vvEPA
United States
Environmental Protection
Agency
Solid Waste and Emergency
Response
(5305W)
EPA530-D-99-001A
August 1999
www.epa.gov/osw
Screening Level
Ecological Risk
Assessment Protocol for
Hazardous Waste
Combustion
Volume One
Peer Review Draft
Printed on paper that contains at least 20 percent postconsumer fiber
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EPA530-D-99-001A
August 1999
Screening Level Ecological Risk Assessment
Protocol for Hazardous Waste Combustion
Facilities
Volume One
U.S. EPA, OFFICE OF SOLID WASTE
U.S. ENVIRONMENTAL PROTECTION AGENCY
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DISCLAIMER
This document provides guidance to U.S. EPA Regions and States on how best to implement RCRA and
U.S. EPA's regulations to facilitate permitting decisions for hazardous waste combustion facilities. It also
provides guidance to the public and to the regulated community on how U.S. EPA intends to exercise its
discretion in implementing its regulations. The document does not substitute for U.S. EPA's regulations,
nor is it a regulation itself. Thus, it cannot impose legally-binding requirements on U.S. EPA, States, or
the regulated community. It may not apply to a particular situation based upon the circumstances. U.S.
EPA may change this guidance in the future, as appropriate.
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ACKNOWLEDGMENTS
Jeff Yurk (U.S. EPA Region 6), the primary author/editor of this document, would like to acknowledge that
the development of this document could not have been accomplished without the support, input, and work
of a multitude of U.S. EPA and support contractor personnel. The foundation for the combustion-related
guidance and methodologies outlined in this document were first developed by the Office of Research and
Development (ORD) and the Office of Solid Waste (OSW) in previous versions of combustion risk
assessment guidance. The State of North Carolinas' combustion risk assessment methodology was also
evaluated in preparation of this document. The foundation for the ecological risk-related procedures and
methodologies outlined in this document were based on previous guidance developed by the Office of
Research and Development (ORD) and EPA's Superfund program. This version of the protocol was
originally initiated in response to the desire of the Region 6 Multimedia Planning and Permitting Division to
implement an up-to-date and technically sound hazardous waste combustion permitting program. The
decision to incorporate guidance on a full range of national combustion risk assessment issues into the
document was encouraged and supported by the Director of the Office of Solid Waste.
The development of this document was significantly enhanced by a number of capable organizations and
personnel within U.S. EPA. Karen Pollard, Stephen Kroner and David Cozzie of the Economic Methods
and Risk Analysis Division in conjunction with Rosemary Workman of the Permits and State Programs
Division, Fred Chanania of the Hazardous Waste Minimization and Management Division, and Karen
Kraus of the Office of General Council provided overall policy, technical and legal comment on this
document. Anne Sergeant, Randy Bruins, David Reisman, Glenn Rice, Eletha Brady Roberts and
Matthew Lorber of the National Center for Environmental Assessment (NCEA), Office of Research and
Development, John Nichols of the National Health and Environmental Effects Research Laboratory, Vince
Nabholtz of the Office of Prevention, Pesticides and Toxic Substances, and Dorothy Canter, Science
Advisor to the Assistant Administrator for the Office of Solid Waste and Emergency Response, provided
key input on breaking scientific developments in the areas of ecological risk assessment, mercury
speciation, the dioxin reassessment, endocrine disrupters, toxicity factors, sulfur and brominated dioxin
analogs, as well as technical comment on the overall methodologies presented in the document.
Contributions by Larry Johnson of the National Exposure Research Laboratory of ORD and Jeff Ryan and
Paul Lemieux of the National Risk Management Research Laboratory of ORD were significant in
providing methodologies for conducting TO analysis and defining appropriate detection limits to be used in
the risk assessment. Donna Schwede of the National Exposure Research Laboratory of ORD and Jawad
Touma of the Office of Air Quality Planning and Standards provided technical review comments to
strengthen the air modeling section of the document. Review and comment on the soil and water fate and
transport models was provided by Robert Ambrose of EPA's Environmental Research Laboratory in
Athens, GA.
All U.S. EPA Regional Offices contributed valuable comments which have significantly improved the
usability of this document. In particular, staff from Region 4 aided in making sure guidance for conducting
trial burns was consistent with this document, and staff from Region 8 provided significant input on the
overall approach. The authors would be remiss if they did not acknowledge significant contributions from
the Texas Natural Resource and Conservation Commission through both comments and discussions of real-
world applications of risk assessment methodologies. Additionally, useful comments were received from
the State of Utah. The Region 6 Superfund Division is to be commended for its valuable review of the
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early document. Region 6 apologizes and bears full responsibility for any mistakes made in the
incorporation of comments and input from all reviewers into the document.
Finally, this work could not have been completed without the tireless efforts of support contractor
personnel. Tetra Tech EM Inc. (Tetra Tech), performed the bulk of the background research. The Air
Group, under subcontract to Tetra Tech, helped develop the chapter on air dispersion modeling. Also,
PGM, under subcontract to Tetra Tech, helped validate fate and transport models utilized in the document
as well as provide recommendations on the overall quality assurance/quality control of the document. The
work of these contractors was performed under the technical direction of staff from the Region 6 Center for
Combustion Science and Engineering, as well as key Agency project and contracting officers.
Region 6 looks forward to the insight and input yet to be provided by the public and other interested parties
during the full external peer review of the document.
in
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REVIEWERS
Preliminary drafts of this ecological risk assessment document, as well as its companion human health risk
assessment document, have received extensive internal Agency and State review. The following is a list of
reviewers who have commented on these documents prior to their release as a peer review draft.
Environmental Protection Agency Reviewers:
Office of Solid Waste
David Cozzie
Virginia Colten-Bradley
Becky Daiss
Steve Kroner
Dave Layland
Alec McBride
Karen Pollard
Rosemary Workman
Val De LaFuente
Bill Schoenborn
Fred Chanania
Economics, Methods, and Risk Analysis Division
Economics, Methods, and Risk Analysis Division
Economics, Methods, and Risk Analysis Division
Economics, Methods, and Risk Analysis Division
Economics, Methods, and Risk Analysis Division
Economics, Methods, and Risk Analysis Division
Economics, Methods, and Risk Analysis Division
Permits and State Programs Division
Permits and State Programs Division
Municipal and Industrial Solid Waste Division
Hazardous Waste Minimization and Management Division
Office of Solid Waste and Emergency Response
Dorothy Canter Office of the Assistant Administrator
Office of Research & Development
Eletha Brady-Roberts
Randy Bruins
David Reisman
Glenn Rice
Sue Schock
Jeff Swartout
David Cleverly
Jim Cogliano
Matthew Lorber
Anne Sergeant
Judy Strickland
Robert Ambrose
Larry Johnson
Donna Schwede
Paul Lemieux
Jeffrey Ryan
John Nichols
National Center for Environmental Assessment/Cincinnati
National Center for Environmental Assessment/Cincinnati
National Center for Environmental Assessment/Cincinnati
National Center for Environmental Assessment/Cincinnati
National Center for Environmental Assessment/Cincinnati
National Center for Environmental Assessment/Cincinnati
National Center for Environmental Assessment/DC
National Center for Environmental Assessment/ DC
National Center for Environmental Assessments/DC
National Center for Environmental Assessment/ DC
National Center for Environmental Assessments/RTP
National Exposure Research Laboratory
National Exposure Research Laboratory
National Exposure Research Laboratory
National Risk Management Research Laboratory
National Risk Management Research Laboratory
National Health and Environmental Effects Research
Laboratory/RTP
Office of Air Quality Planning and Standards
Joe Touma Air Quality Monitoring Group
IV
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Environmental Protection Agency Reviewers (cont):
Office of Pollution, Prevention and Toxics
Vince Nabholtz Risk Assessment Division
Office of General Council
Karen Kraus
Region 1
Jui-Yu Hsieh
Region 2
John Brogard
Region 3
Gary Gross
Region 4
Beth Antley
Rick Gillam
Region 5
Mario Mangino
Gary Victorine
Region 6
Ghassan Khoury
Jon Rauscher
Susan Roddy
JeffYurk
Region 7
John Smith
Region 8
Carl Daly
Tala Henry
Region 9
Mary Blevins
Stacy Braye
Patrick Wilson
Solid Waste and Emergency Response Law Office
Office of Ecosystem Protection Division
Division of Environmental Planning and Protection
Waste and Chemicals Management Division
Waste Management Division
Waste Management Division
Waste, Pesticide and Toxic Division
Waste, Pesticide and Toxic Division
Superfund Group
Superfund Group
Superfund Group
Multimedia Planning and Permitting Division
Air, RCRA and Toxics Division
Hazardous Waste Program
Hazardous Waste Program
Waste Management Division
Waste Management Division
Waste Management Division
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Region 10
Marcia Bailey Office of Environmental Assessment
Roseanne Lorenzana Office of Environmental Assessment
Catherine Massimino Office of Waste and Chemicals
State Reviewers
Texas Natural Resource Conservation Commission
Larry Champagne Toxicology and Risk Assessment Section
Lucy Frasier Toxicology and Risk Assessment Section
Loren Lund Toxicology and Risk Assessment Section
Arkansas Department of Pollution Control and Ecology
Tammi Hynum Hazardous Waste Division
Phillip Murphy Hazardous waste Division
Colorado Department of Health
Joe Schieffelin Hazardous Materials and Waste Management Division
R. David Waltz Hazardous Materials and Waste Management Division
Utah Department of Environmental Quality
Christopher Bittner Division of Solid and Hazardous Waste
Alabama Department of Environmental Management
Nathan Hartman Air Division
Brian Hughes Division of Epidemiology
John Rogers Air Division
VI
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Screening Level Ecological Risk Assessment Protocol
Contents August 1999
TABLE OF CONTENTS
Chapter Page
1 INTRODUCTION 1-1
1.1 OBJECTIVE AND PURPOSE 1-7
1.2 RELATED TRIAL BURN ISSUES 1-12
1.3 REFERENCE DOCUMENTS 1-13
2 FACILITY CHARACTERIZATION 2-1
2.1 COMPILING BASIC FACILITY DATA 2-1
2.2 IDENTIFYING EMISSION SOURCES 2-2
2.2.1 Estimating Stack Emission Rates for Existing
Facilities 2-3
2.2.1.1 Estimates from Trial Burns 2-4
2.2.1.2 Normal Operation Emission
Rate Data 2-6
2.2.1.3 Estimates of the Total Organic Emission (TOE) Rate 2-8
2.2.2 Estimating Stack Emission Rates for Facilities with
Multiple Stacks 2-12
2.2.3 Estimating Stack Emission Rates for Facilities Not
Yet Operational 2-13
2.2.4 Estimating Stack Emission Rates for Facilities
Previously Operated 2-13
2.2.5 Emission from Process Upsets 2-14
2.2.6 RCRA Fugitive Emissions 2-16
2.2.6.1 Quantitative Estimation of RCRA Fugitive Emissions from Process
Upsets 2-17
2.2.6.2 Fugitive Emissions from Combustion Unit Leaks 2-27
2.2.7 RCRA Fugitive Ash Emissions 2-28
2.2.7.1 Quantitative Estimation of RCRA Fugitive Ash
Emissions 2-28
2.2.8 Cement Kiln Dust (CKD) Fugitive Emissions 2-29
2.2.8.1 Composition and Characteristics of CKD 2-30
2.2.8.2 Estimation of CKD Fugitive Emissions 2-31
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering vii
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Screening Level Ecological Risk Assessment Protocol
Contents
August 1999
Chapter
TABLE OF CONTENTS (Continued)
Page
2.3
IDENTIFYING COMPOUNDS OF POTENTIAL CONCERN 2-32
2.3.1 Polychlorinated Dibenzo(p)dioxins and
Dibenzofurans 2-38
2.3.1.1 Toxicity Equivalency Factors for PCDDs and PCDFs 2-40
2.3.1.2 Exposure Assessment for Community Measurement
Receptors 2-43
2.3.1.3 Exposure Assessment for Class-specific Guild Measurement
Receptors 2-45
2.3.1.4 Bioaccumulation Equivalency Factors 2-46
2.3.1.5 Flourine, Bromine, and Sulfur PCDD/PCDF Analogs 2-48
2.3.2 Polynuclear Aromatic Hydrocarbons 2-49
2.3.2.1 Exposure Assessment for PAHs 2-50
2.3.3 Polychlorinated Biphenyls 2-50
2.3.3.1 Exposure Assessment for PCBs 2-52
2.3.4 Nitroaromatics 2-54
2.3.5 Phthalates 2-55
2.3.6 Hexachlorobenzene and Pentachlorophenol 2-56
2.3.7 Metals 2-57
2.3.7.1 Chromium 2-58
2.3.7.2 Mercury 2-59
2.3.8 Particulate Matter 2-67
2.3.9 Hydrogen Chloride/Chlorine Gas 2-68
2.3.10 Endocrine Disrupters 2-68
2.3.11 Radionuclides 2-69
2.4 ESTIMATING COPC CONCENTRATIONS FOR NON-DETECTS 2-71
2.4.1 Definitions of Commonly Reported Detection Limits 2-71
2.4.2 Use in the Risk Assessment of Data Reported as
Non-Detect 2-74
2.4.3 Statistical Distribution Techniques 2-76
2.4.4 U.S. EPA OSW Recommendations on Quantifying
Non-Detects 2-76
2.4.5 Estimated Maximum Possible Concentration (EMPC) 2-77
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
Vlll
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Screening Level Ecological Risk Assessment Protocol
Contents August 1999
TABLE OF CONTENTS (Continued)
Chapter Page
2.5 CONCENTRATIONS DETECTED IN BLANKS 2-78
3 AIR DISPERSION AND DEPOSITION MODELING 3-1
3.1 DEVELOPMENT OF AIR MODELS 3-3
3.1.1 History of Risk Assessment Air Dispersion Models 3-3
3.1.2 Preprocessing Programs 3-5
3.1.3 Expert Interface (Exlnter Version 1.0) 3-6
3.2 SITE-SPECIFIC INFORMATION REQUIRED TO SUPPORT AIR
MODELING 3-7
3.2.1 Surrounding Terrain Information 3-8
3.2.2 Surrounding Land Use Information 3-9
3.2.2.1 Land Use for Dispersion Coefficients 3-9
3.2.2.2 Land Use for Surface Roughness Height (Length) 3-11
3.2.3 Information on Facility Building Characteristics 3-12
3.3 USE OF UNIT EMISSION RATE 3-15
3.4 PARTITIONING OF EMISSIONS 3-15
3.4.1 Vapor Phase Modeling 3-16
3.4.2 Particle Phase Modeling (Mass Weighting) 3-16
3.4.3 Particle-Bound Modeling (Surface Area Weighting) 3-21
3.5 METEOROLOGICAL DATA 3-22
3.5.1 Surface Data 3-25
3.5.
3.5.
3.5.
3.5.
3.5.
3.5.
3.5.
. 1 Wind Speed and Wind Direction 3-27
.2 Dry Bulb Temperature 3-27
.3 Opaque Cloud Cover 3-28
.4 Cloud Ceiling Height 3-28
.5 Surface Pressure 3-29
.6 Precipitation Amount and Type 3-29
.7 Solar Radiation (Future Use for Dry Vapor Deposition) 3-29
3.5.2 Upper Air Data 3-30
3.6 METEOROLOGICAL PREPROCESSORS AND INTERFACE
PROGRAMS 3-30
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering ix
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Screening Level Ecological Risk Assessment Protocol
Contents
August 1999
TABLE OF CONTENTS (Continued)
Chapter Page
3.6.1 PCRAMMET 3-30
3.6.
3.6.
3.6.
3.6.
3.6.
3.6.
3.6.
3.6.
. 1 Monin-Obukhov Length 3-32
.2 Anemometer Height 3-32
.3 Surface Roughness Height at Measurement Site 3-33
.4 Surface Roughness Height at Application Site 3-33
.5 Noon-Time Albedo 3-33
.6 Bowen Ratio 3-36
.7 Anthropogenic Heat Flux 3-36
.8 Fraction of Net Radiation Absorbed at the Ground 3-36
3.6.2
MPRM 3-40
3.7 ISCST3 MODEL INPUT FILES 3-40
3.7.1 COntrol Pathway 3-42
3.7.2 SOurce Pathway 3-46
3.7.2.1 Source Location 3-47
3.7.2.2 Source Parameters 3-48
3.7.2.3 Building Parameters 3-48
3.7.2.4 Particle Size Distribution 3-49
3.7.2.5 Particle Density 3-50
3.7.2.6 Scavenging Coefficients 3-50
3.7.3 REceptor Pathway 3-52
3.7.4 MEteorological Pathway 3-54
3.7.5 Terrain Grid (TG) Pathway 3-55
3.7.6 OUtput Pathway 3-56
3.8 ISCST3 MODEL EXECUTION 3-57
3.9 USE OF MODELED OUTPUT 3-58
3.9.1 Unit Rate Output vs. COPC-Specific Output 3-58
3.9.1.1 Determination of the COPC-Specific Emission Rate (0 .... 3-60
3.9.1.2 Converting Unit Output to COPC-Specific Output 3-60
3.9.2 Output from the ISCST3 Model 3-61
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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Screening Level Ecological Risk Assessment Protocol
Contents August 1999
TABLE OF CONTENTS (Continued)
Chapter Page
3.9.3 Use of Model Output in Estimating Media Equations 3-62
3.9.3.1 Vapor Phase COPCs 3-62
3.9.3.2 Particle Phase COPCs 3-63
3.9.3.3 Particle-Bound COPCs 3-63
3.10 MODELING OF FUGITIVE EMISSIONS 3-63
3.11 ESTIMATION OF COPC CONCENTRATIONS IN MEDIA 3-68
3.11.1 Calculation of COPC Concentrations in Soil 3-69
3.11.1.1 Calculating Highest Average COPC Concentration in Soil ... 3-71
3.11.1.2 Calculating the COPC Soil Loss Constant (ks) 3-71
3.11.1.3 Deposition Term (Ds) 3-79
3.11.1.4 Site-Specific Parameters for Calculating Soil Concentration . . 3-80
3.11.2 Calculation of COPC Concentrations in Surface Water and Sediment.... 3-83
3.11.2.1 Total COPC Loading to a Water Body (LT) 3-85
3.11.2.2 Total Water Body COPC Concentration (Cwtot) 3-93
3.11.2.3 Total COPC Concentration in Water Column (Cwctot) 3-104
3.11.3 Calculation of COPC Concentrations in Plants 3-107
3.11.3.1 Plant Concentration Due to Direct Deposition (Pd) 3-109
3.11.3.2 Plant Concentration Due to Air-to-Plant Transfer (Pv) 3-110
3.11.3.3 Plant Concentration Due to Root Uptake (Pr) 3-110
3.12 REPLACING DEFAULT PARAMETER VALUES 3-111
4 PROBLEM FORMULATION 4-1
4.1 EXPOSURE SETTING CHARACTERIZATION 4-1
4.1.1 Selection of Habitats 4-2
4.1.1.1 Selection of Exposure Scenario Locations Within
Terrestrial Habitats 4-4
4.1.1.2 Selection of Exposure Scenario Locations Within
Aquatic Habitats 4-7
4.1.1.3 Special Ecological Areas 4-9
4.1.2 Identification of Ecological Receptors 4-10
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering xi
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Screening Level Ecological Risk Assessment Protocol
Contents August 1999
TABLE OF CONTENTS (Continued)
Chapter Page
4.2 FOOD WEB DEVELOPMENT 4-11
4.2.1 Grouping Receptors into Feeding Guilds and
Communities 4-12
4.2.2 Organizing Food Web Structure by Trophic Level 4-12
4.2.3 Defining Dietary Relationships between Guilds and
Communities 4-13
4.2.4 Example Habitat-Specific Food Webs 4-14
4.3 SELECTING ASSESSMENT ENDPOINTS 4-22
4.4 SELECTING MEASUREMENT ENDPOINTS 4-27
4.4.1 Procedures for Identifying Measurement Endpoint
Receptors 4-28
4.4.2 Measurement Receptors for Guilds 4-28
4.4.3 Measurement Receptors for Example Food Webs 4-29
5 ANALYSIS 5-1
5.1 EXPOSURE ASSESSMENT 5-1
5.2 ASSESSING EXPOSURE TO COMMUNITY MEASUREMENT
RECEPTORS 5-2
5.3 ASSESSING EXPOSURE TO CLASS-SPECIFIC GUILD MEASUREMENT
RECEPTORS 5-3
5.3.1 Ingestion Rates for Measurement Receptors 5-5
5.3.2 COPC Concentrations in Food Items of Measurement
Receptors 5-11
5.3.2.1 COPC Concentrations in Invertebrates, Phytoplankton, and Rooted
Aquatic Plants 5-11
5.3.2.2 COPC Concentrations in Terrestrial Plants 5-13
5.3.2.3 COPC Concentrations in Fish 5-14
5.3.2.4 COPC Concentrations in Mammals, Birds, Amphibians, and
Reptiles 5-19
5.4 ASSESSMENT OF TOXICITY 5-24
5.4.1 General Guidance on Selection of Toxicity Reference
Values 5-25
5.4.1.1 Evaluation of Toxicity Test Data 5-26
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering xii
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Screening Level Ecological Risk Assessment Protocol
Contents August 1999
TABLE OF CONTENTS (Continued)
Chapter Page
5.4.1.2 Best Professional Judgement for Evaluating Toxicity
Values 5-27
5.4.1.3 Uncertainly Factors for Extrapolation from Toxicity Test
Values 5-29
6 RISK CHARACTERIZATION 6-1
6.1 RISK ESTIMATION 6-1
6.2 RISK DESCRIPTION 6-3
6.2.1 Magnitude and Nature of Ecological Risk 6-3
6.2.1.1 Target Levels 6-4
6.2.2 Fate and Exposure Assumptions 6-5
6.3 UNCERTAINTY AND LIMITATIONS OF THE RISK ASSESSMENT
PROCESS 6-6
6.3.1 Types of Uncertainty 6-7
6.3.1.1 Variable Uncertainty 6-7
6.3.1.2 Model Uncertainty 6-8
6.3.1.3 Decision-rule Uncertainty 6-9
6.3.2 Description of Qualitative Uncertainty 6-9
6.3.3 Description of Quantitative Uncertainty 6-10
6.3.4 Risk Assessment Uncertainty Discussion 6-11
6.3.5 Limitations and Uncertainties Specific to a Screening
Level Ecological Risk Assessment 6-13
6.3.5.1 Limitations Typical of a
Screening Level Ecological
Risk Assessment 6-13
6.3.5.2 Uncertainties Typical of a
Screening Level Ecological
Risk Assessment 6-14
REFERENCES R-l
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering xiii
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Screening Level Ecological Risk Assessment Protocol
Contents August 1999
APPENDICES
Appendix
A CHEMICAL SPECIFIC DATA
B ESTIMATING MEDIA CONCENTRATION EQUATIONS AND VARIABLE VALUES
C MEDIA-TO-RECEPTOR BIOCONCENTRATION FACTORS (BCFs)
D BIOCONCENTRATION FACTORS (BCFs) FOR WILDLIFE MEASUREMENT
RECEPTORS
E TOXICITY REFERENCE VALUES
F EQUATIONS FOR COMPUTING COPC CONCENTRATIONS AND COPC DOSE
INGESTED TERMS
G STATE NATURAL HERITAGE PROGRAMS
H TOXICOLOGICAL PROFILES
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering xiv
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Screening Level Ecological Risk Assessment Protocol
Contents August 1999
FIGURES
Figure Page
1-1 SCREENING LEVEL ECOLOGICAL RISK ASSESSMENT PROCESS 1-10
2-1 EXAMPLE FACILITY PLOT MAP 2-22
2-2 EXAMPLE EMISSIONS INVENTORY 2-23
2-3 COPC IDENTIFICATION 2-35
2-4 PHASE ALLOCATION AND SPECIATION OF MERCURY IN THE AIR 2-62
3-1 SOURCES OF METEOROLOGICAL DATA 3-24
3-2 EXAMPLE INPUT FILE FOR 'PARTICLE PHASE' 3-44
3-3 EXAMPLE PLOT FILE 3-65
3-4 COPC CONCENTRATION IN SOIL 3-70
3-5 COPC LOADING TO THE WATER BODY 3-84
3-6 COPC CONCENTRATION IN PLANTS 3-108
4-1 EXAMPLE FOREST FOOD WEB 4-15
4-2 EXAMPLE TALLGRASS PRAIRIE FOOD WEB 4-16
4-3 EXAMPLE SHORTGRASS PRAIRIE FOOD WEB 4-17
4-4 EXAMPLE SHRUB/SCRUB FOOD WEB 4-18
4-5 EXAMPLE FRESHWATER FOOD WEB 4-19
4-6 EXAMPLE BRACKISH/INTERMEDIATE MARSH FOOD WEB 4-20
4-7 EXAMPLE SALT MARSH FOOD WEB 4-21
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering xv
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Screening Level Ecological Risk Assessment Protocol
Contents August 1999
TABLES
Table Page
2-1 EXAMPLE CALCULATION OF TOTAL FUGITIVE EMISSION RATES FOR EQUIPMENT
IN WASTE FEED STORAGE AREA 2-18
2-2 EXAMPLE CALCULATION OF SPECIATED FUGITIVE EMISSION RATES FOR
EQUIPMENT IN WASTE FEED STORAGE AREAS 2-20
2-3 POLYCHLORINATED DIBENZO(P)DIOXIN AND DIBENZOFURAN CONGENER
TOXICITY EQUIVALENCY FACTORS (TEFs) FOR FISH, MAMMALS, AND BIRDS . 2-42
2-4 PCDD AND PCDF BIOACCUMULATION EQUIVALENCY FACTORS (BEFs) 2-48
2-5 POLYCHLORINATED BIPHENYL CONGENER TOXICITY EQUIVALENCY FACTORS
(TEFs) FOR FISH, MAMMALS, AND BIRDS 2-53
3-1 GENERALIZED PARTICLE SIZE DISTRIBUTION, AND PROPORTION OF AVAILABLE
SURFACE AREA, TO BE USED AS A DEFAULT IN DEPOSITION MODELING IF
SITE-SPECIFIC DATA ARE UNAVAILABLE 3-19
3-2 ALBEDO OF NATURAL GROUND COVERS FOR LAND USE TYPES
AND SEASONS 3-35
3-3 DAYTIME BOWEN RATION BY LAND USE, SEASON, AND PRECIPITATION
CONDITIONS 3-37
3-4 ANTHROPOGENIC HEAT FLUX AND NET RADIATION FOR SEVERAL
URBAN AREAS 3-39
3-5 AIR PARAMETERS FROM ISCST3 MODELED OUTPUT 3-59
4-1 ASSESSMENT ENDPOINTS FOR GUILDS AND COMMUNITIES IN EXAMPLE
FOOD WEBS 4-24
5-1 INGESTION RATES FOR EXAMPLE MEASUREMENT RECEPTORS 5-7
5-2 FOOD CHAIN MULTIPLIERS 5-17
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering xvi
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Screening Level Ecological Risk Assessment Protocol
Contents
August 1999
LIST OF ACRONYMS
Mg/L
Mg/s
|im
|im/s
|im2
°C
op
°K
ADOM
AET
APCS
atm-m3/mol-K
ATSDR
AWFCO
AWQC
BAF
BaP
BCF
BD
BEF
BEHP
BIF
BPIP
BS
BSAF
BTAG
BW
CARB
CAS
CERM
CKD
COMPDEP
COMPLEX I
COPC
CPF
CRQL
CWA
Microgram
Micrograms per kilogram
Micrograms per liter
Micrograms per second
Micrometer
Micrometers per second
Square micrometers
Degrees Celsius
Degrees Fahrenheit
Degrees Kelvin
Acid Deposition and Oxidant Model
Apparent effects threshold
Air pollution control system
Atmosphere-cubic meters per mole-degrees Kelvin
Agency for Toxic Substances and Disease Registry
Automatic waste feed cutoff
Ambient water quality criteria
Bioaccumulation factor
Benzo(a)pyrene
Bioconcentration factor
Soil bulk density
Bioaccumulation equivalency factor
Bis(2-ethylhexyl)phthalate
Boiler and industrial furnace
Building profile input program
Benthic solids
Sediment bioaccumulation factor
Biological Technical Assistance Group
Body weight
California Air Resources Board
Chemical Abstracts Service
Conceptual ecological risk model
Cement kiln dust
COMPLEX terrain model with DEPosition
COMPLEX terrain model, Version 1
Compound of potential concern
Cumulative probability density function
Contract required quantitation limit
Clean Water Act
U.S. EPA Region 6
Multimedia Planning and Permitting Division
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DEHP
DEM
DNOP
DOE
DQL
ORE
EDQL
EEL
EPA
EPC
EQL
EQP
ERA
ERL
ERT
ESP
ESI
ESQ
FCM
FWS
LIST OF ACRONYMS (Continued)
Diethylhexylphthalate (same as Bis(2-ethylhexl)phthalate)
Digital Elevation Model
Di(n)octylphthalate
U.S. Department of Energy
Data quality level
Destruction and removal efficiency
Ecological data quality levels
Estimated exposure level
U.S. Environmental Protection Agency
Exposure point concentration
Estimated quantitation limit
Equilibrium partitioning
Ecological risk assessment
Effects range low
Environmental Research and Technology
Electrostatic precipitator
Ecological screening index
Ecological screening quotient
Food chain multiplier
U.S. Fish and Wildlife Service
g/s
g/cm3
g/m3
GAQM
GC
GEP
HBC
HgCl2
HQ
HSDB
Grams per second
Grams per cubic centimeter
Grams per cubic meter
Guideline on Air Quality Models
Gas chromatography
Good engineering practice
Hexachlorobenzene
Mercuric chloride
Hazard quotient
Hazardous substances data base
IDL
IBM
IRIS
ISCST3
ISCSTDFT
kg
kg/L
Instrument detection limit
Indirect exposure model
Integrated risk information system
Industrial source complex short-term model
Industrial Source Complex Short Term Draft
Kilogram
Kilograms per liter
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L
LC50
LCD
LD50
LEL
LFI
LOAEL
LOD
LOEL
LIST OF ACRONYMS (Continued)
Liter
Lethal concentration to 50 percent of the test population
Local Climatological Data Annual Summary with Comparative Data
Lethal dose to 50 percent of the test population
Lowest effect level
Log fill-in
Lowest observed adverse effect level
Level of detection
Lowest observed effect level
m
m/s
mg
mg/kg
mg/kg/day
mg/L
mg/m3
MACT
MDL
MLE
MPRM
MPTER
MPTER-DS
NC DEHNR
NCDC
NCEA
NEL
NFI
NOAA
NOAEL
NOEC
NOEL
NRC
NTIS
NWS
Meter
Meters per second
Milligram
Milligrams per kilogram
Milligrams per kilogram per day
Milligrams per liter
Milligrams per cubic meter
Maximum achievable control technology
Method detection limit
Maximum likelihood estimation
Meterological Processor for Regulatory Models
Air quality model for multiple point source gaussian dispersion algorithm with
terrain adjustments
Air quality model for multiple point source gaussian dispersion algorithm with
terrain adjustments including deposition and sedimentation
North Carolina Department of Environment, Health, and Natural Resources
National Climatic Data Center
National Center for Environmental Assessment
No effect level
Normal fill-in
National Oceanic and Atmospheric Administration
No observed adverse effect level
No observed effect concentration
No observed effect level
U.S. Nuclear Regulatory Commission
National technical information service
National weather service
OAQPS
OAQPS TTN
OC
OCDD
ORD
ORNL
OSW
Office of Air Quality Planning and Standards
Office of Air Quality and Planning Standards and Technology Transfer
Network
Organic carbon
Octachlorodibenzodioxin
Office of Research and Development
Oak Ridge National Laboratory
Office of Solid Waste
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OV
PAH
PCB
PCDD
PCDF
PCRAMMET
PDF
PIC
PM
PM10
POHC
PQL
PRC
PU
LIST OF ACRONYMS (Continued)
Deposition output values
Polycyclic aromatic hydrocarbon
Polychlorinated biphenyl
Polychlorinated dibenzo(p)dioxin
Polychlorinated dibenzofuran
Personal computer version of the meterological preprocessor for the old RAM
program
Probability density function
Product of incomplete combustion
Particulate matter
Particulate matter less than 10 micrometers in diameter
Principal organic hazardous constituent
Practical quantitation limit
PRC Environmental Management, Inc.
Polyurethane
QA/QC
QAPjP
QSAR
RCRA
REACH
RME
RTDM
RTDMDEP
RTECS
SAMSON
SCRAM BBS
SFB
SMDP
SO
SQL
SVOC
TAL
TCDD
TDA
TEF
TG
TIC
TL
TOC
TRY
TSS
Quality assurance/Quality control
Quality assurance project plan
Quantitative structure activity relationship
Resource Conservation and Recovery Act
Reasonable maximum exposure
Rough terrain diffusion model
Rough terrain diffusion model deposition
Registry of Toxic Effects of Chemical Substances
Solar and Meterological Surface Observational Network
Support Center for Regulatory Air Models Bulletin Board System
San Francisco Bay
Scientific management decision point
Source
Sample quantitation limit
Semivolatile organic compound
Target analyte list
Tetrachlorodibenzo(p)dioxin
Toluene diisocyanate
Toxicity equivalent factor
Terrain grid
Tentatively identified compound
Trophic level
Total organic carbon
Toxicity reference value
Total suspended solids
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Contents August 1999
LIST OF ACRONYMS (Continued)
UF Uncertainty factor
UFI Uniform fill-in
USGS U.S. Geological Survey
USLE Universal soil loss equation
UTM Universal transverse mercator
VOC Volatile organic compound
watts/m2 Watts per square meter
WRPLOT Wind Rose PLOTing program
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LIST OF VARIABLES
Pa
A
P»
e
a
A
b
BAFt
BCFa/s
BCF,
BCFPi_H
BCF,
BCFPi_OM
BCFS/BS_C
BCFS/BS_H
BCFW_C
BCFW_HM
BCFm
BCFr
BD
BMFn
BS
BSAF
Bv
BW
C
CAl
Cc
CF
CF02
*-^gen
CH
G
Dimensionless viscous sublayer thickness (unitless)
Viscosity of air (g/cm-s)
Viscosity of water corresponding to water temperature (g/cm-s)
Air density (g/cm3 or g/m3)
Bed sediment density (kg/L)
Density of water corresponding to water temperature (g/cm3)
Temperature correction factor (unitless)
Bed sediment porosity (unitless)
Soil volumetric water content (mL/cm3 soil)
Empirical intercept coefficient (unitless)
Surface area of affected area (m2)
Empirical slope coefficient (unitless)
Bioaccumulation factor reported on a lipid-normalized basis using the freely
dissolved concentration of a chemical in the water (L/kg)
Aquatic-sediment bioconcentration factor (unitless)
Bioconcentration factor reported on a lipid-normalized basis using the freely
dissolved concentration of a chemical in the water (L/kg)
Bioconcentration factor for plant-to-herbivore for /'th plant food item (unitless)
Soil-to-soil invertebrate bioconcentration factor (unitless)
Bioconcentration factor for plant-to-omnivore for /th plant food item (unitless)
Bioconcentration factor for soil- or bed sediment-to-carnivore (unitless)
Bioconcentration factor for soil-to-plant or bed sediment-to-plant (unitless)
Bioconcentration factor for water-to-carnivore (L/kg)
Bioconcentration factor for water-to-herbivore (L/kg)
Bioconcentration factor for water-to-invertebrate (L/kg)
Plant-soil biotransfer factor (unitless)
Soil bulk density (g soil/cm3 soil)
Biomagnification factor for nth trophic level
Benthic solids concentration (kg/L or g/cm3)
Sediment bioaccumulation factor (unitless)
Air-to-plant biotransfer factor (|ig COPC/g DW plant)/((ig COPC/g air)
Body weight (kg)
USLE cover management factor (unitless)
COPC concentration in /'th animal food item (mg/kg)
COPC concentration in carnivore (mg/kg)
Drag coefficient (unitless)
Dissolved phase water concentration (mg/L)
COPC concentration in fish (mg/kg)
Correction factor for conversion to 4.5 percent O2 (unitless)
Generic chemical concentration (mg/kg or mg/L)
COPC concentration in herbivore (mg/kg)
Stack concentration of/th identified COPC (carbon basis) (mg/m3)
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Screening Level Ecological Risk Assessment Protocol
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August 1999
Q
Q
^PREY
c
sed
^-s/sed
C-TOC
C
TP
Cyp
Cyv
Cywv
A
D2
DDTEQ
DD,
Ds"n
Dyd
Dydp
Dytwp
Dywp
Dywv
Dywwv
Ev
ER
LIST OF VARIABLES (Continued)
COPC concentration in rth plant or animal food item (mg COPC/kg)
COPC concentration in soil or benthic invertebrate (mg/kg)
COPC concentration in soil or sediment interstitial water (mg/L)
COPC concentration in media (mg COPC/kg [soil, sediment] or mg COPC/L
[water])
COPC concentration in omnivore (mg/kg)
COPC concentration in /th plant food item (mg/kg)
Concentration in prey
COPC concentration in bed sediment (g COPC/cm3 sediment or mg COPC/kg
sediment)
COPC concentration in soil or bed sediment (mg/kg)
Stack concentration of TOC, including speciated and unspeciated compounds
(mg/m3)
COPC concentration in terrestrial plants (mg COPC/kg WW)
Total COPC concentration in water column (mg/L)
Total water body COPC concentration (including water column and bed
sediment) (g/m3 or mg/L)
Unitized yearly air concentration from particle phase (//g-s/g-m3)
Unitized yearly air concentration from vapor phase (^g s/g m3)
Unitized yearly watershed air concentration from vapor phase (|lg-s/g-m3)
Lower bound of a particle size density for a particular filter cut size
Upper bound of a particle size density for a particular filter cut size
Diffusivity of COPC in air (cm2/s)
Depth of upper benthic sediment layer (m)
Daily dose of 2,3,7,8-TCDD TEQ (^g/kg BW/d)
Daily dose of rth congener (jWg/kg BW/d)
Mean particle size density for a particular filter cut size
Deposition term (mg/kg-yr)
Diffusivity of COPC in water (cm2/s)
Depth of water column (m)
Unitzed yearly dry deposition rate of COPC (g/m2-yr)
Unitized yearly dry deposition from particle phase (s/m2-yr)
Unitized yearly watershed total deposition (wet and dry) from particle phase
(s/m2-yr)
Unitized yearly wet deposition from particle phase (s/m2-yr)
Unitized yearly wet deposition from vapor phase (s/m2-yr)
Unitized yearly watershed wet deposition from vapor phase (s/m2-yr)
Total water body depth (m)
Average annual evapotranspiration (cm/yr)
Soil enrichment ratio (unitless)
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Screening Level Ecological Risk Assessment Protocol
Contents
August 1999
FAJ
FCM
FCMTLn
FCM,
TLn-Ai
FCMTL3
Jwc
F
oe
Fw
H
•"MEDIUM
I
IR
k
K
KG
KL
Kdbs
Kdl}
Kds
Kdsw
Kocj
kp
ks
kse
ksg
ksl
ksr
ksv
kv
Kv
L
LIST OF VARIABLES (Continued)
Fraction of diet consiting of rth animal food item (unitless)
Fraction of total water body COPC concentration in benthic sediment (unitless)
Trophic level-specific food-chain multiplier (unitless)
Food chain multiplier for nth trophic level
Food chain multiplier for trophic level of rth animal food item (unitless)
Food chain multiplier for trophic level 3 (unitless)
Fraction of total water body COPC concentration in the water column (unitless)
Fraction of COPC air concentration in vapor phase (unitless)
Fraction of organic carbon (unitless)
Fraction of diet consisting of rth plant food item (unitless)
Fraction of COPC wet deposition that adheres to plant surfaces (unitless)
Henry's law constant (atm-m3/mol)
Ingestion rate of soil, surface water, or sediment
Average annual irrigation (cm/yr)
Ingestion rate (kg/day)
von Karman's constant (unitless)
USLE erodibility factor (ton/acre)
Benthic burial rate (yr :)
Gas phase transfer coefficient (m/yr)
Liquid phase transfer coefficient (m/yr)
Bed sediment/sediment pore water partition coefficient (L/kg or cmVg)
Partition coefficient for COPC i associated with sorbing material j (unitless)
Soil-water partition coefficient (cmVg or mg/L)
Suspended sediments/surface water partition coefficient (L/kg)
Organic carbon partition coefficient (mg/L)
Sorbing material-independent organic carbon partition coefficient for COPC j
Octanol-water partition coefficient (unitless)
Plant surface loss coefficient (yr :)
COPC soil loss constant due to all processes (yr :)
COPC loss constant due to soil erosion (yr :)
COPC loss constant due to biotic and abiotic degradation (yr :)
COPC loss constant due to leaching (yr :)
COPC loss constant due to runoff (yr :)
COPC loss constant due to volatilization (yr :)
Water column volatilization rate constant (yr :)
Overall transfer rate coefficient (m/yr)
Overall total water body COPC dissipation rate constant (unitless)
Monin-Obukhov Length (m)
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Screening Level Ecological Risk Assessment Protocol
Contents _ August 1999
LIST OF VARIABLES (Continued)
LDEP = Total (wet and dry) particle phase and wet vapor phase direct deposition load to
water body (g/yr)
Ldif = Dry vapor phase diffusion load to water body (g/yr)
LE = Soil erosion load (g/yr)
LR = Runoff load from pervious surfaces (g/yr)
Lm = Runoff load from impervious surfaces (g/yr)
LT = Total COPC load to water body (g/yr)
LS = USLE length-slope factor (unitless)
MW = Molecular weight of COPC (g/mol)
= Organic carbon content of sorbing material / (unitless)
OV = Deposition output values
P = Average annual precipitation (cm/yr)
PAi = Proportion of /th animal food item in diet that is contaminated (unitless)
Pd = COPC concentration in plant due to to direct deposition (mg/kg WW)
PF = USLE supporting practice factor (unitless)
PPi = Proportion of /th plant food item in diet that is contaminated (unitless)
Pr = COPC concentration in plant due to root uptake (mg/kg WW)
PS/BS = Proportion of soil or bed sediment in diet that is contaminated (unitless)
Pv = COPC concentration in plant due to air-to-plant transfer (mg/kg WW)
Pw = Proportion of water in diet that is contaminated (unitless)
Q = COPC emission rate (g/s)
Qi = Emission rate of COPC (i) (g/s)
Qi(adj) = Adjusted emission rate of COPC (i) (g/s)
Qf = Anthropogenic heat flux (W/m2)
Q* = Net radiation absorbed (W/m2)
r = Interception fraction-the fraction of material in rain intercepted by vegetation
and initially retained (unitless)
R = Universal gas constant (atm-m3/mol-K)
RO = Average annual runoff (cm/yr)
RF = USLE rainfall (or erosivity) factor (yr :)
Sc = Average soil concentration over exposure duration (mg/kg)
ScTc = Soil concentration at time Tc (mg/kg)
SD = Sediment delivery ratio (unitless)
SGC = COPC stack gas concentration as measured in the trial burn (|ig/dscm)
SGF = Stack gas flow rate at 7 percent O2 (dscm/s)
Ta = Ambient air temperature (K) = 298. 1 K
Tp = Length of plant exposure to deposition per harvest of the edible portion of the rth
plant group (yr)
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Contents August 1999
LIST OF VARIABLES (Continued)
tD = Total time period over which deposition occurs (time period of combustion) (yr)
Tm = Melting point temperature (K)
TSS = Total suspended solids concentration (mg/L)
Tw = Water body temperature (K)
M = Current velocity (m/s)
V = Volume
Vdv = Dry deposition velocity (cm/s)
Vfx = Average volumetric flow rate through water body (m3/yr)
VGag = Empirical correction factor for aboveground produce (unitless)
VP = Vapor pressure (atm)
W = Average annual wind velocity (m/s)
WAj = Area of impervious watershed receiving COPC deposition (m2)
WAL = Area of watershed receiving COPC deposition (m2)
WAW = Water body surface area (m2)
Xe = Unit soil loss (kg/m2"yr)
Yp = Standing crop biomass (productivity) (kg/m2 DW)
Zs = Soil mixing zone depth (cm)
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Contents August 1999
CONVERSIONS
0.001 = Units conversion factor (g/mg)
106 = Units conversion factor (|ig/g)
907.18 = Units conversion factor (kg/ton)
3.1536x107 = Conversion constant (s/year)
4,047 = Units conversion factor (m2/acre)
100 = Units conversion factor (m2-mg/cm2-kg)
10"6 = Units conversion factor (g/|ig)
0.12 = Dry weight to wet weight (plants) conversion factor (unitless)
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Chapter 1
Introduction
Related Trial Burn I
4 Reference!
Overview of the
Relationship to U.S. EPA HHRAP
Definitions
Risk assessment is a science used to evaluate the potential hazards to the environment that are attributable
to emissions from hazardous waste combustion units. There is general guidance available regarding the
general ecological risk assessment process including problem formulation, analysis, and risk
characterization (U.S. EPA 1997c; 1998d). This document expands on that general guidance with respect
to the ecological screening level procedures and provides a prescriptive tool to support permitting of
hazardous waste burning combustion facilities under the Resource Conservation and Recovery Act
(RCRA). It is not intended to be used to perform screening or baseline ecological risk assessments (ERA)
in other areas of the RCRA program, such as corrective action.
The following definitions were adopted from Superfund: Process for Designing and Conducting
Ecological Risk Assessments. Interim Final (U.S. EPA 1997c) and Guidelines For Ecological Risk
Assessment (U.S. EPA 1998d), and identify key terms used throughout this guidance. Some of the terms
are annotated with additional information to clarify the definition and explain its use in this protocol.
Area Use Factor: A ratio of an organism's home range, breeding range, or feeding and foraging range to
the area of contamination of the assessment area.
Assessment Endpoint: An explicit expression of the environmental value that is to be protected; it
includes both an ecological entity and specific attributes of that entity. The assessment endpoint in this
protocol is used to link the risk assessment to management concerns and ultimately development of a
protective RCRA operating permit. One or more assessment endpoints may be selected for performing a
risk assessment.
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Screening Level Ecological Risk Assessment Protocol
Chapter 1: Introduction August 1999
Bioaccumulation: The net accumulation of a substance by an organism as a result of uptake directly from
all environmental sources, including food. Bioaccumulation occurs through all exposure routes.
Bioaccumulation Factor (BAF). BAF represents the ratio of the concentration of a chemical to its
concentration in a medium. The factor must be measured at steady-state when the rate of uptake is
balanced by the rate of excretion. In this protocol a bioaccumulation factor (BAF) is estimated by
multiplying a bioconcentration factor (BCF) by a food chain multiplier (FCM) derived based on the trophic
level of the prey ingested by a measurement receptor.
Bioconcentration: A process by which there is a net accumulation of a chemical directly from an exposure
medium into an organism.
Bioconcentration Factor (BCF). BCF represents the ratio of the concentration of a chemical in an
aquatic organism to the concentration of the chemical in surface water, sediment, or soil. The factor must
be measured at steady-state when the rate of uptake is balanced by the rate of excretion. BCFs are used in
this protocol to estimate the body burden of a COPC in producers, primary consumers, and fish consumed
by mid- or upper-trophic level measurement receptors.
Biomagnification: The process by which the concentration of some chemicals increase with increasing
trophic level; that is, the concentration in a predator exceeds the concentration in its prey. In this protocol,
a ratio of FCM's are used to account for biomagnification.
Biotransfer Factor: COPC accumulation factor between a food item and its consumer. In this protocol
biotransfer factors are used to evaluate transport of contaminants in plants to mammals and birds.
Depuration: The loss of a compound from an ecological receptor as a result of any active or passive
process.
Direct Uptake: Direct uptake is a term applied to producers, primary consumers, and detritivores. Direct
uptake includes all exposure routes for aquatic receptors, benthic receptors, soil invertebrates, and
terrestrial plants. Direct uptake is used in this manner because it is difficult, given feeding and habitat
niches of these receptors and limited availability of empirical information, to discern the relative importance
of exposure through ingestion, respiration, dermal uptake, or root uptake. In addition, toxicity tests (used
as the basis of risk assessment toxicity reference values) on these receptors (except some aquatic fauna)
usually do not make a distinction between exposure routes or tend to overemphasize or isolate a particular
route.
Ecological Effects Assessment: A portion of the analysis phase of the risk assessment that evaluates the
ability of a stressor to cause adverse effects under a particular set of circumstances. Toxicity reference
values identified in ecological effects assessment are used in risk characterization.
Ecological Risk Assessment: The process that evaluates the likelihood that adverse ecological effects may
occur or are occurring as a result of exposure to one or more stressors.
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Screening Level Ecological Risk Assessment Protocol
Chapter 1: Introduction August 1999
Ecological Screening Quotient (ESQ): A quotient used to assess risk during the risk assessment in which
protective assumptions are used. Generally, the numerator is the reasonable worst-case COPC
concentration at the point of exposure, and the denominator is the no-adverse-effects-based toxicity
reference value.
Environmental Attribute: Characteristic of a food web functional group (e.g., herbivorous mammal) that
is relevant to the ecosystem. Examples of environmental attributes include seed dispersal, decompositon,
pollination, and food source.
Exposure Assessment: A portion of the analysis phase of ERA that evaluates the interaction of the
stressor with one or more ecological components. Exposure can be expressed as co-occurrence or contact,
depending on the stressor and ecological component involved. Information from the exposure assessment is
used in risk characterization.
Exposure Pathway: A pathway by which a compound travels from a combustion facility to an ecological
receptor. A complete exposure pathway occurs when a chemical enters or makes contact with an
ecological receptor through one or more exposure routes.
Exposure Route: A point of contact or entry of a chemical from the environment into an organism. The
exposure routes for terrestrial wildlife are ingestion, dermal absorption, and inhalation. The exposure
routes for aquatic fauna are ingestion, dermal absorption, and respiration. The exposure routes for
terrestrial plants are root absorption or foliar uptake. Exposure routes for aquatic plants are direct contact
with water and sediments.
Food Chain: The transfer of food energy from the source in plants through a series of organisms with
repeated eating and being eaten (Odum 1971).
Food Web: The interlocking patterns of food chains (Odum 1971).
Food-Chain Multiplier (FCM): The FCM is used to account for dietary uptake of a compound by an
ecological receptor. It may be used to estimate a BAF from a BCF in the absence of reliable BAF data.
The FCM values in Table 5-1 have been adopted from Water Quality Guidance for the Great Lakes
System (U.S. EPA 1995J).
Guild: A group of species occupying a particular trophic level and exploiting a common resource base in a
similar fashion (Root 1967).
Habitat: The physical environment in which a species is distributed. Habitat location depends on several
factors, such as chemical conditions, physical conditions, vegetation, species eating strategy, and species
nesting strategy. By analogy, the habitat is an organism's "address."
Measure of Effect: A measurable ecological characteristic that is related to the valued characteristic
chosen as the assessment endpoint. It is the measure used to evaluate the response of the assessment
endpoint when exposed to a chemical (U.S. EPA 1998d). This protocol proposes, for each class/guild,
representative receptors (measurement receptors) for characterizing risk from exposure to compounds
emitted from a combustion facility.
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Screening Level Ecological Risk Assessment Protocol
Chapter 1: Introduction August 1999
Measure of Effect: A measurable ecological characteristic that is related to the valued characteristic
chosen as the assessment endpoint.
Measure of Exposure: A measurable stressor characteristic that is used to help quantify exposure.
Measurement Receptor: A species, population, community, or assemblage of communities (such as
"aquatic life") used to characterize ecological risk to an assessment endpoint.
Problem Formulation: A systematic planning step that identifies the focus and scope of the risk
assessment. Problem formulation includes ecosystem characterization, pathway analysis, assessment
endpoint development, and measurement endpoint identification. Problem formulation results in the
development of a problem statement that is addressed in the analysis step.
Scientific and Management Decision Point: A point during the risk assessment at which the risk assessor
and risk manager discuss results. The risk manager determines whether the information is sufficient to
arrive at a decision regarding the significance of the results and whether additional information is needed
before proceeding forward in the risk assessment.
Special Ecological Area: Habitats and areas for which protection and special consideration has been
conferred legislatively (federal or state), such as critical habitat for federally or state-designated endangered
or threatened species. In characterizing media concentrations of COPCs, special emphasis is placed on
estimating concentrations and, therefore, exposure potential, in sensitive areas.
Stressor: Any physical, chemical, or biological entity that can induce an adverse response.
Trophic Level: One of the successive levels of nourishment in a food web or food chain. Plant producers
constitute the first (lowest) trophic level, and dominant carnivores constitute the last (highest) trophic level.
Uncertainty Factor: Quantitative values used to adjust toxicity values from laboratory toxicity tests to
toxicity values representative of chronic no-observed-adverse-effect-levels (NOAELs). In this guidance,
uncertainty factors (UF) are used to extrapolate from acute and subchronic test duration to chronic
duration, and to extrapolate from point estimated (e.g., LD50) and lowest-observed-adverse-effect-level
(LOAEL) endpoints to an NOAEL endpoint.
Uptake: Acquisition by an ecological receptor of a compound from the environment as a result of any
active or passive process.
This Screening Level Ecological Risk Assessment Protocol (SLERAP) has been developed as national
guidance to consolidate information presented in other risk assessment guidance and methodology
documents previously prepared by U.S. EPA and state environmental agencies. In addition, this guidance
also addresses issues that have been identified while conducting risk assessments for existing hazardous
waste combustion units. The overall purpose of this document is to explain how ecological risk
assessments should be performed at hazardous waste combustion facilities. This document is intended as
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Screening Level Ecological Risk Assessment Protocol
Chapter 1: Introduction August 1999
(1) guidance for personnel conducting risk assessments, and (2) an information resource for permit writers,
risk managers, and community relations personnel.
The RCRA "omnibus" authority of §3005(c)(3) of RCRA, 42 U.S.C. §6925(c)(3) and 40 CFR
§270.32(b)(2) gives the Agency both the authority and the responsibility to establish risk-based
permit conditions on a case-by-case basis as necessary to protect human health and the
environment. These risk-based site-specific permit conditions are in addition to the national
technical standards required in the hazardous waste incinerator and boiler/industrial furnace
regulations of 1981 and 1991, respectively. Often, the determination of whether or not a permit is
sufficiently protective can be based on its conformance to the technical standards specified in the
regulations. Since the time that the regulations for hazardous waste incinerators and boilers/industrial
furnaces were issued, however, additional information became available which suggested that technical
standards may not fully address potentially significant risks. For example, many studies (including the
Draft Health Reassessment of Dioxin-Like Compounds, Mercury Study Report to Congress, Risk
Assessment Support to the Development of Technical Standards for Emissions from Combustion Units
Burning Hazardous Wastes: Background Information Document, and the Waste Technologies Industries
(WTI) Risk Assessment} indicate that there can be significant risks from indirect exposure pathways (e.g.,
pathways other than direct inhalation). The food chain pathway appears to be particularly important for
bioaccumulative pollutants which may be emitted from hazardous waste combustion units. In many cases,
risks from indirect exposure may constitute the majority of the risk from a hazardous waste combustor.
This key portion of the risk from hazardous waste combustor emissions was not directly taken into account
when the hazardous waste combustion standards were developed. In addition, uncertainty remained
regarding the types and quantities of non-dioxin products of incomplete combustion emitted from
combustion units and the risks posed by these compounds.
As a result, until such time that the technical standards could be upgraded to more completely
address potential risk from hazardous waste combustion, U.S. EPA recommended, pursuant to
the "omnibus" authority, that site-specific risk assessments be performed for all combustion
facilities as a part of the RCRA permitting process. Performance of a site-specific risk assessment can
provide the information necessary to determine what, if any, additional permit conditions are necessary for
each situation to ensure that operation of the combustion unit is protective of human health and the
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environment. Under 40 C.F.R. §270.10(k), U.S. EPA may require a permit applicant to submit additional
information (e.g., a site-specific risk assessment) that the Agency needs to establish permit conditions under
the omnibus authority. In certain cases, the Agency may also seek additional testing or data under the
authority of RCRA §3013 (where the presence or release of a hazardous waste "may present a substantial
hazard to human health or the environment") and may issue an order requiring the facility to conduct
monitoring, testing, analysis, and reporting. Any decision to add permit conditions based on a site-specific
risk assessment under this authority must be justified in the administrative record for each facility, and the
implementing agency should explain the basis for the conditions.
U.S. EPA promulgation of the Maximum Achievable Control Technology (MACT) standards for
hazardous waste incinerators, cement kilns and light-weight aggregate kilns effectively upgraded the
existing national technical standards for these combustion units. U.S. EPA intends to similarly upgrade the
technical standards for other types of hazardous waste combustors in a later rulemaking. Since the MACT
standards are more protective than the original standards for incinerators, cement kilns and light-weight
aggregate kilns, U.S. EPA revised its earlier recommendation regarding site-specific risk assessments. As
discussed in the preamble to the final MACT rule, U.S. EPA recommended that the permitting authority
determine if a site-specific risk assessment is needed in addition to the MACT standards in order to meet
the RCRA statutory obligation of protection of human health and the environment. For hazardous waste
combustors not subject to the Phase I MACT standards, U.S. EPA continues to recommend that site-
specific risk assessments be conducted as part of the RCRA permitting process. If the permitting authority
determines a risk assessment is warranted, it should be conducted as part of the RCRA permitting process.
The permitting agency should consider several factors in its evaluation of the need to perform a risk
assessment (human health and ecological). These factors include:
• whether any proposed or final regulatory standards exist that U.S. EPA has shown to be
protective for site-specific receptors
• whether the facility is exceeding any final technical standards
• the current level of hazardous constituents being emitted by a facility, particularly in
comparison to proposed or final technical standards, and to levels at other facilities where
risks have been estimated
• the scope of waste minimization efforts and the status of implementation of a facility waste
minimization plan
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• particular site-specific considerations related to the exposure setting (such as physical,
land use, presence of threatened or endangered species and special subpopulation
characteristics) and the impact on potential risks
• the presence of significant ecological considerations (e.g., high background levels of a
particular contaminant, proximity to a particular sensitive ecosystem)
• the presence of nearby off-site sources of pollutants
• the presence of other on-site sources of pollutants
• the hazardous constituents most likely to be found and those most likely to pose significant
risk
• the identity, quantity, and toxicity of possible non-dioxin PICs
• the volume and types of wastes being burned
• the level of public interest and community involvement attributable to the facility
This list is by no means exhaustive, but is meant only to suggest significant factors that have thus far been
identified. Others may be equally or more important.
The companion document of the SLERAP is the Human Health Risk Assessment Protocol (HHRAP) (U.S.
EPA 1998c). U.S. EPA OSW has prepared these guidance documents as a resource to be used by
authorized agencies developing risk assessment reports to support permitting decisions for facilities with
hazardous waste combustion units.
1.1 OBJECTIVE AND PURPOSE
This protocol is a multipathway screening tool based on reasonable, protective assumptions about the
potential for ecological receptors to be exposed to, and to be adversely affected by, compounds of potential
concern (COPC) emitted from hazardous waste combustion facilities. The U.S. EPA OSW risk assessment
process is a prescriptive analysis intended to be performed expeditiously using (1) measurement receptors
representing food web-specific class/guilds and communities, and (2) readily available exposure and
ecological effects information. To avoid the time-intensive and resource-consuming process of collecting
site-specific information on numerous constituents, this guidance provides a process to obtain and evaluate
various types of technical information that will enable a risk assessor to perform a risk assessment
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relatively quickly. Additionally this guidance provides: (1) example food webs; (2) example measurement
receptor natural history information; (3) fate and transport data, bioconcentration factors, and toxicity
reference values for 38 COPCs. In lieu of this information, a facility may substitute site-specific
information where appropriate and approved by the applicable permitting authority.
U.S. EPA OSW's objective is to present a user-friendly set of procedures for performing risk assessments,
including (1) a complete explanation of the basis of those procedures, and (2) a comprehensive source of
data needed to complete those procedures. The first volume of this document provides the explanation
(Chapters 1 through 6); and the second and third volumes (Appendices A-H) provides the data sources.
Appendix A presents compound-specific information necessary to complete the risk assessment. Appendix
B presents equations for calculating media concentrations. Appendices C and D provide chemical and
media-specific bioconcentration factors (BCFs). Appendix E provides toxicity reference values (TRVs) for
38 compounds of potential concern (COPCs) and several possible measurement receptors. Appendix F
presents equations for calculating risk. Appendix G provides contact information for obtaining site-specific
species information, and Appendix H provides toxicological profiles for 38 COPCs. Figure 1-1
summarizes the steps needed to complete a screening level ecological risk assessment.
Implementation of this guidance will demonstrate that developing defensible estimates of compound
emission rates is one of the most important elements of the risk assessment. As described in Chapter 2,
traditional trial burns conducted to measure destruction and removal efficiency (DRE) do not sufficiently
characterize organic products of incomplete combustion (PIC) and metal emissions for use in performing
risk assessments. In some instances, a facility or regulatory agency may want to perform a pretrial burn
risk assessment, following the procedures outlined in this document, to ensure that sample collection times
during the trial burn or risk burn are sufficient to collect the sample volumes needed to meet the detection
limits required for the risk assessment. The decision to perform such an assessment should consider
regulatory permitting schedules and other site-specific factors.
U.S. EPA OSW anticipates that ecological risk assessments will be completed for new and existing
facilities as part of the permit application process. The SLERAP recommends a process for evaluating
reasonable—not theoretical worst-case maximum—potential risks to receptors posed by emissions from
RCRA regulated units. The use of existing and site-specific information early in, and throughout, the risk
assessment process is encouraged; protective assumptions should be made only when needed to ensure that
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emissions from combustion units do not pose unacceptable risks. More protective assumptions may be
incorporated to make the process fit a classical "screening level" approach that is more protective and may
be easier to complete.
Regardless of whether theoretical worst case or more reasonable protective assumptions are used in
completing the risk assessment process, every risk assessment is limited by the quantity and quality of:
• site-specific environmental data
• emission rate information
• other assumptions made during the risk estimation process (for example, fate and transport
variables, exposure assumptions, and receptor characteristics)
These limitations and uncertainties are described throughout this document and the appendixes, and are
summarized in Chapter 6.
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FIGURE 1-1
SCREEN ING-LEVEL ECOLOGICAL RISK ASSESSMENT PROCESS
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Potentially, unacceptable risks or other significant issues identified by collecting preliminary site
information and completing risk assessment calculations can be addressed by the permitting process or
during an iteration of the risk assessment. After the initial ecological risk assessment has been completed,
it may be used by risk managers and permit writers in several ways:
• If the initial risk assessment indicates that estimated ecological risks are below regulatory
levels of concern, risk managers and permit writers will likely proceed through the
permitting process without adding any risk-based unit operating conditions to the permit.
• If the initial ecological risk assessment indicates potentially unacceptable risks, additional
site-specific information demonstrated to be more representative of the exposure setting
may be collected and additional iterations of risk assessment calculations can then be
performed.
• If the initial risk assessment or subsequent iterations indicate potentially unacceptable
risks, risk managers and permit writers may use the results of the risk assessment to revise
tentative permit conditions (for example, waste feed limitations, process operating
conditions, and expanded environmental monitoring). To determine if the subject
hazardous waste combustion unit can be operated in a manner that is protective of the
environment, an additional iteration of the risk assessment should be completed using the
revised tentative operating conditions. If the revised conditions still indicate unacceptable
risks, this process can be continued in an iterative fashion until acceptable levels are
reached. In some situations, it may be possible to select target risk levels and
back-calculate the risk assessment to determine the appropriate emission and waste feed
rate levels. In any case, the acceptable waste feed rate and other appropriate conditions
can then be incorporated as additional permit conditions.
• If the initial ecological risk assessment, or subsequent iterations, indicate potentially
unacceptable risks, risk managers and permit writers may also choose to deny the permit.
This process is also outlined in Figure 1-1. As stated earlier, in some instances, a facility or regulatory
agency may want to perform a pretrial burn risk assessment—following the procedures outlined in this
document—to ensure that sample collection times during the trial burn or risk burn are sufficient to collect
the sample volumes necessary to meet the appropriate detection limits for the risk assessment. This is
expected to reduce the need for additional trial burn tests or iterations of the risk assessment due to
problems caused when detection limits are not low enough to estimate risk with certainty sufficient for
regulatory decision making.
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1.2 RELATED TRIAL BURN ISSUES
In the course of developing this guidance and completing risk assessments across the country, U.S. EPA
OSW has learned that developing defensible estimates of compound of potential concern (COPC) emission
rates is one of the most important parts of the risk assessment process. As described in Chapter 2,
traditional trial burns conducted to measure destruction and removal efficiency (DRE) do not sufficiently
characterize organic products of incomplete combustion (PIC) and metal emissions for use in performing
risk assessments.
U.S. EPA OSW considers the trial burn and risk assessment planning and implementation processes as
interdependent aspects of the hazardous waste combustion unit permitting process. In addition, U.S. EPA
OSW advocates that facility planning, regulatory agency review, and completion of tasks needed for both
processes be conducted simultaneously to eliminate redundancy or the need to repeat activities. U.S. EPA
OSW expects that the following guidance documents will typically be used as the main sources of
information for developing and conducting appropriate trial burns:
U.S. EPA. 1989f Handbook: Guidance on Setting Permit Conditions and Reporting
Trial Burn Results. Volume II of the Hazardous Waste Incineration Guidance Series.
Office of Research and Development (ORD). EPA/625/6-89/019. January.
U.S. EPA. 1989g. Handbook: Hazardous Waste Incineration Measurement Guidance
Manual. Volume III of the Hazardous Waste Incineration Guidance Series. Office of
Solid Waste and Emergency Response (OSWER). EPA/625/6-89/021. June.
U. S. EPA. 1992e. Technical Implementation Document for EPA 's Boiler and Industrial
Furnace Regulations. OSWER. EPA-530-R-92-011. March.
U.S. EPA. 1994n. Draft Revision of Guidance on Trial Burns. Attachment B, Draft
Exposure Assessment Guidance for Resource Conservation and Recovery Act (RCRA)
Hazardous Waste Combustion Facilities. OSWER. April 15.
U.S. EPA. 1998b. Guidance on Collection of Emissions Data to Support Site-Specific
Risk Assessments at Hazardous Waste Combustion Facilities. Prepared by EPA Region
4 and the Office of Solid Waste.
Generic Trial Burn Plan and QAPPs developed by EPA regional offices or states.
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1.3 REFERENCE DOCUMENTS
This section describes, in chronological order, the primary guidance documents used to prepare this
guidance. Some of the guidance documents received a thorough review from EPA's Science Advisory
Board, which mostly supported the work. Additional references used to prepare this guidance are listed in
the References chapter of this document. These documents have been developed over a period of several
years; in most cases, revisions to the original guidance documents address only the specific issues being
revised rather than representing a complete revision of the original document. The following discussion
lists and briefly describes each document. Overall, each of the guidance documents reflects a continual
enhancing of the methodology.
This ecological assessment portion of this protocol is based on protecting the functions of ecological
receptors in ecosystems and protecting special ecological areas around a hazardous waste combustion
facility. It is generally consistent with current U.S. EPA guidance, including the Risk Assessment Forum's
Guidelines for Ecological Risk Assessment (U.S. EPA 1998d), as well as the interim final Ecological Risk
Assessment Guidance for Superfund (U.S. EPA 1997c) The most current methodology for assessing fate
and transport of COPC's frequently referenced in this guidance is the U.S. EPA document, Methodology
for Assessing Health Risks Associated with Multiple Exposure Pathways to Combustor Emissions (In
Press).
The following document was the first U.S. EPA NCEA guidance document for conducting risk assessments
at combustion units:
• U.S. EPA. 1990a. Interim Final Methodology for Assessing Health Risks Associated
with Indirect Exposure to Combustor Emissions. Environmental Criteria and Assessment
Office. ORD. EPA-600-90-003. January.
This document outlined and explained a set of general procedures recommended in this guidance for
determining media concentrations utilized in ecological risk assessments. This document was subsequently
revised by the following:
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• U.S. EPA. 1993h. Review Draft Addendum to the Methodology for Assessing Health
Risks Associated with Indirect Exposure to Combustor Emissions. Office of Health and
Environmental Assessment. ORD. EPA-600-AP-93-003. November 10.
U.S. EPA (1993h) outlined recommended revisions to previous U.S. EPA guidance (1990a), which have
been used by the risk assessment community since the release of the document; however, these
recommended revisions were never formally incorporated into the original document.
Finally, U.S. EPA Region 5 contracted for development of a Screening Ecological Risk Assessment of
Waste Technologies Industries (WTI) Hazardous Waste Incinerator, in Liverpool, Ohio (U.S. EPA
19951). This document was extensively peer reviewed and represents the most current application of
ecological risk assessment guidance at a combustion facility. The WTI screening ecological risk
assessment was reviewed and considered throughout the development of the approach presented in this
guidance document.
U.S. EPA. 1998d. Proposed Guidance for Ecological Risk Assessment. Risk Assessment Forum,
Washington, D.C. EPA/630/R-95/002B. August.
U.S. EPA. 1997c. Ecological Risk Assessment Guidance for Superfund: Process for Designing and
Conducting Ecological Risk Assessments. Interim Final. Environmental Response Team, Office
of Emergency and Remedial Response, Edison, New Jersey. June 5.
Root, R.B. 1967. "The Niche Exploitation Pattern of the Blue-Gray Gnatcatcher." Ecological
Monographs. Volume 37, Pages 317-350.
Odum, E.P. 1971. Fundamentals of Ecology. Third Edition. W.B. Saunders Company, Philadelphia.
574 pp.
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Chapter 2
Facility Characterization
What's Covered in Chapter 2:
4 Compiling Basic Facility'
4 Identifying Emission,
4 Estimating Emission;RateJ'jif
4 Identifying Compounds bf Pbtfential Cdncerh (COPCs)"
4 Estimating COPC Concentrations for Non-Detects
4 Evaluating Contamination In Blanks
This chapter provides guidance on characterizing the nature and magnitude of emissions released from
facility sources. The characterization includes (1) compiling basic facility information, (2) identifying
emission sources, (3) estimating emission rates, (4) identifying COPCs, (5) estimating COPC
concentrations for non-detects, and (6) evaluating contamination in blanks.
2.1
COMPILING BASIC FACILITY INFORMATION
Basic facility information should be considered in conducting the risk evaluation, and provided to enable
reviewers to establish a contextual sense of the facility regarding how it relates to other facilities and other
hazardous waste combustion units. At a minimum, the basic facility information listed in the highlighted
box at the end of this and other sections should be considered in the risk evaluation. The following sections
and chapters describe the collection of this information in more detail; however, users may want to consult
these discussions so that all site-specific information needed to complete the risk assessment can be
collected simultaneously, when appropriate, for up front consideration. The risk assessor is also referred to
Briefing the BTAG: Initial Description of Setting, History, and Ecology of a Site (U.S. EPA 1992a) (see
web site www.epa.gov/superfund/program/risk/tooleco.htm) for more guidance on compiling basic facility
information.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Principal business and primary production processes
• Normal and maximum production rates
• Types of waste storage and treatment facilities
• Type and quantity of wastes stored and treated
• Process flow diagrams showing both mass and energy inputs and outputs
• Type of air pollution control system (APCS) associated with each unit
2.2 IDENTIFYING EMISSION SOURCES
Combustion of a hazardous waste generally results in combustion by-products being emitted from a stack.
In addition to emissions from the combustion stack, additional types of emissions of concern that may be
associated with the combustion of hazardous waste include (1) process upsets, (2) general RCRA fugitive
emissions, (3) cement kiln dust (CKD) fugitive emissions, and (4) accidental releases. Each of these
emission source types are defined below with regards to the context and scope of this guidance.
Stack Emissions - Release of compounds or pollutants from a hazardous waste combustion unit
into the ambient air while the unit is operated as intended by the facility and in compliance with a
permit and/or regulation (for interim status).
Process Upset Emissions - Release of compounds or pollutants from a hazardous waste
combustion unit into the ambient air while the unit is not being operated as intended, or during
periods of startup or shutdown. Upset emissions usually result from an upset in the hazardous
waste combustion process and are often known as process upset emissions. Upset emissions are
generally expected to be greater than stack emissions because the process upset results in
incomplete destruction of the wastes or other physical or chemical conditions within the
combustion system that promote the formation and/or release of hazardous compounds from
combustion stacks. Upset emissions usually occur during events and times when the hazardous
waste combustion unit is not operating within the limits specified in a permit or regulation.
RCRA Fugitive Emissions - Release of compounds or pollutants into the ambient air from RCRA
regulated sources other than hazardous waste combustion stacks. RCRA fugitive emissions are
typically associated with the release of compounds or pollutants from leaks in the combustion
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chamber (e.g., "puffs"); tanks, valves, flanges, and other material handling equipment used in the
storage and handling of RCRA hazardous wastes; residues from the combustion process such as
ash or quench water; and other RCRA treatment, storage, or disposal units (e.g., landfills).
CKD Fugitive Emissions - Release of compounds or pollutants into the ambient air caused by the
handling, storage, and disposal of cement kiln dust.
Accidental Release - Accidental release is defined in Section 112(r) of the Clean Air Act as an
unanticipated emission of a regulated substance or other extremely hazardous substance into the
ambient air from a stationary source. Accidental releases are typically associated with non-routine
emissions from RCRA facilities; such as the failure of tanks or other material storage and handling
equipment, or transportation accidents.
Consistent with previous U.S. EPA guidance (U.S. EPA 1994d), U.S. EPA OSW recommends that, with
the exception of accidental releases, all of these emission source types be addressed in the risk assessment,
as applicable. Accidental releases are not considered within the scope of this guidance, and should be
evaluated as recommended in Section 112(r) of the CAA and current U.S. EPA guidance (U.S. EPA
1996k) or the RMP Offsite Consequence Analysis Guidance, dated May 24, 1996. A decision to consider
accidental releases in risk assessments for hazardous waste combustion facilities should be made on a site
specific basis by the relevant permitting authority.
The following subsections contain guidance for estimating emissions for the source types specified for
inclusion in the risk assessment. Guidance on air dispersion modeling of stack and fugitive emissions is
presented in Chapter 3.
2.2.1 Estimating Stack Emission Rates for Existing Facilities
Stack emission rates (in grams per second) need to be determined for every compound of potential concern
(COPC) identified using the procedures outlined in Section 2.3. U.S. EPA OSW expects that emission
rates used to complete the risk assessment will be (1) long-term average emission rates adjusted for upsets,
or (2) reasonable maximum emission rates measured during trial burn conditions in order to assure that risk
assessments are conservative. Maximum emission rates measured during trial burn conditions (see
Section 2.2.1.1) represent reasonable maximum emission rates. These emission rates can be controlled by
hourly rolling average permit limits traditionally found in combustion unit operating permits, and are more
conservative than emission estimates that are based on long-term average emission rates. Long-term
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average emission rates (see Section 2.2.1.2) are based on tests of the combustion unit burning worst-case
wastes at operating conditions that are representative of normal operating conditions over a long-term
period. If long-term average emission rate estimates are used in the risk assessment, the final permit will
likely specify limitations in addition to any hourly rolling average limit typically used to regulate hazardous
waste combustion facilities.
A permitting agency's decision to allow a facility to use emission rate data developed from either normal or
maximum operating conditions will be made on a case-by-case basis. Some facilities may be required to
use emission rate data developed from maximum operating conditions because the variability in waste feed
and operating conditions is too great to make permit decisions based on emission data collected during
normal operating conditions, or because the emissions from combustion of the waste feed material are
anticipated to be highly toxic and only a conservative risk assessment can adequately ensure protection.
2.2.1.1 Estimates from Trial Burns
For existing facilities (such as those built and operational), emission rate information will generally be
determined by direct stack measurements during pretrial burn or trial burn tests, because trial burn tests are
generally part of the permitting process to burn hazardous wastes. This policy is consistent with U.S. EPA
1998 Guidance on Collection of Emissions Data to Support Site-Specific Risk Assessments at Hazardous
Waste Combustion Facilities, prepared by U.S. EPA Region 4 and OSW (U.S. EPA 1998b). For new
facilities (see Section 2.2.3), estimated emission rates used to complete pretrial burn risk assessments
should be compared to the emission rates estimated from actual trial burns completed after the new facility
receives a permit and is constructed. Trial burn tests are designed to produce emission rates higher than
those anticipated under normal operating conditions. U.S. EPA OSW recommends that sampling be
conducted, in accordance with U.S. EPA guidance on conducting trial burns, by using compound-specific
stack sampling, analytical, and quality assurance/quality control (QA/QC) protocols and procedures
approved by the permitting authority. An alternative to a trial burn test is the submittal of data "in lieu of
a trial burn. U.S. EPA OSW will consider this type of data for on-site units on a case-by-case basis. U.S.
EPA OSW expects that this data to be based on recent stack test measurements from a similar type of
combustion unit with similar waste feed, capacity, operating conditions, and air pollution control systems
(APCSs) to ensure comparable emission rates and destruction and removal efficiencies (DREs).
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U.S. EPA OSW expects that using data from a trial burn as a basis for estimating COPC emission rates
will tend to overestimate risk. COPC emission rates measured during trial burns are expected to be greater
than emission rates during normal unit operations, because a facility "challenges" its combustion unit
during a trial burn to develop a wide range of conditions for automatic waste feed cutoff (AWFCO)
systems. Trial burn tests are usually conducted under two conditions: (1) a high-temperature test, in which
the emission rate of metals is maximized, and (2) a low-temperature test, in which the ability of the
combustion unit to destroy principal organic hazardous constituents (POHCs) in the waste feed is
challenged. The lessor of the 95th percentile of the mean or maximum stack gas concentration from the
three trial burn runs should be used to develop the emission rate estimate used in the risk assessment.
High POHC feed rates and extreme operating conditions tested during the low-temperature trial burn test
are usually expected to result in greater product of incomplete combustion (PIC) emission rates. However,
this is not true in all cases. For example, the formation of PCDDs and PCDFs does not necessarily depend
on "POHC incinerability" low temperature conditions. Polychlorinated dibenzo(p)dioxins (PCDDs) and
polychlorinated dibenzofurans (PCDFs) can be formed as a result of (1) catalytic formation in the
low-temperature regions of the combustion unit or APCS during the low temperature test, or (2) catalytic
formation that is dependent on high APCS temperatures typically experienced during the high temperature
test.
Because the amount of testing required to develop estimates of COPC emission rates is so extensive and
time consuming, U.S. EPA OSW places the responsibility for selecting the test conditions first on the
facility and then on the permit writer. If a facility desires to receive a permit with no limits other than those
traditionally based on hourly rolling average data gathered during a trial burn, then risk testing should be
conducted during trial burn or "worst case" conditions. Whether the permit writer requires testing to be
conducted at low, high, or both temperature conditions is a decision that must be made by the permit writer
based on the characteristics of the facility and policy set forth by the senior management of the appropriate
regulatory agency.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
All (current and historical) stack sampling information regarding rates of emissions from the
combustion unit during normal or trial burn conditions
Description of the waste feed streams burned during the stack sampling, including chemical
composition and physical properties, which demonstrate that the waste feeds are representative
of worst case site-specific "real" wastes
* * * NOTICE * * *
Although U.S. EPA OSW will not require a risk assessment for every possible metal
or PIC from a combustion unit, this does not imply that U.S. EPA OSW will allow
only targeted sampling for COPCs during trial burn tests. Based on regional
permitting experience and discussions with regional analytical laboratories, U.S. EPA
OSW maintains that complete target analyte list analyses conducted when using U.S.
EPA standard sampling methods (e.g., 0010 or 0030), do not subject facilities to
significant additional costs or burdens during the trial burn process. Facilities
conducting stack emission sampling should strive to collect as much information as
possible which characterizes the stack gases generated from the combustion of
hazardous waste. Therefore, every trial burn or "risk burn" should include, at a
minimum, the following tests: Method 0010, Method 0030 or 0031 (as appropriate),
total organic compounds (using the Guidance for Total Organics, including Method
0040), Method 23A, and the multiple metals train. Other test methods may be
approved by the permitting authority for use in the trial burn to address detection limit
or other site-specific issues.
2.2.1.2 Normal Operation Emission Rate Data
Facilities with limited waste feed characteristics and operational variability may be allowed to conduct risk
testing at normal operational conditions (U.S. EPA 1994c). The collection of COPC data during normal
operating conditions is referred to as a "risk burn" throughout the remainder of this guidance. It is
important to note, however, that a risk burn does not replace a traditional trial burn conducted to measure
DRE. Instead, U.S. EPA OSW considers a risk burn as an additional operating condition of the trial burn
during which data is collected for the purpose of completing a risk assessment.
Because operational data collected during the risk burn would not normally be extrapolated to hourly
rolling average AWFCO limits specified in an operating permit; the regulatory agency permit writer should
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craft the permit with conditions designed to ensure that the facility does not operate at conditions in
"excess" of the normal conditions over the long-term operation of the facility (for example, waste feed rate
or stack gas flowrate). These additional permit limits are anticipated to take the form of quarterly or
annual mass feed limitations on the waste feed, quarterly or annual average temperatures or stack gas flow
rates, and other appropriate limitations.
It may also be necessary for the permit to contain appropriate reporting requirements to ensure that the
regulatory agency can verify that the facility does not normally operate at conditions in excess of those
tested during the risk burn. Monthly, quarterly, or annual reports which document long-term operations
will likely be required of the facility. If a facility violates a long-term permit condition, the permit writer
may also include language that requires the facility to cease waste burning immediately until a new test,
risk assessment, and/or revised permit are completed. More detailed guidance on the development of
permit limits can be found in U.S. EPA Region 6's Hazardous Waste Combustion Permitting Manual,
which can be obtained from the U.S. EPA Region 6 web page (www.epa.gov/region06/).
