SURFACE IMPOUNDMENT STUDY
TECHNICAL PLAN FOR HUMAN HEALTH AND
ECOLOGICAL RISK ASSESSMENT
Work Assignment Manager
and Technical Direction: Jan Young
U.S. Environmental Protection Agency
Office of Solid Waste
Washington, DC 20460
Prepared by: Research Triangle Institute
3040 Cornwallis Road
Research Triangle Park, NC 27709-2194
Tetra Tech, Inc.
3746 Mount Diablo Boulevard, Suite 300
Lafayette, CA 94549
Under Contract No. 68-W-98-085, WA B-12
U.S. Environmental Protection Agency
Office of Solid Waste
Washington, DC 20460
February 2000
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Acknowledgments
A number of individuals have been involved in the development of the methodologies and
computer programs described herein. Jan Young of the U.S. Environmental Protection Agency, Office
of Solid Waste (EPA/OSW), provided overall technical direction and review throughout this work.
DISCLAIMER
The work presented in this document has been funded by the U.S. Environmental Protection
Agency. Mention of trade names or commercial products does not constitute endorsement or
recommendation for use by the Agency.
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Table of Contents
Section Page
Acknowledgments iii
List of Figures v
List of Tables vii
1.0 Executive Summary 1-1
1.1 Background 1-1
1.1.1 Legislative Mandate for the Surface Impoundment Study and
Consent Decree 1-1
1.1.2 Maj or Obj ectives of the Surface Impoundment Study 1-1
1.1.3 Study Design and Components 1-2
1.1.4 Selection of Representative Sample for Data Collection 1-3
1.1.5 Current Knowledge of Study Population 1-3
1.1.6 Additional Data Sources for Study 1-3
1.1.7 Science Advisory Board Comments 1-5
1.1.8 Purposes of Technical Plan 1-5
1.2 Summary of Technical Plan for the Analysis 1-5
1.2.1 Phase I. Screening and Prioritization of Constituents, Units, and
Pathways of Potential Concern 1-6
1.2.2 Phase II Modeling Approaches for Constituents and Units of
Potential Concern 1-11
1.2.3 Anticipated Outcome of Analysis 1-13
1.3 Peer Review Process and Comments Solicited 1-13
1.4 Organization of the Technical Plan 1-14
2.0 Phase I Screening Assessment 2-1
2.1 Introduction 2-1
2.2 Phase I Human Health Risk Screening 2-3
2.2.1 Phase IA Human Health Initial Risk Screening 2-3
2.2.2 Phase IB Human Health Screening 2-26
2.2.3 Special Cases 2-36
2.3 Phase I: Ecological Screening Assessment 2-38
2.3.1 Phase I Ecological Risk Screening 2-38
2.4 Phase IC Initial Prioritization 2-62
2.4.1 Design Goals and Overview 2-62
2.4.2 Approach 2-63
2.4.3 Risk Characterization Outputs 2-66
iii
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Table of Contents (continued)
Section Page
3.0 Phase II Risk Assessment 3-1
3.1 Overview 3-2
3.1.1 Key Features 3-3
3.1.2 Decision Methodology 3-4
3.1.3 Anticipated Outcome 3-5
3.2 Conceptual Model and Approach 3-6
3.2.1 Spatial Scale and Layout 3-8
3.2.2 Temporal Scale, Frame, and Integration 3-12
3.2.3 Human Health 3-18
3.2.4 Ecological Health 3-28
3.2.5 Risk Metrics 3-44
3.3 Overview of Simulation Modules 3-46
3.3.1 Source Modules 3-48
3.3.2 Air Module 3-51
3.3.3 Watershed Module 3-51
3.3.4 Groundwater (Vadose and Aquifer) Modules 3-52
3.3.5 Surface Water Module 3-53
3.3.6 Farm Food Chain Module 3-54
3.3.7 Terrestrial Food Web Module 3-55
3.3.8 Aquatic Food Web Module 3-56
3.3.9 Human Exposure Module 3-56
3.3.10 Human Risk Module 3-57
3.3.11 Ecological Exposure Module 3-61
3.3.12 Ecological Risk Module 3-63
3.4 3MRA Modifications and Data Collection Requirements 3-65
3.4.1 Model Modifications 3-65
3.4.2 Data Collection Requirements 3-67
Appendices
A Comprehensive List of Toxicity Benchmarks Assembled by RTI A-1
B Statistical Analysis Weights and Variance Estimation for the Surface Impoundment
Study Screening Survey B-l
C Examples of Toxicity Benchmarks for Ecological Risk Assessment C-l
D 3MRA Simulation Modules: Assumptions, Limitations, Inputs, and Outputs D-l
iv
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Figures
Number Page
1-1 Estimated distribution of study-eligible facilities with surface impoundments 1-4
1 -2 Universe of surface impoundment facilities with in-scope impoundments by
industry category 1-4
1-3 Sample representation of risk ranges based on Phase IA screening results 1-10
2-1 Overview of Phase I decision rules 2-2
2-2 Human health risk conceptual site model and potential exposure pathways 2-6
2-3 Flow diagram for Phase IA human health risk screening 2-11
2-4 Decision tree for calculating Phase IA human health risk estimates 2-12
2-5 Decision tree for determining Phase IA air concentrations 2-14
2-6 Decision tree for determining Phase IA water concentration 2-15
2-7 Steps to calculate impoundment water concentration if no survey data available 2-16
2-8 Decision tree for determining sludge concentration 2-16
2-9 Decision tree for performing Phase I human health risk screening 2-19
2-10 Example risk distributions for three industry types 2-25
2-11 Example risk screening results 2-26
2-12 Decision tree for Phase IB human health risk screening 2-30
2-13 Decision tree for Phase IB air screening using IWAIR 2-32
2-14 Decision tree for calculating influent waste concentration for IWAIR 2-33
2-15 Decision tree for Phase IB groundwater screening using IWEM 2-34
2-16 Example combined Phase IA and IB risk distributions for three industry types 2-35
2-17 Overview of the revised WMPT scoring algorithm 2-37
2-18 General food web model for aquatic and terrestrial systems 2-44
2-19 Decision tree for performing Phase I ecological risk screening 2-58
2-20 Decision diagram for evaluating special cases 2-59
2-21 Example Phase I ecological screening assessment output 2-61
3-1 Overview of the SI study risk analysis 3-3
3-2 Phase II decision process 3-6
3-3 Conceptual exposure model for human receptors 3-7
3-4 Conceptual exposure model for ecological receptors 3-9
3-5 Dimensions of the 3MRA conceptual model for surface impoundments 3-10
3-6 Area of interest (AOI) and risk rings for SI Study 3MRA 3-11
3-7 Typical watershed layout for HWIR 3MRA 3-13
3-8 Transfer of watershed polygons to 100- by 100-m template grid 3-13
3-9 Example human receptor placement for HWIR 3MRA 3-14
3-10 Example ecological habitat and home range bins 3-14
3-11 Illustration of concurrent time aggregation of risks 3-16
3-12 Finding Tcrit (year with maximum risk) 3-17
3-13 Diagram of 3MRA Model as applied to surface impoundments 3 -47
3-14 Schematic of general surface impoundment module construct 3-48
3-15 Local watershed containing WMU 3-50
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Figures (continued)
Number Page
3-16a Local watershed 3-50
3-16b Cross-section view 3-50
3-17 Conceptual model of vadose zone and saturated zone 3-53
3-18a Looping structure to calculate risk or HQ 3-58
3-18b Looping structure to build cumulative frequency histograms 3-59
3-18c Looping structure to determine critical year 3-59
3-19 WMU with three radial distance rings 3 -62
3 -20 Area of interest for multiple SI site illustrating overlay of topographic and
U.S. census data 3-68
vi
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Tables
Number Page
2-1 Equations for Development of Human Health Screening Factors 2-7
2-2 Exposure Parameter Values 2-9
2-3 Example Screening Risks for a Facility 2-21
2-4 Facility Distribution by Regulatory Category 2-23
2-5 Facility Distribution by Industry Type 2-24
2-6 Assessment Endpoints and Measures of Effects 2-41
2-7 Representative Habitats, Receptor Groups, and Representative Species 2-43
2-8 Examples of Primary Data Sources for Derivation of Screening Factors
for Community Receptors 2-48
2-9 Selected Sources of Toxicity Data 2-53
2-10 Examples of Screening Factors for Selected Receptor Populations and Communities
Associated with Freshwater Systems 2-57
2-11 Phase IC Prioritization Scoring System 2-64
2-12 Option 2 Ranking System 2-65
2-13 Phase IC Ranking System 2-65
3-1 Alternative Stages for Phase II SI Risk Analysis 3-5
3-2 Meteorological Data Time Scales, by 3MRA Model Module 3-15
3-3 Matrix of Human Receptor Types and Age Cohorts 3-19
3-4 Human Exposure Pathways by Receptor Type 3-21
3-5 Ecological Exposure Routes Evaluated by Receptor Type 3-32
3-6 Conversion Factors for Dissolved Metal 3-39
3-7 Applicable Receptor/Pathway Combinations 3-60
3-8 Pathway Aggregations 3-61
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Section 1.0
Executive Summary
1.0 Executive Summary
1.1 Background
1.1.1 Legislative Mandate for the Surface Impoundment Study and Consent Decree
The 1996 Land Disposal Program Flexibility Act (LDPFA) amended section 3004(g) of
the Resource Conservation and Recovery Act (RCRA) to exempt decharacterized wastes from
provisions of the RCRA land disposal restrictions. Decharacterized nonhazardous wastes are
RCRA hazardous wastes whose hazardous characteristics of ignitability, corrosivity, reactivity,
or toxicity have been removed through dilution or other treatment prior to being managed in
land-based waste management units. The LDPFA exemption allows decharacterized wastes to
be either (1) placed in surface impoundments that are part of wastewater treatment systems
whose ultimate discharge is regulated under the Clean Water Act (CWA), or (2) disposed of in
Class 1 nonhazardous injection wells regulated under the Safe Drinking Water Act (SDWA).
Congress also mandated in the 1996 LDPFA that a 5-year study be conducted to evaluate any
human health or environmental risks posed by the exempted wastes that are managed in these
ways and to evaluate the extent to which existing state regulations address any such risks.
The Office of Solid Waste (OSW) in the U.S. Environmental Protection Agency (EPA, or
the Agency) is conducting this study to assess those exempted wastes placed in surface
impoundments and regulated under the Clean Water Act. (EPA's Office of Water is separately
preparing a study to evaluate any risks through disposal of the exempted wastes in Class 1
nonhazardous injection wells.) In addition, the Surface Impoundment (SI) Study scope was
expanded from decharacterized wastewater only to include all nonhazardous industrial
wastewaters managed in surface impoundments whether or not they were once characteristic
hazardous wastes to satisfy a requirement of Civ. No. 89-0598, Environmental Defense Fund us.
Browner. The expanded scope thus covers two regulatory status categories based on RCRA:
impoundments with decharacterized wastes and impoundments with other nonhazardous
industrial wastes that have never exhibited any of the characteristics of corrosivity, ignitability,
reactivity, or toxicity.
1.1.2 Major Objectives of the Surface Impoundment Study
After reviewing the legislative history of the LDPFA, in 1997 OSW defined the principal
study question as "Determine, with an acceptable degree of certainty, what risks to human health
and the environment are posed by industrial wastewaters managed in surface impoundments."
Together with a randomized two-stage sample design and a separate field sampling effort
intended to "ground-truth" the data on existence of specific chemicals and their quantities, this
technical plan provides the analytic approach for answering the principal study question.
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Section 1.0
Executive Summary
To answer the study question, EPA will (1) develop descriptive statistics of nonhazardous
wastes managed in surface impoundments, the geographic distribution of these surface
impoundments, and their proximity to human populations and sensitive ecosystems; and (2)
develop a screening-level risk profile (for human and ecological receptors) that presents risk
ranges associated with different constituents, unit types, and facility types. These two steps will
satisfy the legislative and consent decree requirements to conduct a study of human and
ecological risks associated with surface impoundments. In addition, the Agency may conduct a
more detailed risk analysis based on multimedia modeling for constituents, units, and pathways
of potential concern either during the time frame of this study or subsequent to this study.
1.1.3 Study Design and Components
The Surface Impoundment Study is designed to be based to the extent possible on data
collected from a representative sample of the study population. This section briefly describes the
definition of facilities, impoundments, and constituents within the scope of the study; the
selection of a representative sampling of facilities to survey and assess; other data sources that
will be used in addition to the survey results; and the analytic procedure for assessing these data
and drawing conclusions about potential risks presented by industrial wastewaters managed in
surface impoundments.
1.1.3.1 Definition of "In-scope" Facilities and "In-scope" Surface Impoundments.
A facility is within the scope of this study if it meets the following criteria:
# It is a direct discharger (i.e., has a National Pollutant Discharge Elimination
System [NPDES] permit), a zero discharger (generally, designed to infiltrate or
evaporate wastewater), or an indirect discharger (discharges wastewater to a
publicly owned treatment works).
# It conducts activities within the manufacturing sector (Standard Industrial
Classification [SIC] codes 20-39), selected transportation subsectors (SIC codes
4212 - Local Trucking Without Storage, and 4581 - Airports, Flying Fields, and
Airport Terminal Services), the waste management service sector (SIC codes 4953
- Refuse Systems and 4952 - Sewerage Systems, but excluding publicly owned
treatment works), or selected wholesale trade subsectors (SIC codes 5085 -
Industrial Supplies, 5093 - Scrap and Waste Materials, and 5171 - petroleum bulk
terminals).
# It has one or more "in-scope" surface impoundments. A surface impoundment is
within the scope of the study if it has been used to manage certain nonhazardous
wastes since June 1990.
1.1.3.2 Definition of "In-scope" Wastes (Based on Type of Constituents and pH).
Nonhazardous wastes are within the scope of the study if they either contain at least one of 256
constituents of potential concern or have a typical pH that falls just outside the range of
hazardous wastes. The pH criterion is included because one of the common "hazardous
characteristics" that is removed when wastewaters are treated is the corrosivity characteristic,
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Section 1.0
Executive Summary
defined as the condition when a representative sample of a waste has a pH below 2 or above
12.5. EPA is interested in knowing the extent to which wastewaters in surface impoundments
are managed in the pH range just outside the hazardous characteristic range. EPA developed the
list of 256 constituents to include constituents that are of interest from a policy standpoint and
constituents required to be studied based on the EDF vs Browner consent decree. Appendix A
lists the constituents within the scope of this study.
1.1.4 Selection of Representative Sample for Data Collection
In February 1999, EPA conducted a "screener" survey of over 2,000 facilities to identify
facilities and surface impoundments that are potentially within the scope of the survey. Selecting
from among the facilities that reported having in-scope impoundments and wastes, EPA
developed a representative sample of 215 facilities to participate in a more detailed survey. EPA
developed this representative sample of facilities for the study by selecting a stratified random
sample of the direct and zero dischargers and a purposive sample of the indirect dischargers
(since there were relatively few indirect dischargers that reportedly used surface impoundments).
The 215 selected facilities received a second detailed survey in November 1999, the
Survey of Surface Impoundments, with responses due in February and March 2000. This survey
requests detailed data on impoundment design, operation, and closure; the constituents that are
present in impoundments and emissions; and data on the subsurface hydrogeology and activities
of nearby humans. Both the long survey and the list of facilities that received it (excluding
facilities whose information is being handled as confidential business information) can be viewed
at http://www.epa.gov/epaoswer/hazwaste/ldr/icr/ldr-impd.htm.
Figure 1-1 shows the estimated distribution of study-eligible facilities used to select the
sample population of 215 facilities completing the detailed survey.
1.1.5 Current Knowledge of Study Population
Based on the responses to the 1999 screener survey, EPA estimates that the number of
nonhazardous waste surface impoundments in the United States that meet the criteria for
inclusion in the Surface Impoundment Study is 19,000 impoundments, located at 8,500 facilities.
Major industries represented include the food processing, paper, chemical, petroleum, and
stone/clay/glass/concrete industries. An estimated 1,000 facilities, predominantly in the paper,
chemical, and petroleum refining sectors, have at least one impoundment with decharacterized
wastewaters.
Figure 1-2 shows the universe of surface impoundment facilities having in-scope
impoundments by industry category.
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Section 1.0
Executive Summary
I nsrtf Gt Alaska
Inse* of
Puerto Rico
51
lns<5t of
Virgin Islands
LegeiKl
Shading corresponds to csbmated
number of faditaes per square mite
Shacing classes are grouped
v>. th na tural breaks
States are labeled with estimated
riwrib^r of ifetdSfres
Figure 1-1. Estimated distribution of study-eligible
facilities with surface impoundments
Sanitary Services
555
Wholesale Chemicals
370
Other
830
Stone/Clay/Glass/
Concrete
Paper/Lumber
870
Petroleum
632
Industrial Machinery
233
Food
1509
Fabricated Metal
236
Chemical
Manufacturing
810
Note: Values shown are national estimates of the number of "study-eligible" facilities (facilities using
surface impoundments that meet criteria for being in the study).
Figure 1-2. Universe of surface impoundment facilities
with in-scope impoundments by industry category.
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Section 1.0
Executive Summary
1.1.6 Additional Data Sources for Study
In addition to the survey results, EPA will also use data from EPA Region and state files
(for those facilities that are permitted under RCRA and under EPA authorities); publicly
available data sources including census tract data to help evaluate the potential for population
exposures; and geographic information system (GIS) data to assist in describing Regional and
site-specific geological and ecological conditions. EPA will also collect and analyze wastewater
and sludge samples at 15 to 20 facilities selected from among the 215 facilities being surveyed.
This is a relatively major sampling effort that will provide important information to supplement
that submitted under the survey and will also serve as a quality assurance step. In addition, EPA
will include six facilities that were the subject of a 1997-1998 pilot study. Thus, in all, there are
221 facilities under review in this study.
1.1.7 Science Advisory Board Comments
The Agency has submitted the overall design and methodology for the Surface
Impoundment Study to EPA's Science Advisory Board (SAB). SAB commented that they found
"considerable technical merit in the proposed study structure . . ." (U.S. EPA, 1998c). The SAB
also commented that it would be extremely resource-intensive to undertake comprehensive site
characterization for multiple units and constituents, and that therefore EPA should use a
screening approach to prioritize its efforts.
1.1.8 Purposes of Technical Plan
The major purposes of this technical plan are to provide the analytic blueprint for
assessing potential risks associated with the surface impoundment universe (using the data from
the surveys and other sources) and to obtain peer review comment on the approaches and
methodologies described.
1.2 Summary of Technical Plan for the Analysis
EPA will conduct the technical risk analysis in two stages, with the basic objective of
screening all the reported surface impoundment units and constituents during Phase I and
conducting more detailed multimedia modeling on some units and constituents in Phase II.
Phase I will be the screening and prioritization stage, during which the large number of surface
impoundments and constituents anticipated to be reported under the survey will be examined,
screened, and prioritized using clear and straightforward science decision rules. During Phase I,
EPA will screen out from further analysis those constituents or units that pose negligible risks
and will prioritize for additional analysis the remaining units and constituents. Those units and
constituents that merit additional analysis of potential risks will proceed to Phase II. In addition,
some ambiguous cases will proceed to Phase II to ensure that the Agency is not overlooking areas
of potential risk. During Phase II, EPA will use the multimedia model developed for the
Hazardous Waste Identification Rule (HWIR), updated for the Surface Impoundment Study, to
conduct fate and transport modeling and assess potential risks associated with these units and
constituents. (Development of this model is near completion and the model is currently
undergoing independent verification to support its use in this and other analyses.) The risk
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Section 1.0
Executive Summary
results for the Phase II stage of the analysis will be used to revise the appropriate portions of the
overall risk profile generated during Phase I.
EPA anticipates that the screening-level risk profile of the universe of surface
impoundments generated during Phase I and the risk analyses conducted on higher priority units,
constituents, and pathways during Phase II will be completed by March 2001 and will be
included in the final study that satisfies the statutory and consent decree requirements described
above. The Phase I risk profile, because it is a screening-level assessment and based on
protective assumptions, will not be a precise representation of risks. As work progresses on
Phase II multimedia analyses, the new risk estimates that are based on more realistic exposure
assumptions will replace the corresponding Phase I risk estimates, and EPA anticipates that the
overall risk profile will generally decrease. (One exception will be for constituents with a high
potential to bioaccumulate or persist, for which the Phase II multimedia modeling may show
potential risks not identified during Phase I.) The Agency also anticipates that the relative risks
will remain approximately the same in the two phases (e.g., comparing different industry
categories, constituents, or unit types.) This two-stage analysis is designed to optimize EPA's
ability to identify areas of potential concern within a limited period of time.
1.2.1 Phase I. Screening and Prioritization of Constituents, Units, and Pathways of
Potential Concern
1.2.1.1 Background. Given the large number of constituents (256 chemicals), in-scope
impoundments (anticipated to be approximately 600 surface impoundments at 221 reporting
facilities), EPA must conduct a screening in order to prioritize those units, constituents, and
pathways of most concern. This point was reinforced by the comments of EPA's Science
Advisory Board that it would be extremely resource-intensive to undertake comprehensive site
characterization for multiple units and constituents and that EPA would need to prioritize areas
for review. In addition, the Agency anticipates that a significant number of units may contain
very low levels of constituents and not require lengthy and costly fate and transport modeling.
Therefore, two major purposes of Phase I of the analysis are to screen the reported constituents,
units, and pathways to identify those of negligible concern that will not require fate and transport
modeling and to prioritize those that proceed for further analysis.
At appropriate points throughout the screening and prioritization process, EPA will
verify the presence of expected constituents by comparing with constituents that are identified in
NPDES and RCRA permits (where applicable), and by cross checking within particular industry
categories. If a constituent has not been reported that one might reasonably have expected would
be reported, one of several actions could occur: the facility might be considered a candidate for
sampling conducted by EPA; the facility might receive followup queries concerning the nature of
the waste; and/or EPA might infer the presence of the constituent for modeling purposes.
EPA considers that a sound screening process is a fundamental and integral part of the
analysis and is proposing to base the screening on clear science decisions rules related to
threshold concentrations of potential concern.
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Section 1.0
Executive Summary
1.2.1.2 Development and Use of Science Decision Rules. EPA proposes to
sequentially apply a series of decision rules in determining whether to retain a reported
constituent at a reported surface impoundment for further analysis in Phase II.
1.2.1.3 Phase IA: Initial Screening
Basic Approach: Use of Screening Factors. The first step will be for EPA to compare
the reported concentration data (in surface impoundment water, sludge, and emissions) collected
from the facility survey with threshold concentrations that are protective of human health
(residential exposures) and protective of selected representative ecological receptors. The
threshold concentrations, or "screening factors," will be developed to provide a suitable margin
of protection that, in most cases, will encompass the potential for indirect exposures and
background exposures, as discussed below. Surface impoundment units reporting concentrations
below the screening factors will be screened out from any further analysis for that particular
constituent or pathway. However, the screening level risk that is calculated will be included in
the overall risk profile for the surface impoundment universe.
Appendix A provides a table of the health-based benchmarks selected to use in this study
for the constituents within the scope of this study; and Section 2.2 the methodology used to
calculate screening factors from these benchmarks. This methodology includes the use, at this
stage, of highly protective exposure factors, such as direct ingestion of surface impoundment
wastewaters and direct inhalation of emissions.
Section 2.3 describes the screening process for ecological risks in detail, including the
selection of existing benchmarks for species representative of major taxonomic groupings and
protective exposure assumptions. In selecting benchmarks, the preferred toxicological responses
will be those related to reproductive fitness and the stability of populations. In addition, EPA
will highlight for special evaluation any facilities that are proximate to sensitive habitats such as
managed wildlife preserves.
At this stage, EPA's objective is to ensure a suitable margin of protection to human
health and the environment in the screening decisions because of the final nature of the screening
action in removing a particular constituent, unit, or pathway from any further analysis, with the
consequence that there will be no further consideration of the contributions to indirect and
cumulative exposures. EPA considers that, for many constituents, screening based on direct
ingestion of the surface impoundment influent and direct inhalation of the emissions is, by its
nature, very protective. That is, if fate and transport modeling were to be conducted, the
potential risks would invariably be lower. EPA may also provide an additional margin of
protection to recognize the potential for indirect exposures and for exposures to background
levels (i.e., sources other than those within the scope of the survey). In addition, special
consideration will be given to constituents with relatively high potential to bioaccumulate or
persist in the environment (see discussion below).
Special-Case Constituents. There are a number of constituents with a relatively high
potential to be persistent or bioaccumulative that require special consideration. These
constituents may represent human health or ecological risks, yet not be identified as constituents
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Section 1.0
Executive Summary
with potential risks through the Phase I screening process. To ensure that these types of
constituents are identified, each constituent will be ranked on the basis of persistence (P),
bioaccumulation (B), and toxicity (T) to human and ecological receptors, using the procedures
and criteria developed for the Revised Waste Management Prioritization Tool (WMPT)
(U.S. EPA, 1998e). "Special-case" constituents will be those identified as having high-ranking
PBT scores and will be reported along with the relative-risk distributions developed under
Phase I. These special-case constituents will proceed to the Phase II multimedia risk analysis.
Risk Criteria. The risk criteria—the levels above which the risk to an individual are
considered significant—are as follows:
# For carcinogens: excess cancer risk = 10"5
# For noncarcinogens: hazard index (HI) =1.
These criteria apply to a specific constituent-unit-pathway combination as well as to summations
of risks for a constituent, an impoundment, or a facility. Summations of His will be considered
only if to the same target organ. By separating risks according to target organ, the resulting HI
can be summed across the ingestion and inhalation exposure pathways for each of the potentially
affected target organs.
Margin of Protection. For purposes of its initial screening, EPA will apply an additional
margin of protection of 10"1. This is intended to allow reasonable certainty that constituents and
units screened out from further consideration present a negligible risk. Among the
considerations leading to the use of a margin of protection are that EPA is not assessing
multimedia risks through indirect pathways in Phase I or background levels of exposures that are
not within the scope of this study. Note, however, that special-case constituents with high
potential to bioaccumulate or persist in the environment will automatically proceed to Phase II
analysis.
Representing Risk Ranges and Cumulative Risks. The calculated screening risks for
each constituent for a specific impoundment and facility will be combined to generate three
cumulative risk estimates: constituent risk, impoundment risk, and facility risk. The risk
estimates generated during Phase I for specific constituents, impoundments, and facilities will be
accumulated into risk ranges or "bins" to portray potential risk distributions for the surface
impoundment universe and various subsets (such as zero dischargers, particular industries, or
particular constituents). The human health risk bins currently under consideration include:
# Excess Cancer Risk (6 bins): < 10"8, > 10"8 and < 10"7, > 10"7 and < 10"6, > 10"6" and
< 10"5, >10"5and < 10"4, >10"4.
# Noncancer HI (6 bins, by target organ): < 0.01, > 0.01 and <0.1, > 0.1 and
< 1, > 1 and < 10, > 10 and < 100, > 100).
# Ecological HQ (5 bins): <0.1, > 0.1 and < 1, > 1 and < 10, > 10 and < 100,
>100).
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Section 1.0
Executive Summary
Section 2.2.1 describes the process of aggregating risks for purposes of presenting an initial risk
profile after completion of Phase I.
The execution of the screening process (as a practical matter) occurs in a tiered approach,
beginning at the facility level and proceeding down through individual units and constituents.
The net effect is that constituent, unit, and pathway combinations that are of negligible concern
do not proceed to Phase II. For a unit to reach Phase II analysis, it must manage wastes with at
least one constituent exceeding the decision criteria; for a facility to reach Phase II analysis, it
must manage wastes with at least one constituent exceeding the decision criteria.
The constituents and units of negligible concern will be included in the lowest risk bins at
this stage and that risk description will carry forward for them. Even though they will not be
analyzed further based on more realistic exposure assumptions, the risks associated with them are
already projected to be negligible.
This "binning" approach is taken to serve the dual objectives of (1) screening out
particular constituent/unit/pathway combinations of negligible concern from further analysis; and
(2) developing an initial overall relative risk ranking of the surface impoundment universe.
Figure 1-3 provides a sample representation of risk ranges based on Phase IA screening
results.
Phase IB Human Health Screening Models. EPA expects to use screening-level fate
and transport models IWAIR and IWEM1 (developed for use under the Industrial D guidance) to
supplement the initial screening performed under Phase IA. In general these screening models
will be appropriate where the major routes of exposure are expected to be direct ingestion of
drinking water or direct inhalation (i.e. indirect pathways are not expected to contribute
significantly). Also, IWAIR will be used in cases where volatile constituents are known to be
present, but there are no air concentration data provided in the surveys. At this stage, EPA will
use a limited amount of data from the surveys for the most sensitive parameters, including
constituent concentrations, unit size, and close-in receptors. Because some constituents and units
may be screened from further analysis, EPA is proposing to use some protective modeling
approaches, such as assessing risks for close-in receptors. This stage of screening-level fate and
transport modeling is expected to improve the precision of the risk estimates and likely will yield
lower risk results than use of the screening factors in Phase IA. The relative-risk profile
developed under Phase IA will be revised and updated based on the results of the Phase IB
modeling.
1 The Agency is reviewing the IWAIR and IWEM models to ensure they are updated based on major
comments received during a recent peer review, such as the need to correctly incorporate biodegradation rates in
IWAIR. This process is expected to be completed in time to use these models in the Surface Impoundment Study.
Alternative models that may be used include SCREEN 3 or CHEMD AT 8 for inhalation pathway and EPACMTP
for ingestion of drinking water.
1-9
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Section 1.0
Executive Summary
01
©
O
(0
Li.
0
A
E
3
z
Constituents^
at these facilities
not assessed further
CO
'C
(1)
-4—I
O
D)
C
'c
(1)
L_
o
w
^-Constituents at these
facilities proceed to Phase IB
(0
1
U
w
k
MP=0.1
<0.01 0.01-0.1
0.1-1 1-10
Noncancer Risks
10-100 >100
Figure 1-3. Sample representation of risk ranges based on Phase IA screening results.
Phase IC Prioritization. If the number of constituents, units, and facilities that could
move into Phase II is large (e.g., more than 25 percent of the study sample), then EPA will
prioritize them for further analysis based on the relative risk rankings that were developed
during Phases IA and IB. Alternatively, if the number of constituents, units, and facilities that
move to Phase II is small and the resulting Phase II effort is within allocated resources, then the
prioritization scheme will not be necessary.
Prioritization will occur by assigning overall scores of 1 (highest priority), 2, or 3 based
on the degree to which a constituent/unit or facility exceeds the risk criteria for human and
ecological risks. For example, an HI of 100 would receive a higher score (1) than an HI of 10
(score = 2). The ranking will also integrate the human health and ecological scores into one
score, placing greater emphasis on human health considerations but also addressing high-priority
ecological concerns. A qualitative review of Phase I relative risk distributions may also lead the
Agency to focus on particular industries, unit types, or constituents of concern. The result of
Phase IC will be to identify high-priority constituents, units, and facilities for comprehensive
multimedia risk modeling during Phase II.
Anticipated Outcome of Phase I. The outcome of Phase I will be development of an
initial risk profile, which represents a relative ranking of potential risks associated with different
constituents, unit types, or facilities within the surface impoundment universe. Because this will
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Section 1.0
Executive Summary
be a screening-level risk profile based on protective exposure assumptions, subsequent analysis
will develop greater precision for these initial risk estimates. Another outcome of Phase I will be
the identification of a subset of surface impoundment units, constituents, and pathways that will
proceed for further risk analysis during Phase II.
Units and constituents screened out during Phase I will be presented as having negligible
risks in the overall risk profile in the final Surface Impoundment Study. The units and
constituents to be studied further, as well as those screened out from further analysis, will be
profiled according to significant patterns (if any) such as industry type, unit characteristic, and
constituent type.
1.2.2 Phase II Modeling Approaches for Constituents and Units of Potential Concern
1.2.2.1 Background. There will be some units and constituents identified during Phase I
that meet the criteria for proceeding to the Phase II analysis and merit further analysis to
characterize with greater precision any potential risks to health and the environment. The
magnitude of the Phase II effort is unknown at this time, and, depending on the number of units
and constituents that require further analysis, EPA intends to undertake one of two approaches.
If, as anticipated, a fairly limited number of units and constituents proceed to Phase II, EPA will
conduct multimedia fate and transport modeling of potential human and ecological risks using
the HWIR multimedia model, as modified for the Surface Impoundment Study and using, to the
extent possible, the site-specific hydrogeologic data, watershed parameters, and receptor data
provided in the surveys and available through other data sources such as GIS files. This is a
fairly intensive modeling approach that will be possible only for a relatively limited number of
cases. Alternatively, in the event that a large number of sites meet the criteria for proceeding to
Phase II, EPA will develop a range of appropriate hydrogeologic and watershed "scenarios"
(approximately 20 to 30 representative scenarios) to simplify the process of data file
development and modeling for a large number of sites. This will greatly streamline the use of
the HWIR model while maintaining the advantages of this powerful tool to describe multimedia
fate and transport. The Agency is also considering extending the "representative scenario"
approach to include representative ranges of populations exposed.
Phase II results will be used to revise the risk profile for the surface impoundment
universe based on more realistic exposure assumptions and multimedia fate and transport
modeling.
1.2.2.2 Choice of Models to Use during Phase II. EPA's preferred approach for the
multimedia modeling during Phase II will be to use its multimedia, multipathway, multireceptor
risk analysis (3MRA) model being developed to support the HWIR rulemaking and other analytic
efforts. An alternative approach considered was to use the MMSOILS model, as currently
updated. Major considerations leading to the recommendation that 3MRA be used were the
ability to use many of the same data files for default parameters that had been developed to
support the HWIR effort; the automatic integration of the various modules for different media
thereby minimizing the quality assurance/quality control (QA/QC) necessary for manual
integration of modules; and (3) the feasibility of using the system both in screening-level
multimedia analyses and comprehensive multimedia analyses. The MMSOILS model met many
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Section 1.0
Executive Summary
of these criteria; however, the integration of the modules for various media is not fully automated
and the manual integration is expected to be too time-consuming to consider for this analysis.
1.2.2.3 Alternative Approaches for Data File Development. At this point it is not
possible to foresee the number of constituents and units that will require modeling during
Phase II after completion of the screening and prioritization that occurs during Phase I.
Depending on the number, the Agency proposes to manage the effort in one of two ways that
differ primarily in the level of site-specific data required to be developed and entered for each
unit.
# Screening-level multimedia analysis for a large number of units and
constituents: EPA will use the information reported in the surveys to choose the
closest matching from among a series of representative scenarios of hydrogeologic
and watershed conditions. The representative scenarios will be defined using the
HWIR database, and EPA will ensure that the scenarios span the appropriate
range of conditions reported in the Surface Impoundment Survey. EPA will
review data reported in the surveys to select the best representative scenario,
including number and proximity of waterbodies, hydrogeologic information, soil
types, and receptor types and ranges. In the modeling, EPA will directly use a
significant amount of site-specific data from the surveys, such as the location of
actual close-in receptors and direction of groundwater flow, information on
constituent concentrations, and information of sensitive ecological habitats.
# Comprehensive multimedia analysis for a small number of units and
constituents: EPA will develop input parameters for the 3MRA model using as
much site-specific data as possible from the surveys, Regional and state files, and
publicly available databases. A comprehensive data file will be developed for
each site, which will be modeled to account, with the greatest possible precision,
for any potential risks associated with that site.
Under either alternative, EPA will use the 3MRA model in a deterministic mode for as many
iterations as possible within the allocated time and resources. The basic approach will be to
model at least one "high-end" and central tendency analysis for each pathway, based on the
closest real receptors ("high-end") and central tendency receptors as reported in the surveys or
identified from other data sources.
1.2.2.4 Risk Profile Generated. EPA anticipates that the risk estimates generated
during Phase II will provide a comprehensive National profile of potential risks posed by the
universe of surface impoundments for several reasons:
# The sample of 215 facilities is a statistically representative sample of the universe
# The potential risks modeled for major pathways at each surface impoundment are
based on real concentration and exposure data reported by facilities and Regions
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Section 1.0
Executive Summary
# The high-end and central tendency scenarios-based on these real
receptors-provide a realistic span of potential risks.
This risk profile generated during Phase II will also serve to identify any unit types, constituents,
or facilities (industries) that may require additional followup analysis.
1.2.3 Anticipated Outcome of Analysis
The Agency will prepare a study by March 2001 that characterizes the potential risks
associated with the surface impoundment universe for 256 constituents.2 This will include a
discussion of any constituents, unit types, or facility categories for which additional analysis is
recommended subsequent to the study. The study will provide a profile of the surface
impoundment universe by unit type, industry type, and constituent; provide a descriptive profile
of the subset of the universe that is of negligible concern and requires no further risk analysis;
and provide a relative-risk profile for the entire universe of surface impoundments for these 256
constituents. The study is expected to also include risk analyses based on multimedia modeling
for a limited number of high-priority constituents, units, and facilities.
1.3 Peer Review Process and Comments Solicited
EPA is providing this technical plan to several peer reviewers with expertise in human
and ecological risk analyses, fate and transport modeling, and screening and statistical procedures
for identifying potential risks from managing industrial wastewaters in surface impoundments.
The Agency is seeking comments on the technical merits of the overall approach and is
specifically soliciting comments in the following areas:
# Derivation of human health screening factors: EPA is seeking comment on
whether the methodology for calculating screening factors is a suitable
methodology and whether there are other data sources that are readily available
that would provide credible benchmarks for the limited number of cases for which
we do not have benchmarks.
# Derivation of ecological screening factors: EPA is seeking comment on
whether the list of representative species is suitable for achieving a screening level
assessment that will highlight possible ecological risks associated with surface
impoundments and whether there are any serious gaps in the consideration of
potential ecological risks that can be addressed by readily available data and
benchmarks not identified here.
# Level of protectiveness: EPA has designed the screening process to ensure that
constituents and units proceeding to subsequent stages of the analysis merit
2 There are a few constituents in the list of 256 in-scope constituents that currently lack human health
benchmarks. EPA is attempting to identify suitable benchmarks to allow their inclusion in this screening analysis.
If benchmarks are not available, then EPA will include these constituents in the overall description of the survey
results but not conduct any risk analyses on them.
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Section 1.0
Executive Summary
further analysis and that those that do not proceed to subsequent stages have
negligible risks (or alternatively, have very well-characterized risks and do not
require further analysis). The intent is that the early stages of the analysis will
include protective assumptions and that there will be a negligible possibility of
overlooking potential risks. The Agency is seeking comment on whether the
proposed screening approach achieves these objectives.
# Approach for dealing with lack of information on chemical composition of
wastewater in the impoundments or emissions: The technical plan proposes a
number of approaches for dealing with situations where facilities do not report
concentration or emissions data for constituents that are present in impoundments.
These include approaches for the use of data from other impoundments at the
same facility, the use of data from other facilities in the same industrial category,
the use of sampling data acquired by EPA from facilities in the same industrial
category and modeling or backcalculating to infer concentrations. EPA is
soliciting comment on these various approaches for estimating concentration data
that are not reported.
# Approach for representing cumulative risks: EPA has designed an approach
for representing cumulative risks for each constituent, unit, and facility reported
under this survey. (An assessment of background risks, i.e., due to sources other
than in-scope surface impoundments, is beyond the scope of this survey and
analysis.) The Agency is seeking comment on the methodology for accumulating
and representing risks within the scope of this survey.
# Modeling approaches: EPA is seeking comment on the overall approach and
methodology for conducting screening-level modeling during Phase I and
multimedia modeling during Phase II. For example, at this point EPA is uncertain
how many units and constituents will merit multimedia modeling. As time and
resources allow, EPA will undertake comprehensive multimedia modeling for
each site. Alternatively, if a large number of sites merit modeling, EPA may use
predefined representative scenarios for hydrogeologic conditions and watersheds
(while still using as much site-based data as possible.) EPA seeks comments on
these alternatives as well as the other modeling strategies outlined in this
Technical Plan.
1.4 Organization of the Technical Plan
The remainder of this Technical Plan is organized as follows:
# Section 2 describes the Phase IA and IB human health and Phase I ecological risk
screening process, and the Phase IC method of prioritizing results of Phase I.
# Section 3 describes the Phase II multimedia, multipathway modeling approach to
estimate risks to human and ecological receptors.
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Section 1.0 Executive Summary
# References are included in Section 4.
Supporting documentation is included in the following appendixes:
# Appendix A, Comprehensive List of Toxicity Benchmarks for Human Health
Risk Assessment
# Appendix B, Statistical Analysis Weights and Variance Estimation for the Surface
Impoundment Study Screening Survey
# Appendix C, Examples of Toxicity Benchmarks for Ecological Risk Assessment
# Appendix D, 3MRA Simulation Modules: Assumptions, Limitations, Inputs, and
Outputs
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Section 2.0
Phase I Screening Assessment
2.0 Phase I Screening Assessment
2.1 Introduction
Experience to date suggests that comprehensive risk analysis, based on multimedia fate
and transport modeling, is both time consuming and costly and should be used where screening
analyses point to the need for sophisticated modeling. The Agency expects that a number of
responses to the detailed questionnaires sent to 215 facilities will describe very low levels of
some constituents in some impoundments. Two major purposes of this stage of the analysis are to
screen (i.e., reduce the number of) constituents, impoundments, and facilities that require fate
and transport modeling and to prioritize those remaining for subsequent analysis. The screening
and prioritization will be based on clear science decision rules related to threshold concentrations
of potential concern and low likelihood of exposures. These decision rules will allow EPA to
screen out those constituents, impoundments, and facilities presenting negligible potential risks
and to focus on analyzing those that may present higher potential risks.
EPA proposes to apply a series of decision rules sequentially in determining the
constituents, impoundments, and facilities that will be evaluated further in Phase II, as shown in
Figure 2-1. Both human health and ecological risk decision rules will be applied.
Human health risk screening consists of two phases:
# Phase IA compares reported constituent concentrations in surface impoundments
to concentrations protective of human health.
# Phase IB estimates human health risk levels based on modeled exposure
concentrations. Phase IB risk screening will be done for all constituents not
eliminated from further evaluation based on Phase IA.
Ecological risk screening consists of one phase that parallels the human health Phase IA
screening:
# Phase I compares reported constituent concentrations to concentrations protective
of ecological receptors.
Risk screening identifies those constituents, impoundments, and facilities that have risks
that are greater than the identified risk criteria and therefore require further analysis. Risk
screening may still result in a large number of constituents, impoundments, and facilities for
Phase II analysis. Therefore, Phase IC decision rules will be applied:
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Section 2.0
Phase I Screening Assessment
Phase I
ecological
risk screening
Figure 2-1. Overview of Phase I decision rules.
# Phase IC ranks the human health and ecological screening risks to prioritize
constituent impoundments and facilities for Phase II analysis.
Key features of the human health risk screening approach are as follows:
# There are two stages in human health risk screening.
# The first stage will compare the arithmetic mean of reported concentration data
(water, sediment, and air) collected from the facility survey to backcalculated
health screening factors protective of residential exposure.
# The second stage will include more realistic evaluations of air and groundwater
risks by evaluating fate and transport processes in the environment. The EPA
screening models Industrial Waste Air Model (IWAIR) and Industrial Waste
Evaluation Model (IWEM) (U.S. EPA, 1998a, b; 1999a, b) will be used to
calculate risks.
The ecological risk screening approach can be characterized by the following key features:
Phase IA
human health
risk screening
2-2
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Section 2.0
Phase I Screening Assessment
# The screening will compare the arithmetic mean of reported concentration data
(water and sediment) collected from the facility survey to screening factors.
# Screening factors will be existing threshold concentrations for adverse ecological
effects such as the Ambient Water Quality Criteria for the protection of aquatic
life.
# Screening factors for a limited set of representative species and representative
habitats will be established using peer-reviewed data from HWIR (i.e., wildlife
exposure factors, biological uptake factors, toxicity benchmarks) supplemented by
toxicity data gathered for constituents not evaluated in HWIR (U.S. EPA, 1999n).
2.2 Phase I Human Health Risk Screening
The Phase I human health risk screening consists of a series of two decision rules,
Phase IA and IB, applied to evaluate whether a constituent, impoundment, or facility can be
excluded from further evaluation or must continue to Phase II. The Phase IA decision rule will
calculate risk estimates for groundwater ingestion, soil ingestion, and air inhalation based on
reported impoundment concentrations. The Phase IB decision rule will calculate risk estimates
for groundwater ingestion and air inhalation based on air and groundwater screening model
exposure concentration estimates. The risk estimates will be compared to risk criteria to
determine if the constituent, impoundment, or facility can be excluded from further evaluation.
Phase IB will be invoked for two conditions: (1) constituents that volatilize but for which there
are no air concentration data provided in the responses to the survey questionnaire, and
(2) constituents that were not excluded based on the Phase IA risk screening.
2.2.1 Phase IA Human Health Initial Risk Screening
The Phase IA risk screening is described in four sections:
# Design goals and overview
# Development of screening factors
# Procedure for risk screening
# Results of risk screening.
2.2.1.1 Phase IA Human Health Design Goals and Overview. The goal of the
Phase IA initial screening assessment is to identify constituents, impoundments, and facilities
that have negligible risks and do not need further assessment in the SI Study. The Phase IA
approach will calculate screening risks to an individual receptor based on concentrations of
constituents in and emissions from surface impoundments reported in the survey questionnaire
and risk screening factors. The human health risk screening factors will be based on toxicity
benchmarks for direct ingestion of drinking water, direct air inhalation, and direct soil ingestion
(for the in-place closure scenario). These screening risks will be protective because
they are based on the highly protective assumptions that a resident will drink the impoundment
water, inhale the air at the impoundment, and eat the sludge. To account for indirect exposures
and cumulative exposures from multiple constituents or impoundments, the Agency may choose
2-3
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Section 2.0
Phase I Screening Assessment
to use a margin of protection (MP) in determining whether a constituent, impoundment, or
facility should be excluded from further evaluation. If risks are less than the selected risk criteria
times the MP, then they will be considered negligible risks and no longer considered in
subsequent phases.
The screening risks for each constituent, impoundment, and facility will also be
accumulated to provide initial risk distributions. Risk distributions will provide the basis for
prioritizing for Phase II (see Section 2.4). These risk distributions, while protective, will also
provide the initial risk profiles that describe the national scale surface impoundment population.
That is, although we expect the risk estimates to decrease as more site-specific data collection
and multimedia exposure modeling are performed in subsequent phases, the overall distribution
should be similar. For instance, if a certain industry has Phase I screening risks that are high
relative to other industries, then the relative risk level will likely remain in Phase II. One major
exception may be constituents that tend to persist and bioaccumulate. The relative risks for these
constituents may be very different in Phase II because indirect (rather than direct) exposure
pathways are driving the risks. These risk distributions will be refined with each subsequent
phase.
2.2.1.2 Development of Phase IA Human Health Screening Factors. The Phase IA
screening risk will be calculated as the ratio of the reported concentrations to the screening
factors multiplied by the risk criteria, as follows:
a . , Reported Concentration ... , . .
Screening Risk = — x Risk Criteria .
Risk Screening Factor
It should be noted that the screening risk calculation is mathematically equivalent to a standard
forward calculation of risk, whereby the exposure concentration is converted to a dose and
multiplied by the cancer slope factor (if carcinogenic) or divided by the reference dose (if
noncarcinogenic). The risk calculation process is also equivalent to the standard approach for
evaluating airborne chemical exposures wherein exposure concentrations are divided by the
reference concentration (if noncarcinogenic). The above calculation is easier to use for this SI
Study screening phase. Also, development of the risk screening factor as a threshold
concentration provides a simple comparison method that EPA, regulated parties, and the public
can use to examine the survey data and screening results.
To develop a human health risk screening factor for each constituent, EPA will
# Identify human receptors
# Identify exposure pathways
# Define toxicity benchmarks
# Identify the risk criteria
# Calculate screening factors.
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Section 2.0
Phase I Screening Assessment
Human Receptor Types. The human health-based risk screening factors will be based
on residential exposure. The exposure factors used to characterize the residents will be based on
the receptor, toxicological endpoint, and exposure pathways evaluated as follows:
# For carcinogenic risks, a time-weighted child/adult resident
# For noncarcinogenic risks, a child resident for the soil and groundwater ingestion
pathways and an adult resident for the air inhalation pathway.
A time-weighted resident is considered for carcinogenic risks because of concerns that a child
with a small body weight typically has higher levels of exposure for the same levels of intake as
an adult. Inclusion of multiple age groups more accurately reflects potential residential
exposures and is supported by data provided in EPA guidance for different age groups (Exposure
Factors Handbook, U.S. EPA, 1997a). A child resident is considered for noncarcinogenic risks
for the soil and groundwater ingestion pathways to ensure a protective level of exposure, since a
child with a small body weight typically has higher levels of exposure for the same levels of
intake as an adult. An adult resident is considered for noncarcinogenic risks for the air inhalation
pathway only because development of the toxicological benchmark for the inhalation pathway,
the reference concentration (RfC), is based on the assumption that an exposed individual would
be comparable to an adult resident (i.e., have a default inhalation rate of 20 m3 per day and a
body weight of 70 kg). EPA considers the RfC protective of child and adult receptors. Although
this approach to evaluating inhalation risks may result in some level of uncertainty in the risk
estimates, EPA considers this acceptable for screening purposes.
Human Exposure Pathways. Risk screening factors will be developed for the following
exposure pathways:
# Ingestion of impoundment water (as a protective estimate of groundwater
ingestion)
# Ingestion of sludge/soil (as a protective estimate of postclosure in place)
# Inhalation of volatile emissions from an impoundment (as a protective estimate of
inhalation).
Inhalation of particulate emissions from sludge (postclosure in place) will not be considered
because this pathway typically represents negligible risks.
Figure 2-2 presents the Phase I risk conceptual model and potential exposure pathways
for human receptors.
Toxicity Benchmarks. Human health risk screening factors will be based on toxicity
benchmarks (i.e., cancer slope factors, reference doses, and reference concentrations) in the
Integrated Risk Information System (IRIS) and Health Effects Assessment Summary Tables
(HEAST) (U.S. EPA, 1999c; U.S. EPA, 1997c). If benchmarks are not available in IRIS or
HEAST, then other EPA or alternative (e.g., ATSDR) health benchmarks will be considered. If
2-5
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Section 2.0
Phase I Screening Assessment
RELEASE EXPOSURE EXPOSURE POTENTIAL
SOURCE MECHANISM SOURCE ROUTE(S) RECEPTOR(S)
~ Pathway evaluated.
- - *¦ Pathway not evaluated.
Figure 2-2. Phase I human health risk conceptual model and
potential exposure pathways.
no health benchmarks are available, a regulatory standard (e.g., maximum contaminant level
[MCL]) may be used to develop the risk screening factor. If benchmarks or regulatory standards
are not available, then the Agency proposes using the provisional approach developed for HWIR
to develop interim human health benchmarks for screening purposes. This process may also
incorporate use of risk-based criteria, such as the preliminary remediation goal for residential
exposure to lead in soil, to ensure that all toxicants of concern are addressed.
Risk Criteria. The risk criteria, the levels above which the risk to an individual are
considered significant, are as follows:
# For carcinogens, excess cancer risk = 10"5
# For noncarcinogens, hazard index (HI) = 1.
These criteria apply to a specific constituent-impoundment-pathway combination as well as to
summations of risks for a constituent, an impoundment, and a facility. Summations of His will
be considered only if to the same target organ. By separating risks according to target organ, the
resulting His can be summed across the ingestion and inhalation exposure pathways for each of
the potentially affected target organs.
Calculation of Human Health Screening Factors. The carcinogenic and
noncarcinogenic risk screening factors will be developed using the equations shown in Table 2-1.
The equations for the three pathways are based on EPA guidance (U.S. EPA, 1989).
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Section 2.0
Phase I Screening Assessment
Table 2-1. Equations for Development of Human Health Screening Factors
C'iirciii()<>onic
N oncii ici n o«oii ic
Inhalation
CRSF =¦
RC-AT- 365
SF ¦ EF ¦ Yd-
BWj
where
CRSFair =
RCc
AT
SF
EF
i =
INR,
BW,
ED;
carcinogenic risk screening factor
for air (mg/m3)
risk criteria for carcinogens
averaging time (yr) = 70
slope factor (kg-d/mg)
exposure frequency (d/yr)
index on age group (e.g., <1 yr,
1-5 yr, 6-11 yr, 12-18 yr, adult)
inhalation rate of air for age group i
(m3/d)
body weight for age group i (kg)
exposure duration for age group i
(yr)
Inhalation
NCRSFair = RCn ¦ RfC
where
NCRSFair =
RCn
RfC
noncarcinogenic risk screening
factor for air (mg/m3)
risk criteria for noncarcinogens
reference concentration (mg/m3)
Ingestion of Water
CRSF =¦
RC-AT -365
SF ¦ EF ¦
Ingestion of Water
NCRSFwater =
BW>
. /'=1
RCn ¦BWC -RfD- 365
EF -IRW„
where
CRSFW
RCc
AT
EF
SF
i
IRW,
BW,
ED;
carcinogenic risk screening factor
for water (mg/L)
risk criteria for carcinogens
averaging time (yr) = 70
exposure frequency (d/yr)
slope factor (kg-d/mg)
index on age group (e.g., <1 yr,
1-5 yr, 6-11 yr, 12-18 yr, adult)
ingestion rate of water for age
group i (L/d)
body weight for age group i (kg)
exposure duration for age group i
(yr)
where
NCRSFwater =
noncarcinogenic risk screening
factor for water (mg/L)
RCn
risk criteria for noncarcinogens
RfD
reference dose (mg/kg-d)
BW,
body weight for child (kg)
EF
exposure frequency (d/yr)
IRWC
ingestion rate of water for child,
ages 1-5 yrs (L/d)
(continued)
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Section 2.0
Phase I Screening Assessment
Table 2-1. (continued)
C'iirciii()<>onic
N oil c:i ici n (>i»oii i c
Ingestion of Soil
CRSFsoll =-
RC-AT- 365
SF-EF-IO6-\YJIRSg^
where
where
CRSFsoll =
carcinogenic risk screening factor
NCRSFsoll
= noncarcinogenic risk screening
for soil (mg/kg)
factor for soil (mg/kg)
RCc
risk criteria for carcinogens
RCn
= risk criteria for noncarcinogens
AT
averaging time (yr) = 70
RfD
= reference dose (mg/kg-d)
EF
exposure frequency (d/yr)
BWC
= body weight for child (kg)
SF
slope factor (kg-d/mg)
EF
= exposure frequency (d/yr)
i =
index on age group (e.g., <1 yr, 1-5
IRSC
= ingestion rate of soil for child,
yr, 6-11 yr, 12-18 yr, adult)
ages 1-5 yr (mg/d)
IRS,
ingestion rate of soil for age group i
(mg/d)
BW,
body weight for age group i (kg)
ED,
exposure duration for age group i
(yr)
Ingestion of Soil
NCRSFsoil =
RC-RfD ¦ BWC ¦ 365
EF ¦ IRS„ -10
-5
The values of the exposure parameters used in the equations are provided in Table 2-2.
All data are from the Exposure Factors Handbook (U.S. EPA, 1997b). The means for inhalation
rate, water intake rate, and body weight are consistent with the data means identified or
calculated in HWIR's Human Exposure Factors (U.S. EPA, 1999q). All human exposure factors
were developed for the following subpopulations:
# Adult resident
# Children ages 12 to 19 years
# Children ages 6 to 11 years
# Children ages 1 to 5 years
# Children ages <1 year (infants).
For noncarcinogenic risks for the soil and groundwater ingestion pathways, the age group that
produces the lowest screening factor (i.e., has the highest ratio of ingestion rate to body weight)
will be used. For the soil ingestion pathway and the groundwater ingestion pathway, this is the
1- to 5-yr-old age group.
The age ranges for children are consistent with the age groups for which most data are
provided in the Exposure Factors Handbook. With the exception of exposure duration and soil
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Section 2.0
Phase I Screening Assessment
Table 2-2. Exposure Parameter Values
Ueeeptor
Inhalation
Kale
(in7d)
Ingestion
Kale of
Water
(l-/d)
Ingestion
Kale of
Soil
(mg/d)
Kxposure
l-'rcquencv
(tl/vr)
Kxposure
Duration
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Section 2.0
Phase I Screening Assessment
sample size) across the ranges used here. For mean adult body weight, the weighted average
(based on sample size) of the values presented in the Exposure Factors Handbook for males and
females ages 18 to 74 were used.
If a carcinogenic or noncarcinogenic risk screening factor cannot be developed for a
chemical based on an EPA or alternative health benchmark, a risk screening factor that is
equivalent to the regulatory standard (e.g., maximum contaminant level or MCL) may be used. If
a regulatory standard is unavailable, then an interim toxicity benchmark will be developed and
used. The Agency considers that, for a screening level analysis, it is appropriate to use draft
provisional benchmarks for constituents lacking established health benchmarks.
Cumulative Effects. The cumulative effects for air, groundwater, and soil exposure to
the same resident will be evaluated, although the exposures may occur at different times. This is
a protective assumption to ensure that risks are not underestimated.
2.2.1.3 Procedure for Phase IA Human Health Risk Screening. The overall human
health screening process is shown in Figure 2-3. The human health risk screening calculation
will be performed for each constituent in each surface impoundment for each of the 221 sample
facilities. At this point, the screening risk estimates will be constituent-specific risks or His that
have been summed across exposure pathways. Cumulative risks will then be calculated for each
impoundment and each facility and for each constituent (summed over all impoundments at the
facility) . The cumulative risk estimates will be used to build initial risk distributions for the
surface impoundments within the scope of the study. The units within the study scope are
defined in Section 1. These fall within six categories ("populations") depending upon their
regulatory status under the Clean Water Act and RCRA. Risk distributions will be generated for
impoundment functional type, impoundment treatment type, industry type, and constituent. The
risk distributions will be used to exclude constituents, impoundment types, or facilities from
further analysis. Further, the risk distributions will provide the basis for prioritizing evaluations
conducted in Phase II (see Phase IC discussion in Section 2.4).
The four main elements to the Phase IA human health screening process are
# Human health risk calculation
# Cumulative risk calculation
# Risk distribution development
# Risk screening.
Human Health Risk Calculation. The risk calculation process is shown in Figure 2-4.
The groundwater ingestion pathway will always be evaluated; surface impoundment wastewater
concentrations will be used in Phase IA as conservative estimates of potential groundwater
exposure levels. The air inhalation pathway will be evaluated for a constituent in Phase IA if the
constituent is a volatile organic chemical (VOC) and airborne chemical concentration or
emissions data are provided by the survey. The soil ingestion pathway will be evaluated for a
constituent if sludge accumulates in the impoundment. Once the air, water, and sludge
2-10
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Section 2.0
Phase I Screening Assessment
(:
For each of the 6 CWA/RCRA populations'
>
f
Calculate human health risk
for each impoundment and constituent
(see Figure 2-4)
'
Generate Cumulative Risk Estimates
• Determine impoundment risk/Hi (e.g., sum of carcinogenic risk and
His for same target organ; select maximum target organ HI)
• Determine constituent risk/Hi (e.g., select maximum risk/maximum HI
across all impoundments)
• Determine facility risk/Hi (e.g., sum constituent risks for carcinogens,
sum constituent His by target organ, select maximum target organ HI)
i r
Perform risk screening
(see Figure 2-9)
c
For all facilities
y
'
Build industry type risk distribution
with facility risk
f
Build regulatory population risk
distribution with facility risk
) c
For all surface impoundments
>
'
Build treatment type risk distribution
with impoundment risk
'
Build functional class risk distribution
with impoundment risk
) (:
For every constituent
y
>
'
Build constituent risk distribution
with constituent risk
Notes:
* Direct discharger
Direct discharger
Indirect discharger
Indirect discharger
Zero discharger
Zero discharger
- decharacterized
- nondecharacterized
- decharacterized
- nondecharacterized
- decharacterized
- nondecharacterized
Figure 2-3. Flow diagram for Phase IA human health risk screening.
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Section 2.0
Phase I Screening Assessment
Air inhalation pathway-
volatile constituents
D
Groundwater ingestion
pathway
Soil ingestion pathway-
impoundments that
accumulate sludge
)
Figure 2-4. Decision tree for calculating Phase IA human health risk estimates.
2-12
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Section 2.0
Phase I Screening Assessment
concentrations are determined from the survey results, the risks will be calculated by dividing the
concentration by the appropriate screening factor and then multiplying by the risk criteria. If the
screening factor is based on a regulatory standard, then the ratio of concentration to the screening
factor will be calculated. Finally, the constituent risk and HI will be calculated by summing the
risks and hazard quotients (HQs) for all pathways for that particular constituent. If the screening
for the constituent has used a regulatory standard, then the maximum ratio of all pathways for
that constituent will be selected.
Concentration data from the facility survey questionnaire will provide exposure
concentrations for the Phase IA risk estimates. Figures 2-5 through 2-8 describe how the air,
water, and sludge concentrations will be used or calculated from the facility survey data. The
decision trees outline the order of preference for using concentration data. The specific survey
questions that will provide the data are noted in the decision trees. A special condition exists for
calculating air inhalation risks from survey data. If the survey questionnaire does not provide an
air concentration or emission rate for a VOC constituent but does provide water concentrations,
then the air emissions and concentration will be estimated using screening models and will be
performed in Phase IB (see Section 2.2.2).
In cases when the air, wastewater, or sludge concentration is not provided in the survey
questionnaire, a concentration will be estimated. An air concentration will be estimated from the
survey-reported emission rate using the worst case dispersion factor from IWAIR (see Section
2.2.2 and Figure 2-5). A sludge concentration will be estimated from the leachate concentration
assuming equilibrium partitioning to sludge (see Figure 2-8). If the survey questionnaire does
not provide water concentration data for a particular impoundment, data from other
impoundments will be used to estimate the water concentration (see Figure 2-7).
Average concentrations of constituents in impoundments have been requested as part of
the survey questionnaire. The average concentration is considered to be a reasonable level of
exposure to use in evaluating long-term chronic exposures because an individual is unlikely to be
exposed to the maximum concentration of a constituent for 30 years. Thus, average
concentrations are likely to represent long-term exposures. This approach is considered
protective for this screening health risk analysis because risks are based on highly protective
exposure assumptions, such as assuming that an individual would directly consume water from
the surface impoundments.
Cumulative Risk Calculation. The
calculated screening risks for each constituent
for a specific impoundment and facility will
be combined to generate three cumulative risk
estimates: impoundment risk, constituent risk,
and facility risk. The cumulative risks will be
used in the risk screening and risk
distributions, as follows.
HQ - Hazard quotient is the ratio of estimated
exposure (dose or concentration) and the appropriate
toxicity value (reference dose or reference
concentration) for a single exposure pathway and
chemical.
HI - Hazard index is the summation of HQs across
pathways and across chemicals affecting the same
target organ.
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Section 2.0
Phase I Screening Assessment
Key:
Shaded boxes indicate information needed from survey questionnaire.
Survey question number appears in parentheses.
Figure 2-5. Decision tree for determining Phase IA air concentrations.
2-14
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Section 2.0
Phase I Screening Assessment
Key:
Shaded boxes indicate information needed from survey questionnaire.
Survey question number appears in parentheses.
Figure 2-6. Decision tree for determining Phase IA water concentration.
2-15
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Section 2.0
Phase I Screening Assessment
*Leachate, impoundment water, influent, or effluent data.
Figure 2-7. Steps to calculate impoundment water concentration
if no survey data available.
The impoundment risk (i.e., risk for a particular impoundment for a particular facility)
Key:
Shaded box indicates information needed from survey questionnaire.
Survey question number appears in parentheses.
Figure 2-8. Decision tree for determining sludge concentration.
2-16
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Section 2.0
Phase I Screening Assessment
will be determined as follows:
# For carcinogenic risks, sum risks from all carcinogenic constituents.
# For noncarcinogenic risks, sum the His for all constituents potentially affecting
the same target organ; then select the maximum HI from the target organ His.
# To represent constituents with risks based on regulatory standards, select the
maximum ratio.
The constituent risk (i.e., risk for a particular constituent for a particular facility) will be
determined as follows:
# For carcinogenic risks, select the maximum risk for the constituent across all
impoundments for the particular facility.
# For noncarcinogenic risks, select the maximum HI for the constituent across all
impoundments for the particular facility.
# To represent constituents with risks based on regulatory standards, select the
maximum ratio across all impoundments for the particular facility.
Facility risks will be calculated as follows:
# For carcinogenic risks, sum the constituent risks.
# For noncarcinogenic risks, sum the His from all constituents potentially affecting
the same target organ; then select the maximum HI from the target organ His.
# To represent chemicals with risks based on regulatory standards, select the
maximum ratio from all constituents.
Note that this approach takes into account that an individual receptor's exposure factors will only
be counted once for the entire facility (e.g., 1.4 L ingested per day or 11 m3 inhaled per day).
Risk Distribution Development. Cumulative frequency histograms of the risks/His will
be developed from the impoundment, constituent, and facility cumulative risks. A risk
cumulative histogram will be defined by a set of six class intervals or "bins." The carcinogenic
risk ranges defining those bins are: 0 to 10"8, 10"8to 10"7, 10"7to 10"6, 10"6to 10"5, 10"5to 10"4, and
10"4. An HI cumulative histogram will be defined by six bins: 0 to 0.01, 0.01 to 0.1, 0.1 to 1.0,
1.0 to 10, 10 to 100, and greater than 100.
Impoundment risks will be used to develop the impoundment type risk and treatment type
risk distributions. For a given impoundment, the impoundment risk will be compared to the risk
bins and the appropriate bin identified. A unitary value (1), representing the impoundment, will
then be placed in the appropriate bin in the risk distribution. If the impoundment is in a
statistically sampled population, the unitary value will be multiplied by the facility sample weight
(see Appendix B for a discussion of the facility sample weights before being added to the bin.
2-17
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Section 2.0
Phase I Screening Assessment
Constituent risks will be used to develop the chemical-specific risk distribution. Facility
risks will be used to develop the industry-type-specific risk distributions and the regulatory
population-specific risk distributions. The development of these risk distributions follows the
procedure described above for the impoundment risk distribution.
EXAMPLE: Adding an impoundment risk to a treatment type risk distribution.
An impoundment for a particular facility (ID 6123) is a biological treatment unit and has
a cumulative impoundment cancer risk of 2 x 10"5 and a cumulative impoundment HI of
0.06. The facility is in the CWA direct discharger population. The risks will be added to
the biological treatment type risk distributions. There will be two risk distributions, one
for cancer risk and one for HI. For the cancer risk distribution, the cancer risk falls in the
10"5to 10"4 risk bin. Because the CWA direct discharger population is a statistically
sampled population, the unitary value is multiplied by the sample weight for that facility
(weight = 38.7). This value is added to the risk bin. For the noncancer risk distribution,
the HI falls in the 0.01 to 0.1 bin. The value of 38.7 is added to the 0.01 to 0.1 bin.
Risk Screening. The Phase IA risk screening will use the three cumulative risk
distributions to identify
# Constituents, impoundments, and facilities that have risks below a decision
criterion and therefore are considered to have negligible risks and are not assessed
in any further phases.
# Constituents, impoundments, and facilities that have risks above a decision
criterion and that will be assessed in Phase IB.
The risk screening procedure is outlined in the decision tree shown in Figure 2-9. A
three-tiered approach is taken because each descending tier provides less information on the
overall facility risk characterization but more detail on what constituents or impoundments can
be excluded from further evaluation. The facility risk accounts for cumulative effects of multiple
impoundments and constituents. Therefore, it is used as the first tier. However, the first tier
indicates only if a facility must be assessed further or not; it does not indicate whether specific
impoundments or constituents can be excluded. The second tier screens using the cumulative
impoundment risk. This tier, however, can only address cumulative effects for multiple
constituents at a single impoundment; it does not indicate whether specific constituents can be
excluded. However, this second tier provides the subcategory of impoundments for a facility that
2-18
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Section 2.0
Phase I Screening Assessment
FRAMEWORK FOR
HEALTH RISK SCREENING
Tier I
Facility Screen
Tier II
Impoundment Screen
r no
Constituent,
impoundment,
and facility proceed
to next phase
Figure 2-9. Decision tree for performing Phase I human health risk screening.
2-19
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Section 2.0
Phase I Screening Assessment
must proceed to Phase IB. The third tier identifies the constituents for the Tier 2 impoundments
that must proceed to Phase IB.
The screening procedure first screens facilities by comparing the facility cumulative risk
to the risk decision criteria, which may be adjusted by a margin of protection of 0.1 (risk decision
criteria x MP = "screening criteria"). If the facility has a risk above the screening criteria, then
the impoundment cumulative risk for each impoundment for that facility is compared to the
screening criteria. If the impoundment has a risk above the screening criteria, then the
constituent cumulative risk for that facility is compared to the screening criteria. If the
constituent has a risk above the screening criteria, then the constituent passes to Phase IB for
further screening. The constituent will be further evaluated only for those impoundments at the
facility that have risks above the screening criteria. The risk screening will be performed for both
cancer and noncancer risks.
EXAMPLE. Calculating the cumulative risks and risk screening for a facility.
The example facility has the risk estimates shown in Table 2-3. The first table presents
the risk estimates for each chemical in each of the four impoundments.
The second table shows the cumulative facility, impoundment, and constituent risks. The
impoundment risk is the sum of the chemical risks for the impoundment; the
impoundment HI is the maximum HI of the two target organ His. For instance, for
Impoundment A, the carcinogenic risk of 3.7 x 10"4 is the sum of Chemicals 1 and 4. The
HI of 0.5 is the HI for Target Organ B.
The constituent risks and His are the maximum of risks and HI for all four
impoundments. For instance, Chemical 1 is detected in Impoundments A, B, and D.
Impoundment A has the maximum risk of 3.7 x 10"4 (from Impoundment A).
The facility risk of 3.7 x 10"4 is the summation of all carcinogenic constituent risks
(Chemicals 1, 4, and 6). The facility HI of 11.05 is the summation of constituent His for
target organ A. Specifically, this is Chemical 2 from Impoundment A and Chemical 5
from Impoundment B.
The third table shows the risk screening results for the facility. One impoundment and
three chemicals are screened from further assessment at this facility. Three chemicals at
three impoundments move on for further assessment in Phase IB.
This example illustrates that the decision criteria incorporate a margin of protection in
determining which units and constituents proceed to Phase IB and II; however, the calculated
risks with no MP are added to the risk distribution (see further discussion below).
2-20
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Section 2.0
Phase I Screening Assessment
Table 2-3. Example Screening Risks for a Facility
HI
Impoundment
Chemical
Risk
Target Organ A Target Organ B
Impoundment A
Chemical 1
3.7E-04
Chemical 2
0.05
Chemical 3
0.3
Chemical 4
1.1E-08
Impoundment B
Chemical 1
2.0E-05
Chemical 3
0.007
Chemical 4
8.0E-08
Chemical 5
11.00
Impoundment C
Chemical 2
0.0004
Chemical 3
0.8
Chemical 5
0.003
Impoundment D
Chemical 1
5.0E-12
Chemical 6
3.0E-08
Cumulative Risk
Risk
HI
Impoundment Risk
Impoundment A
3.7E-04
0.3
Impoundment B
2.0E-05
11.00
Impoundment C
-
0.80
Impoundment D
3.0E-08
-
Constituent Risk
Chemical 1
3.7E-04
Chemical 2
0.05
Chemical 3
0.8
Chemical 4
8.0E-08
Chemical 5
11
Chemical 6
3.0E-08
Facility Risk
3.7E-04
11.05
Risk Screening Results:
Tier 1
Facility
risk and HI > decision criteria*
Tier 2
Impoundment A
risk and HI > decision criteria*
Impoundment B
risk and HI > decision criteria*
Impoundment C
HI > decision criteria*
Impoundment D
risk < decision criteria*
Tier 3
Chemical 1
risk > decision criteria*
Chemical 2
HI < decision criteria*
Chemical 3
HI > decision criteria*
Chemical 4
risk < decision criteria*
Chemical 5
HI > decision criteria*
Chemical 6
risk < decision criteria*
Conclusion:
Impoundment A: Chemicals 1 and 3 to be assessed in next phase
Impoundment B: Chemicals 1 and 5 to be assessed in next phase
Impoundment C: Chemical 3 to be assessed in next phase
Impoundment D: No further assessment of chemicals 1 and 6; no further
assessment at this facility
*Decision criteria: 10"5 for cancer risk; 0.1 for noncancer risk.
2-21
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Section 2.0
Phase I Screening Assessment
Decision Criteria. As described above, the Agency will use risk criteria of 10"5 for
carcinogenic risk and HI = 1 for noncarcinogenic risk throughout the analysis. The decision to
remove a particular constituent, unit, or facility from any further analysis is final (i.e., no further
analysis is warranted). Therefore, EPA may apply a margin of protection of 0.1 in Phase I to
determine whether Phase IB and II modeling is necessary. EPA may consider applying
alternative MPs depending on the screening results presented in the risk distributions.
EPA considers that, for many constituents, screening based on direct ingestion of the
surface impoundment influent and direct inhalation of the emissions is by its nature very
protective; that is, if fate and transport modeling were to be conducted, the potential risks would
invariably be lower. The intent of a margin of protection is to address potential concerns for
indirect exposures. In addition, special consideration will be given to constituents known to
bioaccumulate in setting priorities during Phase IC.
2.2.1.4 Results of Phase IA Human Health Risk Screening. The risk characterization
resulting from the Phase IA analyses will consist of two primary outputs:
# Phase IA risk distributions
# Screening of constituents, impoundments, and facilities.
Risk Distributions. The Phase IA screening risks for each constituent, impoundment,
and facility will provide initial risk distribution profiles that describe the national scale surface
impoundment population. The risk distributions will be provided for the categories that are of
concern to the SI Study:
# Six regulatory status categories of interest
# Three functional classes (storage, treatment, and disposal)
# Types of treatment (e.g., biological, settling)
# Types of industry (by SIC code)
# Types of constituents.
By providing risk distributions for these categories, EPA can determine what types of
industries or impoundments have the highest potential risks and merit additional analysis.
The distribution of the sample population of facilities is shown in Tables 2-4 and 2-5 by
regulatory category and industry type. The direct discharger and zero discharger types are
statistically sampled populations and have the largest number of facilities. The indirect
discharger population is smaller and consists of preselected and purposively selected facilities.
Risk distributions will be provided for the CWA discharger type and RCRA nonhazardous waste
type.
The facility distribution by industry type (see Table 2-4) shows that certain industries
were given higher priority in the SI Study; these industries (e.g., SIC group 26, 28, 29, 32, and
33) are sampled at a higher rate. Development of risk distributions by industrial type may be
grouped by high- or low-priority industry groups, and, because the CWA discharger types are
2-22
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Section 2.0
Phase I Screening Assessment
Table 2-4. Facility Distribution by Regulatory Category
Tvpeol'CWA l-'acilily Type of KC'KA Nonlia/arrious Waste Number ol'l-'acililies
Direct discharger
Decharacterized
79
Nondecharacterized a
82
Zero discharger
Decharacterized
6
Nondecharacterized a
34
Indirect discharger
Decharacterized
2
Nondecharacterized a
12
TOTAL
215
aCould include some missing responses.
three distinct populations, risk distributions by industry type will be developed separately for
each CWA discharger type.
EXAMPLE: An example distribution by industry type is provided in Figure 2-10. Risk
distributions are shown for three different industry types for cancer risks. The figure
indicates that industry type B has the highest percentage of its facilities with cancer risks
less than the screening decision criteria of 10"6. In contrast, industry type C has a large
percentage of its facilities with risks greater than the risk decision criteria.
The distributions of the sample population of facilities by functional class, treatment type, and
constituent will be defined by the survey questionnaire responses.
These risk distributions will be refined with each subsequent phase (see Phase IB Risk
Characterization Outputs for approach). The risk distributions will also provide the basis for the
prioritization for evaluations to be conducted in Phase II (see Section 2.4).
Risk Screening. The Phase IA risk screening approach identifies
# Constituents, impoundments, and facilities that have risks below the screening
criteria and therefore are considered to have negligible risks and are not assessed
in any further phases.
# Constituents for specific impoundments and facilities that have risks above the
screening criteria and will proceed to Phase IB risk screening.
2-23
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Section 2.0
Phase I Screening Assessment
Table 2-5. Facility Distribution by Industry Type
SIC \l:ijor
Croup
SIC Code
Description
Nilmher of
Industry
litcililies
20
-
Food and Kindred Products
19
22
-
Textile Mill Products
5
24
-
Lumber and Wood Products, Except Furniture
7
26
-
Paper and Allied Products
31
28
-
Chemicals and Allied Products
38
29
-
Petroleum Refining and Related Industries
25
30
-
Rubber and Miscellaneous Plastics Products
9
32
-
Stone, Clay, Glass, and Concrete Products
20
33
-
Primary Metal Industries
24
34
-
Fabricated Metal Products, Except Machinery and
Transportation Equipment
5
35
-
Industrial and Commercial Machinery and
Computer Equipment
3
36
-
Electronic and Other Electrical Equipment and
Components, Except Computer Equipment
5
37
-
Transportation Equipment
5
49
4952
(Except POTWs)
Sewerage Systems
1
4953
Refuse Systems
3
50
5085
Industrial Supplies
1
51
5171
Petroleum Bulk Stations and Terminals
7
97
-
National Security and International Affairs
2
TOTAL
210a
aTotal does not include five CBI facilities
2-24
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Section 2.0
Phase I Screening Assessment
U)
o
o
(O
o
a>
G)
(U
a>
p
i
O
OJ
I I Industry Type A
•v< Industry Type B
[iij Industry Type C
P
/
X
<10-8 10-8-10-7 10-7-10-6 10-6-10-5
Cancer Risk
10-5-10-4
>10-4
Figure 2-10. Example risk distributions for three industry types.
The results of the risk screening are best presented using the risk distributions based on
the constituent, impoundment, and facility risks. These are unweighted distributions because the
purpose is to present the number of facilities that must be assessed further. (In comparison, the
risk distributions in Figure 2-10 show percentage of facilities because we want to characterize the
national population.)
EXAMPLE. An example of the screening results is shown in Figure 2-11. The facility
risk distribution for noncancer risks clearly delineates the facilities that will be assessed
further in Phase IB and those that are considered to have negligible risks and will not be
further evaluated. The screening criteria separate the two categories. The impoundment
risk distribution is also divided into two categories based on the screening criteria. The
impoundments proceeding to Phase IB are only associated with the facilities that must be
assessed further. Some of the impoundments for a facility that must go to Phase IB are
not further analyzed; these impoundments fall to the left of the criteria. The third
distribution is on a constituent basis and shows the constituents that will be assessed in
Phase IB. This constituent distribution applies to the subset of impoundments and
facilities that have proceeded to Phase IB. Note that a margin of protection will be
considered at each screening decision.
2-25
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Section 2.0
Phase I Screening Assessment
Facility Risk Distribution
Facilities not assessed -
further
"O
-S2
<5
-Facilities proceed to Phase IB
MP=0.1
<0.01 0.01-0.1 0.1-1 1-10
Noncancer Risks
10-100
>100
Impoundment Risk Distribution
Impoundments not-
assessed further
•-Impoundments proceed
to Phase IB
o
tn
CCL
MP=0.1
<0.01 0.01-0.1 0.1-1 1-10 10-100 >100
Noncancer Risks
Constituent Risk Distribution
Constituents-^ ^-Constituents at these
at these facilities facilities proceed to Phase IB
not assessed further
MP=0.1
CXI
<0.01 0.01-0.1 0.1-1 1-10 10-100 >100
Noncancer Risks
Figure 2-11. Example risk screening results.
2.2.2 Phase IB Human Health Screening
The Phase IB risk screening is described in four sections:
# Design goals and overview
# Screening models
# Procedure for risk screening
# Results of risk screening.
2-26
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Section 2.0
Phase I Screening Assessment
2.2.2.1 Phase IB Human Health Screening Design Goals and Overview. EPA will
use screening models to supplement the initial screening performed under Phase IA. Use of
screening models provides additional characterization of exposure by evaluating the fate and
transport of constituents from their release from the surface impoundment through the
environmental media to the point of exposure. Therefore, the Phase IB screening will provide a
more realistic tier in the phased approach.
The use of screening models will be necessary when there is uncertainty about
eliminating a constituent from further evaluation. Two main categories of uncertainty will be
addressed: (1) constituents that volatilize, but for which there are no air concentration data
provided in the survey questionnaire, and (2) constituents that were not eliminated for the
Phase IA screening. Phase IB modeling will be performed on all constituents identified by the
Phase IA screening.
The Phase IB screening will address only the major routes of exposure that are expected
to contribute significantly to potential risks (i.e., ingestion of drinking water and inhalation of
air). This phase will use a limited amount of site-specific data from the survey questionnaire.
However, because constituents from specific units may be screened from further analysis, the
Phase IB modeling approach will use protective assumptions, such as assessing risks for close-in
receptors.
EPA screening models IWAIR and IWEM, developed for use under the Industrial D
guidance, will be used to calculate screening risk estimates. These risk estimates will replace or
supplement the corresponding Phase IA screening risk estimates and therefore will refine and
improve the overall Phase IA risk distributions.
