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Technical Background Document for
Soil Screening Guidance
Review Draft
Office of Emergency and Remedial Response
U.S. Environmental Protection Agency
December 1994
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TABLE OF CONTENTS
Section Page
List of Figures vi
List of Tables vii
Preface ix
Acknowledgments x
Disclaimer xi
Part 1: Definition and Application
1.1 Background 1-1
1.2 Purpose of Soil Screening Framework 1-1
1.3 Soil Screening Framework 1-2
1.3.1 Site-Specific SSLs: Simple Method 1-3
1.3.2 Site-Specific SSLs: Detailed Approach 1-4
1.3.3 Generic SSLs 1-4
1.4 Scope of the Soil Screening Framework 1-4
1.4.1 Exposure Pathways 1-4
1.4.2 Exposure Assumptions 1-6
1.4.3 Risk Level 1-7
1.5 How To Use the Soil Screening Framework 1-7
1.5.1 Developing a Conceptual Site Model 1-8
1.5.2 Considering Background Concentrations 1-9
1.5.3 Sampling Exposure Area 1-9
1.5.4 Comparing Soil Levels with SSLs 1-10
1.5.5 Use of SSLs as Preliminary Remediation
Goals/Cleanup Levels 1-10
Part 2: Development
2.1 Human Health Basis 2-1
2.1.1 Direct Ingestion and Inhalation 2-1
. 2.1.2 Migration to Ground Water 2-8
2.1.3 Other Pathways 2-8
2.2 Direct Ingestion 2-9
2.3 Inhalation of Volatiles and Fugitive Dusts 2-11
2.3.1 Screening Level Equations for Direct Inhalation 2-12
2.3.2 Volatilization Factor 2-13
2.3.3 Dispersion Model 2-16
2.3.4 Soil Saturation Limit 2-17
2.3.5 Paniculate Emission Factor 2-19
2.3.6 Intrusion of Volatiles into Basements: Johnson and Ettinger Model 2-20
2.4 Migration to Ground Water 2-21
2.4.1 Development of Soil/Water Partition Equation 2-23
2.4.2 Organic Compounds—Partition Theory 2-25
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TABLE OF CONTENTS (continued)
Section Page
2.4.3 Inorganics (Metals)—Partition Theory 2-27
2.4.4 Assumptions for Soil/Water Partition Theory 2-28
2.4.5 Dilution/Attenuation Factor Development 2-29
2.4.6 Dilution-Attenuation Factor Development: Generic SSLs 2-34
Part 3: Determining SSLs
3.1 Conceptual Site Model : . 3-2
3.1.1 SSL Conceptual Model 3-2
3.1.2 Developing the Conceptual Site Model 3-4
3.1.3 Comparing Conceptual Models 3-6
3.2 Site-Specific SSLs: Simple Method 3-9
3.2.1 Soil Parameters 3-17
3.2.2 Leach Test Option 3-21
3.2.3 Meteorological Related Variables (Inhalation Pathway) 3-21
3.2.4 Hydrogeologic Variables for Dilution Model
(Migration to Ground Water Pathway) 3-22
3.2.5 Considering C^ 3-26
3.3 Generic SSLs • 3-28
3.4 Detailed Site-Specific Method 3-33
3.4.1 Inhalation Pathway 3-33
3.4.2 Migration to Ground Water Pathway 3-36
3.4.3 Considerations for Model Selection 3-38
3.4.4 Model Applicability 3-41
Part 4: Measuring Contaminant Concentrations in Soil
4.1 Data Quality Objectives for the Soil Screening Framework 4-1
4.1.1 State the Problem 4-2
4.1.2 Identify the Decision 4-2
4.1.3 Identify Inputs to the Decision 4-2
4.1.4 Specify the Study Boundaries 4-2
4.1.5 Develop a Decision Rule 4-5
4.1.6 Specify Limits on Decision Errors 4-5
4.1.7 Optimize the Design 4-7
4.2 Site-Specific Sampling Approach 4-10
4.2.1 Determining Sample Size for Simple Random Sampling 4-11
4.2.2 Determining Sample Size for Composite Sampling 4-11
4.2.3 Analyzing the Data 4-12
4.3 Prescriptive Sampling Approach 4-12
4.3.1 Composite Sampling for Nonvolatile Contaminants in Surface Soils 4-15
4.3.2 Discrete Sampling of Volatile Contaminants in Surface Soils 4-16
IV
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. TABLE OF CONTENTS (continued)
Section Page
Part 5: Chemical-Specific Parameters
5.1 Introduction 5-1
5.2 Solubility and Kow 5-1
5.3 Vapor Pressure and Air Diffusivity 5-2
5.4 Henry's Law Constant 5-2
5.5 Soil Organic Carbon/Water Partition Coefficients (K^ 5-10
5.5.1 KO,. for Nonionizing Organic Compounds 5-10
5.5.2 K^. for Ionizing Organic Compounds 5-13
5.6 Soil-Water Distribution Coefficients (Kj) for
