Guidance for Developing 

Ecological Soil Screening Levels

OSWER Directive 9285.7-55

U.S. Environmental Protection Agency

Office of Solid Waste and Emergency Response

1200 Pennsylvania Avenue, N.W.

Washington, DC 20460

November 2003

Revised February 2005
This Page Intentionally Left Blank
ES - 1

EXECUTIVE SUMMARY

This document describes the process used to derive a set of risk-based ecological soil screening

levels (Eco-SSLs) for many of the soil contaminants that are frequently of ecological concern for

plants and animals at hazardous waste sites and provides guidance for their use. The Eco-SSL

derivation process represents the group effort of a multi-stakeholder workgroup consisting of

federal, state, consulting, industry, and academic participants led by the U.S. Environmental

Protection Agency Office of Superfund Remediation and Technology Innovation (OSRTI). The

Eco-SSLs are concentrations of contaminants in soil that are protective of ecological receptors

that commonly come into contact with soil or ingest biota that live in or on soil. These values

can be used to identify those contaminants of potential concern in soils requiring further

evaluation in a baseline ecological risk assessment. The Eco-SSLs should be used during Step 2

of the Superfund Ecological Risk Assessment process, the screening-level risk calculation. The

Eco-SSLs are not designed to be used as cleanup levels and EPA emphasizes that it is

inappropriate to adopt or modify these Eco-SSLs as cleanup standards. 

EPA derived the Eco-SSLs in order to conserve resources by limiting the need for EPA and other

risk assessors to perform repetitious toxicity data literature searches and data evaluations for the

same contaminants at every site. This effort should also allow risk assessors to focus their

resources on key site-specific studies needed for critical decision-making. EPA also expects that

the Eco-SSLs will increase consistency among screening risk analyses and decrease the

possibility that potential risks from soil contamination to ecological receptors will be

overlooked.

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 in recent

Record of Decisions at Superfund National Priority List sites. The Eco-SSL contaminant list

also included contaminants nominated by the EPA regional Biological Technical Assistance

Group Coordinators. The list of 24 Eco-SSL contaminants contained 17 metals including

aluminum, antimony, arsenic, barium, beryllium, cadmium, chromium, cobalt, copper, iron, lead,

manganese, nickel, selenium, silver, vanadium, and zinc. The organic contaminants on the list

were dieldrin, Hexahydro -1,3,5-trinitro-1,3,5-triazine (RDX), trinitrotoluene (TNT),

1,1,1-Trichloro-2,2-bis (p-chlorophenyl)ethane (DDT) and metabolites (DDE and DDD),

pentachlorophenol, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls

(PCBs). 

The omission of other contaminants, such as phthalates, cyanides, dioxins and mercury, does not

imply that these contaminants can be excluded from the ERA screening process for soil

contamination. The processes and procedures established here for developing the Eco-SSLs

were intended to be sufficiently transparent to allow others to derive values for additional

contaminants, as needed. PCBs were included by the workgroup in the original Eco-SSL

contaminant list. However, it became apparent early in the process, that development of a PCB

soil screening value was not warranted. Because of the known high persistence and toxicity of

PCBs, and the conservative nature of the Eco-SSLs, it was acknowledged that soil screening
ES - 2

levels derived for PCBs would normally be lower than the soil analyses detection limits. EPA

believes that if PCBs are detected in soil above background levels, the PCBs are probably siterelated and therefore should be included as a contaminant of potential concern in the baseline

risk assessment. 

The approach developed for deriving the Eco-SSLs for plants and soil invertebrates was similar

to the approach taken for deriving the wildlife Eco-SSLs (specifically the toxicity reference

values). The general approach included four steps: (1) conduct literature searches, (2) screen

identified literature with exclusion and acceptability criteria, (3) extract, evaluate, and score test

results for applicability in deriving an Eco-SSL, and (4) derive the value. These procedures were

finalized as standard operating procedures prior to initiating any work to derive the actual values. 

Chapter 3 provides a description of the procedures used for deriving plant and soil invertebrate

Eco-SSL values. The values were derived directly after an evaluation of all available plant and

soil invertebrate chronic toxicity test data (measured toxicity related to soil contaminant

concentrations). Chapter 4 provides a description of the procedures for deriving the wildlife

Eco-SSLs. The wildlife Eco-SSLs were the result of back-calculations from a hazard quotient of

1.0. The hazard quotient is equal to the estimated exposure dose divided by the toxicity

reference value (TRV). An HQ of 1.0 is the condition where the exposure and the dose

associated with no adverse chronic effects are equal, indicating adverse effects at or below 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 (dose) for the respective contaminant for chronic exposure.

The Eco-SSLs apply 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, plants, and soil microbes and their processes. After investigation, the toxicity data

for amphibians and reptiles were deemed insufficient to derive Eco-SSLs. EPA recognizes that

the Eco-SSL may not be protective of these receptor groups. Eco-SSLs protective of microbes

and soil microbial processes were also not derived. Like amphibians and reptiles, EPA

recognizes their importance within terrestrial systems, but concurs with the workgroup that data

are insufficient and the interpretation of test results too uncertain for establishing risk-based

thresholds. 

Eco-SSLs are appropriate to all sites where key soil parameters fall within a certain range of

chemical and physical parameters. The Eco-SSLs for plants and soil invertebrates were derived

to 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%. Based on these stated parameters, it is

expected that there are certain soils and situations to which Eco-SSLs do not apply. These

situations include (but may not be limited to) wetland soils that are regularly flooded (i.e.,

sediments), sewage sludge amended soils where the organic matter content is > 10%, and waste

types where the pH is < 4.0.
ES - 3

Because of the diversity of the workgroup scientists, the process developed to derive the EcoSSLs underwent constant peer review. There were also two external peer reviews performed

during the development process. 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 on April 6, 1999. At this meeting, the SAB provided verbal comments to the presenters

which were subsequently addressed, as appropriate, by the workgroup as they prepared the

guidance. 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. Each of the comments

received was carefully considered by the workgroup Steering Committee and appropriate

changes were made to both the procedures for establishing Eco-SSLs and to the guidance

document. 

After developing the procedures and completing the peer review process, the workgroup focused

primarily on deriving Eco-SSL values for the list of contaminants. The results of the application

of the derivation procedures reported in this document are provided as separate contaminantspecific documents. In cases where data were limited or not available and Eco-SSL values could

not be derived for specific contaminants and receptor groups, EPA may at some point in the

future revise the contaminant-specific documents or add contaminants as appropriate. 
This Page Intentionally Left Blank
ACKNOWLEDGMENTS

The development of this guidance was a team effort led by the U.S. Environmental Protection Agency Office of

Superfund Remediation and Technology Innovation (OSRTI). A Steering Committee coordinated the activities of

four task groups. A team of scientists from Syracuse Research Corporation (SRC) managed by Janet Burris

provided technical and administrative support in the development of this guidance. Listed below are the members of

the Steering Committee and each task group.

Steering Committee

Steve Ells, U.S. EPA OSRTI, Co-Chair

Ralph Stahl, DuPont, Co-chair

Randy Wentsel, U.S. EPA ORD, Co-chair

Bill Adams Rio Tinto

Doris Anders U.S. Air Force Center for Environmental Excellence (AFCEE)

John Bascietto U.S. Department of Energy (DOE)

David Charters U.S. EPA OSRTI

Ron Checkai U.S. Army Edgewood Chemical Biological Center (ECBC)

Stiven Foster U.S. EPA, Office of Research and Development (ORD)

Dale Hoff U.S. EPA, Region 8

Charlie Menzie Menzie-Cura & Associates

Chris Russom U.S. EPA, ORD

Brad Sample CH2MHill

Jason Speicher North Division of the Naval Facilities Engineering Command

Mike Swindoll Exxon Mobile Biomedical Sciences, Inc.

Task Group on Soil Chemistry

Randy Wentsel, U.S. EPA, Co-Chair

Charlie Menzie, Menzie-Cura & Associates, Co-Chair

Bill Berti DuPont

Ron Checkai U.S. Army Edgewood Chemical Biological Center (ECBC)

Mary Goldade U.S. EPA, Region 8

Roman Lanno Oklahoma State University

Charles R. Lee U.S. Army Corps of Engineers, Waterways Experiment Station

Linda Lee Purdue University

Mike Ruby Exponent

John Samuelian OGDEN Environmental and Energy Services

Task Group on Eco-SSLs for Soil Invertebrates and Plants

Ron Checkai, U.S. Army ECBC, Co-Chair

Mike Swindoll, Exxon Mobile 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 U.S. EPA, OSRTI

Charlene Falco Illinois EPA

Stiven Foster U.S. EPA, ORD.

Dave Gannon Zeneca Corp.

Andrew Green International Lead Zinc Research Org. (ILZRO)

Larry Kapustka ecological, planning & toxicology, Inc.

Roman Kuperman U.S. Army ECBC

Les Morrow Illinois EPA

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 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

David Charters U.S. EPA OSRTI

David Cozzie U.S. EPA Office of Solid Waste

Dave Cragin Elf-Atochem North America

Steve Dole Exponent

Anne Fairbrother U.S. EPA, ORD, Corvallis, Oregon

Bob Fares Environmental Standards, Inc.

Gary Friday Westinghouse Savannah River Co.

Kinzie Gordon Parsons Engineering Science, Inc.

Gerry Henningsen U.S. EPA, Region 8 (retired)

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

Task Group on Exposure Models for Wildlife Species

John Bascietto, U.S. Department of Energy (DOE), Co-Chair

Brad Sample, CH2M Hill, Co-Chair

Amber Brenzikofer Parsons Engineering Science

Bridgette Deshields Harding Lawson Associates

Will Gala Chevron

Tracy Hammon Colorado Department of Health and Environmental Protection

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
Guidance for Developing Eco-SSLs i November 2003

TABLE OF CONTENTS

EXECUTIVE SUMMARY 

LIST OF FIGURES

LIST OF TABLES

LIST OF ATTACHMENTS

LIST OF ACRONYMS AND ABBREVIATIONS

1.0 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.1 Scope of the Eco-SSLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

1.2 The General Process for Establishing Eco-SSLs . . . . . . . . . . . . . . . . . . . . . . . . 1-7

1.3 Peer Review Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11

1.4 Quality, Objectivity, Utility, and Integrity of Information . . . . . . . . . . . . . . . 1-12

1.5 Using Eco-SSLs to Screen Contaminated Soils . . . . . . . . . . . . . . . . . . . . . . . 1-12

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 . . . . . . . . . . . . . . . . . . . . . 2-8

3.0 DERIVATION OF PLANT AND SOIL INVERTEBRATE ECO-SSLs . . . . . . . . . . . 3-1

3.1 Literature Search, Acquisition, and Screening . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3.2 Identification of Potentially-Acceptable Literature . . . . . . . . . . . . . . . . . . . . . 3-3

3.3 Extraction of Data and Scoring Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

3.4 Derivation of Eco-SSLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.5 Soil Toxicity Test Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8

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 Derivation of Toxicity Reference Values (TRVs) . . . . . . . . . . . . . . . . . . . . . . 4-11

4.5 Calculation of Wildlife Eco-SSLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18

5.0 ECO-SSL DOCUMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

6.0 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1
Guidance for Developing Eco-SSLs ii November 2003

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 1.3 Comparison of Eco-SSL Derivation Procedures for Wildlife and Plant and Soil

Invertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9

Figure 3.1 The Four-Step Process for Deriving Eco-SSLs for Plants and Soil Invertebrates3-1

Figure 3.2 Literature Exclusion Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

Figure 3.3 Eleven Study Acceptance Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

Figure 4.1 The Wildlife Risk Model for Eco-SSLs (Equation 4-1) . . . . . . . . . . . . . . . . . . 4-2

Figure 4.2 Summary of Method Used for Estimation of Contaminant Concentrations 

in Biota Types (Bi ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7

Figure 4.3 Comparison of Soil-to-Foilage and Soil-to-Seed Bioaccumulation for Cadmium,

Copper, Lead and Zinc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

Figure 4.4 Comparison of Mean Concentrations in Multiple Species near a Smelter . . . 4-10

Figure 4.5 Steps of the Wildlife TRV Derivation Process . . . . . . . . . . . . . . . . . . . . . . . . 4-11

Figure 4.6 Ten Attributes Scored as Part of the Wildlife Toxicological Data 

Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

Figure 4.7 Example of a Toxicological Plot for the TRV Derivation Process (Cobalt) . . 4-15

Figure 4.8 Procedure for Deriving the Wildlife Toxicity Reference Value (TRV) . . . . . 4-16

Figure 4.9 Example of Wildlife TRV Derivation (Cobalt) . . . . . . . . . . . . . . . . . . . . . . . . 4-17
Guidance for Developing Eco-SSLs iii November 2003

LIST OF TABLES

Title Page

Table 1.1 Comparison of Eco-SSL Derivation Procedures . . . . . . . . . . . . . . . . . . . . . . . 1-10

Table 2.1 General Contaminant Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

Table 2.2 Log Kow Values for Organic Contaminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5

Table 2.3 Mean Reported Soil Metal Background Concentrations (ppm dry weight) by 

State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

Table 2.4a Qualitative Bioavailability of Metal Cations in Natural Soils to Plants . . . . . 2-10

Table 2.4b Qualitative Bioavailability of Metal Cations in Natural Soils to Soil 

Invertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10

Table 2.5 Qualitative Bioavailability of Non-Ionizing Organic Contaminants in Natural

Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

Table 2.6 Qualitative Bioavailability of Metal Anions in Natural Soils . . . . . . . . . . . . . 2-11

Table 3.1 Ecologically Relevant Endpoints (EREs) for Soil Invertebrate Eco-SSLs . . . . 3-4

Table 3.2 Ecologically Relevant Endpoints (EREs) for Plant Eco-SSLs . . . . . . . . . . . . . 3-5

Table 3.3 Summary of Nine Study Evaluation Criteria for Plant and Soil Invertebrate

Eco-SSLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

Table 3.4 Standard Methods Appropriate for use in Generating Data for the Derivation of 

Plant or Soil Invertebrate Eco-SSLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9

Table 4.1 Parameterization of the Eco-SSL Wildlife Exposure Model . . . . . . . . . . . . . . . 4-5

Table 4.2 Cases where the Median of the BAF Distribution for Metals is Greater 

or Less than One . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10

Table 4.3 Example of Extracted and Scored Toxicity Data for Wildlife TRV (Cobalt) . 4-14

Table 4.4 Summary of Conservatism Associated with the Wildlife Eco-SSL Risk Model

Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-20
Guidance for Developing Eco-SSLs iv November 2003

LIST OF ATTACHMENTS

Attachment 1-1 Review of Existing Soil Screening Guidelines 

Attachment 1-2 Discussion Concerning Soil Microbial Processes

Attachment 1-3 Review of Dermal and Inhalation Exposure Pathway for Wildlife

Attachment 1-4 Review of Background Concentrations for Metals

Attachment 3-1 Eco-SSL Standard Operating Procedure #1: Plant and Soil

Invertebrate Literature Search and Acquisition

Attachment 3-2 Eco-SSL Standard Operating Procedure #2: Plant and Soil

Invertebrate Literature Evaluation and Data Extraction, Eco-SSL

Derivation, Quality Assurance Review, and Technical Write-up

Attachment 4-1 Exposure Factors and Bioaccumulation Models for Derivation of

Wildlife Eco-SSLs

Attachment 4-2 Eco-SSL Standard Operating Procedure # 3: Wildlife TRV: 

Literature Search and Retrieval

Attachment 4-3 Eco-SSL Standard Operating Procedure # 4: Wildlife TRV

Literature Review, Data Extraction and Coding

Attachment 4-4 Eco-SSL Standard Operating Procedure # 5: Wildlife TRV Data

Evaluation 

Attachment 4-5 Eco-SSL Standard Operating Procedure #6: Wildlife TRV

Derivation 

Due to the length of many of these Attachments, they are only available on the EPA Superfund

web site at www.epa.gov/oswer/riskassessment/ecorisk/ecossl.htm.
Guidance for Developing Eco-SSLs v

LIST OF ACRONYMS AND ABBREVIATIONS

AEC Anion exchange capacity

ASTM American Society of Testing and Materials

AUF Area use factor

AFij Absorbed fraction of contaminant (j) from biota type (i)

AFsj Absorbed fraction of contaminant (j) from soil (s)

Bi Contaminant concentration in biota type (i)

B0ij Intercept from log-linear bioaccumulation model for contaminant (j) for biota type (i)

B1ij Slope from log-linear bioaccumulation model for contaminant (j) for biota type (i)

BAF Bioaccumulation factor

BEH Behavior

BIO Biochemical

BTAG Biological Technical Assistance Group

BW Body weight

Csoil Concentration of contaminant in soil

CCME Canadian Council of Ministers of the Environment

CEC Cation exchange capacity

CI Confidence interval

COC Contaminant of concern

COPC Contaminant of potential concern

DDT 1,1,1-Trichloro-2,2-bis(p-chlorophenyl)ethane

DQO Data quality objective

dw Dry weight

ECxx Effect concentration for the xx% of the test population

Eco-SSL Ecological soil screening level

EPA U.S. Environmental Protection Agency

ERA Ecological risk assessment

ERAGS Ecological Risk Assessment Guidance for Superfund

ERE Ecologically relevant endpoint

foc fraction organic carbon content

FBRC Federal Biology Research Cooperative

FIR Food ingestion rate

g Grams

GRO Growth

HQ Hazard quotient

HQj Hazard quotient for contaminant (j)

HSDB Hazardous Substance Data Bank

ISO International Standards Organization

kg Kilogram

Koc Organic carbon-normalized partition coefficient

Kow Octanol water partition coefficient

LC50 Concentration lethal to 50 percent of test population 

LOAEC Lowest-observed adverse effect concentration

LOAEL Lowest-observed adverse effect level
Guidance for Developing Eco-SSLs vi

LIST OF ACRONYMS AND ABBREVIATIONS

(Continued)

MATC Maximum acceptable toxicant concentration

mg Milligram

mmol Millimole

MOR Mortality

MPA Maximum permissible addition

MPC Maximum permissible concentration

NC Negligible concentration

NOAEL No-observed adverse effect level

NOAEC No-observed adverse effect concentration

NOC Non ionic organic compounds

OC Organic carbon

OECD Organization for Economic Cooperation and Development

OM Organic matter

ORNL Oak Ridge National Laboratory

OSRTI Office of Superfund Remediation and Technology Innovation

Pi Proportion of biota type (i) in diet

Ps Soil ingestion as proportion of diet

pKa Acid dissociation constant

PAH Polycyclic aromatic hydrocarbon

PCB Polychlorinated biphenyl

PCP Pentachlorophenol

PHY Physiology

PTH Pathology

QA Quality assurance

QSAR Quantitative Structure Activity Relationship

RDX Hexahydro-1,3,5-trinitro-1,3,5-triazine

REP Reproduction

RIVM Dutch National Institute of Public Health and the Environment

ROD Record of decision

SAB Science Advisory Board

SMDP Scientific Management Decision Point

Soilj the Eco-SSL for contaminant j for wildlife

SOP Standard operating procedure

SQG Soil quality guideline

Tij Soil-to-biota bioaccumulation factor for contaminant (j) for biota type (i)

Tver diet-to-biota BAF

TNT Trinitrotoluene

TRV Toxicity reference value

TRVj Toxicity reference value for contaminant (j)

UCL Upper confidence limit

U.S. United States

μm micrometer
Guidance for Developing Eco-SSLs vii

LIST OF ACRONYMS AND ABBREVIATIONS

(Continued)

VOC Volatile organic compound

ww Wet weight
This Page Intentionally Left Blank
Guidance for Developing Eco-SSLs 1 - 1 November 2003

1.0 INTRODUCTION

This guidance describes the process used to derive a set of risk-based ecological soil screening

levels (Eco-SSLs) for many of the soil contaminants that are frequently of ecological concern for

plants and animals at hazardous waste sites and further provides guidance on using Eco-SSLs. The

specific Eco-SSL values and the data upon which they were derived are described in separate

contaminant specific Eco-SSL documents. The Eco-SSL derivation process represents the group

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 Superfund

Remediation and Technology Innovation (OSRTI). 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). Although these screening levels were developed specifically to be used during

Step 2 of the Superfund ecological risk assessment process, EPA envisions that any federal, state,

or private environment assessment or cleanup program could use these values to screen soil

contaminants and sites in order to determine if additional ecological site study was warranted. The

Eco-SSLs were not designed to be used as cleanup levels and EPA emphasizes that it would be

inappropriate to adopt or modify these Eco-SSLs as 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 and state

personnel may use and accept other technically sound approaches, either on their own initiative, or

at the suggestion of potentially responsible parties, or other interested parties. Therefore, interested

parties are free to raise questions and objections about the substance of this guidance and the

appropriateness of the application of this document to a particular situation. EPA welcomes public

comments on this guidance at any time and may consider such comments in future revisions of this

guidance.