2.2.2.2 Phase IB Human Health Screening Models. IWAIR and IWEM assess the
risks from potential exposure of air and groundwater, respectively, due to constituents released
from surface impoundments. The screening models, as described below, follow different
approaches. However, both models will provide screening analyses that are useful in
characterizing exposure and incorporate more site-specific data. Despite the difference in
modeling approaches, the results from each of the Phase IB models constitute a defensible basis
to provide screening-level estimaters of risk. Use of IWAIR and IWEM for Phase IB assumes
that any software model errors that were identified by the previous peer review will have been
addressed.
IWAIR. The IWAIR model will be used to calculate risks due to inhalation of airborne
volatile constituents released from surface impoundments. IWAIR incorporates the
CHEMDAT8 volatile emission model to calculate the constituent release (i.e., emission rate)
from an impoundment, uses dispersion factors developed from Industrial Source Complex Short
Term (ISCST3) modeling simulations to calculate an air concentration, uses exposure and risk
calculations following EPA guidance {Risk Assessment Guidance for Superfund, U.S. EPA,
1989), and uses a chemical and toxicological database for 95 chemicals to calculate carcinogenic
and noncarcinogenic chronic inhalation risks. CHEMDAT8 has undergone extensive review by
both EPA and industry representatives and is publicly available. ISCST3 is another regulatory
2-27
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Section 2.0
Phase I Screening Assessment
standard model that has undergone substantial review and use by industry. The dispersion factor
approach for risk screening purposes is recommended by EPA guidance (Soil Screening
Guidance, U.S. EPA, 1996d, e). Dispersion factors for multiple source area sizes, receptor
distances, and meteorological conditions are provided.
IWAIR uses the same exposure factors as Phase IA from the Exposure Factors Handbook
(U.S. EPA, 1997b). An age-weighted resident is considered for carcinogenic chemicals. This
approach is also generally consistent with the establishment of risk-based criteria such as ambient
water quality criteria. An adult resident is considered for noncarcinogenic chemicals. Phase IA
toxicological benchmarks will be used (in place of IWAIR toxicological benchmarks) to
calculate screening risks with IWAIR. For SI Study constituents that are not included in the
IWAIR chemical database, the physicochemical properties from CHEMDAT8 and Phase IA
toxicity benchmarks will be added to IWAIR. IWAIR will then be used to calculate the
constituent risks and His.
The IWAIR model is computationally fast and easy to use and requires minimal input
data. The site-specific data required will be obtained from the survey and include: constituent
waste concentration, impoundment depth, area, annual wastewater flow rate, and whether or not
aeration occurs. Default or additional site-specific data can be provided for aeration parameters
and wastewater parameters important for biodegradation. These are attributes required by the
Phase IB screening because a large number of constituents and units may be assessed in
Phase IB.
The IWAIR model is currently in the public comment period. Peer review comments
have been favorable to the approach and the computer program. Use of the IWAIR model in the
SI Study Phase IB calculations is outlined in Section 2.2.2.3.
IWEM. The IWEM Tier 1 model will be used to calculate the risks due to exposure to
groundwater containing constituents released from surface impoundments. IWEM Tier 1 is a
table containing leachate concentration threshold values for a specific chemical based on a
dilution attenuation factor (DAF) and the toxicity reference levels for 190 constituents. The
toxicity reference level is based on the toxicological benchmark or the MCL. The DAFs were
generated by modeling the migration of waste constituents from an impoundment through the
underlying soil to a monitoring point in the aquifer using the EPA Composite Module for
Leachate Migration with Transformation Products (EPACMTP) in a national Monte Carlo
probabilistic analysis. The DAFs are multiplied by the toxicity benchmark to provide the
leachate concentration threshold value for each chemical.
Leachate concentration threshold values and DAFs are provided for three impoundment
liner scenarios: no liner, single liner, and a composite liner. The no liner scenario represents an
impoundment that is relying upon location-specific conditions such as low-permeability native
soils beneath the unit or low annual precipitation rates to mitigate the release of contaminants to
the groundwater. The single liner scenario represents a 3-foot-thick clay liner with a low
hydraulic conductivity (10"7 cm/s) beneath the impoundment. The composite liner scenario
consists of a 3-foot-thick clay liner beneath a 40-mil-thick high-density polyethylene (HDPE)
flexible membrane liner.
2-28
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Section 2.0
Phase I Screening Assessment
IWEM Tier 1 is based on a health-protective Monte Carlo probabilistic analysis that
accounts for the nationwide variability of groundwater modeling parameters. The Monte Carlo
procedure randomly drew input parameter values from representative statistical distributions for
each parameter. A set of input parameter values was developed and the model was run to
compute the groundwater monitoring well concentration and the DAF. This process was
repeated thousands of times until a distribution of thousands of output values (DAFs) was
produced. The DAF values were ranked from high to low, and the 90th percentile DAF was
determined. The 90th percentile DAF represents the amount of dilution and attenuation that
would occur in at least 90 percent of the cases modeled. In other words, the DAF is protective in
at least 90 percent of the modeled cases. The selection of 90th percentile DAF is based on
# The need to choose a level of protection that is protective and consistent with
other EPA analyses, including the proposed Hazardous Waste Identification Rule
(HWTR) of 1995 (U.S. EPA, 1995b) and hazardous waste listing evaluations (e.g.,
the Petroleum Refinery Waste Listing Determination, U.S. EPA, 1997d)
# The desire to have a large degree of confidence that the results are adequately
protective of human health and the environment given the degree of uncertainty
inherent in the data and the analyses.
The Monte Carlo approach used in EPACMTP has been applied in various EPA
regulatory efforts, including the proposed 1995 HWIR and hazardous waste listing evaluations,
such as those mentioned previously. As such, the Monte Carlo procedure and its applicability to
national analyses has been extensively reviewed within EPA and by the Science Advisory Board
and has been subject to public review and comment (U.S. EPA, 1999aa). The model is currently
in the public comment period.
To maintain consistency with Phase IA in the risk calculation, only the DAFs from
IWEM will be used. For each chemical, the DAF from each liner scenario will be multiplied by
the carcinogenic or noncarcinogenic risk screening factor from Phase IA to develop a new SI
Study-modified IWEM Tier 1 table containing the leachate concentration threshold values. This
approach ensures that receptors are evaluated with the same exposure factors (e.g., amount
ingested and inhaled) used in Phase IA.
There are a number of SI Study constituents that are not included in the IWEM Tier 1
table. For these constituents, a leachate concentration threshold value using a DAF from a
surrogate chemical will be calculated. The leachate concentration threshold value will be
calculated by using the IWEM procedure for estimating DAFs of chemicals for which
EPACMTP was not simulated, as follows: the DAF will be determined by interpolating between
the DAFs of chemicals whose hydrolysis rate and retardation factor are in the same range as the
hydrolysis rate and retardation factor of the new chemical.
Use of the SI Study-modified IWEM Tier 1 table in the SI Study Phase IB calculations is
outlined in Section 2.2.2.3.
2-29
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Section 2.0
Phase I Screening Assessment
2.2.2.3 Procedure for Phase IB Risk Screening. The overall human health screening
process is shown in Figure 2-12. The overall process is the same as Phase IA:
# Risks will be calculated for each constituent, impoundment, facility, and
regulatory population.
# Cumulative risks will be calculated.
# Risk distributions will be developed.
# Risk screening will be performed.
The analyses, however, will be performed only for a subset of constituents, impoundments, and
facilities defined by the Phase IA screening results. Each element is outlined in more detail
below. The primary difference between Phase IA and IB is the procedures used for calculating
risks.
Human Health Risk Calculation. Phase IB risk estimates will be for the air inhalation
and groundwater ingestion pathways only (see Figure 2-12). Phase IB does not include the soil
ingestion pathway. Risks for the air exposure pathway will be estimated using IWAIR if the
Phase IA risk estimate is greater than the screening criteria or if the constituent is a VOC, but air
concentration or emission data were not provided in the survey response. Risks for the
groundwater exposure pathway will be estimated using IWEM if the Phase IA risk estimate is
greater than the screening criteria.
Figure 2-12. Decision tree for Phase IB human health risk screening.
2-30
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Section 2.0
Phase I Screening Assessment
The decision tree for calculating the Phase IB air exposure risks using IWAIR is
presented in Figure 2-13. The types of site-specific data that will be used are outlined; the survey
question that will provide the data is noted in the figure. The decision tree for defining the
influent waste concentration from survey data is shown in Figure 2-14. IWAIR default data will
likely be used for the aeration and waste characteristics data. Because IWAIR must represent
wind conditions across the continental United States, IWAIR contains wind dispersion data
based on 29 meteorological stations. Because the wind pattern may not be representative of the
actual site conditions, a close-in receptor at 25 m will be assumed for the Phase IB screen. If a
constituent is not currently in IWAIR, its physicochemical and toxicological data will be added
to the IWAIR chemical database.
The decision tree for calculating the Phase IB groundwater exposure risks using the
modified IWEM Tier 1 table is presented in Figure 2-15. The Phase IB groundwater risk
calculation will consider the type of lining at each impoundment in determining the appropriate
groundwater screening factor, called the leachate concentration threshold value (LCTV) in
IWEM. The site-specific liner questions are outlined in Figure 2-15. The calculation of the
leachate concentration is shown in Figure 2-6. The risk calculation mirrors the Phase IA
calculation: calculate the ratio of the leachate concentration to the LCTV and multiply by the risk
criteria.
Cumulative Risk Calculation. The calculated screening risks for each constituent for a
specific impoundment and facility will be combined to generate three cumulative risk estimates:
impoundment risk, constituent risk, and facility risk. The calculation of the cumulative risks is
defined in Figure 2-3 and Section 2.2.1.3. It is important to note that the cumulative risks are a
combination of the Phase IA and Phase IB calculated risks for each constituent, because the
Phase IB risk estimate is considered a refinement of the initial Phase IA risk estimate.
Risk Distribution Development. The risk distribution approach is identical to that
defined in Phase IA (see Section 2.2.1.3 for the development approach). Because the Phase IB
cumulative risks are a combination of Phases IA and IB results, the risk distributions also
represent the combined analysis of Phase IA and IB.
Risk Screening. The risk screening approach is also identical to that defined in Phase IA
(see Section 2.2.1.3 for the development approach).
2.2.2.4 Results of Phase IB Risk Screening. The risk characterization resulting from
the Phase IB analyses will consist of two primary outputs:
# Combined Phase IA and IB risk distributions
# Combined Phase IA and IB screening of constituents, impoundments, and
facilities.
2-31
-------
Section 2.0
Phase I Screening Assessment
Enter facility zip cudo
in IWAIR to s«jl«jct
motoorolo* iici.il station
(A2)
Key:
Shaded boxes indicate information
needed from survey questionnaire.
Survey question number appears
in parentheses.
Figure 2-13. Decision tree for Phase IB air screening using IWAIR.
2-32
-------
Section 2.0
Phase I Screening Assessment
Yes
Assume impoundment
concentration =
effluent concentration
Yes
Use same surrogate
impoundment as
chosen in Phase IA and
depicted in Figure 2-7
Calculate initial
estimate of influent
concentration as
follows:
Effluent concentration
Removal efficiency \
I 100 /
Key:
Shaded boxes indicate information needed from survey questionnaire.
Survey question number appears in parentheses.
Figure 2-14. Decision tree for calculating influent waste concentration for IWAIR.
2-33
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Section 2.0
Phase I Screening Assessment
Key:
1 Leachate concentration threshold value.
2IWEM lookup table modified to include HWIR toxicity data and all SI study chemicals.
Shaded boxes indicate information needed from survey questionnaire.
Survey question number appears in parentheses.
Figure 2-15. Decision tree for Phase IB groundwater screening using IWEM.
2-34
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Section 2.0
Phase I Screening Assessment
Risk Distributions. The combined Phase IA and IB screening risks for each constituent,
impoundment, and facility will provide initial screening-level risk distribution profiles for the
national scale surface impoundment population. The risk distributions will be provided for the
categories that are of concern to the SIS, as outlined in Section 2.2.1.4.
The refinement of the screening-level risk distribution from Phase IA to Phase IB is
shown in Figure 2-16. The example risk distribution shown previously for Phase IA (see
Figure 2-10) is now updated to include the Phase IB risk screening results. The new figure
shows the modified risk distributions for the three industry types. It is expected that the Phase IB
analyses, by including the attenuating factor of exposure modeling, will result in more accurate
and generally lower risk estimates. Therefore, percentages below the screening criteria are
expected to increase. The shaded portions of the percentages of facilities in each risk bin are the
added Phase IB percentages. Above the screening criteria, the percentages are expected to
decrease. For the most part, the facilities with risks above the screening criteria are from Phase
IB analyses. Only risks from sludge ingestion, which is a Phase IA analysis only, would
contribute to the facilities with risks above the screening criteria.
Combined Phase IA
and IB risks
Phase IB risks
(0
o
<0
Li.
-------
Section 2.0
Phase I Screening Assessment
Risk Screening. The combined Phase IA and IB risk screening approach identifies
# Constituents, impoundments, and facilities that have risks below the screening
criteria and therefore are considered to have negligible risks and are not assessed
in any further phases
# Constituents for specific impoundments and facilities that have risks above the
screening criteria and should proceed to Phase II analysis, depending on the
factors described in the Phase IC initial prioritization.
In addition to the risk screening, constituents that are considered special cases will
automatically proceed to Phase II. The definition and categorization of special cases is discussed
in the next section.
2.2.3 Special Cases
Certain constituents may present human health or ecological risks yet not be identified as
constituents with high risks in the screening process described above. These constituents are
likely to be persistent or bioaccumulative. To ensure that these types of constituents are
identified, each constituent will be ranked according to a special set of criteria. EPA developed
the Revised Waste Minimization Prioritization Tool (U.S. EPA, 1998e), which scores
constituents on the basis of their persistence (P) in the environment, bioaccumulation (B)
potential, and toxicity (T) to humans and ecological receptors. Because the SI Study is
concerned with the same issues, the procedures used in the WMPT will be used to score
constituents (see Figure 2-17). The persistence (P) scoring is based on a steady-state,
nonequilibrium multimedia partitioning model to estimate constituent half-life. The potential for
bioaccumulation (B) is scored using either measured or estimated bioaccumulation factors
(BAFs) or bioconcentration factors (BCFs). Human and ecological toxicity (T) of the
constituents is also scored. For human health, the highest score for either the carcinogenic or
noncarcinogenic health effects is selected. For ecological effects, the highest toxic effect to
aquatic organisms is identified. Depending on the data available to assess constituent
characteristics, P, B, and T scores are qualified to indicate which data from the established
hierarchy (i.e., high, medium, or low data preferences) have been used in the scoring process.
These factors are consolidated into an overall score for human health effects from each
constituent by summing the P and B scores and summing this score with the highest T score for
either noncarcinogenic or carcinogenic chemicals. A similar process is used to develop scores
for ecologically important constituents, with aquatic toxicity data used to develop T scores for
the ecologically important constituents. The final score is the higher of either the human or
ecological scores.
The SIS will use the PBT scores developed by the WMPT after reviewing the toxicity
data to determine whether the data used in both studies are consistent. If necessary, new scores
will be developed for constituents with new toxicity data or for constituents not evaluated
previously. Furthermore, since EPA intends to continue revising the WMPT in response to the
public and EPA comments, changes in the WMPT procedures will also be addressed, if
necessary. Thus, special case constituents will be identified as those with the highest PBT
2-36
-------
Overall Chemical Score
(3-9)
T
Use higher of two scores
t
Sum the two scores
I
Use higher of two scores
. i .
Cancer
Effects
(1-3)
Non-cancer
Effects
(1-3)
e.g., oral
cancer
slope
factor
e.g., RfD,
chronic
LOAEL
Human Exposure
Potential
(2-6)
Sum the two scores
_L
Bioaccumulation
Potential
(1-3)
Human Health Concern
(3-9)
Ecological Concern
(3-9)
t
Sum the two scores
Use score directly
Aquatic
Toxicity
(1-
3)
i
k
Derive scores by comparing data element values with "fencelines"
e.g., Final
Chronic
Value, LC,„
Ecological Exposure
Potential
(2-6)
Sum the two scores
Bioaccumulation
Potential
(1-3)
Persistence
(1-3)
i
k
i
k
BAF or BCF
Regional
half-life
Figure 2-17. Overview of the revised WMPT scoring algorithm (U.S. EPA, 1998e).
-------
Section 2.0
Phase I Screening Assessment
ranking. The list of special case constituents will be reported with the risk distributions reported
for the Phase I screening process.
2.3 Phase I: Ecological Screening Assessment
The Phase I ecological risk screening is somewhat different from the Phase I human
health screening in that a single comparison between screening factors and constituent
concentrations is conducted to determine whether a constituent, impoundment, or facility should
be included for further evaluation in Phase II. Depending on the ecological receptor of concern,
the Phase I analysis will either estimate risks from the ingestion of contaminated plants, prey, and
media, or it will estimate risks associated primarily with direct contact with a contaminated
medium such as sediment or soil. The ecological risk estimates will be compared to risk criteria
to prioritize the list of constituents, impoundments, and facilities that may warrant further
evaluation to determine the likelihood of adverse ecological effects. It is important to note that
the ecological priority list will be used in conjunction with the Phase I human health screening
results to delineate the universe for the Phase II analysis. The Phase I results from the ecological
screening will be used to inform the selection of constituents, impoundments, and facilities that
are most likely to pose significant risks to both human and ecological receptors.
2.3.1 Phase I Ecological Risk Screening
The Phase I ecological risk screening is described in five sections:
# Design goals and overview
# Management goals and assessment endpoints
# Development of screening factors
# Procedure for risk screening
# Results of risk screening/prioritization.
2.3.1.1 Design Goals and Overview. As suggested above, the primary goal of the Phase
I ecological screening assessment is to establish a priority list of constituents, impoundments, and
facilities based on the potential for adverse ecological effects. The secondary goal of this phase
is to use the screening-level results to generate ecological risk profiles for the universe of surface
impoundments included in the SI Study. These risk profiles provide a "snapshot" of the potential
for environmental effects and will be used to identify constituents, impoundments, and facilities
that have negligible ecological risks. The Phase I approach considers the potential for adverse
effects to a suite of ecological receptors that may be attributed to terrestrial, freshwater, and
wetland habitats including, for example, mammals, birds, and soil and benthic fauna. The
habitats and receptors considered in this study are consistent with the national assessment
strategy developed to support the Hazardous Waste Identification Rule (HWIR) proposed in
November 1999. Because the HWIR risk assessment framework was intended to support
national studies of waste management practices, the SI Study has adopted this framework as the
basis for selecting receptors and habitats.
As with the Phase I screening approach for human health, the ecological screening
analysis calculates risks to individual ecological receptors (e.g., red fox, aquatic biota) based on
the ratio between ecological risk screening factors and the reported concentrations of constituents
2-38
-------
Section 2.0
Phase I Screening Assessment
in surface impoundments reported in the survey questionnaire. Consequently, ecological risk
screening factors are given in units of concentration (e.g., mg/kg or mg/L). The ecological risk
screening factors will include both standard ecological benchmarks such as the Ambient Water
Quality Criteria (AWQC) as well as benchmarks developed for other EPA analyses such as
HWIR. The use of screening factors is considered to be protective because the factors are:
# Derived using established EPA protocols for use in evaluating ecological risk
(e.g., sediment quality criteria)
# Based on highly protective assumptions regarding the toxicological potency of a
constituent (e.g., no adverse effects levels)
# Calculated assuming that all media and food items originate from a contaminated
source.
In addition, the application of the screening factors assumes that ecological receptors are exposed
directly to chemical concentrations in the sludge and wastewater found in the surface
impoundment. For mammals, birds, and selected herpetofauna, these screening factors reflect
ingestion of contaminated media, plants, and prey. For other receptor groups such as soil fauna,
these screening factors are intended to reflect both the direct contact and ingestion routes of
exposure. The results of the screening assessment for these representative species will be used to
infer potential risks to taxonomically and ecologically similar receptors.
2.3.1.2 Management Goals and Assessment Endpoints. Perhaps the most important
step in developing the assessment strategy (often referred to as the problem formulation phase) is
the selection of assessment endpoints. The selection of assessment endpoints, defined as "explicit
expressions of the actual environmental value that is to be protected" (U.S. EPA, 1998f) serves
as a critical link between the ecological risk assessment (ERA) and the management goals. For
the SI Study, the management goals may be summarized as follows: "prioritize the constituents,
impoundments, and facilities based on the potential for adverse ecological effects, and describe
the national distribution of ecological risks associated with the management of wastes in surface
impoundments." Candidates for assessment endpoints often include threatened/endangered
species, commercially or recreationally important species, functional attributes that support food
sources or flood control, or aesthetic values, such as the existence of charismatic species like
eagles (U.S. EPA, 1998f). However, it should be emphasized that two key elements are required
to define an assessment endpoint: (1) a valued ecological entity (e.g., a species, a community)
and (2) an attribute of that entity is important to protect (e.g., reproductive fitness).
Given the similarity in the management goals for HWIR, the assessment endpoints for the
SI Study were chosen to be consistent with those selected for the proposed Hazardous Waste
Identification Rule. As with the HWIR risk analysis, ecological exposures are presumed to occur
at facilities that may be located anywhere within the contiguous United States. Consequently, a
suite of assessment endpoints was chosen based on: (1) their significance to the ecosystem,
(2) their ability to represent a variety of habitat types, (3) their position along a continuum of
trophic levels, and (4) their susceptibility to chemical stressors managed in surface
impoundments meeting certain regulatory criteria. In Table 2-6, the assessment endpoints (i.e.,
values to be protected) selected for the SI Study analysis are defined in terms of: (1) the
2-39
-------
Section 2.0
Phase I Screening Assessment
significance of an ecological entity, (2) the ecological receptor representing that entity, (3) the
characteristic about the entity that is important to protect, and (4) the measures of effect used to
predict risk. The intent of including multiple receptors is that, by protecting producers (i.e.,
plants) and consumers (i.e., predators) at different trophic levels, as well as certain structural
components (e.g., benthic community), a degree of protection from chemical stressors may be
inferred to the ecosystem as a whole. Consequently, the selection of the assessment endpoints
for each receptor taxon is critical to the development of ecological screening factors.
In addition to using screening factors to infer risks to representative species populations
and communities, it is also important to consider the potential effects on managed lands (e.g.,
National Wildlife Refuges), critical habitats (e.g., wetlands), and threatened and endangered
species. Although metrics to evaluate the impacts on the ecological "health" of these entities are
not available for use in screening analyses, the presence of valued habitats and species may
require alternative risk modeling approaches to determine the likelihood of adverse effects.
These assessment endpoints will not be evaluated in Phase I; however, the intrinsic value of
managed lands and critical habitats will be considered in Phase IC and Phase II.
2.3.1.3 Development of Ecological Screening Factors. The development of ecological
screening factors will involve four basic steps:
# Select representative species and receptor groups.
# Identify relevant exposure pathways.
# Select appropriate ecotoxicological studies:
studies used in population-inference
studies used in community-inference.
# Calculate ecological screening factors
screening factors for receptor populations
screening factors for receptor communities.
Examples of ecological screening factors and the studies selected to support their development
are presented in Appendix C. Because these data will be used in both the Phase I and Phase II
analysis, Appendix C uses the generic term of "toxicity benchmarks" to refer to screening factors
as well as ecotoxicological study data. The following discussion describes the methods and data
sources used in the development of screening factors shown in Appendix C.
Selection of Representative Species/Receptor Groups. The HWIR ecological risk
assessment approach included a series of representative habitats for terrestrial (five), freshwater
margins (three), and wetlands (three permanently flooded). These habitats were selected to
capture the variability in ecological systems throughout the United States and to provide a
2-40
-------
Table 2-6. Assessment Endpoints and Measures of Effects
].x;i m pi is of Keologieal Sijiniticiiiue
Assessment I '.ml point
Representative
Receptors
(.'ha rsiete ristie(s)
Measure of I .fleet
# Multiple trophic levels represented
# Represent species with large foraging ranges
# Represent species with longer life spans
# Variety of dietary exposures represented
Viable mammalian wildlife
populations
Deer mouse, meadow
vole, red fox, e.g.
Reproductive and
developmental success
Chronic or subchronic NOAEL(s) for
developmental and reproductive
effects
Viable avian wildlife
populations
Red-tailed hawk,
northern bobwhite,
e-g-
Reproductive and
developmental success
Chronic or subchronic NOAEL(s) for
developmental and reproductive
effects
# Species represent unique habitat niches
# Many species are particularly sensitive to exposure
Protection of amphibian and
reptile populations ("herps")
against acute effects
Frog, newt, snake,
turtle, e.g.
Lethality and percent
deformity
Acute LC50s for developmental effects
resulting from early life stage
exposures
# Represents base food web in terrestrial systems
# Habitat vital to decomposers and soil aerators
# Crucial to nutrient cycling
Sustainable soil community
structure and function
Nematodes, soils
mites, springtails,
annelids, arthropods,
e-g-
Growth, survival, and
reproductive success
95% of species below no effects
concentration at 50th percentile
confidence interval
# Primary producers
# Act as food base for herbivores
# Constitute essential habitat for virtually all receptor groups
(e.g., nests)
Maintain terrestrial primary
producers (plant community)
Soy beans, alfalfa, rye
grass, e.g.
Growth, yield,
germination
10th percentile from LOEC data
distribution
# Important food source for animals that live in waterbody
margins
# Diverse aquatic life important to maintain biotic integrity
Sustainable aquatic community
structure and function
Fish (salmonids),
aquatic invertebrates
(daphnids), e.g.
Growth, survival,
reproductive success
National Ambient Water Quality
Criteria for aquatic life (95% species
protection)
# Provide habitat for reproductive lifestages (e.g., eggs, larval
forms)
# Act to process nutrients and decompose organic matter
Sustainable benthic community
structure and function
Protozoa, flat worms,
ostracods, e.g.
Growth, survival,
reproductive success
10th percentile from LOEC data
distribution
# Primary producers
# Base food source in the aquatic system
Maintain primary aquatic
producers (algal and plant
community)
Algae and vascular
aquatic plants, e.g.
Growth, mortality,
biomass, root length
EC20 for algae; lowest LOEC for
aquatic plants
-------
Section 2.0
Phase I Screening Assessment
meaningful "ecological context" for receptor selection. A detailed description of the criteria used
to identify representative habitats is provided in the HWIR documentation, Ecological Receptors
and Habitats (U.S. EPA, 1999n); however, it is important to recognize that the intent of a
representative habitat scheme was to develop a site-based framework to perform spatially explicit
risk analyses. In the Phase I screening analysis for the SI Study, the representative habitats will
simply be used to support the inclusion of representative species and receptor groups.
Because one of the major goals of the Phase I assessment is to prioritize facilities,
impoundments, and constituents for further analysis, a strategy was developed to: (1) organize
receptors into feeding guilds of taxonomically similar organisms (e.g., herbivorous birds,
carnivorous mammals), and (2) select a species to represent each guild. Habitat-receptor
correlations, food webs, and available exposure factors will be used to identify "screening-
indicator" species that could be expected to receive the highest exposure to constituents, thus
ensuring a protective screening assessment. Common species generally will be preferred as
indicator species because they are found in a variety of habitats and may be used to represent
different guilds. Table 2-7 presents the crosswalk of some of the likely indicator species for
various guilds and the representative habitats with which they are associated.
Identification of Relevant Exposure Pathways. Ecological exposure pathways for the
Phase I screening analysis will be identified based on: (1) both active and postclosure scenarios
for surface impoundments, and (2) likely routes of exposure for receptors assigned to simple food
webs. Chemical constituents may volatilize from active surface impoundments and deposit onto
adjacent soils, plants, or surface waters. In addition, constituents may leach into ground water
and contaminate nearby surface waters and sediments. Following closure, a surface
impoundment may be integrated with local habitats (assuming the contaminant concentration
does not prevent vegetative growth) and serve as a long-term source of exposure to certain types
of constituents (e.g., metals). As shown in Figure 2-18, receptors may be exposed to
contaminated media and/or prey and plants in both terrestrial and aquatic systems. Consequently,
the exposure pathways that will be represented in Phase I are:
# Direct contact with contaminated sludge/soil (e.g., plants, soil fauna)
# Ingestion of contaminated sludge/soil (e.g., mammals, birds)
# Ingestion of plants/prey on contaminated sludge/soil (e.g., mammals, birds)
# Direct contact with contaminated surface water (e.g., fish, amphibians)
# Direct contact with contaminated sludge (e.g., benthos)
# Ingestion of aquatic plants/prey in contaminated surface water (e.g., birds)
# Ingestion of contaminated surface water (e.g., mammals)
Exposure routes that will be not addressed in the Phase I ecological screening assessment
include
# Dermal absorption from contaminated surface water or sludge (e.g., mammals)
# Inhalation of volatile constituents in air.
2-42
-------
Section 2.0
Phase I Screening Assessment
Table 2-7. Representative Habitats, Receptor Groups, and Representative Species
Kepresenlalhe 1 lahilals
A(|ii;ilk
Welland
Terreslrial
*
1 lahilals
1 lahilals
1 lahilals
f
*
%
~
Is
~
^ = c
s/5
*
=
~ *
*
rz i rz zz 7
~
*
*
*
£_
" ^
1 P —
£
I S 1
p
y
T-
Kepresenlalhe Species =
1 55 1 J
Is
W
'¦*1 — '¦*> Z. .1
I a. iA \ o. > IO
1
-------
Section 2.0
Phase I Screening Assessment
Tertiary
Consumers
Secondary
Consumers
Primary
Consumers
Producers
Figure 2-18. General food web model for aquatic and terrestrial systems.
2-44
-------
Section 2.0
Phase I Screening Assessment
Dermal absorption of constituents is considered to be an insignificant exposure pathway
for potentially exposed wildlife receptors and will not be assessed because
# Dense undercoats or down effectively prevents chemicals from reaching the skin
of wildlife species and significantly reduces the total surface area of exposed skin
(Peterle, 1991; U.S. ACE, 1996)
# Results of exposure studies indicate that exposures due to dermal absorption are
insignificant compared to ingestion for terrestrial receptors (Peterle, 1991).
Inhalation of volatile compounds will not be assessed for wildlife receptors because
# Concentrations of volatile chemicals released from soil to aboveground air are
drastically reduced, even near the soil surface (U.S. ACE, 1996)
# Significant concentrations of VOCs would be required to induce noncarcinogenic
effects in wildlife based on inhalation toxicity data for laboratory rats and mice
(U.S. ACE, 1996).
Selection of Appropriate Ecotoxicological Studies—Population Inference. As
suggested in Table 2-6, risks to four groups of receptors (mammals, birds, amphibians, and
reptiles) will be estimated based on endpoints relevant to population sustainability. It is
important to note that screening factors will not be developed based on population-level studies.
Rather, we will use ecotoxicological data on selected physiological endpoints (e.g.,
developmental effects) to infer risks to wildlife populations.
For amphibians, the development of screening factors is severely limited by data
availability. After a review of several compendia presenting amphibian ecotoxicity data (e.g.,
U.S. EPA, 1996g; Power et al., 1989) as well as primary literature sources, it was determined that
there was a general lack of chronic or subchronic ecotoxicological studies. Consequently, studies
on acute exposures during sensitive amphibian life stages will be selected to develop screening
factors. The potential sensitivity of this receptor group warrants their inclusion even though
chronic study data are not yet available. Amphibian studies considered appropriate for
development of Phase I screening factors must include the following information:
# Test organism
# Toxicological endpoint
# Exposure duration
# Life stage at which exposure occurred (e.g., embryo, tadpole).
Appropriate toxicity data for amphibians will include reproductive effects, developmental
effects, or lethality from studies conducted for an exposure duration of less than 8 days. Limiting
the study duration to short exposures will allow use of a larger data set in deriving the screening
factors.
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Section 2.0
Phase I Screening Assessment
For mammals, birds, and reptiles, only toxicity studies relevant to ingestion will be
reviewed (e.g., gavage); studies where the chemical was administered via injection or
implantation will not be reviewed. At a minimum, studies must report the following to be
considered for use in developing the ecological screening factors:
# Test organism
# Toxicological endpoint
# Dose-response information
# Exposure duration
# Exposure route
# Sample size
Preferred Studies. Toxicity studies that reported reproductive impairment,
developmental abnormalities, and mortality will be preferred to studies on other physiological
endpoints because these endpoints are highly relevant to the assessment endpoints selected for
the SI Study (e.g., population sustainability). In addition, the use of reproductive and
developmental toxicity data has been recommended in guidance across several federal agencies
(U.S. EPA, 1998f; Department of the Air Force, 1997; U.S. ACE, 1996). Studies that report no
observed adverse effects levels (NOAELs) will be preferred to those that include only effects
levels and low observed adverse effects levels (LOAELs). Several other important aspects of
study selection are summarized below.
Duration of exposure - Duration is critical in assessing the potential for adverse effects
to wildlife. However, since definitive guidance is not available on subchronic versus chronic
exposures, we will define chronic exposures as greater than 50 percent of the life span of
mammalian wildlife representative species. Little information exists concerning the life span of
birds used in toxicity studies, and a standard study duration has not been established for avian
toxicity tests. Therefore, exposures greater than 10 weeks will be considered chronic for birds;
exposures less than 10 weeks will be considered subchronic (Sample et al., 1996).
Timing of exposure - The timing of exposure is critical in assessing the potential for
adverse effects to wildlife. For example, early development is a particularly sensitive life stage
due to the rapid growth and differentiation occurring within the embryo and juvenile. For many
species, exposures of a few hours to a few days during gestation and early fetal development may
produce severe adverse effects (Sample et al., 1996). Therefore, in the absence of chronic studies
on developmental or reproductive effects (e.g., multigenerational studies), studies that report
exposures during reproductive and/or developmental stages may be selected for use in
developing ecological screening factors.
Endpoint of interest - Our review of toxicity data indicated that reproductive or
developmental effects were frequently observed at doses that were lower than those causing
mortality . Therefore, chronic mortality studies will only be used when reproductive or
developmental data are not available. Physiological (e.g., enzyme activity), systemic, and
behavioral responses will be less preferred because it is often difficult to relate these responses to
quantifiable decreases in reproductive fitness or the persistence of wildlife populations.
Tumorigenic and carcinogenic toxicity studies will not be considered ecologically relevant and
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Section 2.0
Phase I Screening Assessment
will not be used to develop toxicity benchmarks because debilitating cancers in wildlife are
exceedingly rare under field conditions.
Data gaps - From previous analyses such as HWIR, it is apparent that there will be a
number of data gaps in the ecotoxicological database on mammals, birds, and reptiles. In fact,
chronic studies on reptiles are generally unavailable. Similarly, there is a paucity of relevant
studies on birds that meet the selection criteria described above. Two alternatives will be
considered in developing screening factors:
1. Use of Surrogate Chemicals. For some classes of constituents, toxicity data
exist for only a few, well-studied constituents (e.g., polycyclic aromatic
hydrocarbons). Research on qualitative (QSARs) suggests that chemicals with
similar molecular or physicochemical properties have similar biological reactivity
and toxicity (Donkin, 1994; Nirmalakhandan and Speece, 1988). Therefore, these
chemicals may be used as surrogates for other detected members of the chemical
class.
2. Use of Uncertainty Factors. In screening ecological risk assessments, it is often
standard practice to adopt uncertainty factors to derive benchmarks intended to
represent chronic exposures. We will use these factors to ensure that critical
receptors are not eliminated from the Phase I screening.
Selection of Appropriate Ecotoxicological Studies—Community Inference. The
community-based screening factors generally reflect direct exposures to a contaminated medium,
which, in the Phase I screening analysis, is represented by actual impoundment concentrations in
water and sludge. As shown in Table 2-6, risks to five groups will be estimated based on
endpoints relevant to sustainability of community structure and function: soil fauna, terrestrial
plants, aquatic biota, algae and aquatic plants, and benthos. It should be noted that the screening
factors for communities generally are not based on community-level studies in the sense that
they do not reflect endpoints relevant to community dynamics (e.g., predator-prey interactions).
Rather, they are based on the theory that protection of 95 percent of the species in the community
will provide a sufficient level of protection for the community (see, for example, Stephan et al.,
1985, for additional detail). As with the wildlife populations, ecotoxicological data on individual
species will be used to infer risks to the community.
Appropriate ecotoxicological studies to derive screening factors for these receptor groups
are available in a number of compendia and, as a result, it is often not necessary to conduct
primary literature reviews to identify suitable studies. These compendia frequently present
threshold concentrations for effects that may be used directly as screening factors with little or no
modification. Table 2-8 presents the primary data sources that will be used to support the
derivation of screening factors for the community receptors. The selection process for screening
factors among different sources and the screening factor calculations are discussed in the
following section.
Calculation of Ecological Screening Factors—Receptor Populations. The calculation
of ecological screening factors for receptor populations is based on the implicit assumption that
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Section 2.0
Phase I Screening Assessment
Table 2-8. Examples of Primary Data Sources for Derivation of Screening Factors
for Community Receptors
Source
Contents
l'liint C (immunity
Efroymson, R.A., M.E. Will, G.W. Suter II, and A.C. Wooten.
1997a. Toxicological Benchmarks for Screening Contaminants of
Potential Concern for Effects on Terrestrial Plants: 1997 Revision.
This document provides effects data for terrestrial plants exposed in
soil and solution mediums. Approximately 45 constituents have
proposed soil criteria.
PHYTOTOX Database. Office of Research and Development.
Environmental Protection Agency.
This database contains over 49,000 toxicity tests on terrestrial plants
for more the 1,600 organic and inorganic chemicals and 900 species.
Freshwater ( (immunity / Algiic mid Aquutic l'lunts
AQUIRE (AOUatic toxicity Information REtrieval) Database. 1997.
Environmental Research Laboratory, Office of Research and
Development, U.S. EPA, Duluth, MN
This database contains over 145,000 toxicity tests for more than
5,900 organic and inorganic chemicals and 2,900 aquatic species.
U.S. EPA. Ambient Water Quality Criteria. U.S. EPA, Washington,
DC.
These chemical-specific documents provide the ecotoxicity data
and derivation methodologies used to develop the National Ambient
Water Quality Criteria (NAWQC).
U.S. EPA. 1995a. Great Lakes Water Quality initiative Criteria
Documents for the Protection of Aquatic Life in Ambient Water.
Office of Water. (U.S. EPA, 1996a Update)
For a limited number of constituents, the GLWQI has proposed
surface water criteria for aquatic biota using analogous methods as
implemented in the derivation of the NAWQC.
Suter II, G.W. and C. Tsao. 1996. Toxicological Benchmarks for
Screening Contaminants of Potential Concern for Effects on Aquatic
Biota: 1996 Revision
This compendia reference provides acute and chronic water quality
criteria for freshwater species including algae.
Soil ( (immunity
Efroymson, R.A., M.E. Will, and G.W. Suter II. 1997b.
Toxicological Benchmarks for Contaminants of Potential Concern
for Effects on Soil and Litter Invertebrates and Heterotrophic
Process: 1997 Revision. Oak Ridge National Laboratory.
This document provides effects data for soil biota (i.e., microbial
processes and earthworms). Approximately 35 constituents have
proposed soil criteria, and some field studies are included.
CCME (Canadian Council of Ministers of the Environment), 1997.
Recommended Canadian Soil Quality Guidelines.
The criteria developed by the CCME are concentrations above
which effects are likely to be observed.
Sediment ( (immunity
U.S. EPA. 1993. Technical Basis for Deriving Sediment Quality
Criteria for Nonionic Organic Contaminants for the Protection of
Benthic Organisms by Using Equilibrium Partitioning.
This document supplies toxicological criteria for nonionic
hydrophobic organic chemicals using FCVs (final chronic values)
and SCVs (secondary chronic values) developed for surface water
(Sediment Quality Criteria, SQC).
Long and Morgan. 1991. The Potential for Biological Effects of
Sediment-Sorbed Contaminants Tested in the National Status and
Trends Program. National Oceanic and Atmospheric
Administration (NOAA) Technical Memorandum. Update: (Long et
al., 1995)
Field measured sediment concentrations are correlated with impacts
to sediment biota in estuarine environments. Measures of
abundance, mortality, and species composition are the primary
toxicity endpoints.
Jones, D.S., G.W. Sutter III, and R.N. Hall. 1997. Toxicological
Benchmarks for Screening Contaminants of Potential Concern for
Effects on Sediment-Associated Biota: 1997 Revision. Oak Ridge
National Laboratory.
This document proposes sediment criteria for both organic and
inorganic constituents using both field and estimation
methodologies.
MacDonald, D.D. 1994. Approach to the Assessment of Sediment
Quality in Florida Coastal Waters. Florida Department of
Environmental Protection (FDEP), Tallahassee.
This approach applies statistical derivation methods to determine
sediment criteria using NOAA data. The resulting criteria are more
conservative than NOAA values.
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Section 2.0
Phase I Screening Assessment
each receptor species forages only within the contaminated area, regardless of the size of its
home range. For smaller animals, this assumption has little impact on the estimates of exposure.
However, for larger animals with more extensive foraging areas, this assumption may
overestimate exposure if the animal's foraging patterns tend to be evenly spread over the home
range. Thus, it is important to recognize both the explicit and implicit sources of protection in
this methodology.
For amphibian populations, a screening factor for water (SFwater) will be derived as the
geometric mean of acute studies meeting the data requirements discussed above (i.e., relevant
endpoint, acute exposure, high effect level). However, it is important to point out that this
screening factor should be construed as only "protective" of gross effects to amphibian
populations (e.g., lethality to 50 percent of the population). As a result, careful consideration
will be given in interpreting the results of the screening results during the risk characterization.
The remainder of this section outlines the basic technical approach used to convert avian or
mammalian benchmarks (in daily doses) to soil and water screening factors (in units of
concentration) that will be compared with surface impoundment sludge and water concentrations,
respectively.
Once the appropriate ecotoxicological study is identified for mammals and/or birds,1 the
screening factors will be calculated for each medium of interest using a four-step process:
1. Adjust study benchmark using uncertainty factor.
2. Scale benchmark from test species to receptor species.
3. Identify uptake/accumulation factors.
4. Calculate protective concentration (i.e., screening factor).
STEP 1: Adjust Study Benchmark Using Uncertainty Factor
For benchmarks that are based on acute studies, uncertainty factors may be used to
extrapolate from acute exposures to chronic exposures. Based on the review of a toxicity
database of over 4,000 records, we propose using the following:
Extrapolation Uncertainty Factor
Acute LD50 to chronic NOAEL 100
Acute LOAEL to chronic NOAEL 50
These uncertainty factors are consistent with DTSC (1996) guidance and an independent review
of toxicity data by other authors (Calabrese and Baldwin, 1993; Sample et al., 1996). To convert
the acute benchmark to a chronic benchmark, Equation 2-1 will be used:
Chronic Benchmarkstudy Species = Acute Benchmarkstudy Species / Uncertainty Factor (2-1)
The intent of this conversion is to provide a benchmark for the study species that represents a no
observed adverse effects level. Consequently, we refer to this value as the NOAELss to indicate
the effects level and the fact that it applies to the study species.
1 Reptiles are not discussed in this section because of the data deficiencies for this receptor group.
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Phase I Screening Assessment
STEP 2: Scale Benchmark from Study Species to Receptor Species
The benchmark chosen to represent the mammalian or avian taxa will be extrapolated
from the study species to the receptor species (NOAELRS) within the same taxa using a cross-
species scaling equation (Sample et al., 1996). For population-inference benchmarks for
mammals, the extrapolation is performed using Equation 2-2.
NOAELm = NOAELss
( bwss \ 1/4
bwRS-
(2-2)
where NOAELss is the NOAEL for the study species, bwRS is the body weight of the receptor
species, and bwss is the body weight of the study species. This is the default methodology EPA
proposed for carcinogenicity assessments and reportable quantity documents for adjusting animal
data to an equivalent human dose.
For avian species, new research suggests that the cross-species scaling equation used for
mammals is not appropriate (Mineau et al., 1996). Mineau et al. (1996) used a database that
characterized acute toxicity of pesticides to avian receptors of various body weights. The results
of the regression analysis revealed that applying mammalian scaling equations may not predict
sufficiently protective doses for avian species. Mineau et al. (1996) suggested that a scaling
factor of 1 provides a better dose estimate for birds, as shown in Equation 2-3. This
recommendation will be adopted for avian receptors in this assessment.
( bwvv\ i
NOAELw = NOAELss ¦ —- (2-3)
V bwj
STEP 3: Identify Uptake/Accumulation Factors
As suggested in Figure 2-18, movement of contaminants through the food web is an
important exposure vector for mammals and birds. Consequently, estimates of chemical
accumulation in the tissues of plants and prey items are required. For receptors likely to rely on
aquatic systems for food (e.g., kingfisher), bioaccumulation factors and/or bioconcentration
factors are required for aquatic biota such as fish, benthos, and aquatic plants. These data may be
identified in the open literature or they may be estimated for organic constituents using
regression equations such as that shown in Equation 2-4 (Lyman et al., 1990):
log BCF = 0.76 [log („K)\ - 0.23
(2-4)
where BCF is the estimated bioconcentration factor for fish and the Kow is the constituent-
specific octanol-water partition coefficient.
For receptors found primarily in terrestrial systems, bioconcentration factors are required
for terrestrial plants, soil invertebrates (e.g., earthworms), and vertebrates that report the
relationship between tissue concentrations and soil concentrations. As with aquatic
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Section 2.0
Phase I Screening Assessment
accumulation factors, these data may be identified in the literature or estimated using recently
developed methods for earthworms and small mammals (Sample et al., 1998a, 1998b).
In short, these values are typically identified in the open literature and EPA references or
calculated based on the relationship between log Kow and accumulation in lipid tissue. The
primary source of data is the methodology developed for HWIR and described more fully in
Section 3 of this technical plan. To ensure that the Phase I ecological screening assessment is
protective, a default value of 1 will be assigned to each uptake/accumulation factor that cannot be
derived through estimation methods or identified in the literature.
STEP 4: Calculate Protective Concentration for Receptor
Based on the NOAELRS, the screening factor for a receptor that relies on aquatic biota as
the primary food source will be calculated as a function of the receptor's body weight, the
receptor's ingestion rate for food and water, and the bioaccumulation potential of the constituent.
As shown in Equation 2-5:
SF„
NOAELpg x bw
(/, S BAF x F x AB) + (I , )
v jooa L-i j j j/ \ water'
(2-5)
where
bw
Afood
BAF: =
AB:
body weight (kg)
total daily intake of aquatic biota (kg WW/d)
total daily soil intake (kg/d)
bioaccumulation factor for food item j (L/kg WW))
fraction of diet consisting of food item j (unitless)
absorption of chemical in the gut from food item j (assumed = 1).
Equation 2-5 can also be used to derive an "impoundment use only" screening factor for sites that
do not have any fishable waterbodies identified in the survey data. For these cases, only Iwater
would be included in the denominator to reflect use of the impoundment as a drinking water
source.
For terrestrial systems, Equation 2-6 is simply modified to account for soil or sludge
intake:
where
SF
soil/sludge
NOAEL^ x bw
bcf¦
. x F x AB)
j j jJ
+
^soil/ sludge)
(2-6)
bw
lood
BCF
ABj
I,
soil/sludge
body weight (kg)
total daily food intake of terrestrial biota (kg/d)
bioconcentration factor for food item j (assumed unitless)
fraction of diet consisting of food item j (unitless)
absorption of chemical in the gut from food item j (assumed = 1)
total daily soil intake (kg/d).
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Phase I Screening Assessment
Information sources to develop the input values for body weight (bw), ingestion rates (Ixx), and
dietary fractions (Fj) will generally be taken from the extensive HWIR databases. The HWIR
databases were developed using EPA's Wildlife Exposure Factors Handbook (U.S. EPA, 1993c)
and augmented by substantial literature review and synthesis of a variety of information sources.
However, it may be necessary to expand the data collection efforts beyond the current HWIR
universe of constituents; in particular, the identification of suitable ecotoxicological studies to
support the development of receptor species benchmarks (NOAELRS) will require significant
effort. In addition to the review of primary literature, Table 2-9 presents some examples of key
data sources that may be used to identify suitable ecotoxicological studies.
Calculation of Ecological Screening Factors—Receptor Communities. The
calculation of ecological screening factors for receptor communities relies heavily on existing
data sources, many of which have produced peer-reviewed concentrations for soils and surface
water presumed to be protective of ecological receptors. Example include
# Aquatic Biota: U.S. EPA's National Ambient Water Quality Criteria
# Sediment-Associated Biota: National Oceanic and Atmospheric
Administration's (NOAA) Effects Range-Low (ER-Ls)
# Soil Invertebrates: Dutch National Institute of Public Health and Environmental
Protection's (RIVM) Ecotoxicological Intervention Values (EIVs)
To the extent possible, we will rely on existing data sources as well as the ecotoxicity databases
developed under HWIR and other studies conducted by EPA. For constituents lacking readily
available sources, we will use the following approach to calculate ecological screening factors.
Aquatic community—For aquatic biota in freshwater systems, the final chronic value
(FCV) developed for the National Ambient Water Quality Criteria (AWQC) will be chosen as
the screening factor. If an AWQC is not available, the continuous chronic criterion (CCC)
developed for the Great Lakes Water Quality Initiative (GLWQI) will be used as the screening
factor (U.S. EPA, 1995a, 1996a). If neither of these criteria are available, we will calculate a
secondary chronic value (SCV) using the Tier II methods developed through the Great Lakes
Initiative (Stephan et al., 1985; Suter and Tsao, 1996; RTI, 1995a, 1995b).
The SCV is calculated using methods analogous to those applied in calculating the FCV.
However, the Tier II methods: (1) require chronic data on only one of the eight family
requirements, (2) use a secondary acute value (SAV) in place of the FAV, and (3) are derived
based on a statistical analysis of AWQC data conducted by Host et al. (1991). Host et al. (1991)
developed adjustment factors (AFs) depending on the number of taxonomic families that are
represented in the database. The Tier II methodology was designed to generate SCVs that are
below FCVs (for a complete data set) with a 95 percent confidence limit.
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Section 2.0
Phase I Screening Assessment
Table 2-9. Selected Sources of Toxicity Data
Databases
# Hazardous Substances Data Bank (HSDB). National Library of Medicine, National Toxicology
Information Program. Bethesda, MD.
# PHYTOTOX. Chemical Information System (CIS) Database.
# Registry of Toxic Effects of Chemical Substances (RTECS). National Institute for Occupational
Safety and Health (NIOSH), Washington, D.C.
Compilations
# Agency for Toxic Substances and Disease Registry (ATSDR). 1997. Toxicological Profiles. On
CD-ROM. CRC press. U.S. Public Health Service. Atlanta, GA.
# Devillers, J. and J.M. Exbrayat. 1992. Ecotoxicity of Chemicals to Amphibians. Grodon and
Breach Science Publishers. Philadelphia, PA.
# Eisler, R. 1985-1993. Hazards to fish, wildlife, and invertebrates: A synoptic review. U.S. Fish
Wildlife Service Biological Reports
# Hudson, R.H., R.K. Tucker, and M.A. Haegele. 1984. Handbook of toxicity ofpesticides to
wildlife. U.S. Fish and Wildlife Serv. Resour. Publ. 153. 90 pp.
# Sample, B.E., D.M. Opresko, and G.W. Suter II. 1996. Toxicological benchmarks for wildlife:
1996 Revision. Prepared for the U.S. Department of Energy.
Algae and aquatic plants—For algae and aquatic plants, toxicological data are available
in the open literature and in data compilations such as the Toxicological Benchmarks for
Screening Potential Contaminants of Concern for Effects on Aquatic Biota: 1996 Revision (Suter
and Tsao, 1996). Studies on freshwater vascular plants are seldom available; however, toxicity
data are available from standard algal tests. In order of preference, the screening factors for algae
and aquatic plants will be based on either (1) a lowest observed effects concentration (LOEC) for
vascular aquatic plants or (2) an effective concentration (ECVV.) for a species of freshwater algae,
generally a species of green algae.
Benthic community—Two methods will be applied to develop screening factors for the
sediment community. The first and preferred method will use measured sediment concentrations
that resulted in de minimis effects to the composition and abundance of the sediment community.
The second derivation method uses the equilibrium partitioning relationship between sediments
and surface waters to predict a protective concentration for the benthic community using the
chronic FCV. A brief discussion of each method is provided below.
# Screening Factors from Measured Data: The premier sources of measured
sediment toxicity data are the National Oceanic and Atmospheric Administration
(NOAA) and the Florida Department of Environmental Protection (FDEP). These
data are used by NOAA to estimate the 10th percentile effects concentration
effects range-low (ER-L) and a median effects concentration effects range-median
(ER-M) for adverse effects in the sediment community. The FDEP sediment
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Phase I Screening Assessment
criteria are developed from the ER-L and ER-M values to approximate a threshold
effects level (TEL) (estimated from ER-L data). The TELs are preferable to the
ER-L primarily because they have been shown to be analogous to TELs observed
in freshwater organisms (Smith et al., 1996).
# Predicted Sediment CSCLs. If neither a TEL nor an ER-L is available for
nonionic, organic constituents, the screening factor will be calculated using the
sediment quality criteria (SQC) method (U.S. EPA, 1993b). This method assumes
equilibrium-partitioning between the sediment and water column is a function of
the organic carbon fraction (foc) in sediment and the organic carbon partition
coefficient of the constituent. The screening factor is calculated as shown in
Equation 2-7, assuming that the foc is equivalent to 1 percent total organic carbon
(Jones et al., 1997).
Terrestrial plant community—For the terrestrial plant community, screening factors for
soil will generally be derived according to the methodology presented in the Toxicological
Benchmarks for Screening Contaminants of Potential Concern for Effects on Terrestrial Plants:
1997Revision (Efroymson et al., 1997a). The authors derive ecologically relevant benchmarks
by rank ordering the phytotoxicity data according to the lowest observed effects concentrations
(LOECs). We are proposing to adopt that same approach and select screening factors for
constituents with 10 or fewer values at the lowest LOEC. For constituents with more than 10
LOEC values, the 10th percentile LOEC will be selected. Because the toxicity endpoints reflect
endpoints such as plant growth and yield reduction, the screening factors are presumed to be
relevant to sustaining "healthy" plant communities.
Soil community—For the soil community, screening factors will be calculated using the
methodology developed under HWIR. In brief, the screening factors for soil fauna are estimated
to protect species found in a typical soil community, including earthworms, insects, and other
soil fauna. Eight taxa of soil fauna are represented to reflect the key structural (e.g., trophic
elements) and functional (e.g., decomposers) components of the soil community. The
methodology presumes that protecting 95 percent of the soil species will ensure long-term
sustainability of a functioning soil community. The toxicity data on soil fauna will be gleaned
from several major compendia and supplemented with additional studies identified in the open
literature. The mathematical construct shown in Equation 2-8 was developed by Dutch scientists
(i.e., the RIVM methodology) and will be used to calculate screening factors at a 50th percentile
level of confidence (Sloof, 1992). For the screening factors for soil biota (SFsoil50/o), the 50th
percentile level of confidence will be selected because the 95th percentile has been shown to be
overly conservative (e.g., well below background levels).
SF.
sediment
f x K x FCV
J oc oc
(2-7)
SF 0/ = \x - k, s 1
soil5% L m I mA
(2-8)
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Section 2.0
Phase I Screening Assessment
where
xm = sample mean of the log LOEC data
k, = extrapolation constant for calculating the one-sided leftmost confidence limit
sm = sample standard deviation of the log LOEC data.
When data are insufficient to calculate screening factors using this methodology, two other
sources of screening factors will be used. First, the ecotoxicological data presented on indicator
species such as earthworms will be used to select a protective soil concentration (Efroymson et
al., 1997b). Second, the criteria developed by the Canadian Council of Ministers of the
Environment (CCME, 1997) for the protection of soil organisms will be adopted as screening
factors.
2.3.1.4 Procedure for Phase I Ecological Risk Screening. In most respects, the
ecological risk screening procedure mirrors the framework presented in Figure 2-9 for the risks
from noncancer constituents to human health. Therefore, this discussion is intentionally brief to
avoid duplicating the technical plan described in Section 2.2. The salient features of the Phase I
ecological risk screening are summarized below.
Select Appropriate Screening Factors. The underlying strategy for the Phase I
assessment is to identify screening factors that are appropriate for a given facility. Screening
factors for terrestrial receptors (e.g., plants, raccoons) will be used routinely at each site since
these "common" receptors were selected to be broadly applicable across the contiguous United
States. However, surface impoundments are not intended to support aquatic plants, aquatic
invertebrates, fish, or sediment-associated biota; therefore, aquatic and sediment-associated biota
will be assessed only if a potentially affected waterbody is identified near the surface
impoundment. Although not intended to support amphibians, birds, and mammals, surface
impoundments are likely to be attractive to these receptors (especially if impoundments support
vegetation); therefore amphibians, birds, and mammals will be assessed for all surface
impoundments. Consequently, not all screening factors will be applied to each facility.
Select Appropriate Surface Impoundment Concentrations. Whenever possible,
reported mean concentrations for impoundment water and sludge will be used in the Phase I
ecological screening assessment. If impoundment water concentrations are not reported,
available data will be used in the same order of preference as shown in Figure 2-6 to estimate
impoundment water concentrations.
If sludge concentrations are not reported for the surface impoundment, available data will
be used in the following order of preference as shown in Figure 2-8 to estimate impoundment
sludge concentrations.
Calculate Risks. To evaluate the receptor risks (defined as the ratio between the
impoundment concentration and the screening factor, or hazard quotient) from exposure to a
chemical constituent at a particular surface impoundment, Equation 2-9 will be used:
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Section 2.0
Phase I Screening Assessment
HO' , = CmP-" or C,mp"" or „.9)
^ constituent C7-' np C7-' ^ '
Ui' , Ui' 7 j 1JJ • 7
water sludge soil
where Cimp water, and Cimp sludge, and Cimp S0il(sludge) are the impoundment water concentration and
the impoundment sludge concentration, respectively; and SFwater, SFsludge, and SFsoil are the
ecological screening factors applicable to that site. The is the risk to receptor i
associated with that impoundment and facility. The HQ values for each receptor i may be
summed across the entire facility in generating facility risks because (1) the screening factors for
each receptor are based on the same study data (and endpoints) and (2) receptors may be exposed
through both terrestrial and aquatic systems.
2.3.1.5 Results of Phase I Risk Screening/Prioritization. Risk estimates generated by
the Phase I ecological screening assessment must be suitable to characterize, screen, and
prioritize constituents, surface impoundments, and facilities by the following categories of
interest (see also Section 2.4, Phase IC Initial Prioritization):
Facility
# Regulatory status
# Industry type
Surface Impoundment
# Treatment type
# Functional class
Constituent
# Constituent type
The facility risk is defined as the maximum surface impoundment risk ro receptor i for a
particular facility. Facility risk estimates are used to develop industry type and regulatory type
risk distributions.
The surface impoundment risk is defined as the cumulative risk to receptor i from
exposure to all constituents at a particular surface impoundment. Surface impoundment risk
estimates will be used to develop treatment type- and functional class type-specific distributions.
For the Phase I ecological screening assessment, the constituent risk is defined as risk to
the most sensitive receptor across all impoundments at a facility. Constituent risk estimates will
be used to develop constituent-specific risk distributions.
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Section 2.0
Phase I Screening Assessment
Construct Risk Distributions. Separate risk distributions will be constructed from risk
estimates to evaluate categories of interest. Proposed Phase I risk distributions will consist of the
following five risk intervals (risk bin):
# <0.1
# >0.1 and < 1
# >1 and <10
# > 10 and < 100
# >100.
A unitary value (1), representing the constituent, surface impoundment, or facility, will be
added to the appropriate risk bin. Since sample facilities represent a number of facilities
nationwide, unitary values may be weighted by the facility sample weight before being added to
the bin. The Agency may modify risk intervals to provide a more suitable distribution of risks to
evaluate categories of interest.
The facility- and surface impoundment-related risk distributions will be constructed from
risk estimates for all receptors considered at a particular surface impoundment or facility. These
risk distributions will be used to screen facilities, surface impoundments, and constituents. Risk
distributions constructed from maximum risk estimates (i.e., risk estimate for the most sensitive
receptor) will be compared to risk distributions for all receptors to determine if the number of
receptors affects the facility- and impoundment-level risk distributions. In addition, risk
distributions for each trophic level will be developed to evaluate potential impacts on food webs.
These risk distributions for receptor groups and trophic levels will provide useful metrics for the
Phase I risk characterization.
Establish Risk Criteria. A risk criterion of 1 is proposed to screen ecological risk
estimates. Risk estimates less than 1 (e.g., HQ1 < 1) indicate a negligible potential for adverse
ecological impacts. Alternatively, risk estimates of 1 or greater indicate a potential for adverse
ecological effects. Surface impoundments and facilities with risk estimates of one or greater
may be assigned for further evaluation in Phase II, depending on the results of the Phase I human
health screening.
Conduct Risk Screening. The ecological risk screening process is outlined in the
decision tree shown in Figure 2-19. As expected, the decision tree is very similar to the health
risk decision tree illustrated in Figure 2-9. However, there are three distinct differences in the
ecological risk screening procedure. First, the decision tree does not include a margin of
protection (MP) for ecological receptors. Whereas, the human health risk screening is
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Section 2.0
Phase I Screening Assessment
2-58
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Section 2.0
Phase I Screening Assessment
intended to protect individuals, the ecological risk screening is intended to protect species
populations and communities from adverse effects. Although the Agency considered adding an
MP to the risk screening for sites co-located with sensitive habitats (e.g., wildlife refuges), these
"special cases" will be considered prior to the conduct of the ecological risk screen, as shown in
Figure 2-20. The Agency may consider the use of an MP for ecological risk in special cases in
which facilities identify threatened and endangered species located within a 2 km radius of the
impoundments. Second, the ecological risk screening does not include cancer effects; only the
endpoints described under Section 2.3.1.3 on the development of screening factors will be
considered. Third, the figure includes an additional decision point that pertains to the receptor
group for which risk is indicated. The results of the surface impoundment pilot study suggested
that, for each facility, at least one constituent will fail the ecological risk screening for the
terrestrial plant receptor group. Because impoundment sludge/soils are not intended to support
terrestrial habitats, and because the screening factors for terrestrial plants are based on a data set
that does not reflect adaptation by plant communities, EPA determined that a simple exceedance
of the plant screening factor does not provide an adequate basis to determine the potential for
adverse ecological effects. Thus, constituents, surface impoundments, or facilities will only
proceed to the Phase IC analysis if: (1) the hazard quotient for plants exceeds 10 (indicating a
greater potential for adverse effects than a simple exceedance) and (2) the hazard quotient for at
least one other receptor group (e.g., amphibians, birds, or mammals) exceeds the risk criterion
of 1.
Yes
No
Perform Risk Screen
(Figure 2-19)
Yes
Go to Phase IC
Perform Risk Screen
(Figure 2-19)
Figure 2-20. Decision diagram for evaluating special cases.
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Section 2.0
Phase I Screening Assessment
The risk characterization for ecological receptors will be used to determine the priority of
facilities, impoundments, and constituents within the context of the human health risk screening
results (see discussion on Phase IC). That is, the results of the Phase I ecological risk screening
will be used in support of the prioritization, not as an independent screen to identify facilities,
impoundments, and constituents for Phase II. The ecological risk characterization will:
# Characterize facility-level risks to address cumulative effects of multiple surface
impoundments and constituents
# Characterize impoundment-level risks to address the cumulative effects of
multiple constituents at a single surface impoundment.
# Characterize constituent-level risks to address the effects of a single constituent.
The risk estimates (i.e., hazard quotients) will be the primary tool used to prioritize
constituents, surface impoundments, and facilities according to the potential for adverse
ecological effects. The output from the risk screening will be presented in cumulative frequency
histograms similar to those shown in Figure 2-21 to provide EPA with several descriptions of
ecological risk that are relevant to ecological receptors as well as the impoundments and
facilities. In addition, the presence of protected or potentially sensitive habitats will be
considered when prioritizing constituents, surface impoundments, and facilities for further
evaluation in Phase IC. These "special cases" will include managed areas and permanently
flooded wetlands as designated by the National Wildlife Inventory (NWI). As shown in the
decision tree in Figure 2-20, the presence of either managed areas or designated wetlands will be
sufficient to assign a high priority risk score to the facility and impoundments found at that site.
Notice that the figure allows for the performance of a risk screen for high priority sites as well as
those sites that are not co-located with managed areas or designated wetlands. This will support
development of risk distributions for this subset of sites and provide a more complete risk
distribution for the national data set. A brief description of the criteria for managed areas and
designated wetlands is included below.
Managed Areas. Managed areas are specifically protected by law to ensure that plants
and wildlife are preserved. Thus, managed areas may need to be evaluated in further detail than
that provided in the Phase I ecological screening assessment. A high priority score will be
assigned to those facilities and impoundments that are within 2 kilometer of the following areas,
irrespective of the risk estimates:
# National or state parks
# National or state wildlife reserves
# Critical Habitats (designated by the U.S. Fish and Wildlife Service)
# Wild and Scenic Rivers (designated by the U.S. Department of the Interior)
GIS coverages from the Managed Areas Database provide the data needed to quickly
identify surface impoundments within 2 kilometers of a managed area. A distance of 2 kilometer
is proposed because, even for a screening level assessment, the presumption of wildlife use and
exposure to a surface impoundment becomes increasingly tenuous as the distance from the
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Section 2.0
Phase I Screening Assessment
Example Facility Risk Distribution
•-B
Risk Criterion
<0.1
0.1 to <1.0 1.0 to <10
Risk
10 to <100
> 100
Example Impoundment Risk Distribution
£
¦d
I
Risk Criterion
<0.1
0.1 to <1.0
1.0 to < 10
Risk
10 to < 100
> 100
¦ Plants
~ Mammals
~ Soil Biota
Figure 2-21. Example Phase I ecological screening assessment output.
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Section 2.0
Phase I Screening Assessment
impoundment increases. In short, it is not possible to determine whether foraging patterns of
receptors living in managed areas might include the impoundments at a given facility.
Designated Wetlands. If surface impoundments are located within 1 kilometer of
wetlands designated as permanently flooded by the NWI then these surface impoundments and
facilities will receive a high priority score for Phase IC. The NWI GIS coverages will provide
the data needed to quickly identify surface impoundments within 1 kilometer of a designated
wetland. A distance of 1 kilometer is proposed because wetlands near the impoundment are
more likely to be influenced through surface water recharge or drainage systems and, as a result,
they may be significantly affected by the impoundment.
2.4 Phase IC Initial Prioritization
The Phase IA and IB human health and Phase I ecological risk screening will identify the
constituents, impoundments, and facilities that should proceed to Phase II. The screening
process, as described in the Phase IA and IB human health and Phase I ecological risk screening
(Sections 2.2 and 2.3), defines constituents, impoundments, and facilities that will be considered
for proceeding to Phase II analysis as those that have risks greater than the risk decision criteria
(risk > 10"5 and HI > 1). Phase IC provides a method of analyzing the Phase I risk distributions
for this subset of constituents, impoundments, and facilities to aid in defining the scope of Phase
II given the limited resources of the SI Study. A prioritization scheme is proposed because of
concern that the number of constituents, units, and facilities that could move into Phase II will be
large (e.g., greater than 25 percent of the study sample) and may exceed the resources allocated
for Phase II. If the number of constituents, units, and facilities that could move to Phase II is
small and the resulting Phase II effort is within its allocated resources, the prioritization scheme
will not be necessary.
2.4.1 Design Goals and Overview
The goal of Phase IC is to prioritize the Phase II analysis of these constituents,
impoundments, and facilities. The Phase IC prioritization scheme is based on the risk
distributions generated by the Phase I screening analyses. The prioritization scheme consists of
five key features:
# Scoring system for facility risks and constituent risks
# Options for combining facility and constituent scores depending on resource
limitations
# Separate scores for human health and ecological risks
# Ranking system combining human health and ecological scores
# Prioritization based on a qualitative review of risk distributions for the categories
that are of concern to the SI Study (e.g., industry type, treatment type).
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Section 2.0
Phase I Screening Assessment
Priority will be given to those facilities and constituents with the highest screening risks.
Constituents that were not screened in Phase IA and IB because they were considered special
cases (e.g., constituents that bioaccumulate) are also given highest priority in the Phase II
prioritization scheme.
2.4.2 Approach
The Phase IC prioritization scheme consists of four steps:
# Score facility risks and constituent risks
# Rank facility and constituent scores
# Rank combined human health and ecological scores
# Prioritize based on a qualitative review of risk distributions for the categories that
are of concern to the SI Study (e.g., industry type, treatment type).
2.4.2.1 Facility and Constituent Risks Scoring System. The Phase IC prioritization
system is based on the Phase IA and IB risk distribution by facility and constituent. The facility
risk is the cumulative risk from all constituents and impoundments. For human health, a separate
facility risk is provided for both carcinogenic and noncarcinogenic risks for the individual
resident. For ecological risk, a single facility risk based on the maximally impacted receptor type
is provided. The constituent risk for a facility is the cumulative risk for all impoundments at a
facility. For human health, the cumulative risk is a summation of carcinogenic or
noncarcinogenic risks by toxic endpoint for the individual resident. For ecological risk, the
cumulative risk is the maximum risk from any impoundment for the maximally impacted
receptor type. Facility and constituent risk are defined in Sections 2.2 and 2.3.
The risk distributions are risk histograms. For Phase II, only risk bins above the risk
decision criteria are of concern. The risk decision criteria are 10"5 for excess individual human
cancer risk and 1 for human noncancer risk and ecological risk. Each risk bin in the risk
histogram that exceeds the risk decision criteria will be given an integer score. A score of 1 is
given to the largest numeric risk bin and so forth. The scoring system for both facility risks and
constituent risks is shown in Table 2-11.
For example, if a constituent for a facility has a human noncancer hazard index in the
>100 risk bin, then it has a score of 1. If the constituent has a hazard index in the >10 and <100
risk bin, then it has a score of 2. Finally, if the constituent has a hazard index in the > 1 and <10
risk bin, then it has a score of 3.
If the constituent is considered a human health special case (i.e., a constituent that
bioaccumulates), then it receives a score of 1. If the constituent does not receive an ecological
score because the risk screening can not be performed due to a lack of suitable toxicity data, then
the ecological score is 3.
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Section 2.0
Phase I Screening Assessment
Table 2-11. Phase IC Prioritization Scoring System
Score
IIiiin;iii Health Individual
Kxccss Cancer Kisk ISin
Human Health Noncancer
Hazard Index
Ideological
Hazard Index
1
> 10"4
> 100
> 100
2
> 10"5 and < 10"4
> 10 and < 100
> 10 and < 100
3
> 10"6 and < 10"5
> 1 and < 10
>1 and <10; NA
NA = Not available.
Human health and ecological risk screening will be scored separately. For human health
risk screening, the maximum of the cancer and noncancer risks provides the constituent's score.
For ecological risk, the maximum of all receptor types provides the constituent's score.
2.4.2.2 Ranking System for Facility and Constituent Scores. There are currently two
options under consideration to rank facilities and constituents:
# Option 1: The facility scoring alone will provide the ranking system. The
constituent score is not used. This option automatically addresses the issue of
cumulative effects of multiple chemicals and multiple impoundments. This
approach also recognizes that the resource efforts to set up the modeling
evaluation for a facility will be the most resource intensive. Therefore, including
all constituents (of all three ranks) will add minimal level of effort to the
modeling setup effort.
# Option 2: The facility and constituent scores will be combined to provide a
single rank. In this option, the constituents that score the highest are given highest
priority (see Table 2-12). This option recognizes that some constituents (and
impoundments) may constitute more of the facility risk than others. Therefore,
the constituent score will be used to prioritize the rank.
2.4.2.3 Ranking System Combining Human Health and Ecological Scores. The
Phase 1C ranking will combine the human health and ecological risk scores. The ranking of the
nine possible combinations is shown in Table 2-13. This ranking process assumes that the
human health score will be the primary ranking factor and the ecological score will be the
secondary ranking factor. The ranking system is used only to establish order and priority to our
Phase II modeling, not to characterize risks. We expect that high potential risks to ecological
receptors will also be captured under this approach; multimedia modeling includes ecological
receptors; and prioritization based upon a qualitative review (i.e., special cases) will address high
ecological risk scenarios. If resources are limited, then the number of ranks assessed in Phase II
could be reduced (e.g., evaluate only Ranks 1 through 5).
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Section 2.0
Phase I Screening Assessment
Table 2-12. Option 2 Ranking System
Uank
l-'acilitv Score
Constituent Score
la
1
1
lb
2
1
lc
3
1
2a
1
2
2b
2
2
2c
3
2
3a
1
3
3b
2
3
3c
3
3
Table 2-13. Phase IC Ranking System
Uank
Human Health Score
Ideological Score
1
1
1
2
2
3
1
3
4
2
1
5
2
2
6
2
3
7
3
1
8
3
2
9
3
3
2.4.2.4 Prioritization Based on Qualitative Review of Risk Distributions for SI
Study Categories. Consideration will also be given to a qualitative review of the Phase IA and
IB risk screening distributions for the SI Study categories. The risk distributions may provide
interesting conclusions on which EPA may want to focus or reduce attention.
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Section 2.0
Phase I Screening Assessment
# Example 1: If 95 percent of the risk distribution for a certain industry or treatment
type is below the risk decision criteria, then all facilities in the industry or
treatment type could be reprioritized with the lowest prioritization or the
industry or treatment type could be omitted entirely from Phase II. If the same
is true for any constituent's risk distribution, then the constituent could be
demoted in rank or omitted from Phase II.
# Example 2: If comparison of industry-type risk distributions indicates that a
particular industry type (e.g., chemicals and allied products) has a risk distribution
that is skewed toward high risks, then facilities that are in that industry type will
be given highest priority.
2.4.3 Risk Characterization Outputs
The outcome of Phase IC will be the identification of a subset of surface impoundments,
constituents, pathways, and facilities that will be given high priority for further risk
characterization during Phase II. The surface impoundments will be profiled according to
significant patterns (if any), such as industry type, unit characteristic, and constituent type, using
the risk distributions developed during Phase I.
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Section 3.0
Phase II Risk Assessment
3.0 Phase II Risk Assessment
Phase I of the surface impoundment risk assessment will likely identify a number of units
and constituents that need further risk analysis. EPA has designed Phase II to provide that
analysis by characterizing risk for constituents, units, and facilities of concern from Phase 1 using
state-of-the-science risk assessment models. Phase II will expand the Phase I risk profile of the SI
universe based on a true multimedia and multiple exposure pathway model and will take full
advantage of site-based data available from the SI Survey results. These data will be
supplemented, as necessary and practicable, by site-based, regional, and national data collected
using methodologies developed for the Hazardous Waste Identification Rule.
Because of run-time constraints, EPA is considering developing central tendency and
high-end risk results during Phase II, with a Monte Carlo analysis conducted as a possible third
phase. Thus, EPA still considers Phase II to be a screening analysis. However, the risk estimates
generated during Phase II will provide a comprehensive national profile of potential risks posed
by the universe of surface impoundments for several reasons:
# It is based on a sample of facilities selected to statistically represent the SI
universe.
# It covers the significant exposure pathways and receptors likely to be present at an
SI site.
# The potential risks modeled for the major pathways at each site will be based, to
the extent possible, on real concentration and exposure data reported by facilities
and regions in the survey.
# Because the high-end and central tendency scenarios will be based on real
receptors, they will provide a realistic span of potential risks.
The risk profile generated during Phase II will also serve to identify a smaller subset of units and
constituents to be characterized, if necessary, using a full Monte Carlo analysis in future analysis.
To develop Phase II risk estimates, EPA has selected the state-of-the-art, multimedia,
multiple exposure pathway, multiple receptor risk assessment (3MRA) model it developed for
HWIR. This section of the technical plan
# Provides an overview of the Phase II technical framework that summarizes how
the risks will be modeled and how the 3MRA modeling system will be
implemented (Section 3.1)
3-1
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Section 3.0
Phase II Risk Assessment
# Describes the conceptual model and technical approach for the risk assessment,
including sources, exposure pathways, receptors, spatial and temporal scale, and
risk benchmarks and metrics (Section 3.2)
# Reviews the modeling system proposed to be used for the Phase II assessment,
including summaries of the functionality, inputs, and outputs for each 3MRA
component module (Section 3.3)
# Describes modifications to the HWIR 3MRA modeling system and data collection
methodologies that may be necessary for SI Study application, including data
collection requirements (Section 3.4).
Phase II is based heavily on the HWIR 3MRA model and data collection methodologies; as a
result, this writeup depends upon the HWIR 3MRA background documents that are cited
throughout. To fully understand the 3MRA model and its many components, the reader is
encouraged to review these documents, which may be found at: http://www.epa. gov/
epaoswer/hazwaste/id/hwirwste/ri sk. htm.
3.1 Overview
Phase II of the Surface Impoundment Study risk assessment will include a multimedia,
multiple exposure pathway, multiple receptor risk assessment of the management of industrial
wastes in surface impoundments. The simulation model EPA selected for this phase is the
3MRA model, which was developed by EPA to support the proposed Hazardous Waste
Identification Rule. The 3MRA model provides the core functionality required for the Surface
Impoundment Study, including:
# Spatial scale - 2-km radius or less
# Temporal scale - future risk, extended as needed to capture peak exposure
concentrations in media of concern; hourly, daily, monthly, or annual time steps
used in specific models as appropriate for source type or exposure pathway
# Receptors - human and ecological
# Exposure pathways - direct and indirect, including air, surface water,
groundwater, soil, and both terrestrial and aquatic food chains
# Model components - designed to characterize risk by estimating contaminant
release, multimedia fate and transport, exposure, and dose response.
Figure 3-1 shows the Phase II analysis in relation to Phase I.
3-2
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Section 3.0
Phase II Risk Assessment
Figure 3-1. Overview of the SI study risk analysis.
3.1.1 Key Features
Key features of human health and ecological risk assessment approach for the SI study
include the following.
# Receptor types and exposure pathways are the same as for HWIR (Section 3.2.3).
# Models are the same as for HWIR, except that the SI source module will need
specific modifications in use and possibly function to address multiple
impoundment, postclosure, and possibly other scenarios (such as catastrophic
failures). In addition, the exit level processor (ELP) and 3MRA system will need
modification to compile and save risk results in a manner consistent with the
study goals (see Sections 3.4.1 and 3.4.2).
# Data collection will begin with the SI survey that will characterize the facilities,
units, waste streams, and constituents to be modeled, including receptor locations.
HWIR data and data collection methodologies will be applied as needed to
supplement these site-specific data with site-based, regional, and national data.
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Section 3.0
Phase II Risk Assessment
# Modifications to data collection methodologies will be necessary for human
receptor placement to update 1990 U.S. Census and land use data with SI Survey,
map, and aerial photo data available for each SI site. In addition, modification to
HWIR waterbody and watershed delineation methods is recommended to take
advantage of newly available hydrography and elevation data sets (see
Section 3.4.3).
# Risk metrics will be similar to HWIR (i.e., risk distributions based on risk
cumulative frequency distributions [cdfs]), modified as necessary to satisfy SI
Study objectives. Concern will be defined by excess cancer risk or HQ above
EPA-set action levels (see Sections 3.2.3 and 3.2.4).
# Phase II results will update Phase I risk bins for sites not screened out in Phase I.
This will require comparability between phases to be considered during design.
# Two alternative data collection approaches have been developed: representative
and site-specific:
The representative method uses a limited number of site layouts to
represent conditions at a larger number of sites.
The site-specific approach develops site layouts for all surface
impoundment sites of potential concern.
# Alternatives also exist in terms of necessary modifications to the 3MRA modules
and modeling system.
Table 3-1 summarizes options currently envisioned for the alternative approaches to Phase II.
The 3MRA will be used for both alternatives; differences in implementation for the second
alternative can include changes to the source models and system to better represent SI study
objectives and an increase in site-specific resolution for data collection efforts (i.e., all sites
represented).
The representative data collection alternative will be needed only if there are a large
number of sites to evaluate in Phase II and resources (time or budget) limit the number of site
layouts and model revisions that can be developed. Note that because the representative
scenarios will be carefully chosen to span the range of conditions reported under the SI Survey,
and because the modeling takes advantage of a powerful multimedia model, the results will be
more realistic than in Phase I. Still, if undecided between two possible representative scenarios
for a given site, EPA intends to choose the one with the more protective assumptions, i.e., the
less protective environmental settings.
3.1.2 Decision Methodology
Phase II will follow a similar decision process as Phase I, with facilities routed through
Phase II as follows:
3-4
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Section 3.0
Phase II Risk Assessment
Table 3-1. Alternative Stages for Phase II SI Risk Analysis
Acli\ it v
Representing e Allcrn:ili\e
Site-Specific Allcrniilne
Data
Use representative site layout data,
Collect data and define site layouts for all
Collection
population profiles for a subset of SI
SI sites
Study sites to reflect surface
impoundment settings
Process additional meteorological station
data as necessary to cover SI sites.
Use existing HWIR regional data for SI
Study sites
Use SI Survey data as available
Use SI Survey data as available and
practicable.
Model
Use 3MRA models "as is" except
(Optional) Revise SI model and 3MRA
Revisions
adjustments to exit level processor to
system to better fit SI Study goals and
fit SI Study goals/objectives.
objectives.
Model multiple units separately or as mega-
Model revisions could include
units.