Inorganic Constituents 5-23
5.6.1 Modeling Scope and Approach 5-25
5.6.2 Input Parameters 5-26
5.6.3 Assumptions and Limitations 5-28
5.6.4 Results and Discussion 5-29
Part 6: References
References 6-1
Appendices
A Development of a Soil Screening Level Methodology for
the Soil-Plant-Human Exposure Pathway A-l
B Evaluation of the Effect of the Draft SSLs if the Johnson and Ettinger (1991)
Model for the Intrusion of Contaminant Vapors into Buildings B-l
C Limited Validation of the Hwang and Falco Model for Emissions of
Soil-Incorporated Volatile Organic Compounds C-l
D Revisions of VF and PEF Equations D-l
E -Determination of Groundwater Dilution Attenuation Factors for
Fixed Waste Site Areas Using EPACMTP E-l
F Dilution Factor Model Results F-l
G Characterization of Wind Erosion Potential G-l
H Synthetic Precipitation Leaching Procedure (SPLP) (SW-846 Method-1312) H-l
I Derivation of Sample Sizes 1-1
J KO,. Values for Ionizing Organics as a Function of pH J-l
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LIST OF FIGURES
Number Page
1-1 • Risk management spectrum for contaminated soil ........................ 1-2
1-2 Components of the Soil Screening framework ........................... 1-3
1-3 Exposure pathways addressed by the Soil Screening framework .............. 1-4
1-4 Migration to ground water pathway — conceptual model .................... 1-8
2-1 Migration to ground water pathway — EPACMTP modeling effort ............ 2-36
3-1 The SSL conceptual model ......... . .............................. 3-2
3-2 Ground water regions of the United States . . . .......................... 3-6
3-3 Sample conceptual site model diagram for contaminated soil ........ ........ 3-8
3-4 Metal K
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LIST OF TABLES
Number Page
2-1 Regulatory and Human Health Benchmarks Used for SSL Development 2-2
2-2 SSL Chemicals with Noncarcinogenic Effects on Specific
Target Organs 2-6
2-3 Variation of DAF with Size of Source Area for SSL EPACMTP Modeling
Effort 2-36
2-4 Recharge Estimates for DNAPL Site Hydrogeologic Regions 2-39
2-5 SSL Dilution Model Results: DNAPL and HGDB Sites 2-40
3-1 SSL Chemicals Known To Pose Risk Through Non-SSL Pathways 3-5
3-2 Chemical-Specific Properties Used in SSL Calculations 3-13
3-3 KOJ. Values for Ionizing Organics as a Function of pH 3-15
3-4 Parameters for Developing Simple Site-Specific SSLs 3-18
3-5 Parameter Estimates for Calculating Average Soil Moisture Content (6W) 3-20
3-6 Q/C Values by Source Area, City, and Climatic Zone 3-23
3-7 HELP Input Parameters for Determining SSL
Site-Specific Infiltration Rates 3-25
3-8 Physical State of Organic SSL Chemicals 3-27
3-9 Generic SSLs: Default Parameters and Assumptions .. .. '. 3-29
3-10 Generic Soil Screening Levels for Superfund 3-30
3-11 Input Parameters Required for RTTZ Model 3-38
3-12 Input Parameters Required for VIP Model 3-39
3-13 Input Parameters Required for CMLS 3-40
3-14 Input Parameters Required for HYDRUS 3-40
3-15 Input Parameters Required for SUMMERS 3-40
3-16 Input Parameters Required for MULTIMED 3-41
3-17 Input Parameters Required for VLEACH 3-42
3-18 Input Parameters Required for SESOIL (Monthly Option) 3-43
3-19 Input Parameters Required for PRZM 3-44
3-20 Input Parameters Required for VADOFT 3-45
3-21 Characteristics of Unsaturated Zone Models Evaluated 3-46
4-1 Soil Screening Framework DQOs 4-3
4-2 Sample Sizes for Site-Specific Approach 4-11
4-3 Values of H(0.95) for Computing a One-Sided Upper 95% Confidence
Limit on a Lognormal Mean 4-15
5-1 Range, Geometric Mean, and Number of Reported Solubility Values 5-3
5-2 Summary Statistics for Kow Values 5-5
5-3 Summary Statistics for Reported Vapor Pressure Values 5-7
5-4 Air Diffusivity (Dj) Values for SSL Chemicals 5-9
5-5 Henry's Law Constants - SSL Chemicals 5-11
5-6 Comparison of Calculated and Measured Henry's Law Constants 5-12