One of the most important criteria which should be evaluated when considering the collection of data
during a risk burn rather than a trial burn is the ability of the facility to document that the test is conducted
with "worst case" waste. Worst case waste should be the waste feed material or combination of materials
that are most likely to result in significant emissions of COPCs. The potential for both PIC and metal
emissions should be considered in the selection of the worst case waste. For example, if a facility burns
two types of waste—one waste with a high chlorine content and a significant concentration of aromatic
organic compounds and a second with a low chlorine content and a significant concentration of
alkanes—the former waste should be considered to be the "worst case" for PIC formation and should be
used during the risk burn. A similar evaluation should be considered when selecting the worst case waste
for metal emissions.
If a facility chooses to develop—and the appropriate regulatory agency allows the use of—emission rate
estimates from a risk burn rather than a trial burn, the data set for each COPC should be the 95th
percentile of the mean COPC emission rate over all the acceptable test runs or the maximum COPC
emission rate value from all acceptable test runs, whichever value is lower. U.S. EPA OSW does not
believe that it is reasonable to perform a risk assessment with the 95th percentile of the mean emission rate
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if the maximum rate is less than this value. U.S. EPA OSW also recommends that, where possible, the
COPC emission rate value from the trial burn test and the risk burn test be compared in the risk assessment
report along with a comparison of the operational conditions at these two test conditions. For example, if
the POHC used for the DRE test in the trial burn is a semivolatile organic compound (SVOC), the facility
should analyze for all SVOCs (Method 0010) during the trial burn, and compare these values to those
reported for the risk burn. The difference between the emission rates from the trial burn and risk burn
should be evaluated in the uncertainty section of the risk assessment.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Sampling and analytical data for trial burn and risk burn (if the risk assessment is completed
by using risk burn data) operating conditions
Description of the operating conditions, under which each set of emission rate data being used
was developed
Complete evaluation of the differences between trial burn and risk burn operating conditions,
with an explanation of the expected resultant risk differences
2.2.1.3 Estimates of the Total Organic Emission (TOE) Rate
Organic compounds that cannot be identified by laboratory analysis will not be treated as COPC's in the
risk calculations. However, these compounds still may contribute significantly to the overall risk, and
therefore, should be considered in the risk assessment (DeCicco 1995; U.S. EPA 1994d). U.S. EPA
developed the total organic emissions (TOE) test to account for unidentified organic compounds because
existing methods, such as total hydrocarbon analyzers, do not fully determine the total mass of organics
present in stack gas emissions (Johnson 1996). U.S. EPA OSW anticipates that trial and risk burns will
include sampling for TOE in order to provide permitting authorities with the information needed to address
concerns about the unknown fraction organic emissions. The TOE can be used in conjunction with the
identified organic compounds to calculate a TOE factor which can then be used to facilitate a evaluation of
potential risks from the unidentified fraction of organic compounds in the stack gas.
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The TOE test is the subject of other guidance; see the Guidance for Total Organics (U.S. EPA 1996b).
Use of the TOE data is dependent on a good understanding of the test method and how the data is reported.
The TOE method defines total organics as the sum of three fractions:
Fraction 1: Total Volatile Organic Compounds (TOVOc) (referred to as Field GC Component
in the TO Guidance) - TOVOC is defined as the fraction of organic compounds having a boiling
point less than 100°C. This VOC fraction is collected using U.S. EPA Method 0040. U.S. EPA
Method 0040 allows for quantification of the total mass of organic compounds with boiling points
less than 100°C, determined by summing the gas chromatograph/flame ionization detector results
as described in the TO Guidance.
Fraction 2: Total Chromatographical Semivolatiles (TOSVOc) (referred to as Total
Chromatographical Organics Component in the TO Guidance) - TOSVOC is defined as the
fraction of organic compounds having boiling points between 100°C and 300 °C. This VOC
fraction is collected using modified U.S. EPA Method 0010 procedures as defined by U.S. EPA
(1996b). The total mass of organic compounds with boiling points 100°C to 300°C is determined
by summing the total gas chromatorgraph/flame ionization detector results as described in the TO
Guidance.
Fraction 3: Total Gravimetric Compounds (TOGRAV) (referred to as Gravametric component
in the TO Guidance) - TOGRAV is defined as the fraction of organic compounds having boiling
points greater than 300 °C. This fraction includes two types of compounds: (1) Identified SVOCs
collected using U.S. EPA Method 0010 having boiling points greater than 300°C and (2)
unidentified nonvolatile organics having boiling points greater than 300°C. This fraction is
determined by using modified U.S. EPA Method 0010 procedures defined by U.S. EPA (1996b),
which quantifies the mass, above this fractions boiling point, by measuring the total mass by
evaporation and gravimetry (weighing) for nonvolatile total organics.
It should be noted that the TO total (TOTOTAL) is the sum of the sums of each fraction. The sum of the TO
fractions are described as follows:
TOTOTAL ~ TOvoc + TOsvoc + TOGRAv Equation 2-1
where
TOTOTAL = stack concentration of TO, including identified and unidentified
compounds (mg/m3)
TOVOC = stack concentration of volatile TO, including identified and
unidentified compounds (mg/m3)
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TOSVOC = stack concentration of SVOC TO, including identified and
unidentified compounds (mg/m3)
TOORAV = stack concentration of GRAY TO, including identified and
unidentified compounds (mg/m3)
The TOE data is used in conjunction with the identified data to compute a TOE factor. TOE factors have
been computed which range from 2 to 40. The TOE factor is defined by this guidance as the ratio of the
TOTOTAL mass to the mass of identified organic compounds and calculated by the following equation:
TO
JU TOTAL
TOE
Equation 2-2
where
FTOE = TOE factor (unitless)
TOTOTAL = total organic emission (mg/m3)
Cj = stack concentration of the rth identified COPC (mg/m3)
One of the most critical components of the TOE factor is the identification of the organic compounds in the
denominator of Equation 2-2. Although the permitting authority may not require a facility to analyze the
organic compounds with all possible analytical methods, facilities should consider the effects that gaps in
compound specific identification may have on the computation of the TOE factor. For example, hazardous
waste burning cement kilns have expressed concern about the amount of light hydrocarbons that may be
evolved from the raw materials processed in the cement kilns because these light hydrocarbons have not
typically been identified in trial burns. If such concerns are significant, permitting authorities and facilities
may choose to use additional test methods in the trial burn in order to speciate the maximum number of
organic compounds.
U.S. EPA OSW also recommends that permitting authorities include tentatively identified compounds
(TICs) in the denominator when computing the TOE factor to ensure that appropriate credit is given to
defensible efforts at identifying the maximum number of organic compounds. Finally, U.S. EPA OSW
recommends that non-detect compounds of potential concern be treated consistently between the risk
assessment and TOE evaluation. That is, if a non-detected constituent is deleted as a compound of
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potential concern (See Section 2.3), then it would not be included in the identified fraction of the TOE
equation. Compounds of potential concern identified as per Section 2.3, but not detected, should be
included in the TOE factor equation at the reliable detection limit (non-isotope dilution methods) or the
estimated detection limit (isotope dilution methods).
The results of the gravimetric fraction should also be carefully evaluated when using the TOE factor. Both
regulated industry and U.S. EPA scientists have expressed some concern that the gravimetric fraction of
TOE test may contain materials that are not organic. U.S. EPA Office of Research and Development
National Risk Management Research Laboratory (NRMRL) recently completed a study conducted to
identify products of incomplete combustion (U.S. EPA 1997a). U.S. EPA NRMRL suggested in the study
report that the gravimetric fraction of the TOE test may consist of organic and/or inorganic mass not
directly attributable to organic incinerator emissions. U.S. EPA NRMRL theorized that these artifacts
could consist of inorganic salts, super-fine particulate, or fractured XAD-2 resin. U.S. EPA NRMRL also
concluded in this study report that the vast majority of the non-target semivolatile organic compounds
detected, but not fully identified, were alkanes with more than 10 carbon atoms, esters of high molecular
weight carboxylic acids, and phthlates. Most problems associated with accurately determining the
gravimetric fraction attributable to incinerator emissions can be minimized; see the U.S. EPA 1998
Guidance on Collection of Emissions Data to Support Site-Specific Risk Assessments at Hazardous
Waste Combustion Facilities (U.S. EPA 1998b) for minimizing sample errors.
The TOE factor is used in the uncertainty section of the risk assessment report to evaluate the risks from
the unknown fraction of organics. Permitting authorities can evaluate the TOE factor and assess to what
extent actual risks may be greater than estimated risks. For example, if the risk from the known portion of
the emissions show that risks may be borderline and/or the TOE method shows that the unknowns are a
significant portion of the emission profile, the permitting authority may decide to do any or all of the
following:
1. Describe in a narrative form what is known of the unknown portion of the emissions.
2. Attribute a risk to the unknown portion of the emissions. An example was presented as a
preferred option in U.S. EPA (1994d) which assumed that the unknown compounds are
similar in toxicity and chemical properties to the known compounds taken as a whole. The
referenced equation is as follows:
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TO
TOTAL
Equation 2-2A
where
Qiadj = adjusted emission rate of compound/' (g/s)
Qi = emission rate of compound/ (g/s)
TOTOTAL = total organic emission (mg/m3)
Cj = stack concentration of the rth identified COPC (mg/m3)
3. Require additional testing to identify a greater fraction of the organic compounds.
4. Specify permit conditions that further control total organic emissions or that further
control the risks associated with known emissions.
Permitting authorities may use variations of the TOE factor to address site-specific concerns. For example,
some permitting authorities may compute three separate TOE factors based on the apportioning provided
by the TOE test (i.e., TOmc, TOsmc, and TOGRAv). The unknowns associated with each separate fraction
of unidentified organic compounds can then be evaluated separately.
2.2.2 Estimating Emission Rates for Facilities with Multiple Stacks
Emissions from all combustion units burning hazardous waste at a facility, not just the unit currently
undergoing the permitting process, should be considered in the risk assessment. As discussed further in
Chapter 3, air dispersion modeling for each combustion unit (source) should be conducted separate from
the other combustion units, to allow evaluation of risk on a stack or source-specific basis. A case example
is where a chemical manufacturing facility may operate both an on-site incinerator and several hazardous
waste burning boilers. Whether it is the incinerator or the boilers undergoing the permitting process, the
risk assessment should consider the emissions from all the combustion units in the estimate of facility risk.
In addition to RCRA combustion units, emissions from other RCRA treatment, storage, or disposal units
(e.g., open burning/open detonation and thermal desorption) may also be included in the risk evaluation in
some cases.
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2.2.3 Estimating Stack Emission Rates for Facilities Not Yet Operational
New hazardous waste combustion facilities should submit a Part B permit application, go through an
extensive permitting process, and, if successful, receive a final permit to commencement of operation. The
permitting process requires submittal of sufficiently detailed information for the regulatory authorities to
evaluate compliance with existing regulations, guidance, and protectiveness. Stack (source) locations and
dimensions, design flow and emission rate estimates, waste feed characteristics, surrounding building
dimension data, facility plot plans, and terrain data should be reviewed and used in a pre-operation risk
assessment. This will assist in decision-making and designing permit requirements.
The design emission rates, waste feed characteristics, and other design data should be reviewed along with
supplementing documentation to assure they are representative, accurate, and comprehensive. Good
engineering practice dictates a check of, and comparison with, data from similar existing units. Stack test
reports for facilities of similar technology, design, operation, capacity, auxiliary fuels, waste feed types,
and APCSs should be used to estimate COPC emission rates for new facilities that have not been
constructed.
If the preferred option of using surrogate data from similar facilities is not available, some state
environmental agencies enforce emission rate limits based on state laws. Since these limits cannot be
exceeded, they can be used to develop emission rate estimates for the risk assessment. The facility will
demonstrate that its emissions are less than the those considered in the permit and risk assessment during
the trial or risk burn.
Other data which may cause problems when performing risk assessments for new facilities is particle size
distribution. A default particle size distribution is presented in Chapter 3 for use if particle size distribution
data from a similar type of facility are not available.
2.2.4 Estimating Stack Emission Rates for Facilities Previously Operated
Emissions from the historical operation of combustion units burning hazardous waste at a facility, not just
the unit currently undergoing the permitting process, may also be considered in the risk assessment on a
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case-by-case basis as determined by the permitting authority. Such a case may be when the emissions from
historical operation of a source or sources may have already resulted in potential risk concerns at or
surrounding the facility. Emissions from historical operations could be taken into consideration by
modeling as a separate source or, if applicable, in the fate and transport equations by adding the previous
years of operation to the anticipated time period of combustion for a new or existing operating source. In
addition to RCRA combustion units, historical emissions from other RCRA treatment, storage, or disposal
units (e.g., open burning/open detonation and thermal desorption) at the facility under evaluation may also
be included in the risk assessment in some cases.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
All stack test reports for combustion units used to develop emission rate estimates
If using surrogate data for a new facility, descriptions of how the combustion data used
represent similar technology, design, operation, capacity, auxiliary fuels, waste feed types, and
APCSs
Demonstration that the data used to develop the emission rate estimates were collected by
using appropriate U.S. EPA sampling and analysis procedures
The range of data obtained, and values used, in completing the risk assessment
* * * NOTICE * * *
Facilities may use estimated emission rate data from other combustion units only to
determine whether the construction of anew combustion unit should be completed. After
a combustion unit has been constructed, U.S. EPA OSW will require an additional risk
assessment using emission rates collected during actual trial burn conditions.
2.2.5 Emissions From Process Upsets
Uncombusted hazardous waste can be emitted through the stack as a result of various process upsets, such
as start-ups, shutdowns, and malfunctions of the combustion unit or APCS. Emissions can also be caused
by operating upsets in other areas of the facility (e.g., an upset in a reactor which vents gases to a boiler
burning hazardous waste could trigger a process upset in the boiler, resulting in increased emissions). U.S.
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EPA (1994d) indicates that upsets are not generally expected to significantly increase stack emissions over
the lifetime of the facility.
Process upsets occur when the hazardous waste combustion unit is not being operated as intended, or
during periods of startup or shutdown. Upset emissions are generally expected to be greater than stack
emissions (over short periods of time) because the process upset results in incomplete destruction of the
wastes or other physical or chemical conditions within the combustion system that promote the formation
and/or release of hazardous compounds from combustion stacks. Upset emissions usually occur during
events and times when the hazardous waste combustion unit is not operating within the limits specified in a
permit or regulation.
To account for the increased emissions associated with process upsets, the stack emission rate estimated
from trial burn data (upset factor is not applied to non-PIC emission rate estimates where the total mass of
a constituent in the waste feed is assumed to be emitted) is multiplied by an upset factor. When available,
facilities should use site specific emissions or process data to estimate the upset factor. The following
types of data may be considered and evaluated to derive the upset factor:
• Data for continuous emissions monitoring systems that measure stack carbon monoxide,
oxygen, total hydrocarbon (if required), or opacity (if appropriate)
• Data on combustion chamber, APCS, or stack gas temperature
• Frequency and causes of automatic waste feed cutoffs (AWFCO)
• Ratio of AWFCO frequency and duration to operating time
• APCS operating variables, such as baghouse pressure drop, liquid scrubber flow rate, or
electrostatic precipitator voltage
• Stack test collected while the combustion unit was operated under upset conditions
This information may be analyzed with the objective of estimating the magnitude of the increase in
emissions and the percentage of time on an annual basis that the unit operates at upset conditions.
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When site specific data are not available or are inappropriate for deriving an upset factor, consistent with
previous guidance (U.S. EPA 1993h), U.S. EPA OSW recommends that upset emissions be estimated by
using a procedure based on work by the California Air Resources Board (CARB) (1990).
Estimating Emissions from Process Upsets: To represent stack emission rates during process
upsets, multiply the emission rate developed from the trial burn data by 2.8 for organics and
1.45 for metals. These factors are derived by assuming that emissions during process upsets are
10 times greater than emissions measured during the trial burn. Since the unit does not operate
under upset conditions continually, the factor must be adjusted to account for only the period of
time, on an annual basis, that the units operates under upset conditions. For organic compounds,
the facility is assumed to operate as measured during the trial burn 80 percent of the year and
operate under upset conditions 20 percent of the year [(0.80)(1)+(0.20)(10)=2.8]. For metals, the
combustion unit is assumed to operate as measured during the trial burn 95 percent of the year and
operate under upset conditions the remaining 5 percent of the year [(0.95)(1)+(0.05)(10)=1.45].
Catastrophic process upsets brought about by complete failure of combustion and air pollution control
systems resulting from non-routine events such as explosions, fires, and power failures are considered
accidental releases and are not addressed by this guidance.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Historical operating data demonstrating the frequency and duration of process upsets
• A discussion on the potential cause of the process upsets
• Estimates of upset magnitude or emissions
• Calculations which describe the derivation of the upset factor.
2.2.6 RCRA Fugitive Emissions
RCRA fugitive emission sources that should be evaluated in the risk assessment include waste storage
tanks; process equipment ancillary to the combustion unit; and the handling and disposal of combustion
system residues such as ash. Fugitive emissions from other RCRA treatment, storage, or disposal units
(e.g., landfills) may also require evaluation in some cases.
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This section contains guidance for quantitatively estimating fugitive emissions on the basis of procedures
outlined by other U.S. EPA guidance. Guidance regarding air dispersion modeling of fugitive emissions is
presented in Chapter 3.
2.2.6.1 Quantitative Estimation of RCRA Fugitive Emissions from Process Equipment
Quantitative estimation of RCRA fugitive emissions includes (1) identifying equipment to be evaluated as
fugitive emission source(s), (2) grouping equipment, as appropriate, into a combined source, and
(3) estimating compound specific emission rates for each source. Figure 2-1 is an example of a facility plot
plan that includes one RCRA combustion unit (CU-1), two hazardous waste feed storage tanks (WST-1
and WST-2), and ancillary equipment identified in a RCRA Part B permit application for a hypothetical
example facility. This figure, as well as Tables 2-1 and 2-2, have been provided as an example to facilitate
understanding of each of the steps presented for estimating fugitive emissions.
Step 1: Identifying Fugitive Emission Sources - Generally, RCRA fugitive emission sources to be
evaluated in the risk assessment should include waste storage tanks and process equipment that
comes in contact with a RCRA hazardous waste such as equipment specified in Title 40, Code of
Federal Regulations (40 CFR) Part 265, Subpart BB. Equipment covered under Subpart BB
includes the following:
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TABLE 2-1
EXAMPLE CALCULATION
TOTAL FUGITIVE EMISSION RATES
FOR EQUIPMENT IN WASTE FEED STORAGE AREA
1
Fugitive
Emission
Source
Waste
Feed
Storage
Area
2
Waste
Stream
Process
A
Wastes
Process
B
Wastes
3
Type of Waste
Stream In
Service
Light Liquid
Light Liquid
Light Liquid
Light Liquid
Light Liquid
Heavy Liquid
Heavy Liquid
Heavy Liquid
Heavy Liquid
Heavy Liquid
4
Equipment
Type
Pumps
Valves
Connectors
Tank WST-1
Tank WST-2
Pumps
Valves
Connector
Tank WST-1
Tank WST-2
5
Number of
Each
Equipment
Type Per Waste
Stream
3
70
30
1
1
2
75
50
1
1
6
Equipment Emission
Factors
(kg/hr)
0.01990
0.00403
0.00183
~
~
0.00862
0.00023
0.00183
~
~
(g/sec)
0.00553
0.00112
0.00051
~
~
0.00239
0.00112
0.00051
~
~
7
Total VOC
Weight
Fraction
0.9
0.9
0.9
0.9
0.9
0.6
0.6
0.6
0.6
0.6
8
Operational
Time Period of
Equipment
(days)
180
180
180
180
180
180
180
180
0
0
9
Total VOC
Emissions Rate by
Equipment (g/sec)
0.01493
0.07056
0.01377
0.02
0.03
0.00287
0.0504
0.0153
0
0
10
Total Fugitive
Emission
Rate (g/sec)
0.14926
0.06857
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Notes:
Column 1 Equipment in the Waste Feed Storage Area was identified and grouped as a combined RCRA fugitive emission source with an area extent
defined by UTM coordinates (NAD83).
Column 2 The waste streams serviced by equipment in the Waste Feed Storage Area can be determined through review of the facility's RCRA Part B
Permit Application, Air Emission Standards.
Column 3 The type of waste stream in service, defined as light or heavy for determination of equipment specific emission factors, can be determined
from review of waste stream vapor pressure.
Column 4 Similar types of equipment can be grouped according to the most applicable equipment specific emission factor and type of waste stream
service (light or heavy) provided in U.S. EPA (1995f).
Column 5 The number of equipment per type at the source was multiplied by the equipment specific emission factor (Column 6) to obtain equipment
specific emission rate for that respective type of equipment (Column 7).
Column 6 Emission factors specific to each type of equipment can be obtained from U.S. EPA (1995f), with the exception of storage tanks.
Column 7 Weight fraction of total volatile organic compounds was obtained from dividing the concentration of VOCs (mg/L) by the density of the
waste stream (mg/L).
Column 8 Assumed the equipment is operational for 180 days a year.
Column 9 Equipment specific fugitive emission rates were determined by multiplying Columns 5, 6, and 7. Emission rates for tanks were obtained from
Title V air permit application. In the absence of such data, emission rates for tanks can be calculated using U.S. EPA's TANKS Program or
by following the procedures outlined in U.S. EPA (1995a).
Column 10 The total fugitive emission rate for each waste stream is determined by summing emission rates for all the equipment. Table 2-2 presents
calculations for estimating speciated fugitive emissions.
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TABLE 2-2
EXAMPLE CALCULATION
SPECIATED FUGITIVE EMISSIONS
FOR EQUIPMENT IN WASTE FEED STORAGE AREA
1
Fugitive
Emission
Source
Waste Feed
Storage Area
2
Waste Stream
Process A Wastes
Process B Wastes
3
Waste Stream
Composition
Acetaldehyde
Acetonitrile
2-Nitropropane
Nitromethane
Acetaldehyde
Acetonitrile
Methanol
Propionitrile
4
Weight Fraction
ofEachVOCIn
Waste Stream
(%)
0.20
0.25
0.25
0.20
0.20
0.10
0.20
0.05
5
Total
Fugitive
Emission
Rate (g/sec)
0.14926
0.06857
6
Speciated
Fugitive
Emissions
(g/sec)
0.0030
0.0037
0.0037
0.0030
0.0137
0.0069
0.0137
0.0034
Notes:
Column 1 Equipment in the Waste Feed Storage Area was identified and grouped as a combined
RCPxA fugitive emission source with an aerial extent defined by UTM coordinates
(NAD83).
Column 2 The waste streams serviced by equipment in the Waste Feed Storage Area can be
determined through review of the facility's RCPxA Part B Permit Application, Air
Emission Standards.
Column 3 The waste stream composition can be determined from analytical data
Column 4 Weight fraction of compounds in the waste stream can be determined from analytical
data or review of the facility's Title V Air Permit Application, Emissions Inventory
Questionnaire (EIQ) for Air Pollutants (see example in Figure 2-2).
Column 5 The total fugitive emission rate for each waste stream was obtained from Column 10,
Table 2-1.
Column 6 Speciated fugitive emissions were obtained by multiplying Column 4 and 5.
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• Pumps
• Valves
• Connectors (flanges, unions, tees, etc.)
• Compressors
• Pressure-relief devices
• Open-ended lines
• Product accumulator vessels
• Sampling connecting systems
• Closed vent systems
• Agitators
Each fugitive emission source should be identified on a facility plot map with a descriptor and the location
denoted with Universal Transverse Mercator (UTM) coordinates (specify if North American Datum [NAD]
of27orNAD83).
Step 2: Grouping Equipment Into a Combined Source - To significantly reduce the effort required to
complete air dispersion modeling and subsequent risk assessment, equipment in close proximity
may be grouped and evaluated as a single combined source with the speciated emission rates for
each piece of equipment summed. The area extent of the grouped or combined source, as defined
by UTM coordinates (specify if NAD27 or NAD83), should be clearly denoted on a facility plot
map. The area extent of the combined source should be defined by the actual locations of the
equipment being grouped, without exaggeration to cover areas without fugitive sources.
Consideration should also be made for how fugitive emission sources are to be defined when
conducting the air dispersion modeling (see Chapter 3).
As shown in Figure 2-1, equipment in two areas at the hypothetical facility have been grouped into
combined sources; these consist of the Waste Feed Storage Area and the RCRA Combustion Unit Area.
Step 3: Estimating Fugitive Emissions from Tanks - Fugitive emission rates for waste storage tanks can
be obtained from the facility's emission inventory or Title V air permit application prepared in
compliance with Clean Air Act Amendments of 1990 (see example provided as Figure 2-2). If the
facility does not have such information available, fugitive emissions from storage tanks can be
calculated using U.S. EPA's TANKS Program or by following the procedures outlined in U.S.
EPA guidance document (1995a), "Compilation of Air Pollution Emission Factors,
January 1995. "
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FIGURE 2-1
EXAMPLE FACILITY PLOT MAP
FACILITY BOUNDARY
\
\ •
\l
0
3617500
3617400
3617300
WASTEFEED
i STORAGEAREA
~i
Bupric
wsr-i
wsr-2
r
3617200
COMBUSTION UNIT AREA
CU-1
AREA EXTENT OF
WASTEFEED STORAGE
LL X=585873 7=3617184
LR X=585896 7=3617184
UR X=585896 7=3617208
UL X=585873 7=3617208
AREA EXTENT OF
COMBUSTION UNIT AREA
LL X=585952 7=3617114
LR X=58S962 7=3617114
UR X=S8S962 7=3617124
UL X=585952 7=3617124
3617100
3617000
3616900
3616800
NOTE: UTM COORDINATE GRID
IS 100METERNAD83
SCALEBfFEET
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FIGURE 2-2
EXAMPLE EMISSIONS INVENTORY
Department of Environmental Qfialtiy
Air Qfiality Division
P.O. Box 82135
Baton Rouge, LA 70884-2135
(504)765-0219
LOUISIANA
SINGLEPOINTSOURCE/AREA SOURCE
Emission Inventory Questionnaire (EIQJ
for Air Pollutants
LADEQ
Company Name
Hypothetical Chemical Company
Plant location and name (if any)
Eaton Rouge, LA Plant
February 1996
Source ID Number
WST-1
Descriptive name of the equipment served by this stack or vent
Waste Feed Tank
Locationof stack or vent (see instructions on how to determine
location of area sources)
Horizontal Coordinate 589100 m E
UTMzone no. 15 Vertical coordinate 3616200 mN
STACK mdDlSCHARGE
PHYSICAL
CHARACTERISTICS
Change f] yes ft/no
Height of stack
above grade [ft]
Diameter or stack
discharge area
0.167ft
Stack gas exit
temperature ("F)
125
Stack gas flow at process
conditions, not at standard (cfm)
2A27
Stack gas exit velocity
1&22
For tanks. Kst volume
Date of construction
Fuel
Type, of fuel used and heat input (see instructions)
Type of Fuel
Heat input (MMBtu/hr)
Operating
Characteristics
Percent of annual throughout of
pollutants through this emission point
Normal operating time
of this point
Dee-Feb
25
Mar-May
25
Jun-Aug
25
Sep-Nov
25
hrs/ days/ weeks/
day week year
24.00 7 52.0
Normal
operating rate
100%
Air Pollutant Specific Information
PoUutant
Control
equipment
code
Control
equipment
efficiency
Emission Rate
Average
Maximum
(Ibsfar)
Annual
(tons/yr)
estimation
method.
Add,
change,
delete
code
Concentration in gases
exiting at stack
2-Nitropropane
Acetalddtyde
Acetaiiitrite
Methanol
Non-Toxic Voc
000
0.0000
0.0023
0.0041
0.0023
0.0023
0.0062
03463
125.00
21.1266
4.502
195.3347
0.01
0.081
0.01
0.01
0.028
3
3
3
3
3
N/A ppmbyvol
N/A ppmbyvol
N/A ppmbyvol
N/A ppmbyvol
N/A ppmbyvol
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The information required for estimating fugitive emission rates from storage tanks includes, but is
not limited to, the following:
• Dimensions of the tanks
Shell height and diameter
• Characteristics of the tank roof
Color and shade
Condition (e.g., poor, good)
Type (e.g., cone, dome)
Height
Radius or slope
Fixed or floating
• Characteristics of the shell
Color and shade
Condition (e.g., poor, good)
Heated
• Settings on breathe vents
Vacuum setting
Pressure setting
• Characteristics of the stored liquids
Maximum and annual average liquid height
Working volume
Turnovers per year
Net throughput
Average annual temperature
Vapor pressures of speciated constituents (at annual average temperature)
Step 4: Estimating Fugitive Emissions from Process Equipment - Based on guidelines provided in U.S.
EPA (1995f), "Protocol for Equipment Leak Emission Estimates, EPA-453/R-93-017, " fugitive
emissions for each equipment listed under 40 CFR Part 265, Subpart BB can be estimated by the
following four approaches, in order of increasing refinement and data requirements:
• Average Emission Factor Approach (AEFA)
• Screening Ranges Approach (SRA)
U.S. EPA Correlation Approach (EPACA)
• Unit-Specific Correlation Approach (USCA)
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These four approaches can be used at any facility to estimate fugitive emission rates of volatile organic
compounds (VOCs) from equipment. Except for the AEFA method, all of the approaches require screening
data collected by using a portable monitoring device (PMD). Because data on fugitive emissions at a
facility is generally limited, the AEFA method will apply in most cases, and therefore, has been selected for
use in the example demonstrated in Figure 2-1, and Tables 2-1 and 2-2. However, U.S. EPA OSW
recommends that facilities use more refined approaches such as SRA, EPACA, or USCA, if sufficient data
is available. U.S. EPA (1995f) provides a detailed discussion on these three approaches.
An Example Calculation Using the AEFA Method
Information for estimating fugitive emission rates using the AEFA method is as follows:
• Type of waste stream associated with each equipment type (Columns 2 and 3, Table 2-1)
light liquids are those in which the sum of the concentration of individual
constituents with a vapor pressure over 0.3 kilopascals (kPa) at 20°C is greater
than or equal to 20 weight percent
heavy liquids are all others liquids not meeting the definition of light liquids as
specified above
• Number of each equipment type associated with each waste stream (Columns 4 and 5,
Table 2-1)
• Total VOC weight fraction of each waste stream (Column 7, Table 2-1)
• Weight fraction of each VOC in each waste stream (Columns 3 and 4, Table 2-2)
• Operational time period of equipment (Column 8, Table 2-1)
When this approach is used, equipment can be grouped by waste streams of similar characteristics and
VOC composition (Columns 1 and 2, Table 2-1). However, the AEFA approach does not account for
different site-specific conditions such as temperature, vapor pressure, or screening values, among process
units within a source category. Site-specific factors can significantly influence fugitive emission rates of
leaks from equipment.
The average emission factors for synthetic organic chemicals manufacturing industry process units,
refineries, and natural gas plants are presented in U.S. EPA (1995f) (Column 6, Table 2-1). The following
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table is an excerpt from this guidance document. These emission factors are most valid for estimating rates
of emissions from a grouping of equipment over a long time period.
SOCMI AVERAGE EMISSION FACTORS
Equipment type
Valves
Pump seals
Compressor seals
Pressure relief valves
Connectors
Open-ended lines
Sampling connectors
Service
Gas
Light liquid
Heavy liquid
Light liquid
Heavy liquid
Gas
Gas
All
All
All
Emission factor
(kg/hr/source)
0.00597
0.00403
0.00023
0.0199
0.00862
0.228
0.104
0.00183
0.0017
0.0150
Source: U.S. EPA (1993e)
The total VOC emissions rate for a specified equipment type can be calculated by multiplying the
equipment emission factor by the total VOC weight fraction and the number of each equipment type per
waste stream (Column 9, Table 2-1 = Column 6 x Column 7 x Column 5).
The total VOC emission rates for each equipment type are summed to generate the total fugitive emission
rate for the waste stream by (Column 10, Table 2-1). Speciated fugitive emissions can then be calculated
by multiplying the weight fraction of each VOC in the waste stream and the total fugitive emission rate for
the waste stream (Column 6, Table 2-2 = Column 4 x Column 5). This speciated emission rate is the
emission rate used in the risk assessment.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Summary of the step-by-step process conducted to evaluate fugitive emissions
• Facility plot map clearly identifying each fugitive emission source with a descriptor and the
location denoted with UTM coordinates (specify if NAD27 or NAD83).
• Speciated emission rate estimates for each waste stream serviced by each source, with
supporting documentation
• Applicable discussion of monitoring and control measures used to mitigate fugitive emissions
2.2.6.2 Fugitive Emissions from Combustion Unit Leaks
Fugitive emissions that result from the construction, design, or operation of a combustion unit burning
hazardous waste should be evaluated, as appropriate. Examples of fugitive emissions from combustion
unit leaks include the following:
• Combustion units that operate under negative pressure may experience temporary positive
pressures ("puffing") that cause fugitive emissions. This condition can occur when a slug
of high BTU waste is combusted, causing a rapid expansion in the volume of combustion
gases that exceeds the volume of the combustion chamber.
• Fugitive emissions resulting from the day-to-day operation of the combustion unit and
APCS. These emissions will typically include (1) leaks that occur due to a positive
pressure in the APCS, and (2) routine maintenance activities such as replacement of
baghouse collection bags.
Currently, U.S. EPA OSW does not offer any specific quantitative guidance on how to estimate fugitive
emissions from hazardous waste combustion units. However, risks associated with emissions from
hazardous waste combustion unit leaks can be addressed in the uncertainty section of the risk assessment if
no site specific quantitative methods are available. Specifically, the permitting authority can review facility
specific data to determine whether or not the design addresses equipment leaks and whether the operational
data indicates that equipment leaks may be a problem.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Process design information and drawings (if necessary)
• Past operating data indicating the frequency, duration, and magnitude of combustion unit leaks
• Information regarding the probable cause of combustion unit leaks
• Summary of procedures in place to monitor or minimize fugitive emissions resulting from
combustion unit leaks
2.2.7 RCRA Fugitive Ash Emissions
The combustion of hazardous waste materials may result in the production of flyash. Fugitive particle
emissions may result from the subsequent collection, handling, and disposal of the flyash. Typically,
fugitive emissions of flyash, collected from an air pollution control device (APCD) will occur during
transfer into covered trucks or other conveyance mechanisms prior to disposal. Emissions generated during
the loading process can be controlled by APCDs or other types equipment, however, a fraction of the flyash
may still escape into the atmosphere as fugitive emissions.
2.2.7.1 Quantitative Estimation of RCRA Fugitive Ash Emissions
Steps for the quantitative estimation of RCRA fugitive ash emissions include (1) determining an empirical
emission factor, (2) estimating the flyash generation rate, and (3) accounting for air pollution control
equipment, if applicable. As demonstrated in the example calculation below, the fugitive ash emission rate
can then be estimated by multiplying the empirical emission factor by the flyash generation rate and the
control deficiency of the air pollution control equipment, if applicable.
Step 1: Determining an Empirical Emission Factor - Particle emissions associated with flyash loading
and unloading can be estimated using an empirical emission factor of 1.07 Ib per ton flyash. This
factor is based on a field testing program conducted at a coal fired power plant equipped with an
electrostatic precipitator (ESP) (Muleski and Pendleton 1986). Because the combustion of coal
and hazardous wastes are similar activities, flyash generated from similar control devices is
expected to behave similarly under the same conditions, with respect to fugitive emissions. In
general, particle behavior is dependent more on the physical form of the flyash than on the feed (or
waste) stream being combusted. The emission factor determined during the empirical study
(0.107 Ib per ton flyash) can be adjusted by a factor (e.g., 10) to account for the fact that the flyash
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from the combustion of coal (as in the study) was wetted. Flyash from the hazardous waste
combustion facility may not be wetted depending on the facility.
Step 2: Estimating the Flyash Generation Rate - The flyash generation rate from the APCD can be
obtained from the Part B Permit Application and the total ash content of the "generic" waste
streams created from the waste profile. Both values should be approximately the same. Since a
major portion of ash fed to the combustor is converted to bottom ash, it is likely that this value is a
conservatively high estimate of the actual flyash generation rate.
Step 3: Accounting for Air Pollution Control Equipment - If an APCD is used for controlling emissions
during flyash handling operations, an efficiency factor (e.g., 99.5 percent) can be applied to the
emission rate. An efficiency factor of 99.5 percent is based on U.S. EPA (1995a) for typical
collection efficiencies of particulate matter control devices, for the particle sizes in the range of 2.5
to 10 um.
Example Calculation
The fugitive ash emission rate is calculated by multiplying the empirical emission factor (Step 1) times the
estimated flyash generation rate (Step 2) [(1.07 Ib per ton) * (5,000 tons per year) = 5,350 Ibs per year].
Accounting for the air pollution control equipment, the product of Steps 1 and 2 is multiplied times one
minus the fabric filter efficiency (Step 3) to obtain the final RCRA fugitive ash emission rate for use in the
risk assessment [(5,350 Ibs per year) * (1 - 0.995) = 26.75 Ibs per year].
2.2.8 Cement Kiln Dust (CKD) Fugitive Emissions
CKD is the particulate matter (PM) that is removed from combustion gas leaving a cement kiln. This PM
is typically collected by an APCS—such as a cyclone, baghouse, ESP—or a combination of APCSs.
Many facilities recycle a part of the CKD back into the kiln. Current and applicable guidance on
evaluating CKD includes (1) the Technical Background Document for the Report to Congress (U.S. EPA
1993g), and (2) the more recent regulatory determination of CKD (60 FR 7366, February 7, 1995).
Most CKD constituents (for example, metals) are not volatile but could be released to air through fugitive
dust emissions as a volatile or semivolatile organic that can be released in gaseous form and present in
relatively low concentrations, if at all (U.S. EPA 1993a). Dust particles may be suspended in the air by
either wind erosion or mechanical disturbances. The extent to which dust is blown into the air by wind
erosion depends on several site-specific characteristics, including (1) the texture (particle size distribution)
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and moisture content of the CKD on the surface of piles, (2) nonerodible elements, such as clumps of grass
or stones on the pile, (3) a surface crust, and (4) wind speeds. Mechanical disturbances that can suspend
CKD constituents in the air include (1) vehicular traffic on and around CKD piles, (2) CKD dumping and
loading operations, and (3) transportation of CKD around a plant site in uncovered trucks. Cement plants
may use various control measures to limit the release of CKD to the air. For example, CKD may be
pelletized in a pug mill, compacted, wetted, and covered to make the material less susceptible to wind
erosion.
To keep the dust down, many facilities add water to CKD, before disposal, to agglomerate individual
particles. In addition, as CKD sits in a pile exposed to the elements, occasional wetting by rainfall may
form a thin surface crust in inactive areas of the pile. This acts to mitigate air entrainment of particles.
However, based on field observations by U.S. EPA (1993g), neither surface wetting nor natural surface
crusting eliminates the potential for CKD to be blown into the air. Wetting the dust before disposal
provides incomplete and temporary control, because (1) infrequent application of water, and (2) the dust
ultimately dries and returns to a fine particulate that is available for suspension and transport. Similarly, a
surface crust may develop, but (1) the crust breaks when vehicles or people move on the pile, and (2) fresh
dust is regularly added to the pile, providing a continual, exposed reservoir of fine particles. It should be
noted that a crust does not always form for a variety of reasons such as weather and chemistry of the CKD.
CKD constituents that are released to the air are transported and dispersed by the winds, and are ultimately
deposited onto land or water, either by settling in a dry form or by being entrained in precipitation.
2.2.8.1 Composition and Characteristics of CKD
U.S. EPA (1993g) highlighted the limited amount of available information regarding the variation in
chemical constituents of CKD generated by facilities burning hazardous waste as fuel and by facilities
burning only fossil or nonhazardous waste fuels. There may also be differences in composition between the
"as-generated" CKD that is recycled back into the system and the "as-managed" CKD that is disposed on
or offsite.
Transport in air is of concern for CKD, because the dust is a fine PM that is readily suspendable,
transportable, and respirable in air. In general, particles that are < 100 micrometers may be suspended in
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the wind and transported. Within this range, particles that are <30 micrometers can be transported for
considerable distances downwind. Virtually all of the dust generated at the 15 facilities evaluated by U.S.
EPA (1993g) in the Cement Kiln Dust Report to Congress may be suspended and transported in the wind
(that is, the vast majority of particles are < 100 micrometers), and over two-thirds of all CKD particles
generated may be transported over long distances. Additionally, a significant percentage of the total dust
generated (from 22 to 95 percent, depending on kiln type) comprises particles that are < 10 micrometers.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Physical data, including particle size distribution and density
Chemical data, including organic and inorganic analytical tests similar to those used for
sampling combustion gases
Plant net CKD generation rate (how much CKD per year that is available for disposal)
Ambient air monitoring data
CKD management, transportation, storage, and disposal methods
Containment procedures, including fugitive dust prevention measures and the area of exposed
CKD
Meteorological data, including wind speed and precipitation
2.2.8.2 Estimation of CKD Fugitive Emissions
In general, this guidance does not address CKD risks in a quantitative fashion. However, risk assessments
conducted for cement manufacturing facilities should, at a minimum, evaluate the fugitive emissions due to
CKD on a qualitative basis. Readers are referred to the Technical Background Document for the Report
to Congress (U.S. EPA 1993g), for methods to estimate the magnitude of fugitive emissions from the
handling, storage, and disposal of CKD. In addition, an analysis of a specific facility's compliance with
other environmental statutes and regulations may be an appropriate method to qualitatively evaluate risks
associated the handling, storage, and disposal of CKD.
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2.3 IDENTIFYING COMPOUNDS OF POTENTIAL CONCERN
Compounds of potential concern (COPCs) are those compounds evaluated throughout the risk assessment.
The purposes of identifying COPCs are to focus the risk assessment on those compounds that are likely to
pose the most risk to ecological receptors exposed to hazardous waste combustion emissions. The COPC
identification process is conservative by design to avoid not including compounds that might pose an
ecological risk.
There is no one definition of a COPC, because a compound that is a COPC at one hazardous waste
combustion unit may not be a COPC at another combustion unit. COPCs in the emissions from hazardous
waste combustion units vary widely, depending on (1) the type of combustion unit, (2) the type of
hazardous waste feed being burned, and (3) the type of APCS used. Also considered as COPCs are
products of incomplete combustion (PICs); which are any organic compounds emitted from a stack, such as
(1) compounds initially present in the hazardous waste feed stream and not completely destroyed in the
combustion process, and (2) compounds that are formed during the combustion process. Because PICs
may be formed by trace toxic organic compounds in the waste feed stream, these compounds should be
evaluated as PIC precursors, in addition to those compounds that constitute most of the hazardous waste
feed.
PICs should not be confused with principal organic hazardous constituents (POHC), which are compounds
in the waste feed stream used to measure DRE of the combustion unit during a trial burn test. Unburned
POHCs and partially destroyed or reacted POHCs are PICs, but PICs are not necessarily related to
POHCs.
Table A-l (Appendix A) presents a comprehensive list of compounds typically identified (1) in hazardous
waste, and (2) in hazardous waste combustion stack gas emissions. For each compound, Table A-l
identifies the Chemical Abstracts Service (CAS) number and also indicates whether a compound has been
identified as a potential COPC by (1) U.S. EPA and state risk assessment reference documents,
(2) emission test results that have identified the compound in the emissions from hazardous waste
combustion facilities, or (3) other literature that suggests that the compound may be significant from a risk
perspecitve. Table A-l has been provided in this guidance in order to help risk assessors ensure that the
trial burn considers the full range of compounds potentially emitted from a combustion unit and the
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appropriate analytical method. Once the trial burn stack tests are completed, the COPC selection process
is initiated based on the universe of stack test data, not Table A-l. The purpose of a risk assessment is not
to arbitrarily evaluate every potential compound listed in Table A-l.
Based on U.S. EPA OSW review, COPCs previously identified in ecological isk assessments at combustion
facilities are as follows:
• Polychlorinated dibenzo(p)dioxins (PCDD) and polychlorinated dibenzofurans (PCDF)
• Polynuclear aromatic hydrocarbons (PAH)
• Polychlorinated biphenyls (PCB)
• Pesticides
• Nitroaromatics
• Phthalates
• Other organics
• Metals
This list was compiled based on professional experience and is not meant to be either limiting or inclusive.
The list enabled U.S. EPA OSW to focus on (1) developing receptor-specific and compound-specific
biocentration factors as provided in Appendicies C and D, (2) developing compound- and receptor-specific
TR Vs as provided in Appendix E, and (3) developing receptor exposure parameters and exposure equations
discussed in Chapter 5 and provided in Appendix F. These focused compound-specific parameters and
information are included to facilitate the performance of ecological risk assessments, and are not meant to
be either limiting or inclusive for hazardous waste combustion facilities. Experience has shown that
developing compound-specific and receptor-specific parameters for risk assessments can be one of the most
labor- and time-intensive parts of completing the risk assessment, and U.S. EPA OSW intends that the
information included in the Appendicies of this guidance facilitates the risk process.
COPCs are identified from the trial burn data based on their potential to pose an increased risk. This
identification process should focus on compounds that (1) are likely to be emitted, based on the potential
presence of the compound or its precursors in the waste feed, (2) are potentially toxic to ecological
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receptors, and/or (3) have a definite propensity for bioconcentrating in ecological receptors and
bioaccumulating in food chains. Appendix E presents toxicity reference values of specific compounds to
specific receptors. The toxicity information provided in this guidance is for informational purposes to help
permitting authorities explain the basis for identifying compounds as COPCs and facilitate completing the
risk assessment. Since toxicity information may change as additional research is conducted, permitting
authorities should review the most current available information before completing a risk assessment to
ensure that the toxicity data used in the risk assessment is based upon the most current Agency consensus.
As illustrated in Figure 2-3, the following steps should be used to identify the COPCs that will be evaluated
for each facility (U.S. EPA 1993h; 1994d).
Step 1: Evaluate analytical data from the stack tests performed during the trial burn and compounds
associated with fugitive emissions (see Section 2.2.5). Prepare a list which includes all the
compounds specified in the analytical methods performed in the trial burn, and fugitive emission
evaluation. Describe whether the compound was detected or not detected.
A detection in any one of the sample components (e.g., front half rinse, XAD resin, condensate, Tenax
tube) in any run constitutes a detection for that specific compound. Evaluation of blank contamination
results, included in the quality assurance (QA) data section of the trial burn report, should be considered
when determining the non-detect status of the compounds (see Section 2.5).
Step 2: Evaluate the type of hazardous waste burned in the combustion unit—including all wastes that the
unit will be permitted to burn—to determine whether any of the non-detect compounds should be
retained for evaluation as COPCs because they are potentially present in the waste.
For example, if a facility is permitted to burn explosives which characteristically include nitroaromatic
compounds, yet the stack test showed non-detect status for all nitroaromatic compounds, nitroaromatic
compounds should still be evaluated in the risk assessment. This evaluation should also consider other
materials fed to the combustion unit (e.g., raw materials or coal in a cement kiln). Regardless of the type of
hazardous waste being burned in the combustion unit, every risk assessment should include PCDD/PCDFs
and PAHs (the rationale for including these compounds is discussed in greater detail in Sections 2.3.1 and
2.3.2).
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COPC IDENTIFICATION
Analysis at trial bum that considers the
Ml range of compounds potentially emitted
Prepare COPC list that includes all
compounds specified in the analytical
methods performed during trial bum, and
identified in the fugitive emissions evaluation
Evaluate 30 largest TICs to
determine if they have toxicities
similar to any detected compounds
DetermineiCuFCaeKciionslaius
including consideration of blank
contamination
Non-Detected
Compounds
Is the non-detect
compound present
in the waste being
burned?
Does the non-detect
compound
bioaccumulate or
bioconcentrate?
Does the non-detect
compound have a
high potential
to be emitted
as a PIC?
Is the non-detect
compound a concern
due to site specific
factors, and is it
possibly emitted?
Detected
Compounds
Yes
Yes
Yes
Yes
Is toxicological data
available for COPC or
appropriate surrogate
compound?
No
Retain as COPC; evaluate
qualitatively in the risk
assessment
Yes
quantitatively in the risk
assessment
No
Delete from
the COPC list
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Step 3: Include as COPCs those compounds that are non-detect, but have a high potential to be emitted as
PICs.
Although some compounds (nitroaromatics, pthalates, hexachlorobenzene, and petachlorphenol) have
traditionally been automatically identified as PICs in previous U.S. EPA guidance, inclusion of these
compounds should be based on consideration of potential to be emitted and waste feed composition
(e.g., nitrogenated wastes, plastics, or highly chlorinated organic waste streams) (see Sections 2.3.4
through 2.3.6).
Step 4: Include as COPCs those compounds that are non-detect, but have a tendancy to bioaccumulate or
bioconcentrate. This includes organic chemicals with log Kow values equal to or greater than 4.0
(Connolly and Pederson 1987), and inorganic compounds with a whole-body BCF equal to or
greater than 100.
U.S. EPA OSW understands that this step would not retain some nondetected compounds (such as VOCs
with log Kow values less than 4.0) for further evaluation in the risk assessment and appears to provide the
opportunity for detection limits for these compounds to be increased intentionally by the facility to escape
the risk assessment process. However, U.S. EPA OSW anticipates that stack test data used in conducting
the risk assessment will also be subject to evaluation in the human health risk assessment process, which
would subsequently determine increased risk due to nondetected compounds with high detection limits.
Therefore, the lowest achievable detection limits possible with standard U.S. EPA methods for all
compounds are recommended, ensuring that the risk assessment process will result in the risk manager
obtaining the information necessary to conclude that the facility has not potentially overlooked a serious
risk.
Step 5: Evaluate the 30 largest tentatively identified compound (TIC) peaks obtained during gas
chromatography (GC) analysis, to determine whether any of the TICs have toxicities similar to the
detected compounds. If they do, consider surrogate toxicity data, as recommended for detected
COPCs without toxicity information.
Step 6: Evaluate any compound that may be of concern due to other site-specific factors (e.g., community
and regulatory concern, high background concentrations). Include as COPCs those compounds
that (1) are a concern due to site-specific factors, and (2) may be emitted by the combustion unit.
If the compound in question does not have a reasonable potential of being present in the stack emissions,
the risk assessment report should justify this assertion. This information will provide the risk manager with
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the information necessary to evaluate potential for risk. By following Steps 1 through 6, the risk assessor
will be able to identify COPCs from the typically exhaustive list of compounds tested in during the trial
burn. To complete Step 4, log Kow and BCF values for compounds typically identified in risk assessments
as COPCs and listed at the beginning of this section are located in Appendicies A and C, respectively.
The following subsections also focus on compounds that can drive risk assessments as indicated by past
experience. These compounds include polychlorinated dibenzo(p)dioxins and dibenzofurans, polynuclear
aromatic hydrocarbons, polychlorinated biphenyls, nitroaromatics, phthalates, hexachlorobenzene and
pentachlorophenol, and metals. Volatile organic compounds are also discussed. Specific issues that affect
the COPC identification process and evaluation of these compounds in the risk assessment are discussed.
Because U.S. EPA's boiler and industrial furnace (BIF) regulations also regulate emission rates of PM and
hydrochloric acid and chlorine gas, the risks associated with these compounds are also discussed. There is
also a discussion of the emerging issues surrounding the class of compounds called "endocrine disrupters."
U.S. EPA OSW recognizes that, for many compounds, only limited information is available regarding
potential effects. In addition, for some compounds for which effects have been identified, the relationship
between dose and response may be poorly understood. U.S. EPA OSW advocates that the risk assessment
use the sum of the available toxicological information and evaluate the uncertainty associated with these
issues. As stated previously, toxicity benchmarks and information may change as additional research is
conducted, permitting authorities should consult with the most current information before completing a risk
assessment. Toxicity profiles for many of the compounds typically evaluated in ecological risk assessments
are presented in Appendix H. U.S. EPA OSW prepared these profiles to promote consistency in risk
assessments and to assist the uncertainty analysis.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Complete evaluation of hazardous wastes to be burned in the combustion unit
• Complete evaluation of any raw materials or primary fuels burned in the combustion unit
• Waste analysis procedures used to monitor the composition of hazardous waste feed streams
• Analytical data and calculations used to complete the COPC identification process
2.3.1 Poly chlorinated Dibenzo(p)dioxins and Dibenzofurans
Based on their combustion properties and toxicity, U.S. EPA OSW recommends that PCDDs and PCDFs
should be included in every risk assessment. The general combustion properties and guidance for
addressing toxicity of PCDDs and PCDFs are discussed in the following paragraphs and subsections,
respectively.
One mode in which PCDDs and PCDFs form in dry APCSs is fly ash catalyzed reactions between halogens
and undestroyed organic material from the furnace. PCDDs and PCDFs were first discovered as thermal
decomposition products of polychlorinated compounds, including (1) the herbicide 2,4,5-T,
(2) hexachlorophene, (3) PCBs, (4) pentachlorophenol, and (5) intermediate chemicals used to manufacture
these compounds. In recent years, as chemical analytical methods have become more sensitive, additional
sources of PCDDs and PCDFs have been identified, including (1) effluent from paper mills that use
chlorine bleaches, and (2) combustion sources, including forest fires, municipal waste and medical
incinerators, and hazardous waste combustion units. Duarte-Davidson et al. (1997) noted that the
combustion of chlorine-containing materials in municipal solid waste is responsible for about two-thirds of
the total annual emissions of newly formed TCDDs and TCDFs in the United Kingdom. In the United
States, U.S. EPA (1998a) estimated that emissions of dioxin TEQs from municipal solid waste incinerators
accounted for 37 percent of all emissions of dioxins into the environment in 1995.
PCDDs and PCDFs are formed at these combustion sources from the reaction of chlorine-containing
chemicals and organic matter. Predicting the production of PCDDs and PCDFs in a specific situation is
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difficult, because dechlorination, which produces PAHs from PCDDs and PCDFs, occurs under similar
conditions. Recent studies (Addink et al. 1996; Environment Canada 1987; Froese and Hutzinger 1996a,
1996b; Gullett et al.1994; Kilgroe et al. 1991; Luijk et al. 1994; Robert 1994) have explored some of these
complexities, including (1) the formation of PCDDs and PCDFs from simple organics (such as ethane) and
complex organics (such as dibenzofuran), and (2) the catalysis of these organic compound reactions by
various common metals, such as copper. Wikstrom et al. (1996) found that the form of chlorine—whether
organic, as with chlorinated solvents, or inorganic, as with bleach and salts—has little effect on the
quantity of PCDDs and PCDFs formed. However, their study found that the total concentration of chlorine
is important. In particular, if the waste being burned exceeds 1 percent chlorine, the PCDD and PCDF
formation rate increases significantly. The formation rate of PCDDs and PCDFs may also depend on the
physical characteristics of the waste feed stream. Solid waste streams or high-ash-content liquid waste feed
streams may increase particulate levels in the combustion system between the combustion unit and the
APCS. The increased particulate levels provide additional surfaces for catalysis reactions to occur.
A review of currently available dioxin data for combustion units reveals that total PCDD/PCDF emission
rates vary by more than 28-fold between different facilities, even though they use similar combustion units
and APCSs (U.S. EPA 1996h). Site-specific emission data are needed to enable completion of a more
refined risk assessment at each combustion unit.
In evaluating fate-and-transport pathways, it is important to consider the chemical and physical properties
of dioxins. In soil, sediment, and the water column, PCDDs and PCDFs are primarily associated with
particulate and organic matter because of their high lipophilicity and low water solubility of the PCDDs
and PCDFs. Evaluation of ambient air monitoring studies, in which researchers evaluated the partitioning
of dioxin-like compounds between the vapor and particle phases, suggests that the higher chlorinated
congeners (the hexa through hepta congeners) were principally sorbed to airborne particulates, whereas the
tetra and penta congeners were significantly, if not predominantly, partitioned to the vapor phase (U.S.
EPA 1994e). This finding is consistent with vapor/particle partitioning as theoretically modeled in
Bidleman (1988). Dioxin-like compounds exhibit little potential for significant leaching or volatilization
after they have been sorbed to particulate matter (U.S. EPA 1994e).
The guidance in Chapter 5 for modeling exposure to a COPC also applies generally to exposure assessment
for PCDDs and PCDFs. However, procedures specific for these compounds should be followed because
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congener-specific toxicity and bioaccumulation information is limited. As discussed below, exposure of
receptors to PCDDs and PCDFs should be assessed using 2,3,7,8-TCDD toxicity equivalency factors
(TEF) and 2,3,7,8-TCDD bioaccumulation equivalency factors (BEF) to convert the exposure media
concentration of individual congeners to a 2,3,7,8-TCDD Toxicity Equivalent (TEQ).
U.S. EPA OSW is also aware of growing concern regarding the risks resulting from (1) fluorine- and
bromine-substituted dioxins and furans, and (2) sulfur analogs of PCDDs and PCDFs. U.S. EPA guidance
on considering these compounds as potential COPCs is discussed in Section 2.3.1.5.
2.3.1.1 Toxicity Equivalency Factors for PCDDs and PCDFs
There are 210 individual compounds or "congeners" of PCDDs and PCDFs. Evidence indicates that low
levels of PCDD and PCDF congeners adversely affect ecological receptors, especially the
2,3,7,8-substituted congeners (U.S. EPA 1993p; Hodson et al. 1992; Walker and Peterson 1992). The
17 congeners containing chlorine substituents in at least the 2-, 3-, 7-, and 8-ring positions have been found
to display dioxin-like toxicity (U.S. EPA 1993g; 1994h). Therefore, U.S. EPA OSW and other U.S. EPA
guidance (1998; 1993h) recommend that all risk assessments include all PCDDs and PCDFs with chlorine
molecules substituted in the 2,3,7, and 8 positions. In Appendix A, the 17 PCDD and PCDF congeners
that should be evaluated in every risk assessment for potential risk are listed. Any other PCDD and PCDF
congener identified as a COPC should be treated as an uncertainty (see Chapter 6).
As noted above, the toxicity of PCDDs and PCDFs is related to their structure and chlorine substitution
pattern. The 17 listed congeners are known to share a common mechanism of toxicity involving binding to
the Ah-receptor. Planar PCDDs and PCDFs are characteristic for high Ah-receptor affinity. Toxicity is
also related to the chlorine substitution pattern, especially for chlorine atoms in the 2,3,7,8-positions. By
extension, it is assumed that an additivity model may be used to characterize the toxicity of mixtures of
these PCDDs and PCDFs. While these congeners share a similar toxicity mechanism, available
information indicates that the toxicity of these PCDDs and PCDFs is congener-specific, resulting in a wide
range of toxicities (U.S. EPA 1993p, World Health Organization [WHO] 1997). This has resulted in the
development of TEFs for these 17 congeners to convert the exposure media concentration of individual
congeners to a 2,3,7,8-TCDD TEQ; which are widely used to assess the risk of dioxin and dioxin-like
compounds (U.S. EPA 1993p; WHO 1997).
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The procedure used to assess risk on the basis of the relative toxicity of 2,3,7,8-TCDD, which is assumed
the most toxic dioxin (U.S. EPA 1994f), assigns a TEF value to each congener relative to its toxicity in
relation to 2,3,7,8-TCDD. For example, 2,3,7,8-TCDD has a TEF of 1.0, and the other PCDDs and
PCDFs have TEF values between 0.0 and 1.0. To estimate the exposure media concentration, U.S. EPA
OSW recommends that a risk assessment for PCDDs and PCDFs be completed using the
congener-specific emission rates from the stack and fate and transport properties in the media concentration
equations (see Chapter 3 and Appendix B) and food web equations (see Chapter 5 and Appendix F). For
quantifying risk, the exposure media (e.g., may be sediment for evaluating risk to sediment community
measurement receptors, or it may be the dose of one or more prey species for evaluating risk to
class-specific guild measurement receptors) concentrations of the individual congeners should be converted
to a 2,3,7,8-TCDD TEQ by multiplying by the congener-specific TEFs corresponding to the respective
measurement receptor being evaluated. Use of the TEFs allows for the combined risk resulting from
exposure to a mixture of the 17 dioxin-like congeners to be computed assuming that the risks are additive.
WHO (1997) recently convened a conference to discuss the derivation of TEFs for humans and wildlife.
WHO (1997) discussed the compilation and review of relevant scientific information on the PCDD and
PCDF toxicity to wildlife, and utilized this information to assist in identifying TEFs. The following table
(see Table 2-3) lists congener-specific TEFs reported for fish, mammals, and birds (WHO 1997). U.S.
EPA OSW believes that these conference proceedings reflect the best available information for screening
the ecological risk of PCDDs and PCDFs. However, it should be noted that TEFs based on long term
in-vivo studies should be used when available.
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TABLE 2-3
POLYCHLORINATED DIBENZO-P-DIOXIN AND POLYCHLORINATED DIBENZOFURAN CONGENER
TOXICITY EQUIVALENCY FACTORS (TEFs) FOR FISH, MAMMALS, AND BIRDS
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Fish TEF
1.0
1.0
0.5
0.01
0.01
0.001
Not available
0.05
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Receptor
Mammal TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Bird TEF
1.0
1.0
0.05
0.01
0.1
<0.001a
Not available
1.0
0.1
1.0
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Notes:
For exposure assessment, a value of 0.001, which estimates upper range of true value, should be used.
Toxicity Equivalency Factors for Fish
WHO (1997) reported the review of three scientific studies on the relative overt toxicity of PCDDs and
PCDFs to fish from which TEFs could be determined. These included evaluation of rainbow trout sac fry
mortality after egg injection (Walker and Peterson 1991; Zabel et al. 1995) and evaluation of rainbow trout
sac fry mortality following waterborne exposure (Bol et al. 1989). WHO (1997) concluded that TEFs
from the egg injection studies were more appropriate than the waterborne exposure study. WHO (1997)
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also noted that since these TEFs were determined from the toxicity of each congener in relation to
concentration in eggs, site-specific differences in exposure and bioavailability, and species-specific
differences in toxicokinetic factors (deposition and metabolism) are accomodated. TEFs for PCDD and
PCDF congeners in fish are presented in Table 2-3.
Toxicity Equivalency Factors for Mammals
Current TEFs for mammals (for evaluating human health risk to PCDDs and PCDFs) are largely based on
studies in rodents. To supplement existing rodent-based TEFs, WHO (1997) discussed a mink
reproductive study (Tillitt et al. 1996) and a study which analyzed available data from mink reproductive
toxicity tests (Leonard et al. 1994). WHO (1997) reported that the relative potencies of PCDD and PCDF
congeners toward mink reproductive toxicity were similar to the rodent models. WHO (1997) also
discussed recent information on in vivo tumor promotion and in vivo ethoxyresorufm-o-deethylase (EROD)
induction potency. However, specific studies reporting this information were not cited. Based on their
review, WHO (1997) reported updated TEFs for mammals, including new values for 1,2,3,7,8-PeCDD,
OCDD, and OCDF. TEFs for PCDD and PCDF congeners in mammals are presented in Table 2-3.
Toxicity Equivalency Factors for Birds
The experimental design of studies on the overt toxicity of PCDDs and PCDFs to birds precluded
determination of the relative potency of these congeners. Other types of studies evaluated included embryo
mortality following egg injection, in vivo biochemical effects following egg injection, biochemical effects in
in vitro systems (Kennedy et al. 1996), and quantitative-structure activity relationship (QSAR) studies
(Tysklind et al. 1995). The reviewed information indicated no significant differences between the TEF
ranges for EROD induction and embryo mortality. Based on these results, WHO (1997) reported TEFs
determined from EROD induction and QSAR studies. TEFs for PCDD and PCDF congeners in birds are
presented in Table 2-3.
2.3.1.2 Exposure Assessment for Community Measurement Receptors
To evaluate exposure of water, sediment, and soil communities to PCDDs and PCDFs, congener-specific
concentrations in the respective media to which the community is exposed should be converted to a
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2,3,7,8-TCDD TEQ; which allows for direct comparison to 2,3,7,8-TCDD toxicity benchmarks. A
media-secific 2,3,7,8-TCDD TEQ is calculated and used in the exposure assessment because limited
congener-specific toxicity information is available for community receptors (WHO 1997). The
congener-specific concentrations in the media to which the community being evaluated is exposed, should
be calculated consistent with the guidance presented in Chapters 4 and 5, and Appendix F, for assessing
exposure of community measurement receptors to other COPCs. The concentration of each PCDD and
PCDF congener in the media of exposure should then be multiplied by the congener-specific TEF for fish
(see Table 2-3), and summed, to obtain the 2,3,7,8-TCDD TEQ (see Equation 2-3).
TEQ = £(CM • TEF) Equation 2-3
where
TEQ = 2,3,7,8-TCDD toxicity equivalence concentration (jWg/1 [water] or
[soil or sediment])
CMi = Concentration of rth congener in abiotic media (Aig/L [water] or
[soil or sediment])
TEFj = Toxicity equivalency factor (fish) for rth congener (unitless)
U.S. EPA OSW assumes that TEFs for fish accurately reflect the relative toxicity of PCDD and PCDF
congeners to community receptors. This assumption is based on the requirement for congener-specific
TEFs for this analysis, as an alternative to the overly conservative assumption that all congener
concentrations in the media be evaluated directly as 2,3,7,8-TCDD. Evaluation of all congeners directly as
2,3,7,8-TCDD is assumed overly conservative based on the limited evidence of the Ah receptor or
TCDD-like toxicity in invertebrates, and that invertebrates appear to be less sensitive to the toxic effects of
dioxin-like compounds (WHO 1997). For the same reasons, TEF values specific to invertebrate have not
been developed; requiring use of the surrogate TEF values for fish. The reported findings in WHO (1997)
support the use of TEFs, in combination with chemical residue data, for the calculation of TEQ
concentrations in various media, including animal tissues, soil, sediment, and water. However, in relation
to the use of TEFs for abiotic media, it should be noted that the biological meaning of these values is
obscure due to the fact that the assumed biological or toxicological effect is influenced by many
physico-chemical factors before uptake occurs (WHO 1997). Nevertheless, TEF values can be used as
relative measurements of concentrations within media.
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Use of the TEFs allows for the combined risk resulting from exposure to a mixture of the 17 dioxin-like
congeners to be computed assuming that the risks are additive. As discussed in Chapters 5 and 6, risk to
the water, sediment, or soil community being evaluated is then subsequently estimated by comparing the
media-specific 2,3,7,8-TCDD TEQ to the corresponding media-specific toxicity benchmark for
2,3,7,8-TCDD.
2.3.1.3 Exposure Assessment for Class-Specific Guild Measurement Receptors
To evaluate the exposure of class-specific guilds to PCDDs and PCDFs, congener-specific daily doses of
all food items (i.e., media, plants, and animals) ingested by a measurement receptor should be converted to
a 2,3,7,8-TCDD TEQ daily dose (DDTE<^); which allows for direct comparison to 2,3,7,8-TCDD toxicity
benchmarks. The congener-specific daily doses of food items ingested by a measurement receptor should
be calculated consistent with the guidance presented in Chapters 4 and 5, and Appendix F, for assessing
exposure of class-specific guild measurement receptors to other COPCs. This includes the use of
congener-specific media concentrations, congener-specific bioconcentration factors (BCF), and
congener-specific food chain multipliers (FCM). The daily dose of each PCDD and PCDF congener
ingested by a measurement receptor should then be multiplied by the congener-specific TEFs (see
Table 2-3) that correspond to the respective measurement receptor, and summed, to obtain the DDTEQ. Use
of the TEFs allows for the combined risk resulting from exposure to a mixture of the 17 dioxin-like
congeners to be computed assuming that the risks are additive. Following the general guidance provided in
Chapters 5 and 6, risk to the class-specific guild being evaluated is then subsequently estimated by
comparing the dose ingested term (represented by DDTEQ) of the measurement receptor to the receptor
specific toxicity benchmark for 2,3,7,8-TCDD.
The DDTEQ for each measurement receptor should be determined as indicated in the following equation:
DDTEQ = , DD, ' TEF(MeaSurementReceptor) Equation 2-4
where
DDTEQ = Daily dose of 2,3,7,8-TCDD TEQ 0/g/kg BW/d)
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= Daily dose of rth congener (jWg/kg BW/d)
TEF = Toxicity equivalency factor (specific to measurement receptor) (unitless)
As noted above, the congener-specific daily doses ingested by the measurement receptor should be
determined following guidance in Chapter 5 and using equations in Appendix F. These equations include
the use of congener-specific BCF and FCM values. As discussed in Section 23.1 A, the limited availability
of congener-specific BCFs requires that media to receptor BCF values for 2,3,7,8-TCDD be utilized in
conjunction with congener-specific BEF values to obtain estimated congener-specific BCF values. The
estimation of congener-specific BCFs and their resulting numeric values are further discussed in
Appendicies C and D. Calculation of a congener-specific daily dose also requires the use of
congener-specific FCMs. Guidance on the appropriate use ofFCMs in modeling exposure and
congener-specific values are provided in Chapter 5 and Appendix A-2, respectively.
2.3.1.4 Bioaccumulation Equivalency Factors
As discussed in Section 2.3.1.3, modeling the exposure of PCDD and PCDF congeners through the food
web requires the quantification of bioaccumulation potential. However, similar to the limited availability of
congener-specific toxicity information, measured bioaccumulation data specific to each congener is also
limited. Therefore, for use with TEFs in the development of wildlife water quality criteria for the Great
Lakes, U.S. EPA (1995J) developed bioaccumulation equivalency factors (BEFs) as a measure of a
congeners bioaccumulation potential relative to 2,3,7,8-TCDD. As indicated in Equation 2-5, BEFs are
estimated as a ratio between each PCDD and PCDF congener-specific BASFto that of 2,3,7,8-TCDD
(Lodge et al. 1994; U.S. EPA 1995J).
BSAFt
BEF' = TT7T7 Equation 2-5
' TCDD
where
= Bioaccumulation equivalency factor for rth congener (unitless)
BSAFj = Biota-sediment accumulation factor for rth congener (unitless)
BSAFTCDD = Biota-sediment accumulation factor for 2,3,7,8-TCDD
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BEF values reported by U.S. EPA (1995k) for the 17 PCDD and PCDF congeners are provided in
Table 2-4. Although developed based on concentration data of PCDDs and PCDFs in sediment and
surface water for application ofTEFs in fish, U.S. EPA OSW assumes that these BEFs are applicable to
other pathways and receptors. The estimation of PCDD and PCDF congener-specific BCF values using
BEFs is indicated in Equation 2-5. Further discussion and resulting numeric values for congener-specific
BCFs are provided in Appendicies C and D.
BCFt = BCFTCDD • BEFt Equation 2-6
where
BCFt = Media-to-animal or media-to-plant bioconcentration factor for rth
congener (L/kg [water], unitless [soil and sediment])
BCFTCDD = Media-to-receptor BCF for 2,3,7,8-TCDD (L/kg [aquatic receptor],
unitless [soil and sediment receptor])
= Bioaccumulation equivalency factor for rth congener (unitless)
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TABLE 2-4
PCDD AND PCDF BIO AC CUMULATION EQUIVALENCY FACTORS (BEFs)
PCDD Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
Bioaccumulation
Equivalency Factor
(unitless)
1.0
0.92
0.31
0.12
0.14
0.051
0.012
PCDF Congener
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Bioaccumulation
Equivalency Factor
(unitless)
0.80
0.22
1.6
0.076
0.19
0.67
0.63
0.011
0.39
0.016
Source:
U.S. EPA 1995k
2.3.1.5 Fluorine, Bromine, and Sulfur PCDD/PCDF Analogs
U.S. EPA (U.S. EPA 19961; 1996m) is currently evaluating the potential for the formation of (1) fluorine-
and bromine-substituted dioxins and furans, and (2) sulfur analogs of PCDDs and PCDFs. Available
information indicates that fluorinated dioxins and furans are not likely to be formed as PICs; however, the
presence of free fluorine in the combustion gases may increase the formation of chlorinated dioxins
(U.S. EPA 19961). U.S. EPA OSW is not aware of any studies conducted to evaluate this relationship.
Available information indicates the potential for the formation of brominated or chlorobrominated dioxins
(U.S. EPA 1996d).
Although chlorinated dibenzothiophenes (the sulfur analogs of dibenzofurans) have been reported to form,
no information is available to indicate the formation of chlorinated dioxin thioethers (the sulfur analogs of
dibenzofp]dioxins) (U.S. EPA 19961). This may be because the carbon-oxygen bond is stronger than the
carbon-sulfur bond, and the compound furan (which is part of the dibenzofuran structure) is more stable
than thiophene (which is part of the dibenzothiophene structure) (U.S. EPA 1996n). Another possible
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reason that chlorinated dioxin thioethers have not been observed is the potential instability of these
compounds, which contain two carbon-sulfur bonds in the central ring of the structure (U.S. EPA 19961).
The likelihood of the formation or associated toxicity of these compounds is not currently well understood.
Therefore, a quantitative toxicity assessment of fluorine, bromine, and sulfur analogs is not required for
inclusion in the risk assessment report. Instead, the uncertainty section of the risk assessment report should
discuss the potential for the formation of these analogs. It should be noted that there is currently no U.S.
EPA approved method for the sampling or analysis of these dioxin analogs. The use of the method for total
organics (see Section 2.2.1.3) is currently recommended to account for the potential presence of these
compounds.
TEF values for brominated dioxins or furans have not been developed (U.S. EPA 1994e; WHO 1997).
However, the toxicity of bromo- and chlorobromo-substituted dioxin analogs is comparable to that of
chlorinated dioxins in short-term toxicity assays (U.S. EPA 1996m).
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Description of any combustion unit-specific operating conditions that may contribute to the
formation of dioxins
• Any facility specific sampling information regarding PCDD and PCDF concentrations in air,
soil, sediment, water, or biota
• Information regarding the concentration of sulfur, fluorine, and bromine in the combustion
unit feed materials
2.3.2 Polynuclear Aromatic Hydrocarbons
Based on their combustion properties and toxicity, U.S. EPA OSW recommends that PAHs be included in
every risk assessment. The following are commonly detected PAHs: benzo(a)pyrene (BaP);
benzo(a)anthracene; benzo(b)fluoranthene; benzo(k)fluoranthene; chrysene; dibenz(a,h)anthracene; and
indeno(l,2,3-cd)pyrene. The general combustion properties and guidance for addressing toxicity of PAHs
are discussed in the following paragraph and subsection, respectively.
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PAHs are readily formed in combustion units by either (1) dechlorination of other PAHs present in the
waste feed or emissions stream (such as dioxins), or (2) the reaction of simple aromatic compounds
(benzene or toluene) present in the waste feed or emissions stream. PAHs are well-known as the principal
organic components of emissions from all combustion sources, including coal fires (soot), wood fires,
tobacco smoke ("tar"), diesel exhaust, and refuse burning (Sandmeyer 1981). They are generally the only
chemicals of concern in particulate matter (Manahan 1991), although the presence of metals and other
inorganics in the waste feed can add other contaminants of concern. Therefore, based on the toxicity and
combustion chemistry of PAHs, the absence of these compounds from stack emissions should always be
confirmed via stack gas testing.
2.3.2.1 Exposure Assessment for PAHs
U.S EPA OSW recommends that individual PAH compounds be modeled from the emission source to
media (i.e., soil, surface water, soil) and plants, using compound-specific emission rates and fate and
transport properties, as required in the media concentration equations (see Chapter 3 and Appendix B).
Evaluation of exposure of community and class-specific guild measurement receptors to individual PAHs,
should be conducted consistent with guidance provided in Chapters 4 and 5, and utilizing equations in
Appendix F.
2.3.3 Polychlorinated Biphenyls
The use and distribution of polychlorinated biphenyls (PCBs) were severely restricted in the United States
in the late 1970s—with additional bans and restrictions taking effect over the next decade (ATSDR 1995d).
PCBs were produced commercially by the reaction of the aromatic hydrocarbon biphenyl with chlorine gas
in the presence of a suitable catalyst, generally ferric chloride or another Lewis acid (ATSDR 1995d). The
degree of chlorination was controlled by manipulation of the reaction conditions, including temperature,
pressure, and the ratio of the reactants (Erickson 1992; Grayson 1985).
The most commercially useful property of PCBs is that they are chemically stable in relatively adverse
conditions, such as a temperature of several hundred degrees in an oxygen-containing atmosphere; the
more chlorinated congeners are more resistant to reaction. Therefore, destruction of PCBs by combustion
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generally requires conditions of high temperatures (at least 1,200 °C) and an extended contact time (more
than 2 seconds) in that temperature with adequate oxygen (Erickson 1992).
Limited data and studies, including laboratory and field, show that PCBs may be formed from the
combustion of hazardous waste. Stack tests performed in U.S. EPA Region 10 on a boiler and an
incinerator burning waste with 0.07 and 1.4 percent chlorine, respectively, confirmed the presence of PCBs
in the stack gases (Kalama Chemical, Inc. 1996; Idaho National Engineering Laboratory 1997). The
concentration of detected coplanar PCBs (see definition in Section 2.3.3.1) found in the boiler stack gas
was 0.55 ng/dscm @ 7% O2 at low temperature conditions (1,357° F) and 1.12 ng/dscm @ 7% O2 at high
temperature conditions (1,908° F). The concentration of total PCBs detected in the incinerator stack gas
was 211 ng/dscm @ 7% O2 at low temperature conditions (1,750 °F) and 205 ng/dscm @ 7% O2 at high
temperature conditions (2,075 ° F). PCBs with more than four chlorines comprised 51 percent of the total
PCBs in the low temperature test and 59 percent of the total PCBs in the high temperature test.
Other laboratory studies suggest the possible formation of PCBs as PICs from the combustion of
hazardous waste with a high chlorine content. Bergman et al. (1984) heated samples of two chlorinated
paraffins (CP) in conditions similar to incinerator conditions. A CP containing 70 percent chlorine did
produce PCB (up to 0.3 percent of the amount of CP), as well as chlorinated benzenes (up to 0.5 percent),
chlorinated toluenes (up to 0.6 percent), and chlorinated naphthalenes (up to 0.2 percent). Similar
treatment of a CP containing 59 percent chlorine produced only chlorinated benzenes (up to 0.1 percent of
the amount of CP, based on a detection limit of 0.0005 percent for each individual compound) and almost
all of those (about 90 percent) were monochlorobenzene (Bergman et al. 1984). This study indicates that
the combustion of highly chlorinated (60 percent or greater chlorine) wastes can produce PCBs.
PCBs should automatically be included as COPCs for combustion units that burn PCB-contaminated
wastes or waste oils, highly variable waste streams such as municipal and commercial wastes for which
PCB contamination is reasonable, and highly chlorinated waste streams.
Due to the toxicity and uncertainties associated with combustion chemistries the permitting authority may
choose to confirm that the absence of these compounds from stack emissions via stack gas testing for units
burning hazardous wastes.
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2.3.3.1 Exposure Assessment for PCBs
Previous U.S. EPA combustion risk assessment guidance (1994b; 1994d; 1994c; 19941) has recommended
that all PCB congeners (209 different chemicals) be treated in a risk assessment as a mixture having a
single toxicity. This recommendation was based on the U.S. EPA drinking water criteria for PCBs (U.S.
EPA 1988).
However, since the compilation of U.S. EPA (1988), additional research on PCBs has been reported. The
most important result of this research is the demonstration that some of the moderately chlorinated PCB
congeners can have dioxin-like effects (U.S. EPA 1992f; U.S. EPA 1994i; ATSDR 1995d; WHO 1997).
WHO (1997) recently convened a conference to discuss the derivation ofTEFs for humans and wildlife.
Conference participants discussed the compilation and review of relevant scientific information on the PCB
toxicity to wildlife, and utilized this information to assist in identifying TEFs for congeners that can have
dioxin-like effects. U.S. EPA OSW believes that these conference proceedings reflect the best available
information for screening the ecological risk of PCBs. The following table (see Table 2-5) lists PCB TEFs
reported for fish, mammals, and birds (WHO 1997).
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TABLE 2-5
PCB CONGENER TOXICITY EQUIVALENCY FACTORS (TEFs) FOR FISH, MAMMALS, AND BIRDS
PCB Congener
3,4,4',5-TCB
3,3',4,4'-TCB
3,3',4,4',5-PeCB
3,3',4,4',5,5'-HxCB
2,3,3',4,4-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5-HxCB
2,3',4,4',5,5'-HxCB
2,3,3',4,4',5,5'-HpCB
2,2',3,3',4,4',5'-HpCB
2,2',3,4,4',5,5'-HpCB
Receptor
Fish TEF
0.0005
0.0001
0.005
0.00005
<0.000005
<0.000005
<0.000005
<0.000005
<0.000005
<0.000005
0.000005
<0.000005
Not Available
Not Available
Mammals TEF
0.0001
0.0001
0.1
0.01
0.0001
0.0005
0.0001
0.0001
0.0005
0.0005
0.00001
0.0001
Not Available
Not Available
Birds TEF
0.1
0.05
0.1
0.001
0.0001
0.0001
0.00001
0.00001
0.0001
0.0001
0.00001
0.00001
Not Available
Not Available
Source: WHO (1997)
The listed congeners have four or more chlorine atoms with few substitutions in the ortho positions
(positions designated 2, 2', 6, or 6). They are sometimes referred to as coplanar PCBs, because the rings
can rotate into the same plane if not blocked from rotation by ortho-substituted chlorine atoms. In this
configuration, the shape of the PCB molecule is very similar to that of a PCDF molecule. Studies have
shown that these dioxin-like congeners can then react with the aryl hydrocarbon receptor; this same
reaction is believed to initiate the adverse effects of PCDDs and PCDFs. Additional congeners are
suspected of producing similar reactions, but there is not yet enough data to derive TEF values for them.
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High resolution gas chromatograph test methods (e.g., draft Method 1668), available at most commercial
laboratories with dioxin/ftiran analytical capabilities, should be used to identify the specific concentration
of individual coplanar PCBs in stack gas. U.S. EPA OSW recommends that permitting authorities
estimate risks to community and class-specific guild measurement receptors from coplanar PCBs by
computing a TEQ for PCBs, and then comparing to the appropriate toxicity benchmark for 2,3,7,8-TCDD.
