-FD-l-PTH-S
960-Dld-Fitzh-ML-FD - 1 -PTH-3
1 122-Dld-Steve-ML-FD-l -PTH-2
1 139-Dld-van R-ML-FD-1-PTH-2
1056-Dld-Murph-ML-FD-l-PTH-9
1 146-Dld-Walke-ML-FD-l-PTH-7
1096-Dld-Reube-ML-FD-l-PTH-2
960-Dld-Fitzh-ML-FD - 1 -PTH-4
1 122-Dld-Steve-ML-FD-l-PTH-5
1023-Dld-Kolaj-ML-FD-2-PTH-3
932-Dld-Chern-ML-GV-l-PTH-5
998-Dld-Hurka-ML-GV-l -PTH-2
961 -Dld-Foste-ML-FD-1 -PTH-2
1 146-Dld-Walke-ML-OR-2-PTH-7
960-Dld-Fitzh-ML-FD - 1 -PTH-2
1141-Dld-Virgo-ML-FD-l-PTH-l
1 040-Dld-Mehro-ML-FD-l -PTH-2
1 01 8-Dld-Keane-ML-OR- 1 -PTH- 1
999-Dld-Hurka-ML-GV-l -PTH-2
999-Dld-Hurka-ML-GV-l-PTH-l
1095-Dld-Reube-ML-FD-l-PTH-3
1 027-D Id-Krish-ML-FD -1 -PTH-5
1 027-D Id-Krish-ML-FD -1 -PTH-2
998-Dld-Hurka-ML-GV-l-PTH-l
1 020-Dld-Kimbr-ML-FD-l -PTH-4
1 020-Dld-Kimbr-ML-FD-l -PTH-2
972-Dld-Gelle-ML-GV-l-PTH-2
911 -Dld-Bandy-ML-GV-1 -PTH-1
911 -Dld-Bandy-ML-GV-1 -PTH-2
1 01 6-Dld- Jones -ML-FD-1 -PTH-3
988-Dld-Harr -ML-FD-l-REP-1
1056-Dld-Murph-ML-FD-l-REP-4
1143-Dld-Virgo-ML-FD-l-REP-l
1142-Dld-Virgo-ML-FD-l-REP-3
978-Dld-Good -ML-FD-l-REP-2
1056-Dld-Murph-ML-FD-l-REP-3
932-Dld-Chern-ML-GV-l-REP-2
936-Dld-Coste-ML-GV- 1 -REP- 1
972-Dld-Gelle-ML-GV-l -REP-3
953 -Dld-Dix-ML-GV- 1 -REP- 1
1142-Dld-Virgo-ML-FD-l-REP-l
978-Dld-Good -ML-FD-l-REP-3
1142-Dld-Virgo-ML-FD-l-REP-2
EXPOSURE INFORMATION
S
dog
dog
dog
dog
mouse
mouse
deer
rat
rat
rat
rat
rat
rat
mouse
rat
rabbit
rabbit
rat
rat
rat
rat
deer
rat
rat
mouse
rat
mouse
mouse
rat
rat
deer
dog
rat
dog
rat
mouse
mouse
deer
rat
mouse
mouse
deer
rat
rat
rat
mouse
rat
mouse
rat
rat
dog
rat
mouse
rat
dog
rabbit
rabbit
mouse
rat
rat
rat
rat
rat
rat
rat
rat
rat
rat
deer
mouse
mouse
mouse
deer
mouse
mouse
rat
mouse
mouse
mouse
mouse
# of Cone/ Doses
3
3
3
3
4
4
3
5
4
4
2
3
5
5
2
2
2
2
2
2
2
3
4
11
5
5
2
5
3
2
3
3
5
3
4
4
5
3
7
4
4
3
4
8
7
4
5
4
2
3
3
7
5
2
3
2
2
2
2
2
2
3
3
2
2
2
2
11
3
7
4
2
3
4
2
2
3
4
2
4
U
1
M
M
M
M
U
U
U
M
M
M
M
U
M
U
U
M
M
M
U
M
M
U
M
M
M
M
U
U
U
U
U
M
M
M
M
U
M
U
U
U
U
U
M
U
U
U
M
M
M
U
M
U
U
U
M
M
M
U
U
U
M
U
U
M
M
M
M
M
U
U
U
U
U
M
M
M
M
U
U
U
i
1
OR
OR
OR
OR
FD
FD
FD
GV
FD
FD
GV
FD
GV
FD
FD
GV
GV
GV
FD
GV
GV
FD
FD
FD
FD
FD
FD
FD
FD
FD
FD
OR
GV
OR
FD
FD
FD
FD
FD
FD
FD
FD
FD
FD
FD
FD
FD
GV
GV
FD
OR
FD
FD
FD
OR
GV
GV
FD
FD
FD
GV
FD
FD
GV
GV
GV
FD
FD
FD
FD
FD
FD
FD
GV
GV
GV
GV
FD
FD
FD
_§
o
1
104
104
104
104
28
14
3
13
104
104
100
6
13
10
60
100
100
100
8
15
15
3
104
400
90
90
3
10
8
60
3
104
13
104
104
28
90
3
2
28
14
3
104
2
2
28
90
10
100
6
104
2
10
60
85
100
100
104
24
24
100
8
8
7
15
15
8
400
3
13
1
120
3
10
18
7
9
1
120
1
g
1
3
Q
w
w
w
w
d
mo
y
d
W
W
d
W
d
w
d
d
d
d
w
d
d
y
w
d
d
d
mo
w
w
d
y
w
d
w
w
d
d
y
y
d
y
w
y
y
d
d
d
d
w
w
y
w
d
d
d
d
w
w
w
d
w
w
d
d
d
w
d
y
w
g
d
y
d
d
d
d
g
d
g
Oil
5.5
5.5
5.5
5.5
4
4.5
1
NR
5
5
NR
NR
NR
13
NR
NR
NR
NR
NR
NR
NR
1
5
28
8
8
3.5
13
3.5
NR
1
5.5
NR
5.5
5
4
8
1
NR
4
1
5
3
NR
4
8
NR
NR
NR
5.5
NR
13
NR
25.5
NR
NR
3
NR
NR
NR
3.5
3.5
NR
NR
NR
5
28
1
5
5
6
1
NR
9
NR
7
5
6
5
Age Units
mo
mo
mo
mo
w
w
y
NR
w
w
NR
NR
NR
w
NR
NR
NR
NR
NR
NR
NR
y
W
d
w
w
mo
w
mo
NR
y
mo
NR
mo
w
w
w
y
NR
w
y
w
w
NR
w
w
NR
NR
NR
mo
NR
w
NR
mo
NR
NR
w
NR
NR
NR
mo
mo
NR
NR
NR
w
d
y
w
w
w
y
NR
w
NR
w
w
w
w
Lifestage
MU
MU
MU
MU
NR
JV
MU
NR
MU
MU
NR
NR
NR
NR
NR
NR
NR
NR
MA
YO
YO
MU
MU
MU
NR
NR
NR
NR
AD
NR
MU
MU
NR
MU
MU
NR
NR
MU
JV
NR
JV
MU
MU
NR
JV
NR
NR
SM
NR
NR
MU
JV
NR
NR
AD
NR
NR
NR
JV
JV
NR
AD
AD
MU
YO
YO
NR
MU
MU
SM
SM
NR
MU
SM
NR
MU
SM
SM
NR
SM
1
BH
BH
BH
BH
M
F
F
M
BH
BH
NR
M
M
F
M
NR
NR
NR
F
M
M
F
BH
BH
M
M
NR
F
M
M
F
M
M
BH
BH
M
M
F
M
M
F
F
BH
BH
BH
M
M
F
NR
M
BH
F
F
M
NR
NR
NR
BH
BH
BH
NR
M
M
BH
M
M
BH
BH
F
F
F
BH
F
F
F
BH
F
F
BH
F
EFFECT INFORMATION
General Effect Group
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BIO
BEH
BEH
BEH
BEH
BEH
BEH
BEH
BEH
BEH
PHY
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
PTH
REP
REP
REP
REP
REP
REP
REP
REP
REP
REP
REP
REP
REP
a.
i
w
CHM
ENZ
CHM
ENZ
ENZ
ENZ
ENZ
ENZ
CHM
ENZ
ENZ
HRM
ENZ
CHM
ENZ
CHM
ENZ
CHM
ENZ
CHM
ENZ
FOB
FOB
FOB
FOB
FOB
BEH
BEH
BEH
FOB
PHY
ORWT
ORWT
ORWT
fflS
fflS
ORWT
ORWT
ORWT
ORWT
fflS
ORWT
ORWT
fflS
fflS
fflS
ORWT
ORWT
fflS
ORWT
ORWT
ORWT
ORWT
ITX
ITX
fflS
ORWT
fflS
fflS
ORWT
fflS
fflS
ORWT
ORWT
fflS
ORWT
fflS
REP
REP
REP
REP
REP
REP
REP
REP
REP
REP
REP
REP
REP
Effect Measure
TOPR
ALPH
HMGL
CEST
EROD
AATT
ALPH
Other
HMGL
ALPH
ALPH
CORT
PNAD
TOPR
Other
CHOL
ALPH
GLYC
CEST
Other
Other
FCNS
FCNS
FCNS
FCNS
FCNS
FRZG
INST
INST
FCNS
OTHR
ORWT
SMIX
ORWT
GLSN
GfflS
SMIX
ORWT
SMIX
SMIX
GSLN
ORWT
ORWT
NPHR
GfflS
GfflS
SMIX
SMIX
NCRO
ORWT
ORWT
SMIX
ORWT
INTX
CONV
NCRO
ORWT
Other
HYPL
SMIX
GfflS
GfflS
SMIX
ORWT
NCRO
ORWT
NCRO
NSNT
PRWT
RSUC
RBEH
PERT
TPRD
TERA
PRWT
PRWT
PLBR
RSUC
NTSZ
RBEH
Response Site
SR
PL
BL
ER
LI
LI
SR
LI
BL
PL
LI
AR
LI
MC
BR
LI
LI
LI
PL
LI
LI
WO
WO
WO
WO
WO
WO
WO
WO
WO
KI
SP
LI
KI
KI
LI
LI
LI
LI
LI
LI
KI
BR
KI
LI
LI
LI
LI
LI
AR
HE
LI
LI
WO
WO
LI
LI
LI
LI
LI
LI
LI
LI
AR
LI
LI
BR
WO
WO
WO
WO
WO
WO
WO
WO
WO
WO
WO
wo
wo
NOAEL Dose (mg/kg/day)
0.005
0.005
0.05
0.05
0.127
0.13
0.14
0.25
0.79
0.79
2.5
9.8
0.69
0.79
0.85
1.27
1.27
0.69
0.005
0.05
0.05
0.082
0.127
0.127
0.14
0.16
0.3812
0.64
0.69
0.79
0.79
0.80
1.27
1.27
1.5
2.5
9.8
0.054
0.14
0.34
0.65
0.66
0.69
1.5
2
3
4
LOAELDose (mg/kg/day)
0.05
0.05
0.3812
0.64
0.69
1.25
19.6
0.05
0.64
0.92
1.25
1.25
2.5
5
5
5
1.7
1.3
0.64
2.64
0.92
0.05
0.25
0.79
0.3812
0.3812
0.69
0.79
1.27
1.3
3.96
4.1
3
19.6
0.005
0.043
0.64
0.92
1
1.25
1.25
1.3
1.6
1.6
2.5
2.6
2.6
3
5
5
8.0
0.21
0.69
0.67
1.29
3
0.65
0.66
1.29
DATA EVALUATION SCORES
Data Source
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Dose Route
8
8
8
8
10
10
10
8
10
10
8
10
8
10
10
8
8
8
10
8
8
10
10
10
10
10
10
10
10
10
10
8
8
8
10
10
10
10
10
10
10
10
10
10
10
10
10
8
8
10
8
10
10
10
8
8
8
10
10
10
8
10
10
8
8
8
10
10
10
10
10
10
10
8
8
8
8
10
10
10
Test Substance
10
10
10
10
5
5
5
10
10
10
10
5
10
5
5
10
10
10
5
10
10
5
10
10
10
10
5
5
5
5
5
10
10
10
10
5
10
5
5
5
5
5
10
5
5
5
10
10
10
5
10
5
5
5
10
10
10
5
5
5
10
5
5
10
10
10
10
10
5
5
5
5
5
10
10
10
10
5
5
5
1
U
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
i
•-3
s
Q
10
10
10
10
5
5
10
10
6
6
10
7
10
5
6
10
10
10
6
10
10
10
6
7
5
5
5
5
10
6
10
10
10
10
6
5
5
10
6
5
5
10
6
5
6
5
5
10
10
7
10
6
5
6
10
10
10
5
7
7
10
10
10
10
10
10
10
7
10
5
5
5
10
10
10
10
10
5
5
5
Endpoint
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
10
10
10
10
10
10
10
10
10
10
10
10
10
?