5-7 Summary Statistics for Measured K^ Values: Nonionizing Organics 5-14
5-8 Comparison of Measured and Calculated K^, Values 5-17
VII
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LIST OF TABLES (continued)
Section Page
5-9 Degree of lonization (Fraction of Neutral Species, <&)
as a Function of pH 5-21
5-10 Soil Organic Carbon/Water Partition Coefficients and pKa Values for 15
Ionizing Organic Compounds 5-22
5-11 Predicted Soil Organic Carbon/Water Partition Coefficients
(K^., L/kg) as a Function of pH: Ionizing Organics 5-23
5-12 Summary of Collected Kj Values Reported in Literature 5-24
5-13 Summary of Geochemical Parameters Used in the SSL MINTEQ Modeling
Effort 5-28
5-14 Background Pore-Water Chemistry Assumed for the SSL MINTEQ Modeling
Effort 5-28
5-15 Estimated Metal K<, Values for SSL Application 5-30
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PREFACE
This document provides the technical background behind the development of the November 1994 Soil
Screening Guidance for Superfund. These documents define the Soil Screening framework, a suite of
methodologies for developing Soil Screening Levels (SSLs) for 107 chemicals commonly found at
Superfund sites. This document is an updated version of the background document developed in support
of the September 30, 1993, draft SSL guidance. This document and the Guidance is available for public
comment and is currently undergoing extensive peer review. Because the guidance is still under review,
it and this document should not be used until they arc finalized following this rigorous technical review
and public comment
This background document is presented in five parts. Part 1 defines the Soil Screening Framework and
describes its application and implementation at Superfund sites. Pan 2 describes the development of the
suite of methodologies used to develop SSLs for direct ingestion of soil, inhalation of volatiles and
fugitive dust, and the migration to ground water exposure pathways, including assumptions and theories
used in their development Part 3 provides guidance on implementing these methodologies to determine
SSLs, including the simple site-specific methodology, generic SSLs for 107 chemicals, and. the detailed
site-specific methodology. Part 4 addresses sampling schemes for measuring soil contaminant levels
within the framework. Part 5 provides technical background on the determination of chemical-specific
properties for calculating SSLs.
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ACKNOWLEDGMENTS
This technical background document was prepared by Research Triangle Institute (RTI) under EPA
Contract 68-W1-0021, Work Assignment D2-24, for the Office of Emergency and Remedial Response
(OERR), U.S. Environmental Protection Agency (EPA). Janine Dinan, of OERR's Hazardous Site
Evaluation Division (HSED), and Loren Henning, of OERR's Hazardous Site Control Division (HSCD),
the EPA Work Assignment Managers for this effort, guided the effort and are also principal EPA authors
of the document Sherri Gill of OERR/HSCD also contributed to the document. Robert Truesdale is the
RTI Work Assignment Leader and principal RTI author of the document. RTI contributors include Mary
Siedlecki (partition coefficients and MINTEQ modeling); Jeff Reynolds and Jack Eggleston (dilution
modeling and model review); Steve Beaulieu (alternative exposure pathways, human health criteria);
Malcolm Bertoni, Brenda Odum, and Larry Myers (sampling); and Nancy Jones, Anne Crook, Amy
Reynolds, Michael James, and Jim Sporty. Craig Mann of Environmental Quality Management, Inc. (EQ),
conducted the modeling effort for the inhalation pathway and provided background information on that
effort. Dr. Zubair Saleem of EPA's Office of Solid Waste conducted the EPACMTP modeling effort and
provided the discussion on the use of this model for generic DAP development.