The specific guidance, provided in Sections 2.3.1.2 and 2.3.1.2 for evaluating exposure to PCDDs and
PCDFs, should be followed in evaluating exposure to dioxin-like PCBs. However, TEF values listed in
Table 2-5 should be utilized in the TEQ calculations. Also, since congener-specific fate and transport and
bioaccumulation data are not available for each of the PCBs listed in Table 2-5, U.S. EPA OSW
recommends that the fate and transport properties for Aroclor 1254 be used in the modeling. This
approach is reasonable because approximately 77 percent of Aroclor 1254 is composed of PCB congeners
with more than 4 chlorines (Hutzinger et al. 1974).
In addition to the coplanar (dioxin-like) PCB congeners, the remaining PCBs should also be evaluated in
the risk assessment consistent with the guidance provided in Chapters 4 and 5. When evaluating PCB
mixtures containing isomers with more than 4 chlorines in quantities greater than 0.5 percent of the total
PCBs, U.S. EPA OSW recommends that the fate and transport properties for Aroclor 1254 be used in the
modeling. As discussed above for evaluating coplanar PCBs, this approach is reasonable because
approximately 77 percent of Aroclor 1254 is composed of PCB congeners with more than 4 chlorines
(Hutzinger et al. 1974). When assessing risks from PCB mixtures which contain less than 0.5 percent of
PCB congeners with more than 4 chlorines, U.S. EPA OSW recommends that the fate and transport
properties of Aroclor 1016 be used in the modeling. This approach is reasonable because approximately
99 percent of Aroclor 1016 is comprised of PCB congeners with 4 or less chlorines (Hutzinger et al. 1974).
2.3.4 Nitroaromatics
Careful consideration should be made before the automatic inclusion of nitroaromatic organic compounds,
including 1,3-dinitrobenzene; 2,4-dinitrotoluene; 2,6-dinitrotoluene; nitrobenzene; and
pentachloronitrobenzene, in risk assessments for combustion units. These compounds or close relatives
(such as toluenediamine [TDA] and toluene diisocyanate [TDI]—derivatives of dinitrotoluene) are typically
associated with explosives and other highly nitrogenated hazardous wastes. Dinitrotoluene is used to make
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two products: trinitrotoluene and TDA. TDA is, in turn, used to make TDI, which readily reacts with
water and is, therefore, very unstable at ambient conditions; TDI is typically reacted with a polyol to form
polyurethane (PU) plastics.
Combustion properties of these nitroaromatic compounds indicate that they will not be formed as PICs if
they are not present in the waste feed stream, mainly because of the thermodynamic and chemical difficulty
of adding a nitro group to an aromatic. The process requires that (1) nitronium ions be generated, and
(2) an aromatic ring be reacted with the nitronium ion, resulting in the attachment of the nitronium ion to
the ring. This reaction process is not likely to occur in a hazardous waste combustion unit because (1) the
reaction is typically carried out by using a "nitrating acid" solution consisting of three parts concentrated
nitric acid to one part sulfuric acid, and (2) nitronium ions are not usually formed in a combustion unit
environment (if they are, a further thermodynamically favorable reaction will occur, thereby eliminating the
nitronium ion) (Hoggett et al. 1971; Schofield 1980; March 1985).
Nitroaromatics should be included as COPCs if the hazardous waste feed streams include nitroaromatic
compounds or close relatives (TDA and TDI). Also, combustion of feed streams containing unusually high
amounts of fuel-bound nitrogen (greater than 5 percent) may lead to increased levels of nitrogenated PICs
(U.S. EPA 1994c). Examples of waste feeds identified include heavy distillation fractions and bottoms
streams from the production of coal tars and petroleum distillation. Combustion conditions most likely to
result in nitrogenated PICs are associated with premature quenching of the primary flame—resulting from
low temperature or excess air in the primary combustion chamber of the unit (U.S. EPA 1994c). Sampling
for hydrogen cyanide is also recommended (U.S. EPA 1994c).
2.3.5 Phthalates
Careful consideration should be made before the automatic inclusion of phthalates, including
bis(2-ethylhexyl)phthalate (BEHP) and di(n)octyl phthalate (DNOP), in risk assessments for combustion
units. Among all phthalate plasticizers, BEHP—also referred to as di(2-ethylhexyl)phthalate or dioctyl
phthalate)—is produced in the largest volume; it is used in the manufacturing of polyvinyl chloride, which
is the most widely produced plastic. DNOP is a plasticizer that is produced in large volumes and is used in
the manufacture of plastics and rubber materials. Because plastics have become so widely used in society,
phthalate plasticizers such as BEHP and DNOP have become widely distributed in food, water, and the
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atmosphere (Howard 1990). Phthalate plasticizers are commonly found in the environment and are
practically impossible to avoid, especially at the trace concentrations that modern analyses can detect.
Phthalates are synthesized by reacting alcohol with phthalic anhydride in the presence of an acidic catalyst
in a nonaqueous solvent (ATSDR 1993; ATSDR 1995b). Phthalates and their predecessors are readily
combusted compounds, as indicated by their flash points of 150 to 225 °C (NIOSH 1994). There is no
apparent mechanism for phthalate PICs to be formed by the combustion of other chemical compounds.
Therefore, phthalates are very unlikely to be emissions from a combustion unit, although some degradation
products, such as PAHs, are likely to be emitted when phthalates are included in the waste feed. However,
facilities that burn plastics or materials with phthalate plasticizers should carefully consider the potential
for phthalate plasticizers to exist in the stack gas emissions due to incomplete combustion.
The evaluation of phthalate plasticizers in risk assessments should not be automatically discounted due to
the toxicity and biaccumulative potential of these compounds. Moreover, the uncertainties associated with
combustion chemistry suggest that the absence of these compounds from stack emissions should always be
confirmed via stack gas testing rather than process knowledge or waste feed characterization data. U.S.
EPA OSW recommends that careful consideration should be given to including phthalates as COPCs based
on the information presented above.
2.3.6 Hexachlorobenzene and Pentachlorophenol
Careful consideration should be made before the automatic inclusion of hexachlorobenzene and
pentachlorophenol in risk assessments for combustion units. Hexachlorobenzene and pentachlorophenol,
like all chlorinated aromatics, are synthesized by the reaction of elemental chlorine with the parent aromatic
(Deichmann and Keplinger 1981; Grayson 1985). The addition of the first chlorine atom to the benzene or
phenol molecule is rapid, but further chlorination becomes progressively more difficult, requiring ferric
chloride or another Lewis acid catalyst to complete the reaction (March 1985); therefore, these chlorinated
compounds are difficult to make under controlled conditions. Hexachlorobenzene, but not
pentachlorophenol, has been reported in emissions from the combustion of municipal solid waste and from
other processes (such as the chlorination of wood pulp) that also produce PCDDs and PCDFs (ATSDR
1994a; ATSDR 1994b). Hexachlorobenzene is an impurity in pentachlorophenol while pentachlorophenol
is formed from hexachlorobenzene in some factories (ATSDR 1994a; ATSDR 1994b). The combustion
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properties of these chlorinated compounds indicate that they are not likely to be formed as PICs if they are
not present in the waste feed stream.
Hexachlorobenzene and pentachlorophenol should be included as COPCs for units that burn waste feeds
containing hexachlorobenzene and pentachlorophenol, wood preservatives, pesticides, and highly variable
waste streams such as municipal solid waste. However, precluding these compounds from analytical
testing during the trial burn based on process knowledge and waste feed characterization is not
recommended. Because PCDDs and PCDFs can be formed from fly ash-catalyzed reactions between
halogens and undestroyed organic material from the furnace, U.S. EPA guidance (U.S. EPA 1993h; 1994d)
has recommended that potential precursor compounds be included in the risk assessment and trial burn (see
Section 2.3). These precursor compounds may include chlorinated phenols (such as pentachlorophenol)
and chlorinated aromatics (such as hexachlorobenzene). Furthermore, the toxicity and uncertainties
associated with combustion chemistry suggest that the absence of these compounds from stack emissions
should always be confirmed via stack gas testing. U.S. EPA OSW recommends that careful consideration
should be given to including hexachlorobenzene and pentachlorophenol as COPCs based on the
information presented above.
2.3.7 Metals
U.S. EPA OSW recommends that the following inorganic substances be considered for evaluation in the
risk assessment: aluminum, antimony, arsenic, barium, beryllium, cadmium, hexavalent chromium, copper,
lead, mercury (elemental and divalent), nickel, selenium, silver, thallium, and zinc. All of these substances,
except aluminum, copper, nickel, selenium, and zinc, are regulated by 40 CFR Part 266, Subpart H (the
BIF regulations). In the case of metals not regulated by the BIF regulations, U.S. EPA has recommended
that these metals be evaluated, to determine whether additional terms and conditions should be incorporated
into the permit, by using U.S. EPA's omnibus authority provided under 40 CFR Part 270.32(b)(2) (U.S.
EPA 1992c). Facilities may also apply the BIF regulation Tier I assumptions, that assume all metals in the
waste feed pass through the combustion unit and APCS and are passed through to the emission stream
(U.S. EPA 1992e).
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It should be noted, that the presence of metals in the combustion unit's feed stream is not required for
inclusion in the risk assessment. Although metals cannot be formed as PICs, U.S. EPA OSW is aware of
combustion units with metal emissions resulting from waste feed leaching of stainless steel feed piping.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Waste feed, raw material, and secondary fuel stream analytical data
Metal emission rate sampling data or assumptions based on waste feed data
Explanations for excluding specific metals from evaluation in the risk assessment
The following subsections provide additional information regarding U.S. EPA-recommended procedures for
evaluating metals—chromium, mercury, and nickel—that may be specifically altered during the
combustion process or require specific considerations in the risk assessment.
2.3.7.1 Chromium
The oxidation state of chromium is a crucial issue in evaluating the toxicity of this metal and the risks
associated with exposure. Hexavalent chromium (Cr+6) is the most toxic valence state of chromium.
Trivalent chromium (Cr+3), a commonly found less oxidized and toxic form of chromium, is more
commonly found in the environment. U.S. EPA (1990c; 1990d) has indicated that chromium emitted from
a combustion unit is not likely to be in the hexavalent form; however, there is not sufficient evidence to
reliably estimate the partitioning of chromium emissions into these two valence states. Also,
media-specific chromium speciation information is often difficult to obtain within the scope of a screening
risk assessment. However, U.S. EPA OSW recognizes that chromium may exist partially or in some cases
entirely as trivalent chromium in various media. Therefore, unless site-sampling or process-specific
information is provided to support a less conservative approach, the worst-case assumption—that
100 percent of the facility chromium emissions are in the hexavalent form—should be used as the initial
assumption that all exposure is to hexavalent chromium.
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The assumption that receptors are exposed to hexavalent chromium should be maintained in the absence of
site specific data. However, permitting authorities may prepare supplemental calculations (that is, in
addition to the site-specific data described above) considering chromium speciation at the points of
potential exposure.
2.3.7.2 Mercury
Consistent with previous U.S. EPA combustion risk assessment guidance (U.S. EPA 1993h, 1994d, 1994c,
19941), U.S. EPA OSW recommends that mercury be evaluated as COPCs in the risk assessment. Air
emissions of mercury contribute to local, regional, and global deposition. The U.S. Congress explicitly
found this to be the case and required U.S. EPA to prioritize maximum achievable control technology
(MACT) controls for mercury (U.S. Congress 1989).
Anthropogenic mercury releases are thought to be dominated on the national scale by industrial processes
and combustion sources that release mercury into the atmosphere (U.S. EPA 1997b). Stack emissions
containing mercury include both vapor and particulate forms. Vapor mercury emissions are thought to
include both elemental (Hg°) and oxidized (e.g., Hg+2) chemical species, while particulate mercury
emissions are thought to be composed primarily of oxidized compounds due to the relatively high vapor
pressure of elemental mercury (U.S. EPA 1997b). While coal combustion is responsible for more than
half of all emissions of mercury in the U.S. anthropogenic sources, the fraction of coal combustion
emissions in oxidized form is thought to be less that from waste incineration and combustion (U.S. EPA
1997b).
The analytical methods for mercury speciation of exit vapors and emission plumes are being refined, and
there is still controversy in this field. Chemical reactions occurring in the emission plume are also possible.
The speciation of mercury emissions is thought to depend on the fuel used, flue gas cleaning, and operating
temperatures. The exit stream is thought to range from almost all divalent mercury to nearly all elemental
mercury; with true speciation of mercury emissions from the various source types still uncertain and
thought to vary, not only among source types, but also for individual plants as feed stock and operating
conditions change (U.S. EPA 1997b). Most of the total mercury emitted at the stack outlet is found in the
vapor phase; although exit streams containing soot or particulate can bind up some fraction of the mercury
(U.S. EPA 1997b). Total mercury exiting the stack is assumed to consist of elemental and divalent species,
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with no emissions of methylmercury assumed. The divalent fraction is split between vapor and
particle-bound phases (Lindqvist et al. 1991). Much of the divalent mercury is thought to be mercuric
chloride (HgCl2) (U.S. EPA 1997b); this is particularly the case for the combustion of wastes containing
chlorine.
It should be noted that data on mercury speciation in emissions exiting the stack is very limited, as well as,
the behavior of mercury emissions close to the point of release has not been extensively studied. This
results in a significant degree of uncertainty implicit in modeling of mercury emissions. Additional
examples of uncertainties include the precision of measurement techniques, estimates of pollution control
efficiency, limited data specific to source class and activity level. Discussions of uncertainty and sensitivity
analyses of several of the assumptions used in the modeling of mercury emissions are presented in the
Mercury Study Report to Congress (U.S. EPA 1997b).
Phase Allocation and Speciation of Mercury Exiting the Stack
As discussed above, stack emissions are thought to include both vapor and particle-bound forms; and
speciated as both divalent and elemental mercury. Based on review of mercury emissions data presented
for combustion sources in U.S. EPA (1997b) and published literature (Peterson et al. 1995), estimates for
the percentage of vapor and particle-bound mercury emissions range widely from 20 to 80 percent.
Therefore, at this time U.S. EPA OSW recommends a conservative approach that assumes phase allocation
of mercury emissions from hazardous waste combustion of 80 percent of the total mercury in the vapor
phase and 20 percent of total mercury in the particle-bound phase. This allocation is:
• Consistent with mercury emissions speciation data for hazardous waste combustion
sources reported in literature (Peterson et al. 1995); and
• Believed to be reasonably conservative, since it results in the highest percentage of total
mercury being deposited in proximity to the source, and therefore, indicative of the
maximum exposure.
As indicated in the global cycle mass percentages in Figure 2-4, mercury exits the stack in both the
elemental and divalent vapor forms. Based on U.S. EPA (1997b), a vast majority of mercury exiting the
stack does not readily deposit and is transported outside of the U.S. or vertically diffused to the free
atmosphere to become part of the global cycle (see Figure 2-4). The divalent form emitted, either in the
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vapor phase or particle-bound, are thought to be subject to much faster atmospheric removal than elemental
mercury (Lindberg et al. 1992; Peterson et al. 1995; and Shannon and Voldner 1994). In addition, vapor
phase divalent mercury is thought to be more rapidly and effectively removed by both dry and wet
deposition than particle-bound divalent mercury, as a result of the reactivity and water solubility of vapor
divalent mercury (Lindberg et al. 1992; Peterson et al. 1995; and Shannon and Voldner 1994).
Vapor Phase Mercury
As illustrated in Figure 2-4, of the 80 percent total mercury in the vapor phase, 20 percent of the total
mercury is in the elemental vapor form and 60 percent of the total mercury is in the divalent vapor form
(Peterson et al. 1995). A vast majority (assumed to be 99 percent) of the 20 percent vapor phase elemental
mercury does not readily deposit and is transported outside of the U.S. or is vertically diffused to the free
atmosphere to become part of the global cycle (U.S. EPA 1997b). Only a small fraction (assumed to be
one percent) of vapor-phase elemental mercury either is adsorbed to particulates in the air and is deposited
or converted to the divalent form to be deposited (assumed to be deposited as elemental mercury, see
Figure 2-4). Of the 60 percent vapor phase divalent mercury, about 68 percent is deposited and about
32 percent is transported outside of the U.S. or is vertically diffused to the free atmosphere to become part
of the global cycle (U.S. EPA 1997b).
Particle-bound Mercury
Of the 20 percent of the total mercury that is particle-bound, 99 percent (assumed to be 100 percent in
Figure 2-4) is in the divalent form. U.S. EPA (1997b) indicates that only 36 percent of the particle-bound
divalent mercury is deposited, and the rest is either transported outside of the U.S. or is vertically diffused
to the free atmosphere to become part of the global cycle.
Deposition and Modeling of Mercury
Consistent with U.S. EPA (1997b) and as shown in Figure 2-4, it is assumed that deposition to the various
environmental media is entirely divalent mercury in either the vapor or particle-bound form. Without
consideration of the global cycle, mercury speciations will result in 80 percent of the total mercury emitted
being deposited as divalent mercury and the remaining 20 percent being deposited as elemental mercury.
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FIGURE 2-4
PHASE ALLOCATION AND SPECIATION
OF MERCURY IN AIR
Total Mercury Emissions
Existing Stack Into Air
[10.0 gj
• 80% Vapor Phase (or 0.8)
• 20% Particle Bound Phase
Fv (Total Mercury) = 0.8
LEGEND
Hg° -Elemental Mercury
Hg*+ - Divalent Mercury
[ ] - Example Mass Allocation
Without Consideration of Global Cycle
• 80% of Total Mercury Emitted
is Deposited as Hg+ [(6g + 2g)/10gJ
• 20% of Total Mercury Emitted
is Deposited as Hi [2g / lOgJ
Calculated^
*) = [6g / (6g +2g)] = ft 75
^) = [2g/2g] = 1.0
[0.020g]
> 1% Depositedas HgVapor
[1.980g]
> 99% Enters Global Cycle as Hg Vapor
[4.080g]
68% Deposited as Hg+ Vapor
[1.920g]
> 32% Enters Global Cycle as Hg
[0.720g]
> B6% Deposited as Hg^+Parttculate
[1.280g]
> 64% Enters Global Cycle as Hg+ Particulate
With Consideration of Global Cycle
• 48% of Total Mercury Emitted
is Deposited as H^ [(4. 08g + 0. 72g) /IQgJ
• 0.2% of Total Mercury Emitted
is Deposited as Hg [0. 02g / IQgJ
Calculate
) = [4.08g/(4.08g + 0.72g)] = 0.85
[0. 02g / (0. 02g + Og)J = 1.0
Compound Specific Emission Rate O
•Actual Q (Hg*) = 48% * Q (Total Mercury)
•Actual Q (Hg) = 0.2% * Q (Total Mercury
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U.S. EPA OSW recommends utilizing the percentages provided in U.S. EPA (1997b) to account for the
global cycle, the percentage of total mercury deposited is reduced to a total of 48.2 percent (40.8 percent as
divalent vapor, 7.2 percent as divalent particle-bound, and 0.2 percent as elemental vapor). As discussed in
Appendix A-2, these speciation splits result in fraction in vapor phase (Fv) values of 0.85 (40.8/48.2) for
divalent mercury, and 1.0 (0.2/0.2) for elemental mercury. Also, to account for the remaining 51.8 percent of
the total mercury mass that is not deposited, the deposition and media concentration equations (presented in
Appendix B), multiply the compound-specific emission rate (0 for elemental mercury by a default value of
0.002; and divalent mercury by a default value of 0.48.
Consistent with U.S. EPA (1997b) and as shown in Figure 2-4, it is assumed that deposition to the various
environmental media is entirely divalent mercury in either the vapor or particle-bound form. Deposited
divalent mercury is also considered as a source of methyl mercury, which is assumed as a media-specific
percentage of the total mercury deposited.
Also, only a small fraction (assumed to be one percent) of elemental mercury is in the vapor phase and is
assumed to be deposited in its original form. Therefore, any resulting exposure to elemental mercury is
considered to be much less significant, and will not be considered in the pathways of the ecological risk
assessment.
Appendix A-2 provides the parameter values specific to the various forms of mercury, and Appendix B
provides media concentration equations for modeling mercury through the exposure pathways assuming
steady-state conditions.
Methylation of Mercury
The net mercury methylation rate (the net result of methylation and demethylation) for most soils appears to
be quite low; with much of the measured methyl mercury in soils potentially resulting from wet deposition
(U.S. EPA 1997b). Consistent with U.S. EPA (1997b), a fraction of the divalent mercury that is deposited is
assumed to speciate to organic mercury (methyl mercury) in soil. In soil, 98 percent of total mercury is
assumed to be divalent mercury and the remaining mass as methyl mercury (U.S. EPA 1997b). A significant
and important exception to mercury methylation rate being low in soils appears to be wetland soils. Wetlands
appear to convert a small but significant fraction of the deposited mercury into methyl mercury; which can be
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exported to nearby water bodies and potentially bioaccumulated in the aquatic food chain (U.S. EPA 1997b).
Therefore, the assumed percentage of methyl mercury in wetland soils may be higher than the 2 percent
assumed for non-wetland soils, and may closer approximate the 15 percent assumed for sediments.
Both watershed erosion and direct atmospheric deposition can be important sources of mercury to a water
body (U.S. EPA 1997b). There appears to be a great deal of variability in the processing of mercury among
water bodies. This variability is primarily a result of the characteristically wide range of chemical and
physical properties of water bodies that influence the levels of methylated mercury. Some of the mercury
entering the water body is methylated predominately through biotic processes (U.S. EPA 1997b). In the
absence of modeling site-specific water body properties and biotic conditions, consistent with U.S. EPA
(1997b), U.S. EPA OSW recommends 85 percent of total mercury in surface water is assumed to be divalent
mercury and the remaining mass as methyl mercury.
For most environmental systems, the literature suggests that various physical and chemical conditions may
influence the methylation of mercury. Consideration of these conditions, and the magnitude of their potential
impact, may be required in some cases to assess the potential for over or under predicting mercury
methylation in media and subsequent biotransfer up the food chain. Due to the extreme variance between
environmental systems modeled, and at times disagreement, identified in literature reviewed regarding the
quantitative influence of specific conditions on methylation, U.S. EPA OSW recommends that extensive
research of literature, specific to the conditions prevalent at the site, be conducted before application and
deviation from the conservative assumptions recommended above. The following table summarizes the
qualitative effect some of the physical and chemical conditions, as reported in literature, may have on
methylating:
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Physical or Chemical Condition
Low dissolved oxygen
Decreased pH
Decreased pH
Increased dissolved organic carbon
(DOC)
Increased dissolved organic carbon
(DOC)
Increased salinity
Increased nutrient concentrations
Increased selenium concentrations
Increased temperature
Increased sulfate concentrations
Increased sulfide concentrations
Qualitative Influence on
Methylation
Enhanced methylation
Enhanced methylation in water column
Decreased methylation in sediment
Enhanced methylation in sediment
Decreased methylation in water
column
Decreased methylation
Enhanced methylation
Decreased methylation
Enhanced methylation
Enhanced methylation
Enhanced methylation
Referenced Literature
Rudd et al. 1983; Parks et al. 1989
Xun 1987; Gilmour and Henry 1991;
Miskimmin et al. 1992
Ramlal et al. 1985; Steffan et al. 1988
Chois and Bartha 1994
Miskimmin et al. 1992
Blum and Bartha 1980
Wright and Hamilton 1982;
Jackson 1986; Regnell 1994;
Beckvaretal. 1996
Beckvaretal. 1996
Wright and Hamilton 1982; Parks et
al. 1989
Gilmour and Henry 1991; Gilmour et
al. 1992
Beckvaretal. 1996
To account for methylation of mercury in the media and its subsequent biotransfer assuming steady-state
conditions, the deposition and media concentration equations (presented in Appendix B) have been modified
specifically for modeling methyl mercury. Appendix A-2 provides the parameter values specific for
methylmercury, and additional discussion and reference on their origin.
As noted above, methylation can be highly variable between environmental systems. This results in a
significant degree of uncertainty implicit in modeling of mercury methylation. To expand on the qualitative
information presented in the above table, and better understand conditions that may influence mercury
methylation specific to a site, U.S. EPA OSW recommends review of information on this subject presented
in the Mercury Study Report to Congress (U.S. EPA 1997b).
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Exposure Assessment for Mercury
For assessing exposure of community and class-specific guild measurement receptors to mercury, guidance
provided in Chapters 4 and 5 should generally be followed. However, special consideration is required in
evaluating the various forms of mercury modeled to the point of exposure.
To evaluate exposure of water, sediment, and soil communities to mercury, species-specific concentrations of
divalent mercury and methyl mercury, in the respective media to which the community is exposed, should be
directly compared to toxicity benchmarks specific to those compounds. The species-specific media
concentrations should be calculated using equations and guidance presented in Chapter 3 and Appendix B.
Media-specific toxicity benchmarks for divalent and methyl mercury are provided in Appendix E.
To evaluate the exposure of class-specific guilds to mercury, the media-specific concentrations of both
divalent and methyl mercury should be modeled as independent COPCs through the food web, assuming no
methylation of divalent mercury to the methyl mercury form within organisms. Therefore, the daily doses of
all food items (i.e., media, plants, and animals) ingested by a measurement receptor should be considered for
both divalent and methyl mercury, and compared to the respective toxicity benchmarks that are representative
of the measurement receptor (see Appendix E). The daily doses of food items ingested by a measurement
receptor should be calculated consistent with the guidance presented in Chapters 4 and 5, and Appendix F,
for assessing exposure of class-specific guild measurement receptors to other COPCs. This includes the use
of species-specific media concentrations, and methyl mercury bioconcentration factors (BCF) and food chain
multipliers (FCM).
Conclusion
In the event risks associated with mercury exceed target levels based on modeling with equations and initial
conservative assumptions presented in this guidance, the permitting authority may approve use of more
complex models that utilize more extensive site-specific data to predict transformation of chemical forms and
biotransfer of mercury for evaluation at points of potential exposure. For example, the draft version of the
ISCST3 dry gas algorithm for estimating dry gas deposition may be utilized. This draft model can be found
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on the SCRAM bulletin (see Chapter 3); and specific default parameter values for mercury are presented in
U.S. EPA (1997b). While this guidance does not address what models should be used or how data to support
such models should be collected, the decision to use site-specific mercury models in a risk assessment is not
precluded just because it is different; nor does this guidance automatically approve the use of such models. A
permitting authority that chooses to use complex mercury models should carefully identify and evaluate their
associated limitations, and clearly document these limitations in the uncertainty section of the risk assessment
report.
U.S. EPA OSW encourages all facilities to implement a combination of waste minimization and control
technology options to reduce mercury emission rates on an ongoing basis. Realistic expectations for mercury
emission reduction efforts may be established by considering various technology-based mercury emission
limits that apply to waste combustors (for example, standards for European combustors, the proposed
MACT standards for hazardous waste combustors, or the MACT standards for municipal waste
combustors). U.S. EPA OSW acknowledges that site-specific risk assessments as currently conducted may
not identify the entire potential risk from mercury emissions. Mercury that does not deposit locally will
ultimately enter the global mercury cycle for potential deposition elsewhere.
2.3.8 Particulate Matter
PM is all condensed material suspended in air that has a mean aerodynamic diameter of 10 micrometers or
less (PM10). PM can be classified as aerosols, dusts, fogs, fumes, mists, smogs, or smokes, depending on its
physical state and origin. Anecdotal evidence suggests that uncontrolled particulate emissions from coal-
burning industries has adversely affected local populations of wildlife (U.S. Fish and Wildlife Service [U.S.
FWS] 1980). For wildlife, PM can adsorb to external surfaces or membranes, for example causing corneal
damage. Wildlife exposure can also occur through ingested of contaminated food, water, and hair (through
grooming) (U.S. FWS 1980). However, PM dose-response information to evaluate risk of particulate matter
to ecological receptors is limited. For this reason, U.S. EPA OSW does not recommend that PM be
evaluated as a separate COPC in a risk assessment. However, PM is useful as an indicator parameter for
other contaminants because it can be measured in real time and is sensitive to changes in combustion
conditions.
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2.3.9 Hydrogen Chloride/Chlorine Gas
Hydrogen chloride (which becomes hydrochloric acid when dissolved in water) and chlorine are two of the
major products of the chemical industry, with uses too numerous to list. When chlorine gas dissolves in
water (whether during drinking water treatment or when someone inhales chlorine), it hydrolyzes to form
equal amounts of hydrochloric acid and hypochlorous acid.
Hydrogen chloride, as all other strong acids and bases, is an irritant on contact; adverse effects are seen only
in the upper respiratory tract (including the nose, mouth, and throat). High concentrations can become
corrosive and destroy tissues, producing chemical burns. Unless it is highly concentrated, ingested
hydrochloric acid has only minimal adverse effects.
Because of the high concentrations of these compounds needed to produce observable effects, they are not
expected to pose an ecological risk. Therefore, U.S. EPA OSW does not recommend that hydrogen chloride
and chlorine gas be included as separate COPCs in the risk assessment.
2.3.10 Endocrine Disruptors
Endocrine disrupters are chemical compounds that interfere with the endocrine system's normal function and
homeostasis in cells, tissues, and organisms. It has been hypothesized by U.S. EPA OSW that endocrine
disrupters adversely affect the reproductive system by interfering with production, release, transport, receptor
binding action, or elimination of natural blood-borne hormones and ligands.
Several studies have been conducted and serve as the basis for further experimentation to determine whether
the hypothesis is correct. These studies include (1) wildlife reproduction (feminization of birds, alligators,
and certain terrestrial mammals), (2) wildlife population ecology (population decline), (3) human
reproductive physiology (decreased sperm count in males in industrialized nations), (4) molecular biology
(data on receptor-mediated mode of action), and (5) endocrinology (increased understanding of mechanisms
of hormone regulation and impacts of perturbations).
Some have attempted to classify chemical compounds as endocrine disrupters; however, several problems
have been encountered. Only limited empirical data are available to support the designation of specific
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chemicals as endocrine disrupters, and some of the data are conflicting. An absence of a clear structure-
activity relationship is evident among the diverse groups of chemicals considered as endocrine disrupters.
There is a lack of unifying dose-response relationship among the diverse group of chemicals. Also, multiple
modes of action for chemicals are currently considered as endocrine disrupters.
Because the information currently available on endocrine disrupters is inconsistent and limited, U.S. EPA has
not yet developed a methodology for quantitative assessments of risk resulting from potential endocrine
disrupters (U.S. EPA 1996d). Currently, no quantitative U.S. EPA methods exist to specifically address the
effects of endocrine disrupters in a risk assessment. Because the methods for addressing endocrine disrupters
are developing at a rapid pace, permits writers and risk assessors should contact the Economics, Methods and
Risk Analysis Division (EMRAD) of the Office of Solid Waste for the latest policy on how to deal with
endocrine disrupters in site specific risk assessments. Additional information can also be obtained from
review of available publications (e.g., EPA Special Report on Endocrine Disruption) at the web site
"www.epa.gov/ORDAVebPubs/endocrine/".
2.3.11 Radionuclides
Radionuclides exist in (1) naturally occurring radioactive materials such as coal and other rocks, as
(2) radioactive by-products of industrial processes. This risk assessment guidance does not consider the
naturally occurring radioactive materials such as uranium and thorium (and their decay elements) based on
U.S. EPA doctrine and technical limitations for measuring such low levels. However, radioactive wastes and
materials, as defined by the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy
(DOE), are subject to evaluation through interagency agreements on this subject. The U.S. NRC considers
"radioactive waste" as waste that is, or contains, by-product material, source material, or special nuclear
material (as defined in 10 CFR Part 20.1003). The U.S. NRC considers "mixed waste" as waste that is
radioactive waste and hazardous waste defined by U.S. EPA. Radioactive and mixed waste must be handled
in accordance with all relevant regulations, including U.S. EPA and U.S. NRC (10 CFR Part 20.2007)
regulations. In particular, U.S. NRC licensees must comply with 10 CFR Part 20.2004—"Treatment or
Disposal by Incineration"—and applicable U.S. EPA regulations.
U.S. EPA OSW recommends that the combustion of mixed waste and radioactive material should be
evaluated in the risk assessment. Direct radiation (e.g., radiation from sealed sources such as instruments
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that are not released to the environment) does not need to be evaluated in the risk assessment. Risk from both
radiological and non-radiological contaminants should be presented along side each other in a risk summary
table. Results should include a discussion of additivity and the uncertainties of additivity when combining
risks from radiological and non-radiological contaminants. A radionuclide should be included as a COPC if
it is in the combustion unit's waste feed.
U.S. EPA OSW recommends using the ISCST3 air dispersion model, utilizing the exponential decay option
to calculate air concentrations and ground deposition rates. Intake should then be calculated with
appropriate exposure scenario equations and parameters. ISCST3 is a good choice for facilities with
multiple sources, complex terrain, building downwash and wet/dry deposition requirements.
A special consideration in integrating radioactive materials into risk calculations is related to decay and
ingrowth of radionuclides, especially the few decay processes that involve a change of state. Decay should
always be considered, both over the air transport time and the surface exposure duration. Ingrowth may be
important, and special care must be taken in the use of radionuclide slope factors that include contributions
from daughters ('+D" slope factors). Ingrowth involving change of physical states is another situation that
will require special handling in the fate and transport modeling. For instance, solid radium-226 decays to
gaseous radon-222, which then decays through solid polonium-218 to further decay elements.
Equations for fate and transport of radionuclides in soil and water should be consistent with those presented
for non-radionuclides factoring in decay (and ingrowth if applicable). Food chain biotransfer parameters
necessary to determine food concentrations are available in the Handbook of Parameter Values for the
Prediction of Radionuclide Transfer in Temperate Environments; IAEA Technical Report Series No. 364
(International Atomic Energy 1994).
Because the information currently available on ecological fate and effects for radionuclides is very limited,
U.S. EPA OSW has not yet developed a methodology for quantitative assessments of ecological risk resulting
from exposure. Ecological screening levels currently being used in some regions include 1 rad/day for
aquatic receptors, based on population effects, (National Council on Radiation Protection and Measurements
1991), and 0.1 rad/day for terrestrial receptors (with the exception of pine trees and mammalian embryos)
(International Atomic Energy Agency 1992). Additional references on evaluating ecological exposures to
radiation include Barnthouse (1995) and Blaylock et al. (1993).
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USER NOTE
Prescriptive methodology for calculating risk from combustion facilities burning
mixed waste is beyond the scope of the current document. The above information is
provided to outline the methodology recommended by U.S. EPA OSW.
2.4 ESTIMATES OF COPC CONCENTRATIONS FOR NON-DETECTS
The lowest level of an analyte that can be detected using an analytical method is generally termed the
"detection limit." One particularly difficult issue is the treatment of data in the risk assessment that are
reported as below the "detection limit." The following subsections (1) define commonly reported "detection
limits," (2) describe use in the risk assessment of data reported as non-detect, (3) describe statistical
distribution techniques applied to address this issue, (4) summarize U.S. EPA OSW recommendations
regarding quantification of non-detect issues in preparation of a risk assessment, and (5) clarify data flagged
as estimated maximum possible concentration (EMPC) in the risk assessment.
2.4.1 Definitions of Commonly Reported Detection Limits
U.S. EPA's commonly-used definition for the detection limit for non-isotope dilution methods has been the
method detection limit (MDL), as promulgated in 40 CFR Part 136, Appendix B (U.S. EPA 1995i). A level
above the MDL is the level at which reliable quantitative measurements can be made; generically termed the
"quantitation limit" or "quantitation level." In practice, numerous terms have been created to describe
detection and quantitation levels. The significance and applicability of the more widely reported of these
detection and quantitation levels by analytical laboratories are summarized below. These levels—listed
generally from the lowest limit to the highest limit—include the following:
• Instrument Detection Limit (IDL) is the smallest signal above background that an instrument
can reliably detect, but not quantify. Also, commonly described as a function of the
signal-to-noise (S/N) ratio.
• Method Detection Limit (MDL) is the minimum concentration of a substance that can be
measured (via non-isotope dilution methods) and reported with 99 percent confidence that the
analyte concentration is greater than zero, and is determined from analysis of a sample in a
specific matrix type containing the analyte. The MDL is considered the lowest level at
which a compound can be reliably detected. The MDL is based on statistical analyses of
laboratory data. In practice, the MDLs are determined on analytical reagents (e.g., water)
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and not on the matrix of concern. MDLs for a given method, are laboratory and compound
specific.
To determine the MDL as specified in 40 CFR Part 136, Appendix A, for example,
at least seven replicate samples with a concentration of the compound of interest
near the estimated MDL are analyzed. The standard deviation among these analyses
is calculated and multiplied by 3. 14. The result of the calculation becomes the
MDL. The factor of 3 . 14 is based on a t-test with six degrees of freedom and
provides a 99 percent confidence that the analyte can be detected at this
concentration (U.S. EPA 1995i).
It should be noted that 40 CFR Part 136 is specific to the Clean Water Act, and
therefore, it identifies the use of water as the matrix for the MDL determination. The
MDL was promulgated in 1984, and is incorporated in more than 130 U.S. EPA
analytical methods for the determination of several hundred analytes.
• Reliable Detection Level (RDL) is a detection level recommended by the National
Environmental Research Laboratory in Cincinnati. It is defined as 2.623 times the MDL
(U.S. EPA 1995i). The RDL is a total of 8 standard deviations above the MDL
developmental test data (3.14 times 2.623).
• Estimated Detection Limit (EDL) is a quantitation level defined in SW-846 that has been
applied to isotope dilution test methods (e.g., SW-846 Method 8290). A variation of the
SW-846 defined EDL is also commonly reported by commercial laboratories, however, with
the addition of a multiplication factor that generally elevates the EDL value by 3.5 to 5 times
that of the SW-846 definition. Commercial laboratories sometimes report EDLs for
non-isotope dilution methods such as SW-846 Method 8270, even though an EDL is not
defined by the method.
As defined in SW-846: The EDL is defined in SW-846 (presented in various methods,
e.g., Method 8280A) as the estimate made by the laboratory of the concentration of a given
analyte required to produce a signal with a peak height of at least 2.5 times the background
signal level. The estimate is specific to a particular analysis of the sample and will be
affected by sample size, dilution, etc. The presented equation defining EDL is as follows:
- H + H • D
f
IS
s~f is
Equation 2-7
where
EDL = Estimated detection limit (ng/L)
2.5 = Peak height multiplier (unitless)
Qis = Nanograms of the appropriate internal standard added to
the sample prior to extraction (ng)
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Hn'andffn2
His' and His2
D
V
RF
The peak heights of the noise for both of the quantitation
ions of the isomer of interest
The peak heights of both the quantitation ions of the
appropriate internal standards
Dilution factor - the total volume of the sample aliquot in
clean solvent divided by the volume of the sample aliquot
that was diluted (unitless)
Volume of sample extracted (L)
Calculated relative response factor from calibration
verification (unitless)
Common commercial laboratory practice: The EDL, generally reported by commercial
laboratories, is defined as the detection limit reported for a target analyte that is not detected
or presents an analyte response that is less than 2.5 times the background level. The area of
the compound is evaluated against the noise level measured in a region of the chromatogram
clear of genuine GC signals times an empirically derived factor. This empirical factor
approximates the area to height ratio for a GC signal. This factor is variable between
laboratories and analyses performed, and commonly ranges from 3.5 to 5. The equation is
as follows:
2.5-QQ-(F-H)-D
W-A-RRF
Equation^
where
EDL
2.5
Qp
F
H
D
W
RRF
Estimated detection limit
Minimum response required for a GC signal
The amount of internal standard added to the sample before
extraction
An empirical factor that approximates the area to height
ratio for a GC signal
The height of the noise
Dilution factor
The sample weight or volume
The mean analyte relative response factor from the initial
calibration
• Practical Quantitation Limit (PQL) is a quantitation level that is defined in 50 FR 46908 and
52 FR 25699 as the lowest level that can be reliably achieved with specified limits of
precision and accuracy during routine laboratory operating conditions (U.S. EPA 1992g;
1995i). The PQL is constructed by multiplying the MDL, as derived above, by a factor
(subjective and variable between laboratories and analyses performed) usually in the range
of 5 to 10. However, PQLs with multipliers as high as 50 have been reported
(U.S. EPA 19951).
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The PQL has been criticized because of the ambiguous nature of the multiplier and
because the resulting levels have been perceived as too high for regulatory
compliance purposes (U.S. EPA 1995i).
• Target Detection Limit (TDL) is a quantitation level constructed similar to the PQL.
• Reporting Limit (RL) is a quantitiation level constructed similar to the PQL.
• Estimated Quantitation Limit (EQL) is a quantitiation level constructed similar to the PQL.
• Sample Quantitation Limit (SQL) is a quantitation level that is sample-specific and highly
matrix-dependent because it accounts for sample volume or weight, aliquot size, moisture
content, and dilution. SQLs for the same compound generally vary between samples as
moisture content, analyte concentration, and concentrations of interfering compounds vary.
The SQL is generally 5 to 10 times the MDL, however, it is often reported at much higher
levels due to matrix interferences.
• Contract Required Quantitation Limit (CRQL)/Contract Required Detection Limit (CRDL)
is a quantitation pre-set by contract, which may incorporate U.S. EPA (1986b) SW-846
methods, Office of Water methods, or other methods deemed necessary to meet study
objectives. These limits are typically administrative limits and may actually be one or two
orders of magnitude above the MDL.
2.4.2 Use In the Risk Assessment of Data Reported As Non-Detect
In collecting data for use in risk assessments or in setting regulatory compliance levels, the permitting
authority is often faced with data quality objectives that require analyses near or below analytical detection or
quantitation levels. In such situations, permittees often argue that the detection levels should be set with a
large factor of certainty in order to be confident that measurements are reliable. Environmental groups
frequently argue that a level of zero or a level at which a single researcher can demonstrate that the
compound can be detected should be used as the set level. Because measurements made below analytical
detection and quantitation levels are associated with increased measurement uncertainty, an understanding of
these levels is important to the comprehension of the impact they may have when they are applied.
As a result of the quantitative differences between the various types of detection levels, "non-detected"
compounds pose two questions: (1) Is the compound really present?, and (2) If so, at what concentration?
The first question is generally hard to answer, and is dependent mainly on the analytical resources available.
For the second question, the answer is "somewhere between true zero and the quantitation level applied." For
samples obtained during the trial burn that report compounds at below the detection limit, earlier U.S. EPA
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(1994i) guidance has recommended that emission rates for non-detects be developed using one-half of the
"detection limit" and applied in conducting the risk assessment. However, which detection limit should be
used has not been explicitly defined or presented in quantitative terms.
To increase consistency and reproducibility in dealing with non-detects, U.S. EPA OSW recommends
application of the MDL-derived RDL to quantify non-detects for COPCs analyzed with non-isotope dilution
methods, and application of the method-defined EDL to quantify non-detects for COPCs analyzed with
isotope dilution methods. Procedures for these applications are as follows:
Non-isotope Dilution Methods: Non-detects for COPCs analyzed with non-isotope dilution
methods should be quantified for use in the risk assessment using an MDL-derived RDL.
Commonly used non-isotope dilution methods include SW-846 Method 8260 (volatiles), SW-846
Method 8270 (semivolatiles),
1. Require the laboratory to report the actual MDL for every non-detect compound analyzed, in
addition to the commonly used reporting limit, such as an EDL, EQL, or PQL. The MDL
should be derived in a manner consistent with 40 CFR Part 136 Appendix B. This would
also apply for analysis of each individual component of multiple component samples (e.g.,
front half rinse, XAD resin, condensate, Tenax tube).
Note: Laboratories typically produce MDLs specific to each non-isotope dilution method
performed by the laboratory on an annual basis.
2. Calculate an MDL-derived RDL for each COPC non-detect for quantitative application in
the risk assessment. This would be obtained by multiplying the MDL, as reported by the
laboratory, times 2.623 (interim factor) (U.S. EPA 1995i).
3. Adjust the RDL, as appropriate, to account for sample-specific volumetric treatments (e.g.,
splits and dilutions) that differ from those utilized in the Part 136 MDL determinations.
Isotope Dilution Methods (SW-846 Methods 8290,1624, 1625; and CARB 429, etc.): Non-detects
for COPCs analyzed with isotope dilution methods should be quantified for use in the risk
assessment using the EDL as defined by the analytical method without the use of empirical factors or
other mathematical manipulations specific to the laboratory (e.g., EDL as defined in SW-846).
Commonly used isotope dilution methods include SW-846 Methods 8290, 1624, and 1625.
It should be noted that the MDL definition used in 40 CFR Part 136 (see Section 2.4.1) addresses errors of
the first type, false negatives. The 99 percent confidence limit stating that the MDL has only a 1 percent
chance the detects will be misidentified as negative, when the compound of concern was present. Errors of
the second type, false positives are not addressed. By not addressing false positives, or errors of the second
type, the statistically defined default value become 50 percent. In other words, where 40 CFR did not
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address false positives, the system required that 50 percent of the detects at the MDL would be false
positives. This is a very conservative approach, and biased toward not missing any compounds of potential
concern that may be present. The use of the MDL-derived RDL, and to a lesser extent the EDL, somewhat
indirectly addresses the false positive issue. As described in defining the RDL (see Section 2.4.1), by the
time the standard deviation has been multiplied by 8, the possibility of false positives is usually less than
1 percent.
2.4.3 Statistical Distribution Techniques
Many statistical distribution techniques are available for calculating a range of standard deviations to
quantify non-detect concentrations of COPCs. These include random replacement scenarios, such as: (1) the
uniform fill-in (UFI) method, in which each LOD value is replaced with a randomly generated data point by
using a uniform distribution; (2) the log fill-in LFI method which is the same as UFI, except for using a
logarithmic distribution; (3) the normal fill-in (NFI) method which is the same as UFI, except for using a
log-normal distribution; and (4) the maximum likelihood estimation (MLE) techniques (Cohen and Ryan
1989; Rao et al. 1991). If determined to be applicable by the permitting authority, a Monte Carlo simulation
may also be used to determine a "statistical" value for each non-detect concentration.
2.4.4 U.S. EPA OSW-Recommendations on Quantifying Non-Detects
Use of non-detects in risk assessments is dependent on the analytical method used to produce the data. In
most cases, U.S. EPA will estimate emission rates for undetected COPCs (see Section 2.3) by assuming that
COPCs are present at a concentration equivalent to the MDL-derived RDL for non-isotope dilution methods,
or the method-defined EDL for isotope dilution methods. U.S. EPA OSW believes that these methods are
reasonable and conservative, and that they represent a scientifically sound approach that allows maximum
protection of the environment while recognizing the uncertainty associated with analytical measurements at
very low concentrations in a real world sample matrix. It is also recognized that there are subjective
components and limitations to each of the non-detect methodologies presented in this and previous guidance,
including the recommended methods.
Some state permitting authorities have expressed the desire to obtain and use non-routine data
(e.g., uncensored data) of defensible quality in the risk assessment as a way to deal with non-detect issues.
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While this guidance does not address what forms or how such data may be used, the decision to use
non-routine data in a risk assessment is not precluded just because it is different; nor does this guidance
automatically approve the use of non-routine data. A permitting authority that chooses to use non-routine
data should carefully identify and evaluate the limitations associated with non-routine data and clearly
document this discussion in the uncertainty section of the risk assessment report.
For collection of data to be used in a risk assessment, U.S. EPA OSW recommends comprehensive sampling
using typical sampling and analytical methods for VOCs, SVOCs, metals, PCDDs, PCDFs, total organics,
and other appropriate constituents as necessary based on the type of waste that will be burned by the unit. A
pretrial burn risk assessment can help to ensure that the desired quantitation limit (and, therefore, DREs and
COPC stack gas emission rates) will be achieved during the trial burn test.
2.4.5 Estimated Maximum Possible Concentration (EMPC)
The EMPC, as defined in SW-846 Methods 8280A and 8290, is in most cases only used with the isotope
dilution methods as stated. An EMPC is calculated for dioxin isomers that are characterized by a response
with a signal to noise ratio of at least 2.5 for both the quantitation ions, and meet all the relevant
identification criteria specified in the method, except the ion abundance ratio. Ion abundance ratios are
affected by co-eluting interferences that contribute to the quantitative ion signals. As a result, one or both of
the quantitative ions signals may possess positive biases.
An EMPC is a worst case estimate of the concentration. An EMPC is not a detection limit and should not be
treated as a detection limit in the risk assessment. U.S. EPA OSW recommends that EMPC values be used
as detections without any further manipulation (e.g., dividing by 2). However, because EMPCs are worst
case estimates of stack gas concentrations, permitting authorities and facilities should consider techniques to
minimize EMPCs when reporting trial and risk burn results, especially when the EMPC values result in risk
estimates above regulatory levels of concern. Some techniques that may be applied to minimize EMPCs
include performing additional cleanup procedures (as defined by the analytical method) on the sample or
archived extract, and/or reanalyzing the sample under different chromatographic conditions.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Actual MDLs for all non-detect results
• Description of the method applied to quantify the concentration of non-detects
2.5 CONCENTRATIONS DETECTED IN BLANKS
Blank samples are intended to provide a measure of any contamination that may have been introduced into a
sample either in the field while the samples were being collected, in transport to the laboratory, or in the
laboratory during sample preparation or analysis. Blank samples are analyzed in the same manner as the site
samples from the trail burn. In order to prevent the inclusion of non-site related compounds in the risk
assessment, the concentrations of compounds detected in blanks should be compared to concentrations
detected in site samples collected during the trial burn. Four types of blanks are defined in the Risk
Assessment Guidance for Superfund (U.S. EPA 1989e): trip blanks, field blanks, laboratory calibration
blanks, and laboratory reagent of method blanks. Detailed definitions of each are provided below.
Trip Blank - A trip blank is used to indicate potential contamination due to migration of volatile
organic compounds from the air on the site or in sample shipping containers, through the septum or
around the lid of sampling vials, and into the sample. The blank accompanies the empty sample
bottles to the field as well as with the site samples returning to the laboratory for analysis. The blank
sample is not opened until it is analyzed in the lab with the site samples, thus making the laboratory
"blind" to the identity of the blanks.
Field Blank - A field blank is used to determine if field sampling or cleaning procedures
(e.g., insufficient cleaning of sample equipment) result in cross-contamination of site samples. Like
the trip blank, the field blank is transported to the field with empty sample bottles and is analyzed in
the laboratory along with the site samples. Unlike the trip blank, however, the field blank sample is
opened in the field and recovered in the same manner as the collected samples. As with trip blanks,
the field blanks' containers and labels should be the same as for site samples and blind to the
laboratory.
Instrument Blank - An instrument blank is distilled, deionized water injected directly into an
instrument without having been treated with reagents appropriate to the analytical method used to
analyze actual site samples. This type of blank is used to indicate contamination in the instrument
itself.
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Laboratory Reagent of Method Blank - A laboratory reagent of method blank results from the
treatment of distilled, deionized water with all of the reagents and manipulations (e.g., degestions or
extractions) to which site samples will be subjected. Positive results in the reagent blank may
indicate either contamination of the chemical reagents or the glassware and implements used to store
or prepare the sample and resulting solutions. Although a laboratory following good laboratory
practices will have its analytical processed under control, in some instances method blank
contaminants cannot be entirely eliminated.
Water Used for Blanks - For all the blanks described above, results are reliable only if the water
comprising the blank was clean. For example, if the laboratory water comprising the trip blank was
contaminated with VOCs prior to being taken to the field, then the source of VOC contamination in
the trip blank cannot be isolated.
Blank data should be compared with the results with which the blanks are associated. However, if the
association between blanks and data can not be made, blank data should be compared to the results from the
entire sample data set.
U.S. EPA (1989e) makes a division in comparison between blanks containing common laboratory
contaminants and blanks containing contaminants not commonly used in laboratories. Compounds
considered to be common laboratory contaminants are acetone, 2-butanone (methyl ethyl ketone), methylene
chloride, toluene, and the phthalate esters. If compounds considered to be common laboratory contaminants
are detected in the blanks, then sample results are not considered to be detected unless the concentrations in
the sample are equal to or exceed ten times the maximum amount detected in the applicable blanks. If the
concentration of a common laboratory contaminant in a sample is less than ten times the blank concentration,
then the compound is treated as a non-detect in that particular sample.
In some limited cases, it may be appropriate to consider blanks which contain compounds that are not
considered by U.S. EPA to be common laboratory contaminants as identified above. In these limited cases,
sample results are not considered to be detected unless the concentrations in the sample exceed five times the
maximum amount detected in the applicable blanks. If the concentration in a sample is less than five times
the blank concentration, then the compound is treated as a non-detect in that particular sample.
Permitting authorities should carefully consider the evaluation of blank data in the overall context of the risk
assessment and permitting process. U.S. EPA OSW expects that issues related to non-laboratory
contaminant blanks to be minimal because data collection and analysis efforts in support of trial and risk
burns are expected to be of high quality in strict conformance to QA/QC plans and SOPs. The trial and risk
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burn data should be carefully evaluated to ensure that the level of contamination present in the blanks does
not compromise the integrity of the data for purposes of risk assessment, or result in retesting in order to
properly address data quality issues.
When considering blank contamination in the COPC selection process, permitting authorities should ensure
that:
(1) The facility or data gatherer has made every reasonable attempt to ensure good data quality
and has rigorously implemented the QA/QC Plan and good industry sampling and testing
practices.
(2) Trial and risk burn data has not been submitted to the permitting authority as "blank
corrected." Rather, the permitting authority has the full opportunity to review the data
absent additional manipulation by the data gatherer.
(3) The effect of the blank correction on the overall risk estimates, if such an effect is
considered, is clearly described in the uncertainty section of the risk assessment report.
(4) The risk assessment reports emissions rates both as measured, and as blank corrected, in
situations where there is a significant difference between the two values.
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Chapter 3
Air Dispersion and Deposition Modeling
What's Covered in Chapter 3:
4 U.S. EPA-Recommended Air Dispersion and Deposition Model
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Use of Modeled Output
Modeling Fugitive Emissions
Estimating Media Concentrations
Combustion of materials produces residual amounts of pollution that may be released to the environment.
Estimation of potential ecological risks associated with these releases requires knowledge of atmospheric
pollutant concentrations and annual deposition rates in the areas around the combustion facility at
habitat-specific scenario locations. Air concentrations and deposition rates are usually estimated by using
air dispersion models. Air dispersion models are mathematical constructs that approximate the physical
processes occurring in the atmosphere that directly influence the dispersion of gaseous and particulate
emissions from the stack of a combustion unit. These mathematical constructs are coded into computer
programs to facilitate the computational process.
This chapter provides guidance on the development and use of the standard U.S. EPA air dispersion model
that U.S. EPA expects to be used in most situations—the Industrial Source Complex Short-Term
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Model (ISCST3). ISCST3 requires the use of the following information for input into the model, and
consideration of output file development:
Site-specific characteristics required for air modeling (Section 3.2)
Surrounding terrain (Section 3.2.1)
Surrounding land use (Section 3.2.2)
Facility building characteristics (Section 3.2.3)
Unit emission rate (Section 3.3)
• Partitioning of emissions (Section 3.4)
Meteorological data (Section 3.5)
Source Characteristics (Section 3.7)
ISCST3 also requires the use of several preprocessing computer programs that prepare and organize data
for use in the model. Section 3.6 describes these programs. Section 3.7 describes the structure and format
of the input files. Section 3.8 describes limitations to be considered in executing ISCST3. Section 3.9
describes use of the air modeling output in the risk assessment computations. Section 3.10 discusses air
modeling of fugitive emissions. Section 3.11 describes how to estimate the media concentrations of COPCs
in media.
If applicable, readers are encouraged to consult the air dispersion modeling chapter (Chapter 3) of the U.S.
EPA OSW guidance document Human Health Risk Assessment Protocol (HHRAP) (U.S. EPA 1998c)
before beginning the air modeling process to ensure the consideration of specific issues related to human
health risk assessment. Additionally, the Guideline on Air Quality Models (GAQM) (U.S. EPA 1996c) is
a primary reference for all US EPA and state agencies on the use of air models for regulatory purposes.
The GAQM is incorporated in 40 CFR Part 51 as Appendix W. The Office of Air Quality Planning and
Support (OAQPS) provides the GAQM and extensive information on air dispersion models, meteorological
data, data preprocessors, user's guides, and model applicability on the Support Center for Regulatory Air
Models (SCRAM) web site at address "http://www.epa.gov/scram001/index.htm". General questions
regarding air modeling or information on the web site should be addressed to
"atkinson.dennis@epamail.epa.gov". Specific questions on the use of this guidance should be addressed to
the appropriate permitting authority.
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3.1 DEVELOPMENT OF AIR MODELS
This section (1) briefly describes the history of air model development, (2) introduces some data
preprocessing programs developed to aid in preparing air model input files (these preprocessing programs
are described in more detail in Sections 3.2.4 and 3.6, and (3) introduces Exlnter Version 1.0, a
preprocessor to ISCST3.
3.1.1 History of Risk Assessment Air Dispersion Models
Before 1990, several air dispersion models were used by U.S. EPA and the regulated community. These
models were inadequate for use in risk assessments because they considered only concentration, and not the
deposition of contaminants to land. The original U.S. EPA guidance (1990a) on completing risk
assessments identified two models that were explicitly formulated to account for the effects of deposition.
• COMPLEX terrain model, version 1 (COMPLEX I), from which a new model—
COMPLEX terrain model with DEPosition (COMPDEP)—resulted
• Rough Terrain Diffusion Model (RTDM), from which a new
model—RTDMDEP—resulted
COMPDEP was updated to include building wake effects from a version of the ISCST model in use at the
time. Subsequent U.S. EPA guidance (1993h; 1994b) recommended the use of COMPDEP for air
deposition modeling. U.S. EPA (1993h) specified COMPDEP Version 93252, and U.S. EPA (1994b)
specified COMPDEP Version 93340. When these recommendations were made, a combined
ISC-COMPDEP model (a merger of the ISCST2 and COMPLEX I model) was still under development.
The merged model became known as ISCSTDFT. U.S. EPA guidance (19941) recommended the use of the
ISCSTDFT model. After reviews and adjustments, this model was released as ISCST3. The ISCST3
model contains algorithms for dispersion in simple, intermediate, and complex terrain; dry deposition; wet
deposition; and plume depletion.
The use of the COMPDEP, RTDMDEP, and ISCST models is described in more detail in the following
user's manuals; however, all models except the current version of ISCST3 are obsolete:
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• Environmental Research and Technology (ERT). 1987. User's Guide to the Rough
Terrain Diffusion Model Revision 3.20. ERT Document P-D535-585. Concord,
Massachusetts.
• Turner, D.B. 1986. Fortran Computer Code/User's Guide for COMPLEX I Version
86064: An Air Quality Dispersion Model in Section 4. Additional Models for
Regulatory Use. Source File 31 Contained in UNAMAP (Version 6). National Technical
Information Service (NTIS) PB86-222361/AS.
• U.S. EPA. 1979. Industrial Source Complex Dispersion Model User's Guide, Volume I.
Prepared by the H.E. Cramer Company. Salt Lake City, Utah. Prepared for the Office of
Air Quality Planning and Standards. Research Triangle Park, North Carolina. EPA
450/4-79/030. NTIS PB80-133044.
• U.S. EPA. 1980b. User's Guide for MPTER: A Multiple Point Gaussian Dispersion
Algorithm with Optional Terrain Adjustment. Environmental Sciences Research
Laboratory. Research Triangle Park, North Carolina. EPA 600/8-80/016. NTIS
PB80-197361.
U. S. EPA. 1982a. MPTER-DS: The MPTER Model Including Deposition and
Sedimentation. Prepared by the Atmospheric Turbulence and Diffusion Laboratory.
National Oceanic and AtmosphericAdministration (NOAA). Oak Ridge, Tennessee.
Prepared for the Environmental Sciences Research Laboratory. Research Triangle Park,
North Carolina. EPA 600/8-82/024. NTIS PB83-114207.
• U.S. EPA. 1987b. On-Site Meteorological Program Guidance for Regulatory Modeling
Applications. Office of Air Quality Planning and Standards. Research Triangle Park,
North Carolina.
• U.S. EPA. 1995c. User's Guide for the Industrial Source Complex (ISC3) Dispersion
Models, Volumes I and II. Office of Air Quality Planning and Standards. Emissions,
Monitoring, and Analysis Division. Research Triangle Park, North Carolina.
EPA 454/B-95/003a. September.
Users of this document are advised that a draft version of ISCST3 that includes algorithms for estimating
the dry gas deposition (currently referred to as the "Draft Dry Gas Deposition Model: GDISCDFT,
Version 96248") is available on the SCRAM web site. Use of this version to support site specific air
modeling applications is not required, because many of the parameters needed to execute the model are not
available in guidance or the technical literature. Therefore, until the draft version is reviewed and
approved, and the data is provided by U.S. EPA or in the technical literature, U.S. EPA OSW recommends
that the current version of ISCST3, in conjunction with the procedure presented in this guidance
(Appendix B) for estimating dry gas deposition using deposition velocity and gas concentration, should be
used for risk assessments.
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3.1.2 Preprocessing Programs
ISCST3 requires the use of additional computer programs, referred to as "preprocessing" programs. These
programs manipulate available information regarding surrounding buildings and meteorological data into a
format that can be used by ISCST3. Currently, these programs include the following:
• PCRAMMET (Personal Computer Version of the Meteorological Preprocessor for the old
RAM program) prepares meteorological data for use in ISCST3. The program organizes
data—such as precipitation, wind speed, and wind direction—into rows and columns of
information that are read by ISCST3. The PCRAMMET User's Guide contains detailed
information for preparing the required meteorological input file for the ISCST3 model
(U.S. EPA 1995b).
• Building Profile Input Program (BPIP) calculates the maximum crosswind widths of
buildings, which ISCST3 then uses to estimate the effects on air dispersion. This effect on
dispersion by surrounding buildings is typically known as building downwash or wake
effects. The BPIP User's Guide contains detailed information for preparing the required
building dimensions (length, height, and width) and locations for the ISCST3 model (U.S.
EPA 1995d).
Meteorological Processor for Regulatory Models (MPRM) prepares meteorological data
for use in the ISCST3 by using on-site meteorological data rather than data from
government sources (National Weather Service [NWS] or the Solar And Meteorological
Surface Observational Network [SAMSON]). MPRM merges on-site measurements of
precipitation, wind speed, and wind direction with off-site data from government sources
into rows and columns of information that are read by ISCST3. The MPRM User's Guide
contains information for preparing the required meteorological input file for the ISCST3
model (U.S. EPA 1996e).
Most air dispersion modeling performed to support risk assessments will use PCRAMMET and BPIP.
MPRM will generally not be used unless on-site meteorological information is available. However, only
MPRM is currently scheduled to be updated to include the meteorological parameters (solar radiation and
leaf area index) required to execute the dry deposition of vapor algorithms included in the new version of
ISCST3. The draft version of MPRM is available for review and comment on the SCRAM web site as
GDMPRDFT (dated 96248).
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3.1.3 Expert Interface (Exlnter Version 1.0)
Exlnter is an expert interface system enhanced by U.S. EPA Region 6 for the ISCST3 model. By
enhancing Exlnter, the goal of U.S. EPA Region 6 was to support the in-house performance of air
dispersion modeling by regional U.S. EPA and state agency personnel at hazardous waste combustion units
necessary to support risk assessments conducted at these facilities. Exlnter enables the user to build input
files and run ISCST3 and its preprocessor programs in a Windows-based environment. Specific
procedures for developing input files are stored in an available knowledge database. The underlying
premise of the Exlnter system is that the knowledge of an "expert" modeler is available to "nonexpert"
modeling personnel at all times. However, some air modeling experience is required to use Exlnter and its
components as recommended in this guidance. The Exlnter program has been written in Microsoft Visual
C++ in a Microsoft Windows environment.
Exlnter allows for a generic source category that comprises point, area, and volume sources. For each
source type, the program queries the relevant variables for the user. In addition to asking about the inputs
regarding the source types, Exlnter also asks about control options, receptors, meteorology, and output
formats. Exlnter then creates an input file, as required by the ISCST3 dispersion model. Exlnter also
allows the user to run the ISCST3 model and browse the results file.
Version 1.0 of Exlnter provides for input parameters to model dry gas deposition included in a draft
version of ISCST3. However, the data required for dry gas deposition requires a literature search and prior
regulatory approval. The procedure presented in this guidance (Appendix B) for estimating dry gas
deposition using deposition velocity and gas concentration is appropriate without prior approval. More
detailed information on how to use Exlnter can be found in the following:
U.S. EPA. 1996L User's Guide for Exlnter 1.0. Draft Version. U.S. EPA Region 6
Multimedia Planning and Permitting Division. Center for Combustion Science and
Engineering. Dallas, Texas. EPA/R6-096-0004. October.
Exlnter is available on the SCRAM web site at "http://www.epa.gov/scram001/index.htm" under the
Modeling Support section "Topics for Review". Six self-extracting compressed files contain all
components for installation and use. The user's guide is accessed interactively using the help command.
Individual user's guides to ISCST3, BPIP, PCRAMMET, and MPRM also provide good references for
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using Exlnter components. Exlnter requires a minimum of 15 megabytes of free hard disk space, Windows
3.1, 8 megabytes of system memory, and a 486 processor.
3.2 SITE-SPECIFIC INFORMATION REQUIRED TO SUPPORT AIR MODELING
Site-specific information for the facility and surrounding area required to support air dispersion modeling
includes (1) the elevation of the surrounding land surface or terrain, (2) surrounding land uses, and
(3) characteristics of on-site buildings that may affect the dispersion of COPCs into the surrounding
environment.
Often, site-specific information required to support air dispersion modeling can be obtained from review of
available maps and other graphical data on the area surrounding the facility. The first step in the air
modeling process is a review of available maps and other graphical data on the surrounding area. U.S.
Geological Survey (USGS) 7.5-minute topographic maps (1:24,000) extending to 10 kilometers from the
facility, and USGS 1:250,000 maps extending out to 50 kilometers, should be obtained to identify site
location, nearby terrain features, waterbodies and watersheds, ecosystems, special ecological habitats, and
land use. Aerial photographs are frequently available for supplemental depiction of the area. An accurate
facility plot plan—showing buildings, stacks, property and fence lines—is also needed. Facility
information including stack and fugitive source locations, building corners, plant property, and fence lines
should be provided in Universal Transverse Mercator (UTM) grid coordinates in meters east and north in
both USGS reference systems.
Most USGS paper 7.5-minute topographic maps are published in the North American Datum system
established in 1927 (NAD 27). However, most digital elevation data (e.g., USGS Digital Elevation
Mapping) is in the 1983 revised system (NAD 83). Special consideration should be given not to mix
source data obtained from USGS maps based on NAD 27 with digital terrain elevation data based on
NAD 83. Emission source information should be obtained in the original units from the facility data, and
converted to metric units for air modeling, if necessary. Digital terrain data can be acquired from USGS or
another documented source.
The specific information that must be collected is described in the following subsections. Entry of this
information into the ISCST3 input files is described in Section 3.7.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• All site-specific maps, photographs, or figures used in developing the air modeling approach
• Mapped identification of facility information including stack and fugitive source locations,
locations of facility buildings surrounding the emission sources, and property boundaries of the
facility
3.2.1 Surrounding Terrain Information
Terrain is important to air modeling because air concentrations and deposition rates are greatly influenced
by the height of the plume above local ground level. Terrain is characterized by elevation relative to stack
height. For air modeling purposes, terrain is referred to as "complex" if the elevation of the surrounding
land within the assessment area—typically defined as anywhere within 50 kilometers from the stack—is
above the top of the stack evaluated in the air modeling analysis. Terrain at or below stack top is referred
to as "simple." ISCST3 implements U.S. EPA guidance on the proper application of air modeling methods
in all terrain if the modeler includes terrain elevation for each receptor grid node and specifies the
appropriate control parameters in the input file.
Even small terrain features may have a large impact on the air dispersion and deposition modeling results
and, ultimately, on the risk estimates. U.S. EPA OSW recommends that most air modeling include terrain
elevations for every receptor grid node. Some exceptions may be those sites characterized by very flat
terrain where the permitting authority has sufficient experience to comfortably defer the use of terrain data
because its historical effect on air modeling results has been shown to be minimal.
In addition to maps which are used to orient and facilitate air modeling decisions, the digital terrain data
used to extract receptor grid node elevations should be provided in electronic form. One method of
obtaining receptor grid node elevations is using digital terrain data available from the USGS on the Internet
at web site "http://www.usgs.gov". An acceptable degree of accuracy is provided by the USGS "One
Degree" (e.g., 90 meter data) data available as "DEM 250" 1:250,000 scale for the entire United States
free of charge. USGS 30-meter data is available for a fee. Either 90-meter or 30-meter data is sufficient
for most risk assessments which utilize 100 meter or greater grid spacing. Digital terrain data may also be
purchased from a variety of commercial vendors which may require vendor-provided programs to extract
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the data. The elevations may also be extracted manually at each receptor grid node from USGS
topographic maps.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Description of the terrain data used for air dispersion modeling
• Summary of any assumptions made regarding terrain data
Description of the source of any terrain data used, including any procedures used to manipulate
terrain data for use in air dispersion modeling
3.2.2 Surrounding Land Use Information
Land use information in the risk assessment is used for purposes of air dispersion modeling and the
identification or selection of exposure scenario locations (see Chapter 4) in the risk assessment. Land use
analysis for purposes of selecting exposure scenario locations usually occurs out to a radius of 50
kilometers from the centroid of the stacks to ensure identification of all receptors that may be impacted.
However, in most cases, air modeling performed out to a radius of 10 kilometers allows adequate
characterization for the evaluation of exposure scenario locations. If a facility with multiple stacks or
emission sources is being evaluated, the radius should be extended from the centroid of a polygon drawn
from the various stack coordinates.
Land use information is also important to air dispersion modeling, but at a radius closer (3 kilometers) to
the emission source(s). Certain land uses, as defined by air modeling guidance, effect the selection of air
dispersion modeling variables. These variables are known as dispersion coefficients and surface roughness.
USGS 7.5-minute topographic maps, aerial photographs, or visual surveys of the area typically are used to
define the air dispersion modeling land uses (www.usgs.gov).
3.2.2.1 Land Use for Dispersion Coefficients
The Auer method specified in the Guideline on Air Quality Models (40 CFR Part 51, Appendix W) is used
to define land use for purposes of specifying the appropriate dispersion coefficients built into ISCST3.
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Land use categories of "rural" or "urban" are taken from the methods of Auer (Auer 1978). Areas
typically defined as rural include residences with grass lawns and trees, large estates, metropolitan parks
and golf courses, agricultural areas, undeveloped land, and water surfaces. Auer typically defines an area
as "urban" if it has less than 35 percent vegetation coverage or the area falls into one of the following use
types:
Urban Land Use
Type
11
12
Cl
R2
R3
Use and Structures
Heavy industrial
Light/moderate industrial
Commercial
Dense single/multi-family
Multi-family, two-story
Vegetation
Less than 5 percent
Less than 5 percent
Less than 1 5 percent
Less than 30 percent
Less than 35 percent
In general, the Auer method is described as follows:
Step 1 Draw a radius of 3 kilometers from the center of the stack(s) on the site map.
Step 2 Inspect the maps, and define in broad terms whether the area within the radius is rural or
urban, according to Auer's definition.
Step 3 Classify smaller areas within the radius as either rural or urban, based on Auer's
definition. (It may be prudent to overlay a grid [for example, 100 by 100 meters] and
identify each square as primarily rural or urban)
Step 4 Count the total of rural squares; if more than 50 percent of the total squares are rural, the
area is rural; otherwise, the area is urban.
Alternatively, digital land use databases may be used in a computer-aided drafting system to perform this
analysis.
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RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Description of the methods used to determine land use surrounding the facility
Copies of any maps, photographs, or figures used to determine land use
Description of the source of any computer-based maps used to determine land use
3.2.2.2 Land Use for Surface Roughness Height (Length)
Surface roughness height—also referred to as (aerodynamic) surface roughness length—is the height above
the ground at which the wind speed goes to zero. Surface roughness affects the height above local ground
level that a particle moves from the ambient air flow above the ground (for example in the plume) into a
"captured" deposition region near the ground. That is, ISCST3 causes particles to be "thrown" to the
ground at some point above the actual land surface, based on surface roughness height. Surface roughness
height is defined by individual elements on the landscape, such as trees and buildings.
U.S. EPA (1995b) recommended that land use within 5 kilometers of the stack be used to define the
average surface roughness height. For consistency with the method for determining land use for dispersion
coefficients (Section 3.2.2.1), the land use within 3 kilometers generally is acceptable for determination of
surface roughness. Surface roughness height values for various land use types are as follows:
Surface Roughness Heights for Land Use Types and Seasons (meters)
Land Use Type
Water surface
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
Spring
0.0001
1.00
1.30
0.20
0.03
0.05
1.00
0.30
Summer
0.0001
1.30
1.30
0.20
0.20
0.10
1.00
0.30
Autumn
0.0001
0.80
1.30
0.20
0.05
0.01
1.00
0.30
Winter
0.0001
0.50
1.30
0.05
0.01
0.001
1.00
0.15
Source: Sheih, Wesley, and Hicks (1979)
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If a significant number of buildings are located in the area, higher surface roughness heights (such as those
for trees) may be appropriate (U.S. EPA 1995b). A specific methodology for determining average surface
roughness height has not been proposed in prior guidance documents. For facilities using National
Weather Service surface meteorological data, the surface roughness height for the measurement site may be
set to 0.10 meters (grassland, summer) without prior approval. If a different value is proposed for the
measurement site, the value should be determined applying the following procedure to land use at the
measurement site. For the application site, the following method should be used to determine surface
roughness height:
Step 1 Draw a radius of 3 kilometers from the center of the stack(s) on the site map.
Step 2 Inspect the maps, and use professional judgment to classify the areas within the radius
according to the PCRAMMET categories (for example water, grassland, cultivated land,
and forest); a site visit may be necessary to verify some classifications.
Step 3 Calculate the wind rose directions from the 5 years of meteorological data to be used for
the study (see Section 3.4.1.1); a wind rose can be prepared and plotted by using the U.S.
EPA WRPLOT program from the U.S. EPA's Support Center for Regulatory Air Models
bulletin board system (SCRAM BBS).
Step 4 Divide the circular area into 16 sectors of 22.5 degrees, corresponding to the wind rose
directions (for example, north, north-northeast, northeast, and east-northeast) to be used
for the study.
Step 5 Identify a representative surface roughness height for each sector, based on an
area-weighted average of the land use within the sector, by using the land use categories
identified above.
Step 6 Calculate the site surface roughness height by computing an average surface roughness
height weighted with the frequency of wind direction occurrence for each sector.
Alternative methods of determining surface roughness height may be proposed for agency approval prior to
use in an air modeling analysis.
3.2.3 Information on Facility Building Characteristics
Building wake effects have a significant impact on the concentration and deposition of COPCs near the
stack. Building wake effects are flow lines that cause plumes to be forced down to the ground much sooner
than they would if the building was not there. Therefore, the ISCST3 model contains algorithms for
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evaluating this phenomenon, which is also referred to as "building downwash." The downwash analysis
should consider all nearby structures with heights at least 40 percent of the height of the shortest stack to
be modeled. The 40 percent value is based on Good Engineering Practice (GEP) stack height of 2.5 times
the height of nearby structures or buildings (stack height divided by 2.5 is equal to 0.40 multiplied by the
stack height [40 CFR Part 51 Appendix W]). Building dimensions and locations are used with stack
heights and locations in BPIP to identify the potential for building downwash. BPIP and the BPIP user's
guide can be downloaded from the SCRAM web site and should be referred to when addressing specific
questions. The BPIP output file is in a format that can be copied and pasted into the source (SO) pathway
of the ISCST3 input file. The following procedure should be used to identify buildings for input to BPIP:
Step 1 Lay out facility plot plan, with buildings and stack locations clearly identified (building
heights must be identified for each building); for buildings with more than one height or
roof line, identify each height (BPIP refers to each height as a tier).
Step 2 Identify the buildings required to be included in the BPIP analysis by comparing building
heights to stack heights. The building height test requires that only buildings at least 40
percent of the height of a potentially affected stack be included in the BPIP input file. For
example, if a combustion unit stack is 50 feet high, only buildings at least 20 feet (0.40
multiplied by 50 feet) tall will affect air flow at stack top. Any buildings shorter than 20
feet should not be included in the BPIP analysis. The building height test is performed for
each stack and each building.
Step 3 Use the building distance test to check each building required to be included in BPIP from
the building height test. For the building distance test, only buildings "nearby" the stack
will affect air flow at stack top. "Nearby" is defined as "five times the lesser of building
height or crosswind width" (U.S. EPA 1995d). A simplified distance test may be used by
considering only the building height rather than the crosswind width. While some
buildings with more height than width will be included unnecessarily using this
simplification, BPIP will identify correctly only the building dimensions required for
ISCST3.
As an example, if a plot plan identifies a 25-foot tall building that is 115 feet from the
50-foot tall combustion unit stack center to the closest building corner. The building
distance test, for this building only, is five times the building height, or 125 feet (five
multiplied by the building height, 25 feet). This building would be included in the BPIP
analysis, because it passes the building height test and building distance test.
Step 4 Repeat steps 2 and 3 for each building and each stack, identifying all buildings to be
included in the BPIP. If the number of buildings exceeds the BPIP limit of eight buildings,
consider combining buildings, modifying BPIP code for more buildings, or using third-
party commercial software which implements BPIP. If two buildings are closer than the
height of the taller building, the two buildings may be combined. For example, two
buildings are 40 feet apart at their closest points. One building is 25 feet high, and the
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other building is 50 feet high. The buildings could be combined into one building for input
to BPIP. For input to BPIP, the corners of the combined building are the outer corners of
the two buildings. For unusually shaped buildings with more than the eight corners
allowed by BPIP, approximate the building by using the eight corners that best represent
the extreme corners of the building. The BPIP User's Guide contains additional
description and illustrations on combining buildings, and BPIP model limitations (U.S.
EPA 1995d).
Step 5 Mark off the facility plot plan with UTM grid lines. Extract the UTM coordinates of each
building corner and each stack center to be included in BPIP input file. Although BPIP
allows the use of "plant coordinates," U.S. EPA OSW requires that all inputs to the air
modeling be prepared using UTM coordinates (meters) for consistency. UTM coordinates
are rectilinear, oriented to true north, and in metric units required for ISCST3 modeling.
Almost all air modeling will require the use of USGS topographic data (digital and maps)
for receptor elevations, terrain grid files, location of plant property, and identification of
surrounding site features. Therefore, using an absolute coordinate system will enable the
modeler to check inputs at each step of the analysis. Also, the meteorological data are
oriented to true north. Significant errors will result from ISCST3 if incorrect stack or
building locations are used, plant north is incorrectly rotated to true north, or incorrect
base elevations are used. With computer run times of multiple years of meteorological
data requiring many hours (up to 40 hours for one deposition run with depletion),
verification of locations at each step of preparing model inputs will prevent the need to
remodel.
Several precautions and guidelines should be observed in preparing input files for BPIP:
• Before BPIP is run, the correct locations should be graphically confirmed. One method is
to plot the buildings and stack locations by using a graphics program. Several commercial
programs incorporating BPIP provide graphic displays of BPIP inputs.
• U.S. EPA OSW recommends, in addition to using UTM coordinates for stack locations
and building corners, using meters as the units for height.
Carefully include the stack base elevation and building base elevations by using the BPIP
User's Guide instructions.
• Note that the BPIP User's Guide (revised February 8, 1995) has an error on page 3-5,
Table 3-1, under the "TIER(ij)" description, which incorrectly identifies tier height as
base elevation.
• BPIP mixes the use of "real" and "integer" values in the input file. To prevent possible
errors in the input file, note that integers are used where a count is requested (for example,
the number of buildings, number of tiers, number of corners, or number of stacks).
• The stack identifications (up to eight characters) in BPIP must be identical to those used in
the ISCST3 input file, or ISCST3 will report errors.
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For most sites, BPIP executes in less than 1 minute. The array of 36 building heights and 36 building
widths (one for each of 36 10-degree direction sectors) are input into the ISCST3 input file by cutting and
pasting from the BPIP output file. The five blank spaces preceding "SO" in the BPIP output file must be
deleted so that the "SO" begins in the first column of the ISCST3 input file.
One use of BPIP is to design stack heights for new facilities or determine stack height increases required to
avoid the building influence on air flow, which may cause high concentrations and deposition near the
facility. The output for BPIP provides the GEP heights for stacks. Significant decreases in concentrations
and deposition rates will begin at stack heights at least 1.2 times the building height, and further decreases
occur at 1.5 times building height, with continual decreases of up to 2.5 times building height (GEP stack
height) where the building no longer influences stack gas.
3.3 USE OF UNIT EMISSION RATE
The ISCST3 model is usually run with a unit emission rate of 1.0 g/s in order to preclude having to run the
model for each specific COPC. The unitized concentration and deposition output from ISCST3, using a
unit emission rate, are adjusted to the COPC-specific air concentrations and deposition rates in the
estimating media concentration equations (see Section 3-11) by using COPC-specific emission rates
obtained during the trial burn (see Chapter 2). Concentration and deposition are directly proportional to a
unit emission rate used in the ISCST3 modeling.
For facilities with multiple stacks or emission sources, each source must be modeled separately. The key to
not allowing more than one stack in a single run is the inability to estimate stack-specific risks, which limits
the ability of a permitting agency to evaluate which stack is responsible for the resulting risks. Such
ambiguity would make it impossible for the agency to specify protective, combustion unit-specific permit
limits. If a facility has two or more stacks with identical characteristics (emissions, stack parameters, and
nearby locations), agency approval may be requested to represent the stacks with a single set of model runs.