What are Eco-SSLs?

Eco-SSLs are concentrations of contaminants in soil 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. 
Guidance for Developing Eco-SSLs 1 - 2 November 2003

STEP 1: SCREENING LEVEL

• Site Visit

• Problem Formulation

• Toxicity Assessment

Risk Assessor 

and Risk 

Manager 

Agreement

STEP 2: SCREENING LEVEL

• Exposure Estimate

• Risk Characterization 

Compile Existing Information 

SMDP

Toxicity Evaluation

Assessment 

Endpoints

Conceptual Model

Exposure Pathways 

Questions/ Hypotheses

Data Collection

STEP 4: STUDY DESIGN AND DQO PROCESS

• Lines of Evidence

• Measurement Endpoint

• Workplan and Sampling and Analyses Plan

STEP 3: PROBLEM FORMULATION

STEP 5: VERIFICATION OF FIELD SAMPLING DESIGN

STEP 6: SITE INVESTIGATION AND DATA ANALYSES

STEP 7: RISK CHARACTERIZATION

STEP 8: RISK MANAGEMENT

SMDP

SMDP

SMDP

SMDP

SMDP

SMDP = Sample Management Decision Point

DQO = Data Quality Objective

STEP 1: SCREENING LEVEL

• Site Visit

• Problem Formulation

• Toxicity Assessment

Risk Assessor 

and Risk 

Manager 

Agreement

STEP 2: SCREENING LEVEL

• Exposure Estimate

• Risk Characterization 

Compile Existing Information 

SMDP

Toxicity Evaluation

Assessment 

Endpoints

Conceptual Model

Exposure Pathways 

Questions/ Hypotheses

Toxicity Evaluation

Assessment 

Endpoints

Conceptual Model

Exposure Pathways 

Questions/ Hypotheses

Data Collection

STEP 4: STUDY DESIGN AND DQO PROCESS

• Lines of Evidence

• Measurement Endpoint

• Workplan and Sampling and Analyses Plan

STEP 3: PROBLEM FORMULATION

STEP 5: VERIFICATION OF FIELD SAMPLING DESIGN

STEP 6: SITE INVESTIGATION AND DATA ANALYSES

STEP 7: RISK CHARACTERIZATION

STEP 8: RISK MANAGEMENT

SMDP

SMDP

SMDP

SMDP

SMDP

SMDP = Sample Management Decision Point

DQO = Data Quality Objective

Figure 1.1 Eight Step Process Recommended in Ecological Risk Assessment

Guidance for Superfund (ERAGs) (U.S. EPA, 1997)
Guidance for Developing Eco-SSLs 1 - 3 November 2003

Protection of Rare, Endangered and

Threatened Species

Eco-SSLs should be protective of rare,

endangered, and threatened species. However,

the final decision should be made on a sitespecific basis in consultation with the United

States Fish and Wildlife Service and other

natural resource trustees.

As such, these values are presumed to provide adequate

protection of terrestrial ecosystems. Eco-SSLs for

wildlife are derived to be protective of the

representative of the conservative end of the

distribution in order to make estimates for local

populations. The Eco-SSLs are conservative and are

intended to be applied at the screening stage of the

assessment. 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 was written with

the assumption that the reader is familiar with Superfund’s guidance on performing ERAs

(Ecological Risk Assessment Guidance for Superfund (ERAGS)), U.S. EPA, 1997, Figure 1.1),

with Superfund’s ecological risk assessment and risk management principles (U.S. EPA, 1999) and

with EPA’s risk assessment guidelines (U.S. EPA, 1998).

The Eco-SSLs 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 believes that the Eco-SSLs generally provide an appropriate

balance of protectiveness and reasonableness. 

Why are Eco-SSLs Needed?

EPA derived the Eco-SSLs with the intent to

conserve resources by limiting 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 are also intended to 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

workgroup examined currently available soil

screening guidelines (see text box) for their

potential use within the Superfund process. 

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, 1997).

The Dutch National Institute of Public Health and the

Environment (RIVM). Maximum permissible

concentrations (MPCs), maximum permissible 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 et al., 1997).
Guidance for Developing Eco-SSLs 1 - 4 November 2003

Because these existing guidelines were developed in response to country-specific legislation and/or

included policies not totally consistent with current EPA policies, EPA believes use of these

guidelines may not be appropriate. A summary and evaluation of the available guidelines was

completed and provided as Attachment 1-1. 

How Are the Eco-SSLs Derived?

Eco-SSLs were derived 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. The wildlife Eco-SSLs were 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 (TRV). 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 or below 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 (milligrams per

kilogram body weight per day (mg/kg bw/d)). 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 included contaminants

nominated by the EPA regional Biological

Technical Assistance Group (BTAG)

Coordinators. The list of 24 Eco-SSL

contaminants contained seven organics and17

metals (see Figure 1.2). 

The omission of other contaminants, such as

phthalates, cyanides, dioxin, and mercury, does

not imply that 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 in soil. The process and procedures

Figure 1.2 Eco-SSL Contaminants

Organics

• Dieldrin

• Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX)

• Trinitrotoluene (TNT)

• 1,1,1-Trichloro-2,2-bis (p-chlorophenyl)ethane (DDT) 

 and metabolites DDE and DDD.

• Pentachlorophenol (PCP)

• Polycyclic Aromatic Hydrocarbons (PAHs)

• Polychlorinated biphenyls (PCBs)

Metals

• Aluminum • Iron

• Antimony • Lead

• Arsenic • Manganese

• Barium • Nickel

• Beryllium • Selenium

• Cadmium • Silver

• Chromium • Vanadium

• Cobalt • Zinc

• Copper
Guidance for Developing Eco-SSLs 1 - 5 November 2003

established here for developing the Eco-SSLs are intended to be sufficiently transparent to allow

others to derive values for additional contaminants, as needed. Polychlorinated biphenyls (PCBs)

were included by the workgroup in the original 24 identified Eco-SSL contaminants. However, it

became apparent early in the process that development of a screening value would not be

appropriate. Because of the known persistence and toxicity of PCBs, and the conservative nature of

the Eco-SSLs, any soil screening level derived for PCBs would often be lower than the detection

limits used in a screen. EPA recommends that if PCBs are detected in soil above background

levels, then they should be considered site-related and therefore should be included as a COPC in a

baseline ERA.

Ecological Receptors of Concern 

The Eco-SSLs generally apply 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 initial groups included mammals, birds, reptiles,

amphibians, soil invertebrates, plants, and soil microbes and their processes. After further

investigation, the toxicity data for amphibians and reptiles were deemed insufficient to derive

Eco-SSLs. EPA recognizes that the Eco-SSL values do not address possible risks for reptiles and

amphibians and may not be protective of these receptor groups. The user should consider including

these receptors in the site-conceptual model for the site-specific risk assessment.

Eco-SSLs protective of microbes and soil microbial processes were also not derived. Like

amphibians and reptiles, EPA recognized their importance within terrestrial systems, but believes

that data are insufficient and the interpretation of test results too uncertain for establishing riskbased thresholds for risk screening purposes. While Eco-SSLs for microbes and soil microbial

processes were not established at this time, they may be considered in the future as the science

develops and appropriate studies are completed. A summary of the task group discussion

concerning establishing Eco-SSLs for soil microbes and their processes was documented as

Attachment 1-2. 

Eco-SSLs were derived for four general groups of ecological receptors: mammals, birds, plants,

and soil invertebrates. By deriving conservative soil screening values considered protective of

these groups, it is assumed that these receptor groups are protected from possible adverse effects

associated with soil contamination. This assumption 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." 

Exposure pathways can be classified as incomplete, complete, or potentially complete. An

exposure pathway is not considered complete if natural habitat for ecological receptors is not

present and is not expected to be present in the future.
Guidance for Developing Eco-SSLs 1 - 6 November 2003

The Eco-SSLs for plants considered direct

contact of contaminants in soils. The Eco-SSLs

for soil invertebrates considered ingestion of soil

and direct contact exposures with a preference

for conditions of high bioavailability (refer to

Chapters 2 and 3).

The Eco-SSLs for birds and mammals considered

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. Two potentially complete

exposure pathways (dermal contact and

inhalation) were not considered in the derivation

of wildlife Eco-SSLs. The rationale for this

decision included the following:

• Burrowing animals could be exposed to

relatively high concentrations of volatile

organic compounds (VOCs) in their burrows via inhalation. However, with the exception of

some of the polynuclear aromatic hydrocarbons (PAHs), none of the Eco-SSL contaminants

were VOCs. At sites with high VOC and/or certain PAH concentrations in soils with

burrowing mammals present, the inhalation exposure pathway should be considered in the

baseline ERA. In this case, the contaminants would not be excluded in the screening step.

• Soil particles containing non-VOC contaminants

(by either adsorption or absorption) could also be

inhaled by wildlife. Respirable particles (greater

than five μm) are, however, most likely ingested

as a result of mucocilliary clearance rather than

being inhaled (Witschi and Last, 1996). 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 (Attachment 1-3).

• Birds and mammals may also be exposed to

contaminants in soils via dermal contact. Studies

investigating dermal exposures to birds 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

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:

T Soil contamination exists only below the root

zone, and deep burrowing mammalian species 

are not identified as potential receptors in the

site conceptual model.

T The site is within urban and/or industrialized

areas where natural habitat and receptors are

absent.*

*Urban settings may in some cases be used by protected

species. The appropriate trustees should be consulted. 

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
Guidance for Developing Eco-SSLs 1 - 7 November 2003

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 one percent to 11 % of

the total risk (Attachment 1-3) compared to oral exposures.

This approach is consistent with Section 9.2.4 of ERAGS (U.S. EPA, 1997), 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 the Eco-SSLs does

not preclude their inclusion in the site-specific baseline ERA. If it is expected that receptors may

be more exposed to contaminant(s) 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. 

Soils for Which Eco-SSLs are Applicable

It is recommended that Eco-SSLs be considered at sites where key soil parameters fall within a

certain range of chemical and physical parameters. The Eco-SSLs for plants and soil invertebrates

are usually appropriate for application 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 expected to be

broadly applicable (i.e., conservative enough for most soils) as preference was given to studies with

high bioavailability of the contaminants 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. 

Based on these stated parameters, it is expected that there are certain soils and situations to which

Eco-SSLs may not be appropriate. These situations include (but may not be limited to):

• Wetland soils that are regularly flooded (i.e., sediments).

• Sewage sludge amended soils where the organic matter (OM) content is > 10 %.

• Waste types where the pH is < 4.0.

1.2 The General Process for Establishing Eco-SSLs

Four separate task groups were formed to develop the procedures for establishing Eco-SSLs and to

accomplish the work necessary to derive the Eco-SSL values for the 24 listed contaminants. The

task groups were composed of a cross-section of the stakeholders represented in the larger work
Guidance for Developing Eco-SSLs 1 - 8 November 2003

Eco-SSL Task Groups

Soil Chemistry. This task group provided information on the

factors that influence bioavailability of contaminants from soils. 

The task group developed the soils matrix used to select studies

for establishing plant and soil invertebrate Eco-SSLs, the

background soils database, and Chapter 2 of this guidance

document.

Plant and Soil Invertebrate Eco-SSLs. This task group

developed the procedures for establishing plant and soil

invertebrate Eco-SSLs (Chapter 3). The task group derived the

plant and soil invertebrate Eco-SSL values according to these

procedures. 

Wildlife Eco-SSLs. The wildlife Eco-SSLs were developed by

the combined efforts of two separate task groups:

Wildlife Toxicity Reference Values (TRVs). This task

group developed the procedures for establishing the

wildlife toxicity reference values as presented in

Chapter 4.

Wildlife Exposure Model. This task group developed

the equations, assumptions, and factors for establishing

the exposure model for wildlife. The model is presented

in Chapter 4. The model estimates the soil contaminant

concentration associated with no adverse effects to

representative wildlife species.

group with efforts directed by a steering

committee. One task group focused on

information related to the factors that

influence the bioavailiblity of

contaminants from soils. A second task

group focused on deriving the Eco-SSLs

protective of plants and soil invertebrates. 

The third and fourth task groups focused

on deriving Eco-SSLs protective of

wildlife. One group developed the

procedures for deriving wildlife TRVs

(mammalian and avian receptors) while

the other group developed the models

necessary for estimating contaminant

transfer from soils to the terrestrial food

chain and for deriving exposures for

wildlife. 

Although the task groups worked

independently, the approach taken for

deriving the Eco-SSLs for plants and soil

invertebrates was similar (see Figure 1.3)

to the approach taken for deriving the

wildlife Eco-SSLs (specifically the

TRVs). The general approach included

four steps (1) conduct literature searches,

(2) screen identified literature with exclusion and acceptability criteria, (3) extract, evaluate, and

score test results for applicability in deriving an Eco-SSL, and (4) derive the value. 

Step 1: Literature Search

For all receptors, potentially relevant publications were identified through literature searches of

computerized abstracting databases combined with the examination of citations associated with

published literature review articles. The literature searches for avian and mammalian species

included all publication years, while those for plants and soil invertebrates included only

publications after 1987 (Table 1.1). The task group theorized that most of the pre-1988

publications would be identified through the review bibliographies, and this was confirmed in

subsequent analysis of the literature search results with approximately 40 % of potentially

applicable papers identified through non-computerized search techniques. In cases where

contaminant/receptor pairings had less than 20 potentially applicable articles, searches were

completed for all publication years. 

Step 2: Determine Acceptability of Study for Use in Deriving Eco-SSL

Both task groups used similar exclusion criteria to assess the potential applicability of publications

identified through the literature searches. Publications were excluded for similar reasons as 
Guidance for Developing Eco-SSLs 1 - 9 November 2003

Plant and 

Soil Invertebrate Eco-SSL

Conduct Electronic 

Step 1 Literature Search

Determine 

Acceptability of 

Study for use in 

Deriving an EcoSSL

Step 2

Complete according to 

established process 

(Attachment 4-3)

Complete according to 

established process 

(Attachment 3-1)

Wildlife TRV

Apply Literature Rejection 

Criteria (Attachment 4-3)

Apply Literature Rejection 

Criteria (Attachment 3-1) 

Apply 11 Literature 

Acceptance Criteria 

(Attachment 3-1)

Step 3

Extract, Evaluate 

and Score Data 

from Accepted 

Studies

Data extracted and scored 

according to established 

procedures (Attachments 4-

3 and 4-4)

Data extracted and scored 

according to established 

procedures (Attachment 3-2)

Studies that score 66 or 

higher (66%) can be used to 

derive TRV used to calculate 

Eco-SSL

Studies that score 11 or 

higher (61%) can be used to 

derive Eco-SSL

Step 4 Derive Value

Derive according to 

established procedures 

(Attachment 3-2) 

Geometric mean of NOAEL 

values for growth and 

reproduction. In some cases, 

the highest bounded NOAEL 

below the lowest bounded 

LOAEL for growth, 

reproduction or survival

Geometric mean of EC20, 

MATC or EC10 values

Derive according to 

established procedures 

(Attachment 4-5)

Plant and 

Soil Invertebrate Eco-SSL

Conduct Electronic 

Step 1 Literature Search

Determine 

Acceptability of 

Study for use in 

Deriving an EcoSSL

Step 2

Complete according to 

established process 

(Attachment 4-3)

Complete according to 

established process 

(Attachment 3-1)

Wildlife TRV

Apply Literature Rejection 

Criteria (Attachment 4-3)

Apply Literature Rejection 

Criteria (Attachment 3-1) 

Apply 11 Literature 

Acceptance Criteria 

(Attachment 3-1)

Step 3

Extract, Evaluate 

and Score Data 

from Accepted 

Studies

Data extracted and scored 

according to established 

procedures (Attachments 4-

3 and 4-4)

Data extracted and scored 

according to established 

procedures (Attachment 3-2)

Studies that score 66 or 

higher (66%) can be used to 

derive TRV used to calculate 

Eco-SSL

Studies that score 11 or 

higher (61%) can be used to 

derive Eco-SSL

Step 4 Derive Value

Derive according to 

established procedures 

(Attachment 3-2) 

Geometric mean of NOAEL 

values for growth and 

reproduction. In some cases, 

the highest bounded NOAEL 

below the lowest bounded 

LOAEL for growth, 

reproduction or survival

Geometric mean of EC20, 

MATC or EC10 values

Derive according to 

established procedures 

(Attachment 4-5)

NOAEL = No-Observed Adverse Effect Level

LOAEL = Lowest-Observed Adverse Effect Level

EC20 = Effect Concentration 20%

EC10 = Effect Concentration 10%

MATC = Maximum Acceptable Toxicant Concentration

Figure 1.3 Comparison of Eco-SSL Derivation Procedures for Wildlife and Plant and Soil

Invertebrates
Guidance for Developing Eco-SSLs 1 - 10 

Table 1.1. Comparison of Eco-SSL Derivation Procedures

Step in EcoSSL Process Common to Both Approaches Unique to Wildlife Approach Unique to Plant and 

Step 1:Conductelectronicliteraturesearch

Searches were conducted with appropriate abstractingdatabases finding the intersection of contaminant,species and toxicological terms. The final electronicsearch result was reviewed to determine potentiallyapplicable studies. Bibliographies of review articleswere skimmed to identify additional publications. 

All publication years were searched. Searches were limited to 198

searches included literature pU. S. EPA MED-Duluth libr

Step 2:Determineacceptability ofstudy for usein deriving anEco-SSL 

• Must be the primary source of the data• Exposure to single contaminant• Control must be included• Duration of exposure must be reported• Effects must be reported for relevant endpoints

• Dose or concentration must be reported• Laboratory or field studies accepted• Beneficial effects not considered• Species must be reported• Percent metal or purity used to calculate nominal

concentrations

 • Oral route of exposure • At least two exposures: 1 control and 1 contaminant• Endpoints are behavioral, biochemical, growth,

mortality, pathology, population, physiology, andreproduction

• Chronic studies only ( > 3 days exposure duration)

• Application rates not use• Natural or artificial soils• At least three exposures• Test media must have: % 

%; pH $ 4 and # 8.5

• Endpoints are growth , p

and reproduction

• Priority given to chronic 

sublethal effects or plan

Step 3:Extract,evaluate andscore data fromacceptedstudies

Papers were reviewed and the results were extractedaccording to established guidelines.

Separate results were used if any of the following varied: test organism (species or strains), contaminant form, testlocation, control type, doses, application frequency, orroute of exposure. 

Separate results were used if varied: test species (not strain(natural vs. artificial), pH, or 

Each result was scored according to establishedguidelines.

All endpoints were scored in order of preference based on: source of data; chemical form, measurement or nomeasurement in substrate; ability to calculate a dose;bounded vs unbounded NOAEL/LOAEL, route ofexposure, endpoint type, duration of exposure, statisticalpower, and adherence to test guidelines. Studies that scoreed > 65 % were used to derive a TRV.