Special thanks to Jenny Lloyd, who compiled and checked the numbers, and to Kathleen Mohar, Linda
Gaydosh, and Jan Shirley who provided technical editing and word processing support
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DISCLAIMER
Notice: This guidance is based on policies in the Final Rule of the National Oil and Hazardous
Substances Pollution Contingency Plan (NCP), which was published on March 8, 1990 (55 Federal
Register 8666). The NCP should be considered the authoritative source.
The policies set out in this document are intended solely as guidance to the U.S. Environmental Protection
Agency (EPA) personnel; they are not final EPA actions and do not constitute rulemaking. These policies
are not intended, nor can they be relied upon, to create any rights enforceable by any party in litigation
with the United States. EPA officials may decide to follow the guidance provided in this document, or
to act at variance with the guidance, based on an analysis of specific site circumstances. EPA also
reserves the right to change the guidance at any time without public notice.
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Technical Background Document for
Soil Screening Guidance
Parti: DEFINITION AND APPLICATION
1.1 Background
On June 19,1991, the U.S. Environmental Protection Agency's (EPA's) Administrator charged the Office
of Solid Waste and Emergency Response (OSWER) with conducting a 30-day study to outline options
for accelerating the rate of cleanups at National Priorities List (NPL) sites. One of the specific proposals
of the study was for OSWER to "examine the means to develop standards or guidelines for contaminated
soils."
On June 23,1993, EPA announced the development of "Soil Trigger Levels" as one of the Administrative
Improvements to the Superfund program. On September 30, 1993, a draft fact sheet was released mat
presented generic Soil Screening Levels (SSLs) for 30 chemicals (U.S. EPA, 1993c). The fact sheet
presented standardized equations to model exposures to soil contaminants via ingestion, inhalation, and
migration to ground water. The fact sheet provided generic defaults for each parameter in the equations
and a sampling methodology to measure soil contaminant levels. The SSL initiative underwent widespread
review both within and outside the Agency. Suggestions were made on how to improve the methodology
and increase the usefulness of screening levels by finding simple ways to modify them using site-specific
data.
Based on that review, EPA modified the SSLs into a Soil Screening framework that emphasizes the
application of standardized equations for the site-specific evaluation of soil contaminants. This framework
provides an overall approach for developing SSLs for specific contaminants and exposure pathways at a
site under a residential land use scenario. Areas with soil contaminant concentrations below SSLs
generally would not warrant further study or action under the Comprehensive Environmental Response,
Compensation, and Liability Act (CERCLA).
The Soil Screening framework's point of departure is a simple methodology for calculating site-specific
SSLs using easily obtained site data with standardized equations. An option for conducting a more
detailed site-specific analysis is also included in the framework. In addition, default parameters are used
in the standardized equations to produce a table of generic Soil Screening Levels for 107 chemicals that
update those presented in the September 30,1993, draft SSL fact sheet. These generic SSLs are included
in the framework as a default option for use when site-specific values are not available.
1.2 Purpose of Soil Screening Framework
The Soil Screening framework represents the first of several tools EPA plans to develop to standardize
the evaluation and cleanup of contaminated soils. SSLs streamline the remedial investigation/feasibility
study (Rl/FS) process by accelerating and increasing consistency in decisions concerning soil
contamination. As a future companion to the Soil Screening framework, EPA also intends to develop a
methodology to identify levels of contamination that clearly warrant a response action or, possibly,
concentrations for which treatment would be required. The screening levels at the low end and the higher
concentration values that warrant response can be used to identify the bounds of a risk management
continuum (Figure 1-1). Generally, within this continuum lies a range of possible cleanup levels that will
continue to be determined on a site-specific basis.