3.4 PARTITIONING OF EMISSIONS
COPC emissions to the environment occur in either vapor or particle phase. In general, most metals and
organic COPCs with very low volatility (refer to fraction of COPC in vapor phase [Fv] less than 0.05, as
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presented in Appendix A-2) are assumed to occur only in the particle phase. Organic COPCs occur as
either only vapor phase (refer to Fv of 1.0, as presented in Appendix A-2) or with a portion of the vapor
condensed onto the surface of particulates (e.g., particle-bound). COPCs released only as particulates are
modeled with different mass fractions allocated to each particle size than the mass fractions for the organics
released in both the vapor and particle-bound phases. Due to the limitations of the ISCST3 model,
estimates of vapor phase COPCs, particle phase COPCs, and particle-bound COPCs cannot be provided in
a single pass (run) of the model. Multiple runs are required. An example of this requirement is the risk
assessment for the WTI incinerator located in East Liverpool, Ohio. The study used three runs; a vapor
phase run for organic COPCs, a particle run with mass weighting of the particle phase metals and organic
COPCs with very low volatility, and a particle run with surface area weighting of the particle-bound
organic COPCs .
3.4.1 Vapor Phase Modeling
ISCST3 output for vapor phase air modeling runs are vapor phase ambient air concentration and wet vapor
deposition at receptor grid nodes based on the unit emission rate. Vapor phase runs do not require a
particle size distribution in the ISCST3 input file. One vapor phase run is required for each receptor grid
that is modeled (see Section 3.7).
3.4.2 Particle Phase Modeling (Mass Weighting)
ISCST3 uses algorithms to compute the rate at which dry and wet removal processes deposit
particulate-phase COPCs emitted from a combustion unit stack to the Earth's surface. Particle size is the
main determinant of the fate of particles in air flow, whether dry or wet. The key to dry particle deposition
rate is the terminal, or falling, velocity of a particle. Particle terminal velocity is calculated mainly from
the particle size and particle density. Large particles fall more rapidly than small particles and are
deposited closer to the stack. Small particles have low terminal velocities, with very small particles
remaining suspended in the air flow. Wet particle deposition also depends on particle size as larger
particles are more easily removed, or scavenged, by falling liquid (rain) or frozen (snow or sleet)
precipitation. An ISCST3 modeling analysis of particle phase emissions for deposition rate requires an
initial estimate of the particle size distribution, distinguished on the basis of particle diameter.
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The diameters of small particulates contained in stack emissions are usually measured in micrometers. The
distribution of particulate by particle diameter will differ from one combustion process to another, and is
greatly dependent on (1) the type of furnace, (2) the design of the combustion chamber, (3) the composition
of the feed fuel, (4) the particulate removal efficiency, (5) the design of the APCS, (6) the amount of air, in
excess of stoichiometric amounts, that is used to sustain combustion, and (7) the temperature of
combustion. However, based on these variables, the particle size distribution cannot be calculated, but
only directly measured or inferred from prior data. Unfortunately, few studies have been performed to
directly measure particle size distributions from a variety of stationary combustion sources (U.S. EPA
1986a).
U.S. EPA OSW recommends that existing facilities perform stack tests to identify particle size distribution.
These data should represent actual operating conditions for the combustion unit and air pollution control
device (APCD) that remove particulate from the stack gas. A table of particle size distribution data should
be prepared using stack test data in the format in Table 3-1.
U.S. EPA OSW expects that stack test data will be different from the values presented in Table 3-1
because of the use of particle "cut size" for the different cascade impactor filters (or Coulter counter-based
distributions) used during actual stack sampling. The test method will drive the range of particle sizes that
are presented in the results of the stack test. However, because ISCST3 requires mean particle diameter
for each particle size distribution, and the stack test data identifies only the mass ("weight") of particles in
a range bounded by two specific diameters, stack test data must be converted into a mean particle diameter
which approximates the diameter of all the particles within a defined range. Consistent with U.S. EPA
1993h, the mean particle diameter is calculated by using the following equation:
Dmean = [0.25 • (Dl +D?D2 +D^ +JD23)f33 Equation 3-1
where
Dmean = Mean particle diameter for the particle size category
D, = Lower bound cut of the particle size category (//in)
D2 = Upper bound cut of the particle size category (fj,m)
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For example, the mean particle diameter of 5.5 /^m in Table 3-1 is calculated from a lower bound cut size
(assuming a cascade impactor is used to collect the sample) of 5.0 jwm to an upper bound cut size of
6.15 //in. In this example, the mean particle diameter is calculated as:
Dmean = [°-25 (5-°3 + (5.0)2(6.15) + (5.0)(6.15)2 + 6.153)]0'33 = 5.5
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TABLE 3-1
GENERALIZED PARTICLE SIZE DISTRIBUTION, AND PROPORTION OF
AVAILABLE SURFACE AREA, TO BE USED AS A DEFAULT IN DEPOSITION MODELING
IF SITE-SPECIFIC DATA ARE UNAVAILABLE
1
Mean Particle
Diameter a
(//m)
>15.0
12.5
8.1
5.5
3.6
2.0
1.1
0.7
<0.7
2
Particle
Radius
(Mm)
7.50
6.25
4.05
2.75
1.80
1.00
0.55
0.40
0.40
3
Surface
Area/
Volume
Cum1)
0.400
0.480
0.741
1.091
1.667
3.000
5.455
7.500
7.500
4
Fraction of
Total
Massb
0.128
0.105
0.104
0.073
0.103
0.105
0.082
0.076
0.224
5
Proportion
Available
Surface
Area
0.0512
0.0504
0.0771
0.0796
0.1717
0.3150
0.4473
0.5700
1.6800
6
Fraction
of Total
Surface
Area
0.0149
0.0146
0.0224
0.0231
0.0499
0.0915
0.1290
0.1656
0.4880
Notes:
a Geometric mean diameter in a distribution from U.S. EPA (1980a), as presented in U.S. EPA (1993h)
b The terms mass and weight are used interchangeably when using stack test data
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From Table 3-1, the mean particle diameter is 5.5 /^m. The mass of participate from the 5.0 fj,m stack test
data is then assigned to the 5.5 jwm mean particle diameter for the purpose of computing the "fraction of
total mass."
Typically, eight to ten mean particle diameters are available from stack test results. As determined from a
sensitivity analysis conducted by The Air Group-Dallas under contract to U.S. EPA Region 6
(www.epa.gov/region06), a minimum of three particle size categories (> 10 microns, 2-10 microns, and < 2
microns) detected during stack testing are generally the most sensitive to air modeling with ISCST-3 (U.S.
EPA 1997). For facilities with stack test results which indicate mass amounts lower than the detectable
limit (or the filter weight is less after sampling than before), a single mean particle size diameter of 1.0
microns should be used to represent all mass (e.g., particle diameter of 1.0 microns or a particle mass
fraction of 1.0) in the particle and particle-bound model runs. Because rudimentary methods for stack
testing may not detect the very small size or amounts of COPCs in the particle phase, the use of a 1.0
micron particle size will allow these small particles to be included properly as particles in the risk
assessment exposure pathways while dispersing and depositing in the air model similar in behavior to a
vapor.
After calculating the mean particle diameter (Column 1), the fraction of total mass (Column 4) per mean
particle size diameter must be computed from the stack test results. For each mean particle diameter, the
stack test data provides an associated mass of particulate. The fraction of total mass for each mean
particle diameter is calculated by dividing the associated mass of particulate for that diameter by the total
mass of particulate in the sample. In many cases, the fractions of total mass will not sum to 1.0 due to
rounding errors. In these instances, U.S. EPA OSW advocates that the remaining mass fraction be added
into the largest mean particle diameter mass fraction to force the total mass to 1.0.
Direct measurements of particle-size distributions at a proposed new facility may be unavailable, so it will
be necessary to provide assumed particle distributions for use in ISCST3. In such instances, a
representative distribution may be used. The unit on which the representative distribution is based should
be as similar as practicable to the proposed unit. For example, the default distribution provided in
Table 3-1 is not appropriate for a hazardous waste burning boiler with no APCD or a wet scrubber,
because it is based on data from different type of unit. However, the generalized particle size (diameter)
distribution in Table 3-1 may be used as a default for some combustion facilities equipped with either ESPs
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or fabric filters, because the distribution is relatively typical of particle size arrays that have been measured
at the outlet to advanced equipment designs (Buonicore and Davis 1992; U.S. EPA 1986a; U.S. EPA
1987a).
After developing the particulate size distribution based on mass, this distribution is used in ISCST3 to
apportion the mass of particle phase COPCs (metals and organics with Fv values less than 0.05) based on
particle size. Column 4 of Table 3-1 (as developed from actual stack test data) is used in the ISCST3 input
file to perform a particulate run with the particle phase COPCs apportioned based on mass weighting.
3.4.3 Particle-Bound Modeling (Surface Area Weighting)
A surface area weighting, instead of mass weighting, of the particles is used in separate particle runs of
ISCST3. Surface area weighting approximates the situation where a semivolatile organic contaminant that
has been volatilized in the high temperature environment of a combustion system and then condensed to the
surface of particles entrained in the combustion gas after it cools in the stack. Thus, the apportionment of
emissions by particle diameter becomes a function of the surface area of the particle that is available for
chemical adsorption (U.S. EPA 1993h).
The first step in apportioning COPC emissions by surface area is to calculate the proportion of available
surface area of the particles. If particle density is held constant (such as 1 g/m3), the proportion of
available surface area of aerodynamic spherical particles is the ratio of surface area (S) to volume (V), as
follows:
Assume aerodynamic spherical particles.
Specific surface area of a spherical particle with a radius, r—S = 4 nr2
Volume of a spherical particle with a radius, r—V = 4/3 Ttr3
Ratio of S to V—S/V = 4 nr2/ (4/3 nr3) = 3/r
The following uses the particle size distribution in Table 3-1 as an example of apportioning the emission
rate of the particle-bound portion of the COPC based on surface area. This procedure can be followed for
apportioning actual emissions to the actual particle size distribution measured at the stack. In Table 3-1, a
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spherical particle having a diameter of 15 /^m (Column 1) has a radius of 7.5 /^m (Column 2). The
proportion of available surface area (assuming particle density is constant) is 0.400 (S/V = 3/7.5), which is
the value in Column 3. Column 4 shows that particles with a mean diameter of 15 //in, constitute
12.8 percent of the total mass. Multiplication of Column 3 by Column 4 yields a value in Column 5 of
0.0512. This value is an approximation of the relative proportion of total surface area, based on the
percent of particles that are 15 jwm in diameter. The sum of Column 5 yields the total surface area of all
particles in the particle size distribution. In this example, the sum is 3.4423. Column 6 is the fraction of
total surface area represented by the specific particle diameter in the distribution, and is calculated by
dividing the relative proportion of surface area (Column 5) for a specific diameter by the total relative
proportion of surface area (3.4423 square micrometers Lwm2]). In the example of the 15 //m-diameter
particle, the fraction of total surface area available for adsorption is 0.0149 (0.0512/3.4423). This
procedure is then repeated for all particle sizes in the array.
After developing the particulate size distribution based on surface area, this distribution is used in ISCST3
to apportion mass of particle-bound COPCs (most organics) based on particle size. Column 6 of Table 3-1
(as developed from actual stack test data) is used in the ISCST3 input file to perform a particulate run for
the particle-bound COPCs apportioned based on surface area weighting.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Copies of all stack test data used to determine particle size distribution
Copies of all calculations made to determine particle size distribution, fraction of total mass, and
fraction of total surface area
3.5 METEOROLOGICAL DATA
To model air concentration and deposition, the ISCST3 model requires a variety of meteorological
information:
1. Air concentration
a. Hourly values
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(1) Wind direction (degrees from true north)
(2) Wind speed (m/s)
(3) Dry bulb (ambient air) temperature (K)
(4) Opaque cloud cover (tenths)
(5) Cloud ceiling height (m)
b. Daily values
(1) Morning mixing height (m)
(2) Afternoon mixing height (m)
2. Deposition
a. Dry particle deposition—hourly values for surface pressure (millibars)
b. Wet particle deposition—hourly values
(1) Precipitation amount (inches)
(2) Precipitation type (liquid or frozen)
c. Dry vapor deposition (when available)—hourly values for solar radiation
(watts/m2)
As shown in Figure 3-1, these data are available from several different sources. For most air modeling,
five years of data from a representative National Weather Service station is recommended. However, in
some instances where the closest NWS data is clearly not representative of site specific meteorlogical
conditions, and there is insufficient time to collect 5 years of onsite data, 1 year of onsite meteorological
data (consistent with GAQM) may be used to complete the risk assessment. The permitting authority
should approve the representative meteorological data prior to performing air modeling.
The following subsections describe how to select the surface and upper air data that will be used in
conjunction with the ISCST3 model. Section 3.7 describes the computer programs used to process the
meteorological data for input to the ISCST3 model.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Identification of all sources of meteorological data
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FIGURE 3-1
SOURCES OF METEOROLOGICAL DATA
/
JF
' |M'i
y
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3.5.1 Surface Data
Surface data can be obtained from SAMSON in CD-ROM format. SAMSON data are available for 239
airports across the U.S. for the period of 1961 through 1990. The National Climate Data Center (NCDC)
recently released the update to SAMSON through 1995 surface data. However, since the upper air (mixing
height) data available from the U.S. EPA SCRAM web site has not been updated to cover this recent data
period, it is acceptable to select the representative 5 years of meteorological data from the period up
through 1990. SAMSON data contain all of the required input parameters for concentration,
dry and wet particle deposition, and wet vapor deposition. SAMSON also includes the total solar radiation
data required for dry vapor deposition, which may be added to ISCST3 in the future. Alternatively, some
meteorological files necessary for running ISCST3 are also available on the SCRAM BBS for NWS
stations located throughout the country (SCRAM BBS is part of the Office of Air Quality and Planning
and Standards Technology Transfer Network [OAQPS TTN]). The meteorological data, preprocessors,
and user's guides are also located on the SCRAM web site at "http://www.epa.gov/scram001/index.htm".
However, these files do not contain surface pressure, types of precipitation (present weather), or
precipitation amount. Although the ISCST3 model is not very sensitive to surface pressure variations, and
a default value may be used, precipitation types and amounts are necessary for air modeling wet deposition.
Precipitation data are available from the National Climatic Data Center (NCDC), and are processed by
PCRAMMET to supplement the SCRAM BBS surface data. NCDC also has surface data in CD-144
format, which contains all of the surface data, including precipitation.
The SAMSON CD-ROM for the eastern, central, or western (Volumes I, II, and III) United States may be
purchased from NCDC in Asheville, North Carolina.
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National Climatic Data Center
Federal Building
37 Battery Park Avenue
Asheville, NC 28801-2733
Customer Service: (704) 271-4871
File type:
Hourly precipitation amounts
Hourly surface observations with precipitation type
Hourly surface observations with precipitation type
Twice daily mixing heights from nearest station
File name:
NCDC TC-3240
NCDC TD-3280
NCDC SAMSON CD-ROM (Vol. I, H, and/or HI)
NCDC TD-9689
(also available on SCRAM web site for 1984 through 1991)
PCRAMMET and MPRM are the U.S. EPA meteorological preprocessor programs for preparing the
surface and upper air data into a meteorlogical file of hourly parameters for input into the ISCST3 model.
Most air modeling analyses will use PCRAMMET to process the National Weather Service data.
However, both preprocessors require the modeler to replace any missing data. Before running
PCRAMMET or MPRM, the air modeler must fill in missing data to complete 1 full year of values. A
procedure recommended by U.S. EPA for filling missing surface and mixing height data is documented on
the SCRAM BBS under the meteorological data section. If long periods of data are missing, and these data
are not addressed by the U.S. EPA procedures on the SCRAM BBS, then a method must be developed for
filling in missing data. One option is to fill the time periods with "surrogate place holder" data in the
correct format with correct sequential times to complete preparation of the meteorological file. Place
holder data are typically considered the last valid hourly data of record. Then, when ISCST3 is running,
the MSGPRO keyword in the COntrol pathway can be used to specify that data are missing. Note that the
DEFAULT keyword must not be used with MSGPRO. Since the missing data keyword is not approved
generally for regulatory air modeling, the appropriate agency must provide approval prior to use. All
processing of meteorological data should be completely documented to include sources of data, decision
criteria for selection, consideration for precipitation amounts, preprocessor options selected, and filled
missing data.
The most recently available 5 years of complete meteorological data contained on SAMSON, or more
recent sources, should be used for the air modeling. It is desirable, but not mandatory, that the 5 years are
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consecutive. The use of less than five years of meteorological data should be approved by appropriate
authorities. The following subsections describe important characteristics of the surface data.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Electronic copy of the ISCST3 input code used to enter meteorological information
• Description of the selection criteria and process used to identify representative years used for
meteorological data
• Identification of the 5 years of meteorological selected
• Summary of the procedures used to compensate for any missing data
3.5.1.1 Wind Speed and Wind Direction
Wind speed and direction are two of the most critical parameters in ISCST3. The wind direction promotes
higher concentration and deposition if it persists from one direction for long periods during a year. A
predominantly south wind, such as on the Gulf Coast, will contribute to high concentrations and
depositions north of the facility. Wind speed is inversely proportional to concentration in the ISCST3
algorithms. The higher the wind speed, the lower will be the concentration. If wind speed doubles, the
concentration and deposition will be reduced by one-half. ISCST3 needs wind speed and wind direction at
the stack top. Most air modeling is performed using government sources of surface data. Wind data are
typically measured at 10 meters height at NWS stations. However, since some stations have wind speed
recorded at a different height, the anemometer height must always be verified so that the correct value can
be input into the PCRAMMET meteorological data preprocessing program. ISCST3 assumes that wind
direction at stack height is the same as measured at the NWS station height. ISCST3 uses a wind speed
profile to calculate wind speed at stack top. This calculation exponentially increases the measured wind
speed from the measured height to a calculated wind speed at stack height (U.S. EPA 1995d).
3.5.1.2 Dry Bulb Temperature
Dry bulb temperature, or ambient air temperature, is the same temperature reported on the television and
radio stations across the country each day. It is measured at 2 meters above ground level. Air temperature
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is used in ISCST3 in the buoyant plume rise equations developed by Briggs (U.S. EPA 1995c). The model
results are not very sensitive to air temperature, except at extremes. However, buoyant plume rise is very
sensitive to the stack gas temperature. Buoyant plume rise is mainly a result of the difference between
stack gas temperature and ambient air temperature. Conceptually, it is similar to a hot air balloon. The
higher the stack gas temperature, the higher will be the plume rise. High plume heights result in low
concentrations and depositions as the COPCs travel further and are diluted in a larger volume of ambient
air before reaching the surface. The temperature is measured in K, so a stack gas temperature of 450°F is
equal to 505 K. Ambient temperature of 90°F is equal to 305 K, and 32°F is 273 K. A large variation in
ambient temperature will affect buoyant plume rise, but not as much as variations in stack gas temperature.
3.5.1.3 Opaque Cloud Cover
PCRAMMET uses opaque cloud cover to calculate the stability of the atmosphere. Stability determines
the dispersion, or dilution, rate of the COPCs. Rapid dilution occurs in unstable air because of surface
heating that overturns the air. With clear skies during the day, the sun heats the Earth's surface, thereby
causing unstable air and dilution of the stack gas emission stream. Stable air results in very little mixing,
or dilution, of the emitted COPCs. A cool surface occurs at night because of radiative loss of heat on clear
nights. With a cloud cover, surface heating during the day and heat loss at night are reduced, resulting in
moderate mixing rates, or neutral stability. Opaque cloud cover is a measure of the transparency of the
clouds. For example, a completely overcast sky with 10/10ths cloud cover may have only I/10th opaque
cloud cover if the clouds are high, translucent clouds that do not prevent sunlight from reaching the Earth's
surface. The opaque cloud cover is observed at NWS stations each hour.
3.5.1.4 Cloud Ceiling Height
Cloud height is required in PCRAMMET to calculate stability. Specifically, the height of the cloud cover
affects the heat balance at the Earth's surface. Cloud ceiling height is measured or observed at all NWS
stations provided on the SAMSON CD-Roms and the U.S. EPA SCRAM web site.
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3.5.1.5 Surface Pressure
Surface pressure is required by ISCST3 for calculating dry particle deposition. However, ISCST3 is not
very sensitive to surface pressure. SAMSON and NCDC CD-144 data include surface pressure. SCRAM
BBS surface data do not include surface pressure. U.S. EPA believes that, if SCRAM BBS surface data
are used, a default value of 1,000 millibars can be assumed, with little impact on modeled results.
3.5.1.6 Precipitation Amount and Type
The importance of precipitation to ISCST3 results was discussed in the selection of the meteorological data
period (see Section 3.5.1). Precipitation is measured at 3 feet (1 meter) above ground level. Precipitation
amount and type are required to be processed by PCRAMMET or MPRM into the ISCST3 meteorological
file to calculate wet deposition of vapor and particles. The amount of precipitation, or precipitation rate,
will directly influence the amount of wet deposition at a specific location. Particles and vapor are both
captured by falling precipitation, known as precipitation scavenging. Scavenging coefficients are required
as inputs to ISCST3 for vapors with a rate specified for liquid and frozen precipitation. The precipitation
type in a weather report in SAMSON or CD-144 data file will identify to ISCST3 which event is occurring
for appropriate use of the scavenging coefficients entered (see Section 3.7.2.6). SCRAM BBS surface data
do not include precipitation data. Supplemental precipitation files from NCDC may be read into
PCRAMMET for integration into the ISCST3 meteorological file.
3.5.1.7 Solar Radiation (Future Use for Dry Vapor Deposition)
The current version of ISCST3 does not use solar radiation. Several U.S. EPA models, including the Acid
Deposition and Oxidant Model (ADOM), incorporate algorithms for dry vapor deposition. At such time as
U.S. EPA approves the draft version of ISCST3 which includes dry gas deposition, the hourly total solar
radiation will be required. Solar radiation affects the respiratory activity of leaf surfaces, which affects the
rate of vapor deposition. With a leaf area index identified in the ISCST3 input file in the future, the model
will be able to calculate dry vapor deposition.
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3.5.2 Upper Air Data
Upper air data, also referred to as mixing height data, are required to run the ISCST3 model. ISCST3
requires estimates of morning and afternoon (twice daily) mixing heights. PCRAMMET and MPRM use
these estimates to calculate an hourly mixing height by using interpolation methods (U.S. EPA 1996e).
The mixing height files are typically available for the years 1984 through 1991 on the U.S. EPA SCRAM
web site. U.S. EPA OSW recommends that only years with complete mixing height data be used as input
for air modeling. In some instances, data may need to be obtained from more than one station to complete
five years of data. The selection of representative data should be discussed with appropriate authorities
prior to performing air modeling.
Mixing height data for years prior to 1983, in addition to current mixing height data, may be purchased
from NCDC as described in Section 3.5.1. The years selected for upper air data must match the years
selected for surface data. If matching years of mixing height data are not available from a single upper air
station, another upper air station should be used for completing the five years.
3.6 METEOROLOGICAL PREPROCESSORS AND INTERFACE PROGRAMS
After the appropriate surface and upper air data is selected following the procedures outlined in
Section 3.5, additional data manipulation is necessary before the data is used with the ISCST3 model. The
following subsections describe the meteorological preprocessors and interface programs used for these
manipulation tasks. To eliminate any need to repeat air modeling activities, U.S. EPA OSW recommends
that the selection of representative mixing height and surface data be approved by the appropriate
regulatory agency before preprocessing or air modeling is conducted. Permitting authority approval also is
recommended in the selection of site-specific parameter values required as input to the meteorological data
preprocessors.
3.6.1 PCRAMMET
U.S. EPA OSW recommends preparing a meteorological file for ISCST3 that can be used to calculate any
concentration or deposition. By preparing a file that PCRAMMET terms a "WET DEPOSITION" file, all
required parameters will be available to ISCST3 for any subsequent concentration or deposition modeling.
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For example, if only the concentration option is selected in ISCST3 for a specific run, ISCST3 will ignore
the precipitation values in the meteorological file. For subsequent air deposition modeling, ISCST3 will
access the precipitation data from the same preprocessed meteorological file.
PCRAMMET may use SAMSON, SCRAM web site, and NCDC CD-144 surface data files. U.S. EPA
OSW recommends using the SAMSON option in PCRAMMET to process the SAMSON surface data and
U.S. EPA SCRAM web site mixing height data. The PCRAMMET User's Guide in the table "Wet
Deposition, SAMSON Data" (U.S. EPA 1995b) identifies the PCRAMMET input requirements for
creating an ASCII meteorological file for running ISCST3 to calculate air concentration, and wet and dry
deposition. The meteorological file created for ISCST3 will contain all of the parameters needed for air
modeling of concentration and deposition.
PCRAMMET requires the following input parameters representative of the measurement site:
• Monin-Obukhov length
• Anemometer height
• Surface roughness height (at measurement site)
• Surface roughness height (at application site)
• Noon-time albedo
• Bowen ratio
• Anthropogenic heat flux
• Fraction of net radiation absorbed at surface
The PCRAMMET User's Guide contains detailed information for preparing the required meteorological
input file for the ISCST3 model (U.S. EPA 1995b). The parameters listed are briefly described in the
following subsections. These data are not included in the surface or mixing height data files obtained from
the U.S. EPA or NCDC. Representative values specific to the site to be modeled should be carefully
selected using the tables in the PCRAMMET User's Guide or reference literature. The selected values
should be approved prior to processing the meteorological data.
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3.6.1.1 Monin-Obukhov Length
The Monin-Obukhov length (L) is a measure of atmospheric stability. It is negative during the day, when
surface heating causes unstable air. It is positive at night, when the surface is cooled with a stable
atmosphere. In urban areas during stable conditions, the estimated value of L may not adequately reflect
the less stable atmosphere associated with the mechanical mixing generated by buildings or structures.
However, PCRAMMET requires an input for minimum urban Monin-Obukhov length, even if the area to
be analyzed by ISCST3 is rural. A nonzero value for L must be entered to prevent PCRAMMET from
generating an error message. A value of 2.0 meter for L should be used when the land use surrounding the
site is rural (see Section 3.2.2.1). For urban areas, Hanna and Chang (1991) suggest that a minimum value
of L be set for stable hours to simulate building-induced instability. The following are general examples of
L values for various land use classifications:
Land Use Classification
Agricultural (open)
Residential
Compact residential/industrial
Commercial (19 to 40-story buildings)
Commercial (>40-story buildings)
Minimum L
1 meters
25 meters
50 meters
100 meters
150 meters
PCRAMMET will use the minimum L value for calculating urban stability parameters. These urban
values will be ignored by ISCST3 during the air modeling analyses for rural sites.
3.6.1.2 Anemometer Height
The height of the wind speed measurements is required by ISCST3 to calculate wind speed at stack top.
The wind sensor (anemometer) height is identified in the station history section of the Local Climatological
Data Summary available from NCDC for every National Weather Service station. Since 1980, most
National Weather Service stations measure wind speed at the height of 10 meters. However, some stations
operate at other heights or have valid representative data during years of operation at more than one height.
The modeler must verify the correct measurement height for each year of data prior to processing with
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PCRAMMET and running the ISCST3 model. ISCST3 modeled results are very sensitive to small
variations in wind speed.
3.6.1.3 Surface Roughness Height at Measurement Site
Surface roughness height is a measure of the height of obstacles to wind flow. It is important in ISCST3
because it determines how close a particle must be above the ground before it is "captured" for deposition
on the ground. Dramatic differences in ISCST3 calculations may result from slight variations in surface
roughness. For surface meteorological data from a National Weather Station, a value of 0.10 meters for
the "measurement site" typically may be used without prior approval. Surface roughness is proportional,
but not equal, to the physical height of the obstacles. The table in Section 3.2.2.2 lists the roughness
heights that can be used as input values. These values are based on the general land use in the vicinity of
the measurement site. These values should be considered in discussions with the appropriate agency
modeler prior to air modeling.
3.6.1.4 Surface Roughness Height at Application Site
Determination of surface roughness height is also required at the facility (application site) for performing
PCRAMMET processing to prepare an ISCST3 meteorological file. ISCST3 model results are very
sensitive to the value used in PCRAMMET for this parameter. The table in Section 3.2.2.2 is applicable to
the application site. A site-specific computation of a single surface roughness value representative of the
site is required using the method described in Section 3.2.2.2. The computed value of surface roughness
height for the application site, along with maps or photographs illustrating land use, must be approved by
the appropriate agency prior to use.
3.6.1.5 Noon-Time Albedo
"Noon-time albedo" is the fraction of the incoming solar radiation that is reflected from the ground when
the sun is directly overhead. Albedo is used in calculating the hourly net heat balance at the surface for
calculating hourly values of Monin-Obukhov length. PCRAMMET automatically adjusts for the variation
in albedo with solar elevation angle. Experience suggests that ISCST3 modeling results are not sensitive to
the value selected for this parameter. Typical albedo values are presented in Table 3-2. As shown in Table
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3-2, albedo values vary from 0.10 to 0.20 on water surfaces from summer to winter. The most variability
is for cultivated farmland, which varies from 0.14 during spring when land is tilled to expose dark earth, to
0.60 in winter when areas are snow-covered.
Based on the information in Table 3-2, albedos are estimated to vary in rural areas from 0.14 to 0.20 for
cultivated land, and from 0.18 to 0.20 for grassland. For urban areas, the variation without snow is from
0.14 to 0.18. For practical purposes, the selection of a single value for noon-time albedo to process a
complete year of meteorological data is desirable. For example, the single value of 0.18 may be
appropriate to process all meteorological data for an urban site. For rural sites, a single albedo value of
0.18 representative of grassland and cultivated land may be appropriate for areas without significant snow
cover during winter months. For desert shrubland, a single value of 0.28 may be appropriate. A single
value of 0.12 could be representative of forested areas. The permitting authority should review proposed
values used in the processing of the meteorological data.
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TABLE 3-2
ALBEDO OF NATURAL GROUND COVERS FOR LAND USE TYPES AND SEASONS
Land Use Type
Water surface
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
Season"
Spring
0.12
0.12
0.12
0.12
0.14
0.18
0.14
0.30
Summer
0.10
0.12
0.12
0.14
0.20
0.18
0.16
0.28
Autumn
0.14
0.12
0.12
0.16
0.18
0.20
0.18
0.28
Winter
0.20
0.50
0.35
0.30
0.60
0.60
0.35
0.45
Notes:
Source—Iqbal (1983)
a The various seasons are defined by Iqbal (1983) as follows:
Spring: Periods when vegetation is emerging or partially green; this is a transitional situation that applies
for 1 to 2 months after the last killing frost in spring.
Summer: Periods when vegetation is lush and healthy; this is typical of mid-summer, but also of other
seasons in which frost is less common.
Autumn: Periods when freezing conditions are common, deciduous trees are leafless, crops are not yet
planted or are already harvested (bare soil exposed), grass surfaces are brown, and no snow is
present.
Winter: Periods when surfaces are covered by snow and temperatures are below freezing. Winter albedo
depends on whether a snow cover is present continuously, intermittently, or seldom. Albedo
ranges from about 0.30 for bare snow cover to about 0.65 for continuous cover.
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3.6.1.6 Bowen Ratio
The Bowen ratio is a measure of the amount of moisture at the surface. The presence of moisture affects
the heat balance resulting from evaporative cooling, which, in turn, affects the hourly Monin-Obukhov
length calculated by PCRAMMET. Surface moisture is highly variable. Daytime Bowen ratios are
presented in Table 3-3.
Bowen ratio values vary throughout the country. For example, in urban areas where annual rainfall is less
than 20 inches, a single Bowen ratio value of 4.0 may be representative. For rural areas, a Bowen ratio
value of 2.0 may be appropriate for grassland and cultivated land. For areas where annual rainfall is
greater than 20 inches, U.S. EPA OSW recommends a single Bowen ratio value of 2.0 for urban areas;
and 0.7 for rural forests, grasslands, and cultivated lands. The applicable permiting authority should
review proposed values used in the processing of the meteorological data.
3.6.1.7 Anthropogenic Heat Flux
Anthropogenic heat is the surface heating caused by human activity, including automobiles and heating
systems. It is used to calculate hourly L values (Monin-Obukhov lengths). Table 3-4 presents
anthropogenic heat flux (Qj) values that have been calculated for several urban areas around the world. In
rural areas, U.S. EPA OSW recommends that a value of 0.0 Watts/m2 be used for the Qf. A value of 20.0
Watts/m2 is appropriate for large urban areas based on the annual value from Table 3-4 for Los Angeles.
3.6.1.8 Fraction of Net Radiation Absorbed at the Ground
Also used for calculating hourly values of Monin-Obukhov length, fraction of net radiation absorbed at the
ground is the last component of radiative heat balance. Based on the net radiation (Q*) values presented in
Table 3-4, and recommendations presented in the PCRAMMET User's Manual based on Oke (1982),
U.S. EPA OSW recommends values of 0.15 for rural areas and 0.27 for urban areas (U.S. EPA 1995b).
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TABLE 3-3
DAYTIME BOWEN RATIOS BY LAND USE, SEASON,
AND PRECIPITATION CONDITIONS
Land Use
Season"
Spring
Summer
Autumn
Winter
Dry Conditions
Water (fresh and salt)
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
0.1
1.5
1.5
0.2
1.0
1.0
2.0
5.0
0.1
0.6
0.6
0.2
1.5
2.0
4.0
6.0
0.1
2.0
1.5
0.2
2.0
2.0
4.0
10.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Average Conditions
Water (fresh and salt)
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
0.1
0.7
0.7
0.1
0.3
0.4
1.0
3.0
0.1
0.3
0.3
0.1
0.5
0.8
2.0
4.0
0.1
1.0
0.8
0.1
0.7
1.0
2.0
6.0
1.5
1.5
1.5
1.5
1.5
1.5
1.5
6.0
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TABLE 3-3
DAYTIME BOWEN RATIO BY LAND USE, SEASON,
AND PRECIPITATION CONDITIONS
(Continued)
Land Use
Season"
Spring
Summer
Autumn
Winter
Wet Conditions
Water (fresh and salt)
Deciduous forest
Coniferous forest
Swamp
Cultivated land
Grassland
Urban
Desert shrubland
0.1
0.3
0.3
0.1
0.2
0.3
0.5
1.0
0.1
0.2
0.2
0.1
0.3
0.4
1.0
5.0
0.1
0.4
0.3
0.1
0.4
0.5
1.0
2.0
0.3
0.5
0.3
0.5
0.5
0.5
0.5
2.0
Note:
Source—Paine (1987)
a The various seasons are defined by Iqbal (1983) as follows:
Spring: Periods when vegetation is emerging or partially green; this is a transitional situation
that applies for 1 to 2 months after the last killing frost in spring.
Summer: Periods when vegetation is lush and healthy; this is typical of mid-summer, but also of
other seasons in which frost is less common.
Autumn: Periods when freezing conditions are common, deciduous trees are leafless, crops are
not yet planted or are already harvested (bare soil exposed), grass surfaces are brown,
and no snow is present
Winter: Periods when surfaces are covered by snow and temperatures are below freezing.
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TABLE 3-4
ANTHROPOGENIC HEAT FLUX (Qf) AND NET RADIATION
FOR SEVERAL URBAN AREAS
Urban Area
(Latitude)
Manhattan
(40 "North)
Montreal
(45 "North)
Budapest
(47° North)
Sheffield
(53 "North)
West Berlin
(52 "North)
Vancouver
(49 "North)
Hong Kong
(22 "North)
Singapore
(1° North)
Los Angeles
(34 "North)
Fairbanks
(64 "North)
Population
(Millions)
1.7
1.1
1.3
0.5
2.3
0.6
3.9
2.1
7.0
0.03
Population
Density
(Persons/km2)
28,810
14,102
11,500
10,420
9,830
5,360
3,730
3,700
2,000
810
Per Capita
Energy Use
(MJ x 103/year)
128
221
118
58
67
112
34
25
331
740
(^(Watts/m2)
(Season)
117 (Annual)
40 (Summer)
198 (Winter)
99 (Annual)
57 (Summer)
153 (Winter)
43 (Annual)
32 (Summer)
51 (Winter)
19 (Annual)
21 (Annual)
19 (Annual)
15 (Summer)
23 (Winter)
4 (Annual)
3 (Annual)
21 (Annual)
19 (Annual)
Q-
(Watts/m2)
93 (Annual)
52 (Annual)
92 (Summer)
13 (Winter)
46 (Annual)
100 (Summer)
-8 (Winter)
56 (Annual)
57 (Annual)
57 (Annual)
107 (Summer)
6 (Winter)
110 (Annual)
110 (Annual)
108 (Annual)
18 (Annual)
Note:
Source—Oke (1978)
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3.6.2 MPRM
For on-site data, a new version of MPRM is used to mesh on-site data with NWS data in the preparation of
the meteorological input file. MPRM performs the same meteorological file preparation as PCRAMMET,
except the source of the surface data in MPRM consists of on-site measurements (U.S. EPA 1996e).
MPRM includes extensive QA/QC for values that are out of range. MPRM also checks for missing data
and summarizes values that require editing to fill missing data. After a complete surface file passes the
quality checks, it is processed with NCDC mixing height data. NCDC data are purchased to correspond to
the collection period of the on-site surface data. Mixing height data available on SCRAM's web site ends
in 1991. A delay of about 3 months can occur for obtaining mixing height data from NCDC to process
with recent on-site surface data.
Inputs to MPRM for preparing an ISCST3 meteorological file for concentration and deposition are the
same as for PCRAMMET. Section 3.6.1 provides methods for determining values for these parameters.
Draft versions of ISCST3 and MPRM are available for review which implement dry vapor deposition.
These versions are GDISCDFT (dated 96248) and GDMPRDFT (dated 96248), respectively. They may
be found on the U.S. EPA SCRAM web site under "Topics for Review". These draft models are not the
current regulatory versions and should not be used without approval from the appropriate permitting
authority.
3.7 ISCST3 MODEL INPUT FILES
A thorough instruction of how to prepare the input files for ISCST3 is presented in the ISC3 User's Guide,
Volume I (U.S. EPA 1995c), which is available for downloading from the SCRAM BBS. The example
ISCST3 input file is provided in Figure 3-2 from the air dispersion modeling chapter (Chapter 3) of the
U.S. EPA HHRAP (U.S. EPA 1998). This example illustrates a single year run (1984), for particle phase
COPC emissions from a single stack, to compute acute (1-hour average) and chronic (annual average) and
provide single year results in one hour and annual average plot files for post-processing. For ecological
risk assessments, only the annual average air parameters are required, not the 1-hour values. However, by
modeling both the 1-hour and annual averages in a single set of runs, the ISCST3 air dispersion model will
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provide the necessary air parameters for use in both the human health and ecological risk assessments. The
specification of a terrain grid file in the TG pathway is optional. Each air modeling analysis has unique
issues and concerns that should be addressed in the risk assessment report. U.S. EPA OSW recommends
that the air modeling methodology be consistent in data collection, model set-up, and model output. This
consistency will assist both the modeler and U.S. EPA in communicating and interpreting model results.
The risk assessment report should document each section of the ISCST3 input file to identify consistent
methods.
Three sets of ISCST3 runs are required for each COPC emission source. As discussed in Section 3.4,
separate ISCST3 runs are required to model vapor phase COPCs, particle phase COPCs, and
particle-bound phase COPCs for each source (stack or fugitive) of COPCs. The ISCST3 "Control
Secondary Keywords" used for these three runs are:
Vapor Phase: CONG WDEP
Particle Phase: CONG DDEP WDEP DEPOS
Particle-Bound Phase: CONG DDEP WDEP DEPOS
For ISCST3 modeling to provide air parameters for ecological risk assessments, only the total deposition
(DEPOS) of the particle and particle-bound phases are required. The control secondary keywords for
concentration in the air (CONC) and the components of deposition to the ground, dry deposition (DDEP)
and wet deposition (WDEP), are not required to be output separately by ISCST3. However, by specifying
these control secondary keywords as illustrated, the ISCST3 model will compute the needed air parameters
for both human health and ecological risk assessments. ISCST3 requires site-specific inputs for source
parameters, receptor locations, meteorological data, and terrain features. The model is prepared for
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execution by creating an input file. The input file is structured in five (or six if a terrain grid file is used)
sections, or pathways, designated by two-letter abbreviations:
ISCST3 INPUT FILE SECTIONS
Section
Control
Source
Receptor
Meteorology
Terrain Grid (Optional)
Output
Abbreviation
CO
so
RE
ME
TG
OU
The following subsections describe how to specify the parameters for each pathway in the ISCST3 input
file.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Electronic and hard copies of ISCST3 input file for all air modeling runs
3.7.1 COntrol Pathway
Model options (MODELOPT) are specified in the COntrol pathway to direct ISCST3 in the types of
computations to perform. U.S. EPA OSW recommends that air modeling specify the DFAULT parameter
to use the following regulatory default options:
• Use stack-tip downwash (except for Schulman-Scire downwash).
• Use buoyancy-induced dispersion (except for Schulman-Scire downwash).
• Do not use final plume rise (except for building downwash).
• Use the calms processing routines.
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Use upper-bound concentration estimates for sources influenced by building downwash
from super-squat buildings.
• Use default wind speed profile exponents.
• Use default vertical potential temperature gradients.
The CONG parameter specifies calculation of air concentrations for vapor and particles. The DDEP and
WDEP parameters specify dry and wet deposition. The DEPOS specifies computation of total (wet and dry)
deposition flux. Since ISCST3 currently does not include an algorithm for the dry deposition of vapor
phase COPCs, only wet deposition is specified for vapor phase runs. Note that dry deposition of vapor
phase is addressed in the pathway equations during the risk assessment using the concentration of the vapor
phase and a deposition velocity. DRYDPLT and WETDPLT are used for plume depletion resulting from dry
and wet removal. U.S. EPA OSW recommends the following command lines for each of the three runs
(these are for rural areas; substitute URBAN for urban areas):
Vapor: CO MODELOPT DFAULT CONG WDEP WETDPLT RURAL
Particle Phase: CO MODELOPT DFAULT CONG DDEP WDEP DEPOS DRYDPLT WETDPLT
RURAL
Particle-Bound: CO MODELOPT DFAULT CONG DDEP WDEP DEPOS DRYDPLT WETDPLT
RURAL
Note that only the total deposition (DEPOS) air parameter values are required for the ecological risk assessment
pathways. The modeler may elect not to include CONC, DDEP and WDEP as separate output components
from ISCST3 if the air modeling results will not be used for a human health risk assessment. However, the
control secondary keywords must always be specified for plume depletion through the dry deposition
(DRYDPLT) and wet deposition (WETDPLT) processes.
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FIGURE 3-2
EXAMPLE INPUT FILE FOR "PARTICLE PHASE"
CO STARTING
CO TITLEONE Example input file, particle phase run
CO TITLETWO 1984 met data, Baton Rouge Surface, Boothville Upper Air
CO MODELOPT DFAULT CONG DDEP WDEP DEPOS DRYDPLT WETDPLT RURAL
CO AVERTIME 1 ANNUAL
CO POLLUTID UNITY
CO TERRHGTS ELEV
CO RUNORNOT RUN
CO SAVEFILE 84SAVE1 5 84SAVE2
** Restart incomplete runs with INITFILE, changing '**' to 'CO'
** INITFILE 84SAVE1
CO FINISHED
SO STARTING
SO LOCATION STACK1 POINT 637524. 567789. 347.
SO SRCPARAM STACK1 1.0 23.0 447.0 14.7 1.9
SO
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
BUILDHGT
BUILDHGT
BUILDHGT
BUILDHGT
BUILDHGT
BUILDWID
BUILDWID
BUILDWID
BUILDWID
BUILDWID
PARTDIAM
MASSFRAX
PARTDENS
PARTSLIQ
PARTS ICE
SRCGROUP
FINISHED
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
ALL
18.29
18.29
18.29
18.29
18.29
14 . 02
12 . 10
14 . 02
15 . 51
16 . 53
0 .35
0 .22
1 . 0
7E-5
2E-5
18 .
18 .
18 .
18 .
18 .
15 .
14 .
12 .
14 .
15 .
0 . 70
0 . 08
1 . 0
5E-5
2E-5
29 18 .
29 18 .
29 18 .
29 18 .
29 18 .
51 16 .
02 15 .
10 14 .
02 12 .
51 14 .
1 . 10
0 . 08
1 . 0
6E-5
2E-5
.29 18.29 18
.29 18.29 18
.29 18.29 18
.29 18.29 18
.29 18 .29
.53 17.05 17
.51 16.53 17
.02 15.51 16
.10 14.02 15
.02 12.10
2.00 3.60 5
0.11 0.10 0
1.0 1.0 1
1 .3E-4 2 . 6E
4E-5 9E
.29
.29
.29
.29
. 05
. 05
. 53
. 51
. 50
. 07
. 0
-4
-5
18 .29
18 .29
18 .29
18 .29
16 . 53
17 . 05
17 . 05
16 . 53
8 . 10
0 . 10
1 . 0
3 . 9E-4
1 .3E-4
18.29 18
18.29 18
18.29 18
18.29 18
15 .51 14
16 .53 15
17 .05 16
17 .05 17
12 . 5 15.0
0.11 0 . 13
1.0 1.0
5 .2E-4 6
1 . 7E-4 2
.29
.29
.29
.29
. 03
. 51
. 53
. 05
.7E-4 6
.2E-4 2
. 7E-4
.2E-4
RE STARTING
RE ELEVUNIT METERS
RE DISCCART 630000. 565000. 352.
RE DISCCART 630500. 565000. 365.
RE DISCCART 631000. 565000. 402.
(ARRAY OF DISCRETE RECEPTORS)
RE DISCCART 635000. 570000. 387.
RE FINISHED
ME STARTING
ME INPUTFIL 84BTR.WET
ME ANEMHGHT 10.0
ME SURFDATA 13970 1984 BATON_ROUGE
ME UAIRDATA 12884 1984 BOOTHVILLE
ME FINISHED
TG STARTING
TG INPUTFIL TERRAIN.TER
TG LOCATION 0.0 0.0
TG ELEVUNIT METERS
TG FINISHED
OU STARTING
OU RECTABLE ALLAVE FIRST
OU PLOTFILE 1 ALL FIRST BTR841.PLT
OU PLOTFILE ANNUAL ALL BTR84A.PLT
OU FINISHED
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For each of the three runs for each emission source, 5 years of off-site (e.g., National Weather Service
from SAMSON) meteorological data are completed. For sites with meteorological data collected on-site,
the appropriate permitting authority should be notified for the data period required for a risk assessment.
The averaging times (AVERTIME) should be specified as 'ANNUAL' to compute long-term (annual
average) ecological risk. Optionally, the ' 1' may be specified for convenience in modeling for the
maximum 1-hour averages used in computing acute human health risks. Each phase run may be repeated
five times (one for each year, or a total of 15 ISCST3 runs) to complete a set of 15 runs for the full five
years of meteorological data.
Alternatively, the modeler may combine the 5 years of meteorological data into a single meteorological data
file and complete only 3 runs for each emission source (one run for each phase). Section 3.5.1.1 of the
ISC3 User's Guide (U.S. EPA 1995c), includes a complete discussion of combining multiple years of
meteorological data into a single file prior to running ISCST3. The modeler should select the 'ANNUAL'
averaging time for all risk assessment runs, regardless of the number of years in the meteorological data
file. The incorrect selection of 'PERIOD' will not compute the correct deposition rates required by the risk
assessment equations (refer to Section 3.2.3 of the ISC3 User Guide, Volume I). No additional ISCST3
model execution time is required to obtain 1-year or 5-year air modeling values.
In addition, ISCST3 allows the specification of COPC half-life and decay coefficients. Unless approved by
the permitting authority with documentation of COPC-specific data, these keywords should not be used
when conducting air modeling to support risk assessments. The TERRHGTS keyword with the ELEV
parameter typically should be used to model terrain elevations at receptor grid nodes. The FLAGPOLE
keyword specifies receptor grid nodes above local ground level and is not typically used for most air
modeling to perform impacts at ground level.
U.S. EPA OSW also recommends that SAVEFIL be used to restart ISCST3 in the event of a computer or
power failure during long runs. SAVEFIL is best used by specifying two save files, each with a different
name. The save interval should be no longer than 5 days for large runs. If two save files are used, and a
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failure occurs during writing to the savefile, no more than 10 days will be lost. The INITFILE command
should be used to restart the runs after the failure, as shown in the following example:
CO SAVEFILE SAVE1 5 SAVE2
** INITFILE SAVE1
ISCST3 will save the results alternately to SAVE1 and SAVE2 every 5 days. If the run fails after
successfully writing to SAVE1, the ISCST3 run can be restarted by replacing the two asterisks (*) in the
INITFILE line with CO and running ISCST3 again. The run will begin after the last day in SAVE1. The
modeler should change the names of the save files (e.g., SAVES and SAVE4) in the 'CO SAVEFILE'
command line prior to restarting ISCST3 to avoid overwriting the SAVE1 and SAVE2 files containing
valid data from the interrupted run. Note that the MULTYEAR keyword is not used for computing
long-term averages and should not be specified.
The following is an example of the COntrol pathway computer code for a single-year ISCST3 particle run:
CO STARTING
CO TITLEONE Example input file, particle pahse run, 1 year
CO TITLETWO 1984 met data, Baton Rouge Surface, Boothville Upper Air
CO MODELOPT DFAULT CONG DDEP WDEP DEPOS DRYDPLT WETDPLT RURAL
CO AVERTIME 1 ANNUAL
CO POLLUTID UNITY
CO TERRHGTS ELEV
CO RUNORRUN RUN
CO SAVEFILE 84SAVE1 5 84SAVE2
** Restart incomplete runs with INITFILE, changing '**' to 'CO'
** INITFILE SAVE1
CO FINISHED
Additional runs for the other 4 years are set up with the same COntrol pathway, except for the title
description and SAVEFILE filenames.
3.7.2 SOurce Pathway
As discussed in Section 3.3, ISCST3 normally uses a unit emission rate of 1.0 g/s. Additional source
characteristics required by the model (typically obtained from the Part B permit application and trial burn
report) include the following:
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• Source type (point source for stack emissions; area or volume for fugitive emissions)
• Source location (UTM coordinates, m)
• Source base elevation
• Emission rate (1.0 g/s)
• Stack height (m)
• Stack gas temperature (K)
• Stack gas exit velocity (m/s)
• Stack inside diameter (m)
• Building heights and widths (m)
• Particle size distribution (percent)
• Particle density (g/cm3)
• Particle and gas scavenging coefficients (unitless)
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
• Input values with supporting documentation for each parameter identified in Section 3.7.2
3.7.2.1 Source Location
The location keyword of the SOurce pathway (SO LOCATION) identifies source type, location, and base
elevation. The source type for any stack is referred to as a point source in ISCST3. Fugitive source
emissions are discussed in section 3.10. The source location must be entered into ISCST3. Locations
should be entered in UTM coordinates. The easterly coordinate is entered to the nearest meter; for
example, 637524 meters UTM-E (no commas are used). The northerly coordinate is entered to the nearest
meter; for example, a northerly coordinate of 4,567,789 meters UTM-N is entered as 4567789. The base
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elevation of each stack must be entered in meters. Base elevation may be obtained from a USGS
topographic map, facility plot plans or USGS digital data base.
An example input for the location keyword on the SOurce pathway includes source type, location, and base
elevation in the following format:
SO LOCATION STACK1 POINT 637524. 4567789. 347.
3.7.2.2 Source Parameters
The source parameters keyword of the SOurce pathway (SO SRCPARAM) identifies the emission rate,
stack height, stack temperature, stack velocity, and stack diameter. The unit emission rate is entered as
1.0 g/s. Stack height is the height above plant base elevation on the SO LOCATION keyword. Stack
exit temperature is the most critical stack parameter for influencing concentration and deposition. High
stack temperatures result in high buoyant plume rise, which, in turn, lowers concentration and deposition
rates. Stack temperatures should be based on stack sampling tests for existing stacks. For new or
undefined stacks, manufacturer's data for similar equipment should be used. Stack exit velocity should be
calculated from actual stack gas flow rates and stack diameter. Actual stack gas flow rates should be
determined for existing stacks during stack sampling. Representative values for new or undefined sources
should be obtained from manufacturer's data on similar equipment. Stack diameter is the inside diameter
of the stack at exit.
Following is an example of the source parameter input in the SOurce pathway for emission rate (grams per
second), stack height (meters), stack temperature (K), stack velocity (meters per second), and stack
diameter (meters):
SO SRCPARAM STACK1 1.0 23.0 447.0 14.7 1.9
3.7.2.3 Building Parameters
The building height and width keywords of the SOurce pathway (SO BUILDHGT; SO BUILDWID)
identify the building dimensions that most influence the air flow for each of the 36 10-degree directions
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surrounding a stack. The dimensions are calculated by using the U.S. EPA program BPIP, as described in
Section 3.2.4.
The BPIP output file is input as follows:
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29 18.29 18.29 18.29 18.29
SO BUILDHGT STACK1 18.29 18.29 18.29 18.29
SO BUILDWID STACK1 14.02 15.51 16.53 17.05 17.05 16.53 15.51 14.03
SO BUILDWID STACK1 12.10 14.02 15.51 16.53 17.05 17.05 16.53 15.51
SO BUILDWID STACK1 14.02 12.10 14.02 15.51 16.53 17.05 17.05 16.53
SO BUILDWID STACK1 15.51 14.02 12.10 14.02 15.51 16.53 17.05 17.05
SO BUILDWID STACK1 16.53 15.51 14.02 12.10
3.7.2.4 Particle Size Distribution
ISCST3 requires particle size distribution for determining deposition velocities. U.S. EPA OSW
recommends site-specific stack test data for existing sources. New or undefined sources may use the
particle size distribution presented in Table 3-1.
The following example is the ISCST3 input for particle phase run. From Table 3-1, the distribution for
9 mean diameter sizes includes the data required for the keywords of the SOurce pathway
(SO PARTDIAM; SO MASSFRAX). The PARTDIAM is taken from Column 1 (Mean Particle Diameter).
The MASSFRAX is taken from Column 4 (Fraction of Total Mass).
SO PARTDIAM STACK1 0.35 0.70 1.10 2.00 3.60 5.50 8.10 12.5 15.0
SO MASSFRAX STACK1 0.22 0.08 0.08 0.11 0.10 0.07 0.10 0.11 0.13
The example for the ISCST3 input for the particle-bound run is described below. From Table 3-1, the
PARTDIAM is the same. The MASSFRAX is taken from Column 6 (Fraction of Total Surface Area).
SO PARTDIAM STACK1 0.35 0.70 1.10 2.00 3.60 5.50 8.10 12.5 15.0
SO MASSFRAX STACK1 0.49 0.17 0.13 0.09 0.05 0.02 0.02 0.01 0.02
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3.7.2.5 Particle Density
Particle density is also required for modeling the air concentration and deposition rates of particles.
Site-specific measured data on particle density should be determined for all existing sources when possible.
For new or undefined sources requiring air modeling, a default value for particle density of 1.0 g/cm3 may
be used. Particles from combustion sources, however, may have densities that are less than 1.0 g/cm3
(U.S. EPA 1994a), which would reduce the modeled deposition flux.
Following is an example of the particle density input in the SOurce pathway (SO PARTDENS) for the
9 mean particle size diameters of the previous example:
SO PARTDENS STACK1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
3.7.2.6 Scavenging Coefficients
Wet deposition flux is calculated within ISCST3 by multiplying a scavenging ratio by the vertically
integrated concentration. The scavenging ratio is the product of a scavenging coefficient and a
precipitation rate. Studies have shown that best fit values for the scavenging coefficients vary with particle
size. For vapors, wet scavenging depends on the properties of the COPCs involved. However, not enough
data are now available to adequately develop COPC-specific scavenging coefficients. Therefore, vapors
are assumed to be scavenged at the rate of the smallest particles with behavior in the atmosphere that is
assumed to be influenced more by the molecular processes that affect vapors than by the physical processes
that may dominate the behavior of larger particles (U.S. EPA 1995c).
To use the wet deposition option in ISCST3, users must input scavenging coefficients for each particle size
and a file that has hourly precipitation data. For wet deposition of vapors, a scavenging coefficient for a
0.1-jwm particle may be input to simulate wet scavenging of very small (molecular) particles. Alternatively,
site-specific measured washout data or a calculation based on Henry's Law constant may be approved by
the appropriate permitting authority prior to analysis. Wet deposition results only during precipitation.
Scavenging coefficients should be determined for each particle size from the best fit curve based on the
work of Jindal and Heinhold (1991) presented in the ISC3 User's Guide (U.S. EPA 1995c). The curves are
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limited to a maximum particle size of 10-ywm, so all scavenging coefficients for particle sizes greater than
or equal to 10-jwm are assumed to be equal. This assumption follows research on wet scavenging of
particles (Jindal and Heinhold 1991).
The ISCST3 model input also differentiates between frozen and liquid scavenging coefficients. As a
conservative estimate, the frozen scavenging coefficients are assumed to be equal to the liquid scavenging
coefficients (PEI and Cramer 1986). If desired, the user may input separate scavenging coefficients for
frozen precipitation. Research on sulfate and nitrate data has shown that frozen precipitation scavenging
coefficients are about one-third of the values of liquid precipitation (Scire, Strimaitis, and Yamartino 1990;
Witby 1978).
Following is an example of the particle liquid (rain) and frozen (sleet or snow) scavenging coefficients
input in the SOurce pathway for 9 mean particle size diameters assuming particles are scavenged by frozen
precipitation at 1/3 the rate of liquid precipitation:
SO PARTSLIQ STACK1 7E-5 5E-5 6E-5 1.3E-4 2.6E-4 3.9E-4 5.2E-4 6.7E-4 6.7E-4
SO PARTSICE STACK1 2E-5 2E-5 2E-5 4E-5 9E-5 1.3E-4 1.7E-4 2.2E-4 2.2E-4
The complete SOurce pathway for the example particle phase input file is as follows:
SO
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
so
STARTING
LOCATION
SRCPARAM
BUILDHGT
BUILDHGT
BUILDHGT
BUILDHGT
BUILDHGT
BUILDWID
BUILDWID
BUILDWID
BUILDWID
BUILDWID
PARTDIAM
MASSFRAX
PARTDENS
PARTSLIQ
PARTSICE
SRCGROUP
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
STACK1
ALL
POINT 637524
1.0 23.0 447
18.29 18.29
18.29 18.29
18.29 18.29
18.29 18.29
18.29 18.29
14.02 15.51
12.10 14.02
14.02 12.10
15.51 14.02
16.53 15.51
0.35 0.70 1.
0.22 0.08 0.
1.0 1.0 1.
7E-5 5E-5 6E
2E-5 2E-5 2E
. 4567789
.0
18.
18.
18.
18.
18.
16.
15.
14.
12.
14.
10
08
0
-5
-5
14
.29
.29
.29
.29
.29
.53
.51
.02
.10
.02
2.
0.
1.
1 .
.7
18
18
18
18
18
17
16
15
14
12
00
11
0
3E-
4E-
1
3
0
1
4
5
. 347
.9
29
29
29
29
29
05
53
51
02
10
.60
.10
.0
2.
18
18
18
18
17
17
16
15
5
0
1
6E
9E
29 18.29 18.29 18.29
29 18.29 18.29 18.29
29 18.29 18.29 18.29
29 18.29 18.29 18.29
05 16.53 15.51 14.03
05 17.05 16.53 15.51
53 17.05 17.05 16.53
51 16.53 17.05 17.05
50 8.10 12.5 15.0
07 0.10 0.11 0.13
0 1.0 1.0 1.0
•4 3.9E-4 5.2E-4 6.7E-4 6.7E-4
•5 1.3E-4 1.7E-4 2.2E-4 2.2E-4
SO FINISHED
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When modeling air vapors using ISCST3, the following is an example of the SOurce pathway input for wet
vapor scavenging coefficients that replaces the PARTDIAM, MASSFRAX, PARTDENS, PARTSLIQ and
PARTSICE lines in the above example:
SO GAS-SCAV STACK1 LIQ 1.7E-4
SO GAS-SCAV STACK1 ICE 0.6E-4
3.7.3 REceptor Pathway
The REceptor pathway identifies sets or arrays of receptor grid nodes identified by UTM coordinates for
which ISCST3 generates estimates of air parameters including air concentration, dry and wet deposition,
and total deposition. Previous U.S. EPA guidance (U.S. EPA 1994a) recommended using a polar receptor
grid to identify maximum values, because polar grids provide coverage over large areas with a reduced
number of receptor grid nodes, thereby reducing computer run times. However, U.S. EPA Region 6
experience indicates that, although the use of polar grids may reduce computer run times, air modelers
typically choose a different option, because the benefit of reduced run time is offset by difficulties in
identifying polar grid locations in absolute UTM coordinates for (1) extracting terrain values from digital
terrain files, and (2) selecting receptor grid node locations for evaluation of ecosystems and special
ecological habitats (see Chapter 4).
Receptor grid node arrays may be generated by using ISCST3 grid generation. However, assigning terrain
elevations for each receptor grid node in an array associated with the generated grid can result in errors.
One method of obtaining a Cartesian grid with terrain elevations is to open the USGS DEM file in a
graphics program (e.g., SURFER®). Selection of the grid option samples the DEM file, at the
user-specified spacing, over a range of east (x) and north (y) values. The specified x and y locations
extract terrain elevation (z) from the DEM file at the desired receptor grid node for air modeling with the
appropriate terrain elevations at each receptor grid node. These x, y, and z values are saved as a text file
with one receptor grid node per line. A text editor is used to prefix each line with "RE DISCCART" to
specify a discrete receptor grid node in ISCST3 format. Commercial receptor grid generators are also
available. One commercial program (Lakes Environmental Software) generates the recommended receptor
grid node array and extracts terrain elevations from the USGS DEM downloaded files, or any terrain file in
x-y-z format.
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The following is an example of the REceptor pathway for discrete receptor grid nodes at 500-meter spacing
and including terrain elevations (in meters):
RE STARTING
RE ELEVUNIT METERS
RE DISCCART 630000. 565000. 352.
RE DISCCART 630500. 565000. 365.
RE DISCCART 631000. 565000. 402.
1
RE DISCCART 635000. 570000. 387.
RE FINISHED
U.S. EPA OSW recommends that air modeling for each risk assessment include, at a minimum, an array of
receptor grid nodes covering the area within 10 kilometers of the facility with the origin at the centroid of a
polygon formed by the locations of the stack emission sources. This receptor grid node array should
consist of a Cartesian grid with grid nodes spaced 100 meters apart extending from the centroid of the
emission sources out to 3 kilometers from the centroid. For the distances from 3 kilometers out to
10 kilometers, the receptor grid node spacing can be increased to 500 meters. The single grid node array
contains both grid node spacings. This same receptor grid node array is included in the REceptor pathway
for all ISCST3 runs for all years of meteorological data and for all emission sources.
Terrain elevations should be specified for all receptor grid nodes. Several methods are available for
assigning terrain elevations to grid nodes using digital terrain data. The 1:250,000 scale DEM digital data
are available for download at the USGS Internet site:
Worldwide Web: http://edcwww.cr.usgs.gov/pub/data/dem/250
FTP (two options): ftp://edcwww.cr.usgs.gov/pub/data/dem/250
ftp ://edcftp .cr .usgs .gov/pub/data/dem/25 0
This data has horizontal spacing between digital terrain values of approximately 90 meters which provides
sufficient accuracy for air modeling.
In addition to the receptor grid node array evaluated for each facility out to 10 kilometers, other grid node
arrays may be considered for evaluation of water bodies and their watersheds, ecosystems and special
ecological habitats located beyond 10 kilometers. Grid node spacing of 500 meters between nodes is
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recommended for grid node arrays positioned at distances greater than 10 kilometers from the emission
source. An equally spaced grid node array facilitates subsequent computation of area averages for
deposition rates.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT REPORT
Summary of all information regarding the coordinates and placement of the receptor grid node
array used in air modeling
Copies of any maps, figures, or aerial photographs used to develop the receptor grid node array
Map presenting UTM locations of receptor grid nodes, along with other facility information.
3.7.4 MEteorological Pathway
The file containing meteorological data is specified in the MEteorological pathway. PCRAMMET creates
individual files for each of 5 years, as ASCII files, to be read into ISCST3 for computing hourly
concentrations and deposition rates. The modeler may specify a single year of meteorological data in each
ISCST3 run, or combine the total period of meteorological data into a single meteorological file for
processing by ISCST3 in a single 5-year run. When combining meteorological files, the modeler is
cautioned to consider the following:
• Preprocess each year separately using PCRAMMET or MPRM into an ASCII format
• Combine the years into a single file (using a text editor or DOS COPY command)
• The first line (header) of the combined file is read by ISCST3 for comparison to the
Surface and Upper Air Station ID numbers specified in the input file ME pathway
The header for subsequent years is read by ISCST3 only if not deleted in the combined
file. If subsequent year headers are included in the combined file, ISCST3 will compare
the station IDs to the input file station ID. For air modeling analysis which use
meteorological data from more than one surface station or upper air station (e.g., the upper
air station is moved after the third year of the period and assigned a new station ID by the
National Weather Service), the modeler should delete the headers for subsequent years in
the combined file.
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For sites where the anemometer height is changed during the 5 year period (e.g., for the
period 1984-1988, the anemometer was relocated from 20 feet to 10 meters on December
15, 1985), the modeler should run each year separately to specify the correct anemometer
height in the ISCST3 input file ME pathway which corresponds to the correct height for
that year of meteorological data.
Details of specifying the meteorological data file are in the ISC3 User's Guide (Section 3.5.1.1). Each year
within the file must be complete with a full year of data (365 days, or 366 days for leap years). The
anemometer height must be verified for the surface station from Local Climate Data Summary records, or
other sources, such as the state climatologist office. U.S. EPA OSW recommends that the anemometer
height ANEMHGHT for the wind speed measurements at the surface station be correctly identified before air
modeling.
The following is an example input section for the MEteorological pathway, using the 1984 Baton Rouge
file, with an anemometer height of 10 meters and station identification numbers:
ME STARTING
ME INPUTFIL 84BR.WET
ME ANEMHGHT 10.0
ME SURFDATA 13970 1984 BATON_ROUGE
ME UAIRDATA 12884 1984 BOOTHVILLE
ME FINISHED
3.7.5 Terrain Grid (TG) Pathway
The computation of dry plume depletion is sensitive to terrain elevation. In the absence of a terrain grid
file, ISCST3 automatically assumes that the terrain slope between the stack base and the receptor grid node
elevation is linear. In concept, this assumption may underestimate plume deposition. However, based on
experience, the magnitude of the differences in computed concentrations and deposition rates is nominal.
Since the inclusion of a terrain grid file in the TG pathway significantly increases model execution time,
U.S. EPA OSW recommends that a terrain grid file is not necessary for all sites. If a terrain grid file is
desired for a specific site based on highly variable terrain over short distances, the format of the TG file is
described in the ISC3 User's Guide.
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The location keyword of the TG pathway (TG LOCATION) identifies the x and y values to be added to the
source and receptor grid to align with the terrain file coordinates. If the source and receptor grid nodes are
in relative units such that the source is at location 0,0, the location keywords in the TG pathway would be
the UTM coordinates of the source. U.S. EPA OSW requires that all emission sources and receptor grid
nodes be specified in UTM coordinates (NAD27 or NAD83 format), and that the TG file, if used, be in
UTM coordinates. Therefore, the location of the origin of the TG file relative to the source location will be
0,0. Also, U.S. EPA OSW recommends that the terrain elevations in the TG file be presented in meters.
Following is an example of the TG pathway:
TG STARTING
TG INPUTFIL TERRAIN.TER
TG LOCATION 0.0 0.0
TG ELEVUNIT METERS
TG FINISHED
3.7.6 OUtput Pathway
ISCST3 provides numerous output file options in addition to the results in the output summary file
specified in receptor tables (RECTABLE). The plot file is most useful for facilitating post-processing of
the air parameter values in the model output. The plot file lists the x and y coordinates and the
concentration or deposition rate values for each averaging period in a format that can be easily pulled into a
post-processing program (or spreadsheet). Note that the ISCST3 generated 'plot' file is not the same
format as the ISCST3 generated 'post' file. U.S. EPA OSW recommends using the plot file, not the post
file.
Following is an example OUtput file specification for single-year run of 1-hour and annual average plot
files:
OU STARTING
OU RECTABLE ALLAVE FIRST
OU PLOTFILE 1 ALL FIRST BTR841.PLT
OU PLOTFILE ANNUAL ALL BTR84A.PLT
OU FINISHED
For ecological risk assessments, the 1-hour average plot file is not needed. If the modeler has directed in
the ISCST3 control pathway for 1-hour averages to be computed for use in a human health acute risk
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assessment, then the 1-hour average plot file also should be specified (U.S. EPA 1998). The second line in
the example directs ISCST3 to create a table of values for each receptor grid node for all averaging periods
in the model run (annual and optionally 1-hour). The third line directs ISCST3 to create a separate plot file
of the 1-hour average results, if desired by the modeler. The fourth line directs ISCST3 to create another
separate plot file of the annual average results for all sources in the run for each receptor grid node.
3.8 ISCST3 MODEL EXECUTION
Model execution time should be considered for each analysis. A complete air modeling run—including air
concentration, wet and dry deposition, and plume depletion—may require 10 times the run time for the
same source and receptor grid nodes for air concentration only. Even if only the total deposition is
specified, ISCST3 must compute air concentration and the dry and wet deposition components in order to
compute the total deposition air parameter values required for the ecological risk assessment. For example,
an ISCST3 particle run of one source with 800 receptor grid nodes, on 1 year of meteorological data, with
the options for air concentration, wet and dry deposition, and plume depletion required about 40 hours on a
personal computer with a 486 processor running at 66 megahertz (486/66). The same run can be
completed in about 10 hours on a 586/120 personal computer. Five years of meteorological data and an
additional 1,600 receptor grid nodes result in total run times of 120 hours for 1 year, and 600 hours for a
5-year analysis on a 486/66 personal computer. Run time on a 586/120 personal computer is estimated at
about 150 hours. A significant loss of modeling effort and analysis time can be prevented by verifying
input parameters and conducting test runs prior to executing the ISCST3 runs.
Long run times result mainly from two algorithms—plume depletion and terrain grid file. ISCST3 run
times are increased as much as tenfold for runs applying plume depletion. U.S. EPA OSW believes that
constituent mass must be conserved between suspended concentration and deposition rate by allowing for
depletion of deposited mass from the plume concentration in ISCST3. The overestimate of plume
concentration, and the subsequent overestimate of deposition, which results when plume depletion is not
allowed, is too conservative. However, the nominal benefits of including a terrain grid file do not justify
the added run times. Therefore, plume depletion should always be included, but terrain grid files are not
recommended.
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3.9 USE OF MODELED OUTPUT
The ISCST3 modeled output (air concentrations and deposition rates) are provided on a unit emission rate
(1.0 g/s) basis from the combustion unit or emission source, and are not COPC-specific. The estimating
media equations presented in Section 3.11 and Appendix B require the model output (air parameters, see
Table 3-5) directly without converting the unit based output to COPC-specific output. However, there may
be some instances where the risk assessor will need to convert modeled output to COPC-specific output for
the risk assessment. For example, the risk assessor may want to compare modeled COPC concentrations in
ambient media to concentrations actually measured in the field.
3.9.1 Unit Rate Output vs. COPC-Specific Output
The relationship between the unit emission rate and the unit air parameter values (air concentrations and
deposition rates) is linear. Similarly, the relationship between the COPC-specific emission rate (0 and the
COPC-specific air parameter values (air concentrations and deposition rates) would also be linear if the
COPC-specific emission rate was used in the air model. Section 3.3 discussed the use of the unit emission
rate and advanced the theory that a unit emission rate should be used instead of the COPC-specific
emission rate in order to preclude having to run the ISCST3 model separately for each individual COPC.
The use of a unit emission rate in the air modeling is advocated because a common ratio relationship can be
developed between the unit emission rate and the COPC-specific emission rate based on the fact that in the
air model, both individual relationships are linear. This ratio relationship can be expressed by the
following equation:
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TABLE 3-5
AIR PARAMETERS FROM ISCST3 MODELED OUTPUT
Air
Parameter
Cyv
Cyp
Dywv
Dydp
Dywp
Cywv
Dywwv
Dytwp
Description
Unitized yearly average air concentration from vapor
phase
Unitized yearly average air concentration from particle
phase
Unitized yearly average wet deposition from vapor
phase
Unitized yearly average dry deposition from particle
phase
Unitized yearly average wet deposition from particle
phase
Unitized yearly (water body or watershed) average air
concentration from vapor phase
Unitized yearly (water body or watershed) average
wet deposition from vapor phase
Unitized yearly (water body or watershed) average
total (wet and dry) deposition from particle phase
Units
Aig-s/g-m3
Aig-s/g-m3
s/m2-yr
s/m2-yr
s/m2-yr
Aig-s/g-m3
s/m2-yr
s/m2-yr
COPC-Specific Air Concentration Modeled Output Air Concentration
COPC-Specific Emission Rate Unit Emission Rate
Equation 3-2
Use of this equation requires that three of the variables be known. The modeled output air concentration
(or deposition rate) is provided by the air model, the unit emission is 1.0 g/s, and the COPC-specific
emission rate; which is obtained directly from stack or source test data.
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3.9.1.1 Determination of the COPC-Specific Emission Rate (0
The COPC-specific emission rate can usually be determined with information obtained directly from the
trial burn report. The COPC-specific emission rate from the stack is a function of the stack gas flow rate
and the stack gas concentration of each COPC; which can be calculated from the following equation:
SGC•CFO2
Q = SGF- Equation 3-4
IxlO6
where
Q = COPC-specific emission rate (g/s)
SGF = Stack gas flow rate at dry standard conditions (dscm/s)
SGC = COPC stack gas concentration at 7 percent O2 as measured in the trial burn
((ig/dscm)
CFO2 = Correction factor for conversion to actual stack gas concentration O2 (unitless)
1 x 106 = Unit conversion factor ((ig/g)
Guidance for determining COPC-specific emission rates for fugitive emission sources can be found in
Chapter 2. Also, it is sometimes necessary to derive the COPC-specific emission rate from surrogate data,
such as for a new facility that has not yet been constructed and trial burned (see Chapter 2).
3.9.1.2 Converting Unit Output to COPC-Specific Output
Once the three of the four variables in Equation 3-1 are known, the COPC-specific air concentrations and
deposition rates can be obtained directly by multiplication, as follows:
COPC-Specific Modeled Output Air Concentration-COPC-Specific Emission Rate
Air Concentration Unit Emission Rate Equation 3-3
For example, if COPC A is emitted at a rate of 0.25 g/s, and the ISCST3 modeled concentration at a
specific receptor grid node is 0.2 Aig/m3 per the 1.0 g/s unit emission rate, the concentration of COPC A at
that receptor grid node is 0.05 jWg/m3 (0.25 multiplied by 0.2). Deposition is calculated similarly,
proportional to the emission rate of each COPC. Readers are reminded once again that this process of
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converting modeled unitized output into COPC-specific output is taken directly into account in the
estimating media concentration equations in Section 3.11 and Appendix B.
3.9.2 Output from the ISCST3 Model
The ISCST3 output is structured and the risk assessor must understand how to read the output in order to
ensure accurate use of modeled output in the risk assessment. The output from each ISCST3 model run is
written to two separate file formats. The 'output file' is specified by name at run time in the execution
command. Typical command line nomenclature is:
ISCST3 inputfile.INP outputfile.OUT
where
ISCST3: specifies execution of the ISCST3 model
inputf ile . INP: is the input file name selected by the modeler
output file. OUT: is the output file name selected by the modeler, typically the same as the
input file name
For example, the following ISCST3 input line would run the input file (PART84.INP) created by the
modeler for particulate emissions using 1984 meteorological data. The output file (PART84.OUT) from
the run will automatically be written by ISCST3 during model execution.
ISCST3 PART84.INP PART84.0UT
The output 'plot file' is specified by the modeler in the ISCST3 input file OUtput pathway and created by
ISCST3 during the run (see Section 3.7.6). Figure 3-3 is an example of the first few lines in the particle
phase plot file with single-year annual average concentration, total deposition, dry deposition and wet
deposition values for each receptor grid node. The total deposition is the sum of the dry and wet
components of deposition. The single-year values at each receptor grid node being evaluated must be
averaged to a 5-year value. The 5-year averaged values at the receptor grid nodes selected for evaluation in
the risk assessment (see Section 3.9.3), are used in the estimating media concentration equations. This file
is usually imported into a post-processing program (or spreadsheet) before entry into the risk assessment
computations.
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Similar plot files are produced for the particle-bound and vapor phase runs. The output for the vapor
phase runs will be average concentration and wet deposition. The output for the particle and particle-
bound phase runs will be average concentration, dry deposition, wet deposition and total deposition. Again,
the 1-year values at each receptor grid node must be averaged to a 5-year value at each node unless a single
five-year ISCST3 run using a combined meteorological file is used. If the 5-year combined file is used, the
results from the ISCST3 plot file may be used directly in the risk assessment without averaging over the
five years.
All values are defined as used in the estimating media concentration equations (see Section 3.11).
3.9.3 Use of Model Output in Estimating Media Equations
Section 3.4 discussed how consideration of partitioning of the COPCs effects the development of ISCST3
modeling runs. The selection of which air modeled air parameter values (air concentrations and deposition
rates) to use in the estimating media concentration equations is based on this same partitioning theory.
3.9.3.1 Vapor Phase COPCs
ISCST3 output generated from vapor phase air modeling runs are vapor phase air concentrations (unitized
Cyv and unitized Cywv) and wet vapor depositions (unitized Dywv and unitized Dywwv) for organic
COPCs at receptor grid nodes based on the unit emission rate. These values are used in the estimating
media concentration equations for all COPC organics except the polycyclic aromatic hydrocarbons
dibenzo(a,h)anthracene and indeno(l,2,3-cd)pyrene, which have vapor phase fractions, Fv, less than
five percent. The air concentration (unitized Cyv) and wet vapor deposition (unitized Dywv) from the vapor
phase run is also used in the estimating media concentration equations for mercury. Values for these
COPCs are selected from the vapor phase run because the mass of the COPC emitted by the combustion
unit is assumed to have either all or a portion of its mass in the vapor phase (see Appendix A-2).
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3.9.3.2 Particle Phase COPCs
ISCST3 output generated from particle phase air modeling runs are air concentration (unitized Cyp), dry
deposition (unitized Dydp), wet deposition (unitized Dywp), and combined deposition (unitized Dytwp) for
inorganics and relatively non-volatile organic COPCs at receptor grid nodes based on the unit emission
rate. These values are used in the estimating media concentration equations for all COPC inorganics
(except mercury, see Chapter 2 and Appendix A-2) and polycyclic aromatic hydrocarbons with fraction of
vapor phase, Fv , less than 0.05 (e.g., dibenzo(a,h)anthracene and indeno(l,2,3-cd)pyrene). Values for
inorganic and relatively non-volative COPCs are selected from the particle phase run because the mass of
the COPC emitted by the combustion unit is assumed to have all of its mass in the particulate phase (see
Appendix A-2), apportioned across the particle size distribution based on mass weighting.
3.9.3.3 Particle-Bound COPCs
ISCST3 output generated from particle-bound air modeling runs are air concentration (unitized Cyp), dry
deposition (unitized Dydp), wet deposition (unitized Dywp), and combined deposition (unitized Dytwp) for
organic COPCs and mercury (see Chapter 2 and Appendix A-2) at receptor grid nodes based on the unit
emission rate. These values are used in the estimating media concentration equations for all COPC
organics and mercury to account for a portion of the vapor condensed onto the surface of particulates.
Values for these COPCs are selected from the particle-bound run because the mass of the COPC emitted
by the combustion unit is assumed to have a portion of its mass condensed on particulates (see
Appendix A-2), apportioned across the particle size distribution based on surface area weighting.
3.10 MODELING OF FUGITIVE EMISSIONS
Fugitive source emissions, as defined in Chapter 2, should be modeled using the procedures presented
throughout this chapter for stack source emissions. However, the fugitive emissions should be represented
in the ISCST3 input file SOurce pathway as either "area" or "volume" source types. Fugitive emissions of
volatile organics are modeled only in the vapor phase. Fugitive emissions of ash are modeled only in the
particle and particle-bound phases, not vapor phase.
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As discussed in Chapter 2, fugitive emissions of volatile organic vapors are associated with combustion
units that include storage vessels, pipes, valves, seals and flanges. The horizontal area of the fugitive
source (which can be obtained from the facility plot plan) is entered into the ISCST3 input file following
the instructions presented in the ISC3 User's Guide, Volume I (U.S. EPA 1995c). The height of the
fugitive source is defined as the top of the vertical extent of the equipment. If the vertical extent of the
fugitive source is not known, a default height of ground level (release height of zero) may be input,
providing a conservative estimate of potential impacts. The ISCST3 model run time is faster for volume
source types than for area source types, and should be considered for most applications. The methods in
the ISCST3 User's Guide should be followed in defining the input parameters to represent the fugitive
source.
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December 1998
*ISCST3 (96113) : Ex;
*MODELING OPTIONS USI
* CONG DEPOS DDEP
* PLOT FILE 01
* FOR A TOTAL
* FORMAT : ( 6 ( :
* X Y
ID
*
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FIGU
EXAMPLE
ample Particle Phase Run, Single Year :
3D:
WDEP RURAL ELEV DFAULT
T ANNUAL VALUES FOR SOURCE GROUP: ALL
OF 21 RECEPTORS.
LX, F13 . 5) , IX, F8 .2, 2X,A6 . 2X.A8 . 2X. 18 . 2X
AVERAGE CONG
. 00000
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.27475
.22195
.40644
.51388
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DRYDPL WETDPL
GRP NUM HRS NET
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8760
NA
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NA
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NA
NA
NA
NA
U.S. EPA Region 6
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Chapter 3: Air Dispersion and Deposition Modeling
February 28,1997
691800
691900
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692100
692200
692300
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8760
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NA
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NA
NA
NA
NA
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August 1999
The following example is for organic fugitive emissions modeled as a volume source type. For a facility
which may have two stack emission sources (Bl, B2) and two fugitive emission sources (areas Fl, F2);
a total of four runs for each year (or 5-year combined file) of meteorological data is required. One run is
required for each of the two stacks as point sources. One run is required for each of the two fugitive areas
as volume sources (Note: modeler may alternatively model as an area source). Since the emissions are
fugitive volatile organics, only the vapor phase is modeled. The vertical extent of the pipes, valves, tanks
and flanges associated with each fugitive emission area is 15 feet (about 5 meters) above plant elevation.