Within a study with multiple following order of preference> physiology. Score was basexperimental design (i.e.,adhereporting of chemical concenduration of exposure; NOAE

(or can be estimated); statisticorganism.Studies that scored $ 11 out 

Eco-SSL.

Step 4:Calculation ofvalue

Results that scored above a cutoff were used in andestablished approach to determine value.

The TRV was equal to the geometric mean of NOAELvalues for growth and reproduction or the highest boundedNOAEL below the lowest bounded LOAEL for growth,reproduction or survival. The process considered NOAELand LOAEL endpoints and included unbounded NOAELvalues, but not unbounded LOAEL values. 

The Eco-SSL was equal to ge

values. Only EC20, MATC an

NOAEC and LOAEC values Unbounded NOAECs and LOWhen a study reported multipthe following order of prefere
Guidance for Developing Eco-SSLs 1 - 11 November 2003

specified later in Chapters 3 and 4 and summarized in Table 1.1. Acceptable plant and soil

invertebrate studies were further restricted to those that followed state-of-the-art soil testing

requirements (e.g., 4.0 # pH # 8.5, organic matter content # 10 %, use of natural or artificial soil). 

Both task groups agreed that three or more treatment levels (including the control) were preferred,

but a substantial portion of the avian and mammalian studies would have been excluded if this

requirement was applied. Therefore only two treatment levels were required for mammalian and

avian receptors and five were required for plants and soil invertebrates. Only chronic toxicity

studies (greater than a three-day exposure) were accepted for mammalian and avian studies. 

Although acute studies were not excluded for plants and soil invertebrates, the exposure duration

was considered later in the process for selecting the most appropriate test results for deriving the

Eco-SSL.

Step 3: Extract, Evaluate and Score Data

The extraction of toxicity data from the acceptable literature, evaluation of test methods and results,

and scoring of each test result also followed similar processes. When the methods diverged, it was

due mainly to inherent differences in study designs (e.g., direct exposures to soils versus exposures

in the diet or drinking water). Both task groups determined a cutoff for acceptable results for use in

deriving the wildlife TRV or plant or soil invertebrate Eco-SSL by plotting data for various data

sets and identifying logical breaks. For mammalian and avian species this break occurred at an

evaluation score of 66 out of a possible 100 (i.e., studies with a score greater than or equal to 66 %

were used to derive the TRV), while the break for plants and soil invertebrates occurred with a

score of 11 out of 18.

Step 4: Select Value

For both task groups, only the results from studies scoring above the respective cutoffs were used to

determine the value. For mammals and birds the TRV was equal to the geometric mean of NOAEL

values for growth and reproduction, or the highest bounded NOAEL (NOAEL with paired LOAEL)

below the lowest bounded lowest-observed adverse effect level (LOAEL) for growth, reproduction

or survival (whichever was lower). For contaminants with no growth, reproduction, or survival

data, the TRV was derived from biochemical, behavioral, pathology and physiology results. The

methodology considered both NOAEL and LOAEL endpoints including unbounded NOAEL values

but not unbounded LOAEL values. 

For plants and soil invertebrates, the Eco-SSL was equal to the geometric mean of values for the

maximum acceptable toxicant concentration (MATC), the effective concentration that affects 20 %

of the test population (EC20), or the effective concentration that affects 10 % of the test population

(EC10). When a study reported multiple endpoints, the selection of endpoints for use in deriving the

Eco-SSL followed an order of preference of the EC20, then the MATC, then the EC10. The MATC

was either reported in the study or was calculated from the geometric mean of the no-observed

adverse effect concentration (NOAEC) and the lowest-observed adverse effect concentration

(LOAEC). 
Guidance for Developing Eco-SSLs 1 - 12 November 2003

1.3 Peer Review Process

The Eco-SSL procedures received both internal and external peer review. Because of the diversity

of the workgroup scientists, the process developed to derive the Eco-SSLs underwent constant peer

review. There were also two external peer reviews 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

this meeting the SAB provided verbal comments to presenters at the SAB review, which were

passed on to the workgroup and incorporated into the guidance as appropriate. A formal external

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

provided in separate documentation on the Eco-SSL website. Each of the comments received was

carefully considered by the Steering Committee and the individual work groups and appropriate

changes were made to both the procedures for establishing Eco-SSLs and the associated guidance

document. 

1.4 Quality, Objectivity, Utility, and Integrity of Information

The EPA is committed to a policy of ensuring that the information it provides to the public, and

uses to make its decisions, maintain a basic standard of quality, which includes objectivity, utility,

and integrity. The Eco-SSLs are primarily derived from information the EPA obtained from

external sources that may not have used the same standards, guidelines, and controls that EPA

imposes on itself, and those who gather data on behalf of the agency. The agency has recently

proposed five assessment factors for evaluating the quality of information it obtains from external

sources (67 FR 57225, September 9, 2002):

• Soundness The extent to which the procedures, measures, methods, or models

employed to generate the information are reasonable for, and consistent with, the

intended application and are scientifically/technically appropriate.

• Applicability and Utility The extent to which the information is applicable and

appropriate for the Agency’s intended use.

• Clarity and Completeness The degree of clarity and completeness with which the

data, assumptions, methods, quality controls, and analyses employed to generate the

information are documented.

• Uncertainty and Variability The extent to which the variability and uncertainty in

the information or in the procedures, measures, methods, or models are evaluated or

characterized. 

• Evaluation and Review The extent of independent application, replication,

evaluation, validation, and peer review of the information or of the procedures,

measures, methods, or models. 
Guidance for Developing Eco-SSLs 1 - 13 November 2003

Because these five assessment factors were developed after this guidance document was prepared,

they are not addressed by name. However, all five of these principles were embedded in the

standardized procedures for literature review, toxicity data selection, and data evaluation that were

used to derive the Eco-SSLs. 

1.5 Using Eco-SSLs to Screen Contaminated Soils

The Eco-SSLs are intended for use in identifying soil contaminants ( i.e., COPCs) and/or areas of

soil contamination that warrant further consideration in a baseline ERA. Screening is typically

completed during Step 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 initial problem formulation. With the information gathered in Step 1, it is recommended

that the risk assessor completes a screening of soils data using the Eco-SSLs in the risk calculation

performed during Step 2.

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 appropriate is important for adequate

planning and sampling strategies for the ERA. 

Are There Soil Exposure Pathways for Ecological Receptors? 

The Eco-SSLs are designed for use at sites where terrestrial receptors may be exposed directly or

indirectly to contaminated soil. Therefore, generally the first step in determining whether use of the

Eco-SSLs is appropriate is to identify all complete and potentially complete soil pathways present

at the site. 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 (soil invertebrates and plants) 

Soil Invertebrates and Plants

• Direct contact 

• Ingestion of soil (by soil invertebrates)

• Uptake (by plants) 

For surface soils (i.e., those soils within the root zone at the specific site), all the above pathways

were considered. Ecological risks from potential exposure to contaminated subsurface soils were

generally not considered. In some site-specific cases, however, there may be risks to animals that
Guidance for Developing Eco-SSLs 1 - 14 November 2003

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 generally need to know

enough about the site to answer at least the following questions: 

• What contaminants are known or suspected to exist at the site? 

• What complete exposure pathways might exist at the site?

• What habitat types 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 both the root zone and the burrow zone at the site), then additional screening for soil effects

on ecological receptors is generally not needed.

Are There Exposure Pathways at the Site Not Addressed by the Eco-SSLs? 

In some cases, the site-specific conceptual model may identify complete or potentially complete 

ecological soil exposure pathways that are not considered in the derivation of the Eco-SSLs. In

these instances, the additional pathways should be considered in a separate screening analysis or as

part of the baseline ERA. Examples of such instances include: 

• The contaminated soil is near a surface water body or wetland where there is

potential for contamination of surface water and/or sediments by transport during

rain events. 

• 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.

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? 
Guidance for Developing Eco-SSLs 1 - 15 November 2003

• Which geographic areas of soil contamination may result in ecological risks?

• Which receptors/functional groups (e.g., birds or soil invertebrates) appear to be at

most risk and should be the focus of the baseline ERA?

Are the Existing Site Soil Contaminant Data Adequate? 

At this point of the process, the user should make a decision concerning the adequacy of the

available contaminant concentration data for soils for use in completing a screening level analysis. 

This decision, typically made by the site manager and risk assessor, usually considers the

following:

• Are all expected areas of soil contamination 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 DDT is suspected as part of the deposited waste, are soil analyses

available for DDT? Or are data only available for metals?

• Are the quantification limits adequate to measure the contaminants at the Eco-SSL

levels?

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, it is recommended that the maximum soil contaminant

concentrations be 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 (as applied in Superfund).

Which Eco-SSL Should be Used? 

Assuming there is a complete exposure pathway, the lowest of the four reported Eco-SSLs should

generally 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 soil

invertebrates. 
Guidance for Developing Eco-SSLs 1 - 16 November 2003

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 ERA. If soil

concentrations exceed some receptor-specific Eco-SSLs and not others, then it is recommended that

the contaminant be retained as a COPC only for those receptor groups where Eco-SSLs are

exceeded. 

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 should recognize that new information may become available

during the baseline ERA 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 COPCs. If there are no soil contaminant concentrations that exceed the Eco-SSLs, a

baseline ERA for soils is generally not needed for that site.

What if There is No Eco-SSL? 

At this time, Eco-SSLs are not available for all four receptor groups and for all 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 some contaminants, EPA had not

completed the review of toxicity data for derivation of Eco-SSLs. The current Eco-SSLs are

available on the EPA website http://www.epa.gov/oswer/riskassessment/ecorisk/ecossl.htm. The

user should consult this source for the current values. 

For those contaminants with an insufficient number of acceptable toxicity studies to establish an

Eco-SSL, a summary of the toxicity studies evaluated in the Eco-SSL process was made available

in the contaminant specific Eco-SSL documents. The information from these studies can be used

according to the process described in Section 1.3.1 of ERAGS to derive screening values. As more

toxicity information becomes available, EPA may use these processes to revise existing Eco-SSLs

or to develop new ones.

Can I Use Site-specific Data to Modify an Eco-SSL or Should I Proceed to a Baseline Risk

Assessment? 

If one or more Eco-SSL values are exceeded it is recommended that the user proceed to a baseline

ERA. The Eco-SSLs are intended to be conservative values used to identify those contaminants

that should be the focus of a baseline ERA and as such they should not be modified as part of the

screening step. During the baseline ERA, it will be possible for the user to collect and use sitespecific data as part of development of the site-specific exposure and toxicity assessments in the

baseline ERA. 
Guidance for Developing Eco-SSLs 1 - 17 November 2003

Definitions

Contaminants of concern (COCs) are the contaminants that, at

the completion of the risk assessment, are found to pose

unacceptable human or ecological risks. The COCs drive the

need for a remedial action.

Contaminants of potential concern (COPCs) generally

comprise the contaminants that are investigated during the

baseline risk assessment that may or may not pose unacceptable

risks.

Screening is a common approach used by risk assessors to refine

the list of COPCs to those hazardous substances, pollutants and

contaminants that may pose substantial risks to health and the

environment.

Background refers to constituents or locations that are not

influenced by the releases from a site, and is usually described as

naturally occurring or anthropogenic (US EPA, 2002)

• Anthropogenic -natural and human-made

substances present in the environment as a

result of human activities (not specifically

related to the release in question); and,

• Naturally occurring - substances present in

the environment in forms that have not been

influenced by human activity.

Consideration of Background Soil Concentrations

Due to conservative modeling assumptions (e.g., metal exists in most toxic form or highly

bioavailable form, high food ingestion rate, high soil ingestion rate) which are common to

screening processes, several Eco-SSLs are derived below the average background soil

concentration for a particular contaminant as presented in Attachment 1-4. Since Eco-SSLs are not

designed to be used as clean-up levels, EPA is not promoting clean-up to below background

concentrations. It is EPA’s policy to not screen against background levels. Background

concentrations, the speciation of metals, and the effects of conservative modeling assumptions are

generally taken into account in the initial steps of the baseline risk assessment. Specific procedures

for comparing risk information to site specific background levels is addressed in the Office of Solid

Waste and Emergency Response document Role of Background in the CERCLA Cleanup Program

(OSWER 9285.6-07P) (U.S. EPA, 2002). However, if a specific exposure parameter or toxicity

endpoint is found to consistently produce an Eco-SSL that is below background concentrations

(taking into account natural species of metals), EPA may reevaluate specific Eco-SSL values as

they are updated. 

Information on background concentrations of

contaminants in soils was collected and

reviewed during the Eco-SSL derivation

process to examine how the Eco-SSL values

compare to natural soil conditions. These

comparisons were used to guide the process

and are presented as Attachment 1-4. The

review indicated that there are regions of the

country where natural background levels for

some metals exceed Eco-SSLs. For these

regions and for specific local areas, the

acquisition of adequate data on background

soil concentrations is an important step

toward evaluating, on a site-specific basis, if

observed concentrations are related to

releases or are naturally occurring. 

Background concentrations are further

discussed in each of the contaminant-specific

Eco-SSL documents.
This Page Intentionally Left Blank
Guidance for Developing Eco-SSLs 2 - 1 November 2003

2.0 SOIL PROPERTIES 

2.1 Introduction

Soil properties influence the toxicity of contaminants to invertebrates, plants, and wildlife. 

Therefore, they are generally important to consider in the development of Eco-SSLs and 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. This soil property 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 toxicity to and accumulation of contaminants in soils to plants and soil invertebrates. The

absorption of contaminants bound to incidentally ingested soil particles in the animal gut is

influenced by other parameters including gut residence time as well as toxicokinetic and

physiological factors that may affect the uptake of contaminants in wildlife. Because wildlife

species vary in the anatomy and physiology of their digestive systems (e.g., herbivores vs.

carnivores), generalizations can not and should not be made concerning bioavailability of

contaminants on soil incidentally ingested by wildlife at the screening level. 

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 ensure that Eco-SSLs are

adequately conservative for a broad range of soils, an effort is made in the procedures developed

to select studies that favor the bioavailability of the selected contaminants. To accomplish this,

it is first necessary to develop a basic understanding of how various soil properties may

influence bioavailability. Several authors stress the importance of physical and chemical

properties of contaminants that influence the bioavailability in soils and thus exposure and

toxicity (Alexander, 1995; Allen et al., 1999; Linz and Nakles, 1997; and Loehr and Webster,

1996). The behavior and bioavailability of contaminants are greatly influenced by their

interactions with soil parameters, such that not all contaminants are equally available to biota. 

However, estimating the availability of metals and organic contaminants in soil to soil biota and

plant toxicity is not a straightforward process. 

The bioavailability of contaminants depends on their chemical properties and the 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
Guidance for Developing Eco-SSLs 2 - 2 November 2003

contaminant to be removed from solution and become associated with soil particulates

irrespective of mechanism, is generally referred to as "sorption". The exception are precipitation

reactions, which are often discussed independently from sorption processes. Contaminants are

generally considered to be bioavailable when they are released from interactions with the soil

and soil constituents and released into the soil pore-water. The exception to this rule is the direct

ingestion of soil by terrestrial wildlife.

Identifying and quantifying soil properties that control the distribution of a contaminant in

soil/water systems at equilibrium are 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 and 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 are classified into the four groups (Table

2.1).

Metals. Metals occur naturally in soils primarily as amorphous oxides and hydroxides, and to a

lesser extent as carbonates, phosphates, sulfates, and sulfides, which are relatively insoluble. 

The same is generally true for metal-contaminated soils, because metals quickly undergo

precipitation and coprecipitation reactions forming relatively insoluble solid phases, and/or are

strongly complexed by soil minerals or organic matter (Lindsay, 1979). Toxicity testing on the
Guidance for Developing Eco-SSLs 2 - 3 November 2003

other hand usually employs very soluble metals not commonly found in any appreciable amounts

in soils relative to total metal concentrations.

Table 2.1 General Contaminant Classification

Contaminant Class Eco-SSL Contaminant

Metal Cations aluminum, antimony, barium, beryllium, cadmium, cobalt, copper,

iron, lead, manganese, nickel, silver, and zinc

Metal Anions arsenic, chromium, selenium, and vanadium

Nonionic Organics PCBs, DDT and metabolites, dieldrin, PAHs, TNT, and RDX

Ionizable Organics PCP

As identified in Table 2.1, most of the 24 contaminants considered for Eco-SSLs are metals that

typically exist as cationic species. 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 dependant. 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

cations.

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 III), which are

present in soil solution in the form of AsO4

3- 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 CrO4

2-), which is usually considered more soluble, mobile and bioavailable

than the sparingly soluble chromite (chromium (III)), 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 (SeO4

2-) and selenites (SeO32-). For vanadium, vanadate (VO43-) 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 are 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,

bioavailability.
Guidance for Developing Eco-SSLs 2 - 4 November 2003

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

and tend to bioaccumulate and biomagnify in the food chain. The structure and degree of

chlorination of PCBs and associated congeners directly impacts their behavior, persistence, and

bioavailability (e.g., see citations in Hansen et al., 1999). As solubility decreases, sorption

increases, and 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, PAHs and explosives (TNT

and RDX) are generally considered less persistent and 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 carbon and hydrogen. PAHs can be highly retained by soil in a

similar manner as pesticides or PCBs, but are considered less persistent due to their higher

affinity to be degraded microbially. TNT and RDX, a trinitro aromatic and trinitro

nitrogen-heterocyclic respectively, are explosive materials and are more polar than 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., Gertsl, 1990; Lyman et al., 1990;). The greater the affinity of a

contaminant for organic matter, the larger the organic carbon-normalized partition coefficient

(Koc), and a soil with higher amounts of organic matter has a higher propensity to sorb NOCs. 

The hydrophobicity and Koc, of organic compounds increases with the size of the compound and

with increasing chlorine content, as in the case of chlorinated organics. Therefore, sorption of

PAHs by soils 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 are known to have

specific interactions with clay surfaces that are impacted by the inorganic cations present and
Guidance for Developing Eco-SSLs 2 - 5 November 2003

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. 

Table 2.2. Log Kow Values for Organic Contaminants

Analyte CAS no. log Kow Source

RDX 121824 0.87 Syracuse Research Corporation (SRC)

TNT 118967 1.6 SRC

DDT 50293 6.53 U.S. EPA (1996)

DDD 72548 6.1 U.S. EPA (1996)

DDE 72559 6.76 U.S. EPA (1996)

Dieldrin 60571 5.37 U.S. EPA (1996)

Pentachlorophenol (PCP) 87865 5.09 U.S. EPA (1996)

PCBs 4.5 (1 chlorine)

>8 (10 chlorines)

Verschueren (1996)

Schwarzenbach et al. (1993)

PAHs

 Naphthalene (2 rings) 91203 3.36 U.S. EPA (1996)

 Acenaphthene (3 rings) 83329 3.92 U.S. EPA (1996)

 Phenanthrene (3 rings) 85018 4.55 U.S. EPA (1995)

 Anthracene (3 rings) 120127 4.55 U.S. EPA (1996)

 Chrysene (4 rings) 218019 5.7 U.S. EPA (1996)

 Benzo(a)anthracene (4 rings) 56553 5.7 U.S. EPA (1996)

 Benzo(a)pyrene (5 rings) 50328 6.11 U.S. EPA (1996)

 Dibenzo(ah)anthracene (5 rings) 53703 6.69 U.S. EPA (1996)

 Benzo(b)fluoranthene (5 rings) 92240 6.2 U.S. EPA (1996)

 Benzo(k)fluoranthene (5 rings) 207089 6.2 U.S. EPA (1996)

 Benzo(ghi)perylene (6 rings) 191242 6.7 U.S. EPA (1995)

Key Soil Parameters Affecting Contaminant Bioavailability in Soils

From the preceding overview of how 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
Guidance for Developing Eco-SSLs 2 - 6 November 2003

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 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

millimole (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 millimole (mmol) per 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 size ranges from: 20 microns to 2 millimeters (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
Guidance for Developing Eco-SSLs 2 - 7 November 2003

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 contaminants

such as the nitro-substituted explosives. Metals can form precipitates with inorganic soil

constituents, such as carbonate and phosphate minerals under certain soil conditions. Carbonateand phosphate-metal complexes 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 (e.g., 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 partition to 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 (foc), 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 Koc for

estimating contaminant sorption with the soil-specific distribution coefficient estimated by Koc

multiplied by foc. 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, 2000). Therefore, with increasing organic matter content,

retention of an organic contaminant increases and rates of release decrease, thereby, decreasing

overall contaminant bioavailability. 