Q
8
8
4
4
10
8
8
8
4
4
4
10
4
4
4
4
4
4
4
4
4
4
4
10
4
4
4
4
4
4
8
8
4
8
10
10
8
8
8
8
4
4
8
8
4
4
10
4
10
4
4
4
4
4
4
4
4
4
4
4
4
4
4
10
8
10
10
4
4
10
4
4
4
4
4
4
•a
1
10
10
i
i
10
10
10
10
10
10
1
10
10
10
10
10
10
10
10
10
10
1
1
10
1
1
10
10
10
1
10
10
3
10
10
10
10
10
10
10
1
10
10
10
10
1
10
1
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
1
1
10
1
10
1
10
10
8
1
o
1
10
10
10
10
6
10
10
6
10
10
10
6
6
10
6
6
6
10
6
6
6
10
10
10
10
10
10
10
6
10
10
6
10
10
6
10
10
10
6
10
10
10
10
10
6
10
10
10
6
10
10
10
6
6
10
10
10
10
6
6
10
6
6
10
10
10
10
10
10
10
10
10
10
10
10
10
Test Conditions
4
4
4
4
4
4
4
4
2
2
4
4
4
4
4
4
4
4
4
4
4
4
2
4
7
7
4
4
4
4
4
4
4
4
4
7
4
4
4
4
4
2
4
4
4
7
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
H
81
81
68
68
71
73
78
77
73
73
68
73
73
69
66
73
73
77
66
73
73
68
67
85
71
71
72
72
73
69
68
84
80
73
82
74
86
81
77
72
76
68
76
76
77
68
71
86
71
76
80
73
72
69
76
72
74
74
80
73
73
80
76
78
91
87
84
84
69
74
92
77
86
77
78
78
76
-------
Table 4.4
Example of Extracted and Scored Toxicity Data for Wildlife
TEST INFORMATION
*
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
Q
H
936-Dld-Coste-ML-GV- 1 -REP-2
1 146-Dld-Walke-ML-OR-2-GRO-5
1146-Dld-Walke-ML-FD-l-GRO-2
1023-Dld-Kolaj-ML-FD-l-GRO-2
1023-Dld-Kolaj-ML-FD-2-GRO-2
1 027-Dld-Krish-ML-FD-l -GRO-4
932-Dld-Chern-ML-GV-l-GRO-4
953-Dld-Dix-ML-GV-l-GRO-2
1 020-Dld-Kimbr-ML-FD- 1 -GRO- 1
1 01 6-Dld- Jones -ML-FD-1 -GRO-2
1056-Dld-Murph-ML-FD-l-GRO-5
1150-Dld-Wasse-ML-DR-l-GRO-l
911 -Dld-Bandy-ML-GV-1 -GRO-3
1 147-Dld-Walke-ML-FD-l-MOR-l
1 157-Dld-Wiese-ML-FD-l-MOR-l
1 147-Dld-Walke-ML-FD-2-MOR-l
978-Dld-Good -ML-FD-1 -MOR- 1
1056-Dld-Murph-ML-FD-l-MOR-2
1 146-Dld-Walke-ML-FD-l-MOR-4
1096-Dld-Reube-ML-FD-l-MOR-l
960-Dld-Fitzh-ML-FD- 1 -MOR- 1
988-Dld-Harr -ML-FD-1 -MOR- 2
943-Dld-Davis-ML-FD-l-MOR-l
1018-Dld-Keane-ML-OR-l-MOR-2
999-Dld-Hurka-ML-GV- 1 -MOR-3
1095-Dld-Reube-ML-FD-l-MOR-l
918-Dld-Bilds-ML-FD-l-MOR-l
1143-Dld-Virgo-ML-FD-l-MOR-3
932-Dld-Chern-ML-GV-2-MOR-l
932-Dld-Chern-ML-GV-l-MOR-3
961 -Dld-Foste-ML-FD- 1 -MOR-3
1 01 6-Dld-Jones-ML-FD- 1 -MOR- 1
1137-Dld-Uzouk-ML-OR-l-MOR-l
1150-Dld-Wasse-ML-DR-l-MOR-3
1127-Dld-Stoew-ML-FD-l-MOR-l
EXPOSURE INFORMATION
S
mouse
dog
rat
mouse
rat
rat
mouse
mouse
rat
rat
deer
rabbit
rat
mouse
blesbuck
mouse
mouse
deer
rat
rat
rat
rat
sheep
dog
rabbit
mouse
mouse
mouse
rat
mouse
rat
rat
guinea pig
rabbit
rat
# of Cone/ Doses
2
3
4
5
5
2
4
3
3
2
3
2
2
4
6
6
2
3
4
8
7
11
5
3
2
2
2
7
4
4
3
2
2
2
2
U
1
M
M
M
M
M
U
M
M
U
M
U
M
M
M
U
U
U
U
M
U
U
M
M
M
M
U
U
U
M
M
U
M
M
M
U
i
I
(S
GV
OR
FD
FD
FD
FD
GV
GV
FD
FD
FD
DR
GV
FD
FD
FD
FD
FD
FD
FD
FD
FD
FD
OR
GV
FD
FD
FD
GV
GV
FD
FD
OR
DR
FD
_§
o
1
18
104
104
90
90
24
10
9
8
8
3
5
15
132
90
128
120
3
104
2
2
400
32
85
100
104
3
13
10
10
6
8
75
5
42
g
1
3
Q
d
w
w
d
d
w
d
d
w
w
y
w
d
w
d
w
d
y
w
V
y
d
w
d
d
w
mo
w
d
d
w
w
d
w
d
Oil
9
5.5
5
8
8
NR
NR
7
3.5
5
1
NR
NR
3
1
3
6
1
5
3
NR
28
NR
25.5
NR
3
3.5
5
NR
NR
NR
5
NR
NR
NR
Age Units
w
mo
w
w
w
NR
NR
w
mo
w
y
NR
NR
w
y
w
w
y
W
W
NR
d
NR
mo
NR
w
mo
w
NR
NR
NR
w
NR
NR
NR
Lifestage
NR
MU
MU
NR
NR
JV
SM
SM
AD
NR
MU
YO
YO
MU
NR
MU
NR
MU
MU
NR
JV
MU
NR
AD
NR
NR
NR
SM
SM
SM
NR
NR
NR
YO
JV
1
F
BH
BH
M
M
BH
F
F
M
BH
F
M
M
BH
BH
BH
BH
F
BH
BH
BH
BH
M
NR
NR
BH
NR
F
F
F
M
BH
F
M
BH
EFFECT INFORMATION
General Effect Group
REP
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
a.
i
w
REP
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
MOR
Effect Measure
OTHR
BDWT
BDWT
BDWT
BDWT
BDWT
BDWT
BDWT
BDWT
BDWT
BDWT
BDWT
BDWT
MORT
MORT
MORT
MORT
MORT
MORT
MORT
SURV
MORT
MORT
MORT
MORT
MORT
MORT
SURV
MORT
MORT
MORT
MORT
MORT
MORT
MORT
Response Site
WO
WO
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
wo
NOAEL Dose (mg/kg/day)
0.05
0.79
1.27
1.27
1.6
3
4
5.33
8.00
0.13
0.53
0.65
0.66
0.69
0.79
0.79
0.82
0.85
1
1
1.25
1.3
1.3
2
3
6
9.8
8.00
LOAELDose (mg/kg/day)
2
6
0.14
4.6
5
1.3
0.89
1.3
3.95
4.1
1.7
2
2.7
6
19.6
3
4.6
13.5
DATA EVALUATION SCORES
Data Source
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Dose Route
8
8
10
10
10
10
8
8
10
10
10
5
8
10
10
10
10
10
10
10
10
10
10
8
8
10
10
10
8
8
10
10
8
5
10
Test Substance
10
10
10
10
10
5
10
10
5
10
5
5
10
10
5
5
5
5
10
5
5
10
10
10
10
5
5
5
10
10
5
10
10
5
5
1
U
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
1
1
o-
Q
10
10
6
5
5
7
10
10
10
10
10
6
10
5
6
5
5
10
6
5
6
7
10
10
10
5
5
5
10
10
7
10
10
6
5
Endpoint
10
8
8
8
8
8
8
8
8
8
8
8
8
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
?
Q
4
4
4
4
4
4
10
4
4
4
4
4
4
8
10
10
4
4
4
8
8
10
10
4
4
4
4
10
10
4
10
4
4
4
4
•a
1
10
i
10
1
1
1
10
10
10
1
10
10
10
10
10
10
1
1
0
0
0
0
0
0
1
1
1
10
10
1
10
1
10
10
10
1
o
1
10
10
10
10
10
10
10
10
6
6
10
6
6
10
6
10
10
10
10
10
10
10
10
6
6
10
10
10
10
10
6
6
6
6
6
Test Conditions
4
4
2
7
7
4
4
4
4
4
4
4
4
4
4
4
4
4
2
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
H
86
75
80
75
75
69
90
84
77
73
81
68
80
86
80
83
68
73
81
81
82
90
93
81
72
68
68
83
91
76
81
74
81
69
73
-------
Figure 4.6 Example of Mammalian TRV Derivation for Dieldrin
100.000 4
0.001
•BIO-NOAEL OBIO-LOAEL •BEH-NOAEL OBEH-LOAEL OPHY-NOAEL »PHY-LOAEL OPTH-NOAEL PTH-LOAEL •REP-NOAEL OREP-LOAEL OGRO-NOAEL
GRO-LOAEL OMOR-NOAEL MOR-LOAEL
Result number
Reference Number
Test Species Key
10-c
Test Species
D =dog
R =rat
M = mouse
Dr = deer
Rb = rabbit
Ble = blesbuck (antelope)
G = Guinea Pig
S = Sheep
Wildlife TRV Derivation Process
1) There are at least three results available for two test species within the GRO, REP and MOR effect groups.
2) There are three NOAEL results available for calculation of a weighted geometric mean.
3) The weighted geometric mean of the adjusted NOAELs for REP and GRO equals 0.80 mg dieldrin/kg BW/day.
4) The weighted geometric mean NOAEL is slightly lower than the lowest LOAEL for mortality at 0.89 mg dieldrin/kg BW/day.
5) The avian wildlife TRV for dieldrin is equal to 0.80 mg dieldrin/kg BW/day.
-------
NO
Are there at least 3
toxicity values for 2
species for REP, GRO or
MOR groups?
YES
Are there 3 or more
NOAELs in REP and
GRO groups?
TRV = lowest
LOAEL /10
YES
At least 3
LOAELs
for GRO
&REP?
NO
No TRV can be
derived
NO
At least 6
NOAELs and/or
LOAELs for
other
endpoints?
TRV = Highest
NOAEL below
lowest LOAEL
for BEH, PTH,
BIO or MOR
Figure 4.7 TRV Derivation Process
NO
NO
YES
At Least one NOAEL (for
REP and GRO)?
YES
Calculate the weighted
geometric mean of NOAELs
(REP and GRO)
YES
Is NOAEL < lowest
LOAEL for MOR?
NO
TRV = Lowest
NOAEL for
GRO & REP
NO
TRV = Highest
NOAEL below
lowest LOAEL
for MOR
Is weighted mean
NOAEL < lowest
LOAEL for MOR?
NO
TRV = Highest
NOAEL below
lowest LOAEL
for appropriate
effect group
YES
Is Mechanism of
Toxicity Addressed?
YES
TRV = Weighted
geometric mean of
NOAELs for REP &
GRO
-------
The results of the wildlife TRY derivation process for each contaminant are provided as Appendix 4-6.
4.5 Calculation of Wildlife Eco-SSLs
The Eco-SSL wildlife risk model (Equation 4-1) may be expressed in two forms, depending on the
method used to estimate contaminant concentrations in food items (B;).
1) If a BAF was used to estimate the contaminant concentrations in food items (bioaccumulation),
then the equation was:
FIR- A (Equation 4_2)
HQ. • ± ^
J TRY
where:
soil-to-biota BAF (units- dry weight to dry weight) for contaminant (j) for food
type (i)
2) If regression models were used, then the equation was:
[Soil; • Ps • FIR - AFjs]- [ . e 'J . P. . FIR - AF - AUF
(Equation 4-3)
where:
e = Napierian constant (2.7182818),
BOjj = Intercept from log-linear bioaccumulation model for contaminant (j) for biota type
(i), and
Bltj = slope from log-linear bioaccumulation model for contaminant (j) for biota type (i)
The general procedure for calculating the wildlife Eco-SSL involves inverting the BAF or loglinear
forms of the exposure models (Equations 4-2 and 4-3, respectively) to determine the contaminant
concentration in soil that is equivalent to an HQ = 1. Exposure models that employ BAFs are a simple
linear function of the soil concentration and may be inverted algebraically. However, when the
exposure model incorporates the loglinear bioaccumulation models, numerical methods are required.
DRAFT 4-18 June 27,2000
-------
The solution to the Eco-SSL exposure model using a simple BAF is outlined below. Equation 4-2 can
be rewritten as:
N \
[Ps • FIR • AFjs]- [. (7V> Pl • FIR • AFtj] I • Soilj • A UF
H<3j ' "" fgy " (Equation 4-4)
3
1 1
Multiplication of both sides of equation 4-4 by — and 7777 produces:
Soilj
( N }
[Ps • FIR • AF]s\> [. (7V> Pi • FIR • AFtj]
\ i-i }
i • FIR • AFtJ\ • AUF
Soil TRY • HQ (Equation 4-5)
Inversion of equation 4-5 produces:
TRVl • HQ.
Soil • -, i i r
3 ' "r * (Equation 4-6)
[Ps • FIR • AFJS]' [. (Ttj)' Pl • FIR • AFtj\ • AUF
where:
Soilj= the Eco-SSL for contaminant] for wildlife and TRVj is equal to a no-effect level.
Solution of the log-linear form of the wildlife Eco-SSL model is more complex than the BAF-based
model. An algorithm, implemented through a spreadsheet, was derived to facilitate the solution of this
form of the model. A description of the solution to the log-linear form of the wildlife Eco-SSL model
and the code for the algorithm are both presented in Appendix 4-2.
Wildlife Eco-SSLs. In order to calculate wildlife Eco-SSLs, Equation 4-6 was rearranged, with the
removal of all parameters that were set to 1, resulting in the following simplified model:
TRY
(Equation 4-7)
FIR*[P- T ]
DRAFT 4-19 June 27,2000
-------
where:
= Contaminant concentration for contaminant (j) in soil (mg/kg dry weight),
FIR = Food ingestion rate (kg food [dry weight]/ kg BW [wet weight] / d),
PS = Soil ingestion as proportion of diet,
TRVj = Toxicity reference value for contaminant (j) (mg [dry weight]/kg BW [wet
weight] /d),
Ty = Soil-to-biota BAF for contaminant (j) for biota type (i).
In some cases where soil-to-biota BAFs were not available it was necessary to use a string of BAFs
(for example: (BAF for soil to earthworm + BAF for earthworm to shrew) in which case the equation
was reduced to:
j (Equation 4-8)
Eco* SSL , •
pred FIR*[P • ( T.. • T )]
where:
Tver = diet to biota BAF
Eco-SSLs were calculated for each contaminant for each surrogate receptor. The results of the
calculations are presented as Appendix 4-2. The Eco-SSLs currently derived for wildlife are
summarized in Chapter 5.
DRAFT 4-20 June 27,2000
-------
5.0 ECO-SSL SUMMARIES
Presented below are summaries of the Eco-SSL values derived for each contaminant and receptor
group. The summaries provide a brief review of the contaminant including environmental forms,
sources, background concentrations, mechanisms of toxicity, and essential element status (if
applicable). Separate discussion are provided for each receptor group including plants, soil
invertebrates, avian wildlife and mammalian wildlife. Some synopses are not yet complete as Eco-SSL
derivation is pending receipt of lexicological studies for review. The Eco-SSLs are rounded to two
significant digits.
Some Eco-SSLs for metals are within the range of reported background concentrations that may occur
at sites without any contaminant release due to hazardous waste disposal activities. As part of the
Eco-SSL project, available data for the background concentrations of metals are summarized in a
report that is further discussed in Chapter 6. It is anticipated that as the user of the Eco-SSLs performs
other site specific studies as part of the baseline risk assessment, the resulting soil contaminant
concentrations found to be protective may be substantially higher.
5.1 Antimony
Table 5.1 Antimony Eco-SSLs
(mg/kg dry weight in soil)
Plants
Pending
Soil Invertebrates
NA
Wildlife
Avian
NA
Mammalian
21
NA = Not Available. Data was either not available or insufficient to derive Eco-SSL.
Antimony (Sb, stibium) is a semi-metallic element belonging to group VA of the periodic table and
sharing some chemical properities with lead, arsenic, and bismuth (U. S. EPA, 1992). In nature,
antimony is associated with sulfur as stibnite. Antimony also occurs in ores with arsenic, and the two
metals share similar chemical and physical properties. Antimony is a common component of lead and
copper alloys and is used in the manufacture of ceramics, textiles, paints, explosives, batteries, and
semiconductors. Major sources of environmental contamination are smelters, coal combustion, and
incineration of waste and sewage sludge. In the past, antimony compounds have been used
therapeutically as an anti-helminthic and anti-protozoic treatment. This practice has been largely
discontinued as a result of antimony toxicity.
Antimony exists in valences of 0, -3, +3, +5. The tri- and pentavalent forms are the most stable forms
of antimony (U.S. EPA, 1992) and are of the most interest in biological systems. The toxicokinetics
and toxicity of the tri- and pentavalent forms vary, with the trivalent form considered to be more toxic.
DRAFT 5 - 1 June 27, 2000
-------
Ingested antimony is absorbed slowly, and many antimony compounds are reported to be
gastrointestinal irritants. Trivalent antimony is absorbed more slowly than the pentavalent form.
Approximately 15-39% of trivalent antimony is reported to be absorbed in the gastrointestinal tract of
animals (Rossi et al, 1987). The toxic effects of antimony in mammals involves cardiovascular changes.
Observed changes include degeneration of the myocardium, arterial hypotension, heart dysfunction,
arrhythmia, and altered electrocardiogram patterns (Rossi et al. 1987). The mode of action for
antimony-induced cardiotoxicity is unknown.
The Eco-SSL values derived to date for antimony are summarized in Table 5.1. Eco-SSL values for
antimony are not available for plants and soil invertebrates or avian wildlife. For these receptor groups,
data was insufficient to derive soil screening values. An Eco-SSL value for antimony is available for
mammalian wildlife.
Plant Eco-SSL for Antimony
An Eco-SSL value could not be derived for plants at this time. The literature search process (Exhibit
3-1) identified thirteen papers for review. Six of these studies did not pass the Literature Acceptance
Criteria. The remaining seven papers have not been received for review.
Soil Invertebrate Eco-SSL for Antimony
An Eco-SSL value could not be derived for soil invertebrates at this time. The literature search process
(Exhibit 3-1) did not identify any acceptable literature studies for the toxicity of antimony in soil to soil
invertebrates.
Avian Eco-SSLs for Antimony
The literature search process for wildlife TRVs (described in Exhibit 4-1) did not identify any
lexicological studies of antimony and birds. At this time an Eco-SSL can not be derived for avian
receptors for antimony.