To define the sources for input to ISCST3, the release height is specified as 2.5 meters (1A of vertical extent
of fugitive emissions). The initial vertical dimension is specified as 1.16 meters (vertical extent of 5 meters
divided by 4.3 as described in the ISC3 User's Guide).
Plot Plan
B2
A
F2
B1
A
F1
ISC3 Volume
F1A
F1B
F1C
F1D
The initial horizontal dimension is the side length of the square fugitive area (footprint) divided by 4.3. If
fugitive area F2 has a measured side of 30 meters, the initial horizontal dimension is 6.98 (30 meters
divided by 4.3). For fugitive area Fl, the area on the plot plan must be subdivided (ISC3 Volume) to
create square areas for input to ISCST3. The four areas depicted represent subdivision into square areas.
The resulting four square areas are input into a single ISCST3 run for Fugitive source Fl as four separate
volume sources (F1A, FIB, F1C, FID). The initial horizontal dimension for each volume source is the
side of the square divided by 4.3. It is very important to allocate proportionately the unit emission rate
(1.0 gram per second) among the subdivided areas. For example, if the areas of the subdivided squares in
the ISC3 Volume figure results in F1A equal to FIB each with l/8th the total area, the proportion of the
unit emissions allocated to each of these volume sources is 0.125 grams per second. The remaining two
areas are each 3/8ths of the total area of fugitive Fl, so that 0.375 grams per second is specified for the
emission rate from each source. The total emissions for the four volume sources sum to the unit emission
rate for the Fl fugitive source (0.125 + 0.125 + 0.375 + 0.375 = 1.0 g/s). By specifying all sources to be
included in the model results from ISCST3 (SO SRCGROUP ALL), the ISCST3 model will appropriately
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combine all four volume source subdivisions of fugitive source Fl into combined impact results for fugitive
source F1. The resulting air parameter values in the plot files may be used directly in the risk assessment
equations, the same as if a stack emission were modeled as a single point source. The initial vertical
dimension is defined the same as F2, using the vertical extent of 5 meters divided by 4.3 and a release
height of 2.5 meters (1A vertical extent). For volume sources, the location is specified by the x and y
coordinates of the center of each square area.
The COntrol parameters should follow the recommendations for setting up a vapor phase computation.
CO CONG WDEP
Fugitive emissions of ash particles are from the storage piles associated with combustion units. The
horizontal area of the storage pile is entered into the ISCST3 input file following the ISCST3 User's Guide,
Volume I (U.S. EPA 1995c). The height of emissions is input as the top of the pile. If the vertical extent is
not known, the height may be input as ground level (or zero height). Fugitive ash will typically be modeled
as area source type. However, volume source type may be considered by the appropriate regulatory agency
prior to air modeling. The methods in the ISCST3 User's Guide should be followed in defining the input
parameters to represent the ash release as an area source.
The COntrol parameters should follow the recommendations for setting up a particulate phase
computation.
CO CONG DDEP WDEP DEPOS
The emissions characterization and source type must be documented.
3.11 ESTIMATION OF COPC CONCENTRATIONS IN MEDIA
As discussed in Section 3.9 (see also Table 3-5), the ISCST3 modeled output of unitized air parameters
(air concentrations and deposition rates) are provided on a unit emission (1.0 g/s) basis from the
combustion unit, and are not COPC-specific. The estimating media concentration equations, presented in
this section, accept these unitized output values directly to calculate COPC-specific media concentrations
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for use in characterizing ecological risk. Selection of the appropriate ISCST3 modeled output for use in
the equations is discussed in Section 3.9.
This section presents the estimating media concentration equations used for calculating, from the
appropriate ISCST3 unitized model output and COPC-specific emission rates, COPC-specific media
concentrations in soil, surface water, and sediment. Determining COPC media concentrations is relevant to
estimating risks to potentially impacted ecosystems through exposure of ecological receptors to COPCs in
air (plant only), soil, surface water, and sediment. This section also includes equations for calculating
COPC-specific concentrations in terrestrial plants resulting from foliar and root uptake.
Section 3.11.1 describes the equations for calculating COPC-specific concentration in soils. Section 3.11.2
describes the equations for calculating COPC-specific concentrations in surface water and sediment.
Section 3.11.3 describes the equations for calculating COPC-specific plant concentrations from foliar and
root uptake. In addition, Appendix B also provides in more detail the media concentration equations and
default input variables recommended by U.S. EPA OSW.
3.11.1 CALCULATION OF COPC CONCENTRATIONS IN SOIL
As depicted in Figure 3-4, COPC concentrations in soil are calculated by summing the particle and vapor
phase deposition of COPCs to the soil. Wet and dry deposition of particles and vapors are considered, with
dry deposition of vapors calculated from the vapor air concentration and the dry deposition velocity. Soil
concentrations may require many years to reach steady state. As a result, the equations used to calculate
the soil concentration over the period of deposition were derived by integrating the instantaneous soil
concentration equation over the period of deposition. U.S. EPA OSW recommends that the highest 1-year
annual average COPC concentration in soil be used as the soil concentration for estimating ecological risk,
which would typically occur at the end of the time period of combustion (see Section 3.11.1).
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Degradation
(Biotic + Abiotic)
and Volatilization
Figure 3-4 - COPC Concentration in Soil
Following deposition, the calculation of soil concentration also considers losses of COPCs by several
mechanisms, including leaching, erosion, runoff, degradation (biotic and abiotic), and volatilization. All of
these loss mechanisms may lower the soil concentration if included in the soil concentration calculation (see
Section 3.11.1.2). Soil conditions—such as pH, structure, organic matter content, and moisture
content—can also affect the distribution and mobility of COPCs in soil. Loss of COPCs from the soil is
modeled using a combination of default and site-specific values to account for the physical and chemical
characteristics of the soil.
COPCs may also be physically incorporated into the upper layers of soil through tilling. The concentration
in the top 20 centimeters of soil should be computed for estimating a COPC concentration in soils that are
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physically disturbed or tilled. The COPC concentration in the top 1 centimeter of soil should be computed
for estimating a COPC concentration in soils that are not tilled (see Section 3.11.1.4).
3.11.1.1 Calculating Highest Annual Average COPC Concentration in Soil
U.S. EPA OSW recommends the following equation for calculating the highest average annual COPC soil
concentration.
Recommended Equations for Calculating:
Highest Annual Average COPC Concentration in Soil (Cs)
Ds-[l-exp(-ks-tD-)]
; Equation 3-7
ks
where
Cs = COPC concentration in soil (mg COPC/kg soil)
Ds = Deposition term (mg/kg-yr)
ks = COPC soil loss constant due to all processes (yr :)
tD = Total time period over which deposition occurs (time period of combustion) (yr)
This equation calculates the highest annual average soil concentration, which is typically expected to occur
at the end of the time period of deposition (U.S. EPA 19941; 1998c). Derivation of the equation is
presented in U.S. EPA (1998c). Appendix B, Table B-l-1 also describes the equation, definitions of its
terms, and default values for the variables.
3.11.1.2 Calculating the COPC Soil Loss Constant (ks)
COPCs may be lost from the soil by several processes that may or may not occur simultaneously. In
Equation 3-8, the soil loss constant, ks, expresses the rate at which a COPC is lost from soil (U.S.
EPA 1993h; 1998c). The constant ks is determined by using the soil's physical, chemical, and biological
characteristics to consider the losses resulting from:
(1) biotic and abiotic degradation,
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(2) erosion,
(3) surface runoff,
(4) leaching, and
(5) volatilization.
Consistent with earlier U.S. EPA guidance (U.S. EPA 1993h; 19941; 1998c), U.S. EPA OSW recommends
using Equation 3-8 to compute the soil loss constant.
Recommended Equation for Calculating:
COPC Soil Loss Constant (ks)
ks = ksg + kse + ksr + ksl + ksv Equation 3-8
where
ks = COPC soil loss constant due to all processes (yr"1)
ksg = COPC loss constant due to degradation (yr"1)
kse = COPC loss constant due to erosion (yr"1)
ksr = COPC loss constant due to runoff (yr"1)
ksl = COPC loss constant due to leaching (yr"1)
ksv = COPC loss constant due to volatilization (yr"1)
The use of Equation 3-8 assumes that COPC loss can be defined by using first-order reaction kinetics.
First-order reaction rates depend on the concentration of one reactant (Bohn, McNeal, and O'Connor
1985). The loss of a COPC by a first-order process depends only on the concentration of the COPC in the
soil, and a constant fraction of the COPC is removed from the soil over time. Those processes that
apparently exhibit first-order reaction kinetics without implying a mechanistic dependence on a first-order
loss rate are termed "apparent first-order" loss rates (Sparks 1989). The assumption that COPC loss
follows first-order reaction kinetics may be an oversimplification because—at various concentrations or
under various environmental conditions—the loss rates from soil systems will resemble different kinetic
expressions. However, at low concentrations, a first-order loss constant may be adequate to describe the
loss of the COPC from soil (U.S. EPA 1990a).
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COPC loss in soil can also follow zero or second-order reaction kinetics. Zero-order reaction kinetics are
independent of reactant concentrations (Bonn, McNeal, and O'Connor 1985). Zero-order loss rates
describe processes in which the reactants are present at very high concentrations. Under zero-order
kinetics, a constant amount of a COPC is lost from the soil over time, independent of its concentration.
Processes that follow second-order reaction kinetics depend on the concentrations of two reactants or the
concentration of one reactant squared (Bohn, McNeal, and O'Connor 1985). The loss constant of a COPC
following a second-order process can be contingent on its own concentration, or on both its concentration
and the concentration of another reactant, such as an enzyme or catalyst.
Because COPC loss from soil depends on many complex factors, it may be difficult to model the overall
rate of loss. In addition, because the physical phenomena that cause COPC loss can occur simultaneously,
the use of Equation 3-8 may also overestimate loss rates for each process (Valentine 1986). When
possible, the common occurrence of all loss processes should be taken into account.
The following subsections discuss issues associated with the calculation of the ksl, kse, ksr, ksg, and ksv
variables. Appendix B, Tables B-l-2 through B-l-6 present the equations for computing the overall and
individual soil loss constant, except for loss due to degradation, which is discussed below.
COPC Loss Constant Due to Biotic and Abiotic Degradation (ksg)
Soil losses resulting from biotic and abiotic degradation (ksg) are determined empirically from field studies
and should be addressed in the literature (U.S. EPA 1990a). Lyman et al. (1982) states that degradation
rates can be assumed to follow first order kinetics in a homogenous media. Therefore, the half-life of a
compound can be related to the degradation rate constant. Ideally, ksg is the sum of all biotic and abiotic
rate constants in the soil media. Therefore, if the half-life of a compound (for all of the mechanisms of
transformation) is known, the degradation rate can be calculated. However, literature sources do not
provide sufficient data for all such mechanisms, especially for soil. Therefore, Appendix A-2 presents U.S.
EPA OSW recommended values for this COPC specific variable.
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Recommended Values for:
COPC Loss Constant Due to Biotic and Abiotic Degradation (ksg)
See Appendix A-2
The rate of biological degradation in soils depends on the concentration and activity of the microbial
populations in the soil, the soil conditions, and the COPC concentration (Jury and Valentine 1986).
First-order loss rates often fail to account for the high variability of these variables in a single soil system.
However, the use of simple rate expressions may be appropriate at low chemical concentrations (e.g.,
nanogram per kilogram soil) at which a first-order dependence on chemical concentration may be
reasonable. The rate of biological degradation is COPC-specific, depending on the complexity of the
COPC and the usefulness of the COPC to the microorganisms. Some substrates, rather than being used by
the organisms as a nutrient or energy source, are simply degraded with other similar COPCs, which can be
further utilized. Environmental and COPC-specific factors that may limit the biodegradation of COPCs in
the soil environment (Valentine and Schnoor 1986) include:
(1) availability of the COPC,
(2) nutrient limitations,
(3) toxicity of the COPC, and
(4) inactivation or nonexistence of enzymes capable of degrading the COPC.
Chemical degradation of organic compounds can be a significant mechanism for removal of COPCs in soil
(U.S. EPA 1990a). Hydrolysis and oxidation-reduction reactions are the primary chemical transformation
processes occurring in the upper layers of soils (Valentine 1986). General rate expressions describing the
transformation of some COPCs by all non-biological processes are available, and these expressions are
helpful when division into component reactions is not possible.
Hydrolysis in aqueous systems is characterized by three processes: acid-catalyzed, base-catalyzed, and
neutral reactions. The overall rate of hydrolysis is the sum of the first-order rates of these processes
(Valentine 1986). In soil systems, sorption of the COPC can increase, decrease, or not affect the rate of
hydrolysis, as numerous studies cited in Valentine (1986) have shown. The total rate of hydrolysis in soil
can be predicted by adding the rates in the soil and water phases, which are assumed to be first-order
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reactions at a fixed pH (Valentine 1986). Methods for estimating these hydrolysis constants are described
byLymanetal. (1982).
Organic and inorganic compounds also undergo oxidation-reduction (redox) reactions in the soil (Valentine
1986). Organic redox reactions involve the exchange of oxygen and hydrogen atoms by the reacting
molecules. Inorganic redox reactions may involve the exchange of atoms or electrons by the reactants. In
soil systems where the identities of oxidant and reductant species are not specified, a first-order rate
constant can be obtained for describing loss by redox reactions (Valentine 1986). Redox reactions
involving metals may promote losses from surface soils by making metals more mobile (e.g., leaching to
subsurface soils).
COPC Loss Constant Due to Soil Erosion (kse)
U.S. EPA (1993h) recommended the use of Equation 3-8A to calculate the constant for soil loss resulting
from erosion (kse).
0.1 • X -SD -ER Kd -BD
where:
kse = COPC soil loss constant due to soil erosion
0.1 = Units conversion factor (1,000 g-kg/10,000 cm2-m2)
Xe = Unit soil loss (kg/m2-yr)
SD = Sediment delivery ratio (unitless)
ER = Soil enrichment ratio (unitless)
Kds = Soil-water partition coefficient (mL/g)
BD = Soil bulk density (g/cm3 soil)
Zs = Soil mixing zone depth (cm)
6SW = Soil volumetric water content (mL/cm3 soil)
Unit soil loss (Xe) is calculated by using the Universal Soil Loss Equation (USLE), as described in
Section 3.11.2. Variables associated with Equation 3-8A are further discussed in Appendix B,
Table B-1-3.
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U.S. EPA guidance (1994b and 19941) have stated that all kse values are equal to zero. U.S. EPA (19941)
stated that kse is equal to zero because of contaminated soil eroding onto and off of the site.
Consistent with earlier U.S. EPA guidance (1994b and 19941) and U.S. EPA (1998c), U.S. EPA OSW
recommends that the constant for the loss of soil resulting from erosion (kse) should be set equal to zero.
Recommended Value for:
COPC Loss Constant Due to Erosion (kse)
0
For additional information on addressing kse, U.S. EPA OSW recommends consulting the methodologies
described in U.S. EPA document, Methodology for Assessing Health Risks Associated with Multiple
Exposure Pathways to Combustor Emissions (U.S. EPA In Press). The use of kse values is also further
described in Appendix B, Table B-l-3.
COPC Loss Constant Due to Runoff (ksr)
Consistent with earlier U.S. EPA guidance (1993h; 19941) and U.S. EPA (1998c), U.S. EPA OSW
recommends that Equation 3-8B be used to calculate the constant for the loss of soil resulting from surface
runoff (far). The use of this equation is further described in Appendix B, Table B-l-4.
Recommended Equation for Calculating:
COPC Loss Constant Due to Runoff (ksr)
i
l+(Kd-BD/B
sw s s sw
where
ksr = COPC loss constant due to runoff (yr :)
RO = Average annual surface runoff from pervious areas (cm/yr)
9SW = Soil volumetric water content (mL/cm3 soil)
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Zs = Soil mixing zone depth (cm)
Kds = Soil-water partition coefficient (mL/g)
BD = Soil bulk density (g/cm3 soil)
COPC Loss Constant Due to Leaching (ksl)
Consistent with earlier U.S. EPA guidance (1993h and 19941) and U.S. EPA (1998c), U.S. EPA OSW
recommends that Equation 3-8C be used to calculate the COPC loss constant due to leaching (ksl). The
use of this equation is further described in Appendix B, Table B-l-5.
Recommended Equation for Calculating:
COPC Loss Constant Due to Leaching (ksl)
P+ I-RO-E
ksl -
where
ksl = COPC loss constant due to leaching (yr :)
P = Average annual precipitation (cm/yr)
/ = Average annual irrigation (cm/yr)
RO = Average annual surface runoff from pervious areas (cm/yr)
Ev = Average annual evapotranspiration (cm/yr)
9SV = Soil volumetric water content (mL/cm3 soil)
Zs = Soil mixing zone depth (cm)
Kds = Soil-water partition coefficient (mL/g)
BD = Soil bulk density (g/cm3 soil)
Appendix B, Table B-l-5 further describes the variables associated with Equation 3-8C. The average
annual volume of water (P +1 - RO - Ev) available to generate leachate is the mass balance of all water
inputs and outputs from the area under consideration.
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COPC Loss Constant Due to Volatilization (ksv)
Semi-volatile and volatile COPCs emitted in high concentrations may become adsorbed to soil particles and
exhibit volatilization losses from soil. The loss of a COPC from the soil by volatilization depends on the
rate of movement of the COPC to the soil surface, the chemical vapor concentration at the soil surface, and
the rate at which vapor is carried away by the atmosphere (Jury 1986).
Consistent with U.S. EPA (In Press), U.S. EPA OSW recommends that Equation 3-8D be used to
calculate the constant for the loss of soil resulting from volatilization (ksv). The soil loss constant due to
volatilization (ksv) is based on gas equilibrium coefficients and gas phase mass transfer. The first order
decay constant, ksv, is obtained by adapting the Hwang and Falco equation for soil vapor phase diffusion
(Hwang and Falco 1986). The use of this equation is further described in Appendix B, Table B-l-6.
Recommended Equation for Calculating:
COPC Loss Constant Due to Volatilization (ksv)
ksv =
3.1536 x W7-H
D
Z • Kd • R • T • BD I I Z
s s a I \ s
- 0
Equation 3-8D
where
ksv
3.1536 x 107
H
Kds
R
Ta
BD
Dn
COPC loss constant due to volatization (yr :
Units conversion factor (s/yr)
Henry's Law constant (atm-m3/mol)
Soil mixing zone depth (cm)
Soil-water partition coefficient (mL/g)
Universal gas constant (atm-m3/mol-K)
Ambient air temperature (K) = 298.1 K
Soil bulk density (g/cm3 soil)
Diffusivity of COPC in air (cm2/s)
Soil volumetric water content (mL/cm3 soil)
Solids particle density (g/cm3)
Appendix B, Table B-l-5 further describes the variables associated with Equation 3-8C. In cases where
high concentrations of volatile organic compounds are expected to be present in the soil, U.S. EPA OSW
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recommends consulting the methodologies described in U.S. EPA document, Methodology for Assessing
Health Risks Associated with Multiple Exposure Pathways to Combustor Emissions (U.S. EPA In Press).
The use of ksv values is also further described in Appendix B, Table B-l-6.
3.11.1.3
Deposition Term (Ds)
U.S. EPA OSW recommends that Equation 3-9 be used to calculate the deposition term (Ds). This
equation is further described in Appendix B, Table B-l-1. The use of Equation 3-11 to calculate the
deposition term is consistent with earlier U.S. EPA guidance (19941) and U.S. EPA (1998c), which both
incorporate a deposition term (Ds) into Equation 3-7 for the calculation of the COPC concentration in soil
(Cs) (see also Section 3.11.1.1).
Recommended Equation for Calculating:
Deposition Term (Ds)
Ds =
100 -Q
Zs-BD
• [F/0.31536 • Vdv • Cyv + Dywv) + (Dywp + Dydp) • (1 - Fv)] Equation 3-9
where
Ds
100
Q
zs
BD
Fv
0.31536
Vdv
Cyv
Dywv
Dydp
Dywp
Deposition term (mg COPC/kg soil-yr)
Units conversion factor (m2-mg/cm2-kg)
COPC-specific emission rate (g/s)
Soil mixing zone depth (cm)
Soil bulk density (g/cm3 soil)
Fraction of COPC air concentration in vapor phase (unitless)
Units conversion factor (m-g-s/cm-jWg-yr)
Dry deposition velocity (cm/s)
Unitized yearly average air concentration from vapor phase (//g-s/g-m3)
Unitized yearly average wet deposition from vapor phase (s/m2 year)
Unitized yearly average dry deposition from particle phase (s/m2 year)
Unitized yearly average wet deposition from particle phase (s/m year)
Section 3.9 further describes the ISCST3 unitized air parameters (Cyv, Dywv, Dydp, and Dywp) obtained
as output from the air dispersion modeling. Appendix B describes the determination of other variables
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associated with Equation 3-9. The proper use of this equation is also further described in Appendix B,
Table B-l-1.
3.11.1.4 Site-Specific Parameters for Calculating Soil Concentration
As discussed in the previous sections, calculating the COPC concentration in soil (Cs) requires some
site-specific parameter values, which must be calculated or derived from available literature or site-specific
data. These site-specific parameters include the following:
• Soil mixing zone depth (Zs)
Soil bulk density (BD)
Available water (P + I - RO - Ev)
• Soil volumetric water content (6>sw)
Determination of values for these parameters is further described in the following subsections, and in
Appendix B.
Soil Mixing Zone Depth (Zs)
When exposures to COPCs in soils are modeled, the depth of contamination is important in calculating the
appropriate soil concentration. Due to leaching and physical disturbance (e.g., tilling) COPCs may migrate
deeper in the soil in for some areas. Therefore, the value for the depth of soil contamination, or soil mixing
zone depth (ZJ, used in modeling ecological risk should be considered specific to tilled (e.g., large plowed
field) or unfilled soil areas.
In general, previous U.S. EPA combustion risk assessment guidance (1990a) has estimated that if the area
under consideration is tilled or mechanically disturbed, the soil mixing zone depth is about 10 to
20 centimeters depending on local conditions and the equipment used. If soil is not moved, COPCs are
assumed to be retained in the shallower, upper soil layer. In this case, earlier U.S. EPA guidances (U.S.
EPA 1990a; U.S. EPA 1993h) have typically recommended a value of 1 centimeter.
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Consistent with earlier U.S. EPA guidance (1990a) and U.S. EPA (1998c), U.S. EPA OSW recommends
the following values for the soil mixing zone depth (ZJ.
Recommended Values for:
Soil Mixing Zone Depth (Zs)
1 cm - untilled
20 cm - tilled
Soil Bulk Density (BD)
BD is the ratio of the mass of soil to its total volume. This variable is affected by the soil structure, type,
and moisture content (Hillel 1980). Consistent with U.S. EPA (1990a; 1994b) and information presented
in Hoffman and Baes (1979), U.S. EPA OSW recommends the following value for the soil dry bulk density
(BD).
Recommended Value for:
Soil Dry Bulk Density (BD)
1.50 g/cm3 soil
For determination of actual field values specific to a specified location at a site, U.S. EPA (19941)
recommended that wet soil bulk density be determined by weighing a thin-walled, tube soil sample (e.g., a
Shelby tube) of known volume and subtracting the tube weight (ASTM Method D2937). Moisture content
can then be calculated (ASTM Method 2216) to convert wet soil bulk density to dry soil bulk density.
Available Water (P + I-RO- Ev)
The average annual volume of water available (P +1 - RO - Ev) for generating leachate is the mass balance
of all water inputs and outputs from the area under consideration. A wide range of values for these
variables may apply in the various U.S. EPA regions.
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The average annual precipitation (/*), irrigation (7), runoff (RO), and evapotranspiration (Ev) rates and
other climatological data may be obtained from either data recorded on site or from the Station Climatic
Summary for a nearby airport.
Meteorological parameters—such as the evapotranspiration rate and the runoff rate—may also be found in
resources such as Geraghty, Miller, van der Leeden, and Troise (1973). Surface runoff may also be
estimated by using the curve number equation developed by the U.S. Soil Conservation Service (U.S. EPA
1990a). U.S. EPA (1985b) cites isopleths of mean annual cropland runoff corresponding to various curve
numbers developed by Stewart, Woolhiser, Wischmeier, Caro, and Frere (1975). Curve numbers are
assigned to an area on the basis of soil type, land use or cover, and the hydrologic condition of the soil
(U.S. EPA 1990a).
Using these different references may introduce uncertainties and limitations. For example, Geraghty, van
der Leeden, and Troise (1973) present isopleths for annual surface water contributions that include
interflow and ground water recharge; these values should be adjusted downward to reflect surface runoff
only. U.S. EPA (1994b) recommends that these values be reduced by 50 percent.
Soil Volumetric Water Content (0m)
The soil volumetric water content 0SW depends on the available water and the soil structure. A wide range
of values for these variables may apply in the various U.S. EPA regions. Consistent with earlier guidance
documents (U.S. EPA 1994b), U.S. EPA OSW recommends the following value for 6SW.
Recommended Value for:
Soil Volumetric Water Content (0m)
0.2 ml/cm3 soil
Additional information on soil water content is presented in Appendix B, specific to the equations in which
it is used.
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3.11.2 CALCULATION OF COPC CONCENTRATIONS IN SURFACE WATER AND
SEDIMENTS
COPC concentrations in surface water and sediments are calculated for all water bodies selected for
evaluation in the risk assessment. Mechanisms considered for determination of COPC loading of the water
column are:
(1) Direct deposition,
(2) Runoff from impervious surfaces within the watershed,
(3) Runoff from pervious surfaces within the watershed,
(4) Soil erosion over the total watershed,
(5) Direct diffusion of vapor phase COPCs into the surface water, and
(6) Internal transformation of compounds chemically or biologically.
Other potential mechanisms may require consideration on a case-by-case basis (e.g., tidal influences),
however, contributions from other potential mechanisms are assumed to be negligible in comparison with
those being evaluated.
The USLE and a sediment delivery ratio are used to estimate the rate of soil erosion from the watershed.
To evaluate the COPC loading to a water body from its associated watershed, the COPC concentration in
watershed soils should be calculated. As described in Section 3.11.1, the equation for COPC concentration
in soil includes a loss term that considers the loss of contaminants from the soil after deposition. These loss
mechanisms may all lower the soil concentration associated with a specific deposition rate.
Surface water concentration algorithms include a sediment mass balance, in which the amount of sediment
assumed to be buried and lost from the water body is equal to the difference between the amount of soil
introduced to the water body by erosion and the amount of suspended solids lost in downstream flow. As a
result, the assumptions are made that sediments do not accumulate in the water body over time, and an
equilibrium is maintained between the surficial layer of sediments and the water column. The total water
column COPC concentration is the sum of the COPC concentration dissolved in water and the COPC
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concentration associated with suspended solids. Partitioning between water and sediment varies with the
COPC. The total concentration of each COPC is partitioned between the sediment and the water column.
Rmoffto
Soil Elation
(Sediments) tetide
Deposition
VUatiiztion
Direct 1
Deposition 1
Rnofffrani 1
Impervious 1
Surfaces 1
Runoff
Pervii
Surfa
~^^"
from 1
>us 1
ces 1
Soil
Bofiion
fepor
Tiavfer
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Chapter 3: Air Dispersion and Deposition Modeling August 1999
3.11.2.1 Total COPC Loading to a Water Body (LT)
Consistent with earlier U.S. EPA guidance (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends
the use of Equation 3-10 to calculate the total COPC load to a water body (LT). This equation is also
further described in Appendix B, Table B-2-1.
Recommended Equation for Calculating:
Total COPC Load to the Water Body (LT)
LT = LDEp + Ld^ + Lm + LR + LE + Lj Equation 3-10
where
LT = Total COPC load to the water body (including deposition, runoff, and
erosion) (g/yr)
LDEP = Total (wet and dry) particle phase and wet vapor phase COPC direct
deposition load to water body (g/yr)
Ldif = Vapor phase COPC diffusion (dry deposition) load to water body (g/yr)
Lm = Runoff load from impervious surfaces (g/yr)
LR = Runoff load from pervious surfaces (g/yr)
LE = Soil erosion load (g/yr)
Lj = Internal transfer (g/yr)
Due to the limited data and uncertainty associated with the chemical or biological internal transfer, Lh of
compounds into daughter products, U.S. EPA OSW recommends a default value for this variable of zero.
However, if a permitting authority determines that site-specific conditions indicate calculation of internal
transfer should be considered, U.S. EPA OSW recommends following the methodologies described in U.S.
EPA NCEA document, Methodology for Assessing Health Risks Associated with Multiple Exposure
Pathways to Combustor Emissions (U.S EPA In Press). Calculation of each of the remaining variables
(LDEP, Ldif, Lm, LR, and LE) is discussed in the following subsections.
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Total (Wet and Dry) Particle Phase and Wet Vapor Phase Contaminant Direct Deposition Load to
Water Body (LDEP)
Consistent with U.S. EPA (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends Equation 3-11 to
calculate the load to the water body from the direct deposition of wet and dry particles and wet vapors onto
the surface of the water body (LDEP). The equation is also further described in Appendix B, Table B-2-2.
Recommended Equation for Calculating:
Total Particle Phase and Wet Vapor Phase Direct Deposition Load to Water Body (LDEP)
LDEP = Q'\-FV' Dywwv + (1 - FV ) • Dytwp }-Aw Equation 3-11
where
LDEP = Total (wet and dry) particle phase and wet vapor phase COPC direct
deposition load to water body (g/yr)
Q = COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Dywwv = Unitized yearly (water body and watershed) average wet deposition from
vapor phase (s/m2-yr)
Dytwp = Unitized yearly (water body and watershed) average total (wet and dry)
deposition from vapor phase (s/m2-yr)
Aw = Water body surface area (m2)
Section 3.9 describes the unitized air parameters, Dywwv and Dywwv, obtained as output from the ISCST3
air dispersion modeling. The determination of water body surface area, Aw, is described in Chapter 4.
Appendix A-2 describes determination of the compound-specific parameter, Fv.
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Diffusion Load to Water Body (Laf)
Consistent with earlier U.S. EPA guidance (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends
using Equation 3-12 to calculate the dry vapor phase COPC diffusion load to the water body (Ldif). The
equation is described in detail in Appendix B, Table B-2-3.
Recommended Equation for Calculating:
Vapor Phase COPC Diffusion (Dry Deposition) Load to Water Body (LDif)
dlf
H Equation 3-12
where
Ldif = Vapor phase COPC diffusion (dry deposition) load to water body (g/yr)
Kv = Overall COPC transfer rate coefficient (m/yr)
<2 = COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Cywv = Unitized yearly (water body and watershed) average air concentration
from vapor phase (yWg-s/g-m3)
Aw = Water body surface area (m2)
10~6 = Units conversion factor (g/jWg)
H = Henry's Law constant (atm-mVmol)
R = Universal gas constant (atm-m3/mol-K)
Twk = Water body temperature (K)
The overall COPC transfer rate coefficient (Kv) is calculated by using the equation in Appendix B,
Table B-2-13. Consistent with previous U.S. EPA guidance (19941; 1993h) and U.S. EPA (1998c), U.S.
EPA OSW recommends a water body temperature (Twk) of 298 K (or 25 °C). Section 3.9 describes the
determination of the modeled air parameter, Cywv. The determination of water body surface area, Aw, is
described in Chapter 4. Appendix A-2 describes determination of compound-specific parameters, Fv and
H.
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Runoff Load from Impervious Surfaces (Lm)
In some watershed soils, a fraction of the wet and dry deposition in the watershed will be to impervious
surfaces. Dry deposition may accumulate and be washed off during rain events. Consistent with U.S. EPA
(19941) and U.S. EPA (1998c), U.S. EPA OSW recommends the use of Equation 3-13 to calculate
impervious runoff load to a water body (Z,^). The equation is also presented in Appendix B, Table B-2-4.
Recommended Equation for Calculating:
Runoff Load from Impervious Surfaces (Lm)
Lm = Q ' [ Fv ' Dywwv + (1.0 - Fv ) • Dytwp ] • A} Equation 3-13
where
Lm = Runoff load from impervious surfaces (g/yr)
Q = COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Dywwv = Unitized yearly (water body and watershed) average wet deposition from
vapor phase (s/m2-yr)
Dytwp = Unitized yearly (water body and watershed) average total (wet and dry)
deposition from vapor phase (s/m2-yr)
Aj = Impervious watershed area receiving COPC deposition (m2)
Impervious watershed area receiving COPC deposition (A}) is the portion of the total effective watershed
area that is impervious to rainfall (i.e., roofs, driveways, streets, and parking lots) and drains to the water
body.
Runoff Load from Pervious Surfaces (LR)
Consistent with U.S. EPA (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends the use of
Equation 3-14 to calculate the runoff dissolved COPC load to the water body from pervious soil surfaces in
the watershed (LR). The equation is also presented in Appendix B, Table B-2-5.
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Recommended Equation for Calculating:
Runoff Load from Pervious Surfaces (LR)
LR = RO • (AL - Af) • Q ' 0'01 Equation 3-14
sw
where
LR = Runoff load from pervious surfaces (g/yr)
RO = Average annual surface runoff from pervious areas (cm/yr)
AL = Total watershed area receiving COPC deposition (m2)
Aj = Impervious watershed area receiving COPC deposition (m2)
Cs = COPC concentration in soil (in watershed soils) (mg COPC/kg soil)
BD = Soil bulk density (g soil/cm3 soil)
Osw = Soil volumetric water content (mL water/cm3 soil)
Kds = Soil-water partition coefficient (cm3 water/g soil)
0.01 = Units conversion factor (kg-cm2/mg-m2)
Appendix B describes the determination of site-specific parameters, RO, AL, Ah BD, and 0SW. The
calculation of the COPC concentration in soil (Cs) is discussed in Section 3.11.1 and Appendix B. Soil
bulk density (BD) and soil water content (Osw) are described in Section 3.11.1.4.
Soil Erosion Load (LE)
Consistent with U.S. EPA (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends the use of
Equation 3-15 to calculate soil erosion load (LE). The equation is also presented in Appendix B,
Table B-2-6.
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Recommended Equation for Calculating:
Soil Erosion Load (LE)
Cs • Kd • BD
LE = Xe • (AL - A,) -SD-ER- * • 0.001 Equation 3-15
'
"sw ~s
where
LE = Soil erosion load (g/yr)
Xe = Unit soil loss (kg/m2-yr)
AL = Total watershed area (evaluated) receiving COPC deposition (m2)
Aj = Impervious watershed area receiving COPC deposition (m2)
SD = Sediment delivery ratio (watershed) (unitless)
ER = Soil enrichment ratio (unitless)
Cs = COPC concentration in soil (in watershed soils) (mg COPC/kg soil)
BD = Soil bulk density (g soil/cm3 soil)
Osw = Soil volumetric water content (mL water/cm3 soil)
Kds = Soil-water partition coefficient (mL water/g soil)
0.001 = Units conversion factor (k-cm2/mg-m2)
Unit soil loss (Xe) and watershed sediment delivery ratio (SD) are calculated as described in the following
subsections and in Appendix B. COPC concentration in soil (Cs) is described in Section 3.11.1 and
Appendix B, Table B-l-1. Soil bulk density (BD) and soil water content (6^) are described in
Section 3.11.1.4.
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Universal Soil Loss Equation - USLE
U.S. EPA OSW recommends that the universal soil loss equation (USLE), Equation 3-16, be used to
calculate the unit soil loss (XJ specific to each watershed. This equation is further described in
Appendix B, Table B-2-7. Appendix B also describes determination of the site- and watershed-specific
values for each of the variables associated with Equation 3-16. The use of Equation 3-16 is consistent with
U.S. EPA (1994b; 19941) and U.S. EPA (1998c).
Recommended Equation for Calculating:
Unit Soil Loss (Xe)
Equation3-16
where
Xe = Unit soil loss (kg/m2-yr)
RF = USLE rainfall (or erosivity) factor (yr :)
K = USLE credibility factor (ton/acre)
LS = USLE length-slope factor (unitless)
C = USLE cover management factor (unitless)
PF = USLE supporting practice factor (unitless)
907.18 = Units conversion factor (kg/ton)
4047 = Units conversion factor (m2/acre)
The USLE RF variable, which represents the influence of precipitation on erosion, is derived from data on
the frequency and intensity of storms. This value is typically derived on a storm-by-storm basis, but
average annual values have been compiled (U.S. Department of Agriculture 1982). Information on
determining site-specific values for variables used in calculating^ is provided in U.S. Department of
Agriculture (U.S. Department of Agriculture 1997) and U.S. EPA guidance (U.S. EPA 1985b).
Refer to Appendix B, Table B-2-7 for additional discussion of the USLE.
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Sediment Delivery Ratio (SD)
U.S. EPA OSW recommends the use of Equation 3-17 to calculate sediment delivery ratio (SD). The use
of this equation is further described in Appendix B, Table B-2-8.
Recommended Equation for Calculating:
Sediment Delivery Ratio (SD)
SD = a • (AL yb Equation 3-17
where
SD = Sediment delivery ratio (watershed) (unitless)
a = Empirical intercept coefficient (unitless)
b = Empirical slope coefficient (unitless)
AL = Total watershed area (evaluated) receiving COPC deposition (m2)
The sediment delivery ratio (SD) for a large land area, a watershed or part of a watershed, can be
calculated, on the basis of the area of the watershed, by using an approach proposed by Vanoni (1975).
Accordingly, U.S. EPA (1993h) recommended the use of Equation 3-17 to calculate the sediment delivery
ratio.
According to Vanoni (1975), sediment delivery ratios vary approximately with the -0.125 power of the
drainage area. Therefore, the empirical slope coefficient is assumed to be equal to 0.125. An inspection of
the data presented by Vanoni (1975) indicates that the empirical intercept coefficient varies with the size of
the watershed, as illustrated in Appendix B, Table B-2-8.
AL is the total watershed surface area affected by deposition that drains to the body of water. A watershed
includes all of the land area that contributes water to a water body. In assigning values to the watershed
surface area affected by deposition, consideration should be given to (1) the distance from the stack, (2) the
location of the area affected by deposition fallout with respect to the water body, and (3) in the absence of
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any deposition considerations, watershed hydrology. Total sediment in a water body may have originated
from watershed soils that are (or have the potential to be) both affected and unaffected by deposition of
combustion emissions. If a combustor is depositing principally on a land area that feeds a tributary of a
larger river system, consideration must be given to an "effective" area. An effective drainage area will
almost always be less than the watershed.
3.11.2.2 Total Water Body COPC Concentration (Cwtot)
U.S. EPA OSW recommends the use of Equation 3-18 to calculate total water body COPC concentration
(Cwtot). The total water body concentration includes both the water column and the bed sediment. The
equation is also presented in Appendix B, Table B-2-9.
Recommended Equation for Calculating:
Total Water Body COPC Concentration (Cwtot)
-- Equa"on 3'1
where
Cwtot = Total water body COPC concentration (including water column and bed
sediment) (g COPC/m3 water body)
LT = Total COPC load to the water body (including deposition, runoff, and
erosion) (g/yr)
Vfx = Average volumetric flow rate through water body (m3/yr)
fwc = Fraction of total water body COPC concentration in the water column
(unitless)
kwt = Overall total water body COPC dissipation rate constant (yr :)
Aw = Water body surface area (m2)
dwc = Depth of water column (m)
dbs = Depth of upper benthic sediment layer (m)
The total COPC load to the water body (LT)—including deposition, runoff, and erosion—is described in
Section 3.11.2.1 and Appendix B, Table B-2-1. The depth of the upper benthic layer (dbs), which
represents the portion of the bed that is in equilibrium with the water column, cannot be precisely specified;
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however, U.S. EPA (1993h) recommended values ranging from 0.01 to 0.05. Consistent with U.S. EPA
(19941; 1998c), U.S. EPA OSW recommends a default value of 0.03, which represents the midpoint of the
specified range. Issues related to the remaining parameters are summarized in the following subsections.
Fraction of Total Water Body COPC Concentration in the Water Column (fwc) and Benthic Sediment
Consistent with U.S. EPA (1998c), U.S. EPA OSW recommends using Equation 3-19 to calculate fraction
of total water body COPC concentration in the water column (fwc), and Equation 3-20 to calculate fraction
of total water body contaminant concentration in benthic sediment (fbs). The equations are also presented i
Appendix B, Table B-2-10.
in
Recommended Equation for Calculating:
Fraction of Total Water Body COPC Concentration in
the Water Column (fwc) and Benthic Sediment (fbs)
(l+Kdsw-TSS-lxW-6)-dwc/dz
Jwc ~ Equation 3-19
(1 +Kdsw-TSS-1* 1(T6) • djdz + (Qbs+Kdbs-BS) • djdz
fbs ~ 1 fwc Equation 3-20
where
fwc = Fraction of total water body COPC concentration in the water column
(unitless)
fbs = Fraction of total water body COPC concentration in benthic sediment
(unitless)
Kdsw = Suspended sediments/surface water partition coefficient (L water/kg
suspended sediment)
TSS = Total suspended solids concentration (mg/L)
1 x 10"6 = Units conversion factor (kg/mg)
dz = Total water body depth (m)
9bs = Bed sediment porosity (Lwater/Lsediment)
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Kdbs = Bed sediment/sediment pore water partition coefficient (L water/kg bottom
sediment)
BS = Benthic solids concentration (g/cm3 [equivalent to kg/L])
dwc = Depth of water column (m)
dbs = Depth of upper benthic sediment layer (m)
U.S. EPA (1993h) and NC DEHNR (1997) recommended the use of Equations 3-19 and 3-20 to calculate
the fraction of total water body concentration occurring in the water column (fwc) and the bed sediments
(fbs). The partition coefficient Kdsw describes the partitioning of a contaminant between sorbing material,
such as soil, surface water, suspended solids, and bed sediments (see Appendix A-2). NC DEHNR (1997)
also recommended adding the depth of the water column to the depth of the upper benthic layer (dwc + dbs)
to calculate the total water body depth (dz).
NC DEHNR (1997) recommended a default total suspended solids (TSS) concentration of 10 mg/L, which
was adapted from U.S. EPA (1993h). However, due to variability in water body specific values for this
variable, U.S. EPA OSW recommends the use of water body-specific measured revalues representative
of long-term average annual values for the water body of concern. Average annual values for TSS are
generally expected to be in the range of 2 to 300 mg/L; with additional information on anticipated TSS
values available in the U.S. EPA NCEA document, Methodology for Assessing Health Risks Associated
with Multiple Exposure Pathways to Combustor Emissions (U.S. EPA In Press).
If measured data is not available, or of unacceptable quality, a calculated TSS value can be obtained for
non-flowing water bodies using Equation 3-21.
_ Xe • (AL-A^ • SD • 1x103
TSS = Equation 3-21
VfS Dss • Aw
where
TSS = Total suspended solids concentration (mg/L)
Xe = Unit soil loss (kg/m2-yr)
AL = Total watershed area (evaluated) receiving COPC deposition (m2)
Aj = Impervious watershed area receiving COPC deposition (m2)
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SD = Sediment delivery ratio (watershed) (unitless)
Vfx = Average volumetric flow rate through water body (value should be 0 for
quiescent lakes or ponds) (mVyr)
Dss = Suspended solids deposition rate (a default value of 1,825 for quiescent
lakes or ponds) (m/yr)
Aw = Water body surface area (m2)
The default value of 1,825 m/yr provided for Dss is characteristic of Stake's settling velocity for an
intermediate (fine to medium) silt.
Also, to evaluate the appropriateness of watershed-specific values (specific for non-flowing water bodies)
used in calculating the unit soil loss (Xe), as described in Section 3.11.2.1 and Appendix B, the water-body
specific measured TSS value should be compared to the calculated TSS value obtained using Equation 3-21.
If the measured and calculated revalues differ significantly, parameter values used in calculating Xe
should be re-evaluated. This re-evaluation of TSS ar\dXe should also be conducted if the calculated TSS
value is outside of the normal range expected for average annual measured values, as discussed above.
Bed sediment porosity (0fa) can be calculated from the benthic solids concentration by using the following
equation (U.S. EPA 1993h):
h --
Ps
where
9bs = Bed sediment porosity (Lwater/Lsediment)
ps = Bed sediment density (kg/L)
BS = Benthic solids concentration (kg/L)
U.S. EPA OSW recommends the following default value for bed sediment porosity (Obs), which was
adapted from U.S. EPA (1993h) and U.S. EPA (1998c):
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Recommended Value for:
Bed Sediment Porosity (0bs)
"bs ~ 0.6 Lwater/Lsedlment
(assumingps= 2.65 kg/L [bed sediment density] and BS= 1 kg/L [benthic solids concentration])
Values for the benthic solids concentration (BS) and depth of upper benthic sediment layer (dbs) range from
0.5 to 1.5 kg/L and 0.01 to 0.05 meters, respectively. However, consistent with earlier U.S. EPA guidance
(1993h; 19941) and U.S. EPA (1998c), 1 kg/L is a reasonable default for most applications of the benthic
solids concentration (BS), and 0.03 meter is the default depth of the upper benthic layer (dbs). The default
depth of 0.03 meters is based on the midpoint of the range presented above. The use of this equation is
further described in Appendix B, Table B-2-10.
Overall Total Water Body COPC Dissipation Rate Constant (kwt)
Consistent with U.S. EPA (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends the use of
Equation 3-22 to calculate the overall dissipation rate of COPCs in surface water, resulting from
volatilization and benthic burial. The equation is also presented in Appendix B, Table B-2-11.
Recommended Equation for Calculating:
Overall Total Water Body COPC Dissipation Rate Constant (kwt)
kwt = f™ • kv + fbs • kb Equation 3-22
where
kwt = Overall total water body dissipation rate constant (yr :)
fwc = Fraction of total water body COPC concentration in the water column
(unitless)
kv = Water column volatilization rate constant (yr :)
fbs = Fraction of total water body COPC concentration in benthic sediment
(unitless)
kb = Benthic burial rate constant (yr :)
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The variables/^, and^ are discussed in the previous section, Equations 3-19 and 3-20, and calculated by
using the equations presented in Appendix B, Table B-2-10.
Water Column Volatilization Rate Constant (kv)
Consistent with U.S. EPA (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends using
Equation 3-23 to calculate water column volatilization rate constant. The equation is also presented in
Appendix B, Table B-2-12.
Recommended Equation for Calculating:
Water Column Volatilization Rate Constant (kv)
,
K ~
dz-(\+Kdsw-
Equation 3-23
4
where
kv = Water column volatilization rate constant (yr :)
Kv = Overall COPC transfer rate coefficient (m/yr)
dz = Total water body depth (m)
Kdsw = Suspended sediments/surface water partition coefficient (L water/kg
suspended sediments)
TSS = Total suspended solids concentration (mg/L)
1 x 10~6 = Units conversion factor (kg/mg)
Total water body depth (dz), suspended sediment and surface water partition coefficient (Kdsw), and total
suspended solids concentration (TSS), are previously described in this section. Kdsw is discussed in
Appendix A-2. The overall transfer rate coefficient (Kv) is described in the following subsection.
Overall COPC Transfer Rate Coefficient (Kv)
Volatile organic chemicals can move between the water column and the overlying air. The overall transfer
rate Kv, or conductivity, is determined by a two-layer resistance model that assumes that two "stagnant
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films" are bounded on either side by well-mixed compartments. Concentration differences serve as the
driving force for the water layer diffusion. Pressure differences drive the diffusion for the air layer. From
balance considerations, the same mass must pass through both films; the two resistances thereby combine
in series, so that the conductivity is the reciprocal of the total resistance.
Consistent with U.S. EPA (1993h) and U.S. EPA (1998c), U.S. EPA OSW recommends the use of
Equation 3-24 to calculate the overall transfer rate coefficient (Kv). The equation is also presented in
Appendix B, Table B-2-13.
Recommended Equation for Calculating:
Overall COPC Transfer Rate Coefficient (Kv)
H
7?
R
T -
Equation 3-24
where
Kv
KL
KG
H
R
Twk
6
Overall COPC transfer rate coefficient (m/yr)
Liquid phase transfer coefficient (m/yr)
Gas phase transfer coefficient (m/yr)
Henry's Law constant (atm-m3/mol)
Universal gas constant (atm-m3/mol-K)
Water body temperature (K)
Temperature correction factor (unitless)
The value of the conductivity Kv depends on the intensity of turbulence in the water body and the overlying
atmosphere. As Henry's Law constant increases, the conductivity tends to be increasingly
influenced by the intensity of turbulence in water. Conversely, as Henry's Law constant decreases, the
value of the conductivity tends to be increasingly influenced by the intensity of atmospheric turbulence.
The liquid and gas phase transfer coefficients, KL and KG, respectively, vary with the type of water body.
The liquid phase transfer coefficient (KL) is calculated by using Equations 3-25 and 3-26. The gas phase
transfer coefficient (KG) is calculated by using Equations 3-27 and 3-28.
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Henry's Law constants generally increase with increasing vapor pressure of a COPC and generally
decrease with increasing solubility of a COPC. Henry's Law constants are compound-specific and are
presented in Appendix A-2. The universal ideal gas constant, R, is 8.205 x 10~5 atm-m3/mol-K, at 20°C.
The temperature correction factor (6), which is equal to 1.026, is used to adjust for the actual water
temperature. Volatilization is assumed to occur much less readily in lakes and reservoirs than in moving
water bodies.
Liquid Phase Transfer Coefficient (KL)
Consistent with U.S. EPA (1998c), U.S. EPA OSW recommends using Equations 3-25 and 3-26 to
calculate liquid phase transfer coefficient. (KL). The use of these equations is further described in
Appendix B, Table B-2-14.
Recommended Equation for Calculating:
Liquid Phase Transfer Coefficient (KL)
For flowing streams or rivers:
KT =
\
(1 x io-4).£> -u
3.1536xl07
Equation 3-25
For quiescent lakes or ponds:
KL = (C°d5 • W)
3-1536xl°7
Equation 3-26
where
KL
Dw
u
1 x
dz
Cd
W
10"
Liquid phase transfer coefficient (m/yr)
Diffusivity of COPC in water (cm2/s)
Current velocity (m/s)
Units conversion factor (m2/cm2)
Total water body depth (m)
Drag coefficient (unitless)
Average annual wind speed (m/s)
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pa = Density of air (g/cm3)
pw = Density of water (g/cm3)
k = von Karman's constant (unitless)
2.z = Dimensionless viscous sublayer thickness (unitless)
/uw = Viscosity of water corresponding to water temperature (g/cm-s)
3.1536x107 = Units conversion factor (s/yr)
For a flowing stream or river, the transfer coefficients are controlled by flow-induced turbulence. For these
systems, the liquid phase transfer coefficient is calculated by using Equation 3-25, which is the O'Connor
and Dobbins (1958) formula, as presented in U.S. EPA (1993h).
For a stagnant system (quiescent lake or pond), the transfer coefficient is controlled by wind-induced
turbulence. For quiescent lakes or ponds, the liquid phase transfer coefficient can be calculated by using
Equation 3-26 (O'Connor 1983; U.S. EPA 1993h).
The total water body depth (dz) for liquid phase transfer coefficients is discussed previously in this section.
Consistent with U.S. EPA (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends the use of the
following default values. These values are further described in Appendix A-2:
(1) a diffusivity of chemical in water ranging (Dw) from 1.0xl05to8.5xlO~2 Cm2/s5
(2) a dimensionless viscous sublayer thickness (1,) of 4,
(3) a von Karman's constant (k) of 0.4,
(4) a drag coefficient (Q) of 0.0011 which was adapted from U.S. EPA (1993h),
(5) a density of air (pa) of 0.0012 g/cm3 at standard conditions (temperature = 20°C or 293 K,
pressure = 1 atm or 760 millimeters of mercury) (Weast 1986),
(6) a density of water (pw) of 1 g/cm3 (Weast 1986),
(7) a viscosity of water (//„,) of a 0.0169 g/cm-s corresponding to water temperature
(Weast 1986).
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Gas Phase Transfer Coefficient (KG)
U.S. EPA OSW recommends using Equations 3-27 and 3-28 to calculate gas phase transfer coefficient
(Ka). The equation is also discussed in Appendix B, Table B-2-15.
Recommended Equation for Calculating:
Gas Phase Transfer Coefficient (KG)
For flowing streams or rivers:
KG = 36500 m/yr
Equation 3-27
For quiescent lakes or ponds:
KG =
Equation 3-28
where
KG
Cd
W
k
pa
Da
3.1536 x 107
Gas phase transfer coefficient (m/yr)
Drag coefficient (unitless)
Average annual wind speed (m/s)
von Karman's constant (unitless)
Dimensionless viscous sublayer thickness (unitless)
Viscosity of air corresponding to air temperature (g/cm-s)
Density of air corresponding to water temperature (g/cm3)
Diffusivity of COPC in air (cm2/s)
Units conversion factor (s/yr)
U.S. EPA (1993h) indicated that the rate of transfer of a COPC from the gas phase for a flowing stream or
river is assumed to be constant, in accordance with O'Connor and Dobbins (1958) (Equation 3-27).
For a stagnant system (quiescent lake or pond), the transfer coefficients are controlled by wind-induced
turbulence. For quiescent lakes or ponds, U.S. EPA OSW recommends that the gas phase transfer
coefficient be computed by using the equation presented in O'Connor (1983) (Equation 3-28).
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Consistent with U.S. EPA (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends 1.81 x 10'4 g/cm-s
for the viscosity of air corresponding to air temperature.
Benthic Burial Rate Constant (kb)
U.S. EPA OSW recommends using Equation 3-29 to calculate benthic burial rate (kb). The equation is also
discussed in Appendix B, Table B-2-16.
Recommended Equation for Calculating:
Benthic Burial Rate Constant (kb)
If
Kb
' Xe • AL • SD • 1 x 103 - Vfx • TSS
Aw • TSS
\ w
(WO . 1 y 1 A -6 1
-ZOO 1 A L\J T-, ,. « ~r.
1 Fnintmn ^ 'Q
BS'dhs }
where
SD
Vfx
TSS
Aw
BS
dbs
1 x 1(T6
IxlO3
Benthic burial rate constant (yr :)
Unit soil loss (kg/m2-yr)
Total watershed area (evaluated) receiving deposition (m2)
Sediment delivery ratio (watershed) (unitless)
Average volumetric flow rate through water body (mVyr)
Total suspended solids concentration (mg/L)
Water body surface area (m2)
Benthic solids concentration (g/cm3)
Depth of upper benthic sediment layer (m)
= Units conversion factor (kg/mg)
Units conversion factor (g/kg)
The benthic burial rate constant (kb), which is calculated in Equation 3-29, can also be expressed in terms
of the rate of burial (Wb):
Wb=kb- dbs
Equation 3-30
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where
Wb = Rate of burial (m/yr)
kb = Benthic burial rate constant (yr :)
dbs = Depth of upper benthic sediment layer (m)
According to U.S. EPA (19941) and U.S. EPA (1998c), COPC loss from the water column resulting from
burial in benthic sediment can be calculated by using Equation 3-29. These guidance documents also
recommend a benthic solids concentration (BS) value ranging from 0.5 to 1.5 kg/L, which was adapted
from U.S. EPA (1993h). U.S. EPA OSW recommends the following default value for benthic solids
concentration (BS).
Recommended Default Value for:
Benthic Solids Concentration (BS)
1.0 kg/L
The calculated value for kb should range from 0 to 1.0; with low kb values expected for water bodies
characteristic of no or limited sedimentation (rivers and fast flowing streams), and kb values closer to 1.0
expected for water bodies characteristic of higher sedimentation (lakes). This range of values is based on
the relation between the benthic burial rate and rate of burial expressed in Equation 3-30; with the depth of
upper benthic sediment layer held constant. For kb values calculated as a negative (water bodies with high
average annual volumetric flow rates in comparison to watershed area evaluated), a kb value of 0 should be
assigned for use in calculating the total water body COPC concentration (Cwtot) in Equation 3-18. If the
calculated kb value exceeds 1.0, re-evaluation of the parameter values used in calculating^ should be
conducted.
3.11.2.3 Total COPC Concentration in Water Column (Cwctot)
U.S. EPA OSW recommends using Equation 3-31 to calculate total COPC concentration in water column
(Cwctot)- The equation is also discussed in Appendix B, Table B-2-17.
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Recommended Equation for Calculating:
Total COPC Concentration in Water Column (Cwctot)
_ , r wc bs
wctot - Jwc ' *-wtot' —-, Equation 3-31
where
Cwctot = Total COPC concentration in water column (mg COPC/L water column)
fwc = Fraction of total water body COPC concentration in the water column
(unitless)
Cwtot = Total water body COPC concentration, including water column and bed
sediment (mg COPC/L water body)
dwc = Depth of water column (m)
dbs = Depth of upper benthic sediment layer (m)
The use of Equation 3-3 1 to calculate total COPC concentration in water column is consistent with U.S.
EPA (19941; 1998c).
Total water body COPC concentration — including water column and bed sediment (Cwtot) and fraction of
total water body COPC concentration in the water column (fwc) — should be calculated by using
Equation 3-18 and Equation 3-19, respectively. Depth of upper benthic sediment layer (dbs) is discussed
previously.
Dissolved Phase Water Concentration
U.S. EPA OSW recommends the use of Equation 3-32 to calculate the concentration of COPC dissolved in
the water column (Cdw). The equation is discussed in detail in Appendix B, Table B-2-18.
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Recommended Equation for Calculating
Dissolved Phase Water Concentration
C
WCtOt
1 + Kd • TSS • 1 x 1(T6
Equation 3-32
4
where
Cdw = Dissolved phase water concentration (mg COPC/L water)
Cwctot = Total COPC concentration in water column (mg COPC/L water column)
Kdsw = Suspended sediments/surface water partition coefficient (L water/kg
suspended sediment)
TSS = Total suspended solids concentration (mg/L)
1 x 10~6 = Units conversion factor (kg/mg)
The use of Equation 3-32 to calculate the concentration of COPC dissolved in the water column is
consistent with U.S. EPA (19941; 1998c).
The total COPC concentration in water column (Cwctot) is calculated by using the Equation 3-31 (see also
Appendix B, Table B-2-17). The surface water partition coefficient (Kdsw) and total suspended solids
concentration (TSS) are discussed previously.
COPC Concentration in Bed Sediment (Csed)
U.S. EPA OSW recommends the use of Equation 3-33 to calculate COPC concentration in bed sediment
(Csed). The equation is also presented in Appendix B, Table B-2-19.
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Recommended Equation for Calculating:
COPC Concentration in Bed Sediment (Csed)
r r bs we bs
~ Jbs' Lwtot' n , VJ _ D0 ' -, Equation 3-33
where
Csed = COPC concentration in bed sediment (mg COPC/kg sediment)
fbs = Fraction of total water body COPC concentration in benthic sediment
(unitless)
Cwtot = Total water body COPC concentration, including water column and bed
sediment (mg COPC/L water body)
Kdbs = Bed sediment/sediment pore water partition coefficient (L COPC/kg water
body)
Obs = Bed sediment porosity (Lpore water/Lsediment)
BS = Benthic solids concentration (g/cm3)
dwc = Depth of water column (m)
dbs = Depth of upper benthic sediment layer (m)
The use of Equation 3-33 to calculate the COPC concentration in bed sediment is consistent with U.S. EPA
(19941; 1998c).
The total water body COPC concentration—including water column and bed sediment (Cwtot) and the
fraction of total water body COPC concentration that occurs in the benthic sediment (fbs)—is calculated by
using Equations 3-18 and 3-20, respectively. The bed sediment and sediment pore water partition
coefficient (Kdbs) is discussed in Appendix A-2. Bed sediment porosity (dbs), benthic solids concentration
(BS), depth of water column (dwc), and depth of upper benthic layer (dbs) are discussed previously.
3.11.3 CALCULATION OF COPC CONCENTRATIONS IN PLANTS
The concentration of COPCs in plants is assumed to occur by three possible mechanisms:
• Direct deposition of particles—wet and dry deposition of particle phase COPCs
onto the exposed plant surfaces.
• Vapor transfer—uptake of vapor phase COPCs by plants through their foliage.
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• Root uptake—root uptake of COPCs available from the soil and their transfer to
the aboveground portions of the plant.
Deposition
of Particles
Vapor
Transfer
COPC Concentatlon In Plants
Figure 3-6 COPC Concentration in Plants
Root Uptake
from Soil
The total COPC concentration in terrestrial plants, CTP is calculated as a sum of contamination occurring
through all three of these mechanisms.
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3.11.3.1 Calculating Plant Concentration Due to Direct Deposition (Pd)
Consitent with previous U.S. EPA guidance (19941) and U.S. EPA (1998c), U.S. EPA OSW recommends
the use of Equation 3-34 to calculate COPC concentration in plants due to direct deposition. The use of
this equation is further described in Appendix B, Table B-3-1.
Recommended Equation for Calculating:
Plant Concentration Due to Direct Deposition (Pd)
Equation 3-34
Yp-kp
where
Pd = Plant concentration due to direct (wet and dry) deposition (mg COPC/kg
WW)
1,000 = Units conversion factor (mg/g)
Q = COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Dydp = Unitized yearly average dry deposition from particle phase (s/m2-yr)
Fw = Fraction of COPC wet deposition that adheres to plant surfaces (unitless)
Dywp = Unitized yearly wet deposition from particle phase (s/m2-yr)
Rp = Interception fraction of the edible portion of plant (unitless)
kp = Plant surface loss coefficient (yr :)
Tp = Length of plant exposure to deposition per harvest of the edible portion of
the /th plant group (yr)
012 = Dry weight to wet weight conversion factor (unitless)
Yp = Yield or standing crop biomass of the edible portion of the plant
(productivity) (kg DW/m2)
Section 3.9 describes the use of the unitized air parameters, Dydp and Dywp, obtained as output from the
air dispersion modeling. Appendix A-3 describes determination of Fv. Appendix B describes
determination of Fw, Rp, kp, Tp, and Yp. The dry weight to wet weight conversion factor of 0.12 is based
on the average rounded value from the range of 80 to 95 percent water content in herbaceous plants and
nonwoody plant parts (Taiz at al. 1991).
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3.11.3.2 Calculating Plant Concentration Due to Air-to-Plant Transfer (Pv)
Consistent with U.S. EPA (1998c), U.S. EPA OSW recommends the use of Equation 3-35 to calculate the
plant concentration due to air-to-plant transfer (Pv). The use of this equation is further described in
Appendix B, Table B-3-2.
Recommended Equation for Calculating:
Plant Concentration Due to Air-to-Plant Transfer (Pv)
r> /-> T- A 11 Cyv • Bv
Pv = Q • Fv- 0.12 • -^ Equation 3-35
Pa
where
Pv = Plant concentration due to air-to-plant transfer (mg COPC/kg WW)
Q = COPC emission rate (g/s)
Fv = Fraction of COPC air concentration in vapor phase (unitless)
Cyv = Unitized yearly average air concentration from vapor phase ((ig-s/g-m3)
Bv = Air-to-plant biotransfer factor ([mg COPC/g DW plant]/[mg COPC/g
air]) (unitless)
012 = Dry weight to wet weight conversion factor (unitless)
pa = Density of air (g/m3)
Section 3.9 describes the use of the unitized air parameter, Cyv. Appendix A-3 describes determination of
the COPC-specific parameters, Fv and Bv. The dry weight to wet weight conversion factor of 0.12 is
based on the average rounded value from the range of 80 to 95 percent water content in herbaceous plants
and nonwoody plant parts (Taiz at al. 1991). Appendix B further describes use of Equation 3-35,
including determination of Fw and pa.
3.11.3.3 Calculating Plant Concentration Due to Root Uptake (Pr)
Consistent with previous U.S. EPA guidance (1994g; 19941; 1995h) and U.S. EPA (1998c), U.S. EPA
OSW recommends the use of Equation 3-36 to calculate the plant concentration due to root uptake (Pr).
The use of this equation is further described in Appendix B, Table B-3-3.
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Recommended Equation for Calculating:
Plant Concentration Due to Root Uptake (Pr)
Pr = Cs • BCFr • 0.12 Equation 3-36
where
Pr = Plant concentration due to root uptake (mg COPC/kg WW)
BCFr = Plant-soil biotransfer factor (unitless)
Cs = COPC concentration in soil (mg COPC/kg soil)
012 = Dry weight to wet weight conversion factor (unitless)
Equation 3-36 is based on the soil-to-aboveground plant transfer approach developed by Travis and Arms
(1988). The dry weight to wet weight conversion factor of 0.12 is based on the average rounded value
from the range of 80 to 95 percent water content in herbaceous plants and nonwoody plant parts (Taiz at
al. 1991). Appendix A-3 describes determination of the COPC-specific parameter BCFr. Section 3.11.1
and Appendix B describe calculation of Cs.
3.12 REPLACING DEFAULT PARAMETER VALUES
As discussed in Chapter 1, default parameter values are provided in this guidance for numerous inputs to
the fate and transport modeling. After completing a risk assessment based on the default parameter values
recommended in this guidance, risk assessors may choose to investigate replacing default parameter values
with measured or published values if a more representative estimate of site-specific risk can be obtained.
Use of parameter values other than those specified in this guidance should always be clearly described in
the risk assessment report and work plan, and approved by the permitting authority. U.S. EPA OSW
recommends that requests to change default parameter values include the following information, as
appropriate:
1. An explanation of why the use of a measured or published value other than the default
value is warranted (e.g., the default parameter value is based on data or studies at sites in
the northwestern U.S., but the facility is located in the southeast);
2. The supporting technical basis of the replacement parameter value, including readable
copies (printed in English) of any relevant technical literature or studies;
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3. The basis of the default parameter value, as understood by the requestor, including
readable copies (printed in English) of the referenced literature or studies (if available);
4. A comparison of the weight-of-evidence between the competing studies (e.g., the proposed
replacement parameter value is based on a study that is more representative of site
conditions, a specific exposure setting being evaluated, or a more scientifically valid study
than the default parameter value, the proposed replacement parameter is based on the
analysis of 15 samples as opposed to 5 for the default parameter value, or the site-specific
study used more stringent quality control/quality assurance procedures than the study upon
which the default parameter value is based);
5. A description of other risk assessments or projects where the proposed replacement
parameter value has been used, and how such risk assessments or projects are similar to
the risk assessment in consideration.
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Chapter 4
Problem Formulation
What's Covered in
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Identifying Measures of Effect
Problem formulation establishes the exposure setting used as the basis for exposure analysis and risk
characterization. Problem formulation includes (1) characterization of the exposure setting for
identification of potentially exposed habitats in the assessment area (Section 4.1); (2) development of food
webs representative of the habitats to be evaluated (Section 4.2); (3) selection of assessment endpoints
relevant to food web structure and function (Section 4.3); and (4) identification of measurement receptors
(Section 4.4).
4.1
EXPOSURE SETTING CHARACTERIZATION
Exposure setting characterization is important in the identification of habitats consisting of ecological
receptors in the assessment area that may be impacted as a result of exposure to compounds emitted from a
facility. Ecological receptors within a potentially impacted habitat can be evaluated through consideration
of the combination of exposure pathways to which ecological receptors representing a habitat-specific food
web may be exposed to a compound. The habitats identified to be evaluated are selected based on existing
habitats surrounding the facility (see Section 4.1.1); and also support which habitat-specific food webs are
evaluated in risk characterization. Consideration of ecological receptors representative of the habitats also
provides the basis for selecting measurement receptors, as well as, it supports demonstration of the
presence or absence of federal and state species of special interest (see Section 4.1.1.3).
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Exposure setting characterization is generally focused geographically to the assessment area that is defined
as the area surrounding the facility that is impacted from facility emissions as predicted by ISCST3 air
dispersion modeling; with additional consideration typically extending by a 50-km radius, taken from the
centroid of a polygon (also used as the origin of ISCST3 receptor grid node array, see Chapter 3) identified
by the UTM coordinates of the facility's emission sources. A 50-km radius is generally the recognized
limit of the ISCST3 air dispersion model and its predecessors (U.S. EPA 1990a; 1995c). Resources for
characterizing the exposure setting should focus on the areas impacted from emissions as predicted by air
dispersion modeling. As discussed in Section 4.1.1, habitats (potentially including water bodies and their
associated watersheds)—both within and outside the facility property boundary—should be considered for
evaluation.
The following subsections provide information on selection of habitats, and identification of ecological
receptors representative of those habitats, to be considered for evaluation in the risk assessment.
4.1.1 Selection of Habitats
Habitats to be considered in the risk assessment are selected by identifying similar habitats surrounding the
facility that are potentially impacted by facility emissions, and when overlayed with the air dispersion
modeling results, define which habitat-specific food webs should be evaluated in the risk assessment.
Habitats can be defined based on their biotic and abiotic characteristics, and are generally divided into two
major groups (i.e., terrestrial and aquatic) that can be classified as follows:
Terrestrial
Forest
Shortgrass praire
Tallgrass praire
Agricultural/Cropland
Scrub/Shrub
Desert
Aquatic
Freshwater
Brackish/Intermediate
Marine
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Habitat types can typically be identified by reviewing hard copy and/or electronic versions of land use land
classification (LULC) maps, topographic maps, and aerial photographs. Sources and general information
associated with each of these data types or maps are presented below. Also, as noted in Chapter 3, the
UTM coordinate system format (NAD27 or NAD83) for all mapping information should be verified to
ensure consistency and prevent erroneous georeferencing of locations and areas.
Land Use Land Cover (LULC) Maps - LULC maps can be downloaded directly from the USGS
web site (http://mapping.usgs.gov./index.html), at a scale of 1:250,000 in a file type GIRAS
format. LULC maps can also be downloaded from the EPA web site (ftp://ftp.epa.gov/pub), at a
scale of 1:250,000, in an Arc/Info export format. These maps provide detailed habitat information
based upon the classification system and definitions of Level II Land Use and Land Cover
information. Exact boundaries of polygon land use area coverages, in areas being considered for
evaluation, should be verified using available topographic maps and aerial photographic coverages.
Topographic Maps - Topographic maps are readily available in both hard copy and electronic
format directly from USGS or numerous other vendors. These maps are commonly at a scale of
1:24,000, and in a file type of TIFF format with TIFF World File included for georeferencing.
Aerial Photographs - Hard copy aerial photographs can be purchased directly form USGS in a
variety of scales and coverages. Electronic format aerial photographs of Digital Ortho Quarter
Quads (DOQQs) can also be purchased directly form USGS, or from an increasing number of
commercial sources. Properly georeferenced DOQQs covering a 3-km or more radius of the
assessment area, overlays of the LULC map coverage, and the ISCST3 modeled receptor grid node
array provide an excellent reference for identifying land use areas and justifying selection of
exposure locations.
While these data types or maps do not represent the universe of information available on habitats or land
use, they are readily available from a number of governmental sources (typically accessible via the
Internet), usually can be obtained free or for a low cost, and, when used together, provide sufficient
information to reliably identify and define boundaries of habitats to be considered for evaluation in risk
characterization. However, while the use of these or other data can be very accurate, verifying identified
habitats by conducting a site visit is recommended. Also, these data sources may be dated, and may not
reflect current habitat boundaries or land use (i.e., expanded cropland or urban developments, new lakes).
Additional information useful for habitat identification can be obtained from discussions with
representatives of private and government organizations which routinely collect and evaluate ecosystem or
habitat information including the following: (1) Soil Conservation Service, (2) U.S. Fish and Wildlife
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Service (FWS), (3) U.S Department of Agriculture, (4) state natural resource, wildlife, and park agencies,
and (5) local government agencies.
U.S. EPA OSW recommends that habitats identified during exposure setting characterization and selected
for evaluation in the risk assessment be clearly mapped and include the following supporting information:
• Facility boundaries
• Facility emission source location(s)
• Habitat types and boundaries
• Water bodies and their asssociated watersheds
• Special ecological areas (see Section 4.1.1.2)
A facility location map, including land-use and land cover data, which allows for identification of habitats
to support selection of habitat-specific food webs to be evaluated in the risk assessment. The map should
also note the UTM coordinate system format (NAD27 or NAD83) for all information presented to ensure
consistency and prevent erroneous georeferencing of locations and areas; including accurate identification
of exposure scenario locations and water bodies within the habitat to be evaluated, as discussed in the
following subsections.
4.1.1.1 Selection of Exposure Scenario Locations Within Terrestrial Habitats
Exposure scenario locations to be evaluated within the terrestrial habitats identified during the exposure
setting characterization, are selected at specific receptor grid nodes based on evaluation of the magnitude of
air parameter values estimated by ISCST3 (see Chapter 3). U.S. EPA OSW would like to note that the
methodology and resulting selection of receptor grid nodes as exposure scenario locations is one of the most
critical parts of the risk assessment process, ensuring standardization across all facilities evaluated and
reproducibility of results. The estimates of risk can vary significantly in direct response to the receptor grid
nodes that are selected as exposure scenario locations because the grid node-specific ISCST3 modeled air
parameter values are used as inputs into the media equations.
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U.S. EPA OSW recommends that, at a minimum, the procedures described below be used in the selection
of receptor grid nodes as exposure scenario locations; and that the selected exposure scenario locations
correspond to actual ISCST3 modeled receptor grid node locations defined by UTM coordinates. In
addition to consistency and reproducibility, these procedures ensure that the exposure scenario location(s)
selected for evaluation over a specified habitat do not overlook the most highly impacted locations.
Exposure scenario locations, at actual receptor grid nodes, should be selected as follows:
Step 1: Define Terrestrial Habitats To Be Evaluated - All habitats, identified during exposure
setting characterization for evaluation in the risk assessment, should be defined and habitat
boundaries mapped in a format (NAD 27 or NAD 83 UTM) consistent with that used to define
locations of facility emission sources and modeld ISCST3 receptor grid nodes.
Step 2: Identify Receptor Grid Node(s) Within Each Habitat To Be Evaluated - For each
habitat to be evaluated, identify the receptor grid nodes within that area or on the boundary of that
area (defined in Step 1) that represent the locations of highest yearly average concentration for
each ISCST3 modeled air parameter (i.e., air concentration, dry deposition, wet deposition) for
each phase (i.e., vapor, particle, particle-bound). This determination should be performed for each
emission source (i.e., stacks, fugitives) and all emissions sources at the facility combined. This
results in the selection of one or more receptor grid nodes as exposure scenario locations, within a
defined habitat area to be evaluated, and that meet one or more of the following criteria:
• Highest modeled unitized vapor phase air concentration
• Highest modeled unitized vapor phase wet deposition rate
• Highest modeled unitized particle phase air concentration
• Highest modeled unitized particle phase wet deposition rate
• Highest modeled unitized particle phase dry deposition rate
• Highest modeled unitized particle-bound phase air concentration
• Highest modeled unitized particle-bound phase wet deposition rate
• Highest modeled unitized particle-bound phase dry deposition rate
Only ISCST3 modeled air parameters corresponding to a single receptor grid node should be used per
exposure scenario location as inputs into the media equations, without averaging or statistical
manipulation. However, based generally on the number and location of facility emission sources, multiple
exposure scenario locations may be selected to represent the highest potential impact area for a specific
habitat being evaluated.
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Modeling of the above air parameter criteria for habitats at actual sites being evaluated in U.S. EPA
Region 6, using actual modeled air parameters, indicates that only 1 to 3 receptor nodes are typically
selected per habitat. This is because, in most cases, the location of some of the highest air concentration
and deposition rate, within a habitat for several of the modeled air parameters, occurs at the same receptor
grid node. The number of receptor grid nodes with maximum air parameters depends on many factors,
including number of and distance between emissions sources, habitat size and shape, distance and direction
from facility, topographic features, and meteorological patterns. It should also be noted, that while these
criteria minimize overlooking maximum risk within a habitat, they do not preclude the risk assessor from
selecting additional exposure scenario locations within that same habitat based on site-specific risk
considerations.
Also, a water body and associated watershed in close proximity to the exposure scenario location being
evaluated should be identified to represent a drinking water source for applicable receptors (see
Appendix F). Although the locations and type of sources (i.e., free water, consumption of water as part of
food items) of water ingested by an animal through diet are expected to vary depending on the receptor and
availability, COPC intake by the receptor through ingestion of water can be estimated by assuming only
water intake from a defined water body for which a COPC concentration can be calculated. Therefore, a
representative water body should be defined and evaluated following the guidance provided in
Section 4.1.1.2, and a COPC concentration in the water column, Cwctot, calculated as described in Chapter 3
and Appendix B.
If a definable water body is not located within or in close proximity to the terrestrial habitat being
evaluated, receptor drinking water intake terms in the exposure equations presented in Appendix F should
be adjusted accordingly (i.e., ingestion of drinking water set equal to zero). However, for sites where the
permitting authority or risk manager identifies that receptor exposure through ingestion of drinking water
may be significant, an available option is to assume that a small water body exists at the same receptor grid
node as the exposure scenario location being evaluated. If multiple exposure scenario locations within the
habitat are being evaluated, a single assumed water body can be located at the closest receptor grid node
located equal distance from each of the exposure scenario locations being evaluated, and utilized as a
drinking water source for evaluation of each exposure scenario location within the habitat. Since the
assumed water body represents a pool or other drinking source too small for identification on an aerial
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photograph or map, it can be assumed to have a unit volume (i.e., surface area of 1 meter square, water
column depth of 1 meter). The assumed water body should not have flow or an associated watershed.
4.1.1.2 Selection of Habitat Exposure Scenrario Locations Within Aquatic Habitats
Exposure scenario locations to be evaluated within the aquatic habitats identified during the exposure
setting characterization may first require differentiating water bodies from land areas within aquatic
habitiats not typically covered by water (e.g., flood plains or wetland areas transitioning to terrestrial and
upland habitats). Exposure scenario locations within land areas of aquatic habitats not characteristic of a
standing water body are selected following the same steps as for terrestrial habitats (see Section 4.1.1.1).
However, exposure scenario locations for defined water bodies within aquatic habitats should be selected
following the guidance provided in this section. The associated watershed contributing COPC loading to
the water body being evaluated should also be defined.
U.S. EPA OSW recommends that, at a minimum, the following procedures be used in the selection of
exposure scenario locations within defined water body areas of aquatic habitats as follows:
Step 1: Define Aquatic Habitats To Be Evaluated - All habitats, identified during exposure
setting characterization for evaluation in the risk assessment, should be defined and habitat
boundaries mapped in a format (NAD 27 or NAD 83 UTM) consistent with that used to define
locations of facility emission sources and modeled ISCST3 receptor grid nodes. Water body
boundaries should reflect annual average shoreline elevations. The area extent of watersheds
associated with water bodies to be evaluated should also be defined.
Step 2: Identify Receptor Grid Node(s) Within Each Habitat To Be Evaluated - For each water
body and associated watershed to be evaluated, the receptor grid nodes within that area and on the
boundary of that area (defined in Step 1) should be considered. For water bodies, the risk assessor
can select the receptor grid node that represent the locations of highest yearly average
concentration for each ISCST3 modeled air parameter (i.e., air concentration, dry deposition, wet
deposition) for each phase (i.e., vapor, particle, particle-bound), or average the air parameter
values for all receptor grid nodes within the area of the water body. This determination should be
performed for each emission source (i.e., stacks, fugitives), and all emissions sources at the facility
combined. For watersheds, the modeled air parameter values should be averaged for all receptor
grid nodes within the area extent or effective area of the watershed (excluding the area of the water
body).
For evaluating the COPC loading to the water body from its associated watershed, the area extent of the
watershed should be defined and the ISCST3 modeled air parameter values at each receptor grid node
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within the watershed area (excluding the water body) averaged. These averaged air parameter values are
then used in the estimating media equations presented in Chapter 3 and Appendix B for calculating the
COPC loading to the water body.
For water bodies identified as potentially impacted from emission sources and selected for evaluation, the
area extent of the associated watershed that contributes water to the water body should also be identified
and defined by UTM coordinates. The area extent of a watershed is generally defined by topographic highs
that result in downslope drainage into the water body. The watershed can be important to determining the
overall water body COPC loading, because pervious and impervious areas of the watershed, as well as the
soil concentration of COPCs resulting from emissions from facility sources, are also used in the media
concentration equations to calculate the water body COPC concentrations resulting from watershed runoff
(see Chapter 3 and Appendix B). The total watershed area that contributes water to the water body can be
very extensive relative to the area that is impacted from facility emissions. Therefore, it is important that
the area extent of all watersheds to be evaluated should be approved by the permitting authority, to ensure
that the watershed and its contribution to the water body is defined appropriately in consideration of the
aquatic habitat being evaluated and subsequent estimated risk.
For example, if facility emissions impact principally a land area that feeds a specific tributary that drains to
a large swamp system and immediately upstream of the ISCST3 receptor grid nodes identified as exposure
scenario locations for the aquatic habitat defined by the swamp, the risk assessor should consider
evaluating an "effective" watershed area rather than the entire watershed area of the large swamp system.
For such a large swamp system, the watershed area can be on the order of thousands of square kilometers
and can include numerous tributaries draining into the swamp at points that would have no net impact on
the water body COPC concentration at the exposure point(s) of interest.
Similar to large watersheds, water bodies may also be extensive in size relative to the area that is impacted
from facility emissions and exposure point(s) of interest. In such cases, the risk assessor should consider
defining and evaluating an "effective" area of the water body that focuses the assessment specific to areas
potentially impacted and of most concern when considering potential for exposure. Therefore, as with
watersheds, it is important that the area extent of all water bodies to be evaluated should be approved by
the permitting authority, to ensure that potential impacts and exposure are appropriately considered.