# Model multiple Sis
# Model catastrophic failures
Model postclosure as land application unit
# Model postclosure in one run
separately for each site.
# Loop over multiple impoundments, multiple chemicals.
# Compare risk against screening criteria.
# Assign facilities, impoundments, and chemicals to risk bins for identifying and
prioritizing possible followup activities.
Figure 3-2 pictures the Phase II decision process.
3.1.3 Anticipated Outcome
After the completion of Phase II, the Agency will prepare a report by March 2001 that
characterizes the potential risks associated with the SI universe for 256 constituents. This report
will include
# Identification of any constituents, unit types, or facility categories for which
additional comprehensive analysis may be needed.
# A profile of risks for the SI universe by unit type, industry type, and constituent.
# A descriptive profile of the subset of the universe that is of negligible concern and
requires no further risk analysis.
3-5
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Section 3.0
Phase II Risk Assessment
* Direct discharger - decharacterized
Direct discharger - nondecharacterized
Indirect discharger - decharacterized
Indirect discharger - nondecharacterized
Zero discharger - decharacterized
Zero discharger - nondecharacterized
Figure 3-2. Phase II decision process.
# Recommendation for additional analysis for the remaining facilities that show
significant risks.
The Phase II analysis is designed to provide the risk information necessary to formulate these
profiles and risks.
3.2 Conceptual Model and Approach
The approach to conducting the SI Study risk assessment begins with the conceptual
model of a surface impoundment site. Figure 3-3 diagrams this conceptual model for human
exposure, beginning with the multimedia release of chemicals from a surface impoundment,
both during its active life and following closure. Once released, the chemicals travel through
environmental media that define the exposure pathways for the analysis: air, vadose zone and
groundwater, watershed, and surface water. In addition, plants and animals take up the chemicals
3-6
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PRIMARY
SOURCE(S)
PRIMARY
RELEASE
MECHANISM(S)
SECONDARY
SOURCE(S)
SECONDARY
RELEASE
MECHANISM(S)
MIGRATION
PATHWAY(S)
EXPOSURE
ROUTE(S)
POTENTIAL
RECEPTOR(S)
Surface
Impound-
ment
r> Erosion/Runoff
(postclosure only)
Leaching/
Infiltration
4*
Watershed Soils
Groundwater
L* Surface Water
Resuspension
Airborne
Particulates
(postclosure only)
Volatilization
Airborne
Vapors
Erosion/Runoff
Stock Water2
Groundwater
Ingestion of drinking water
Inhalation while showering
Resident
Home gardener
Farmer
Fisher
Agricultural
Field
Home
Garden
Surface Water3
H
K
H
Ingestion of fruits & vegetables
Ingestion of meat
Ingestion of milk
| Farmer
Ingestion of fruits & vegetables { Home gardener
Ingestion of fish { Fisher (all groups)
Deposition/Diffusion
Dispersion
Airborne
Vapors/
Particulates
Inhalation of vapors
Inhalation of particulates
Surface Soil
(Backyard)
Ingestion of soil
\
/I
Resident
Home gardener
Farmer
Fisher
Exposure pathway
1. For each receptor type, adult, three child, and infant cohort included.
2. Stock water consumed by farm animals only.
3. Lakes, permanently flooded wetlands, order 3 streams.
Figure 3-3. Conceptual exposure model for human receptors.
-------
Section 3.0
Phase II Risk Assessment
either directly from the different fate and transport media or by bioaccumulation of chemicals in
terrestrial and aquatic food webs. Eventually, human and ecological receptors are exposed to
chemicals in the environment as a result of inhalation or ingestion of contaminated media and/or
biota.
Human receptor types considered in the SI Study include residents, gardeners, farmers,
and fishers, each divided into five age cohorts. The conceptual model diagram (Figure 3-3)
shows the exposure pathways for each type of receptor. For example, the exposure pathways that
will be evaluated for an adult resident include ingestion of soil and groundwater and inhalation of
airborne vapors and particulates. Contaminated foodstuffs are considered only for resident
gardeners and farmers.
The ecological risk assessment provides descriptions of risk to representative wildlife
species in representative habitats. The ecological receptor types include plants, invertebrates,
amphibians, reptiles, birds, and mammals. The conceptual exposure model demonstrating the
environmental exposure pathways for each type of ecological receptor is shown in Figure 3-4.
For example, the exposure pathways that will be evaluated for a mammal include ingestion of
soil, water, and terrestrial food items.
Figure 3-5 itemizes the dimensions of the 3MRA as it will be implemented to address the
SI Study conceptual model. These dimensions include 11 major categories:
# Chemicals addressed
# Source type (i.e., surface impoundment)
# Source term characteristics
# Source release mechanisms
# Transport media
# Fate processes
# Intermedia contaminant fluxes
# Food chain/food web components
# Receptors and habitats
# Exposure pathways
# Human and ecological risk measures.
These categories characterize the Phase II analysis that will provide a state-of-the-art
representation of risks. A more detailed overview of the 3MRA model, including each
component module and its conceptual basis, is provided in Section 3.3 and the HWIR
background documents referenced therein.
3.2.1 Spatial Scale and Layout
Phase II of the SI Study will adopt the same spatial scale and site layout framework as
used for the HWIR 3MRA. The area of interest (AOI) for the analysis is defined by a 2-km radius
from the corner of a square surface impoundment (Figure 3-6). This distance was determined to
encompass the area of greatest risk for the air and groundwater pathways. In addition, concentric
3-8
-------
PRIMARY
SOURCE(S)
PRIMARY
RELEASE
MECHANISM(S)
SECONDARY
SOURCE(S)
SECONDARY
RELEASE
MECHANISM(S)
MIGRATION
PATHWAY(S)
EXPOSURE
ROUTE (S)
POTENTIAL
RECEPTOR(S)
Terrestrial plants
{
{
Soil invertebrates
Reptiles
Birds
Mammals
^ Aquatic plants
Aquatic
invertebrates
Amphibians
Fish
{
Benthic
invertebrates
Reptiles
Birds
Mammals
Complete pathway (evaluated)
Notes:
1. If intermittently flooded wetland, hydric-soil invertebrates are included.
2. If intermittently flooded wetland, aquatic invertebrates and fish are not included.
Figure 3-4. Conceptual exposure model for ecological receptors.
-------
Section 3.0
Phase II Risk Assessment
Chemicals
Intermedia Contaminant Fluxes
Organic chemicals (227)
Source
Air (volatilization, resuspension)
Metals (17)
Source
Vadose zone (leaching)
Nonmetallic inorganic chemicals (8)
Source
Local watershed soil (erosion, runoff)
Air
Watershed/farm habitat soil (wet/dry
Source Type
deposition, vapor diffusion)
Surface impoundment (operating and
Air
Surface water (wet/dry deposition, vapor
postclosure)
diffusion)
Watershed soil
Surface water (erosion, runoff)
Source Term Characteristics
Surface water
Sediment (sedimentation)
Mass balance
Vadose zone
Groundwater (infiltration)
Multiphase partitioning
Watershed soil
Air (volatilization)
First-order degradation
Groundwater
Surface water
Source depletion
Food Chain/Food Web Fluxes
Source Release Mechanisms
Air
Vegetation (particulate deposition; vapor
Volatilization
diffusion)
Leaching
Farm/habitat/garden soil
Vegetation (root uptake, translocation)
Runoff (postclosure, surface failure)
Vegetation, soil, surface
Animals (uptake)
Erosion (postclosure, surface failure)
water, groundwater
Particle resuspension (postclosure)
Surface water
Aquatic organisms (uptake)
Transport Media
Receptors and Habitats
Atmosphere
Ecological Habitats:
Watershed
Terrestrial
Vadose zone/Groundwater
Freshwater aquatic
Surface water
Human Receptors*:
Ecological Receptors:
Fate Processes
Resident
Plants
Chemical/biological transformation
Home gardener
Invertebrates
(and associated products)
Dairy farmer
Amphibians
Linear partitioning
Beef farmer
Reptiles
(water/air, water/soil, air/plant,
Fisher**
Birds
water/biota)
Mammals
Nonlinear partitioning
*For each human receptor type, consider 5 age cohorts
(metals in vadose zone)
"All receptor types can be fishers.
Chemical reactions/speciation
(mercury in surface waters)
Exposure Pathways
Human
Ingestion (plant, meat, milk, fish, water, soil, breast milk)
Inhalation (gases, particulates)
Ecological
Ingestion (plant, animal, water, soil)
Direct contact (surface water, sediment, soil)
Human And Ecological Risk Measures
Cancer (risk probability)
Noncancer (hazard quotient)
Human: population
Ecological: population
Figure 3-5. Dimensions of the 3MRA conceptual model for surface impoundments.
3-10
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Section 3.0
Phase II Risk Assessment
risk rings are defined within the 2-km radius (Figure 3-6) to provide additional spatial resolution
for the risk results:
# Humans risks are totaled within the 0 to 0.5 km, 0.5 to 1 km, and 1 to 2 km
distances from the edge of the SI.
# Ecological risks are totaled with the 0 to 1 km, 1 to 2 km, and 0 to 2 km distances
from the edge of the surface impoundment.
Human and ecological risk results will be tracked and maintained for each of these risk rings
within the AOI to provide information on the distribution of risk with distance from the SI.
Another important determinant of the spatial scale of the analysis is the size of watershed
subbasins delineated across the 2-km AOI. For the 3MRA model, watershed subbasins define
the areas over which deposition rates and soil concentrations are averaged and assumed to be
uniform. Although watershed size is determined mainly by the topography and hydrography at
the site, the size of the subbasins subdividing these watersheds is defined during watershed
delineation. Currently, these are defined in 3MRA so that there are generally about 10 to 12
watershed subbasins within the AOI at a site, with an average subbasin area of about 1 million
3-11
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Section 3.0
Phase II Risk Assessment
square meters within the AOI (Figure 3-7).1 This provides adequate spatial resolution to map soil
concentration gradients across the site while keeping the total number of watersheds at a given
site to a reasonable number from a run-time perspective. This decision could be revisited when
watersheds are delineated at the SI sites.
The spatial resolution is also limited by the resolution of the underlying data
(topographic, waterbodies, land use, soils) used in the site-based data collection process. Because
many of these data sources (i.e., topographic, land use, soils) have a scale of
1:250,000, that scale was used to define a minimum resolution for the coordinate system used to
pass spatial data from the geographic information system (GIS) data sources to the 3MRA model.
Figure 3-8 shows how the site grid system, based on 100 m x 100 m grid cells, defined the data
resolution used to pass watersheds, waterbodies, farms, and ecological habitats.
In HWIR, human receptor locations at a site are defined by the centroids of 1990 U.S.
Census blocks or by the ring/block centroid where the blocks cross a risk ring. Figure 3-9
illustrates human receptor locations at a typical HWIR site. Note that the density of receptor
points will vary with population density because census blocks are sized by the population they
contain. Because some site-specific receptor location data will be available from the SI Survey
results, there may be a need to modify receptor point placement for SI sites. There also may be
onsite receptors placed within the SI after closure, a scenario not addressed in HWIR. These
issues are discussed in Section 3.4 along with other modifications to the HWIR data collection
methodologies.
Ecological habitats are defined in the 3MRA primarily by land use and waterbody
coverages (see U.S. EPA, 1999n, for description of the habitat delineation methodology). Four
receptor home range bins are placed within each habitat in an overlapping fashion, with receptors
assigned to the appropriately sized home range bin. Figure 3-10 illustrates a typical habitat and
home ranges. This approach will likely be used "as is" to place ecological receptors around the SI
Study sites.
3.2.2 Temporal Scale, Frame, and Integration
The SI Study considers in-scope impoundments to be those in active operation on or after
June 1, 1990. Selection of this date ensures that the study will be limited to wastes managed
according to practices that have become more commonplace in recent years. Therefore, the study
will evaluate the release of in-scope constituents starting from June 1, 1990, through the
remainder of its active lifetime and, if appropriate, for the postclosure lifetime of the
impoundment.
3.2.2.1 Model Temporal Scale. The 3MRA model operates on an annual average time
scale, with individual module results being reported as a time series of annual average
concentrations or fluxes. Individual modules may operate on different time scales, depending on
1 Note that some watershed subbasins extend outside of the AOI in Figure 3-7 to capture the entire
drainage area for each watershed draining into the AOI. This is necessary to calculate accurate runoff and solids
loads for the 3MRA surface water module.
3-12
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Section 3.0
Phase II Risk Assessment
Sample Site
NC034
NC028
; /NC035
/ /site Location \ \
NC001 I I
NC027
NC018
WMU boundary
watershed boundary
Soil Map Unit
| NC001
| I NC018
NC027
| NC028
I | NC034
NC035
Figure 3-7. Typical watershed layout for HWIR 3MRA.
Figure 3-8. Transfer of watershed polygons to 100- by 100-m template grid.
3-13
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Section 3.0
Phase II Risk Assessment
Figure 3-9. Example human receptor placement for HWIR 3MRA.
Single
habitat
Range Bin 1
\_Range Bin 3
and 4
Figure 3-10. Example ecological habitat and home range bins.
3-14
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Section 3.0
Phase II Risk Assessment
Table 3-2. Meteorological Data Time Scales, by 3MRA Model Module
3MKA Module
Hourly
'I'wicc Daily
Daily
Monthly
Annual
Long-term
Surface impoundment
!
Land application unit
!
!
!
!
Air
!
!
!
Watershed
!
!
!
Surface water
!
what is appropriate for modeling objectives. This is reflected by which type of meteorological
data each module reads, shown in Table 3-2. Reasons for departures from annual average
conditions include the need for shorter time scales to accurately estimate release or fate and
transport in media sensitive to fluctuations in meteorological data. For example, the surface
impoundment module needs monthly data to capture temperature extremes that can impact
volatilization. The land application unit (LAU)2 and watershed modules require daily
precipitation data to accurately estimate precipitation-driven runoff and erosion events.
3.2.2.2 Study Time Frame. The study time frame for exposure and risk depends on the
migration times of the constituents in the receiving media. For most media (i.e., air, surface
water, soil), the exposure and risk occur in the same time frame as the release from the
impoundment. For media such as groundwater, where the media and chemical properties
attenuate the migration process, the exposure and risk time frame can be tens to thousands of
years after the release. The study time frame, therefore, varies for each chemical and
environmental medium considered for each specific facility and impoundment. A maximum
time limit for considering exposure and risk is defined as 5,000 years. This should capture the
significant impacts of most chemicals included in the analysis.
3.2.2.3 Temporal Integration. A given receptor will be considered subject to exposure
from various but not necessarily all pathways simultaneously. The aggregate risk to any
individual receptor is defined as the sum of the risks from each pathway over a given time period.
Given that the exposure in the different media can occur over significantly different times,
aggregation of risk is performed for exposures that occur at the same time. For instance,
exposures and risks due to contaminated air occurring in the first 10-year time frame is not
aggregated with exposures and risks due to contaminated groundwater occurring in the hundredth
year time frame. Figure 3-11 illustrates how risks of different time periods will be overlaid and
aggregated across exposure pathways for a given receptor and constituent. Note that risks will be
aggregated across different exposure routes (i.e., ingestion, inhalation) only after considering
current EPA practices for route-to-route extrapolation. In general, combining pathways and
routes involves the following considerations:
2 The LAU module is being used to model a postclosure surface impoundment; see Section 3.3.1.
3-15
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Section 3.0
Phase II Risk Assessment
Contact Medium ffl
i = 1; soil
i = 2; groundwater
PRbefgh1,1,1,m,t(C"*
P^bolql^^. 1 ,m,
((C„)
ERbefghl.m.t'0^
P^befghl ,1,1 ,m,t
(a) Risk due to Pathway 1
Pathway ffl
j = 1; soil ingestion
j = 2; groundwater ingestion
Exposure Route fk)
k = 1; ingestion
h1,1,1,m,t,+ rnbefgh2,2,1,m,t,
(c) Exposure Route Risk for Both Pathways
Figure 3-11. Illustration of concurrent time aggregation of risks.
Source: U.S. EPA (1999aa).
3-16
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Section 3.0
Phase II Risk Assessment
# Pathway-specific benchmarks
# All ingestion pathways combined
# All inhalation pathways combined
# Results flagged in database if oral and inhalation routes could be combined
# Cancer risk and hazard quotients not combined.
Once risk estimates are integrated temporally, the 3MRA human risk module determines
and outputs that critical year, Tcrit, during which the maximum cumulative risk and/or hazard
quotient occurs across the population for each receptor/cohort combination and for each exposure
pathway and pathway aggregation. This process involves adding receptors across all risk bins for
each year of the analysis to calculate total risk/hazard for each period. The year with maximum
total risk/hazard is Tcrit. Types of outputs at Tcrit include population-weighted risk or hazard
quotient.
Tcrit is determined by the following steps:
1. Calculate total risk/hazard for each period
2. Find period with maximum total risk/hazard
# by nine exposure pathways
by route (ingestion or inhalation) or
by combined routes
# by distance (3 risk rings)
# by four receptor types
# by five age cohorts.
Tcrit is determined in a similar fashion in the ecological risk module for receptor types, groups,
and distances. An example of a simple Tcrit determination is shown in Figure 3-12.
3.2.3 Human Health
Example of 2-year analysis with Tcrit = year 2
Year 1
10
10
10
10
10
10
5.5E-3
Year 2
0
0
0
20
20
20
1.1 E-2
Bin 1
Bin 2
Bin 3
Bin 4
Bin 5
Bin 6
Total
<10"8
10 "8- <10"7
10 "7- <106
106- <10"5
10"5- <10 "4
10 4 - 1
Figure 3-12. Finding Tcrit (year with maximum risk).
3-17
-------
Section 3.0
Phase II Risk Assessment
The human health risk assessment (HRA) will evaluate the potential for adverse human
health effects that may occur as a result of chemical releases at the surface impoundments
investigated as part of the study. The results of the HRA will be used to assess the potential risks
associated with the impounded wastes. The HRA for the SI Study is designed to be consistent
with EPA guidance (U.S. EPA, 1989, 1991a, 1992c, 1997d, in press) and consists of the
following components:
# Identification of chemicals of potential concern
# Identification of potential human receptors
# Assessment of exposure
# Assessment of chemical toxicity
# Characterization of risk
# Analysis of sources of uncertainty in the predicted risk estimates.
3.2.3.1 Chemicals of Potential Concern. Chemicals of potential concern (COPCs) are
those identified in the environment that may cause adverse health effects in exposed individuals.
The general types of COPCs identified within the scope of the SI Study are listed in Figure 3-5
and include:
# 227 organic chemicals
# 17 metals
# 8 nonmetallic inorganic chemicals
# 4 other.
EPA will select the final list of COPCs based on the results of the SI Survey (i.e.,
chemicals detected in the SI wastes) as well as policy and regulatory concerns (e.g., the three
specific target chemicals beryllium, 1,1,2-trichloroethane, 1,1,2,2-tetrachloroethane). In addition,
the Phase II analysis will address only those chemicals that were not screened out in the Phase I
analysis.
3.2.3.2 Human Receptor Types. EPA is concerned with the potential risk to the
exposed population within a 2-km radius of the surface impoundments at a facility. Census and
land use data will be used in conjunction with the SI Survey results to identify receptor types and
populations that are potentially exposed in the AOI for each SI site. Receptor types and age
cohorts to be evaluated using 3MRA are shown in Table 3-3.
EPA risk assessments are expected to address or provide descriptions of individual risk to
important subgroups of the population such as highly exposed or highly susceptible groups or
individuals, if known. EPA plans to evaluate the risk burden carried by different subgroups of
the exposed population, with particular concern over the potentially disproportionate risks to
children and subsistence populations (e.g., Native Americans who rely on indigenous fish species
as a major portion of their diets) in the study area. EPA believes that these concerns will exhibit
themselves on a site-specific basis and plans to address such risk in separate, site-specific
analyses.
3-18
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Section 3.0
Phase II Risk Assessment
Table 3-3. Matrix of Human Receptor Types and Age Cohorts3
Age Cohort
Ueeeptor Type In Inn 111 Child (1-5) Child (6-11) Child (12-19) Adult
Resident0
/
/
/
/
/
Home
gardener0
/
/
/
/
/
Beef farmer
/
/
/
/
/
Dairy farmer
/
/
/
/
/
Fishersd
/
/
/
/
/
" There is no overlap between receptor types or age cohorts (unique sets).
b Infant is less than 1 year old and is evaluated only for dioxin-like compounds in breast milk.
c May include onsite future resident for time periods after closure of an impoundment.
d Fishers defined for all four receptor types: resident, home gardener, beef farmer, dairy
farmer, and across noninfant age cohorts.
3.2.3.3 Human Exposure Pathways. The human exposure assessment will estimate the
type, timing, and magnitude of exposures that receptors may experience due to contact with the
chemicals of potential concern (these exposures are calculated using the human exposure
module, which is described in Section 3.3.9). Exposures will be evaluated for potentially
complete exposure pathways. An exposure pathway describes the course that a chemical takes
from a source to an exposed individual. An exposure pathway is complete when there is a route
by which a human receptor takes up a chemical that was released from the source of concern (in
this case, a surface impoundment).
Exposure routes include uptake mechanisms such as ingestion, dermal contact, and
inhalation. When modeling human exposure, the exposure routes that will be considered are
# Direct ingestion of soil
# Direct ingestion of contaminated groundwater (private groundwater wells only)
# Inhalation of contaminated shower air (private groundwater wells only)
# Inhalation of volatile emissions from impoundment
# Inhalation of particulate emissions from sludge (postclosure in place)
3-19
-------
Section 3.0
Phase II Risk Assessment
# Indirect exposure through ingestion of produce (gardeners, farmers) and meat and
dairy (farmers only) contaminated from air deposition or sludge erosion/runoff to
soil and subsequent plant uptake and consumption
# Indirect exposure (all recreational fishers) through ingestion of T3 and T4 fish
contaminated through the aquatic food web from air deposition onto or sludge
erosion/runoff into surface waterbodies surrounding each SI.
These routes define the exposure media to be modeled in the risk analysis (i.e.,
groundwater, soil, air, vegetables, meat, dairy products, and fish). OSW considered the inclusion
of dermal routes of exposure but decided that health benchmarks for dermal toxicity derived
from oral toxicity studies are not sufficiently developed at this time for use in analyses that could
support regulatory decisions and, therefore, has decided not to include dermal exposure routes.
The exposure pathways and routes that will be evaluated for each receptor type at each
site are shown in Table 3-4. These pathways were previously pictured in the conceptual model
diagram (Figure 3-3) for human receptors. Residents are exposed to some level of contaminant in
the air (inhalation) and the soil (incidental ingestion) and are assumed to be exposed to
potentially contaminated groundwater (inhalation and ingestion) if the house is not on a public
water supply. Home gardeners are residents who also grow some portion of their fruits and
vegetables. Farmers have the same exposure pathways as home gardeners with the additional
exposure to either contaminated beef or contaminated dairy products (depending on the type of
farms present at a site). Recreational fishers are any of the above receptors with the added
pathway of eating contaminated fish from local streams or lakes. Thus, some fraction of
residents, home gardeners, and farmers also are fishers.
As shown in Figure 3-3, a receptor may be exposed simultaneously via multiple
pathways, each involving different combinations of contact media and exposure routes. The
human exposure model component will aggregate exposures across exposure pathways and
routes, when appropriate (e.g., daily doses of beef contaminated by uptake from forage, silage,
grain, soil, and drinking water), and provide estimates of total exposure for the eight routes listed
above. Because human health benchmarks are pathway-specific, pathways and routes are
combined in the analysis as follows in the 3MRA model:
# All ingestion pathways are combined.
# All inhalation pathways are combined
# Flag placed in database if oral and inhalation routes could be combined.
# Cancer risk and hazard quotients are not combined.
The evaluation of human exposure must include evaluation of spatial variability and
temporal variability in exposure across a site and also variability and uncertainty in exposure
factors for each receptor type. The exposure for each of these receptor types is estimated at each
receptor location across the study area to capture spatial variability in exposure and for every year
over the modeling time frame to capture temporal variability at each location. In addition, each
receptor type has distributions for all exposure factors for each of the age groups. In HWIR,
these age cohort-specific distributions were derived from percentile data for contact rates, body
3-20
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Section 3.0
Phase II Risk Assessment
Table 3-4. Human Exposure Pathways by Receptor Type
Reeeplors (;is oulpul l)\ 11iiin;in Risk Modulo)
Resident
Resident (iiirdener l-'isher l-'iirmer
Reeeplors cis oulpul In I Inniiiii r.xposure Module)
Residenl Beef l);iii\
Residenl Residenl (iiirdener l-'iirmer l-";i rmer Keel' l);iir\
Piillm ;i\
Residenl
(iiirdener
l-'isher
l-'isher
l-'isher
l-'isher
l-'iirmer
l-'iirmer
Air inhalation
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Shower air
inhalation
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Soil ingestion
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Water ingestion
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Crop ingestion
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Beef ingestion
\n
\o
V.
V.
Yes
V.
Yes
V.
Milk ingestion
\o
\o
\o
V.
V.
Yes
V.
Yes
Fish ingestion
\o
\o
Yes
Yes
Yes
Yes
\o
V.
weight, etc. in the Exposure Factors Handbook (U.S. EPA, 1997b), as described in U.S. EPA
(1999v). Exceptions include fixed values assumed for exposure duration (9 years for
carcinogens, 1 year for noncarcinogens), and medium or food-specific estimates of fraction
contaminated.
Lead exposures and risk evaluations will differ from those used with other exposure
pathways. This set of evaluations will be developed as a separate model because EPA (FR
56[110]:26460-26564) evaluates lead exposures in terms of potential blood lead (Pb)
concentrations (//g/dL-blood) rather than as intake or absorbed doses (i.e., //g/kg-d). The reasons
for using this different protocol are discussed in Section 3.2.3.4.
3.2.3.4 Human Health Effect Benchmarks. The health effect benchmarks that will be
used in the human health risk assessment are chemical- and exposure pathway-specific and
include
Cancer Risk
# Oral cancer slope factor
# Inhalation cancer slope factor
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Section 3.0
Phase II Risk Assessment
Noncancer (Toxic) Effects
# Oral reference dose (RfD)
# Inhalation reference concentration (RfC).
The Agency has a comprehensive human health benchmark database to support risk
assessment projects, which will be used to populate the SI Study database and is based on the
following sources, listed in order of preference:
# Integrated Risk Information System (IRIS)
# Health Effects Assessment Summary Tables (HEAST)
# EPA-approved toxicity equivalency factors (TEFs)
# Superfund Technical Support Center Provisional Benchmarks
# Various EPA criteria documents.
When benchmarks are not available from the above sources, alternative sources include the
following:
# Agency for Toxic Substances and Disease Control (ATSDR) minimal risk levels
(MRLs)
# California Environmental Protection Agency (CalEPA)
# Interim benchmarks developed from primary scientific literature.
Each of these sources is briefly reviewed below.
IRIS is EPA's electronic database containing information on human health effects (U.S.
EPA, 1999s). Each chemical file contains descriptive and quantitative information on potential
health effects. Health benchmarks for chronic noncarcinogenic health effects include RfDs and
RfCs. Cancer classification and oral and inhalation CSFs are included for carcinogenic effects.
IRIS is the official repository of Agency-wide consensus of human health risk information.
HEAST is a listing of provisional noncarcinogenic and carcinogenic health toxicity
values (RfDs, RfCs, and CSFs) derived by EPA (U.S. EPA, 1997c). Although the health toxicity
values in HEAST have undergone review, they have not been updated in several years and do not
represent Agency-wide consensus information.
Cancer slope factors for some dioxin-like compounds and polychlorinated aliphatic
hydrocarbons (PAHs) were calculated by using the TEF approach. For the TEF approach, the
toxicity of a group of chemically related constituents that typically occur in the environment as
mixtures is based on estimates of the toxic potency of each constituent as compared with a
reference compound within the group. TEF estimates are based on a knowledge of the
mechanism of action, available experimental data, and other structure-activity information. TEFs
have been established for a number of polychlorinated dibenzodioxins, polychlorinated
dibenzofurans, and polychlorinated biphenyl (PCB) congeners thought to have dioxin-like
3-22
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Section 3.0
Phase II Risk Assessment
toxicity (Ahlborg et al., 1994; U.S. EPA, 1998d). TEFs for several PAHs also have been
established (U.S. EPA, 1993b).
The Superfund Technical Support Center (U.S. EPA, National Center for Environmental
Assessment or NCEA) derives provisional RfCs, RfDs, and CSFs for certain chemicals. These
provisional health benchmarks can be found in Risk Assessment Issue Papers. These provisional
values have not undergone EPA's formal review process for finalizing benchmarks, and do not
represent Agency-wide consensus information.
EPA has also derived health benchmark values in other risk assessment documents such
as Health Assessment Documents (HADs), Health Effect Assessments (HEAs), Health and
Environmental Effects Profiles (HEEPs), Health and Environmental Effects Documents
(HEEDs), Drinking Water Criteria Documents, and Ambient Water Quality Criteria Documents.
Evaluations of potential carcinogenicity of chemicals in support of reportable quantity
adjustments were published by EPA's Carcinogen Assessment Group (CAG) and may include
cancer potency factor estimates. Health toxicity values identified in these EPA documents are
usually dated and are not recognized as Agency-wide consensus information or verified
benchmarks, however.
The ATSDR minimal risk levels are substance-specific health guidance levels for
noncarcinogenic endpoints. An MRL is an estimate of the daily human exposure to a hazardous
substance that is likely to be without appreciable risk of adverse noncancer health effects over a
specified duration of exposure. MRLs are derived for acute, intermediate, and chronic exposure
durations for oral and inhalation routes of exposure. Inhalation and oral MRLs are similar to
EPA's RfCs and RfDs, respectively; however, MRLs are intended to serve as screening levels.
CalEPA has developed cancer potency factors for chemicals regulated under California's
Hot Spots Air Toxics Program (CalEPA, 1999a). The cancer potency factors are analogous to
EPA's oral and inhalation CSFs. CalEPA has also developed chronic inhalation reference
exposure levels (RELs), analogous to EPA's RfC, for 120 substances (CalEPA, 1999b). CalEPA
used EPA's 1994 inhalation dosimetry methodology in the derivation of inhalation RELs. The
cancer potency factors and inhalation RELs have undergone internal peer review by various
California agencies and have been the subject of public comment.
Appendix B provides a comprehensive list of EPA and non-EPA benchmarks available
for the 256 SI study constituents. At least one benchmark was available from EPA sources for all
but the following 25 constituents:
#
Ammonium perchlorate
#
Cyclohexanol
#
Ammonium vanadate
#
tris(2,3-Dibromo propyl)phosphate
#
Chloral hydrate
#
cis-1,3 -Dichloropropylene
#
Chlordecone
#
trans-1,3 -Di chl oropropy 1 ene
#
Chloromethyl methyl ether
#
7,12-Dimethyl benz[a]anthracene
#
Copper
#
Dimethylphthalate
#
Cresol mixtures
#
Ethyl methanesulfonate
#
Cyanide (total)
#
Fluoride
3-23
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Section 3.0
Phase II Risk Assessment
# Lead and compounds # Styrene-7,8-oxide
# 3-Methyl cholanthrene # Sulfide
# N-Nitrosopiperidine # Thallium
# Perchlorate # p-Xylene
# Safrole
Possible benchmark options for several of these constituents are discussed in the remainder of
this section.
Alternatives for addressing constituents without EPA benchmarks include omitting them
from the quantitative risk assessment, using provisional benchmarks from other sources (e.g.,
ATSDR, CalEPA), or estimating benchmarks (for screening purposes) if suitable toxicological
data are available.
EPA considers that, for a screening-level analysis, it is appropriate to identify or develop
and use draft provisional benchmarks where necessary. For 9 of the chemicals listed above, IRIS
benchmarks for similar chemicals could be used as their benchmarks. The rationale for these
chemicals is as follows:
# Chloral hydrate could be based on chloral. The IRIS RfD for chloral (2E-03
mg/kg-d) was based on a study that used chloral hydrate.
# Chloromethyl methyl ether could be based on bischloromethylether. The IRIS file
for chloromethyl methyl ether states that the risk is not likely to be greater than for
bischloromethylether (a contaminant of chloromethyl methyl ether). The CSF for
bischloromethylether is 220 (mg/kg-d)"1 and the URF is 6.2E-02 (//g/m3)"1.
# cis-l,3-Dichloropropylene and trans-l,3-dichloropropylene could be based on 1,3-
dichloropropene. The studies cited in the IRIS file for 1,3-dichloropropene used a
technical-grade chemical that contained about a 50/50 mixture of the cis- and
trans-isomers. The RfD is 3E-04 mg/kg-d and the RfC is 2E-02 mg/m3.
# Cresol mixtures could be based on m-cresol. Cresol mixtures contain all three
cresol isomers; therefore, it is appropriate to use the lowest RfD (5E-02 mg/kg-d
for m-cresol) from the cresol isomers to represent the mixture.
# Total cyanide could be based on amenable cyanide. The IRIS RfD is
0.02 mg/kg-d.
# Fluoride could be based on fluorine. The RfD for fluorine (6E-02 mg/kg-d) is
based on soluble fluoride.
# Thallium could be based on thallium chloride. There are several thallium salts that
have RfDs in IRIS. The lowest value among the thallium salts is routinely used to
represent thallium in risk assessments.
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# p-Xylene could be based on total xylenes. An RfD of 2 mg/kg-d is listed for total
xylenes, m-xylene, and o-xylene in IRIS. Total xylenes contains a mixture of all
three isomers; therefore, the RfD likely is appropriate for p-xylene.
Copper and lead present special cases. Although HEAST lists an RfD of 1.3 mg/L for
copper, HEAST notes that there is a drinking water standard and that data are inadequate to
calculate an RfD. Nevertheless, the drinking water standard is sometimes used to calculate an
RfD of 0.037 mg/kg-d (assuming a 70-kg body weight and ingestion of 2 L of water per day).
This RfD has previously been used.
Even though lead toxicity has been investigated for decades, the data do not fit into the
typical RfD methodology. EPA has determined that lead exposure can result in various health
effects, depending on the level of exposure. Also, potential health effects differ, depending on
whether exposure occurs to an adult or a child and, at blood-lead levels of 10 to 15 //g/dL or
possibly lower, effects may include inhibited activity of enzymes involved in red blood cell
metabolism, interference with heme synthesis, interference with vitamin D hormone synthesis,
altered brain wave activity, deficits in IQ and other mental indices, early childhood growth
reductions, and increases in blood pressure (FR 56[110]:26460-26564). Some of these effects
may not have a threshold. The EPA RfD workgroup concluded that it was inappropriate to
develop an RfD for lead. Consequently, a biokinetic uptake model was developed to predict
blood lead levels in young children exposed to lead. A soil screening guidance level of
400 mg/kg (U.S. EPA, 1996d) was developed based on the lead model. EPA is considering use
of a screening value (for sludge) of 400 mg/kg in the surface impoundment study and 10 to
15 mg/L for water.
EPA is in the process of developing a revised RfD for perchlorate. A peer review
workshop was held in February 1999 to review the toxicological data used as the basis for the
revised RfD (0.0009 mg/kg-d). This value is still undergoing review; however, if it were to be
finalized in the near future it could be used in the risk assessment. A provisional RfC of 2.0E-3
mg/m3 was derived for cyclohexanol in the listing rule for solvents (63 FR 64371) and could be
used in the SI study. The Agency will use a screening value (for sludges) of 400 mg/kg in the
surface impoundment study.
In summary, EPA benchmarks exist for 240 SI constituents. Of the 24 without
benchmarks, suitable draft benchmarks may be readily identified for about one-third and
subsequent literature searches may identify suitable studies to develop benchmarks for several
others. These special cases will be clearly identified in presenting analytic findings based on
draft benchmarks. For the few remaining chemicals for which benchmarks cannot be developed,
a quantitative risk assessment will not be possible; however, information concerning the
frequency of detection and concentrations should be considered carefully in a qualitative
assessment.
3.2.3.5 Human Health Risk Measures. Risk characterization integrates the exposure
and toxicity assessments to produce quantitative estimates of potential health risks associated
with the chemicals of potential concern. Risks will be determined for individual chemical
parameters as well as for additive effects (across pathways) and cumulative effects for multiple
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chemicals. The following are key features of the calculation of cancer and noncancer risk in the
SI Study:
# Excess cancer risk criterion = 10"5
# HQ criterion = 1
# Cancer risks can be cumulative across chemicals
# HQs can be summed across chemicals if effects are to same target organs.
Because of fundamental differences in the calculation of critical toxicity values, the estimates of
potential excess carcinogenic risks and noncarcinogenic health effects are calculated separately.
Risk probabilities determined for each carcinogen generally will be considered to be
additive over all exposure pathways so that an overall risk of cancer will be estimated for each
group of potentially exposed receptors. Cancer risks will not be summed if inhalation or
ingestion of a chemical results in health effects at the point of exposure or has other immediate
effects.
Consistent with previous EPA practice in the assessment of human cancer risks from
constituents, if an individual's probability of developing cancer due to an exposure to the
constituent in question is estimated to be in the range of 1 in 100,000 (1 x 10"5), with a
confidence level consistent with the levels that can be achieved with the existing state of
chemical constituent risk assessment methodologies, then that exposure would be of concern.
In a manner similar to carcinogens, HQ values will be summed for chemical exposures
causing the same health effect (i.e., affecting the same target organ) to develop hazard indices
(His). HQs and His are not risk probabilities but are accepted by EPA as quantitative levels of
risks for noncarcinogens. Consistent with previous EPA practice in the assessment of human
noncancer health effects from constituents, if the ratio of the individual's exposure and the
applicable toxicity value is greater than 1, again with a confidence level consistent with the levels
that can be achieved with the existing state of chemical constituent risk assessment
methodologies, then that exposure would be of concern.
Lead Risk Evaluations. A separate model will be developed for risks from potential
exposures to lead. Blood-lead levels for potentially exposed receptors will be compared with a
selected action level. EPA typically considers that action may be warranted if the 95th percentile
of blood-lead levels exceeds 10 //g/dL (i.e., action may be considered if there is a 5 percent
chance that a sensitive receptor exposed to lead would have a blood-lead level greater than
10 jUg/dL). To be health protective, predicted blood-lead levels exceeding this criterion would be
noted for receptors at each facility.
To accommodate this method in the SI Study requires integrating potential lead exposures
calculated by the 3MRA model with the EPA model used to estimate blood-lead levels
corresponding to different levels of exposure and then comparing the predicted blood-lead levels
with a selected action level. A suggested approach to implement this method would be to use the
3MRA model to estimate multimedia lead exposure point concentrations and evaluate risks to
children using EPA's Integrated Exposure and Uptake Biokinetic (IEUBK) Model (U.S. EPA,
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1994a). The IEUBK Model combines estimates of environmental exposures with
pharmocokinetic modeling to predict blood-lead levels in children. Blood lead levels could also
be estimated using an EPA method for estimating adult blood lead levels.
Infant Risks. Risk to infants will be estimated only for exposures to dioxin and dioxin-
like compounds via the breast milk pathway. This exposure will be calculated on the basis of the
total exposure to the adult (maternal) exposures of each receptor type. The risk will be
characterized as a margin of error (MOE) using a measure of background exposures for
comparison purposes.
Risk Estimates. Carcinogenic risk estimates and noncarcinogenic HI values will be
estimated for each receptor type in each exposure area and, as appropriate, for each exposure
route in the vicinity of a facility. In the case of carcinogens (or noncarcinogens where inhalation
and ingestion act on the same organ), the individual exposure route risks can be aggregated to
estimate the aggregate risks. Each set of risk analyses will provide a determination of the
contribution of each exposure pathway and the other dimensions for the SI Study risk analysis.
Individual vs. Population-Weighted Risk Estimates. EPA risk assessments are
expected to address or provide descriptions of individual risk and population risk. The estimates
of the potential distribution of human health risk will be communicated, or described, in terms of
the number of individuals who (currently) have or (in the future) can reasonably be expected to
incur risks from the constituents from the impoundments in either the central tendency range or
the high end of the estimated risk distribution. The risk summary processor will include the
proportion of the population whose exposures are modeled who can reasonably be expected to
incur risks above the levels described previously for cancer and noncancer effects, the proportion
of people within the modeled exposed population with risks estimated to be above those levels,
and pathways, receptors, and age cohorts exceeding identified risk levels.
Population-weighted risk estimates will be produced by multiplying the population
estimates for each exposure area to the individual risk estimate and summing the adjusted risk
estimates for all exposure areas. Normalized population risk estimates will be provided for each
receptor type to provide the distribution of risk across all receptors in the study area. The
population-weighted risk estimate is shown as
Ne Ne
PRr= £(Rr,exNr) / £Nr (3-1)
where
PRr = Population-weighted risk estimate for receptor type r
Rr,e = Risk estimate for receptor type r in exposure area e
Nr = Number of receptors of receptor type r in exposure area e
Ne = Number of exposure areas in the study area.
3.2.3.6 Uncertainty Analysis. Potential sources of uncertainty and variability will be
identified and evaluated using a semiquantitative uncertainty analysis. The uncertainty analysis
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will provide an evaluation of the variability in the estimated risks and allow the identification of
sources of uncertainty that are potentially reducible.
3.2.4 Ecological Health
The ecological risk assessment (ERA) for the SI Study will evaluate the potential for
adverse ecological impacts that may occur as result of chemical releases at surface
impoundments. The results of this ERA may provide information to assist in evaluating the need
for future regulation of surface impoundments. The ERA for the SI Study is consistent with EPA
guidance (U.S. EPA, 1998) and, as such, will be structured around the following main sections:
# Problem formulation
# Analysis phase
# Risk characterization.
3.2.4.1 Problem Formulation. The problem formulation establishes the scope of the
ERA by (1) ensuring that ecological receptors (e.g., plants, wildlife) likely to be exposed are
evaluated and (2) evaluating exposure scenarios relevant to different habitats. The problem
formulation for the SI Study will describe the following elements, which are detailed in this
section:
# Representative ecological receptors
# Chemicals of potential concern
# Potential and relevant exposure pathways
# Assessment and measurement endpoints.
Representative Ecological Receptors. In general, ecological receptors are species of
plants and wildlife that may be exposed and adversely impacted by chemicals released from
surface impoundments. The ecological receptors that will be evaluated in this ERA include the
following:
# Terrestrial wildlife using habitats at or near surface impoundments can be exposed
by either drinking directly from the surface impoundment and/or consuming
plants, soil, or prey items that bioaccumulate chemicals present in the
impoundment.
# Aquatic plants and other biota may be exposed to chemicals that are transported
from surface impoundments to nearby aquatic habitats.
# Vascular plants and other terrestrial biota may be exposed to chemicals that are
transported from the surface impoundment to nearby terrestrial habitats (e.g.,
surficial soils).
Because all potentially affected species cannot be individually assessed, ecological
receptors will be identified by organizing potentially affected plants and wildlife into guilds of
taxonomically and functionally related organisms (e.g., herbivorous birds, insectivorous birds,
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carnivorous mammals). Receptors will be selected to represent each guild based on taxonomic
relatedness, function in the ecosystem, and availability of wildlife exposure factors and toxicity
data. For example, the American robin may be selected to represent insectivorous birds at an
impoundment because (1) it is a bird, (2) it eats insects and worms, (3) it is one of many thrushes
observed at or near the impoundment, and (4) wildlife exposure factors have been established
(U.S. EPA, 1993d).
For the SI Study, a 2-km radius around the impoundment is considered a conservative
estimate of the habitat area in which receptors may be affected by the surface impoundment.
Habitats within the 2-km radius of the site will be characterized and assigned based on
site-specific and regional data. Risks to aquatic biota (i.e., fish and aquatic invertebrates) living
in surface impoundments will not be evaluated in the SI Study since impoundments are expected
to provide poor habitat for these organisms, and impoundments are not intended to support
freshwater aquatic biota. Habitats that will be considered in the SI Study include
Terrestrial Habitats
# Grasslands
# Shrub/scrub
# Forest
# Crop fields and pastures
# Residential
Freshwater Habitats
# Rivers/streams
# Ponds
# Lakes
Wetland Habitats
# Permanently or intermittently flooded grassland
# Permanently or intermittently flooded shrub/scrub
# Permanently or intermittently flooded forest.
These habitats are intended to represent habitats across the United States that may be
found at or near surface impoundments and that support wildlife receptors. Although estuarine
and marine ecosystems may potentially be impacted, evaluating these systems would require
substantial effort in data collection on estuarine receptors (e.g., ecotoxicity data) as well as in
adapting the multimedia modeling construct to simulate the complex environmental behavior of
chemicals in the brackish and marine waters of estuarine systems. Currently, there is no system
developed to support this type of assessment; as such, these habitats will not be included in the
analyses.
Once site-specific habitats are identified, representative ecological receptors will be
assigned to appropriate habitats based on documented foraging and feeding behavior and habitat
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usage. Representative ecological receptors for the following groups of taxa will be used to
populate habitats:
Terrestrial Habitats
# Terrestrial plants
# Soil invertebrates
# Reptiles
# Herbivorous birds, soil invertebrate-consuming birds, and carnivorous birds
# Herbivorous mammals, soil invertebrate-consuming mammals, and carnivorous
mammals
Freshwater Habitats
# Aquatic plants
# Aquatic invertebrates
# Benthic invertebrates
# Fish
# Amphibians
# Herbivorous birds, benthic invertebrate-consuming birds, piscivorous birds, and
carnivorous birds (including migratory birds and waterfowl)
# Herbivorous mammals, benthic invertebrate-consuming mammals, and
carnivorous mammals
Wetland Habitats
# Wetland plants
# Hydric soil-associated invertebrates
# Aquatic invertebrates (permanently flooded wetlands only)
# Fish (permanently flooded wetlands only)
# Amphibians
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# Reptiles
# Herbivorous birds, invertebrate-consuming birds, and carnivorous birds (including
migratory birds and waterfowl)
# Herbivorous mammals, piscivorous mammals, and carnivorous mammals.
Chemicals of Potential Concern. Chemicals of potential concern are detected chemicals
in exposure media (e.g., soil, surface water, prey tissue) that may adversely impact ecological
receptors. Specific COPCs within the study scope are discussed in Section 3.2.3.1.
Ecological Exposure Pathways. The complete exposure pathways to be evaluated in
this analysis must conform to the following elements:
# A source and mechanism of COPC release
# A transport medium
# A point or area where ecological receptors may be exposed to COPCs
# An exposure route through which COPC uptake occurs.
Figure 3-4 graphically presents the risk conceptual model and potentially complete exposure
pathways for ecological receptors. Table 3-5 lists the media and exposure routes that will be
evaluated in the analysis.
Direct contact to COPCs by terrestrial wildlife will not be evaluated because (1) dense
undercoats or down effectively prevents chemicals from reaching the skin of wildlife species and
significantly reduces the total surface area of exposed skin (Peterle, 1991; U.S. ACE, 1996) and
(2) results of exposure studies indicate that exposures due to dermal absorption are insignificant
compared to ingestion for terrestrial wildlife (Peterle, 1991). Similarly, inhalation of volatile
organic chemicals (VOCs) will not be evaluated because (1) concentrations of volatile chemicals
released from soil to aboveground air are drastically reduced, even near the soil surface
(U.S. ACE, 1996) and (2) VOC concentrations in soils would have to be great to induce
noncarcinogenic effects in wildlife based on inhalation toxicity data for laboratory rats and mice
(U.S. ACE, 1996). In addition, availability of inhalation benchmarks for ecological receptors is
limited.
Assessment and Measurement Endpoints. Assessment endpoints describe attributes of
ecological receptors that are considered environmentally important and that reflect environmental
values to be protected. Assessment endpoints are selected to reflect regulatory and policy goals
as well as the environmental conditions addressed by the risk assessment. In selecting
assessment endpoints, it is crucial to establish the relationship between the assessment endpoints
(i.e., the ecological values to be protected) and the measures of effect (e.g., the ecotoxicity data
used to support benchmarks). The measures of effect generally reflect toxicity to individual
organisms while the assessment endpoints represent ecological values that go beyond the
individual receptor.
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Table 3-5. Ecological Exposure Routes Evaluated by Receptor Type
Rool I pliiko Direct ( onliicl Ingestion
SurfiUT SurliKT Surl'sicc
Receptor Wilier Soil Wilier Sediment Soil Wilier Soil I'ooil
Plants
Aquatic plants
X
Terrestrial plants
X
Invertebrates
Aquatic invertebrates
X
Sediment-associated
invertebrates
X
Soil invertebrates
X
Wildlife
Fish
X
Amphibians
X
Reptiles
X
X
X
Birds
X
X
X
Mammals
X
X
X
Assessment endpoints are defined based on the identification of potentially exposed
ecological receptors and potentially complete exposure pathways. Development of assessment
endpoints is based on the assumption that protection of a population or community can be
inferred from protection of developmental and reproductive functions in individuals. Toxicity
endpoints that can reasonably be assumed to influence the potential of a population to sustain
itself (e.g., developmental and reproductive effects) are used to infer a level of protection to
populations. This inference, however, has yet to be validated from field or microcosm studies on
exposed populations. This is currently a limitation in the state-of-the-science and limits our
ability to interpret ecological risk results.
3.2.4.2 Analysis. The analysis phase for the SI Study will estimate COPC exposures and
establish toxicity benchmarks for ecological receptors.
Exposure Assessment. Estimates of exposure (doses or medium concentrations) for
each representative receptor will be calculated according to EPA guidance (U.S. EPA, 1993d).
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Exposure Point Concentrations. Concentrations in accessible media (including food
items) are needed to estimate exposures to representative species. Because the risk assessment is
intended to be an evaluation of receptors at large (rather than of the maximally exposed receptor),
estimates of the mean exposure point concentrations in accessible environmental media will be
predicted using the 3MRA fate and transport models described in Section 3.3.
For receptors that receive significant exposures through their food, simple food webs will
be constructed to show the major trophic levels and pathways through which chemicals are
transferred up the food chain. Estimates of COPC concentrations in terrestrial and aquatic food
items will be calculated using COPC concentrations in environmental media and biological
uptake factors such as bioaccumulation factors, bioconcentration factors, and biotransfer factors.
Estimates of exposure will include evaluations of spatial and temporal variability in exposure
point concentrations.
Wildlife Exposure Factors. Species-specific wildlife exposure factors will be established
for each representative receptor. These factors include body weight; food, soil, and water
ingestion rates; dietary preferences; and foraging range. For receptors with foraging areas
smaller than the habitat, the foraging area will be randomly located within the habitat boundaries.
For receptors with foraging areas larger than the habitat, the estimated dose is weighted as the
ratio of the habitat area to the foraging area (habitat area/foraging area). This weighting is used
to adjust the dose for the fraction of the receptor's diet taken from the habitat that is potentially
affected by the surface impoundment.
The exposure assessment will include an algorithm to construct a unique, randomly
selected diet for each receptor species at each site where it occurs, thus reflecting the variability
in receptor species' dietary composition. The algorithm requires dietary preference data
consisting of a list of potential diet items for each species and the maximum and minimum
proportion of the species' diet that each item can constitute. The prey preference algorithm ranks
diet items by maximum potential dietary fraction, and then constructs the diet from these ranges,
starting with the most preferred food item (largest maximum) and randomly selecting dietary
fractions from within the given ranges. The dietary composition is habitat-specific because the
same species (e.g., raccoon) may be assigned to aquatic and terrestrial habitats, resulting in
different dietary preferences.
Because the risk assessment is intended to be an evaluation of receptors at large (rather
than of the maximally exposed receptor), estimates of the mean wildlife exposure factors will be
used in the SI Study. Ingestion rates are a function of body weight and, thus, will also reflect
central tendency values.
Effects Assessment. Ecological benchmarks based on the toxicological effects of
chemicals on representative ecological receptors will be used to evaluate whether estimated
exposures are likely to result in adverse ecological effects. Population-level effects will be
inferred from benchmarks developed from toxicity studies examining reproductive,
developmental, or mortality endpoints. Community-level effects will be inferred from toxicity
benchmarks established to protect a specified percentage of the community (e.g., ambient water
quality criteria).
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Benchmark Development. The specific methods used to calculate the protective level
(i.e., benchmarks and chemical stressor concentration limits [CSCLs]) are taken from the HWIR
methodology and vary with the receptor taxa.3 For the HWIR analysis, protective CSCLs were
derived (in ppm) for specific communities and populations in direct contact with contaminated
media (i.e., terrestrial plants, soil biota, sediment biota, fish/aquatic invertebrates, and
herpetofauna). Protective benchmark doses (mg/kg-d) were developed for mammals and birds
based on exposure through the food web by ingestion of contaminated prey items.
Ecological Benchmarks for Representative Receptors (ChemEBRec). Ecological
benchmarks (EBs), derived in units of dose (mg/kg-d), were developed for representative taxa of
mammals and birds. The EBs were appropriate for upper-trophic-level consumers because the
primary exposure route occurs through ingestion of contaminated prey items. The approach
adopted for HWIR uses a hierarchy for the selection of ecotoxicity data and extrapolates from a
test species to the species of interest (in this case, wildlife).
Benchmark studies for mammals and birds were selected using a few key guidelines.
These guidelines represent the minimum requirements for a study to be of sufficient rigor for
benchmark derivation.
# Measurement Endpoints—Studies containing measurement endpoints reported as
either a no-observed-adverse-effect level (NOAEL) or a lowest-observed-adverse-
effect level (LOAEL) in units of daily dose were preferred. From these results,
the geometric mean between the NOAEL and the LOAEL (i.e., maximum
acceptable toxicant concentration [MATC]) was calculated. The MATC was the
preferred benchmark for representative mammalian and avian species.
# Toxicity Endpoints—Because population viability in mammals and birds was
selected as the assessment endpoint, the benchmarks were developed from toxicity
endpoints of reproductive or developmental success or, if unavailable, other
effects that could conceivably impair population dynamics.
# Methods—No specific test methodologies were required in studies used for
benchmark derivation. Standard laboratory practices (e.g., control dose groups),
however, were required. Field data may not be appropriate to develop a daily
dose exposure.
# Receptor Requirements—Ecotoxicity data for wildlife species were preferred
(e.g., mallards or mink); however, because of the paucity of studies exposing
wildlife species, rats and mice were typically the surrogate species exposed in
benchmark studies.
3 For this analysis, CSCLs refer to constituent concentrations (e.g., mg/kg soil) in environmental media that
are presumed to cause de minimis effects on ecological receptors. Benchmarks, in mg/kg-d, provide protective
ingestion doses that are estimated to cause de minimis effects to mammalian and avian receptors.
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# Durations—Studies were selected that reflected chronic or subchronic exposure
durations extending over a large percentage of the test species' lifetime, over
multiple generations, or over a particularly sensitive life stage of a species.
# Exposure Routes—Studies indicating oral exposure (e.g., dietary, gavage) were
preferred to studies using other exposure routes (e.g., intraperitoneal injection).
Mammals and birds in the field are typically more highly exposed through
ingestion of contaminated prey than through inhalation or direct contact, although
there are exceptions (e.g., burrowing animals).
# Dosing Scheme—Dose-response curves characterized by at least three data points
were selected over studies exposing animals to one dose level. This helped
identify both a NOAEL and a LOAEL for MATC calculations.
Mammalian and avian benchmarks represent population-inference benchmarks. By
developing benchmarks from NOAELs and LOAELs in mammals and birds, benchmarks were
estimated to provide protection from ingested doses that may inhibit the reproductive capacities
of these populations. The ability of the population to sustain itself (within normal biological
variation) was inferred from individual effects such as fecundity. This inference, however, has
yet to be validated from field or microcosm studies on exposed populations. Without validation,
it is likely that some benchmarks are overprotective and others are underprotective of wildlife
populations. Although this method does not confirm protection of populations, by protecting
individuals from adverse effects to reproductive and developmental endpoints, some level of
protection is provided to populations.
Once the benchmark study was identified, a scaled benchmark was calculated for
representative receptors of mammals. This method used an allometric scaling equation based on
body weight to extrapolate test species doses to estimate wildlife species doses. For mammals, a
scaling factor of 3/4 was used (Equation 3-2). This is the default methodology EPA proposes for
carcinogenicity assessments and reportable quantity documents for adjusting animal data to an
equivalent human dose (U.S. EPA, 1992b). For birds, recent research suggests that the cross-
species scaling equation used for mammals is not appropriate for avian species (Mineau et al.,
1996). Using a database that characterized acute toxicity of pesticides to avian receptors of
various body weights, Mineau et al. (1996) concluded that applying mammalian scaling
equations may not sufficiently predict protective doses for avian species. Benchmarks scaled for
small-bodied avian species using the mammalian equation generated scaled doses that were not
protective enough for small birds. Mineau et al. (1996) suggested that a scaling factor of 1
provided a better dose estimate for birds. Therefore, a scaling factor of 1 was applied for avian
receptors (Equation 3-3).
bw
1/4
EB = MATC.x
w t
bw
(3-1)
V
w /
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where
EB = MATC.x
1 bwt^
bw
(3-2)
EBW = scaled ecological benchmark for species w (mg/kg-d)
MATC, = maximum acceptable toxicant concentration (mg/kg-d)
bwt = body weight of the surrogate test species (kg)
bww = body weight of the representative wildlife species (kg).
Total Surface Water CSCLs. The CSCLs developed for surface water based on total
concentrations of the constituent covered the following receptor taxa: freshwater community (i.e.,
fish and aquatic invertebrates), algae/aquatic plants, and herpetofauna.4 The methods used to
derive CSCLs are reviewed here for each receptor taxon. The CSCL developed for the
freshwater community was derived to reflect both total and dissolved water concentrations.
Freshwater community - The
freshwater community CSCL was developed
to protect species of fish and aquatic
invertebrates. The CSCL does not extend to
protect species of mammals and birds that
may forage in freshwater ecosystems. The
methods adopted to develop freshwater
community CSCLs are consistent with those
supported across EPA offices. The CSCLs
were derived using methodologies founded
through the development of the National
Ambient Water Quality Criteria (NAWQC).
These methods require the compilation of
appropriate acute and chronic ecotoxicity data
reporting effects to survival, growth, and
reproduction in aquatic biota for specific
members of the freshwater community. The
NAWQC method uses a list of ecotoxicity
data requirements for eight taxonomic
families that represent typical freshwater
species (see text box). Whether a final
chronic value (FCV) or a secondary chronic
value (SCV) is calculated depends on how
well the eight taxonomic families are
represented by the data.
Data Requirements for FCV Calculation
# The family Salmonidae in the class Osteichthyes
# One other family (preferably a commercially or
recreationally important warmwater species) in
the class Osteichthyes (e.g., bluegill, channel
catfish)
# A third family in the phylum Chordata (e.g.,
fish, amphibian)
# A planktonic crustacean (e.g., a cladoceran,
copepod)
# A benthic crustacean (e.g., ostracod, isopod,
amphipod)
# An insect (e.g., mayfly, dragonfly, damselfly,
stonefly, midge)
# A family in a phylum other than Arthropoda or
Chordata (e.g., Rotifera, Annelida, Mollusca)
# A family in any order of insect or any phylum
not already presented.
4 Herpetofauna includes species of amphibians and reptiles. Insufficient ecotoxicity data were identified to
derive CSCLs for reptiles. Therefore, continued discussions only review amphibians.
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For populations of the freshwater community (e.g., fish, aquatic invertebrates), the FCV
developed for the NAWQC or the criterion continuous concentration (CCC) developed for the
Great Lakes Water Quality Initiative (GLWQI) was the preferred CSCL to use for this analysis
(U.S. EPA, 1995a, 1996f). If neither a CCC nor an FCV was available, an SCV was calculated
using Tier II methods developed through the GLWQI (Stephan et al., 1985; Suter and Tsao,
1996).
Aleae and aquatic plants - For algae and aquatic plants, toxicological benchmarks were
identified in the open literature or from data compiled in Toxicological Benchmarks for
Screening Potential Contaminants of Concern for Effects on Aquatic Biota: 1996 Revision (Suter
and Tsao, 1996). For most contaminants, studies were not available for aquatic vascular plants,
but lowest-effects concentrations were identified for algae. The criteria for algae and aquatic
plants were based on a lowest-observed-effect concentration (LOEC) for vascular aquatic plants
or an effective concentration (EC11;) for a species of freshwater algae, frequently a species of
green algae (e.g., Selenastrum capricornutum). Because of the lack of data in this receptor group
and the differences between vascular aquatic plants and algae sensitivity, usually the lowest value
of those identified was used. In instances where only a median effective concentration (EC50)
was identified to characterize effects to algae growth and survival, a safety factor of 5 was
applied to generate an estimated low effects concentration.
Amphibians - Amphibians appear to be highly sensitive to a number of toxicants (e.g.,
trace metals) during the developmental stages of their life cycle. Amphibians are essential parts
of a number of food webs (particularly wetlands) and are likely to provide a fairly sensitive
indicator for chemical stressors relevant to higher levels of biological organization. Though
amphibians are a significant ecological receptor, ecotoxicity data characterizing the chronic dose-
response relationship for chemicals of concern are limited. After a review of several compendia
presenting amphibian ecotoxicity data (e.g., Devillers and Exbrayat, 1992; Power et al., 1989;
U.S. EPA, 1996g) as well as primary literature sources, no suitable subchronic or chronic studies
were identified that reported effects to reproductive or developmental endpoints in amphibian
species. Therefore, a CSCL based on chronic endpoints and exposure durations was not derived.
Instead, the CSCL was developed from a geometric mean of acute (i.e., LC50, lethal water
concentration resulting in 50 percent mortality) amphibian ecotoxicity data. A few general
guidelines were followed in selecting analogous acute studies for developing the CSCL:
# Test duration was usually less than 15 d.
# Toxicity endpoints included mortality (LC50)
# Exposure occurred during early life stages (i.e., embryo, larvae, and tadpole).
Because the criteria are based on acute data (i.e., lethality), the severity of the potential
adverse effects that this criterion indicates is significant. Incorporating the amphibian data into
the NAWQC within the data requirement categories is currently under consideration. Because
amphibian species are more likely to breed in standing waters such as wetlands, ponds, or
temporary puddles, the appropriateness of combining protection of amphibian receptors with the
freshwater community CSCL is unclear.
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Dissolved Surface Water CSCLs. Conversion factors were available for several of the
metal constituents to convert total metal concentrations in the water column to total dissolved
concentrations (U.S. EPA, 1999y). Although the total concentrations supplied by the NAWQC
and GLWQI are still deemed scientifically defensible by EPA, the Agency recommends the use
of dissolved metal concentrations when they are available (Prothro, 1993).
Methods are currently available to develop dissolved CSCLs only for metals in the
freshwater community. Dissolved CSCLs were derived from total water CSCLs using a
conversion factor. The conversion factors applicable to chronic criteria in freshwater are
presented in Table 3-6. The conversion factors were developed by EPA using a series of
filtration experiments that measured the difference between filtered and unfiltered concentrations
of metals in surface waters. Dissolved CSCLs were derived by multiplying the total CSCL by
the conversion factor (Equation 3-4).
Metal CSCLdissolved = (Metal CSCLtotal) x (iConversion Factor) (3-4)
where
Metal CSCL total = either an FCV or an SCV in freshwater
Conversion Factor = the fraction of dissolved metal.
Sediment CSCL. Two methods were applied in developing the CSCL for the benthic
community (e.g., worms, amphipods). The first and preferred method used measured sediment
concentrations that resulted in minimal effects to the composition and abundance of the sediment
community. The sediment criteria were derived from the upper limit of the range of sediment
contaminant concentrations that are derived from no-effects data, species diversity, and
abundance endpoints. Measurements to derive the CSCLs were taken at the national scale and
reflected a variety of sediment types and benthic community species. The second CSCL
derivation method used the equilibrium partitioning (EqP) relationship between sediments and
surface waters to predict a protective concentration for the benthic community. This method was
used only for nonionic organic constituents. For the benthic community, the approach used to
establish CSCLs was based on a complete assessment of several sources proposing protective
sediment CSCLs. A discussion of each method (i.e., measured and estimated CSCLs) is
provided.
Measured sediment CSCLs - The premier sources of measured sediment CSCLs are the
National Oceanic and Atmospheric Administration (NOAA) and the Florida Department of
Environmental Protection (FDEP) sediment documents. NOAA annually collects and analyzes
sediment samples from sites located in coastal marine and estuarine environments throughout the
United States as part of the National Status and Trends (NS&T) Program. Data collected by
NOAA include measured sediment concentrations and the corresponding measures of toxicity in
resident species such as amphipods, arthropods, and bivalves on a variety of community-based
endpoints (e.g., abundance, mortality, species composition, and species richness). These data
are used by NOAA to estimate the 10th percentile effects concentration (ER-L) and a median
effects concentration (ER-M) for adverse
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Table 3-6. Conversion Factors for Dissolved Metal3
Constituent Conversion l-'aclor
Arsenic
1.00
Cadmiumb
1.1017-[(ln hardness)(0.04184)]
Chromium IIIb
0.860
Chromium VI
0.960
Leadb
1.4620-[(ln hardness)(0.14571)]
Mercury
0.850
Zincb
0.986
Conversion factor for chronic CSCLs in freshwater.
bDependent on the water hardness (assumed to be 100 mg CaC03/L for this analysis).
effects in the sediment community. These values are not NOAA standards; rather, they are used
to rank sites based on the potential for adverse ecological effects. In contrast, the FDEP sediment
criteria were developed from the ER-L and ER-M data to approximate a probable effects level
(PEL, estimated from ER-M data) and a threshold effects level (TEL, estimated from ER-L data).
PELs and TELs correspond to the statistically derived upper limit of contaminated sediment
concentrations that demonstrate probable effects and no effects to the benthic community,
respectively. Generally, FDEP values are more conservative than NOAA values. Even though
these criteria were developed for a marine community, researchers have demonstrated that
marine TELs have good correlation with no-effects levels found for freshwater systems (Smith et
al., 1996). In order of preference, TELs were adopted as CSCLs if available; if not, ER-L values
were used. The FDEP criteria were chosen above the NOAA criteria for the following reasons:
The same database was used for both the NOAA criteria and the FDEP criteria
development.
In most cases, the FDEP criteria were more conservative than the NOAA criteria
because a larger portion of the low-effects data was used in benchmark
development.
The marine TELs developed by the FDEP were found to be analogous to TELs
observed in freshwater organisms (Smith et al., 1996).
Estimated sediment CSCLs. When measured effects data were not available for organic
constituents using the TEL or ER-L approach, the value was derived using the EqP approach to
estimate the sediment CSCL (U.S. EPA, 1993c). The surface water FCV or SCV was used to
generate a sediment CSCL using the partitioning relationships among surface water, pore water,
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and organic carbon in sediment. This method assumes that the equilibrium partitioning between
the sediment and the water column is a function of the organic carbon. Equations 3-5 and 3-6
were used to calculate the sediment CSCL depending on whether an FCV or an SCV was
available. In calculating sediment CSCL for nonionic chemicals, the fraction organic carbon (foc)
was assumed to be 1 percent total organic carbon and Kocs (organic carbon partitioning
coefficients) were adopted as reported in Jones et al. (1997). However, because sediment CSCLs
were derived for organic constituents based on site-specific foc, some of the CSCLs were
recalculated within the HWIR modeling framework on a site-specific basis.
Sediment CSCL= f x K x FCV (3'5)
J oc oc
Sediment CSCL= foc x Koc x SCV (3-6)
Soil CSCLs. Soil CSCLs were derived for the terrestrial plant community and the soil
community. Each of the specific methods, including the rationale and the derivation methods, is
outlined in the following sections.
Terrestrial plants - For the terrestrial plant community, toxicological benchmarks were
identified from a summary document prepared at the Oak Ridge National Laboratory (ORNL):
Toxicological Benchmarks for Screening Potential Contaminants of Concern for Effects on
Terrestrial Plants: 1997 Revision (Efroymson et al., 1997). The measurement endpoints were
generally limited to growth and yield parameters for the following reasons:
# They are the most common class of responses reported in phytotoxicity studies
and, therefore, allow for criterion calculations for a large number of constituents.
# They are ecologically significant responses, both in terms of plant populations
and, by extension, the ability of producers to support higher trophic levels.
As presented in Efroymson et al. (1997), criteria for phytotoxicity were selected by rank-ordering
the LOEC values and then approximating the 10th percentile. If there were 10 or fewer values
for a chemical, the lowest LOEC was used. If there were more than 10 values, the 10th
percentile LOEC was used.
Soil community - Two methods were used in deriving soil community CSCLs: a
community-based CSCL and an earthworm/microbial CSCL.
# Community-Based CSCL—The first, and preferred, method was based on a
community-level approach similar to that applied in deriving the NAWQC. This
method developed a CSCL based on NOECs to reproductive and development
endpoints in a number of key functional taxa in the soil community. The CSCL
was designed to protect the structure and function of the soil community and its
critical role in the overall nutrient processing that occurs in the terrestrial food
web.
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Two key uncertainties were noted in the development of community-based
CSCLs. First, the ecotoxicity data used in the method are based on NOECs. The
CSCLs developed using the earthworm/microbial method for the soil community
were based on low-effects levels. Because these CSCLs are based on no-effects
soil concentrations, some added conservatism was generated in the soil
community CSCLs for lead and cadmium. Second, the species taxa groups
designed to represent key compartments in the soil community did not include
microbes. This introduces some uncertainty in the soil CSCL because microflora
make up approximately 80 to 90 percent of the biomass in soil and microflora are
responsible for the majority of the biological activity in soil (e.g., N
mineralization).
# Earthworm and Microbial CSCLs—The second method used to derive soil
CSCLs required the identification of LOECs for earthworms and microbial
endpoints. However, because a single species alone cannot predict the potential
toxicological impacts to the soil community, the community-based method was
preferred over using an earthworm or microbial CSCL.
Earthworms have been recognized to play important roles in promoting soil
fertility, releasing nutrients, and providing aeration and aggregation of soil, as
well as being an important food source for higher trophic level organisms. In
addition, their constant contact with soil media and permeable epidermis makes
them more susceptible to contaminant exposures. Likewise, microbial
communities play a key functional role in soil fertility, decomposition processes,
and nutrient cycling, providing nutrients in available forms to plants. Microbial
CSCLs were only used when they indicated a significantly higher sensitivity to a
particular constituent than the corresponding earthworm toxicity data.
The earthworm and microbial CSCLs were developed using the ER-L approach,
which was also applied to develop terrestrial plant CSCLs. When more than 10
studies were identified reporting LOECs, then the 10th percentile of the values
was derived as the CSCL. When less than 10 values were identified, however, the
lowest LOEC was selected as the CSCL.
3.2.4.3 Risk Characterization. Risk characterization integrates the results of the
analysis phase to evaluate the likelihood of adverse ecological impacts associated with exposures
to COPCs (U.S. EPA, 1992b). The potential for adverse ecological impacts will be characterized
using hazard quotients HQs. HQ is the ratio of the estimated exposure dose to the toxicity
benchmark for ecological receptors:
An HQ less than 1 (HQ < 1) indicates a negligible potential for adverse ecological impacts due to
exposure to a particular COPC; whereas, an HQ of 1 or greater (HQ >1) indicates a potential for
adverse ecological impacts due to exposure to a particular COPC.
Risk results will be considered in the context of two additional site-specific
Hazard Quotient
Estimated Exposure Dose
(3-7)
Toxicity Benchmark
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characteristics:
# Presence or proximity of managed areas (e.g., state or national parks, wildlife
refuges, or wild and scenic rivers
# Presence or proximity of federally listed threatened or endangered (T&E) species.
Locations of managed areas will be determined using the Managed Area Database. At sites
where significant managed areas are potentially impacted, ecological risks may be considered
greater than equivalent risks at other sites. Presence of T&E species can be determined from
U.S. Fish and Wildlife Service (U.S. FWS) data or from State Natural Heritage Program data. In
either case, precise locations of T&E populations are not likely to be available. Therefore, the
significance of relevant T&E documentation will be weighed on a case-by-case basis.
Uncertainty Analysis. Where wildlife exposure factors, biological uptake factors, and/or
toxicity benchmarks are not available, HQs will not be evaluated. Representative ecological
receptors and chemicals that cannot be assessed using HQs will be identified. A methodology for
a qualitative analysis of uncertainty was developed for the HWIR analysis and will be applied to
the surface impoundments ecological risk assessment.
For ecological risks, the constituent-specific concentrations in wastes generated in the
HWIR assessment that are determined to pose de minimis risk (i.e., the exemption criteria) were
based on two types of risk metrics: (1) the ecological hazard quotient, that is, the ratio between
exposure concentrations or dose and appropriate ecological benchmarks,5 and (2) the probability
of protection for ecological receptors. However, the ecological benchmarks include a variety of
receptors (e.g., soil fauna, mammals, plants), and, because the quality and quantity of relevant
data vary widely across receptors, the ecological exemption criteria represent different levels of
knowledge regarding the exposure and toxicity of chemical stressors. The variability in
supporting data suggests that the level of confidence in the exemption criteria is dependent on the
quantity and quality of available data. In short, the ecological exemption criteria do not reflect a
standard data set; rather, they reflect a continuum of data on toxicity and exposure (e.g.,
bioaccumulation factors) of varying levels of quality. To provide an effective tool to characterize
where on the continuum a given exemption criterion falls, a framework was developed to assign
confidence indicators based on the sufficiency of the data set supporting an exemption criterion.
Sufficiency, in this framework, is determined according to how well an exemption criterion (1)
captures risks to all relevant receptor groups in a habitat, (2) is supported by ecotoxicity data of
high quality, and (3) represents all significant routes of exposure to ecological receptors.
Consequently, the confidence indicators reflect all three of these "attributes" of an ecological
exemption criterion.
5 The term "ecological benchmarks" is used here in a broad sense to refer to two descriptors that were used
to identify protective levels: ecotoxicological benchmarks and chemical stressor concentration limits. Briefly, this
distinction was made to clearly represent the differences in the level of biological organization. Ecotoxicological
benchmarks are threshold doses intended to protect wildlife populations from significant adverse effects from the
ingestion of contaminated media and prey and are expressed in units of mg/kg-d. In contrast, chemical stressor
concentration limits are medium-specific concentrations (e.g., sediment) intended to protect assemblages of species
in contact with a contaminated medium.
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The development of confidence indicators must address the completeness of the data set
with regard to receptors of concern (in each type of habitat), as well as the confidence in data on
toxicity and on uptake and accumulation. Consequently, the confidence indicator consists of
three parts: (1) the habitat confidence indicator, (2) the benchmark confidence indicator, and (3)
the exposure confidence indicator. A brief discussion is provided here of each of these three
parts; a more extensive treatment of the confidence indicators and methods used in their
development is provided in Data Requirements and Confidence Indicators for Ecological
Benchmarks Supporting Exemption Criteria for the Hazardous Waste Identification Rule
(HWIR99) (U.S.EPA, 1999z).
Habitat Confidence Indicator. The confidence indicator for habitats is a qualitative
statement used to convey the relationship between chemical properties and data availability on
receptor groups in terrestrial and aquatic habitats, respectively. For example, to achieve a high
level of confidence in assessing a persistent, bioaccumulative constituent, we would require
ecotoxicological data on all receptor groups assigned to a habitat. In contrast, we may achieve a
high level of confidence in assessing a readily biodegradable constituent that does not
bioaccumulate with data on fewer receptor groups because: (1) the spatial impact of the
constituent will be extremely limited by rapid breakdown (assuming nontoxic daughter products)
and (2) food web exposures are unlikely to be significant. Three habitat confidence indicators
were used to establish the data requirements for all combinations of persistence and
bioaccumulation potential: "A" indicates high confidence, "B" indicates moderate confidence,
and "C" indicates low confidence in the data set. Hence, a habitat confidence indicator of "B"
means that, given the persistence and bioaccumulation rating for the constituent, there is
moderate confidence in the ability of the data set to assess all appropriate receptors. To afford
the maximum flexibility in this indicator, terrestrial and aquatic habitat indicators are reported
separately for each constituent.
Benchmark Confidence Indicator. The secondary confidence indicator reflects specific
criteria for ecotoxicological data used to develop ecological benchmarks (EBs) and chemical
stressor concentration limits (CSCLs). For each EB and CSCL, a data quality confidence
indicator was established: a "1" indicates high confidence in the study data, a "2" indicates
moderate confidence in the study data, and a "3" indicates low confidence in the study data. For
those receptors for which data are available, an average confidence indicator is calculated and
assigned to the exemption criterion for the terrestrial and aquatic habitats, as appropriate. This is
a critical distinction in interpreting the benchmark confidence indicator. The indicator only
refers to the confidence in those data that were actually used to support the development of EBs
and CSCLs; it does not address the quantity of receptors for which data are available. For
instance, a confidence indicator of "CI" for an aquatic habitat means that, although we have low
confidence in the data set to represent a sufficient number of receptors in freshwater systems, we
have high confidence in the benchmarks (or CSCLs) that were developed.
Exposure Confidence Indicator. The tertiary confidence indicator reflects the quality of
the data and models that are available to predict exposures through the food chain. In addition,
this indicator also acknowledges the importance of these pathways given the bioaccumulation
potential and persistence of a constituent. For instance, lack of empirical data on
bioaccumulation for a constituent rated as having low potential for bioaccumulation should not
necessarily result in a lower indicator of confidence. If exposure via the food web is determined
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to be insignificant, the data requirements on uptake and accumulation in plants and prey may be
lower. Thus, the exposure confidence indicator addresses our ability to evaluate the exposure
pathways of concern that are likely to be of concern and acknowledges that, for certain
constituents, exposure pathways through the food chain may not be completed. A confidence
indicator of "exp-1" indicates that we have high confidence in our ability to evaluate relevant
exposure pathways. However, this indicator may be applied to bioaccumulative as well as
nonbioaccumulative constituents. For example, an overall confidence indicator of "B2exp-1"
could describe two different situations: (1) moderate confidence in sufficiency of the data set
across receptors as well as in the toxicity data, and high confidence in the bioaccumulation data
for use in terrestrial systems, or (2) moderate confidence in sufficiency of the data set across
receptors as well as in the toxicity data and low potential for exposure via the food web. In either
case, our confidence is high that we are able to evaluate relevant exposure pathways of concern.
In contrast, an "exp-3" indicator would suggest that the data/models are insufficient to evaluate
potential exposure pathways of concern and that our confidence in the exposure profile is low.
3.2.5 Risk Metrics
Risk metrics for Phase II will be shaped by current capabilities of the 3MRA system,
output needs for the SI study, and EPA decisions to modify the 3MRA system. These metrics
represent the outputs of the risk analysis. The 3MRA system currently contains the exit level
processor to store, process, and display risk metrics. The ELP contains three components:
# The ELP-I reads the human health and ecological risk/hazard results from the
human risk and ecological risk modules and stores these results in a series of Risk
Summary Output Files (RSOFs).
# The Risk Visualization Processor graphically displays the RSOFs.
# The ELP-II provides chemical-specific waste stream concentrations that meet a
prespecified level of protectiveness.
Additional detail on each of these components can be found in U.S. EPA (1999v, 1999w),
including example outputs.
Because the objective of the SI study is to characterize risk from impoundments in the SI
universe rather than calculate protective waste concentrations, the ELP-II will not be required in
its current form. EPA will decide on whether to modify the 3MRA RVP and ELP-II to meet SI
Study objectives or develop a different system to process and analyze the 3MRA RSOFs
depending on resources and system capabilities.
3.2.5.1 Risk Bins. The Phase II analysis will use the same risk bins as those used in
Phase I. This will allow the Phase II results to be used to update risk bins used in Phase I. The
human health risk bins currently under consideration include:
# Excess Cancer Risk (6 bins): < 10"8, > 10"8 and < 10"7, > 10"7 and < 10"6, > 10"6" and
< 10"5, >10"5and < 10"4, >10"4.
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# Noncancer HI (6 bins, by target organ): < 0.01, > O.Oland <0.1, > 0.1 and < 1,
> 1 and < 10, > 10 and < 100, > 100).
# Ecological HQ (5 bins): < 0.1, > 0.1 and < 1, > 1 and < 10, > 10 and < 100,
>100).
The current HWIR risk bins are not quite compatible with the bins above - additional resolution
is available in the 10"7to 10"5 cancer risk range and there are only four human HQ bins. HWIR
risk bins are as follows:
# Excess Cancer Risk (7 bins): < 10"8, >10"8and < 5 x 10"7, > 5 xl0"7and < 10"6,
> 10"6and < 5 xl0"5,> 5 xl0"5and< 1 xlO"5, > 10"5and< 10"4, >10"4.
# Noncancer HQ (4 bins, by target organ): <0.1, >0.1 and <1, >1 and <10, > 10)
# Ecological HQ (5 bins): < 0.1, > 0.1 and < 1, > 1 and < 10, > 10 and < 100,
> 100).
However, the number of bins and the bin ranges are specific inputs to the HWIR 3MRA model
and can be easily adjusted to meet SI Study objectives.
3.2.5.2 Reporting and Maintenance of Results. As with Phase I, all Phase II risk
cumulative probability functions (cdfs) will be reported and maintained according to the SI Study
risk attributes:
# Five regulatory status categories
# Three functional classes (storage, treatment, and disposal)
# Ttreatment types (e.g., biological, settling)
# Iindustry types
# Contaminants.
These represent five dimensions by which risk outputs will need to be organized and compiled
and will require modifications to the 3MRA system as described below. Additional categories
can be included and maintained as necessary (prior to the analysis) to support regulatory
decision-making.