Other Factors

Background. Background refers to constituents or locations that are not influenced by the

releases from a site, and is usually described as naturally occurring or anthropogenic background

(US EPA, 2002). Background contaminant concentrations can vary due to soil type, depth, and

region of the country. Due to this variation, background metal levels in soils are addressed on a
Guidance for Developing Eco-SSLs 2 - 8 November 2003

site-specific basis in the baseline ERA. As part of the development of Eco-SSLs, background

metal concentrations from the literature were compiled to provide a general comparison of the

screening levels with current available information. Background metal concentrations were

reviewed and compiled in Attachment 1-4. For reference, background levels by state are

provided in Table 2.3. In the contaminant specific Eco-SSL documents, the Eco-SSL values are

compared to a summary box-and-whisker chart depicting background concentrations in eastern

and western United States soils. A discussion on the quality of available toxicity data and the

reasonableness of the Eco-SSL values when compared to background concentrations is

presented. Background data is site-specific and should be derived for each site investigated. 

Aging. The issue of adsorption, complexation, lability of contaminants in soils, and the

corresponding reduction in toxicity over time is an important issue in understanding the fate of

contaminants in soils. In the evaluation of the available toxicological literature for plants and

soil biota, few studies incorporated a step to age or weather spiked contaminants in soils. The

use of contaminated soils from the field in laboratory tests is a viable option; however, due to the

common presence of mixtures and the specific soil chemistry parameters, the use of field

contaminated soils is primarily useful only for site-specific studies. While no standard setting

organization has established methods for aging contaminants in soils, an aging step has been

added by some investigators in plant and soil invertebrate test methods that involves several

cycles of wetting and drying of a freshly spiked soil (Kuperman et al., 2002 and Phillips et al.,

2002).

2.3 Using Soil Properties to Guide Eco-SSL Derivation

In identifying 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 plant and soil invertebrate task group evaluated the current

literature, they observed that CEC and clay content were not routinely reported. Thus, these

parameters were not used and the 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 within that typically found in soils. Tests that used soils with

characteristics that fell outside the selected ranges were not considered. Although other soil

factors can be significant, combinations of these two soil parameters are sufficient for use in this

screening process as a qualitative guide to address how most soils across the United States may

influence bioavailability of contaminants. Qualitative rankings of high, medium, and low

bioavailability were used to categorize each combination of the soil parameters and their ranges. 

Information on bioavailability was used to help select and score studies to include in the

derivation of the Eco-SSL values. Greater weight was given to those studies that had 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 2.4a and

2.4b, 2.5, and 2.6 are provided 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 between pH values 
Guidance for Developing Eco-SSLs 2 - 9 November 2003

Table 2.3. Mean Reported Soil Metal Background Concentrations (mg/kg dry weight) by State* Aluminum Antimony Arsenic Barium Beryllium Cadmium Chromium Cobalt Copper Iron Manganese Nickel Lead Selenium Silver Vanadium

Zinc

Alabama 23100 3.6 4.7 200 0.6 30.6 4.4 9.6 11950 420 11 9.3 0.3 38 26

Arkansas 33429 1.2 9.7 336 0.9 53.1 12 17 19857 731 18 21 0.7 52 39

Arizona 32933 1.4 9.6 364 1.0 0.4 37.3 9.9 23 20787 447 23 16 0.4 0.5 42 51

California 75633 0.8 5.1 598 1.1 0.4 119.9 14 39 36867 640 48 26 0.2 0.8 118 113

Colorado 61557 1.1 6.7 662 1.4 41.7 6.8 21 23048 343 13 31 0.4 74 87

Connecticut 85000 4.1 400 0.5 40.0 7.5 15 17500 450 13 5.0 0.8 60 40

Delaware 22500 1.0 1.4 400 0.5 30.0 3.3 5.0 7500 85 6.0 15 0.3 20 23

Florida 9944 0.9 3.0 48 0.6 0.1 15.4 1.6 5.6 3705 86 8.5 12 0.3 0.5 11 12

Georgia 38250 1.0 5.0 232 0.6 32.4 6.9 21 16976 252 17 19 0.4 43 47

Iowa 64667 1.0 7.3 617 1.3 64.7 11 31 23278 603 26 19 0.4 97 57

Idaho 58500 1.0 6.4 757 1.1 52.1 12 28 32000 580 22 22 0.3 90 83

Illinois 48714 1.1 7.1 551 0.7 48.4 9.8 24 19159 646 19 39 0.5 62 67

Indiana 50000 1.0 7.5 500 0.7 46.8 10 27 21364 518 18 18 0.4 74 56

Kansas 61818 1.1 6.8 694 1.0 49.0 8.9 25 18788 452 17 32 0.4 77 67

Kentucky 54123 1.0 7.8 349 1.1 79.8 11 17 30432 483 23 16 0.5 66 35

Louisiana 42188 1.0 7.6 441 0.6 60.8 8.6 33 19688 470 33 16 0.7 76 55

Massachusetts 34083 1.0 8.6 203 1.3 0.2 39.5 7.8 16 19000 439 13 13 1.9 87 54

Maryland 39167 1.2 3.8 393 1.3 47.9 7.5 20 28571 291 13 22 0.2 63 39

Maine 65385 1.0 9.4 319 1.6 71.2 10 28 45385 581 30 19 0.7 98 80

Michigan 10964 1.3 4.2 127 0.7 0.9 13.8 4.6 12 10520 230 12 9.2 0.3 0.5 44 33

Minnesota 49457 1.0 5.5 571 0.7 0.3 25.4 7.2 20 19581 583 14 9.9 0.3 72 38

Missouri 42094 1.0 10 499 1.0 50.0 12 19 24733 940 20 23 0.5 72 53

Mississippi 45368 1.0 8.8 390 0.9 53.2 12 20 19684 471 21 18 0.5 68 45

Montana 70938 1.1 8.8 739 1.1 63.3 7.5 29 27766 366 20 14 0.4 101 69

Nebraska 59474 1.0 5.5 711 1.1 32.5 5.9 15 16000 306 15 16 0.4 62 54

North Carolina 60105 1.0 4.8 356 0.6 64.8 15 34 37053 563 24 17 0.4 107 56

North Dakota 62857 1.0 7.0 682 0.9 53.2 6.9 23 25357 530 20 13 0.4 83 64

New Hampshire 66667 4.4 500 2.3 0.6 18.4 5.3 12 33333 633 10 28 0.3 57 23

New Jersey 10075 1.4 7.0 54 0.3 0.3 13.9 1.7 14 11632 221 3.8 35 0.9 30 22

New Mexico 54423 1.0 5.9 727 1.0 55.5 8.8 21 20898 367 28 18 0.3 72 44

Nevada 66078 1.0 9.0 822 1.3 36.8 8.4 25 22725 481 15 25 0.3 78 69

New York 58800 1.0 6.4 666 1.4 0.2 66.9 9.1 36 38900 418 21 20 0.3 132 82

Ohio 54615 12 469 1.0 55.0 13 28 27308 550 25 23 0.6 88 69

Oklahoma 39200 1.0 7.0 430 1.1 46.0 7.1 16 19320 465 15 18 0.3 50 50

Oregon 94412 1.2 5.1 682 0.9 121.6 16 53 50147 725 23 15 0.3 168 70

Pennsylvania 63438 1.0 13 366 1.4 52.8 15 37 36063 609 24 23 0.5 80 81

Rhode Island 100000 3.5 500 0.5 50.0 10 15 30000 500 15 15 0.9 70 30

South Carolina 39143 3.9 151 1.4 21.4 3.5 16 12500 87.1 7.8 5.0 0.3 45 25

South Dakota 74333 1.3 8.5 1043 1.4 58.7 7.7 29 25667 1013 28 16 0.5 108 75

Tennessee 31894 0.7 16 193 0.8 0.2 40.3 14 17 28479 1112 18 23 0.6 1.2 49 57

Texas 41958 1.1 6.4 404 0.9 39.6 5.3 15 16328 303 12 14 0.3 52 39

Utah 45638 1.1 8.0 493 0.9 45.6 6.6 26 18830 371 13 35 0.3 70 96

Virginia 60438 1.2 5.1 436 0.9 54.3 9.7 33 27750 441 17 36 0.4 77 233

Vermont 56667 3.6 333 1.7 66.7 12 18 30000 800 25 20 0.4 70 43

Washington 66834 1.0 4.5 606 0.9 0.8 49.9 18 31 42635 760 23 14 0.3 0.7 160 78

Wisconsin 48000 1.0 4.4 543 2.0 40.3 7.7 12 15667 365 14 12 0.3 48 44

West Virginia 67000 1.3 8.6 360 1.0 46.0 14 22 28500 770 23 17 0.5 65 60

Wyoming 56125 1.1 6.5 756 0.7 47.9 8.3 21 25250 416 16 17 0.5 84 57

* Summary of background soil concentration data provided as Attachment 1-4.
Guidance for Developing Eco-SSLs 2 - 10 November 2003

of 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 2.4a, 2.4b, 2.5, and 2.6). For example, a soil with a pH between 5.5 to 7.0, and organic

matter content between 2 and 6%, would bind metal cations to a moderate extent. Therefore, a

bioavailability index of ‘medium' for metal cations was assigned (see Table 2.4a and 2.4b).

These tables simplified and facilitated the use of soil chemistry information in the derivation of

Eco-SSLs for plants and soil invertebrates. The ranges given in these tables were used in

selecting the most appropriate plant and soil invertebrates toxicity data for deriving Eco-SSLs

(Chapter 3). 

Table 2.4a. 

Qualitative Bioavailability of Metal Cations in Natural Soils 

to Plants

Soil Type

Soil pH

Low Organic Matter

(< 2% )

Medium Organic Matter

(2 to < 6%)

High Organic Matter 

(6 to 10%) 

4 # Soil pH # 5.5 Very High High Medium

5.5 < Soil pH < 7 High Medium Low

7 # Soil pH # 8.5 Medium Low Very Low

Table 2.4b. 

Qualitative Bioavailability of Metal Cations in Natural Soils 

to Soil Invertebrates

Soil Type

Soil pH

Low Organic Matter

(< 2% )

Medium Organic Matter

(2 to <6%)

High Organic Matter 

(6 to 10%) 

4 # Soil pH #5.5 Very High High Medium

5.5 < Soil pH < 7 High Medium Low

7 # Soil pH # 8.5 Medium Medium Very Low
Guidance for Developing Eco-SSLs 2 - 11 November 2003

Table 2.5 

Qualitative Bioavailability of Non-Ionizing Organic Contaminants in Natural Soils

Soil Type Log Kow

Organic Matter (%)

< 2 2 to <6 6 to 10

4 # Soil pH #5.5

Log Kow > 3.5 High Medium Low

Log Kow < 3.5 Very High High Medium

5.5 < Soil pH < 7

Log Kow > 3.5 Medium Low Low

Log Kow < 3.5 High Medium Low

7 # Soil pH # 8 .5

Log Kow > 3.5 Low Low Low

Log Kow < 3.5 Medium Low Low

Table 2.6 

Qualitative Bioavailability of Metal Anions in Natural Soils

Soil Type

Soil pH

Low Organic Matter

(< 2%) 

 Medium Organic Matter

(2 to <6% )

High Organic Matter

(6 to 10%) 

4 # Soil pH #5.5 Medium Low Very Low

5.5 < Soil pH < 7 High Medium Low

7 # Soil pH # 8.5 Very High High Medium
This Page Intentionally Left Blank
Guidance for Developing Eco-SSLs November 2003 3 - 1

Figure 3.1 The Four-Step Process for Deriving

Eco-SSLs for Plants and Soil Invertebrates

Step 1. Literature search, acquisition, and

screening (Attachment 3-1). Apply 22

Literature Exclusion Criteria.

Step 2. Identify acceptable literature by

applying eleven Study Acceptance

Criteria to retrieved papers (Attachment

3-1).

Step 3. Extract and score data from acceptable

literature according to nine Study

Evaluation Criteria (Attachment 3-2). 

Step 4. Derive soil invertebrate and plant EcoSSLs according to specified procedures 

(Attachment 3-2).

• Sort study data by bioavailability score

• Complete QA review

• Calculate value 

3.0 DERIVATION OF PLANT AND SOIL INVERTEBRATE ECO-SSLs

Eco-SSLs for plants and soil invertebrates were derived using a four-step process. These

procedures built upon previous efforts (CCME, 1997; Efroymson et al., 1997 a,b), and

established a process for evaluating published studies and selecting relevant data from the

literature. The process (Figure 3.1) was guided by a set of standard operating procedures (SOPs)

that specified the steps for identifying and evaluating data for appropriateness in deriving an

Eco-SSL as well as the actual calculation of the screening values (Attachments 3-1 and 3-2). 

The process was intended to ensure that the Eco-SSLs for plants and soil invertebrates were

based on sound science. The process required data from ecotoxicity studies that met prescribed

requisites including but not limited to a thorough experimental design, appropriate quality

control, and specific soil boundary conditions.

When appropriate and sufficient data were available for a specific contaminant, Eco-SSLs for

plants and soil invertebrates generally could be derived using the prescribed process. 

Alternatively, if sufficient data does not exist, then toxicity testing could be completed under the

preferred Eco-SSL experimental conditions (refer to Section 3.5) to generate the data needed to

derive an Eco-SSL for the contaminant of interest.

Eco-SSLs for plants and soil invertebrates were

derived using data from tests performed within

soil boundary conditions favoring relatively

high bioavailability for upland aerobic soils

(refer to Tables 2.4 - 2.6 of Chapter 2). The soil

chemistry conditions of relatively high

bioavailability were defined by low soil pH and

organic matter. These parameters were

frequently found to be the predominant factors

affecting contaminant bioavailability to plants

and soil invertebrates in aerobic soils. Extreme

pH values (< 4 and > 8.5) will substantially

affect the solubility, precipitation, speciation,

and sorption processes of contaminants, and

therefore were not appropriate for use in EcoSSL derivation. Similarly, organic matter,

composed primarily of carbon, oxygen, and

nitrogen, at elevated levels in the soils (i e.

>10%) has a high affinity to bind organic

compounds as well as some metals, thereby

reducing their bioavailability. 
Guidance for Developing Eco-SSLs November 2003 3 - 2

Figure 3.2 Literature Exclusion Criteria

Biological Product Biological toxins (venoms, etc.)

Chemical Methods Methods for measuring contaminants

Drug Testing for drug effects

Effluent Effluent, sewage, polluted run-off

Contaminant Fate Fate and transport of substance in the

environment (only)

Human Health Human or primate subjects

In Vitro In vitro studies, including cell cultures and

excised tissues

Methods Methods reported but no usable specific

toxicity test results

Mixture Combinations of chemicals in laboratory

testing

Modeling Only modeling results reported

No Conc No dose or concentration reported, or not

able to calculate from information given 

No Duration No exposure duration reported

No Effect No effect reported for a biological test

species

No Species No viable plant or animal present or tested

No Toxicant No toxicant used

No Tox Data Toxicant used, but no results reported that

had a negative impact

Nutrient Nutrition studies reporting no concentrationrelated negative impact

Oil Oil and petroleum products

Publ As Author states information is published in

another source

QSAR Data developed only from

quantitative-structure activity relationships

Review Data reported are not primary data

Survey Assessment of toxicity in the field over a

period of time

QSAR = Quantitative Structure Activity Relationship

3.1 Literature Search, Acquisition, and Screening

The first step in the process of developing Eco-SSLs for plants and soil invertebrates was to

identify, retrieve, and screen published papers that reported data for soil toxicity to terrestrial

plants or soil invertebrates. The procedures used to complete the literature search, retrieval and

review are provided as Attachment 3-1. 

The literature search included both paper-based searches and searches of computerized

abstracting databases. The paper-based literature search process consisted primarily of the

manual review of bibliographies, guidance documents, and review articles. This manual search

was not limited by publication year. Searches of computerized abstracting databases included

the use of DIALOG, SilverPlatter

and Ovid commercial database

vendors. Within DIALOG, the

targeted databases included

AGRICOLA, BIOSIS and

ChemAbstracts. The searches were

supplemented with other electronic

databases including Toxline,

PolTox1, Toxnet, and Current

Contents. Searches of computerized

abstracting databases were limited

to papers published after 1988,

except in cases when fewer than 20

publications were identified for a

contaminant- receptor pairing (e.g., 

cadmium- plants). In these cases,

the electronic search was expanded

to include all publication years in

the databases. It was assumed that

relevant studies prior to 1988 would

be identified in the bibliographies of

review articles. A list of 22

Literature Exclusion Criteria (see

Figure 3.2) was then used to screen

out those studies not appropriate for

use in deriving Eco-SSLs. These

Literature Exclusion Criteria were

applied to retrieved abstracts, or to

the entire publication if the needed

information was not available in the

abstract. 
Guidance for Developing Eco-SSLs November 2003 3 - 3

Figure 3.3 Eleven Study Acceptance Criteria

1. The document is the primary source of the test

result.

2. Adverse effects are caused by an identified

chemical stressor (i.e., no mixture testing in

laboratory studies).

3. The chemical form (e.g., metal salt used) and

concentration are reported by the author(s).

4. The test medium used in the study is a natural

or artificial soil.

5. The study reports the organic matter content

and it is #10 % of the composition of the soil;

or equivalent concentration reported on the

basis of organic carbon.

6. Except for studies on non-ionizing substances

(e.g., PCP), the study reports the pH of the soil,

and the soil pH is within the range 4.0 # soil

pH # 8.5.

7. The study includes at least one control

treatment.

8. The duration of the exposure is reported, or a

standard study method with a defined duration

is used.

9. For studies conducted in a laboratory setting, at

least three treatment levels are used (i.e.,

control plus two chemical exposures). 

10. Biological effects are reported for ecologically

relevant endpoints (ERE).

11. Either the test species' scientific name,

common name, variety, or strain is reported.

For the 24 contaminants listed in the Chapter 1, EPA completed the literature searches according

to the SOP (Attachment 3-1). The searches identified more than 7,600 papers. These

publication abstracts and titles were screened to determine if they were likely to meet the EcoSSL requirements using the 22 Literature Exclusion Criteria. This process resulted in the

acquisition of more than 5,200 papers. 

3.2 Identification of PotentiallyAcceptable Literature

The second step of the process identified

which publications acquired through Step 1

included at least the minimum information

necessary for deriving an Eco-SSL. Eleven 

Study Acceptance Criteria (see Figure 3.3)

were applied to each of the acquired

publications. Publications that meet all 11

Study Acceptance Criteria were further

evaluated in Step 3. Detailed descriptions of

the Study Acceptance Criteria are presented

in Attachment 3.1. Approximately seven

percent of the plant and soil invertebrate

publications reviewed by EPA (identified

during Step 1) met all 11 Study Acceptance

Criteria. 

3.3 Extraction of Data and Scoring

Studies

In Step 3, each of the publications that met

all 11 Study Acceptance Criteria were

reviewed and study data extracted and

scored. If a publication contained more than

one study, each study was evaluated and

scored separately. For each study reviewed,

a set of critical notes were recorded in a

spreadsheet. A separate "study" was defined

if any of the following parameters varied: 

test species (not strain), contaminant (not

form), soil (natural vs. artificial), pH or

percent organic matter (% OM) content. 