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No further study Site-specific Response
warranted under cleanup action clearly
CERCLA goal/level warranted
JL. A X
t V \r ~\
| 1 1 1 ^
"Zero" Screening Response Very high
concentration level level concentration
Figure 1-1. Risk management spectrum for contaminated soil.
EPA anticipates the use of the Soil Screening framework as a tool to facilitate prompt identification of
the contaminants and exposure areas of concern during both remedial actions and some removal actions
under CERCLA. SSLs do not trigger the need for response actions or define "unacceptable" levels of
contaminants in soil. SSLs may serve as Preliminary Remediation Goals (PRGs) under certain conditions
(see Section 1.5.5). In the future, EPA will consider expanding the guidance to address the Resource
Conservation and Recovery Act (RCRA) Corrective Action program.
The SSLs are, as noted above, intended for use as a tool; their use is not mandatory at sites being
addressed under CERCLA. The framework leaves a broad range of discretion to the site manager, both
on whether the SSL approach is appropriate for a site and, if it is used, on the appropriate method. It
comprises three optional approaches—simple site-specific, detailed site-specific, and generic. In the first
two some or all default values would be replaced, as appropriate, with site-specific data Furthermore,
the models themselves are not codified as rules and can be modified if appropriate, although some
explanation should be provided if such modification is made.
1.3 Soil Screening Framework
Highlight 1-1: Soil Screening Framework
• Soil Screening Level (SSL): a chemical concentration in soil below which there is no concern
under CERCLA for ingestion, inhalation, and migration to ground water exposure pathways,
provided certain conditions are met.
• Simple Site-Specific Method: standardized equations to calculate SSLs with easily obtained
site-specific data (inhalation and migration to ground water pathways only).
• Detailed Site-Specific Method: full-scale model evaluation to develop SSLs for the inhalation
and migration to ground water pathways.
• Generic SSLs: derived using national default parameters in SSL equations for use at sites
where a simple site investigation is not warranted (all three SSL pathways).
A Soil Screening Level is a chemical concentration in soil that represents a level of contamination below
which there is no concern under CERCLA, provided conditions associated with the SSLs are met.
Generally, if contaminant concentrations in soil fall below the SSL, the site meets specific residential use
and human exposure conditions, and there are no ecological receptors of concern, then no further study
or action is warranted for residential land use of that area. (Some States have developed screening
numbers that are more stringent than the generic SSLs presented in this document; therefore further study
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may be warranted under State programs.) Concentrations in soil above either the generic or site-specific
SSL described in this section and shown in Highlight 1-1 would not automatically designate a site as
"dirty" nor trigger a response action. However, exceeding a screening level suggests that further
evaluation of the potential risks that may be posed by site contaminants is appropriate to determine the
need for a response action.
The Soil Screening framework presents three approaches for establishing screening levels (Highlight 1-1).
The option emphasized within the framework is a simple method whereby readily obtainable, site-specific
data are incorporated into standardized equations to derive site-specific screening levels for selected
contaminants. When questions still exist at a site regarding whether or not contaminant levels are of
concern, a second approach is to derive more tailored screening levels by plugging additional site data into
more complex fate and transport models (the detailed site-specific method). The third approach is to use
generic SSLs, which are derived using conservative default values in the standardized equations. Although
the default parameters are not necessarily "worst case," they are conservative. Therefore, the framework
promotes the option of using site-specific data to derive screening levels.
For the direct soil ingestion pathway, only generic SSLs were developed. Simple and full-scale site-
specific methods were not developed because cost and complexity make developing site-specific data for
this pathway, such as soil ingestion rates or chemical-specific bioavailability, generally impracticable.
However, EPA is evaluating the data available to support adjustment of the exposure frequency term based
on regional climatic conditions.
Generally, the decision of which method to
use will be driven by time and cost, with the Conservatism
site manager weighing the cost of conducting
a more site-specific investigation against the
potential for deriving a higher (but equally . Simple
protective) SSL (Figure 1-2). In other words, Method'^
the site manager must balance the potential for ^/* ^s.
eliminating the site (or portion of the site) Generic ~ ^ Detailed Site-
from further concern (and the associated cost SSL -4 > Specific Method
savings) with the cost and time required to
conduct the site charactenzation activities Investigation Costs
necessary to apply the method. Part 3 of this Less ^ f ^ More
document provides a more detailed discussion
of the level of effort required to conduct
further study of site risks using the simple or Figure 1-2. Components of the Soli Screening
detailed site-specific methods. framework.