Human Health Risk Summary Output. For human risk, the HWIR 3MRA ELP1 stores
and maintains, by chemical and WMU type, the number of "site and iteration" pairs that protects
at least some percentile of the human population (currently 0%, 5%, 25%, 50%, 75%, 85%, 90%,
95%), 98%), and 99%>) for each "risk bin/waste concentration" pair by distance, pathway, receptor,
cohort,6 and critical-year (Tcrit) method. The ELP2 takes these results and determines the
Protective Summary Output File (PSOF) that specifies which waste concentrations will provide
risks below the target risk level for the selected 95 percent receptors for the exposure at 80
6 Note that the 3MRA ELP1 rolls the 5 age cohorts into 4 age cohorts by combining the age 1-6 and 7-12
child age ranges.
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percent of the sites. The SI Study does not need this functionality, and, furthermore, needs to
segment the SI universe into the five categories or dimensions listed above. For these reasons, a
new exit level processor may be needed to produce the human risk summary outputs needed for
the SI study.
The population risks estimated across facilities will be summarized for each CWA
population. The aggregation will include the facility weighting for the statistically sampled
CWA populations (direct and zero dischargers). For each CWA population, multiple ways of
viewing the risk summaries may be needed to answer the study objectives. Most of the ways to
view the risk summaries, called dimensions, are provided by the existing HWIR model (see
above). These include: by exposure pathway, by receptor type, and by chemical. The five
additional dimensions needed to address the SI Study study objectives (by regulatory status
category, industry, treatment type, and chemical) represent a new functionality that will be
needed in the 3MRA ELP. In addition, there may be the need to adjust the 3MRA RVP to
graphically display risk summaries for ease of interpretation of SI risk results.
Ecological Risk Summary Output. For ecological risks, the current 3MRA ELP1
produces RSOFs with ecological hazard quotient bins that store, by chemical and WMU type, the
number of "site and iteration" pairs that protects at least some percentile of the population for
each critical-year method and "hazard-bin/Cw" pair by distance and habitat group, distance and
habitat type, distance and receptor group, distance and trophic level, receptor group and habitat
group, or trophic level and habitat group. As with the ELP1 human risk RSOFs, these outputs
should be adequate for SI Study purposes, but a new ELP2 and possibly RVS will be needed to
accommodate the five additional dimensions needed to address the SI Study study objectives.
3.3 Overview of Simulation Modules
The SI Study will use the 3MRA model for the Phase II risk assessment. To address
multiple exposure simultaneously, the 3MRA model includes 17 functional modules. Figure 3-13
shows the 14 component modules of the 3MRA model that will be applied to the SI Study risk
assessment.7 These modules are briefly described in this section, with assumptions and
limitations, detailed input and output requirements, and functionality provided for each module
in Appendix A.
7 Three 3MRA source modules are not needed for the SI Study risk assessment: landfill, wastepile, and
aerated tank.
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Source Media Food Chain Exposure/Risk
Figure 3-13. Diagram of 3MRA Model as applied to surface impoundments.
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3.3.1 Source Modules
The source module employed for the SI Study risk analysis must model multimedia
releases both before and after closure of the impoundment. The SI module currently in the 3MRA
model only models releases up to closure. For the SI Study risk assessment, sludge from SI
operation will be assumed to be left in place after closure where it is subject to volatilization,
wind and water erosion, and leaching. Currently, the best option to model these processes is the
3MRA land application unit module, which includes all needed release mechanisms and can be
adapted to the SI postclosure scenario simply by adjusting input variables.
The remainder of this section provides an overview of the 3MRA SI and LAU modules to
be used in the SI Study risk analysis. This information was extracted and adapted from the
HWIR 3MRA background documents for source modules (U.S. EPA, 1999a, 1999b), which
contain additional detail, including all assumptions, governing equations, boundary conditions,
solution techniques, and supporting references.
3.3.1.1 Overview - SI Operating Module. The SI module divides an operating SI into
two primary compartments: a "liquid" compartment and a "sediment" compartment. Mass
balances are performed on these primary compartments at time intervals small enough that the
hydraulic retention time in the liquid compartment is not significantly impacted by the solids
settling and accumulation. Figure 3-14 provides a general schematic of a module construct for an
SI.
I Rainfall
Influent -~
t Emissions (aerated and nonaerated surfaces)
-~ Effluent
Liquid Compartment
Aerobic biodegradation
First-order chemical degradation (e.g., hydrolysis)
Biomass growth
I Contaminant diffusion; I Solids settling/resuspension
Sediment Compartment
Anaerobic degradation/decay
1 Solids burial; 1 Leachate
I Leachate to groundwater
Figure 3-14. Schematic of general surface impoundment module construct.
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In the liquid compartment, there is flow both in and out of the waste management unit.
There is also a leachate flow to the sediment compartment and out the bottom of the surface
impoundment. The leachate flow rate is estimated from liquid depth and from the hydraulic
conductivities and thicknesses of the sediment compartment, the clogged native soil layer, and
the underlying soil layer.
Within the liquid compartment, there is contaminant loss through volatilization,
hydrolysis, biodegradation (presumably aerobic), and particle burial (net sedimentation). The
sediment compartment has contaminant losses due to (anaerobic) biodegradation and hydrolysis.
Some contaminant mixing between the liquid and sediment compartments occurs due to
contaminant diffusion and due to particle sedimentation and resuspension.
Solids generation occurs in the liquid compartment due to biological growth; solids
destruction occurs in the sediment compartment due to sludge digestion. Using a well-mixed
assumption, the suspended solids concentration within the WMU is assumed to be constant
throughout the WMU. However, some stratification of sediment is expected across the length
and depth of the WMU so that the effective total suspended solids (TSS) concentration within the
tank is assumed to be a function of the WMU's TSS removal efficiency rather than equal to the
effluent TSS concentration. The liquid (dissolved) phase contaminant concentration within the
tank, however, is assumed to be equal to the effluent dissolved phase concentration (i.e., liquid is
well mixed). Consequently, the term "mostly well mixed" describes the liquid compartment.
The procedure used to determine the leaching rate follows the method outlined in EPA
Composite Module for Leachate Migration with Transformation Products (EPACMTP)
Background Document (U.S. EPA, 1996a). There are two important differences: (1) the liquid
depth is known and (2) there is a sediment layer between the liquid and the liner.
3.3.1.2 LAU (SI Postclosure) Module. At the end of its operating life, an SI may be
closed with sludges removed or in place. After that, remaining contaminants in the sludge and/or
subsoil solid matrices are subject to release and migration through leaching, volatilization,
erosion, and transport by wind and water. Although the current 3MRA SI module currently
cannot model these processes from a solid matrix, the LAU module does, and can be adapted to
model the SI post-closure period by modifying the model inputs.
The watershed including an LAU (or postclosure SI) is termed here the "local" watershed
and is illustrated in Figure 3-15. A local watershed is defined as that drainage area that just
contains the LAU (or a portion thereof— there can be multiple local watersheds where a LAU
crosses a drainage divide) in the lateral (perpendicular to runoff flow) direction. The local
watershed extends downslope to the point that runoff flows and eroded soil loads would enter a
waterbody. Areas downslope of the LAU within the local watershed are subject to chemical
contamination from the LAU through overland runoff and soil erosion.
Figure 3-16 illustrates how the local watershed is conceptualized within the LAU module,
that is, as a two-dimensional, two-medium system. The dimensions are longitudinal, i.e.,
downslope or in the direction of runoff flow, and vertical, i.e., through the soil column. The
media are the soil column
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Figure 3-15. Local watershed containing WMU.
Figure lb
View
Watershed Divide
Sheet Flow
•
T
.
>
f
Waterbody
Figure 3-16a. Local watershed.
Figure 3-16b. Cross-section view.
and, during runoff events, the overlying runoff water column. The local watershed is assumed to
be made up of, in the longitudinal direction, an arbitrary number of land subareas that may have
differing surface or subsurface characteristics, e.g., land uses, soil properties, and chemical
concentrations. For example, subarea 2 might be a WMU, subarea 1 would then represent an
upslope area, and subareas 3 through N would be downslope buffer areas extending to the
waterbody.
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3.3.2 Air Module
The purpose of the atmospheric modeling for the 3MRA is to estimate, at various
receptor points in the area of interest, the annual average air concentration of dispersed
constituents (particles and vapors) and the annual deposition rates for vapors and particles. The
area of interest is defined by a 2-km radius measured from the edge of the largest area source
(WMU) at the site. Additional detail on the air module can be found in U.S. EPA (1999c and
1999d).
The atmospheric module simulates the transport and diffusion of chemical constituents,
which are in the form of volatilized gases or fugitive dust emitted from area sources. The
simulated air concentrations are used to estimate bio-uptake from plants and human exposures
due to direct inhalation. The predicted deposition rates are used to determine chemical loadings
to watershed soils, farm crop areas, and surface waters.
The atmospheric concentration and deposition of constituents can be determined in
several ways. However, the selected procedure has to be computationally efficient to satisfy the
HWIR requirements of numerous simulations within a Monte Carlo framework. Because the
modeling is site-based, the steady-state Gaussian plume modeling approach was considered to be
appropriate and, the ISCST3 model was selected. The model provides estimates of contaminant
concentration, dry deposition (particles only), and wet deposition (particles and gases) for
user-specified averaging periods (i.e., annually).
ISCST3 is used as legacy code in the 3MRA framework. That is, the model is left intact
and the necessary interfacing to the framework is handled using pre- and postprocessors.
Together, the EPA air quality model (ISCST3) and the pre- and postprocessing code that
integrates ISCST3 into the 3MRA environment are referred to as the Air Module. The pre- and
postprocessing code also provides additional functionality to support other 3MRA framework
requirements.
3.3.3 Watershed Module
Chemical mass can be released from a surface impoundment in the form of volatile
emissions from an operating impoundment or volatile and particulate emissions from a closed
unit (modeled by the source module, Section 3.3.1). These emissions can then be transported and
deposited onto the soils of nearby land areas as wet or dry deposition (modeled by the air
module, Section 3.3.2). Once deposited, a chemical is then subject to fate and transport
processes within the watershed on which it is deposited and it is available either for direct
exposure to human or ecological receptors or indirect exposure through a food chain. It is the
purpose of the watershed module to model these fate and transport processes. Additional detail,
including all governing equations, can be found in U.S. EPA (1999f).
Fate and transport processes simulated by the watershed module are volatilization,
leaching, runoff, erosion, and biological and/or chemical degradation. Transport of chemical by
runoff and erosion is into adjacent waterbodies. Because the surface transport processes are
hydrologically related, the land areas surrounding the surface impoundment are disaggregated
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into watershed subbasins. A watershed subbasin can vary in size from a portion of a hillside,
similar to the local watershed construct of the land application unit module (see Section 3.3.1.2),
to much larger areas encompassing regional stream or river networks. In all cases, a watershed is
modeled as a single, homogeneous area with respect to soil characteristics, runoff and erosion
characteristics, and chemical concentrations in soil. No spatial disaggregation below the
watershed level is made; that is, no spatial chemical concentration gradients are simulated within
a given watershed.
3.3.4 Groundwater (Vadose and Aquifer) Modules
The HWIR 3MRA vadose and aquifer modules are a modified version of EPACMTP
(U.S. EPA, 1996a,b,c). This code simulates the fate and transport of contaminants released from
land-based waste management units through the underlying unsaturated or vadose zone (soil) and
saturated zone (surficial aquifer). EPACMTP replaced EPACML (U.S. EPA, 1993a) as the best
available tool to predict potential exposure at a downstream receptor well. EPACMTP offers
improvements to EPACML by considering: (1) the formation and transport of transformation
products; (2) the impact of groundwater mounding on groundwater velocity; (3) finite source as
well as continuous source scenarios; and (4) metal transport.
The composite vadose/aquifer model consists of a one-dimensional module that simulates
infiltration and dissolved constituent transport through the vadose zone, which is coupled with a
three-dimensional saturated zone module. The saturated zone module consists of a three-
dimensional groundwater flow submodule and three-dimensional transport submodules. The
saturated zone groundwater flow submodule accounts for the effects of leakage from the land
disposal unit and regional recharge on the magnitude and direction of groundwater flow. The
saturated zone transport submodule accounts for three-dimensional advection and dispersion and
linear or nonlinear equilibrium sorption.
Interrelationships between the vadose and saturated zone modules and other modules
(through water and chemical mass fluxes) under the 3MRA framework are shown in Figure 3-17.
As shown in the figure, the vadose zone module receives infiltration and solute mass fluxes from
the source module. The migration of contaminants in the vadose zone is terminated at the water
table where the contaminant fluxes, in the form of concentrations, are transferred to the saturated
zone module. The SZM also receives areal recharge from the watershed module. The SZM
provides time-dependent, annual average contaminant concentrations at receptor wells and
annual average contaminant fluxes at an intercepting stream located somewhere in the modeled
domain.
Detailed descriptions of both modules, including their purpose and scope of application,
mathematical formulations, and use in HWTR99, are provided in U.S. EPA (1999g). Additional
information relating to the EPACMTP and its verification is provided in the background
documents for EPACMTP (U.S. EPA, 1996a,b,c, 1997a).
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Receptor Well Concentration
Figure 3-17. Conceptual model of vadose zone and saturated zone.
3.3.5 Surface Water Module
The 3MRA surface water module models streams, lakes, ponds, and wetlands. Chemical
mass released from a WMU can enter a nearby surface waterbody network in runoff and erosion
directly from the WMU, from atmospheric deposition to the water surface, in runoff and erosion
from adjoining watershed subbasins, and by interception of contaminated groundwater. The
chemical is then subject to transport and transformation processes occurring within the
waterbody network, resulting in variable chemical concentrations in the water column and in the
underlying sediments. These chemical concentrations are the basis for direct exposure to human
and ecological receptors and indirect exposure through uptake in the aquatic food web.
The 3MRA surface water module consists of the core model Exams II (Burns, 1997;
Burns et al., 1982) and the interface module ExamsIO. More detailed documentation can be
found in U.S. EPA (1999u), from which the following material was extracted.
The surface water module estimates annual average total and dissolved chemical
concentrations in the water column and in the underlying sediments at various receptor points
within the affected waterbody. Transport/transfer processes modeled include advection, vertical
diffusion, volatilization, deposition to the sediment bed, resuspension to the water column, and
burial to deep sediments. Transformation processes modeled include hydrolysis and
biodegradation as pseudo-first-order reactions influenced by temperature and pH. Outputs from
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the surface water module include water column and sediment concentrations that are used by the
aquatic food web module and the ecological exposure module.
In the HWIR 3MRA, stream reaches of orders 3 to 5 are evaluated, as defined by the
Strahler ordering system (Strahler, 1957). Order three reaches are assumed to support fish
populations. Reaches of order greater than 5 are assumed to carry sufficient annual average
dilution flow that concentrations of chemicals of interest can be reasonably assumed to be at
background levels (U.S. EPA, 1999u).
3.3.6 Farm Food Chain Module
The farm food chain (FFC) module calculates the concentration of a chemical in
homegrown produce (fruits and vegetables), farm crops for cattle (forage, grain, and silage), beef,
and milk. The concentrations in homegrown produce, beef, and milk are inputs to the human
exposure module and are used to calculate the applied dose to human receptors who consume
them. The modeling construct for the FFC module is based on recent and ongoing research
conducted by EPA's Office of Research and Development (ORD) and presented in Methodology
for Assessing Health Risks Associated with Multiple Exposure Pathways to Combustor
Emissions (U.S. EPA, in press). Additional detail about the background and implementation of
the model is available in U.S. EPA (1999i).
The FFC module is designed to predict the accumulation of a contaminant in the edible
parts of a plant from uptake of contaminants in soil and through transpiration and direct
deposition of the contaminant in air. Concentrations are predicted for three main categories of
food crops presumed to be eaten by humans: exposed fruits and vegetables (i.e., those without
protective coverings, such as lettuce), protected vegetables (e.g., those with protective covering,
such as corn), and root vegetables (e.g., potatoes). In addition, the module estimates the
contaminant concentration from the biotransfer of contaminants in feed (i.e., forage, grain, and
silage), soil, and drinking water to beef and dairy cattle through ingestion.
The FFC module contains two separate programs; one predicts the concentration of
contaminants in produce grown by home gardeners and the other predicts the concentration of
contaminant in food crops, beef, and milk produced on farms. The module was designed with
this functionality because not all study areas contain farms, and the methodology developed for
farms is different than that developed for the home gardener. The program for home gardeners
uses point estimates of air and soil concentrations at the residential receptor location assigned to
each census block. In contrast, the program used for farms calculates an area-weighted average
soil concentration for the farm and uses an interpolation subroutine to estimate the average air
concentration across the area of the farm. Thus, the predicted concentrations in farm food crops
reflect the spatial average for the farm. Similarly, the feed concentrations for the cattle are
derived using spatial averages. In predicting concentrations in beef and milk, the contribution
from contaminated drinking water sources, such as farm ponds or wells on the farm, is also
considered. However, irrigation of crops and home gardens is not modeled.
Because the behavior of each chemical constituent is, to a large degree, determined by
chemical properties, the module includes a series of chemical-specific switches that turn on the
appropriate subroutines, depending on whether the chemical is an organic (O), metal (M), special
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(S), or dioxin-like (D). For most organic chemicals, the industrial exposure module calculates
chemical-specific values for the biotransfer factors used in the various equations, including air-
to-plant biotransfer factor, root concentration factor, and soil-to-plant biotransfer factor. Metals,
dioxin-like chemicals, and special chemicals generally use literature values for these various
biotransfer factors, when available. For dioxin-like compounds and special chemicals, the
biotransfer factors are calculated in the same way as for organics if literature values are not
available.
3.3.7 Terrestrial Food Web Module
The terrestrial food web module (TerFW) calculates chemical concentrations in soil,
terrestrial plants, and various prey items consumed by ecological receptors, including
earthworms, other soil invertebrates, and vertebrates. These concentrations are used as input to
the ecological exposure (EcoEx) module to determine the applied dose to each receptor of
interest (e.g., deer, kestrel). The module is designed to calculate spatially averaged soil
concentrations in the top layer of soil (i.e., surficial soil) as well as deeper soil horizons (i.e.,
depth-averaged over approximately 5 cm). The spatial averages are defined by the home ranges
and habitats that are delineated within the area of interest at each site. Once the average soil
concentrations are calculated, these values are multiplied by empirical bioconcentration factors
(for animals) and biotransfer factors (for plants) to predict the tissue concentrations for items in
the terrestrial food web. Supporting detail about the background and implementation of the
model is available in U.S. EPA (1999j).
The conceptual approach used in developing the TerFW module was designed to predict a
range of concentrations in plants and prey items to which a given receptor may be exposed. The
predator and various prey are represented in the site layout by allowing the respective home
ranges to overlap. For plants and soil fauna, the TerFW estimates concentrations based on the
spatially averaged soil and air concentrations across each home range. Receptors that ingest
plants and soil invertebrates as part of the diet are presumed to forage only within that part of the
home range that is contained within the AOI at a given site. Consequently, home range defines
the spatial scale for concentrations in soil, plants, and prey (both mobile and relatively immobile)
to which a given receptor is exposed.8
As with the Farm Food Chain module, the TerFW modeling construct is based on recent
and ongoing research conducted by EPA ORD and presented in Methodology for Assessing
Health Risks Associated with Multiple Exposure Pathways to Combustor Emissions (U. S. EPA,
in press). The model subroutine distinguishes among different types of chemicals, using
empirically derived algorithms for some chemicals and bio-uptake data from field or greenhouse
studies for other chemicals. The TerFW module accounts for uptake via root-to-plant
translocation, air-to-plant transfer for volatile and semivolatile chemicals, and particle-bound
deposition to edible plant surfaces. Specific differences exist between the FFC module and the
TerFW module in predicting plant concentrations.
8 Exposures are prorated depending upon the relationship of the home range to the habitat contained within
the area of interest. For details, see U.S. EPA, 19991.
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To estimate the concentrations in other categories of terrestrial prey items (e.g.,
earthworms, small birds), the TerFW relies on soil-to-organism bioconcentration factors
identified from empirical studies and/or generated using regression methods developed by the
Oak Ridge National Laboratory (see, for example, Sample et al., 1998). A thorough discussion
of these data is provided in the data collection documentation for the FFC and TerFW modules.
3.3.8 Aquatic Food Web Module
The aquatic food web (AqFW) module calculates chemical concentrations in aquatic
organisms that are consumed by human and ecological receptors (e.g., fish filet, aquatic
macrophytes). These concentrations are used as input to the human and ecological exposure
modules to determine the applied dose to receptors of interest. The module is designed to predict
concentrations in aquatic organisms for cold water and warm water aquatic habitats. Supporting
detail about the background and implementation of the model is available in U.S. EPA (1999k).
The underlying framework for the AqFW module is the development of representative
freshwater habitats for warmwater and coldwater systems. Four basic types of freshwater
systems were included for the two temperature categories: streams/rivers, permanently flooded
wetlands, ponds, and lakes. Simple food webs were constructed for each of the eight freshwater
habitats (four cold water and four warm water) that specify: (1) the predator-prey interactions,
(2) the physical and biological characteristics of the species that are assigned to each habitat (e.g.,
size, lipid content), and (3) the dietary preferences for fish in trophic levels 3 (TL3) and 4 (TL4).
Prey preferences are based on optimal foraging theory (Gerking, 1994). For each freshwater
habitat, the feeding guilds for various types and sizes of fish (e.g., medium benthivore) were used
to construct a simple food web and to map dietary preferences for organisms in each habitat (U.S.
EPA, 1999o). The habitat types are less important for some constituents (e.g., metals) for which
empirical data are used to relate the water concentration to tissue concentration. However, the
food web structure and species assignments are critical in determining concentrations of
hydrophobic constituents in aquatic organisms.
The AqFW methodology introduces several new approaches to modeling representative
aquatic systems. First, the AqFW module uses a probabilistic algorithm that cycles through the
database on prey preferences to select dietary fractions for TL3 and TL4 fish for predicting tissue
concentrations. Second, the AqFW module implements a flexible matrix that allows for the
simultaneous solution of all compartments (e.g., benthos, zooplankton, fish) in the system. This
functionality allows the module to perform calculations efficiently and provides the flexibility for
adding additional compartments and/or interactions to the food web structure.
3.3.9 Human Exposure Module
The human exposure module calculates the applied dose (milligram of constituent per
killogram of body weight) to human receptors from media and food concentrations calculated by
other modules in the 3MRA methodology. These calculations are performed for each receptor,
cohort, exposure pathway, and year at each exposure area.9
9 See Section 3.3.10 for the list of receptors and cohorts and applicable exposure pathways.
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The human exposure module calculates exposures for two basic receptor types:
residential receptors (residents and home gardeners) and farmers. Residential receptors may also
be recreational fishers in addition to being a resident or home gardener. Farmers may be beef
farmers or dairy farmers, and either type of farmer may also be a recreational fisher. The
subcategories within residential receptors and farmers differ in the particular exposures they
incur. For example, a resident (only) differs from a home gardener in that home gardeners are
exposed to contaminated fruits and vegetables, but residents are not. Within each of the two
basic receptor types, the human exposure module calculates exposures for five age cohorts:
infants (ages 0-1 year), children ages 1-5 years, children ages 6-11 years, children ages 12-19
years, and adults (ages 20 years and up). Additional detail about the background and
implementation of the model is available in U.S. EPA (1999q).
For HWIR, the human exposure module uses distributions for most exposure factors (e.g.,
contact rates, body weight) that are derived from data in the Exposure Factors Handbook
(U.S. EPA, 1997b). Exceptions include exposure duration (9 years for carcinogens, 1 year for
noncarcinogens) and "fraction contaminated," specified as point estimates for each medium/feed
item.
3.3.10 Human Risk Module
The essence of the 3MRA for human health is an evaluation of total risks to receptors
incurred as a result of simultaneous exposure from different pathways. To calculate risks from
multiple pathway exposures, the 3MRA human risk module considers two basic human receptor
types: residential receptors (residents and home gardeners) and farmers. Residential receptors
may also be recreational fishers in addition to being a resident or home gardener. Farmers may
be beef farmers or dairy farmers, and either type of farmer may also be a recreational fisher.
These receptor categories were developed considering the exposure pathways of concern, and the
category names suggest the associated exposure pathway(s). For example, a resident is exposed
only to the baseline exposure pathways, i.e., inhalation via ambient air and shower along with
soil and groundwater ingestion, with the home gardener being exposed through these exposure
pathways plus ingestion of homegrown produce. Additional detail about the background and
implementation of the model is available in U.S. EPA (1999r).
In total there are eight categories of human receptors: resident, home gardener, resident
fisher, home gardener fisher, beef farmer fisher, dairy farmer fisher, beef farmer, and dairy
farmer. The human exposure module models each of these eight categories and provides outputs
for each. The human risk module uses these outputs to calculate risks and/or HQs for each
category. However, to maintain output storage at reasonable levels, it aggregates results into
four10 composite receptor categories (resident, resident gardener, fisher, and farmer) to develop
the cumulative population11 frequency histograms and critical years. These three basic human
risk module functions (calculating risk/HQ, building cumulative frequency histograms, and
10 The 3MRA Exit Level Processor (ELP) preserves this receptor resolution, but also aggregates these four
receptors into a fifth, "all receptors" category.
11 Site-specific receptor populations identified as part of HWIR data collection activities are specified by
receptor category, exposure area (farm or census block), and distance ring (U.S. EPA, 1999t).
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determining critical year) are performed for each radial distance in a series of nested loops, as
illustrated in Figures 3-18a through 3-18c.12
For example, the composite "fisher" receptor population consists of subpopulations from
the resident fisher, resident gardener fisher, beef farmer fisher, and dairy farmer fisher receptor
categories. Similarly, beef farmers and dairy farmers are aggregated into a single, composite
"farmer." A complete mapping of the eight receptor categories as output by the Human exposure
module to their composited receptor categories as output by the human risk module is presented
in Table 3-7. Table 3-7 also shows the applicable exposure pathways for the receptor categories.
In this section, the term "receptor" is used to mean either one of the eight most disaggregated
receptor categories, or one of the four composited receptor categories, as appropriate to the
context. In addition, a cohort is defined as a receptor subpopulation based on age. Five cohort
classes are considered in the human risk module13: Child 1 (0 to 1 year old), Child 2 (1 to 5 years
old), Child 3 (6 to 11 years old), Child 4 (12 to 19 years old), and adult (greater than 19 years
old).
Receptor loop
-~ Cohort loop
—~ Exposure pathway loop
-~ Exposure area loop
Year loop
Calculate risk or HQ,
given receptor,
cohort, pathway,
area, and year
Next year
Next exposure area
1 Next exposure pathway
Next cohort
1 Next receptor
Figure 3-18a. Looping structure to calculate risk or HQ.
12 These illustrations are intended only to facilitate overall understanding of the module; the implementing
computer code is significantly different to optimize performance.
13 For purposes of storage efficiency, the ELP combines the Child 2 and 3 cohort classes as output by the
human risk module into a single composite cohort class (ages 1 to 11). Child 4 is also combined with the adult
cohort class by the ELP.
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Receptor loop
-~ Cohort loop
—~ Exposure pathway loop
-~ Year loop
I—~ Exposure area loop
Construct cumulative frequency
histogram across areas, given
receptor, cohort, pathway, and
year
Next exposure area
— Next year
Next exposure pathway
—- Next cohort
Next receptor
Figure 3-18b. Looping structure to build cumulative frequency histograms.
Table 3-7. Applicable Receptor/Pathway Combinations
Receptor loop
—~ Cohort loop
-~ Exposure pathway loop
I—~ Year loop
Determine year of maximum
cumulative risk and/or HQ to all
receptor/cohort individuals, given
receptor, cohort, and pathway
1 Next year
— Next exposure pathway
•— Next cohort
Next receptor
Figure 3-18c. Looping structure to determine critical year.
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1. Air inhalation
yes
yes
yes
yes
yes
yes
yes
yes
2. Soil ingestion
yes
yes
yes
yes
yes
yes
yes
yes
3. Water ingestion
yes
yes
yes
yes
yes
yes
yes
yes
4. Crop ingestion
no
yes
no
yes
yes
yes
yes
yes
5. Beef ingestion
no
no
no
no
yes
no
yes
no
6. Milk ingestion
no
no
no
no
no
yes
no
yes
7. Fish ingestion
no
no
yes
yes
yes
yes
no
no
8. Shower inhalation3
yes
yes
yes
yes
yes
yes
yes
yes
9. Infant breast milkb
yes
yes
yes
yes
yes
yes
yes
yes
s Shower inhalation applies only to child 4 and adult cohorts. All other pathways shown apply to all noninfant cohorts.
b Applies only to child 1 (infant) cohorts.
The nine individual exposure pathways considered by the human risk module are shown
in Table 3-7. All exposure pathways do not apply to all receptors; those that are applicable are
indicated by "yes" in the table. With respect to applicability among receptor cohorts, if a
pathway applies to a receptor, then it applies to all cohorts of that receptor, with two exceptions:
(1) shower inhalation applies only to child 4 and adult cohorts (younger children are assumed to
bathe as opposed to shower), and (2) the infant breast milk pathway applies only to breastfeeding
infants.14'15
Because the essence of a multipathway risk assessment is the evaluation of total risks
incurred as a result of simultaneous exposure from different pathways, the human risk module
considers four different pathway aggregations as shown in Table 3-8. The inhalation route
aggregates over the two inhalation pathways. The ingestion route aggregates over all ingestion
14 For HWIR99, the infant breast milk pathway is evaluated only for a single chemical, the dioxin species
2,3,7,8 -TCDD TEQ [CAS No. 1746-01-6], (That is, the logical flag ChemBreastMilkExp will be set to "true" only
for this chemical in the chemical properties input file, cp.ssf.)
15 For the infant breast milk pathway, the margin of exposure [MOE] (mg/kg-d) is analogous to HQ for
infant breast milk exposure. Hereinafter, HQ will be understood to mean "MOE" for the Child 1 cohort and breast
milk pathway.
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Table 3-8. Pathway Aggregations
Pathway
Inhalation
Uoute
Ingestion
Uoule
(iroundwaler
Mullipalhwav
1. Air inhalation
/
/
2. Soil ingestion
/
/
3. Water ingestion
/
/
/
4. Crop ingestion
/
/
5. Beef ingestion
/
/
6. Milk ingestion
/
/
7. Fish ingestion
/
/
8. Shower inhalation
/
/
/
pathways. The groundwater aggregation includes the two groundwater-based pathways (drinking
water ingestion and shower inhalation). Finally, the multipathway aggregation includes all
combinable16 pathways. (The infant breast milk pathway is a separate pathway that is not
relevant to the route aggregations and is not included in the table.)
The radial distance rings are set at 500 meters, 1,500 meters, and 2,000 meters from a
circle circumscribing the (square) WMU (Figure 3-19). Thus, the first set of risk and/or HQ
results is applicable only to those receptors residing within 500 meters of the WMU boundary.
The second set applies to all receptors within 1,000 meters, including those previously
considered within 500 meters. The third set applies to all receptors within 2,000 meters, which
includes all receptors within the overall Area of Interest.
3.3.11 Ecological Exposure Module
The ecological exposure (EcoEx) module, initially developed in support of the HWIR
3MRA, calculates the applied dose (in mg/kg-d) to ecological receptors that are exposed to
contaminants via ingestion of contaminated plants, prey, and media (i.e., soil, sediment, and
surface water). These dose estimates are then used as inputs to the ecological risk module. The
EcoEx module calculates exposures for each receptor placed within a terrestrial or freshwater
aquatic habitat (as defined in the site layout). Thus, exposure is a function of: (1) the habitat to
which the receptor is assigned; (2) the spatial boundaries of the species home range, (3) the food
items (plants and prey) that are available in a particular home range, (4) the dietary preferences
for food items that are available, and (5) the media concentrations in the receptor's home range.
16 The feasibility of pathway additivity is chemical-specific and is specified by the logical variables
ChemC add and ChemNC add in the chemical properties input file.
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Figure 3-19. WMU with three radial distance rings.
In essence, the module estimates an applied dose for birds, mammals, and selected herpetofauna
that reflects the spatial and temporal characteristics of the exposure (i.e., exposure is tracked
through time and space). Additional detail about the background and implementation of the
model is available in U.S. EPA (19991).
The conceptual approach in developing the ecological exposure assessment for HWIR
was to reflect the major sources of variability in ecological exposures. In particular, the approach
considers variability through: (1) the development of representative habitats; (2) selection of
receptors based on ecological region; (3) the recognition of opportunistic feeding and foraging
behavior using probabilistic methods; (4) the creation of a dietary scheme specific to region,
habitat, and receptor; and (5) the application of appropriate graphical tools to capture spatial
variability in exposure. The underlying framework for the EcoEx module is based on a
representative habitat scheme to increase the resolution of general terrestrial and freshwater
systems. The spatial characteristics of the site-based database were determined using a
geographic information system (GIS) delineation tool to define habitat boundaries and linkages,
home ranges, wetland areas, and surface waterbodies. A cross-referencing database was
developed to automate the selection of receptors and assign them to habitats based on habitat
characteristics and ecological region. A complete description of the habitats, home ranges,
receptors, and delineation scheme implemented in the GIS format is found in U.S. EPA (1999n).
Depending on the type of habitat and chemical-specific uptake and accumulation, animals
may be exposed through the ingestion of plants (both aquatic and terrestrial), soil invertebrates,
aquatic invertebrates, fish, terrestrial vertebrates, media, or any combination that is reflected by
the dietary preferences of the particular species. For example, an omnivorous vertebrate that
inhabits a freshwater stream corridor habitat may ingest fish, small terrestrial vertebrates found in
the stream corridor, terrestrial and aquatic plants, surface water, and soil. The dietary preferences
are independent of the chemical type and, therefore, contaminant concentrations in some food
items may be near zero for chemicals that do not bioaccumulate. The dietary preferences for
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each receptor are supported by an extensive exposure factors database containing information on,
for example, dietary habits and natural history for over 50 representative species of interest. The
module includes an innovative approach to characterizing the diet: a probabilistic algorithm that
cycles through the database on minimum and maximum prey preferences to simulate dietary
variability.
3.3.12 Ecological Risk Module
The ecological risk (EcoRisk) module calculates HQs17 for a suite of ecological receptors
assigned to habitats delineated for study sites. These receptors fall into eight receptor groups:
mammals, birds, herpetofauna, terrestrial plants, soil community, aquatic plants and algae,
aquatic community, and benthic community. The spatial resolution of the EcoRisk module is, to
a large degree, determined by both the home ranges and habitats delineated at each site.
Additional detail about the background and implementation of the ecological risk module is
available in U.S. EPA (1999m).
Home range areas are defined in terms of species-specific foraging area size as well as the
habitat and predator-prey interactions, that is, the home ranges are constrained by habitat
boundaries18 and represent predator-prey interactions. Spatially averaged concentrations in
media, plants, and prey items are calculated for each home range and used to estimate the applied
dose to receptors in the ecological exposure (EcoEx) module. In addition, soil concentrations for
each home range are compared to threshold concentrations for adverse effects in plants and soil
biota.
The habitat area is important in assessing risks to several receptor groups (e.g., benthic
community); exposures and associated risks are considered across the entire habitat rather than
for one or more home ranges. For example, contaminant concentrations to which the aquatic
community is exposed are represented by a habitat-wide average that may include multiple
stream reaches. The temporal resolution is based on annual average applied doses (for
comparison with EBs) and media concentrations (for comparison with CSCLs).
The HQs for all receptors assigned to the study site are calculated and placed into one of
five risk bins developed to assist decision-makers in creating appropriate risk metrics. The HQ
risk bins are used in developing cumulative distribution functions of risk and are defined as: (1)
below 0.1, (2) between 0.1 and 1, (3) between 1 and 10, (4) between 10 and 100, and (5) above
100. Each of the HQs calculated by the EcoRisk module has a series of attributes associated with
it that allows ecological risks to be interpreted in a number of ways. For instance, distance from
the source (i.e., 1 km, 1 to 2 km, or across the entire site) is important in understanding the
spatial character of potential ecological risks.
17 Hazard quotients are defined as: (1) the ratio between applied dose received from the ingestion of
contaminated media and food items and an ecological benchmark (EB in units of dose), and (2) the ratio between
the concentration in the medium of interest (soil, sediment, or surface water) and a chemical stressor concentration
limit (CSCL in units of concentration).
18
If the home range area is larger than the area of the habitat, the home range is presumed to extend
beyond the 2-km radius that defines the area of interest; that is, habitats are exclusive. If the home range is smaller
than the habitat, the entire home range is presumed to fall within the habitat boundaries within the area of interest.
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Other attributes considered relevant to ecological risks and regulatory decision-making
include the following:
# Habitat type (e.g., grassland, pond, permanently flooded forest)
# Habitat group (i.e., terrestrial, aquatic, and wetland)
# Rreceptor group (e.g., mammals, amphibians, soil community)
# Ttrophic level (i.e., producers, TL1, TL2, TL3 top predators).
The maximum HQ across the site is also reported along with its ecological risk attributes. This
metric was added for use in "pass/fail" analyses that may be needed to prioritize sites for
additional analyses.
In calculating receptor-specific HQs, the EcoRisk module does all of the necessary
accounting to develop distributions based on the specific receptor and habitat groupings of
interest. The EcoRisk module reads in information about the chemical concentrations that each
receptor is exposed to, calculates hazard quotients based on the EB or CSCL and the chemical
exposure information, and provides summaries of ecological risk information for the simulation
to determine when critical years with maximum HQs are experienced. For any given year, the set
of HQ data is stored as a series of distributions along with their attributes. As indicated above,
the cumulative frequency distributions are composed of a series of bins for different ranges of
HQ values. The bins are populated based on the number of receptors with HQ values in the range
defined for the given bin.
Each site is constructed as a set of habitats, each located within one or more distance
rings at the site, and a set of receptors inhabiting ranges within each of those habitats. Habitats
have a variety of characteristics, including a unique index identifier, a habitat type and group, a
number of reaches, a number of ranges containing receptors, and the receptors associated with
each range. Reaches, habitats, and ranges also have chemical concentrations associated with
them. Each receptor has an index, type, name, group, trophic level. To a large degree, the
habitats reflect differences in vegetative communities based on various land use and land cover
data layers. Home ranges are assigned to each habitat based on the median size of receptor
species' foraging and feeding ranges. Ecological receptors were grouped into four different home
range sizes: 1,000,000,000, 10,000,000, 1,000,000, and 100,000 square meters. These home
ranges were approximated for size (by an expanding a circular polygon) and randomly placed
within each habitat polygon so that they overlap to reflect predator-prey relationships.
Outputs are generated for three areas of the site relative to the distance from the edge of
the waste management unit. These distances are termed EcoRings and depict the following:
(1) habitats that fall within 1 km of the WMU, (2) habitats that fall between 1 and 2 km from the
WMU, and (3) habitats within 2 km of the WMU (i.e., across the entire site). It is important to
note that the HQ results for habitats that intersect both EcoRings are attributed to the risk results
for both of those distances. In other words, the habitat risks are not apportioned by distance, they
are reported as though they are positioned entirely within each distance ring. Because the
fundamental unit of this analysis is the representative habitat (not distance to the waste
management unit), it was considered inappropriate to truncate risks by distance.
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3.4 3MRA Modifications and Data Collection Requirements
As described previously, the 3MRA Model has the basic functionality and features
necessary to characterize risks for the purposes of the SI Study. However, because the specific
needs of the SI Study are somewhat different than those for HWIR, there will be some necessary
modifications to the 3MRA component models and modeling system. In addition, data
availability is better for the SI Study, both in terms of site-specific data from the SI Survey and
better, more complete data from other sources. Using these improved data sources in the 3MRA
model for the SI Study will require some modifications to the HWIR data collection
methodologies.
This section summarizes the model and data collection modifications that may be
necessary to implement the 3MRA model for the SI Study. It is intended to outline the major
topic areas and alternatives for planning purposes. Although this represents the major areas to be
addressed, other changes may be necessary as the analysis evolves. As the Phase I screening
analysis progresses, EPA will continue to investigate and expand on these modifications,
addressing those necessary before Phase II modeling begins.
3.4.1 Model Modifications
The basic functionality of the 3MRA model will be maintained for the SI Study.
Modifications of the HWIR modeling construct for the SI Study that can be handled with simple
changes in model inputs (e.g., risk bins, risk distance rings) are not addressed here. More
substantive revisions to the current model construct are needed to address the following
limitations.
# The current 3MRA system cannot model multiple units at a site.
# The current surface impoundment model cannot model a postclosure scenario or
catastrophic failures.
# The Exit Level Processor 2 (ELP2), which produces exit level waste
concentrations from the ELP1 risk database, does not meet SI Study objectives.
Current objectives require that the SI Study Phase II risk results be organized in terms of the SI
Study risk attributes that are beyond the dimensions currently addressed by 3MRA.
3.4.1.1 Multiple Surface Impoundments. Multiple surface impoundments cannot be
modeled simultaneously by the 3MRA Model, and modifying the current system to allow this
would require an extensive, system-wide redesign that is well beyond the scope of this effort. To
address risks from multiple units will require running the model separately for each unit and
combining risk results after modeling during postprocessing. This will require cognizance of this
need during data collection and site layout definition (i.e., an identical site layout for all units at a
site), as well as consideration of this need during the development of postprocessing techniques
for Phase II SI Study risk results.
3.4.1.2 SI Postclosure Scenario. As discussed in Section 3.3.1, the postclosure scenario
for the SI Study will be handled by using the LAU module to model a closed SI with sludge left
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Phase II Risk Assessment
in place. The LAU module has the functionality necessary to model a closed SI with just changes
in module inputs. Currently envisioned alternatives for using the LAU module in this context
include
# Combining the SI and LAU modules into a single source module
# Modification of the 3MRA Site Definition Processor to run the LAU module after
the SI module to model the SI post closure period and to combine results of the
two modules into a single source module output file
# Separate 3MRA model runs for operating and postclosure periods.
The latter option could be problematic because SI module outputs (i.e., sludge concentrations)
will be needed as inputs for the LAU module.
3.4.1.3 SI Catastrophic Failure. The SI Survey is collecting data on occurrences of
catastrophic failures within the SI universe. EPA will evaluate these results in deciding whether
to analyze the risks that could result from such events.
3.4.1.4 ELP - Postprocessing Options. The postprocessing functionality of the ELP2 is
not needed for the SI Study and this module will not be used during Phase II modeling. The
ELP1 does provide and access a database of the results needed for the SI Study objectives, but
additional data processing will be required to summarize results by the SI Study risk attributes:
# Multiple impoundments at a site
# Five regulatory status categories
# Three functional classes (storage, treatment, and disposal)
# Treatment types (e.g., biological, settling)
# Industry types
# Contaminants.
These represent additional dimensions to those currently provided by the 3MRA ELP1. Risk
outputs will need to be organized and compiled by these dimensions; this will require either
modifications to the 3MRA system or separate postprocessing of the Access database (ouput by
the ELP1) containing the Protective Summary Output Files (PSOFs). EPA will decide on the best
option, considering the need for automation (i.e., system modifications) in light of the number of
SI facilities modeled and data analyses necessary to meet decision-making needs.
3.4.2 Data Collection Requirements
In general, data collection methods developed and documented for the HWIR
implementation of 3MRA will be adequate for the SI Study. National and regional data collected
for HWIR will likely be suitable for SI Study purposes, although it may be necessary to employ
automated HWIR methodologies to collect data to characterize regions with SI sites not covered
for the HWIR sites. The main effort necessary for the SI Study will be the collection of site-based
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Section 3.0
Phase II Risk Assessment
data to characterize the SI Study sites. Specific issues associated with this effort are discussed in
the following section.
3.4.2.1 Utilization of SI Survey Data. The SI Survey provides a rich source of data for
the Phase II analyses, providing accurate facility locations, SI dimensions and operating
characteristics, waste properties and constituents, soil and hydrogeologic information, and
receptor locations. Incorporation of these data will require some modification to current 3MRA
data collection methods, but will result in a more robust and accurate dataset. EPA is currently
designing the survey data coding and database to be compatible with 3MRA data needs. Most of
the following points relate to the use of these data.
3.4.2.2 Modificationof the Area of Interest (API). Because multiple Sis are present at
many facilities, and because the SI Study design requires risks to be combined for such multiple
units, a single AOI will be needed so that receptors and other aspects of the site layout will be
identical for all units at the site. This, and knowledge of the exact shape of each SI will require an
irregular AOI, as shown in the example in Figure 3-20. This is not a problem from the system
standpoint, but does represent a change from the circular 3MRA AOI.
3.4.2.3 Receptor Locations. Ecological receptors and habitats will be delineated as they
were for the HWTR analysis. Human receptor data are being collected in the survey as residence
locations on topographic maps. Initial evaluation of the survey results suggests that these data are
variable in extent, quality, and time frame from respondent to respondent. It is possible that the
3MRA automated data collection methodologies for U.S. Census data will be needed to
supplement the survey receptor data. The combined dataset will provide improved resolution and
accuracy of receptor locations over the use of the Census data alone, especially in sparsely
populated areas where residences are far apart. In more populated areas, the Census data will be
valuable in providing additional information on the number of households in dense residential
areas. The combination of these data at an SI site with varying residential densities is shown in
Figure 3-20.
3.4.2.4 Watershed and Waterbodv Delineation. Limited resolution and data quality
problems of the topographic and hydrographic base data posed a challenge during watershed and
waterbody delineation for the HWIR 3MRA. Fortunately, higher-resolution, better-quality data
are now available as the National Elevation Dataset (NED) and the National Hydrography
Dataset (NHD), as well as in the topographic maps available for every SI facility. These data,
combined with the now finalized 3MRA site layout system, should greatly improve the efficiency
of waterbody and watershed delineations for the SI Study sites addressed in the Phase II analysis.
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Section 3.0
Phase II Risk Assessment
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Figure 3-20. Area of interest for multiple SI site illustrating overlay of
topographic and U.S. census data.
3-68
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Section 4.0
References
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Risk Assessment. Risk Assessment Forum, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1991b. Risk Assessment Guidance for
Superfund, Volume 1: Human Health Evaluation Manual (Part B, Development of Risk-
Based Preliminary Remediation Goals). Interim. Publication 9355.0-30. Office of
Emergency and Remedial Response, Washington, DC. NTIS PB91-921359/CCE.
U.S. EPA (Environmental Protection Agency). 1992a. Draft report: A cross-species scaling
factor for carcinogen risk assessment based on equivalence of mg/kg3/4/day. Federal
Register 57 FR 24152, June 5, 1992.
U.S. EPA (Environmental Protection Agency). 1992b. Framework for Ecological Risk
Assessment. Risk Assessment Forum, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1992c. Guidelines for Exposure Assessment.
Office of Research and Development, Office of Health and Environmental Assessment,
Washington, DC.
U.S. EPA (Environmental Protection Agency). 1993a. Parameter values for the EPA's
composite module for landfills (EPACML) used in developing nationwide regulations:
Toxicity Characteristic Rule, Office of Solid Waste, Washington, D.C., 20460.
4-6
-------
Section 4.0
References
U.S. EPA (Environmental Protection Agency). 1993b. Provisional Guidance for Quantitative
Risk Assessment of Polycyclic Aromatic Hydrocarbons. EPA/600/R-93-089. Office of
Health and Environmental Assessment, Environmental Criteria and Assessment Office,
Cincinnati, OH.
U.S. EPA (Environmental Protection Agency). 1993c. Technical Basis for Deriving Sediment
Quality Criteria for Nonionic Organic Contaminants for the Protection ofBenthic
U.S. EPA (Environmental Protection Agency). 1993d. Wildlife Exposure Factors Handbook.
EPA/600/R-93/187. Washington, DC.
U.S. Environmental Protection Agency (Environmental Protection Agency). 1994a. Guidance
Manual for the Integrated Exposure Uptake Biokinetic Model for Lead in Children.
EPA/540/R-93/081.Office of Solid Waste and Emergency Response, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1994b. Integrated Risk Information System
(IRIS) Database.
U.S. EPA (Environmental Protection Agency). 1995a. Hazardous waste management system:
identification and listing of hazardous waste—Hazardous Waste Identification Rule
(HWIR). 60 Federal Register 66344.
U.S. EPA (Environmental Protection Agency). 1995b. Great Lakes Water Quality Initiative
Criteria Documents for the Protection of Wildlife DOT, Mercury, 2,3,78-TCDD, and
PCBs. EPA-820-B-95-008. Office of Water, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1996a. EPA's Composite Model for Leachate
Migration with Transformation Products (EPACMTP), Background Document. Office of
Solid Waste, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1996b. EPA 's Composite Model for Leachate
Migration with Transformation Products (EPACMTP), Background Document for
Metals. Office of Solid Waste, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1996c. EPA's Composite Model for Leachate
Migration with Transformation Products (EPACMTP), Background Document for Finite
Source Methodology for Chemical with Transformation Products. Office of Solid Waste,
Washington, DC.
U.S. EPA (Environmental Protection Agency), 1996d. Soil Screening Guidance: Fact Sheet.
Publication 9355.4-14FSA. Office of Emergency and Remedial Response, Washington,
DC.
U.S. Environmental Protection Agency (U.S. EPA). 1996e. Soil Screening Guidance: Technical
Background Document. EPA/540/R-95/128. Office of Solid Waste and Emergency
Response, Washington, DC.
4-7
-------
Section 4.0
References
U.S. EPA (Environmental Protection Agency). 1996f. 1995 Updates: Water Quality Criteria
Documents for the Protection of Aquatic Life in Ambient Water. EPA-820-B-96-001.
Office of Water, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1996g. Amphibian Toxicity Data for Water
Quality Criteria Chemicals. EPA/600/R-96/124. National Health and Environmental
Effects Research Laboratory, Corvallis, OR.
U.S. EPA (Environmental Protection Agency). 1997a. EPA's Composite Model for Leachate
Migration with Transformation Products (EPACMTP), User's Guide. Office of Solid
Waste, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1997b. Health Effects Assessment Summary
Tables (HEAST), FY 1997 Update. EPA-540-R-97-036. Office of Emergency and
Remedial Response, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1997a. Exposure Factors Handbook. EPA
600/P-95/002Fa. Office of Research and Development, Washington, DC. August.
U.S. EPA (Environmental Protection Agency). 1997b. Supplemental Background Document;
Nongroundwater Pathway Risk Assessment; Petroleum Process Waste Listing
Determination. 68-W6-0053. Office of Solid Waste, Washington, DC. March 20.
U.S. Environmental Protection Agency (U.S. EPA). 1997c. Health Effects Assessment Summary
Tables (HEAST). FY-1997 Annual. EPA 540/R-94/020. Office of Solid Waste and
Emergency Response, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1998a. Industrial Waste Air Model Technical
Background Document. EPA 530-R-99-004. Office of Solid Waste, Washington, DC.
December.
U.S. Environmental Protection Agency (U.S. EPA). 1998b. Industrial Waste Air Model (IWAIR)
User's Guide. EPA530-R-99-005. Office of Solid Waste, Washington, DC. December.
U.S. EPA (Environmental Protection Agency). 1998c. An SAB Report: Review of the Surface
Impoundments Study (SIS) Plan. Science Advisory Board, Washington, DC., Page 2
cover letter.
U.S. EPA (Environmental Protection Agency). 1998d. The U.S. EPA TEF Values. Office of
Research and Development, National Center for Environmental Assessment. Available
online at http://www.epa.gov/nceawwwl/dchem.htm.
U.S. EPA (Environmental Protection Agency). 1998e. Waste Minimization Prioritization Tool
Spreadsheet Document for the RCRA Waste Minimization PBT Chemical List Docket (#
F-98-MMLP-FFFFF). Office of Solid Waste, Washington, DC.
http://www.epa.gov/epaoswer/ hazwaste/minimize/ chemlist/index.htm. September.
4-8
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Section 4.0
References
U.S. EPA (Environmental Protection Agency). 1998f. Guidelines for Ecological Risk
Assessment. EPA/630/R-95/002F. Risk Assessment Forum, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1999a. Source Modules for Tanks And Surface
Impoundments: Background and Implementation for the Multimedia, Multipathway, and
Multireceptor Risk Assessment (3MRA) for HWIR99. Office of Solid Waste, Washington,
DC. October.
U.S. EPA (Environmental Protection Agency). 1999b. Source Modules for Nonwastewater Waste
Management Units (Land Application Units, Wastepiles, And Landfills): Background and
Implementation for the Multimedia, Multipathway, and Multireceptor Risk Assessment
(3MRA) for HWIR99. Office of Solid Waste. Washington, DC. October.
U.S. EPA (Environmental Protection Agency). 1999c. User's Guide For The Industrial Source
Complex (ISC3) Dispersion Models for Use in the Multimedia, Multipathway and
Multireceptor Risk Assessment (3MRA) for HWIR99. Volume I: User Instructions.
Volume II: Description of Model Algorithms. Office of Research and Development,
Research Triangle Park, NC. June.
U.S. EPA (Environmental Protection Agency). 1999d. Air Module Pre- and Postprocessor:
Background and Implementation for the Multimedia, Multipathway, and Multireceptor
Risk Assessment (3MRA) for HWIR99. Office of Solid Waste, Washington, DC. October.
U.S. EPA (Environmental Protection Agency). 1999e. Data Collection for the Hazardous Waste
Identification Rule. Section 4.0 Meteorological Data. Office of Solid Waste,
Washington, DC.
U.S. EPA (Environmental Protection Agency). 1999f. Watershed Module: Background and
Implementation for the Multimedia, Multipathway, and Multireceptor Risk Assessment
(3MRA) for HWIR99. Office of Solid Waste, Washington, DC. October.
U.S. EPA (Environmental Protection Agency). 1999g. The Vadose and Saturated Zone Modules
Extractedfrom EPACMTP for HWIR99. Office of Solid Waste, Washington, DC. August.
U.S. EPA (Environmental Protection Agency). 1999i. Farm FoodChainModule: Background
and Implementation for the Multimedia, Multipathway, and Multireceptor Risk
Assessment (3MRA) for HWIR99. Draft Report. July. Located at http://www.epa.gov/
epaoswer/hazwaste/id/hwirwste/ri sk. htm
U. S. EPA (EPA). 1999j. Terrestrial Food Chain Module: Background and Implementation for
the Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) for HWIR99
for the Hazardous Waste Identification Rule. Draft Report. July. Located at
http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk.htm
4-9
-------
Section 4.0
References
U.S. EPA ((Environmental Protection Agency). 1999k. Aquatic Food Web Module: Background
and Implementation for the Multimedia, Multipathway, and Multireceptor Risk
Assessment (3MRA) for HWIR99for the Hazardous Waste Identification Rule. Draft
Report. July. Located at http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk.htm
U.S. EPA (Environmental Protection Agency). 19991. Ecological Exposure Module: Background
and Implementation for the Multimedia, Multipathway, and Multireceptor Risk
Assessment (3MRA) for HWIR99. Draft Report. July. Located at
http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk.htm
U.S. EPA (Environmental Protection Agency). 1999m. Ecological Risk Module: Background
and Implementation for the Multimedia, Multipathway, and Multireceptor Risk
Assessment (3MRA) for HWIR99 for the Hazardous Waste Identification Rule. Draft
Report. July. Located at http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk.htm
U.S. EPA (Environmental Protection Agency). 1999n. Data Collection for the Hazardous Waste
Identification Rule, Section 13: Ecological Receptors and Habitats. Draft Report.
October. Located at http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk.htm
U.S. EPA (Environmental Protection Agency). 1999o. Data Collection for the Hazardous Waste
Identification Rule, Section 11: Aquatic Food Web Data. Draft Report. October. Located
at http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk.htm
U.S. EPA (Environmental Protection Agency). 1999p. The HWIR Surface Water Module. Draft
Report. July. Located at http://www.epa.gov/epaoswer/hazwaste/id/hwirwste /risk.htm
U.S. EPA (Environmental Protection Agency). 1999q. Human Exposure Module for HWIR99
Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Model. Office of
Solid Waste, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1999r. Human Risk Module for HWIR99
Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Model. Office of
Solid Waste, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1999s. Integrated Risk Information System
(IRIS). National Center for Environmental Assessment, Office of Research and
Development, Washington, DC. Website at http://www.epa.gov/iris/subst/index.html.
U.S. EPA (Environmental Protection Agency). 1999t. Data Collection for the Hazardous Waste
Identification Rule. Section 9.0 Human Receptor Data. Office of Solid Waste,
Washington, DC.
U.S. EPA (Environmental Protection Agency). 1999u. The HWIR Surface Water Module. Office
of Solid Waste, Washington, DC.
4-10
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Section 4.0
References
U.S. EPA (Environmental Protection Agency). 1999v. Documentation for the FRAMES-HWIR
Technology Software System, Volume 7: Exit Level Processor-I. Office of Research and
Development. Athens, GA. October.
U.S. EPA (Environmental Protection Agency). 1999w. Documentation for the FRAMES-HWIR
Technology Software System, Volume 15: Risk Visualization Processor and Exit Level-II
Processor. Office of Research and Development. Athens, GA. October.
U.S. EPA (Environmental Protection Agency). 1999x. Data Collection for the Hazardous
Waste Identification Rule: Section 8. Human Exposure Factors. Office of Solid Waste,
Washington, DC.
U.S. EPA (Environmental Protection Agency). 1999y. National Recommended Water Quality
Criteria-Correction. EPA/822-Z-99-001. Office of Water, Washington, DC.
U. S. EPA. 1999z. Data Requirements and Confidence Indicators for Ecological Benchmarks
Supporting Exemption Criteria for the Hazardous Waste Identification Rule (HWIR99).
(Environmental Protection Agency), Office of Solid Waste, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1999aa. Framework for Finite-Source
Multimedia, Multipathway, andMultireceptor Risk Assessment 3MRA. Office of Solid
Waste, Washington, DC. Located at http://www.epa.gov/epaoswer/hazwaste/id/
hwirwste/ri sk. htm
U.S. EPA (Environmental Protection Agency). In press. Methodology for Assessing Health
Risks Associated with Indirect Exposure to Combustor Emissions. Update to EPA/600/6-
90/003, Methodology for Assessing Health Risks Associated with Indirect Exposure to
Combustor Emissions. National Center for Environmental Assessment, Cincinnati, OH.
Located at http://www.epa.gov/ncea/combust.htm
Veith, G. D., K. J. Macek, S. R. Petrocelli, and J. Carroll. 1980. An evaluation of using partition
coefficients and water solubility to estimate bioconcentration factors for organic
chemicals in fish. In: Aquatic Toxicology, ASTMSTP 707, J. G. Eaton, P. R. Parrish, and
A. C. Hendricks (eds.). American Society for Testing and Materials, pp. 116-129.
Venkatram, A. 1988. A Simple Model for Dry Deposition and Particle Settling. Subcontractor
Progress Report 2 (including addendum). EPA Contract No. 68D70002, Work
Assignment No. 1-001.
Zaranko, Danuta T., Ronald W. Griffiths, and Narinder K. Kaushik. 1997. Biomagnification of
polychlorinated biphenyls through a riverine food web. Environmental Toxicology and
Chemistry, 16(7): 1463-1471.