The specific procedures used to extract and

record study data are provided as

Attachment 3-2.
Guidance for Developing Eco-SSLs November 2003 3 - 4

Ecologically Relevant Endpoints

For each of the studies reviewed, the measures of toxic effects to either plants or soil

invertebrates were grouped into one of four ecologically relevant endpoints (EREs) (see Table

3.1). If a study reported toxic effects for multiple EREs, only the preferred ERE and

corresponding contaminant concentration were recorded in the critical notes. For soil

invertebrates, the preferred ERE followed the order: reproduction > population > growth. For

soil invertebrates, reproduction was the preferred ERE because it is necessary for sustaining

populations, and reproductive endpoints are good indicators of longer term (i.e., chronic)

exposure. Although population endpoints were considered less robust than reproductive

endpoints, they were next in preference because screening ecological risk assessments focus on

protection at the population level. Growth measurements on individuals were the traditional

measurement endpoints, and are frequently extrapolated to represent impact at the population

level. 

For plants, the preferred ERE was biomass production as it is normally the most sensitive

measurement. If no measurement of biomass production was reported, some physiological

endpoints were accepted as there were established linkages between certain measures of

photosynthesis and productivity (refer to Table 3.1). 

Table 3.1 Ecologically Relevant Endpoints (EREs) for Soil Invertebrate Eco-SSLs

Ecologically Relevant Endpoint Definition

Reproduction

Measures of the effect of toxicants on offspring production. Examples of

EREs associated with reproduction included changes in fecundity, number

of progeny produced (eggs, cocoons, etc.), rate of reproduction (hatching

rates, etc.), rate of maturation, sexual development, change in sex

expression, and sterility number or proportion of abnormal progeny. 

Population

Measurements and endpoints regarding a group of soil invertebrates of the

same species occupying the same area at a given time. Measurement

included population dynamics. Examples of EREs associated with

population included changes in size and age class structures, changes in sex

ratio, intrinsic population growth rate, survivability of subsequent

generations, diversity, evenness, index to population size (count, number,

abundance), life table data, population density (number/area).

Growth

Broad category which encompassed measures of weight/mass and length.

EREs associated with growth and development included responses such as a

change in body weight.
Guidance for Developing Eco-SSLs November 2003 3 - 5

Table 3.2 Ecologically Relevant Endpoints (EREs) for Plant Eco-SSLs

Ecologically Relevant Endpoint Definition

Growth (Biomass) Measurement of plant products including standing crop biomass, seedling

emergence, shoot length/growth, root elongation/growth, fresh or dry mass,

yield or production (e.g., seed production).

Physiology For the purposes of developing Eco-SSLs, plant studies reporting EREs

associated with physiological responses were used. Physiological endpoints

for plants included net photosynthesis (CO2 uptake, oxygen release),

decrease in chlorophyll content or chlorophyll fluorescence, increased

deformation, membrane damage, desiccation/decrease in water content,

detrimental changes in dormancy measures, decreased flowering, and

increased senescence. 

Toxicity Parameters

For each study, a toxicity parameter was recorded. The toxicity parameter was the

concentration-response measurement associating the adverse effect with the contaminant

exposure. Toxicity parameters considered acceptable for deriving Eco-SSLs were the EC20

(effective concentration that affects 20% of the test population), the MATC and the EC10

(effective concentration that affects 10% of the test population). The MATC was equal to the

geometric mean of the NOAEC and the LOAEC. Some toxicity data were not used to derive

Eco-SSLs including acute toxicity data such as the concentration lethal to 50% of the test

population (LC50) or the effective concentrations affecting more than 50% of the test population

(EC50). Effect concentrations affecting less than 5% of the test population (ECx <5) were also

not considered acceptable for deriving Eco-SSLs. The LC50 and EC50 values were not considered

sufficiently protective of ecological resources, while EC5 values have low levels of confidence

due to natural variability. 

When NOAEC and LOAEC values were reported, these data were used to calculate an MATC. 

A bounded value was a study result with both a NOAEC and LOAEC reported (other than

control). Unbounded NOAEC or LOAEC values do not describe a dose-response curve and thus

were not used due to the high uncertainty over where the real threshold of toxicity lies. If a

study reported multiple toxicity parameters, a single preferred toxicity parameter was selected. 

Selection of the preferred toxicity parameter followed the order:

EC20 > MATC > EC10

If a study reported more than one adverse effect concentration with the “preferred” ERE and

toxicity parameter, the lowest effect concentration was then selected. If a publication did not

report an EC20, MATC or EC10, but sufficient data were provided, the reviewer calculated and

recorded the toxicity value under the appropriate toxicity parameter. Toxicity data were reported

as mg/kg of the chemical on the critical notes form (Attachment 3-2). Data not reported in these

units were converted to mg/kg or converted from formulation to active ingredient. When metal
Guidance for Developing Eco-SSLs November 2003 3 - 6

concentrations were reported, these were converted to an elemental basis. The only data

extracted were for results where an adverse effect was significantly different from the control. 

Scoring Each Study

Each study was scored according to nine specific Study Evaluation Criteria (Table 3.3) and the

results documented in the critical notes. If a single publication contained data for multiple

studies (e.g., reports toxicity data for more than one species or soil type, etc.), each study within

the publication was scored separately. The information recorded for each study included test

species, soil characteristics (e.g., pH, %OM), relative bioavailability score, the ERE, toxicity

parameter, and toxicity value (Attachment 3.2). The specific procedures used to score studies

and document the results are provided as Attachment 3-2.

Scoring for each Study Evaluation Criterion used a three-point scale (i.e., 0, 1, or 2). The

maximum score for a study was 18. Studies were deemed inappropriate for deriving Eco-SSLs

for plants and soil invertebrates if they did not score above ten. Studies with an evaluation score

of 10 or less were presumed to lack sufficient detail about the study to enable reviewers to assess

the quality of the data. These studies results were not included in the pool of data considered for

deriving the Eco-SSL values. 

3.4 Derivation of Eco-SSLs

Sorting Data by Bioavailability Score

The first step in deriving an Eco-SSL was to sort the studies accepted at Step 3 (receiving a

Study Evaluation Score greater than ten out of 18 possible points) by their bioavailability score. 

The bioavailability score was determined as part of the scoring process in Step 3 (Table 3.3). 

This score was based on the soil matrix tables presented in Chapter 2 (Tables 2.4 to 2.6), the type

of soil (natural versus artificial), pH and OM content. A score of two was applied to natural

soils with relatively high or very high bioavailability, a score of one was applied to natural soil

with medium bioavailability and to standard artificial soil, and a score of zero was applied for

natural soil with low or very low relative bioavailability. 

Quality Control Review 

A quality assurance review of the sorted data was preformed by a panel of experts. The

reviewers verified that all studies used to derive the Eco-SSLs were correctly evaluated and

scored. All studies were reviewed by at least two individuals (other than the original reviewer). 

The quality control review provided a forum for confirming that the appropriate data were

identified and documented, resolving any comments or concerns, and ensuring consensus on data

selection. For example, in cases where a study reported data for multiple test species and for

several endpoints, the quality control review provided a forum to reach a consensus that the most 
Guidance for Developing Eco-SSLs November 2003 3 - 7

 Table 3.3 Summary of Nine Study Evaluation Criteria for Plant and Soil Invertebrate Eco-SSLs

Criteria Title Rationale Scoring

#1: Testing was Done Under

Conditions of High

Bioavailability

Bioavailability 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 were based on the bioavailability matrix

(see Chapter 2). Scored 2 if bioavailability of

natural soil was high or very high. Scored 1 for

natural soil with medium bioavailability or

standard artificial soil. Scored 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.

Scored based on experimental design and

methods used for statistical analyses. Scored 2,

1 or 0. Specific criteria used provided in

Attachment 3-2.

#3: Concentration of Test

Substance in Soil is Reported

The concentration of the contaminant tested must be

reported unambiguously. 

Scored 2 if measured concentrations were

reported. Scored 1 for nominal concentrations

and scored 0 in all other cases.

#4: Control Responses are

Acceptable

Negative controls are critical to distinguish treatment effects

from non-treatment effects.

Scored 2 if a standardized procedure were used

and control values were within procedural

guidelines or acceptable range (if non-standard

procedure used). Scored 1 if results of control

were not reported or were ambiguous. Score d0

if control results were not within an acceptable

range.

#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. 

Scored 2 if chronic exposures were used. 

Scored 1 if acute tests were used. Scored 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; (B) 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 applied based on how well the study

reports the four contaminant dosing procedures

(A to D). Scored 2 if study reported all. Scored

1 if information for items A and B, but not C or

D; Scored 0 if details were not provided and

could not be inferred.

#7: A Dose-Response

Relationship is Reported or can

be Established from Reported

Data

Two methodologies can be used to identify this benchmark

concentration. The first method generates a no observed

adverse effect concentration (NOAEC) and a lowest

observed adverse effect concentration (LOAEC). The

second method uses a statistical model to calculate a dose

response curve and estimate an effect concentration for

some percentage of the population (ECx), usually between

an EC5 and an EC50.

Scored 2 if an EC10- EC20; or a NOEC and

LOEC were within a factor of 3. Scored 1 if

the difference between the NOEC and LOEC

was > 3x but < 10x. Scored 0 if an ECx was not

reported or the difference between the NOEC

and LOEC was > 10, or only a NOEC or LOEC

was reported.

#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. 

Scored 2 if ANOVA or statistical method were

based on a P = 0.05; or the 95% CI of the ECx. 

Scored 1 if an ANOVA was completed but P

level not provided or > 0.05; or if EC data did

not include the 95% CI or used a 90% CI. 

Scored 0 if a NOEC, LOEC, or EC/LCx were

not reported, or were reported without a

description of the method used to calculate the

values.

#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).

Scored 2 if the source and condition of the test

organisms were known and described.

Scored 1 for a non-commercial source not

adequately described, or if insufficient

information was provided about a commercial

source. Scored 0 if organisms were from a

known contaminated site, or insufficient

information was provided on the a commercial

source.
Guidance for Developing Eco-SSLs November 2003 3 - 8

appropriate data were used to derive the Eco-SSL. The quality assurance review consisted of

verifying the following information:

• Adherence to all eleven Study Acceptance Criteria

• Accuracy of study evaluation scores

• Accuracy of soil pH, OM, and relative bioavailability score for each study

• Selection of preferred EREs, toxicity parameter(s), and toxicity results

• Units of the toxicity result (wet weight/dry weight, contaminant form)

Calculation of the Eco-SSL Values

The Eco-SSL was calculated as the geometric mean of all the toxicity values at the highest

relative bioavailability score for which sufficient data existed (i.e., $ three data points). If less

than three data values were available at the highest relative bioavailability level, data from the

next highest bioavailability level were included in that Eco-SSL data set. This process

proceeded until a combined data set of three or more data values were identified for calculating

the Eco-SSL. For example, if there were only two toxicity values from studies using soils with

relatively high bioavailability (i.e., score = two), but there were data from four medium

bioavailability studies (i.e., score = one), then an Eco-SSL was calculated using the combined

six data values. If there were less than three acceptable studies (score > ten) an Eco-SSL was not

calculated.

3.5 Soil Toxicity Test Methods

If sufficient data for deriving an Eco-SSL were not available from the published literature,

additional plant or soil invertebrate data could be generated through completion of appropriately

designed soil toxicity studies. The Agency recommends that such studies be designed with

consideration of the nine Study Evaluation Criteria (Table 3.3) to generate the highest quality

data to derive an Eco-SSL. For example, the ideal studies would be conducted using natural

soils within the specified soil chemistry conditions, have the highest relative bioavailability (i.e.,

score = two), and satisfy the other evaluation criteria.

Several standardized soil test procedures are available that can be used to generate the data for

deriving Eco-SSLs. Alternative test methods or designs could also be used if they use the

appropriate EREs. Whether standardized or other methods are used, experimental design

options should be selected that best fit the Study Evaluation Criteria outlined in Table 3.3. To

obtain data that best represent natural conditions, investigators are encouraged to incorporate

aging/weathering of contaminated soil into their study design. Examples of standard procedures

that may be used are described in the following subsections and are listed in Table 3.4. 

Soil Invertebrate Toxicity Testing

Several internationally standardized soil invertebrate toxicity tests may be used to generate data

for Eco-SSLs. Specifically, three soil toxicity tests are identified as generally appropriate: 1) a

21-day chronic earthworm reproduction (cocoon production) toxicity test (ISO/11268-2:1998),
Guidance for Developing Eco-SSLs November 2003 3 - 9

2) the enchytraeid reproduction test (ISO/16387:2001), and 3) the collembolan reproduction test

(ISO/11267:1998) (Table 3.4). These specific tests are recommended as they measure

contaminant toxicity during chronic assays, and include at least one reproductive component

among the measurement endpoints. Guidelines for these methods have been approved by the

International Standards Organization (ISO), and similar efforts are in the final stages of review

or approval by one of several national and international organizations (e.g., the Organization for

Economic Cooperation and Development (OECD), the American Society for Testing and

Materials (ASTM), the European Community, and the Federal Biology Research Cooperative

(FBRC)).

Plant Toxicity Testing

There are relatively few standardized plant toxicity test procedures that EPA expects would

generate data acceptable for deriving Eco-SSLs (Table 3.4). The EPA guidelines for the early

seedling growth and vigor plant test may be used, as well as similar ASTM methods (e.g.,

ASTM E 1963-98). The use of these procedures may require a modified design to best meet the

nine Study Evaluation Criteria. 

Table 3.4 Standard Methods Appropriate for use in Generating Data for the Derivation of 

Plant or Soil Invertebrate Eco-SSLs

Species Citation

Eisenia fetida ISO (International Standardization Organization). 1998. Soil Quality – Effects of Pollutants

on Earthworms (Eisenia fetida) – Part 2: Determination of Effects on Reproduction. ISO

11268-2:1998

Enchytraeus sp. ISO (International Standardization Organization) (2001). Soil Quality – Effects of

Pollutants on Enchytraeidae (Enchytraeus sp.) – Determinations of effects on reproduction

and survival. ISO/CD 16387.

Folsomia candida ISO (International Standardization Organization) (1999). Soil Quality – Inhibition of

Reproduction of Collembola (Folsomia candida) by Soil. ISO 11267:1999.

Terrestrial Plants ASTM (American Society for Testing and Materials). 2002. Standard Guide for

Conducting Terrestrial Plant Toxicity Tests. Designation: E 1963-98 Annual Book of

Standards. American Society for Testing and Materials. West Conshohocken, PA.

Terrestrial plants U.S. EPA (U.S. Environmental Protection Agency). 1996. Ecological effects test

guidelines. OPPTS 850.4230: Early seedling growth toxicity test. Report Number EPA

712–C–96–347. Office of Prevention, Pesticides and Toxic Substances, Washington, DC.

Terrestrial plants U.S. EPA (U.S. Environmental Protection Agency). 1996. Ecological effects test

guidelines. OPPTS 850.4250. Vegetative vigor, Tier II. Report Number EPA

712–C–96–364. Office of Prevention, Pesticides and Toxic Substances, Washington, DC.

Terrestrial plants U.S. EPA (U.S. Environmental Protection Agency). 1996. Ecological effects test

guidelines. OPPTS 850.4150. Terrestrial Plant Toxicity, Tier 1: Vegetative vigor. Report

Number EPA 712–C–96–163. Office of Prevention, Pesticides and Toxic Substances,

Washington, DC.
This Page Intentionally Left Blank
Guidance for Developing Eco-SSLs 4 - 1 Revised February 2005

Hazard Quotient (HQ) Exposure Dose (mg / kg bw / day)

Toxicity Reference Value (mg / kg bw / day) =

4.0 DERIVATION OF WILDLIFE ECO-SSLs

Eco-SSLs for wildlife were derived using a five-part process that included: selecting a wildlife

risk model, selecting surrogate species, estimating an exposure dose, deriving the TRVs, and

calculating the Eco-SSL. Wildlife Eco-SSLs were derived for two groups of receptors: mammals

and birds. Eco-SSLs were not derived for amphibians or reptiles at this time, as described in

Chapter 1. 

4.1 The Wildlife Risk Model for Eco-SSLs

The basic equation that was used for estimating potential risks to wildlife was:

Contaminant exposure for terrestrial wildlife was expressed as an Exposure Dose in mg/kg

bw/day, and the Toxicity Reference Value (TRV) was expressed in the same units.

The Eco-SSL is the soil concentration that

results in an HQ=1, that is, when the TRV

and the Exposure Dose are equal. The

Exposure Dose for wildlife was equal to the

amount of contaminant in the diet that is

taken up or transferred from the soil. 

Therefore, it was necessary to model the

contaminant specific soil concentration that

would result in a dietary concentration equal

to the Exposure Dose that was equal to the

TRV. Estimation of the Exposure Dose is

described in Section 4.3. Derivation of the

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. In this

model, incidental oral soil exposure was

added to the total dietary (food-based)

exposure, making the total oral exposure

greater than 100%. This equation also

included terms for the absorbed fraction (AF) of the contaminant from soil and the diet as well as

an area use factor (AUF) (representing the fraction of time an animal would be exposed). For

the purposes of establishing Eco-SSLs, which are conservative screening values, these terms (AF

and AUF) were set equal to one.

Steps for Establishing a Wildlife Eco-SSL

1. Identify the Wildlife Risk 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 EcoSSLs by solving equation for an HQ=1.
Guidance for Developing Eco-SSLs 4 - 2 Revised February 2005

4.2 Selection of Surrogate Wildlife Species

It was 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 representatives within generic trophic groups included, but were not

limited to, the following: 

• This approach provided generic screening values that could be applied to any site,

regardless of the presence or absence of a particular species. The trophic groups

selected were expected to be present or potentially present at all sites across the

nation.

• This approach provided results that could be used to examine comparative risks

associated with different exposure routes (e.g., ingestion of food versus ingestion

of soil) 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 was consistent with ERAGS which states: "for the screening-level

ERA, 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; U.S. EPA, 1997)

Figure 4.1. The Wildlife Risk Model for Eco-SSLs (Equation 4-1)

where:

HQj = Hazard quotient for contaminant (j) (unitless), 

Soilj

 = Concentration of contaminant (j) in soil (mg/kg dry weight), 

N = Number of different biota types in diet,

Bij = Concentration of contaminant (j) 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] /day),

AFij = Absorbed fraction of contaminant (j) from biota type (i) (for screening purposes set equal to 1),

AFsj = Absorbed fraction of contaminant (j) from soil (s) (for screening purposes set equal to 1),

TRVj = Toxicity reference value (mg/kg BW/day) (Section 4.4),

Ps = Soil ingestion as proportion of diet,

AUF = Area use factor (for screening purposes set equal to 1).
Guidance for Developing Eco-SSLs 4 - 3 Revised February 2005

Criteria for Selection of Surrogate Taxa 

Three general trophic groups (e.g., herbivore, ground insectivore, and carnivore) for both

mammals and birds were used for the Eco-SSL wildlife exposure model. Within each of these

trophic groups, a specific species was identified as a "surrogate" species. Surrogate species were

selected to provide a conservative representation of their respective trophic groups. Selected

species were 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

was considered to be associated with higher metabolic rates (Nagy et al., 1999) and smaller

home ranges (McNab, 1963), exposure for small receptors was assumed to be high. Eco-SSLs

based on these species were therefore likely to be protective of other, larger species in their

trophic group.

Selection of specific surrogate species was necessary for parameterizing the Eco-SSL wildlife

model, which required 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 had to have a clear direct or

indirect exposure pathway link to soil. Direct exposure pathways to soil included

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 were assumed to be the most highly exposed to soil

contamination with the exception of contaminants that biomagnify. Indirect exposure

included ingestion by carnivores of prey that have direct contact with soil. 