1.3.1 Site-Specific SSLs: Simple Method. The simple method for developing site-specific
SSLs requires the collection of a small number of easily obtained site parameters for use in the
standardized SSL equations so that the calculated screening levels can be appropriately conservative for
the site but not as conservative as the generic values. Once derived, the user compares measured site or
area contaminant concentrations to the site-specific screening levels. If concentrations do not exceed the
SSLs for each pathway of concern, it would generally be appropriate to exclude the area from further
investigation. If the levels are exceeded, the site manager may decide that a more comprehensive
evaluation is needed to determine the risk posed via a particular exposure pathway.
To address the site-specific potential for volatilization of contaminants, soil conditions such as fraction
organic carbon, average soil moisture content, and dry bulk density must be determined or estimated for
a site, along with measurement of the area of contamination (i.e., source area). To address the potential
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for generation of fugitive dusts, other data must be collected, such as roughness height, mean annual
windspeed, and threshold friction velocity. Addressing potential contaminant migration to ground water
requires the same parameters mat are needed to address volatilization, along with simple hydrogeologic
variables necessary to estimate contaminant dilution in the saturated zone. A leach test also is included
as an option for estimating soil leachate concentrations for this pathway.
Section 3.2 presents detailed guidance on the site characterization activities required to apply the simple
site-specific method. Part 2 of this document provides more detail on the development of the pathway-
specific equations, assumptions, and methodology that form the basis for both the simple site-specific
approach and the generic SSLs.
1.3.2 Site-Specific SSLs: Detailed Approach. A more detailed method for developing site-
specific SSLs involves conducting a full-scale model evaluation at a site. This option involves the
application of more complex transport and fate models and allows for consideration of a finite contaminant
source. Applying such models can more accurately define the risk of exposure via inhalation or migration
to ground water and may show that there is no further concern from the pathway in question. However,
these more complex models also can require collection of considerably more site data than the simple site-
specific option. Section 3.4 presents information on the selection and use of models for the detailed
method.
1.3.3 Generic SSLs. Generic SSLs can be used in place of site-specific screening levels where the
cost or time of additional investigation is not warranted. Table 3-9 in Part 3 provides generic SSLs for
107 chemicals for three exposure pathways: ingestion, inhalation of volatiles and fugitive dusts, and
migration to ground water. Part 2 describes development of the specific default input values used to
calculate generic SSLs. If the generic SSLs are not exceeded for any of the three pathways, the user may
eliminate those pathways, or areas of the site, from further investigation. If more than one exposure
pathway is of concern, the lowest SSL should be used.
1.4 Scope of the Soil Screening Framework
1.4.1 Exposure Pathways. The Soil Screening framework has been developed for 107 chemicals
using assumptions for residential land use activities for three pathways of exposure to site contaminants
(see Figure 1-3):
• Ingestion of soil
• Inhalation of volatiles and fugitive
dusts
• Ingestion of contaminated ground
water caused by migration of
contaminants through soil to an
underlying potable aquifer.
Reviews of risk assessments at hazardous
waste sites indicate that these pathways are
the most common routes of human exposure
to contaminants in the residential setting.
These are also the pathways for which
generally accepted methods, models, and
assumptions have been developed that lend
Direct Ingestion
of Ground
Water and Soil
Inhalation
Blowing
Oust and
Volatilization
Figure 1-3. Exposure pathways addressed by the
Soil Screening framework.
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Highlight 1-2: Key Attributes of the Soil Screening Framework
• Standardized equations are presented to address three individual human exposure
pathways.
• Parameters are identified for which site-specific information is needed to develop site-
specific SSLs.
• Default values are provided to calculate generic SSLs that are consistent with Superfund's
concept of "Reasonable Maximum Exposure" (RME).