4-11
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Appendix A
Comprehensive List of Toxicity Benchmarks for
Human Health Risk Assessment
-------
Appendix A
Table A-l. Comprehensive List of Toxicity Benchmarks
Benchmarks Selected for Use in Deriving Phase 1A Screening Levels
Oral CSF Oral CSF Inh CSF
RfC (food) (per (H20) (per Inh URF (per
Constituent Name CAS No. Oral (RfD) (mg/m') mg/kg/d) mg/kg/d) (per ug/m') mg/kg/d)
Acenaphthene
83329
6.0E-02
Acetaldehyde
75-07-0
9.0E-03
2.2E-6
Acetone
67641
1.0E-01
Acetonitrile
75-05-8
6.0E-02
Acetophenone
98-86-2
1.0E-01
Acrolein
107-02-8
2.0E-02
2.0E-05
Acrylamide
79-06-1
2.0E-04
4.5E+0
4.5E+0
1.3E-3
4.5E+0
Acrylic acid
79-10-7
5.0E-01
1.0E-03
Acrylonitrile
107-13-1
1.0E-03
2.0E-03
5.4E-1
5.4E-1
6.8E-5
2.4E-1
Aldicarb
116-06-3
1.0E-03
Aldrin
309-00-2
3.0E-05
1.7E+1
1.7E+1
4.9E-3
1.7E+1
Allyl alcohol
107-18-6
5.0E-03
Allyl chloride (3-chloropropene)
107-05-1
1.0E-03
Ammonium vanadate
7803-55-6
Amonium perchlorate
7790-98-9
Aniline
62-53-3
1.0E-03
5.7E-3
5.7E-3
Anthracene
120-12-7
3.0E-01
Antimony
7440-36-0
4.0E-04
Antimony trioxide
1309-64-4
4.0E-04
2.0E-04
Aramite
140-57-8
5.0E-02
2.5E-2
2.5E-2
7.1 E-6
2.5E-2
Arsenic, inorganic
7440-38-2
3.0E-04
1.5E+0
1.5E+0
4.3E-3
Barium
7440-39-3
7.0E-02
5.0E-04
Benz(a)anthracene
56-55-3
7.3E-01
7.3E-01
Benzene
71-43-2
2.9E-23
2.9E-23
8.3E-63
2.9E-23
Benzidine
92-87-5
3.0E-03
2.3E+2
2.3E+2
6.7E-2
2.3E+2
Benzo(a)pyrene
50-32-8
7.3E+0
7.3E+0
Benzo(b)fluoranthene
205-99-2
7.3E-01
7.3E-01
Benzyl alcohol
100-51-6
3.0E-01
Benzyl chloride
100-44-7
1.7E-1
1.7E-1
Beryllium
7440-41-7
2.0E-03
2.0E-05
2.4E-3
8.4E+0
Bis(2-chloroethyl)ether
111-44-4
1.1 E+0
1.1 E+0
3.3E-4
1.1 E+0
Bis(2-chloroisopropyl)ether
39638-32-9
4.0E-02
7.0E-2
7.0E-2
1 .OE-5
3.5E-2
Bis(2-ethylhexyl)phthalate
(DEHP; also di-)
117-81-7
2.0E-02
1.4E-2
1.4E-2
Bis(chloromethyl)ether
542-88-1
2.2E+2
2.2E+2
6.2E-2
2.2E+2
Bromoform
75-25-2
2.0E-02
7.9E-3
7.9E-3
1.1 E-6
3.9E-3
Bromomethane (methyl bromide)
74-83-9
1.4E-03
5.0E-03
Butadiene, 1,3-
106-99-0
2.8E-4
1.8E+0
Butanol, n- (n-butyl alcohol)
71-36-3
1.0E-01
(continued)
A-3
-------
Appendix A
Table A-l. (continued)
Benchmarks Selected for Use in Deriving Phase 1A Screening Levels
Oral CSF Oral CSF Inh CSF
RfC (food) (per (H20) (per Inh URF (per
Constituent Name CAS No. Oral (RfD) (mg/m') mg/kg/d) mg/kg/d) (per ug/m') mg/kg/d)
Butyl benzyl phthalate
85-68-7
2.0E-01
Cadmium
7440-43-9
5.0E-04
1.8 E-3
Carbon disulfide
75-15-0
1.0E-01
7.0E-01
Carbon tetrachloride
(tetrachloromethane)
56-23-5
7.0E-04
1.3E-1
1.3E-1
1.5E-5
5.3E-2
Chloral
75-87-6
2.0E-03
Chloral hydrate
[trichloroacetaldehyde hydrate]
302-17-0
Chlordane
57-74-9
5.0E-04
7.0E-04
3.5E-1
3.5E-1
1 .OE-4
1.3E+0
Chlordecone
143-50-0
Chlorine cyanide (cyanogen
chloride)
506-77-4
5.0E-02
Chloro-1,3-butadiene, 2-
(chloroprene)
126-99-8
2.0E-02
7.0E-03
Chloroaniline, 4- (p-)
106-47-8
4.0E-03
Chlorobenzene
108-90-7
2.0E-02
2.0E-02
Chlorobenzilate
510-15-6
2.0E-02
2.7E-1
2.7E-1
7.8E-5
2.7E-1
Chlorodibromomethane
(dibromochloromethane)
124-48-1
2.0E-02
8.4E-2
8.4E-2
Chloroform (trichloromethane)
67-66-3
1.0E-02
6.1 E-3
6.1 E-3
2.3E-5
8.1 E-2
Chloromethane (methyl chloride)
74-87-3
1.3E-2
1.3E-2
1.8E-6
6.3E-3
Chloromethyl methyl ether
107-30-2
Chloronaphthalene, beta-
91-58-7
8.0E-02
Chlorophenol, 2-
95-57-8
5.0E-03
Chromium (III), insoluble salts
16065-83-1
1.5E+00
Chromium (VI)
18540-29-9
3.0E-03
1.2E-2
4.1 E+1
Chromium (VI) - chromic acid
mists & dissolved Cr aerosols
18540-29-9
8.0E-06
Chromium (VI) - Cr particulates
18540-29-9
1.0E-04
Chrysene
218-01-9
7.3E-03
7.3E-03
cis-1,3-Dichloropropylene
10061-01-5
Cobalt (and cmpds)
7440-48-4
6.0E-02
Copper
7440-50-8
Cresol mixtures
1319-77-3
Cresol, m- (3-methylphenol)
108-39-4
5.0E-02
Cresol, o- (2-Methylphenol)
95-48-7
5.0E-02
Cresol, p- (4-methylphenol)
106-44-5
5.0E-03
Cumene
98-82-8
1.0E-01
4.0E-01
Cyanide (amenable)
57-12-5
2.0E-02
Cyanogen bromide
506-68-3
9.0E-02
(continued)
A-4
-------
Appendix A
Table A-l. (continued)
Benchmarks Selected for Use in Deriving Phase 1A Screening Levels
Oral CSF Oral CSF Inh CSF
RfC (food) (per (H20) (per Inh URF (per
Constituent Name CAS No. Oral (RfD) (mg/m') mg/kg/d) mg/kg/d) (per ug/m') mg/kg/d)
Cyclohexanol
108-93-0
Cyclohexanone
108-94-1
5.0E+00
DDD (p,p-
dichlorodiphenyldichloroethane)
72-54-8
2.4E-1
2.4E-1
DDE (p,p'-
dichlorodiphenyldichloroethylene)
72-55-9
3.4E-1
3.4E-1
DDT (p,p'-
dichlorodiphenyltrichloroethane)
50-29-3
5.0E-04
3.4E-1
3.4E-1
9.7E-5
3.4E-1
Diallate
2303-16-4
6.1 E-2
6.1 E-2
Dibenzo(a,h)anthracene
53-70-3
7.3E+00
7.3E+00
Dibromo-3-chloropropane, 1,2-
(DBCP)
96-12-8
2.0E-04
1.4E+0
1.4E+0
6.9E-7
2.4E-03
Dibromoethane, 1,2- (ethylene
dibromide)
106-93-4
2.0E-04
8.5E+1
8.5E+1
2.2E-4
7.6E-1
Dichlorobenzene, 1,2- (o-)
95-50-1
9.0E-02
2.0E-01
Dichlorobenzene, 1,4-
106-46-7
8.0E-01
2.4E-2
2.4E-2
Dichlorobenzidine, 3,3'-
91-94-1
4.5E-1
4.5E-1
Dichlorobromomethane
(bromodichloromethane)
75-27-4
2.0E-02
6.2E-2
6.2E-2
Dichlorodifluoromethane [CFC-
12]
75-71-8
2.0E-01
2.0E-01
Dichloroethane, 1,1-
75-34-3
1.0E-01
5.0E-01
Dichloroethane, 1,2- (ethylene
dichloride)
107-06-2
9.1 E-2
9.1 E-2
2.6E-5
Dichloroethylene, 1,1-
75-35-4
9.0E-03
6.0E-1
6.0E-1
5.0E-5
2.0E-1
Dichloroethylene, 1,2- (cis)
156-59-2
1.0E-02
Dichloroethylene, 1,2- (trans)
156-60-5
2.0E-02
Dichlorophenol, 2,4-
120-83-2
3.0E-03
Dichlorophenoxyacetic acid, 2,4-
(2,4-D)
94-75-7
1.0E-02
Dichloropropane, 1,2-
78-87-5
4.0E-03
6.8E-2
6.8E-2
Dieldrin
60-57-1
5.0E-05
1.6E+1
1.6E+1
4.6E-3
1.6E+1
Diethyl phthalate
84-66-2
8.0E-01
Diethylstilbestrol
56-53-1
4.7E+3
4.7E+3
Dimethoate
60-51-5
2.0E-04
Dimethoxybenzidine, 3,3'-
119-90-4
1.4E-2
1.4E-2
Dimethylbenz[a]anthracene,
7,12-
57-97-6
Dimethylbenzidine, 3,3'-
119-93-7
9.2E+0
9.2E+0
Dimethylformamide, N,N-
68-12-2
1.0E-01
3.0E-02
Dimethylphenol, 2,4-
105-67-9
2.0E-02
Dimethylphenol, 3,4-
95-65-8
1.0E-03
Dimethylphthalate
131-11-3
Di-n-butyl phthalate
84-74-2
1.0E-01
(continued)
A-5
-------
Appendix A
Table A-l. (continued)
Benchmarks Selected for Use in Deriving Phase 1A Screening Levels
Oral CSF Oral CSF Inh CSF
RfC (food) (per (H20) (per Inh URF (per
Constituent Name CAS No. Oral (RfD) (mg/m') mg/kg/d) mg/kg/d) (per ug/m') mg/kg/d)
Dinitrobenzene, 1,3- (m-)
99-65-0
1.0E-04
Dinitrophenol, 2,4-
51-28-5
2.0E-03
Dinitrotoluene, 2,4-
121-14-2
2.0E-03
Dinitrotoluene, 2,6-
606-20-2
1.0E-03
Di-N-octyl phthalate
117-84-0
2.0E-02
Dinoseb
88-85-7
1.0E-03
Dioxane, 1,4-
123-91-1
1.1 E-2
1.1 E-2
Diphenylamine, N,N-
122-39-4
2.5E-02
Diphenylhydrazine, 1,2-
122-66-7
8.0E-1
8.0E-1
2.2E-4
8.0E-1
Direct Black 38
1937-37-7
8.6E+0
8.6E+0
Direct Blue 6
2602-46-2
8.1 E+0
8.1 E+0
Direct Brown 95
16071-86-6
9.3E+0
9.3E+0
Disulfoton
298-04-4
4.0E-05
Endosulfan
115-29-7
6.0E-03
Endothall
145-73-3
2.0E-02
Endrin
72-20-8
3.0E-04
Epichlorohydrin
106-89-8
2.0E-03
1.0E-03
9.9E-3
9.9E-3
1.2E-6
4.2E-3
Epoxybutane, 1,2-
106-88-7
2.0E-02
Ethoxyethanol acetate, 2-
111-15-9
3.0E-01
Ethoxyethanol, 2- (ethylene glycol
monoethyl ether)
110-80-5
4.0E-01
2.0E-01
Ethyl acetate
141-78-6
9.0E-01
Ethyl chloride (chloroethane)
75-00-3
1.0E+01
Ethyl ether
60-29-7
2.0E-01
Ethyl methacrylate
97-63-2
9.0E-02
Ethyl methanesulfonate
62-50-0
Ethylbenzene
100-41-4
1.0E-01
1.0E+00
Ethylene glycol
107-21-1
2.0E+00
Ethylene oxide
75-21-8
1.0E+0
1.0E+0
1 .OE-4
3.5E-1
Ethylene thiourea
96-45-7
8.0E-05
1.1 E-1
1.1 E-1
Fluoranthene
206-44-0
4.0E-02
Fluorene
86-73-7
4.0E-02
Fluoride
16984-48-8
Formaldehyde
50-00-0
2.0E-01
1.3E-5
4.5E-2
Formic acid
64-18-6
2.0E+00
Furan
110-00-9
1.0E-03
Furfural
98-01-1
3.0E-03
5.0E-02
Glycidaldehyde
765-34-4
4.0E-04
1.0E-03
Heptachlor
76-44-8
5.0E-04
4.5E+0
4.5E+0
1.3E-3
4.5E+0
Heptachlor epoxide
1024-57-3
1.3E-05
9.1 E+0
9.1 E+0
2.6E-3
9.1 E+0
Hexachlorobenzene
118-74-1
8.0E-04
1.6E+0
1.6E+0
4.6E-4
1.6E+0
(continued)
A-6
-------
Appendix A
Table A-l. (continued)
Benchmarks Selected for Use in Deriving Phase 1A Screening Levels
Oral CSF Oral CSF Inh CSF
RfC (food) (per (H20) (per Inh URF (per
Constituent Name CAS No. Oral (RfD) (mg/m') mg/kg/d) mg/kg/d) (per ug/m') mg/kg/d)
Hexachlorobutadiene
87-68-3
2.0E-04
7.8E-2
7.8E-2
2.2E-5
7.8E-2
Hexachlorocyclohexane, alpha-
319-84-6
6.3E+0
6.3E+0
1.8E-3
6.3E+0
Hexachlorocyclohexane, beta-
319-85-7
1.8E+0
1.8E+0
5.3E-4
1.8E+0
Hexachlorocyclohexane, gamma-
(lindane)
58-89-9
3.0E-04
1.3E+0
1.3E+0
Hexachlorocyclopentadiene
77-47-4
7.0E-03
7.0E-05
Hexachloroethane
67-72-1
1.0E-03
1.4E-2
1.4E-2
4.0E-6
1.4E-2
Hexachlorophene
70-30-4
3.0E-04
Hexane, n-
110-54-3
6.0E-02
2.0E-01
Hydrazine
302-01-2
3.0E+0
3.0E+0
4.9E-3
1.7E+1
ldeno[1,2,3-cd]pyrene
193-39-5
7.3E-01
7.3E-01
Isobutyl alcohol
78-83-1
3.0E-01
Isophorone
78-59-1
2.0E-01
9.5E-4
9.5E-4
Lead and cmpds (inorganic)
7439-92-1
Maleic anhydride
108-31-6
1.0E-01
Maleic hydrazide
123-33-1
5.0E-01
Manganese
7439-96-5
1.4E-01
5.0E-05
Mercuric chloride
7487-94-7
3.0E-04
Mercury (elemental)
7439-97-6
3.0E-04
Met hacrylonit rile
126-98-7
1.0E-04
7.0E-04
Methanol
67-56-1
5.0E-01
Methomyl
16752-77-5
2.5E-02
Methoxychlor
72-43-5
5.0E-03
Methoxyethanol acetate, 2-
110-49-6
2.0E-03
Methoxyethanol, 2- (ethylene
glycol methyl ether)
109-86-4
1.0E-03
2.0E-02
Methyl ethyl ketone
78-93-3
6.0E-01
1.0E+00
Methyl isobutyl ketone
108-10-1
8.0E-02
8.0E-02
Methyl mercury
22967-92-6
1.0E-04
Methyl methacrylate
80-62-6
1.4E+00
7.0E-01
Methyl parathion
298-00-0
2.5E-04
Methyl tert-butyl ether
1634-04-4
3.0E+00
Methylaniline, 2- (o-toluidine)
95-53-4
2.4E-1
2.4E-1
Methylcholanthrene, 3-
56-49-5
Methylene bromide
74-95-3
1.0E-02
Methylene chloride
(dichloromethane)
75-09-2
6.0E-02
3.0E+00
7.5E-3
7.5E-3
4.7E-7
Methylene-bis(2-chloroaniline),
4,4'- (MBOCA)
101-14-4
7.0E-04
1.3E-1
1.3E-1
3.7E-5
1.3E-1
Molybdenum
7439-98-7
5.0E-03
Naphthalene
91-20-3
2.0E-02
3.0E-03
Nickel subsulfide
12035-72-2
4.8E-4
1.7E+0
(continued)
A-7
-------
Appendix A
Table A-l. (continued)
Benchmarks Selected for Use in Deriving Phase 1A Screening Levels
Oral CSF Oral CSF Inh CSF
RfC (food) (per (H20) (per Inh URF (per
Constituent Name CAS No. Oral (RfD) (mg/m') mg/kg/d) mg/kg/d) (per ug/m') mg/kg/d)
Nickel, soluble salts
7440-02-0
2.0E-02
Nitrobenzene
98-95-3
5.0E-04
2.0E-03
Nitropropane, 2-
79-46-9
2.0E-02
2.7E-3
9.4E+0
N-Nitrosodiethylamine
55-18-5
1.5E+2
1.5E+2
4.3E-2
1.5E+2
N-Nitrosodimethylamine (N-
methyl-N-nitroso-methanamine)
62-75-9
5.1 E+1
5.1 E+1
1.4E-2
5.1 E+1
N-Nitroso-di-n-butylamine
924-16-3
5.4E+0
5.4E+0
1.6E-3
5.4E+0
N-Nitrosodi-n-propylamine
621-64-7
7.0E+0
7.0E+0
N-Nitrosodiphenylamine
86-30-6
4.9E-3
4.9E-3
N-Nitroso-N-methylethylamine
10595-95-6
2.2E+1
2.2E+1
N-Nitrosopiperidine
100-75-4
N-Nitrosopyrrolidine
930-55-2
2.1 E+0
2.1 E+0
6.1 E-4
2.1 E+0
Octamethylpyrophosphoamide
152-16-9
2.0E-03
Parathion
56-38-2
6.0E-03
Pentachlorobenzene
608-93-5
8.0E-04
Pentachlorodibenzofuran,
1,2,3,7,8-
57117-41-6
7.5E+3
7.5E+3
1.7E+0
7.5E+3
Pentachlorodibenzofuran,
2,3,4,7,8-
57117-31-4
7.5E+4
7.5E+4
1.7E+1
7.5E+4
Pentachlorodibenzo-p-dioxin,
1,2,3,7,8-
40321-76-4
1.5E+5
1.5E+5
3.3E+1
1.5E+5
Pentachloronitrobenzene
82-68-8
3.0E-03
2.6E-1
2.6E-1
Pentachlorophenol
87-86-5
3.0E-02
1.2E-1
1.2E-1
Perchlorate
14797-73-0
Phenol
108-95-2
6.0E-01
Phenylenediamine , m-
108-45-2
6.0E-03
Phorate
298-02-2
2.0E-04
Phthalic anhydride
85-44-9
2.0E+00
1.2E-01
Polychlorinated biphenyls
1336-36-3
2
0.4
0.0001
see note
Pronamide
23950-58-5
7.5E-02
Propylene oxide
75-56-9
3.0E-02
2.4E-1
2.4E-1
3.7E-6
1.3E-2
Pyrene
129-00-0
3.0E-02
Pyridine
110-86-1
1.0E-03
Safrole
94-59-7
Selenium
7782-49-2
5.0E-03
Silver
7440-22-4
5.0E-03
Strychnine and salts
57-24-9
3.0E-04
Styrene
100-42-5
2.0E-01
1.0E+00
Styrene-7,8-oxide
96-09-3
Sulfide
18496-25-8
TCDD, 2,3,7,8-
1746-01-6
1.5E+5
1.5E+5
3.3E+1
1.5E+5
Tetrachlorobenzene, 1,2,4,5-
95-94-3
3.0E-04
(continued)
A-8
-------
Appendix A
Table A-l. (continued)
Benchmarks Selected for Use in Deriving Phase 1A Screening Levels
Oral CSF Oral CSF Inh CSF
RfC (food) (per (H20) (per Inh URF (per
Constituent Name CAS No. Oral (RfD) (mg/m') mg/kg/d) mg/kg/d) (per ug/m') mg/kg/d)
Tetrachlorodibenzodioxins
41903-57-5
0
0
0
0
Tetrachlorodibenzofurans
55722-27-5
0
0
0
0
Tetrachloroethane, 1,1,1,2-
630-20-6
3.0E-02
2.6E-2
2.6E-2
7.4E-6
2.6E-2
Tetrachloroethane, 1,1,2,2-
79-34-5
2.0E-1
2.0E-1
5.8E-5
2.0E-1
Tetrachloroethylene
(perchloroethylene)
127-18-4
1.0E-02
5.2E-2
5.2E-2
5.8E-7
2.0E-3
Tetrachlorophenol, 2,3,4,6-
58-90-2
3.0E-02
Tetraethyldithiopyrophosphate
3689-24-5
5.0E-04
Thallium
7440-28-0
Thallium (I) acetate
563-68-8
9.0E-05
Thallium (I) carbonate
6533-73-9
8.0E-05
Thallium (I) chloride
7791-12-0
8.0E-05
Thallium (I) nitrate
10102-45-1
9.0E-05
Thallium (I) sulfate
7446-18-6
8.0E-05
Thiram
137-26-8
5.0E-03
Toluene
108-88-3
2.0E-01
4.0E-01
Toluene-2,4-diamine (2,4-
diaminotoluene)
95-80-7
3.2E+0
3.2E+0
Toluidine, p-
106-49-0
1.9E-1
1.9E-1
Toxaphene
8001-35-2
1.1 E+0
1.1 E+0
3.2E-4
1.1 E+0
trans-1,3-Dichloropropylene
10061-02-6
T richloro-1,2,2-trifluoroethane,
1,1,2- (Freon 113)
76-13-1
3.0E+01
3.0E+01
Trichlorobenzene, 1,2,4-
120-82-1
1.0E-02
2.0E-01
Trichloroethane, 1,1,1-(methyl
chloroform)
71-55-6
1.0E+00
Trichloroethane, 1,1,2- (vinyl
trichloride)
79-00-5
4.0E-03
5.7E-2
5.7E-2
1.6E-5
5.7E-2
Trichloroethylene
79-01-6
1.1E-02
1.1E-02
1.7E-06
6.0E-03
Trichlorofluoromethane (CFC-11)
75-69-4
3.0E-01
7.0E-01
Trichlorophenol, 2,4,5-
95-95-4
1.0E-01
Trichlorophenol, 2,4,6-
88-06-2
1.1 E-2
1.1 E-2
3.1 E-6
1 .OE-2
Trichlorophenoxy) propionic acid,
2 (2,4,5-
93-72-1
8.0E-03
Trichlorophenoxyacetic acid,
2,4,5-
93-76-5
1.0E-02
Trichloropropane, 1,2,3-
96-18-4
6.0E-03
7.0E+0
7.0E+0
Triethylamine
121-44-8
7.0E-03
Trinitrobenzene, 1,3,5- (sym-)
99-35-4
3.0E-02
tris(2,3-Dibromopropyl)phosphate
126-72-7
Vanadium
7440-62-2
7.0E-03
Vinyl acetate
108-05-4
1.0E+00
2.0E-01
Vinyl chloride
75-01-4
1.9E+0
1.9E+0
8.4E-5
3.0E-1
(continued)
A-9
-------
Appendix A
Table A-l. (continued)
Benchmarks Selected for Use in Deriving Phase 1A Screening Levels
Oral CSF Oral CSF Inh CSF
RfC (food) (per (H20) (per Inh URF (per
Constituent Name CAS No. Oral (RfD) (mg/m') mg/kg/d) mg/kg/d) (per ug/m') mg/kg/d)
Warfarin
81-81-2
3.0E-04
Xylene, m-
108-38-3
2.0E+00
Xylene, o-
95-47-6
2.0E+00
Xylene, p-
106-42-3
Xylenes (total)
1330-20-7
2.0E+00
Zinc
7440-66-6
3.0E-01
(continued)
A-10
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Acenaphthene
83329
6.0E-02
IRIS
Acetaldehyde
75-07-0
9.0E-03
IRIS
2.2E-6
IRIS
Acetone
67641
1.0E-01
IRIS
Acetonitrile
75-05-8
6.0E-02
IRIS
Acetophenone
98-86-2
1.0E-01
IRIS
Acrolein
107-02-8
2.0E-02
HEAST
2.0E-05
IRIS
Acrylamide
79-06-1
2.0E-04
IRIS
4.5E+0
IRIS
1.3E-04
IRIS
1.3E-3
IRIS
4.5E+0
Acrylic acid
79-10-7
5.0E-01
IRIS
1.0E-03
IRIS
Acrylonitrile
107-13-1
1.0E-03
HEAST
2.0E-03
IRIS
5.4E-1
IRIS
1.5E-05
IRIS
6.8E-5
IRIS
2.4E-1
Aldicarb
116-06-3
1.0E-03
IRIS
Aldrin
309-00-2
3.0E-05
IRIS
1.7E+1
IRIS
4.9E-04
IRIS
4.9E-3
IRIS
1.7E+1
Allyl alcohol
107-18-6
5.0E-03
IRIS
Allyl chloride (3-chloropropene)
107-05-1
1.0E-03
IRIS
Ammonium vanadate
7803-55-6
Amonium perchlorate
7790-98-9
Aniline
62-53-3
1.0E-03
IRIS
5.7E-3
IRIS
1.6E-07
IRIS
Anthracene
120-12-7
3.0E-01
IRIS
Antimony
7440-36-0
4.0E-04
IRIS
Antimony trioxide
1309-64-4
4.0E-04
HEAST
2.0E-04
IRIS
Aramite
140-57-8
5.0E-02
HEAST
2.5E-2
IRIS
7.1E-07
IRIS
7.1 E-6
IRIS
2.5E-2
Arsenic, inorganic
7440-38-2
3.0E-04
IRIS
1.5E+0
IRIS
5.0E-05
IRIS
4.3E-3
IRIS
Barium
7440-39-3
7.0E-02
IRIS
5.0E-04
HEAST2
Benz(a)anthracene
56-55-3
7.3E-01
TEF
2.1E-05
TEF
Benzene
71-43-2
2.9E-23
IRIS
8.3E-073
IRIS
8.3E-63
IRIS
2.9E-2
6E-2
mg/m3
(subchro
nic RfC)
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Benzidine
92-87-5
3.0E-03
IRIS
2.3E+2
IRIS
6.7E-03
IRIS
6.7E-2
IRIS
2.3E+2
Benzo(a)pyrene
50-32-8
7.3E+0
IRIS
2.1E-04
IRIS
Benzo(b)fluoranthene
205-99-2
7.3E-01
TEF
2.1E-05
TEF
Benzyl alcohol
100-51-6
3.0E-01
HEAST
Benzyl chloride
100-44-7
1.7E-1
IRIS
4.9E-06
IRIS
Beryllium
7440-41-7
2.0E-03
IRIS
2.0E-05
IRIS
2.4E-3
IRIS
8.4E+0
Bis(2-chloroethyl)ether
111-44-4
1.1E+0
IRIS
3.3E-05
IRIS
3.3E-4
IRIS
1.1E+0
Bis(2-chloroisopropyl)ether
39638-32-9
4.0E-02
IRIS
7.0E-2
HEAST
2.0E-06
HEAST
1 .OE-5
HEAST
3.5E-2
Bis(2-ethylhexyl)phthalate (DEHP;
also di-)
117-81-7
2.0E-02
IRIS
1.4E-2
IRIS
4.0E-07
IRIS
subchron
ic RfC=
1E-2
mg/m3
Bis(chloromethyl)ether
542-88-1
2.2E+2
IRIS
6.2E-03
IRIS
6.2E-2
IRIS
2.2E+2
Bromoform
75-25-2
2.0E-02
IRIS
7.9E-3
IRIS
2.3E-07
IRIS
1.1E-6
IRIS
3.9E-3
Bromomethane (methyl bromide)
74-83-9
1.4E-03
IRIS
5.0E-03
IRIS
Butadiene, 1,3-
106-99-0
2.8E-4
IRIS
1.8E+0
Butanol, n- (n-butyl alcohol)
71-36-3
1.0E-01
IRIS
Butyl benzyl phthalate
85-68-7
2.0E-01
IRIS
Cadmium
7440-43-9
5.0E-04
IRIS
1.8E-3
IRIS
Carbon disulfide
75-15-0
1.0E-01
IRIS
7.0E-01
IRIS
Carbon tetrachloride
(tetrachloromethane)
56-23-5
7.0E-04
IRIS
1.3E-1
IRIS
3.7E-06
IRIS
1.5E-5
IRIS
5.3E-2
subchron
ic
RfC=2E-
2 mg/m3
Chloral
75-87-6
2.0E-03
IRIS
Chloral hydrate
[trichloroacetaldehyde hydrate]
302-17-0
Chlordane
57-74-9
5.0E-04
IRIS
7.0E-04
IRIS
3.5E-1
IRIS
1.0E-05
IRIS
1 .OE-4
IRIS
1.3E+0
Chlordecone
143-50-0
Chlorine cyanide (cyanogen
chloride)
506-77-4
5.0E-02
IRIS
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Chloro-1,3-butadiene, 2-
(chloroprene)
126-99-8
2.0E-02
HEAST2
7.0E-03
HEAST
Chloroaniline, 4- (p-)
106-47-8
4.0E-03
IRIS
Chlorobenzene
108-90-7
2.0E-02
IRIS
2.0E-02
HEAST2
subchron
ic RfC=
2E-1
mg/m3
Chlorobenzilate
510-15-6
2.0E-02
IRIS
2.7E-1
HEAST
7.8E-06
HEAST
7.8E-5
HEAST
2.7E-1
Chlorodibromomethane
(dibromochloromethane)
124-48-1
2.0E-02
IRIS
8.4E-2
IRIS
2.4E-06
IRIS
Chloroform (trichloromethane)
67-66-3
1.0E-02
IRIS
6.1 E-3
IRIS
1.7E-07
IRIS
2.3E-5
IRIS
8.1 E-2
RfC not
current
Chloromethane (methyl chloride)
74-87-3
1.3E-2
HEAST
3.7E-07
HEAST
1.8E-6
HEAST
6.3E-3
RfC=0.3
mg/m3
Chloromethyl methyl ether
107-30-2
Chloronaphthalene, beta-
91-58-7
8.0E-02
IRIS
Chlorophenol, 2-
95-57-8
5.0E-03
IRIS
Chromium (III), insoluble salts
16065-83-1
1.5E+00
IRIS
Chromium (VI)
18540-29-9
3.0E-03
IRIS
8E-6 or 1E-4
IRIS
1.2E-2
IRIS
4.1E+1
subchron
ic RfC=
4E-6
mg/m3
Chromium (VI) - chromic acid mists
& dissolved Cr aerosols
18540-29-9
8.0E-06
IRIS
Chromium (VI) - Cr particulates
18540-29-9
1.0E-04
IRIS
Chrysene
218-01-9
7.3E-03
TEF
2.1E-07
TEF
cis-1,3-Dichloropropylene
10061-01-5
Cobalt (and cmpds)
7440-48-4
0.06 mkd
(chronic
RfD)
Copper
7440-50-8
*MCL only
HEAST
Cresol mixtures
1319-77-3
Cresol, m- (3-methylphenol)
108-39-4
5.0E-02
IRIS
Cresol, o- (2-Methylphenol)
95-48-7
5.0E-02
IRIS
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Cresol, p- (4-methylphenol)
106-44-5
5.0E-03
HEAST
Cumene
98-82-8
1.0E-01
IRIS
4.0E-01
IRIS
Cyanide (amenable)
57-12-5
2.0E-02
IRIS
Cyanogen bromide
506-68-3
9.0E-02
IRIS
Cyclohexanol
108-93-0
Cyclohexanone
108-94-1
5.0E+00
IRIS
DDD (p,p'-
dichlorodiphenyldichloroethane)
72-54-8
2.4E-1
IRIS
6.9E-06
IRIS
DDE (p,p'-
dichlorodiphenyldichloroethylene)
72-55-9
3.4E-1
IRIS
9.7E-06
IRIS
DDT (p,p'-
dichlorodiphenyltrichloroethane)
50-29-3
5.0E-04
IRIS
3.4E-1
IRIS
9.7E-06
IRIS
9.7E-5
IRIS
3.4E-1
Diallate
2303-16-4
6.1 E-2
HEAST
1.7E-06
HEAST
Dibenzo(a,h)anthracene
53-70-3
7.3E+0
TEF
2.1E-04
TEF
Dibromo-3-chloropropane, 1,2-
(DBCP)
96-12-8
2.0E-04
IRIS
1.4E+0
HEAST
4.0E-05
HEAST
6.9E-7
HEAST
2.4E-03
Dibromoethane, 1,2- (ethylene
dibromide)
106-93-4
2.0E-04
HEAST
8.5E+1
IRIS
2.5E-03
IRIS
2.2E-4
IRIS
7.6E-1
Dichlorobenzene, 1,2- (o-)
95-50-1
9.0E-02
IRIS
2.0E-01
HEAST2
Dichlorobenzene, 1,4-
106-46-7
8.0E-01
IRIS
2.4E-2
HEAST
6.8E-07
HEAST
Dichlorobenzidine, 3,3'-
91-94-1
4.5E-1
IRIS
1.3E-05
IRIS
Dichlorobromo methane
(bromodichloromethane)
75-27-4
2.0E-02
IRIS
6.2E-2
IRIS
1.8E-06
IRIS
Dichlorodifluoromethane [CFC-12]
75-71-8
2.0E-01
IRIS
2.0E-01
HEAST2
Dichloroethane, 1,1-
75-34-3
1.0E-01
HEAST
5.0E-01
HEAST2
Dichloroethane, 1,2- (ethylene
dichloride)
107-06-2
9.1 E-2
IRIS
2.6E-06
IRIS
2.6E-5
IRIS
Dichloroethylene, 1,1-
75-35-4
9.0E-03
IRIS
6.0E-1
IRIS
1.7E-05
IRIS
5.0E-5
IRIS
2.0E-1
Dichloroethylene, 1,2- (cis)
156-59-2
1.0E-02
HEAST
Dichloroethylene, 1,2- (trans)
156-60-5
2.0E-02
IRIS
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Dichlorophenol, 2,4-
120-83-2
3.0E-03
IRIS
Dichlorophenoxyacetic acid, 2,4-
(2,4-D)
94-75-7
1.0E-02
IRIS
Dichloropropane, 1,2-
78-87-5
4.0E-03
IRIS
6.8E-2
HEAST
1.9E-06
HEAST
Dieldrin
60-57-1
5.0E-05
IRIS
1.6E+1
IRIS
4.6E-04
IRIS
4.6E-3
IRIS
1.6E+1
Diethyl phthalate
84-66-2
8.0E-01
IRIS
Diethylstilbestrol
56-53-1
4.7E+3
HEAST
1.3E-01
HEAST
Dimethoate
60-51-5
2.0E-04
IRIS
Dimethoxybenzidine, 3,3'-
119-90-4
1.4E-2
HEAST
4.0E-07
HEAST
Dimethylbenz[a]anthracene, 7,12-
57-97-6
Dimethylbenzidine, 3,3'-
119-93-7
9.2E+0
HEAST
2.6E-04
HEAST
Dimethylformamide, N,N-
68-12-2
1.0E-01
HEAST
3.0E-02
IRIS
Dimethylphenol, 2,4-
105-67-9
2.0E-02
IRIS
Dimethylphenol, 3,4-
95-65-8
1.0E-03
IRIS
Di methyl phthalate
131-11-3
Di-n-butyl phthalate
84-74-2
1.0E-01
IRIS
Dinitrobenzene, 1,3- (m-)
99-65-0
1.0E-04
IRIS
Dinitrophenol, 2,4-
51-28-5
2.0E-03
IRIS
Dinitrotoluene, 2,4-
121-14-2
2.0E-03
IRIS
Dinitrotoluene, 2,6-
606-20-2
1.0E-03
HEAST
Di-N-octyl phthalate
117-84-0
2.0E-02
HEAST
Dinoseb
88-85-7
1.0E-03
IRIS
Dioxane, 1,4-
123-91-1
1.1E-2
IRIS
3.1E-07
IRIS
Diphenylamine, N,N-
122-39-4
2.5E-02
IRIS
Diphenylhydrazine, 1,2-
122-66-7
8.0E-1
IRIS
2.2E-05
IRIS
2.2E-4
IRIS
8.0E-1
Direct Black 38
1937-37-7
8.6E+0
HEAST
2.4E-04
HEAST
Direct Blue 6
2602-46-2
8.1 E+0
HEAST
2.3E-04
HEAST
Direct Brown 95
16071-86-6
9.3E+0
HEAST
2.6E-04
HEAST
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Disulfoton
298-04-4
4.0E-05
IRIS
Endosulfan
115-29-7
6.0E-03
IRIS
Endothall
145-73-3
2.0E-02
IRIS
Endrin
72-20-8
3.0E-04
IRIS
Epichlorohydrin
106-89-8
2.0E-03
HEAST
1.0E-03
IRIS
9.9E-3
IRIS
2.8E-07
IRIS
1.2E-6
IRIS
4.2E-3
Epoxybutane, 1,2-
106-88-7
2.0E-02
IRIS
Ethoxyethanol acetate, 2-
111-15-9
3.0E-01
HEAST2
Ethoxyethanol, 2- (ethylene glycol
monoethyl ether)
110-80-5
4.0E-01
HEAST
2.0E-01
IRIS
Ethyl acetate
141-78-6
9.0E-01
IRIS
Ethyl chloride (chloroethane)
75-00-3
1.0E+01
IRIS
Ethyl ether
60-29-7
2.0E-01
IRIS
Ethyl methacrylate
97-63-2
9.0E-02
HEAST
Ethyl methanesulfonate
62-50-0
Ethylbenzene
100-41-4
1.0E-01
IRIS
1.0E+00
IRIS
Ethylene glycol
107-21-1
2.0E+00
IRIS
Ethylene oxide
75-21-8
1.0E+0
HEAST
2.9E-05
HEAST
1 .OE-4
HEAST
3.5E-1
Ethylene thiourea
96-45-7
8.0E-05
IRIS
1.1E-1
HEAST
3.4E-06
HEAST
Fluoranthene
206-44-0
4.0E-02
IRIS
Fluorene
86-73-7
4.0E-02
IRIS
Fluoride
16984-48-8
Formaldehyde
50-00-0
2.0E-01
IRIS
1.3E-5
IRIS
4.5E-2
Formic acid
64-18-6
2.0E+00
HEAST
Furan
110-00-9
1.0E-03
IRIS
Furfural
98-01-1
3.0E-03
IRIS
5.0E-02
HEAST2
Glycidaldehyde
765-34-4
4.0E-04
IRIS
1.0E-03
HEAST
Heptachlor
76-44-8
5.0E-04
IRIS
4.5E+0
IRIS
1.3E-04
IRIS
1.3E-3
IRIS
4.5E+0
Heptachlor epoxide
1024-57-3
1.3E-05
IRIS
9.1 E+0
IRIS
2.6E-04
IRIS
2.6E-3
IRIS
9.1 E+0
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Hexachlorobenzene
118-74-1
8.0E-04
IRIS
1.6E+0
IRIS
4.6E-05
IRIS
4.6E-4
IRIS
1.6E+0
Hexachlorobutadiene
87-68-3
2.0E-04
HEAST
7.8E-2
IRIS
2.2E-06
IRIS
2.2E-5
IRIS
7.8E-2
Hexachlorocyclohexane, alpha-
319-84-6
6.3E+0
IRIS
1.8E-04
IRIS
1.8E-3
IRIS
6.3E+0
Hexachlorocyclohexane, beta-
319-85-7
1.8E+0
IRIS
5.3E-05
IRIS
5.3E-4
IRIS
1.8E+0
Hexachlorocyclohexane, gamma-
(lindane)
58-89-9
3.0E-04
IRIS
1.3E+0
HEAST
3.7E-05
HEAST
Hexachlorocyclopentadiene
77-47-4
7.0E-03
IRIS
7.0E-05
HEAST
Hexachloroethane
67-72-1
1.0E-03
IRIS
1.4E-2
IRIS
4.0E-07
IRIS
4.0E-6
IRIS
1.4E-2
Hexachlorophene
70-30-4
3.0E-04
IRIS
Hexane, n-
110-54-3
6.0E-02
HEAST
2.0E-01
IRIS
Hydrazine
302-01-2
3.0E+0
IRIS
8.5E-05
IRIS
4.9E-3
IRIS
1.7E+1
ldeno[1,2,3-cd]pyrene
193-39-5
7.3E-1
TEF
2.1E-05
TEF
Isobutyl alcohol
78-83-1
3.0E-01
IRIS
Isophorone
78-59-1
2.0E-01
IRIS
9.5E-4
IRIS
2.7E-08
IRIS
Lead and cmpds (inorganic)
7439-92-1
Maleic anhydride
108-31-6
1.0E-01
IRIS
Maleic hydrazide
123-33-1
5.0E-01
IRIS
Manganese
7439-96-5
1.4E-01
IRIS
5.0E-05
IRIS
Mercuric chloride
7487-94-7
3.0E-04
HEAST
Mercury (elemental)
7439-97-6
3.0E-04
IRIS
Methacrylonitrile
126-98-7
1.0E-04
IRIS
7.0E-04
HEAST2
Methanol
67-56-1
5.0E-01
IRIS
Methomyl
16752-77-5
2.5E-02
IRIS
Methoxychlor
72-43-5
5.0E-03
IRIS
Methoxyethanol acetate, 2-
110-49-6
2.0E-03
HEAST2
Methoxyethanol, 2- (ethylene glycol
methyl ether)
109-86-4
1.0E-03
HEAST2
2.0E-02
IRIS
Methyl ethyl ketone
78-93-3
6.0E-01
IRIS
1.0E+00
IRIS
Methyl isobutyl ketone
108-10-1
8.0E-02
HEAST
8.0E-02
HEAST2
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Methyl mercury
22967-92-6
1.0E-04
IRIS
Methyl methacrylate
80-62-6
1.4E+00
IRIS
7.0E-01
IRIS
Methyl parathion
298-00-0
2.5E-04
IRIS
Methyl tert-butyl ether
1634-04-4
3.0E+00
IRIS
Methylaniline, 2- (o-toluidine)
95-53-4
2.4E-1
HEAST
6.9E-06
HEAST
Methylcholanthrene, 3-
56-49-5
Methylene bromide
74-95-3
1.0E-02
HEAST2
Methylene chloride
(dichloromethane)
75-09-2
6.0E-02
IRIS
3.0E+00
HEAST
7.5E-3
IRIS
2.1E-07
IRIS
4.7E-7
IRIS
Methylene-bis(2-chloroaniline),
4,4'- (MBOCA)
101-14-4
7.0E-04
HEAST
1.3E-1
HEAST
3.7E-06
HEAST
3.7E-5
HEAST
1.3E-1
Molybdenum
7439-98-7
5.0E-03
IRIS
Naphthalene
91-20-3
2.0E-02
IRIS
3.0E-03
IRIS
RfD= 4E-
2
mg/kg/d
Nickel subsulfide
12035-72-2
4.8E-4
IRIS
1.7E+0
Nickel, soluble salts
7440-02-0
2.0E-02
IRIS
Nitrobenzene
98-95-3
5.0E-04
IRIS
2.0E-03
HEAST2
Nitropropane, 2-
79-46-9
2.0E-02
IRIS
2.7E-3
HEAST
9.4E+0
N-Nitrosodiethylamine
55-18-5
1.5E+2
IRIS
4.3E-03
IRIS
4.3E-2
IRIS
1.5E+2
N-Nitrosodimethylamine (N-methyl-
N-nitroso-methanamine)
62-75-9
5.1E+1
IRIS
1.4E-03
IRIS
1.4E-2
IRIS
5.1E+1
N-Nitroso-di-n-butylamine
924-16-3
5.4E+0
IRIS
1.6E-04
IRIS
1.6E-3
IRIS
5.4E+0
N-Nitrosodi-n-propylamine
621-64-7
7.0E+0
IRIS
2.0E-04
IRIS
N-Nitrosodiphenylamine
86-30-6
4.9E-3
IRIS
1.4E-07
IRIS
N-Nitroso-N-methylethylamine
10595-95-6
2.2E+1
IRIS
6.3E-04
IRIS
N-Nitrosopiperidine
100-75-4
N-Nitrosopyrrolidine
930-55-2
2.1E+0
IRIS
6.1E-05
IRIS
6.1 E-4
IRIS
2.1E+0
Octamethylpyrophosphoamide
152-16-9
2.0E-03
HEAST
Parathion
56-38-2
6.0E-03
HEAST
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Pentachlorobenzene
608-93-5
8.0E-04
IRIS
Pentachlorodibenzofuran,
1,2,3,7,8-
57117-41-6
7.5E+3
TEF
2.3E-01
TEF
1.7E+0
TEF
7.5E+3
Pentachlorodibenzofuran,
2,3,4,7,8-
57117-31-4
7.5E+4
TEF
2.3E+00
TEF
1.7E+1
TEF
7.5E+4
Pentachlorodibenzo-p-dioxin,
1,2,3,7,8-
40321-76-4
1.5E+5
TEF
4.5E+00
TEF
3.3E+1
TEF
1.5E+5
Pentachloronitrobenzene
82-68-8
3.0E-03
IRIS
2.6E-1
HEAST
7.4E-06
HEAST
Pentachlorophenol
87-86-5
3.0E-02
IRIS
1.2E-1
IRIS
3.0E-06
IRIS
Perchlorate
14797-73-0
Phenol
108-95-2
6.0E-01
IRIS
Phenylenediamine , m-
108-45-2
6.0E-03
IRIS
Phorate
298-02-2
2.0E-04
HEAST
Phthalic anhydride
85-44-9
2.0E+00
IRIS
1.2E-01
HEAST
Polychlorinated biphenyls
1336-36-3
2.0
(food),
0.4 (H20)
IRIS
0.0001
(evap
congenr)
IRIS
2.0 (dust,
aerosol), 0.4
(evap c)
Pronamide
23950-58-5
7.5E-02
IRIS
Propylene oxide
75-56-9
3.0E-02
IRIS
2.4E-1
IRIS
6.8E-06
IRIS
3.7E-6
IRIS
1.3E-2
Pyrene
129-00-0
3.0E-02
IRIS
Pyridine
110-86-1
1.0E-03
IRIS
Safrole
94-59-7
Selenium
7782-49-2
5.0E-03
IRIS
Silver
7440-22-4
5.0E-03
IRIS
Strychnine and salts
57-24-9
3.0E-04
IRIS
Styrene
100-42-5
2.0E-01
IRIS
1.0E+00
IRIS
Styrene-7,8-oxide
96-09-3
Sulfide
18496-25-8
TCDD, 2,3,7,8-
1746-01-6
1.5E+5
HEAST
4.5E+00
HEAST
3.3E+1
HEAST
1.5E+5
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Tetrachlorobenzene, 1,2,4,5-
95-94-3
3.0E-04
IRIS
Tetrachlorodibenzodioxins
41903-57-5
0.0E+0
TEF
0.0E+00
TEF
0.0E+0
TEF
0.0E+0
Tetrachlorodibenzofurans
55722-27-5
0.0E+0
TEF
0.0E+00
TEF
0.0E+0
TEF
0.0E+0
Tetrachloroethane, 1,1,1,2-
630-20-6
3.0E-02
IRIS
2.6E-2
IRIS
7.4E-07
IRIS
7.4E-6
IRIS
2.6E-2
Tetrachloroethane, 1,1,2,2-
79-34-5
2.0E-1
IRIS
5.8E-06
IRIS
5.8E-5
IRIS
2.0E-1
Tetrachloroethylene
(perchloroethylene)
127-18-4
1.0E-02
IRIS
5.2E-02
HAD
1.5E-06
HAD
5.8E-07
HAD
Tetrachlorophenol, 2,3,4,6-
58-90-2
3.0E-02
IRIS
Tetraethyldithiopyrophosphate
3689-24-5
5.0E-04
IRIS
Thallium
7440-28-0
Thallium (I) acetate
563-68-8
9.0E-05
IRIS
Thallium (I) carbonate
6533-73-9
8.0E-05
IRIS
Thallium (I) chloride
7791-12-0
8.0E-05
IRIS
Thallium (I) nitrate
10102-45-1
9.0E-05
IRIS
Thallium (I) sulfate
7446-18-6
8.0E-05
IRIS
Thiram
137-26-8
5.0E-03
IRIS
Toluene
108-88-3
2.0E-01
IRIS
4.0E-01
IRIS
Toluene-2,4-diamine (2,4-
diaminotoluene)
95-80-7
3.2E+0
HEAST
9.1E-05
HEAST
Toluidine, p-
106-49-0
1.9E-1
HEAST
5.4E-06
HEAST
Toxaphene
8001-35-2
1.1E+0
IRIS
3.2E-05
IRIS
3.2E-4
IRIS
1.1E+0
trans-1,3-Dichloropropylene
10061-02-6
Trichloro-1,2,2-trifluoroethane,
1,1,2- (Freon 113)
76-13-1
3.0E+01
IRIS
3.0E+01
HEAST
Trichlorobenzene, 1,2,4-
120-82-1
1.0E-02
IRIS
2.0E-01
HEAST
Trichloroethane, 1,1,1- (methyl
chloroform)
71-55-6
RfC= 1.0
mg/m3
Trichloroethane, 1,1,2- (vinyl
trichloride)
79-00-5
4.0E-03
IRIS
5.7E-2
IRIS
1.6E-06
IRIS
1.6E-5
IRIS
5.7E-2
(continued)
-------
Table A-l. (continued)
EPA Benchmarks
Drinking Inhal Unit Inhal HEAST Super-
RfD RfD RfC RfC Oral CSF Oral CSF H20 URF Oral URF Risk URF inhal CSF fund
Constituent Name CAS No. (mg/kg/d) Source (mg/m3) source (mkd)-1 Source (per ug/L) Source (ug/m3)-1 Source (mkd)-1 values
Trichloroethylene
79-01-6
oral
URF=3.2
E-7; oral
CSF=1.1
E-2; inh
URF=1.7
E-6; inh
CSF=6E-
3
Trichlorofluoromethane (CFC-11)
75-69-4
3.0E-01
IRIS
7.0E-01
HEAST2
Trichlorophenol, 2,4,5-
95-95-4
1.0E-01
IRIS
Trichlorophenol, 2,4,6-
88-06-2
1.1E-2
IRIS
3.1E-07
IRIS
3.1 E-6
IRIS
1 .OE-2
Trichlorophenoxy) propionic acid, 2
(2,4,5-
93-72-1
8.0E-03
IRIS
Trichlorophenoxyacetic acid, 2,4,5-
93-76-5
1.0E-02
IRIS
Trichloropropane, 1,2,3-
96-18-4
6.0E-03
IRIS
7.0E+0
HEAST
2.0E-04
HEAST
Triethylamine
121-44-8
7.0E-03
IRIS
Trinitrobenzene, 1,3,5- (sym-)
99-35-4
3.0E-02
IRIS
tris(2,3-Dibromopropyl)phosphate
126-72-7
Vanadium
7440-62-2
7.0E-03
HEAST
Vinyl acetate
108-05-4
1.0E+00
HEAST
2.0E-01
IRIS
Vinyl chloride
75-01-4
1.9E+0
HEAST
5.4E-05
HEAST
8.4E-5
HEAST
3.0E-1
Warfarin
81-81-2
3.0E-04
IRIS
Xylene, m-
108-38-3
2.0E+00
HEAST
Xylene, o-
95-47-6
2.0E+00
HEAST
Xylene, p-
106-42-3
Xylenes (total)
1330-20-7
2.0E+00
IRIS
Zinc
7440-66-6
3.0E-01
IRIS
(continued)
-------
Table A-l. (continued)
Constituent Name
CAS No.
Other EPA
values
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
ATSDR ATSDR inhal CalEPA97 CalEPA99 CalEPA99 CalEPA9
oral MRL MRL - chronic chronic inhal unit 9 inhal CalEPA99
-chronic chronic inhal REL inhal REL risk CSF Oral CSF
Source(s) (mg/kg/d) (ppm) (mg/m3) (mg/m3) (ug/m3)-1 (mkd)-1 (mkd)-1
Acenaphthene
83329
Acetaldehyde
75-07-0
9.0E-03
not
updated
2.7E-06
1.0E-02
Acetone
67641
13
Acetonitrile
75-05-8
Acetophenone
98-86-2
Acrolein
107-02-8
0.0005
2.0E-05
2.0E-05
Acrylamide
79-06-1
7.0E-04
not
updated
1.3E-03
4.5E+00
Acrylic acid
79-10-7
1.0E-03
not
updated
Acrylonitrile
107-13-1
0.04
2.0E-03
2.0E-03
2.9E-04
1.0E+00
Aldicarb
116-06-3
Aldrin
309-00-2
0.00003
Allyl alcohol
107-18-6
Allyl chloride (3-chloropropene)
107-05-1
1.0E-03
not
updated
6.0E-06
2.1E-02
Ammonium vanadate
7803-55-6
Amonium perchlorate
7790-98-9
Aniline
62-53-3
1.0E-03
not
updated
1.6E-06
5.7E-03
Anthracene
120-12-7
Antimony
7440-36-0
Antimony trioxide
1309-64-4
2.0E-04
not
updated
Aramite
140-57-8
Arsenic, inorganic
7440-38-2
0.0003
3.0E-05
3.0E-05
3.3E-03
1.2E+01
1.5E+00
(continued)
-------
Table A-l. (continued)
Constituent Name
CAS No.
Other EPA
values
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
ATSDR ATSDR inhal CalEPA97 CalEPA99 CalEPA99 CalEPA9
oral MRL MRL - chronic chronic inhal unit 9 inhal CalEPA99
-chronic chronic inhal REL inhal REL risk CSF Oral CSF
Source(s) (mg/kg/d) (ppm) (mg/m3) (mg/m3) (ug/m3)-1 (mkd)-1 (mkd)-1
Barium
7440-39-3
Benz(a)anthracene
56-55-3
1.1E-04
3.9E-01
1.2E+00
Benzene
71-43-2
6.0E-02
6.0E-02
2.9E-05
1.0E-01
Benzidine
92-87-5
1.0E-02
not
updated
1.4E-01
5.0E+02
Benzo(a)pyrene
50-32-8
1.1E-03
3.9E+00
1.2E+01
Benzo(b)fluoranthene
205-99-2
1.1E-04
3.9E-01
1.2E+00
Benzyl alcohol
100-51-6
Benzyl chloride
100-44-7
4.9E-05
1.7E-01
Beryllium
7440-41-7
1.0E-06
1.0E-06
2.4E-03
8.4E+00
Bis(2-chloroethyl)ether
111-44-4
7.1E-04
2.5E+00
Bis(2-chloroisopropyl)ether
39638-32-9
Bis(2-ethylhexyl)phthalate
(DEHP; also di-)
117-81-7
1.0E-02
1.0E-02
2.4E-06
8.4E-03
Bis(chloromethyl)ether
542-88-1
1.3E-02
4.6E+02
Bromoform
75-25-2
0.2
Bromomethane (methyl bromide)
74-83-9
0.005
5.0E-03
5.0E-03
Butadiene, 1,3-
106-99-0
8.0E-03
8.0E-03
1.7E-04
3.4E+00
Butanol, n- (n-butyl alcohol)
71-36-3
Butyl benzyl phthalate
85-68-7
Cadmium
7440-43-9
0.0002
1.0E-05
2.0E-05
4.2E-03
1.5E+01
Carbon disulfide
75-15-0
0.3
7.0E-01
7.0E-01
Carbon tetrachloride
(tetrachloromethane)
56-23-5
4.0E-02
4.0E-02
4.2E-05
1.5E-01
Chloral
75-87-6
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Chloral hydrate
[trichloroacetaldehyde hydrate]
302-17-0
Chlordane
57-74-9
0.0006
0.00002 mg/m3
Chlordecone
143-50-0
0.0005
Chlorine cyanide (cyanogen
chloride)
506-77-4
Chloro-1,3-butadiene, 2-
(chloroprene)
126-99-8
Chloroaniline, 4- (p-)
106-47-8
Chlorobenzene
108-90-7
1.0E+00
1.0E+00
Chlorobenzilate
510-15-6
Chlorodibromomethane
(dibromochloromethane)
124-48-1
Inh CSF = 8.4E-2
per mkd;
URF=2.4E-5 per
M|jg/m3
Air Char. Study0.03
Chloroform (trichloromethane)
67-66-3
0.01
0.02
3.0E-01
3.0E-01
5.3E-06
1.9E-02
Chloromethane (methyl chloride)
74-87-3
0.05
Chloromethyl methyl ether
107-30-2
Chloronaphthalene, beta-
91-58-7
Chlorophenol, 2-
95-57-8
RfC=0.0014
mg/m3
Air Char. Study
Chromium (III), insoluble salts
16065-83-1
Chromium (VI)
18540-29-9
8.0E-07
8.0E-07
1.5E-01
5.1E+02
4.2E-01
Chromium (VI) - chromic acid
mists & dissolved Cr aerosols
18540-29-9
1E-4 mg/3
Chromium (VI) - Cr particulates
18540-29-9
Chrysene
218-01-9
1.1E-05
3.9E-02
1.2E-01
cis-1,3-Dichloropropylene
10061-01-5
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Cobalt (and cmpds)
7440-48-4
RfC=1 E-5 mg/m3
Air Char. Study5.0E-06not updated
Copper
7440-50-8
2.0E-05
not
updated
Cresol mixtures
1319-77-3
RfC=4E-4mg/m3
Air Char. Study4.0E-031.8E-01
Cresol, m- (3-methylphenol)
108-39-4
Cresol, o- (2-Methylphenol)
95-48-7
Cresol, p- (4-methylphenol)
106-44-5
Cumene
98-82-8
Cyanide (amenable)
57-12-5
Cyanogen bromide
506-68-3
Cyclohexanol
108-93-0
prov RfD=1.7E-5
mkd; prov
RfC=6.0E-5 or
2.0E-5 mg/m3
61FR 42317-354; 63FR 64371-402
Cyclohexanone
108-94-1
DDD (p,p-
dichlorodiphenyldichloroethane)
72-54-8
DDE (p,p'-
dichlorodiphenyldichloroethylene)
72-55-9
DDT (p,p'-
dichlorodiphenyltrichloroethane)
50-29-3
Diallate
2303-16-4
Dibenzo(a,h)anthracene
53-70-3
1.2E-03
4.1E+00
Dibromo-3-chloropropane, 1,2-
(DBCP)
96-12-8
2.0E-03
7.0E+00
Dibromoethane, 1,2- (ethylene
dibromide)
106-93-4
8.0E-04
8.0E-04
7.1E-05
2.5E-01
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Dichlorobenzene, 1,2- (o-)
95-50-1
Dichlorobenzene, 1,4-
106-46-7
0.1
8.0E-01
8.0E-01
1.1E-05
4.0E-02
Dichlorobenzidine, 3,3'-
91-94-1
3.4E-04
1.2E+00
Dichlorobromomethane
(bromodichloromethane)
75-27-4
inh CSF=6.2E-2
per mkd;
URF=1.8E-5 per
M|jg/m3
Air Char. Study0.02
Dichlorodifluoromethane [CFC-
12]
75-71-8
1.0E+00
not
updated
Dichloroethane, 1,1-
75-34-3
1.6E-06
5.7E-03
Dichloroethane, 1,2- (ethylene
dichloride)
107-06-2
4.0E-01
4.0E-01
2.2E-05
7.0E-02
Dichloroethylene, 1,1-
75-35-4
0.009
2.0E-02
2.0E-02
Dichloroethylene, 1,2- (cis)
156-59-2
Dichloroethylene, 1,2- (trans)
156-60-5
Dichlorophenol, 2,4-
120-83-2
Dichlorophenoxyacetic acid, 2,4-
(2,4-D)
94-75-7
Dichloropropane, 1,2-
78-87-5
0.09
Dieldrin
60-57-1
0.00005
Diethyl phthalate
84-66-2
Diethylstilbestrol
56-53-1
Dimethoate
60-51-5
Dimethoxybenzidine, 3,3'-
119-90-4
Dimethylbenz[a]anthracene,
7,12-
57-97-6
inh CSF=84 per
mkd; URF=2.4E-2
per M|Jg/m3
Air Char. Study
7.1E-02
2.5E+02
(continued)
-------
Table A-l. (continued)
TO
S
S-
Constituent Name
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Dimethylbenzidine, 3,3'-
119-93-7
Dimethylformamide, N,N-
68-12-2
3.0E-02
3.0E-02
Dimethylphenol, 2,4-
105-67-9
Dimethylphenol, 3,4-
95-65-8
Dimethylphthalate
131-11-3
Di-n-butyl phthalate
84-74-2
Dinitrobenzene, 1,3- (m-)
99-65-0
Dinitrophenol, 2,4-
51-28-5
Dinitrotoluene, 2,4-
121-14-2
inh CSF=6.8E-1
per mkd;
URF=1.9E-4 per
M|jg/m3
Air Char. Study0.002
7.0E-03
not
updated
8.9E-05
3.1E-01
Dinitrotoluene, 2,6-
606-20-2
Di-N-octyl phthalate
117-84-0
Dinoseb
88-85-7
Dioxane, 1,4-
123-91-1
RfC=0.8 mg/m3
Air Char. Study3.0E+003.0E+00
7.7E-06
2.7E-02
Diphenylamine, N,N-
122-39-4
Diphenylhydrazine, 1,2-
122-66-7
Direct Black 38
1937-37-7
Direct Blue 6
2602-46-2
Direct Brown 95
16071-86-6
Disulfoton
298-04-4
0.00006
Endosulfan
115-29-7
0.002
Endothall
145-73-3
Endrin
72-20-8
0.0003
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Epichlorohydrin
106-89-8
1.0E-03
1.0E-03
2.3E-05
8.0E-02
Epoxybutane, 1,2-
106-88-7
2.0E-02
2.0E-02
Ethoxyethanol acetate, 2-
111-15-9
RfC=0.3 mg/m3
Air Char. Study3.0E-013.0E-01
Ethoxyethanol, 2- (ethylene glycol
monoethyl ether)
110-80-5
2.0E-01
2.0E-01
Ethyl acetate
141-78-6
Ethyl chloride (chloroethane)
75-00-3
1.0E+01
1.0E+01
Ethyl ether
60-29-7
Ethyl methacrylate
97-63-2
Ethyl methanesulfonate
62-50-0
Ethyl benzene
100-41-4
1.0E+00
1.0E+00
Ethylene glycol
107-21-1
RfC=0.6 mg/m3
Air Char. Study2
4.0E-01
4.0E-01
Ethylene oxide
75-21-8
5.0E-03
3.0E-02
8.8E-05
3.1E-01
Ethylene thiourea
96-45-7
3.0E-03
not
updated
1.3E-05
4.5E-02
Fluoranthene
206-44-0
Fluorene
86-73-7
Fluoride
16984-48-8
Formaldehyde
50-00-0
0.2
0.008
2.0E-03
3.0E-03
6.0E-06
2.1E-02
Formic acid
64-18-6
Furan
110-00-9
Furfural
98-01-1
Glycidaldehyde
765-34-4
Heptachlor
76-44-8
Heptachlor epoxide
1024-57-3
(continued)
-------
Table A-l. (continued)
Constituent Name
CAS No.
Other EPA
values
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
ATSDR ATSDR inhal CalEPA97 CalEPA99 CalEPA99 CalEPA9
oral MRL MRL - chronic chronic inhal unit 9 inhal CalEPA99
-chronic chronic inhal REL inhal REL risk CSF Oral CSF
Source(s) (mg/kg/d) (ppm) (mg/m3) (mg/m3) (ug/m3)-1 (mkd)-1 (mkd)-1
Hexachlorobenzene
118-74-1
0.00002
3.0E-03
not
updated
5.1E-04
1.8E+00
Hexachlorobutadiene
87-68-3
9.0E-02
not
updated
Hexachlorocyclohexane, alpha-
319-84-6
0.008
2.0E-02
not
updated
Hexachlorocyclohexane, beta-
319-85-7
2.0E-03
not
updated
Hexachlorocyclohexane, gamma-
(lindane)
58-89-9
3.0E-04
not
updated
3.1E-04
1.1E+00
Hexachlorocyclopentadiene
77-47-4
0.0002
7.0E-04
not
updated
Hexachloroethane
67-72-1
8.0E-02
not
updated
Hexachlorophene
70-30-4
Hexane, n-
110-54-3
0.6
2.0E-01
2.0E-01
Hydrazine
302-01-2
2.0E-04
2.0E-04
4.9E-03
1.7E+01
3.0E+00
ldeno[1,2,3-cd]pyrene
193-39-5
1.1E-04
3.9E-01
1.2E+00
Isobutyl alcohol
78-83-1
Isophorone
78-59-1
RfC=0.012 or
0.0037 mg/m3
63FR 64371 and 61FR 423170.2
2.0E+00
2.0E+00
Lead and cmpds (inorganic)
7439-92-1
1.2E-05
4.2E-02
8.5E-03
Maleic anhydride
108-31-6
2.0E-04
1.0E-03
Maleic hydrazide
123-33-1
Manganese
7439-96-5
0.00004 mg/m35.0E-05
5.0E-05
Mercuric chloride
7487-94-7
Mercury (elemental)
7439-97-6
0.0002 mg/m33.0E-04
3.0E-04
(continued)
-------
Table A-l. (continued)
TO
S
S-
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Methacrylonitrile
126-98-7
Methanol
67-56-1
RfC=13 mg/m3
Air Char. Studyl .0E+011.0E+01
Methomyl
16752-77-5
Methoxychlor
72-43-5
Methoxyethanol acetate, 2-
110-49-6
prov RfD=0.0057
mkd; prov
RfC=0.03 mg/m3
61FR 42317 and Air Char. Study9.0E-029.0E-02
Methoxyethanol, 2- (ethylene
glycol methyl ether)
109-86-4
prov RfD=0.0057
mkd
61 FR 42317 2.0E-022.0E-02
Methyl ethyl ketone
78-93-3
1.0E+00
1.0E+01
Methyl isobutyl ketone
108-10-1
Methyl mercury
22967-92-6
0.5
ug/kg/d
Methyl methacrylate
80-62-6
1.0E-01
not
updated
Methyl parathion
298-00-0
0.0003
Methyl tert-butyl ether
1634-04-4
0.7
3.0E+00
3.0E+00
Methylaniline, 2- (o-toluidine)
95-53-4
inh CSF=2.4E-1
per mkd;
URF=6.9E-5 per
M|jg/m3
Air Char. Study
Methylcholanthrene, 3-
56-49-5
inh CSF=7.4 per
mkd; URF=2.1E-3
per M|Jg/m3
Air Char. Study
6.3E-03
2.2E+01
Methylene bromide
74-95-3
Methylene chloride
(dichloromethane)
75-09-2
0.2
0.3
3.0E-01
4.0E-01
1.0E-06
3.5E-03
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Methylene-bis(2-chloroaniline),
4,4'- (MBOCA)
101-14-4
0.003
4.3E-04
1.5E+00
Molybdenum
7439-98-7
Naphthalene
91-20-3
0.002
9.0E-03
9.0E-03
Nickel subsulfide
12035-72-2
Nickel, soluble salts
7440-02-0
0.0002 mg/m35.0E-05
5.0E-05
2.6E-04
9.1E-01
Nitrobenzene
98-95-3
3.0E-02
not
updated
Nitropropane, 2-
79-46-9
2.0E-02
not
updated
N-Nitrosodiethylamine
55-18-5
1.0E-02
3.6E+01
N-Nitrosodimethylamine (N-
methyl-N-nitroso-methanamine)
62-75-9
4.6E-03
1.6E+01
N-Nitroso-di-n-butylamine
924-16-3
3.1E-03
1.1E+01
N-Nitrosodi-n-propylamine
621-64-7
2.0E-03
7.0E+00
N-Nitrosodiphenylamine
86-30-6
2.6E-06
9.0E-03
N-Nitroso-N-methylethylamine
10595-95-6
6.3E-03
3.7E+00
N-Nitrosopiperidine
100-75-4
2.7E-03
9.4E+00
N-Nitrosopyrrolidine
930-55-2
6.0E-04
2.1E+00
Octamethylpyrophosphoamide
152-16-9
Parathion
56-38-2
Pentachlorobenzene
608-93-5
Pentachlorodibenzofuran,
1,2,3,7,8-
57117-41-6
1.9E+00
6.5E+03
Pentachlorodibenzofuran,
2,3,4,7,8-
57117-31-4
1.9E+01
6.5E+04
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Pentachlorodibenzo-p-dioxin,
1,2,3,7,8-
40321-76-4
1.9E+01
6.5E+04
Pentachloronitrobenzene
82-68-8
Pentachlorophenol
87-86-5
0.001
1.0E-01
not
updated
5.1E-06
1.8E-02
Perchlorate
14797-73-0
Phenol
108-95-2
prov RfC=0.02 or
0.006 mg/m3
61FR
42317 and
63FR
24596
6.0E-01
6.0E-01
Phenylenediamine , m-
108-45-2
Phorate
298-02-2
Phthaiic anhydride
85-44-9
1.0E-02
1.0E-02
Polychlorinated biphenyls
1336-36-3
5.7E-4 (high) 2.0E-5
(low)
2.0E+0
(high) 7.0E-
2 (low)
Pronamide
23950-58-5
Propylene oxide
75-56-9
3.0E+00
3.0E-02
3.7E-06
1.3E-02
2.4E-01
Pyrene
129-00-0
Pyridine
110-86-1
Safrole
94-59-7
Selenium
7782-49-2
0.005
8.0E-05
2.0E-02
Silver
7440-22-4
2.0E-02
not
updated
Strychnine and salts
57-24-9
Styrene
100-42-5
0.06
1.0E+00
1.0E+00
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal
CSF
(mkd)-1
CalEPA99
Oral CSF
(mkd)-1
Styrene-7,8-oxide
96-09-3
6.0E-03
not
updated
Sulfide
18496-25-8
TCDD, 2,3,7,8-
1746-01-6
0.000001 ug/kg/d
3.8E+01
1.3E+05
Tetrachlorobenzene, 1,2,4,5-
95-94-3
Tetrachlorodibenzodioxins
41903-57-5
Tetrachlorodibenzofurans
55722-27-5
Tetrachloroethane, 1,1,1,2-
630-20-6
Tetrachloroethane, 1,1,2,2-
79-34-5
0.04
5.8E-05
2.0E-01
Tetrachloroethylene
(perchloroethylene)
127-18-4
oral URF=1.5E-6;
oral CSF =5.2E-2;
inh URF=5.8E-7;
inh CSF=2E-3
1985 HAD
& 1987
addend
0.04
4.0E-02
not
updated
5.9E-06
2.1E-02
5.1E-02
Tetrachlorophenol, 2,3,4,6-
58-90-2
9.0E-02
not
updated
Tetraethyldithiopyrophosphate
3689-24-5
Thallium
7440-28-0
Thallium (I) acetate
563-68-8
Thallium (I) carbonate
6533-73-9
Thallium (I) chloride
7791-12-0
Thallium (I) nitrate
10102-45-1
Thallium (I) sulfate
7446-18-6
Thiram
137-26-8
Toluene
108-88-3
0.4
4.0E-01
4.0E-01
Toluene-2,4-diamine (2,4-
diaminotoluene)
95-80-7
1.1E-03
4.0E+00
Toluidine, p-
106-49-0
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Toxaphene
8001-35-2
trans-1,3-Dichloropropylene
10061-02-6
Trichloro-1,2,2-trifluoroethane,
1,1,2- (Freon 113)
76-13-1
9.0E+01
not
updated
Trichlorobenzene, 1,2,4-
120-82-1
Trichloroethane, 1,1,1- (methyl
chloroform)
71-55-6
1.0E+00
1.0E+00
Trichloroethane, 1,1,2- (vinyl
trichloride)
79-00-5
4.0E-01
not
updated
1.6E-05
5.7E-02
Trichloroethylene
79-01-6
6.0E-01
6.0E-01
2.0E-06
1.0E-02
1.5E-02
Trichlorofluoromethane (CFC-11)
75-69-4
2.0E+01
not
updated
Trichlorophenol, 2,4,5-
95-95-4
Trichlorophenol, 2,4,6-
88-06-2
2.0E-05
7.0E-02
Trichlorophenoxy) propionic acid,
2 (2,4,5-
93-72-1
Trichlorophenoxyacetic acid,
2,4,5-
93-76-5
Trichloropropane, 1,2,3-
96-18-4
Triethylamine
121-44-8
7.0E-03
7.0E-03
Trinitrobenzene, 1,3,5- (sym-)
99-35-4
tris(2,3-Dibromopropyl)phosphate
126-72-7
Vanadium
7440-62-2
RfC=7E-5 mg/m3
Air Char. Study
Vinyl acetate
108-05-4
2.0E-01
2.0E-01
(continued)
-------
Table A-l. (continued)
Additional Benchmarks (non-IRIS. non-HEAST. non-Superfund)
Constituent Name
CAS No.
Other EPA
values
ATSDR
oral MRL
- chronic
Source(s) (mg/kg/d)
ATSDR inhal
MRL -
chronic
(ppm)
CalEPA97
chronic
inhal REL
(mg/m3)
CalEPA99
chronic
inhal REL
(mg/m3)
CalEPA99
inhal unit
risk
(ug/m3)-1
CalEPA9
9 inhal CalEPA99
CSF Oral CSF
(mkd)-1 (mkd)-1
Vinyl chloride
75-01-4
0.00002
5.0E-03
not
updated
7.8E-05
2.7E-01
Warfarin
81-81-2
Xylene, m-
108-38-3
Xylene, o-
95-47-6
Xylene, p-
106-42-3
Xylenes (total)
1330-20-7
0.1
2.0E-01
7.0E-01
Zinc
7440-66-6
0.3
9.0E-04
not
updated
References
References for TEFs:
EPA98= http://www.epa.gov/nceawwwl/dchem.htm
EPA93=Provisional guidance for quantitative risk assessment of PAHs, EPA/600/R-93-089
a Benzene oral CSF (food, water) = 0.015 to 0.055 per mg/kg/day; inhalation URF = 2.2 x 10"6 to 7.8 x 10"6 per ng/m3;
inhalation CSF = 7.7 x 10"3 to 2.7 x 10"2 per mg/kg/day (revised on IRIS 1/00).
-------
Appendix B
Statistical Analysis Weights and Variance
Estimation for the Surface Impoundment Study
Screening Survey
-------
Appendix B
Appendix B
Statistical Analysis Weights and Variance
Estimation for the Surface Impoundment Study
Screening Survey
The statistical analysis weights for the observational units in any probability-based
sample survey are the initial sampling weights adjusted to reduce the potential for bias due to
survey nonresponse. The initial sampling weight for each unit is the reciprocal of the probability
that the unit was selected into the sample. If each unit could have more than one linkage to the
sampling frame (or list) from which the sample was selected, the initial sampling weights must
be adjusted to compensate for this multiplicity. Finally, a model-based estimate of the
probability of responding is usually used to reduce the potential for bias due to nonresponse. In
the sections that follow, we discuss each of these steps for computing the statistical analysis
weights for the Surface Impoundment Study screening survey. In the last section, we discuss
estimation of sampling variances using the screening survey data.
B.l Initial Sampling Weights
Because of major differences in the sources and availability of sampling frame data, three
primary sampling strata were defined for selection of facilities for the screening survey based on
the facility's regulatory status under the Clean Water Act:
1. Direct discharge (Section 402) impoundments: These impoundments treat
waste in systems that ultimately discharge directly into surface waters. This
subpopulation is regulated under CWA Section 402, which requires National
Pollution Discharge Elimination System (NPDES) permits for all facilities that
discharge to "waters of the United States."
2. "Zero discharge" impoundments: These impoundments are not designed to
discharge waste into the environment except through infiltration into soil or
evaporation. Facilities that use infiltration or evaporation ponds for waste
treatment or disposal may be regulated under a variety of state laws addressing
both waste handling and groundwater protection. Specific regulations regarding
these impoundments vary by State.
B-3
-------
Appendix B
3. Indirect discharge (Section 307) impoundments: These impoundments treat or
hold waste prior to discharging to a publicly owned treatment works (POTW).
Facilities that discharge significant waste flows to POTWs must comply with
federal and local standards for pretreatment of waste in order to prevent adverse
impacts on the public treatment plants. Local POTWs are the principal permitting
authorities for CWA Section 307 facilities.
For direct discharge facilities, RTI constructed an essentially complete sampling frame of
43,050 facilities from the NPDES permits in the EPA's Permit Compliance System (PCS)
database. We partitioned the sampling frame into three primary sampling strata, defined as
follows:
1. Facilities in high-priority SICs (26, 2819, 2824, 2834, 2869, 2897, 2911, 30, 33,
or 36).
2. All other facilities with in-scope SICs.
3. The six pilot study facilities.
Stratum 1, the high-priority SICs, were expected to contain a higher proportion of facilities that
use surface impoundments to manage decharacterized waste waters. Hence, this stratum was
sampled at a higher rate than Stratum 2, the remainder of the in-scope SICs, to ensure that the
Phase 1 screening survey would include an adequate number of facilities using surface
impoundments to manage decharacterized waste waters. Each of these strata was then
partitioned into substrata based on SIC codes, and the substrata were all sampled at the same rate
within each primary sampling stratum. Hence, a stratified simple random sample of 2,000
facilities was selected from 15 sampling strata, and the six pilot study facilities were retained
with certainty.
For zero discharge facilities, IEc constructed a sampling frame of 5,807 facilities from
available state data and two federal databases: EPA's Toxics Release Inventory (TRI) and the
AIRS Facility Subsystem (AFS). We stratified the sampling frame according to the general
categories of completeness for the different state and federal data sources, and according to high
and low priority SIC codes. Table B-l summarizes the sampling strata for the zero discharge
facilities. A stratified random sample of 250 facilities was selected using the same sampling rate
for all strata except for the Oklahoma database of private sewage treatment facilities. We
expected this group of facilities to be mostly out-of-scope, and, if in-scope, to be relatively
homogeneous. Hence, we sampled them at one-half the rate used for the other strata.
Because local POTWs are the principal permitting authorities for indirect discharge
facilities, IEc used anecdotal information collected from EPA, state and local personnel, and
database information from EPA Region 7 to construct a purposive sampling frame of 35
facilities. All 35 facilities were included in the screening sample.
B-4
-------
Appendix B
Table B-l. Zero Discharge Sample Frame Stratification
lli<>li Low
litcililies Priority Priority litcililies
in SIC C'oilo SIC C'oilo with No litcililies in
Database Strata TKI/AI S l itcililies l itcililies SIC Code SIC Code 44>52
Complete Databases:
CA, KY, MI, NV, NH, NM,
NC, OK(l), OK(2),a PA, TN,
VT, WI
228
61
306
1155
886 facilities in
OK(2)a
General Databases:
FL, KS, MD, MN, MI, NI,
NY, OR, VA, WA
128
127
543
1592
95
Partial Databases:
AR, HI, ME, MA, MT, RI,
TX, UT
116
121
117
138
No State Information:
AL, AK, AZ, CO, CT, DE,
GA, ID, IL, IN, 10, LA, MO,
NE, ND, OH, SC, SD, WY,
WV, PR
194
N/A
N/A
N/A
N/A
Notes:
3 The Oklahoma (2) database includes an unusually extensive listing of private sewage treatment facilities.
We expect most of these facilities to be out of scope, and we therefore sampled only this database at a
rate lower then the rest ofthe frame in order to avoid spending considerable project resources examining
these facilities.
Subsequent to selection of this sample for the screening survey, EPA and IEc determined
that some of the sample facilities were ineligible for Phase 2 of the study, and those facilities
were removed from the sample before mailing the screening questionnaires. Hence, we have
computed the initial sampling weight for each of the 2,019 facilities that were mailed the
screening questionnaire. The sampling weight for each of the 35 indirect discharge facilities is
undefined (missing) because these facilities were purposively selected. If the six pilot study
facilities are included in any statistical analyses their analysis weight will unity (1.00) because
they were included in the sample with certainty. For each of the other 1,984 facilities mailed a
screener, the initial sampling weight was computed as
™i0) =NI(j)/n,(j),
B-5
-------
Appendix B
where
Ni(j) = Total number of facilities in stratum j, and
ni0) = Number of facilities selected into the sample from stratum j.
The frame count, N,(j), sample size, n,(j), and initial sampling weight, w,(j), are shown for each
stratum in B-Table 2.1
B.2 Multiplicity Adjustments
The PCS data used to construct the sampling frame for the direct discharger sample were
outfall- or pipe-level records. We first collapsed the pipe-level records to the permit level by
permit number (NPID). We then combined permits to the facility level, but there was no unique
facility ID to guide this process. Hence, we conservatively merged permits to the facility level
only when it was quite clear that there were multiple permits for the same facility. We merged
up to three different permits into a single facility-level record. Any facilities that had multiple
permits that did not get merged into a single facility-level record on the sampling frame had
multiple chances to be selected into the sample.
Therefore, in the screening questionnaire we listed all permits that had been used to
define the facility on the sampling frame, and we asked each facility to list any additional permits
that had been active for the facility at any time since June 1, 1990. Partway through data
collection, we discovered that some facilities did not understand that these additional permits
should include stormwater permits. Hence, we set up a computer-assisted telephone interviewing
(CATI) application to call the screener respondents and probe for any additional permits that had
not been listed in their questionnaire responses. We used both the responses to the original
question, Question 7, as well as the responses to the supplemental CATI question to make the
weight adjustments for frame multiplicity.
We first cleaned the responses to the questions regarding the additional permits. NPDES
permit numbers are all 9-digit ID numbers for which the first two digits are a U.S. State or
Territory abbreviation. Of course, the permit numbers reported in the survey did not all conform
to this format. The data cleaning consisted of removing extraneous characters (e.g., blank, dash,
#, NPDES, etc.) as needed to produce ID numbers in the proper format for matching against the
NPDES permit numbers on the sampling frame. Leading zeros and/or state abbreviations were
inserted into other reported numbers when those edits produced ID numbers in the NPDES
format. In addition, when two permits numbers had been keyed as a single response in the
survey, those permit numbers were moved into separate variables, which resulted in one
additional Question 7 response variable that was not in the raw survey responses. Permit
numbers that clearly were not in the format of an NPDES permit number received only minimal
editing.
1 The sample of 250 facilities was selected as an initial sample of 150 facilities plus 10 independent
supplemental samples of 10 facilities each, resulting in some unintended variation in the stratum sampling rates for
the combined sample.
B-6
-------
Appendix B
Table B-2. Initial Sampling Weights
Sampling
I vpo of l iicililv Stratum l-Yamc Count Sample Size Initial Weight
Direct Dischargers
(DISCHARG=1)
126
927
142
6.528
128
1019
156
6.532
129
440
67
6.567
130
1478
226
6.540
133
1752
268
6.537
136
919
141
6.518
2A
5169
141
36.660
2B
3442
95
36.232
2C
3000
82
36.585
2D
3212
88
36.500
2E
2680
73
36.712
2F
3642
100
36.420
2G
2688
74
36.324
2H
9276
254
36.520
21
3400
93
36.559
3
6
6
1.000
Zero Dischargers
(DISCHARG=2)
1
228
13
17.539
2
128
6
21.333
3
116
4
29.000
4
194
6
32.333
5
61
5
12.200
6
127
6
21.167
7
121
6
20.167
8
301
13
23.154
9
543
25
21.720
10
1155
55
21.000
11
1592
74
21.514
12
117
4
29.250
13
891
22
40.500
-------
Appendix B
After having completed the cleaning of the permit numbers for all screeners, we
determined if any facilities reported any of the preloaded permit numbers as additional permits.
For this task, we can confined our attention to the completed screeners for direct discharge
facilities (DISCHARG=1), including the screeners completed by former owners. For each
sampled direct discharge facility, we first compared the permit numbers reported in the screeners
as additional permit numbers against the up to three NPDES permit numbers that defined the
facility on the sampling frame. If the facility incorrectly reported any of these permit numbers as
additional permit numbers, they were deleted from the cleaned variables identifying the
additional permit numbers reported in the survey.
The next step was to determine the sample multiplicity for each of the 1,603 direct
discharge facilities for which the current or most recent owner was a respondent (P1RESP=1).
The multiplicity, m(j), of the j-th sample facility is the number of facilities on the full sampling
frame of 43,050 facilities that were linked to the sample facility. We merged the additional
permits numbers (not preloaded) that were reported by each facility against the facility-level
sampling frame permit numbers to determine the number of additional facilities on the sampling
frame that were linked to the sample facility. The facility's multiplicity, m(j), is then this count
plus one (i.e., the number of additional facilities plus the one originally selected). For most
direct discharge facilities, the multiplicity is one (1.00) because there were no additional facilities
on the sampling frame that were linked to the sample facility.
The nonresponse adjustments require that the frame multiplicity be known for every
sample facility, not just the responding facilities. Therefore, for each direct discharger sampling
stratum, we computed the average multiplicity among the facilities with known multiplicity (the
respondents) and imputed the multiplicity for each nonresponding facility within each sampling
stratum to be the average multiplicity for the stratum. After having computed or imputed the
multiplicity, m(j), for each direct discharge sample facility, we computed the multiplicity-
adjustment to the sampling weight for they'-th facility as follows:
w2(j) = 1 /m(j) for direct discharge facilities
w2(j) = 1 for zero discharge facilities.
Lessler and Kalsbeek (1992, Section 5.2.2) show how this using this multiplicity adjustment
produces survey estimates that are design-unbiased.
B.3 Nonresponse Adjustments
Weight adjustments to reduce the potential bias due to survey nonresponse are based on
models for the probability of responding, using data that are available for both the respondents
and the nonrespondents. Since the sampling stratum was the only thing we knew about the
nonresponding facilities, we used sample-based ratio adjustments based on the sampling strata
(Kalton and Maligalig, 1991). The nonresponse adjustments were defined only for the direct and
zero discharge facilities because the indirect discharger sample was not a probability-based
sample.
B-8
-------
Appendix B
The weight adjustment for nonresponse is simply the reciprocal of the weighted response
rate. Therefore, strata for which the number of respondents is small (e.g., less than 20) must be
collapsed with other strata to form weighting classes. Moreover, combining strata to form
weighting classes reduces the variance inflation that results from variability in the analysis
weights. Hence, in order to determine weighting classes for the screening survey, we reviewed
the following statistics for each sampling stratum:
# number of sample facilities, ns
# number of facilities with known eligibility status, nk
# response rate for eligibility determination, rk = (nk/nj * 100
# number of eligible facilities, ne
# number of responding facilities, nr
# unweighted response rate, ru = (nr n ) * 100.
After reviewing the pattern of survey responses and eligibility by sampling strata, we
decided that each of the 15 sampling strata for the direct discharger sample contained sufficient
numbers of respondents to be a separate weighting class. These are the first 15 weighting
classes. However, because of the smaller sample size for the zero discharger sample, we
combined strata to form weighting classes as follows:
# Weighting class 16 consists of zero discharger strata 1 through 4: the facilities
from the TRI or AFS portion of the sampling frame;
# Weighting class 17 consists of zero discharger strata 5, 6, and 9: the facilities with
high-priority SICs; and
# Weighting class 18 consists of the remainder of the zero discharger facilities.
Having defined the weighting classes for nonresponse adjustment, we implemented the
weight adjustments for nonresponse in two stages. We first made an adjustment for inability to
determine whether or not a facility was eligible for the Phase 1 screening interview (i.e., was in
operation at any time since June 1, 1990). The second stage of nonresponse adjustment was an
adjustment for nonresponse among the facilities known to be eligible for the screening survey.
The weight adjustment factor for inability to determine eligibility for the screening
interview was computed for the c-th weighting class follows:
E wi 0)w20)
(C) = tr^eC >
E m'i (/) "s (/) 4 (/)
jec
where Ik(j) is an indicator that the eligibility status or the j-th facility is known, i.e.,
hO) = 1 if the eligibility status of the j-th facility is known (P1ELIG=1 or 2)
Ir(j) = 0 otherwise.
B-9
-------
Appendix B
This adjustment is equivalent to assuming that the proportion of sample facilities that are eligible
for the screening survey (i.e., in operation at any time since June 1, 1990) is the same among
those with known and unknown eligibility status.
Similarly, the weight adjustment factor for survey nonresponse was defined for the c-th
weighting class as follows:
where Ir and Ic are indicators of response and eligibility status, respectively, i.e.,
Ir(j) = 1 if the j-th facility was a screener respondent (P1RESP=1)
Ir(j) = 0 otherwise, and
Ie(j) = 1 if the j-th facility was eligible for Phase 1 (P1ELIG=1)
I/j) = 0 otherwise.
The final statistical analysis weight was then be defined for the /-th facility in the c-th
weighting class as the product of the various weight components, as follows:
Wj 0) = W1 0) 0) Ws(c) W4(c) W •
One property of this analysis weight is that the sum of this weight for the respondents in each
weighting class is identical to the sum of the multiplicity-adjusted weights of all eligible sample
facilities in that weighting class, exactly as if all the facilities had responded.
After computing these analysis weights, we performed several weight checks to verify
that each weight component had been computed correctly.
B.4 Variance Estimation
Since sample facilities were selected using a stratified simple random sampling design,
standard textbook formulae for stratified random sampling designs can be used to compute
sampling variances, except that the observations must be weighted by the statistical analysis
weights to account for survey nonresponse. However, the number of responding facilities is as
small as two in some of the sampling strata used for the zero discharger sample because of the
small sample sizes and high rates of ineligibility for zero dischargers. Therefore, we recommend
that the collapsed sampling strata used for the weighting classes (P1WTCLAS) be used as the
analysis strata when computing sampling variances.
If one wishes to compute sample means and proportions, those estimates are ratio
estimates. Ratio estimators are nonlinear statistics, which require special-purpose software. One
option is to use RTI's SUDAAN® software package. If one uses SUDAAN to analyze the data,
we recommend that the following design options be used to compute sampling variances.
DE SIGN= S TRWR
NEST=P1WTCLAS.
B-10
-------
Appendix B
These options specify that the design is a stratified random sampling design in which units were
selected with replacement and that the variable P1WTCLAS defines the analysis strata. The
with-replacement option is recommended because the survey results will be used to make
inferences regarding the super-population of all survey-eligible facilities, whether or not they
were included on the sampling frame constructed for this study and because the zero discharger
frame is known to be incomplete.
References
Lessler, J.T. and W.D. Kalsbeek (1992). NonsamplingError in Surveys. New York, NY:
Wiley.
Kalton, G. and D.S. Maligalig (1991). "A Comparison of Methods of Weighting Adjustment for
Nonresponse." Bureau of the Census 1991 Annual Research Conference Proceedings,
pp. 105-110.
B-ll
-------
Appendix C
Examples of Toxicity Benchmarks for
Ecological Risk Assessment
-------
Table C-l. Selected Sources of Toxicity Data.
DATABASES
• Hazardous Substances Data Bank (HSDB). National Library of Medicine, National Toxicology Information
Program. Bethesda, MD.
• Integrated Risk Information System (IRIS). U.S. EPA, Office of Health and Environmental Assessment,
Environmental Criteria and Assessment Office.
• PHYTOTOX. Chemical Information System (CIS) Database.
• Registry of Toxic Effects of Chemical Substances (RTECS). National Institute for Occupational Safety and
Health (NIOSH), Washington, D.C.
COMPILATIONS
• Agency for Toxic Substances and Disease Registry (ATSDR). 1997. Toxicological Profiles . On CD-ROM.
CRC press. U.S. Public Health Service. Atlanta, GA.
• Calow, P. (ed.). 1994. Handbook of Ecotoxicology. Volume 2. Blackwell Scientific Publications. London,
England.
• Devillers, J. and J.M. Exbrayat. 1992. Ecotoxicity of Chemicals to Amphibians . Grodon and Breach Science
Publishers. Philadephia, PA.
• Eisler, R. 1985-1993. Hazards to fish, wildlife, and invertebrates: A synoptic review . U.S. Fish Wildlife
Service Biological Reports
• Friberg, L., G.F. Nordberg, and V.B. Vouk (eds). 1986. Handbook on the Toxicology of Metals. Second
Edition. Volume II: Specific Metals . Elsevier Science Publishers. New York, NY.
• Hill, E.F., R.G. Heath, J.W. Spann, and J.S. Williams. 1975. Lethal Dietary Toxicities of Environmental
Pollutants to Birds . Special Scientific Report - Wildlife 191. U.S. Department of the Interior, Fish and
Wildlife Service. Washington, DC.
• Hudson, R.H., R.K. Tucker, and M.A. Haegele. 1984. Handbook of toxicity ofpesticides to wildlife. U.S.
Fish and Wildlife Serv. Resour. Publ. 153. 90 pp.
• Humphreys, D.J. 1989. Veterinary Toxicology. Baliliere Tindall. London, England.
• Kabata-Pendias, A., and H. Pendias. 1992. Trace Elements in Soils and Plants . 2nd edition. CRC Press, Ann
Arbor, MI.
• Klaassen, C.D., M.O. Amdur, J. Doull. 1986. Casarett and Doull's Toxicology. The Basic Science of
Poisons . 3rd edition. Macmillan Publishing Company. New York, NY.
• Lewis, R.J., Sr. 1992. Sax's Dangerous Properties of Industrial Materials . Eighth Edition. Van Nostrand
Reinhold. New York, NY.
• Regional Water Quality Control Board, Central Coast District (RWQCBCC). 1989. Water Quality Control
Plan, Central Coast Basin. Regional Water Quality Control Board, Central Coast District. San Luis Obispo,
CA.
• Sample, B.E., D.M. Opresko, and G.W. Suter II. 1996. Toxicological benchmarks for wildlife: 1996
Revision. Prepared for the U.S. Department of Energy.
• Schafer, E.W. 1972. The acute oral toxicity of 369 pesticidal, pharmaceutical and other chemicals to wild
birds. Toxicol. Appl. Pharm . 21: 315-330.
• Suter, G.W. II, and C.L. Tsao. 1996. Toxicological benchmarks for screening potential contaminants of
concern for effects on aquatic biota: 1996 revision. Prepared for the U.S. Department of Energy.
• U.S. Environmental Protection Agency (U.S. EPA). 1986. Quality Criteria for Water 1986. EPA 440/5-86-
001. Office of Water Regulations and Standards, Criteria and Standards Division, U.S. Environmental
Protection Agency. Washington, DC.
• U.S. Environmental Protection Agency (U.S. EPA). 1995. Great Lake Water Quality Criteria Documents for
the Protection of Wildlife. EPA 820/b-85/008. Office of Water. Washington, D.C.
• U.S. Navy (U.S. Navy). 1997. Development of toxicity reference values as part of a regional approach for
conducting ecological risk assessments at naval facilities in California. Draft Technical Memorandum.
Prepared for the U.S. Navy
• Venugopal, B. and T.D. Luckey. 1978. Metal Toxicity in Mammals. 2. Plenum Press. New York, NY.
• Will, M.E. and G.W. Suter II. 1995. Toxicological benchmarks for screening potential contaminants of
concern for effects on terrestrial plants: 1995 revision. Prepared for the U.S. Department of Energy.
PRIMARY LITERATURE
• Over 400 citations
App_l_Tabs_SIS.xls
2/12/00
-------
Table C-2
Toxicity Benchmarks for Mammals
Uncertainty Factor
Age Observed Effect
Food Water Ingest.
Effect Exposure Cone. Cone. % Rate
Level Duration (mg/kg) (mg/I) C'heiii. (g/d)
Drink Body Toxicity
Rate Conv. Weight Benchmark
(mL/d) Factor (kg) (mg/kg-d)
Subchronic- Endpoint-to-
to-Chronic NOAEL
Chromium HI
Chromium VI
Cobalt
Copper
Mercury, Organo-
Molybdenum
Silver
Thallium
Vanadium
Zinc
• No adverse effect on survivorship, and no
histopathologjcal changes and only minor
reductions in growth at 540 days and median lil
Mouse Weanling • No adverse effects on maternal survival, mean
litter size, number of runts, and number of still
• No adverse effect on growth or food and water
consumption
• No adverse effect on survival, growth, median life NOAEL
span, and longevity
• No adverse effects on number of copulating NOAEL
females, number of pregnant females, total
implants, live fetuses, average fetal weight, and
• No adverse effects on reproduction and longevity NOAEL
Mouse Not given •
Weanling •
Weanling •
NOAEL Lifetime
NOAEL 3 generations
NOAEL 16 months
Adult and
offspring
Adult
2 years 50,000
n growth and no adverse
NOAEL
LOAEL
NOAEL
30 days
1 year
No adverse effect o
systemic pathologies
Newborn • Decreased growth in rat pups
Kit • No adverse effects on kit mortality, length of
gestation, and kit weight
Adult • Increased young deaths and number of runts; by the Effects 3 generations
second generation (F2), there were insufficient Level
numbers to continue the investigation
14-day- • No effect on growth, percent pregnant, litter s
oldpups ovulations, fetal resorptions, pre-implantation
to adult deaths, or fetal weight
Adult • No adverse effects on fertility, kit weight, and kit NOAEL 6 months
survivorship
Adult • No adverse effects on prenatal development
Adult • Reduced reproductive success and increased
incidence of runts
Adult • No adverse effects on fertility, gestation, offspring NOAEL 3 generations
viability, and lactation indices
Adult • No adverse effects on fertility, number of young,
juvenile growth, andjuvenile survival
Adult • "Excellent clinical condition" was reported and nc
adverse effect on behavior and fluid consumption
Adult • No adverse effects on male reproductive tract
morphology or function
Adult • No adverse effects on fertility, number of litters,
number of dead offspring/litter, and average body
Adult • No adverse effect on fetal development
NOAEL > 100 days 1100
NOAEL 3 generations
LOAEL 3 generations
NOAEL 2 generations
1 NOAEL 12 weeks
NOAEL 60 days
NOAEL
NOAEL
60 days
(gestation)
16 days
(gestation)
0.739 137
0.7989 15
5 0.001 0.025
5 0.001 0.025
22 0.001 0.435
25 0.001 0.25
0.001 0.25
25 0.001 0.25
0.001 0.25
0.001 0.25
5 0.001 0.025
0.001 0.25
25 0.001 0.25
25 0.001 0.25
0.001 0.25
1.0 Schroeder et al. 1968 —
1.0 Schroeder & Mitchener 1971 —
5.1 Perry et al. 1983 —
0.5 Schroeder & Mitchener 1975 —
1.0 Sutou et al. 1980b —
2,000 Ivankovic & Preussmann 197; —
2.5 MacKenzie et al. 1985 —
12 Domingo et al. 1985 —
12 Aulerich et al. 1982 —
1.0 Azar et al. 1973 —
Schoreder & Mitchener
1971
88 Laskey et al. 1982 —
1.0 Aulerich et al. 1974 —
0.024 Verschuuren et al. 1976 —
2.1 Schroeder & Mitchener 1971 —
15 Ambrose et al. 1976 —
0.15 Rosenfeld & Beath 1954 —
65 Walker 1971 —
0.74 Formigli et al. 1986 —
2.1 Domingo et al. 1986 —
120 Schlicker & Cox 1968 —
Volatile Organic Compounds
RTFY
Benzene
Adult • Increased maternal mortality and embryonic EL 10 days
resorption (gestation)
Adult • Increased maternal mortality and embryonic EL 10 days
1,000 0.025 264 Nawrot & Staples 1979
1,000 0.25 260 Nawrot & Staples 1979
Ethylbenzene
Xylene
'-Related VDC.S
Dichloroethene, 1,2- Rat
Dichloroethane, 1,2- Mouse
• No adverse histop_ 0 ,.ianges in
reproductive organs
• No adverse histopathological changes in
reproductive organs
• No adverse effects on fertility, number of live
fetuses, litter survival and growth, malformation
pathology, or adult mortality
NOAEL 10 days
(gestation)
NOAEL 90 days
NOAEL 2 generations
(24 to 25
weeks each)
0.25
500 NTP1'
0.25 206 McCauley et al. 1990
1,000 0.035 50 Lane et al. 1982
-------
Table C-2
Toxicity Benchmarks for Mammals
Uncertainty Factor
Chemical
Observed Effect
Effect
Food
Exposure Cone.
Duration (mg/kg)
Water
(mgX)
Ingest.
Rate
(g/d)
Drink
Rate
(mL/d)
Factor
Body
Weight
(hg)
Toxicity
Benchmark
(mg/kg-d)
Subchronic- Endpoint-to-
to-Chronic NOAEL
Tetrachloroethene
Trichloroethane
Trichloroethene
Chlorohenzene-Related VPCs
Chlorobenzene Mouse
Dichlorobenzene Rat
Acetone
• Increased mortality, however,
on testes and ovaries
• No effects on fertility, incidence of implants
resorptions, number of live fetuses, frequency of
live litters, litter size, postnatal growth, postnatal
survival, or incidence of congenital malformations
• No adverse maternal toxicity, fetal mortality, and NOAEL
teratogenic effects
o adverse effects LOAEL 78 weeks
NOAEL 2 generations
(24 to 25
weeks each)
Adult • No decrease in survival.
Fetus • Decrease in maternal weight and a
the incidence of an extra rib in the fetuses of
pregnant rats
NOAEL
LOAEL
35 days
(gestation)
103 weeks
10 days
1,000 0.035
0.025
0.25
385 NCI 1977
1,000 Lane et al. 1982
100 Manson et al. 1984
Kluwe etal. 1985
Giavini et al. 1986
Dichloropropane, 1,2-
Polycyclic Aromatic Hydrocarbons and Semivolatile Organic Compounds
Benzo[a]pyrene
Naphthalene
Methylphenol, 2-
Pentachlorophenol
Adult
Adult
• Reduced F1 fertility, fewer and smaller F2 litters, LOAEL
and decreased weight of 42-day-old pups
• No adverse effects on body weight and water NOAEL
consumption
• No adverse effect oi
• No adverse effects <
development
reproduction
l number of pups bom alive o
NOAEL
NOAEL
10 days
(gestation)
10 days
(gestation)
6 months
4 months
0.001 0.25
MacKenzie and Angevine
1981
Navarro et al. 1991
Homshaw etal. 1986
Schwetz et al. 1978
Chlorinated Pesticides
Aldrin / Dieldrin
Chlordane
DDT / DDD / DDE
Endosulfan
Heptaclor
Adult • No adverse effects on number of pregnancies,
number of pups per litter, offspring mortality, and
weight of young at weanling
Adult • No adverse effects on survival, fertility, litter size,
birth weights, and pup and weanling body weights
Adult • No adverse effects viability and abundance of
Adult • No adverse effects on number of young produced
Adult • No adverse effect on development
Adult • No adverse effect on percentage of successful
litters, percent of kits born alive, kit birth weight,
kit survival, body weight of the parental
generation, and central nervous system effects
NOAEL 3 generations
NOAEL 4 generations
NOAEL 6 generations
NOAEL 2 years
NOAEL 84 days
NOAEL 181 days
0.001 0.25
0.001 0.025
0.001 0.35
0.80
0.20
Treon and Cleveland 1954,
1955
Grant et al. 1977
Keplinger et al. 1968
Fitzhugh 1948
Hoeschst 1984
Crum et al. 1993
Polychlorinated Biphenyls
Adult • No adverse effects on maternal mortality, number NOAEL 4.5 months
of offspring, or offspring survival
0.14 Aulerich & Ringer 1977
Dioxins and Furans
2,3,7,8-TCDD
Adult/
Fetus
• Decreased gestational survival index; decreased
fetal weight
LOAEL 3 generations
0.25 0.000001 Murray et al. 1979
Definitions:
BHC
% Chem.
g/d
kg
mg/kg
mg/kg- d
mg/L
mL/d
NOAEL
LOAEL
PAHs
Benzene hexachloride.