2) Diet Composition. The selected,

surrogate species must forage in

terrestrial, upland habitats. This

criteria ensured that only potential

exposures related to soil

contamination were considered and

consumption of aquatic prey items

(exposures to the aquatic

environment) were not considered. 

3) Diet composition can be

simplistically classified. The

dietary composition of each

surrogate species had to be easily

classified into one of the three

selected trophic groups (herbivore,

ground insectivore, carnivore). 

Clear classification of diet served to

What Wildlife Groups are not Considered

Appropriate for Eco-SSLs?

Some specific wildlife groups were not considered suitable

for deriving wildlife Eco-SSLs. These groups include:

V 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. 

V Piscivores (e.g., herons, otter) were excluded due to

the lack of a direct exposure pathway to soil.

V Aerial Insectivores (e.g., swallows) and Arboreal

Insectivores (e.g., warblers) were excluded as they do

not forage primarily from terrestrial environments. 
Guidance for Developing Eco-SSLs 4 - 4 Revised February 2005

simplify the exposure assumptions related to dietary composition into three classes: 

plants, invertebrates and animals. Further, the dietary composition for the surrogate

species needed to be realistically assumed to consist of a single food type. This

assumption allowed 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) produced results that are protective of

species with more varied diets. Omnivorous species were assumed to be more likely to

consume foods with differing contaminant concentrations. As a result, their total

exposure would be less than that for species whose diets that consisted of the single most

contaminated food type. By selecting surrogate species that foraged exclusively on a

specific food type (plants, invertebrates, or vertebrates), maximum risks were expressed

for any given contaminant. This helped to ensure protectiveness of all other species.

4) Mammalian and avian species identified. Because toxic responses for the same

contaminant could differ among wildlife taxa, surrogate species were selected for both

mammals and birds. Based upon the above factors, three mammalian and three avian

species (listed in Table 4.1) were selected to represent some of the most highly exposed

species. It was assumed that the use of these six species also protected other herbivores,

ground insectivores, and carnivores.

4.3 The Exposure Dose

Estimation of the exposure dose associated with contaminant concentrations in soil required

parameterization of the general model provided as Equation 4-1 (see Figure 4.1). 

Reduced Wildlife Risk Model for Screening

The Eco-SSLs are intended to be conservative screening values that can be used to eliminate

contaminants clearly not associated with unacceptable risks. Therefore, several simplifying,

conservative assumptions were made in the parameterization of the general wildlife risk model

(Equation 4-1). These assumptions included:

• Surrogate species were assumed to reside and forage exclusively on and within

the contaminated site. Therefore, the area use factor (AUF) was set equal to 1. 

• Bioavailability of the contaminant in both soil and food was 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’ (AFij) were both equal to 1.

• The surrogate species' diet consisted of just one food type. Therefore, the

proportion of biota type ‘i’ in the diet (Pi) was equal to 1 and the number of biota

types (N) in the diet was equal to 1.
Guidance for Developing Eco-SSLs 4 - 5 Revised February 2005

Parameterizing the Model for Estimating the Exposure Dose 

Parameterization of the model included using exposure factors related to the surrogate species

(see Table 4.1) and estimating the contaminant concentrations in biota items (Bij) consumed in

the diet. The exposure factors identified and derived for surrogate species-specific exposure

factors are described in Attachment 4-1. The food and soil ingestion rates used in the exposure

model were represented by high-end values. For the food ingestion rate a high end point

estimate was selected based on measured data. For soil ingestion, the 90th percentile from the

distribution was selected. 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)

Food Ingestion Rate

(kg dw/kg bw day) 1

Soil Ingestion 

(Ps) 2 Assumed Diet

Mammalian Herbivore

(Meadow Vole) 0.0875 0.032 100% foliage

Mammalian Ground Insectivore

(Short-tailed shrew) 0.209 0.030 100% earthworms

Mammalian Carnivore

(Long-tailed weasel) 0.130 0.043 100% small mammals

Avian Grainivore

(Mourning dove) 0.190 0.139 100% seeds

Avian Ground Insectivore

(American woodcock) 0.214 0.164 100% earthworms

Avian Carnivore 

(Red-tailed hawk) 0.0353 0.057

100% small mammals

that consume 100%

earthworms

Parameterization details provided in Attachment 4-1. 1 High end point estimate based on measured data. Derivation presented in Attachment 4-1. 2 Soil ingestion as proportion of diet. 90th percentile. Derivation presented in Attachment 4-1.

dw = dry weight

Estimating Contaminant Concentrations in Biota (Dietary Items)

The contaminant concentrations in biota types (Bij) composing the wildlife diets (plants,

earthworms, or small mammals) were estimated by assuming that the concentration of the

contaminant ‘j’ in the food type ‘i’ could be predicted from the concentration of the contaminant

in the soil (Soilj). 
Guidance for Developing Eco-SSLs 4 - 6 Revised February 2005

The concentration of contaminant (j) in biota or food type type (i) (Bij) is related to the

concentration in soil (Soilj

) by an uptake model which has one of the following forms:

Case 1) Bij = BAFij * Soilj (constant)

Case 2) ln(Bij) = Iij + Sij * ln(Soilj

) (log-linear)

Case 3) Bij = Iij + Sij * Soilj (linear)

where:

Bij = Concentration of contaminant (j) in food type (i) (where i = plant,

earthworm, or small mammal) 

BAFij = Soil-to-biota bioaccumulation factor (BAF) for contaminant (j) for biota

type (i) 

Iij = Intercept from bioaccumulation model for contaminant (j) for food type (i)

Sij = Slope from bioaccumulation model for contaminant (j) for food type (i)

Soilj = Concentration of contaminant (j) in soil

For the predatory surrogate species that are assumed to consume 100% small mammals, BAFs or

regression equations were not available for all contaminants relating soil concentration to

concentration in small mammal tissue. In these instances, it was necessary to estimate small

mammal tissue concentrations (Bij) from dietary-based BAFs or regressions:

Case 4) Bij = Cdiet * BAFdm

Case 5) ln(Bij) = Iij + Sij * ln(Cdiet)

Case 6) Bij = Iij + Sij * Cdiet

where:

Bij = Concentration of contaminant (j) in food type (i) (where i = small

mammal)

Cdiet = Concentration of contaminant (j) in diet where diet is 100% earthworms

estimated as in Case 1, 2 or 3, above

BAFdm = Diet-to-biota BAF for contaminant (j) in mammal or bird tissue

A hierarchy was established for decision-making concerning the use of available data to estimate

contaminant concentrations in biota types (Bij). The following values were used in order of

preference:

1) Existing Regression (R). If regression equations were available from the literature and

the r-square values were > 0.2, then these were preferentially used. The primary sources

of existing regression equations were: Sample et al. (1999) for earthworms; Sample et al.

(1998a) for small mammals; and Bechtel-Jacobs (1998) for plants.

2) New Regression (R). If regressions were not available from the literature, then paired

data (contaminant concentration in soil versus concentration in plant foliage;

concentration in soil versus soil invertebrate; or concentration in diet versus mammal or
Guidance for Developing Eco-SSLs 4 - 7 Revised February 2005

bird tissue) were identified from the literature. If the paired data were sufficient to

establish a regression and the regression was significant with r-square values > 0.2, then

the regression was used to estimate uptake. 

3) Ratio (BAF). If regressions were not significant then BAFs (or ratios of the contaminant

in soil to the contaminant in the food item) were used to estimate uptake. BAFs were

identified either from the literature or from the paired data compiled in the attempt to

establish a regression in Step 2. The median BAF value was selected to estimate uptake. 

4) Modeled BAFs or Bij (M for

modeled). If BAFs were not

available in the literature or

the paired data were not

available to derive the BAF,

then Bij was estimated using

relationships established

between physical or chemical

parameters of the contaminant

‘j’ and Bij. Existing models

from the literature were used

where possible. In some

cases, new models were

derived. The models are

provided in Attachment 4-1. 

5) Assumptions (A). In

instances where data were 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 biota

types. These assumptions are

discussed in following

subsections.

Figure 4.2 summarizes the method (based on the hierarchy) used for estimating contaminant

concentrations in biota types (dietary items) for each of the Eco-SSL contaminants. The

following subsections describe how contaminant concentrations were estimated in each of the

dietary items including plants, soil invertebrates and small mammals.

Figure 4.2 Summary of Method Used for Estimation of

Contaminant Concentrations in Biota Types (B i )

COC Soil to Soil to Diet to Soil to

Plant Earthworm Mammal Mammal

Antimony R A BAF NA

Arsenic BAF R NA R

Barium BAF BAF BAF NA

Beryllium R BAF BAF NA

Cadmium R R NA R

Chromium BAF BAF NA R

Cobalt BAF BAF NA R

Copper R BAF NA R

Lead R R NA R

Manganese BAF R NA R

Nickel R BAF NA R

Selenium R R NA R

Silver BAF BAF NA BAF

Vanadium BAF BAF NA BAF

Zinc R R NA R

Dieldrin BAF R R NA

DDT R R R NA

DDD R R R NA

DDE R R R NA

PCP R R R NA

PAHs R or BAF M A A

TNT BAF TBD A A

RDX BAF TBD A A

A = Assumption .

BAF = Bioaccumulation Factor.

M = Estimated based on equation relating physical-chemical factor to

bioaccumulation (model).

NA = Not applicable. Only one method is needed for estimating tissue

concentrations in mammals based on either diet or soil concentrations.

R = Either existing or new regression equation (Attachment 4-1)

TBD = To be determined in the chemical specific Eco-SSL document.
Guidance for Developing Eco-SSLs 4 - 8 Revised February 2005

Cadmium

0.001 0.01 0.1 1 10 100 1000

0.001

0.01

0.1

1

10

100

1000

Foliage

95% prediction 

limit - foliage

Seeds

0.1 1 10 100 1000 10000

0.1

1

10

100

1000

Concentration in soil (mg/kg dry wt.)

0.1 1 10 100 1000 10000

Concentration in plant (mg/kg dry wt.)

0.001

0.01

0.1

1

10

100

1000

10000

1 10 100 1000 10000 100000

0.1

1.0

10.0

100.0

1000.0

10000.0

100000.0

Copper

Lead Zinc

Figure 4.3 Comparison of Soil-to-Foilage and Soil-to-Seed Bioaccumulation 

for Cadmium, Copper, Lead and Zinc

How Contaminant Concentrations Are Estimated for Plants and Soil Invertebrates as Dietary

Items

The specific information concerning how contaminant concentrations were estimated for the

plant, earthworm and small mammal biota types (Bij) of the diets of the surrogate species is

provided as Attachment 4-1. Regressions (R), BAFs, models (M) and assumptions (A) were all

used (Figure 4.2).

The existing models for plants produce estimated contaminant concentrations in foliage. 

Although these data are suitable for receptors that consume foliage (e.g., meadow vole), their

suitability for receptors whose diets consist of seeds (e.g., mourning dove) is unknown. To

address this issue, a literature search was performed to locate studies in which contaminant

concentrations in seeds and in soil were evaluated. The few data located (limited to metals) were

plotted over reported soil-to-plant foliage bioaccumulation data (Figure 4.3). 
Guidance for Developing Eco-SSLs 4 - 9 Revised February 2005

For cadmium, copper, lead, and zinc soil-to-seed bioaccumulation mirrors the pattern observed

for soil-to-foliage bioaccumulation. The degree of spread in the seed bioaccumulation data was

comparable to that for foliage data. Based on these data, the models estimating contaminant

concentrations in foliage were assumed to be suitable surrogates (at least for metals) for

estimating contaminant concentrations in seeds.

How Contaminant Concentrations Are Estimated for Mammals as Dietary Items

Empirical soil-to-whole body log-linear regression models and BAFs were available from

Sample et al. (1998a) for 11 of the 24 contaminants. For the remaining contaminants for which

empirical regression models or BAFs were not available, bioaccumulation was estimated using

the methods presented in Attachment 4-1.

Although many species of predatory wildlife consume both birds and mammals as prey, few data

were available to estimate bioaccumulation of contaminants into birds. As a consequence, the 

bioaccumulation models for mammals were assumed to produce estimates that adequately

represent concentrations in birds. The validity of this assumption was 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 were available for carcasses (tissue remaining after removal of the

gastrointestinal 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 4.4. Based on these data, concentrations in birds appear to be approximately

equivalent to or less than those found in omnivorous or insectivorous small mammals.

What if Data are not Available to Estimate Concentrations in Dietary Items?

For some contaminants and biota types (i.e., earthworms for antimony), data were not available

to derive BAFs (as described in Attachment 4-1). For these contaminants, an assumption or

default BAF of 1 was used. This assumption was 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). Approximately 90% of plant BAFs, 68% of

earthworm BAFs and 83% of small mammal BAFs were less than one. Assuming BAFs for

inorganic contaminants when data are absent generally over-estimate uptake and are, therefore,

suitable for screening purposes only. A default BAF of 1 was also used for organic compounds.
Guidance for Developing Eco-SSLs 4 - 10 Revised February 2005

Birds1 Birds2 Mice1 Mice2 Shrew1 Shrew2

Concentration in Carcass (mg/kg dry)

1

10

100

1000

Lead

Birds1 Birds2 Mice1 Mice2 Shrew1 Shrew2

10

100

1000

Birds1 Birds2 Mice1 Mice2 Shrew1 Shrew2

Concentration in Carcass (mg/kg dry)

0.1

1

10

100

Birds1 Birds2 Mice1 Mice2 Shrew1 Shrew2

4

6

8

10

12

14

Zinc

Cadmium Copper

Figure 4.4 Comparison of Mean Concentrations in Multiple Species near a Smelter

Table 4.2 Cases where the Median of the BAF Distribution for Metals 

is Greater or Less than One

Biota Type Total Number of

Contaminants BAFs < 1 BAFs > 1

Plants 21 19 2

Earthworms 31 21 10

Small Mammals 24 20 4
Guidance for Developing Eco-SSLs 4 - 11 Revised February 2005

4.4 Derivation of Toxicity Reference Values (TRVs)

The wildlife TRV is defined as:

Dose 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.

As presented in Figure 4.5, a four-step process was used to select TRVs appropriate for

calculating wildlife Eco-SSLs. The four steps included: 1) conduct a literature search, 2)

complete a review of the literature and extract data, 3) complete an evaluation of the extracted

data and score data, and 4) derive a TRV. 

Literature Search and Retrieval

The first step in deriving the TRVs was to

conduct a literature search to identify

toxicological studies for mammals and

birds for retrieval and review. The search

procedure was described in detail in

Attachment 4-2. EPA completed a

literature search for 23 of the Eco-SSL

contaminants (PCBs were excluded). The

search process identified over 44,000

records for review. The literature search

process was documented in sufficient

detail that others may use it to identify

relevant data for additional contaminants

not completed by EPA.

Literature Review and Data Extraction

The toxicological literature identified

from the literature search was next

reviewed for usefulness in establishing

wildlife TRVs. Literature exclusion

criteria (similar to those discussed in

Chapter 3 for plants and soil

invertebrates) and listed in detail in Attachment 4-3 were applied to the identified literature. 

Additional types of studies excluded specifically for wildlife included those that only report

genotoxic, mutagenic, or carcinogenic effects, acute or non-oral exposures (inhalation, injection,

dermal, etc.), or studies unrelated to the contaminant and receptor groups of interest. Where

possible, the literature exclusion criteria were applied to identified titles and abstracts prior to

retrieval of the paper. For retrieved studies that passed the literature exclusion criteria, the

Figure 4.5. Steps of the Wildlife TRV Derivation

Process

The wildlife TRV derivation process is composed of four

general steps that are documented as separate standard operating

procedures (SOPs):

• Literature Search and Retrieval

Wildlife TRV Literature Search and Retrieval (Attachment

4-2). A literature search identifies dose-response literature 

for retrieval. 

• Literature Review and Data Extraction

Wildlife TRV Literature Review, Data Extraction and

Coding (Attachment 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 Data Evaluation (Attachment 4-4). Each of

the results identified in the reviewed literature is scored for

quality and applicability for TRV derivation.

• TRV Derivation

Wildlife TRV Derivation (Attachment 4-5). This procedure

plots the collective dose-response information and

establishes the process for estimating the TRV. 
Guidance for Developing Eco-SSLs 4 - 12 Revised February 2005

relevant toxicological data were extracted and

entered into an electronic database according to

established extraction and coding procedures

detailed as Attachment 4-3. These extraction and

coding procedures were consistent with EPA's

EcoTox database (USEPA, 2003). 

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.

Data Evaluation 

Each test result extracted during the literature

review process was scored for quality and

applicability for TRV derivation. The data

evaluation process was provided as Attachment

4-4. In instances where more than one

"experiment" (i.e., different combinations of test

organism (species or strains), contaminant form,

test location, control type, doses, application

frequency, or route of exposure) were reported in a

paper, 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 was

based on the evaluation of ten attributes of the toxicological study (Figure 4.6) 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 the most appropriate for deriving an oral TRV. The total

score was calculated by adding the results of the evaluation of each attribute. The use of total

Figure 4.6 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 were

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. Physiology, behavioral,

biochemical and pathology changes were scored

lower and biomarkers scored lowest.

7. Dose Range

Studies with both NOAEL and LOAEL values

scored higher than studies which reported only

one value. Narrower ranges between NOAEL

and LOAEL scored higher.

8. Statistical Power

The statistical power of a NOAEL was scored.

9. Exposure Duration

Exposure durations encompassing multiple

generations and critical life stages scored higher

than chronic, subchronic, and acute.

10. Test Conditions

Studies that reported standard exposure

conditions scored higher then those that

reported fewer or none.
Guidance for Developing Eco-SSLs 4 - 13 Revised February 2005

Data Evaluation Score was interpreted as follows: 

80 to 100 High confidence

71 to 79 Medium confidence

66 to 70 Low confidence

0 to 65 Not used in Eco-SSL derivation

A web-based data entry system and database was created by EPA as a tool to facilitate efficient,

accurate, and consistent data extraction from individual reviewed toxicological studies as well as

data evaluation scoring. Extraction of the data directly into an electronic database facilitated

necessary sorting, searching, and presentation of the data for the purposes of TRV derivation. 

The TRV database was focused on extracting the NOAEL and LOAEL doses from each of the

toxicological studies.

TRV Derivation 

The dose-response information for mammals and birds was plotted separately, and a TRV was

derived for each class using an established procedure. The process used was documented in

Attachment 4-5. The following general steps were completed to derive the TRVs:

Data Sorted. The toxicity data (effect doses) 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 tables provided 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.

Data Plotted. Summary plots were constructed depicting the NOAELs and LOAELs for

each contaminant. Separate plots were completed for mammalian and avian data. The

data plots (example provided as Figure 4.7) were organized by General Effect Group in

order from left to right as: biochemical (BIO), behavior (BEH), physiology (PHY),

pathology (PTH), reproduction (REP), growth (GRO), and mortality (MOR). Within

each toxicological 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

Group were recorded on the plots. For example, a study may report more the one adverse

reproductive effect such as reduced progeny weight as well as progeny survival and

reduced number of litters. In this case, only one effect was coded for the most

conservative "worst case" result.