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The recommended ISCST3 modeled receptor grid node array extends out about 10 km from facility
emission sources (see Chapter 3). To address evaluation of habitat areas, water bodies, or watersheds
located beyond the coverage provided by the recommended receptor grid node array (greater than 10 km
from the facility), the ISCST3 modeling can be conducted with an additional receptor grid node array
specified to provide coverage of the area of concern, or the steps above can be executed using the closest
receptor grid nodes from the recommended array. However, using the closest receptor grid nodes from the
recommended receptor grid node array will in most cases provide a conservative estimate of risk since the
magnitude of air parameter values at these receptor grid nodes would most likely be higher than at receptor
grid nodes located further from the facility sources and actually within the area of concern.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT
Identification and/or mapping of habitats, water bodies, and associated watersheds potentially
impacted by facility emissions of COPCs, including surface area of the water body and area
extent of the contributing watershed defined by UTM coordinates
Rational for selection or exclusion from evaluation, habitats within the assessment area
Description of rational and assumptions made to limit the watershed area to an "effective" area
Copies of all maps, photographs, or figures used to define characteristics of habitats, water
bodies, and watersheds
4.1.1.3 Special Ecological Areas
A special ecological area is a habitat that could require protection or special consideration on a site-specific
basis because (1) unique and/or rare ecological receptors and natural resources are present, or
(2) legislatively-conferred protection (e.g., a national monument) has been established. All potentially
exposed special ecological areas in the assessment area should be identified for consideration. The
following are examples of special ecological habitats (U.S. EPA 1997c):
Marine Sanctuaries
National river reaches
Spawning areas critical for maintenance offish/shellfish species
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Terrestrial areas utilized for breeding by large or dense aggregations of animals
Migratory pathways and feeding areas critical for maintenance of anadromous fish species
National Preserves
Federal lands designated for protection of natural ecosystems
National or State Wildlife Refuges
Critical areas identified under the Clean Lakes Program
Habitats known to be used by Federal or State designated or proposed endangered or
threatened species
Areas identified under the Coastal Zone Management Act
Sensitive areas identified under the National Estuary Program or Near Coastal Waters
Program
Designated Federal Wilderness Areas
State lands designated for wildlife or game management
Federal- or State-designated Scenic or Wild Rivers, or Natural Areas
Wetlands
RECOMMENDED INFORMATION FOR RISK ASSESSMENT
Identification and mapping of habitats in the assessment area, information on which the
identification is based, and information on any special ecological areas. Maps, photographs, or
additional sources used to determine habitats and define boundaries should be referenced. Maps
and figures should also note the UTM coordinate system format (NAD27 or NAD83) for all
information presented to ensure consistency and prevent erroneous georeferencing of locations
and areas.
4.1.2 Identification of Ecological Receptors
Identification of ecological receptors during exposure setting characterization is used to define food webs
specific to potentially impacted habitats to be evaluated in the risk assessment. Ecological receptors for
each habitat potentially impacted should be identified to ensure (1) plant and animal communities
representative of the habitat are represented by the habitat-specific food web, and (2) potentially complete
exposure pathways are identified. Examples of sources and general information available for identification
of site-specific ecological receptors are presented below:
Government Organizations - U.S. Fish and Wildlife Service (National Wetland Inventory Maps -
http://nwi.fws.gov) and State Natural Heritage Programs (see Appendix H) provide maps or lists
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of species based on geographic location, and are very helpful in identifying threatened or
endangered species or areas of special concern.
General Literature (field guides) - Examples of information describing the flora and fauna of
North America and useful in the development of habitat-specific food webs (see Section 4.2)
include the following: Wharton 1982; Craig et al. 1987; Baker et al. 1991; Carr 1994; Ehrlich et
al. 1988; National Geographic Society (1987, 1992); Whitaker 1995; Burt and Grossenheider
1980; Behler 1995; Smith and Brodie 1982; Tyning 1990; and Farrand Jr. 1989.
Private or Local Organizations - Additional private or professional organizations that are
examples of sources of information include: National Audubon Society, National Geographic
Society, Local Wildlife Clubs, State and National Parks Systems, and Universities.
Ecological receptor identification should include species both known and expected to be present in a
specific habitat being evaluated, and include resident and migratory populations. Identification of flora
should be based on major taxonomic groups represented in the assessment area. Natural history
information may also be useful during food web development in assigning individual receptors to specific
habitats and guilds based on feeding behavior (as discussed in Section 4.2.).
4.2 FOOD WEB DEVELOPMENT
Information obtained during exposure setting characterization should be used to develop one or more
habitat-specific food web(s) that represent communities and guilds of receptors potentially exposed to
emissions from facility sources. Food webs are interlocking patterns of food chains, which are the straight-
line transfer of energy from a food source (e.g., plants) to a series of organisms feeding on the source or on
other organisms feeding on the food source (Odum 1971). While energy and, therefore, transfer of a
compound in a food chain, is not always linear, it is assumed in this guidance that energy and, thus,
compounds, are always transferred to a higher trophic level. The importance of a food chain as an
exposure pathway primarily depends on receptor dietary habits, the receptors in the food chain, and other
factors including bioavailability and depuration of the compound evaluated.
Habitat-specific food webs are developed for use in the risk assessment to:
Define direct and indirect exposure pathways
Formulate assessment endpoints
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Develop mathematical relationships between guilds
Perform quantitative exposure analysis for ecological receptors
Food webs can be developed using the "community approach" (Cohen 1978), which includes
(1) identification of potential receptors in a given habitat (see Section 4.1.2) for grouping into feeding
guilds by class and communities (see Section 4.2.1), (2) organizing food web structure by trophic level
(e.g., primary producer, secondary consumer; see Section 4.2.2), and (3) defining dietary relationships
between guilds and communities (see Section 4.2.3). The result is a complete food web for a defined
habitat, which should be developed for each habitat in the assessment area to be evaluated in risk
characterization. An example of food web development is presented in Section 4.2.4.
4.2.1 Grouping Receptors into Feeding Guilds and Communities
The first step in developing a habitat-specific food web is to identify, based on the dietary habits and
feeding strategies of receptors compiled in Section 4.1.2, the major feeding guilds for birds, mammals,
reptiles, amphibians, and fish. A guild is a group of species that occupies a particular trophic level and
shares similar feeding strategies. Invertebrates and plants are not assigned to guilds, rather these receptors
are grouped into their respective community by the environmental media they inhabit. The distinction for
grouping upper-trophic-level receptors into class-specific guilds, and invertebrates and plants into
communities, is made because the risk to these groups is characterized differently (see Chapter 5).
Once the major feeding guilds are identified (e.g., herbivore, omnivore, carnivore, insectivore), receptors
should be grouped by class (e.g., mammals, birds, amphibians and reptiles, and fish). As noted,
invertebrates and plants are grouped into their respective community by the media they inhabit (i.e, soil
invertebrates, terrestrial vegetation, sediment fauna, water column invertebrates, phytoplankton, and rooted
aquatic vegetation).
4.2.2 Organizing Food Web Structure By Trophic Level
The structure of a food web should be organized by trophic level. A trophic level is one of the successive
levels of nourishment in a food web or food chain. The first trophic level (TL1) contains the primary
producers or the green plants. Members of this trophic level produce their own food from nutrients,
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sunlight, carbon dioxide, and water. These primary producers are also the source of food for members of
the second trophic level (TL2). The second trophic level is often refered to as the primary consumers and is
composed of animals that eat plants (herbivores) and animals that subsist on detritus (decaying organic
matter) found in sediment and soil (detritivores). The third trophic level (TL3), contains both omnivores
and carnivores. Omnivores are animals that eat both plant and animal matter, while carnivores eat
primarily animal matter. The fourth trophic level (TL4), contains only carnivores and is sometimes refered
to as the dominant carnivores. TL4 contains animals at the top of the food chain (e.g., raptorial birds).
Some species can occupy more than one trophic level at a time depending on life stage. For this reason,
professional judgement should be used to categorize receptors without making the food web unduly
complex.
4.2.3 Defining Dietary Relationships Between Guilds and Communities
After species have been grouped into the appropriate guilds and communities, and organized by trophic
level, dietary relationships between guilds and communities should be defined. Guilds and communities
should be linked together based on dietary relationships by evaluating the dietary composition of the
receptors for each guild and community. Although most organisms have a complex diet, it should be
assumed that the majority of their diet is composed of a limited number of prey items and, therefore, a
limited number of feeding guild interactions occur. Therefore, U.S. EPA OSW recommends that generally
only those interactions that contribute more than five percent of the total diet should be considered for
development of a food web. This recommendation of five percent of the total diet as a general cutoff is
based on the assumption that the food web can be simplified without underestimating exposure.
RECOMMENDED INFORMATION FOR RISK ASSESSMENT
Habitat-specific food web(s) that include identification of (1) media (e.g., soil, sediment, water),
(2) trophic levels that include at a minimium producers (TL 1), primary consumers (TL 2),
secondary consumers (TL 3), and carnivores (TL 4), (3) guilds divided into classes (e.g.,
herbivorous mammals, omnivorous birds) and communities, and (4) major dietary interactions.
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4.2.4 Example Habitat-Specific Food Webs
To better illustrate food web development as discussed in the previous sections (see Sections 4.2.1 through
4.2.3), seven habitat-specific example food webs are presented as Figures 4-1 through 4-7. The habitats
represented include:
Forest
Tallgrass prairie
Shortgrass prairie
Shrub/Scrub
Freshwater/Wetland
Salt marsh
• Brackish/Intermediate marsh
The terrestrial and aquatic example food webs are based on information describing the flora and fauna of
North America (U.S. FWS 1979; Wharton 1982; Craig et al. 1987; Baker et al. 1991). Supplemental
information was collected from field guides and U.S. EPA's Wildlife Exposure Factors Handbook (Carr
1994; Ehrlich et al. 1988; National Geographic Society 1987; U.S. EPA 1993o; Whitaker 1995; Burt and
Grossenheider 1980; Behler 1995; Smith and Brodie 1982; Tyning 1990; National Geographic Society
1992;FarrandJr. 1989).
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Carnivorous Mammals
Long-tailed weasel, Coyote,
Red fox, Gray fox, Marten, Fisher
Carnivorous Birds
Red-tailed hawk,
Great horned owl, Coopers hawk,
Barn owl
Carnivorous Reptiles
Eastern yellowbellied race; Eastern
coral snake, Texas rat snake,
Western diamondback rattlesnake
Omnivorous Mammals
Short-tailed shrew, Opossum,
Southeastern shrew, Vagrant shrew,
Pacific shrew, Ornate shrew, Dwarf
shrew, Smoky shrew
NOTE: PATHWAYS NOT REPRESENTED
MATHEMATICALLY IN EQUATIONS
RECEPTORS LISTED IN ITALICS
ARE MEASUREMENT RECEPTORS
Omnivorous Amphibians /
Reptiles
Ornate box turtle; Marbled salamander, Slendei
glass lizard, Rough earth snake, Hunters
spadefbot toad
Omnivorous Birds
American Robin, Carolina wren,
Red cockaded woodpecker,
Yellow warbler
Invertebrates
Nematods, Arachnids,
Gastropods,
Oligochaetes, Arthropods
Herbivorous Birds
Mourning dove,
Chipping sparrow
Terrestrial Plants
Loblolly pine, Dwarf palmetto,
Southern bayberry, Yellowstar
thistle, Bluegrama, Forbes
Soil
Nutrients, Detritus
Herbivorous Mammals
Deer mouse, Pika, Eastern
cottontail, Townsend's chipmunk
Gray squirrel, Red squirrel,
Woodland vole, Porcupine, Elk
FIGURE 4-1
EXAMPLE
FOREST FOOD WEB
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Carnivorous Mammals
Long-tailed weasel, Swift fox,
Coyote, Badger, Spotted skunk
Carnivorous Birds
American kestrel, Golden eagle,
Coopers hawk, Prairie hawk,
Ferruginous hawk, Swainsons hawk
Carnivorous Reptiles
Eastern yellowbelly racer, Great plains
ratsnake, Bullsnake,
Western diamondback rattlesnake
Omnivorous Mammals
Least shrew, Pygmy shrew,
Townsend's mole, Eastern mole,
Idaho ground squirrel
NOTE: PATHWAYS NOT REPRESENTED
MATHEMATICALLY IN EQUATIONS
RECEPTORS LISTED IN ITALICS
ARE MEASUREMENT RECEPTORS
Omnivorous Amphibians /
Reptiles
Ornate box turtle, Texas toad, Eastern hognose
snake, Plains blind snake, Texas spotted
whiptail, Short-lined skink, Six-lined racerunne
Omnivorous Birds
Western meadowlark, Scissor-tailed
flycatcher, Sandhill crane, Dickcissel
Greater prairie chicken
Herbivorous Birds
Mooring dove
Chipping sparrow,
Canada, goose
Invertebrates
Nematodes, Gastropods,
Oligochaetes, Arthropods
Terrestrial Plants
Big bluestem, Switchgrass, Little
bluestem, Johnson grass, Indian
Soil
Nutrients, Detritus
Herbivorous Mammals
Deer Mouse, Eastern cottontail,
White-tailed jackrabbitt, Plains
harvest mouse, Black-tailed
woodchuck, Plains pocket mouse
Meadow vole, Gopher
FIGURE 4-2
EXAMPLE
TALLGRASS PRAIRIE FOOD WEB
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Carnivorous Mammals
Swift Fa* Coyote, Red fox,
Badger, Spotted skunk, Bobcat
Carnivorous Birds
American kestrel, Burrowing owl,
White-tailed hawk, Coopers hawk,
Ferruginous hawk, Swainsons hawk
Carnivorous Reptiles
Eastern yellowbelly racei; Great plains
ratsnake, Bullsnake,
Western diamondback rattlesnake
Omnivorous
Least shrew, Pygmy shrew,
Townsend's mole, Eastern mole,
Thirteen-lined ground squirrel,
Hispid pocket mouse, Striped skunl:
Omnivorous Amphibians /
Reptiles
Ornate box turtle, Texas toad, Eastern hognoae
snake, Plains blind snake, Texas spotted
whiptail, Short-lined skink, Six-lined racerunnet
Omnivorous Birds
Northern bobwhite, Lesser prairie chicken,
Lesser golden plover, Mountain plover,
American pipit
Invertebrates
Arachnids, Gastropods,
Oligochaetes, Arthropod
Herbivorous Birds
Mourning Dove, Canada
goose, Chipping Sparrow
Terrestrial Plants
Blue grama,Hairy grama, Broom
weed, Purple three-awn, Mesquite,
Side-oats grama, Yucca, Buffalo
grass, Alkali sacaton, Little bluesteir
Soil
Nutrients, Detritus
Herbivorous Mammals
Deer mouse, Eastern Cottontail,
White-tailed jackrabbitt, Black-
tailed woodchuck, Black-tailed
prairie dog, Plains harvest
mouse, Meadow vole
NOTE: PATHWAYS NOT REPRESENTED
MATHEMATICALLY IN EQUATIONS
RECEPTORS LISTED IN ITALICS
ARE MEASUREMENT RECEPTORS
FIGURE 4-3
EXAMPLE
SHORTGRASS PRAIRIE FOOD WEB
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H
O >
M W
E- J
ffi J
OH W
o >
NOTE:
V
Carnivorous Mammals
Long-tailed weasel, Coyote, Red fox
Gray fox, Badger, Spotted skunk
Carnivorous Birds
American kestrel, Burrowing owl,
Rough-legged hawk, Mississippi
kite, Black shouldered kite,
Crested caracara
Carnivorous Reptiles
Eastern yellowbelly racer, Great plains
ratsnake,Texas rat snake, Bullsnake,
Western diamondback rattlesnake
Omnivorous Mammals
White-footed mouse, Opossum,
Southeastern shrew, Merriam's
shrew, Arizona shrew, Desert shrew
Eastern chipmunk, Least chipmunk
Omnivorous Amphibians /
Reptiles
Ornate box turtle, Texas toad, Texas spotted
whiptail, Eastern hognose snake, Short-lined
skink, Six-lined racerunner, Eastern green toad
Omnivorous Birds
Northern bobwhite,
Horned lark, American pipit
Dickcissel
Invertebrates
Arachnids, Gastropods,
Oligochaetes, Arthropods
Nematodes
Herbivorous Birds
Mourning Dove,
Canada goose
Terrestrial Plants
Cotton, Soy bean, Corn,
Sunflower, Thistle, Forbes
Sugarcane
Soil
Nutrients, Detritus
Herbivorous Mammals
Deer mouse, Pygmy rabbit,
Brush rabbit, Eastern cottontail,
Nuttall's cottontail, Desert
cottontail
PATHWAYS NOT REPRESENTED
MATHEMATICALLY IN EQUATIONS
RECEPTORS LISTED IN ITALICS
ARE MEASUREMENT RECEPTORS
FIGURE 4-4
EXAMPLE
SHRUB/SCRUB FOOD WEB
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Carnivorous Mammals
Mink, River otter, Jaguar,
Mountain lion, Bobcat
NOTE:
Carnivorous Birds
American kestrel, Northern
harrier, Short-eared owl,
Merlin
Carnivorous
Shore Birds
Spotted sandpiper, Great blue
heron, Belted kingfisher,
Black rail, Greater yellowlegs
Carnivorous Reptiles
American alligator, Alligator
snapping turtle, Spiny softshell
turtle, Speckled king snake,
Cotton mouth
Carnivorous Fish
Largemouth bass, Spotted gar,
Alligator gar, Grass pickerel,
Chain pickerel
Omnivorous
Amphibians / Reptiles
Green frog, Small-mouthed
salamander, Painted turtle,
Three-toed amphiuma, Lesser siren
Omnivorous Fish
Carp, Channel catfish,
Blue catfish,
Black bullhead
Omnivorous Mammals
Least shrew, Masked shrew,
Southeastern shrew, Duskey
shrew, Ornate shrew
Omnivorous Birds
Mallard, Marsh wren,
Red-winged blackbird, Swamp
sparrow, Northern shoveler,
Herbivorous / Planktivorous
Fish
Carp, Golden shiner, Threadfin
shad, Mosquito fish, Sailfin
Invertebrates
Herbivorous Birds
Canvasback,
Canada Goose, Northern pintail
Herbivorous Mammals
Muskrat, Marsh rabbit, Swamp
rabbit, Fox squirrel
Invertebrates
Aquatic Vegetation
Vascular plants, Maidencane, Saltmeadow
cordgrass, Bull tongue, Alligator weed, Sedges
Phytoplankton
Algae
Water and Sediment
Nutrients, Detritus
PATHWAYS NOT REPRESENTED
MATHEMATICALLY IN EQUATIONS
RECEPTORS LISTED IN ITALICS
ARE MEASUREMENT RECEPTORS
FIGURE 4-5
EXAMPLE
FRESHWATER FOOD WEB
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Carnivorous Mammals
Mink, River otter,
Jaguar, Bobcat
Carnivorous Birds
American kestrel, Northern
Harrier, Short-eared owl,
Merlin, Osprey, White-tailed
hawk
Carnivorous
Shore Birds
Spotted sandpiper,
Belted kingfisher, Great blue
heron, Greater yellowlegs,
Dunlin
Omnivorous
Amphibians / Reptiles
Green frog, Dwarf salamander, Green
tree frog, Southern leopard frog,
Snapping turtle, Diamondback terrapin
Omnivorous Mammals
Marsh rice rat, Masked shrew,
Broad-footed mole, Star-nosed
mole. Cotton mouse. Raccoon
Omnivorous Birds
Mallard, Marsh wren, Red-winged
blackbird, Swamp sparrow,
Northern shoveler, Herring gull
Herbivorous Mammals
Muskrat, Marsh rabbit, Swamp
rabbit, Fox squirrel, Beaver
Herbivorous Birds
Canvasback, Northern pintail,
Canada goose, Fulvous
whistling Duck
Carnivorous Reptiles
American alligator, Gulf
salt marsh snake, Diamondback
water snake, Cottonmouth
Carnivorous Fish
Bull shark, Stingray,
Atlantic stingray, Spotted gar,
Alligator gar, American eel
Aquatic Vegetation
(Vascular plants), Wiregrass, Three cornered
grass, Saltmarsh bulrush, Saltmeadow cordgrass
Saltgrass, Blackrush
NOTE:
PATHWAYS NOT REPRESENTED
MATHEMATICALLY IN EQUATIONS
RECEPTORS LISTED IN ITALICS
ARE MEASUREMENT RECEPTORS
Water
Invertebrates
Arthropods,
Gastropods,
Decapods
AA >
-'^
/ 1
Vv
--
Omnivorous Fish
Carp, Channel catfish,
Blue catfish,
Black bullhead
A A A AA
/ / /
' / /
Herbivorous / Planktivorous
Fish
Carp, Gulf killifish, Golden shiner,
Threadfin shad, Mosquito fish, Sailfin
molly, Red shiner
A A
AA A A AAA
Water and Sediment
Nutrients, Detritus
FIGURE 4-6
EXAMPLE
BRACKISH / INTERMEDIATE
MARSH FOOD WEB
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o >
c^ w
E- J
Carnivorous Mammals
Red fox, Sea otter
z
Carnivorous Birds
Northern Harrier, Merlin,
Osprey, White-tailed hawk
Carnivorous
Shore Birds
Spotted sandpiper,
Black rail, Great blue
heron
Carnivorous Reptiles
American alligator, Gulf
salt marsh snake, Diamondback
water snake, Mobile cooler
Carnivorous Fish
Bull shark, Fine toothed shark,
Spotted eagle ray, Spotted
moray eel, redfish
A A A. A
Omnivorous Fish
Sea catfish, Gafftopsail
catfish, Feather blenny,
Atlantic midshipman,
Gulftoadfish
Omnivorous Birds
Marsh wren, Short-billed
dowitcher, Least sandpiper
Roseate spoonbill
Omnivorous Mammals
Marsh rice rat, Cotton
mouse. Wild boar
Herbivorous / Planktivorous
Fish
Gulf pipefish, Sharptail goby
Clown goby, Gulf killifish, Carp
Invertebrates
Herbivorous Mammals
Salt-marsh harvest mouse,
Marsh rabbit, Swamp rabbit
Invertebrates
Herbivorous Birds
Canvasback,
Great blue heron, Dunlin
Aquatic Vegetation
Vascular plants), Smooth cordgrass, Wiregrass,
Saltmeadow cordgrass, Saltgrass, Blackrush
Phytoplankton
Water and Sediment
Nutrients, Detritus
NOTE:
PATHWAYS NOT REPRESENTED
MATHEMATICALLY IN EQUATIONS
RECEPTORS LISTED IN ITALICS
ARE MEASUREMENT RECEPTORS
FIGURE 4-7
EXAMPLE
SALT MARSH FOOD WEB
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4.3 SELECTING ASSESSMENT ENDPOINTS
An assessment endpoint is an expression of an ecological attribute that is to be protected (U.S. EPA
1997c). A critical ecological attribute of a guild or community is a characteristic that is relevant to
ecosystem (food web) structure and function. Protection of the critical ecological attributes of each guild
and community is assummed to also ensure the protectiveness of habitat-specific food web structure and
function. Therefore, assessment endpoints should be identified specific to each class-specific guild and
community within each trophic level of the habitat-specific food web.
Examples of assessment endpoints for guilds include:
• Seed disperser
• Major food source for predator
• Decomposer/detritivore
• Pollinator
• Regulate populations of prey (e.g., forage fish, small rodents)
Examples of assessment endpoints for communities include:
• Diversity or species richness
• Community composition
• Productivity
• Major food source for consumer
• Habitat for wildlife
Descriptions of ecological attributes to be protected (i.e., assessment endpoints) associated with several
guilds and communities in a terrestrial ecosystem are provided as examples below.
• Herbaceous plant abundance, habitat, and productivity are attributes to be preserved in a
terrestrial ecosystem. As food, herbaceous plants provide an important pathway for
energy and nutrient transfer from soil to herbivorous (e.g., rabbit) and omnivorous
(e.g., mouse) receptors. Herbaceous plants also provide critically important habitat for
small animals.
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• Woody plant habitat and productivity are critical attributes to be protected. As food,
woody plants provide an important pathway for energy and nutrient transfer from soil to
herbivorous and omnivorous vertebrates (e.g., white-tailed deer, yellow-bellied sapsucker).
Woody plants also provide critically important habitat for terrestrial wildlife.
• Herbivore productivity is an attribute to be protected in the terrestrial ecosystem because
herbivores incorporate energy and nutrients from plants and transfer it to higher trophic
levels, such as first- and second-order carnivores (e.g., snakes and owls, respectively).
Herbivores also are integral to the success of terrestrial plants, through such attributes as
seed dispersal.
• Omnivore productivity is an attribute to be protected in the terrestrial ecosystem because
omnivores incorporate energy and nutrients from lower trophic levels and transfer it to
higher levels, such as first- and second-order carnivores.
• First-order carnivore productivity is an attribute to be protected in the terrestrial ecosystem
because these carnivores provide food to other carnivores (both first- and second-order),
omnivores, scavengers, and microbial decomposers. They also affect the abundance,
reproduction, and recruitment of lower trophic level receptors, such as vertebrate
herbivores and omnivores, through predation.
• Second-order carnivore productivity is an attribute to be protected in the terrestrial
ecosystem because carnivores affect the abundance, reproduction, and recruitment of
species in lower trophic levels in the food web.
• Soil invertebrate productivity and function as a decomposer are attributes to be preserved
in a terrestrial ecosystem, because they provide a mechanism for the physical breakdown
of detritus for microbial decomposition, which is a vital function. Soil invertebrates also
function as a major food source for omnivorous birds.
Selection of assessment endpoints represents a scientific and management decision point. Since risk
characterization, and subsequently final risk management decisions, are dependent on the selection of
assessment endpoints, they should be developed with input from risk managers and other stakeholders.
Table 4-1 lists the assessment endpoints for guilds and communities in the three aquatic and four terrestrial
example habitat-specific food webs.
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TABLE 4-1
ASSESSMENT ENDPOINTS FOR GUILDS AND COMMUNITES IN EXAMPLE FOOD WEBS
Representative Receptors
Example Critical Ecological Attributes
Aquatic Receptors
Aquatic Plants
Water Invertebrates
Herbivorous /
Planktivorous Fish
Omnivorous Fish
Carnivorous Fish
Phytoplankton, Vascular plants
Crustaceans, Rotifers, Amphipods
Carp, Gulf killifish, Threadfin shad, Molly, Golden Shiner,
Goby, Mosquito Fish, Red Shiner
Carp, Channel catfish, Gafftopsail fish, Atlantic midshipman,
Feather blenny, Gulf toad fish, Bluecat, Bullhead
Largemouth bass, Spotted gar, Bull shark, Redfish, Grass
pickerel, Alligator gar, Chain pickerel, American eel, Atlantic
stingray, Spotted moray eel, Fine toothed shark
Primary producers convert light energy into biomass, and are the first link in
aquatic food chains supporting higher trophic level aquatic consumers and
wildlife. Rooted vegetation also provides habitat and bottom stability.
Aquatic invertebrates are an important food source for many higher trophic
level consumers. Zooplankton regulate phytoplankton populations, and are a
critical link in energy transfer to higher trophic levels in aquatic ecosystems.
Herbivorous/Planktivorous Fish are an important prey species for higher
trophic level predators in the aquatic and terrestrial ecosystems, and provide a
critical link for energy transfer from primary producers to higher trophic level
consumers. They generally comprise the majority of tissue biomass in
aquatic ecosystems, and provide an important role to the ecosystem through
regulating algae and plankton biomass.
Omnivorous fish are an important prey item for higher trophic level
predators. Through predation, they may also regulate population levels in
lower trophic level fish and invertebrates.
Carnivorous fish provide an important function for the aquatic environment
by regulating lower trophic populations through predation. They are also an
important prey item for many top level mammal and bird carnivores.
Sediment Receptors
Sediment Invertebrates
Oligochaetes, Pelecypods, Amphipods, Decapods, Polychaetes,
Gastropods
Sediment invertebrates are an important food source for many higher trophic
level predators. They also provide an important role as
decomposers/detritivores in nutrient cycling.
Soil Receptors
Terrestrial Plants
Vascular plants, Grasses, Forbs, Lichens
Primary producers provide a critical food source and are the first link in the
terrestrial food chain for higher trophic level consumers, hi addition,
vegetation provides critical habitat for wildlife.
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TABLE 4-1 (Continued)
ASSESSMENT ENDPOINTS FOR GUILDS AND COMMUNITES IN EXAMPLE FOOD WEBS
Representative Receptors
Soil Invertebrates
Nematodes, Gastropods, Oligochaetes, Arthropods
Example Critical Ecological Attributes
Soil invertebrates provide an important food source for many higher trophic
level species. As decomposers/detritivores they play a critical role in nutrient
cycling. They also aid in soil aeration and infiltration by increasing macro,
and micro porosity.
Upper Trophic Level Avian and Mammalian Wildlife
Herbivorous Mammals
Herbivorous Birds
Omnivorous Mammals
Omnivorous Birds
Omnivorous
Amphibians and
Reptiles
Deer mouse, Nutria, Eastern cottontail, Prairie vole, Fox
squirrel, Grey squirrel, Swamp rabbit, Eastern wood rat,
White-tailed deer, Fulvous harvest mouse, Black-tailed
jackrabbit, Hispid cotton rat, Hispid pocket mouse, Black-
tailed prairie dog,
Mourning dove, Canada goose, Chipping sparrow, Northern
pintail
Least shrew, Raccoon, Muskrat, Marsh rice rat, Wild boar,
Cotton mouse, Eastern spotted skunk, Coyote, Nine-banded
armadillo, Virginia opossum, Elliot's short-tailed shrew,
Striped skunk, Golden mouse, Seminole bat.
American robin, Northern bobwhite, Marsh wren, Carolina
wren, Swamp sparrow, Yellow warbler, Lesser prairie chicken,
Roadrunner, Mallard, Least sandpiper, Red cockaded wood
pecker, Roseate spoonbill, Greater prairie chicken, Scissor-
tailed flycatcher, Sandhill crane, Dickcissel, Canada goose,
Red-winged blackbird, Hooded merganser, Northern shovler.
Ornate box turtle, Green frog, Texas toad, Eastern hognose
snake, Plains blind snake, Small-mouthed salamander,
Diamondback terrapin, Short-lined skink, Six-lined racerunner,
Eastern green toad, Marbled salamander, Slender glass lizard,
Herbivorous mammals are an important prey item for many higher trophic
level predators. They provide an important link for energy transfer between
primary producers and higher trophic level consumers. In addition, these
organisms generally comprise the majority of the terrestrial tissue biomass,
and are important in seed dispersal and pollination for many plant species.
Herbivorous birds are an important prey item for many higher trophic level
predators. They are important in seed dispersal for many plants in both
terrestrial and aquatic ecosystems. Aquatic herbivorous birds may also play
an important role in egg dispersion for fish and invertebrate species.
Omnivorous mammals are an important prey item for higher trophic level
predators, and influence lower trophic level populations through predation.
They play an important role in seed dispersal for many types of terrestrial
vegetation and aquatic plants.
Omnivorous birds are an important prey item for higher trophic level
predators. They play an important role in seed dispersal and pollination for
many types of terrestrial vegetation and aquatic plants. In addition, aquatic
species provide egg dispersal for some fish and invertebrate species.
Omnivorous amphibians and reptiles provide an important food source for
predators. They also provide seed dispersal for many plants and regulate
lower trophic level populations through predation.
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TABLE 4-1 (Continued)
ASSESSMENT ENDPOINTS FOR GUILDS AND COMMUNITES IN EXAMPLE FOOD WEBS
Representative Receptors
Example Critical Ecological Attributes
Carnivorous Mammals
Grey fox, Swift fox, River otter, Bobcat, Mountain lion, Long-
tailed weasel, American badger, Red fox, American mink, Red
wolf
Carnivorous mammals provide an important functional role to the
environment by regulating lower trophic level prey populations.
Carnivorous Birds
Red-tailed hawk, American kestrel, Marsh hawk, Great-horned
owl, Barn owl, Burrowing owl, White-tailed hawk, Ferruginous
hawk , Swansons hawk, Golden eagle, Mississippi kite, Prairie
hawk, Merlin
Carnivorous Birds provide an important functional role to the environment by
regulating lower trophic level prey populations.
Carnivorous Shore
Birds
Great blue heron, Belted kingfisher, Spotted sandpiper, Black
rail, Greater yellowlegs, Dunlin,
Carnivorous Shore Birds provide an important functional role to the
environment by regulating lower trophic level prey populations, and
influencing species composition in terrestrial and aquatic ecosystems. They
also provide egg dispersal for some fish and aquatic invertebrates.
Carnivorous Reptiles
Eastern yellowbelly racer, Eastern coral snake, Texas rat snake,
Western Diamondback rattlesnake, American alligator,
Bullsnake, Alligator snapping turtle, Cotton mouth, Speckled
king snake, Spiny softshell turtle, Gulf salt marsh snake,
Carnivorous Reptiles provide an important functional role to the environment
by regulating lower trophic level prey and are an important prey item for
other upper trophic level predators.
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4.4 IDENTIFYING MEASUREMENT RECEPTORS TO EVALUATE MEASURES OF
EFFECT
Measures of effect are measures used to evaluate "the response of the assessment endpoint when exposed to
a stressor (formerly measurement endpoints)" (U.S. EPA 1997c). Measures of exposure are measures of
how exposure may be occurring, including how a stressor may co-occur with the assessment endpoint
(U.S. EPA 1997c). Measures of effect, in conjunction with measures of exposure, are used to make
inferences about potential changes in the assessment endpoint (U.S. EPA 1997c).
Measures of effect are selected as: (1) toxicity values developed and/or adopted by federal or state
agencies (e.g., ambient water quality criteria [AWQC], NOAA effects range low [ERL] values) for
protection of media-specific communities, or (2) receptor-specific chronic
no-observed-adverse-effects-levels (NOAELs) or their equivalent for ecologically relevant endoints (see
Chapter 5) for this screening assessment. Measures of exposure are selected as the COPC concentrations
in media or dose (e.g., ingestion of contaminated media and/or tissue) to which exposure occurs, and
determined as discussed in Chapter 5.
The evaluation of the measure of effect to the assessment endpoint (see Chapters 5 and 6) requires
identification of a measurement receptor representive of the assessment endpoint. The measurement
receptor is selected based on consideration of factors such as (1) ecological relevance, (2) exposure
potential, (3) sensitivity, (4) social or economic importance, and (5) availability of natural history
information.
A measurement receptor, specific to each guild, may be selected as a species, population, community, or
assemblage of communities. For communities (i.e., soil, surface water, sediment), the community or
assemblage of communities is selected as the measurement receptor, and no specific species is selected.
For guilds, individual species are selected as measurement receptors. Sections 4.4.1 and 4.4.2 discuss
measurement receptors for communities and for mammals and birds, respectively. Section 4.4.3 discusses
selection of measurement receptors for the example food webs (see Section 4.2).
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4.4.1 Measurement Receptors for Communities
For communities (i.e., soil, surface water, sediment), the community or assemblage of communities are
selected as the measurement receptors, and no specific species are selected. Therefore, it is inferred that
critical ecological attributes of these communities are not adversely affected if a COPC concentration in
that respective media does not exceed the toxicity benchmark specific for that community (see Section 5.1).
Representative measurement receptors for soil, surface water, sediment communities include:
• Soil—Soil invertebrate community and terrestrial plant community
• Surface Water—Phytoplankton community, water invertebrate community
• Sediment—Benthic invertebrate community
4.4.2 Measurement Receptor for Guilds
A measurement receptor should be selected for each class-specific guild to model (1) COPC dose ingested,
and (2) whole body COPC concentration in prey eaten by predators. The selected measurement receptor
should be representative of other species in the guild, with respect to the guild's feeding niche in the
ecosystem. The risk assessment should demonstrate that using the measurement receptor ensures that risk
to other species in the guild is not underestimated. The following factors should be evaluated to identify a
measurement receptor:
• Ecological Relevance - Highly relevant receptors provide an important functional or
structural aspect in the ecosystem. Attributes of highly relevant receptors typically fall
under the categories of food, habitat, production, seed dispersal, pollination, and
decomposition. Critical attributes include those that affect or determine the function or
survival of a population. For example, a sustainable population of forage fish might be
critical to the sustainability of a population of carnivorous game fish.
• Exposure Potential - Receptors with high exposure potentials are those that, due to their
metabolism, feeding habits, location, or reproductive strategy, tend to have higher
potentials for exposure than other receptors. For example, the metabolic rates of small
receptors are generally higher than those for large animals. This results in a higher
ingestion per body weight (i.e., increased exposure potential).
Sensitivity - Highly susceptible receptors include those with low tolerances to a COPC as
well as receptors with enhanced COPC susceptibility due to other concomitant stressors
that may not be related to a COPC, such as reduced habitat availability. For example,
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raptorial birds are highly sensitive to the effects of chlorinated pesticides that
bioaccumulate through the food chain.
• Social or Economic Importance - An assessment endpoint may also be based on socially
or economically important receptors. These types of receptors include species valued for
economic importance such as crayfish and game fish. For these receptors, critical
attributes include those that affect survival, production, and fecundity characteristics. For
example, swamp crayfish are highly sensitive to some heavy metals through adverse
effects to behavioral characteristics.
• Availability of Natural History Information - Natural history information is essential to
quantitaviliy evalate risk to measurement receptors. If this information such as body
weight, food, water, soil, and sediment ingestion rates is unavailable for the desired
measurement receptor, a surrogate species should be selected. Uncertainty associated with
using a surrogate species should be discussed.
It should be noted that more than one measurement receptor can be selected per assessment endpoint.
Also, although each of these factors should be evaluated when selecting the measurement receptor, at least
one of the measurement receptors selected to represent a class-specific guild should have the highest
exposure potential (i.e., ingestion rate on a body weight basis). This ensures that risk to other species in
the guild is not underestimated.
U.S. EPA's Wildlife Exposure Factors Handbook (U.S. EPA 1993o) is an example of an excellent source
of dietary and other natural history information. However, it is recommended that receptor information
obtained from it or any source be verified and documented during measurement receptor identification.
4.4.3 Measurement Receptors for Example Food Webs
Consistent with the discussions presented in Section 4.4, measurement receptors were selected for the
example food webs presented in Section 4.2. Receptor information documented in Wildlife Exposure
Factors Handbook (U.S. EPA 1993o) and available literature was evaluated to determine suitable
measurement receptors for each class-specific guild represented in the example food webs.
Ecological relevance, exposure potential, sensitivity, social or economic importance and availability of
natural history information (see Section 4.4.3) were evaluated to identify measurement receptors for the
example food webs. It should be noted that since these measurement receptors have been provided as
examples to facilitate understanding of the previously described selection process, not every assessment
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endpoint has been represented (e.g., TL3 omnivorous fish, TL3 omnivorous amphibians and reptiles, and
TL4 carnivorous fish) as may be expected for a complete ecological risk assessment at a site. Discussions
on each of the example measurement receptors follow.
American Kestrel
The American kestrel (Falco sparverius), or sparrow hawk, was selected as the measurement receptor for
the carnivorous bird guild in the example shortgrass prairie, tallgrass prairie, shrub/scrub, freshwater
wetland, and brackish/intermediate marsh food webs based on the following information:
• The kestrel is important in regulating small mammal populations through predation.
Predators of the kestrel include larger raptors such as red-tailed hawks, golden eagles, and
great horned owls.
• The kestrel's prey include a variety of invertebrates such as worms, spiders, scorpions,
beetles, and other large insects, as well as an assortment of small to medium-sized birds
and mammals. Winter home ranges vary from a few hectares to hundreds of hectares,
depending on the amount of available prey in the area.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
American Robin
The American robin (Turdus migratorius) was selected as the measurement receptor for the omnivorous
bird guild in the example forest food web based on the following information:
• The robin serves an important function in seed dispersion for many fruit species, making it
a valuable component of the ecosystem.
• Habitats include forests, wetlands, swamps, and habitat edge where forested areas are
broken with agricultural and range land. The robin forages on snails and other soil
invertebrates, seeds, and fruit.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
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Canvasback
The Canvasback (Aythya valisineria) was selected as the measurement receptor for the herbivorous bird
guild in all three example aquatic food webs based on the following information:
• The Canvasback provides a valuable functional role to aquatic habitats by dispersing seeds
for aquatic vegetation.
• The Canvasback is the largest member of the Pochards (bay ducks) and is common
throughout North America. They breed from Alaska to Nebraska, and in intermountain
marshes of Washington, Oregon, and northern California. Their diet consists of aquatic
vegetation, and small invertebrates, which they obtain by digging in sediments. Although
the canvasback consumes aquatic invertebrates during certain times of the year, in winter
when they are present along coastal regions, a large portion of their diet is aquatic
vegetation and was therefore selected to represent the herbivorous bird guild.
• Since natural history information on the canvasback was scarce, the Lesser Scaup (Aythya
affinis), for which natural history information is readily available, was selected as a
surrogate receptor.
Deer Mouse
The deer mouse (Peromyscus maniculatus) was selected as the measurement receptor for the herbivorous
mammal guild in the example forest, shortgrass prairie, tallgrass prairie, shrub/scrub food webs based on
the following information:
• The deer mouse is preyed upon by owls, snakes, and small carnivorous mammals, making
it a very important prey item. This animal also plays an important ecological role in seed
and fruit dispersion for many types of vegetation. In addition, their burrowing activities
influence soil composition and aeration.
• The deer mouse is almost strictly nocturnal and feeds chiefly on seeds, fruits, bark, roots,
and herbage. Due to its burrowing and dietary habits, there is a high potential for direct
and indirect exposure. The home range for a deer mouse is rarely over 100 meters, and it
spends most of its day in an underground burrow.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
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Least Shrew
The least shrew (Cryptotis parva) was selected as the measurement receptor for the omnivorous mammal
guild in the example tallgrass prairie, shortgrass prairie, and freshwater wetland food webs based on the
following information:
• Because of the shrews abundance and high population density, they make up a large
portion of the diet of owls, hawks, and snakes.
• Shrews feed on snails, insects, sow bugs, and other small invertebrates. The home range
size is on average 0.39 hectares. Their diet of invertebrates and their burrowing behavior
result in a high potential of direct and indirect exposure to contaminants.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Long-tailed Weasel
The long-tailed weasel (Mistily Renatd) was selected as the measurement receptor for the carnivorous
mammal guild in the example forest, tallgrass prairie and shrub/scrub food webs based on the following
information:
• The long-tailed weasel is important in regulating small mammal populations through
predation. Predators of the weasel include cats, foxes, snakes, and large raptors such as
hawks and owls.
• Habitats are varied and include forested, brushy, open areas including farm lands
preferably near water, where they prey on rabbits, chipmunks, shrews, mice, rats and
birds.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Mallard Duck
The mallard duck (Anas platyrhynchos} was chosen as the measurement receptor for the omnivorous bird
guild for the freshwater wetland and brackish/intermediate marsh food webs based on the following
information:
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• The mallard serves as a valuable component in aquatic food webs providing dispersion of
seeds for aquatic vegetation, and due to their role in the nutrient cycle of wetlands. In
addition, the mallard is a major prey item for carnivorous mammals, birds, and snakes.
• The mallard is present in a diverse amount of aquatic habitats throughout the United
States. Although their diet is considered omnivorous, 90 percent of their diet may be plant
material at some times of the year. Mallards are surface feeders that will often filter
through soft mud and sediment searching for food items.
• The mallard is very important game species, representing approximately one-third of all
waterfowl harvested.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Marsh Rice Rat
The marsh rice rat (Oryzomys palustris) was selected as the measurement receptor for the omnivorous
mammal guild in the example brackish/intermediate and salt marsh food web based on the following
information:
• The marsh rice rat inhabits marsh and wetland areas where it feeds on crabs, insects,
fruits, snails, and aquatic plants. The rice rat plays an important role in seed dispersal and
is a major food item for many predators including raptors, cats, weasels and snakes.
• The marsh rice rat has a high potential for exposure due to their aquatic diet and direct
contact with media.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Marsh Wren
The marsh wren (Cistothorus palustris) was selected as the measurement receptor for the omnivorous bird
guild in the example salt marsh food web based on the following information:
• The marsh wren consumes large numbers of aquatic insects thus regulating their
populations, which make it a valuable component of the ecosystem. Main predators are
snakes and turtles which prey heavily upon the eggs.
• The marsh wren is common throughout the United States, inhabiting freshwater, brackish,
and saltwater marshes. Its diet consists mainly of aquatic invertebrates, although snails
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and spiders may be taken. In addition, its diet of aquatic invertebrates makes it susceptible
to accumulation and toxicity of bioaccumulative chemicals
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Mink
The mink (Mustela vison) was selected as the measurement receptor for the carnivorous mammal guild in
the example brackish/intermediate marsh and freshwater food webs based on the following information:
• As a high trophic level predator, the mink provides an important component to the
ecosystem by influencing the population dynamics of their prey. Their main predators
include fox, bobcats, and great-horned owls.
• The mink is one of the most abundant carnivorous mammals in North America, inhabiting
rivers, creeks, lakes, and marshes. They are distributed throughout North America, except
in extreme north Canada, Mexico, and areas of the southwestern United States. Mink are
predominantly nocturnal hunters, although they are sometimes active during the day. They
are opportunistic feeders and will consume whatever prey is most abundant including:
small mammals, fish, birds, reptiles, amphibians, crustaceans, and insects.
• They have been shown to be sensitive to PCBs and similar chemicals, and have a high
potential for exposure due to their aquatic diet and direct contact with the media.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Mourning Dove
The Mourning Dove (Zenaida macroura) was selected as the measurement receptor for the herbivorous
bird guild in all four example terrestrial food webs based on the following information:
• The dove plays an important functional role in seed dispersion for many grasses and
forbs. Doves provide an important prey item for many higher trophic level omnivores and
carnivores. Predators of the mourning dove include falcons, hawks, fox, and snakes.
• The mourning dove inhabits open woodlands, forests, prairies, and croplands. It feeds
mostly on seeds, which comprise 99 percent of its diet. It may ingest insignificant amounts
of animal matter and green forage incidently.
• Mourning doves have a high potential for exposure through ingestion of inorganic
contaminants.
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Mourning doves are an important game species, contributing significantly as a food and
economic resource.
The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Muskrat
The muskrat (Ondrata zibethicus) was selected as the measurement receptor for the herbivorous mammal
guild in the example freshwater wetland and brackish/intermediate marsh food webs based on the following
information:
• The muskrat is important to the overall structure of the aquatic ecosystem by regulating
aquatic vegetation diversity and biomass, resulting in stream bank stability and increased
habitat diversity for aquatic organisms including fish. It was also chosen as the
measurement receptor based on its value to the ecosystem including its large population
densities and importance as a prey species (e.g., prey for hawks, mink, otters, owls, red
fox, snapping turtles, alligators, and water snakes).
• The muskrat spends a large part of its time in the water, and is common in fresh, brackish,
and saltwater habitats. It has relatively high food and water ingestion rates, and a diet that
consists mainly of aquatic vegetation, clams, crayfish, frogs, and small fish.
• Due to the large numbers, the muskrat plays an important economic role in the fur
industry, and as a food item for some cultures.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Northern Bobwhite
The northern bobwhite (Colinus virginianus) was selected as the measurement receptor for the omnivorous
bird guild in the example shortgrass prairie and shrub/scrub food webs based on the following information:
• The bobwhite plays an important role in seed dispersion for many plant species, and is an
important prey item for snakes, and other small mammals. If habitat conditions permit,
their numbers will increase rapidly, providing an additional food source for many
predators. They also are valuable in controlling insect populations during certain times of
the year.
• The bobwhite's diet consists mainly of seeds and invertebrates, although in the winter
green vegetation can dominate its diet. During breeding season, the bobwhite's home
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range may encompasses several hectares, including areas for foraging, cover, and a nest
site. In non-breeding season, the bobwhite's home range can be as large as 16 hectares. It
has a high potential for exposure through ingestion and dermal contact with soil during
dust bathing.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Northern Harrier
The Northern harrier (Circus cyaneus), also called the Marsh hawk was selected as the measurement
receptor for carnivorous bird guild in the example salt marsh food web based on the following information:
• The marsh hawk plays an important role in the ecosystem in regulating small mammal
populations through predation.
• The marsh hawks diet consists of small mammals, birds, and occasionally snakes, frogs,
and insects. Their habitat preferences include wetlands or marshes.
• In addition, the marsh hawk has demonstrated sensitivity to pesticides, which
bioaccumulate through food chains.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Red Fox
The red fox (Vulpes vulpes) was selected as the measurement receptor for the carnivorous mammal guild in
the example salt marsh food web based on the following information:
• Red fox have a high potential for exposure due to bioaccumulation though the food chain,
and are a valuable component to ecosystem structure by regulating the abundance,
reproduction, distribution, and recruitment of lower trophic level prey.
• Although omnivorous in dietary habits, the majority of the diet consists of cottontail
rabbits, voles, mice, birds, and other small mammals. This animal was chosen because of
its status as a top carnivore and its widespread distribution in the United States, inhabiting
chaparral, wooded and brushy areas, coastal areas and rim rock country.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
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Red-tailed Hawk
The red-tailed hawk (Buteo jamaicensis) was selected as the measurement receptor in the carnivorous bird
guild in the example forest food web based on the following information:
• The red-tailed hawks position as a high trophic level predator makes them a valuable
component of terrestrial food webs through their regulation of populations of lower trophic
level prey species.
• The red-tailed hawk is widely distributed in the United States among a diverse number of
habitat types ranging from woodlands to pastures. Its diet includes small mammals (such
as mice, shrews, voles, rabbits, and squirrels), birds, lizards, snakes, and large insects. It
is an opportunistic feeder, preying on whatever species is most abundant. Red-tailed
hawks are territorial throughout the year, and have home ranges that can be over 1,500
hectares.
• Red-tailed hawks have shown sensitivity to many chemicals which disrupt reproduction
or egg development.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Salt Marsh Harvest Mouse
The salt marsh harvest mouse (Reithrodontomys raviventris) was selected as the measurement receptor for
the herbivorous mammal guild in the example salt marsh food web based on the following information:
• The salt marsh harvest mouse plays an important functional role in aquatic habitats
through seed dispersal for aquatic vegetation.
• Predators include owls, snakes, and many mammals including weasels, fox, and cats.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Short-tailed Shrew
The short-tailed shrew (Blarina brevicaudd) was selected as the measurement receptor for the omnivorous
mammal guild in the example forest food web based on the following information:
• The short-tailed shrews value as a prey species for many high level predators is very
important to the health of an ecosystem. They also play an important role in soil recycling
and aeration, through tunnel excavation.
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• The short-tailed shrew is one of the most common mammals in the United States. It is a
small insectivorous mammal that represents secondary consumers (insectivores) present in
terrestrial ecosystems. Their diet of invertebrates such as earthworms and their burrowing
behavior result in a high potential of direct and indirect exposure to contaminants It has a
very high metabolism rate which requires almost constant feeding. The most common
habitats are wooded and wet areas in the drier parts of the range.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Spotted Sandpiper
The spotted sandpiper (Actitis macularid) was selected as the measurement receptor for the carnivorous
shore bird guild in the example freshwater wetland, brackish/intermediate, and salt marsh food webs based
on the following information:
• The spotted sandpiper inhabits a wide variety of habits usually associated with water or
marsh.
• Spotted sandpipers have a high potential for exposure through ingestion of aquatic insects,
worms, fish , crustaceans, mollusks, and carrion.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
Swift Fox
The Swift Fox (Vulpes velox) was selected as the measurement receptor for the carnivorous mammal guild
in the example shortgrass prairie food web based on the following information:
• The swift fox fills an important functional role by regulating the population dynamics of
many prey species.
• The swift fox is mainly nocturnal and its diet consists of small mammals, insects, birds,
lizards, and amphibians. It spends most of its days in a den, emerging at night to hunt.
Their home range extends several kilometers.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
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Western Meadow Lark
The western meadow lark (Sturnella neglecta) was selected as the measurement receptor for the
omnivorous bird guild in the example tallgrass prairie food web based on the following information:
• The western meadow lark serves an important function in seed dispersion for many forb
and grass species, making it a valuable component of the ecosystem.
• Habitats include grassland, savanna, pasture, and cultivated fields. The western meadow
lark forages on spiders, sowbugs, snails, and grass and forb seeds.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
White-footed Mouse
The white-footed mouse (Peromyscus polionotus) was selected as the measurement receptor for the
omnivorous mammal guild in the example shrub/scrub food web based on the following information:
• The white-footed mouse plays an important role in seed dispersal and provide an important
food source for raptors, snakes and other mammals including cats, weasels and fox.
• The white-footed mouse feeds on nuts, seeds, fruits, beetles, caterpillars, and other insects.
Due to its burrowing and dietary habits, there is a high potential for direct and indirect
exposure.
• The availability of natural history information (e.g., home range, ingestion rates, body
weights) also support selection as a measurement receptor.
U.S. EPA Region 6 U.S. EPA
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Chapter 5
What's Covered in
4 Exposure Assessmetf!
4 Toxicity Assessment
The analysis phase of a risk assessment consists of assessing (1) exposure of a measurement receptor to a
compound of potential concern (COPC), and (2) toxicity of a COPC to a measurement receptor. The
exposure assessment (Section 5.1), and the toxicity assessment (Section 5.4) are used to characterize
ecological risk, as discussed in Chapter 6.
5.1 EXPOSURE ASSESSMENT
Exposure is the contact (e.g., ingestion) of a receptor with a COPC. Exposure of ecological receptors to
COPCs emitted from facility sources are evaluated through consideration of exposure pathways. All
exposure pathways that are potentially complete should be evaluated. The existence of a potentially
complete exposure pathway indicates the potential for a receptor to contact a COPC; it does not require
that a receptor be adversely affected. Exposure pathways considered in this guidance include all direct
uptake pathways of a COPC from media (e.g., soil, sediment, and surface water) for lower trophic level
receptors evaluated at the community level, and ingestion of a COPC contaminated organism (plant or
animal food item) or media for higher trophic level receptors evaluated as class-specific guilds. It should
be noted that exposure pathways currently not addressed in this guidance due to the limitation of data
include (1) inhalation and dermal exposure pathways for upper trophic level organisms, (2) ingestion via
grooming and preening, and (3) foliar uptake of dissolved COPCs by aquatic plants.
Exposure assessment consists of quantifying exposure of a measurement receptor to a COPC. As
previously noted (see Chapter 4), exposure to community and class-specific guild measurement receptors is
assessed using different approaches. This is because the available toxicity reference values (TRVs) used in
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Chapter 5: Analysis August 1999
risk characterization (see Chapter 6) for lower trophic level communities are media specific; whereas TRVs
for upper trophic level class-specific guilds are provided in terms of dose ingested.
For community measurement receptors (e.g., water, sediment, and soil communities), the exposure
assessment consists of determining the COPC concentration in the media that the particular community
inhabits. For example, the COPC concentration in soil is determined during the exposure assessment for
comparison to the NOAEL for terrestrial plants and soil invertebrates during risk characterization. For
class-specific guild measurement receptors, exposure is assessed by quantifying the daily dose ingested of
contaminated media and/or organism (expressed as the mass of COPC ingested per kilogram body weight
per day). The following sections provide guidance on assessing exposure to community and class-specific
guild measurement receptors.
5.2 Assessing Exposure to Community Measurement Receptors
Since exposure to communities is assumed to be primarily through contact with COPCs within the media
they inhabit, the assessment of exposure for community measurement receptors is simply the determination
of the COPC concentration in the media that they inhabit. Exposure for water, sediment, and soil
community measurement receptors should be determined as follows:
Water Community - Exposure to the water community as a measurement receptor (e.g., water
invertebrates or phytoplankton in the freshwater/wetland food web) is assessed by determining the
COPC dissolved water concentration (Cdw) (see Chapter 3 and Appendix B) at the specific
location being evaluated (see Chapter 4).
Sediment Community - Exposure to the sediment community as a measurement receptor
(e.g., sediment invertebrates in the brackish/intermediate food web) is assessed by determining the
COPC concentration in bed sediment (Csed) (see Chapter 3 and Appendix B) at the specific
location being evaluated (see Chapter 4).
Soil Community - Exposure to the soil community as a measurement receptor (e.g., soil
invertebrates or terrestrial plants in the forest food web) is assessed by determining the COPC
concentration in soil (Cs) (see Chapter 3 and Appendix B) at the specific location being evaluated
(see Chapter 4).
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Chapter 5: Analysis August 1999
5.3 Assessing Exposure to Class-Specific Guild Measurement Receptors
Exposure to measurement receptors of class-specific guilds is assessed by quantifying the daily dose
ingested of contaminated food items (i.e., plant and animal), and media. COPC daily dose ingested
(expressed as the mass of COPC ingested per kilogram body weight per day) depends on the COPC
concentration in plant and animal food items and media, the measurement receptor's trophic level
(i.e., consumer), the trophic level of animal food items (i.e., prey), and the measurement receptor's
ingestion rate of each food item and media. The complexity of the daily dose equation will depend on
(1) the number of food items in a measurement receptor's diet, (2) the trophic level of each food item and of
the measurement receptor. The daily dose of COPC ingested by a measurement receptor, considering all
food items and media ingested, can be calculated from the following generic equation:
DD = £ IRF • C, • Pt • F,. + £ IRM • CM • PM Equation5-l
where
DD = Daily dose of COPC ingested (mg COPC/kg BW-day)
IRP = Measurement receptor plant or animal food item ingestion rate (kg/kg
BW-day)
Cj = COPC concentration in rth plant or animal food item (mg COPC/kg)
Pi = Proportion of/th food item that is contaminated (unitless)
Fj = Fraction of diet consisting of plant or animal food item /' (unitless)
IRM = Measurement receptor media ingestion rate (kg/kg BW-day [soil or bed
sediment] or L/kg BW-day [water])
CM = COPC concentration in media (mg/kg [soil or bed sediment] or mg/L
[water])
PM = Proportion of ingested media that is contaminated (unitless)
Sections 5.3.1 through 5.3.2 (also see Appendix F) provide guidance for determining values for the above
parameters; including (1) the determination of measurement receptor food item and media ingestion rates,
and (2) the calculation of COPC concentrations in plant and animal food items. The use of BCFs and
FCMs in calculating COPC concentrations in plant and animal food items is also discussed in the following
sections. The daily dose should be computed using COPC media (i.e., soil, sediment, surface water, air)
concentrations, at the location within the habitat supporting the food web being evaluated (see Chapter 4),
for determination of (1) the COPC concentration in the plant or animal food item ingested, and (2) the
U.S. EPA Region 6 U.S. EPA
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Chapter 5: Analysis August 1999
COPC concentration in the media ingested. Guidance on the calculation of COPC concentrations in media
being ingested is provided in Chapter 3 and Appendix B.
The daily dose of COPC ingested by a measurement receptor should be determined by summing the
contributions from each contaminated plant, animal, and media food item. Equation 5-1 and consumer
specific equations in Appendix F, are derived to account for 100 percent of the measurement receptor's diet
(total daily mass of food items ingested) which can potentially be contaminated. However, if a food item or
media at an actual site location is not contaminated (i.e., the COPC concentration in the media or resulting
food item is zero), then the daily mass of that food item or media ingested does not contribute to the daily
dose of COPC ingested. Also, Equation 5-1 does not directly include a term for home range, as defined
spatially. However, the term accounting for the proportion of plant or animal food item that is
contaminated, Ph numerically accounts for the fraction of a respective food item that may potentially be
obtained from outside the geographical limits of the impacted habitat (i.e., outside the area of
contamination) being evaluated.
For measurement receptors ingesting more than one plant or animal food item, U.S. EPA OSW
recommends that exposure be separately quantified assuming that the measurement receptor ingests both
"equal" and "exclusive" diets. Not only does this constitute the most complete evaluation of exposure
potential for a measurement receptor; if warranted, it also identifies which pathways are driving risk
specific to a COPC and measurement receptor, and allows risk management efforts to be prioritized.
Guidance for calculating DD assuming "equal diet" and "exclusive diet" is provided below.
Equal Diet - To evaluate exposure to a measurement receptor based on an equal diet, the daily
dose of COPC ingested is calculated assuming that the fraction of daily diet consumed by the
measurement receptor is equal among food item groups. This is computed by setting the value for
fraction of diet consisting of plant and/or animal food items, Fh equal to 1.0 divided by the total
number of plant and animal food item groups ingested. Therefore, Ft values within a specific DD
equation would be the same numerically.
Exclusive Diet - To evaluate exposure to a measurement receptor based on exclusive diets, the
daily dose of COPC ingested is calculated assuming that the fraction of daily diet consumed by the
measurement receptor is exclusively (100 percent) one food item group. This is computed by
setting the value for Ft equal to 1.0 for each food item group at a time, while the Ft values for the
remaining food item groups are set equal to zero. The food item designated as exclusive is
alternated to each respective food item represented in the DD equation to obtain a numeric range of
exposure values based on exclusive diets. If the daily diet of a food item (i.e., prey) of a
measurement receptor (i.e., consumer) also consists of more than one plant or animal food item,
U.S. EPA Region 6 U.S. EPA
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then an equal diet should be assumed for the food item being consumed while evaluating exposure
to the measurement receptor.
In addition to quantifying exposure based on equal and exclusive diets for measurement receptors, U.S.
EPA OSW recommends that the following assumptions be applied in a screening level risk assessment.
The COPC concentrations estimated to be in food items and media ingested are
bioavailabile.
• Only contributions of COPCs from the sources (e.g., combustion stacks, fugitives)
included in the risk assessment are considered in estimating COPC concentrations in food
items and media.
• The measurement receptor's most sensitive life stage is present in the assessment area
being evaluated in the risk assessment.
The body weights and food ingestion rates for measurement receptors are conservative.
Each individual species in a community or class-specific guild is equally exposed.
The proportion of ingested food items and ingested media that is contaminated is assumed
to be 100 percent (i.e., Pt is asigned a value of 1.0); which assumes that a measurement
receptor feeds only in the assessment area.
Although conservative in nature, U.S. EPA OSW recommends use of these assumptions considering that
the results of a screening level risk assessment are intended to support development of permits and focus
risk management efforts. Site-specific exposure characterization that my warrant deviation from these
screening level assumptions should be reviewed and approved by the appropriate permitting authority
following recommendations provided in Section 3.12.
5.3.1 Ingestion Rates for Measurement Receptors
As indicated in Equation 5-1 above, species specific ingestion rates of food items and media, on a body
weight basis, are required for calculating the daily dose of COPC ingested for each measurement receptor.
As specified for use in the equations presented in Appendix F, it is important to ensure that food
(i.e., plants and animals) and water ingestion rates are on a wet weight basis, and ingestion rates for soil
and sediment are on a dry weight basis (see Appendix F). Table 5-1 provides values for ingestion rates for
measurement receptors identified in the example food webs presented in Chapter 4. These values are
primarily obtained from the allometric equations presented in the Wildlife Exposure Factors Handbook
U.S. EPA Region 6 U.S. EPA
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(U.S. EPA 1993o). Soil ingestion rates were calculated using the percent soil in estimated diets of wildlife
as described in Beyer et al. (1994).
Species specific ingestion rates including food and water have been measured for few wildlife species.
Therefore, allometric equations presented in the Wildlife Exposure Factors Handbook were used to
calculate species specific food and media ingestion rates. Allometry is defined as the study of the
relationship between the growth and size of one body part to the growth and size of the whole organism,
including ingestion rates, and can be used to estimate species specific values for ingestion (U.S. EPA
1993o). Allometric equations should only be used for those taxonomic groups used to develop the
allometric relationship. For example, equations developed for carnivorous mammals should not be used to
calculate food ingestion rates for herbivorous mammals. For a detailed discussion on the development and
limitations of the allometric equations used to obtain ingestion rate values presented in Table 5-1, see U.S.
EPA (1993o) and Nagy (1987).
The use of individual species body weights may result in some uncertainty, since individual species usually
exhibit values somewhat different from those predicted by allometric modeling derived using multiple
species. However, this uncertainty is expected to be minimal since measurement receptors were selected to
maximize exposure for each class-specific guild, as discussed in Section 4.4.2.
If species specific values are not available in U.S. EPA (1993o), or can not be represented by the allometric
equations presented, other sources to evaluate include:
U.S. Fish and Wildlife Service (FWS) publications (e.g., U.S. FWS 1979)
• State wildlife resource management agencies
• Published scientific literature
• Publications by wildlife conservation organizations (such as The National Audubon
Society)
U.S. EPA Region 6 U.S. EPA
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Screening Level Ecological Risk Assessment Protocol
Chapter 5: Analysis
August 1999
TABLE 5-1
INGESTION RATES FOR EXAMPLE MEASUREMENT RECEPTORS
Measurement
Receptor
American Kestrel
American Robin
Canvas Back
Deer Mouse
Least Shrew
Long Tailed Weasel
Mallard Duck
Marsh Rice Rat
Marsh Wren
Mink
Example
Food Weba
SG, TG, SS,
FW,BR
F
FW, BR,
SW
TG, F, SG,
SS
SG, FW,
TG
TG ,F, SS
BR,FW
BR, SW
SW
FW,BR
Body
Weight (kg)
l.OOE-01
8.00E-02
7.70E-01 b
1.48E-02
4.00E-03
8.50E-02
1.04E+00
3.00E-02
l.OOE-02
9.74E-01
Reference
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
National
Audubon Society
1995
National
Audubon Society
1995
U.S. EPA 1993o
National
Audubon Society
1995
U.S. EPA 1993o
U.S. EPA 1993o
Food IR e
(kgWW/
kg BW-day)
4.02E-01 f
4.44E-01 f
1.99E-01 f
5.99E-01 s
6.20E-01 h
3.33E-01 '
1.79E-01 f
4.40E-01 g
9.26E-01 f
2.16E-011
Reference
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
Water IR
(L /kg BW-
day)
1.25E-01k
1.37E-01k
6.43E-02 k
1.51E-011
1.72E-011
1.27E-011
5.82E-02 k
1.41E-011
2.75E-01 k
9.93E-02 '
Reference
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
Soil/Sed IR m
(kgDW/
kg BW-day)
1.39E-0311
1.43E-020
1.82E-03P
1.44E-031
1.36E-020
2.98E-03 r
3.18E-03
2.33E-03 s
1.96E-020
1.93E-031
Reference
Pascoeetal. 1996
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
U.S. EPA Region 6
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TABLE 5-1
INGESTION RATES FOR EXAMPLE MEASUREMENT RECEPTORS
Measurement
Receptor
Mourning Dove
Muskrat
Northern Bobwhite
Northern Harrier
Red Fox
Red-tailed Hawk
Salt-marsh Harvest
Mouse
Short-tailed Shrew
Spotted Sandpiper
Swift Fox
Western Meadow
Lark
Example
Food Weba
F, SS, TG,
SG
BR,FW
SG, SS
SW
SW
F
SW
F
SW, BR,
FW
SG
TG
Body
Weight (kg)
1.50E-01 c
1.09E+00
1.50E-01
9.60E-01
3.94E+00
9.60E-01 d
9.10E-03
1.50E-02
4.00E-02
1.40E+00
9.00E-02
Reference
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
Food IR e
(kgWW/
kg BW-day)
3.49E-01 f
2.67E-01 '
3.49E-01 f
1.85E-01 f
1.68E-011
1.85E-01 f
7.41E-01 s
6.20E-01 h
5.69E-01 f
1.93E-011
4.21E-01 f
Reference
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
U.S. EPA 1993o;
Nagy 1987
Water IR
(L /kg BW-
day)
1.09E-01k
9.82E-02 '
1.09E-01k
5.99E-02 k
8.63E-02 '
5.99E-02 k
1.58E-011
1.51E-011
1.74E-01k
9.34E-02 '
1.31E-01k
Reference
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
U.S. EPA 1993o
Soil/Sed IR m
(kgDW/
kg BW-day)
7.01E-03 °
6.41E-04
1.20E-02'
9.95E-03 n
1.51E-03
9.95E-03 n
1.78E-031
1.36E-020
4.15E-02"
1.73E-031
1.39E-020
Reference
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
Beyer et al. 1994
U.S. EPA Region 6
Multimedia Planning and Permitting Division
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U.S. EPA
Office of Solid Waste
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TABLE 5-1
INGESTION RATES FOR EXAMPLE MEASUREMENT RECEPTORS
Measurement
Receptor
White-footed Mouse
Example
Food Weba
SS
Body
Weight (kg)
l.OOE-02
Reference
U.S. EPA 1993o
Food IR e
(kgWW/
kg BW-day)
6.14E-018
Reference
U.S. EPA 1993o;
Nagy 1987
Water IR
(L /kg BW-
day)
1.52E-011
Reference
U.S. EPA 1993o
Soil/Sed IR m
(kgDW/
kg BW-day)
2.70E-03
Reference
Beyer et al. 1994
Notes: IR- Ingestion Rate; WW- Wet weight; DW-Dry Weight; BW- Body Weight; kg - kilogram; L - Liter
a = Food Webs: BR - Brackish/Intermediate Marsh; F - Forest; FW - Freshwater/Wetland; SG - Shortgrass Prairie; SS - Shrub/Scrub;
SW - Saltwater Marsh; TG - Tallgrass Prairie.
b = The body weight reported for the mallard is used as a surrogate value for the canvas back.
c = The body weight reported for the northern bobwhite is used as a surrogate value for the morning dove.
d = The body weight reported for the red-tailed hawk is used as a surrogate value for the northern harrier.
e = Food ingestion rate (IR) values are reported in Table 5-1 as kg WW/kg BW-day. To convert IR from a dry weight (as calculated using allometric
equations) to a wet weight basis, the following general equation is used:
IR kg WW/kg BW-day = (IR kg DW/BW-day)/(l - % moisture/100)
Ingestion rate values provided in Table 5-1 are calculated based on assumed percent moisture content of food items of measurement receptors
specified. For herbivores, the moisture content of ingested plant matter is assumed to be 88.0 percent (Taiz et al. 1991). For carnivores, the
moisture content of ingested animal matter is assumed to be 68.0 percent (Sample et al. 1997). For omnivores, an equal fraction of plant and
animal matter is assumed ingested with an overall average moisture content of 78.0 percent [(88.0 + 68.0)/2].
f = Food ingestion rates generated using the following allometric equation for all birds: IR (g/day) = 0.648 Wt °651 (g).
g = Food ingestion rates generated using the following allometric equation for rodents: IR (g/day) = 0.621 Wt °564 (g).
h = Allometric equations reported in U.S. EPA (1993o) do not represent intake rates for shrews; therefore, measured field values from the referenced
sources are presented.
i = Food ingestion rates generated using the following allometric equation for all mammals: IR (g/day) = 0.235 Wt °822 (g).
= Food ingestion rates generated using the following allometric equation for herbivores: IR (g/day) = 0.577 Wt °727 (g).
= Water ingestion rates generated using the following allometric equation for all birds: IR (L/day) = 0.059 Wt ° 67° (kg).
= Water ingestion rates generated using the following allometric equation for all mammals: IR (L/day) = 0.099 Wt ° 90° (kg).
= Soil and sediment ingestion rates calculated based on percent soil in diet as reported in Beyer et al. 1994.
= Percent soil in diet reported for the bald eagle is used as a surrogate value for the american kestrel, northern harrier, and red-tailed hawk.
= Percent soil in diet is assumed as 10.0 percent of diet based on range presented in Beyer et al. 1994.
J
k
1
m
n
o
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p = Percent soil in diet reported for the mallard is used as a surrogate value for the canvas back.
q = Percent soil in diet reported for the white-footed mouse is used as a surrogate value for the deer mouse and salt-marsh harvest mouse.
r = Percent soil in diet reported for the red fox is used as a surrogate value for the long-tailed weasel, mink, and swift fox.
s = Percent soil in diet is assumed as 2.0 percent of diet based on range presented for herbivores.
t = Percent soil in diet reported for the wild turkey is used as a surrogate value for the northern bobwhite.
u = Percent soil in diet reported for the western sandpiper is used as a surrogate value for the spotted sandpiper.
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5.3.2 COPC Concentrations in Food Items of Measurement Receptors
Determination of COPC concentrations in food items is required for calculating the daily dose of COPC
ingested for each class-specific guild measurement receptor being evaluated. Since the risk assessment
considers potential future exposure that may occur as a result of facility emissions over time, these
concentrations are generally expected to be estimated mathmatically. The following subsections provide
guidance for estimating COPC concentrations in the following groups of food items:
• Invertebrates, phytoplankton, and rooted aquatic plants;
• Terrestrial plants;
• Fish; and
• Mammals, birds, reptiles, and amphibians.
5.3.2.1 COPC Concentration in Invertebrates, Phytoplankton, and Rooted Aquatic Plants
COPC concentrations in invertebrate, phytoplankton, and rooted aquatic plants can be calculated by
rearranging the mathmatical expression for a bioconcentration factor (BCF). Equation 5-2 is the
mathmatical definition of a BCF, which is the ratio, at steady-state, of the concentration of a compound in a
food item to its concentration in a media. Equation 5-3 is the same equation expressed in terms of a COPC
concentration in a food item.
Cl
BCF = Equation 5-2
Ci - CM • BCF Equation 5-3
where
BCF = Bioconcentration factor (unitless [soil, sediment], or L/kg [water])
Ct = COPC concentration in /th plant or animal food item (mg COPC/kg)
CM = COPC concentration in media (mg/kg [soil, sediment], or mg/L [water])
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Equation 5-3 estimates a COPC concentration in an invertebrate, phytoplankton, and rooted aquatic plant
to evaluate dose ingested to the measurement receptor. Calculation of COPC concentrations in media is
further discussed in Chapter 3 and Appendix B. Media-to-receptor BCFs are receptor- and media-specific,
and values along with supporting discussion are provided in Appendix C. Appendix F provides specific
equations and supporting discussion for calculating COPC concentrations in plant and animal food items.
Equilibrium Partitioning (EqP) Approach
When adequate site-specific characterization data is available, specifically organic carbon fraction data for
soil and sediment, the permitting authority may elect in some cases to allow the calculation of COPC
concentrations in soil invertebrate (Cornell and Markwell 1990) or sediment invertebrate (U.S. EPA
1993q) using the equilibrium partitioning (EqP) approach. However, the EqP approach is not prefered
over use of measured BCF values multiplied by the COPC concentration in the media (i.e., sediment or
soil), following the approach previously discussed.
The EqP approach utilizes the correlation of the concentrations of nonionic organic compounds in sediment,
on an organic carbon basis, to their concentrations in the interstitial water, to determine the observed
biological effects on sediment invertebrate (U.S. EPA 1993q). The EqP approach is only applicable for
(1) hydrophobic nonionic organic compounds, (2) soil- and sediment-invertebrates, and (3) COPCs with
empirical water bioconcentration factors (U.S. EPA 1993q). Also, the EqP approach assumes that the
partitioning of the compound in sediment organic carbon and interstitial water are in equilibrium, and the
sediment—interstitial water equilibrium system provides the same exposure as a water-only exposure (U.S.
EPA 1993q).
To calculate the COPC concentration in an invertebrate using the EqP approach, the soil or sediment
interstitial water concentration should be multiplied by the BCF determined from a water exposure for a
benthic invertebrate:
Cj = CIW • BCFm Equation 5-4
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where
Cj = COPC concentration in soil or benthic invertebrate (mg/kg)
CIW = COPC concentration in soil or sediment interstitial water (mg/L)
BCFm = Bioconcentration factor for water-to-invertebrate (L/kg)
Equation 5-5 is used to calculate the COPC concentration in soil or sediment interstitial water for this
approach:
c
Ciw = ~ — Equation 5-5
Joe ' Ko
oc
where
CIW = COPC concentration in soil or sediment interstitial water (mg/L)
CM = COPC concentration in media (mg/kg [soil, sediment])
foc = Fraction of organic carbon in soil or sediment (unitless)
Koc = Organic carbon partitioning coefficient (L/kg)
5.3.2.2 COPC Concentration in Terrestrial Plants
The COPC concentration in terrestrial plants (CTP) is calculated by summing the plant concentration due to
direct deposition (Pd), air-to-plant transfer (Pv), and root uptake (Pr). Equation 5-6 should be used to
compute a COPC concentration in terrestrial plants:
CTP = Pd + Pv + Pr Equation 5-6
where
CTP = COPC concentration in terrestrial plants (mg COPC/kg WW)
Pd = COPC concentration in plant due to to direct deposition (mg/kg WW)
Pv = COPC concentration in plant due to air-to-plant transfer (mg/kg WW)
Pr = COPC concentration in plant due to root uptake (mg/kg WW)
Calculation of Pd, Pv, and Pr is presented in Chapter 3 and Appendix B. Calculation of CTP is further
discussed in Appendix F.
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5.3.2.3 COPC Concentration in Fish
The COPC concentration in fish is calculated by multiplying a COPC-specific BCF and trophic
level-specific FCM by the dissolved water concentration, as follows:
CF = BCF • FCM • Cdw Equation 5-7
where
CF = COPC concentration in fish (mg/kg)
BCF = Bioconcentration factor for water-to-fish (L/kg)
FCM = Food-chain multiplier (unitless)
Cdw = Dissolved phase water concentration (mg/L)
The COPC concentration in fish is calculated using dissolved phase water concentrations, since
bioconcentration, or estimated bioaccumulation, values are typically derived from studies based on
dissolved phase water concentrations. The FCM used to calculate a COPC concentration in fish should be
appropriate for the trophic level of the fish ingested by a measurement receptor. Development of FCM
values is discussed in the following subsection, and actual recommended values are provided in Table 5-2.
The dissolved phase water concentration is calculated as discussed in Chapter 3 and Appendix B. Values
for bioconcentration factors for water-to-fish, and discussion on their determination, can be found in
Appendix C. Calculation of CF is further discussed in Appendix F.
Food-Chain Multipliers
FCMs presented in Table 5-2 were adopted directly from U.S. EPA (1995k), which determined them for
Kow values ranging from 3.5 through 9.0 using the Gobas (1993) model. U.S. EPA determined FCMs to
develop water criteria protective to wildlife of the Great Lakes (U.S. EPA 1995J). As presented in
Equation 5-8, U.S. EPA (1995k) calculated trophic level specific FCMs (see Table 5-2) utilizing BAF
values obtained from the Gobas (1993) model and compound specific Km values.
BAFi
FCM = Equation 5-8
K
ow
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where
FCM = Food-chain multiplier (unitless)
BAFj = Bioaccumulation factor reported on a lipid-normalized basis using the
freely dissolved concentration of a chemical in the water (L/kg)
Kow = Octanol-water partition coefficient (L/kg)
BAF values predicted using the Gobas (1993) model were based on chemical concentrations in both the
water column and surface sediment. Bioaccumulation values for fish were determined from the rate of
chemical uptake, the rate of chemical depuration (including excretion), metabolism, and dilution due to
growth. As reported in U.S.. EPA (1995k), data on physicochemical parameters and species
characteristics reported by Oliver and Niimi (1988), Flint (1986), and Gobas (1993) were used.
For each Kow value, the Gobas (1993) model reported correlating BAFt values specific to each organism in
the food web. U.S. EPA (1995k) determined trophic level-specific FCMs by calculating the geometric
mean of the FCM for each organism in each respective trophic level. The FCMs were developed assuming
no metabolism of a compound. Thus, for compounds where metabolism may occur (i.e., some PAHs), the
COPC concentration in fish ingested by a measurement receptor may be overestimated. This information
should be noted as an uncertainty in risk characterization. It should also be noted that the FCM values
presented in Table 5-2 were developed using Kow values reported in U.S. EPA (1995k); which may differ
from Kow values specified in Appendix A-2 of this guidance.
Using the U.S. EPA (1995k) assumption that a compound's log Kow value approximates its BCFh
Equation 5-8 for determining FCM values can also be expressed as follows:
BAFi
FCM = Equation 5-9
BCFl
where
FCM = Food-chain multiplier (unitless)
BAF! = Bioaccumulation factor reported on a lipid-normalized basis using the
freely dissolved concentration of a chemical in the water (L/kg)
BCFl = Bioconcentration factor reported on a lipid-normalized basis using the
freely dissolved concentration of a chemical in the water (L/kg)
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Equation 5-9 can also be written to demonstrate the relation of a BCF multiplied by a FCMto estimate a
BAF, as shown in the following equation:
BAF = BCF • FCM Equation 5-10
where
BAF = Bioaccumulation factor (L/kg)
BCF = Bioconcentration factor (L/kg)
FCM = Trophic level-specific food-chain multiplier (unitless)
FCMs are specified for use in this guidance to model a COPC concentration in fish, and also mammalian
and bird food items, that are ingested by a measurement receptor. The BCF-FCM approach accounts for
the uptake or bioaccumulation of COPCs into organisms, typically represented in equations as a BAF (U.S.
EPA 1995J). The availability of data allows the BCF-FCM approach to be more consistently applied
across class-specific guilds within food webs being evaluated.
U.S. EPA OSW recognizes the limitations and uncertainties of applying FCMs derived from aquatic food
web data to terrestrial receptors, as well as all top level consumers, whether their food is chiefly aquatic or
not. However, the BCF-FCM approach is recommended in this guidance because (1) evaluation of multiple
food chain exposure pathways is typically required to estimate risk to multiple mammalian and avian guilds
in several food webs, (2) screening level risk assessment results are intended to support develoment of
permits and focus risk management efforts, rather than as a final point of departure for further evaluation,
and (3) U.S. EPA OSW is aware of no other applicable multipathway approaches for consistently and
reproducibly estimating COPC concentrations in prey ingested by upper-trophic-level ecological receptors,
considering current data limitations. Therefore, U.S. EPA OSW believes the BCF-FCM approach is the
best available quantitative method for estimating COPC concentrations in upper trophic level food items
ingested by measurement receptors, considering data availabilty and the objectives inherent to a screening
level risk assessment.