Percent of chemical.
Grams per day.
Kilogram.
Milligrams per kilogram.
Milligrams per kilogram per day.
Milligrams per liter.
Milliliters per day.
No observed adverse effect level.
Low observed adverse effect level.
Polycyclic aromatic hydrocarbons.
-------
Table C-2
Toxicity Benchmarks for Mammals
Uncertainty Factor
Food Water Ingest. Drink Body Toxicity
Effect Exposure Cone. Cone. % Rate Rate Conv. Weight Benchmark Subchronic- Endpoint-to-
Observed Effect Level Duration (mg/kg) (mg/I) C'heiii. (g/d) (mL/d) Factor (kg) (mg/kg-d) Reference to-Chronic NOAEL
PCBs - Polychlorinated biphenyls.
RTV - Reference toxicity value.
TCDD - Tetrachlorodibenzo-p -dioxi
VOCs - Volatile organic chemicals.
-------
Table C-3
Toxicity Benchmarks for Birds
Uncertainty Factor
Observed Effect
Food Water Ingest.
Effect cone. Cone. % Rate
Level Duration (mg/kg) (mgfi.) Chem. (g/d)
Drink Body Toxicity
Rate Conv. Weight Benchmark
(ml/d) Factor (kg) (mg/kg-d)
Subchronic- Endpoint-to-
to-Chronic NOAEL
Antimony
Arsenic
1 year • No effect on egg weight, duckling production, NOAEL 4 weeks prior to
duckling body weight, duckling growth, or
number of days between pairing and 1st egg.
• Well tolerated by the chicks
pairing through
multiple
hatchings
NOAEL 4 weeks
5.5 Stanley, Jr. et al. 1994
0.001 0.121 100 Johnson et al. 1960
Beryllium
Cadmium
Chromium in
Chromium VI
Cobalt
Copper
Lead, metallic
Lead, organo-
Manganese
Mercury
Mercury, Organo-
Molybdenum
Nickel
Chicken
American black
American Adult
Japanese quail Adult
Seler
Japanese quail
Japanese quail
Mallard
Chicken
Mallard
Mallard
Adult
Adult
and 9-
day-old
7 months
• No adverse effects on egg production NOAEL
• No adverse systemic pathologies and no NOAEL
adverse effect on growth
• No adverse effects on survival and NOAEL
• No adverse effect on growth and hatching NOAEL
success, and minor reductions in number of
• No adverse effect on growth NOAEL
• Reduced fertility and hatchability NOAEL
• Fewer eggs and ducklings LOAEL
• Fewer eggs were laid and embryo viability LOAEL
was reduced
• No adverse effects on survivorship and growth NOAEL
• No adverse effects on growth, adult survival, NOAEL
8 months
10 months
6 months
12 weeks
75 days
1 year
3 generations
21 days
90 days
78 days
0.001 0.8 2.1 Leach et al. 1979
0.001 1.25 1.0 Haseltine et al. 1985
0.001
0.001
0.001
0.001
0.13
0.15
0.072
0.15
0.45
0.06
0.001 0.8
0.001 0.782
Patee 1984
Edens et al. 1976
Laskey & Edens 1985
Hill and Schaffner 1976
Heinz 1979
88 Lepore & Miller 1965
18 Cain & Pafford 1981
0.5 Heinz et al. 1987
Silver
Thallium
Vanadium
Mallard Adult
Chicken Adult
• No adverse effects on mortality, body weight, NOAEL
or blood chemistry
• No adverse effects on fertility, egg NOAEL
hatchability, and body weight of 3-week old
0.001 1.935
11 White & Dieter 1978
14 Stahl et al. 1990
Volatile Organic Compounds
RTF.Y
Benzene
Toluene
Ethylbenzene
Xylene
Tnchlnmethene-Related I
Dichloroethene, 1,2-
Dichloroethane, 1,2-
Tetrachloroethene
Trichloroethane
Trichloroethene
rhlnmhen yene-Rel.
Chlorobenzene
Dichlorobenzene
-------
Table C-3
Toxicity Benchmarks for Birds
Uncertainty Factor
Test Species Age
Observed Effect
Effect
Level Duration
Food Water
Cone. Cone. %
(mg/kg) (mg/L) C'heill.
Ingest.
Rate
(g/d)
Drink
Rate
(ml/d)
Factor
Body Toxicity
Weight Benchmark
(kg) (mg/kg-d)
Subchronic- Endpoint-to-
to-Chronic NOAEL
Other VOCs
Acetone "
Dichloropropane, 1,2-
Polycyclic Aromatic Hydrocsii'liiuis snnl Scinivnhiiilc Or-isiiiic ( i>iii|m>iiihIs
Benzo[a]pyrene
Naphthalene
Methylphenol, 4-
Pentachlorophenol •
o toxicity
Chlorinated Pesticides
Aldrin / Dieldrin
BHC
DDT / DDD / DDE
Endosulfan
Heptachlor
Mallard
Mallard
Adult
Adult
Red-winged Adult
blackbird
Pelican Adult
Gray partridge Adult
Bobwhite quail Adult
• Some mortality
• No adverse effects on food intake, water
consumption, body weight, egg laying
frequency, egg size and weight, eggshell
thinning, and eggshell porosity
• No mortality
• Adverse effects on reproductive success
• No reproductive effect
• Mortality
LOAEL 30 days
NOAEL 8 weeks
LOAEL
NOAEL
6 years
4 weeks
0.001 0.064
0.001 3.5
0.001 0.4
Hudson et al. 1984
Chakravarty & Lahiri
2.2 Stickel et al. 1983
0.028 Anderson et al. 1975
10 Abiola 1992
11 Hill et al. 1975
Polychlorinated Biphenyls
Screech owl Adult • No adverse effects on fertility and
NOAEL 2 generations
0.001 0.2 0.42 McLane & Hughes 1980
Dioxins and Furans
2,3,7,8-TCDD
2,3,7,8-TCDF
Chicken 3-day-
old
Chicken 1-day-
old
• No mortality or chick edema
• No mortality or chick edema
Subchronic 21 days
Subchronic 21 c
0.203 0.0001 Schwetz et al. 1973
0.178 0.001 McKinney etal. 1976
BHC
% Chen
mUd
NOAEL
LOAEL
PAHs
PCBs
RTV
TCDD
TCDF
VOCs
-------
Table C-4
Toxicity Benchmarks for Amphibians
T oxicity Amphibian
Effect Benchmark Duration-to- Endpoint-to- RTVs
Chemical Test Species Age Observed Effect Level Duration (ug/L) Reference Chronic NOAEL (ug/L)
Inorganics
Antimony • •
Arsenic • •
Barium • •
Beryllium Spoiled l,;irv;i • \K>ii;ilii\ l,i" °o hoins 3.100 Slonim ;iiul R;i> 100 31
Cadmium • •
Chromium • •
Cobalt
Copper ClawedToad 3-4weeks • Mortality LC50 48hours 1,700 de Zwart and Sloof 1987 — 100 17
Lead Argentine Embryo • Arrested development EC50 4 70 Perez-Coll et cd. 1988 100 4.7
Manganese • •
Mercury l];Njni imrrou- Kmhivo • Aiio-^iocl clo\oU-«piiioiii IV °o hoins po-si- 1.3 liirizo rial. I°S3 100 0.013
Molybdenum • •
Nickel /
Selenium •
Silver v ^ ^/#;. ;i" /
Thallium
Vanadium • •
Zinc ClawedToad Embryo • Arrested development EC50 96 hours 3,600 Dawson et al. 1988 — 100 36
Volatile Organic Compounds
RTFY
Benzene Leopard frog 3-4 weeks • Mortality
Toluene ~ ' ~ •
Ethylbenzene
Xylene
Trinhlnrnethene-Relater.
Dichloroethene, 1,2-
Dichloroethane, 1,2-
Tetrachloroethene
Trichloroethane
Trichloroethene ClawedToad 3-4weeks • Mortality
C.hlnrnhpnzpnp-Rplated VOCs
Chlorobenzene Northern leopard Tadpole • Mortality
frog
Dichlorobenzene
Other vac.
Acetone Axoloil l,;irv;i • \1«>ii;ilii\
Dichloropropane,
Polvcyclic Aromatic Hydrocarbons and Semivolatile Organic G impmiiidx
Benzo[a]pyrene Ribbed salamander Larva • Claslogonic ollocl I-1'.. 8tki\> 10 Siboulet etcd. 1984 — 10 1.0
Naphthalene Clawedtoad Larva • Absence of swimming EC50 6 hours 1,700 Edmisten e? a/. 1982 — 100 17
Methylphenol, 2- Clawedtoad Tadpole • Mortality NOAEL 48hours 24,000 Sloof et al. 1983 10 — 2,400
Pentachlorophenol Bullfrog Tadpole * Mortality LC50 96 hours 210 Thurston et al. 1985 10 10 2.1
Chlorinated Pesticides
Aldrin / Dieldrin Fowler's toad Tadpole • Mortality
BHC Chorus frog Tadpole • Mortality
App_l_Tabs_SIS.xls
2/12/00
LC50
LC50
48 hours
> 4 days
3,700
390
Sloof et al. 1983
100
100
37
3.9
NOAEL 48 hours
Sloof et al. 1983
LC50 Fertilization to 4 1,200
days post-
hatching
Birge and Cassidy 1983
LC50 96 hours 68 Mayer & Ellersieck 1986 — 100 0.68
LC50 96 hours 2,600 Mayer & Ellersieck 1986 — 100 26
-------
Table C-4
Toxicity Benchmarks for Amphibians
Test Species Age Observed Effect
Toxicity
Effect Benchmark
Level Duration (ug/L)
Amphibian
Duration-to- Endpoint-to- RTVs
Chronic NOAEL (ug/L)
Chlordane Northern leopard Adult • Mortality
frog
DDT/DDD/DDE Fowler'stoad 7weeks • Molality
Endosulfan
Heptachlor
LC50
96 hours
96 hours
500 Kaplan and Overpeck 1964
Maye
100
100
5.0
0.3
Polychlorinated l>i|ilu iiv l\
Total PCBs American toad Embyros • Mortality
Fertilization to 4
days post-
hatching
Birge & Cassidy 1983
Dioxins and Furans
PCDDs and PCDFs
Definitions:
BHC - Benzene hexachloride.
EC50 - Effect concentration (50%).
LC50 - Lethal concentration (50%).
mg/L - Micrograms per liter.
NOAEL - No observed adverse effect level.
PAHs - Polycyclic aromatic hydrocarbons.
PCBs - Polychlorinated biphenyls.
PCDDs - Polychlorinated dibenzo-/?-dioxins.
PCDFs - Polychlorinated dibenzofurans.
RTV - Reference toxicity value.
VOCs - Volatile organic chemicals.
App_l_Tabs_SIS.xls
2/12/00
-------
Table C-5
Toxicity Benchmarks for Soil Invertebrates
Toxicity
Benchmark
Chemical (mg/kgsoll) Reference
Inorganics
Antimony
•
No toxicity benchmark is proposed •
Arsenic
40
van den BergcV ul. 1993
Barium
620
van den BergcV ul. 1993
Beryllium
•
No toxicity benchmark is proposed •
Cadmium
12
van den Berg et ul. 1993
Chromium
230
van den Berg et ul. 1993
Cobalt
240
van den Berg et ul. 1993
Copper
190
van den Berg et ul. 1993
Lead
290
van den Berg et ul. 1993
Manganese
•
No toxicity benchmark is proposed •
Mercury
10
van den Berg et ul. 1993
Molybdenum
480
van den Berg et ul. 1993
Nickel
210
van den Berg et ul. 1993
Selenium
•
No toxicity benchmark is proposed •
Silver
•
No toxicity benchmark is proposed •
Thallium
•
No toxicity benchmark is proposed •
Vanadium
•
No toxicity benchmark is proposed •
Zinc
720
van den Berg et ul. 1993
Volatile Organic Compounds
BTEX
Benzene
Toluene
Ethylbenzene
Xylene
Trichloroethene-Related VOCs
Dichloroethene, 1,2-
Dichloroethane, 1,2-
T etrachloroethene
Trichloroethane
Trichloroethene
Chlorobenzene-Related VOCs
Chlorobenzene
Dichlorobenzene
Other VOCs
Acetone
Dichloropropane, 1,2-
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
30 van den Berg el ul. 1993
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
• No toxicity benchmark is proposed •
Polycyclic Aromatic Hydrocarbons
-------
Table C-5
Toxicity Benchmarks for Soil Invertebrates
Toxicity
Benchmark
Chemical (mg/kgs„n) Reference
Chlorinated Pesticides
Aldrin / Dieldrin
• \.>
toxicity
benchmark
is proposed
BHC
• No
toxicity
benchmark
is proposed
Chlordane
•No
toxicity
benchmark
is proposed
DDT /DDD/DDE
• \.>
toxicity
benchmark
is proposed
Endosulfan
•No
toxicity
benchmark
is proposed
Heptachlor
• V-
toxicity
benchmark
is proposed
Polychlorinated Biphenyls
Total PCBs • No toxicity benchmark is proposed •
Dioxins and Furans
2,3,7,8-TCDD 5 Remecke and Nash 1984
Definitions:
BHC - Benzene hexachloride.
mg/kgjoi! _ Milligrams per kilogram soil.
PAHs - Polycyclic aromatic hydrocarbons.
PCBs - Polychlorinated biphenyls.
TCDD - Tetrachlorodibenzo-p-dioxin.
VOCs - Volatile organic chemicals.
-------
Table C-6
Toxicity Benchmarks for Plants
Toxicity
Effect Cone, in leaf BCF Benchmark Endpoint-to- Plant RTV
Observed Effect Level (mg/kgieaf) (kgsoii/kgieafi (mg/kgsoil) Reference NOAEL (mg/kgsoii)
Inorganic;
Antimony
Normal concentration in leaves
NOAEL
50
0.2
250
Kabata-Pendias & Pendias 1984
250
Arsenic
Normal concentration in leaves
NOAEL
1.7
0.04
43
Kabata-Pendias & Pendias 1984
43
Barium
Excessive concentration in leaves
LOAEL
500
0.15
3,300
Kabata-Pendias & Pendias 1984
10
330
Beryllium
Normal concentration in leaves
NOAEL
7.0
0.01
700
Kabata-Pendias & Pendias 1984
—
700
Cadmium
Normal concentration in leaves
NOAEL
0.2
0.36
0.56
Kabata-Pendias & Pendias 1984
—
0.56
Chromium
Normal concentration in leaves
NOAEL
0.5
0.0075
67
Kabata-Pendias & Pendias 1984
—
67
Cobalt
Normal concentration in leaves
NOAEL
1.0
0.02
50
Kabata-Pendias & Pendias 1984
—
50
Copper
Normal concentration in leaves
NOAEL
30
0.40
75
Kabata-Pendias & Pendias 1984
—
75
Lead
Normal concentration in leaves
NOAEL
10
0.045
220
Kabata-Pendias & Pendias 1984
220
Manganese
Normal concentration in leaves
NOAEL
300
0.25
1,200
Kabata-Pendias & Pendias 1984
1,200
Mercury
Excessive concentration in leaves
LOAEL
1.0
0.9
1.1
Kabata-Pendias & Pendias 1984
10
0.11
Molybdenum
Normal concentration in leaves
NOAEL
1.0
0.25
4.0
Kabata-Pendias & Pendias 1984
—
4.0
Nickel
Normal concentration in leaves
NOAEL
5.0
0.06
83
Kabata-Pendias & Pendias 1984
—
83
Selenium
Normal concentration in leaves
NOAEL
2.0
0.025
80
Kabata-Pendias & Pendias 1984
80
Silver
Normal concentration in leaves
NOAEL
0.5
0.4
1.3
Kabata-Pendias & Pendias 1984
1.3
Thallium
Excessive concentration in leaves
LOAEL
20
0.004
5,000
Kabata-Pendias & Pendias 1984
10
500
Vanadium
Normal concentration in leaves
NOAEL
1.5
0.0055
270
Kabata-Pendias & Pendias 1984
—
270
Zinc
Normal concentration in leaves
NOAEL
150
1.5
100
Kabata-Pendias & Pendias 1984
—
100
Volatile Organic Compounds
mux
Benzene •
Toluene
Ethylbenzene •
Xylene
Triphlnrnpthpn.p-Rpln.tpfj. V
Dichloroethene, 1,2- •
Dichloroethane, 1,2- •
Tetrachloroethene •
Trichloroethane •
Trichloroethene •
C.hlnrnhpnzpnp-Rplatpd VOl
Chlorobenzene •
Dichlorobenzene •
Other VQCs
Acetone •
Dichloropropane, 1,2- •
•oposec
Polycyclic Aromatic II>i «.\inic Compound:
Benzo[a]pyrene • posed •
Naphthalene
Methylphenol, 4-
Pentachlorophenol
Reduced lettuce seed germination and
growth
Will A: Suior 1°
Hulzebos et al. 1993
Chlorinated Pesticide*
Aldrin / Dieldrin
BHC
Chlordane
DDT/DDD/DDE
Endosulfan
Heptachlor
App_l_Tabs_SIS.xls
2/12/00
-------
Table C-6
Toxicity Benchmarks for Plants
Chemical
Observed Effect
Effect
Level
Cone, in leaf BCF
(mgfligleaf) (kg,.u/kgi,«
1 OXICliy
Benchmark
(mgfcgsoil)
Reference
Endpoint-to-
NOAEL
Plant RTV
(nig/kg^)
Polychlorinated Biphenyls
Total PCBs
No effect on growth
NOAEL
40
Strek& Weber 1980
—
40
Dioxins and Furans
PCDDj
BHC
Benzene hexachloride.
ec50
Effect concentration (50%).
l®souAgl«f
Kilograms in soil per kilograms in leaf.
mg/kgieaf
Milligrams per kilogram leaf.
mg/kgS01i
Milligrams per kilogram soil.
NOAEL
No observed adverse effect level.
LOAEL
Low observed adverse effect level.
PAHs
Polycyclic aromatic hydrocarbons.
PCBs
Polychlorinated biphenyls.
PCDDs
Polychlorinated dibenzo-p -dioxins.
PCDFs
Polychlorinated dibenzofurans.
VOCs
Volatile organic chemicals.
App_l_Tabs_SIS.xls
2/12/00
-------
Table C-7
Toxicity Benchmarks for Freshwater Aquatic Biota
Chemical Toxicity Benchmark (|lg/L)
Reference
Inorganics
Antimony
30
U.S. EPA 1986
Arsenic
150
U.S. EPA 1999
Barium
4.0
U.S. EPA 1993b
Beryllium
0.66
U.S. EPA 1993b
Cadmium
TBC
U.S. EPA 1999
Chromium III
TBC
U.S. EPA 1999
Chromium VI
11
U.S. EPA 1999
Cobalt
23
U.S. EPA 1993b
Copper
TBC
U.S. EPA 1999
Lead
TBC
U.S. EPA 1999
Manganese
120
U.S. EPA 1993b
Mercury
0.77
U.S. EPA 1999
Molybdenum
370
U.S. EPA 1993b
Nickel
TBC
U.S. EPA 1999
Selenium
5.0
U.S. EPA 1999
Silver
3.4a
U.S. EPA 1999
Thallium
12
U.S. EPA 1993b
Vanadium
20
U.S. EPA 1993b
Zinc
TBC
U.S. EPA 1999
Volatile Organic Compounds
BTEX
Benzene
130
U.S. EPA 1993b
Toluene
9.8
U.S. EPA 1993b
Ethylbenzene
7.3
U.S. EPA 1993b
Xylene
13
U.S. EPA 1993b
Trichloroethene-Related VOCs
Dichloroethene, 1,2-
590
U.S. EPA 1993b
Dichloroethane, 1,2-
910
U.S. EPA 1993b
T etrachloroethene
98
U.S. EPA 1993b
Trichloroethane
11
U.S. EPA 1993b
Trichloroethene
47
U.S. EPA 1993b
Chlorobenzene-Related VOCs
Chlorobenzene
64
U.S. EPA 1993b
Dichlorobenzene
15
U.S. EPA 1993b
Other VOCs
Acetone
1,500
U.S. EPA 1993b
Dichloropropane, 1,2-
5,7006
U.S. EPA 1986
-------
Table C-7
Toxicity Benchmarks for Freshwater Aquatic Biota
Chemical
Toxicity Benchmark (|ig/L)
Reference
Polycyclic Aromatic Hydrocarbons and Semivolatile Organic Compounds
Benzo[a]pyrene
0.014
U.S. EPA 1993b
Naphthalene
12
U.S. EPA 1993b
Methylphenol, 2-
13
U.S. EPA 1986
Pentachlorophenol
• No toxicity benchmark
is proposed •
Chlorinated Pesticides
Aldrin / Dieldrin
0.056
U.S. EPA 1999
BHC
0.953
U.S. EPA 1986
Chlordane
0.0043
U.S. EPA 1999
DDT /DDD/DDE
0.1
U.S. EPA 1993b
Endosulfan
0.051
U.S. EPA 1993b
Heptachlor
0.0038
U.S. EPA 1993b
Polychlorinated Biphenyls
Total PCBs
0.14
U.S. EPA 1993b
Dioxins and Furans
PCDDs
• No toxicity benchmark
is proposed •
PCDFs
• No toxicity benchmark
is proposed •
Definitions:
BHC
PAHs
PCBs
PCDDs
PCDFs
TBC
Hg/L
VOCs
Benzene hexachloride.
Polycyclic aromatic hydrocarbons.
Polychlorinated biphenyls.
Poly chlorinated dibenzo-/> -dioxins.
Polychlorinated dibenzofurans.
To be calculated. (The toxicity benchmark for this metal is expre:
and will be calculated based on the hardness of impoundment wat
Micrograms per liter.
Volatile organic chemicals.
Notes:
a = Criterion maximum concentration (U.S. EPA 1999)
b = lowest chronic value
-------
Table C-8
Toxicity Benchmarks for Sediment Associated Biota
Toxicity Benchmark
Chemical
(mg/kgsed)
Reference
Inorganics
Antimony
2.0
Longer al. 1995
Arsenic
8.2
Long etal. 1995
Barium
• No toxicity benchmark is proposed •
Beryllium
• No toxicity benchmark is proposed •
Cadmium
1.2
Long etal. 1995
Chromium
81
Long etal. 1995
Cobalt
• No toxicity benchmark is proposed •
Copper
34
Long etal. 1995
Lead
47
Long etal. 1995
Manganese
• No toxicity benchmark is proposed •
Mercury
0.15
I ,ong el al. 1995
Molybdenum
• No toxicity benchmark is proposed •
Nickel
21
I ,ong el al. 1995
Selenium
• No toxicity benchmark is proposed •
Silver
1.0
I ,ong el al. 1995
Thallium
• No toxicity benchmark is proposed •
Vanadium
• No toxicity benchmark is proposed •
Zinc
150
Long etal. 1995
Volatile Organic Compounds
BTEX
Benzene
• No toxicity benchmark is proposed •
Toluene
• No toxicity benchmark is proposed •
Ethylbenzene
• No toxicity benchmark is proposed •
Xylene
• No toxicity benchmark is proposed •
Trichloroethene-Related VOCs
Dichloroethene, 1,2-
• No toxicity benchmark is proposed •
Dichloroethane, 1,2-
• No toxicity benchmark is proposed •
T etrachloroethene
• No toxicity benchmark is proposed •
Trichloroethane
• No toxicity benchmark is proposed •
Trichloroethene
• No toxicity benchmark is proposed •
Chlorobenzene-Related VOCs
Chlorobenzene
• No toxicity benchmark is proposed •
Dichlorobenzene
• No toxicity benchmark is proposed •
Other VOCs
Acetone
• No toxicity benchmark is proposed •
Dichloropropane, 1,2-
• No toxicity benchmark is proposed •
Polycyclic Aromatic Hydrocarbons and Semivolatile Organic Compounds
Benzo[a]pyrene
0.43
Long etal. 1995
Naphthalene
0.16
hong etal. 1995
Methylphenol, 4-
• No toxicity benchmark is proposed •
Pentachlorophenol
• No toxicity benchmark is proposed •
-------
Table C-8
Toxicity Benchmarks for Sediment Associated Biota
Chemical
Toxicity Benchmark
(mg/kgsed)
Reference
Chlorinated Pesticides
Aldrin / Dieldrin
BHC
Chlordane
DDT/DDD/DDE
Endosulfan
Heptachlor
0.0016
No toxicity benchmark is proposed •
No toxicity benchmark is proposed •
No toxicity benchmark is proposed •
I ,ong el at. I995
No toxicity benchmark is proposed •
No toxicity benchmark is proposed •
Poly chlorinated Biphenyls
_ Total PCBs
0.023
Longer at. 1995
Dioxins and Furans
PCDDs
PCDFs
No toxicity benchmark is proposed ¦
No toxicity benchmark is proposed ¦
Definitions:
BHC - Benzene hexachloride.
mg/kg,ed - Milligrams per kilogram sediment.
PAHs - Polycyclic aromatic hydrocarbons.
PCBs - Polychlorinated biphenyls.
PCDDs - Polychlorinated dibenzo-p -dioxins.
PCDFs - Polychlorinated dibenzofurans.
VOCs - Volatile organic chemicals.
-------
Appendix D
3MRA Simulation Modules
Assumptions, Limitations, Inputs, and Outputs
-------
Appendix D
Appendix D
3MRA Simulation Modules
Assumptions, Limitations, Inputs, and Outputs
The SI Study will utilize the HWIR 3MRA Model for the Phase II risk assessment. To
address multiple exposure simultaneously, the 3MRA Model includes 17 functional modules.
Fourteen of these component modules will be applied to the SI Study risk assessment1. The
assumptions and limitations and detailed input and output requirements are provided for each
module in this Appendix.
D.l System Input/Output Specifications
Within the 3MRA system, data are passed to and between modules using prespecified
formats and file structures.There are three types of sequence- independent, comma-separated
value (CSV) files used by the 3MRA module: site simulation files (*.SSF), global result files
(*.GRF), and dictionary files (*.DIC). Sequence-independence means that a parameter can be
read from anywhere in a file without having to query the file line by line. Sequence
independence also implies that the ordering of parameters within the file is inconsequential.
Using this convention, established by PNNL (PNNL 1998) for HWTR99, a parameter may
be read directly from a file with the assistance of functions in 3MRA (PNNL 1998). These
ASCII, comma-delimited files are accessed by the module using a specially-provided dynamic
link library provided in the HWIRIO.DLL. Each file has a corresponding data dictionary file in
the /SSF/ subdirectory (i.e., si.die, cp.dic, and sw.dic).
Figure 3-11 shows how data is passed between modules in the 3MRA model; these data
are transferred via *.GRF files. Dictionary files (DIC) define the parameters found in
corresponding *.SSFs and *.GRFs. The represents a module abbreviation. For example,
VZ.DIC defines all parameters which may appear in VZ.SSF and VZ.GRF. A detailed
discussion of each file type follows.
1 Three 3MRA source modules are not needed for the SI Study risk assessment: landfill, waste pile, and
aerated tank.
D-3
-------
Appendix D
# *.DIC files define what parameters are allowable as well as their specifications.
These dictionary files list the variable names along with their dimensions, data
type (i.e., character or "string", real or "float," or integer), minimum and
maximum limits, units, and a brief description. For some arrays, the maximum
number of entries are also given.
# Site simulation files (*.SSF) contain parameters for input only (e.g., read-only
files) used by specific modules. Their access is limited to one module except for
the site layout SSF (SL.SSF), which includes site layout and other variables
needed by several simulation modules, and the chemical properties SSF (CP.SSF)
that contains all chemical-specific properties and benchmarks.
# Global result files (*.GRF) are files generated by modules, which, in turn, may be
used as input files for other modules (e.g., read and write files).
The file name (*.SSF or *.GRF) is provided for each of the inputs and outputs listed below by
module.
D-4
-------
Appendix D
D.2 Source Modules
The source module employed for the Surface Impoundment (SI) Study risk analysis must
model multimedia releases both before and after closure of the impoundment. The SI module
currently in the HWTR 3MRA Model only models releases up to closure. For the SI Study risk
assessment, sludge from SI operation will be assumed to be left in place after closure, where it is
subject to volatilization, wind and water erosion, and leaching. Currently, the best option to
model these processes is the 3MRA land application unit (LAU) module, which includes all
needed release mechanisms and can be adapted to the SI post-closure scenario simply by
adjusting input variables.
The remainder of this section provides the assumptions and limitations of the 3MRA SI
and LAU modules and details their input requirements and outputs. This information was
extracted and adapted from the HWTR 3MRA background documents (U.S. EPA, 1999a, 1999b),
which contain additional detail, including all assumptions, governing equations, boundary
conditions, solution techniques, and supporting references.
D.2.1 Functionality - SI Operating Module
The SI operating module functionality may be summarized as follows:
# Models single unit (cannot model multiple impoundments at a site)
# Uses mass balance approach considering contaminant removal by volatilization,
biodegradation, hydrolysis, leaching, and partitioning to solids
# Estimates volatilization rates for both aerated and quiescent surfaces
# Estimates infiltration rate and contaminant leachate flux rates
# Estimates suspended solids removal (settling) efficiency
# Estimates temperature effects on contaminant degradation and volatilization
# Cannot model catastrophic failures.
The SI module calculates volatile emissions flux from a simulated wastewater treatment
tank. The unit has only volatile air emissions (no particulates) and is assumed to have a pervious
bottom so that contaminant leaching to the subsurface can occur. There is no runoff and
overland flow of contaminant. The module is a quasi-steady-state module, and the emissions
occur only while the unit operates.
D.2.2 Assumptions and Limitations - SI Module
The general module construct used for the SI module includes losses due to volatilization
from aerated and/or quiescent surfaces, biodegradation, hydrolysis, solids settling/accumulation,
D-5
-------
Appendix D
and leaching. This general module construct can be useful for a wide variety of types of Sis.
However, certain processes, such as chemical precipitation, however, may not be explicitly
modeled with this module construct. However, with judicious selection of the input parameters,
the general module construct can provide accurate fate estimates for most tank and SI waste
applications. For example, if the precipitation rate for chemical precipitation is known, the input
parameters used for "biomass" growth could be manipulated to simulate the solids generation
rate caused by precipitation (rather than biomass growth).
The following assumptions are used in the development of the SI module solution:
# Two-compartment module: "mostly" well-mixed liquid compartment and a well-
mixed sediment compartment, which includes a temporary accumulating solids
compartment
# First-order kinetics for volatilization in liquid compartment
# First-order kinetics for hydrolysis in both liquid and sediment compartment
# First-order kinetics for biodegradation with respect to both contaminant
concentration and biomass concentration in liquid compartment
# First-order kinetics for biodegradation in sediment compartment
# Darcy's law for calculating the infiltration rate
# First-order kinetics for solids settling
# First-order biomass growth rate with respect to total biological oxygen demand
(BOD) loading
# First-order biomass decay rate within the accumulating sediment compartment
# No contaminant in precipitation/rainfall
# Linear contaminant partitioning among adsorbed solids, dissolved phases, and
vapor phases
# Consideration of only one contaminant at a time (does not simulate fate and
transport of reaction products or multiple chemicals).
# Limitation of the maximum infiltration rate to that which does not cause ground
water "mounding" and to no more than 99 percent of the maximum influent rate.
# Limitation of the minimum effluent rate to no less than one percent of the influent
rate..
D-6
-------
Appendix D
Due to the simplicity of the biodegradation rate module employed and the use of Henry's
law partitioning coefficients, the module is most applicable to dilute aqueous wastes. At higher
contaminant concentrations, biodegradation of toxic constituents may be expected to exhibit
zero-order or even inhibitory rate kinetics. For waste streams with high contaminant or high total
organic concentrations, vapor phase contaminant partitioning may be better estimated using
partial pressure (Raoult's law) rather than Henry's law. Also, because daughter products are not
included in the module, any contaminant emissions or leachate generated as a reaction
intermediate or end product from either biodegradation or hydrolysis are not included in the
module output.
D.2.3 Functionality - LAU (SI Postclosure) Module
The source term module developed for LAUs estimates annual average surface soil
constituent concentrations and constituent mass emission rates to air, surface water, and
groundwater. The LAU source emission module includes a local watershed module to estimate
constituent mass flux rates from runoff and erosion to a downslope waterbody, as well as surface
soil constituent concentrations in buffer areas downslope of a land-based waste management unit
(WMU). The LAU module also includes a Generic Soil Column Module (GSCM) that describes
the dynamics of constituent mass fate and transport within nonwastewater WMUs and near-
surface soils in watershed subareas.2 The LAU module functionality may be summarized as
follows:
# Performs constituent mass balance
# Can consider waste additions/removals to simulate active facilities
# Jointly estimates constituent mass losses due to a variety of first-order
mechanisms, including:
— Volatilization of gas-phase constituent mass from the surface to the air
— Leaching of aqueous-phase constituent mass by advection or diffusion
from the bottom of the WMU or vadose zone
— Hydrolysis and biodegradation
— Suspension of constituent mass adsorbed to surface particles due to wind
action and vehicular activity
— Suspension of constituent mass adsorbed to surface particles due to water
erosion
— Surface runoff of aqueous-phase constituent mass.
2 The term "soil" is used loosely here to refer to a porous medium, whether it is sludge left in place after SI
closure or near-surface soil in a watershed subarea downslope from the SI.
D-7
-------
Appendix D
Governing equations for the LAU module are similar to those used by Jury et al. (1983,
1990) and Shan and Stevens (1995) modified to allow for the periodic addition of constituent
mass and enhanced constituent mass loss rates in the surface soil (e.g., due to runoff, erosion,
wind, and mechanical processes) (U.S. EPA, 1999b).
D.2.4 Assumptions and Limitations - LAU (SI Post-Closure) Module
The following assumptions were made in the development of the LAU module to be used
to model the SI after closure:
# The contaminant partitions to three phases: adsorbed (solid), dissolved (liquid),
and gaseous (as in Jury et al., 1983, 1990). The model is not applicable if
nonaqueous phase liquid (NAPL) is present. Similarly, with metals, the presence
of a precipitate is not allowed.
# The contaminant undergoes reversible, linear equilibrium partitioning between the
adsorbed and dissolved phases (as in Jury et al., 1983, 1990). The sorptive
capacity of the soil column solids is considered to be infinite with respect to the
total mass of contaminant over the duration of the simulation (i.e., the soil column
sorptive capacity does not become exhausted).
# Contaminant in the dissolved and gaseous phases is assumed to be in equilibrium
and to follow Henry's law (as in Jury et al., 1983, 1990).
# The total water flux or infiltration rate (I, m/d) is constant in space and time (as in
Jury et al., 1983, 1990) and greater than or equal to zero. It is specified as an
annual average.
# Although some inputs are annual average, others (e.g., wind velocities) are long-
term annual averages. Accordingly, the outputs are not strictly applicable to
individual years (i.e., the module is designed to only support estimation of
chronic, long-term average risk estimates).
# Material in the soil column (including waste) can be approximated as
unconsolidated homogeneous porous media whose basic properties (density,
organic carbon, water content, porosity) are average annual values, constant in
space.
# Contaminant mass may be lost from the soil column due to one or more first-order
loss processes.
# The total chemical flux is the sum of the vapor flux and the flux of the dissolved
solute (as in Jury et al., 1983, 1990).
# The chemical is transported in one dimension (up or down) through the soil
column (as in Jury et al., 1983, 1990).
D-8
-------
Appendix D
# The vapor-phase and liquid-phase porosity and tortuosity factors obey the module
of Millington and Quirk (1961) (as in Jury et al., 1983, 1990).
# The modeled spatial domain of the soil column remains constant in volume and
fixed in space with respect to a vertical reference, e.g., the water table.
# The module allows consideration of only one contaminant at a time and does not
simulate fate and transport of reaction products or multiple chemicals.
D.2.5 Inputs for the Source Modules
Table D-l summarizes the input variables for the SI module and Table D-2 lists the
values used by the LAU module. Both modules use data provided from the header file (hd.ssf),
site layout file (sl.ssf), source module-specific file (i.e., si.ssf, la.sf), and the daily (LAU),
monthly (SI), annual (LA), and long-term annual average (LAU) meteorological data files. All
SSF files are expected to be in an SSF subdirectory; the meteorological data are expected to be in
a MetData subdirectory. For the SI, because the operating temperature in the unit may vary as a
function of the ambient temperature and hydraulic residence time, the module also uses chemical
property information calculated as a function of the unit temperature. Much of these data are
provided through calls to the chemical property dll functions (which use data files stored in a
chemical properties subdirectory). Some temperature correction routines are embedded within
the SI module program. The LAU receives chemical data corrected to the long-term annual
average temperature at the site.
D.2.6 Outputs from the Source Modules
Table D-3 lists the outputs from the SI module. The primary outputs of the SI module
include:
# Outputs to Air Module - the annual average volatilization rate, which provides
input for calculations of air transport of contaminant using the air module.
# Outputs to the Vadose Zone Module - the average annual infiltration rate and the
average annual leachate contaminant flux rate, which are inputs used by the
vadose zone module to calculate contaminant flux to groundwater.
The volatilation rate is calculated for a number of years specified either as the total number of
years of the simulation or the number of years the unit is operated. Infiltration to the vadose zone
is assumed to be driven by the hydrostatic pressure head produced by the wastewater in the unit
and when the unit ceases operation it is assumed that no additional contaminant leaches from the
source. Annual liquid infiltration rates and contaminant leachate flux rates are both calculated at
the base of the unit.
The SI module generates a results file (sr.grf) in the grf subdirectory containing all
module outputs used as inputs for other modules. The program may also generate warning
messages (e.g., if the calculated unit temperature is below freezing, a warning is generated).
D-9
-------
Appendix D
Table D-4 lists the outputs from the LAU module. Time series outputs to the various
other HWIR99 modules include:
# Outputs to Air Module. All annual time series outputs to the Air Module are
reported up to and including the last year that there is nonzero volatile or
particulate emission flux. After this all emissions will be zero and are not
reported. Thus, the annual time series outputs to the Air Module are all the same
length.
# Outputs to the Vadose Zone Module. The annual time series of leachate flux for
each local watershed is reported up to and including the last year that there is a
nonzero flux in any local watershed. This results in the same time series length
for all local watersheds. After this, all leachate flux values for all local
watersheds will be zero and are not reported. Annual infiltration rate is reported
from year one to the last year that meteorological data are available for the
simulation.
# Outputs to the Surface Water Module. The annual time series of contaminant
loading to the waterbody are reported up to and including the last year that there is
nonzero loading from any local watershed. This results in the same reported time
series length for all local watersheds. Solids loads and runoff from all local
watershed(s) are reported up to the last year that meteorological data are available.
# Outputs to Exposure Modules (Human and Ecological). The annual time series of
depth-weighted average soil contaminant concentrations in the LAU and in the
buffer area downslope from the LAU is reported to the the last year of nonzero
concentrations in each local watershed and subarea. Thus, the length of the
reported time series for soil concentrations in each local watershed and subarea
may differ. The same is true for surface soil contaminant concentrations.
The LAU module generates a results file (sr.grf) in the grf subdirectory containing all
module outputs used as inputs for other modules. The program may also generate warning
messages (e.g., if constituent solubility is exceeded in the WMU a warning is generated).
D-10
-------
Appendix D
Table D-l. Summary of Inputs for Surface Impoundment Source Module
Tile \ iiriiihlo Nsimo
I nils
Diilii
Type
\ iiriithlc \sime in
Modulo C'oilo
Doscriplion
HD.SSF CPDirectory
String
m_pathname, pathname
Path for location of
chemical properties files
MetData
String
MetPath
Path for location of
meteorological files
SL.SSF SrcArea
m2
Real
m_A_wmu, A wmu, A tot
Area of the waste
management unit
SiteLatitude
degrees
Real
m_Lat
Latitude of the site
SiteLongitude
degrees
Real
mLong
Longitude of the site
MetSta
String
mMetSta, MetSta
ID number for
meteorological station
associated with site
NyrMax
years
Integer
mNyrMax
Maximum module
simulation time
SrcPh
pH units
Real
m_pH. pH
Waste pH
SrcTemp
degrees
Celsius
Real
m T waste, T waste
Temperature of the
waste
SrcType
String
m WMUType, WMUType
Type of waste
management unit (AT or
SI)
SrcNumLWS
Integer
mSrcNumLWS,
SrcNumLWS
Number of local
watersheds (SI only)
SrcL WSNumSub Area
Integer
mSrcL WSNumSub Area[ ]
Number of subareas in
the local watershed (SI
only)
SrcL W S Sub Arealndex
unitless
Integer
m_SrcLWSSubAreaIndex[ ]
Local watershed subarea
containing WMU (SI
only)
SrcL W S Sub AreaArea
?
111
Real
m_SrcLWSSubAreaArea[ ]
Area of a subarea in the
local watershed (SI
only)
TermFrac
fraction
Real
mTermFrac
Peak output fraction for
simulation termination
SrcDepth
m
Real
mSrcDepth
Depth of source (0 for
AT)
NumVad
Integer
m NumVad, NumVad
Number of vadose zones
(SI only)
(continued)
D-l 1
-------
Appendix D
Table D-l. (continued)
l-'ile
\ iiriiihlo Nsimo
I nils
Diilii
Type
\ iiriithlc \sime in
Modulo C'oilo
Doscriplion
N_stof
unitless
Integer
mNstot, Nstot
Number of subsurface
soil layers (currently
hardwired to 1) (SI
only)a
VadSATK
cm/h
Real
m_hydc_s[ ], hydc_s[ ]
Saturated hydraulic
conductivity in the
subsurface soil layer (SI
only)
VadThick"
m
Real
m_d_s[ ][ ], d_s[ ]
Thickness of the
subsurface soil layer (SI
only)a
VadALPHA
1/cm
Real
m_alpha_s[ ], alpha_s[ ]
Alpha soil parameter for
subsurface soil (SI only)
VadBETA
unitless
Real
m_beta_s[ ], beta_s[ ]
Beta soil parameter for
subsurface soil (SI only)
SI.SSF
VadSATK
cm/h
Real
m hydc liner, hydc liner
Hydraulic conductivity
of the liner (SI only)
dliner
m
Real
m d liner, d liner
Thickness of SI liner
(currently hardwired to
0.5 m) (SI only)
VadALPHA
1/cm
Real
m alpha liner, alpha liner
Alpha soil parameter for
SI liner (SI only)
VadBETA
unitless
Real
mbetaliner, betaliner
Beta soil parameter for
SI liner (SI only)
hydcsed
m/s
Real
m hyde sed, hydc sed
Hydraulic conductivity
of the sediment that
accumulates in the unit
(SI only)
bio_yield
g/g
Real
m_bio_yield, bio_yield
Biomass yield in g dry
wt biomass/g CBOD
CBOD
g/cm3
Real
m CBOD, CBOD
Carbonaceous
biochemical oxygen
demand for the chemical
C_in
mg/L
Real
m_C_in, C_in
Concentration of
chemical in hazardous
waste
EconLife
year
Integer
m EconLife, EconLife
Economic life of the
unit
(continued)
D-12
-------
Appendix D
Table D-l.
(continued)
I'ilo \ iiriiihlo Nsimo
1 nils
Diilii
Type
\ iiriithlc \sime in
Modulo C'oilo
Doscriplion
NumEcon
Integer
mNumEcon, NumEcon
Number of economic
lifetimes that the unit
operates
dimp
cm
Real
mdimp. d imp
Diameter of the impeller
used to aerate the unit
dmeanTSS
cm
Real
m m, m
Mean particle of an
influent particle
dsetpt
fraction
Real
mdsetpt, d setpt
Fraction full of sediment
at which unit is dredged
dwmu
m
Real
mdwmu, d wmu, d tot
Depth of the waste
management Unit
Faer
fraction
Real
m_F_aer, F aer
Fraction of the unit
surface area that is
aerated
focW
mass
fraction
Real
m foc, foe
Fraction of organic
carbon in the waste
fwmu
mass
fraction
Real
m fwmu, fwmu
Fraction of waste that is
hazardous
J
lb 02/h-hp
Real
m_J, J
02 transfer rating of
aerator
kbal
unitless
Real
m kbal, kbal
Ratio of biologically
active solids to the total
solids concentration
kdec
1/s
Real
m k dec, k dec
Anaerobic
digestion/decay constant
of the organic sediment
u_1
g/cm-s
Real
m_mu_H20, mu_H20
Viscosity of water
MWt_H20
g/mol
Real
m_MWt_H20, MWt_H20
Molecular weight of
water
nimp
unitless
Integer
ln n iinp. n imp
Number of
impellers/aerators
02Eff
unitless
Real
m_02eff, 02eff
02 transfer correction
factor
Powr
hp
Real
m Powr, Powr
Total power to
aerators/impellers
(continued)
D-13
-------
Appendix D
Table D-l. (continued)
I'ilo \ ;iri:ihlo Nsimo
I nils
Diilii
Type
\ iiriithlc \sime in
Modulo C'oilo
Doscriplion
Qwmu
m3/s
Real
m_Q_\vmu. Q wmu, Q_in
Total influent flow rate
into the unit
rhol
g/cm3
Real
m_rho_H20. rho_H20
Density of water
rho_part
g/cm3
Real
m_rho_part, rho_part
Density of particles in
the influent waste
TSSin
g/cm3
Real
mTSSin, TSS in
Total suspended solids
concentration in the
influent
wimp
rad/s
Real
m w imp, w imp
Rotational speed of
impellers
CP.SSF NumChem
Integer
mNumChem,
Number of chemical
species
ChemType
String
mChemType, ChemType
Type of chemical
ChemADiff
cm2/s
Real
m_Da, Da
Diffusivity of chemical
in air
ChemWDiff
cm2/s
Real
m_Dw, Dw
Diffusivity of chemical
in water
ChemHLC
(atm m3) /
mol
Real
m_HLC, HLC
Henry's law constant for
the chemical
ChemKoc
mL/g
Real
m_Koc, Koc
Soil-water partitioning
coefficient for the
chemical
ChemAnaBioRate
1/day
Real
m kbiou, kbs
Biodegradation / decay
rate of contaminant in
sediment compartment
ChemAerBioRateb
1/day
Real
m kbioa, kbm
Complex first-order
biodegradation rate
constant for the
chemicalb
ChemHydRate
1/day
Real
m k hyd, k hyd
Hydrolysis rate for the
chemical
ChemSol
mg/L
Real
m_Sol, Sol
Chemical solubility
ChemCASID
String
m_CAS, CAS
Chemical CAS ID
number
ChemName
String
m ChemName, ChemName
Chemical name
(continued)
D-14
-------
Appendix D
Table D-l. (continued)
Tile \ iiriiihlo Nsimo I nils
Diilii
Type
\ iiriithlc \sime in
Modulo C'oilo
Description
ChemKd L/kg
Real
m_Kds, Kds
Solid/water partition
coefficient
Met data — °C
file
Real
m_AvgTemp[ ] [ ]
Average monthly
temperature
m/s
Real
m_um[y] [z]
Monthly average
windspeed
m/d
Real
m_AvgPpt[ ][ ]
Average monthly
precipitation
m/d
Real
m_E[][]
Average monthly
evaporation
—
Integer
NyrMet
Number of years of
meteorological data
" The module currently assumes there is one native soil layer and that the thickness of the underlying soil layer is
assumed to be a minimum of 1 meter thick. If the regional vadose zone thickness is less than l+dwmll. then the
impoundment is assumed to be built up (via an earthen berm) so that there is 1 meter of soil between the bottom
of the SI and the ground water.
b Note: If normalized biodegradation rate constants are unavailable, normalized biodegradation rates constants
are estimated from first-order biodegradation rate constants developed for soil systems by assuming the
effective biomass in the soil system is 2.0/10 " Mg/m3. This value was developed by RTI as an interim
estimate until a more rigorously developed value for this parameter is available from EPA.
D-15
-------
Appendix D
Table D-2. Summary of Inputs for Land Application Unit (LAU) Source Module
Hie
Vsiriiihlc Niimo
I nils
Diilii
Type
Description
HD.SSF
CPDirectory
String
Path for location of chemical properties files
MetData
String
Path for location of meteorological files
SL.SSF
SrcArea
m2
Real
Area of LAU
SiteLatitude
degrees
Real
Latitude of the site
MetSta
String
ID number for meteorological station associated with
site
NyrMax
years
Integer
Maximum module simulation time
SrcNumLWS
Integer
Number of local watersheds
SrcL WSNumSub Area
Integer
Number of subareas in the local watershed
SrcL W S Sub Arealndex
unitless
Integer
Local watershed subarea containing LAU
SrcL W S Sub AreaArea
m2
Real
Area of a subarea in the local watershed
TermFrac
fraction
Real
Peak output fraction for simulation termination
SrcDepth
m
Real
Depth of source (tilling depth)
LA.SSF
asdm
mm
Float
Mode of the aggregate size distribution (LAU surface)
bcm
unitless
Float
Boundary condition multiplier (lower boundary)
BDw
g/cm3
Float
Waste density (wet waste)
C
unitless
Float
USLE cover factor (all local watershed subareas except
LAU)
CN
unitless
Float
SCS curve number (by local watershed subarea)
CNwmu
unitless
Float
SCS curve number (LAU)
ConVs
m/d
Float
Settling velocity (suspended solids)
CTPwaste
ug/g
Float
Waste constituent concentration (LAU)
CutOffYr
year
Integer
Operating life
Cwmu
unitless
Float
USLE cover factor (LAU)
deltDiv
unitless
Integer
Time step divider (for debugging)
DRZ
cm
Float
Root zone depth (by local watershed subarea)
effdust
unitless
Float
Dust suppression control efficiency
fcult
unitless
Float
Number of cultivations per application
(continued)
D-16
-------
Appendix D
Table D-2. (continued)
l-'ile \ nriiihlo \:imc
I nils
Diilii
Type
Description
fd
1/mo
Float
Frequency of surface disturbance per month, active
LAU
focS
mass
fraction
Float
Surface soil fraction organic carbon (by local watershed
subarea)
focW
mass
fraction
Float
Fraction organic carbon (waste solids)
fwmu
mass
fraction
Float
Fraction hazardous waste applied to LAU
Infild
m/d
Float
Input infiltration rate (for debugging)
K
kg/m2
Float
USLE soil erodibility factor (by local watershed
subarea)
Ksat
cm/h
Float
Soil saturated hydraulic conductivity (by local
watershed subarea)
Kwmu
kg/m2
Float
USLE soil erodibility factor (LAU)
Lc
unitless
Float
Roughness ratio (LAU)
mt
m
Float
Distance vehicle travels on LAU surface
LA.SSF Nappl
1/year
Integer
Waste applications per year
nv
1/d
Float
Vehicles/day (mean annual)
nw
unitless
Float
Wheels per vehicle (mean)
P
unitless
Float
USLE soil erosion control factor (all local watershed
subareas except LAU)
Pwmu
unitless
Float
USLE soil erosion control factor (LAU)
Rappl
Mg/m2-
year
Float
wet waste application rate
RunID
string
Run identification label (optional)
SMb
unitless
Float
soil moisture coefficient b (by local watershed subarea)
SMFC
volume %
Float
soil moisture field capacity (by layer and local
watershed subarea)
SMWP
volume %
Float
soil moisture wilting point (by layer and local
watershed subarea)
solid
mass
percent
Float
percent solids (waste)
(continued)
D-17
-------
Appendix D
Table D-2. (continued)
l-'ile \ nriiihlo \:imc
I nils
Diilii
Type
Description
Ss
mass
percent
Float
surface soil silt content
Sw
mass
percent
Float
waste solids silt content (as applied)
Theta
degrees
Float
slope (by local watershed)
thetawZld
volume
fraction
Float
input volumetric water content in till zone (for
debugging)
thetawZ2d
volume
fraction
Float
input volumetric water content in subsoil zone (for
debugging)
veg
fraction
Float
post-closure fraction vegetative cover
vs
km/h
Float
vehicle speed (mean)
vw
Mg
Float
vehicle weight (mean)
WCS
volume
fraction
Float
soil saturated water content or total porosity (by local
watershed subarea)
X
m
Float
flow length (by local watershed)
zava
m
Float
averaging depth upper (depth averaged soil
concentration)
zavb
m
Float
averaging depth lower (depth averaged soil
concentration)
zruf
cm
Float
post-closure roughness height(LAU)
zZlsa
m
Float
modeled soil column depth (local watershed subareas
other than LAU)
zZlWMU
m
Float
tilling depth (LAU)
zZ2WMU
m
Float
subsoil layer thickness
CP.SSF ChemCASID
String
CAS number of chemical
ChemName
String
Chemical name
ChemType
String
Type of chemical
ChemTemp
degrees C
Real
Temperature for chemical properties
ChemFracNeutral
Fraction
Real
Fraction of chemical in the neutral form
ChemADiff
cm2/s
Real
Diffusivity of chemical in air
(continued)
D-18
-------
Appendix D
Table D-2. (continued)
l-'ile \ nriiihlo \:imc
I nils
Diilii
Type
Description
ChemWDiff
cm2/s
Real
Diffusivity of chemical in water
ChemHLC
(atm m3) /
mol
Real
Henry's law constant for the chemical
ChemKoc
mL/g
Real
Soil organic carbon - water partitioning coefficient
(organic compounds)
ChemAnaBioRate
1/day
Real
Biodegradation / decay rate of contaminant in sediment
compartment
ChemAerBioRateb
1/day
Real
Complex first-order biodegradation rate constant for the
chemicalb
ChemHydRate
1/day
Real
Hydrolysis rate for the chemical
ChemSol
mg/L
Real
Chemical solubility
ChemKd
L/kg
Real
Solid/water partition coefficient (inorganic compounds;
by media)
Met file temp
degrees C
Real
Average daily air temperature
jday
Integer
Julian day
dailyR
1/t
Real
Daily USLE rainfall erosivity factor
dailyppt
cm/d
Real
Total daily precipitation
AvgTemp
degrees C
Real
Monthly mean temperature
maxtemp
degrees C
Real
Maximum daily average temperature for month
mintemp
degrees C
Real
Minimum daily average temperature for month
PE
Real
Thornthwaite Precipitation Evaporation Index
fwl
%
Real
Percent time wind speed is greater than 5.4 m/s
up
m/s
Real
Long term mean annual windspeed
Uplus
m/s
Real
Annual average fastest mile of wind
pdays
d/yr
Real
Days per year with precipitation > 0.01 in
PP
d/yr
Real
Mean number of days per year with >0.01 in
precipitation
Ed
m/d
Real
Average daily evaporation
tsc
degrees C
Real
Long term average soil column temperature
(continued)
D-19
-------
Appendix D
Table D-2. (continued)
Diilii
1 ile
Vsiriiihlc Niimo
I nils Type
Description
—
Integer
Number of years of meteorological data
" The module currently assumes there is one native soil layer and that the thickness of the underlying soil layer is
assumed to be a minimum of 1 meter thick. If the regional vadose zone thickness is less than l+dwmll. then the
impoundment is assumed to be built up (via an earthen berm) so that there is 1 meter of soil between the bottom
of the SI and the ground water.
b Note: If normalized biodegradation rate constants are unavailable, normalized biodegradation rates constants
are estimated from first-order biodegradation rate constants developed for soil systems by assuming the
effective biomass in the soil system is 2.0 x 10"6 Mg/m3. This value was developed by RTI as an interim
estimate until a more rigorously developed value for this parameter is available from EPA.
D-20
-------
Appendix D
Table D-3. Output Summary (SR.GRF) for Surface Impoundment Source Module
lile Code
Diilii V:iri:iblc \:ime in
I nils Typo Module Code
Description
SR.GRF VENY
VEYR
VE
LeachFluxNY
LeachFluxYR
LeachFlux
NyrMet
Annlnfil
SrcOvl
SrcSoil
SrcLeachSrc
SrcLeachMet
SrcVE
SrcCE
SrcH20
year
g/m2/d
year
g/m2/d
year
m/d
Integer VENumOut
Integer VEOutYear[ ]
Real E_wmu_t[ ]
Integer LeachFluxNumOut[ ]
Integer LeachFluxOutY ear[ ] [
Real L_wmu_t[ ]
Integer nyrs
Real Infil_t[ ]
Logic ISrcOvl
Logic ISrcSoil
Logic ISrcLeachSrc
Logic ISrcLeachMet
Logic ISrcVE
Logic ISrcCE
Logic l_SrcH20
number of years in VE
outputs
Year associated with VE
output
Volatile emission rate
Number of years in leach
flux outputs (SI only)
Year associated with leach
flux output (SI only)
Leachate contaminant flux
(SI only)
Number of years in the
available met record (set
equal to number of year unit
operates)
Annual average leachate
infiltration rate (SI only)
Flag for overland flow
presence
Flag for soil presence
Flag for leachate presence
when leachate is not
met-driven (unit is active)
Flag for leachate presence
when leachate is met-driven
Flag for volatile emissions
presence
Flag for chemical sorbed to
particulates emissions
presence
Flag for surface water
presence
D-21
-------
Appendix D
Table D-4. Output Summary (SR.GRF) for the LAU Source Module (Post-Closure SI)
\ ill
isihle \sime"1
Kile Modulo
Code
Definition
I nils
SR.GRF I
Annlnfil
Leachate infiltration rate (annual avg., WMU
subarea(s) only)
m/d
Jvol
VE
Volatile emission rate
g/m2/d
VEYR
Year associated with output
Year
VENY
Number of years in outputs
Unitless
CE30
CE
Constituent mass emission rate-PM30
g/m2/d
CEYR
Year associated with output
Year
CENY
Number of years in outputs
Unitless
E30
PE30
Eroded solids mass emission rate-PM30
g/m2/d
PE30YR
Year associated with output
Year
PE30NY
Number of years in outputs
Unitless
pmf
PMF
Particulate emission particle size distribution
Mass frac.
PMFYR
Year associated with output
Year
PMFNY
Number of years in outputs
Unitless
Q
Runoff
Runoff flow to waterbody
m3/d
Jlch
LeachFlux
Leachate contaminant flux
g/m2/d
LeachFluxYR
Year associated with output
Year
LeachFlux
NY
LeachFluxNY
Number of years in outputs
Unitless
SWLoadChem
Chemical load to waterbody
g/d
S WLoadChemY r
Year associated with output
year
SWLoadChemNY
Number of years in outputs
Unitless
CSL
SWLoadSolid
Total suspended solids load to waterbody
g/d
CI
SWConcTot
Total chemical concentration in surface water
runoff
mg/L
SWConcTotYR
Year associated with output
Year
SWConcTotNY
Number of years in outputs
Unitless
CT
CTss
Soil concentration in surface soil layer
l-ig/g
CTssYR
Year associated with output
Year
(continued)
D-22
-------
Appendix D
Table D-4. (continued)
\ ill
isihlc Niimo'1
lilo Modulo
C ode
DoI'i 11 it ion
I nils
CTssNY
Number of years in outputs
Unitless
CT
CTda
Depth-weighted average soil concentration
(from zava to zavb)
l-ig/g
CTdaYR
Year associated with output
Year
CTdaNY
Number of years in outputs
Unitless
SrcSoil
Flag for soil presence (true)
Logical
SrcOvl
Flag for overland flow presence (true)
Logical
SrcLeachMet
Flag for leachate presence when leachate is
met-driven (true)
Logical
SrcLeachSrc
Flag for leachate presence when leachate is not
met-driven (false)
Logical
SrcVE
Flag for volatile emissions presence (true)
Logical
SrcCE
Flag for chemical sorbed to particulates
emissions presence (true)
Logical
SrcH20
Flag for surface water presence for eco-exposure
(false)
Logical
NyrMet
Number of years in the available met record
Unitless
" Where the variable name is used in the code but not in the documentation, the first column is left blank.
D-23
-------
Appendix D
D.3 Air Module
The HWIR 3MRA model air module is the Industrial Source Complex-Short Term
(ISCST3) model, with pre- and postprocessors incorporated to adapt it to the 3MRA system,.
The air module provides estimates of contaminant concentration, dry deposition (particles only),
and wet deposition (particles and gases) for user-specified averaging periods (i.e., annually for
HWIR99).
ISCST3 is used as legacy code in the 3MRA framework. That is, the model is left intact
and system interfacing is handled using the pre- and postprocessors. The pre- and postprocessing
code also provides additional functionality to support other 3MRA framework requirements. This
section provides an overview of the assumptions, limitations, inputs, and outputs of the 3MRA
air module as applied in HWIR; additional detail can be found in U.S. EPA (1999c and 1999d).
D.3.1 Functionality
ISCST3 is a steady-state Gaussian plume model. The model provides point estimates of
ambient air concentration, dry deposition (particles only), and wet deposition (particles and
gases) for user-specified averaging periods (e.g., annual). The regulatory version of the model
has been modified to sample from a file of hourly meteorological data at regular intervals (SCIM
function) and thus will only model a fraction of the hours for the period of record (e.g., 20 years).
Sampling intervals of every 8 hours for wet deposition and every 8 days for dry deposition have
been tested and are currently being used in HWIR. Sampling enables the model to execute more
quickly while producing long-term annual averages comparable to those obtained from the full
data set.
Because of computational time burden, the air model component is run outside of the
FRAMES-HWIR system. The air concentrations and deposition rates are saved into a database
that the FRAMES-HWIR system accesses. During 3MRA implementation, normalized emission
rates (from the 3MRA source modules) are used as inputs along with the sampled meteorological
data. Chemical-specific and temporal-specific concentrations and deposition rates are calculated
in the ISCST3 postprocessor by multiplying the normalized concentration and deposition
predictions by these chemical-specific annual emission rates.
For the outside 3MRA runs, ISCST3 predicts normalized concentrations and depositions
at a set of grid points within the 2-km area of interest surrounding an impoundment, locations
that are optimized by 3MRA to represent concentrations at human receptor points and deposition
rates for watersheds, waterbodies, farms, and ecological habitats. Although a spline interpolation
routine is available in the 3MRA postprocessor ISC3, this feature is not being used.
The major functionality provided by the pre- and postprocessing code is the following:
D-24
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Appendix D
# Decision to execute ISCST3. If a given site/WMU combination has already been
run by the Air Module in a set of model runs, the preexisting (saved) ISCST3
output file is used to calculate parameters for the Air Module output file, ar.grf. If
not, ISCST3 is executed with pre- and postprocessing, which saves the ISC output
files for future use in the grfMfo directory and calculates parameters for the Air
Module output file.
# MASSFRAX calculation. The preprocessor can calculate a source-specific,
long-term average particulate mass fraction distribution (ISCST3 input variable,
MASSFRAX) from the land-based source modules (landfill, wastepile, or land
application unit) output time series PMF (the particle size distribution) and PE30
(the mass flux of 30 |im or smaller particles). If the internal calculation of
MASSFRAX is not exercised, then MASSI'RAXxs, read as a fixed distribution (not
WMU-specific) from the ar.ssf file.
# Spline interpolation decision. The preprocessor determines whether to execute
ISCST3 to model directly ("ISCST3-model") to all site-specific output x-y
coordinates requested by the 3MRA framework or, alternatively, to model to a
prespecified (fixed) set of polar coordinates and then use a two-dimensional cubic
spline method to interpolate from this polar set to the (larger) 3MRA-requested set
of interest. The spline option was not used for HWIR99.
Other new features added to ISCST3 include a revised plume depletion scheme that
replaces the computationally intensive Horst (1983) plume depletion algorithm with a faster,
more robust plume depletion and settling algorithm developed by Venkatram (1988). This
algorithm depletes material in a surface-based internal boundary layer that grows with distance
from the source. In conjunction with this change, the deposition velocity algorithm was also
modified by removing the inertial impaction term, which overestimates deposition velocity for
some particle sizes. Also, a new output option was required to allow examination of
concentration and deposition by particle size so that inhalation risks can be determined for
pollutants with particle sizes <10 |im.
D.3.2 Assumptions and Limitations
# The ISCST3 modeling does not simulate chemical-specific fate processes such as
photolysis and degradation.
# To conserve mass in the 3MRA framework, ISCST3 includes a source depletion
algorithm that adjusts for the mass lost to deposition. However, module
simulations showed that less than 1 percent of emitted mass is deposited within a
2-km radius with source depletion on. Therefore, to reduce computational burden,
source depletion for deposition loss was not implemented in HWIR.
D-25
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Appendix D
# One of the largest areas of uncertainty in the 3MRA air modeling is related to gas
deposition. Although previous modeling exercises used a transfer coefficient to
model the dry deposition of gases it is a challenge to preserve conservation of
mass. Therefore, dry deposition of gases is not considered in HWIR
# Although chemical-specific scavenging coefficients should be used to calculate
the wet deposition of gases, these values are not readily available. Instead, HWIR
uses a single scavenging coefficient for all gases that approximates gases as very
small particles. This approach may underpredict wet deposition for some gases
and overpredict for others.
D.3.3 Inputs for the Air Module
Table D-5 summarizes the inputs passed by the 3MRA model system and read by the air
module preprocessor. The input file for ISCST3-HWTR is generated by the Air Module
preprocessor based on the module-specific input file, ar.ssf, the generic site layout file, sl.ssf,
modeled inputs from the generic source output file, sr.grf, and certain "hard-wired" options set
by certain variables in the ar.ssf file. The following options used by ISCST3-HWIR are identical
for each model run:
# Calms processing routine is used
# Missing data processing routine is used
# SCIM is not used
# Plume depletion due to dry (particles only) and wet (vapors and particles)
deposition is considered.
The use of rural or urban dispersion parameters is selected on a site-by-site basis. The
type of model output depends on the facility, with concentration, dry deposition, and wet
deposition being calculated for land application units, waste piles, and landfills. Only
concentration and wet deposition were calculated for the surface impoundments and aerated
tanks because the pollutants are only in the gaseous phase.
Source Parameters. Surface impoundments and land application units are modeled as
ground-level area sources by ISCST3-HWIR. The air model receives inputs that are output by
these source module(s) in the SR.GRF model file. These inputs include volatile constituent
emission rates (VE) from both unit types and, from the LAU, particulate mass emission rates
(PE30), and constituent mass emission rates for particulates (CE). Becaue a variable mass
fraction distribution for particulates (MASSFRAXOption on) would make it necessary to rerun
ISCST3 with each Monte Carlo sampling, a fixed MASSFRAX distribution for all sources was
used to enable complete reuse of ISCST3 outputs from sample to sample. ISCST3 requires
scavenging coefficients by particle size category for liquid precipitation one for frozen
precipitation. Scavenging coefficients are assigned based on the size of the particles. Frozen and
liquid precipitation were assumed to scavenge particles at the same rate.
D-26
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Appendix D
Receptor Locations. Air modeling points (AirLocX, AirLocY) depend on the layout of
the waterbodies, watersheds, human receptors, farms, and habitats around a site. For HWIR,
ISCST3 model directly to all site-specific output x-y coordinates requested by the 3MRA
framework.
Meteorological Inputs. Meteorological data are collected regionally by meteorological
station, with a modeled site assigned to the nearest station with similar weather conditions and
adequate weather data for the analysis. Hourly meteorological data are sent to the air module in
separate ASCII files prepared by PCRAMMET and read directly by the model. These files were
created for the meteorological surface stations and associated upper air stations necessary to
cover meteorlogical condtions for the facilities being modeled. The hourly files are designated as
#######h.dat, where ####### is the surface meteorological station number, and are formatted as
follows.
Header Record
Sfc Station # Sfc Sta. Year Mixing Ht. Station # Mixing Ht. Sta. Year
Data Records (in each row, with each row corresponding to one hour)
Variable
Description
Units
001
Year
002
Month
003
Day
004
Hour
005
Random flow vector (wind dir)
(degrees)
006
Wind Speed
(m/s)
007
Ambient Temperature
(K)
008
Stability Category
009
Rural Mixing Height
(m)
010
Urban Mixing Height
(m)
011
Friction Velocity at Application Site
(m/s)
012
Monin-Obukhov Length at app. Site
(m)
013
Roughness length at application site
(m)
014
Precipitation Code
015
Precipitation Amount
(mm)
The length of the hourly meteorological files varies based on availability of data with a minimum
file length of 10 years.
Most meteorological data were extracted from Solar and Meteorological Surface
Observation Network (SAMSON; U.S. DOC and U.S. DOE, 1993) hourly data files and
converted as necessary to daily time series, monthly time series, annual time series, and
long-term averages for use with the various media modules. Because SAMSON precipitation
data were inadequate, precipitation data were obtained from cooperative station daily summaries
(NCDC et al., 1995), with SAMSON data used to help allocate these daily data to hourly time
series. Mixing heights were obtained from upper air station data. Programs were used to fill in
data where it was missing in SAMSON or NCDC datasets. Land use data also were required in
D-27
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Appendix D
the vicinity of each meteorological station to derive air model inputs such as Bowen ratio, surface
roughness height, minimum Monin-Obukhov length, noontime albedo, and the fraction of net
radiation absorbed by the ground. Additional details on meteorological data collection and
processing using PCRAMMET can be found in U.S. EPA (1999e).
D.3.4 Outputs from the Air Module
The air model output file (ar.grf) includes the following output variables, along with
variables necessary to define the time series /receptor location arrays for each:
# PM10 concentrations
# Volatile concentrations (CVap)
# Vapor wet deposition (VapWDep)
# Particulate dry deposition (ParDDep)
# Particulate wet deposition (ParWDep).
Table D-6 summarizes the ouputs from the air module post processor.
D-28
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Appendix D
Table D-5. Air Module Input Parameters
111 put I'iinimcUT I nils Description
Module-Specific Parameters (AR.SSF)
NumAirSplineDist
unitless
Number of distances used to construct the polar mesh used to construct
the spline
NumAirSpline Angle
unitless
Number of angles used to construct the polar mesh used to construct the
spline
AirSplineDistance
m
Array of radial distances of polar mesh
AirSplineAngle
degrees
Array of angles used in polar mesh
SplineOption
unitless
Whether or not splining is an option. 0=no spline, l=spline
StartYr
Required by ISCST3. Starting year of Met. file
ScimStr
Required by ISCST3. SCIM option
RuralStr
Required by ISCST3. Rural or Urban
DiyDpStr
Required by ISCST3. Dry Depletion Option
WetDpStr
Required by ISCST3. Wet Depletion Option.
AirData
Required by ISCST3. Upper Air (Met.) Station number
SurfData
Required by ISCST3. Surface (Met.) Station number
AnemHght
m
Required by ISCST3. Anemometer height
SHight
m
Required by ISCST3. Source height
Array Len
unitless
Required by ISCST3. Length of array
MASSFRAXOption
unitless
Logical flag for whether to internally calculate MASSFRAX distribution
(false) or read a fixed MASSFRAX (true) from AR.SSF.
MASSFRAX
fraction
Required by ISCST3. Fraction of particle size (1 dim. array for each
particle-emitting source type, i.e. LAU, LF, and WP in that order)
PARTDIAM
,wm
Required by ISCST3. Particle diameter (1 dim. array)
PARTSLIQ
h/s-mm
Required by ISCST3. Particle scavenging coefficient by liquid
precipitation (1 dim. array)
PARTSICE
h/s-mm
Required by ISCST3. Particle scavenging coefficient by frozen
precipitation (1 dim. array)
LiqScav
h/s-mm
Required by ISCST3. Gas scavenging coefficient by liquid precipitation
(1 dim. array)
IceScav
h/s-mm
Required by ISCST3. Gas scavenging coefficient by frozen precipitation
(1 dim. array)
(continued)
D-29
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Appendix D
Table D-5. (continued)
Input I'liriimcler
I nils
Description
SCIMBYHR
unitless
Required by ISCST3. Sets model to skim through Metfile, picking
certain hours according to array specifications
Site Layout Inputs (SL.SSF)
AirLocX
m
Array for easting in site coordinate system (for each receptor location)
AirLocY
m
Array for northing in site coordinate system (for each receptor location)
MetSta
Met. Station identifier (L.dat (L: long-term), A.dat
(A: annual time series), M.dat (M: monthly time series),
D.dat (D: daily time series), H.dat (H: hourly time
series))
NumAir
Number of air points
SettingID
Srctype (*One of LAU, LF, WP, AT, SI) + SitelD
SrcArea
?
111
Area of source
SrcLocX
m
Easting in site coordinate system (Source location). 0 at source centroid
SrcLocY
m
Northing in site coordinate system (Source location). 0 at source centroid
SrcType
One of {LAU, LF, WP, AT, SI}
Inputs from Source Module (SR.GRF)
CE
float
g/m2/d
PE30
float
g/m2/d
PE30NY
integer
PMF
float
VE
float
g/m2/d
VENY
integer
SrcVE
logical
SrcCE
logical
D-30
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Appendix D
Table D-6. AR.GRF Output Variables (Air Module Outputs)
Viiriiihlc \:imc I nil Description
PM10
Mg/m3
2-dimensional array that provides information on total number of
years of activity for PM10 at each receptor location and PM10
concentration in each year.
PM10YR
Year
Time series of years corresponding to this variable.
PM10NY
unitless
Number of years in the time series corresponding to this variable.
CVap
Mg/m3
2-dimensional array that provides information on total number of
years of activity for volatiles at each receptor location and volatile
concentration in each year.
CVapYR
Year
Time series of years corresponding to this variable.
CVap NY
unitless
Number of years in the time series corresponding to this variable.
VapWDep
g/m2/d
2-dimensional array that provides information on total number of
years of activity for vapor wet deposition at each receptor location
and wet deposition flux for each year.
VapWDepYR
Year
Time series of years corresponding to this variable.
VapWDepNY
unitless
Number of years in the time series corresponding to this variable.
ParDDep
g/m2/d
2-dimensional array that provides information on total number of
years of activity for particulate dry deposition at each receptor
location and dry deposition flux for each year.
ParDDepYR
Year
Time series of years corresponding to this variable.
ParDDepNY
unitless
Number of years in the time series corresponding to this variable.
ParWDep
g/m2/d
2-dimensional array that provides information on total number of
years of activity for particulate wet deposition at each receptor
location and particulate wet deposition flux for each year.
ParWDepYR
Year
Time series of years corresponding to this variable.
ParWDepNY
unitless
Number of years in the time series corresponding to this variable.
SrcVE
Flag to tell if vapor
SrcCE
Flag to tell if particulate
D-31
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Appendix D
D.4 Watershed Module
The watershed module models chemical fate and transport processes within a watershed
subbasin, including erosion, runoff, leaching, volatilization, and degradation. It also estimates
runoff and solids loads to surface waterbodies and recharge as an input for the aquifer module.
This section summarizes the assumptions, limitations, inputs, and outputs. Additional detail,
including all governing equations, can be found in U.S. EPA (1999f).
D.4.1 Functionality
The watershed module estimates soil chemical concentrations for each subbasin;
streamflow and chemical and solids loading estimates for the surface water module (Section
3.3.5); and regional infiltration (recharge) estimates for the vadose zone module (Section 3.3.4).
In summary, the watershed module addresses the following specific objectives:
# Simulate the time series of annual average chemical concentrations in surficial
soil (top 1 cm) resulting from aerial deposition throughout the area of interest
surrounding the WMU. (Note that, although chemical mass losses due to
volatilization and leaching from the soil column are evaluated, these losses are
simulated only for the purpose of estimating soil concentrations and waterbody
loads; that is, these losses are not subsequently received as inputs by the Air or
Vadose modules because they are secondary sources.)
# Simulate the time series of annual average chemical loadings in surface runoff and
erosion that will enter individual waterbody reaches throughout the AOI.
# Simulate the time series of annual average runoff that will enter waterbodies
throughout the AOI.
# Simulate the time series of annual average stream baseflow (dry weather
streamflow) in waterbodies throughout the AOI. (Runoff plus baseflow represents
total streamflow.)
# Simulate the time series of annual average eroded solids loads that will enter
waterbodies throughout the AOI.
# Simulate the time series of annual average infiltration (recharge) rates for each
watershed in the AOI.
Note that the watershed module simulates only indirect chemical loads to the waterbody;
that is, the sole source of chemical to the watershed soils is aerial deposition. Chemical loads to
the waterbody resulting from direct runoff and erosion from a closed surface impoundment are
simulated by the LAU source module. Similarly, if a receptor is located in a buffer area between
the closed impoundment and the downslope waterbody (i.e., in the local watershed), the total
surficial soil concentration to which the receptor is exposed includes the aerial deposition-related
D-32
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Appendix D
concentration simulated by the watershed module (for the subbasin containing the local
watershed) plus the runoff/erosion-related concentration simulated by the LAU module.
D.4.2 Assumptions and Limitations
The watershed module's conceptual and mathematical models are very similar to those
already described for the LAU module — the combined "local watershed/soil column". This
algorithm is a dynamic, two-dimensional, fate and transport model that also includes
hydrological functionality; assumptions and limitations associated with its application in either
module are listed in Section D.2.1. However, the watershed module differs in that algorithm is
applied to each watershed subbasin as a whole and subbasins are not disaggregated into
"subareas" as are the local watersheds. Hence, the watershed module is a one-dimensional
[vertical], lumped model that simulates each watershed individually. In the watershed module,
although the depth of the soil column is a user-specified input, set at a default of 5 cm. Each soil
column layer is 1 cm thick.
Another difference with the LAU module involves the size of the computational time step
used to determine contaminant concentrations in runoff water. Because indirect soil
concentrations resulting from aerial deposition are likely to be significantly less than soil
concentrations resulting from direct runoff/erosion from a source, and aerial because deposition
rates in 3MRA are known only on an average annual basis (not daily) the watershed module
includes the following features.
# Soil erosion and runoff models are executed on a daily time step, with daily
results rolled up to annual average soil erosion (CSL) and runoff volume (Q),
which are used to estimate chemical losses in erosion and runoff as well as runoff
flow and suspended solids loading to waterbodies.
# The computational time step used by the watershed/soil column algorithm is
based on numerical considerations but does not exceed 1 year.
# Annual average runoff-related parameters and the annual average aerial deposition
rates are used in applying the watershed/soil column algorithm at each
computational time step.
In summary, annual average soil erosion and runoff are estimated on a daily time step,
while the remainder of the model (contaminant mass fate and transport simulation) is executed
on a computational time step that is typically much larger than one day and can vary each year of
the simulation. All outputs are ultimately reported as annual averages, regardless of their
individual computational time steps.
The watershed module uses the identical hydrology submodel used in the LAU model to
estimate stormwater runoff and ground water infiltration. Streamflows are assumed to be made
up of both stormwater runoff and baseflow. Baseflow is streamflow occurring during nonrunoff
periods and is derived from ground water discharge to streams or interflow (shallow infiltration
flowing parallel to the ground surface). Although baseflow can vary seasonally, or even near
D-33
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Appendix D
continuously, as ground water levels and/or interflow varies, it was considered unnecessary (and
computationally impractical) to attempt to estimate within-year variability in baseflows. Rather,
a single estimate was derived based on 30Q2 low flow data, i.e., the minimum 30-day average
flow occurring, on average, at least once every other year. A descriptions of how this flow
statistic was derived can be found in U.S. EPA (1999f).
Like the LAU module, the watershed module also uses the (modified) Universal Soil
Loss Equation (MUSLE), as described in U.S. EPA (1999a, 1999f), to predict soil erosion from
watersheds considered in their entirety. In applying this model, watersheds are assumed to be
homogeneous in terms of erosion characteristics, including sheet flow length and slope, as
described in U.S. EPA (1999a, 1999f).
D.4.3 Inputs for the Watershed Module
Because of its similar design and construction, the watershed module inputs are similar to
those previously shown in Table D-2 for the LAU module, except without the waste and WMU-
related parameters. Table D-7 summarizes the watershed inputs. Note that like other modules, the
watershed module receives inputs from the site layout and header files (sl.ssf, hd.ssf), as well as a
module-specific input file (ws.ssf) containing inputs specific to the watershed module, and
separate meteorological data files for daily and long term average meteorological data. In
addition, the watershed module reads the ar.grf output file for dry and wet deposition rates.
D.4.4 Outputs from the Watershed Module
Table D-8 summarizes the outputs of the watershed module. These include, for each
watershed subbasin, soil concentrations, infiltration rates, runoff to the downslope waterbodies,
and chemical and solids loadings in this runoff. In addition the module provides stream baseflow
estimates for each watershed subbasin. Note that time series reporting is subbasin-specific; that
is, all outputs for a given subbasin are reported, including zeros, up to the year that the subbasin
simulation is terminated.