Mammalian Eco-SSLs for Antimony
The electronic and manual literature search process for wildlife toxicity data (Exhibit 4-1) for antimony
identified 46 studies. Of these, ten studies contained data used to derive the TRVs used to calculate the
Eco-SSL, 34 studies were rejected for use and two studies could not be located for review. As
described in Chapter 4, three separate Eco-SSL values are calculated for mammalian wildlife, one each
for three surrogate species representing different trophic levels: herbivores (vole), ground insectivores
(shrew) and carnivores (weasel). The lowest value for these three species is the mammalian Eco-SSL.
DRAFT 5 - 2 June 27, 2000
-------
The mammalian Eco-SSLs for antimony derived for the following surrogate species are as calculated as
follows:
Calculation of Wildlife Eco-SSLs
Antimony
Surrogate
Receptor Group
Mammalian
herbivore (vole)
Mammalian
ground insectivore
(shrew)
Mammalian
carnivore (weasel)
TRVj
(mg dw/kg
BW/d)
4.4
4.4
4.4
FIR
(kg/kg/d)
0.58
0.2
0.1
Ps
0.029
0.03
0.04
Tv
Estimated by log-
linear uptake
model solved for
HQ=1
1
1
Tver
NA
0.001
Eco-SSL
(mg/kg dw)
(Soilj)
120
21
1100*
Sources and derivatio n of the exposure parameters (FIR, P, and T) are provided in Appendix 4-1.
The process for derivation of wildlife TRVs is described in Appendix 4-5 and the results are provided in
Appendix 4-6.
Eco-SSL = Soilj - TRVj / FIR * [Ps +Tjj]
*Eco-SSLpred= TRVj / FIR * [Ps + (T,j + T¥er)]
Soilj = Contaminant concentration for contaminant (j) in soil (mg/kg dry weight),
FIR = Food ingestion rate (kg food [dry weight]/ kg BW [wet weight] /d),
ps = Soil ingestion as proportion of diet,
TRVj = Toxicity reference value for contaminant (j) (mg [dry weight]/kg BW [wet weight] /d) for
contaminant (j),
TtJ = Soil-to-biota BAF for contaminant (j) for biota type (i),
rver = Diet to biota BAF.
5.2 Arsenic
Table 5.2 Arsenic Eco-SSLs
(mg/kg dry weight in soil)
Plants
37
Soil Invertebrates
Pending
Wildlife
Avian
Pending
Mammalian
Pending
DRAFT
5-3
June 27, 2000
-------
Arsenic is naturally present in rock and soils with concentrations in soils reflecting by the geology of the
region as well as anthropogenic inputs. Higher concentrations are associated with igneous and
sedimentary rocks, particularly with sulfidic ores (API, 1998). Extensive discussions of the sources,
concentrations and chemical species are presented in NAS (1977) and Cullen and Reimer (1989).
Arsenic is used in multiple manufacturing and industrial processes including the production of wood
treating chemicals, herbicides, pesticides, desiccants, metal alloys, glass, pharmaceuticals and semi-
conductors. Elevated arsenic soil concentrations are often associated with mining activities, smelters,
pesticide/herbicide manufacturing facilities and agricultural lands (API, 1998).
Arsenic can exist in four oxidation stats: +5, +3, 0 and -3. In soil, arsenic is a constituent of numerous
minerals and is found frequently associated with sulfur, most commonly as arsenopyrite (FeAsS).
Inorganic arsenate can also be bound to iron
and aluminum cations, or any other cation
that may be present (e.g., calcium, zinc,
magnesium, lead) as well as organic matter in
soils (API, 1998).
Typical Background Concentrations of
75-
. - 60 -
&
| 45~
1
1 30-
g
U 15-
0-
Arsenic in U. S. Soils
X
X
CERCLIS-3 East West
Arsenic occurs in contaminated soils
primarily as the inorganic arsenic (V) and
arsenic (HI) but soil microorganisms can
produce organic forms (Cullen and Reimer,
1989; Huang, 1994; CCME, 1996a).
Transformations among inorganic and organic
forms are controlled by the oxidation-
reduction, precipitation/adsorption, and
biomethylation processes in addition to the
biological production and volatilization of the
arsines (API, 1998). The availability or solubility of arsenic in soils depends on the source (natural vs.
anthropogenic) and the soil's clay content, redox potential and pH. Generally, factors that tend to
increase arsenic availability are anthropogenic source (e.g., pesticides), low clay content, low redox
potential (reducing conditions) and high pH (alkaline conditions) (Cullen and Reimer, 1989, API,
1998).
The Eco-SSL values derived to date for arsenic are summarized in Table 5.2. Eco-SSL values for
arsenic are not yet available for soil invertebrates, avian wildlife or mammalian wildlife. An Eco-SSL
value for arsenic is available for plants.
DRAFT
5-4
June 27, 2000
-------
Plant Eco-SSLfor Arsenic
The following table and graph summarize the data used to derive the plant Eco-SSL for arsenic.
Summary of Data used to Derive Plant Eco-SSL for
Arsenic
Study
ID
1
2
3
4
5
6
7
8
9
Reference
Jacobs (1970)
Jacobs (1970)
Jacobs (1970)
Jacobs (1970)
Jiang (1994)
Jiang (1994)
Jiang (1994)
Jiang (1994)
Jiang (1994)
Test Organism
Zea mays
Phaseolus vulgaris
Pisium sativum
Solanum tuberosum
Lolium perenne
Lolium perenne
Hordeum vulgare
Hordeum vulgare
Hordeum vulgare
Bio-
availability
Score
2
2
2
2
2
2
2
2
2
ERE
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
Tox
Parameter
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
Soil Cone.
(mg/kg dw)
40
40
97
135
22
22
22
112
4
Level
A
A
A
A
A
A
A
A
A
ERE = Ecologically Relevant Endpoint, described in Appendix 3-1
Tox Parameter = Maximum Acceptable Threshold Concentration (MATC) or ECXX described in Appendix 3-1
Soil Cone. = Concentration of contaminant in soil (mg/kg ) for the corresponding ERE and Tox parameter.
Level = Preference level (described in Appendix 3-1 ).
160 •
•a 14° "
'3
to
.S 120 •
tj
ue for Arsen
(mg/kg)
N 00 O
3 O O
C3 OU
•fe1
'Q 4U
'S
^ 20 '
0 •
(
Arsenic Plant Data for
*
*
A A
Eco-SSL
* MATC
^^"Geometric mean =37
^^
^
) 1 2 3 4 5
6 7 8 9 10
Study ID
The plant Eco-SSL for arsenic was derived from "A" level data (described in Chapter 3 and Appendix
3-1). The data set of nine records was obtained from two papers and six species. All of the toxicity
data were based on growth (GRO) effects, a chronic endpoint. The experiments were conducted with
natural soils under conditions of high or very high bioavailability.
DRAFT
5-5
June 27, 2000
-------
The plant Eco-SSL for arsenic of 37 mg/kg dw is greater than the background concentration of arsenic
in most locations (Exhibit 5-1), and higher than most other soil screening values (Exhibit 1-1).
Soil Invertebrate Eco-SSL for Arsenic
An Eco-SSL value for arsenic could not be derived for soil invertebrates at this time. The literature
search process (Exhibit 3-1) identified some acceptable literature studies but the review of these is not
yet complete.
Avian and Mammalian Eco-SSLs for Arsenic
The literature search process for avian and mammalian toxicity data (Exhibit 4-1) is in progress for
arsenic.
5.3 Cadmium
Table 5.3 Cadmium Eco-SSLs (mg/kg dry weight in soil)
Plants
29
Soil Invertebrates
110
Wildlife
Avian
Pending
Mammalian
Pending
Pending = Derivation not complete
Cadmium is a naturally occurring rare
element that does not have any known
essential or beneficial biological function
(Eider, 1985; OSHA, 1992). Cadmium is
used as an anticorrosive electroplated onto
steel, as an electrode component in alkaline
batteries, as a component of solders and
welding electrodes and as a stabilizer of
plastics, ceramics and paint. Cadmium is
also released to the environment by
anthropogenic activities including mining, and
the production of sewage-sludges and
phosphate fertilizers (Hutton, 1983; Shore
and Douben, 1994 and Van Enk, 1983).
Typical Background Concentrations of
Cadmium in US Soils
10 -
9 -
8 -
7 -
6 -
5 -
4 -
g 3-
u J
§ 2-
"Sfc
a
o
1
x
CERCLIS-3
East
West
Cadmium is a divalent metal that is insoluble in water but its chloride and suphate salts are freely
soluble. The availability of cadmium to organisms in the environment is dependant on a number of
factors including pH, Eh, and chemical speciation (Eisler, 1985). Cadmium is taken up by plants from
DRAFT
5-6
June 27, 2000
-------
soils and translocated with subsequent transfer through the terrestrial food chain (Shore and Douben,
1994). The main routes of cadmium absorption for mammals are via respiration and ingestion. Factors
that are reported to affect dietary cadmium absorption from the GI tract include age, sex, chemical
form, levels of protein, levels of calcium and the presence of other elements (Nriagu, 1981). Cadmium-
induced effects associated with oral intake include nephrotoxicity and also possible effects on the liver,
hematopoietic, reproductive organs, immune, skeletal and cardiovascular systems (Shore and Douben,
1994).
The Eco-SSL values derived to date for cadmium are summarized in Table 5.3. Eco-SSL values for
cadmium are not yet available for avian or mammalian wildlife. Eco-SSLs are available for plants and
soil invertebrates.
Plant Eco-SSL for Cadmium
The following table and graph summarize the data used to derive the plant Eco-SSL for cadmium:
Summary of Data used to Derive Plant Eco-SSL for
Cadmium
Study
ID
1
2
3
4
5
6
7
8
9
Reference
Kelly (1979)
Kelly (1979)
Kelly (1979)
Kelly (1979)
Kelly (1979)
Dixonl988
Adema(1989)
Adema(1989)
Adema(1989)
Test Organism
Firms strobus
Firms taeda
Betula allenghaniensis
Primus virginiana
Firms strobus
Querus rubras
Lactuca saliva
Lycopersicum esculentum
Avena saliva
Bio-
availability
Score
2
2
2
2
2
2
2
2
2
ERE
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
Tox
Parameter
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
Soil Cone.
(mg/kg dw)
39
39
39
39
39
14
10
57
18
Level
A
A
A
A
A
A
A
A
A
ERE = Ecologically Relevant Endpoint, described in Appendix 3-1
Tox Parameter = Maximum Acceptable Threshold Concentration (MATC) or ECXX described in Appendix 3-1
Soil Cone. = Concentration of contaminant in soil (mg/kg ) for the corresponding ERE and Tox parameter.
Level = Preference level (described in Appendix 3-1 ).
DRAFT
5-7
June 27, 2000
-------
s
's
•d
u
.s
o
60
50 -
40 -
30 -
20 -
10 -
Cadmium Plant Data for Eco-SSL
* MATC
^^"Geometric mean
= 29
1234567
Study ID
10
The plant Eco-SSL for cadmium was derived from "A" level data (described in Chapter 3 and
Appendix 3-1). The data set of nine records was obtained from three papers and eight species. All of
the toxicity data were based on growth (GRO) effects, a chronic endpoint. The experiments were
conducted with natural soils under conditions of high or very high bioavailability.
The plant Eco-SSL for cadmium of 29 mg/kg is greater than the reported background concentrations
of cadmium (Exhibit 5-1), and higher than most other available soil screening values (Exhibit 1-1).
Soil Invertebrate Eco-SSL for Cadmium
The following table and graph summarize the data used to derive the soil invertebrate Eco-SSL for
cadmium.
Summary of Data used to Derive Soil Invertebrate Eco-SSL for
Cadmium
Study
ID
1
2
3
4
5
6
7
Reference
Crommentuijin (1993)
Kammenga (1994)
Sandifer(1996)
Sandifer(1996)
Sandifer(1997)
Van Gestel( 1997)
Van Gestel( 1997)
Test
Organism
F. Candida
P. acuminatus
F. Candida
F. Candida
F. Candida
F. Candida
F. Candida
Bio-
availability
Score
1
1
1
1
1
1
1
ERE
REP
POP
REP
REP
REP
POP
POP
Tox
Parameter
MATC
MATC
MATC
MATC
MATC
EC10
EC10
Soil Cone.
(mg/kg dw)
220
57
600
600
447
6
19
Level
B
B
B
B
B
B
B
DRAFT
5-
June 27, 2000
-------
Study
ID
Summarj
Reference
of Data used to Derive Soil Invertebrate Eco-SSL for
Cadmium
Test
Organism
Bio-
availability
Score
ERE
Tox
Parameter
Soil Cone.
(mg/kg dw)
Level
ERE = Ecologically Relevant Endpoint, described in Appendix 3-1
Tox Parameter = Maximum Acceptable Threshold Concentration (MATC) or ECXX described in Appendix 3-1
Soil Cone. = Concentration of contaminant in soil (mg/kg ) for the corresponding ERE and Tox parameter.
Level = Preference level (described in Appendix 3-1).
Cadmium Soil Invertebrate Data for Eco-SSL
Toxicity Value for Cadmium (mg/kg'
i— ' lS> L*J -ti L/l C^ ^
,8888888
i. i i iii
* MATC
• EC10
^^™ Geometric mean =113
•
01234
Study ID
567
i
}
The invertebrate Eco-SSL for cadmium was derived from "B" level data (described in Chapter 3 and
Appendix 3-1). The data set of seven records was obtained from five papers and two species. The
toxicity data were based on reproductive (REP) and population (POP) effects, both chronic endpoints.
All of the data were from experiments conducted under conditions of medium bioavailability.
The invertebrate Eco-SSL for cadmium of 110 mg/kg is much greater than the reported background
concentrations of cadmium (Exhibit 5-1), and higher than most other available soil screening values
(Exhibit 1-1).
Avian and Mammalian Eco-SSLsfor Cadmium
The literature searches were completed for the identification of toxicity data for cadmium and avian and
mammalian wildlife. This search identified over 544 total citations for retrieval and review. To date,
228 citations have been rejected for use in deriving the wildlife TRVs. The review of the remaining
literature has not, however, been completed.
DRAFT
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June 27, 2000
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5.4 Chromium
Table 5.4 Chromium Eco-SSLs (mg/kg dry weight in soil)
Plants
5
Soil Invertebrates
Pending
Wildlife
Avian
21- Chromium (III)
NA - Chromium (VI)
Mammalian
360 - Chromium (III)
330 - Chromium (VI)
NA = Not Available. Data was either not available or insufficient to derive Eco-SSL.
Chromium is the 21st most common element in the earth's crust. Chromium ore deposits are primarily
used for metallurgical applications such as the production of stainless steel. Other uses include wood
preservation, leather tanning, pigments and refractories (Earnhardt, 1997). In the natural environment,
chromium occurs as two oxidation states or valences: chromium (IE) and chromium (VI).
Chromium speciation in soils is complex. Among the factors that affect the speciation of chromium in
soil and water and its uptake into animals and plants include: organic matter content, ferrous ion
content, and redox state, and pH (Outridge and Scheuhammer, 1993; CCME, 1996b). In general,
chromium (VI) is favored by higher pH, aerobic conditions, low amounts of organic matter and the
presence of manganese and iron oxides which oxidize chromium (El). Transformation of chromium
(VI) to the trivalent form tends to occur in acidic, anoxic soils with high organic content. Chromium
(HI) is cationic and adsorbs onto clay particles, organic matter, metal oxyhydroxides and other
negatively charged particle in contrast to chromium (VI) which does not interact significantly with clay
or organic matter. As a result, chromium (VI) is more water-soluble and mobile than chromium (HI)
(Outridge and Scheuhammer, 1993).
Plants are reported to play a major role in
the geochemistry of chromium as they
contain a significant fraction of the
biologically active pool of chromium,
approximately three orders of magnitude
greater than that found in animal tissues. In
contrast to animals, chromium (HI) uptake
by plants occurs more rapidly than
chromium (VI). It is uncertain, however, if
chromium is an essential element for plant
nutrition although some investigators have
observed a stimulatory effect of chromium
on plant growth (Outridge and
Scheuhammer, 1993).