Exclusion of Data with Limited Utility in Establishing an Eco-SSL. Each NOAEL

and LOAEL result was evaluated according to the Data Evaluation process (Attachment
Result #

Reference

Ref 

No. Test Organism

# of Conc/ DosesMethod of AnalysesRoute of ExposureExposure DurationDuration UnitsAgeAge UnitsLifestageSexGeneral Effect GroupEffect MeasureResponse SiteNOAEL Dose (mg/kg/day) LOAEL Dose (mg/kg/day)Data Evaluation Score

1 Maro et al., 1980 171 Cow (Bos taurus) 2 M FD 45 d 7 mo JV F BIO HMGL BL 0.30 70

2 Chetty et al., 1979 115 Rat (Rattus norvegicus) 6 U FD 4 w NR NR NR B BIO HMGL BL 19 29 75

3 Kadiiska et al., 1985 19290 Rat (R. norvegicus) 2 U DR 30 d NR NR JV M BIO P450 LI 20 69

4 Derr et al., 1970 129 Rat (R. norvegicus) 2 U DR 35 d NR NR JV M BIO HMCT BL 118 65

5 Gershbein et al., 1983 136 Rat (R. norvegicus) 2 U FD 80 d 44 d JV M BEH NMVM WO 1.5 66

6 Huck and Clawson, 1976 86 Pig (Sus scrofa) 4 U FD 28 d NR NR NR NR BEH FCNS WO 7.1 69

7 Bourg et al., 1985 111 Rat (R. norvegicus) 2 M DR 57 d 80 d JV M BEH ACTP WO 20 77

8 Haga et al., 1996 105 Rat (R. norvegicus) 2 U FD 16 w NR NR NR M PHY Other HE 8.8 77

9 Gershbein et al., 1983 136 Rat (R. norvegicus) 2 U FD 80 d 44 d JV M PTH GHIS NR 1.5 73

10 Chetty et al., 1979 116 Rat (R. norvegicus) 6 U FD 4 w NR NR NR B PTH SMIX TS 4.8 9.6 78

11 Haga et al., 1996 105 Rat (R. norvegicus) 2 U FD 16 w NR NR NR M PTH BDWT WO 8.8 77

12 Van Vleet et al., 1981 149 Pig (S. scrofa) 2 U FD 10 w NR NR JV M PTH GLSN HE 19 73

13 Seidenberg et al., 1986 113 Mouse (Mus musculus) 2 U GV 5 d NR NR GE F PTH BDWT WO 82 75

14 Derr et al., 1970 129 Rat (R. norvegicus) 2 U DR 35 d NR NR JV M PTH SMIX HE 118 67

15 Nation et al., 1983 126 Rat (R. norvegicus) 3 U FD 69 d 80 d MA M REP TEWT TE 5.0 20 83

16 Domingo et al., 1985 124 Rat (R. norvegicus) 4 U GV 28 d NR NR MA F REP PRWT WO 5.4 11 87

17 Paternain et al., 1988 109 Rat (R. norvegicus) 4 U GV 9 d NR NR GE F REP PRWT WO 25 81

18 Seidenberg et al., 1986 113 Mouse (M. musculus) 2 U GV 5 d NR NR GE F REP PROG WO 82 72

19 Pedigo et al., 1988 121 Mouse (M. musculus) 4 U DR 13 w 10 w MA B REP RSUC WO 10 78

20 Anderson et al., 1992 120 Mouse (M. musculus) 2 U DR 9 w 12 w MA M REP TEWT TE 14 73

21 Corrier et al., 1985 123 Rat (R. norvegicus) 2 U FD 70 d 100 d SM M REP TEDG TE 20 77

22 Mollenhauer et al., 1985 119 Rat (R. norvegicus) 2 U FD 98 d 100 d MA M REP TEWT TE 24 73

23 Anderson et al., 1993 139 Mouse (M. musculus) 2 U DR 13 w 12 w MA M REP TEWT TE 43 78

24 Pedigo et al., 1993 187 Mouse (M. musculus) 2 U DR 10 w 8 to 10 w JV M REP PRFM WO 56 73

25 Maro et al., 1980 171 Cow (Bos taurus) 2 M FD 45 d 7 mo JV F GRO BDWT WO 0.30 77

26 Gershbein et al., 1983 136 Rat (R. norvegicus) 2 U FD 80 d 44 d JV M GRO BDWT WO 1.5 68

27 Huck and Clawson, 1976 86 Pig (S. scrofa) 4 U FD 16 w NR NR NR NR GRO BDWT WO 2.4 74

28 Pedigo et al., 1988 121 Mouse (M. musculus) 4 U DR 5 w 6 to 7 w SM M GRO BDWT WO 19 33 82

29 Mohiuddin et al., 1970 132 Guinea pig (Cavia porcellus) 2 U OR 5 w NR NR MA M GRO BDWT WO 20 72

30 Bourg et al., 1985 111 Rat (R. norvegicus) 2 M DR 57 d 80 d JV M GRO BDWT WO 20 72

31 Chetty et al., 1979 116 Rat (R. norvegicus) 6 U FD 4 w NR NR NR B GRO BDWT WO 0.96 77

32 Paternain et al., 1988 109 Rat (R. norvegicus) 4 U GV 9 d NR NR GE F GRO BDWT WO 6.2 79

33 Van Vleet et al., 1981 149 Pig (Sus scrofa) 2 U FD 5 w NR NR JV M GRO BDWT WO 20 77

34 Anderson et al., 1993 139 Mouse (M. musculus) 2 U DR 13 w 12 w MA M GRO BDWT WO 43 76

35 Derr et al., 1970 129 Rat (R. norvegicus) 2 U DR 24 d NR NR JV M GRO BDWT WO 122 72

36 Van Vleet et al., 1981 149 Pig (S. scrofa) 2 U FD 10 w NR NR JV M MOR MORT NR 19 78

37 Seidenberg et al., 1986 113 Mouse (M. musculus) 2 U GV 5 d NR NR GE F MOR MORT NR 82 80

38 Mohiuddin et al., 1970 132 Guinea pig (Cavia porcellus) 2 U OR 5 w NR NR MA M MOR SURV WO 20 73

ACTP = activity level; B = both; BIO = biochemical; BL = blood; d = days; BDWT = body weight changes; BEH = behavior; DR = Drinking water; F = 

female; FCNS = food consumption; FD = food; GE = gestation; GRO = growth; GLSN = gross lesions; GV = gavage; HE = heart; HMCT = hematocrit; 

HMGL = hemoglobin; JV = juvenile; LI = liver; MA = mature; M = male; M = measured; m = months; MOR = mortality, NMVM = number of movements; 

NR = Not reported; OR = other oral; PHY = physiology; PTH = pathology; PRFM = sexual performance; REP = reproduction; SM = sexually mature; 

SMIX = weight relative to body weight; SURV = survival; TE = testes; TEDG = testes degeneration; TEWT = testes weight; U = unmeasured; w = weeks; 

WO = whole organism

Table 4.3. Example of Extracted and Sorted Data for Wildlife TRV Derivation (Cobalt)

Mortality Data

Growth Effects

Reproductive Effects

Biochemical Effects

Behavioral Effects

Physiology Effects

Pathology Effects

Guidance for Developing Eco-SSLs 4-14 Revised February 2005
Result number Test Species Key

1) 10 - C Lowest Observed Adverse Effect Dose

Reference Number Test Species Paired values from same study when joined by line

No Observed Adverse Effect Dose

Figure 4.7 Example of a Toxicological Plot for the TRV Derivation Process (Cobalt)

70

75

75

69

65

66

69

77

77 78 77

73

75

87

81

72

83

87 78

73

77 73

78

73

77

68

74

72 72

82

77

79

72

78

80

73

78

67

83

82

76

77 73

0

1

10

100

1000 Dose (mg Co/kgBW/day)

BIO-NOAEL BIO-LOAEL BEH-NOAEL BEH-LOAEL PHY-NOAEL PHY-LOAEL PTH-NOAEL

PTH-LOAEL REP-NOAEL REP-LOAEL GRO-NOAEL GRO-LOAEL MOR-NOAEL MOR-LOAEL 10) 116-R8) 105-R 6) 86-Pg 5) 136-R 9) 136-R

Biochemical (BIO) Behavior (BEH) Pathology (PTH) Reproduction (REP) Growth (GRO) Mortality (MOR) 2) 116-R 4) 129-R 1) 171-Cw 7) 111-R 11) 105-R 12) 149- Pg 13) 113-M 14) 129-R 15) 126-R 16) 124-R 17) 109-R 18) 113-M 19) 121-M 20) 120-M22) 119-R 23) 139-M 24) 187-M 25) 171-Cw 26) 136-R 27) 86-Pg 28) 121-M 29) 132- Gp 30) 111-R 31) 116-R 32) 109-R 33) 149-Pg34) 139-M 35) 129-R 36) 149-Pg 37) 113-M 38) 132-Gp 21) 123-R 3) 19290-R

R = rat

M = mouse

Pg = pig

Gp = guinea pig

Cw = cow

83

Guidance for Developing Eco-SSLs 4-15 Revised February 2005
Guidance for Developing Eco-SSLs 4 - 16 Revised February 2005

NO

NO

YES

YES

NO

YES

YES

NO

NO YES

YES

NO

NO YES

Step 2:

Are there 3 or more 

NOAELs in REP 

and GRO?

Step 4:

Calculate the geometric mean of 

NOAELs for REP and GRO

Step 3:

At Least one NOAEL 

for REP and GRO?

Step 5: 

At least 3 

LOAELs for 

GRO & REP?

TRV = lowest 

LOAEL / 10

YES

Step 6: 

At least 6 NOAELs or 

LOAELs for all 

endpoints?

TRV = Highest NOAEL 

below lowest LOAEL 

for all endpoints

No TRV can 

be derived

Is NOAEL < 

lowest bounded 

LOAEL for REP, 

GRO or MOR?

TRV = Lowest 

NOAEL for GRO, REP 

or MOR

TRV = Highest bounded 

NOAEL below lowest 

bounded LOAEL for 

REP, GRO or MOR

Is geometric mean 

NOAEL < lowest 

bounded LOAEL for 

REP, GRO or MOR?

TRV = geometric mean 

of NOAELs for REP & 

GRO

Is Mechanism of 

Toxicity Addressed?

NO

TRV = Highest bounded 

NOAEL below lowest 

bounded LOAEL for 

appropriate effect group

Step 1:

Are there at least 3 

toxicity values for 2 

species for REP, GRO or 

MOR?

NO

NO

YES

YES

NO

YES

YES

NO

NO YES

YES

NO

NO YES

Step 2:

Are there 3 or more 

NOAELs in REP 

and GRO?

Step 4:

Calculate the geometric mean of 

NOAELs for REP and GRO

Step 3:

At Least one NOAEL 

for REP and GRO?

Step 5: 

At least 3 

LOAELs for 

GRO & REP?

TRV = lowest 

LOAEL / 10

YES

Step 6: 

At least 6 NOAELs or 

LOAELs for all 

endpoints?

TRV = Highest NOAEL 

below lowest LOAEL 

for all endpoints

No TRV can 

be derived

Is NOAEL < 

lowest bounded 

LOAEL for REP, 

GRO or MOR?

TRV = Lowest 

NOAEL for GRO, REP 

or MOR

TRV = Highest bounded 

NOAEL below lowest 

bounded LOAEL for 

REP, GRO or MOR

Is geometric mean 

NOAEL < lowest 

bounded LOAEL for 

REP, GRO or MOR?

TRV = geometric mean 

of NOAELs for REP & 

GRO

Is Mechanism of 

Toxicity Addressed?

NO

TRV = Highest bounded 

NOAEL below lowest 

bounded LOAEL for 

appropriate effect group

Step 1:

Are there at least 3 

toxicity values for 2 

species for REP, GRO or 

MOR?

Figure 4.8 Procedure for Deriving the Wildlife Toxicity Reference Value (TRV)

 4-4) and scored within a range of 0 to 100 (worst to best) for usefulness in establishing

an oral TRV. 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 TRV derivation used the most

suitable data.

TRV Selected. The general steps and conditional statements of the derivation process

are outlined in Figure 4.8. These steps are an a priori framework for selection of the

TRV value based on the results of the toxicological plots. The flow chart (Figure 4.8) is

used with the toxicity data plots to derive the TRV according to the described steps. The

TRV is equal to the geometric mean of the NOAEL values for growth (GRO) and

reproductive (REP) effects. In cases where the geometric mean NOAEL is higher than

the lowest bounded LOAEL (bounded refers to a LOAEL that has a paired NOAEL) for

either, GRO, REP or mortality (MOR), the TRV is equal to the highest bounded NOAEL

below the lowest bounded LOAEL. An example is provided using the mammalian cobalt

plot depicted as Figure 4.9. 
Result number Test Species Key

1) 10 - C Lowest Observed Adverse Effect Dose

Reference Number Test Species Paired values from same study when joined by line

No Observed Adverse Effect Dose

Wildlife TRV Derivation Process

1) There are at least three results available for two test species within the GRO, REP and MOR effect groups. 

 There is enough data to derive TRV.

2) There are are at least three NOAEL results available for calculation of a geometric mean. 

3) The geometric mean of the NOAEL values for GRO and REP equals 7.3 mg Co/kg BW/day. 

4) The geometric mean NOAEL value is less than the lowest bounded LOAEL for REP, GRO or MOR. 

5) The mammalian wildlife TRV for cobalt is equal to 7.3 mg Co/kg BW/day. 

Figure 4.9 Example of a TRV Derivation (Cobalt)

70

75

75

69

65

66

69

77

77 78 77

73

75

87

81

72

83

87 78

73

77 73

78

73

77

68

74

72 72

82

77

79

72

78

80

73

78

67

83

82

76

77 73

0

1

10

100

1000 Dose (mg Co/kgBW/day)

BIO-NOAEL BIO-LOAEL BEH-NOAEL BEH-LOAEL PHY-NOAEL PHY-LOAEL PTH-NOAEL

PTH-LOAEL REP-NOAEL REP-LOAEL GRO-NOAEL GRO-LOAEL MOR-NOAEL MOR-LOAEL 10) 116-R8) 105-R 6) 86-Pg 5) 136-R 9) 136-R

Biochemical (BIO) Behavior (BEH) Pathology (PTH) Reproduction (REP) Growth (GRO) Mortality (MOR) 2) 116-R 4) 129-R 1) 171-Cw 7) 111-R 11) 105-R 12) 149- Pg 13) 113-M 14) 129-R 15) 126-R 16) 124-R 17) 109-R 18) 113-M 19) 121-M 20) 120-M22) 119-R 23) 139-M 24) 187-M 25) 171-

C

26) 136-R

27) 86-Pg

28) 121-M

29) 132-

Gp 30) 111-R

31) 116-R

32) 109-R

33) 149-Pg

34) 139-M

35) 129-R

36) 149-Pg

37) 113-M

38) 132-Gp

21) 123-R

3) 19290-R

Geometric Mean of 

NOAELs for REP and 

GRO = 7.3

R = rat

M = mouse

Pg = pig

Gp = guinea pig

Cw = cow

83

Guidance for Developing Eco-SSLs 4-17 Revised February 2005
Guidance for Developing Eco-SSLs 4 - 18 Revised February 2005

4.5 Calculation of Wildlife Eco-SSLs

Based on the assumptions described in Section 4.3, the Eco-SSL wildlife risk model (Equation

4-1) was reduced to the following after removal of the parameters set equal to 1:

Equation 4-2

where:

Soilj = Concentration for contaminant (j) in soil (mg/kg [dry weight]), 

FIR = Food ingestion rate (kg food [dry weight]/ kg bw [wet weight] / d),

Ps = Proportion of diet that is soil,

TRVj

 = Toxicity reference value for contaminant (j) (mg [dry weight]/kg bw [wet

weight] /d),

Bij = Concentration of contaminant (j) in food type (i) mg/kg [dry weight].

As described previously, the concentration of contaminant (j) in biota or food type type (i) (Bij)

was related to the concentration in soil (Soilj

) by an uptake model which has one of the following

forms:

Case 1) Bij = BAFij * Soilj (constant)

Case 2) ln(Bij) = Iij + Sij * ln(Soilj) (log-linear)

Case 3) Bij = Iij + Sij * Soilj (linear)

where:

BAFij = Soil-to-biota Bioaccumulation factor (BAF) for contaminant (j) for biota

type (i), 

Iij = Intercept from bioaccumulation model for contaminant (j) for food type (i)

Sij = Slope from bioaccumulation model for contaminant (j) for food type (i)

In instances where it was necessary to estimate small mammal tissue concentrations (Bij) based

on dietary based BAFs or regressions, the uptake model may have one of the following forms:

Case 4) Bij = Cdiet * BAFdm 

Case 5) ln(Bij) = Iij + Sij * ln(Cdiet)

Case 6) Bij = Iij + Sij * Cdiet

where:

Bij = Concentration of contaminant (j) in food type (i) (where i = small

mammal)
Guidance for Developing Eco-SSLs 4 - 19 Revised February 2005

(Equation 4-3)

Cdiet = Concentration of contaminant (j) in diet where diet is 100% earthworms

estimated as in Case 1, 2 or 3, above

BAFdm = Diet-to-biota BAF for contaminant (j) in mammal or bird tissue

The general procedure for calculating the wildlife Eco-SSL for a contaminant (j) was to solve

Equation 4-2 to determine the concentration in soil (Soilj

) equivalent to an HQj equal to 1. In

most cases, solution of Equation 4-2 was accomplished by use of a computer spreadsheet

program. 

In cases where Bij was estimated using a simple BAF from soil to food type, Equation 4-2 was

rearranged resulting in the following simplified equation:

Conservatism of Model Parameterization

The purpose of the Eco-SSLs is to identify concentrations of contaminants in soil that may

present a risk from those concentrations that clearly do not. As a screening tool, the Eco-SSLs

should be conservative, but not so much so that no concentrations pass the screen. To this end,

the wildlife Eco-SSL model was parameterized using a combination of conservatively skewed

and non-conservatively skewed parameter values. The level of conservatism associated with the

model parameter values is summarized in Table 4.4. Whereas the TRVs, food and soil ingestion

rates, diet composition, area use, and bioavailability were all conservatively skewed, all

bioaccumulation values were based on central tendency estimates.
Guidance for Developing Eco-SSLs 4 - 20 Revised February 2005

Table 4.4 Summary of Conservatism Associated with the Wildlife Eco-SSL Risk Model Parameters. 

Parameter Value selected for use in

calculation of Eco-SSL

Conservatism of

Value Rationale

Toxicity Reference Value

(TRV)

the geometric mean of

NOAELs for REP or GRO

or the highest bounded

NOAEL below the lowest

bounded LOAEL for REP,

GRO or MOR

moderate Represents highest dose that did not cause

any adverse effects in any test species

Food Ingestion Rate (FIR) 90th percentile high

Conservatively-skewed value selected to

represent majority of individuals within

population without being over protective. 

Soil Ingestion as Fraction of

Diet (Ps) 90th percentile high

Conservatively-skewed value selected to

represent majority of individuals within

population without being over protective. 

Soil to Biota

Bioaccumulation Factor median

neither conservative

nor anticonservative

Lowest uncertainty - not expected to overpredict concentrations

Diet to Biota

Bioaccumulation Factor median

neither conservative

nor anticonservative

Lowest uncertainty - not expected to overpredict concentrations

log-linear regression

equations for

bioaccumulation

value predicted by

regression equation

neither conservative

nor anticonservative

Lowest uncertainty - not expected to overpredict concentrations

Diet composition (Pi

)

100% plant, small mammal,

or soil invertebrate

(depending on trophic

group)

high

Maximum pathway-specific exposure to

allow screening of risks by pathway.

Consistent with ERAGs (U.S. EPA 1997)

Area Use Factor (AUF) 100% high Consistent with ERAGs (U.S. EPA 1997)

Bioavailability (AFij, AFsj)

Equivalent to that for the

chemical form used to

develop the TRV

high Consistent with ERAGs (U.S. EPA 1997)
Guidance for Developing Eco-SSLs 5 - 1 November 2003

5.0 ECO-SSL DOCUMENTS

Presented in this chapter is a description of the format and content of the Eco-SSL documents

which report the results of the Eco-SSL derivation procedures as described in the previous

chapters. Each document is structured with the following standard sections. However, the

structure is not followed for iron and aluminum where the documents specifically review the

chemistry, bioavailability, and toxicity in soils instead of providing numeric screening levels. 

EPA is continuing to evaluate existing toxicity studies (primarily for birds and mammals) and

intends to issue additional Eco-SSL documents in the future. These documents will be posted on

the afore mentioned website as they become available. The following subsections describe the

basic components of each of the documents.