D-34
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Appendix D
Table D-7. Summary of Inputs for Watershed Module
Hie
\ iiriiihlo Niimo
I nils
Diilii
Type
Description
HD.SSF
CPDirectory
String
Path for location of chemical properties files
MetData
String
Path for location of meteorological files
AR.GRF
SrcCE
Integer
Flag to tell if particulate
VapWDep
g/m2/d
Array of number of years of activity for vapor wet
deposition and wet deposition flux for each year (by air
point)
VapWDepYR
Year
Time series of years for VapWDep
VapWDepNY
unitless
Number of years in VapWDep time series
ParDDep
g/m2/d
Array of number of years of activity for particulate dry
deposition by dry deposition flux for each year (by air
point)
ParDDepYR
Year
Time series of years for ParDDep
ParDDepNY
unitless
Number of years in the ParDDep time series
ParWDep
g/m2/d
Array of total number of years of activity for particulate
wet deposition by particulate wet deposition flux for
each year (by air point)
ParWDepYR
Year
Time series of years for ParWDep
ParWDepNY
unitless
Number of years in the ParWDep time series
SL.SSF
SiteLatitude
degrees
Float
Latitude of the site
MetSta
String
ID number for meteorological station associated with
site
NyrMax
years
Integer
Maximum module simulation time
TermFrac
fraction
Float
Peak output fraction for simulation termination
AirTemp
degrees C
Float
Long term annual air temperature for site
NumAir
Integer
Number of air points at site
WSSubAirFrac
fraction
Float
Fraction of watershed subbasin represented by air
points
WSSubAirlndex
Integer
Index of air points representing subbasin (by subbasin)
WSSubNumAir
Integer
Number of air points that represent subbasin (by
subbasin)
NumWSSub
Integer
Number of watershed subbasins
(continued)
D-35
-------
Appendix D
Table D-7. (continued)
l-'ile \ nriiihlo \:imc
I nils
Diilii
Type
Description
WS Sub Area
m2
Float
Area of subbasin (by watershed subbasin)
W S SubNumSub Area
Integer
Number of subbasin subareas (= 1; by WS subbasin)
WSTemp
degrees C
Float
Average watershed temperature (by site)
focS
fraction
Float
soil fraction organic carbon (by WS subbasin)
WS.SSF a_BF
m/day
Float
regression coefficient a for baseflow model
b_BF
m/day
Float
regression coefficient b for baseflow model
bcm
unitless
Float
Boundary condition multiplier (lower boundary)
C
unitless
Float
USLE cover factor (by watershed subbasin)
CN
unitless
Float
SCS curve number (by watershed subbasin)
ConVs
m/d
Float
Settling velocity (suspended solids)
deltDiv
unitless
Integer
Time step divider (for debugging only)
DRZ
cm
Float
Root zone depth (by watershed subbasin)
Infild
m/d
Float
Input infiltration rate (for debugging only)
K
kg/m2
Float
USLE soil erodibility factor (by watershed subbasin)
Ksat
cm/h
Float
Soil saturated hydraulic conductivity (by watershed
subbasin)
P
unitless
Float
USLE soil erosion control factor (by watershed
subbasin)
RunID
string
Run identification label (optional)
SMb
unitless
Float
soil moisture coefficient b (by watershed subbasin)
SMFC
volume %
Float
soil moisture field capacity (by layer and watershed
subbasin)
SMWP
volume %
Float
soil moisture wilting point (by layer and watershed
subbasin)
Theta
degrees
Float
slope (by local watershed)
thetawZld
volume
fraction
Float
input volumetric water content in till zone (for
debugging)
WCS
volume
fraction
Float
soil saturated water content or total porosity (by
watershed subbasin)
X
m
Float
flow length (by watershed subbasin)
(continued)
D-36
-------
Appendix D
Table D-7. (continued)
l-'ile
Vsiriiihlc Niimo
I nils
Diilii
Type
Description
zava
m
Float
averaging depth upper (depth-averaged soil
concentration)
zavb
m
Float
averaging depth lower (depth-averaged soil
concentration)
zZlsa
m
Float
modeled soil column depth (by watershed subbasin)
CP.SSF
ChemName
String
Name of chemical
ChemCASID
String
Chemical CAS number
ChemType
String
Type of chemical
ChemTemp
degrees C
Real
Temperature for chemical properties
ChemFracNeutral
fraction
Real
Fraction of chemical in neutral form
ChemADiff
cm2/s
Float
Diffusivity of chemical in air
ChemWDiff
cm2/s
Float
Diffusivity of chemical in water
ChemHLC
(atm m3) /
mol
Float
Henry's law constant for the chemical
ChemKoc
mL/g
Float
Soil organic carbon - water partitioning coefficient
(organic compounds)
ChemAnaBioRate
1/day
Float
Biodegradation / decay rate of contaminant in sediment
compartment
ChemAerBioRateb
1/day
Float
Complex first-order biodegradation rate constant for the
chemicalb
ChemHydRate
1/day
Float
Hydrolysis rate for the chemical
ChemSol
mg/L
Float
Chemical solubility
ChemKd
L/kg
Float
Solid/water partition coefficient (inorganic compounds;
by media)
Met file
temp
degrees C
Float
Average daily air temperature
jday
Integer
Julian day
dailyR
1/t
Float
Daily USLE rainfall erosivity factor (daily)
dailyppt
cm/d
Float
Total daily precipitation
AvgTemp
degrees C
Float
Mean monthly temperature
maxtemp
degrees C
Float
Maximum daily average temperature for month
mintemp
degrees C
Float
Minimum daily average temperature for month
(continued)
D-37
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Appendix D
Table D-7. (continued)
Diilii
Tile \ iiriiihlo \:imc I nils Type Description
PE
Float
Thornthwaite Precipitation Evaporation Index
Ed
m/d
Float
Average daily evaporation
tsc
degrees C
Float
Long term average soil column temperature
—
Integer
Number of years of meteorological data
" The module currently assumes there is one native soil layer and that the thickness of the underlying soil layer is
assumed to be a minimum of 1 meter thick. If the regional vadose zone thickness is less than l+dwmll. then the
impoundment is assumed to be built up (via an earthen berm) so that there is 1 meter of soil between the bottom
of the SI and the ground water.
b Note: If normalized biodegradation rate constants are unavailable, normalized biodegradation rates constants
are estimated from first-order biodegradation rate constants developed for soil systems by assuming the
effective biomass in the soil system is 2.0/10 " Mg/m3. This value was developed by RTI as an interim
estimate until a more rigorously developed value for this parameter is available from EPA.
D-38
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Appendix D
Table D-8. Summary of Outputs (WS.GRF) for the Watershed Module
Output \ iiriiihlo
Description
I nits
NyrMet
Number of years in the available meteorological record
Year
CTdaR
Depth-averaged soil concentration (from zava to zavb)
Mg/g
CTdaRYR
Year associated with output
Year
CTdaRNY
Number of years in outputs
Unitless
CTssR
Surface soil concentration
Mg/g
CTssRYR
Year associated with output
Year
CTssRNY
Number of years in outputs
Unitless
RunoffR
Annual average runoff flow to waterbody
m3/d
BFann
Long-term annual average baseflow to waterbody
m3/d
Annlnfil
Annual average recharge rate
m/d
SWLoadChemR
Chemical load (resulting from deposition only) to waterbody
g/d
SWLoadChemRYR
Year associated with output
year
SWLoadChemRNY
Number of years in outputs
Unitless
SWLoadSolidR
Total suspended solids in runoff
g/d
D-39
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Appendix D
D.5 Vadose and Aquifer Modules
The HWIR 3MRA vadose and aquifer modules are a modified version of EPA's
Composite Model for leachate migration with Transformation Products (EPACMTP) (US EPA,
1996a,b,c). This code simulates the fate and transport of contaminants released from land-based
waste management units through the underlying unsaturated or vadose zone (soil) and saturated
zone (surficial aquifer). EPACMTP replaced EPACML (US EPA, 1993) as the best available
tool to predict potential exposure at a downstream receptor well. EPACMTP offers
improvements to EPACML by considering: 1) the formation and transport of transformation
products; 2) the impact of groundwater mounding on groundwater velocity; 3) finite source as
well as continuous source scenarios; and 4) metal transport.
Detailed descriptions of both modules, including their purpose and scope of application,
mathematical formulations, and use in HWIR99 may be found in U.S. EPA (1999g). Additional
information relating to the EPACMTP and its verification is provided in the background
documents for EPACMTP (US EPA, 1996a,b,c, 1997).
D.5.1 Functionality
EPACMTP comprises three major simulation components:
# A module that performs one-dimensional analytical and numerical solutions for
water flow and contaminant transport in the vadose zone underlying a waste
management unit.
# A numerical module that simulates steady-state groundwater flow subject to
recharge from the vadose zone.
# A module comprising analytical and numerical solutions for contaminant
transport in the saturated zone.
For 3MRA, portions of the EPACMTP code pertaining to the vadose zone make up the vadose
zone module (VZM), and portions pertaining to the saturated zone make up the saturated zone
module (SZM).
The VZM simulates infiltration and contaminant transport between the top of the vadose
zone and the water table (see Figure 3-17). Flow in the vadose zone is modeled as steady-state,
one-dimensional (vertical) flow from underneath the source (the WMU). Recharge occurs from
the soil outside the WMU toward the water table. The lower boundary of the vadose zone is the
water table. The flow in the vadose zone is predominantly gravity-driven, and therefore the
vertical flow component accounts for most of the fluid flux between the source and the water
table. The flow rate is determined by the long-term average infiltration rate through the waste
management unit and recharge downgradient from the WMU.
Contaminant is transported in the vadose zone by advection and dispersion. The vadose
zone is assumed to be initially contaminant-free, and it is assumed that contaminants migrate
D-40
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Appendix D
vertically downward. The VZM can simulate both steady-state and transient transport, with
single or multiple species chain decay reactions and linear or nonlinear sorption. The VZM
consists of two submodules: one for flow calculations and one for transport.
The SZM simulates groundwater flow using either a three- or one-dimensional steady-
state solution for predicting hydraulic head and Darcy velocities. The saturated groundwater
system is assumed to be of constant thickness, subject to recharge along the top of the aquifer and
a regional gradient defined by upstream and downstream head boundary conditions. The
saturated zone transport module describes the advective-dispersive transport of dissolved
contaminants in a three-dimensional, constant thickness aquifer. The initial contaminant
concentration is set to zero. The concentration gradient along the downstream boundary is zero,
and the lower aquifer boundary is taken to be impermeable. A zero concentration condition is
used for the upstream aquifer boundary. Contaminants enter the saturated zone through a patch
source on the upper aquifer boundary directly beneath the source. Recharge of contaminant-free
infiltration water occurs along the upper aquifer boundary outside the patch source. Transport
mechanisms considered are advection, dispersion, linear or nonlinear equilibrium adsorption, and
first-order decay with daughter product formation. As in the unsaturated zone, the saturated zone
transport module can simulate multispecies transport involving chain decay reactions. The
saturated zone module performs a fully three-dimensional transport simulation.
The VZM and SZM, derived from the EPACMTP code, are, together, capable of
simulating the fate and transport of dissolved contaminants from a point of release at the base of
a waste disposal unit, through the unsaturated zone and underlying aquifer, to one or more
receptor wells at arbitrary downstream locations in the groundwater system. The modules
account for the major mechanisms affecting contaminant migration, including: transport by
advection and hydrodynamic dispersion, retardation due to reversible linear or nonlinear
equilibrium adsorption onto the soil and aquifer solid phase, and biochemical degradation
processes. The latter may involve chain decay reactions if the contaminant(s) of concern form a
decay chain.
D.5.2 Assumptions and Limitations
As is true of any model, EPACMTP and, its modules are based on a number of
simplifying assumptions which make the code easier to use and ensure its computational
efficiency. These assumptions, however, may cause application of the model to be inappropriate
in certain situations. The inherent assumptions and limitations of the vadose zone module
(VZM) and saturated zone module (SZM) are summarized below:
1) Soil and Aquifer Medium Properties. Soil and aquifer are uniform porous media,
and flow and transport are governed by Darcy's law (Bear, 1972) and the
advection-dispersion equation, respectively. The model does not account for the
presence of preferential pathways such as fractures and macro-pores. Although
the aquifer properties are assumed to be uniform, the model does allow for
anisotropy in the hydraulic conductivity. Also, in the saturated zone module,
effects due to the presence of fractures and heterogeneity are superimposed onto
the base homogeneous model.
D-41
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Flow in the Unsaturated Zone. Flow in the unsaturated zone is steady-state, one-
dimensional vertical flow from underneath the source toward the lower boundary
of the unsaturated zone, that is, toward the water table. The flow in the
unsaturated zone is predominantly gravity-driven, and, therefore, the vertical flow
component accounts for most of the fluid flux between the source and the water
table. The flow rate is determined by the long-term average infiltration rate
through the waste management unit. The long-term average infiltration rate is
calculated from a series of annual average infiltration rates.
Flow in the Saturated Zone. The SZM is designed to simulate flow in an
unconfined aquifer with constant saturated thickness. The concept employed is
that of regional flow in the horizontal direction, with vertical disturbance due to
recharge and infiltration from the overlying unsaturated zone and waste disposal
facility. The lower boundary of the aquifer is assumed to be impermeable. Flow
in the saturated zone is assumed to be steady-state.
The SZM accounts for different recharge rates underneath and outside the source
area. Groundwater mounding beneath the source is represented in the flow system
by increased hydraulic head values at the top of the aquifer. This approach is
reasonable as long as the height of the mound is small relative to the thickness of
the saturated zone.
Transport in the Unsaturated Zone. Contaminant transport in the unsaturated zone
is by advection and dispersion. The unsaturated zone is assumed to be initially
contaminant-free and contaminants are assumed to migrate vertically downward
from the disposal facility. The VZM simulates transient transport in the
unsaturated zone, with single species or multiple species chain decay reactions,
and linear or nonlinear sorption.
Transport in the Saturated Zone. Contaminant transport in the saturated zone is
due to advection and dispersion. The aquifer is assumed to be initially
contaminant-free and contaminants enter the aquifer only from the unsaturated
zone immediately underneath the waste disposal facility, which is modeled as a
rectangular horizontal plane source. The SZM simulates transient transport in a
fully three-dimensional mode in order to obtain a scientifically rigorous analysis.
The concentration at the water table must be specified as a function of time. The
SZM is capable of simulating transient transport in a quasi-three dimensional
mode when computational efficiency is desired (e.g., Monte Carlo simulations).
The SZM can consider linear sorption and the transport of a single species or
multiple species chain decay reactions.
Contaminant Phases. The VZM and SZM simulate constituent transport in the
aqueous phase only, and disregard interphase mass transfer processes other than
adsorption onto immobile solids. The modules do not account for volatilization in
the unsaturated zone; this is a conservative approach for volatile chemicals in the
aqueous phase. The modules also do not account for the presence of a NAPL (e.g.
-------
Appendix D
oil) or transport in gas phase. When a mobile oil phase is present, significant
migration may occur within it, so that the VZM and SZM may under predict the
movement of hydrophobic chemicals.
7) Chemical Reactions. The groundwater pathway (VZM and SZM) modules take
into account chemical reactions by adsorption and decay processes. The VZM
allows sorption of organic compounds in the unsaturated zone to be represented
by linear or nonlinear adsorption isotherms, while sorption in the saturated zone is
always linear. It is assumed that the adsorption of contaminants onto the soil or
aquifer solid phase occurs instantaneously and is entirely reversible.
The effect of geochemical interactions is especially important in the fate and
transport analyses of metals. For the simulation of metals with non-linear
adsorption, both modules utilize sorption isotherms generated by MINTEQA2
(Allison et al., 1991, a metal speciation model). MINTEQA2 generates
concentration-dependent effective partition coefficients for various combinations
of geochemical conditions. This procedure is described in the background
document for the modeling of metals transport (EPA, 1996b).
The VZM and SZM also account for chemical and biological transformation
processes. All transformation reactions are represented by first-order decay
processes. An overall decay rate is determined within the modules, so that the
modules cannot explicitly consider the separate effects of multiple degradation
processes such as oxidation, hydrolysis and biodegradation. In order to increase
their flexibility, both modules have the capability to determine the overall decay
rate from chemical-specific hydrolysis constants and soil and aquifer temperature
and pH values, and from biodegradation rates selected from a database. It is
assumed that biodegradation is aerobic in the unsaturated zone and anaerobic in
the saturated zone.
Both modules assume that the reaction stoichiometry is prescribed for scenarios
involving chain decay reactions and applys to all transformation processes. The
speciation factors are specified as constants by the user (see the EPACMTP
Background Document, EPA, 1996a). In reality, these coefficients may change as
functions of aquifer conditions (e.g., temperature and pH) and/or concentration
levels of other chemical components.
D.5.3 Inputs for the Vadose and Saturated Zone Modules
A list of vadose zone-specific input parameters is provided in Table D-9, showing
variables by file name. The VZM requires input about the site from the site layout SSF (sl.ssf),
inputs specific to the vadose zone (e.g.,control parameters, soil characteristics, etc.) from the
vadose zone module SSF (vz.ssf), chemical-specific data from the chemical properties SSF
(cp.ssf), and outputs from the source module (i.e., chemical and water fluxes) from the source
GRF (sr.grf).
D-43
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Appendix D
A list of saturated zone-specific input parameters is provided in Table D-10. The SZM
requires input about the site (sl.ssf), information specific to the aquifer (aq.ssf), and chemical-
specific data from the chemical properties SSF (cp.ssf), as well as outputs from the source
(sr.grf), watershed (ws.grf), and vadose zone (vz.grf) modules. Tables D-9 through D-12 specify
source and destination files for all parameters in the VZM and SZM (SZM is also referred to as
the aquifer module).
D-44
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Appendix D
Table D-9. Vadose Zone Module Input Parameters
Diilii
l-'ile C'oilo I nits l \pe Description
SL.SSF
AquFEOX
fraction
Float
Hydrous ferric oxide (HFO) adsorbent content
AquLOM
mg/L
Float
Leachate organic matter
NumVad
Integer
Number of vadose zones = number of local watersheds
NyrMax
years
Integer
Maximum model simulation time
SrcArea
mA2
Float
Area of source
SrcL W S Sub AreaArea
m2
Float
Area of local watershed subarea
SrcL W S Sub Arealndex
unitless
Integer
Local watershed subarea containing WMU
SrcNumLWS
Integer
Number of local watersheds
TermFrac
fraction
Float
Termination peak fraction criteria
VadALPHA
1/cm
Float
Soil retention paramter alpha
VadBETA
unitless
Float
Soil retention paramter beta
VadID
String
Setting ID for vadose zone
VadPh
pH units
Float
Average vadose zone pH
VadSATK
cm/hr
Float
Saturated hydraulic conductivity
VadTemp
degrees C
Float
Average vadose zone temperature
VadThick
m
Float
Vadose zone thickness
VadWCR
L/L
Float
Residual water content
VadWCS
L/L
Float
Saturated water content
VZ.SSF
DISPR
m
Float
Longitudinal dispersivity
POM
g/g
Float
Percent organic matter
RHOB
g/cm3
Float
Bulk density of soil
CP.SSF
ChemCASID
String
CASID
ChemHydNumProd
Integer
Number of products
ChemHydProdCASID
String
Product CASID
ChemHydProdYield
moles/mole
s
Float
Product yield coefficient
ChemHydRate
1/day
Float
Hydrolysis rate
ChemKoc
mL/g
Float
Koc
ChemMolWt
g/mole
Float
Molecular weight
NumChem
Integer
Number of chemicals assc. W/parent
ChemAerBioRate
1/day
Float
Aerobic biodegradation rate
SR.GRF
Annlnfil
m/d
Float
Leachate infiltration rate (annual avg. WMU only)
LeachFlux
g/m2/d
float
Leachate contaminant flux
LeachFluxNY
integer
Number of years in outputs
LeachFluxYR
year
integer
Year associated with output
NyrMet
year
Integer
Number of years in the available met record
SrcLeachMet
Logical
Flag for leachate presence when leachate is met-driven
SrcLeachSrc
Logical
Flag for leachate presence when leachate is not met-
driven (active surface impoundments)
TWT
yr
Float
Times for CWT
D-45
-------
Appendix D
Table D-10. Aquifer Module Input Parameters
Diitii
Tile Code I nits l \po Description
SL.SSF
AquDir
degrees
Float
Groundwater flow direction in degrees from North
AquFEOX
fraction
Float
Hydrous ferric oxide (HFO) adsorbent content
AquGrad
Float
Regional groundwater gradient
Aquld
String
Environmental setting ID for aquifer
AquLOM
mg/L
Float
Leachate organic matter concentration
AquPh
pH units
Float
Average aquifer pH
AquSatk
m/yr
Float
Saturated hydraulic conductivity (aquifer)
AquTemp
degrees C
Float
Average Aquifer Temperature
AquThick
m
Float
Saturated zone thickness
AquVadlndex
Integer
Index of vadose zone per aquifer
AquWellFracZ
fraction
Float
Fractional depth of well in aquifer measured from
watertable
AquWellLocX
m
Float
Easting in UTM
AquWellLocY
m
Float
Northing in UTM
NumAqu
Integer
Number of aquifers
NumAquWell
Integer
Number of drinking water wells
NumWBN
Integer
Number of waterbody networks
NumWSSub
Integer
Number of watershed sub basins
NyrMax
years
Integer
Maximum model simulation time
SrcArea
mA2
Float
Area of source
SrcLocX
m
Float
Easting in site coordinate system (0)
SrcLocY
m
Float
Northing in site coordinate system (0)
TermFrac
fraction
Float
Termination peak fraction criteria
VadID
String
Environmental setting ID for aquifer
WBNNumRch
Integer
Number of reaches for this network
WBNRchAquIndex
Integer
Index of aquifer that impacts this reach
WBNRchLength
m
Float
Reach length
WBNRchLocX
m
Float
Easting in UTM
WBNRchLocY
m
Float
Northing in UTM
WBNRchNumAqu
Integer
Number of aquifer that impact this reach
WBNRchNumLoc
unitless
Integer
Number of x,y points associated with watershed
WS Sub Area
mA2
float
Area of watershed subbasin
AQ.SSF
AL
m
Float
Longitudinal dispersivity
ALATRatio
m
Float
Horizontal transverse dispersivity
ALAVRatio
m
Float
Vertical transverse dispersivity
ANIST
Float
Anisotropy ratio
(continued)
D-46
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Appendix D
Table D-10. (continued)
Diitii
Tile Code I nits l \po Description
AquAnaBioRandUnif
Integer
Uniformly distributed random number used to
choose the anaerobic biodegradation regime:
O=methanogenic; 1= sulfate reducing
AquDoFracture
Logical
Logical flag to turn fractures on or off
AquDoHetero
Logical
Logical flag to turn heterogeneity on or off
AquFracturelD
Integer
Indicator for degree of fracturing of saturated porous
media
AquRandFractU nif
Float
Uniformly distributed random number-used when
AquDoFracture==TRUE
AquRandHeteroNorm
Float
Normally distributed random numbers with 0 mean
and std of 1-used when AquDoHetero==TRUE
AquRandHeteroU nif
Float
Uniformly distributed random number-used when
AquDoHetero==TRUE
BDENS
g/cm3
Float
Bulk density of soil
FOC
fraction
Float
Fraction organic carbon
POR
Float
Effective porosity
CP.SSF
ChemCASID
String
CASID
ChemHydNumProd
Integer
Number of products
ChemHydProdCASID
String
Product CASID
ChemHy dProdY ield
moles/mole
Float
Product yield coefficient
ChemHydRate
1/day
Float
Hydrolysis Rate
ChemKoc
mL/g
Float
Koc
ChemMetB ioRate
1/day
Float
Anaerobic biodegradation under methanogenic red.
ChemMolWt
g/mole
Float
Molecular weight
ChemS04BioRate
1/day
Float
Anaerobic biodegradation under S04 reduction
NumChem
Integer
Number of chemicals assc. w/parent
SR.GRF
SrcLeachMet
Logical
Flag for leachate presence when leachate is met-
driven
SrcLeachSrc
Logical
Flag for leachate presence when leachate is not met-
driven (active surface impoundment)
WS.GRF
Annlnfil
m/d
I'lual
Inleuer
Annual average recharge rate (time series by
watershed subbasin)
NyrMet
year
Number of years in the available met record
VZ.GRF
CWT
mg/L
Float
Concentration at water table
NTS
yr
Integer
Number of time-conc/flux pairs in TWT and CWT
SINFIL
m/yr
Float
Long term average waterflux beneath source
TSOURC
yr
Float
Duration of source boundary condition
TWT
yr
Float
Times for CWT
D-47
-------
Appendix D
Table D-ll. Vadose Zone Module Outputs
lile
Code
I nils
Type
Description
VZ.GRF
CWT
mg/L
Float
Concentrations at water table
NTS
yr
Integer
Number of time-conc/flux pairs in TWT and CWT
SINFIL
m/yr
Float
Longterm average waterflux beneath source
TSOURC
yr
Float
Duration of source boundary condition
Table D-12. Aquifer Module Outputs
File
C ode
I nils
Diilii
Type
Description
AQ.SSF
AquRchMassFlux
g/yr
Float
Mass flux from aquifer to reach (time series by
reach)
AquRchMassFluxNY
Integer
Number of time - mass-flux-to-reach pairs
AquRchMassFluxYR
year
Float
Time of mass flux from aquifer to reach
AquRchWaterFlux
m3/day
Float
Total GW flux to reach
AquWellConc
mg/L
Float
Observed well concentration
AquWellConcFlag
Logical
Flag indicating well is within plume: T - yes, F -
no (by well)
AquWellConcNY
Integer
Number of time - observed well cone pairs
AquWellConcY r
year
Integer
Time of observed well concentration
D-48
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Appendix D
D.6 Surface Water Module
The HWIR 3MRA surface water module models streams, lakes, ponds, and wetlands and
consists of the core model Exams II (Burns, 1997, Burns et al., 1982) and the interface module
ExamsIO. This section describes the assumptions, limitations, inputs, and outputs of this module.
Detailed documentation can be found in U.S. EPA (1999u), from which the following material
was extracted.
D.6.1 Functionality
The 3MRA surface water module takes the loadings calculated by the source,
atmospheric, watershed, and groundwater modules, along with data on meteorology, hydrology,
environmental conditions, and chemical reactivity, and calculates the chemical concentrations
throughout the waterbody network over time. The 3MRA surface water module contains the core
model Exams II (Burns, 1997; Burns et al., 1982), which is a general surface water fate model
for organic chemicals. This compartment model has been used routinely by both EPA and
industry analysts for the analysis of expected pesticide concentrations in generically defined
environments, such as farm ponds. It has also been used for site-specific analysis of pesticide
concentrations in various waterbodies around the world. The interface module ExamsIO was
developed specifically for this 3MRA project. It reads data from other 3MRA modules and
databases and builds Exams input files describing the waterbody environment and chemical
properties, along with the command file that specifies the chemical loading history and controls
the Exams simulation. ExamsIO passes control to Exams, which conducts the simulation and
produces intermediate results files. ExamsIO then processes the intermediate files and passes the
output data back to the proper 3MRA database.
Exams II is an interactive modeling system that allows a user to specify and store the
properties of chemicals and ecosystems, modify either via simple commands, and conduct rapid
evaluations and sensitivity analyses of the probable aquatic fate of synthetic organic chemicals.
Exams combines chemical loadings, transport, and transformation into a set of differential
equations using the law of conservation of mass as an accounting principle. It accounts for all the
chemical mass entering and leaving a system as the algebraic sum of external loadings, transport
processes that export the compound from the system, and transformation processes within the
system that convert the chemical to daughter products. The program produces output tables and
simple graphics describing chemical exposure, fate, and persistence.
Exams represents each waterbody via a set of segments or distinct zones in the system.
The program is based on a series of mass balances for the segments that give rise to a single
differential equation for each segment. Working from the individual transport and transformation
process equations, Exams compiles an overall equation for the net rate of change of chemical
concentration in each segment. The resulting system of differential equations describes the mass
balance for the entire system, which is then solved by the method of lines. Exams includes a
descriptor language that simplifies the specification of system geometry and connectedness.
Exams includes process models of the physical, chemical, and biological phenomena
governing the transport and fate of compounds. Each of the unit process equations used to
D-49
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Appendix D
compute the kinetics of chemicals accounts for the interactions between the chemistry of a
compound and the environmental forces that shape its behavior in aquatic systems. This
"second-order" or "system-independent" approach lets one study the fundamental chemistry of
compounds in the laboratory and then, based on independent studies of the levels of driving
forces in aquatic systems, evaluate the probable behavior of the compound in systems that have
never been exposed to it. Most of the process equations are based on standard theoretical
constructs or accepted empirical relationships. The user can specify reaction pathways for the
production of transformation products of concern, whose further fate and transport can then be
simultaneously simulated by Exams.
Exams contains process modules for several chemical reactions. Equilibrium reactions
are used for sorption and ionization. Kinetic reactions are used for volatilization, hydrolysis
(acid, base, and neutral), biodegradation (water column and sediments), photolysis, oxidation,
and reduction. Exams uses these modules as determined by the input chemical properties.
Exams has been designed to accept standard water quality parameters and system characteristics
that are commonly measured by limnologists throughout the world and chemical datasets
conventionally measured or required by EPA regulatory procedures.
The contaminant fate algorithms in Exams include sorption to suspended solids, biotic
solids, and sediment solids, but Exams does not simulate a solids balance. Solids concentrations
are specified as input data. The effects of settling and resuspension on chemical fate are
accounted for in a bulk sediment-water exchange term.
Exams can be run in three modes: steady-state, quasi-dynamic with steady environmental
data, and quasi-dynamic with monthly environmental data. H-Exams implements mode 2, in
which the model integrates the equations over specified time periods with given environmental
and loading conditions. Pulse loadings are allowed by Exams in mode 2 simulations, but this
capability is not implemented by H-Exams. The EXAMS simulation proceeds in yearly
increments using yearly-average loadings and environmental conditions.
While Exams can be run interactively or as a batch program, H-Exams is implemented
solely as a batch process. H-Exams does not consider transformations due to photolysis or
oxidation. Transformation rate constants for hydrolysis, biodegradation, and reduction are
calculated by the HWIR chemical processor and passed through the batch chemical database to
Exams. Internal Exams algorithms for calculating rate constants are bypassed.
D.6.2 Assumptions and Limitations
Exams incorporates a few major assumptions. The model was designed to evaluate the
consequences of longer-term, primarily time-averaged chemical loadings that ultimately result in
trace-level contamination of aquatic systems. Exams generates a steady-state, average flow field
(long-term or monthly) for the ecosystem. The program cannot then fully evaluate the transient,
high concentrations that arise from chemical spills, although spills under average hydrological
conditions can be studied. An assumption of trace-level chemical concentrations was used to
design the process equations. The chemical is assumed not to radically change the environmental
variables that drive its transformations. Exams uses linear sorption isotherms, and second-order
D-50
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Appendix D
(rather than Michaelis-Menten-Monod) expressions for biotransformation kinetics, which is
known to be valid for low concentrations of pollutants. Sorption is treated as a thermodynamic
or constitutive property of each compartment in the system, that is, sorption-desorption kinetics
are assumed to be rapid compared to other processes. While this assumption may be violated by
extensively-sorbed chemicals, they tend to be captured by benthic sediments, where their release
to the water column is controlled by benthic exchange processes.
In addition to the assumptions incorporated by EXAMS, the 3MRA implementation of
Exams (H-Exams) employs several simplifications in order to meet requirements and constraints
associated with the 3MRA system . The project design calls for repeated long simulations (200 to
10,000 years) executed quickly (seconds to minutes). This requirement limits the temporal
resolution at which simulations can be conducted. Another important constraint is limited site-
specific data. This constraint limits the accuracy with which a particular site can be described.
The major model simplifications made in response to these project constraints include the use of
average-yearly hydrological and loading inputs, the use of national distributions to specify some
site-specific environmental conditions, and the use of a simple solids balance with no settling and
burial. For sites that experience periodic drying, a small positive flow equivalent to 5 mm/year
of direct precipitation onto the water body surface is maintained in order to keep the model
functioning.
These simplifications lead to a degree of model error in the calculated concentrations.
Using annual-average loadings and flows rather than daily loadings and flows will lead to
calculated annual-average concentrations that are biased somewhat high, depending on the
correlation between flow and loading at a particular site. This bias is somewhat mitigated for
reactive and volatile chemicals where the loss rate is proportional to the concentration. The use
of national distributions rather than site-specific environmental data could cause calculated
concentrations to be low or high at a given location, with no known general bias. The simple
solids balance will overestimate suspended solids concentrations slightly in streams and more
significantly in ponds, wetlands, and lakes. Calculated total water column chemical
concentrations will be high, while the dissolved chemical fraction will be low. The net result for
dissolved water column chemical concentrations, which are used for fish exposure, is not
expected to be biased significantly high or low.
The procedure for preventing drying of surface water reaches is more difficult to evaluate.
This procedure conducts chemical loads downstream within a remnant aquatic reach rather than
within runoff over a dry bed or subsurface flow within the bed. While the mass balance is
maintained, the chemical and solids concentrations will tend to be elevated within the remnant
reach. These elevated concentrations are probably realistic for years in which evaporation
exceeds all hydrologic inflows.
D.6.3 Inputs for the Surface Water Module
Three site simulation files are generated for each execution of the surface water module -
the site layout file sl.ssf, the chemical property file cpstream.ssf, and the surface water body
file(s) sw*.ssf (where * stands for the water body number at a site). Table D-13 lists the input
parameters contained in the various SSF and GRF files read by the surface water module. The
D-51
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Appendix D
site layout SSF contains 35 variables used by the surface water module. The surface water SSF
files contain 25 variables that are relevant only to the surface water module. The chemical SSF
file contains 44 variables reead by the surface water module.
In addition the surface water model reads several global results files (GRFs) containing
water, solids, contaminant loadings from other modules: 9 variables from the air module (ar.ssf),
7 variables from the source module (sr.grf), 7 variables from the watershed module (ws.grf), and
4 variables from the groundwater module (aq.grf).
The groundwater (aquifer) results file contains 9 variables, 4 of which are used by the
surface water module.
D.6.4 Outputs from the Surface Water Module
The surface water model produces water and sediment chemical concentrations
(dissolved and total) that are used by the aquatic foodweb, farm food chain, ecological exposure,
and ecological risk modules, along with the number of values and output years. These variables
are contained in the sw.grf and are listed in Table D-14.
D-52
-------
Appendix D
Table D-13. Surface Water Module Inputs
I'ilc
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1 'j PC-
Description
sw.ssf
ahydd
m
FLOAT
hydraulic coefficient depth multiplier
sw.ssf
bhydd
FLOAT
hydraulic coefficient depth exponent
sw.ssf
ahydW
m
FLOAT
hydraulic coefficient width multiplier
sw.ssf
bhydW
FLOAT
hydraulic coefficient width exponent
sw.ssf
DepthSedRes
cm
float
underlying sediment layer depth
sw.ssf
DepthBenthos
cm
float
surficial sediment layer depth
sw.ssf
d_pond
m
float
depth of pond
sw.ssf
dwtlnd
m
float
depth of wetland
sw.ssf
depil
m
FLOAT
depth of epilimnion
sw.ssf
dhypol
m
FLOAT
depth of hypolimnion
sw.ssf
E_sw
cm2/sec
float
sediment-water column diffusion coefficient
sw.ssf
Ethermocline
cm2/sec
FLOAT
thermocline diffusion coefficient
sw.ssf
rhoDSedRes
g/mL
FLOAT
underlying sediment layer dry bulk density
sw.ssf
rhoDBenthos
g/mL
FLOAT
surficial sediment layer dry bulk density
sw.ssf
porSedRes
Lw/L
FLOAT
underlying sediment layer porosity
sw.ssf
porBenthos
Lw/L
FLOAT
surficial sediment layer porosity
sw.ssf
kPlankCMin
yrM
FLOAT
Plankton carbon mineralization rate constant; not
used in this HWIR application.
sw.ssf
k_SedG2
yrM
FLOAT
Sediment mineralization rate constant, G2 fraction;
not used in this HWIR application.
sw.ssf
k_SedG3
yrM
FLOAT
Sediment mineralization rate constant, G3 fraction;
not used in this HWIR application.
sw.ssf
vbury
mm/yr
FLOAT
underlying sediment layer burial rate
sw.ssf
Trophiclndex
INTEGER
trophic index
sw.ssf
Supstream
mg/L
FLOAT
[upstream suspended solids concentration]
sw.ssf
Cupstream
mg/L
FLOAT
upstream chemical concentration
sw.ssf
Qupstream
m3/day
FLOAT
upstream flow
sl.ssf
WBNDOC
mg/L
Float
DOC of stream, lake, and wetland reaches in
waterbody network
sl.ssf
WBNfocAbS
fraction
Float
fraction organic carbon of abiotic solids in water
column
sl.ssf
WBNfocBioS
fraction
Float
fraction organic carbon of biotic solids in water
column
sl.ssf
WBNfocSed
fraction
Float
fraction organic carbon in sediments of stream,
lake, and wetland reaches
sl.ssf
WBNId
Integer
Environmental Setting Id for WBN
sl.ssf
WBNNumRch
Integer
Number of reaches for this network
sl.ssf
WBNpH
pH units
Float
pH of stream, lake, and wetland reaches in the
waterbody network
(continued)
D-53
-------
Appendix D
Table D-13. (continued)
I'ilc
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Description
sl.ssf
WBNRchAirF rac
fraction
Float
Fraction of this reach impacted by air point
sl.ssf
WBNRchAirlndex
Integer
Index of air point that impacts this reach
sl.ssf
WBNRchAquFrac
fraction
Float
Fraction of this reach impacted by the aquifer
sl.ssf
WBNRchAquIndex
Integer
Index of aquifer that impacts this reach
sl.ssf
WBNRchArea
m2
Float
reach surface area (nonstream reaches)
sl.ssf
WBNRchBodyType
String
Type of waterbody (Stream, Lake, Wetland)
sl.ssf
WBNRchHypoAreaFrac
fraction
Float
fraction of total surface area for hypolimnion
sl.ssf
WBNRchLength
m
Float
Reach Length
sl.ssf
WBNRchNumAir
Integer
Number of points that impact this reach
sl.ssf
WBNRchNumAqu
Integer
Number of aquifer that impact this reach
sl.ssf
WBNRchNumLoc
unitless
Integer
number of x,y points associated with watershed
sl.ssf
WBNRchNumRch
Integer
Number of reaches that impact this reach
sl.ssf
WBNRchNumW S Sub
Integer
Number of watersheds that impacts this reach
sl.ssf
WBNRchOrder
unitless
Integer
stream order
sl.ssf
WBNRchRchFrac
fraction
Float
Fraction of this reach impacted by another reach
sl.ssf
WBNRchRchlndex
Integer
Index of reach that impacts this reach
sl.ssf
WBNRchSrcLWSFrac
fraction
Float
fraction of waterbody network reach impacted by
the source local watershed
sl.ssf
WBNRchSrcLWSIndex
Integer
index of local watershed from source
sl.ssf
WBNRchType
String
Type of reach (Headwater, exiting, other)
sl.ssf
WBNRchWSSubFrac
fraction
Float
Fraction of this reach impacted by watershed
sl.ssf
WBNRchW S Sublndex
Integer
Index of watershed that impacts this reach
sl.ssf
WBNTemp
degrees
Celsius
Float
median temperature of stream, lake, and wetland
reaches in waterbody network
sl.ssf
WBNTOC
mg/L
Float
TOC of stream, lake, and wetland reaches in
waterbody network
sl.ssf
WBNTSS
mg/L
Float
TSS of stream, lake, and wetland reaches in
waterbody network
cp.ssf
ChemName
String
Chemical Name
cp.ssf
ChemActBioNumProd
Integer
Number of products
cp.ssf
ChemActBioProdCASID
String
Product CASID
cp.ssf
ChemActBioProdName
String
Product Name
cp.ssf
ChemActBioProdYield
moles/moles
Float
Product Yield Coefficient
cp.ssf
Chem ActB ioRate
1/day
Float
Activated Biodegradation
cp.ssf
ChemADiff
cmA2/s
Float
Air Diffusion Coefficient
cp.ssf
ChemAerBioNumProd
Integer
Number of products
cp.ssf
ChemAerBioProdCASID
String
Product CASID
cp.ssf
ChemAerBioProdName
String
Product Name
(continued)
D-54
-------
Appendix D
Table D-13. (continued)
I'ilc
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Tj po
Description
cp.ssf
ChemAerBioProdYield
moles/moles
Float
Product Yield Coefficient
cp.ssf
Chem AerB ioRate
1/day
Float
Aerobic Biodegradation rate
cp.ssf
ChemAnaBioNumProd
Integer
Number of products
cp.ssf
ChemAnaBioProdCASID
String
Product CASID
cp.ssf
ChemAnaBioProdName
String
Product Name
cp.ssf
Chem AnaB ioProdYield
moles/moles
Float
Product Yield Coefficient
cp.ssf
Chem AnaB ioRate
1/day
Float
Anaerobic Biodegradation
cp.ssf
ChemAnaRedNumProd
Integer
Number of products
cp.ssf
ChemAnaRedProdCASID
String
Product CASID
cp.ssf
ChemAnaRedProdName
String
Product Name
cp.ssf
ChemAnaRedProdYield
moles/moles
Float
Product Yield Coefficient
cp.ssf
ChemAnaRedRate
1/day
Float
Anaerobic Reduction
cp.ssf
ChemDen
g/mL
Float
Density
cp.ssf
Chemfoc
fraction
Float
Fraction Organic Content of Medium
cp.ssf
ChemHLC
(atm
mA3)/mol
Float
Henry's Law Constant
cp.ssf
ChemHydNumProd
Integer
Number of products
cp.ssf
ChemHydProdCASID
String
Product CASID
cp.ssf
ChemHydProdName
String
Product Name
cp.ssf
ChemHy dProdY ield
moles/moles
Float
Product Yield Coefficient
cp.ssf
ChemHydRate
1/day
Float
Catalyzed Hydrolysis
cp.ssf
ChemKd
L/kg
Float
Partition Coefficient for Med
cp.ssf
ChemKoc
mL/g
Float
Koc
cp.ssf
ChemKow
Float
Kow
cp.ssf
ChemMed
String
Solubility Media (Soil, Sediment,Surface Water,
Waste)
cp.ssf
ChemMolWt
g/mole
Float
Molecular weight for the chemical
cp.ssf
ChemName
String
Name
cp.ssf
ChemPh
pH units
Float
[pH assumed for these properties]
cp.ssf
ChemSol
mg/L
Float
Solubility for each media
cp.ssf
ChemTemp
degrees
Celsius
Float
Temperature assumed for these properties
cp.ssf
ChemType
string
Chemical Type (0, M, Hg, S, D)
cp.ssf
ChemVol
mL
Float
Volume
cp.ssf
ChemVp
torr
Float
Vapor Pressure
cp.ssf
ChemWDiff
cmA2/s
Float
Water Diffusion Coefficient
cp.ssf
NumChem
Integer
Number of chemicals described
sr.grf
Runoff
m3/d
float
runoff
(continued)
D-55
-------
Appendix D
Table D-13. (continued)
I'ilc
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Description
sr.grf
SWLoadChem
g/d
float
chemical load to waterbody
sr.grf
SWLoadChemYR
year
integer
year associated with output
sr.grf
SWLoadChemNY
integer
number of years in outputs
sr.grf
SrcOvl
logical
flag for overland flow presence
sr.grf
SrcH20
logical
flag for surface water presence
sr.grf
NyrMet
year
integer
number of years in the available met record
ar.grf
VapWDep
g/m2/d
FLOAT
vapor wet deposition flux
ar.grf
VapWDepYR
Year
Integer
year corresponding to vapor wet deposition flux
value
ar.grf
VapWDepNY
Integer
number of annual vapor wet deposition flux values
ar.grf
ParDDep
g/m2/d
FLOAT
particle dry deposition flux
ar.grf
ParDDepYR
Year
Integer
year corresponding to particle dry deposition flux
value
ar.grf
ParDDepNY
Integer
number of particle dry deposition flux values
ar.grf
ParWDep
g/m2/d
FLOAT
particle wet deposition flux
ar.grf
ParWDepYR
Year
Integer
year corresponding to particle dry deposition flux
value
ar.grf
ParWDepNY
Integer
number of particle dry deposition flux values
ws.grf
NyrMet
year
integer
number of years in the available met record
ws.grf
RunoffR
m3/d
float
runoff flow to waterbody
ws.grf
BFann
m3/d
float
long-term avg baseflow to waterbody
ws.grf
SWLoadChemR
g/d
float
chemical load (deposition only) to waterbody
ws.grf
SWLoadChemRYR
year
integer
year associated with output
ws.grf
SWLoadChemRNY
integer
number of years in outputs
ws.grf
SWLoadSolidR
g/d
float
total suspended solids (runoff)
aq.grf
AquRchMassFlux
g/yr
Float
Mass Flux from Aquifer to Reach
aq.grf
AquRchMassFluxNY
Integer
Number of Time - Mass-Flux-to-Reach Pairs
aq.grf
AquRchMassFluxYR
year
Integer
Time of Mass Flux from Aquifer to Reach
aq.grf
AquRchWaterFlux
m3/day
Float
Total GW Flux to Reach
D-56
-------
Appendix D
Table D-14. Surface Water Module Outputs (SW.GRF)
Cock'
I nils
Diilii
Tj |K-
Description
WBNConcBenthDiss
mg/L
Float
Dissolved chemical concentration in the surficial benthic layer
WBNConcBenthDissNY
Integer
Number of dissolved chemical concentration values in the surficial
benthic layer
WBNConcBenthDissYr
year
Integer
Year corresponding to dissolved chemical concentration in benthic
layer
WBNConcBenthTot
ug/g
Float
Total chemical concentration in the surficial benthic layer
WBNConcBenthTotNY
Integer
Number of total chemical concentration values in the surficial
benthic layer
WBNConcBenthTotYr
year
Integer
Year corresponding to total chemical concentration in benthic layer
WBNConcWaterDiss
mg/L
Float
Dissolved chemical concentration in the water column
WBNConcWaterDissNY
Integer
Number of dissolved chemical concentration values in the water
column
WBNConcWaterDissYr
year
Integer
Year corresponding to dissolved chemical concentration in the water
column
WBNConcWaterTot
mg/L
Float
Total chemical concentration in the water column
WBNConcWaterTotNY
Integer
Number of total chemical concentration values in the water column
WBNConcWaterTotYr
year
Integer
Year corresponding to dissolved chemical concentration in the water
column
WBNfocBenth
fraction
Float
Organic carbon content of benthic sediments
WBNfocBenthNY
Integer
Number of organic carbon content values
WBNfocBenthYr
year
Integer
Year corresponding to organic content values
WBNNumChem
Integer
Number of chemicals in output file
WBNTSS Water
mg/L
Float
Total suspended solids concentration in the water column
WBNTSSWaterNY
Integer
Number of suspended solids concentration values
WBNTSSWaterYr
year
Integer
Year corresponding to suspended solids values
D-57
-------
Appendix D
D.7 Farm Food Chain Module
The farm food chain (FFC) module calculates the concentration of a chemical in
homegrown produce (fruits and vegetables), farm crops for cattle (forage, grain, and silage), beef,
and milk.. The module is designed to predict the accumulation of a contaminant in the edible
parts of a plant from uptake of contaminants in soil and through transpiration and direct
deposition of the contaminant in air. In addition, the module estimates the contaminant
concentration from the biotransfer of contaminants in feed (i.e., forage, grain, and silage), soil,
and drinking water to beef and dairy cattle through ingestion.
The modeling construct for the FFC module is based on recent and ongoing research
conducted by the U.S. Environmental Protection Agency (EPA) Office of Research and
Development (ORD) and presented in Methodology for Assessing Health Risks Associated with
Multiple Exposure Pathways to Combustor Emissions (U.S. EPA, in press). Additional detail
about the background and implementation of the model is available in U.S. EPA (1999i).
D.7.1 Functionality
The major computational functions performed by the Farm Food Chain Module are the
following:
# Concentrations of contaminants in homegrown produce. The concentrations of
contaminants in fruits and vegetables are calculated for home gardens and for
farms.
# Concentrations of contaminants in cattle feed. The concentrations of
contaminants in pasture grass (i.e., forage), silage, and grain are calculated for
beef and dairy farms.
# Concentrations of contaminants in locally grown beef and milk. The
concentrations of contaminants in beef and milk produced on local farms are
calculated.
D.7.2 Assumptions and Limitations
The contaminant concentration calculations used in the Farm Food Chain Module reflect
a number of assumptions and/or limitations:
# Study area is bounded at 2 km. It is assumed that all significant contaminant
concentrations occur within 2 km of the source. Concentrations are not
determined outside the 2-km study area.
# Homogeneous concentrations in fruits and vegetables are assumed. For
unprotected fruits and vegetables, the exposure methodology makes no provision
for the possible chemical concentration gradients within the produce that might
result in different concentrations in edible portions.
D-58
-------
Appendix D
# Re suspension and redeposition on plants are not considered. Plant
concentrations are a function of the deposition of the contaminants that have been
emitted from the waste management unit (WMU). Plant concentrations do not
consider resuspension and redeposition. These processes can occur due to tillage,
wind erosion, vehicular resuspension, and rainsplash, but will not be examined by
this model.
# Inhalation and dermal exposure are not considered in cattle. Beef and dairy
cattle calculations consider only contaminant pathways of food, soil, and water
ingestion. Other pathways such as inhalation or dermal exposure are not
considered in this module.
D.7.3 Inputs for the Farm Food Chain Module
The farm food chain module receives inputs from its module-specific input file (ff.ssf),
the generic site layout file (Sl.ssf), the generic chemical properties file (cp.ssf), and modeled
inputs from the following other modules: aquifer module (aq.grf), air module (ar.grf), surface
water module (sw.grf), watershed module (ws.grf), and those source modules outputting (to a
common grf file, sr.grf) a "true" for the soil-presence logical flag, srcsoil. These sources are the
land application unit, landfill, wastepile, and surface impoundment. Input variables are listed
and described in Table D-15.
D.7.4 Outputs from the Farm Food Chain Module
The farm food chain module outputs are written to the ff.grf file. The soil, plant, beef and
milk outputs are 2-dimensional arrays indexed on time and space. Output variables are listed and
described in Tables D-16.
D-59
-------
Appendix D
Table D-15. Summary of Inputs for Farm Food Chain Module
File
Input Parameters
Units
Description
ff.ssf
Fforage
Fraction
Fraction of forage grown in contaminated soil. (Note:
"" is replaced with beef and milk.)
ff.ssf
Fgrain
Fraction
Fraction of grain grown in contaminated soil. (Note:
"" is replaced with beef and milk.)
ff.ssf
Fsilage
Fraction
Fraction of silage grown in contaminated soil. (Note:
"" is replaced with beef and milk.)
ff.ssf
Fw
Unitless
Fraction of wet deposition that adheres to the plant. (Note:
"" is replaced with exfruit, exveg, forage, and
silage.)
ff.ssf
MAF
Percent
Moisture adjustment factor to convert DW into WW.
(Note: "" is replaced with exfruit, exveg, leaf,
profruit, proveg, and root.)
ff.ssf
MAFleaf
Percent
Moisture content in leaf.
ff.ssf
QpJorage
kg DW/d
Consumption rate of forage by cattle. (Note: "" is replaced with beef and milk.)
ff.ssf
Qpjgrain
kg DW/d
Consumption rate of grain by cattle. (Note: ""
is replaced with beef and milk.)
ff.ssf
Qp silage
kg DW/d
Consumption rate of silage by cattle. (Note: "" is replaced with beef and milk.)
ff.ssf
Qs
kg/d
Consumption rate of contaminated soil. (Note: "" is replaced with beef and milk.)
ff.ssf
Qw
L/d
Consumption rate of water. (Note: "" is
replaced with beef and milk.)
ff.ssf
rho leaf
g/L
Density of the leaf.
ff.ssf
Rp
Unitless
Interception fraction. (Note: "" is replaced
with exfruit, exveg, forage, and silage.)
ff.ssf
tp
Year
Length of plant exposure to deposition. (Note: "" is replaced with exfruit, exveg, forage, and silage.)
ff.ssf
VapDdv
cm/s
Vapor-phase dry deposition velocity.
ff.ssf
VGag
Unitless
Empirical correction factor. (Note: "" is
replaced with exfruit, exveg, forage, and silage.)
ff.ssf
VGbg root
Unitless
Empirical correction factor for roots.
ff.ssf
Yp
kg DW/m2
Yield or standing crop biomass. (Note: "" is
replaced with exfruit, exveg, forage, and silage.)
sl.ssf
FarmAirFrac
Fraction
Fraction of farm or crop area impacted by air points.
(continued)
D-60
-------
Appendix D
Table D-15. (continued)
File
Input Parameters
Units
Description
sl.ssf
FarmAirlndex
NA
Index of points that impacts farm or crop area.
sl.ssf
FarmAquIndex
NA
Index of aquifer that impacts farm or crop area.
sl.ssf
FarmAqu WellFrac
Fraction
Fraction farm uses aquifer well as animal DW source.
sl.ssf
FarmAqu Welllndex
NA
Index of contributing subarea in local watershed indices
associated with each farm.
sl.ssf
FarmL WSIndex
NA
Local watershed indices associated with each farm.
sl.ssf
FarmL WSSubAreaFrac
Fraction
Fraction of contribution of subarea in local watershed
indices associated with each farm.
sl.ssf
FarmNumAir
Unitless
Number of air points that impact farm or crop area.
sl.ssf
FarmNumA qu Well
Unitless
Number of wells in each aquifer impacting farm.
sl.ssf
FarmNumL WS
Unitless
Number of local watersheds impacting farm or crop area.
sl.ssf
FarmNum WBNRch
Unitless
Number of WBN reach that impact farm or crop area.
sl.ssf
FarmNum WSSub
Unitless
Number of watersheds that impact farm or crop area.
sl.ssf
Farm WBNIndex
NA
Index of WBN that impacts farm or crop area.
sl.ssf
Farm WBNRchFrac
Fraction
Fraction of farm or crop area impacted by WBN reach.
sl.ssf
Farm WBNRchlndex
NA
Index of WBN reach that impacts farm or crop area.
sl.ssf
Farm WSSubFrac
Fraction
Fraction of each watershed on farm.
sl.ssf
Farm WSSublndex
NA
Index of watershed on farm.
sl.ssf
focS
Mass fraction
Fraction organic carbon (soil).
sl.ssf
HumRcpAirlndex
NA
Index of air points that impact receptor.
sl.ssf
HumRcpL WSArealndex
NA
Local watershed index for each human receptor.
sl.ssf
HumRcpL WSSubArealnd
ex
NA
Local watershed subarea index for each human receptor.
sl.ssf
HumRcp WSSublndex
NA
Index of watershed that impacts receptor.
sl.ssf
NumFarm
Unitless
Number of farm or crop areas.
cp.ssf
ChemBa <#>
d/g
Biotransfer factor. (Note: "" is replaced with
beef and milk.)
cp.ssf
ChemBa water
d/g
Biotransfer factor for dissolved contaminant in surface
water.
(continued)
D-61
-------
Appendix D
Table D-15. (continued)
File
Input Parameters
Units
Description
cp.ssf
ChemBr
(Mg/gDW
plant) / (,'g/g
soil)
Soil-to-plant bioconcentration factor. (Note: "" is replaced with exfruit, exveg, forage, grain,
profruit, proveg, root, and silage.)
cp.ssf
ChemBs
Fraction
Bioavailability fraction of contaminant in soil relative to
vegetation.
cp.ssf
ChemBv ecfjlant
Unitless
Empirical correction factor for Bv.
cp.ssf
ChemBv
(Mg/gDW
plant) / (,'g/g
air)
Mass-based air-to-plant biotransfer factor. (Note: "" is replaced with exfruit, exveg, forage, and silage.)
cp.ssf
ChemHLC
(atm-m3)/mol
Henry's law constant.
cp.ssf
ChemKoc
mL/g
Organic carbon partition coefficient
cp.ssf
ChemKow
Unitless
Octanol/water partition coefficient
cp.ssf
ChemkpPar
1/ yr
Plant surface loss of particle-bound constituent. (Note:
"" is replaced with exfruit, exveg, forage, and
silage.)
cp.ssf
ChemkpVap
1/ yr
Degradation loss of vapor-phase constituents. (Note:
"" is replaced with exfruit, exveg, forage, and
silage.)
cp.ssf
ChemRCF
(Mg/gWW
plant) / (. ' g/iriL
soil water)
Root concentration factor.
cp.ssf
ChemType
NA
Chemical type (0, M, Hg, S, or D)
aq.grf
AquWellConc
mg/L
Concentration of contaminant in the water of an aquifer
well.
aq.grf
Aqu WellConcNY
Year
Number of years in the time series corresponding to this
variable.
aq.grf
Aqu WellConcYR
Unitless
Time series of years corresponding to this variable.
ar.grf
CVap
Mg/m3
Concentration of chemical in air vapor.
ar.grf
CVapNY
Unitless
Number of years in the time series corresponding to this
variable.
ar.grf
CVapYR
Year
Time series of years corresponding to this variable.
ar.grf
ParDDep
g/m2/d
Particle dry deposition rate.
ar.grf
ParDDepNY
Unitless
Number of years in the time series corresponding to this
variable.
ar.grf
ParDDepYR
Year
Time series of years corresponding to this variable.
(continued)
D-62
-------
Appendix D
Table D-15. (continued)
File
Input Parameters
Units
Description
ar.grf
ParWDep
g/m2/d
Particle wet deposition rate.
ar.grf
ParWDepNY
Unitless
Number of years in the time series corresponding to this
variable.
ar.grf
ParWDepYR
Year
Time series of years corresponding to this variable.
ar.grf
VapWDep
g/m2/d
Vapor wet deposition rate.
ar.grf
VapWDepNY
Unitless
Number of years in the time series corresponding to this
variable.
ar.grf
VapWDepYR
Year
Time series of years corresponding to this variable.
sr.grf
CTda
Mg/g
Depth-averaged soil concentration across farm area.
sr.grf
CTdaNY
Year
Number of years in the time series corresponding to this
variable.
sr.grf
CTdaYR
Unitless
Time series of years corresponding to this variable.
sr.grf
CTss
Mg/g
Surficial soil concentration across farm area.
sr.grf
CTssNY
Year
Number of years in the time series corresponding to this
variable.
sr .grf
CTssYR
Unitless
Time series of years corresponding to this variable.
sw.grf
CTdaR
Mg/g
Depth-averaged soil concentration for the regional
watershed area.
sw.grf
CTdaRNY
Year
Number of years in the time series corresponding to this
variable.
sw.grf
CTdaRYR
Unitless
Time series of years corresponding to this variable.
sw.grf
CTssR
Mg/g
Surface soil concentration for the regional watershed area.
swrf
CTssRNY
Year
Number of years in the time series corresponding to this
variable.
sw.grf
CTssRYR
Unitless
Time series of years corresponding to this variable.
NA = Not applicable
D-63
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Appendix D
Table D-16. Summary of Outputs Parameters for the Farm Food Chain Module
File
Code
Units
Description
ff.grf
Abeefjarm
mg/kg WW
Concentration of contaminant in beef.
ff.grf
AbeefJarmNY
Year
Number of years in the time series corresponding to this
variable.
ff.grf
AbeefJarmYR
Unitless
Time series of years corresponding to this variable.
ff.grf
AmilkJarm
mg/kg WW
Concentration of contaminant in milk.
ff.grf
AmilkJarmNY
Year
Number of years in the time series corresponding to this
variable.
ff.grf
AmilkJarmYR
Unitless
Time series of years corresponding to this variable.
ff.grf
CTssAve ^farm
Mg/g
Chemical concentration in surficial soil averaged over farm
area.
ff.grf
CTssAve _farmNY
Year
Number of years in the time series corresponding to this
variable.
ff.grf
CTssAve _farmYR
Unitless
Time series of years corresponding to this variable.
ff.grf
P ^farmYR
Unitless
Time series of years corresponding to this variable.
ff.grf
P^farm
mg/kg WW
Concentration of contaminant for specific fruit and
vegetable categories grown on a farm. (Note: "" is replaced with exfruit, exveg, profruit, proveg, and
root.)
ff.grf
P ^farmNY
Year
Number of years in the time series corresponding to this
variable.
ff.grf
P_garden
mg/kg WW
Concentration of contaminant for specific fruit and
vegetable categories grown in a garden. (Note: "" is replaced with exfruit, exveg, profruit, proveg, and
root.)
ff.grf
P_garden YR
Unitless
Time series of years corresponding to this variable.
ff.grf
P_gardenNY
Year
Number of years in the time series corresponding to this
variable.
D-64
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Appendix D
D.8 Terrestrial Food Web Module
The terrestrial food web module (TerFW) calculates chemical concentrations in soil,
terrestrial plants, and various prey items consumed by ecological receptors, including
earthworms, other soil invertebrates, and vertebrates. These concentrations are used as input to
the ecological exposure (EcoEx) module to determine the applied dose to each receptor of
interest (e.g., deer, kestrel). The module is designed to calculate spatially-averaged soil
concentrations in the top layer of soil (i.e., surficial soil) as well as deeper soil horizons (i.e.,
depth-averaged over approximately 5 cm). The spatial averages are defined by the home ranges
and habitats that are delineated within the area of interest (AOI) at each site. Once the average
soil concentrations are calculated, these values are multiplied by empirical bioconcentration
factors (for animals) and biotransfer factors (for plants) to predict the tissue concentrations for
items in the terrestrial food web. Additional detail on the TerFW module can be found in U.S.
EPA (1999j).
D.8.1 Functionality
The major computational functions performed by the Terrestrial Food Web module are
the following:
# Time series management. The TerFW module determines the overall duration of
the time period to be simulated (including concentration data from discontinuous
time periods) and identifies the individual years within the overall duration that
will be simulated.
# Module loops over the time series, through habitats and home ranges. The
TerFW module has three basic loops: (1) over the time series, (2) over each
habitat delineated at the site, and (3) over the four home range areas delineated
within each habitat.
# Calculation of time series soil and plant concentrations and minimum and
maximum concentrations in terrestrial prey types (e.g., small mammals). This is
the fundamental structure of the TerFW module, namely, to develop soil and
tissue concentrations for each year of the simulation that reflect the range of
potential exposure concentrations. These concentrations are spatially explicit with
regard to the home range for each ecological receptor.
The major steps performed by the Terrestrial Food Web module that are required to
predict concentrations in soil (surficial and depth-averaged), plants, and other prey types can be
summarized as follows:
# Select terrestrial habitat of interest (i.e., cropland, residential area, grassland,
forest, shrub/scrub).
# Select home range within habitat (i.e., one of four home range areas).
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Appendix D
# Calculate average soil concentration within home range for surficial soil and
depth-averaged soil.
# Calculate concentration for all categories of terrestrial plants within home range.
# Calculate tissue concentration in soil fauna within home range (i.e., earthworms
and other soil invertebrates).
# Calculate tissue concentrations in receptors assigned to home range (e.g., small
mammals, omnivores).
# Loop through all home ranges within habitat of interest and repeat calculations of
soil and tissue concentrations.
# Report minimum and maximum values for tissue concentrations in prey types
other than terrestrial plants and soil fauna.
D.8.2 Assumptions and Limitations
The contaminant concentration calculations used in the Terrestrial Food Web module
reflect a number of assumptions and limitations, which are listed below.
D.8.2.1 Assumptions
# Study area is bounded at 2 km. EPA assumed that significant exposures to
source-related contaminants do not occur for ecological receptors that are beyond
2 km of the source. Consequently, tissue concentrations in food items located
outside of the study (measured from the edge of the source to a point 2 km away)
are presumed to be zero.
# Uptake and accumulation of chemicals within categories ofplants (e.g., exposed
vegetables) is assumed to be similar. The algorithms used to estimate biotransfer
factors do not distinguish physiological differences across various kinds of plants.
For example, the category "forage" includes forbs, grasses, fungi, shrubs, trees,
and unclassified plants. Therefore, in estimating biotransfer factors for this
category, it is implicitly assumed that the physiological differences in different
plant species do not significantly affect chemical loadings in plant tissues. The
use of empirical data on selected plant species (typically crops) also assumes
similar mechanisms of uptake and accumulation.
# No less than 10 percent of the diet is attributed to the study area. In many
instances, the home range for a given receptor exceeds the size of the habitat. In
general we assumed that the percent of the home range that "fits" into the habitat
is a suitable surrogate with which to scale exposure and predict tissue
concentration. However, the purpose of this analysis is to determine acceptable
waste concentrations assuming that the study area (e.g., forests) would be used as
habitat by wildlife. Therefore, we assumed that no less than 10 percent of the diet
D-66
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Appendix D
originated from the study area, even if the fraction of the home range inside the
habitat fell below 10 percent.
# A reasonable averaging depth for soil concentrations is 5 cm. In view of the
multiple purposes of this soil concentration (e.g., evaluate risks to soil fauna;
predict tissue concentrations in prey using soil-based bioaccumulation factors),
this was selected as a depth that was ecologically meaningful (with regard to
organisms occupying different soil horizons) and consistent with the goals for the
ecological risk analysis. However, this assumption carries with it some
uncertainty in its application within the exposure and risk modules.
D.8.2.2 Limitations
# Concentrations in terrestrial prey are based on soil-to-prey bioaccumulation
factors (BAFs). The most significant limitation in predicting tissue concentrations
in terrestrial prey is the paucity of mechanistic models and data sources with
which to estimate food web dynamics. For instance, the tissue concentration in
small birds is generally predicted using a BAF for soil rather than a biotransfer
factor (or BAF) from earthworms and insects into birds. As a result, the TerFW
can not rely on the matrix solution technique used by the Aquatic Food Web
module to solve for concentrations in various prey items.
# Some chemicals rely heavily on empirical uptake data. This limitation is similar
to that noted for the Farm Food Chain module. In essence, the paucity of data on
uptake and accumulation of constituents in terrestrial food items introduces
significant uncertainty into this module.
# Estimates of tissue concentrations reflect a single home range setting. The
TerFW module calculates tissue concentrations in prey items for a single random
placement of four home range sizes.3 As a result, the four home ranges in the site
layout may not reflect the spatial variability in soil contamination, particularly for
large habitats (i.e., habitats that cover substantially greater areas than most of the
home ranges).
# Re suspension and redeposition on plants are not considered. Plant
concentrations are a function of the deposition on plants of the contaminants that
have been emitted from the waste management unit. Plant concentrations do not
reflect resuspension and redeposition, which can occur due to tillage, wind
erosion, vehicular resuspension, and rainsplash
3 As described in U.S. EPA (1999n), each receptor is assigned to one of four discrete home range sizes,
depending on the receptor-specific home range size. The four home ranges overlap in a manner that reflects the
predator-prey relationships.
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Appendix D
D.8.3 Inputs
The concentration inputs required by the TerFW module are provided by the Air module,
the Regional Watershed (RW) module, and two source modules: the Wastepile and Land
Application Unit. The Air (Ar) module provides air concentrations and deposition rates relevant
to plant loadings. The RW module provides surficial and depth-averaged soil concentrations for
watersheds within the AOI, and the source modules provide soil concentrations within the
drainage sub-basin that includes the source. The average chemical concentration in soil
calculated for a given home range may include contributions from regional watersheds as well as
from a source-related drainage sub-basin (referred to as the local watershed). These inputs
include:
Air Module
# vapor concentration for each home range and habitat within the AOI
# wet vapor deposition rate for each home range and habitat within the AOI
# dry particle deposition rate for each home range and habitat within the AOI
# wet particle deposition rate for each home range and habitat within the AOI
Regional Watershed
# surficial soil concentration for each watershed within the AOI
# depth-averaged soil concentration for each watershed within the AOI
Source Modules
# surficial soil concentration for the each local watershed within the AOI
# depth-averaged soil concentration for each local watershed within the AOI
The terrestrial food web module receives inputs from its module-specific input file
(tf.ssf), the generic site layout file (sl.ssf), the generic chemical properties file (cp.ssf), and
modeled inputs from the following other modules: air module (ar.grf), watershed module
(ws.grf), and those source modules outputting to a common grf file (sr.grf) a "true" for the soil-
presence logical flag, SrcSoil. These sources are the land application unit, landfill, wastepile,
and surface impoundment. The soil, plant, invertebrate, and worm concentration outputs are
three-dimensional arrays indexed on time, space, and receptor. The small birds, small
herpetofauna, small mammals, herbiverts, and omniverts are two-dimensional arrays indexed on
time and space. All input variables are listed and described in Table D-17.
D.8.4 Outputs from the Terrestrial Food Web Module
The terrestrial food web module outputs are written to the tf.grf file. All output variables
are listed and described in Table D-18.
D-68
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Appendix D
Table D-17. Summary of Inputs for the Terrestrial Food Web Module
Hie
Input I'iinimeters
I nits
Description
tf.ssf
Bv ecfjlant
unitless
Empirical correction factor for Bv.
tf.ssf
Fw
unitless
Fraction of wet deposition that adheres to the plant.
(Note: "" is replaced with exfruit, exveg,
forage, and silage.)
tf.ssf
MAF
percent
Moisture adjustment factor to convert DW into WW.
(Note: "" is replaced with exfruit, exveg,
leaf, profruit, proveg, and root.)
tf.ssf
MAFleaf
percent
Moisture content in leaf.
tf.ssf
rho leaf
g/L
Density of the leaf.
tf.ssf
Rp
unitless
Interception fraction. (Note: "" is replaced
with exfruit, exveg, forage, and silage.)
tf.ssf
tp
year
Length of plant exposure to deposition. (Note: "" is replaced with exfruit, exveg, forage, and silage.)
tf.ssf
VapDdv
cen/sec
Vapor phase dry deposition velocity.
tf.ssf
VGag
unitless
Empirical correction factor. (Note: "" is
replaced with exfruit, exveg, forage, and silage.)
tf.ssf
VGbg root
unitless
Empirical correction factor for roots.
tf.ssf
Yp
kg DW/m2
Yield or standing crop biomass. (Note: "" is
replaced with exfruit, exveg, forage, and silage.)
sl.ssf
focS
mass fraction
Fraction organic carbon (soil).
sl.ssf
HabRangeAirlndex
NA
Index of air points that impacts a home range.
sl.ssf
HabRangeA irFrac
fraction
Fraction of home range impacted by air points.
sl.ssf
HabRangeNum WSSub
unitless
Number of watersheds that impact a home range.
sl.ssf
HabRangeL WSSubAFrac
fraction
Fraction of contributing local watershed subarea.
sl.ssf
HabRange WSSubFrac
fraction
Fraction of home range impacted by watershed.
cp.ssf
ChemBAF
unitless
Bioaccumulation factor for small birds, herbiverts, small
herpetofauna, invertebrates, small mammals, omniverts,
and worms.
cp.ssf
ChemBr
Cwg/g DW plant) /
Cwg/g soil)
Soil-to-plant bioconcentration factor. (Note: "" is replaced with exfruit, exveg, forage, grain,
profruit, proveg, root, and silage.)
cp.ssf
ChemBv ecfjlant
unitless
Empirical correction factor for Bv.
cp.ssf
ChemBv
Cwg/g DW plant) /
Cwg/g air)
Mass-based air-to-plant biotransfer factor. (Note: "" is replaced with exfruit, exveg, forage, and silage.)
(continued)
D-69
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Appendix D
Table D-17. (continued)
Hie
Input I'iinimeters
I nits
Description
cp.ssf
ChemHLC
(atm-m3) / mol
Henry's law constant.
cp.ssf
ChemKoc
mL/g
Organic carbon partition coefficient
cp.ssf
ChemKow
unitless
Octanol/water partition coefficient
cp.ssf
ChemkpPar
1/ year
Plant surface loss of particulate-bound constituent. (Note:
"" is replaced with exfruit, exveg, forage,
and silage.)
cp.ssf
ChemkpVap
1/ year
Degradation loss of vapor phase constituents. (Note:
"" is replaced with exfruit, exveg, forage,
and silage.)
cp.ssf
ChemRCF
Cwg/g WW plant) /
(. g/mL si water)
Root concentration factor.
cp.ssf
ChemType
NA
Chemical type (0, M, Hg, S, or D)
ar.grf
CVap
/ug/m3
Concentration of chemical in air vapor.
ar.grf
CVapNY
unitless
Number of years in the time series corresponding to this
variable.
ar.grf
CVapYR
year
Time series of years corresponding to this variable.
ar.grf
ParDDep
g/m2/d
Particle dry deposition rate.
ar.grf
ParDDepNY
unitless
Number of years in the time series corresponding to this
variable.
ar.grf
ParDDepYR
year
Time series of years corresponding to this variable.
ar.grf
ParWDep
g/m2/d
Particle wet deposition rate.
ar.grf
ParWDepNY
unitless
Number of years in the time series corresponding to this
variable.
ar.grf
ParWDepYR
year
Time series of years corresponding to this variable.
ar.grf
VapWDep
g/m2/d
Vapor wet deposition rate.
ar.grf
VapWDepNY
unitless
Number of years in the time series corresponding to this
variable.
ar.grf
VapWDepYR
year
Time series of years corresponding to this variable.
sr.grf
CTda
Mg/g
Depth-averaged soil concentration across farm area.
sr.grf
CTdaNY
year
Number of years in the time series corresponding to this
variable.
sr.grf
CTdaYR
unitless
Time series of years corresponding to this variable.
(continued)
D-70
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Appendix D
Table D-17. (continued)
Hie
Input I'iinimeters
I nits
Description
sr.grf
CTss
Mg/g
Surficial soil concentration across farm area.
sr.grf
CTssNY
year
Number of years in the time series corresponding to this
variable.
sr.grf
CTssYR
unitless
Time series of years corresponding to this variable.
ws.grf
CTdaR
Mg/g
Depth-averaged soil concentration for the regional
watershed area.
ws.grf
CTdaRNY
year
Number of years in the time series corresponding to this
variable.
ws.grf
CTdaRYR
unitless
Time series of years corresponding to this variable.
ws.grf
CTssR
Mg/g
Surface soil concentration for the regional watershed
area.
ws.grf
CTssRNY
year
Number of years in the time series corresponding to this
variable.
ws.grf
CTssRYR
unitless
Time series of years corresponding to this variable.
NA = not applicable
D-71
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Appendix D
Table D-18. Summary of Outputs from the Terrestrial Food Web Module
Hie
Output I'iimmeters
I nits
Description
tf.grf
C NY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
C
mg/kg
Concentration of contaminant found in herbiverts and
omniverts.
tf.grf
C YR
year
Time series of years corresponding to this variable.
tf.grf
C HabRange
mg/kg
Concentration of contaminant found in invertebrates and
worms.
tf.grf
C HabRangeNY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
C HabRangeYR
year
Time series of years corresponding to this variable.
tf.grf
C sm
mg/kg
Concentration of contaminant found in small birds,
herpetofauna, and mammals.
tf.grf
C sm YR
year
Time series of years corresponding to this variable.
tf.grf
C sm NY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
CTdaAveHabRange
Mg/g
Average depth average soil concentration in each home
range.
tf.grf
CtdaAveHabRangeNY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
CTdaAveHabRange YR
year
Time series of years corresponding to this variable.
tf.grf
CTssAveHabRange
Mg/g
Average depth average soil concentration in each home
range.
tf.grf
CTssAveHabRangeNY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
CTssAveHabRangeYR
year
Time series of years corresponding to this variable.
tf.grf
P HabRangeNY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
P HabRangeYR
year
Time series of years corresponding to this variable.
tf.grf
P HabRange
mg/kg
Concentration of contaminant found in exfruit, exveg,
forage, grain, root, and silage.
D-72
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Appendix D
D.9 Aquatic Food Web Module
The aquatic food web (AqFW) module calculates chemical concentrations in aquatic
organisms that are consumed by human and ecological receptors (e.g., fish filet; aquatic
macrophytes). These concentrations are used as input to the human and ecological exposure
modules to determine the applied dose to receptors of interest. The module is designed to predict
concentrations in aquatic organisms for cold water and warm water aquatic habitats. It uses both
property-based calculations and empirical data to estimate uptake and accumulation. Addition
detail on the model design and construction, including governing equations, may be found in
U.S. EPA (1999o).
D.9.1 Functionality
The major computational functions performed by the Aquatic Food Web module can be
summarized as follows:
# Time series management. The AqFW module determines the overall duration of
the time period to be simulated (including concentration data from discontinuous
time periods) and identifies the individual years within the overall duration that
will be simulated.
# Module loops over the time series, through aquatic habitats, and reaches. The
AqFW module has three basic loops: (1) over the time series, (2) over each
aquatic habitat delineated at the site, and (3) over the "fishable" reaches within
each aquatic habitat. The module considers all reach order 3 streams, ponds,
lakes, and certain types of permanently flooded wetlands as fishable by human
and ecological receptors.
# Calculation of time series tissue concentrations for fish and other aquatic
organisms. The AqFW module predicts concentrations for each year of the
simulation for aquatic organisms assigned to each habitat. These concentrations
are defined spatially for each reach even though a stream habitat or wetland may
contain multiple reaches.4 Similarly, the module predicts concentrations in ponds
and lakes as though the system is fully mixed and at steady state.
The major steps performed by the Aquatic Food Web module that are required to predict
concentrations in aquatic organisms may be summarized as follows:
# Select fishable reach of interest (i.e., stream or wetland reach, pond, or lake).
# Determine temperature and set aquatic habitat type (e.g., cold water stream).
# Construct dietary matrix for fish in aquatic habitat.
4 Reaches are defined in the site layout file and modeled by the Surface Water module as homogeneous
segments (i.e., there is no concentration gradient throughout the reach).
D-73
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Appendix D
# Calculate whole-body tissue concentrations (for ecological receptors)
Identify chemical type (e.g., hydrophobic organic, metal, mercury).
If chemical type is not readily metabolizable (i.e., special), check Kow
value.
If chemical is hydrophobic (log Kow > 4.0 is true), run matrix solution to
estimate whole-body tissue concentrations.
If chemical is hydrophilic (log Kow < 4.0 is true), run regression models to
estimate whole-body tissue concentrations.
If chemical is metal, readily metabolizable, or mercury, get empirical
bioaccumulation data and calculate whole-body tissue concentrations.
# Calculate filet concentrations (for human receptors).
D.9.2 Assumptions and Limitations
The methodology used in the aquatic food web module reflects a number of assumptions
and/or limitations, which are listed below. It should be noted that, because the AqFW module
relies on the surface water module (U.S. EPA, 1999p) to provide concentrations in surface water
and sediment, the assumptions and limitations identified for the SW module are relevant to the
AqFW module. For example, the SW module provides annualized average concentrations for
stream reaches and other waterbodies. Consequently, the methods developed to estimate tissue
concentrations in aquatic organisms were developed to be use the annual average surface water
concentrations predicted with the SW model. The assumptions and limitations implicit in the
SW module are not discussed in this section.
D.9.2.1 Assumptions
# Study area is bounded at 2 km. EPA assumed that significant exposures to
source-related contaminants do not occur for ecological receptors that are beyond
2 km of the source. Consequently, concentrations were not calculated in aquatic
organisms in waterbodies outside of the study area, measured from the corner of
the source to a point 2 km away.
# All waterbodies that define aquatic habitats are fishable. The module assumes
that all third order stream reaches (and above), ponds, lakes, and certain
permanently flooded wetlands support a multi-compartment aquatic food web.
The simple food webs developed for each of these aquatic habitats provide a
useful framework for predicting tissue concentrations in aquatic organisms for a
national assessment. Nevertheless, it is a certainty that not all of the waterbodies
designated as fishable in this analysis will be of sufficient quality to sustain a
multi-compartment food web.
D-74
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Appendix D
# Variability in aquatic systems is reasonably represented. The underlying
framework developed for the AqFW module (as applied in a national analysis) is
the eight representative aquatic habitats. It is implicitly assumed that these eight
habitats provide adequate resolution of the major types of freshwater systems
within the constraints of available data and modeling tools.
# Hydrophobic organics may be defined as organic chemicals with log Km > 4.0.
Although a strict definition for hydrophobic organics has not appeared in the
literature, the AqFW module assumes that a reasonable cutoff is a log Kow value
of 4.0. Comparisons of predicted bioaccumulation factors (BAFs) derived with
mechanistic models versus BAFs derived using regression equations suggests
that, below log Kow = 4.0, the difference in BAF estimates is below the level of
resolution that these models are capable of.
# The model construct is applicable to waterbodies other than coldwater lakes. A
number of journal articles (e.g., Morrison et al., 1997) and reference texts (e.g.,
Rand, 1995) were reviewed in evaluating appropriate mechanistic models to
simulate the uptake and accumulation of hydrophobic organics in aquatic
organisms. From that review, it was determined that the underlying theory for
these models is remarkably similar and that there is no inherent advantage in
selecting one model over another. Although the Gobas (1993) model was
calibrated for coldwater lakes (i.e., Lake Ontario), it was determined that this
model construct was appropriate for use on other aquatic systems under the
general assumption of steady-state conditions.
D.9.2.2 Limitations
# Steady-state conditions are generally assumed. Because annual average
concentrations are provided by the SW module, the AqFW module assumes
steady-state conditions. As a result, the module can not be used to evaluate the
impacts from storm events nor can it be used to distinguish the impacts on tissue
concentrations from peak events and subsequent averaging from long-term, low-
level exposures. For example, a storm event may contaminate a given reach for
relatively short periods of time, probably well below the duration required for
organisms to reach steady-state for most chemicals.