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TABLE 5-2
FOOD-CHAIN MULTIPLIERS
Log Kow
2.0
2.5
3.0
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.0
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
Trophic Level of Consumer
2
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
3
1.0
1.0
1.0
1.0
1.0
1.1
1.1
1.1
1.1
1.1
1.2
1.2
1.3
1.3
1.4
1.5
1.6
1.8
2.0
2.2
2.5
2.8
3.2
3.6
4.2
4.8
5.5
6.3
7.1
4
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.1
1.1
1.1
1.1
12
1.2
1.3
1.5
1.6
1.9
2.2
2.6
3.2
3.9
4.7
5.8
7.1
8.6
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TABLE 5-2
FOOD-CHAIN MULTIPLIERS
Log Kow
5.1
5.8
5.9
6.0
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.0
8.1
8.2
8.3
8.4
8.5
8.6
Trophic Level of Consumer
2
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
3
8.0
8.8
9.7
11
11
12
13
13
14
14
14
14
14
14
14
14
13
13
13
12
11
10
9.2
8.2
7.3
6.4
5.5
4.7
3.9
3.3
4
10
12
14
16
18
20
22
23
25
26
26
27
27
26
25
24
23
21
19
17
14
12
9.8
7.8
6.0
4.5
3.3
2.4
1.7
1.1
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TABLE 5-2
FOOD-CHAIN MULTIPLIERS
Log Kow
8.7
8.8
8.9
9.0
Trophic Level of Consumer
2
1.0
1.0
1.0
1.0
3
2.7
2.2
1.8
1.5
4
0.78
0.52
0.35
0.23
Source: U.S. EPA. 1995k. "Great Lakes Water Quality Initiative Technical Support Document for the Procedure to
Determine Bioaccumulation factors." EPA-820-B-95-005. Office of Water. Washington, D.C. March.
5.3.2.4 COPC Concentration in Mammals, Birds, Amphibians, and Reptiles
The COPC concentration in mammals and birds, as food items ingested by measurement receptors, are
estimated using equations specific to each guild (i.e., herbivores, omnivores, and carnivores), and based on
the plant and animal food items, and media ingested. Similar to calculating the COPC concentration in
fish, a BCF-FCM approach is used to account for bioaccumulation. However, the contribution of COPC
concentrations from each food item ingested must be accounted for directly for wildlife, whereas, the
derivation of BCF-FCM values already accounts for the COPC contributions from all pathways for fish.
Also for wildlife, a ratio of FCMs is applied to each animal food item ingested to account for the increase
in COPC concentration occurring between the trophic level of the prey item (TLn) and the trophic level of
the omnivore (TL3) or carnivore (TL4).
General equations for estimating COPC concentrations of food items in each guild, including use of a FCM
ratio to estimate biomagnification, are described in the following subsections using mammals and birds as
examples. Specific equations and discussion of associated parameters are provided in Appendix F. It
should be noted that due to limited availabilty of biotransfer and toxicity data for reptiles and amphibians,
the equations in the following subsections and in Appendix F have not been specifically described for use to
model exposure to these receptors. However, if site-specific conditions and data warrant evaluation of
reptiles and amphibians, the permitting authority may elect to utilize the same generic equations presented.
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Herbivorous Mammals and Birds
As indicated in Equation 5-11, the COPC concentration in herbivorous mammals and birds is calculated by
summing the contribution due to ingestion of contaminated plant food items and media. The general
equation for computing COPC concentration in herbivores is as follows:
C • RCF • P • F } + ( C • RCF
^-Pi "^ Pi-H * Pi L Pi ' ' ^s/sed S/BS-H
Equation 5-11
+ ( Cwctot • BCFW_H -Pw)
where
CH = COPC concentration in herbivore (mg/kg)
CPi = COPC concentration in /th plant food item (mg/kg)
BCFPi_H = Bioconcentration factor for plant-to-herbivore for /'th plant food
item (unitless)
PPi = Proportion of /th plant food item in diet that is contaminated
(unitless)
FPi = Fraction of diet consisting of /'th plant food item (unitless)
Cs/sed = COPC concentration in soil or bed sediment (mg/kg)
BCFS/BS_H = Bioconcentration factor for soil-to-plant or bed sediment-to-plant
(unitless)
PS/BS = Proportion of soil or bed sediment in diet that is contaminated
(unitless)
Cwctot = Total COPC concentration in water column (mg/L)
BCFW_HM = Bioconcentration factor for water-to-herbivore (L/kg)
Pw = Proportion of water in diet that is contaminated (unitless)
Media-to-herbivore BCF values are COPC and receptor-specific and provided in Appendix C. As
discussed in Appendix D, plant-to-herbivore BCF values are receptor-specific and determined from
biotransfer factors. Calculation of COPC concentrations in plant food items and media is further discussed
in previous sections of Chapter 5, and in Chapter 3 and Appendix B. The variables representing the diet
fraction and proportion of diet contaminated are discussed in Section 5.3 and Appendix F. Appendix F
also provides specific equations and supporting discussion for calculating the COPC concentration in
herbivores.
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Omnivorous Mammals and Birds
As indicated in Equation 5-12, the COPC concentration in omnivorous mammals and birds is calculated by
summing the contribution due to ingestion of contaminated animal and plant food items, and media.
However, unlike herbivores which are TL2 consumers, omnivores are TL3 consumers of animal food
items and a ratio of FCMs is applied to each animal food item ingested to account for the increase in COPC
concentration occurring between the trophic level of the prey item (TLn) and the trophic level of the
omnivore (TL3). In general, the COPC concentration in omnivores depends on the COPC concentration in
each food item ingested, and the trophic level of each food item, as follows:
C
OM
FCM
• PAl • FAI)
TLn-A,
, • BCFpi_OM • Pp, • Fpi)
Equation 5-12
where
COM = COPC concentration in omnivore (mg/kg)
CAi = COPC concentration in /'th animal food item (mg/kg)
FCMTL3 = Food chain multiplier for trophic level 3 (unitless)
FCMTLn_Ai = Food chain multiplier for trophic level of /'th animal food item
(unitless)
PAi = Proportion of /th animal food item in diet that is contaminated
(unitless)
FAi = Fraction of diet consiting of /'th animal food item (unitless)
BCFPi_OM = Bioconcentration factor for plant-to-omnivore for /'th plant food
item (unitless)
CPi = COPC concentration in /'th plant food item (mg/kg)
PPi = Proportion of /th plant food item that is contaminated (unitless)
= Fraction of diet consiting of /'th plant food item (unitless)
= COPC concentration in soil or bed sediment (mg/kg)
= Bioconcentration factor for soil- or bed sediment-to-omnivore
(unitless)
= Proportion of soil or bed sediment in diet that is contaminated
(mg/kg)
Cwctot = Total COPC concentration in water column (mg/L)
BCFW_OM = Bioconcentration factor for water-to-omnivore (L/kg)
Pw = Proportion of water in diet that is contaminated (unitless)
Media-to-omnivore BCF values are COPC and receptor-specific and provided in Appendix C. The use of
an FCM ratio to estimate biomagnification between trophic levels is discussed in a following subsection.
F
;ed
'77
'rS/BS-OM
P
S/BS
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Calculation of COPC concentrations in animal food items is further discussed in previous sections of
Chapter 5. Calculation of COPC concentrations in plant food items and media is further discussed in
previous sections of Chapter 5, and in Chapter 3 and Appendix B. The variables representing the diet
fraction and proportion of diet contaminated are discussed in Section 5.3 and Appendix F. Appendix F
also provides specific equations and supporting discussion for calculating the COPC concentration in
omnivores.
Carnivorous Mammals and Birds
As indicated in Equation 5-13, the COPC concentration in carnivorous mammals and birds is calculated by
summing the contribution due to ingestion of contaminated animal and media food items. In general, the
equation for computing a COPC concentration for carnivorous food items is similar to the corresponding
equation for omnivores; only without the component accounting for ingestion of plant food items.
Similarly, a ratio of FCMs is applied to each animal food item ingested to account for the increase in
COPC concentration occurring between the trophic level of the prey item (TLn) and the trophic level of the
carnivore (TL4). The COPC concentration in carnivores depends on the COPC concentration in media, in
each animal food item ingested, their respective trophic level, as follows:
_
CC =
CA,
FCM
TL4
PAi ' FA,
TLn-A,
C
' ^s/sed ' tf^-^S/BS-C ' "s/BS /
BCFur_^ • Pur )
W_C w
Equation 5-13
where
Cc = COPC concentration in carnivore (mg/kg)
CAi = COPC concentration in /'th animal food item (mg/kg)
FCMTL3 = Food chain multiplier for trophic level 4 (unitless)
FCMTLn_Ai = Food chain multiplier for trophic level of /'th animal food item
(unitless)
PAi = Proportion of /th animal food item in diet that is contaminated
(unitless)
FAi = Fraction of diet consisting of /'th animal food item (unitless)
Cs/sed = COPC concentration in soil or bed sediment (mg/kg)
BCFS/BS_C = Bioconcentration factor for soil- or bed sediment-to-carnivore
(unitless)
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PS/BS = Proportion of soil or bed sediment in diet that is contaminated
(mg/kg)
Cwctot = Total COPC concentration in water column (mg/L)
BCFW_C = Bioconcentration factor for water-to-carnivore (L/kg)
Pw = Proportion of water in diet that is contaminated (unitless)
Media-to-carnivore BCF values are COPC and receptor-specific and provided in Appendix C. The use of
an FCM ratio to estimate biomagnification between trophic levels is discussed in the following subsection.
Calculation of COPC concentrations in animal food items is further discussed in previous sections of
Chapter 5. Calculation of COPC concentrations in plant food items and media is further discussed in
previous sections of Chapter 5, and in Chapter 3 and Appendix B. The variables representing the diet
fraction and proportion of diet contaminated are discussed in Section 5.3 and Appendix F. Appendix F
also provides specific equations and supporting discussion for calculating the COPC concentration in
carnivores.
Use of Food Chain Multiplier Ratio to Estimate Biomagnification
Biomagnification involves the transfer of a chemical in food through successive trophic levels (Hamelink et
al. 1971). Chemicals with greatest potential to biomagnify are highly lipophillic, have low water
solubilities, and are resistant to being metabolized (Metcalf et al. 1975). To account for COPC
biomagnification in the food chain, U.S. EPA OSW recommends the use of FCM ratios as derived by U.S.
EPA (1995k).
FCM ratios are used to estimate the increase in a COPC concentration resulting from the ingestion of TL2
prey (i.e., animal food item) by a TL3 measurement receptor (i.e., omnivore or carnivore), and the ingestion
of TL2 and TL3 prey by a TL4 measurement receptor. Biomagnification, expressed as a biomagnification
factor (BMP), equals the quotient of the FCM of the measurement receptor divided by the FCM of the prey.
It is important to note that the basic difference between the FCM and BMF is that the FCMs relate back to
trophic level one, whereas BMFs always relate back to the preceding trophic level (U.S. EPA 1995k). This
relation is entirely compatible, but confusion can result if the terms specific to trophic level are not used
consistently and clearly (U.S. EPA 1995k). As presented in U.S. EPA (1995k), the following relation of
FCM to BMF can be expressed as follows:
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BMFTL2 = FCMTL2 Equation 5-14
BMFTL3 = FCMTL3IFCMTL2 Equation 5-14A
where
BMFn = Biomagnification factor for nth trophic level
FCMTLn = Food chain multiplier for nth trophic level
5.4 ASSESSMENT OF TOXICITY
Toxicity of a COPC is assessed by identifying toxicity reference values (TRVs) specific to a COPC and the
measurement receptor being evaluated. As discussed in Chapter 6, TRVs are subsequently set as the
denominator for computing COPC ecological screening quotients (ESQs) during risk characterization. The
available TRVs used in risk characterization for lower trophic level communities are media specific;
whereas TRVs for upper trophic level class-specific guilds are provided in terms of dose ingested. TRVs for
community and class-specific guild measurement receptors are further described below:
Community (lower trophic level) TRVs are media specific and used to screen ecological
effects to receptors inhabiting soil, surface water, and sediment. Community TRVs are
expressed on a concentration basis, such as milligrams of COPC per kilogram of soil, and
generally either:
(1) a COPC media concentration that, based on its intended use by a regulatory
agency, confers a high degree or protection to receptor populations or communities
inhabiting the media (these include regulatory values such as federal ambient
water quality criteria, state no-effect-level sediment quality guidelines, and
sediment screening effect concenentrations), or
(2) a laboratory-derived toxicity value representing a COPC media concentration that
causes, over a chronic exposure duration, no adverse effects to a representative
ecological receptor (e.g., no-observed-effect-concentration).
• Class-specific guild (upper trophic level) TRVs are used to screen ecological effects to
wildlife, and expressed as a COPC daily dose ingested that causes, over a chronic
exposure duration, no observed adverse effects to a measurement receptor. Class-specific
guild TRVs are expressed in units of mass (e.g., milligrams or micrograms) of COPC per
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kilogram body weight (wet weight) per day.
Guidance for selection of TRVs for community and class-specific guild measurement receptors is provided
in the following sections. TRVs specific to example measurement receptors presented in the food webs in
Chapter 4 are available in Appendix E.
5.4.1 General Guidance on Selection of Toxicity Reference Values
Compound specific TRVs should be identified for each measurement receptor evaluated to characterize risk
to a community or class-specific guild. U.S. EPA OSW recommends evaluation of the following sources
of toxicity values, listed in order of general preference, in determining TRVs for use in a screening level risk
assessment:
Toxicity values developed and/or adopted by federal and/or state regulatory agencies;
generally provided in the form of standards, criteria, guidance, or benchmarks. Toxicity
values developed and/or adopted by federal or state regulatory agencies are generally media
specific, and reported only for surface water and sediment. Examples include state or federal
ambient water quality criteria (AWQC), National Oceanic and Atmospheric Administration
(NOAA) effects range-low (ERL) values for sediment (Long et al. 1995), and State of Florida
sediment quality guidelines (MacDonald 1993).
Toxicity values published in scientific literature. Appropriate values should be derived from a
laboratory study which characterizes adverse effects on ecologically-relevant endpoints
(e.g., growth, reproduction, mortality). As discussed in Section 5.4.1.3, toxicity values obtained
from scientific literature may also require application of an uncertainty factor (UF) to account for
extrapolation uncertainty.
Toxicity values calculated for sediment using equilibrium partitioning (EqP) approach. The
EqP approach is further described in Section 5.3.2.1. Calculating sediment toxicity values using
the EqP approach requires determination of (1) an organic carbon content of the sediments, and
(2) a corresponding surface water toxicity value.
Toxicity values from surrogate compounds. Surrogate compounds are selected through
evaluation of parameters such as chemical structure and toxicity mechanisms of action. For
example, low molecular weight (i.e. those have two or less rings) polyaromatic hydrocarbons
(PAH's) could be grouped together and evaluated using the toxicity data from a PAH congener
belonging to this group.
The evaluation of toxicity values published in scientific literature should consider (1) ecological relevance
of the study, (2) exposure duration (e.g., chronic, acute), and (3) study endpoints (e.g., NOAEL, LOAEL).
The identification of literature toxicity values used to derive TRVs should focus on toxicological data
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characterizing adverse effects on ecologically relevant endpoints, such as growth, seed germination,
reproduction, and survival. Study endpoints specified for reported toxicity values generally include the
following:
Soil, surface water, and sediment measurement receptors
No-observed-effect-level (NOEL) or no-observed-effect-concentration (NOEC)
Lowest-observed-effect-level (LOEL) or lowest-observed-effect-concentration
(LOEC)
Median lethal concentration to 50 percent of the test population (LC50) or median
effective concentration for 50 percent of the test population (EC50)
Wildlife measurement receptors
No-observed-adverse-effect-level (NOAEL)
Lowest-observed-adverse-effect-level (LOAEL)
Median lethal dose to 50 percent of the test population (LD50)
Evaluation of toxicity test data is further discussed in Section 5.4.1.1.
When multiple studies are assessed equally under the criteria above, professional judgement can be applied
to determine the most appropriate study and corresponding toxicity value to be selected as the TRV(see
Section 5.4.1.2). As discussed in Section 5.4.1.3, toxicity values obtained from scientific literature may
also require application of an UF to account for extrapolation uncertainty (due to differences in test
endpoint and exposure duration) when considering use of the test value as a TR Vm a screening level risk
assessment.
5.4.1.1 Evaluation of Toxicity Test Data
A TRV should represent a COPC concentration or dose that causes no observed adverse effects to an
ecologically relevant endpoint of a receptor exposed for a chronic (long-term) duration. As noted above,
evaluation of test data from ecologically relevant studies should be further assessed based on exposure
duration and study endpoint.
The following hierarchy, in terms of decreasing preference, should be followed to assess exposure duration
and study endpoint:
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1. Chronic NOAEL
2. Subchronic NOAEL
3. Chronic LOAEL
4. Subchronic LOAEL
5. Acute median lethality point estimate
6. Single dose toxicity value
The following guidelines should be used to generally determine exposure duration:
• For fish, mammals, and birds:
A chronic test lasts for more than 90 days
A subchronic test lasts from 14 to 90 days
An acute test lasts less than 14 days
• For other receptors:
A chronic test lasts for 7 or more days
A subchronic test lasts from 3 to 6 days
An acute test lasts less than 3 days
The logic followed to identify the a toxicity value should be fully documented. Sources of toxicity values
include electronic databases, reference compendia, and technical literature. Toxicity values identified from
secondary sources should be verified, wherever possible, by reviewing the original study. If an original
study is unavailable, or multiple studies of similar quality are available, best professional judgment should
be used to determine an appropriate toxicity value.
5.4.1.2 Best Professional Judgement for Evaluating Toxicity Values
If more than one toxicity study meets a set of qualifying criteria applicable for study endpoint and exposure
duration, best professional judgement should be used to identify the most appropriate study and
corresponding toxicity value for TRV selection. The most appropriate study is the one with the least
uncertainty about the accuracy of the value of endpoint (i.e., NOAEL) that, ultimately, provides the
greatest degree of protectiveness to the applicable measurement receptor. The most appropriate study
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should be identified by reviewing the experimental design of each study. Discussed below are important
aspects of experimental design that should be evaluated.
• Number of treatments, spread between treatments, and number of replicates per
treatment. The number of treatments and the spread between exposure concentrations (or
dose groups) will affect the accuracy of the test endpoint (such as the NOAEL). That is,
the smaller the spread between the NOAEL and LOAEL, the less the uncertainty is about
the true concentration or dose at which there is no adverse effect. The statistical power of
a toxicity test (or any test for that matter) is dependent, in large part, on the number of
replicates (or number of animals per dose). That is, the ability of a test to detect statistical
differences (test sensitivity) increases as the number of replicates increase.
Exposure route. The exposure route of the test should coincide with the applicable
exposure route or pathway under consideration in the risk assessment. For example, the
screening level risk assessment may evaluate the risk of contaminated soils to terrestrial
plants due to exposure to bulk soil. Therefore, a terrestrial plant toxicity study that
evaluated the effects of soil solutions on a plant species may be a less appropriate than a
study based on effects of bulk soil.
• Exposure during sensitive life stage. Ideally, all toxicity studies would evaluate the
effects of a toxicant on the most sensitive life stage, such as neonatal zooplankton and first
instar larvae. Therefore, the exposure duration should be receptor- and toxicant-specific.
• Nominal or measured test concentrations. Measured test concentrations more accurately
estimate the true concentration of a toxicant presented to a receptor. Nominal, or
unmeasured, test concentrations do not account for potential losses of the toxicant (such as
toxicant adsorbed to particulate material) or for inaccuracies in preparing test solutions.
In addition, samples for measuring test concentrations should be collected from the
exposure chamber, not the delivery system.
• Use, type, and performance of controls. A positive control (no toxicant) should be used
in each toxicity study. The only difference between a positive control and a treatment is
the absence of the toxicant from the control. Performance in a positive control should meet
pre-existing performance criteria (such as acceptable survival). Treatment performance
should be statistically compared to (or inferred from in some circumstances) to control
performance to identify statistical endpoints (such as the NOAEL and LOAEL). In some
situations, a negative control (toxicant with known toxicity, also called a performance
control) may be appropriate. If a negative control is used, its results should be compared
to standards to determine if test receptor sensitivity was acceptable.
• Method used to determine endpoint (i.e., NOAEL). Ideally, an acceptable number of
replicates should be used so a test has statistical power. An appropriate statistical test
should be performed to identify the NOAEL. In some cases, the NOAEL may have to be
inferred because of insufficient number of replicates. While the latter is not unscientific,
the former method provides a measure that the conclusion might be false. For example, if
test results are statistically analyzed at a probability level of 95 percent, there is a 5
percent chance that the results of the statistical analysis are false.
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5.4.1.3 Uncertainty Factors for Extrapolation From Toxicity Test Values to TRVs
Incomplete knowledge of the actual toxicity of a chemical leads to the use of UFs to reduce the likelihood
that risk estimates do not underestimate risk. Historically, UFs have been used for various extrapolations,
and their applications reflect policy to provide conservative estimates of risk (Chapman et al. 1998). As
discussed below, UFs are used in the risk assessment to reduce the probability of underestimating
ecological risk from exposures to combustor emissions. This is performed by multiplying a toxicity value
by a UF to produce a TRVreflecting an NOAEL for a chronic exposure duration.
UFs should be used to convert a toxicity value to a chronic NOAEL-based TRV. In most cases, the UFs
discussed below should be applicable to available toxicity values. In some cases, however, irregular
toxicity data (such as, a subchronic LC50) may be the only available information. In these cases, the
toxicity data should be thoroughly reviewed and professional judgment should be used to identify
appropriate UFs that are consistent with those listed below. Special attention should be taken with toxicity
values from single oral dose, intraperitoneal, and subchronic lethality tests.
Specifically, UFs should be used to account for extrapolation uncertainty due to differences in test endpoint
and exposure duration:
• Test endpoint uncertainty—extrapolation from a non-NOAEL endpoint (e.g., LOAEL,
LD50) to an NOAEL endpoint
Duration uncertainty—extrapolation from a single dose, acute, or subchronic duration to a
chronic duration
Except as noted above for irregular toxicity data, the following UFs (Calabrese and Baldwin 1993) should
be used to convert a toxicity test endpoint to a TRVequivalent to a chronic NOAEL:
• A chronic LOAEL (or LOEL or LOEC) should be multiplied by a UF of 0.1 to convert it
to a chronic NOAEL
A subchronic NOAEL should be multiplied by a UF of 0.1 to convert it to a chronic
NOAEL.
• An acute lethal value (such as an LC50 or LD50) should be multipled by an UF of 0.01 to
convert it to a chronic NOAEL.
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Chapter 6
Risk Characterisation
•'• '!vy?
What's Covered in
4- Risk Estimation
4- Risk Description ,;''''.v!^^^^ti«;'^^?^C,¥^^4^r^••'^.:'^:
4- Uncertainty and Limitations of the Screening Level Risk Assessment
Risk characterization includes risk estimation and risk description (U.S. EPA 1992b). Risk estimation is
an integration of the exposure assessment (see Section 5.1) and the toxicity assessment (see Section 5.4) to
determine the potential risk to a community or guild from exposure to a COPC. Risk estimation is
quantified using the quotient method to calculate an ecological screening quotient (ESQ) (Suter 1993).
Risk description describes the magnitude and nature of potential risk for each community and guild, based
on the quantitative results of the risk estimation and calculated ESQ values. Risk description also discusses
the significance of the default assumptions used to assess exposure, because they affect the magnitude and
certainty of the calculated ESQ value. The resultant risk characterization should consider any major
uncertainties and limitations associated with results generated in performing the screening level risk
assessment.
Section 6.1 discusses using the quotient method and calculation of ESQs to estimate potential ecological
risk. Section 6.2 discusses various aspects of the risk description. Section 6.3 discusses consideration of
uncertainties and limitations.
6.1 RISK ESTIMATION
To estimate potential ecological risk, an ESQ should be calculated specific to each measurement receptor,
COPC, and exposure scenario location evaluated in the risk assessment. Also, dietary-variable ESQs
should be computed for class-specific guild measurement receptors based on "equal diet" dose and
"exclusive diet" dose, as discussed in Section 5.3. As expressed in Equation 6-1, an ESQ is the quotient of
the COPC estimated exposure level (EEL) divided by the COPC and measurement receptor specific
toxicity reference value (TRV), as follows:
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ESQ = -^r. Equation 6-1
where
ESQ = Ecological screening quotient (unitless)
EEL = COPC estimated exposure level (mass COPC/mass media [communities]
or mass daily dose COPC ingested/mass body weight-day [class-specific
guilds])
TRV = COPC toxicity reference value (mass COPC/mass media [communities]
or mass daily dose COPC ingested/mass body weight-day [class-specific
guilds])
Care should be made to ensure that the units for the EEL value and the TRV are consistent, including
correct use of corresponding wet and dry weights. TRVs specific to organic and inorganic compounds are
typically expressed in units of (ig/kg and mg/kg, respectively. General guidance for determining TRVs is
provided in Chapter 5. Also, Appendix E provides compound specific TRVs for the example measurement
receptors identified in the food webs in Chapter 4.
ESQs for community measurement receptors are calculated using EELs specific to the COPC concentration
in the corresponding media. A COPC specific ESQ should be calculated for each community measurement
receptor at each location evaluated, as appropriate for the food web being analyzed in the risk assessment.
For calculating ESQs for class-specific guild measurement receptors, the EEL is the daily dose of COPC
ingested. A COPC specific ESQ should also be calculated for each class-specific guild measurement
receptor at each location evaluated, as appropriate for the food web being analyzed in the risk assessment.
For class-specific guild measurement receptors, ESQs should be calculated specific to equal and exclusive
diets (see Chapter 5).
To evaluate potential risk resulting from exposure of a measurement receptor to multiple COPCs at a
specific location, each of the COPC-specific ESQ values should be summed to determine a total ESQ.
ESQReceptorTotal = ^ ESQcOPC Specific Equation 6-2
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where
ESQReceptor Totai = Total ecological screening quotient for receptor (unitless)
ESQCOpC specific = COPC specific ecological screening quotient (unitless)
As for COPC-specific ESQs, total ESQs for class-specific guild measurement receptors should be
calculated specific to equal and exclusive diets (see Chapter 5).
6.2 RISK DESCRIPTION
Risk description considers the magnitude and nature of potential risk for community and class-specific
guild measurement receptors evaluated, and provides information for the risk manager and permitting
authority to evaluate the significance of an ESQ value. Also, Section 6.2.2 recognizes some of the default
exposure assumptions that may affect the magnitude of an ESQ value.
6.2.1 Magnitude and Nature of Ecological Risk
The magnitude and nature of potential risk should be further considered for each measurement receptor
with a COPC-specific ESQ value equal to or above risk target levels specified by the appropriate
permitting authority. Interaction between the risk assessor and the risk manager and permitting authority
has been noted throughout the process (See Figure 1 for Scientific Management Decision Points). At the
risk characterization phase of the risk assessment, most of the interaction between the risk assessor and the
risk manager and permitting authority is through description of the certainty of the resulting risk estimates.
Consistent with the NCP and current U.S. EPA guidance (1998c), the risk manager and permitting
authority with input from the risk assessor should also consider the need to collect additional information to
refine risk estimates and/or implement permit requirements (i.e., operating conditions, use of APCDs, waste
feed conditions, or environmental monitoring) at combustion facilities where an ESQ exceeds risk target
levels for ecological communities or guilds that may reasonably be expected to be exposed.
The magnitude and nature of potential risk should also be further considered for each measurement receptor
with a total ESQ value greater than or equal to the target risk levels. While the total ESQ provides the risk
manager and permitting authority with useful information regarding potential risk resulting from exposure
of a measurement receptor to multiple COPCs at a specific location, potential limitations and uncertainties
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associated with the calculation of the total ESQ should be considered before its use. Specifically, the
resulting total ESQ is determined by summing COPC-specific ESQs that will usually be calculated utilizing
TRVs (see Chapter 5) based on different effects (e.g. growth, reproduction), toxicity endpoints
(e.g., NOAEL, LOAEL) and/or exposure durations (e.g., chronic, acute). In considering usability of total
ESQs, U.S. EPA OSW recommends that the risk manager and permitting authority focus on the highest
contributing COPCs, or classes of COPCs which can appropriately be added across effects, toxicity
endpoints and exposure durations, in further evaluating potential risks due to exposure to multiple COPCs.
Broad assessment endpoints rather than toxicologically-specific endpoints are recommended for performing
a screening level ecological risk assessment (see Chapter 5). Therefore, the potential risk to each
community and guild evaluated in the risk assessment should be described. Specifically, potential adverse
effects should be described for each community and guild with a COPC-specific or total ESQ value equal
to or above risk target levels. This should be performed for each selected food web and receptor location
evaluated, and specific to equal and exclusive diets for applicable class-specific guilds. The description
should characterize potential risk to the selected assessment endpoints, based on the measures of effect and
measurement receptors. U.S. EPA OSW recommends that the risk description specific to a measurement
receptor include, at a minimum, the contributing COPCs, emission sources, exposure pathways, and
significant uncertainties.
6.2.1.1 Target Levels
Target levels are risk management based and set by the regulatory authority. Target values are not a
discrete indicator of observed adverse effect. If a calculated risk falls within target values, a regulatory
authority may, without further investigation, conclude that a proposed action does not present an
unacceptable risk. A calculated risk that exceeds these targets, however, would not, in and of itself,
indicate that the proposed action is not safe or that it presents an unacceptable risk. Rather, a risk
calculation that exceeds a target value triggers further careful consideration of the underlying scientific
basis for the calculation.
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6.2.2 Fate and Exposure Assumptions
As noted throughout this guidance, the screening level ecological risk assessment is based on numerous
conservative assumptions affecting the potential for a receptor to be exposed to a compound emitted from a
facility and the numeric magnitude of the resulting estimated risk. These fate and exposure assumptions
are required as a result of current data gaps and uncertainties associated with available scientific
information and data required for risk evaluation. However, U.S. EPA OSW recommends that as
information is available to address data gaps and reduce uncertainties specific to ecological risks identified
at a facility by the screening level risk assessment, it should be provided to the permitting authority for
approval to be incorporated into evaluation of risk. Some of the fate and exposure assumptions utilized in
this guidance to conduct a screening level risk assessment are listed below:
• The estimated COPC concentration in soil and sediment is 100 percent bioavailable. This
includes a COPC that is weakly or strongly adsorbed to particles and a COPC that is
dissolved in interstitial water.
• The estimated dissolved COPC concentration in the water column is 100 percent
bioavailable. For ingestion of water by wildlife, this includes a COPC that is freely
dissolved as an ion or compound, and a COPC that may be adsorbed to another matrix,
such as dissolved organic carbon.
• The total COPC mass estimated to be ingested by a measurement receptor is taken up
across the gut and reaches the site of toxic action. This includes COPC concentrations in
food items and abiotic media. This assumes that no fraction of the COPC mass is
metabolized or otherwise depurated by an ecological receptor, and that there is no
competition for available sites where the toxic action occurs.
• The chemical species present is the most toxic form, and is the form represented by the
TRY.
• Community measurement receptors inhabiting an abiotic medium take up 100 percent of
the COPC concentration to which they are exposed. All COPC mass taken up by a plant
or animal food item of a measurement receptor is assimilated into edible biomass.
• An ecological receptor is continuously exposed during its entire life, including critical life
stage(s).
• A measurement receptor's home range is 100 percent within the assessment area being
evaluated in the risk assessment.
• A measurement receptor's food is 100 percent contaminated.
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The relevance of fate and exposure assumptions specific to COPCs at a site, and their numerical bias to
resulting ESQ values should be considered before application of results. Also, to facilitate the qualitative
assessment of toxicokinetic and toxicodynamic factors (e.g., bioavailability, metabolism), toxicological
profiles of numerous compounds often considered in combustion risk assessments (see Section 2.3) are
included in Appendix H. U.S. EPA OSW prepared these profiles because it believes that these compounds
(1) will be the principal compounds of ecological concern at combustion facilities, and (2) to promote
consistency in presenting and evaluating relevant COPC-specific toxicity information.
6.3 UNCERTAINTY AND LIMITATIONS OF THE RISK ASSESSMENT PROCESS
This section describes how to interpret uncertainties associated with the risk assessment. The discussion of
uncertainties in this section and in Section 6.3.1 was adopted from the U.S. EPA 1996 Risk Assessment
Support to the Development of Technical Standards for Emissions from Combustion Units Burning
Hazardous Waste (EPA Contract Number 68-W3-0028), dated February 20, 1996.
Uncertainty can be introduced into a risk assessment at every step of the process outlined in this document.
Uncertainty occurs, because risk assessment is a complex process, requiring the integration of the
following:
• Release of pollutants into the environment
• Fate and transport of pollutants, in a variety of different and variable environments, by
processes that are often poorly understood or too complex to quantify accurately
• Potential for adverse effects in receptors, as extrapolated from studies of differing species
• Probability of adverse effects in functionality of food web that is made up of species that
are highly variable
Uncertainty is inherent in the process even if the most accurate data with the most sophisticated models are
used. The methodology outlined in this document relies on a combination of point values—some
conservative and some typical—yielding a point estimate of exposure and risk that falls at an unknown
percentile of the full distributions of exposure and risk. For this reason, the degree of conservatism in risk
estimates cannot be known; instead, it is known that the values combine many conservative factors and are
likely to overstate actual risk (Hattis and Burmaster 1994). Therefore, a formal uncertainty analysis is
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required to determine the degree of conservatism. This section discusses the types of uncertainty and the
areas in which uncertainty can be introduced into an assessment. In addition, this section discusses
methods for qualitatively and quantitatively addressing uncertainty in risk assessments.
It should also be noted, variability is often used interchangeably with the term "uncertainty," but this is not
strictly correct. Variability may be tied to variations in physical and biological processes, and cannot be
reduced with additional research or information, although it may be known with greater certainty (for
example, the weight distribution of a species may be known and represented by the mean weight and its
standard deviation). "Uncertainty" is a description of the imperfect knowledge of the true value of a
particular variable or its real variability in an individual or a group. In general, uncertainty is reducible by
additional information-gathering or analysis activities (that is, better data or better models), whereas real
variability will not change (although it may be more accurately known) as a result of better or more
extensive measurements (Hattis and Burmaster 1994).
6.3.1 Types of Uncertainty
Finkel (1990) classified all uncertainty into four types: (1) variable uncertainty, (2) model uncertainty,
(3) decision-rule uncertainty, and (4) variability. Variable uncertainty and model uncertainty are generally
recognized by risk assessors as major sources of uncertainty; decision rule is of greatest concern to the risk
manager.
6.3.1.1 Variable Uncertainty
Variable uncertainty occurs when variables appearing in equations cannot be measured precisely or
accurately, because of either (1) equipment limitations, or (2) spatial or temporal variances between the
quantities being measured. Random, or sample, errors are common sources of variable uncertainty that are
especially critical for small sample sizes. It is more difficult to recognize nonrandom, or systematic, errors
that result from the basis for sampling, experimental design, or choice of assumptions. As stated in Section
6.3, true variability is something we can not do much about (except to know that it exists).
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6.3.1.2 Model Uncertainty
Model uncertainty is associated with all models used in all phases of a risk assessment. For example, the
use of a single species to represent several will introduce uncertainty into the risk assessment because of the
considerable amount of interspecies variability in sensitivity to a COPC. Computer models are
simplifications of reality, requiring exclusion of some variables that influence predictions but cannot be
included in models because of (1) increased complexity, or (2) a lack of data for these variables. The risk
assessor needs to consider the importance, in consultation with the modeler, of excluded variables on a
case-by-case basis. In addition, a model which was developed to use "average" conditions as its inputs,
could result in a large amount of uncertainty when "specific" conditions are used. Finally, choosing the
correct model form is often difficult, because conflicting theories appear to explain a phenomenon equally
well.
The models specified for use in this document were selected on the basis of scientific policy. Therefore, the
air dispersion and deposition model (ISCST3) and the indirect exposure models (IBM) were selected,
because they provide the information needed to conduct indirect assessments and are considered by U.S.
EPA to be state-of-the-science models. This choice of models could also be considered under decision rule
uncertainty. ISCST3—the air dispersion model recommended for use—has not been widely applied in its
present form. Few data are available on atmospheric deposition rates for chemicals other than criteria
pollutants, thereby making it difficult to (1) select input variables related to deposition, and (2) validate
modeled deposition rates. Because dry deposition of vapor phase materials is evaluated external to the air
dispersion model, the plume is not depleted and, as a result, mass balance is not maintained. The effect of
this would be to overestimate deposition, but the magnitude of the overestimation is unknown. Mass
balance is maintained for other forms of deposition (such as wet deposition and particle phase dry
deposition). Long-range transport of pollutants into and out of the areas considered was not modeled,
resulting in an underestimation of risk attributable to each facility.
In addition to air dispersion modeling, the use of other fate and transport models recommended by this
guidance can also result in some uncertainty. For example, the models which estimate COPC
concentrations in waterbodies may be particularly conservative for waterbodies located in estuarine
environments with tidal influence. Because tidal influence is not considered in the models presented in
Chapter 3, the resultant dilution of COPC concentrations in water and sediments likely caused by tidal
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influence will not be considered in the risk assessment. Thus, the risk assessment results will likely be
more conservative for tidally influenced waterbodies than for those waterbodies that are not tidally
influenced. Permitting decisions based on risk estimates for estuarine environments should consider this
uncertainty. The delineation of this uncertainty may be one area that could be addressed in a more refined
site-specific risk assessment, if warranted.
6.3.1.3 Decision-rule Uncertainty
Decision-rule uncertainty is probably of greatest concern to risk managers. This type of uncertainty arises,
for example, out of the need to balance different social concerns when determining an acceptable level of
risk. The uncertainty associated with risk analysis influences many policy and risk management decisions.
Possibly the most important aspect for the risk estimates is the selection of constituents to be included in
the analysis. Constituents identified by this guidance will include compounds that have the potential to
pose the greatest risk to ecological receptors through exposure. For example, many PICs are highly
lipophilic and tend to bioaccumulate, thereby presenting a potentially high risk to upper trophic level
receptors through the consumption of contaminated food items.
6.3.2 Description of Qualitative Uncertainty
Often, sources of uncertainty in a risk assessment can be determined but cannot be quantified. For
example, this can occur when a factor is known or expected to be variable, but no data are available
(e.g., presence of COPCs without toxicity data). In this case, default data may be available that can be
useful in estimating a possible range of values. Uncertainty also often arises out of a complete lack of data.
A process may be so poorly understood that the uncertainty cannot be quantified with any confidence. In
addition, some sources of uncertainty (such as uncertainty in theories used to deduce models) are inherent
qualifications reflecting subjective modes of confidence rather than probabilistic arguments. When
uncertainty can be presented only qualitatively, the possible direction and orders of magnitude of the
potential error should be considered.
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6.3.3 Description of Quantitative Uncertainty
Knowledge of experimental or measurement errors can also be used to introduce a degree of quantitative
information into a qualitative presentation of uncertainty. For example, standard laboratory procedures or
field sampling methods may have a known error level that can be used to quantify uncertainty. In many
cases, uncertainty associated with particular variable values or estimated risks can be expressed
quantitatively and further evaluated with variations of sensitivity analyses. Finkel (1990) identified a
six-step process for producing a quantitative uncertainty estimate:
Define the measure of risk (i.e., assessment endpoint). More than one measure of risk may
result from a particular risk assessment: however, the uncertainty should be quantified or
reached individually.
Specify "risk equations" that present mathematical relationships that express the risk
measure in terms of its components. This step is used to identify the important variables in
the risk estimation process.
Generate an uncertainty distribution for each variable or equation component. These
uncertainty distributions may be generated by using analogy, statistical inference
techniques, expert opinion, or a combination of these.
Combine the individual distributions into a composite uncertainty distribution.
Recalibrate the uncertainty distributions. Inferential analysis could be used to "tighten" or
"broaden" particular distributions to account for dependencies among the variables and to
truncate the distributions to exclude extreme values.
Summarize the output clearly, highlighting the important risk management implications.
Address specific critical factors.
Implication of supporting a point estimate produced without considering
uncertainty
Balance of the costs of under- or over-estimating risks
Unresolved scientific controversies, and their implications for research
When a detailed quantitative treatment of uncertainty is required, statistical methods are employed. Two
approaches to a statistical treatment of uncertainty with regard to variable values are described here and
were used in this analysis where appropriate. The first is to use an appropriate statistic to express all
variables for which uncertainty is a major concern. For example, if a value used is from a sample (such as
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yearly emissions from a stack), the mean and standard deviation should both be presented. If the sample
size is very small, it may be appropriate to (1) give the range of sample values and use a midpoint as a best
estimate in the model, or (2) use the smallest and largest measured value to obtain two estimates that bound
the expected true value. Selection of the appropriate statistic depends on the amount of data available and
the degree of detail required. Uncertainties can be propagated by using analytical or numerical methods.
A second approach is to use the probability distributions of major variables to propagate variable value
uncertainties through the equations used in a risk analysis. A probability distribution of expected values is
then developed for each variable value. These probability distributions are typically expressed as either
probability density functions (PDF) or cumulative probability density functions (CPF). The PDF presents
the relative probability for discrete variable values, whereas the CPF presents the cumulative probability
that a value is less than or equal to a specific value.
A composite uncertainty distribution is created by combining the individual distributions with the equations
used to calculate the probability of particular adverse effects and points. Numerical or statistical methods
are often used. In Monte Carlo simulations, for example, a computer program is used to repeatedly solve
the model equations, under different selections of variable values, to calculate a distribution of exposure (or
risk) values. Each time the equations are calculated, values are randomly sampled from the specified
distributions for each variable. The end result is a distribution of exposure (or risk). These can again be
expressed as PDFs or, more appropriately, as CPFs. The distribution enables the risk assessor to choose
the value corresponding to the appropriate percentile in the overall distribution. For example, the risk
assessor can select an exposure level or risk level that corresponds to the 95th percentile of the overall risk
distribution rather than a point estimate of risk that is based on the 95th percentile values for each variable.
6.3.4 Risk Assessment Uncertainty Discussion
The science of risk assessment is evolving; where the science base is incomplete and uncertainties exist,
science policy assumptions must me made. It is important for risk assessments of facilities that burn
hazardous waste to fully explain the areas of uncertainty in the assessments and to identify the key
assumptions used in conducting the assessments. Toward that end, a table should be added to the end of
each section (e.g., stack emissions, air modeling, exposure assessment, risk characterization) which lists the
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key assumptions in that section, the rationale for those assumptions, their effect on estimates of risk
(overestimation, underestimation, neutral), and the magnitude of the effect (high, medium, low). For
example, it could explain that using a particular input variable, such as exit gas temperature, will under- or
overestimate long-term emissions, and the resulting risks, by a factor of x. These tables can be used to
evaluate the extent to which protective assumptions were used in the risk assessments. They can also help
determine the nature of the uncertainty analysis to be performed. The assumptions listed in the risk
characterization section, which synthesizes the data outputs from the exposure and toxicity analyses,
should be the most significant assumptions from each of the previous sections.
Within this guidance, identification of uncertainties and limitations are also included with the discussion of
specific technical issues (e.g., TOE, estimates of emission rates, COPC selection process, quantification of
non-detects) as they are presented in their respective sections. Limitations associated with parameter
values and inputs to equations are presented in the Appendices.
As an example discussion, the following summarizes some of the uncertainty involved in the air dispersion
modeling component of the risk assessment process.
Although dispersion modeling is a valuable tool for estimating concentration and deposition impacts, it has
many limitations. The accuracy of the models is limited by (1) the ability of the model algorithms to depict
atmospheric transport and dispersion of contaminants, and (2) the accuracy and validity of the input data.
For example, most refined models require input of representative meteorological data from a single
measuring station. In reality, a release will encounter highly variable meteorological conditions that are
constantly changing as it moves downwind. U.S. EPA's Guideline on Air Quality Models—Revised (Title
51 CFR Appendix W) describes two types of model uncertainty. Inherent uncertainty involves deviations
in concentrations that occur even if all of the model input is accurate. Reducible uncertainty is associated
with the model and the uncertain input values that will affect the results. Although it is important to
accurately represent actual conditions by selecting the right model, and using accurate and representative
input data, all model results are subject to uncertainty. Nevertheless, models are generally considered
reasonably reliable in estimating the magnitude of highest concentrations resulting from a release, although
they may not necessarily be time-and space-specific (Title 51 CFR Appendix W). When applied properly,
air dispersion models are typically accurate to ± 10 to 40 percent and can be used to yield a "best estimate"
of air concentrations (Title 51 CFR Appendix W).
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Uncertainties specific to other technical components (e.g., TOE, quantification of non-detects) of the risk
assessment process are further described in their respective chapters or sections of this guidance.
6.3.5 Limitations and Uncertainties Specific to a Screening Level Ecological Risk Assessment
As a screening-level tool, the screening level ecological risk assessment has several inherent limitations.
Some of these limitations are discussed in Section 6.3.5.1. After computing the ESQs and analyzing the
risk assessment results, the risk assessor should evaluate the uncertainty associated with the screening level
risk assessment. Section 6.3.5.2 provides a list of uncertainties that U.S. EPA OSW recommends should
typically be evaluated, at least qualitatively, in a screening level risk assessment.
6.3.5.1 Limitations Typical of a Screening Level Ecological Risk Assessment
The approach used to select the measurement receptors is based, in part, on the premise that if key
components of the ecosystem are protected, protection will be conferred to populations and, by extension,
communities and the ecosystem. Although this approach is reasonable given the nature of the analysis and
the availability of the data, protection of measurement receptors may not always adequately protect all
ecologically significant assessment endpoints. Similarly, the selection process for ecological receptors
relies on a modified trophic element approach. As a result, representative species may not be the most
sensitive to particular compounds, but may have been chosen as a function of their ecological significance
and the availability of natural history information.
COPCs were selected to provide a conservative representation of those compounds in hazardous waste
combustion stack and fugitive emissions that have the highest potential to result in adverse ecological
effects. Due to a lack of data on adverse ecological effects associated with combustion emissions through
all exposure pathways, this list may not be all inclusive.
The toxicity of compounds varies with the measurement receptors and with the availability and form of a
given compound. If a compound is more bioavailable to an organism for absorption or uptake (such as
through increased solubility in the surface soil, surface water, or sediment), then the toxic potential of the
compound increases. Availability and chemical form are affected by factors such as pH, temperature,
alkalinity, seasonal variation, microbial activity, organic carbon content, and complexation with other
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compounds. In the risk assessment, bioavailability of COPCs is assumed to be similar to that observed in
the toxicity studies reported in the literature. Thus, toxicity may be over- or underestimated, depending in
part on the extent to which site-specific compound bioavailability differs from those in studies reported in
the literature.
Attempts to quantify and correct for uncertainty resulting from the use of surrogate species is common, but
controversial. Calabrese and Baldwin (1993) discuss the use of uncertainty factors to adjust for
extrapolations among taxa, between laboratory and field responses, and between acute and chronic
responses. These multipliers are expected to adjust for differences in responses among taxa resulting from
differences in physiology and metabolism. When extrapolating from laboratory to field settings, important
considerations are differences in physical environment, organism behavior, and interactions with other
ecological components. Extrapolation between responses will be necessary in some cases, particularly
when data on relevant endpoints are not available (most commonly when extrapolating from a LOAEL to a
NOAEL). The net effect of uncertainty factors on the accuracy of the risk assessment depends on the
accuracy of the assumptions that underlie the factors themselves.
6.3.5.2 Uncertainties Typical of a Screening Level Ecological Risk Assessment
A screening level risk assessment is typically performed using at least some default parameter values in
place of site-specific measured data (see Sections 3.12 and 6.2.2), and incorporating assumptions (see
Section 6.2) as a result of data gaps. The absence of site-specific information and the need to use these
assumptions may result in uncertainty associated with the calculation of ESQs. An understanding of the
uncertainties associated with the ESQs is necessary for understanding the significance of the ESQs. After
identifying the major uncertainties associated with the risk assessment results, their significance should be
evaluated with respect to the computed ESQs. Uncertainties that generally should be evaluated in a
screening level ecological risk assessment for a combustion facility are listed below:
• Changes in future COPC emissions compared with modeled emission rates used in the risk
assessment.
Quantification of emissions and evaluation of non-detects used in the risk assessment.
The site-specific representativeness of food web(s) used in the risk assessment.
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• The exposure potential of the measurement receptors.
• The representativeness of equal and exclusive diet assumptions for measurement receptors.
• The effect of COPC physicochemical properties on estimates of fate and bioavailability.
• The effect of site-specific environmental conditions affecting the fate, transport, and
bioavailability of the COPCs.
The assumption that once exposed, a measurement receptor does not metabolize or
eliminate a COPC.
• The potential risk to measurement receptors of COPCs with no TRVs.
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Center for Combustion Science and Engineering R-l 5
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-17
-------
Screening Level Ecological Risk Assessment Protocol
References August 1999
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-l 8
-------
Screening Level Ecological Risk Assessment Protocol
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-l 9
-------
Screening Level Ecological Risk Assessment Protocol
References August 1999
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-20
-------
Screening Level Ecological Risk Assessment Protocol
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-21
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Screening Level Ecological Risk Assessment Protocol
References August 1999
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U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering R-22
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United States Solid Waste and EPA530-D-99-001B
Environmental Protection Emergency Response August 1999
Agency (5305W) www.epa.gov/osw
v>EPA Screening Level Ecological
Risk Assessment Protocol
for Hazardous Waste
Combustion
Volume Two
Appendix A
Peer Review Draft
Printed on paper that contains at least 20 percent postconsumer fiber
-------
-------
APPENDIX A
CHEMICAL-SPECIFIC DATA
Screening Level Ecological Risk Assessment Protocol
August 1999
A-l CHEMICALS FOR CONSIDERATION AS COMPOUNDS OF
POTENTIAL CONCERN
A-2 COMPOUND SPECIFIC PARAMETER VALUES
-------
-------
APPENDIX A-l
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
-------
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 1 of 30)
CAS Number
50-00-0
50-06-6
50-07-7
50-18-0
50-29-3
50-32-8
50-55-5
51-28-5
51-43-4
51-52-5
51-79-6
52-85-7
53-70-3
53-96-3
54-11-5
55-18-5
55-38-9
55-63-0
55-91-4
56-04-2
Compound Name
Formaldehyde (methylene oxide)
Phenobarbital
Mitomycin
Cyclophosphamide
4,4'-DDT
Benzo(a)pyrene
Reserpine
2,4-Dinitrophenol
Epinephrine
Propylthiouracil
Ethyl carbamate (urethane)
Famphur
Dibenzo(a,h)anthracene
2-Acetylaminofluorene
Nicotine
Nitrosodiethylamine
Fenthion
Nitroglycerine
Diisopropylfluorophosphate (DFP)
Methylthiouracil
o x
sl
.3 §
"8 &K
•£ < K?
a«t
.§ N 0
IIs
gg
o o
K009, K010, K038, K040, K156, K157
F032, F034, F037, F038, K001, K022, K035, K141,
K142, K144, K145, K147, K148
K001
F032, F034, K022, K141, K142, K144, K145, K147,
K148
Chemical-Specific Data
Available
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-1
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 2 of 30)
CAS Number
56-23-5
56-38-2
56-49-5
56-53-1
56-55-3
56-57-5
56-72-4
57-12-5
57-14-7
57-24-9
57-41-0
57-57-8
57-74-9
57-97-6
58-89-9
58-89-9
58-90-2
59-50-7
59-89-2
60-09-3
60-11-7
Compound Name
Carbon tetrachloride
Parathion
3-Methylcholanthrene
Diethylstilbestrol
Benzo(a)anthracene
Nitroquinoline- 1-oxide
Coumaphos
Cyanide
1,1-Dimethyl hydrazine
Strychnine
5,5-Diphenylhydantoin
beta-Propiolactone
Chlordane
7, 12-Dimethylbenz(a)anthracene
gamma-BHC (Lindane)
Lindane (all isomers)
2,3,4,6-Tetrachlorophenol
4-Chloro-3-methylphenol (p-chloro-m-cresol)
N-Nitrosomorpholine
Aminoazobenzene
Dimethyl aminoazobenzene
o x
sl
.3 §
"8 &K
•s < t*
a«t
.§ N 0
IIs
gg
o o
F001, F024, F025, K016, K019, K020, K021, K073,
K116, K150, K151, K157
F032, F034, K001, K022, K035, K141, K142, K143,
K144, K145, K147, K148
K107, K108, K109, K110
K097
F020, F023, F027, F028, K001
F004, K001
Chemical-Specific Data
Available
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-2
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 3 of 30)
CAS Number
60-34-4
60-35-5
60-51-5
60-57-1
61-82-5
62-38-4
62-44-2
62-50-0
62-53-3
62-55-5
62-56-6
62-73-7
62-74-8
62-75-9
63-25-2
64-17-5
64-18-6
64-64-7
64-67-5
65-85-0
66-27-3
66-75-1
Compound Name
Methyl hydrazine
Acetamide
Dimethoate
Dieldrin
Amitrole
Phenylmercury acetate
Phenacetin
Ethyl methanesulfonate
Aniline
Thioacetamide
Thiourea
Dichlorovos
Fluoroacetic acid, sodium salt
N-Nitrosodimethylamine
Carbaryl
Ethanol
Formic acid (methanoic acid)
Di-n-propylnitrosamine
Diethyl sulfate
Benzoic acid
Methyl methanesulfonate
Uracil mustard
o x
sl
.3 §
"8 SB
•£ < K?
a«t
% n o
IIs
§g
o o
K083, K103, K104, K112, K113
K156
K009, K010
Chemical-Specific Data
Available
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-3
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 4 of 30)
CAS Number
67-56-1
67-64-1
67-66-3
67-72-1
68-12-2
70-25-7
70-30-4
71-43-2
71-55-6
72-20-8
72-33-3
72-43-5
72-54-8
72-55-9
72-57-1
74-83-9
74-87-3
74-88-4
74-90-8
Compound Name
Methanol
Acetone
Chloroform (trichloromethane)
Hexachloroethane (perchloroethane)
Dimethyl formamide
N-Methyl-N'-nitro-N-nitrosoguanidine (MNNG)
Hexachlorophene
Benzene
Methyl chloroform (1,1,1-trichloroethane)
Endrin
Mestranol
Methoxychlor
4,4'-DDD
DDE
Trypan blue
Bromomethane (methylbromide)
Chloromethane (methyl chloride)
Methyl iodide (lodomethane)
Hydrogen cyanide
o x
sl
.3 §
"8 &K
•s < t*
a«t
.§ N 0
IIs
gg
o o
F024, F025, K009, K010, K019, K020, K021, K029,
K073, K116, K149, K150, K151, K158
F024, F025, K016, K030, K073
F005, F024, F025, F037, F038, K085, K104, K105,
K141, K142, K143, K144, K145, K147, K151, K159
F001, F002, F024, F025, K019, K020, K028, K029,
K096
K131, K132
F024, F025, K009, K010, K149, K150, K157
K011, K013
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-4
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 5 of 30)
CAS Number
74-93-1
74-95-3
74-97-5
75-00-3
75-01-4
75-05-8
75-07-0
75-09-2
75-15-0
75-21-8
75-25-2
75-27-4
75-29-6
75-34-3
75-35-4
75-36-5
75-44-5
75-45-6
75-55-8
75-56-9
75-60-5
Compound Name
Thiomethanol
Methylene bromide
Bromochloromethane
Chloroethane
Vinyl chloride
Acetonitrile
Acetaldehyde
Methylene chloride
Carbon disulfide
Ethylene oxide
Bromoform
Bromodichloromethane
2-Chloropropane
1 , 1-Dichloroethane
1,1-Dichloroethene
Acetyl chloride
Phosgene (hydrogen phosphide)
Chlorodifluoromethane
1,2-Propylenimine (2-methyl aziridine)
Propylene oxide
Cacodylic acid
o x
sl
.3 §
"8 SB
•£ < K?
a«t
.§ N 0
IIs
gg
o u
F024, F025, K019, K020, K028, K029
K011, K013, K014
F001, F002, F024, F025, K009, K010, K156, K157,
K158
F005
F024, F025
F024, F025, K019, K020, K029
K116
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-5
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 6 of 30)
CAS Number
75-69-4
75-70-7
75-71-8
75-86-5
75-87-6
76-01-7
76-13-1
76-44-8
77-47-4
77-78-1
78-00-2
78-32-0
78-34-2
78-59-1
78-83-1
78-87-5
78-93-3
78-97-7
79-00-5
79-01-6
79-06-1
Compound Name
Trichlorofluoromethane (Freon 11)
Trichloromethanethiol
Dichlorodifluoromethane
2-Methylactonitrile
Chloral
Pentachloroethane
1 , 1 ,2-Trichloro- 1 ,2,2-trifluoroethane(Freon 1 13)
Heptachlor
Hexachlorocyclopentadiene
Dimethyl sulfate
Tetraethyl lead
Tri-p-tolyl phosphate
Dioxathion
Isophorone
Isobutyl alcohol
1 ,2-Dichloropropane
2-Butanone (methyl ethyl ketone)
2-Hydroxypropionitrile
1 , 1 ,2-Trichloroethane
Trichloroethene
Acrylamide
o x
sl
.3 §
"8 &K
•s < t*
a«t
% n o
IIs
§g
o o
F001, F002
F024, F025
F001, F002
K097
F024, F025, K032, K033, K034
K131
F005
F005
F002, F024, F025, K019, K020, K095, K096
F001, F002, F024, F025, K018, K019, K020
K014
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-6
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 7 of 30)
CAS Number
79-10-7
79-11-8
79-19-6
79-20-9
79-22-1
79-34-5
79-44-7
79-46-9
80-62-6
81-07-2
81-81-2
82-68-8
83-32-9
84-66-2
84-74-2
85-01-8
85-44-9
85-68-7
86-30-6
86-50-0
86-73-7
Compound Name
Acrylic acid
Chloroacetic acid
Thiosemicarbazide
Methyl acetate
Methyl chlorocarbonate
1 , 1 ,2,2-Tetrachloroethane
Dimethyl carbamoyl chloride
2-Nitropropane
Methyl methacrylate
Saccharin
Warfarin
Pentachloronitrobenzene (PCNB)
Acenaphthene
Diethyl phthalate
Dibutyl phthalate
Phenanthrene
Phthalic anhydride (1,2-benzenedicarboxylic
anhydride)
Butylbenzyl phthalate
N-Nitrosodiphenylamine
Azinphos-methyl
Fluorene
o x
sl
.3 §
"8 SB
•£ < K?
a«t
% n o
IIs
§g
o o
F024, F025, K019, K020, K030, K073, K095, K150
F005
K022
K022
K023, K024, K093, K094
K022
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-7
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 8 of 30)
CAS Number
86-88-4
87-65-0
87-68-3
87-86-5
88-06-2
88-74-4
88-75-5
88-85-7
90-04-0
90-13-1
91-20-3
91-22-5
91-57-6
91-58-7
91-59-8
91-80-5
91-94-1
92-52-4
92-67-1
92-87-5
92-93-3
Compound Name
alpha-Naphthylthiourea
2,6-Dichlorophenol
Hexachlorobutadiene (perchlorobutadiene)
Pentachlorophenol
2,4,6-Trichlorophenol
o-Nitroaniline (2-nitroaniline)
2-Nitrophenol
Dinoseb
o-Anisidine
1-CWoronaphthalene
Naphthalene
Quinoline
2-Methylnaphthalene
2-Chloronaphthalene
2-Naphthylamine (beta-naphthylamine)
Methapyrilene
3,3'-Dichlorobenzidine
Biphenyl
4-Aminobiphenyl
Benzidine
4-Nitrobiphenyl
o x
sl
.3 §
"8 &K
•s < t*
a«t
% n o
IIs
§g
o o
K043
F024, F025, K016, K018, K030
F021, F027, F028, F032, K001
F020, F023, F027, F028, K001, K043, K099, K105
F024, F025, F034, K001, K022, K035, K060, K087,
K145
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-8
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 9 of 30)
CAS Number
93-72-1
94-58-6
94-59-7
94-75-7
95-06-7
95-47-6
95-48-7
95-50-1
95-53-4
95-57-8
95-79-4
95-80-7
95-83-0
95-94-3
95-95-4
96-09-3
96-12-8
96-18-4
96-23-1
96-45-7
97-63-2
98-01-1
Compound Name
Silvex
Dihydrosaffrole
Safrole (5-(2-Propenyl)- 1 ,3-benzodioxole)
2,4-D
Sulfallate
o-Xylene (dimethyl benzene)
o-Cresol
1 ,2-Dichlorobenzene
o-Toluidine
2-Chlorophenol
5-Chloro-2-methylaniline
2,4-Toluene diamine
4-Chloro- 1 ,2-phenylenediamine
1 ,2,4,5-Tetrachlorobenzene
2,4,5-Trichlorophenol
Styrene oxide
1 ,2-Dibromo-3-chloropropane
1 ,2,3-Trichloropropane
1 ,3-DicWoro-2-propanol
Ethylene thiourea
Ethyl methacrylate
Furfural
o x
sl
.3 §
"8 &K
•s < t*
a«t
% n o
IIs
§g
o o
F027
F004
F002, F024, F025, K042, K085, K105
K112, K113, K114
K001
K112, K113, K114, K115, K027
K085, K149, K150, K151
F020, F023, F027, F028, K001
K123, K124, K125, K126
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-9
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 10 of 30)
CAS Number
98-07-7
98-82-8
98-83-9
98-86-2
98-87-3
98-95-3
99-09-2
99-35-4
99-55-8
99-59-2
99-65-0
100-01-6
100-02-7
100-25-4
100-41-4
100-42-5
100-44-7
100-51-6
100-52-7
100-75-4
101-05-3
101-14-4
Compound Name
Benzotrichloride
Cumene
Methyl styrene (mixed isomers)
Acetophenone
Benzal chloride
Nitrobenzene
3-Nitroaniline
1,3,5-Trinitrobenzene
5-Nitro-o-toluidine
5-Nitro-o-anisidine
1 ,3-Dinitrobenzene
4-Nitroaniline (p-nitroaniline)
4-Nitrophenol (p-nitrophenol)
1 ,4-Dinitrobenzene (p-dinitrobenzene)
Ethylbenzene
Styrene
Benzyl chloride
Benzyl alcohol
Benzaldehyde
N-Nitrosopiperidine
Anilazine
4,4'-Methylenebis (2-chloroaniline)
o x
sl
.3 §
"8 &K
•s < t*
a«t
% n o
IIs
§g
o o
K015, K149
F004, K083, K103, K104
K025
K015, K085, K149
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-10
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 11 of 30)
CAS Number
101-27-9
101-55-3
101-61-1
101-68-8
101-79-9
101-80-4
102-82-9
103-33-3
103-85-5
105-60-2
105-67-9
106-42-3
106-44-5
106-46-7
106-47-8
106-49-0
106-50-3
106-51-4
106-88-7
106-89-8
106-93-4
106-99-0
Compound Name
Barban
4-Bromophenyl phenyl ether
4,4'-Methylenebis (N,N-dimethylaniline)
Methylene diphenyl diisocyanate (MDI)
4,4-Methylenedianiline
4,4'-Oxydianiline
Tributylamine
Azobenzene
Phenylthiourea
Caprolactam
2,4-Dimethylphenol
p-Xylene (dimethyl benzene)
p-Cresol (4-methyl phenol)
1 ,4-Dichlorobenzene
p-Chloroaniline
p-Toluidine
p-Phenylenediamine
Quinone
1 ,2-Epoxybutane
Epichlorohydrin (l-chloro-2,3 epoxypropane)
Ethylene dibromide
1,3-Butadiene
o x
sl
.3 §
"8 &K
•s < t*
a«t
% n o
IIs
§g
o o
K001
F004
F024, F025, K085, K105, K149, K150
K112, K113, K114
K017
K117, K118, K136
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-11
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 12 of 30)
CAS Number
107-02-8
107-05-1
107-06-2
107-07-3
107-10-8
107-12-0
107-13-1
107-18-6
107-19-7
107-20-0
107-21-1
107-30-2
107-49-3
107-98-2
108-05-4
108-10-1
108-18-9
108-31-6
108-38-3
108-39-4
108-46-3
Compound Name
Acrolein
Allyl chloride
1,2-Dichloroethane (ethylene dichloride)
2-Chloroethanol
n-Propylamine
Propionitrile
Acrylonitrile
Allyl alcohol
Propargyl alcohol
Chloroacetaldehyde
Ethylene glycol (1,2-ethanediol)
Chloromethyl methyl ether
Tetraethyl pyrophosphate
Propylene glycol monomethyl ether
Vinyl acetate
Methyl isobutyl ketone
Diisopropylamine
Maleic anhydride
m-Xylene (dimethyl benzene)
m-Cresol
Resorcinol
o x
sl
.3 §
"8 SB
•£ < K?
a«t
3 N o
IIs
gg
o o
F024, F025
F024, F025, K018, K019, K020, K029, K030, K096
K011, K013
K010
K023, K093
F004
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-12
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 13 of 30)
CAS Number
108-60-1
108-67-8
108-87-2
108-88-3
108-90-7
108-95-2
108-98-5
109-06-8
109-77-3
109-88-4
109-89-7
109-99-9
110-54-3
110-75-8
110-80-5
110-86-1
111-15-9
111-42-2
111-44-4
111-54-6
111-76-2
111-91-1
Compound Name
bis (2-Chloroisopropyl)ether
1,3,5-Trimethylbenzene
Methylcyclohexane
Toluene
Chlorobenzene
Phenol
Thiophenol (benzenethiol)
2-Picoline
Malononitrile
2-Methoxyethanol
Diethylamine
Tetrahydrofuran
n-Hexane
2-Chloroethylvinyl ether
Ethylene glycol monoethyl ether
Pyridine
Ethylene glycol monoethyl ether acetate
Diethanolamine
bis(2-chloroethyl)ether
Ethylene(bis)dithiocarbamic acid
Ethylene glycol monobutyl ether
bis(2-chloroethoxy)methane
o x
sl
.3 §
"8 &K
•£ < K?
a«t
.§ N 0
IIs
gg
o u
F005, F024, F025, K015, K036, K037, K149, K151
F002, F024, F025, K015, K105, K149
K001, K022, K087
K026
F005
F005, K026, K157
K017
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-13
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 14 of 30)
CAS Number
114-26-1
115-02-6
115-29-7
115-90-2
116-06-3
117-79-3
117-80-6
117-81-7
117-84-0
118-74-1
118-96-7
119-90-4
119-93-7
120-12-7
120-58-1
120-62-7
120-71-8
120-80-9
120-82-1
120-83-2
121-14-2
Compound Name
Propoxur (Bayton)
Azaserine
Endosulfan
Fensulfothion
Aldicarb
2-Aminoanthraquinone
Dichlone
bis(2-ethylhexyl)phthalate
Di-n-octylphthalate
Hexachlorobenzene (perchlorobenzene)
2,4,6-Trinitrotoluene
3,3'-Dimethoxybenzidine
3,3'-Dimethylbenzidine
Anthracene
Isosafrole
Piperonyl sulfoxide
p-Cresidine
Catechol
1 ,2,4-Trichlorobenzene
2,4-Dichlorophenol
2,4-Dinitrotoluene
o x
sl
.3 §
"8 &K
•s < t*
a«t
% n o
IIs
§g
o o
F024, F025, K016, K018, K030, K042, K085, K149,
K150, K151
K022
F024, F025, K085, K150
K043, K099
K025, Kill
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-14
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 15 of 30)
CAS Number
121-44-8
121-69-7
121-75-5
122-09-8
122-39-4
122-66-7
123-31-9
123-33-1
123-38-6
123-63-7
123-91-1
124-48-1
126-68-1
126-72-7
126-75-0
126-98-7
126-99-8
127-18-4
129-00-0
130-15-4
131-11-3
Compound Name
Triethylamine
N,N-Diethyl aniline
Malathion
a,a-Dimethylphenethylamine
Diphenylamine
1 ,2-Diphenylhydrazine
Hydroquinone
Maleic hydrazide
Propionaldehyde
Paraldehyde
Dioxane (1,4-dioxane)
Chlorodibromomethane
0,0,0-Triethyl phosphorothioate
tris(2,3-dibromopropyl) phosphate
Demeton-S
Methacrylonitrile
Chloroprene
Tetrachloroethene (Perchloroethylene)
Pyrene
1 ,4-Naphthoquinone
Dimethyl Phthalate
o x
sl
.3 §
"8 &K
•£ < t*
a«t
% n o
IIs
§g
o o
K156, K157
K083, K104
K009, K010, K026
F001, F002, F024, F025, K016, K019, K020, K073,
K116, K150, K151
K022
K024
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-15
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 16 of 30)
CAS Number
131-89-5
131-89-5
132-32-1
132-64-9
133-06-2
133-90-4
134-32-7
137-17-7
137-26-8
140-57-8
140-88-5
141-66-2
143-33-9
143-50-0
145-73-3
148-82-3
151-50-8
151-56-4
152-16-9
156-60-5
156-62-7
189-55-9
Compound Name
2-Cyclohexyl-4,6-dinitro-phenol
2-Cycloyhexyl-4,6-dinitrophenol
3-Amino-9-ethylcarbazole
Dibenzofuran
Captan
Chloramben
1-Naphthylamine (alpha-naphthylamine)
2,4,5-Trimethylaniline
Thiram
Aramite
Ethyl acrylate
Dicrotophos
Sodium cyanide
Kepone
Endothall
Melphalan
Potassium cyanide
Ethylene imine (Aziridine)
Octamethyl pyrophosphoramide
(trans) 1 ,2-dichloroethene
Calcium cyanamide
Dibenzo(a,i)pyrene
o x
sl
.3 §
"8 &K
•s < t*
a«t
% n o
IIs
§g
o o
F007, F008, F009, F010, F011
F007, F008, F009, F010, F011
F024, F025
Chemical-Specific Data
Available
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
PICs in Stack Emissions
Actually Detected
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-16
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 17 of 30)
CAS Number
191-24-2
192-65-4
192-97-2
193-39-5
205-82-3
205-99-2
206-44-0
207-08-9
208-96-8
218-01-9
224-42-0
225-51-4
297-97-2
297-97-2
298-00-0
298-02-2
298-03-3
298-04-4
299-84-3
300-76-5
Compound Name
Benzo(g,h,i)perylene
Dibenzo(a,e)pyrene
Benzo(e)pyrene
Indeno(l ,2,3-cd)pyrene
BenzoQfluoranthene
Benzo(b)fluoranthene (3,4-Benzofluoranthene)
Fluoranthene
Benzo(k)fluoranthene
Acenaphthalene
Chrysene
Dibenz(a,j)acridine
Benz[c]acridine
O,O-Diethyl O-pyrazinyl phosphorothioate
Thionazine
Methyl parathion
Phorate
Demeton-O
Disulfoton
Ronnel
Naled
o x
sl
.3 §
"8 &K
•s < t*
a«t
.§ N 0
IIs
gg
o o
K022
K022
F032, F034, K001, K022, K035, K141, K142, K147,
K148
K022
K001, K022, K035, K141, K142, K143, K144, K147,
K148
K001, K022, K035
F034, K022, K141, K142, K143, K144, K147, K148
K001, K022, K035
F037, F038, K001, K022, K035
K038, K040
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-17
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 18 of 30)
CAS Number
302-01-2
302-17-0
303-34-1
305-03-3
309-00-2
311-45-5
315-18-4
319-84-6
319-85-7
319-86-8
321-60-8
334-88-3
353-50-4
357-57-3
367-12-4
460-00-4
460-19-5
463-58-1
465-73-6
470-90-6
479-45-8
492-80-8
Compound Name
Hydrazine
Chloral hydrate
Lasiocarpine
Chlorambucil
Aldrin
Diethyl-p-nitrophenyl phosphate
Mexacarbate
alpha-Hexachlorocyclohexane (alpha-BHC)
beta-Hexachlorocyclohexane (beta-BHC)
delta-BHC
2-Fluorobiphenyl
Diazomethane
Carbon oxyfluoride
Brucine
2-Fluorophenol
4-Bromofluorobenzene
Cyanogen (oxalonitrile)
Carbonyl sulfide
Isodrin
Chlorfenvinphos
Tetryl
Auramine
o x
sl
.3 §
"8 SB
•£ < K?
a«t
3 N o
IIs
gg
o o
F024
Chemical-Specific Data
Available
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
PICs in Stack Emissions
Actually Detected
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-18
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 19 of 30)
CAS Number
494-03-1
504-24-5
505-60-2
506-61-6
506-64-9
506-68-3
506-77-4
510-15-6
512-56-1
528-29-0
532-27-4
534-52-1
540-36-3
540-73-8
540-84-1
541-53-7
541-73-1
542-62-1
542-75-6
542-76-7
542-88-1
Compound Name
Chlornaphazin
4-Aminopyridine
Mustard gas
Potassium silver cyanide
Silver cyanide
Cyanogen bromide (bromocyanide)
Cyanogen chloride
Chlorobenzilate
Trimethyl phosphate
1 ,2-Dinitrobenzene (o-Dinitrobenzene)
2-Chloroacetophenone
4,6-Dinitro-o-cresol
1 ,4-Difluorobenzene
1 ,2-Dimethylhydrazine
2,2,4-Trimethylpentane
Dithiobiuret
1 ,3-Dichlorobenzene
Barium cyanide
1 ,3-Dichloropropene
3-Chloropropionitrile
bis(Chloromethyl)ether
o x
sl
.3 §
"8 SB
•£ < K?
a«t
.§ N 0
IIs
gg
o u
F006, F007, F008, F009, F010, F011, F012, F019,
K007, K088
F006, F012, F019, K007, K088
F004
F024, F025, K085, K105
K017
Chemical-Specific Data
Available
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-19
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 20 of 30)
CAS Number
544-92-3
557-19-7
557-21-1
563-12-2
563-68-8
584-84-9
590-60-2
591-08-2
591-78-6
592-01-8
593-60-2
598-31-2
602-87-9
606-20-2
608-93-5
615-53-2
621-64-7
623-40-5
624-83-9
628-86-4
630-10-4
630-20-6
Compound Name
Copper cyanide
Nickel cyanide
Zinc cyanide
Ethion
Thallium(I)acetate
2,4-Toluene diisocyanate
Bromoethene
l-Acetyl-2-thiourea
2-Hexanone (butyl methyl ketone)
Calcium cyanide
Vinyl bromide
Bromoacetone
5-Nitroacenaphthene
2,6-Dinitrotoluene
Pentachlorobenzene
N-Nitroso-N-methylurethane
N-Nitroso-di-n-propylamine
Toluene-2,6-diamine
Methyl isocyanate
Mercury fulminate
Selenourea
1,1, 1 ,2-Tetrachloroethane
o x
sl
.3 §
"8 SB
•£ < K?
a«t
.§ N 0
IIs
§g
o u
K027
F024, F025, K085, K149, K150, K151
F024, F025, K019, K020, K030, K095
Chemical-Specific Data
Available
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-20
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 21 of 30)
CAS Number
636-21-5
640-19-7
680-31-9
684-93-5
692-42-2
696-28-6
732-11-6
755-04-5
757-58-4
759-73-9
764-41-0
765-34-4
786-19-6
822-06-0
924-16-3
930-55-2
959-98-8
961-11-5
1024-57-3
1031-07-8
1116-54-7
1120-71-4
Compound Name
o-Toluidine hydrochloride
Fluoroacetamide
Hexamethylphosphoramide
N-Nitroso-N-methylurea
Diethylarsine
Dichlorophenylarsine
Phosmet
Titanium tetrachloride
Hexaethyl tetraphosphate
N-Nitroso-N-ethylurea
1 ,4-Dichloro-2-butene
Glycidylaldehyde
Carbophenothion
Hexamethylene- 1 ,5-diisocyanate
N-Nitroso-di-n-Buetylamine
N-Nitrosopyrrolidine
Endosulfan I
Tetrachlorvinphos
HeptacWor epoxide
Endosulfan sulfate
N-Nitrosodiethanolamine
1,3-Propane sultone
o x
sl
.3 §
"8 &K
•s < t*
a«t
% n o
IIs
§g
o o
Chemical-Specific Data
Available
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-21
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 22 of 30)
CAS Number
1303-28-2
1314-32-5
1314-62-1
1319-77-3
1327-53-3
1330-20-7
1332-21-4
1335-32-6
1336-36-3
1338-23-4
1464-53-5
1563-66-2
1582-09-8
1615-80-1
1634-04-4
1718-51-0
1746-01-6
1836-75-5
1888-71-7
2037-26-5
2104-64-5
Compound Name
Arsenic pentoxide
Thallic oxide
Vanadium pentoxide
Cresols/cresylic acid (isomers and mixtures)
Arsenic trioxide
Xylene (total)
Asbestos
Lead subacetate
Polychlorinated biphenyls (209 congeners)
2-Butanone peroxide
1,2,3,4-Diepoxybutane
Carbofuran
Trifluralin
N,N'-Diethylhydrazine
Methyl tert butyl ether
Terphenyl-dl4
2,3,7,8-Tetrachlorodibenzo(p)dioxin(TCDD)
Nitrofen
Hexachloropropene
Toluene-d8
EPN
o x
sl
.3 §
"8 &K
•s < t*
a«t
.§ N 0
IIs
gg
o u
F004
K156, K158
F020, F022, F023, F026, F027, F028, F032
Chemical-Specific Data
Available
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-22
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TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 23 of 30)
CAS Number
2303-16-4
2310-17-0
2385-85-5
2425-06-1
2763-96-4
2921-88-5
3114-55-4
3288-58-2
3689-24-5
4170-30-3
4549-40-0
5131-60-2
5344-82-1
6533-73-9
6923-22-4
6959-48-4
7005-72-3
7421-93-4
7439-92-1
7439-96-5
7439-97-6
Compound Name
Diallate (cis or trans)
Phosalone
Mirex
Captafol
5-(Aminomethyl)-3-isoxazolol
Chlorpyrifos
Chlorobenzene-d5
O,O-Diethyl S-methyl dithiophosphate
Tetraethyl dithiopyrophosphate
Crotonaldehyde (Propylene aldehyde)
N-Nitrosomethylvinylamine
4-Chloro- 1 ,3-phenylenediamine
l-(o-Chlorophenyl)thiourea
Thallium(I)carbonate
Monocrotophos
3-(Chloromethyl)pyridine hydrochloride
4-Chlorophenyl phenyl ether
Endrin aldehyde
Lead
Manganese
Mercury
o x
sl
.3 §
"8 &K
•s < t*
a«t
.§ N 0
IIs
gg
o u
F020, F023, F027, F028
F035, F037, F038, K002, K003, K005, K046, K048,
K049, K051, K052, K061, K062, K064, K069, K086,
K100
K071, K106
Chemical-Specific Data
Available
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-23
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 24 of 30)
CAS Number
7440-02-0
7440-22-4
7440-28-0
7440-36-0
7440-38-2
7440-39-3
7440-41-7
7440-43-9
7440-47-3
7440-48-4
7440-50-8
7440-62-2
7440-66-6
7446-18-6
7487-94-7
7488-56-4
7647-01-0
7664-38-2
7664-39-3
7664-41-7
7700-17-6
Compound Name
Nickel
Silver
Thallium
Antimony
Arsenic
Barium
Beryllium
Cadmium
Chromium (total)
Cobalt
Copper
Vanadium
Zinc
Thallium(I)sulfate
Mercuric chloride
Selenium sulfide
Hydrogen Chloride (hydrochloric acid)
Phosphoric acid
Hydrogen fluoride
Ammonia
Crotoxyphos
o x
sl
.3 §
"8 SB
•£ < K?
a«t
3 N o
IIs
gg
o o
F006
K021, K161
F032, F034, F035, K031, K060, K084, K101, K102,
K161
F006, K061, K064, K069, K100
F032, F034, F035, F037, F038, K090
Chemical-Specific Data
Available
X
X
X
X
X
X
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
X
X
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-24
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 25 of 30)
CAS Number
7723-14-0
7778-39-4
7782-41-4
7782-49-2
7782-50-5
7783-00-8
7783-06-4
7786-34-7
7791-12-0
7803-51-2
7803-55-6
8001-35-2
8065-48-3
10102-43-9
10102-44-0
10102-45-1
10595-95-6
11096-82-5
11097-69-1
11104-28-2
11141-16-5
12039-52-0
Compound Name
Phosphorus
Arsenic acid
Fluorine
Selenium
Chlorine
Selenium dioxide
Hydrogen sulfide
Mevinphos
Thallium(I)chloride
Phosphine
Ammonium vanadate
Toxaphene (chlorinated camphene)
Demeton
Nitric oxide
Nitrogen dioxide
Thallium® nitrate
N-Nitrosomethylethylamine
Arochlor-1260
Arochlor-1254
Arochlor-1221
Arochlor-1232
Thallium® selenite
o x
sl
.3 §
"8 SB
•£ < K?
a«t
.§ N 0
IIs
gg
o u
K041, K098
Chemical-Specific Data
Available
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
PICs in Stack Emissions
Actually Detected
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-25
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 26 of 30)
CAS Number
12672-29-6
12674-11-2
13071-79-9
13171-21-6
13463-39-3
13765-19-0
16752-77-5
18540-29-9
18883-66-4
19408-74-3
20816-12-0
20830-81-3
20859-73-8
21609-90-5
22967-92-6
23950-58-5
25013-15-4
25265-76-3
25376-45-8
26471-62-5
33213-65-9
Compound Name
Arochlor-1248
Arochlor-1016
Terbufos
Phosphamidon
Nickel carbonyl
Calcium eliminate
Methomyl
Chromium (hexavalent)
Streptozotocin
l,2,3,7,8,9-Hexachlorodibenzo(p)dioxin
Osmium tetroxide
Daunomycin
Aluminum phosphide
Leptophos
Methyl mercury
Pronamide
Methyl styrene
Phenylenediamine
Toluenediamine
Toluene diisocyanate
Endosulfan II
o x
sl
.3 §
"8 SB
•£ < K?
a«t
% n o
IIs
§g
o o
F006, F019, K002, K003, K004, K005, K006, K007,
K008, K048, K049, K050, K051, K061, K062, K069,
K086, K100
F021, F022, F026, F027, F028, F032
K083, K103, K104
Chemical-Specific Data
Available
X
X
X
X
X
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
PICs in Stack Emissions
Actually Detected
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-26
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 27 of 30)
CAS Number
33245-39-5
35822-46-9
39196-18-4
39227-28-6
39300-45-3
40321-76-4
53469-21-9
53494-70-5
55673-89-7
57117-41-6
57117-44-9
57653-85-7
60851-34-5
67562-39-4
70648-26-9
72918-21-9
109719-77-9
125322-32-9
-
-
-
-
Compound Name
Fluchloralin
l,2,3,4,6,7,8-Heptachlorodibenzo(p)dioxin
Thiofanox
l,2,3,4,7,8-Hexachlorodibenzo(p)dioxin
Dinocap
l,2,3,7,8-Pentachlorodibenzo(p)dioxin
Arochlor-1242
Endrin ketone
1,2,3,4,7,8,9-Heptachlorodibenzofuran
2,3,4,7,8-Pentachlorodibenzofuran
1,2,3,6,7,8-Hexachlorodibenzofuran
l,2,3,6,7,8,-Hexachlorodibenzo(p)dioxin
2,3,4,6,7,8-Hexachlorodibenzofuran
1,2,3,4,6,7,8-HeptacWorodibenzofuran
1,2,3,4,7,8-Hexaclilorodibenzofuran
1,2,3, 7,8, 9-Hexachlorodibenzofuran
1,2,3,7,8-Pentaclilorodibenzofuran
2,3,7,8-TetracWorodibenzofuran
Beryllium compounds
Cadmium compounds
Chlorocyclopentadiene
N-CWorodiisopropyl amine
o x
sl
.3 §
"8 &K
•£ < K?
a«t
% n o
IIs
§g
o o
F032
F021, F022, F026, F027, F028, F032
F020, F021, F022, F023, F026, F027, F028, F032
F032
F020, F021, F022, F023, F026, F027, F028, F032
F021, F022, F026, F027, F028, F032
F021, F022, F026, F027, F028, F032
F021, F022, F026, F027, F028, F032
F032
F021, F022, F026, F027, F028, F032
F021, F022, F026, F027, F028, F032
F020, F021, F022, F023, F026, F027, F028, F032
F020, F022, F023, F026, F027, F028, F032
Chemical-Specific Data
Available
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
X
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
X
X
X
X
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-27
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 28 of 30)
CAS Number
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
Compound Name
N-Chloroisopropyl amine
Chromium compounds
Creosote
Cyanide compounds
O-Decyl hydroxylamine
Dibenzo(a,e)fluoranthene
Dibenzo(a,h)fluoranthene
Dibutylchloramine
3,3-Dichloroisopropyl ether
Dichloropentadiene
Dimethylnitrosamine
Lead compounds
Nicotine salts
2-Nitrodiphenylamine
Octachlorodibenzo(p)dioxin
OctachlorodibenzofUran
Phthalic acid esters
Saccharin salts
Sodium O-ethylmethylphosphonate
Diisopropylamine
Strychnine salts
Thioamine
o x
sl
.3 §
"8 SB
•£ < K?
a«t
% n o
IIs
§g
o o
K001, K035
F006, F007, F008, F009, F010, F011, F012, F019,
K007, K060, K088
K022
K022
Chemical-Specific Data
Available
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
X
X
X
X
U.S. EPA Recommended
and Potential PICs
(1994b)
X
X
X
X
X
X
PICs in Stack Emissions
Actually Detected
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-28
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TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 29 of 30)
CAS Number
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
--
Compound Name
O-decyl-hydroxylamine
Acenaphthene-d 1 0
Antimony compounds
Arsenic compounds (inorganic, including arsine)
2-Chloro- 1 ,3-butadiene
Chrysene-dl2
Cobalt compounds
Coke oven emissions
Dibenz(a)anthracene
1 ,4-Dichlorobenzene-d4
Dichloroethylene
Dichloropropane
Dichloropropanols
Dichloropropene
Manganese compounds
Mercury compounds
Naphthalene-d8
Nickel compounds
Nitrobenzene-d5
Perylene-dl2
Phenanthrene-dlO
o x
sl
.3 §
"8 &K
•s < t*
a«t
.§ N 0
IIs
gg
o u
F024, F025
K001, K035
K073
F024, F025
K017
F024, F025
Chemical-Specific Data
Available
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
U.S. EPA Recommended
and Potential PICs
(1994b)
PICs in Stack Emissions
Actually Detected
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-29
-------
TABLE A-1
INFORMATION ON COMPOUNDS OF POTENTIAL INTEREST
(Page 30 of 30)
CAS Number
--
--
--
--
--
--
--
--
--
Compound Name
Phenol-d6
Phenolic compounds
Phosphorodithioic and phosphorothioic acid
esters
2,3,7,8-substitutedPolychlorinated
dibenzo(p)dioxin congeners (2,3,7,8-PCDDs)
2,3,7,8-substituted Polychlorinated dibenzofuran
congeners (2,3,7,8-PCDFs)
Selenium compounds
Tetrachlorobenzene
2,4,6-Tribromophenol
Tricliloropropane
o x
sl
.3 §
"8 &K
•s < t*
a«t
.§ N 0
IIs
gg
o o
K060
K036, K037, K038, K039, K040
F024, F025
K017
Chemical-Specific Data
Available
PICs Recommended by
U.S. EPA (1994a) for Risk
Assessments
U.S. EPA Compounds
Identified in Combustion
Unit Emissions (1993)
U.S. EPA Recommended
and Potential PICs
(1994b)
PICs in Stack Emissions
Actually Detected
Note: See Table A-1 References and Discussion (Appendix A-1) for explanation of the information presented.