Typical Background
175-
^ 150-
"efc 125-
Concentration
NJ Ol ^1 O
0 01 0 01 0
1 1 1 1 1
Chromium in
>
(
CERCLIS-3
Concentrations of
U.
»
S.
r
Soils
East
X
West
DRAFT
5-10
June 27, 2000
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Chromium has, however, been shown to be an essential nutrient for humans and animals (NRC, 1997).
Several reviews are available concerning its role in nutrition (Anderson, 1987; Anderson, 1988, Borel
and Anderson, 1984; Prasad, 1978 and Underwood, 1977). Chromium (ID) has been shown to have
antioxidative properties in vivo and it is integral in activating enzymes and maintaining the stability of
proteins and nucleic acids. Its primarily metabolic role is to potentiate the action of insulin through its
presence in an organometallic molecule called the glucose tolerance factor (GTF).
The hexavalent forms of chromium are absorbed three to five times better in the intestine compared to
chromium (ID) forms. Some evidence suggests that ingested orally, most of the chromium (VI) is
believed to be reduced to chromium (HI) before reaching sites of absorption in the small intestine
(Outridge and Scheuhammer, 1993). Anionic forms of both chromium (HI) and chromium (VT) are
absorbed more rapidly than the cationic forms (Eastin et al, 1980). Chromium in synthetic organic
forms is more readily absorbed and accumulated into tissues compared to the inorganic forms of
chromium (NRC, 1997). Chromium toxicosis in ruminants is associated with severe congestion and
inflammation of the digestive tract, kidney and liver damage with the precipitating properties of
chromium believed to be the basis of the tissue damage (Thompson et al., 1991).
The Eco-SSL values derived to date for chromium are summarized in Table 5.4. Eco-SSL values for
chromium (HI) or chromium (VI) are not yet available for soil invertebrates. The derivation of these
values is pending further review of identified literature studies. Eco-SSL values are not available for
avian wildlife for chromium (VI) as no appropriate dose-response data was identified from the literature
search process to derive a TRV.
Plant Eco-SSL for Chromium
The following table and graph summarize the data used to derive the plant Eco-SSL for cadmium.
Summary of Data used to Derive Plant Eco-SSL for
Chromium
Graph
ID
1
2
3
4
5
6
7
Reference
Gunther(1990)
Gunther(1990)
Gunther(1990)
Gunther(1990)
Gunther(1990)
Gunther(1990)
Gunther(1990)
Test Organism
Avena saliva
Brassica rapa
Avena sativa
Lycopersicon esculentum
Avena sativa
Lycopersicon esculentum
Latuca sativa
Bio-
availability
Score
2
2
2
2
1
1
1
ERE
GRO
GRO
GRO
GRO
GRO
GRO
GRO
Tox
Parameter
EC50
EC50
EC50
EC50
EC50
EC50
EC50
Soil Cone.
(mg/kg dw)
25
8
41
31
27
27
22
Level
D
D
D
D
D
D
D
ERE = Ecologically Relevant Endpoint, described in Appendix 3-1
Tox Parameter = Maximum Acceptable Threshold Concentration (MATC) or ECXX described in Appendix 3-1
Soil Cone. = Concentration of contaminant in soil (mg/kg ) for the corresponding ERE and Tox parameter.
Level = Preference level (described in Appendix 3-1).
DRAFT
5-11
June 27, 2000
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Chromium Plant Data for Eco-SSL
Toxicity Value for Chromium in Soil
(mg/kg dw)
^^•tOtOOJUiJ^-fc
3 Ln o Ln o LnOLnOL/
4
* EC50
^^^ Geometric mean = 24
>
*
0123456789
Study ID
The plant Eco-SSL for chromium was derived from "D" level data (described in Chapter 3 and
Appendix 3-1). The data set of seven records was obtained from one paper and five species. All of
the toxicity data were based on growth (GRO) effects, a chronic endpoint. The experiments were
conducted under conditions medium to high or very high bioavailability. The geometric mean of the data
was divided by 5 to account for use of EC50 data in deriving the Eco-SSL. It is recommended that
further testing on the effects of chromium on plants be completed to strengthen the data set.
The plant Eco-SSL for chromium of 24 mg/kg is within the range of reported background
concentrations of chromium (Exhibit 5-1), and lower than most other available soil screening values for
chromium (Exhibit 1-1).
Soil Invertebrate Eco-SSL for Chromium
Chromium Eco-SSL values for soil invertebrates are not yet available. The literature search process
(Exhibit 3-1) identified some acceptable literature studies but the review of these is not yet complete.
Avian and Mammalian Eco-SSLsfor Chromium
The electronic and manual literature search process (Exhibit 4-1) for chromium identified 113 studies.
Of these, 27 studies contained data used to derive the TRY for the Eco-SSL, 63 studies were rejected
for use and 22 are pending receipt for review. As described in Chapter 4, six separate Eco-SSL
values are calculated for wildlife, one each for six surrogate species representing different trophic levels.
Eco-SSLs are calculated separately for trivalent and hexavalent chromium.
DRAFT
5-12
June 27, 2000
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The avian and mammalian Eco-SSLs for chromium derived for the following surrogate species are as
follows:
Calculation of Wildlife Eco-SSLs
Chromium
Surrogate Receptor Group
Avian herbivore (dove)
Chromium (III)
Chromium (VI)
Avian ground insectivore
(woodcock)
Chromium (III)
Chromium (VI)
Avian carnivore (hawk)
Chromium (III)
Chromium (VI)
Mammalian herbivore (vole)
Chromium (III)
Chromium (VI)
Mammalian ground
insectivore (shrew)
Chromium (III)
Chromium (VI)
Mammalian carnivore
(weasel)
Chromium (III)
Chromium (VI)
TRVj
(mg dw/kg
BW/d)
1.6
NA
1.6
NA
1.6
NA
24.5
22
24.5
22
24.5
22
FIR
(kg/kg/d)
0.23
0.23
0.17
0.17
0.12
0.12
0.58
0.58
0.2
0.2
0.1
0.1
'•
0.16
0.16
0.12
0.12
0.05
0.05
0.029
0.029
0.03
0.03
0.04
0.04
T.
0.041
0.041
0.306
0.306
Estimated by log-
linear uptake model
0.041
0.041
0.306
0.306
Estimated by log-
linear uptake model
Tver
Eco-SSL
(mg/kg dw)
(Soil)
33
NA
21
NA
83*
NA
600
540
360
330
3000
2700*
Sources and derivation of the exposure parameters (FIR, P, and T) are provided in Appendix 4-1.
The process for derivation of wildlife TRVs is described in Appendix 4-5 and the results are provided in Appendix
Eco-SSL = Soilj - TRY, / FIR * [Ps +Tjj]
*Eco-SSLpred= TRVj / FIR * [Ps + (Ty + T¥er)]
Soilj = Contaminant concentration for contaminant (j) in soil (mg/kg dry weight),
FIR = Food ingestion rate (kg food [dry weight]/ kg BW [wet weight] /d),
P, = Soil ingestion as proportion of diet,
TRVj = Toxicity reference value for contaminant (j) (mg [dry weight]/kg BW [wet weight] /d) for contaminant (j),
T,J = Soil-to-biota BAF for contaminant (j) for biota type (i),
rver = Diet to biota BAF.
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June 27, 2000
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5.5 Cobalt
Table 5.5 Cobalt Eco-SSLs (mg/kg dry weight in soil)
Plants
Pending
Soil Invertebrates
NA
Wildlife
Avian
32
Mammalian
340
NA = Not Available. Data was either not available or insufficient to derive Eco-SSL.
Cobalt belongs to Group WI of the periodic classification of elements and shares properties with
nickel and iron. Cobalt is a relatively rare element in the earth's crust (0.0023%) and is usually found in
association with other metals such as copper, nickel, manganese, and arsenic Release of cobalt to the
environment occurs via soil and natural dust, seawater spray, volcanic eruptions, forest fires, and other
continental and marine biogenic emissions. Anthropogenic sources include fossil fuel burning,
processing of cobalt-containing alloys, copper and nickel smelting and refining, sewage sludge, and
agricultural use of phosphate fertilizers.
Cobalt is an essential trace metal that functions as a component of vitamin B12.. Vitamin B12 acts as
coenzyme in many enzymatic reactions, including some involved in hematopoiesis, and is essential to
growth and normal neural function. Non-ruminant animals require dietary intake of
cobalt in the physiologically active form of vitamin B12. Intake of inorganic cobalt is sufficient to meet
the nutritional requirements of ruminant animals, since ruminal microorganisms have the capacity to
biosynthesize vitamin B12 (Henry, 1995). No other essential functions of cobalt have been identified.
Although cobalt is an essential nutrient,
excessive oral doses result in a variety of
adverse responses. The best characterized
toxic responses are increases in red blood cell
counts (polycythemia), cardiomyopathy, and
effects on the male reproductive system
(Paternain et al., 1988; Haga et al., 1996,
Pedigo et al., 1988). In addition, reduced
food and water intake and growth inhibition
are commonly observed (Diaz et al., 1994a;
1994b). At present, the mechanisms
underlying cobalt toxicity are poorly
understood.
Typical Background Concentrations of
Cobalt in U. S. Soils
30
j£ 25
B, 20
I 15H
at
u
o
U
10
5-
CERCLIS-3
East
West
The Eco-SSL values derived to date for cobalt are summarized in Table 5.5. Eco-SSL values for
cobalt are not available for plants and soil invertebrates. For these receptor groups, data was
DRAFT
5-14
June 27, 2000
-------
insufficient to derive soil screening values.
Plant Eco-SSLfor Cobalt
A cobalt Eco-SSL value could not be derived for plants at this time. The literature search process
(Exhibit 3-1) identified 75 papers for review. Of these, 35 did not pass the Literature Acceptance
Criteria. The remaining papers have not been received for review.
Soil Invertebrate Eco-SSLfor Cobalt
A cobalt Eco-SSL value could not be derived for soil invertebrates at this time. The literature search
process (Exhibit 3-1) identified 13 papers for review. Of these, 11 papers did not meet the Literature
Acceptance Criteria, one met the criteria and one has not been received for review.
Avian and Mammalian Eco-SSLsfor Cobalt
The electronic and manual literature search process (Exhibit 4-1) for cobalt identified 115 studies. Of
these, 30 studies contained data extracted and used to derive the Eco-SSL, 85 studies were rejected
for use and two studies could not be located for review. As described in Chapter 4, six separate Eco-
SSL values are calculated for wildlife, one each for six receptor groups representing different trophic
levels. The lowest value for any of the three mammalian receptor groups is equal to the mammalian
Eco-SSL and the lowest of any of the three avian receptor groups is equal to the avian Eco-SSL.
The avian and mammalian Eco-SSLs for cobalt derived for the following surrogate species are as
follows:
Calculation of Wildlife Eco-SSLs for
Cobalt
Surrogate Receptor
Group
Avian herbivore (dove)
Avian ground insectivore
(woodcock)
Avian carnivore (hawk)
Mammalian herbivore
(vole)
Mammalian ground
insectivore (shrew)
TRVj
(mg dw/kg
BW/d)
1.3
1.3
1.3
10.4
10.4
FIR
(kg/kg/d)
0.23
0.17
0.12
0.58
0.2
Ps
0.16
0.12
0.05
0.029
0.03
TV
0.0075
0.122
Estimated by log-
linear uptake model
0.0075
0.122
T
Aver
Eco-SSL
(mg/kg dw)
(Soil)
34
32
170
490
340
DRAFT
5-15
June 27, 2000
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Calculation of Wildlife Eco-SSLs for
Cobalt
Surrogate Receptor
Group
TRVj
(mg dw/kg
BW/d)
FIR
(kg/kg/d)
Eco-SSL
(mg/kg dw)
(Soil)
Mammalian carnivore
(weasel)
10.4
0.1
0.04
Estimated by log-
linear uptake model
1500*
Sources and derivation of the exposure parameters (FIR, P, and T) are provided in Appendix 4-1.
The process for derivation of wildlife TRVs is described in Appendix 4-5 and the results are provided in Appendix 4-6.
Eco-SSL = Soilj - TRVj / FIR * [Ps +Tjj] or
*Eco-SSLpred= TRVj / FIR * [Ps + (Ty + Tver)]
Soilj
FIR
P.,
Contaminant concentration for contaminant (j) in soil (mg/kg dry weight),
Food ingestion rate (kg food [dry weight]/ kg BW [wet weight] /d),
Soil ingestion as proportion of diet,
Toxicity reference value for contaminant (j) (mg [dry weight]/kg BW [wet weight] /d) for contaminant
(i),
Soil-to-biota BAF for contaminant (j) for biota type (i),
Diet to biota BAF.
5.6 Copper
Table 5.6 Copper Eco-SSLs (mg/kg dry weight in soil)
Plants
Pending
Soil Invertebrates
61
Wildlife
Avian
Pending
Mammalian
Pending
Pending = Derivation not complete
Copper (CAS# 744050-8) is a transition metal that belongs to Group IB of the periodic table. Copper
exists in four valence states (Cu°, Cu+1, Cu +2, Cu +3) with Cu+2 (cupric) being the most common form
(CCME, 1997b). Copper is a relatively abundant mineral that occurs in a variety of mineral deposits
including elemental copper, but it is most commonly found in deposits of sulphide minerals (CCME,
1997b).
DRAFT
5-16
June 27, 2000
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Typical Background Concentrations of
175-
150-
|l25-
.2 m'
*-l— *
1 50-
o
° 25-
0-
CopperinU.S.Sofls
X
* ^
CERdJS-3 East West
Copper is released into the environment from
both anthropogenic and natural sources.
Anthropogenic sources include mining
operations, agriculture activities, solid waste,
and sludge. Natural sources of copper include
forest fires and volcanic paniculate (NAS,
1977). Atomospheric transport of copper is
influenced by adsorption rates. Copper is
adsorbed by a wide variety of material,
including organic matter, clays, and Al, Fe,
and Mn oxides (CCME,1997b, WHO,
1997). Copper deposited in soil is strongly
adsorbed by soil particles and has very little
mobility relative to other trace metals (CCME,
1997b). Soil pH is an important regulator of
copper mobility, decreasing pH tends to
increase copper solubility (NAS, 1977,
CCME, 1997b).
Copper is an essential element that is required by wide variety of organisms. Nutrient requirements
vary among species, but within the plant kingdom they typically range from 5 to 30 ppm in soil.
Required levels for soil invertebrates are not readily available. Dietary requirements for birds and
mammals are typically less than 10 ppm (Underwood, 1977).
Most organisms are able to regulate their copper levels. However, if the capacity to regulate uptake
and distribution is exceeded, copper can interfere with electron transfer functions in plastids (plants) and
mitochondria (all organisms). The disruption of electron transport, as well as other secondary toxicity
actions by copper can lead to impaired growth, loss of reproductive capacity, or death. Copper
concentrates in the tissues of certain organisms, but it does not tend to accumulate or magnify in higher
trophic levels.
The Eco-SSL values derived to date for copper are summarized in Table 5.6. Eco-SSL values for
copper are not yet available for plants, avian or mammalian wildlife. The retrieval and review of these
citations is not yet complete. An Eco-SSL value is, however, available for soil invertebrates.
Plant Eco-SSL for Copper
A copper Eco-SSL value for plants is not yet available. The literature search process (Exhibit 3-1)
identified some acceptable literature studies but the review of these is not yet complete.
DRAFT
5-17
June 27, 2000
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Soil Invertebrate Eco-SSL for Copper
The following table and graph summarize the data used to derive the soil invertebrate Eco-SSL for
copper.