# The module relies heavily on empirical data for many chemicals. For chemicals
that have not been shown to be readily metabolizable (e.g., other than PAHs,
selected phthalates), mechanistic models are not used to predict tissue
concentrations. Hence, the AqFW module estimates tissue concentrations by
multiplying empirical factors (primarily bioconcentration factors, or BCFs) by
water concentrations. As discussed in the data collection documentation on the
AqFW parameters, these BCFs are measured under conditions that may not be
relevant to all possible conditions (and species) included in the analysis.
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Appendix D
# The module does not allow for separate treatment of essential metals.
Bioconcentration of essential metals is not linear and modeling approaches are
available to account for nonlinearity (see Bergman and Dorward-King, 1997).
Bioconcentration of essential metals tends to be much greater at low
concentrations than at higher concentrations since organisms actively seek to
sequester necessary nutrients. Because many metals are regulated in biological
systems, the apparent bioconcentration of metals at low concentrations may
simply result in metal accumulation at "healthy" levels.
# The module currently lacks the capability to use sediment concentrations directly
in predicting tissue concentrations. The AqFW module was developed, primarily,
to utilize dissolved and total contaminant concentrations to predict tissue
concentrations. Although sediment concentrations are used in predicting uptake
and accumulation into benthic dwellers, the AqFW module lacks the necessary
algorithms to use these data directly to predict concentrations in plants or fish.
For certain constituents (e.g., dioxins), it may be useful to build this functionality
into the module to provide greater flexibility in data use.
# The module has not been validated in field studies. Much of the modeling theory
on which the AqFW module is based is widely accepted and has been used in
numerous analyses. In particular, the methods used to predict concentrations of
hydrophobic organics have been validated in coldwater lakes. However, the
module has not been validated for other freshwater aquatic habitats, nor has it
been validated in toto for application in a national-scale analysis.
D.9.3 Inputs
The only concentration inputs required by the AqFW module are provided by the surface
water module (SW). These inputs include:
# average, reach-specific total concentration in sediment
# average, reach-specific total concentration in surface water
# average, reach-specific dissolved concentration in surface water
The aquatic food web module receives inputs from its module-specific input file, af.ssf,
the generic site layout file (sl.ssf), the chemical properties file (cp.ssf), and modeled inputs from
the surface water module (sw.grf). All AqFW module outputs are 3-dimensional arrays indexed
on time, waterbody network, and reach. Input variables are listed and described in Table D-19.
D.9.4 Outputs from the Aquatic Food Web Module
The aquatic food web module outputs are written to the af.grf file. Output variables are
listed and described in Table D-20.
D-76
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Appendix D
Table D-19. Summary of Inputs for the Aquatic Food Web Module
Tile
Input I'iimmeters
I nits
Description
af.ssf
aJish
unitless
Slope of BCF regression equation across all tissues in fish.
af.ssf
a mus
unitless
Slope of BCF regression equation for muscle tissue in fish.
af.ssf
b Jish
unitless
Slope (2) of BCF regression equation across all tissues in
fish.
af.ssf
b mus
unitless
Slope (2) of BCF regression equation for muscle tissue in
fish.
af.ssf
BiotaTypelndex
unitless
Numerical index of each biota type.
af.ssf
BwFish
unitless
Fish body weight.
af.ssf
cJish
unitless
Error term in BCF regression equation across all tissues in
fish.
af.ssf
c mus
unitless
Error term in BCF regression equation for muscle tissues in
fish.
af.ssf
FiletFrac
unitless
Fraction of fish that is a filet based on lipid content.
af.ssf
Fish WaterFrac
unitless
Water fraction across all tissues of fish.
af.ssf
LipFrac
unitless
Lipid fraction.
af.ssf
LipFracMus
unitless
Lipid fraction in fish muscle.
af.ssf
MaxPreyPref
unitless
Maximum dietary preference for item in the aquatic food
web.
af.ssf
MinPreyPref
unitless
Minimum dietary preference for item in the aquatic food
web.
af.ssf
Mus WaterFrac
unitless
Water fraction in muscle of fish.
af.ssf
NumBiotaTypes
unitless
Number of biota types in the aquatic food web.
af.ssf
rho lip
kg/L
Density of organic carbon.
af.ssf
rho oc
kg/L
Density of lipids.
af.ssf
T3EdibleFish
unitless
Edible trophic level 3 fish for human consumption.
af.ssf
T3NumEdibleFish
unitless
Number of edible trophic level 3 fish in the aquatic food
web.
af.ssf
T3NumFish
unitless
Number of trophic level 3 fish in the aquatic food web.
sl.ssf
NumWBN
unitless
Number of waterbody networks.
sl.ssf
WBNFishableRchlndex
unitless
Index of reaches that are fishable.
sl.ssf
WBNNumFishableRch
unitless
Number of fishable reaches.
(continued)
D-77
-------
Appendix D
Table D-19. (continued)
Tile
Input I'iimmeters
I nits
Description
sl.ssf
WBNRchArea
?
111
Reach surface area.
sl.ssf
WBNRchOrder
unitless
Reach order of stream.
sl.ssf
WBNRchBodyType
unitless
Type of waterbody (e.g., pond, stream).
sl.ssf
WBNTemp
degrees
Celsius
Median temperature of waterbody network.
sl.ssf
WBNTempMax
degrees
Celsius
Maximum temperature of reaches in the waterbody network.
cp.ssf
ChemT3musBAFm
L/kg ww
Empirical bioaccumulation factor in filet of TL3 fish.
cp.ssf
ChemT3fishBAFm
L/kg ww
Empirical bioaccumulation factor in whole-body of TL3
fish.
cp.ssf
ChemT4musBAFm
L/kg ww
Empirical bioaccumulation factor in filet of TL4 fish.
cp.ssf
ChemT4fishBAFm
L/kg ww
Empirical bioaccumulation factor in whole-body of TL4
fish.
cp.ssf
ChemaqmpBCFm
L/kg ww
Empirical bioconcentration factor for aquatic macrophytes.
cp.ssf
ChembenthffBAFm
L/kg ww
Empirical bioaccumulation factor in benthic filter feeders.
cp.ssf
ChemKm
1/day
Metabolic rate constant for fish.
cp.ssf
ChemKow
unitless
Octanol/water partition coefficient.
cp.ssf
ChemType
NA
Chemical Type
sw.grf
WBNConcBenthTot
Mg/g
Concentration of contaminant in benthic solids.
sw.grf
WBNConcBenth TotNY
unitless
Number of years in the time series corresponding to this
variable.
sw.grf
WBNConcBenth TotYR
year
Time series of years corresponding to this variable.
sw.grf
WBNConc WaterTot
mg/L
Total contaminant concentration in surface water.
sw.grf
WBNConc WaterTotNY
unitless
Number of years in the time series corresponding to this
variable.
sw.grf
WBNConc WaterTotYR
year
Time series of years corresponding to this variable.
sw.grf
WBNConc Water Diss
mg/L
Freely dissolved contaminant concentration in surface water.
sw.grf
WBNConc WaterDissNY
unitless
Number of years in the time series corresponding to this
variable.
sw.grf
WBNConc WaterDissYR
year
Time series of years corresponding to this variable.
sw.grf
WBNfocBenth
fraction
Benthivore fraction of organic carbon.
(continued)
D-78
-------
Appendix D
Table D-19. (continued)
Tile
Input I'itiitmctci s
I nils
Description
sw.grf
WBNfocBenthNY
unitless
Number of years in the time series corresponding to this
variable.
sw.grf
WBNfocBenth YR
year
Time series of years corresponding to this variable.
NA = not applicable
D-79
-------
Appendix D
Table D-20. Summary of Outputs for the Aquatic Food Web Module
Hie
C "ode
I nils
Description
af.grf
Caqmp
mg/kg ww
Concentration of contaminant in aquatic plants.
af.grf
CaqmpNY
unitless
Number of years in the time series corresponding to this variable.
af.grf
CaqmpYR
year
Time series of years corresponding to this variable.
af.grf
Cbenthff
mg/kg ww
Concentration of contaminant in benthic filter feeders.
af.grf
CbenthfJNY
unitless
Number of years in the time series corresponding to this variable.
af.grf
CbenthffYR
year
Time series of years corresponding to this variable.
af.grf
CT3Filet
mg/kg ww
Concentration in filet of contaminant in TL3 fish.
af.grf
CT3FiletNY
unitless
Number of years in the time series corresponding to this variable.
af.grf
CT3FiletYR
year
Time series of years corresponding to this variable.
af.grf
CT3Fish
mg/kg ww
Whole-body concentration of contaminant in TL3 fish.
af.grf
CT3FishNY
unitless
Number of years in the time series corresponding to this variable.
af.grf
CT3FishYR
year
Time series of years corresponding to this variable.
af.grf
CT4Filet
mg/kg ww
Concentration in filet of contaminant in TL4 fish.
af.grf
CT4FiletNY
unitless
Number of years in the time series corresponding to this variable.
af.grf
CT4FiletYR
year
Time series of years corresponding to this variable.
af.grf
CT4Fish
mg/kg ww
Whole-body concentration of contaminant in TL4 fish.
af.grf
CT4FishNY
unitless
Number of years in the time series corresponding to this variable.
af.grf
CT4FishYR
year
Time series of years corresponding to this variable.
D-80
-------
Appendix D
D.10 Human Exposure Module
The human exposure module calculates the applied dose (mg of constituent per kg of
body weight), to human receptors from media and food concentrations calculated by other
modules in the multimedia, multipathway and multiple receptor risk assessment (3MRA)
methodology. These calculations are performed for each receptor, cohort, exposure pathway, and
year at each exposure area.5 The human exposure module calculates exposures for two basic
receptor types: residential receptors (residents and home gardeners) and farmers. Residential
receptors may also be recreational fishers in addition to being a resident or home gardener.
Farmers may be beef farmers or dairy farmers, and either type of farmer may also be a
recreational fisher. The subcategories within residential receptors and farmers differ in the
particular exposures they incur. For example, a resident (only) differs from a home gardener in
that home gardeners are exposed to contaminated fruits and vegetables, but residents are not.
Within each of the two basic receptor types, the human exposure module calculates exposures for
5 age cohorts: infants (ages 0-1 year), children ages 1-5 years, children ages 6-11 years, children
ages 12-19 years, and adults (ages 20 years and up). Additional detail about the background and
implementation of the model is available in U.S. EPA (1999q).
D.10.1 Functionality
The major computational functions performed by the human exposure module are the
following:
# Time series management. The human exposure module determines the overall
duration of the time period to be simulated, which could possibly be
discontinuous, and the individual years within this duration to be simulated.
# Calculation of time series exposure concentrations and doses from time series
media andfood concentrations. This is the fundamental purpose of the human
exposure module and is performed by a set of equations specific to each exposure
pathway and carcinogenic/noncarcinogenic chemical property.
Exposure to humans other than infants may occur through eight pathways: inhalation of
ambient air, inhalation of shower air, ingestion of groundwater, ingestion of soil, ingestion of
fruits and vegetables, ingestion of beef, ingestion of milk, and ingestion of fish. However, not all
receptors are exposed via all of these pathways. Residents are exposed via inhalation of ambient
air, inhalation of shower air, ingestion of groundwater, and ingestion of soil. Home gardeners
have the same exposures as a resident, plus exposure via ingestion of fruits and vegetables. All
farmers are exposed via inhalation of ambient air, inhalation of shower air, ingestion of
groundwater, ingestion of soil, and ingestion of fruits and vegetables. In addition, beef farmers
are exposed via ingestion of beef, and dairy farmers are exposed via ingestion of milk.
Recreational fishers have the same exposures as one of the other receptor types plus fish
ingestion. Not all age cohorts are exposed via all pathways; shower exposures are calculated
only for adults and children ages 12 to 19 years.
5 See Section 3.3.10 for the list of receptors and cohorts and applicable exposure pathways.
D-81
-------
Appendix D
The media inputs for which the human exposure module calculates exposure
concentrations and doses include ambient air concentration (both vapor and particulate), soil
concentration, groundwater concentration, exposed vegetable concentration, protected vegetable
concentration, exposed fruit concentration, protected fruit concentration, root vegetable
concentration, beef concentration, milk concentration, and fish filet concentration for trophic
level 3 and trophic level 4 fish. For vegetables and fruits, the terms "exposed" and "protected"
refer to whether the edible portion of the plant is exposed to the atmosphere.
Infant exposure occurs via breast milk ingestion. The human exposure module tracks
noninfant exposure by the eight pathways described above. For infant exposure via breast milk,
the maternal exposure via all pathways must be summed. Therefore, infant exposures are
calculated for eight maternal exposure configurations: resident, home gardener, beef farmer,
dairy farmer, resident/recreational fisher, home gardener/recreational fisher, beef
farmer/recreational fisher, and dairy farmer/recreational fisher. The mother is assumed to be an
adult (as opposed to a teenager) for the purpose of calculating maternal dose in the infant breast
milk pathway.
D.10.2 Assumptions and Limitations
The exposure characterization methodology used in the human exposure module reflects
a number of assumptions and/or limitations, which are listed below.
# Study area is bounded at 2 km. EPA assumed that all significant exposure by
human receptors occurs within 2 km of the source. Exposures are not evaluated
for individuals residing outside of the 2-km study area, measured from the source
periphery.
# Human receptors are stationary. EPA assumed in characterizing exposure that
human receptors both reside and work at the receptor location identified for them
during site characterization (i.e., the farm area for farmers or residential exposure
area for nonfarmers). The point of exposure is, in general, the census block
centroid for a resident and home gardener and the centroid of a farm for farmers.
This assumption may overestimate or underestimate exposure, because it is
possible that individuals may reside at the identified location within the study area
but commute to work areas outside of the study area or could commute to more or
less contaminated areas within the study area.
# Incremental exposure is modeled. The HWIR model generates incremental
exposures in accordance with standard practice. No provision is made for
considering background exposures for the purpose of generating cummulative or
total risk, HQ, or MOE estimates for modeled receptors.
# Homogeneous concentrations in fruits and vegetables are assumed. The exposure
methodology makes no provision for possible chemical concentration gradients
within fruits or vegetables that might result in different concentrations in edible
portions than when averaged throughout the food item.
D-82
-------
Appendix D
# Food preparation has no effect. No diminution of chemical concentration in food
items is assumed to occur through food preparation, e.g., washing of fruits and
vegetables.
# Annual average concentrations/doses for exposure. No shorter term average or
spikes evaluated. All outputs from this module are on an annual basis.
# Ingestion rates are age cohort specific. The ingestion and inhalation rates, as well
as body weight, are based on the age cohort. For example, a 1 to 5-year-old child
would ingest less water on a daily basis then a 6- to 10-year-old child.
D.10.3 Inputs for the Human Exposure Module
The human exposure module receives inputs from its module-specific input file, he.ssf,
the generic site layout file (sl.ssf), the generic chemical properties file (cp.ssf), and the following
modeled outputs from other 3MRA modules: ground water concentrations from the aquifer
module (aq.grf); fish filet concentration for trophic level 3 and trophic level 4 fish from the
aquatic food web module (af.grf); exposed vegetable concentration, protected vegetable
concentration, exposed fruit concentration, protected fruit concentration, root vegetable
concentration, beef concentration, and milk concentration from the farm food chain module
(ff.grf)6; soil concentrations from the watershed module (ws.grf); ambient air concentration (both
vapor and particulate) from the air module (ar.grf); and soil concentrations from those source
modules outputting (to a common grf file, sr.grf) a "true" for the soil-presence logical flag,
SrcSoil. (SrcSoil = true signifies that contaminated soil is present, which is an exposure
pathway.) These sources are the land application unit, landfill, wastepile, and surface
impoundment. All input variables for the human exposure module are listed and described in
Table D-21.
D.10.4 Outputs from the Human Exposure Module
Human exposure module outputs are written to the he.grf file. All exposure outputs
except infant breastmilk exposures are three-dimensional arrays indexed on time, space, and age
cohort. The spatial component may be x-y coordinate representing receptor locations as the
centroid of a census block or farm represented by a set of x,y coordinates. Infant breast milk
exposures are two-dimensional arrays on time and space. They apply to only one age cohort,
infants, so there is no third dimension. Output variables are listed and described in Table D-22.
6 For vegetables and fruits, the terms "exposed" and "protected" refer to whether the edible portion of the
plant is exposed to the atmosphere.
D-83
-------
Appendix D
Table D-21. Summary of Input Parameters for the Human Exposure Module
I'ilo
Inpul Piii'iimclor
I nils
Description
he.ssf
Bri
m3/d
Cohort-specific inhalation rate for the four child resident
cohorts (Note: "" is replaced with the actual cohort
designation in the variables used by the human exposure
module)
he.ssf
Bri r
m3/d
Inhalation rate for the adult resident.
he.ssf
BW
kg
Cohort-specific body weight for each of the four child
cohorts (Note: this parameter is not differentiated for
farmer versus non-farmer receptor)
he.ssf
BW_r
kg
Body weight for adult receptors
he.ssf
CRb_cf_<#>
gWW/kg/d
Beef consumption rate for the three farmer child cohorts
2-4 (Note: "<#>" is replaced with the actual cohort
number in the variables used by the human exposure
module)
he.ssf
CRb af
gWW/kg/d
Beef consumption rate for the adult farmer
he.ssf
CRbm cf 1
mL/d
Breast milk ingestion rate for the farmer infant
he.ssf
CRfr_cf_<#>
gWW/kg/d
Exposed fruit consumption rate for the three farmer child
cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRfrJ
gWW/kg/d
Exposed fruit consumption rate for the adult farmer
he.ssf
CRfr eg <#>
gWW/kg/d
Exposed fruit consumption rate for the three gardener
child cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRfrjg
gWW/kg/d
Exposed fruit consumption rate for the adult gardener
he.ssf
CRfs c <#>
gWW/kg/d
Home-caught fish consumption rate for the three child
cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRfs a
gWW/kg/d
Home-caught fish consumption rate for the adult
he.ssf
CRl_cf_-#-
gWW/kg/d
Exposed vegetables consumption rate for the three farmer
child cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRIJ
gWW/kg/d
Exposed vegetables consumption rate for the adult farmer
(continued)
D-84
-------
Appendix D
Table D-21. (continued)
I'ilc
Inpul Piii'iimclcr
I nils
Description
he.ssf
CRl_cg_<#>
gWW/kg/d
Exposed vegetables consumption rate for the three
gardener child cohorts 2-4 (Note: "<#>" is replaced with
the actual cohort number in the variables used by the
human exposure module)
he.ssf
CRl_g
gWW/kg/d
Exposed vegetables consumption rate for the adult
gardener
he.ssf
CRpfr cf <#>
gWW/kg/d
Protected fruit consumption rate for the three farmer child
cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRpfrJ
gWW/kg/d
Protected fruit consumption rate for the adult farmer
he.ssf
CRpfr eg <#>
gWW/kg/d
Protected fruit consumption rate for the three gardener
child cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRpfr_g
gWW/kg/d
Protected fruit consumption rate for the adult gardener
he.ssf
CRpl_cf_<#>
gWW/kg/d
Protected vegetables consumption rate for the three farmer
child cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRplJ
gWW/kg/d
Protected vegetables consumption rate for the adult farmer
he.ssf
CRpl eg <#>
gWW/kg/d
Protected vegetables consumption rate for the three
gardener child cohorts 2-4 (Note: "<#>" is replaced with
the actual cohort number in the variables used by the
human exposure module)
he.ssf
CRpl_g
gWW/kg/d
Root vegetables consumption rate for the adult gardener
he.ssf
CRr_cf_<#>
gWW/kg/d
Root vegetables consumption rate for the three farmer
child cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRrJ
gWW/kg/d
Root vegetables consumption rate for the adult farmer
he.ssf
CRr eg <#>
gWW/kg/d
Root vegetables consumption rate for the three gardener
child cohorts 2-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRr_g
gWW/kg/d
Root vegetables consumption rate for the adult gardener
(continued)
D-85
-------
Appendix D
Table D-21. (continued)
I'ilc
Inpul Piii'iimclcr
I nils
Description
he.ssf
CRw cr <#>
gWW/kg/d
Drinking water consumption rate for the four child
cohorts 1-4 (Note: "<#>" is replaced with the actual
cohort number in the variables used by the human
exposure module)
he.ssf
CRw r
gWW/kg/d
Drinking water consumption rate for the adult receptor
he.ssf
CRs cr <#>
gWW/kg/d
Incidental soil ingestion rate for the three child cohorts 2-
4 (Note: "<#>" is replaced with the actual cohort number
in the variables used by the human exposure module)
he.ssf
CRsr
gWW/kg/d
Incidental soil ingestion rate for the adult receptor
he.ssf
CRm cf <#>
gWW/kg/d
Milk consumption rate for the three child farmer cohorts
2-4 (Note: "<#>" is replaced with the actual cohort
number in the variables used by the human exposure
module)
he.ssf
CRm af
gWW/kg/d
Milk consumption rate for the adult farmer receptor
he.ssf
DD
cm
Water droplet diameter
he.ssf
EFr
d/yr
Exposure frequency (adult resident)
he.ssf
F
Unitless
Fraction of consumed (e.g., exposed fruit,
exposed vegetables, beef, drinking water) that is
contaminated (for the "f' farmer or "g" gardener). (Note:
in the actual variable names, and are replaced with acronyms referring to
appropriate terms - e.g., "m" for milk and "f' for farmer,
respectively)
he.ssf
fiP
Unitless
Fraction of whole blood that is plasma
he.ssf
ffin
Unitless
Fraction of mother's weight that is fat
he.ssf
ftnbm
Unitless
Fraction of fat in maternal breastmilk
he.ssf
fpm
Unitless
Fraction of mother's weight that is plasma
he.ssf
Fs
Unitless
Fraction of contaminated soil
he.ssf
FT<#>fish
Unitless
Fraction of fish consumed that is T# (i.e., T3 or T4)
he.ssf
Fin
cm
Shower nozzle height
he.ssf
Rshower
L/min
Shower rate
he.ssf
t sb
min
Time in shower and bathroom
he.ssf
t shower
min
Shower time
he.ssf
Vbath
m3
Bathroom volume
(continued)
D-86
-------
Appendix D
Table D-21. (continued)
I'ilc
Inpul Piii'iimclcr
I nils
Description
he.ssf
Vn
cm/s
Terminal velocity of shower droplet
he.ssf
VRbh
L/min
Bathroom to house ventilation rate
he.ssf
Vrsb
L/min
Shower to bathroom ventilation rate
he.ssf
Vshower
m3
Shower volume
sl.ssf
FarmAquIndex
NA
Index of aquifer that impacts farm or crop area
sl.ssf
FarmL WSIndex
NA
Local watershed indices associated with each farm
sl.ssf
FarmL WSSubAreaFrac
Fraction
Fraction of contribution of subarea to farm
sl.ssf
FarmL WSSubArealndex
NA
Index of contributing subarea in local watershed indices
associated with each farm
sl.ssf
FarmNumL WSSubArea
NA
Contributing subarea in local watershed indices associated
with each farm
sl.ssf
FarmNum WSSub
Unitless
Number of watersheds that impact farm or crop area
sl.ssf
Farm WBNIndex
NA
Index of WBN that impacts farm or crop area
sl.ssf
Farm WBNRchlndex
NA
Index of WBN reach that impacts farm or crop area
sl.ssf
Farm WSSubFrac
Unitless
Fraction of each watershed on farm
sl.ssf
Farm WSSublndex
NA
Index of watersheds that impact farm or crop area
sl.ssf
focS
Mass
fraction
Fraction organic carbon (soil)
sl.ssf
HumRcpAirlndex
NA
Index of air points that impact receptor
sl.ssf
HumRcpA qulndex
Unitless
Index of aquifer that impacts receptor
sl.ssf
HumRcpA qu Welllndex
Unitless
Index of well that impacts receptor for the given aquifer
sl.ssf
HumRcpL WSIndex
NA
Local watershed index for each human receptor
sl.ssf
HumRcpL WSSubArealnde
NA
Local watershed subarea index for each human receptor
sl.ssf
HumRcpPh
pH units
Average shower water pH
sl.ssf
HumRcpTemp
° Celsius
Typical shower temperature
sl.ssf
HumRcp WSSublndex
NA
Index of watershed that impacts receptor
sl.ssf
NumFarm
Unitless
Number of farm or crop areas
sl.ssf
NumHumRcp
Unitless
Number of human receptor points at a site
sl.ssf
NumWBN
Unitless
Number of waterbody networks
sl.ssf
NyrMax
Years
Maximum model simulation time
(continued)
D-87
-------
Appendix D
Table D-21. (continued)
I'ilc
Input Piii'iimclcr
I nils
Description
sl.ssf
TermFrac
Fraction
Peak output fraction for simulation termination
sl.ssf
WBNFishableRchlndex
Unitless
Index of reaches that are fishable
sl.ssf
WBNNumFishableRch
Unitless
Number of fishable reaches
cp.ssf
ChemBreast MilkExp
Unitless
Causes breast milk exposure? (l=yes, 0=no)
cp.ssf
ChemCSFfood
(mg/kg-d)1
Cancer slope factor (food ingestion)
cp.ssf
ChemCSFinhal
(mg/kg-d)1
Cancer slope factor (inhalation)
cp.ssf
ChemCSFwater
(mg/kg-d)1
Cancer slope factor (drinking water ingestion)
cp.ssf
ChemRfC
mg/m3
Reference concentration (inhalation)
cp.ssf
ChemRfDfish
mg/kg-d
Reference dose (fish ingestion)
cp.ssf
ChemRfDfood
mg/kg-d
Reference dose (food ingestion)
cp.ssf
ChemRfDwater
mg/kg-d
Reference dose (drinking water ingestion)
cp.ssf
Chemfai
Fraction
Fraction of ingested contaminant by the infant which is
absorbed
cp.ssf
ChemFam
Fraction
Fraction of contaminant ingested by mother that is
absorbed
cp.ssf
ChemFbl
Fraction
Fraction of contaminant in whole blood compartment
cp.ssf
ChemFf
Fraction
Fraction of contaminant stored in maternal fat
cp.ssf
Chemkpm
Unitless
Concentration proportionality constant between plasma
and breast milk aqueous phase
cp.ssf
ChemKrbc
Unitless
Concentration proportionality constant between red blood
cells and plasma
cp.ssf
Chemt halfb
d
Biological half-life of chemical in lactating women
cp.ssf
ChemADiff
cm2/s
Air diffusion coefficient
cp.ssf
ChemHLC
(atm m3)/
mol
Henry's law constant
cp.ssf
ChemWDiff
cm2/s
Water diffusion coefficient
af.ssf
CT3filet
mg/kg
WW
Chemical concentration in trophic level 3 fish filet
af.ssf
CT4filet
mg/kg
WW
Chemical concentration in trophic level 4 fish filet
ff.ssf
CTssAve ^farm
l-ig/g
Chemical concentration in surficial soil averaged over
farm area
(continued)
D-88
-------
Appendix D
Table D-21. (continued)
I'ilc
Inpul Piii'iimclcr
I nils
Description
ff.ssf
CtssAve ^farmYR
Unitless
Time series of years corresponding to this variable
ff.ssf
CTssAve _farmNY
Unitless
Number of years in the time series corresponding to this
variable
ff.ssf
Abeefjarm
mg/kg
WW
Modeled beef concentration
ff.ssf
AbeefJarmYR
Unitless
Time series of years corresponding to this variable
ff.ssf
AbeefJarmNY
Unitless
Number of years in the time series corresponding to this
variable
ff.ssf
AmilkJarm
mg/kg
WW
Modeled milk concentration
ff.ssf
Ami IkJ'armYR
Unitless
Time series of years corresponding to this variable
ff.ssf
AmilkJarmNY
Unitless
Number of years in the time series corresponding to this
variable
ff.ssf
P ^farm
mg/kg
WW
Modeled concentration for specific fruit and vegetable
categories (e.g., would be
replaced by: "exveg", "proveg" or "root" for exposed
vegetables, protected vegetables or root vegetables,
respectively) raised on farms
ff.ssf
P ^farmYR
Unitless
Time series of years corresponding to this variable
ff.ssf
P ^farmNY
Unitless
Number of years in the time series corresponding to this
variable
ff.ssf
P jgarden
mg/kg
WW
Modeled concentration for specific fruit and vegetable
categories (e.g., would be
replaced by: "exveg", "proveg" or "root" for exposed
vegetables, protected vegetables or root vegetables,
respectively) raised in home gardens
ff.ssf
P_garden YR
Unitless
Time series of years corresponding to this variable
ff.ssf
P jgardenNY
Unitless
Number of years in the time series corresponding to this
variable
sr.ssf
SrcSoil
NA
Flag for soil presence
sr.ssf
CTss
l-ig/g
Soil concentration (annual average, all subareas)
sr.ssf
CTssYR
Unitless
Time series of years corresponding to this variable
sr.ssf
CTssNY
Unitless
Number of years in the time series corresponding to this
variable
(continued)
D-89
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Appendix D
Table D-21. (continued)
I'ilc
liipul Piii'iimclcr
I nils
Description
ar.ssf
PM10
Hg/m3
Concentration of particles = 10 microns
ar.ssf
PM10YR
Unitless
Time series of years corresponding to this variable
ar.ssf
PM10NY
Unitless
Number of years in the time series corresponding to this
variable
ar.ssf
CVap
Hg/m3
Concentration of chemical in air vapor
ar.ssf
CVapYR
Unitless
Time series of years corresponding to this variable
ar.ssf
CVapNY
Unitless
Number of years in the time series corresponding to this
variable
ws.ssf
CTssR
l-ig/g
Surface soil concentrations for modeled watersheds
ws.ssf
CTssYR
Unitless
Time series of years corresponding to this variable
ws.ssf
CTssNY
Unitless
Number of years in the time series corresponding to this
variable
NA = not applicable
D-90
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Appendix D
Table D-22. Summary of Output Parameters for the Human Exposure Module
l-ile
111 pnl I'itlitlllClCI-
I nils
Description
he.grf
IngBM H
mg/kg-d
Chemical- and pathway-specific average daily dose for the
nonfarmer infant resulting from breast milk ingestion. (Note:
"" denotes the actual pathway name in the variables
generated by the human exposure module.)
he.grf
IngBMHYR
Unitless
Time series of years corresponding to this variable.
he.grf
IngBMHNY
Unitless
Number of years in the time series corresponding to this
variable
he.grf
IngBMF
mg/kg-d
Chemical- and pathway-specific average daily dose for the
farmer infant resulting from breast milk ingestion (Note:
"" denotes the actual pathway name in the variables
generated by the human exposure module.)
he.grf
IngBMFYR
Unitless
Time series of years corresponding to this variable
he.grf
IngBMFNY
Unitless
Number of years in the time series corresponding to this
variable
he.grf
Cambient Farm
mg/m3
Farm area-specific modeled ambient air concentration used in
evaluating inhalation risk; separate estimates are generated for
each modeled year
he.grf
Cambient FarmYR
Unitless
Time series of years corresponding to this variable
he.grf
Cambient FarmNY
Unitless
Number of years in the time series corresponding to this
variable
he.grf
Cambient HumRcp
mg/m3
Residential location-specific modeled ambient air
concentration used in evaluating inhalation risk; separate
estimates are generated for each modeled year
he.grf
Cambient HumRcpYR
Unitless
Time series of years corresponding to this variable
he.grf
Cambient HumRcpNY
Unitless
Number of years in the time series corresponding to this
variable
he.grf
Csb Farm
mg/m3
Farm area-specific modeled shower/bath air concentration
used in evaluating inhalation risk; separate estimates are
generated for each modeled year
he.grf
Csb FarmYR
Unitless
Time series of years corresponding to this variable
he.grf
Csb FarmNY
Unitless
Number of years in the time series corresponding to this
variable
he.grf
Csb HumRcp
mg/m3
Residential location-specific modeled shower/bath air
concentration used in evaluating inhalation risk; separate
estimates are generated for each modeled year
he.grf
Csb HumRcpYR
Unitless
Time series of years corresponding to this variable
(continued)
D-91
-------
Appendix D
Table D-22. (continued)
lilo
111 pnl I'itlitlllClCI-
I nils
Description
he.grf
Csb HumRcpNY
Unitless
Number of years in the time series corresponding to this
variable
he.grf
Ing Farm
mg/kg-d
Chemical- and pathway- specific average daily dose for the
farmer resulting from ingestion of the dietary item identified as
the "pathway" by the variable (Note: "" denotes the
actual pathway name in the variables generated by the human
exposure module.)
he.grf
Ing FarmYR
Unitless
Time series of years corresponding to this variable
he.grf
Ing FarmNY
Unitless
Number of years in the time series corresponding to this
variable
he.grf
Ing HumRcp
mg/kg-d
Chemical- and pathway- specific average daily dose for the
non-farmer resulting from ingestion of the dietary item
identified as the "pathway" by the variable (Note:
"" denotes the actual pathway name in the variables
generated by the human exposure module.)
he.grf
Ing HumRcp
YR
Unitless
Time series of years corresponding to this variable
he.grf
Ing HumRcp
NY
Unitless
Number of years in the time series corresponding to this
variable
D-92
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Appendix D
D. 11 Human Risk Module
The human risk module considers two basic human receptor types are considered:
residential receptors (residents and home gardeners) and farmers. Residential receptors may also
be recreational fishers in addition to being a resident or home gardener. Farmers may be beef
farmers or dairy farmers, and either type of farmer may also be a recreational fisher. This results
in eight categories of human receptors: resident, resident gardener, resident fisher, resident
gardener fisher, beef farmer fisher, dairy farmer fisher, beef farmer, and dairy farmer.
The eight receptor categories were developed in consideration of exposure pathways; for
example, a residential resident is a receptor that is exposed only to the baseline exposure
pathways, i.e., inhalation via air and shower and ingestion via soil and water. A resident gardener
is a resident exposed through these exposure pathways plus ingestion of homegrown produce.
The human exposure module's output has eight-receptor resolution; the human risk
module calculates risks and/or HQs for each of these eight receptor categories but then
aggregates these eight categories into four7 composite receptor categories: resident, resident
gardener, fisher, and farmer, for purposes of developing the cumulative population8 frequency
histograms and critical years. For example, the composite "fisher" receptor population consists
of subpopulations from the resident fisher, resident gardener fisher, beef farmer fisher, and dairy
farmer fisher receptor categories. Similarly, beef farmers and dairy farmers are aggregated into a
single, composite "farmer." For every receptor type (four or eight), five age cohort classes are
considered9: Child 1 (0 to 1 year old), Child 2 (1 to 5 years old), Child 3 (6 to 11 years old),
Child 4 (12 to 19 years old), and Adult (greater than 19 years old). For the Child 1 (infant)
cohort, only the breast milk pathway applies.10 The margin of exposure [MOE] (mg/kg-d) for the
infant breast milk pathway is analogous to HQ for infant breast milk exposure.
D.ll.l Functionality
The human risk module processes modeled outputs from the human exposure module
(human receptor exposure estimates) and performs three major functions using these data:
7 The 3MRA Exit Level Processor I (ELP I) preserves this receptor resolution, but also aggregates these
four receptors into a fifth, "all receptors" category.
8 Site-specific receptor populations were identified as part of the HWIR99 data collection activities and are
specified by receptor category, exposure area (farm or census block), and distance ring. See US EPA (1999c) for
discussion of the methodology.
9 For purposes of storage efficiency, the ELP I combines the Child 2 and 3 cohort classes as output by the
human risk module into a single composite cohort class (ages 1 to 11). Child 4 is also combined with the adult
cohort class by the ELP I.
10 For HWIR99, the infant breast milk pathway is evaluated only for a single chemical, the dioxin species
2,3,7,8 -TCDD TEQ [CAS No. 1746-01-6], (That is, the logical flag ChemBreastMilkExp will be set to "true" only
for this chemical in the chemical properties input file, cp.ssf.)
D-93
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Appendix D
1. It calculates risk and/or hazard quotient for each receptor, cohort, exposure
pathway, exposure area, and year. Whether risk, HQ, or both are calculated is
determined as a function of the chemical under consideration. For carcinogenic
chemicals, risks are calculated as average risks over a 9-year exposure duration.
For noncarcinogens, HQs are calculated as a 1-year average.
2. It constructs cumulative frequency histograms that quantify the distributions of
receptor/cohort-specific populations among different levels of risk and/or HQ for
each exposure pathway and aggregation ofpathways11 and year. The populations
consist of individual receptor/cohorts residing at the various exposure areas
(residential areas and farms).
3. It determines and outputs that critical year during which the maximum cumulative
risk and/or HQ occurs across the population for each receptor/cohort
combination andfor each exposure pathway and pathway aggregation.
These functions are performed three times, once for each of three radial distances outward
from the centroid of the waste management unit. The purpose of distance-specific results is to
assess the sensitivity of risk results to distance from the chemical source.
These functions are performed by the human risk module by a series of nested loops.
Figures 3-17a through 3-17c illustrate the general looping structure used for a given radial
distance. These illustrations are intended only to facilitate overall understanding of the module;
the implementing computer code is significantly different to optimize performance.
D.11.2 Assumptions and Limitations
Calculations performed by the human risk module reflect a number of assumptions and/or
limitations including:
# Risks are calculatedfor a 2-km radius study area. EPA assumed that all
significant exposure and risk/HQ to human receptors occurs within 2 km of the
source boundary based on the types of waste management unit sources currently in
the 3MRA model.
# Human receptors are stationary. EPA assumed that human receptors both reside
and work at the receptor location identified for them during site characterization.
(Farmer receptors are on farms, and residential receptors are assumed to be at the
centroid of census blocks within radial distance rings.) This assumption may
overestimate or underestimate exposure, because it is possible that individuals
11 Aggregation of risks occurring simultaneously over multiple pathways is the essence of multipathway
risk assessment. For example, if a receptor is exposed to chemicals from both ingestion of contaminated
groundwater and ingestion of contaminated fish, then it is appropriate to estimate the cumulative risk that is incurred
across these pathways. In some instances, it is appropriate to aggregate risk across portals of entry (i.e., ingestion
and inhalation).
D-94
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Appendix D
may reside at the identified location within the study area but either commute to
work outside of the study area or commute to more or less highly contaminated
areas within the study area.
# Incremental risk is considered. No provision is made for considering background
exposures and their risks for the purpose of generating total risk estimates.
# Risk and HQ estimates are aggregated for certain receptors. As mentioned, the
four receptors considered—resident, residential gardener, farmer, and fisher—are
fewer in number than the number of receptor categories output by the Human
exposure module. Risks are aggregated for certain receptors to maintain output
storage requirements at reasonable levels. Such aggregation results in some loss
in risk resolution. For example, the risks specific to farmers who drink
contaminated milk but do not ingest contaminated beef will not be available.
# Lifetime and exposure duration for carcinogens. For carcinogenic risk
calculations, receptors are assumed to live 76.5 years. Of this lifetime, the
exposure duration is assumed to be 9 years.
# Carcinogenic risks are proportionately disaggregatedfrom lifetime exposure to
the assumed exposure duration. Incurred risks are assumed to be lifetime risks
that are reduced in direct proportion to the fraction of a lifetime actually exposed,
i.e., 350 of 365 days per year (15 days away per year) for each year of exposure
duration.
# Synergistic or antagonistic effects among multiple chemicals and individual
chemical speciation on risk estimates are not considered. The human risk module
is executed by the 3MRA system with a system-level chemical loop so that only
one chemical is considered at any single execution. Chemicals are considered to
be independent.
# Cancer slope factors do not vary with cohort age. Age-specific differences in
exposure responses are not available and consequently are not considered.
# Maximum HQ estimates are conservatively based on a single year of exposure.
Unlike carcinogenic risk estimates, which use a moving average over multiyear
exposure periods (as discussed in the next section), HQ estimates treat each year
independently, i.e., their time series reflects 1-year average values. Thus, a single
high year of maximum exposure would not be "diluted" by a multiyear averaging
period. This is a protective approach.
# If any residents in a census block group ingest ground water, all residents in the
component census blocks are assumed to ingest ground water. The census data
report the number of households within a census block group that are served by
ground water wells. However, this information is available only at the census
block group level, and it is not possible to determine from census data alone
D-95
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Appendix D
whether individual census blocks within a block group with wells have wells or
not. The human risk module loops over census blocks when considering
residential exposure areas. Consequently, the actual fraction of residents on wells
for any individual residential exposure area is uncertain. To resolve this lack of
information, the protective assumption is made that, if any residents in the block
group containing the residential exposure area (or census block) under
consideration are on wells, then all residents in the exposure area are on wells.
That is not to say, of course, that all well water is contaminated. Only those wells
lying within the ground water plume from the source are potentially contaminated.
D.11.3 Inputs for the Human Risk Module
The human risk module receives inputs from its module-specific input file, hr.ssf, the
generic site layout file (sl.ssf), the generic chemical properties file (cp.ssf), and modeled inputs
from the human exposure module's output file, he.grf. Input variables are listed and described in
Table D-23.
D.11.4 Outputs from the Human Risk Module
Human risk module outputs are written to hr.grf, and include all risk estimates necessary
to determine risk by distance from the source, exposure pathway, exposure route, receptor type,
and age cohort as well as total and maximum risk estimates. Output variables are listed and
described in Table D-24.
D-96
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Appendix D
Table D-23. Summary of Inputs for the Human Risk Module
l-ile
Input I'iiriiiiK'ler
I nils
Description
hr.ssf
LifeTime
years
Human lifetime used in carcinogenic risk calculation
hr.ssf
ExDur Car Farm
years
Exposure duration used for modeling carcinogenic risk for all
farming receptor populations
hr.ssf
ExDur Car Block
years
Exposure duration used for modeling carcinogenic risk for all
residential receptor populations
hr.ssf
ExDur NCar Farm
years
Exposure duration used for modeling noncarcinogenic
HQ/MOE for all farming receptors.
hr.ssf
ExDur NCar Block
years
Exposure duration used for modeling noncarcinogenic
HQ/MOE for all residential receptors.
hr.ssf
RegPercentile
percent
Regulatory criterion used in calculating total risk incurred by a
given percentage of the population. RegPercentile is the
percentage.
hr.ssf
DoExposed
unitless
Logical flag indicating whether the output CDFs comprise
(1) only actually exposed receptors (true), or (2) all receptors
(false).
sl.ssf
BinRange Min C
unitless
Carcinogenic risk values used to define the minimum value for
each of the carcinogenic risk bins.
sl.ssf
BinRange Min NC
unitless
Noncarcinogenic HQ values used to define the minimum value
for each of the noncarcinogenic HQ bins.
sl.ssf
NumBinC
unitless
Number of bins used in reporting cancer risk (i.e., number of
bins used to define the cumulative risk distribution)
sl.ssf
NumBinNC
unitless
Number of bins used in reporting noncancer HQs (i.e., number
of bins used to define the cumulative HQ distribution)
sl.ssf
NumHumRcp
unitless
Number of residential locations (i.e., number of human
receptor points excluding farms)
sl.ssf
NumFarm
unitless
Number of beef and/or dairy farms
sl.ssf
NumRing
unitless
Number of concentric rings used to subdivide the site for
purposes of estimating risk or HQ/MOE distributions
conditional on distance from the source.
sl.ssf
FarmPopulation
unitless
Number of farmers associated with each of the farms/crop
areas identified for a given site. These population estimates
are farmer-, type-, and cohort-specific.
sl.ssf
HumRcpPopulation
unitless
Number of residents (nonfarmers) associated with each of the
residential locations identified for a given site. These
population estimates are receptor population- and cohort-
specific.
(continued)
D-97
-------
Appendix D
Table D-23. (continued)
I-ik-
Input I'iiriiiiK'ler
I nils
Description
sl. ssf
RingFarmFrac
fraction
The fraction of a given farm that is located within a given ring.
This variable is used to allocate farm population into different
rings for purposes of ring-specific risk or HQ/MOE estimates
when the farm occurs in multiple rings.
cp.ssf
ChemC Add
unitless
Identifies whether carcinogenic risk for a given chemical can
be added across routes.
cp.ssf
ChemNC Add
unitless
Identifies whether noncarcinogenic HQ/MOEs for a given
chemical can be added across routes.
cp.ssf
ChemBreastMi IkExp
unitless
Identifies whether a given chemical should be assessed for
breast milk exposure (i.e., does it bioconcentrate in breast
milk)
cp.ssf
ChemCSFinhal
(mg/kg-d)1
Inhalation cancer slope factor
cp.ssf
ChemCSFfood
(mg/kg-d)1
Cancer slope factor used to evaluate dietary exposure and
incidental soil ingestion (excluding drinking water)
cp.ssf
ChemCSFwater
(mg/kg-d)1
Cancer slope factor used to evaluate ingestion of drinking
water
cp.ssf
ChemRFC
mg/m3
Inhalation reference concentration
cp.ssf
ChemRFDfood
mg/kg-d
Reference dose used to evaluate dietary exposure and
incidental soil ingestion (excluding drinking water and fish)
cp.ssf
ChemRFDwater
mg/kg-d
Reference dose used to evaluate ingestion of drinking water
cp.ssf
ChemRFDfish
mg/kg-d
Reference dose used to evaluate dietary exposure to fish
cp.ssf
ChemBM
mg/kg-d
Background-drived breast milk exposure value used in
generating margin of exposure estimate for breast milk
consumption in infants
he.grf
IngBM H
mg/kg-d
Chemical- and pathway- specific average daily dose for the
nonfarmer infant resulting from breast milk ingestion. (Note:
"" denotes the actual pathway name in the variables
generated by the human exposure module.)
he.grf
IngBMHYR
unitless
Time series of years corresponding to this variable.
he.grf
IngBMHN
Y
unitless
Number of years in the time series corresponding to this
variable.
he.grf
IngBMF
mg/kg-d
Chemical- and pathway- specific average daily dose for the
farmer infant resulting from breast milk ingestion. (Note:
"" denotes the actual pathway name in the variables
generated by the human exposure module.)
he.grf
IngBMFYR
unitless
Time series of years corresponding to this variable.
(continued)
D-98
-------
Appendix D
Table D-23. (continued)
lilo
Input I'iiriiiiK'ler
I nils
Description
he.grf
IngBMFNY
unitless
Number of years in the time series corresponding to this
variable.
he.grf
Cambient Farm
mg/m3
Farm-specific modeled ambient air concentration used in
evaluating inhalation risk. Separate estimates are generated
for each modeled year.
he.grf
Cambient FarmYR
unitless
Time series of years corresponding to this variable.
he.grf
Cambient FarmNY
unitless
Number of years in the time series corresponding to this
variable.
NA = not applicable
D-99
-------
Appendix D
Table D-24. Summary of Outputs from the Human Risk Module
lile
Cock'
I nils
Description
hr.grf
Risk 1 Index;
Risk 2 Index;
Risk 3 Index
unitless
Number of exposure pathway-specific (Riskllndex), exposure
route-specific (Risk_2_Index), or total (Risk_3_Index) carcinogenic
risk estimates generated for each receptor population/age cohort
combination. Separate exposure pathway-, exposure route-, and
total risk estimates are generated for each ring modeled. Risk
results for each of these three categories are only reported for Tcrit
(i.e., for the maximum risk year) and not for all modeled years.
hr.grf
HQ 1 Index;
HQ 2 Index;
HQ 3 Index
unitless
Number of exposure pathway-specific (HQ l Index), exposure
route-specific (HQ_2_Index), or total (HQ_3_Index) non-
carcinogenic HQ estimates generated for each receptor
population/age cohort/chemical combination. Separate exposure
pathway-, exposure route-, and total HQ estimates are generated for
each ring modeled. HQ results for each of these three categories are
only reported for Tcrit (i.e., for the maximum risk year) and not for
all modeled years.
hr.grf
Risk 1;
Risk 2;
Risk 3
unitless
CDFs of population in risk bins for exposure pathway-specific
(Risk l), exposure route-specific (Risk_2), and total (Risk_3)
carcinogenic risk. Separate CDFs are generated for each
receptor/cohort/ring combination. These estimates are only reported
for Tcrit.
hr.grf
HQ 1;
HQ 2;
HQ_3
unitless
CDFs of population in HQ bins for exposure pathway-specific
(HQ_1), exposure route-specific (HQ_2) and total (HQ_3) non-
carcinogenic HQ estimate. Separate CDFs are generated for each
receptor population/cohort/ring combination. These estimates are
only reported for Tcrit.
hr.grf
Risk 1 Tcrlndex;
Risk 2 Tcrlndex;
Risk 3 Tcrlndex
years
Tcrit (critical year or maximum risk year) for carcinogenic risk.
Separate Tcrits are identified for exposure pathways
(Risk l Tcrlndex), exposure routes (Risk_2_TcrIndex), and for
total risk (Risk_3_TcrIndex).
hr.grf
Risk 1 Ringlndex;
Risk 2 Ringlndex;
Risk 3 Ringlndex
unitless
The specific ring associated with each of the Tcrit values identified
through the Risk_#_TcrIndex variables (i.e., RisklRinglndex,
Risk_2_RingIndex, and Risk_3_RingIndex).
hr.grf
Risk 1 Pathlndex;
Risk 2 Pathlndex;
Risk 3 Pathlndex
unitless
The specific exposure pathway associated with each of the Tcrit
values identified through the Risk_#_TcrIndex variables (i.e.,
Risk l Ringlndex, Risk_2_RingIndex, and Risk_3_RingIndex).
hr.grf
HQ 1 Tcrlndex;
HQ 2 Tcrlndex;
HQ 3 Tcrlndex
years
Tcrit (critical year or maximum HQ year) for noncarcinogenic HQ.
Separate Tcrit values are identified for exposure pathways
(HQ l Tcrlndex), exposure routes (HQ_2_TcrIndex), and for total
HQ (HQ_3_Tcrlndex).
hr.grf
HQ 1 Ringlndex;
HQ 2 Ringlndex;
HQ 3 Ringlndex
unitless
The specific ring associated with each of the Tcrit values identified
through the HQ_#_TcrIndex variables (i.e., HQIRinglndex,
HQ_2_RingIndex, and HQ_3_RingIndex).
hr.grf
HQ 1 Pathlndex;
HQ 2 Pathlndex;
HQ 3 Pathlndex
unitless
The specific exposure pathway associated with each of the Tcrit
values identified through the HQ_#_TcrIndex variables (i.e.,
HQ I Ringlndex, HQ_2_RingIndex, and HQ_3_RingIndex).
D-100
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Appendix D
D.12 Ecological Exposure Module
The ecological exposure (EcoEx) module calculates the applied dose (in mg/kg-d) to
ecological receptors that are exposed to contaminants via ingestion of contaminated plants, prey,
and media (i.e., soil, sediment, and surface water). These dose estimates are then used as inputs
to the ecological risk module. The EcoEx module calculates exposures for each receptor home
range placed within a terrestrial or freshwater aquatic habitat (as defined in the site layout).
Thus, exposure is a function of: (1) the home range (or portion, thereof) to which the receptor is
assigned; (2) the spatial boundaries of the home range, (3) the food items (plants and prey) that
are available in a particular home range, (4) the dietary preferences for food items that are
available, and the media concentrations in the receptor's home range. In essence, the module
estimates an applied dose for birds, mammals, and selected herpetofauna that reflects the spatial
and temporal characteristics of the exposure (i.e., exposure is tracked through time and space).
Supporting detail about the background and implementation of the model is available in U.S.
EPA (19991).
D.12.1 Functionality
The major computational functions performed by the ecological exposure module can be
summarized as follows:
# Time series management. The EcoEx module determines the overall duration of
the time period to be simulated (including concentration data from discontinuous
time periods) and identifies the individual years within the overall duration that
will be simulated.
# Module loops over the time series, through habitats and receptors. The EcoEx
module has three basic loops: (1) over the time series, (2) over each habitat
delineated at the site, and (3) over the mammalian, avian, and selected
herpetofauna receptors assigned to each habitat.
# Calculation of time series exposures from time series media andfood
concentrations. This is the fundamental structure of the EcoEx module, namely,
to develop exposure concentrations for each year of the simulation that include all
relevant receptors, food items, and media. These exposure concentrations are
spatially explicit with regard to the home range for each ecological receptor.
The major calculation steps performed by the ecological exposure module that are
required to calculate an applied dose may be summarized as follows:
# Select receptor of interest.
# Get media concentrations from TerFW module, SW module, and SR module.
# Calculate average media concentrations to which receptor is exposed.
# Construct diet for receptor of interest (i.e., composition and preferences).
# Get plant and prey concentrations for dietary items from TerFW.
# Sum intake from media and food sources.
D-101
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Appendix D
# Calculate potential applied dose by adjusting for body weight.
# Calculate applied dose by prorating dose by habitat / home range ratio.
D.12.2 Assumptions and Limitations
The exposure characterization methodology used in the ecological exposure module
reflects a number of assumptions and/or limitations, which are listed below.
D.12.2.1 Assumptions.
# Study area is bounded at 2 km. EPA assumed that significant exposures to
source-related contaminants do not occur for ecological receptors that are beyond
2 km of the source. Consequently, exposures are not evaluated for receptors
outside of the study area, measured from the edge of the source to a point 2 km
away.
# All areas delineated as habitat support wildlife. EPA assumed that habitats
delineated at each site are capable of sustaining a variety of wildlife. Because the
predator-prey interactions for each habitat are represented by a simple food web,
we assumed each habitat to be of sufficient quality to support multiple trophic
levels and at least one reproducing pair of upper trophic level predators. Hence,
exposure estimates reflect essentially free access to any of the food items
suggested in the database on ecological exposure factors.
# There are no other chemical stressors in the study area. Because this is a site-
based (rather than site-specific) assessment we assumed that ecological receptors
were not subjected to other stressors within the study area. Background
concentrations of constituents were not considered in developing exposure
estimates, nor were other potential nonchemical stressors such as habitat
fragmentation.
# No less than 10 percent of the diet is attributed to the study area. In many
instances, the home range for a given receptor exceeds the size of the habitat. In
general we assumed that the percent of the home range that "fits" into the habitat
is a suitable surrogate with which to scale exposures. However, the purpose of
this analysis is to determine acceptable waste concentrations assuming that
suitable portions of the study area (e.g., forests) would be used as habitat by
wildlife. Therefore, we assumed that no less than 10 percent of the diet originated
from the study area, even if the fraction of the home range inside the habitat fell
below 10 percent.
# Spatial averaging of exposures is defined by habitat and home range. For this
site-based assessment of representative habitats, we assumed that a reasonable
approach to define the spatial extent of exposure for each receptor was to place
the home range within the habitat boundaries. If the home range was larger than
the habitat (i.e., extends beyond AOI) the exposure was averaged across the
D-102
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Appendix D
habitat and then prorated. However, alternative approaches were considered,
including the calculation of exposure point concentrations based on a random
walk across various habitats.
D.12.2.2 Limitations.
# Plant categories were defined by analogy. Vegetation categories relevant to
wildlife were extrapolated from the plant categories defined for use in the Farm
Food Chain (FFC) module. The cross reference for vegetative categories
consumed by wildlife is presented in the Terrestrial Food Web module
documentation.
# Annual average concentrations define exposure. The exposure profiles generated
with the EcoEx module are based on the average annual concentrations in food
items and media. Consequently, concentration spikes due to episodic events (e.g.,
rain storms) or elevated source releases following waste additions are not
evaluated. In addition the annual average approach does not capture elevated
exposures during critical life stages.
# Exposures are predicted only for adult animals. Because concentrations are
annualized, the module predicts exposures only for adult animals; intrayear
contaminant exposures to juveniles, often with very different dietary preferences,
are not predicted.
# Dietary preferences remain constant over the year. The EcoEx module constructs
the dietary preferences for each receptor based on dietary data covering one or
more seasons. Some of the seasonal variability in the diet is captured indirectly
by the hierarchical algorithm used to determine the dietary preferences. However,
the algorithm is implemented on data across multiple seasons and, therefore, does
not necessarily reflect seasonal differences.
# Exposure estimates reflect a single home range setting. The EcoEx module
calculates the applied doses to receptors for a single random placement of four
home range sizes.12 As a result, the four home ranges in the site layout may not
reflect the spatial variability in exposure patterns, particularly for large habitats
(i.e., habitats that cover substantially greater areas than most of the home ranges).
D.12.3 Inputs for the Ecological Exposure Module
The concentration inputs required by the EcoEx module are provided by the terrestrial
food web module (TerFW), the aquatic food web module (AqFW), the surface water module
12 As described in U.S. EPA (1999n), each receptor is assigned to one of four discrete home range sizes,
depending on the receptor-specific home range. The four home ranges are spatially linked in that the ranges overlap
in a manner that reflects the dietary preferences of the predator species.
D-103
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Appendix D
(SW), and the surface impoundment module (a common source output file). These inputs
include:
Terrestrial Food Web (TF.GRF)
# Spatially averaged surficial soil concentration by home range
# Spatially averaged concentration in soil invertebrates by home range
# Spatially averaged concentration in various plant types by home range
# Minimum and maximum concentrations in various categories of vertebrates
across the habitat (e.g., small mammals, small birds, omnivores)
Aquatic Food Web (AF.GRF)
# Average, reach-specific concentration
# Average, reach-specific concentration
# Average, reach-specific concentration
# Average, reach-specific concentration
# Average, reach-specific concentration
Surface Water (SW.GRF)
# Average, reach-specific concentration
# Average, reach-specific concentration
Surface Impoundment (SR.GRF)
# Average concentration in surface impoundment water
The ecological exposure module receives inputs from its module-specific input file,
ee.ssf, the generic site layout file (sl.ssf), and modeled inputs from the following other modules:
terrestrial food web module (tf.grf), aquatic food web module (af.grf), surface water module
(sw.grf), and those source modules outputting (to a common grf file, sr.grf) a "true" for the
surface water logical flag, SrcH20. In the HWIR application, these sources are the land
application unit, landfill, wastepile, and surface impoundment; currently, only the surface
impoundment reports true for this flag. Input variables are listed and described in Table D-25.
D.12.3 Outputs from the Ecological Exposure Module
The ecological exposure module outputs are written to the ee.grf file. All ecological
exposure outputs are 3-dimensional arrays indexed on time, habitat, and receptor. Output
variables are listed and described in Table D-26.
in aquatic (water column) invertebrates
in benthic invertebrates
in aquatic macrophytes
in trophic level 3 (T3) fish
in trophic level 4 (T4) fish
in sediment
in surface water
D-104
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Appendix D
Table D-25. Summary of Input Parameters for the Ecological Exposure Module
I'ilc
Code
I nils
Desei'iplion
ee.ssf
BodyWt rec
kg
Body weight of each receptor.
ee.ssf
CR ^food
kg/d
Consumption rate of food items for each receptor.
ee.ssf
CR water
L/d
Consumption rate of water for each receptor.
ee.ssf
CRJrac sed
mass
fraction
Consumption rate of sediment for each receptor.
ee.ssf
CRJrac soil
mass
fraction
Consumption rate of surficial soil for each receptor.
ee.ssf
Habitatlndex
unitless
Index of habitat types.
ee.ssf
HabitatType
NA
Description of habitat types.
ee.ssf
MaxPreyPref HabRange
unitless
Maximum dietary preference for items found in a habitat
range.
ee.ssf
MinPreyPref HabRange
unitless
Minimum dietary preference for items found in a habitat
range.
ee.ssf
NumHabitat
unitless
Number of habitat types represented.
ee.ssf
NumPrey
unitless
Number of potential prey items.
ee.ssf
Preylndex
unitless
Numerical index of potential prey items.
ee.ssf
PreyType
NA
Description of each prey item.
sl.ssf
HabArea
m2
Area of habitat.
sl.ssf
Hablndex
unitless
Index of habitat type.
sl.ssf
HabNumRange
unitless
Number of home ranges per habitat.
sl.ssf
HabNum WBNRch
unitless
Number of WBN reaches that impact each habitat range.
sl.ssf
HabRangeAreaFrac
fraction
Fraction of total home range area that falls within each
habitat.
sl.ssf
HabRangeFish WBNIndex
unitless
Index of WBN containing fishable reaches that impact
each habitat range.
sl.ssf
HabRangeNumSISrc
unitless
Number of surface impoundments that intersect each
habitat range.
sl.ssf
HabRangeNum WBNRch
unitless
Number of WBN reaches found within each habitat
range.
sl.ssf
HabRangeNum WSSub
unitless
Number of watersheds that impact each habitat range.
sl.ssf
HabRangeRecIndex
unitless
Receptor index associated with each habitat range.
sl.ssf
HabRange WBNIndex
unitless
Index of WBN that impacts each habitat range.
sl.ssf
HabRange WBNRchlndex
unitless
Index of WBN reaches that impact each habitat range.
sl.ssf
HabRange WSSublndex
unitless
Index of watershed that impacts each habitat range.
sl.ssf
HabType
NA
Type of representative habitat.
(continued)
D-105
-------
Appendix D
Table D-25. (continued)
lile
Code
I nils
Description
sl.ssf
Hab WBNIndex
unitless
Index of WBN that impacts each habitat.
sl.ssf
Hab WBNRchFrac
unitless
Fraction of habitat range impacted by each reach.
sl.ssf
Hab WBNRchlndex
unitless
Index of WBN reaches that impact each habitat.
sl.ssf
HRangeFish WBNRchlndex
unitless
Index of fishable reaches that impact each habitat range.
sl.ssf
HRangeNumFish WBNRch
unitless
Number of fishable reaches that cross each habitat range.
sl.ssf
NumHab
unitless
Number of habitats selected for site simulation.
sl.ssf
NumReceptor
unitless
Complete receptor list across all habitat types.
sl.ssf
NumWBN
unitless
Number of waterbody networks.
sl.ssf
Num WSSub
unitless
Number of watershed sub basins.
sl.ssf
Receptorlndex
unitless
Indices assigned to each receptor.
sl.ssf
ReceptorName
NA
Name of receptor.
sl.ssf
ReceptorType
NA
Description of receptor.
sl.ssf
WBNFishableRchlndex
unitless
Index of reaches that are fishable.
sl.ssf
WBNNumFishableRch
unitless
Number of fishable reaches.
sl.ssf
WBNNumRch
unitless
Number of reaches for each waterbody network.
af.grf
Caqmp
mg/kg
Concentration of contaminant in aquatic plants.
af.grf
CaqmpNY
unitless
Number of years in the time series corresponding to this
variable.
af.grf
CaqmpYR
year
Time series of years corresponding to this variable.
af.grf
Cbenthff
mg/kg
Concentration of contaminant in benthic organisms.
af.grf
CbenthfJNY
unitless
Number of years in the time series corresponding to this
variable.
af.grf
CbenthffYR
year
Time series of years corresponding to this variable.
af.grf
CT3Fish
mg/kg
Concentration of contaminants in trophic level 3 fish.
af.grf
CT3FishNY
unitless
Number of years in the time series corresponding to this
variable.
af.grf
CT3FishYR
year
Time series of years corresponding to this variable.
af.grf
CT4Fish
mg/kg
Concentration of contaminants in trophic level 4 fish.
af.grf
CT4FishNY
unitless
Number of years in the time series corresponding to this
variable.
af.grf
CT4FishYR
year
Time series of years corresponding to this variable.
sr.grf
SrcH20
NA
Flag for surface water presence.
sr.grf
SWConcTot
mg/L
Contaminant concentration in surface water.
(continued)
D-106
-------
Appendix D
Table D-25. (continued)
lile
Code
I nils
Description
sr.grf
SWConcTotNY
unitless
Number of years in the time series corresponding to this
variable.
sr.grf
SWConcTotYR
year
Time series of years corresponding to this variable.
sw.grf
WBNConc WaterTot
mg/L
Dissolved concentration in surface water used as
drinking water source by cattle.
sw.grf
WBNConcWaterTotNY
unitless
Number of years in the times series corresponding to this
variable.
sw.grf
WBNConc WaterTotYR
year
Time series of years corresponding to this variable.
tf.grf
C NY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
C
mg/kg
Concentration of contaminant found in herbiverts and
omniverts.
tf.grf
C YR
year
Time series of years corresponding to this variable.
tf.grf
C HabRange
mg/kg
Concentration of contaminant found in invertebrates and
worms.
tf.grf
C HabRangeNY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
C HabRangeYR
year
Time series of years corresponding to this variable.
tf.grf
C sm
mg/kg
Concentration of contaminant found in small birds,
herpetofauna, and mammals.
tf.grf
C sm YR
year
Time series of years corresponding to this variable.
tf.grf
C sm NY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
CTdaAveHabRange
Mg/g
Average depth average soil concentration in each habitat
range.
tf.grf
CTdaAveHabRangeNY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
CTdaAveHabRange YR
year
Time series of years corresponding to this variable.
tf.grf
CTssAveHabRange
Mg/g
Average depth average soil concentration in each habitat
range.
tf.grf
CTssAveHabRangeNY
unitless
Number of years in the time series corresponding to this
variable.
tf.grf
CTssAveHabRangeYR
year
Time series of years corresponding to this variable.
tf.grf
P HabRangeNY
unitless
Number of years in the time series corresponding to this
variable.
(continued)
D-107
-------
Appendix D
Table D-25. (continued)
lile
( ode
I nils
Description
tf.grf
P HabRangeYR
year
Time series of years corresponding to this variable.
tf.grf
P HabRange
mg/kg
Concentration of contaminant found in exfruit, exveg,
forage, grain, root, and silage.
NA = not applicable
Table D-26. Summary of Output Parameters for the Ecological Exposure Module
I'ilc
(ode
I nils
Description
ee.grf
Dose
rec
mg/kg-d
Dose of contaminant to receptor.
ee.grf
Dose
recNY
unitless
Number of years in the time series corresponding to this variable.
ee.grf
Dose
recYR
year
Time series of years corresponding to this variable.
D-108
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Appendix D
D.13 Ecological Risk Module
The ecological risk (EcoRisk) module calculates hazard quotients13 (HQs) for a suite of
ecological receptors assigned to habitats delineated for study sites. These receptors fall into eight
receptor groups: (1) mammals, (2) birds, (3) herpetofauna, (4) terrestrial plants, (5) soil
community, (6) aquatic plants and algae, (7) aquatic community, and (8) benthic community.
The spatial resolution of the EcoRisk module is, to a large degree, determined by both the home
ranges and habitats delineated at each site.
The HQs for the for all receptors assigned to the study site are calculated and placed into
one of five risk bins developed to assist decision-makers in creating appropriate risk metrics.
The HQ risk bins are used in developing cumulative distribution functions of risk and are defined
as: (1) below 0.1, (2) between 0.1 and 1, (3) between 1 and 10, (4) between 10 and 100, and (5)
above 100. Each of the HQs calculated by the EcoRisk module has a series of attributes
associated with it that allows ecological risks to be interpreted in a number of ways. For
instance, distance from the source (i.e., 1 km, 1 km to 2 km, or across the entire site) is important
in understanding the spatial character of potential ecological risks.
Outputs are generated for three areas of the site relative to the distance from the edge of
the waste management unit. These distances are termed EcoRings and depict the following:
(1) habitats that fall within 1 km of the WMU, (2) habitats that fall between 1 and 2 km from the
WMU, and (3) habitats within 2 km of the WMU (i.e. across the entire site). It is important to
note that the HQ results for habitats that intersect both EcoRings are attributed to the risk results
for both of those distances. In other words, the habitat risks are not apportioned by distance, they
are reported as though they are positioned entirely within each distance ring. Because the
fundamental unit of this analysis is the representative habitat (not distance to the waste
management unit), it was considered inappropriate to truncate risks by distance.
D.13.1 Functionality
The major computational functions performed by the ecological risk module may be
summarized as follows:
# Time series management. The EcoRisk module determines the overall duration of
the time period to be simulated (including concentration data from discontinuous
time periods) and identifies the individual years within the overall duration that
will be simulated.
# Module loops over the time series, through habitats, and receptors. The EcoRisk
module has three basic loops: (1) over the time series, (2) over each habitat, and
(3) over each receptor assigned to the habitat.
13 Hazard quotients are defined as: (1) the ratio between applied dose received from the ingestion of
contaminated media and food items and an ecological benchmark (EB in units of dose), and (2) the ratio between
the concentration in the medium of interest (soil, sediment, or surface water) and a chemical stressor concentration
limit (CSCL in units of concentration).
D-109
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Appendix D
# Calculation of time series hazard quotients for ecological receptors. The
EcoRisk module predicts HQs for each year of the simulation for receptors in each
habitat. These HQs are defined in terms of a number of attributes to facilitate
clarity in the risk characterization.
The major steps performed by the EcoRisk module that are required to predict ecological
risks are summarized as follows:
# Select the ecological distance ring of interest (i.e., 0 to 1 km; 1 to 2 km, entire
site).
# Read in all data required to calculate HQs for all receptors (e.g., EBs, CSCLs, site
layout characteristics such as water hardness).
# Calculate HQs for all receptors within the area of interest for each year of the
simulation.
# Calculate probability density functions for each year of the simulation (this is
performed in much the same manner as with the Human Risk module).
# Identify and output the cumulative density functions for various receptor and
habitat groups for the year in which the maximum total HQ was experienced.
# Identify and output information about the receptor experiencing the maximum HQ
across all years of the simulation and the year in which the maximum occurred.
D.13.2 Assumptions and Limitations
The methodology used in the ecological risk module reflects a number of assumptions
and/or limitations, which are listed below. Several key assumptions listed for the ecological
exposure module (see Section D. 12.1) are also relevant to the EcoRisk module. For example, the
assumption that all areas delineated as habitat support wildlife also applies to the EcoRisk
module in that HQs calculated within each habitat are presumed to reflect potential risks to
ecological receptors. For convenience, these assumptions are included below as well as
assumptions and limitations that are unique to the EcoRisk module.
D.13.2.1 Assumptions
# Study area is bounded at 2 km. We assumed that significant risks to source-
related contaminants do not occur for ecological receptors that are beyond 2 km of
the source. Consequently, HQs were not calculated for receptors outside of the
study area, measured from the corner of the source to a point 2 km away.
# All areas delineated as habitat support wildlife. It is assumed that habitats
delineated at each site are capable of sustaining a variety of wildlife. Since the
predator-prey interactions for each habitat are represented by a simple food web,
D-110
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Appendix D
each habitat is assumed to be of sufficient quality to support multiple trophic
levels and, at least, one reproducing pair of upper trophic level predators. Hence,
risk calculations assume that the receptors of interest are present in each habitat.
# There is only one source for each chemical stressors in the study area.
Background concentrations of constituents were not considered in developing
exposure estimates. Contributions to ecological exposures from other sources, or
pre-existing conditions such as a fish advisory were not addressed.
# The most appropriate endpoints for population sustainability are reproductive
and developmental effects. In calculating HQs for populations of mammals and
birds, it is implicitly assumed that endpoints associated with the populations'
ability to reproduce and grow are an appropriate surrogate for true population-
level endpoints (e.g., adverse effects leading to a 10% reduction in the population
size).
# One and only one population of each wildlife species is carried by a given
habitat. For example, although there may be a number of receptors assigned to a
habitat, multiple populations of shrews or robins are not evaluated. Each receptor
population has the same spatial characteristics, as defined by the home range.
Hence, there is one HQ calculated for each receptor in each habitat.
# Maximum HQ estimates are based on a single year of exposure. The ecological
HQ estimates are based on annual averages: the smallest increment of time that
for which the 3MRA system is designed. This time step represents much longer
than lifetime exposures for some receptors, and substantially less than lifetime for
other receptors.
D. 13.2.2 Limitations
# The HQs are not calculated at the population or community level; ecological risks
must be inferred to higher levels of biological organization. Ecosystems are
enormously complex, and our understanding of even simple community dynamics
is limited. Data on chemical stressors are seldom available above the level of an
individual organism; that is, the study endpoints focus on individual organisms
rather than processes crucial to assemblages of organisms. Even the CSCLs
developed to evaluate risks to communities are derived by statistical inference on
toxicity data for individual organisms. Therefore, the data are generally
insufficient to allow us to truly evaluate effects at the population or community
levels. This is currently a limitation in the state-of-the-science, particularly for
national analyses.
# It is not possible to verify that reproductive and developmental endpoints are, in
all cases, sufficient to protect the assessment endpoints for wildlife populations.
The endpoints for certain wildlife populations (i.e., mammals, birds) were almost
exclusively taken from reproductive and developmental studies. Although
D-lll
-------
Appendix D
reproductive and developmental endpoints have been recognized by EPA as
relevant to population sustainability, they are not always the critical effect
associated with a chemical stressor. The assumption that other effects that may
occur at lower environmental concentrations are not significant with respect to the
population sustainability limits confidence in predicting ecological risk. Studies
regarding this question are inconclusive and, therefore, there is some uncertainty
in using only reproductive and developmental studies to address the assessment
endpoint of population sustainability.
# The HQ estimates are generated based on one, and only one, home range area.
For the purposes of creating the site layout file, four home range areas are placed
in each habitat. Once these areas are delineated and appropriate receptors are
assigned, the spatial characteristics of the risk for each home range is established.
Variability associated with exposures in different areas of the habitat is not
reflected in this scheme. This limitation may result in significant differences for
receptors with small home ranges, and can influence the risk estimates for
predators with large home ranges (i.e., home range « habitat) since tissue
concentrations in prey items are constrained by the same spatial characteristics.
As a result, the representativeness of the HQs with regard to the spatial character
of the exposure is limited.
# The effects of multiple stressors (chemical and non-chemical) are not considered
in developing estimates of potential ecological risk. This is a source of
considerable uncertainty in the HQ estimates. The EcoRisk module is executed
within the FRAMES system within a system-level chemical loop such that only a
single chemical is evaluated per iteration of the model. As a result, risks are
predicted assuming a single chemical exposure. Data availability on the
antagonistic and synergistic effects associated with multiple stressors are
extremely limited at this time (with the possible exception narcotic contaminants
in aqueous systems) and prevented the development of a multi-stressor analytical
approach for the HWIR universe of constituents. Data limitations
notwithstanding, the inability to consider multiple stressors is a limitation in our
ability to interpret the risk results generated by this module.
# The HQ estimates for the aquatic and benthic communities, respectively, are
resolved at the habitat, rather than reach level. There is some uncertainty
associated with calculating risks to aquatic life across an entire habitat (as defined
within the study area). Species of fish such as brown trout tend to utilize certain
segments of stream habitats and, therefore, HQs at the reach level may be more
appropriate. Conversely, establishing artificial boundaries between stream
reaches is contrary to the goals of the assessment strategy, namely, to evaluate
ecological risks using the habitat as the fundamental unit.
# The HQ estimates reflect different endpoints at varying levels of effect. The HQ
methodology - the ratio of an exposure to a benchmark - is applied uniformly
across all ecological receptors. However, the data supporting the HQ calculation
D-112
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Appendix D
vary in that they include endpoints from lethality to reproductive fitness and
address and community-level effects by inference. To some degree, the HQ
estimates for different receptor groups represent different risk metrics. The
interpretation of these HQ estimates is, therefore, limited by our understanding of
the potential ecological significance of the measures of effect as well as overall
confidence in the data used to support the calculations.
D.13.3 Inputs for the Ecological Risk Module
The concentration and dose inputs required by the EcoRisk module are provided by the
ecological exposure (EcoEx) module, the Terrestrial Food Web module (TerFW), and the
Surface Water (SW) module. These inputs include:
Ecological Exposure (ee.grf)
# applied dose to receptors by home range and habitat
Terrestrial Food Web (tf.grf)
# spatially-averaged surficial soil concentration by home range
Surface Water (sw.grf)
# average, reach-specific total concentration in sediment
# average, reach-specific total concentration in surface water
# average, reach-specific dissolved concentration in surface water
The ecological risk module receives inputs from its module-specific input file, er.ssf, the
generic site layout file (sl.ssf), the chemical properties file (cp.ssf), and modeled inputs from the
surface water module (sw.grf), terrestrial food web module (tf.grf), and the ecological exposure
module (ee.grf). Input variables are listed and described in Table D-27.
D.13.4 Outputs
The ecological risk module outputs are written to the er.grf file. All ecological risk
outputs are 3-dimensional arrays indexed on time, habitat, and receptor. Output variables are
listed and described in Table D-28.
D-113
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Appendix D
Table D-27. Summary of Inputs for the Ecological Risk Module
File
Code
Units
Description
er.ssf
EcoRegPercentile
unitless
Policy criterion for selecting critical year for maximum HQ
sl.ssf
NumEcoBin
unitless
Number of bins for cumulative distribution function
sl.ssf
EcoRingNumHab
unitless
Number of habitats in each ecoring
sl.ssf
EcoBinRangeMin
unitless
Minimum HQ for each ecobin
sl.ssf
NumEcoRing
unitless
Number of ecorings at the site
sl.ssf
EcoRingHablndex
unitless
Habitat index for a habitat in a given ecoring
sl.ssf
HabNumRange
unitless
Number of ranges in a given habitat
sl.ssf
HabNumWBNRch
unitless
Number of reaches in a given habitat
sl.ssf
HabType
NA
String description of the habitat type for a given habitat
sl.ssf
ReceptorType
NA
String description of the receptor type for a given receptor
sl.ssf
ReceptorName
NA
Receptor name
sl.ssf
RecGroup
NA
String description of receptor group
sl.ssf
RecT rophicLevel
NA
String description of receptor trophic level
sl.ssf
HabRangeRecIndex
unitless
Index for a given receptor in a given habitat
sl.ssf
WBNWaterHardness
mg CaC03
eq/L
Water hardness for a given waterbody network type
sl.ssf
HabWBNIndex
unitless
Waterbody network index for a given reach in a given
habitat
sl.ssf
HabWBNRchlndex
unitless
Reach index for a given reach in a waterbody network in a
given habitat
sl.ssf
WBNRchBodyType
NA
String description of reach body type for a given reach in a
given waterbody network
cp.ssf
ChemCASID
NA
Chemical abstracts service registry number for the chemical
cp.ssf
ChemType
NA
Chemical type
cp.ssf
ChemKoc
mL/g
Organic carbon partition coefficient for the chemical
cp.ssf
ChemEBRec
mg/kg-day
Ecological benchmark for the chemical for a given receptor
cp.ssf
ChemCSCLWaterDis
sRec
mg/L
Chemical stressor concentration limit for the chemical
dissolved in water for a given receptor
cp.ssf
ChemCSCLWaterTot
Rec
mg/L
Chemical stressor concentration limit for the total chemical
in water for a given receptor
(continued)
D-114
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Appendix D
Table D-27. (continued)
File
Code
Units
Description
cp.ssf
ChemCSCLSediment
Rec
ug/g
Chemical stressor concentration limit for the chemical in
sediment for a given receptor
cp.ssf
ChemCSCLSoilRec
ug/g
Chemical stressor concentration limit for the chemical in
soil for a given receptor
sw.grf
WBNNumChem
NA
Number of chemical species for the chemical
sw.grf
WBNConcWaterDiss
mg/L
Dissolved concentration of chemical in a given WBN reach
in a given year
sw.grf
WBNConcWaterTot
mg/L
Total concentration of a chemical in a given WBN reach in a
given year
sw.grf
WBNConcBenthTot
ug/g
Total concentration of a chemical in the benthic column of a
given WBN reach in a given year
sw.grf
WBNfocBenth
fraction
Fraction organic carbon in the benthic column of a given
WBN reach in a given year
tf.grf
CT daAveHabRange
ug/g
Depth-averaged total chemical concentration in soil,
averaged over a given habitat and range in a given year
ee.grf
Doserec
mg/kg-day
The chemical dose experienced by a receptor in a given
habitat and range in a given year
NA = not applicable
D-115
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Appendix D
Table D-28. Summary of Outputs for the Ecological Risk Module
File
Code
Units
Description
er.grf
HQcdfHabGroup
unitless
Cumulative percentile of receptor HQs, by habitat group for each
ecoring for the
er.grf
HQcdfHabType
unitless
Cumulative percentile of receptor HQs, by habitat type
er.grf
HQcdfRecGroup
unitless
Cumulative percentile of receptor HQs, by receptor group
er.grf
HQcdfRGHabGroup
unitless
Cumulative percentile of receptor HQs, by receptor group and
habitat group (ecoring 3 only)
er.grf
HQcdfTLHabGroup
unitless
Cumulative percentile of receptor HQs, by trophic level and habitat
group (ecoring 3 only)
er.grf
HQcdfTrophicLevel
unitless
Cumulative percentile of receptor HQs, by trophic level
er.grf
HQHabTypeTcrit
year
Time output at which maximum HQ occurs for each habitat type
er.grf
HQHabGroupTcrit
year
Time output at which maximum HQ occurs for each habitat group
er.grf
HQMax
unitless
Maximum HQ across the ecoring
er.grf
HQMaxHabGroup
unitless
Habitat group index for the maximum HQ in the ecoring
er.grf
HQMaxHabType
NA
Habitat type for the maximum HQ in the ecoring
er.grf
HQMaxRec
unitless
Receptor index for the maximum HQ in the ecoring
er.grf
HQMaxRecGroup
NA
Receptor group for the maximum HQ in the ecoring
er.grf
HQMaxTcrit
year
Year with maximum HQ across all eco receptors in the ecoring
er.grf
HQMaxT rophicLevel
unitless
Trophic level of receptor for the maximum HQ in the ecoring
er.grf
HQRecGroupTcrit
year
Time output at which maximum HQ occurs for each receptor group
er.grf
HQRGHabGroupTcrit
year
Time output at which maximum HQ occurs for each receptor
group/habitat group combination
er.grf
HQTLHabGroupTcrit
year
Time output at which maximum HQ occurs for each trophic
level/habitat group combination
er.grf
HQT rophicLevelT crit
year
Time output at which maximum HQ occurs for each trophic level
NA = not applicable
D-116
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