Introduction

The introduction to each contaminant summary provides a brief review of the contaminant

including environmental forms, sources, background concentrations, mechanisms of toxicity, and

essential elemental status if applicable. These summaries are intended to provide the reader with

a general introduction only and do not represent an exhaustive review of the environmental fate

and effects of the contaminant. It is recommended that the user of the Eco-SSL values review

site-specific data concerning contaminant sources and possible fate and transport processes to

evaluate as necessary the site-specific properties of the contaminant in soils. The general

introductory material is based on a review of literature obtained during the search process for

plants and soil invertebrates and wildlife. Some review material is based on information

available from the Hazardous Substances Databank (HSDB), a database of the National Library

of Medicine's TOXNET system (http://toxnet.nlm.nih.gov). 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 management activities. As part of the Eco-SSL

project, available data for the background concentrations of metals are summarized in Table 2.3

(see Chapter 2) and Attachment 1-4.

Summary Table

This subsection of each document contains a summary table of the Eco-SSL values calculated

for each receptor group expressed as mg contaminant per kg dw soil. If an Eco-SSL could not

be calculated for a receptor group then “NC” is noted for not calculated. In some cases, the EcoSSL is pending further review of toxicity information. In these cases, “pending” is noted. The

Eco-SSL values are rounded to two significant digits.

Eco-SSL for Plants

This subsection describes the results of the derivation of an Eco-SSL value for terrestrial plants

completed according to the procedures describe in Chapter 3 and appropriate Attachments.
Guidance for Developing Eco-SSLs 5 - 2 November 2003

Eco-SSL for Soil Invertebrates

This subsection describes the results of the derivation of an Eco-SSL value for soil invertebrates

completed according to the procedures described in Chapter 3 and Attachments. 

Eco-SSL for Avian Wildlife

This subsection describes the results of the derivation of an Eco-SSL value for avian wildlife. 

The subsection provides only the data used to derive the Eco-SSL according to the procedures

described in Chapter 4 and Attachments. The results are provided as two parts.

• Toxicity Reference Value

• Estimation of Dose and Calculation of the Eco-SSL

Eco-SSL for Mammalian Wildlife

This subsection describes the results of the derivation of an Eco-SSL value for mammalian

wildlife. The subsection provides only the data used to derive the Eco-SSL according to the

procedures described in Chapter 4 and Attachments. The results are provided as two parts.

• Toxicity Reference Value

• Estimation of Dose and Calculation of the Eco-SSL.

References

The references are provided as the last subsection of each document. The references are

segregated by receptor with separate lists for plants and soil invertebrates and wildlife. The

references for each receptor are further segregated into two lists. The first list provides the

papers used to derive the Eco-SSL and the second list provides the papers that were rejected for

use. For each of the papers rejected the reason for the rejection is listed. 

Appendices

A complete tabulation of the toxicity data used to derive the wildlife toxicity reference values

(TRVs) is provided as an appendix.
Guidance for Developing Eco-SSLs 6 - 1 November 2003

6.0 REFERENCES

Abou-Donia, M.B. and D. G. Graham. 1978. Delayed neurotoxicity from long term low-level

topical administration of Leptophos to the comb of hens. Toxicology and Applied

Pharmacology. 46: 199-213.

Alexander, M. 1995. How toxic are toxic chemicals in soil? Environ. Sci. Technol. 29: 2713-

2717.

Allen, H., S. McGrath, M. McLaughlin, W. Peijnenburg, and S. Suave. 1999. Bioavailability of

Metals in Terrestrial Ecosystems. Draft Document.

Anderson, W.C., R. C. Loehr, and B. P. Smith. 1999. Environmental Availability of

Chlorinated Organics, Explosives, and Heavy Metals in Soils. American Academy of

Environmental Engineers.

Anderson, M. B., Lepak, K., Farinas, V., and George, W. J. 1993. Protective action of zinc

against cobalt-induced testicular damage inthe mouse. Reprod. Toxicol. 7(1): 49-54. 

Anderson, M. B., Pedigo, N. G., Katz, R. P., and George, W. J. 1992. Histopathology of testes

from mice chronically treated with cobalt. Reprod. Toxicol. 6(1): 41-50. 

Barber, S.A. 1995. Nutrient Bioavailability - A Mechanistic Approach. John Wiley and Sons.

Barnhart, J. 1997. Chromium Chemistry and Implications for Environmental Fate and Toxicity. 

1997. Journal of Soil Contamination. 6(6): 561-568.

Betchel-Jacobs. 1998. Empirical Models for the Uptake of Inorganic Chemicals from Soil by

Plants. Oak Ridge National Laboratory BJC/OR-133. Bechtel Jacobs Company L.L.C.

Beyer, W. N., O. H. Pattee, L. Siteo, D. J. Hoffman, and B. M. Mulhern. 1985. Metal

contamination in wildlife living near two zinc smelters. Environ. Poll. Series A. 38(1):

63-86.

Bohn, H., B. McNeal, and G. O’Connor. 1985. Soil Chemistry. 2nd ed. John Wiley and Sons.

Bourg, W. J., Nation, J. R., and Clark, D. E. 1985. The effects of chronic cobalt exposure on

passive-avoidance performance in the adult rat. Bull. Psychon. Soc. 3(6): 527-530.

Canadian Council of Ministers of the Environment (CCME). 1997. Recommended Canadian

Soil Quality Guidelines. March 1997.

Chetty, K. N., Subba, R. A. O. D SV, Drummond, L., and Desaiah, D. 1979. Cobalt-induced

changes in immune response and atpase activities in rats. J Environ. Sci. Health Part B
Guidance for Developing Eco-SSLs 6 - 2 November 2003

Pestic. Food. Contam. Agric. Wastes. 14(5): 525-544. 

Corrier, D. E., Mollenhauer, H. H., Clark, D. E., Hare, M. F., and Elissalde, M. H. 1985. 

Testicular degeneration and necrosis induced by dietary cobalt. Vet Pathol. 22(6): 

610-6. 

Cullen, A.C. and H.C. Frey. 1999. Probabilistic Techniques in Exposure Assessment. A

Handbook for Dealing with Variability and Uncertainty in Models and Inputs. New

York: Plenum Press.

Derr, R. F., Aaker, H., Alexander, C. S., and Nagasawa, H. T. 1970. Synergism between cobalt

and ethanol on rat growth rate. J. Nutr. 100(5): 521-524.

Domingo, J. L., Paternain, J. L., Llobet, J. M., and Corbella, J. 1985. Effects of cobalt on

postnatal development and late gestation in rats upon oral administration. Rev Esp Fisiol. 

Driver, C.J., M. W. Ligotke, P. V. Van Voris, B. D. McVeety, and B. J. Greenspan. 1991. 

Routes of uptake and their relative contribution to the toxicologic response of northern

bobwhite (Colinus virginianus) to an organophosphate pesticide. Environ. Tox. and

Chem. 10: 21-33. 

Efroymson, R. A., M. E. Will, and G. W. Suter II. 1997a. Toxicological Benchmarks for

Contaminants of Potential Concern for Effects on Soil And Litter Invertebrates and

Heterotrophic Process: 1997 Revision. ES/ER/TM-126/R2. Prepared for the U.S.

Department of Energy, Office of Environmental Management by Lockheed Martin

Energy Systems, Inc. managing the activities at the Oak Ridge National Laboratory

(ORNL).

Efroymson, R.A., M.E. Will, G. W. Suter II and A.C. Wooten. 1997b. Toxicological

Benchmarks for Screening Contaminants of Potential Concern for Effects on Terrestrial

Plants: 1997 Revision. ES/ER/TM-85/R3. Prepared for the U.S. Department of Energy,

Office of Environmental Management by Lockheed Martin Energy Systems, Inc. 

managing the activities at the Oak Ridge National Laboratory (ORNL).

Gershbein, L. L., Gershbein, J. D., and French, R. 1983. Behavior of male rats fed low levels of

metallic salts. Res Commun Chem Pathol Pharmacol. 39(3):507-510.

Gertsl, Z. 1990. Estimation of Organic Chemical Sorption by Soils. Journal of Contaminant

Hydrology. 6: 357-375.

Haga, Y., Clyne, N., Hatroi, N., Hoffman-Bang, C., Pehrsson, S. K., and Ryden, L. 1996. 

Impaired myocardial function following chronic cobalt exposure in an isolated rat heart

model. Trace Elements and Electrolytes. 13(2): 69-74. 
Guidance for Developing Eco-SSLs 6 - 3 November 2003

 Hansen, B.G., A. B. Paya-Perez,, M. Rahman, and B. R. Larsen. 1999. QSARs for Kow and

Koc of PCB congeners: a critical examination of data, assumptions, and statistical

approaches. Chemosphere 39: 2209-2228.

Henderson, J. D., J. T. Yamamoto, D.M. Fry, J. N. Seiber, and B. W. Wilson. 1994. Oral and

dermal toxicity of organophosphate pesticides in the domestic pigeon (Columba livia). 

Bulletin of Environ. Contam. Toxicol. 52: 633-640.

Huck, D. W. and Clawson, A. J. 1976. Excess dietary cobalt in pigs. J Anim Sci. 43(6): 

1231-1246.

James, B.R., J.C. Petura, R.J. Vitale, and G.R. Mussoline. 1997. Oxidation-Reduction

Chemistry of Chromium: Relevance to the Regulation and Remediation of ChromateContaminated Soils. Journal of Soil Contamination. 6(6): 569-580.

Jones, D.S., G. W. Suter, and R. N. Hull. 1997. Toxicological Benchmarks for Screening

Contaminants of Potential Concern for Effects on Sediment-Associated Biota: 1997

Revision. ES/ER/TM-95/R4. Prepared for the U.S. Department of Energy, Office of

Environmental Management by Lockheed Martin Energy Systems, Inc. managing the

activities at the Oak Ridge National Laboratory (ORNL).

Kuperman, R. G., Checkai, R. T., Phillips, C. T., and Simini, M. 2002. Toxicity Assessments of

Antimony, Barium, Beryllium, and Manganese for Development of Ecological Soil

Screening Levels (Eco-SSL) Using Enchytraeid Reproduction Benchmark Values.

Rep.Doc.#ECBC-TR-XXX (Draft) , 81 p.

Lagrega, M. 1994. Hazardous Waste Management. McGraw-Hill, Inc.

Lee, L. S., P. S. C. Rao, P. Nkedi-Kizza, and J. J. Delfino. 1990. Influence of solvent and

sorbent characteristics on distribution of pentachlorophenol in octanol-water and soilwater systems. Environ. Sci. Tech. 24: 654-661.

Lindsay, W. L. 1979. Chemical Equilibria in Soils. John Wiley & Sons, New York.

Linz, D. G. and D. V. Nakles. 1997. Environmentally Acceptable Endpoints in Soil: Risk-Based

Approach to Contaminated Site Management Based on Availability of Chemicals in Soil. 

 American Academy of Environmental Engineers. Annapolis, MD.

Loehr, R. C. and M. T. Webster. 1996. Effect of Treatment on Contaminant Availability,

Mobility and Toxicity. Chapter 2. In: Environmentally Acceptable Endpoints in Soils. 

D. Linz, and D. Nakles (eds.), American Academy of Environmental Engineers. 

Annapolis, MD.
Guidance for Developing Eco-SSLs 6 - 4 November 2003

Lyman, W. J., W. F. Reehl, and D. H. Rosenblatt. 1990. Handbook of Chemical Property

Estimation Methods. McGraw-Hill, New York.

McNab, B. K. 1963. Bioenergetics and the determination of home range size. Am. Nat.

97:133-140.

Maro, J. K., Kategile, J. A., and Hvidsten, H. 1980. Studies on copper and cobalt in dairy

calves. British Journal of Nutrition. 44(1): 25-31. 

Miller, R.W. and D. T. Gardiner. 1998. Soil in Our Environment, 8th edition, Prentice Hall,

Upper Saddle River, NJ.

Mohiuddin, S. M., Taskar, P. K., Rheault, M., Roy, P. E., Chenard, J., and Morin, Y. 1970. 

Experimental cobalt cardiomyopathy. Am Heart J. 80(4): 532-543. 

Mollenhauer, H. H., Corrier, D. E., Clark, D. E., Hare, M. F., and Elissalde, M. H. 1985. Effects

of dietary cobalt on testicular structure Virchow Arch [Cell Pathol]. 49(3): 241-248.

Nagy, K. A., I. A. Girard, and T. K. Brown. 1999. Energetics of free-ranging mammals, reptiles,

and birds. Ann. Rev. Nutr. 19: 247-277.

Nation, J. R., Bourgeois, A. E., Clark, D. E., and Hare, M. F. 1983. The effects of chronic

cobalt exposure on behavior and metallothionein levels in the adult rat. Neurobehav

Toxicol Teratol. 5(1): 9-15. 

Paternain, J. L., Domingo, J. L., and Corbella, J. 1988. Developmental toxicity of cobalt in the

rat. J. Toxicol. Environm. Health. 24(2):193-200.

Pedigo, N. G. and Vernon, M. W. 1993. Embryonic losses after 10-week administration of

cobalt to male mice. Reprod. Toxicol. 7: 111-116.

Pedigo, N. G., George, W. J., and Anderson, M. B. 1988. Effects of acute and chronic exposure

to cobalt on male reproduction in mice. Reprod Toxicol . 2(1): 45-53. 

Pignatello, J. J. 2000. The measurement and interpretation of sorption and desorption rates for

organic compounds in soil media. In: Advances in Agronomy, Vol. 69, Academic Press,

San Diego, CA.

Phillips, C. T., Checkai, R. T., Kuperman, R. G., and Simini, M. 2002. Toxicity Assessment of

Antimony, Barium, Beryllium, and Manganese for Development of Ecological Soil

Screening Levels (Eco-SSL) Using Folsomia Reproduction Benchmark Values.

Rep.Doc.#ECBC-TR-XXX (Draft) , 98 p.
Guidance for Developing Eco-SSLs 6 - 5 November 2003

Rijksinstituut Voor Volksgezondheid en Milieu (RIVM). 1997a. National Institute of Public

Health and Environment. Maximum Permissible Concentrations and Negligible

Concentrations for Pesticides. T. Crommentuijn, D.F. Kalf, M.D. Polder, R. Posthumus

and E.J. van de Plassche. October, 1997.

Rijksinstituut Voor Volksgezondheid en Milieu (RIVM). 1997b National Institute of Public

Health and Environment. Maximum Permissible Concentrations and Negligible

Concentrations for Metals. T. Crommentuijn, D.F. Kalf, M.D. Polder, R. Posthumus and

E.J. van de Plassche. 

Sample, B. E., J. J. Beauchamp, R. Efroymson, and G. W. Suter, II. 1999. Literature-derived

bioaccumulation models for earthworms: development and validation. Environmental

Toxicology and Chemistry. 18: 2110-2120.

Sample, B. E., D. M. Opresko, and G. W. Suter II. 1996. Toxicological Benchmarks for

Wildlife: 1996 Revision. ES/ER/TM-86/R3. Prepared for the U.S. Department of

Energy, Office of Environmental Management by Lockheed Martin Energy Systems, Inc. 

managing the activities at the Oak Ridge National Laboratory (ORNL).

Sample, B. E, J. J. Beauchamp, R. A. Efroymson, and G. W. Suter, II. 1998a. Development and

Validation of Bioaccumulation Models for Small Mammals. Oak Ridge National

Laboratory ES/ER/TM-219. Lockheed Martin Energy Systems Environmental

Restoration Program.

Sample, B. E, J. J. Beauchamp, R. A. Efroymson, G. W. Suter, II and T.L. Ashwood. 1998b. 

Development and Validation of Bioaccumulation Models for Earthworms. Oak Ridge

National Laboratory ES/ER/TM-220. Lockheed Martin Energy Systems Environmental

Restoration Program.

Schwarzenbach, R. P., P.M. Gschwend, and D. M. Imboden. 1993. Environmental Organic

Chemistry. In: Methods of Soil Analysis, Part 2, Microbiological and Biochemical

Properties, 2nd Edition.. R. W. Weaver et al (eds.), Agronomy Monograph 9,

Agronomy Science of America. Soil Science Society of America. Wiley-Interscience. 

Madison, WI. p. 63. 

Seidenberg, J. M., Anderson, D. G., and Becker, R. A. 1986. Validation of an in vivo

developmental toxicity screen in the mouse. Teratog Carcinog Mutagen. 6:361-374. 

Sumner, M.E. (ed). 2000. Handbook of Soil Science. CRC Press, Boca, Raton. FL.

Syracuse Research Corp. (SRC). Physical Properties Database. http://esc.syrres.com/interkow

/PhysProp.htm
Guidance for Developing Eco-SSLs 6 - 6 November 2003

United States Environmental Protection Agency (U.S. EPA). 2003. ECOTOX

(ECOTOXicology) database http://www.epa.gov/ecotox/.

United States Environmental Protection Agency (U.S. EPA). 2002. Role of Background in the

CERCLA Cleanup Program. U.S. EPA Office of Solid Waste and Emergency Response,

Office of Emergency and Remedial Response, April 26, 2002. OSWER 9285.6-07P.

United States Environmental Protection Agency (U.S. EPA). 1999. Ecological Risk Assessment

and Risk Management Principles for Superfund Sites. Office of Emergency and

Remedial Response, Washington, DC. OSWER Directive 9285.7-28.P.

United States Environmental Protection Agency (U.S. EPA). 1998. Guidelines for Ecological

Risk Assessment. Risk Assessment Forum. U.S. Environmental Protection Agency,

Washington DC. EPA/630/R-95/002F. April. May 14, 1998 Federal Register 63(93):

26846-26924. 

United States Environmental Protection Agency (U.S. EPA). 1997. Ecological Risk Assessment

Guidance for Superfund: Process for Designing and Conducting Ecological Risk

Assessments. Interim Final. U.S. Environmental Protection Agency, Environmental

Response Team (Edison, NJ). June 5, 1997.

United States Environmental Protection Agency (U.S. EPA). 1996. Soil Screening Guidance:

Technical Background Document. Office of Emergency and Remedial Response,

Washington, D.C., EPA/540/R-95/128. May. 

United States Environmental Protection Agency (U.S. EPA). 1995. Internal Report on

Summary of Measured, Calculated, and Recommended Log Kow Values. U.S.

Environmental Protection Agency, Office of Water, Washington, D.C. 38 pp.

United States Environmental Protection Agency (U.S. EPA). 1993. Wildlife Exposure Factors

Handbook, Volumes 1 and II. U.S. Environmental Protection Agency. 

EPA/600/R-93/187a and b.

Van Vleet, J. F., Boon, G. D., and Ferrans, V. J. 1981. Induction of lesions of selenium-vitamin

E deficiency in weanling swine fed silver, cobalt, tellurium, zinc, cadmium, and

vanadium. Am. J. Vet. Res. 42(5): 789-799. 

Verschueren, K. 1996. Handbook of Environmental Data on Organic Chemicals. 3rd ed. Van

Nostrand Reinhold, New York, NY, USA.

Weissmahr, K.W., S.B. Haderlein, and R.P. Schwarzenbach. 1998. Complex formation of soil

minerals with nitroaromatic explosives and other pi-acceptors. Soil Science Society of

America Journal. 62: 369-378.
Guidance for Developing Eco-SSLs 6 - 7 November 2003

Weissmahr, K.W., M. Hildenbrand, R. P. Schwarzenbach, and S. B.Haderlein. 1999. 

Laboratory and Field Scale Evaluation of Geochemical Controls on Groundwater

Transport of Nitroaromatic Ammunition Residues. Environ. Sci. Technol. 33 (15): 2593

–2600.

Witschi, H.R., and J.A. Last. 1996. Toxic responses of the respiratory system. In: Casarett and

Doull’s Toxicology, 5th Edition, pp 443-462. C.D. Klaassen (ed.), McGraw-Hill, New

York.