Summary of Data used to Derive Soil Invertebrate Eco-SSL for
Copper
Graph
ID
1
2
3
4
5
6
7
8
9
10
11
Reference
Kula and Larink( 1997)
Kula and Larink( 1997)
Kula and Larink( 1997)
Svendsen and Weeks (1 997a)
Scott-Fordsmand et al. (1997)
Korthalsetal. (1996)
Svendsen and Weeks (1997b)
Korthalsetal.(1996)
Ma (1988)
Ma (1988)
Ma (1988
Test Organism
E.fetida
E. andrei
L. rubellus
E. andrei
F. fimertaria
nematodes
L. rubellus
nematodes
A. caliginosa
A. chlorotica
L. rubellus
Bio-
availability
Score
2
2
2
2
2
2
2
2
2
2
2
ERE
REP
REP
REP
REP
REP
REP
GRO
POP
REP
REP
REP
Tox
Parameter
MATC2
MATC
MATC
MATC
EC10
MATC
MATC
MATC
EC10
EC10
EC10
Soil
Cone.
(mg/kg
dry wt.)
18
6
84
113
38
141
226
612
27
28
80
Level
A
A
A
A
A
A
A
A
A
A
A
ERE = Ecologically Relevant Endpoint, described in Appendix 3-1
Tox Parameter = Maximum Acceptable Threshold Concentration (MATC) or ECXX described in Appendix 3-1
Soil Cone. = Concentration of contaminant in soil (mg/kg ) for the corresponding ERE and Tox parameter.
Level = Preference level (described in Appendix 3-1 ).
Copper Soil Invertebrate Data for Eco-SSL
700
600 -
500 -
400 -
300 -
200 -
MATC
EC10
ic mean = 61
1 2 3 4 5 6 7 8 9 10 11 12
The invertebrate Eco-SSL for plants was derived from "A" level data (described in Chapter 3 and
Appendix 3-1). The data set of eleven records was obtained from five papers and seven species. The
DRAFT
5-18
June 27, 2000
-------
toxicity data were based on reproductive (REP) and growth (GRO) effects, both chronic endpoints.
All of the data were from experiments conducted with natural soils under conditions of high or very high
bioavailability. The tests were conducted with highly soluble salts and neither aging nor weathering,
which would lower bioavailability, was included in the experimental designs.
The invertebrate Eco-SSL for copper of 61 mg/kg is above the reported background concentrations of
copper in most locations (Exhibit 5-1), and similar to or less than most other available soil screening
values for copper (Exhibit 1-1).
Avian and Mammalian Eco-SSLsfor Copper
The literature searches were completed for the identification of dose-response data for copper and
mammalian and avian wildlife according to the process specified in Exhibit 4-1. This search identified
over 382 papers for review. The review of this literature, however, is not complete.
5.7 Dieldrin
Table 5.7 Dieldrin Eco-SSLs (mg/kg dry weight in soil)
Plants
Pending
Soil Invertebrates
Pending
Wildlife
Avian
0.011
Mammalian
0.015
Pending = Derivation not complete
The Eco-SSL values derived to date for dieldrin are summarized in Table 5.7. Eco-SSL values for
dieldrin are not yet available for plants and soil invertebrates. For these receptor groups, the review of
the toxicity literature is not yet complete.
Plant Eco-SSL for Dieldrin
A dieldrin Eco-SSL value could not be derived for plants at this time. The literature search process
(Exhibit 3-1) for dieldrin identified 89 papers for review. The review of this literature, however, is not
complete.
Soil Invertebrate Eco-SSL for Dieldrin
A dieldrin Eco-SSL value could not be derived for soil invertebrates at this time. The literature search
process (Exhibit 3-1) for dieldrin for soil invertebrates identified 81 papers for review. The review of
this literature, however, is not complete.
DRAFT
5-19
June 27, 2000
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Avian and Mammalian Eco-SSLs for Dieldrin
The electronic and manual literature search process (Exhibit 4-1) for dieldrin identified 276 studies. Of
these, 101 studies contained data extracted and used to derive the Eco-SSL, 151 studies were rejected
for use and 24 studies are pending retrieval for review. As described in Chapter 4, six separate Eco-
SSL values are calculated for wildlife, one each for six receptor groups representing different trophic
levels. The lowest value for any of the three mammalian receptor groups is equal to the mammalian
Eco-SSL and the lowest of any of the three avian receptor groups is equal to the avian Eco-SSL.
The avian and mammalian Eco-SSLs for dieldrin derived for the following surrogate species are as
follows:
Calculation of Wildlife Eco-SSLs
Dieldrin
Surrogate Receptor
Group
Avian herbivore (dove)
Avian ground insectivore
(woodcock)
Avian carnivore (hawk)
Mammalian herbivore
(vole)
Mammalian ground
insectivore (shrew)
Mammalian carnivore
(weasel)
TRVj
(mg dw/kg
BW/d)
0.48
0.48
0.48
0.8
0.8
0.8
FIR
(kg/kg/d)
0.23
0.17
0.12
0.58
0.2
0.1
Ps
0.16
0.12
0.05
0.029
0.03
0.04
T-
Lv
Estimated by log-
linear uptake model
267
267
Estimated by log-
linear uptake model
267
267
T
Aver
0.9091
0.9091
Eco-SSL
(mg/kg dw)
(Soilj)
10
0.011
0.017*
20
0.015
0.032*
Sources and derivation of the exposure parameters (FIR, P, and T) are provided in Appendix 4-1.
The process for derivation of wildlife TRVs is described in Appendix 4-5 and the results are provided in Appendix 4-6.
Eco-SSL = Soilj - TRVj / FIR * [Ps +1^] or
*Eco-SSLpred= TRVj / FIR * [Ps + (Ty + Tver)]
Soilj = Contaminant concentration for contaminant (j) in soil (mg/kg dry weight),
FIR = Food ingestion rate (kg food [dry weight]/ kg BW [wet weight] /d),
Ps = Soil ingestion as proportion of diet,
TRV, = Toxicity reference value for contaminant (j) (mg [dry weight]/kg BW [wet weight] /d) for contaminant (j),
TJJ = Soil-to-biota BAF for contaminant (j) for biota type (i),
rver = Diet to biota BAF.
DRAFT
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5.8 RDX
Table 5.8 RDXEco-SSLs (mg/kg dry weight in soil)
Plants
Pending
Soil Invertebrates
Pending
Wildlife
Avian
NA
Mammalian
5.8
Pending = Derivation not complete
Hexahydro-l,3,5-Trinitro-l,3,5-Triazine (RDX) is a crystalline high explosive used extensively by the
military in shells, bombs and demolition charges. It is commonly referred to as cyclonite or RDX
(British code name for Research Department Explosive or Royal Demolition Explosive). Manufacture in
the U. S. is by the Bachmann process in which hexamine is reacted with an ammonium nitrate/nitric acid
mixture in the presence of acetic acid and acetic anhydride. Military grades of RDX contain about 10%
octahydro-l,3,5,7-tetranitro-l,3,5,7-tetrazocine (HMX). RDX is released to the environment at sites
where it is manufactured as well as sites where it is converted to munitions. Other releases occur at
military depot facilities through the demilitarization of obsolete munitions, deposition in landfills and open
burning and detonation processes (Talmage et al., 1999).
Once released to soils, RDX does not readily adsorb to soil particles and is resistant to biodegradation
under both aerobic and anaerobic conditions. RDX can undergo aerobic biodegradation under special
conditions where soil microbs are adapted to RDX (Talmage et al., 1999). Plants are reported that
RDX can be taken up from either soil or hydroponic solutions and translocated in plant tissue (Talmage
et al., 1999 and Harvey et al., 1991). For mammals, RDX is slowly but extensively absorbed following
ingestion.
The Eco-SSL values derived to date for RDX are summarized in Table 5.8. Eco-SSL values for RDX
are not yet available for plants and soil invertebrates. The retrieval and review of these citations is not
yet complete. An Eco-SSL value could not be derived for avian wildlife as the literature search did not
identify any toxicity studies. An Eco-SSL value is, however, available for mammalian wildlife.
Plant Eco-SSL for RDX
An Eco-SSL value could not be derived for plants for RDX at this time. The literature search process
(Exhibit 3-1) identified papers for review, however this review is not complete.
Soil Invertebrate Eco-SSL for RDX
An Eco-SSL value could not be derived for plants for RDX at this time. The literature search process
(Exhibit 3-1) identified papers for review, however this review is not complete.
DRAFT
5-21
June 27, 2000
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Avian Eco-SSLsfor RDX
The literature search process for wildlife TRVs (described in Exhibit 4-1) did not identify any
lexicological studies of RDX and birds. At this time an Eco-SSL can not be derived for avian
receptors for RDX.
Mammalian Eco-SSLsfor RDX
The mammalian Eco-SSLs for dieldrin derived for the following surrogate species are as follows:
Calculation of Wildlife Eco-SSLs for
RDX
Surrogate
Receptor Group
TRVj
(mg dw/kg
BW/d)
FIR
(kg/kg/d)
Eco-SSL
(mg/kg dw)
(SoU,)
Mammalian
herbivore (vole)
11.6
0.58
0.029
0.242
74
Mammalian
ground insectivore
(shrew)
11.6
0.2
0.03
9.91
5.8
Mammalian
carnivore (weasel)
11.6
0.1
0.04
9.91
12*
Sources and derivation of the exposure parameters (FIR, P, and T) are provided in Appendix 4-1.
The process for derivation of wildlife TRVs is described in Appendix 4-5 and the results are provided in
Appendix 4-6.
Eco-SSL = Soilj - TRVj / FIR * [Ps +T;j]
*Eco-SSLpred= TRVj / FIR * [Ps + (T, + Tver)]
Soilj
FIR
Ps
TRVj
Contaminant concentration for contaminant (j) in soil (mg/kg dry weight),
Food ingestion rate (kg food [dry weight]/ kg BW [wet weight] /d),
Soil ingestion as proportion of diet,
Toxicity reference value for contaminant (j) (mg [dry weight]/kg BW [wet weight] /d) for
contaminant (j),
Soil-to-biota BAF for contaminant (j) for biota type (i),
Diet to biota BAF.
DRAFT
5-22
June 27, 2000
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5.9 Zinc
Table 5.9 Zinc Eco-SSLs (mg/kg dry weight in soil)
Plants
190
Soil Invertebrates
120
Wildlife
Avian
Pending
Mammalian
Pending
Pending = Derivation not complete
Typical Background Concentrations of Zinc in
U. S. Soils
600
45°"
O
300 -
§ 150-
U
CERCLIS-3
East
West
Zinc is the 25th most abundant element that
is used industrially in the production of
galvanized materials, alloys and other
products. Anthropogenic sources of zinc in
the environment include electroplating,
smelting and ore processing, domestic and
industrial sewage, combustion of solid waste
and fossil fuels, road surface runoff,
corrosion of zinc alloy and galvanized
surfaces, and erosion of agricultural soils
(CCME, 1996c).
Zinc occurs in soil solution under the single
valence state zinc (+2). Zinc is highly
reactive and is present as both soluble and
insoluble compounds. Zinc also forms
stable combination with organic substances. Metallic zinc is insoluble while the solubility of other zinc
compounds range from insoluble (oxides, carbonates, phosphates, silicates) to extremely soluble
(sulphates and chlorides) (CCME, 1996c).
Zinc is an essential element for normal plant growth. Terrestrial plants primarily absorb zinc as zinc
(2+) from soil solution and the uptake is dependant on the availability, solubility and movement of zinc
to plant roots. Zinc availability to plants is a function of soil physico-chemical properties and plant
biological characteristics. Uptake and distribution of zinc is influenced by the form of zinc, other metal
ions present in the system, soil phosphorous level, cation exchange capacity, soil texture, pH and
organic matter content (CCME, 1996c).
Zinc is also an essential element for animal life and is necessary for a wide variety of physiologic
functions (Thompson et al, 1991 and Ammerman et al, 1995). Zinc activates several enzymes and is
a component of many important metalloenzymes. The element is critically involved in cell replication
and in the development of cartilage and bone (Ammerman et al. 1995).
DRAFT
5-23
June 27, 2000
-------
The Eco-SSL values derived to date for zinc are summarized in Table 5.7. Eco-SSL values for zinc
are not yet available for avian or mammalian wildlife. Eco-SSLs are available for plants and soil
invertebrates.
Plant Eco-SSL for Zinc
The following table and graph summarize the data used to derive the plant Eco-SSL for zinc.
Summary of Data used to Derive Plant Eco-SSL for
Zinc
Study
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Reference
Chlopeck(1996)
Chlopecka(1996)
Chlopecka(1996)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Roszyk(1988)
Test Organism
Zea mays
Hordeum vulgare
Zea mays
Avena saliva
Avena saliva
Brassica
Brassica
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Avena saliva
Bio-
availability
Score
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Tox
Parameter
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
MATC
Soil
Cone.
(mg/kg
dw)
87
87
299
155
361
177
155
155
143
335
159
328
169
155
361
162
306
159
169
ERE
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
GRO
Level
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
ERE = Ecologically Relevant Endpoint, described in Appendix 3-1
Tox Parameter = Maximum Acceptable Threshold Concentration (MATC) or ECXX described in Appendix 3-1
Soil Cone. = Concentration of contaminant in soil (mg/kg ) for the corresponding ERE and Tox parameter.
Level = Preference level (described in Appendix 3-1).
DRAFT
5-24
June 27, 2000
-------
400
a 300"
&
•- 250 H
200-
5 150
s
£> 100
u
H
50-
Zinc Plant Data for Eco-SSL
* *
MATC
Geometric mean =189
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Study ID
The plant Eco-SSL for zinc was derived from "A" level data (described in Chapter 3 and Appendix 3-
1). The data set of nineteen records was obtained from two papers and four species. All of the toxicity
data were based on growth (GRO) effects, a chronic endpoint. The experiments were conducted with
natural soils under conditions of high or very high bioavailability.
The plant Eco-SSL for zinc of 190 mg/kg is greater than the reported background concentration of zinc
in most locations (Exhibit 5-1), and is lower than most other available soil screening values (Exhibit 1-
1).
Soil Invertebrate Eco-SSL for Zinc
The following table and graph summarize the data used to derive the soil invertebrate Eco-SSL for zinc.
Summary of Data used to Derive Soil Invertebrate Eco-SSL for
Zinc
Study ID
1
2
o
J
4
5
Reference
Korthals(1998)
Korthals(1996)
Smit(1997)
Smit(1998)
Smit(1998)
Test
Organism
Nematode
Nematode
F. Candida
F. Candida
F. Candida
Bio-
availability
Score
2
2
2
2
2
ERE
REP
POP
REP
REP
REP
Tox
Parameter
MATC
MATC
EC10
EC10
EC10
Soil
Cone.
(mg/kg
dw)
35
141
116
99
159
Level
A
A
A
A
A
DRAFT
5-25
June 27, 2000
-------
Summary of Data used to Derive Soil Invertebrate Eco-SSL for
Zinc
Study ID
6
Reference
Srmt(1998)
Test
Organism
F. Candida
Bio-
availability
Score
2
ERE
REP
Tox
Parameter
EC10
Soil
Cone.
(mg/kg
dw)
305
Level
A
ERE = Ecologically Relevant Endpoint, described in Appendix 3-1
Tox Parameter = Maximum Acceptable Threshold Concentration (MATC) or ECXX described in Appendix 3-1
Soil Cone. = Concentration of contaminant in soil (mg/kg ) for the corresponding ERE and Tox parameter.
Level = Preference level (described in Appendix 3-1 ).
350 -i
OJJ
f 300 -
Si 250 -
.=
S 200 -
S)
h
& 150 -
> 100 -
•a so -
o
o -
c
Zinc Soil Invertebrate Data for Eco-SSL
* MATC
• EC10
^^^"Geometric mean =119
*
•
^H
U
1 2 3
4567
Study ID
The invertebrate Eco-SSL for zinc was derived from "A" preference level data (described in Chapter 3
and Appendix 3-1). The data set of six records was obtained from two papers and two species. The
toxicity data were based on reproductive (REP) and population (POP) effects, both chronic endpoints.
The experiments were conducted with natural soils under conditions of high or very high bioavailability.
The invertebrate Eco-SSL for cadmium of 120 mg/kg is greater than the reported background
concentrations of zinc in most locations (Exhibit 5-1), and is lower than most other available soil
screening values (Exhibit 1-1).