A-1-30
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APPENDIX A-l
COMPOUNDS OF POTENTIAL INTEREST
REFERENCES AND DISCUSSION
This discussion lists reference documents for each of the columns in Table Al-1 and briefly describes the
quality of data associated with these references. This information is only presented for informational
purposes to assist in planning data collection.
Al.l COLUMN 1: CHEMICAL ABSTRACTS SERVICE (CAS) NUMBER
The CAS number is a unique number assigned to each compound in the table. Compounds are listed by
CAS number, in ascending order, to prevent problems with alphabetization procedures or differences in
common nomenclature.
A1.2 COLUMN 2: COMPOUND NAME
The most common compound name is listed. Where appropriate, common synonyms are also listed to
aid the user in identifying particular compounds.
A1.3 COLUMN 3: COMPOUNDS LISTED IN 40 CFR PART 261 APPENDIX VII OR VIII
Appendix VII of Title 40 Code of Federal Regulations (40 CFR) Part 261 identifies compounds for
which specific hazardous wastes, from specific and nonspecific sources, are listed (U.S. EPA 1995).
Appendix VIII of 40 CFR Part 261 identifies acute hazardous wastes and toxic hazardous wastes
associated with commercial chemical products, manufacturing chemical intermediates, and
off-specification commercial chemical products (U.S. EPA 1995). This column lists hazardous waste
codes for the associated compounds. This list is provided for reference purposes only, because it is
commonly cited by other U.S. EPA combustion risk assessment documents as an original source of the
product of incomplete combustion (PIC) lists. An explanation of the reasons for including a COPC on
this list is beyond the scope of this guidance.
A1.4 COLUMN 4: CHEMICAL-SPECIFIC DATA AVAILABLE
This column lists those compounds for which the following are available (as presented in Appendix A-2):
(1) chemical-specific physical and chemical information, and (2) chemical-specific fate-and-transport
information.
A1.5 COLUMN 5: PICS RECOMMENDED BY U.S. EPA (1994a) FOR SCREENING LEVEL
RISK ASSESSMENTS
Compounds in this column marked with an "X" in the appropriate cells identified by U.S. EPA (1994a)
as PICs to be included in screening level risk assessments. U.S. EPA (1994a) does not describe the basis
or references for the inclusion of these PICs in screening level risk assessments. More information
regarding some of these compounds is presented in Chapter 2.
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A1.6 COLUMN 6: PICS IDENTIFIED IN COMBUSTION UNIT EMISSIONS (U.S. EPA 1993)
Compounds in this column marked with an "X" in the appropriate cells are identified in U.S. EPA (1993)
as PICs. The source documents for these tables cited by U.S. EPA (1993) are described in the following
subsections. These references have been cited by this and other U.S. EPA reference documents as
"sources" of information regarding PIC emissions from hazardous waste combustion units. This
document—U.S. EPA (1993)—has, in turn, been cited by later guidance documents as a "source" of
information regarding PIC emissions from hazardous waste combustion units. However, as is indicated
by the listing of the references from Dempsey and Oppelt (1993) (which is a summary of existing
information), many of the reference documents appear to simply cite additional "sources" of information.
The original research and sampling data regarding PIC emissions have not yet been identified but, based
on a preliminary review of the information below, the sources of the "original" information cited by all of
the most common reference documents may be limited and may have been published over 15 years ago.
Al.6.1 Demsey and Oppelt (1993)
The sections of Demsey and Oppelt (1993) regarding PICs from hazardous waste combustion facilities
("Combustion Byproduct Emissions" and "Table XVII: Organics that Could Potentially be Emitted from
Devices Burning Hazardous Waste") cite the following references:
• U.S. EPA (1989b) does not include a list of PICs from combustion sources. U.S. EPA
(1989b) discussed ways of ensuring that PIC emissions do not pose an unacceptable risk
to human health and the environment. Stack gas carbon monoxide (CO) concentration is
a good indicator of combustion efficiency; therefore, controlling CO is a prudent and
reasonable approach to minimizing the potential risk from PICs. The destruction and
removal efficiency (DRE) standard of 40 CFR Part 264.242(a) limits stack emissions of
principal organic hazardous constituents (POHC) to 0.01 percent (or 0.0001 percent for
dioxin-containing waste) of the quantity of POHC in the waste. This standard, however,
does not impose a limit on PICs. Therefore, a limit of 100 parts per million by volume
(ppmv) (Tier I) was imposed, below which PIC emissions do not pose unacceptable risks
to human health. The proposed rule allows a waiver to the 100-ppmv CO limit, by
(1) restricting total hydrocarbon (THC) emissions to 20 ppmv (Tier II), or (2) showing
that THC emissions do not pose an unacceptable risk by using prescribed risk assessment
procedures.
The above limitations were also provided in the Federal Register, dated January 23, 1981
(U.S. EPA 1981) and April 27, 1990 (U.S. EPA 1990b)
• U.S. EPA (1981) does not contain any information regarding PICs not contained in U.S.
EPA (1989b). There is no discussion of "risk" in this document. Although the notice
deals with permitting standards, there is no risk-based approach, and it appears to be an
entirely technical discussion. Specifically, it deals with updated material for specific
parts of 40 CFR.
40 CFR Part 122 (Incinerator Facility Permits)
40 CFR Part 264 (General Standards for Hazardous Waste Incineration)
40 CFR Part 265 (Interim Status Standards for Hazardous Waste
Incineration)
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Standards are technology-based, not risk-based.
• U.S. EPA (1990a) describes amendments to the hazardous waste incinerator regulations
for the following purpose:
Improve control of toxic metal emissions, HC1 emissions, and residual organic
emissions; amend the definitions of incinerators and industrial furnaces; propose
definitions for plasma arc incinerators and infrared incinerators; propose to
regulate carbon regeneration units as thermal treatment devices; and make a
number of minor revisions to permitting procedures.
U.S. EPA (1990a) also states the following:
The database on PIC emissions is limited therefore, the risk assessments may
under-estimate risk. The assessments consider only the organic compounds that
have been actually identified and quantified. Zero to 60 percent of total
unburned hydrocarbon emissions have been chemically identified at any
particular facility. Thus, the bulk of the hydrocarbon emissions have not been
considered in those risk assessments. Although many of the unidentified,
unqualified organic compounds may be non-toxic, some fraction of the organic
emissions is undoubtedly toxic. . . .data on typical PIC emissions from
hazardous waste combustion sources were compiled and assessed in recent EPA
studies. These studies identified 37 individual compounds in the stack gas of the
eight full-scale hazardous waste incinerators tested, out of which 17 were volatile
compounds and 20 semivolatile compounds. Eight volatile compounds
(benzene, toluene, chloroform, trichloroethylene, carbon tetrachloride,
tetrachloroethylene, chlorobenzene, and methylene chloride), and one
semivolatile compound (naphthalene) were identified most frequently in more
than 50 percent of the tests. Some of these compounds are carcinogenic.
The sources for these statements appear to be Wallace and others (1986) and Trenholm
and Lee (1986).
Trenolm and Lee (1986), prepared by Andrew R. Trenholm of Midwest Research
Institute and C.C. Lee at the U.S. EPA Hazardous Waste Engineering Research
Laboratory, discussed that emissions from incinerators are only characterized for
constituents listed in Appendix VIII. However, constituents not listed in Appendix VIII
are also emitted from the stacks.
Data was obtained from HWERL-sponsored tests at eight hazardous waste incinerators,
nine boilers that co-fired hazardous wastes, and five mineral processing kilns that fired
hazardous wastes as fuel. In addition, SVOC emissions data for two municipal solid
waste incinerators and seven coal-fired power plants were also reviewed. The common
PICs are presented in the following table:
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August 1999
Volatile PICs Most Frequently Present in Stack Gases
VOCs
Benzene
Toluene
Carbon Tetrachloride
Chloroform
Methylene Chloride
Trichloroethylene
Tetrachloroethylene
1,1,1 -Trichloroethane
Chlorobenzene
SVOCs
Naphthalene
Phenol
Bis(2-ethylhexyl)phthalate
Diethylphthalate
Butylbenzylphthalate
Dibutylphathlate
Tests were conducted for three incinerator runs to search for constituents not listed in
Appendix VIII. These constituents include:
Non-Appendix VIII Constituents Present in Highest Concentrations in Stack Gases
Acetone
Ethylbenzene
Acetophenone
Benz aldehyde
Benzenedicarboxaldehyde
Benzoic acid
Chlorocyclohexanol
Cyclohexane
Cyclohexanol
Cyclohexene
Dioctyl adipate
Ethenyl ethylbenzene
Ethylbenzaldehyde
Ethylbenzoic acid
Ethylphenol
Ethylphenyl-ethanone
Ethynylbenzene
Phenylacetylene
1,1 '-(1 ,4-phenylene)bisethanone
Phenylpropenol
Propenyhnethylbenzene
Tetramethyloxirane
Trimethylhexane
Emission rates of compounds not in the waste feed were also provided.
• U.S. EPA (1985) does not include a list of PICs from combustion sources. U.S. EPA
(1985) discussed views and reviews by the Environmental Effects, Transport, and Fate
Committee of the Science Advisory Board of issues related to the environmental impacts
of the incineration of liquid hazardous wastes at sea and on land. Several issues were
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addressed, including issues concerning the combustion and incineration of hazardous
waste. Major findings of the committee were as follows:
Fugitive emissions and spills may release as much or more material to the
environment than the direct emissions from waste incineration processes.
Numerous PICs are formed during combustion processes. However, only a
fraction of them are identified or detected. It is possible that the aggregate of all
compound emissions that are not categorized as other POHCs or PICs can be
more toxic and pose greater risks than those listed. Although 99.99 percent DRE
has been claimed, if the unburned or undetected hydrocarbon output is included,
the DRE may actually be less than 99.99 percent. Therefore, the concept of
destruction efficiency used by EPA was found to be incomplete and not useful
for subsequent exposure assessments. All emissions and effluents must be
identified and quantified, including their physical form and characteristics.
Local site-specific conditions must be used in characterizing exposure to
receptors from waste incinerator emissions.
The evaluation of exposure durations and concentrations should be based on a
detailed assessment of transport processes and the habits of the exposed
organisms. The role of food chains needs particular attention.
At a minimum, the toxicities of representative emissions and effluents from
incinerators should be tested on sensitive life stages of representative aquatic and
terrestrial vertebrates, invertebrates, and plants of ecological importance.
• U.S. EPA (1990b) does not include a list of PICs from combustion sources. It was
prepared by the PIC subcommittee of the Science Advisory Board to review the OSW
proposal to control emissions of PICs from hazardous waste incinerators by instituting
process controls that are based on CO and THC emission concentrations. U.S. EPA risk
assessments indicate that emissions of PICs at currently measured levels are not likely to
produce unacceptable risks. However, because the current DRE standard applies only to
designated POHCs, 99.99 percent DRE does not preclude the possibility that emission of
PICs could present significant risk. The following summarizes the major findings of the
subcommittee review.
The concept of using CO and THC as guidance for incinerator operational
control is reasonable.
At low CO levels, CO correlates well with THC; therefore, limiting CO in order
to ensure high combustion efficiency and low THC levels is reasonable. At high
CO concentrations, CO and THC do not correlate well; therefore, relying solely
on the controlling of CO may not provide a reasonable control for THC.
Continuous emissions monitoring of THC is preferred. Quantification of PICs
alone is not practical with the sampling techniques that are available, primarily
because PICs are normally emitted in the range of parts per billion (ppb) to parts
per trillion (ppt).
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A 100-ppmv limit for CO is reasonable. However, supporting documentation
does not demonstrate that a CO concentration of 100 ppmv is better than 50
ppmv or 150 ppmv.
Continuous emissions monitoring of THC with a cold system appears to be
practical for routine operations. However, a hot transfer line produces better
analysis of THC concentrations and detection of a larger fraction of the THCs
emitted.
The database characterizing PICs in emissions would not allow a correlation to
be established with CO or THC levels for various combustion devices and
conditions. Limited data introduces large uncertainties into U.S. EPA's risk
assessment. Therefore, U.S. EPA's site-specific risk assessment process is
limited in its usefulness in establishing acceptable THC levels. However, the
risk assessment procedures are risk-based.
• U.S. EPA (1987) is a report prepared by Andrew R. Trenholm, Acurex Corporation,
California, and staff members from the U.S. EPA Hazardous Waste Engineering
Research Laboratory in Cincinnati, Ohio. The paper discussed the lack of information
on total emissions from combustion of hazardous wastes, particularly under conditions of
less than optimal performance. The focus issue was whether additional constituents that
are listed in Appendix VIII or not listed in Appendix VIII which were not identified in
early tests might be emitted from hazardous waste combustion units. To address this
issue and related issues, U.S. EPA initiated this project to qualitatively and quantitatively
study the characteristics of all possible effluents, under steady-state and transient
conditions. The following summarizes the major findings:
THC emissions detected as specific compounds ranged from 50 to 67 percent for
five runs and were 91 percent for one run. The fraction of THC not detected is
most likely explained by uncertainty in the measurements or other analytical
problems.
Methane accounted for the largest fraction of THC.
Oxygenated aliphatic compounds made up the largest class of compounds among
the SVOCs, both in total mass and number of compounds.
Transient upsets did not cause significant increases in the concentration of
SVOCs or most VOCs. Three VOCs that were increased were methane,
methylene chloride, and benzene.
Particulate and HC1 emissions did not change between the steady-state and
transient test runs.
• Duval and Rubey (1976) was prepared by D.S. Duval and W.A. Rubey of the University
of Dayton Research Institute, Ohio. The objective of the study was to provide data from
which requirements can be assigned for the thermal disposal of kepone. This report was
primarily concerned with the high-temperature destruction of kepone, with DDT and
Mirex used as comparative Analog. Laboratory tests were conducted to establish
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destruction temperature characteristics of the vaporized pesticides at preselected
residence times. The following summarizes the major findings.
Kepone was essentially destroyed at a 1-second residence time and a temperature
range of 500°C to 700°C, depending on the pesticide.
Major decomposition products detected were hexachlorocyclopentadiene and
hexachlorobenzene for both kepone and Mirex. These products were formed in
different thermal regions.
The study demonstrated that the chemical nature of the effluent products depends
on the temperature and residence time that the basic molecule experiences.
• Duval and Rubey (1977) discusses the experimental destruction temperature and
residence time relationships for various PCB compounds and mixtures of PCBs. The
document states that "upon thermal stressing in air, PCBs decomposes to
low-molecular-weight products." However, the document does not identify any of these
low-molecular-weight products. In fact, the document states directly that the products
were not identified in the study. It further recommends that additional research be
conducted on the "degradation products and effluents."
• Bellinger, Torres, Rubey, Hall, and Graham (1984) was prepared by Barry Bellinger and
others of the University of Bayton, Ohio. This paper presented the gas-phase thermal
stability method under controlled laboratory conditions to rank the incinerability of
compounds. The objective of this study was to determine the gas-phase thermal
decomposition properties of 20 hazardous organic compounds.
The compounds were selected on the basis of (1) frequency of occurrence in hazardous
waste samples, (2) apparent prevalence in stack effluents, and (3) representativeness of
the spectrum of hazardous waste organic waste materials. The following summarizes the
major findings.
Gas-phase thermal stability method is a more effective means of ranking the
incinerability of hazardous compounds in a waste.
Numerous PICs were formed during the thermal decomposition of most of the
compounds tested. However, PICs were not identified.
Bestruction efficiency of 99.99 percent is achieved at 2 seconds mean residence
time in flowing air at 600°C to 950 °C.
No single physical or chemical property describes the ranking scheme for
incinerability.
• Bellinger, Hall, Graham, Mazer, Rubey, and Malanchuk (1986) was prepared by Barry
Bellinger, B. Bouglas, L. Hall, John L. Graham, Sueann L. Mazer, and Wayne A. Rubey
of the University of Bayton Research Institute, Bayton, Ohio, and Myron Malanchuk of
U.S. EPA, Cincinnati, Ohio. The paper discussed the development of an incineration
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model based on laboratory studies conducted by using the nonflame mode of hazardous
waste thermal decomposition. The results of these studies were compared to the
flame-mode studies and field tests to evaluate the incineration model proposed. The
model was based on the premise that incinerators do not operate continuously at
optimum conditions. As a result, 1 percent or more of the feed and its flame treatment
products must undergo further decomposition in the nonflame region to meet the DRE
criterion of greater than 99.99 percent.
In the past, several methods were used to rank the incinerability of compounds.
Nonflame studies, however, indicated that tests on compounds conducted at low oxygen
concentrations provided a better correlation with field tests to determine the relative
incinerability of compounds. Four experimental studies were conducted to develop and
expand the database on POHCs and PICs.
Studies were conducted on individual compounds to evaluate degradation compounds
and PICs from the original parent compound. The thermal degradation of
2,3',4,4',5-PCB was studied under four reaction atmospheres (at varying levels of
oxygen) at a constant gas phase residence time of 2.0 seconds. Tests were conducted at
temperatures ranging from 500°C to 1,000°C. Tests indicated that the yield of
combustion products decreased with increased oxygen levels. Numerous major
degradation products were identified from the thermal degradation of 2,3',4,4',5-PCB,
including:
Penta-, tetra-, and trichlorodibenzofurans
Tetra- and trichlorobiphenyls
Tri- and dichlorobenzene
Tetra- and trichloronaphthalene
Tri- and dichlorochlorophenylethlyene
Tetrachlorobiphenylenes
C9H8OC1
C10H3C13
Thermal decomposition of chloroform was studied. Numerous decomposition products
were identified, including:
CC14
C2H4C12
C2HC13
C2HC15
C2C12
C2C14
C3C14
C4C16
Thermal decomposition of poly chlorinated phenols was studied in nitrogen (N2) and
oxygen atmospheres because of the potential formation of poly chlorinated
dibenzodioxins. Pentachlorophenol (PCP) thermal decomposition was studied.
Numerous decomposition products of PCP were identified in N2 and/or air atmospheres,
including:
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Dichlorobutadiyne (in N2)
Tetrachloroethylene (in air)
Tetrachloropropyne (in air)
Trichlorofuran (in air)
Tetrachlorofuran (in air)
Trichlorobenzene (in N2 and air)
Tetrachlorobenzene (in N2 and air)
Pentachlorobenzene (in N2 and air)
Hexachlorobenzene (in N2)
Octachlorostyrene (in N2)
Hexachlorodihydronaphthalene (in N2 and air)
The paper concluded that PICs in the air atmosphere may have formed directly from the
parent material, whereas, in the nitrogen atmosphere, the principal PICs may have
evolved from the thermal decomposition of other PICs.
• Kramlich, Seeker, and Heap (1984) does not include a list of PICs from combustion
sources. It was prepared by J.C. Kramlich, W.R. Seeker, and M.P. Heap of Energy and
Environmental Research Corporation, California; and C.C. Lee of the Industrial Waste
Combustion Group, U.S. EPA. This paper presented a research program to study the
flame-mode incineration of hazardous waste liquids in laboratory scale reactors. The
objective of this study was to supply the flame-mode data that will be used in evaluating
the applicability of various approaches to ranking the ease of incinerability.
Five compounds were tested—chloroform, 1,1-dichloroethane, benzene, acrylonitrile, and
chlorobenzene—because (1) their range of incinerabilities is broad, and (2) they are
representative of liquid hazardous wastes. The following summarizes the findings.
The flame section of the incinerator destroys greater than 99.995 percent of the
wastes.
The post-flame region destroys the remainder of the wastes.
The destruction efficiency is reduced because of flame-related failures.
Incinerability ranking depends on actual failure condition.
No incinerability ranking system completely predicts the destruction efficiency
of the compounds tested for all failure conditions.
• Trenholm and Hathaway (1984) was prepared by Andrew Trenholm and Roger
Hathaway of Midwest Research Institute (MRI) in Missouri, and Don Oberacker, U.S.
EPA, Cincinnati, Ohio. PICs were defined as any Appendix VIII hazardous organic
constituent detected in the stack gas but not present in the waste feed at a concentration
of 100 micrograms per gram or higher. Benzene and chloroform were the most
commonly found PICs. PIC emissions were comparable to POHC emissions in
concentration and total mass output. This document discussed PIC formation
mechanisms and criteria for PIC formations.
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MRI conducted a series of tests at eight operating hazardous waste incineration facilities
and analyzed the collected samples for PICs. These tests were conducted as part of the
technical support of U.S. EPA's preparation of a regulatory impact analysis for
hazardous waste incinerators. Each incinerator had a liquid injection burner, and some
facilities also included a rotary kiln or hearth. Three incinerators had no air pollution
control devices. The remaining five had wet scrubbers for HC1 control, and four of these
had other particulate control devices. Twenty-nine compounds were classified as PICs
from the eight incinerator tests and are presented in Table Al .6-1. In general, PIC
concentrations were slightly higher than POHC concentrations, although this ratio varied
from site to site. PIC output rate very rarely exceeded 0.01 percent of the POHC input
rate. The document stated that the measurement of Appendix VIII compounds at low
concentrations in the waste feed, auxiliary fuel, and inflow streams to control systems is
often necessary to explain the presence of PICs.
• Olexsey, Huffman, and Evans (1985) was prepared by Robert A. Olexsey and others of
the U.S. EPA Hazardous Waste Engineering Research Laboratory in Cincinnati, Ohio.
This document discussed PIC generation mechanisms and criteria for PIC formations.
The paper provided data on emissions of PICs during full-scale tests conducted on
incinerators and boilers burning hazardous waste (Trenholm and others 1984; Castaldini
and others 1984). The documents referenced by this paper summarized a series of
full-scale tests conducted on seven incinerators and five boilers conducted by U.S. EPA
to support its regulatory development for incinerators and boilers. Commonly found
PICs identified in these tests are presented in Tables Al.6-2 and Al.6-3.
• For incinerators, ratios of PIC emissions to POHC input ranged from 0.00007 to
0.0028 percent; and ratios of PIC emissions to POHC emissions ranged from 0.01 to
3.89. For boilers, ratios of PIC emissions to POHC input ranged from 0.0032 to
0.3987 percent, and ratios of PIC emissions to POHC emissions ranged from 5.44 to
22.5. These data indicated that PIC emissions were higher for boilers than for
incinerators; that is, PIC emissions were reduced with increased POHC DRE which is
higher for incinerators. The document proposed seven methods to control PICs and
recommended further research on PIC generation mechanisms and control technologies.
• Trenholm, Kapella, and Hinshaw (1992) was prepared by Andrew R. Trenholm and
David W. Kapella of MRI in North Carolina and Gary D. Hinshaw of MRI in Missouri.
The paper discusses the following issues regarding emissions from incinerators that burn
hazardous waste: (1) emissions of specific constituents presented in Appendix VIII,
(2) emissions of specific compounds or types of compounds, and (3) data on the size and
molecular weight of compounds emitted. The following were among the major issues
discussed.
PICs were studied through U.S. EPA-sponsored tests at eight incinerators, nine
industrial boilers, and five mineral processing kilns. The study was limited to
compounds presented in Appendix VIII. In all, 52 organic compounds
(32 VOCs and 20 SVOCs) were identified. The VOC concentrations were
significantly higher than the SVOC concentrations. PICs listed in this paper
included benzene, toluene, carbon tetrachloride, trichloromethane,
dichloromethane, trichloroethene, tetrachloroethene, 1,1,1-trichloroethane,
cholorobenzene, naphthalene, and phenol.
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TABLE Al.6-1
PICS IDENTIFIED BY TRENHOLM AND HATHAWAY (1984)
PICs Found In Stack Effluents
PIC
Benzene
Chloroform
Bromodichloromethane
Dibromochloromethane
Naphthalene
Bromoform
Chlorobenzene
Tetrachloroethylene
1,1,1 ,-Trichloroethane
Toluene
o-Nitrophenol
Methylene chloride
Phenol
2,4,6-Trichlorophenol
Carbon disulfide
o-Chlorophenol
2,4-Dimethylphenol
Methylene bromide
Bromochloromethane
Trichlorobenzene
Hexachlorobenzene
Diethyl phthalate
Pentachlorophenol
Dichlorobenzene
Chloromethane
Methyl ethyl ketone
Bromomethane
Pyrene
Fluoranthene
Number of Facilities
6
5
4
4
3
3
3
3
3
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Low Concentration (ng/L)
12
1
3
1
5
0.2
1
0.1
0.1
2
2
2
4
110
32
22
21
18
14
7
7
7
6
4
3
3
1
1
1
High Concentration (ng/L)
670
1,330
32
12
100
24
10
2.5
1.5
75
50
27
22
110
32
22
21
18
14
7
7
7
6
4
3
3
1
1
1
Notes:
ng/L
PIC
Nanograms per liter
Product of incomplete combustion
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Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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Appendix A-l
August 1999
TABLE Al.6-2
VOLATILE PICS MOST FREQUENTLY IDENTIFIED IN BOILER EMISSIONS
(OLEXSY, HUFFMAN, AND EVANS 1985)
PIC
Chloroform
Tetrachloroethylene
Chloromethane
Methylene chloride
Benzene
1,1,1 -Trichloroethane
1 ,2-Dichloroethane
Number of Facilities
5
5
4
4
3
3
3
Low Concentration
(ng/L)
4.2
0.3
4.6
83
9.4
5.9
1.3
High Concentration
(ng/L)
1,900
760
410
2,000
270
270
1,200
Notes:
ng/L =
Nanograms per liter
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
A-1-42
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Appendix A-l
August 1999
TABLE Al.6-3
VOLATILE PICS MOST FREQUENTLY IDENTIFIED IN INCINERATOR EMISSIONS
(OLEXSY, HUFFMAN, AND EVANS 1985)
PIC
Benzene
Chloroform
Tetrachloroethylene
1,1,1 -Trichloroethane
Toluene
Methylene chloride
Number of Facilities
6
5
3
3
2
2
Low Concentration
(ng/L)
12
1
0.1
0.1
2
2
High Concentration
(ng/L)
670
1,330
2.5
1.5
75
27
Notes:
ng/L = Nanograms per liter
PIC = Product of incomplete combustion
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
A-l-43
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From the U.S. EPA-sponsored tests, (1) volatile compounds listed in Appendix
VIII identified were only a fraction—sometimes about one-half—of the total
organic compounds identified, and (2) semivolatile compounds not listed in
Appendix VIII identified were three to 30 times the quantity of organic
compounds listed in Appendix VIII. Table A 1.6-4 lists the compounds
identified by the U.S. EPA-sponsored tests.
A study of hazardous waste incinerator stack effluent was conducted to
characterize the types of compounds emitted. Twenty-nine compounds were
identified at a concentration range of 0.1 to 980 nanograms per liter. Methane,
chloromethane, and chloroform accounted for more than one-half of the total
mass of VOCs detected. Other than methane, oxygenated aliphatic hydrocarbons
formed the highest fraction of the total emissions.
Based on the incinerator stack effluent study, it was found that as combustion
conditions deteriorate, increases in mass emissions are first noted with VOCs.
Emissions of these compounds, most notably Cl to C3 compounds, increase
proportionately more than larger compounds. For larger compounds, available
data indicate that emission increases are more likely to be aromatic compounds.
Al.6.3 CARB(1990b)
CAPvB prepared "Technical Support Document of Proposed Dioxins Control Measures for Medical
Waste Incinerators" to meet the requirements of California Health and Safety Code Section 39666 that a
needs report be prepared for proposed rules. The report presents a proposed airborne toxic control
measure for dioxin emissions from medical waste-burning facilities. The report concentrates on dioxin,
furan, and cadmium emissions, although other pollutants detected during the tests are listed. Table
Al.6-5 lists these pollutants.
Al.6.4 CARB(1991)
CARB prepared "Air Pollution Control at Resource Recovery Facilities 1991 Update" to update
information presented in its 1984 report, entitled "Air Pollution Control at Resource Recovery Facilities."
Specifically, the document updates available guidelines concerning incinerator technology, emissions
control technology, and emission limits for municipal waste, hospital waste, biomass, tire, manure,
landfill and digester gas, and sewer sludge incinerators. The document states that its guidelines
represent levels that have been achieved by existing facilities.
In addition, the document summarizes the ultimate analysis of waste types undergoing treatment in the
facilities described above. An appendix summarizes stack gas analysis data for numerous operating
facilities. Pollutants identified in the analyses are summarized in Table A 1.6-6.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering A-1-44
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August 1999
TABLE Al.6-4
MOST FREQUENTLY IDENTIFIED PICS
(TRENHOLM, KAPELLA, AND HINSHAW 1992)
Appendix VIII
Volatile Organic
Compounds
1,1,1 -Trichloroethane
Benzene
Carbon tetrachloride
Chlorobenzene
Chloroform
Methylene chloride
Tetrachloroethylene
Toluene
Trichloroethylene
Appendix VIII
Semivolatile Organic
Compounds
Compounds Not Listed
in Appendix VIII
Bis(2-Ethylhexyl)phthalate
Butylbenzylphthalate
Dibutylphtahlate
Diethylphthalate
Naphthalene
Phenol
1,1 '-(1,4-Phenylene)bisethanone
Acetone
Acetophenone
Benzaldehyde
Benzenedicarboxaldehyde
Benzoic acid
Cyclohexanol
Chlorocyclohexanol
Cyclohexane
Ethylbenzene
Ethylbenzoic acid
Ethylphenol
Ethylphenyl-ethanone
Ethynylbenzene
Phenylpropenol
Propenylmethylbenzene
Tetramethyloxirane
Trimethylhexane
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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August 1999
TABLE Al.6-5
COPCS IDENTIFIED BY CARB (1990b)
COPC
Ammonia
Arsenic
Benzene
Bromodichloromethane
Cadmium
Carbon dioxide
Carbon monoxide
Carbon tetrachloride
Chlorobenzenes
Chlorodibromomethane
Chloroform
Chlorophenols
Chromium, hexavalent
Chromium, total
Copper
Cumene
Notes:
1 ,2-Dibromoethane
Dichloroethane
Dichloromethane
1 ,2-Dichloropropane
Ethylbenzene
Freon
Hydrocarbon, total
Hydrogen chloride
Hydrogen fluoride
Iron
Lead
Manganese
Mercury
Mesitylene
Methyl isobutyl ketone
Napthalene
Nickel
Nitrogen oxides
PM
PAHs
Sulfur dioxide
Tetrachloroethene
Tetratrichloromethylene
Toluene
Tribromomethane
Trichlorethane
1,1,1 -Trichloroethane
Trichloroethylene
Trichlorotrifluroethane
Vinyl chloride
Xylenes
Zinc
PAH = Polynuclear aromatic hydrocarbons
PM = Particulate matter
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
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August 1999
TABLE Al.6-6
STACK GAS ANALYSIS DATA
(CARB 1991)
(Page 1 of 2)
Pollutant
Nitrogen oxides
Sulfur oxides
Particulate matter
Carbon monoxide
Total hydrocarbons
Hydrogen chloride
Hydrogen fluoride
Amonnia
Carbon dioxide
Oxygen
Arsenic
Beryllium
Cadmium
Chromium (total)
Chromium (hexavalent)
Copper
Mercury
Iron
Manganese
Nickel
Lead
Zinc
Polyaromatic
hydrocarbons b
Poly chlorinated
biphenyls b
CPb
CBb
Benzene
Poly chlorinated
dibenzo(p) dioxins b
Polychlorinated
dibenzofurans b
2,3,7,8-Tetrachloro
dibenzo(p)dioxin
equivalents b
Incinerator Type a
Municipal
Waste
(5)
•
•
•
•
•
•
•
NA
•
•
•
•
•
•
ND
•
•
NA
NA
•
•
NA
•
•
•
•
•
•
•
•
Hospital
Waste
(7)
•
•
•
•
•
•
NA
NA
•
•
•
NA
•
•
•
NA
•
NA
NA
•
•
NA
NA
ND
NA
NA
•
•
•
•
Biomass
(4)
•
ND
•
•
•
NA
NA
•
•
•
•
NA
•
•
NA
NA
NA
•
•
•
•
NA
•
•
•
•
•
•
•
•
Manure
(1)
•
•
•
•
•
NA
NA
NA
•
•
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Tire
(1)
•
•
•
•
•
•
NA
•
•
•
•
•
ND
•
•
NA
ND
NA
NA
ND
ND
NA
•
•
•
•
NA
•
•
•
Landfill Gas
(20)
•
•
•
•
•
NA
NA
NA
NA
NA
•
• b
• b
•
NA
•
•
NA
NA
•
•
•
NA
NA
NA
NA
NA
NA
NA
NA
Sewage
Sludge and
Digester Gas
(5)
•
•
•
•
•
NA
NA
NA
•
•
•
•
•
•
NA
NA
•
NA
NA
•
•
NA
NA
NA
NA
NA
NA
NA
NA
•
U.S. EPA Region 6
Multimedia Planning and Permitting Division
Center for Combustion Science and Engineering
U.S. EPA
Office of Solid Waste
A-1-47
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Screening Level Ecological Risk Assessment Protocol
Appendix A-l August 1999
TABLE Al.6-6
STACK GAS ANALYSIS DATA
(CARB 1991)
(Page 2 of 2)
Notes:
• = Detected in at least one emission test
ND = Not detected in any emission test
NA = No analysis
a Number in parentheses indicates the number of facilities for which data were tabulated.
b Isomers and/or homologues that were not detected were added to total values at one-half the detection limit;
pollutant may not have actually been detected.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering A-l-48
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Al.6.5 U.S. EPA (1988)
This document, referenced by some documents as a 1989 document, was prepared in 1988.
U.S. EPA prepared "Hospital Waste Combustion Study: Data Gathering Phase" to assemble available
information on hospital waste combustion so that U.S. EPA can evaluate whether airborne pollutant
emissions from hospital waste combustion should be regulated. While preparing this document, U.S.
EPA reviewed the pertinent literature to determine which studies would be helpful in completing the
database on toxic emissions from medical waste incinerators. The report clearly addresses only those
pollutants for which emissions data were found. The data reviewed were mostly for larger, controlled air
incinerators; and the more commonly used retort incinerators were not evaluated.
The study identified several categories of pollutants that were measured in stack gases; these are
discussed in the following paragraphs.
Where evaluated, acid gases were detected in stack gases. For example, HC1 was detected in 24 of 28
tests; HC1 concentration not recorded in the remaining four tests.
Particulate matter (PM) was detected in all stack tests for 30 facilities at concentrations ranging from
0.001 grains per dry standard cubic foot (gr/dscf), at a facility with PM add-on control devices, to
0.22 gr/dscf at facilities without such control devices.
Trace metals were detected in stack tests for three medical waste incineration facilities. Metals detected
include arsenic, cadmium, chromium, iron, manganese, nickel, and lead. The document also states that
fine-particle enrichment processes could lead to emissions of molybdenum, tin, selenium, vanadium, and
zinc. However, test results for these trace metals are not presented.
With respect to organic emissions, dioxins and furans were detected in emissions from three facilities,
both with and without pollution control devices. Other organic emissions detected in stack tests cited in
this report include CO, THC, trichlorotrifluoroethane, tetrachloromethane, tetrachloroethene, and
trichloroethylene.
In a stack testing conducted on three Canadian biomedical waste incinerators, PCBs and PAHs were
either not detected (one facility) or not analyzed (two facilities).
Al.6.6 CARB(1996)
In May 1996, CARB prepared "Proposed Amendments to the Emission Inventory Criteria and Guidelines
Report Published in Accordance with the Air Toxics 'Hot Spots' Information and Assessment Act of
1987." The purpose of the report is to present the basis of CARB's recommended amendments to the Air
Toxics Hot Spots Program. The report states that California Health and Safety Code (HSC) 44321
requires CARB to compile the list of toxic substances that must be monitored from "designated reference
lists of substances." Therefore, the document is not a primary source of toxics emission information.
The primary sources of information are mandated by California HSC 44321, as follows:
• California HSC 44321(a): National Toxicology Program, International Agency for
Research on Cancer
• California HSC 44321(b): Governor's List of Carcinogens and Reproductive Toxicants
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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• California HSC44321(c): CARB
• California HSC 44321(d): Hazard Evaluation System and Information Service
• California HSC 44321(e): U.S. EPA
• California HSC 4432 l(f): California HSC
The lists of toxic substances presented in the document are not restricted to incinerator facilities, but
apply to any facility discharging airborne pollutants to the atmosphere. The document also removes
numerous substances, primarily medicinal compounds, from lists of toxic chemicals that must always be
evaluated, and places them on lists of toxic compounds that require evaluation only if a facility
manufactures that substance.
A1.7 COLUMN 7: U.S. EPA-RECOMMENDED AND POTENTIAL PICS (1994a; 1994b)
Compounds marked with an "X" in the appropriate cells are identified in U.S. EPA (1994a and 1994b).
Based on information presented in U.S. EPA (1994b), these tables were developed from available U.S.
EPA data and from lists of toxic compounds from various U.S. EPA programs. Because the source lists
were not developed as lists of toxic PICs, U.S. EPA deleted compounds that were not appropriate (U.S.
EPA 1994b). U.S. EPA acknowledged the importance of using focused studies to develop a PIC list that
is (1) appropriately protective of the environment, and (2) not excessively burdensome on the regulated
community. Nevertheless, Tables 1 and 2 in U.S. EPA (1994b) were compiled as draft lists for use
during the interim period. Tables in U.S. EPA (1994b) were to be revised as additional PIC data were
collected. U.S. EPA Permits and State Program Division is currently updating these tables; however, a
target completion date is not available. Tables 1 and 2 are based on the following (U.S. EPA 1994b):
• Hazardous waste constituent list in 40 CFR Part 261, Appendix VIII
• hazardous air pollutants (HAP) list
• Office of Research and Development list of organic compounds found in combustion
devices developed for U.S. EPA (1993)
The following compounds were deleted from this list:
• Pesticide compounds not likely to be a PIC
• Federal Drug Administration-regulated drugs
• Carcinogenic sugar substitutes
• Compounds without chemical-specific listings (for example, "coal tar")
• Compounds without U.S. EPA-established sampling and analysis methods
• Metallic compounds (because of difficulty in analyzing the specific compounds; metals
are still included in elemental totals)
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering A-l-50
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• Compounds with low octanol-water partition coefficients and no inhalation toxicity data
• Compounds with low toxicity values
• Naturally-occurring plant toxins
Specific compounds were retained on Tables 1 and 2 on the following basis:
• Pesticides with a molecular structure simple enough to be of concern as a PIC
• Compounds with very high octanol-water partition coefficients
A1.8 COLUMN 8: PICS ACTUALLY DETECTED IN STACK EMISSIONS
Compounds marked by an "X" in the appropriate cells are PICs that have actually been detected in stack
emissions. U.S. EPA compiled this list by evaluating the studies highlighted in Section A1.6.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering A-1 -51
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California Air Resources Board (CARB). 1990a. "Health Risk Assessment Guidelines for
Nonhazardous Waste Incinerators." Prepared by the Stationary Source Division of the CARB
and the California Department of Health Services.
CARB. 1990b. "Technical Support Document of Proposed Dioxins Control Measures for Medical
Waste Incinerators." May 25.
CARB. 1991. "Air Pollution Control at Resource Recovery Facilities. Update."
CARB. 1996. "Proposed Amendments to the Emission Inventory Criteria and Guidelines Report
Published in Accordance with the Air Toxics 'Hot Spots' Information and Assessment Act of
1987." May.
Castaldini, C., and others. 1984. "Engineering Assessment Report—Hazardous Waste Cofiring in
Industrial Boilers." Report to U.S. Environmental Protection Agency under Contract No.
68-02-3188. June.
Dellinger, B., D.L. Hall, J.L. Graham, S.L. Mazer, W.A. Rubey, and M. Malanchuk. 1986. PIC
Formation Under Pyrolytic and Starved Air Conditions.. Prepared for the U.S. EPA Industrial
Environmental Research Laboratory. Prepared by the University of Dayton Research Institute.
EPA/600/2-86/006. NTIS PB-86-145422. January.
Dellinger, B., J.L. Torres, W.A. Rubey, D.L. Hall, and J.L. Graham. 1984. Determination of the
Thermal Decomposition Properties of 20 Selected Hazardous Organic Compounds. Prepared for
the U.S. EPA Industrial Environmental Research Laboratory. Prepared by the University of
Dayton Research Institute. EPA-600/2-84-138. NTIS PB-84-232487. August.
Demsey, C.R., and E.T. Oppelt. 1993. "Incineration of Hazardous Waste: A Critical Review Update."
Air and Waste. 43:25-73.
Duval, D.S., and W.A. Rubey. 1976. Laboratory Evaluation of High-Temperature Destruction of
Kepone and Related Pesticides. EPA-600/2-76-299. NTIS PB-264892/1. December.
Duval, D.S., and W.A. Rubey. 1977. Laboratory Evaluation of High-Temperature Destruction of
Poly'chlorinated Biphenyls and Related Compounds. EPA-600/2-77-228. NTIS PB-279139/0.
December.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering A-1-5 3
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Appendix A-l August 1999
Kramlich, J.C., W.R. Seeker, and M.P. Heap. 1984. "Laboratory-Scale Flame Mode Study of
Hazardous Waste Incineration." Proceedings of the Ninth Annual Research Symposium on
Incineration and Treatment of Hazardous Waste. Fort Mitchell, Kentucky. May 2 through 4,
1983. EPA-600/9-84/015. NTIS PB-84-234525. Pages 79-94. July.
Olexsey, R.A., G.L. Huffman, and G.M. Evans. 1985. "Emission and Control of By-Products from
Hazardous Waste Combustion Processes." Proceedings of the llth Annual Research Symposium
on Incineration and Treatment of Hazardous Waste. Cincinnati, Ohio. April 29 to May 1,
1985. EPA-600/9-85/028. NTIS PB-86-199403. Pages 8-15. September.
Trenholm, A., and R. Hathaway. 1984. "Products of Incomplete Combustion from Hazardous Waste
Incinerators." Proceedings of the 10th Annual Research Symposium on Incineration and
Treatment of Hazardous Waste. Fort Mitchell, Kentucky. April 3-5. EPA-600/9-84/022. NTIS
PB-85-116291. Pages 84-95. September.
Trenholm, Andrew R., David W. Kapella, and Gary D. Hinshaw. 1992. "Organic Products of
Incomplete Combustion from Hazardous Waste Combustion." Proceedings of the Air and
Waste Management Association 85th Annual Meeting and Exhibition. Kansas City, Missouri.
June 21-26.
Trenholm, A., and C.C. Lee. 1986. "Analysis of PIC and Total Mass Emissions from an Incinerator."
Proceedings of the Twelfth Annual Research Symposium on Land Disposal, Remedial Action,
Incineration, and Treatment of Hazardous Waste. Cincinnati, Ohio. April 21 to 23, 1986.
EPA/60-9-86/022. Pages 376-381. August.
Trenholm, A., and others. 1984. "Performance Evaluation of Full-Scale Hazardous Waste Incinerators."
Report to U.S. EPA under Contract No. 68-02-3177.
U.S. Environmental Protection Agency (EPA). 1981. "Incinerator Standards for Owners and Operators
of Hazardous Waste Management Facilities; Interim Final Rule and Proposed Rule." Federal
Register. 46(15):7666-7690. January 23.
U.S. EPA. 1985. Report on the Incineration of Liquid Hazardous Wastes. Science Advisory Board.
Environmental Effects, Transport, and Fate Committee. April.
U.S. EPA. 1987. Total Mass Emissions from a Hazardous Waste Incinerator. Final Report. Midwest
Research Institute. EPA-600/S2-87/064. NTIS PB-87-228508/AS. June 12.
U.S. EPA. 1988. "Hospital Waste Combustion Study: Data Gathering Phase." Office of Air Quality
Planning and Standards. Research Triangle Park, North Carolina. EPA-450/3-88-008.
December.
U.S. EPA. 1989. Guidance of PIC Controls for Hazardous Waste Incinerators. Volume V of the
Hazardous Waste Incineration Guidance Series. EPA/530-SW-90-040. April 3.
U.S. EPA. 1990a. "Standards for Owners and Operators of Hazardous Waste Incinerators and Burning
of Hazardous Wastes in Boilers and Industrial Furnaces; Proposed Rule, Supplemental Proposed
Rule, Technical Corrections, and Request for Comments." Federal Register.
55(82):17862-17921. April 27.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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U.S. EPA. 1990b. Report of the Products of Incomplete Combustion Subcommittee of the Science
Advisory Board; Review of OSWProposed Controls for Hazardous Waste Incineration Products
of Incomplete Combustion. EPA-SAB EC-90-004. October 24.
U.S. EPA. 1993. Review Draft Addendum to the Methodology for Assessing Health Risks Associated
with Indirect Exposure to Combustor Emissions. OHEA. ORD. EPA-600-AP-93-003.
November 10.
U.S. EPA. 1994a. Revised Draft Guidance for Performing Screening Level Risk Analyses at
Combustion Facilities Burning Hazardous Wastes: Attachment C, Draft Exposure Assessment
Guidance for RCRA Hazardous Waste Combustion Facilities. Office of Emergency and
Remedial Response (OERR). OSW. December 14.
U.S. EPA. 1994b. "Table 1—Chemicals Recommended for Identification and Table 2—Chemicals for
Potential Identification." Draft Exposure Assessment Guidance for Resource Conservation and
Recovery Act Hazardous Waste Combustion Facilities: Attachment. April 15.
U.S. EPA. 1995. "Basis for Listing Hazardous Waste.: Title 40, Code of Federal Regulations, Part 261,
Appendices VII and VIII.
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering A-1-5 5
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APPENDIX A-2
COMPOUND SPECIFIC PARAMETER VALUES
Screening Level Ecological Risk Assessment Protocol
August 1999
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Screening Level Ecological Risk Assessment Protocol
Appendix A-2 August 1999
APPENDIX A-2
TABLE OF CONTENTS
Section Page
LIST OF VARIABLES AND COMPOUND-SPECIFIC PARAMETERS A-2-ii
A2.1 GUIDANCE DOCUMENTS AS PRIMARY REFERENCE SOURCES A-2-1
A2.2 GENERAL ANALYSIS AND METHODOLOGY A-2-2
A2.3 PHYSICAL AND CHEMICAL PROPERTIES A-2-3
A2.3.1 Molecular Weight (MW) A-2-3
A2.3.2 Melting Point Temperature (Tm) A-2-4
A2.3.3 Vapor Pressure (Vp) and Aqueous Solubility (S) A-2-4
A2.3.4 Henry's Law Constant (H) A-2-6
A2.3.5 Diffusivity of COPCs in Air (Da) and Water (Dw) A-2-7
A2.3.6 Octanol-Water Partitioning Coefficient (Km) A-2-8
A2.3.7 Organic Carbon Partition Coefficient (Koc) A-2-10
A2.3.7.1 Ionizing Organic Compounds A-2-10
A2.3.7.2 Nonionizing Organic Compounds A-2-10
A2.3.8 Partitioning Coefficients for Soil-Water (KdJ, Suspended Sediment-Surface
Water (Kdsv), and Bottom Sediment-Sediment Pore Water (Kdbs) A-2-12
A2.3.9 COPC Soil Loss Constant Due to Biotic and Abiotic Degradation A-2-14
A2.3.10 Fraction of COPC Air Concentration in the Vapor Phase (Fv) A-2-15
REFERENCES A-2-17
TABLES OF COMPOUND-SPECIFIC PARAMETER VALUES A-2-25
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
Center for Combustion Science and Engineering A-2-i
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APPENDIX A-2
LIST OF VARIABLES AND COMPOUND-SPECIFIC PARAMETERS
pair = Density of air (g/cm3)
Pforage = Density of forage (g/cm3)
Babeef = Biotransfer factor in beef
(mg COPC/kg FW tissue)/(mg COPC/day) OR (day/kg FW tissue)
Bachicken = Biotransfer factor in chicken
(mg COPC/kg FW tissue)/(mg COPC/day) OR (day/kg FW tissue)
Baegg = Biotransfer factor in eggs
(mg COPC/kg FW tissue)/(mg COPC/day) OR (day/kg FW tissue)
Bamiik = Biotransfer factor in milk
(mg COPC/kg FW tissue)/(mg COPC/day) OR (day/kg FW tissue)
Bapork = Biotransfer factor in pork
(mg COPC/kg FW tissue)/(mg COPC/day) OR (day/kg FW tissue)
BAFfish = Bioaccumulation factor in fish
(mg COPC/kg FW tissue)/(mg COPC/L total water column)
OR (L water/kg FW tissue)
BCFfish = Bioconcentration factor in fish (L/kg FW OR unitless)
Brag = Plant-soil bioconcentration factor in aboveground produce
(Mg COPC/g DW plant)/(Mg COPC/g DW soil)—unitless
Bt'fomge/siiage = Plant-soil bioconcentration factor in forage and silage
(Mg COPC/g DW plant)/(Mg COPC/g DW soil)—unitless
Brgrain = Plant-soil bioconcentration factor in grain
(Mg COPC/g DW grain)/(Mg COPC/g DW soil)—unitless
Brrootveg = Plant-soil bioconcentration factor for belowground produce
(Mg COPC/g DW plant)/(Mg COPC/g DW soil)—unitless
BSAF-flsh = Biota-sediment accumulation factor in fish
(mg COPC/kg lipid tissue)/(mg COPC/kg sediment)—unitless
Bvol = Volumetric air-to-leaf biotransfer factor in leaf
(Mg COPC/L FW plant)/(Mg COPC/L air)—unitless
Bvag = COPC air-to-plant biotransfer factor for aboveground produce
(Mg COPC/g DW plant)/(Mg COPC/g air)—unitless
BVfomge/siiage = Air-to-plant biotransfer factor in forage and silage
(Mg COPC/g DW plant)/(Mg COPC/g air)—unitless
c = Junge constant = 1.7 x 10~04 (atm-cm)
Da = Diffusivity of COPC in air (cm /s)
Dw = Diffusivity of COPC in water (cm /s)
fOCtbs = Fraction of organic carbon in bottom sediment (unitless)
fOC:S = Fraction of organic carbon in soil (unitless)
fOCtSW = Fraction of organic carbon in suspended sediment (unitless)
fwater = Fraction of COPC in water (unitless)
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Fw
H
Inhalation
CSF
Inhalation
URF
Kd
ksg
MW
PL
P°s
Oral CSF
R
RCF
RfC
RfD
Rp
S
ASf
ST
Fraction of COPC air concentration in vapor phase (unitless)
Fraction of wet deposition that adheres to plant surfaces (unitless)
Henry's law constant
Inhalation cancer slope factor (mg/kg-day)"1
Inhalation unit risk factor ((jg/m3)"
Soil-water partition coefficient (mL water/g soil OR cm3 water/g soil)
Suspended sediment-surface water partition coefficient
(L water/kg suspended sediment OR cm3 water/g suspended sediment)
Bed sediment-sediment pore water partition coefficient
(L water/kg bottom sediment OR cm3 water/g bottom sediment)
Octanol/water partitioning coefficient
(mg COPC/L octanol)/(mg COPC/L octanol)—unitless
Soil organic carbon-water partition coefficient (mL water/g soil)
COPC soil loss constant due to biotic and abiotic degradation (yr"1)
Molecular weight of COPC (g/mole)
Liquidphase vapor pressure of COPC (atm)
Solid-phase vapor pressure of COPC (atm)
Oral cancer slope factor (mg/kg-day)";
Universal gas constant (atm-m3/mol-K)
Root concentration factor
(Hg COPC/g DW plant)/((ig COPC/mL soil water)
Reference concentration (mg/m3)
Reference dose (mg/kg/day)
Interception factor of edible portion of plant (unitless)
Solubility of COPC in water (mg COPC/L water)
Entropy effusion [ASf/R = 6.79 (unitless)]
Whitby's average surface area of particulates (aerosols)
= 3.5 x 10~06 cm2/cm3 air for background plus local sources
= 1.1 x 10~05 cm2/cm3 air for urban sources
tin
TEF
Vp
Half-time of COPC in soil (days)
Ambient air temperature (K)
Melting point temperature (K)
Toxicity equivalency factor (unitless)
Vapor pressure of COPC (atm)
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APPENDIX A-2
The following sections provide the methodology and rationale followed for the selection or development
of compound-specific parameter values recommended by U.S. EPA OSW. Compound-specific values
are provided for (1) physical and chemical properties, (2) fate-and-transport parameters, and (3) health
benchmarks. A summary table of all compound-specific parameter values is provided at the end of this
appendix, followed by individual parameter-value tables for each compound. The individual
parameter-value tables cite sources for each parameter value.
A2.1 PRIMARY GUIDANCE DOCUMENTS
Throughout Appendix A-2, the following guidance documents are referenced as the primary sources for
the development and comparision of compound-specific parameter values, and used to the fullest extent
possible to provide consistency. Therefore, in this appendix, the term primary guidance documents
refers to the following documents:
• U.S. EPA. 1994f Revised Draft Guidance for Performing Screening Level Risk
Analyses at Combustion Facilities Burning Hazardous Wastes: Attachment C,
Draft Exposure Assessment Guidance for RCRA Hazardous Waste Combustion
Facilities. Office of Emergency and Remedial Response (OERR). Office of
Solid Waste. December 14.
• U.S. EPA. 1995b. Review Draft Development of Human Health Based and Ecologically
Based Exit Criteria for the Hazardous Waste Identification Project. Volumes I
and II. Office of Solid Waste. March 3.
• North Carolina Department of Environment, Health, and Natural Resources
(NC DEHNR). 1997. North Carolina Protocol for Performing Indirect
Exposure Risk Assessments for Hazardous Waste Combustion Units. January.
To ensure consistency, sources referenced in the primary guidance documents were also evaluated.
Information for certain compounds like PCDDs, PCDFs, and mercury were obtained from the following
documents:
• U.S. EPA. 1994a. Estimating Exposure to Dioxin-Like Compounds. External Review
Draft Report. Volumes I-III. Office of Research and Development. Washington, DC.
EPA/600/6-88/005Ca,b,c.
• U.S. EPA. 1997g. Mercury Study Report to Congress. Volume III: Fate and Transport
of Mercury in the Environment. Office of Air Quality Planning and Standards and Office
of Research and Development. EPA-452/R-97-005. December.
U.S. EPA (1994a) provides various parameter values for (but are not limited to) PCDDs, PCDFs, and
PCBs. U.S. EPA (1997g) provides various parameter values for mercuric compounds including
elemental mercury, mercuric chloride, and methyl mercury.
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A2.2 GENERAL ANALYSIS AND METHODOLOGY
This section describes the general analysis and methodology followed for the development of
compound-specific parameter values presented. Compound-specific parameter values in the primary
guidance documents and other sources generally were evaluated as follows:
1. Compound-specific values for each parameter were compared among the primary
guidance documents and the following observations were noted:
a. Parameter values provided in U.S. EPA (1994f) are limited to 24 compounds.
For these compounds, sources were referenced specifically to each parameter, in
addition to the methodology used to obtain the respective values.
b. U.S. EPA (1995b) provides various parameter values for a comprehensive list of
compounds. The methodology used for determining values was covered in
detail. However, parameter values for each compound were not referenced to a
specific source. Although a comprehensive list of sources was provided, it is
difficult to determine which parameter value for a compound was obtained from
which source.
c. NC DEHNR (1997) provides various parameter values for a comprehensive list
of compounds, including congeners of poly chlorinated dibenzo(p)dioxins
(PCDDs) and poly chlorinated dibenzofurans (PCDFs). However, the sections
citing the methodology and sources of values in the NC DEHNR (1997) were
reproduced directly from U.S. EPA (1994f). Therefore, in NC DEHNR (1997),
the compound-specific parameter values that were provided did not correlate
with the sections citing the methodology and sources of values. In addition, only
a partial list of sources was provided for the values. Therefore, it was not
possible to determine the actual source of values with certainty.
2. Sources of values referenced in the primary guidance documents were further researched
and evaluated. Observations affecting usability are included in parameter-specific
discussions for each compound, as appropriate.
3. Values provided in the primary guidance documents were used only when the sources
and applicability of such values could be verified. Additional sources of parameter
values were evaluated, used, and referenced when technically justified.
4. Recommended parameter values obtained using correlations or equations were calculated
using the recommended values for these variables provided in this SLERAP.
In general, for the selection of parameter values, the following three steps were followed:
1. Whenever measured parameter values were available in published literature studies, they
were preferred for use over other types of data. When multiple measured values were
available, the geometric mean of the parameter values is recommended for use.
2. In the absence of measured values in published literature that could not be directly
evaluated, parameter values compiled or adopted for use by the primary guidance
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documents, U.S. EPA (1994a), and U.S. EPA (1997g) are recommended.
3. If unable to obtain acceptable values from published literature or the primary guidance
documents, parameter values were estimated or calculated using correlation equations
based on sound scientific judgment.
The following sections, A2.3 through A2.5, provide compound-specific parameter values, which are
categorized and discussed as follows: (1) organic compounds, including poly chlorinated biphenyls
(PCB), and excluding methyl mercury, PCDDs and PCDFs, (2) PCDDs and PCDFs, (3) all metals except
mercury, and (4) the mercuric compounds—mercury (elemental; metal), mercuric chloride (divalent
inorganic mercury), and methyl mercury (organic mercury).
For each of the parameters, the sources of values referenced in this SLERAP are followed by a discussion
and justification of their selection. There is also a brief discussion of the methodology followed by each
of the primary guidance documents. This provides a complete evaluation and comparison of the
compound-specific parameter values provided in the primary guidance documents that are currently used
to conduct risk assessments.
A2.3 PHYSICAL AND CHEMICAL PROPERTIES
A2.3.1 Molecular Weight (MW)
Molecular weight (MW) of a compound is defined as the sum of atomic weights of all atoms in the
compound's molecule.
Organics and Metals For most organics (except PCDDs and PCDFs) and metals, this SLERAP provides
MW values that were obtained from the following:
Budavari, S., M.J. O'Neil, A. Smith, and P.E. Heckelman. 1989. The Merck Index: An
Encyclopedia of Chemicals, Drugs, and Biologicals. llth Edition. Merck and
Company, Inc. Rahway, New Jersey.
MW values not provided in Budavari, O'Neil, Smith, and Heckelman (1989) were obtained from the
following document:
• Montgomery, J.H., and L.M. Welkom. 1991. Groundwater Chemicals Desk Reference.
Lewis Publishers. Chelsea, Michigan.
Because Budavari, O'neil, Smith, and Heckelman (1989) provides MW values for most of the compounds
evaluated, it was used as the primary source to ensure consistency. MW values are based on the
compound's formula; and, the values in Budavari, O'Neil, Smith, and Heckelman (1989) are the same as
the values cited in several literature sources. MW values for most of the compounds in the primary
guidance documents were also obtained from Budavari, O'Neil, Smith, and Heckelman (1989).
PCDDs and PCDFs MW values for PCDDs and PCDFs were obtained from U.S. EPA (1994a).
Mercuric Compounds MW values for mercury and mercuric chloride were obtained from Budavari and
others (1989). MW value for methyl mercury was obtained from U.S. EPA (1997g).
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A2.3.2 Melting Point Temperature (Tm)
Melting point temperature (Tm) is the temperature of the compound (in degree Kelvin [K]) at which the
solid state of the compound undergoes a phase change to a liquid phase. At ambient temperatures and at
an atmpospheric pressure of 1 atmosphere, compounds are either in a solid or liquid state. The
compound liquid or solid state is provided in the summary tables of compound-specific parameter values.
Organics and Metals For most organics (except PCDDs and PCDFs) and metals, this SLERAP provides
values for Tm that were obtained from Budavari, O'Neil, Smith, and Heckelman (1989). Tm values not
provided in Budavari, O'Neil, Smith, and Heckelman (1989) were obtained from Montgomery and
Welkolm (1991).
Because Budavari, O'Neil, Smith, and Heckelman (1989) provides Tm values for most of the compounds
evaluated, it was used as the primary source to ensure consistency. Tm values in Budavari, O'Neil,
Smith, and Heckelman (1989) were generally within 2 to 3 degrees of the values provided in literature
sources reviewed. Tm values for most compounds in the primary guidance documents were also obtained
from Budavari, O'Neil, Smith, and Heckelman (1989).
PCDDs and PCDFs Tm values for PCDDs and PCDFs were obtained from U.S. EPA (1994a).
U.S. EPA (1994a) provides Tm values for PCDDs and PCDFs, that were obtained from various literature
sources.
A2.3.3 Vapor Pressure (Vp) and Aqueous Solubility (S)
The vapor pressure (Vp) of a substance is defined as the pressure in atmospheres exerted by the vapor
(gas) of a compound when it is under equilibrium conditions. It provides a semi-quantitative rate at
which it will volatilize from soil and/or water. The aqueous solubility (S) of a compound is defined as
the saturated concentration of the compound in water (mg COPC/L water) at a given temperature and
pressure, usually at soil/water temperatures and atmospheric pressure (Montgomery and Welkom 1991).
Organics For most organics (except PCDDs and PCDFs), values for Vp and S were obtained from the
following:
• U.S. EPA 1994c. Draft Report Chemical Properties for Soil Screening Levels. Prepared
for the Office of Emergency and Remedial Response. Washington, DC. July 26.
U.S. EPA (1994c) provides measured, calculated, and estimated values for Vp and S that were obtained
from various literature sources. Vp values in U.S. EPA (1994c) were generally either measured (at 20°C
to 25 °C) or calculated values obtained from various literature sources. U.S. EPA (1994c), however,
provides values for Vp corrected to 25 C. U.S. EPA (1995b) states that, because the distribution of many
of the parameters is skewed, the geometric mean or the median values were preferable to the arithmetic
mean values. Therefore, when available geometric mean values were preferred over the arithmetic mean
values. The geometric mean of the temperature corrected Vp values, determined from measured and
calculated values, is recommended for use in this SLERAP.
In U.S. EPA (1994c), lvalues were either measured (at 20 C to 30 C) or calculated values obtained from
various literature sources. The geometric mean S value, calculated from measured and calculated values,
is recommended for use in this SLERAP. Although lvalues were measured at temperatures ranging
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from 20°C to 30°C, U.S. EPA (1994c) states that lvalues were not corrected to 25°C, because the
variability in solubilities measured at 20°C to 25 °C was within the overall range of measured values.
U.S. EPA (1994c) is the preferred source, because (1) sources and the conditions at which each value was
obtained are provided, and (2) values were provided to 2 significant figures. Also, U.S. EPA (1994c)
provides multiple Vp and S values for each compound from several different literature sources; providing
a recent, more comprehensive compilation of reported literature values. Vp and S values from U.S. EPA
(1994c) were generally consistent with those provided in U.S. EPA (1994f), U.S. EPA (1995b), and NC
DEHNR(1997).
When Vp and lvalues were not available in U.S. EPA (1994c), they were obtained from one of three
sources, in the following order of preference:
1. U.S. EPA(1994f)
2. U.S. EPA (1995b); values from which were obtained from one of three sources:
a. Mackay, D., W.Y. Shiu, and K.C. Ma. 1992. Illustrated Handbook of
Physical-Chemical Properties and Environmental fate for Organic Chemicals.
Volume I-Monoaromatic Hydrocarbons, Chlorobenzenes, andPCBs.
Volume II-Polynuclear Aromatic Hydrocarbons, Poly chlorinated Dioxins and
Dibenzofurans. Volume III - Volatile Organic Chemicals. Lewis Publishers.
Boca Raton, Florida.
b. Howard, P.H. 1989-1993. Handbook of Environmental Fate and Exposure
Data For Organic Chemicals. Volumes I: Large Production and Priority
Pollutants (1989). Volume II: Solvents (1990). Volume III: Pesticides (1991).
Volume IV: Solvents2 (1993). Lewis Publishers. Chelsea, Michigan.
c. Other referenced literature sources, when values were not available in Mackay,
Shiu, and Ma (1992) or Howard (1989-1993).
3. U.S. EPA. 1994b. Superfund Chemical Data Matrix (SCDM). Office of Emergency
and Remedial Response. Washington, DC. June.
Vp and S values in U.S. EPA (1994f) were geometric mean values obtained from various literature
sources. References specific to sources of values for each compound were provided in U.S. EPA (1994f)
and were, therefore, preferred over U.S. EPA (1995b) values.
Most Vp and lvalues in U.S. EPA (1995b) were obtained from Mackay, Shiu, and Ma (1992) or Howard
(1989-1993). Mackay, Shiu, and Ma (1992) and Howard (1989-1993) obtain the "best" values after
evaluation of various literature sources.
Vp values in U.S. EPA (1994b) were obtained from various literature sources, lvalues in U.S. EPA
(1994b) were the geometric mean of values obtained from various literature sources.
PCDDs andPCDFs Vp and lvalues for PCDDs and PCDFs were obtained from U.S. EPA (1994a). Vp
and S values were either (1) measured, or (2) estimated by using the homologue (compound class with
the same amount of chlorination) average method.
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NOTE: The phase—solid or liquid—of Vp values for all organics, including PCDDs and
PCDFs, was indicated. This is based on whether the compound is in the solid or
liquid phase at ambient soil temperatures.
Metals As cited in the primary guidance documents and in the literature, metals—except mercury—are
considered (1) nonvolatile at ambient temperatures, and (2) insoluble in water, except as certain weak
acids. Therefore, Vp and S values were not available for all metals (except mercury) in any of the
literature sources reviewed.
Mercuric Compounds Mercury is a relatively volatile compound. Vp and S values for elemental
mercury were obtained from Budavari, O'Neil, Smith, and Heckelman (1989); and are comparable to the
values in the primary guidance documents. Vp and S values for mercuric chloride were obtained from
U.S. EPA (1997g) and Budavari, O'Neil, Smith, and Heckelman (1989), respectively. Vp and lvalues
for methyl mercury were not found in the literature.
A2.3.4 Henry's Law Constant (H)
Henry's Law constant (H) is also referred to as the air-water partition coefficient, and is defined as the
ratio of the partial pressure of a compound in air to the concentation of the compound in water at a given
temperature under equilibrium conditions. Henry's Law constant values generally can be (1) calculated
from the theoretical equation defining the constant, (2) measured, or (3) estimated from the compound
structure. Experimental and estimated H values from literature reports, however, are (1) very
temperature-dependent and difficult to measure, (2) generally obtained from various literature sources
that use different experimental and estimation methods, and (3) available for only a limited number of
compounds.
Organics For organics (excluding PCDDs and PCDFs), //values were calculated from the following
theoretical equation (Lyman, Reehl, and Rosenblast 1982) for consistency, using recommended MW, S,
and Vp values provided in this SLERAP:
„ _ Vp • MW
H Equation A2-1
H = Henry's Law constant (atm-m3/mole)
Vp = Vapor pressure of COPC (atm)
S = Solubility of COPC in water (mg COPC/L water)
The primary guidance documents also used theoretical Equation A-3-1 to calculate //values.
PCDDs and PCDFs //values for PCDDs and PCDFs are calculated values obtained from U.S. EPA
(1994a).
Metals For all metals (except mercury), H is zero, because Vp—because of the nonvolatile nature of the
metals—and S are assumed to be zero.
Mercuric Compounds H values for elemental mercury, mercuric chloride, and methyl mercury were
obtained from U.S. EPA (1997g).
U.S. EPA Region 6 U.S. EPA
Multimedia Planning and Permitting Division Office of Solid Waste
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A2.3.5 Diffusivity of COPCs in Air (Da) and Water (Dw)
Diffusivity or diffusion coefficients in air (Z)a) and water (Z)w) are used to calculate the liquid or gas phase
transfer of a COPC into a waterbody.
Or ganics For organics (except PCDDs and PCDFs), diffusivity values were obtained directly from the
CHEMDAT8 model chemical properties database (Worksheet DATATWO.WK1):
• U.S. EPA. 1994d. CHEM8— Compound Properties Estimation and Data. Version 1.00.
CHEMDAT8 Air Emissions Program. Prepared for Chemicals and Petroleum Branch,
OAQPS. Research Triangle Park. North Carolina. November 18.
The U.S. EPA (1994d) database uses empirical correlations with compound density and molecular weight
to calculate diffusivity values. For compounds not in the U.S. EPA (1994d) database, diffusivity values
were obtained by using the WATERS model correlation equations for air and water diffusivities:
U.S. EPA. 1995d. WATERS- -Air Emissions Models Wastewater Treatment.
Version 4.0. OAQPS. Research Triangle Park. North Carolina. May 1.
U.S. EPA(1995d) database values were predicted by using chemical-structural relationships. Diffusivity
values for all compounds in the U.S. EPA (1994d) and (1995d) databases were either predicted or
estimated. The primary guidance documents also recommended U.S. EPA (1994d) and (1995d) database
model values. More recent documents, including the following, also recommended these values:
• U.S. EPA. 1996. Soil Screening Guidance: Technical Background Document and
User 's Guide. Office of Solid Waste and Emergency Response. Washington, DC.
EPA/540/R-95/128. May.
For diffusivity values that were not available in these databases, Dw and Da values were calculated using
the following equations cited and recommended for use in U.S. EPA (1997g):
n 1-9
'' " (MW)m Equation A2-2a
w'' = (MW)2K Equation A2-2b
U.S. EPA (1995b) recommended the use of standard default diffusivity values. U.S. EPA (1995b) stated
that the diffusivity parameters vary slightly, and default values appear to be within the range of typical
values. Values for diffusivity in air range from about 0.01 to 0.1 square centimeters per second (cm2/s);
therefore, U.S. EPA (1995b) recommended a default value of 0.08 cm2/s. Values for diffusivity in water
range from 1 x 10"06 to 1 x 10"05 cm2/s; therefore, U.S. EPA (1995b) recommended a default value of
8 x 10"06 cm2/s. Diffusivity values calculated using Equations A-2-2a and A-2-2b were within the range
specified by U.S. EPA (1995b), and therefore, were adopted for use in this SLERAP.
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PCDDs and PCD Fs Diffusivity values in air and water for (1) 2,3,7,8-TCDD were obtained from
U.S. EPA (1994e), and (2) 2,3,7,8-TCDF were obtained from U.S. EPA (1995d). For all other congeners
of PCDDs and PCDFs, (1) a default Dw value of 8 x 10~06 cm2/s was used, and (2) Da values were
calculated using the following equation recommended by U.S. EPA (1994a):
Equation A2-2c
D MW
y x
where
D = Diffusivities in air of compounds x and y (cm2/s)
= Molecular weights of compounds x and y (g/mol)
Da values for PCDD congeners were calculated by using the Da value and AdWfor 2,3,7,8-TCDD. Da
values for PCDF congeners were calculated using the Da value and AdWfor 2,3,7,8-TCDF. This
approach is consistent with the methodology specified in U.S. EPA (1994a).
Metals and Mercuric compounds For metals (except chromium and mercury), diffusivity values were
not available in the literature. Diffusivity values for chromium and mercury were obtained from the U.S.
EPA (1994d) database. Diffusivity values for mercuric chloride and methyl mercury were calculated
using Equations A-2-2a and A-2-2b.
A2.3.6 Octanol/Water Partitioning Coefficient (Kow)
The «-octanol/water partitioning coefficient (K0J is defined as the ratio of the solute concentration in the
water-saturated «-octanol phase to the solute concentration in the «-octanol-saturated water phase
(Montgomery and Welkom 1991).
Or sanies For organics (except PCDDs and PCDFs), Kov values were obtained from U.S. EPA (1994c).
U.S. EPA (1994c) provides measured, calculated, and estimated Kow values obtained from various
literature sources. The geometric mean Kov value, calculated from all measured and calculated values for
each compound, is recommended in this SLERAP.
Kow values that were not available in U.S. EPA (1994c) were obtained from one of three sources, in the
following order of preference:
1. U.S. EPA (1994f)
2. Karickhoff, S.W. and J.M. Long. 1995. "Internal Report on Summary of Measured,
Calculated, and Recommended Log Kow Values." Environmental Research Laboratory.
Athens. April 10.
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3. U.S. EPA (1995b), values from which were obtained from one of three sources:
a. Mackay, D., W.Y. Shiu, and K.C. Ma. 1992. Illustrated Handbook of
Physical-Chemical Properties and Environmental Fate for Organic Chemicals.
Volume I-Monoaromatic Hydrocarbons, Chlorobenzenes, andPCBs.
Volume II - Polynuclear Aromatic Hydrocarbons, Poly chlorinated Dioxins and
Dibenzofurans. Volume III - Volatile Organic Chemicals. Lewis Publishers.
Boca Raton, Florida.
b. Howard, P.H. 1989-1993. Handbook of Environmental Fate and Exposure
Data For Organic Chemicals. Volumes I: Large Production and Priority
Pollutants (1989). Volume II: Solvents (1990). Volume III: Pesticides (1991).
Volume IV: Solvents2 (1993). Lewis Publishers. Chelsea, Michigan.
c. Other literature sources, when values were not available in Mackay, Shiu, and
Ma (1992) and Howard (1989-1993).
U.S. EPA (1994c) is the preferred source of values because (1) sources were provided, (2) several
literature values were provided, some of which are also cited by the primary guidance documents and
Karickhoff and Long (1995), and (3) the values were provided to 2 significant figures.
U.S. EPA (1994f) is the second-choice source of Kow values recommended; and provides geometric mean
values obtained from various literature sources. Karickhoff and Long (1995) recommended arithmetic
mean values obtained from various literature sources and was, therefore, preferred as the third-choice
source of Km, values when values were not available from the first two sources.
ow
In order to reference specific sources ofKm values for each compound, values from U.S. EPA (1995b)
and NC DEHNR (1997) were used only when values were not available in the literature sources
reviewed.
PCDDs andPCDFs Kow values for the PCDDs and PCDFs were obtained from either U.S. EPA (1994a)
or U.S. EPA (1992d). U.S. EPA (1994a) and U.S. EPA (1992d) provide Kow values for PCDDs and
PCDFs that were either measured values obtained from the literature or calculated by averaging the
literature values within the homologue group. According to U.S. EPA (1994a), Km values for
hexachlorodibenzofurans were not available in the literature. Therefore, as recommended in U.S. EPA
(1994a), due to lack of data, homologue group average values for hexachlorodibenzodioxins were applied
to hexachlorodibenzofurans.
Metals No Km values were available for metals, either in the literature or in the primary guidance
documents. Km values for the metals were assumed to be zero, because the affinity of the metals to the
octanol is almost zero.
Mercuric compounds No Kow values were available in the literature for mercury and methyl mercury.
For mercuric chloride, the Km value was obtained from U.S. EPA (1997g).
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A2.3.7 Soil Organic Carbon-Water Partition Coefficient (Koc)
The soil organic carbon-water partition coefficient (K01) or the organic carbon normalized soil sorption
coefficient is defined as the ratio of adsorbed compound per unit weight of organic carbon to the aqueous
solute concentration (Montgomery and Welkom 1991).
Organics Because of the soil mechanisms that are inherently involved, Koc values for the ionizing
organics and nonionizing organics are discussed separately.
A2.3.7.1 Ionizing Organic Compounds
Ionizing organic compounds include amines, carboxylic acids, and phenols. These compounds contain
the functional groups that ionize under specific pH conditions, and include the following:
• Organic acids (2,4,6-trichlorophenol; pentachlorophenol; 2,3,4,5-tetrachlorophenol;
2,3,4,6-tetrachlorophenol; 2,4,5-trichlorophenol; 2,4-dichlorophenol; 2-chlorophenol;
phenol; 2,4-dimethylphenol; 2-methylphenol; 2,4-dinitrophenol; and benzoic acid)
• Organic bases—n-nitroso-di-n-propylamine; n-nitrosodiphenylamine, and
4-chloroaniline)
Koc values for ionizing organic compounds were obtained from U.S. EPA (1994c). U.S. EPA (1994c)
provides Koc values for the ionizing organic compounds that have been estimated on the basis of the
degree of ionization and the relative proportions of neutral and ionized species. The primary guidance
documents cite one value for the ionizing organics, independent of the pH. The primary guidance
documents calculate Koc values for the ionizing organics by using correlation equations containing Km
that are applicable to nonionizing organics. However, Koc values for ionizing compounds can vary vastly,
depending on the pH conditions in the environment. Therefore, for the aforementioned ionizing organic
compounds, this SLERAP prefers and provides estimated Koc values that are based on pH.
Koc values were estimated on the basis of the assumption that the sorption of ionizing organic compounds
is similar to hydrophobic organic sorption, because the soil organic carbon is |