5.10 Aluminum
Aluminum (Al) is the most commonly occurring metallic element, comprising eight percent of the earth's
crust (Press and Siever, 1974). It is a major component of almost all common inorganic soil particles,
with the exceptions of quartz sand, chert fragments, and ferromanganiferous concretions. The typical
range of aluminum in soils is from 1 percent to 30 percent (10,000 to 300,000 mg Al kg-1) (Lindsay,
DRAFT
5-26
June 27, 2000
-------
1979 and Dragun, 1988), with naturally occurring concentrations varying over several orders of
magnitude.
EPA recognizes that due to the ubiquitous nature of aluminum, the natural variability of aluminum soil
concentrations and the availability of conservative soil screening benchmarks (Efroymson, 1997'a;
1997b), aluminum is often identified as a COPC for ecological risk assessments. The commonly used
soil screening benchmarks (Efroymson, 1997a; 1997b) are based on laboratory toxicity testing using an
aluminum solution that is added to test soils. Comparisons of total aluminum concentrations in soil
samples to soluble aluminum-based screening values are deemed by EPA to be inappropriate (see
Exhibit 5-2).
The standard analytical measurement of aluminum in soils under CERCLA contract laboratory
procedures (CLP) is total recoverable metal. The available data on the environmental chemistry and
toxicity of aluminum in soil to plants, soil invertebrates, mammals and birds (summarized in Exhibit 5-1)
support the following conclusions:
• Total aluminum in soil is not correlated with toxicity to the tested plants and soil
invertebrates.
• Aluminum toxicity is associated with soluble aluminum.
Soluble aluminum and not total aluminum is associated with the uptake and
bioaccumulation of aluminum from soils into plants.
• The oral toxicity of aluminum compounds in soil is dependant upon the chemical form
(Storer and Nelson, 1968). Insoluble aluminum compounds such as aluminum oxides
are considerably less toxic compared to the soluble forms (aluminum chloride, nitrate,
acetate, and sulfate). For example, Storer and Nelson (1968) observed no toxicity to
the chick at up to 1.6% of the diet as aluminum oxide compared to 80 to 100%
mortality in chicks fed soluble forms at 0.5% of the diet.
Because the measurement of total aluminum in soils is not considered suitable or reliable for the
prediction of potential toxicity and bioaccumulation, an alternative procedure is recommended for
screening aluminum in soils. The procedure is intended as a practical approach for determining if
aluminum in site soils could pose a potential risk to ecological receptors. This alternative procedure
replaces the derivation of numeric Eco-SSL values for aluminum. Potential ecological risks associated
with aluminum are identified based on the measured soil pH. Aluminum is identified as a COPC only at
sites where the soil pH is less than 5.5.
DRAFT 5 - 27 June 27, 2000
-------
6.0 USING ECO-SSLs TO SCREEN CONTAMINATED SOILS
This Chapter provides guidance on using the Eco-SSLs to identify those soil contaminants (i.e.
COPCs) and/or areas of soil contamination that warrant further consideration in a baseline ERA.
Screening is completed during Steps 1 and 2 of the 8-step Superfund ERA process, as depicted in
Figure 1.1. Prior to using the Eco-SSLs, it is assumed that the risk assessor has completed Step 1,
including the site visit and problem formulation. With the information gathered in Step 1, the risk
assessor completes a screening of soils data using the Eco-SSLs in the risk calculation performed
during Step 2.
Soil Screening Process Using Eco-SSLs
Complete Site Visit, Initial Problem Formulation,
Toxicity Evaluation and Exposure Assessment (Steps 1 &
2 of ERAGS; U.S. EPA, 1997).
Develop a Preliminary Site-Specific Conceptual Site
Model (U.S. EPA, 1997)
Compare CSM to the General Eco-SSL Model
Identify pathways present at the site addressed by the
Eco-SSL guidance.
Identify pathways present at the site not addressed by
the guidance.
Identify if Available Analytical Data Set for Soils is
Adequate for Screening
Compare Site Soil Concentrations to Eco-SSLs
For Exceedances, Consider Site-Specific Modifications
For Exceedances, Consider Proceeding to a Baseline ERA
6.1 Comparing the Site Conceptual
Model to the General Eco-SSL
Model
The user should compare the
preliminary site conceptual model
developed for their site during Step 1,
with the assumptions and limitations
inherent in the Eco-SSLs to determine if
additional or more detailed assessments
are needed for any exposure pathways
or contaminants. Early identification of
areas, conditions or receptors where
Eco-SSLs are not applicable is
important for adequate planning and
sampling strategies for the ERA.
Are There Soil Exposure Pathways
for Ecological Receptors?
The Eco-SSLs apply only to sites
where terrestrial receptors may be
exposed directly or indirectly to
contaminated soil. The first step is to identify all possible, complete soil pathways present at the site in
order to determine if they can be addressed by the Eco-SSL value. The following are the receptor,
group-specific pathways of exposure to soil contaminants considered in deriving the Eco-SSLs:
Mammals and Birds
• Incidental ingestion of soil
Ingestion of food contaminated via soil invertebrates and/or plant uptake
DRAFT
6-1
June 27, 2000
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Soil Invertebrates and Plants
• Direct contact
Ingestion of soil (by
invertebrates)
• Uptake (by plants)
For surface soils (i.e., those soils within the root
zone at the specific site), all the above
pathways should be considered. Ecological
risks from potential exposure to contaminated
subsurface soils are generally not considered
for ecological receptors. In some cases,
however, there may be risks to animals that
burrow beneath the root zone. It should also
be noted, that for some plants, the root zone
can extend several feet.
As part of Step 1 of the ERAGS process, the site manager and risk assessor
about the site to answer at least the following questions:
What is a Complete Ecological Exposure Pathway
for Contaminants in Soil?
For an exposure pathway to be complete, a contaminant
must be able to travel from the source to ecological
receptors and be taken up by the receptors via one or
more exposure routes (U.S. EPA, 1997).
Exposure pathways may not be complete for ecological
receptors if:
• Soil contamination exists only below the root
zone and deep burrowing mammalian species
are not identified as potential receptors in the
site conceptual model.
• The site is within urban and/or industrialized
areas where natural habitat and receptors are
absent.
need to know enough
1) What contaminants are known or suspected to exist at the site?
2) What complete exposure pathways might exist at the site?
3) Which habitats located on or near the site are potentially contaminated?
If it is determined that there are no complete soil exposure pathways (e.g., the current and future land
use is industrial and there are no terrestrial habitats, or the only soil contamination is well below the root
zone at the site), then additional screening for soil effects on ecological receptors is not needed.
Are There Exposure Pathways Not Addressed by the Eco-SSL?
In some cases, the site-specific conceptual model may have identified potentially complete or complete
ecological soil exposure pathways that were not considered in the derivation of the Eco-SSLs. In these
instances (presented below), the additional pathways need to be considered in a separate screening
analysis or as part of the baseline ERA.
• The contaminated soil is near a surface water body or wetland where there is potential
for contamination of surface water and/or sediments by overland flow of soil.
DRAFT
6-2
June 27, 2000
-------
There are other likely ecological exposure routes not considered in the derivation of the
Eco-SSLs. For example, inhalation of VOCs may be of concern for burrowing
animals.
Some site conditions may be a source of contamination to groundwater. For example,
contaminants from soils may leach to groundwater, which could result in exposures for
ecological receptors upon discharge to surface waters.
6.2 Comparing Site Soil Concentrations to the Eco-SSLs
Comparisons of site soil concentrations to the Eco-SSLs during Step 2 of the ERAGS process may be
used to answer the following questions:
Are there any potential ecological risks associated with soil contamination, and is it
necessary to proceed with a baseline ERA (Steps 3 to 8 of ERAGS)?
• Which contaminants in soil can be dropped from further consideration and which ones
should be the focus of the baseline ERA?
• Which geographic areas of soil contamination may result in ecological risks?
Which receptors/functional groups (i.e., birds or invertebrates) appear to be at most
risk and should be the focus of the baseline ERA?
Are the Existing Site Soil Contaminant Data Adequate?
The user at this point of the process should make a decision concerning the adequacy of the available
contaminant concentration data for completing a screening level analysis. This decision, made by the
site manager and risk assessor, considers the following:
• Are all expected soil contaminant sources sampled, or are there other areas of potential
exposure for ecological receptors for which soil data are not available?
Are the parameters of the soil analyses sufficient to identify the possible contaminants
deposited as part of known waste disposal processes and practices? For example, if
PAHs are suspected as part of the deposited waste, are soil analyses available for
these? Or are data only available for metals?
• Are the quantification limits adequate to measure the contaminants at the Eco-SSL
levels?
DRAFT 6-3 June 27,2000
-------
How do you Calculate the Concentration Term for Comparison to the Eco-SSLs?
The appropriate soil contaminant concentration for comparison to the Eco-SSL is dependent on a
number of factors, including the size of the site, the nature and extent of the contamination, and the level
of confidence in the site sampling data. In most cases, there are limited soil data available at Step 2 of
the ERAGS process; therefore, the maximum soil contaminant concentrations are compared to the
Eco-SSLs. However, if the data set is large, the 95% upper confidence limit (UCL) of the arithmetic
mean may be the appropriate value to use. Decisions concerning concentration terms used for
comparisons should be made in consultation with the site manager, site risk assessor, and the regional
BTAG.
Which Eco-SSL Should be Used?
The lowest of the four reported Eco-SSLs should be used to compare to the site soil concentrations.
The ERA process assumes that complete exposure pathways exist for each of the four receptor groups;
i.e., every terrestrial habitat at or near a hazardous waste site is, or should be, suitable for mammals,
birds, plants and invertebrates.
What if Soil Contaminant Concentrations Exceed Eco-SSLs?
If the appropriate site soil contaminant concentration exceeds an Eco-SSL, then the user should retain
that contaminant as a COPC for further consideration in the baseline ecological risk assessment.
What if Soil Contaminant Concentrations Do Not Exceed Eco-SSLs?
Contaminants in soils with concentrations lower than Eco-SSLs can be excluded as COPCs in the
subsequent ERA. However, the user needs to recognize that new information may become available
during the baseline risk assessment which may show that initial assumptions are no longer valid (e.g.,
site contaminant levels are higher than reported earlier). In this case, contaminants may be placed back
on the list of COPC. If there are no soil contaminant concentrations that exceed the Eco-SSLs, a
baseline ecological risk assessment for soils would generally not be needed for that site.
What if There is No Eco-SSL?
At this time, Eco-SSLs for all four receptor groups are not available for all the 24 soil contaminants.
For some of the Eco-SSL contaminants, there was an insufficient number of acceptable toxicity studies
to establish an Eco-SSL. For these contaminants, a summary of all toxicity studies evaluated in the
Eco-SSL process is available on the Eco-SSL website. The information from these studies can be
used according to the process described in Section 1.3.1 of ERAGS to derive screening values.
Exhibit 3-4 provides the plant and soil invertebrate toxicity data that were judged acceptable for use in
deriving Eco-SSLs, but for which there were only one or two studies available (i.e., score >10).
DRAFT 6-4 June 27,2000
-------
Can I Use Site-specific Data to Modify an Eco-SSL or Should I Proceed to a Baseline Risk
Assessment?
Decisions concerning the derivation and use of modified Eco-SSL values are made by the risk assessor
in consideration of site-specific factors. At some sites, the need to proceed to a baseline ERA to fully
evaluate risks to terrestrial receptors from contaminants in soil may be obvious based on the
comparison of the Eco-SSLs to the soil contaminant concentrations. For example, the screening
assessment may result in hazard quotients (HQs = site soil concentration / Eco-SSL) for one of more
contaminants that are very large (> 100), or there may be obvious signs of stressed vegetation. Some
outcomes are, however, not clear. For example, the HQ for a receptor may be relatively small and the
use of site-specific exposure information may yield an HQ value less than or equal to 1.0. In these
cases, it may be appropriate to collect some limited site exposure data and use this information to
redefine the risk equation, which may screen out some or all of the soil contaminants. Information on
modifying Eco-SSLs is presented in Chapter 7.
6.3 Consideration of Background Soil Concentrations
Background concentrations of contaminants (i.e., naturally occurring inorganic compounds) may be
considered only after the screening process for Superfund Sites. Following screening consideration can
be given to site-specific background levels of contaminants in soils. Guidance on how to determine
background conditions and on how to use this information in the assessment process is being developed
by an EPA workgroup and is expected to be completed in early 2001.
Data on background concentrations of contaminants in soils were collected and reviewed during the
Eco-SSL derivation process to examine how the Eco-SSL values compared to natural soil conditions.
These comparisons were used to guide the process and are presented as Exhibit 5-1. The review also
indicated that there are regions of the country where natural background levels for metals exceed Eco-
SSLs. For these regions and for specific local areas, the acquisition of data on background soil
concentrations is an important step toward evaluating whether observed concentrations are related to
releases or are naturally occurring.
DRAFT 6-5 June 27,2000
-------
7.0 SITE-SPECIFIC CONSIDERATIONS FOR MODIFYING THE ECO-SSLS
The Eco-SSLs were derived to be broadly applicable as screening levels. In order to achieve that
goal, assumptions were made about exposures and effects for plants, invertebrates, and wildlife
to insure that the derived Eco-SSL values were sufficiently conservative such that they could
confidently be used for screening. When contaminant concentrations in soils are lower than Eco-
SSLs, it is presumed that the contaminant will not pose an ecological risk and does not need to be
considered further with respect to that type of risk. However, when a contaminant concentration
in soil exceeds an Eco-SSL, there may or may not be a risk depending on site-specific
considerations. Guidance on how to consider site-specific factors in ecological risk assessments
is given in ERAGs (U.S. EPA,1997). This chapter describes some of the site-specific
considerations specific to soil issues. The intent of this chapter is to give the reader guidance on
possible next steps beyond the application of Eco-SSLs that could be considered as part of a
baseline risk assessment.
7.1 Site-Specific Considerations for Wildlife
Eco-SSLs for wildlife were derived using selected values for exposure assumptions. An effort
was made to insure that these were adequately conservative by choosing values from either the
90th or 10th percentile of distributions of exposure parameters (which ever was more
conservative). Other assumptions concerned the degree to which a local population would use a
site (100%) and the relative bioavailability of contaminants in ingested soils and biota (100%).
One or more of these assumptions can be modified when adequate site-specific information is
available. Such information may relate to characteristics of site-specific receptors or site or soil
characteristics. Examples include the relative proportions of food in a receptor's diet, the size of
a receptor's foraging area, the amount of soil a receptor incidentally ingests, and the
bioavailability of the contaminants.
Modifications of select exposure assumptions could be used to adjust Eco-SSLs to make them
more site-specific. The modifications suggested below could also be used in the baseline risk
assessment. Decisions on whether and how to modify Eco-SSLs are site-specific and should be
discussed between the risk assessor and risk manager in accordance with Step 3 of ERAGS (see
Section 3.2 in USEPA, 1997.) Site-specific considerations for wildlife exposed to contaminants
in soils fall into two categories: wildlife characteristics and site characteristics. It is envisioned
that these site-specific modifications based on these characteristics would be made after initial
site screening.
The various parameters that might be modified on a site-specific basis can be identified within
the general wildlife exposure and risk model (Figure 4.1):
DRAFT 7-1 July 10,2000
-------
[Soilj * Ps * FIR * AFjJ + [^B*P*FIR* AF«]}* AUF
HQ. = ± ll }
TRV
where:
= Hazard quotient for contaminant (j) (unitless),
Soilj = Contaminant concentration for contaminant (j) in soil (mg/kg dry weight) (site
characteristic),
TV = Number of different biota types in diet (wildlife characteristic),
Bt = Contaminant concentration in biota type (i) (mg/kg dry weight) (site
characteristic often dependent on mobility of metals in soil),
P,. = Proportion of biota type (i) in diet (wildlife characteristic),
FIR = Food ingestion rate (kg food [dry weight]/ kg BW [wet weight] / d) (wildlife
characteristic),
AFV = Absorbed fraction of contaminant (j) from biota type (i) (wildlife
characteristic),
AFsj = Absorbed fraction of contaminant (j) from soil (s) (wildlife and site soil
characteristics that influence bioavailability),
TRVj = The no adverse effect dose (mg/kg BW/day) (Section 4.4),
Ps = Soil ingestion as proportion of diet (wildlife characteristic),
AUF = Area use factor (wildlife and site size characteristics)
Wildlife Characteristics
Eco-SSLs for wildlife are derived for six general receptor groups that represent different feeding
strategies for birds and mammals. The degree to which these receptor groups are actually
represented at a site will vary. Site-specific knowledge of the types of wildlife that may use the
site can be used to modify one or more of the exposure parameters of the general wildlife Eco-
SSL exposure model.
Site-Specific Receptor Species. The Eco-SSLs are calculated for surrogate receptor species
that were considered to be protective of other birds and mammals (see text box). However, one
or more of these species may not be present or applicable on a site-specific basis. Eco-SSLs can
be calculated for site-specific species. For example, a particular site may not have habitat to
support raptors. Additionally, species of birds or mammals present at a site may have different
feeding habits and life history than those used to derive the Eco-SSLs. An example would be the
raccoon, which ingests a varied diet and also has a different range of body weights and ingestion
rates than the weasel.
DRAFT 7-2 July 10,2000
-------
Protectiveness of the Wildlife Eco-SSLs
Protectiveness of the wildlife Eco-SSLs is provided
through both the surrogate species selection and the
parameterization of the exposure model.
Surrogate receptor species were selected to provide a
conservative representation of their respective trophic
guilds. These species are generally small in size relative to
other species within their respective trophic groups (e.g.,
weasels and voles vs foxes and coyotes or rabbits and
deer).
Because small size is associated with higher metabolic
rates (Nagy et al. 1999) and smaller home ranges (McNab
1963 ), exposure and risk for small receptors is maximized.
EcoSSLs based on these species are therefore likely to be
protective of other, larger species in their trophic guild.
Parameters for the Exposure Model. The food and soil
ingestion rates used in the exposure model are represented
by the 90th percentiles from their respective distributions.
Use of exposure parameter values from the upper tails of
the distributions further ensures the protectiveness of the
Eco-SSLs for other wildlife species.
Exposure Parameters. Site-specific
information can also be used to adjust
parameters such as ingestion rates (food or
soil) or body weights. For example, site-
specific or regional data may indicate that one
or more of the wildlife species have higher
body weights or lower ingestion rates than the
conservative values used in the Eco-SSL
derivation (10th and 90th percentiles,
respectively).
Dietary Composition. Site-specific or a
more varied dietary composition can be used
to modify the wildlife exposure model. The
Eco-SSLs assume that species consume only
one item in the diet (the most contaminated)
when many species actually have a varied diet
(e.g., 50% plants, 50% invertebrates). For
example, raccoons have a varied diet,
ingesting soil invertebrates, reptiles, aquatic
organisms, and small mammals as well as
plants.
Area Use Factor. The Area Use Factor
(AUF) reflects both wildlife and site
characteristics and is used to judge the extent
to which a wildlife species' exposure comes
from the site. Where the size of the site is significantly smaller than the home range of the
species being evaluated, only a fraction of total exposure may be from the site. For example, the
home range of the red-tailed hawk ranges from 1 to 10 square kilometers (247 to 2471 acres).
For a site that is 50 acres, the exposure could be adjusted using an AUF of 0.2 (or lower). Care
must be taken when selecting an AUF because species may favor particular feeding areas out of
proportion to their reported foraging areas. Therefore, the simple relationships between foraging
areas and site sizes may not always hold.
Site Characteristics
Certain site characteristics can influence exposure of wildlife to contaminants in soils. These
include the spatial distribution and magnitudes of exposure concentrations as well as the degree
to which soil-related parameters have effects on the bioavailability of the contaminants.
Obtaining site-specific information on key soil characteristics such as organic carbon, pH, cation
exchange capacity, and grain size may be valuable information for judging the potential
DRAFT
7-3
July 10, 2000
-------
importance of bioavailability as a factor influencing exposure. Information on parameters such as
bulk density (a measure of compaction) is useful for judging the extent to which the site can
support plants and soil invertebrates.
Exposure Point Concentrations. During site screening, Eco-SSLs are typically compared to the
maximum soil contaminant concentrations at the site. This simple and conservative approach is
appropriate since sampling for screening normally focuses on the more contaminated locations of
a site. However, maximum point values might not be representative of the exposures experienced
by wildlife. Therefore, as additional sampling data become available for the site (i.e., through site
characterization studies), alternative exposure statistics may be considered. In accordance with
USEPA guidance, these exposure statistics usually are estimates of mean exposure
concentrations (e.g., 95% UCL of the mean), which account for uncertainty in the estimates.
Other statistics may be appropriate depending on the extent to which exposure is resolved
through spatially explicit models that account for wildlife exposure and contaminant distribution.
Bioavailability. Key considerations when judging the value of the collection of site-specific
bioavailability information include:
• determining which contaminants are "driving the risk" and for which site-specific
information would be most useful,
• determining which soil-related pathways (uptake in food items, incidental soil ingestion,
dermal contact, etc.) are driving the risk,
examining soil characteristics (e.g., for organic carbon or cation exchange capacity) to
obtain insights into the potential that bioavailability is reduced, evaluating whether
revised risk estimates (including utilizing site-specific bioavailability information) would
change the risk estimate sufficiently to affect decisions.
These considerations can guide the collection of additional site-specific information. Such
information is most likely to be useful when focused on the contaminants and pathways of
concern at a site. Other site-specific factors that may affect exposure estimates and which can be
considered when proceeding beyond screening-level assessment include: (1) more detailed
evaluations of the spatial and vertical extent of contaminants in soils, (2) the distribution of
available habitat, (3) utilization of area use factors (AUFs) that are specific to wildlife species,
and (4) other biological and ecological characteristics of the wildlife being evaluated.
The TRVs used to calculate Eco-SSLs are generally based on studies using highly bioavailable
forms of contaminants. Bioavailability of contaminants under field conditions is generally lower
than in laboratory experiments. As indicated in the general wildlife exposure equation, there are
a few parameters that are influenced by bioavailability: Bt (contaminant concentration in biota
type (i) (mg/kg dry weight)), andAFSJ (absorbed fraction of contaminant (j) from soil (s)).
DRAFT 7-4 July 10,2000
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Soil-related effects on contaminant bioavailability are likely to be important site-specific
variables influencing wildlife exposure. Bioavailability can be manifested through variable
uptake into food items such as soil invertebrates and plants as well as the degree to which
contaminants are released from soils that are incidentally ingested by wildlife (Figure 7-1).
An analysis of the relative importance of various pathways (Exhibit 7-1) points to the importance
of accumulation of contaminants within wildlife food items such as soil invertebrates or plants
for most receptors and contaminants. The pathway can be readily addressed with available site-
specific measurements of uptake into food items (i.e., tissue residue levels), and/or models that
use site-specific measurements of soil properties such as organic carbon. However, because
there are a number of factors that can influence bioavailability of contaminants in soils, site-
specific measurements of uptake into food items and empirical models based on such measures
are likely to provide more accurate information on bioavailability and exposure than that given
by theoretical models. Theoretical models of uptake in plants and invertebrates can be useful for
providing bounding estimates, and these estimates may be sufficient for site evaluation.
However, the uncertainties associated with exposure estimates provided by currently available
models must be recognized (e.g., Sample et al., 1999).
Incidental ingestion of soils by wildlife can be a relatively important source of exposure to
wildlife where the overall movement of a contaminant into food is low. However, this exposure
pathway is often less important than uptake into food and is typically more difficult to measure or
model. For these reasons, value of information analysis is particularly important for judging the
usefulness of site-specific information on the bioavailability of contaminants in incidentally
ingested soils. Evaluating the incidental soil ingestion pathway also requires special
consideration of the digestive systems of receptors. For example, there are different types of
digestive systems (e.g., ruminant vs. mono-gastric species) that influence the bioavailability of
contaminants.
An example approach for incorporating site-specific information on bioavailability is illustrated
in Figure 7-2. The process would be applied to those contaminants that exceed wildlife Eco-SSL
values. Because there are other factors that influence estimates of exposure to wildlife (e.g., area
use factors, soil ingestion rates, other receptor or site-specific information), the range of options
should be considered before deciding on the value of collecting and using bioavailability
information.
The approach involves (1) identifying the pathways for which such information might be useful,
(2) judging the extent to which this information might affect the risk assessment and decision,
and (3) using site-specific data on soil characteristics to discern the likelihood that bioavailability
might be reduced. For example, the bioavailability of organic contaminants would be expected
DRAFT 7-5 July 10,2000
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Contaminant
Figure 7-1. Bioavailability Issues in Wildlife
Pathway Receptor
More
Bioavailable*
RDX
TNT
Zinc
Copper
Cadmium
Lead
Barium
Nickel -
Manganese
PCP
Chromium
Antimony
Silver
PCBs
PAHs
Cobalt
Aluminum
Iron
Vanadium
Less
Bioavailable
— »>
-J
HH
O
VI
1
1
b
Invertebrate
Plant
2
2
2
t.
>
t
>
Omnivore
(Vermivore)
Herbivore
^
2
-V
->
3
Carnivore
3
k
w
3
-*
^
z
o
HH
H
ABSORP
Significant Bioavailabilitv Parameters:
1. Uptake from soil into food items.
2. Uptake from food into receptor (dose)
3. Gastrointestinal absorption
* Based on EcoSSL assumptions
Bioavailability can be reduced by sorption, sequestration and other physical binding.
Absorption depends on anatomy and physiology of the digestive system as well as the presence and composition of
materials in the gut.
DRAFT
7-6
July 10, 2000
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Figure 7-2. Incorporating Unavailability into Exposure Estimates
Consider other Factors
(e.g., regulatory
mandates)
Contaminant exceeds Eco-SSL
Consider potential
influence of key soil
properties (e.g., TOC,
pH, CEC)
Options for modifying exposure or
effects information (Step 3 of ERAGs):
" Site-specific
1 Receptor-specific
Do not invest resources
into developing
bioavailability
information.
Identify key:
" Pathways
" Chemicals
1 Receptors
Is information or bioavailability likely to
be useful for decision-making?
(SMDP for baseline)
No
Yes
Select key pathways for analysis
Uptake into soil
invertebrates and/or plants
Soil Ingestion
Measurements
" Laboratory
" In situ
' Field
Models
Site-specific
receptors
Measured soil
nronerties
Use receptor and/or
chemical-relevant
methods
" In vivo
* In vitro
• Models
Identify receptor and chemical
relevant distinctive characteristics
Herbivores
" Mammals
' Birds
Carnivores/Omnivores
" Mammals
1 Birds
SMDP = Scientific Management Decision Point
DRAFT
7-7
July 10, 2000
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to decline as the organic content of soil increases. When judging the type and value of
information on bioavailability, it is important to identify the degree to which exposure is
influenced by uptake into wildlife food items such as soil invertebrates and plants as compared to
incidental ingestion of soils. The outcome of this analysis might then determine which
information would be most useful to confer a site-specific dimension to the Eco-SSLs.
As illustrated in Figure 7-2, options available for determining site-specific uptake into wildlife
food items (plants and soil invertebrates) include measurements or models. If exposure is driven
by incidental ingestion of soils, determining the relative bioavailability of the contaminants
associated with ingested soil is a possible approach for refining exposure estimates. However,
bioavailability of ingested soils will be affected by different types of wildlife digestive systems.
Finally, there are a limited number of approaches available for assessing the relative
bioavailability of contaminants on ingested soils.
7.2 Site-Specific Considerations for Plants and Invertebrates
An empirical approach has been used to derive Eco-SSLs for plants and invertebrates (Chapter
3). This involved selecting data from toxicity tests that were performed on soils that met specific
physical and/or contaminant criteria. The intent was to include data from soils for which
contaminants are more likely to be bioavailable. Therefore, it is expected that there may be site-
specific soils within which the contaminants are less bioavailable and less toxic. Three
approaches are available for making site-specific adjustments:
1) Literature values
2) Toxicity tests
3) Measurements of bioavailable contaminant fractions
Using Literature Values for Adjusting Eco-SSLs
The Eco-SSLs for plants and invertebrates are based primarily on literature values for soils with
selected ranges of physical and contaminant characteristics. If a site soil falls out of this range,
one option available is to examine existing toxicity data for soils that are more similar to the site
soils. This could involve using studies that were conducted outside the range used to derive the
Eco-SSL values and/or to parse the data set to obtain values that are most representative of the
site soils. A limitation on either approach is the number of available studies. The QA and ranking
principles applied to the derivation of the plant and soil invertebrate Eco-SSLs (Chapter 3)
should be followed to insure that site-specific modifications derived from the literature are
technically supportable.
Using Toxicity Tests for Deriving Site-Specific Eco-SSLs
This option is readily available for plants and soil invertebrates and generally acceptable.
Protocols for the conduct of soil toxicity tests are discussed in Exhibit 7-2. Typically, these
DRAFT 7-8 July 10,2000
-------
would be applied to site soils in order to provide site-specific information on toxicity. Because
there are a number of confounding factors associated with site-specific toxicity tests, care must
be taken in the design of such studies. If the intent of the testing is to identify contaminant
concentrations at which effects are manifested (e.g., an Apparent Effects Threshold Approach),
then the design would need to include sampling along gradients of contamination. If testing is
being performed only to determine whether or not the highest soil concentrations produce any
adverse effects, then range-finding tests are adequate. Further guidance on the design and
conduct of such site-specific studies can be obtained from the regional BTAG.
Using Measurements of Bioavailable Contaminant Fractions
Most measurements of contaminants in soils involve measures of "total" bulk metals or organic
contaminants. Much attention is being given to identifying measures of the bioavailable fractions
of the contaminants. A measure of the contaminant concentration actually available to plants or
soil invertebrates could provide a more relevant estimate of exposure. To this end, a number of
investigators are currently exploring various extraction techniques for measuring the bioavailable
fraction of the contaminants in soils. These methods vary depending on the contaminant and
receptor. Typical categories of measurements include: 1) teachability to and presence of
contaminants in soil pore water (various aqueous extractions), 2) uptake of contaminants through
integument (various solid and liquid extraction methods), 3) uptake of contaminants through the
gut of invertebrates (simulated digestive fluids). Currently, with the possible exception of lead
and mammals, there are no validated methods for measuring bioavailability that have been
accepted by EPA. This is expected to change in the future.
7.3 Site-Specific Applications of Soil Chemistry Data
Site-specific studies offer more flexibility to address soil availability and toxicity issues. For
example, at a given site, plant and soil biota toxicity studies can be conducted according to the
established methods and endpoints (described in Exhibit 7-2) to generate site-specific screening
levels for a given metal or mixture of metals. An example, presented in Table 7.1, shows how an
Eco-SSL for metals established for high availability soils could be adjusted with the results from
site-specific soil toxicity tests for medium and low availability soils. In addition, in this part of
the process, for given soils or COPC, additional soil parameters may more appropriately explain
the relationship between availability and toxicity of COPC to soil biota and plants.
DRAFT 7-9 July 10,2000
-------
Table 7.1. Use of Site-Specific Soil Toxicity Tests for Modifying Screening Levels for
Metal Cations Under Designated Soil Conditions
Soil Type
Low OM (<2%)
Low CEC (<50 mmol/kg)
Low clay content
Medium OM (2-6 %)
Medium CEC (50-500 mmol/kg)
Medium clay content
High OM (6- 10%)
High CEC (>500 mmol/kg)
High clay content
SoilpH
4-5.5
Screening Value
22ppm
5.5-7 7 - 8.5
Site Testing
Value
51 ppm
Site Testing
Value
1 30 ppm
As additional data are generated for specific contaminants, models may be developed that relate
soil chemistry parameters to soil biota toxicity. Where data can support the use and validation of
these techniques, they offer broadly applicable methodologies to address these issues. The
literature, to date, does not present a consistent relationship of COPC concentrations in soils or
soil solutions and biota toxicity to currently utilize these methods in a regulatory arena.
7.4 Soil Sampling Data Requirements
The user should examine the currently available soil data and evaluate if the extent of these data
is sufficient for decision-making using the Eco-SSLs. The Soil Screening Guidance: Users
Guide (U.S. EPA, 1996a and 1996b) provides guidance on defining data collection needs for
soils including the two steps that are reviewed here.
Develop Hypothesis about Distribution of Soil Contamination. The user should identify
which areas of the site may have soil concentrations in excess of the Eco-SSLs.
Develop Sampling and Analysis plan (SAP) for Determining Soil Contaminant
Concentration. The sampling strategy for soils should be designed by completing the data
quality objectives (DQO) process, which includes the: statement of the problem, identification of
the decision, identification of inputs to the decision, definition of study boundaries, development
of a decision rule, specification on decision errors and optimizing the design. Sampling should
also be completed to measure soil characteristics, including bulk density, moisture content,
organic carbon content, porosity, pH and cation exchange capacity (CEC).
The depth over which surface soils are sampled should reflect the type of exposure expected at
the site, the type of receptors expected at the site, the depth of biological activity and the depth of
potential contamination. The size, shape and orientation of sampling volume have an effect on
DRAFT
7-10
July 10, 2000
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the reported measured contaminant concentration values.
Selection of sampling design and methods http://es/epa.gov/ncerqa/qa_docs.html can be
accomplished by use of the Data Quality Objectives (DQO) process. Additional soil sampling
guidance that should be consulted includes: Preparation of Soil Sampling Protocols: Sampling
Techniques and Strategies (U.S. EPA, 1992a), and Guidance for Data Usability in Risk
Assessment (U.S. EPA, 1992b). Reference to relevant soil sampling guidance (and other
documents) is appropriate during Steps 1 and 2 of the ERAGS process for the user to understand
the extent and quality of the existing soil data. These guidance documents may be used to
recommend further soil sampling for application of the Eco-SSLs or completion of a baseline
ERA.
7.5 Soil Properties Suggested For Routine Measurement
When soils are evaluated for potential ecological risks due to the presence of contaminant
contamination, there are several soil properties that should be considered for routine
measurements. These measurements indicate where the soils fall within the ranges of soil
properties given in Tables 2.3 through 2.5 (Chapter 2). This provides insight into the degree to
which site soils reflect the data used to derive the Eco-SSLs. It also is used to guide how to
proceed beyond the application of Eco-SSLs when collecting and evaluating data during a
baseline ERA. Specifically, site-specific information on soil properties indicates the extent to
which contaminants may be bound in the soil matrix. Possible site-specific modifications to the
Eco-SSLs that account for bioavailability are previously discussed in Chapter 7. Based on
discussions within the Eco-SSL work group for plants and soil invertebrates and consideration of
factors that influence exposure and bioavailability, the following soil properties are identified as
important for routine measurement during the baseline ERA:
• pH
Organic matter or organic carbon
Cation exchange capacity (CEC)
Soil texture (particle-size analysis)
• Bulk density as a measure of soil compaction
Other factors may also be important depending on the nature of the ecological stressor and on the
need to consider multiple stressors when evaluating effects. However, the list given above
represents a minimal set of information needed for site-specific assessments.
Of the soil properties suggested for routine measurement, pH, organic matter/organic carbon, and
cation exchange capacity were selected for use in guiding Eco-SSL derivation, thus were
previously defined and discussed in terms of their relative impact on contaminant bioavailability.
The rationales for suggesting routine measurement soil texture and bulk density are provided
below as well as additional comments regarding potential alterations in soil properties over time
and general soil health. Additional information on the soil properties presented below can be
DRAFT 7-11 July 10,2000
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found in the Handbook of Soil Science (Sumner, 2000).
Soil Texture (Particle Size Analysis)
Soil texture influences the types of animals and plants that can live on or in the soil. Thus,
information on soil texture helps an ecological risk assessor understand the types of biota that a
soil can support. At a screening level, this can be important for developing conceptual models of
receptors and exposure pathways. Soil texture also influences the bioavailability of some
contaminants. Thus, a silt or clay soil may bind contaminants differently than a sand soil. Soil
texture refers to the weight proportion of the separates for particles less than 2 mm as determined
from a laboratory particle-size distribution. The finer sizes are called fine earth (smaller than
2mm diameter) as distinct from rock fragments (pebbles, cobbles, stones and boulders). The
texture classes are sand, loamy sands, sandy loams, loam, silt loam, silt, sandy clay loam, clay
loam, silty clay loam, sandy clay, silty clay, and clay. Subclasses of sand are subdivided into
coarse sand, sand, fine sand, and very fine sand. Soil texture, structure, and depth all affect the
water-holding capacity of the soils and need to be considered when determining water retention
requirements or supplemental irrigation requirements as the wetland restores during the dry
periods of the year.
Bulk Density (as a measure of soil compaction)
The bulk density of a soil influences the ability of soil burrowing invertebrates and plants to
utilize that soil as habitat. Highly compacted soils such as those found on some industrial sites
preclude many invertebrates and plants. Therefore, information on bulk density can be used by
ecological risk assessors during the baseline risk assessment to determine a soil's ability to
support flora and fauna. This information can then be used in conceptual models.
Bulk density is the weight of solids per unit volume of soil. The bulk density of a soil will
increase under land uses that result in soil compaction, which is when soil particles are pressed
together, reducing the pore space between them. Soil compaction occurs in response to pressure
(weight per unit area) exerted by field machinery or animals. The risk for compaction is greatest
when soils are wet. Soil compaction is caused by tilling, harvesting, or grazing when the soils
are wet. Compaction restricts rooting depth, which reduces the uptake of water and nutrients by
plants. It affects the activity of soil organisms by decreasing the rate of decomposition of soil
organic matter and subsequent release of nutrients. Compacted soils can be identified by platy or
weak structure, greater penetration resistance, higher bulk density, restricted plant rooting, and/or
flattened, turned, or stubby plant roots.
Soil bulk density depends on the soil texture. Minimum bulk density values for which plant
roots may be restricted at various soil textures are presented in Table 7.2.
DRAFT 7-12 July 10,2000
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Table 7.2 Minimum Bulk Density Values for Which Plant Roots May be Restricted for
Various Soil Textures
Soil Texture
Coarse, medium, and fine sand and loamy sands other than loamy very fine
Very fine sand, loamy, very fine sand
Loam, sandy clay loam
Clay loam
Sandy clay
Silt, silt loam
Silty clay loam
Silly clay
Clay
Silt, silt loam
Soil Bulk Density
(g/cc)
1.8
1.77
1.75
1.65
1.6
1.55
1.5
1.45
1.4
Measurement Techniques
Several federal agencies and others such as the U.S. EPA, the U.S. Department of Agriculture
National Resource Conservation Service (USDA NRCS), and ASTM have developed analytical
methods to measure these soil properties. The methods from these various agencies in some
instances are similar while other methods are quite different because the intended use of the
measurement data is different. For instance, USEPA Office of Solid Waste has a compendium of
test methods for evaluating physical and contaminant properties of soils referred to SW-846.
Several methods for measuring various soil physical and chemical properties can also be found in
three volumes of Methods of Soil Analysis (Klute, 1986; Page et al., 1994; Sparks et al., 1996)
including limitations and interferences.
7.6 Site-Specific Considerations for Wetlands
Wetland soils and sediments typically have different geochemical properties compared to upland
soils. While screening levels for soils may suffice for screening wetland soils because these
screening values were conservatively derived, site-specific conditions may warrant different
approaches for modifying SSLs. Two questions commonly arise when considering wetland
systems:
• Distinguishing wetland soils from wetland sediments, and
Selecting the appropriate methods for site-specific evaluations.
DRAFT
7-13
July 10, 2000
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While there is likely a gradient between wetland soils and sediments, distinguishing between
these categories may be useful at a screening level as well as for more site-specific assessments.
In general, screening levels developed for soils may be applicable to wetland soils, while
screening levels developed for sediments may be applicable to wetland sediments. A few
approaches have been proposed for distinguishing between these environments. The first and
most widely accepted is the classification developed for the National Wetland Inventory (NWI)
which provides some basis for distinguishing between soils and sediments within wetlands.
Various states have also made this distinction in order to help manage these areas.
Massachusetts, for example, includes the following descriptions in its 1996 Guidance for
Disposal Site Risk Characterization:
Given the transitional nature of wetlands between terrestrial and aquatic systems,
sediment and/or soil may be present in a given wetland. The MCP (310 CMR 40.0006)
gives the following definition for sediment:
Sediment means all detrital and inorganic or organic matter situated on the
bottom of lakes, ponds, streams, rivers, the ocean, or other surface water bodies.
Sediments are found:
a) in tidal waters below the mean high waterline as defined in 310
CMR 10.23; and
b) below the upper boundary of a bank, as defined in 310 CMR
10.54(2) which abuts and confines a water body.
All other unconsolidated earth in wetlands, including the lOyearfloodplain, is
considered soil.
Table 7.3 provides a possible approach for applying Eco-SSL values for soils in wetland systems.
The approach makes use of the National Wetlands Inventory (NWI) classification system
(Cowardin et al. 1979). Because the character of wetland systems varies across the country with
different management types across states, Table 7-3 should be viewed only as a rough guide. The
appropriate local and regional wetland regulatory personnel should be consulted concerning the
applicability of soil and/or sediment screening criteria to wetlands. Application of the Eco-SSLs
alone or in tandem with sediment benchmarks requires professional judgement. The regularity,
depth and duration of flooding should be considered as well as the presence or absence of
emergent vegetation in making the determination. If the "soils" are flooded often enough to
qualify as "sediments" and are not vegetated with emergent species then Eco-SSLs should not be
used.
Site-specific modifications of Eco-SSLs for wetlands would need to consider wetland flora and
fauna as well as the properties of the wetland soils. A discussion of such approaches is beyond
the scope of this document. Conceptually, the approach is similar to that used for upland soils.
DRAFT 7-14 July 10,2000
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However, the specifics of laboratory and field testing methods may differ.
DRAFT 7-15 July 10,2000
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Table 7.3. Recommended Application of Eco-SSLs and/or Sediment Benchmarks to NWI Categories of Wetlands and
Deepwater Habitats
NWI Weland Classification: Wetlands and Deepwater Habitats
System
Marine
Estuarine
Riverine
Subsystem
Subtidal
Intertidal
Subtidal
Intertidal
Tidal
Class
Rock Bottom
Unconsolidated Bottom
Aquatic Bed
Reef
Aquatic Bed
Reef
Rocky Shore
Unconsolidated Shore
Rock Bottom
Unconsolidated Bottom
Aquatic Bed
Reef
Aquatic Bed
Reef
Streambed
Rocky Shore
Unconsolidated Shore
Emergent Wetland
Scrub-shrub Wetland
Forested Wetland
Rock Bottom
Unconsolidated Bottom
Aquatic Bed
Streambed
Rocky Shore
Unconsolidated Shore
Emergent Wetland (Non-persistent)
Applicability of Benchmarks
Eco-SSLs
®
optional
-
®
®
®
®
-
-
-
optional
®
optional
Sediment
Benchmarks
®
®
®
®
®
optional
®
®
®
®
®
®
-
optional
optional
optional
optional
-
®
®
®
optional
®
Comments
Assume sample not obtainable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Assume sample not obtainable
Use both for regularly or irregularly flooded shores; Use only
Eco-SSLs for less frequently flooded shores.
Assume sample not obtainable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Use Eco-SSLs also only for frequently dry/exposed and
vegetated streambeds
Assume sample not obtainable
Use both for regularly or irregularly flooded shores; Use only
Eco-SSLs for less frequently flooded shores.
Substitute sediment benchmarks for Eco-SSLs only within
regularly flooded or wetter reaches and unvegetated
streambeds, tidal creeks, pools and hollows, or reaches
dominated by Obligate wetland plant species
Substitute sediment benchmarks for Eco-SSLs only within
regularly flooded or wetter reaches and unvegetated
streambeds, tidal creeks, pools and hollows, or reaches
dominated by Obligate wetland plant species
Substitute sediment benchmarks for Eco-SSLs only within
regularly flooded or wetter reaches and unvegetated
streambeds, tidal creeks, pools and hollows, or reaches
dominated by Obligate wetland plant species
Assume sample not obtainable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Use Eco-SSLs also only for irregularly flooded and/or
frequently dry /exposed, vegetated streambeds used by
foraging widlife.
Assume sample not obtainable
Use both for regularly or irregularly flooded shores; Use only
Eco-SSLs in less frequently flooded shores supporting
wildlife.
Use sediment benchmarks within regularly flooded or wetter
reaches dominated by aquatic macrophytes or Obligate
wetland plant species, and in unvegetated streambeds, tidal
creeks, pools and hollows; Consider Eco-SSLs also for
irregularly flooded or drier reaches used by foraging wildlife.
pmr.Table 7-3.xls
Page 1 of 2
7/11/00 10:27 AM
-------
Table 7.3. Recommended Application of Eco-SSLs and/or Sediment Benchmarks to NWI Categories of Wetlands and
Deepwater Habitats
NWI Weland Classification: Wetlands and Deepwater Habitats
System
Riverine
Lacustrine
Palustrine
Subsystem
Lower Perennial
Upper Perennial
Intermittent
Limnetic
Littoral
Class
Rock Bottom
Unconsolidated Bottom
Aquatic Bed
Rocky Shore
Unconsolidated Shore
Emergent Wetland (Non-persistent)
Rock Bottom
Unconsolidated Bottom
Aquatic Bed
Rocky Shore
Unconsolidated Shore
Streambed
Rock Bottom
Unconsolidated Bottom
Aquatic Bed
Rock Bottom
Unconsolidated Bottom
Aquatic Bed
Rocky Shore
Unconsolidated Shore
Emergent Wetland (Non-persistent)
Rock Bottom
Unconsolidated Bottom
Aquatic Bed
Unconsolidated Shore
Moss-Lichen Wetland
Emergent Wetland (Both Subclasses)
Scrub-shrub Wetland
Forested Wetland
Applicability of Benchmarks
Eco-SSLs
®
optional
®
optional
-
-
-
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Benchmarks
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Comments
Assume sample not obtainable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Assume sample not obtainable
Use only Eco-SSLs in temporarily flooded or drier shores
used by wildlife. Consider using both for seasonally flooded
shores.
Use only sediment benchmarks in seasonally flooded or
wetter reaches dominated by Obligate wetland plant species,
and in unvegetated streambeds; Consider Eco-SSLs also for
temporarily flooded and drier reaches or during drawdown
periods.
Assume sample not obtainable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Assume sample not obtainable
Use only Eco-SSLs in temporarily flooded or drier shores
used by wildlife. Consider using both for seasonally flooded
shores.
Consider Eco-SSLs also for intermittently flooded or more
frequently dry /exposed and vegetated streambeds.
Assume sample not obtainable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Assume sample not obtainable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Assume sample not obtainable
Use only Eco-SSLs in temporarily flooded or drier shores
used by wildlife. Consider using both for seasonally flooded
shores.
Use sediment benchmarks in seasonally flooded or wetter
reaches with Obligate wetland plants, and in unvegetated
streambeds; Consider using Eco-SSLs also in temporarily
flooded and drier reaches or during seasonal drawdown
periods.
Assume sample not obtainable
Eco-SSLs are Not Applicable
Eco-SSLs are Not Applicable
Use only Eco-SSLs in temporarily flooded or drier shores
used by wildlife. Consider using both for seasonally flooded
shores.
Apply Eco-SSLs for soil biota, plants, and wildlife receptors
Use only Eco-SSLs in temporarily flooded and drier reaches
of the Persistent subclass; Use both types of benchmarks in
seasonally flooded or wetter reaches of the Non-persistent
subclass or reaches dominated by Obligate wetland plants
Substitute sediment benchmarks for Eco-SSLs only in
semipermanently flooded or wetter reaches, unvegetated
channels, ponds, or hollows, and areas dominated by
Obligate wetland plants
Substitute sediment benchmarks for Eco-SSLs only in
semipermanently flooded or wetter reaches, unvegetated
channels, ponds, or hollows, and areas dominated by
Obligate wetland plants
pmr.Table 7-3.xls
Page 2 of 2
7/11/00 10:27 AM
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