• Generic SSLs are generally based on a 10"6 risk for carcinogens, or a hazard quotient of
1 for noncarcinogens; SSLs for migration to ground water are based on nonzero
maximum contaminant level goals (MCLGs). or, when MCLGs are not available, maximum
contaminant levels (MCLs). When neither of these are available, the aforementioned risk-
based targets are used.
themselves to a standardized approach. Key attributes of the Soil Screening framework are given in
Highlight 1-2.
Other Pathways. Additional exposure pathways to contaminants in soil—dermal adsorption, plant
uptake, and migration of volatiles into basements—may contribute significantly to the risk to human
health from exposure to specific contaminants in a residential setting. Factoring these exposure pathways
into the Soil Screening framework is limited by the amount of data available to quantify the potential
absorption of chemicals from the soil matrix through skin and the potential for contaminant uptake into
plants (Section 2.1). For dermal exposure, pentachlorophenol is the only SSL chemical for which
available data are adequate to suggest that exposure through the dermal route equals or exceeds ingestion
exposure. The ingestion SSL for this chemical is divided in half to account for this additional exposure.
For the soil-plant-human pathway, empirical data are sufficient at this time only to identify certain
inorganic contaminants that have the greatest potential for uptake into plants (see Table 3-1, Section 3.1.1).
The Office of Emergency and Remedial Response (OERR) is working on methods to estimate exposure
through the soil-plant-human exposure pathway (see Section 2.1 and Appendix A) and may incorporate
these methods into the framework at a later date.
At this time, OERR does not believe that the potential for migration of contaminants into basements can
reasonably be incorporated into the SSL framework. Parameters required for the models (e.g., field soil
air permeability; the number and size of cracks in foundation or basement walls) do not lend themselves
to standardization or national estimates for evaluation of future exposure. In addition, modeling for
organic vapor intrusion into basements has not been adequately validated. See Section 2.3.6 and Appendix
B for a detailed analysis of the available modeling for this pathway.
Ecological Receptors. As part of the baseline risk assessment, an ecological assessment should be
conducted at every Superfund site. The SSL Framework does not attempt to define significant ecological
receptors or quantify ecological risks. However, a comparable list of screening level benchmarks, called
Ecotox Thresholds, is being developed by OERR for application during the ecological risk assessment
addressed in OSWER Directive No. 9285.7-17 (U.S. EPA, 1994J). These values are defined as media-
specific chemical concentrations above which there is sufficient concern regarding adverse effects to
ecological receptors to warrant further site investigation. OERR is developing guidance on designing and
conducting ecological risk assessments that will describe the use of such screening values in the Superfund
Remedial Investigation process.
*
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Other Land Uses. Longer-term efforts will be required to develop standardized tools to address
exposures relevant to industrial and other land uses. The results of these efforts may be included in future
revisions of the Soil Screening guidance.
1.4.2 Exposure Assumptions. The models and assumptions supporting the Soil Screening
framework were developed to be consistent with Superfund's concept of "reasonable maximum exposure"
in the residential setting. The Risk Assessment Guidance for Superfund, Volume 1 (U.S. EPA, 1989d) and
the Standard Default Exposure Factors guidance (U.S. EPA, 1991b) outlined the Superfund program's
approach to calculating a Reasonable Maximum Exposure (RME). Since that time, the Agency (U.S.
EPA, 199la) has coined a new term that the Superfund program believes corresponds to the definition of
RME: "high-end individual exposure."
The Superfund program's method to estimate the RME for chronic exposures on a site-specific basis is
to combine an average exposure point concentration with reasonably conservative values for intake and
duration in the exposure calculations. The default intake and duration assumptions are presented in the
Standard Default Exposure Factors guidance (U.S. EPA, 199 Ib). The duration assumptions were chosen
to represent individuals living in a small town or other nontransient community. (Exposure to members
of a more transient community is assumed to be shorter and thus associated with lower risk.) Exposure
point concentrations are either measured at the site (e.g., ground water concentrations at a receptor well)
or estimated using exposure models with site-specific model inputs. An average concentration term is
used in most assessments where the focus is on estimating long-term, chronic exposures. Where the
potential for acute toxicity is of concern, exposure estimates based on maximum concentrations may be
more appropriate.
The resulting site-specific estimate of RME is then compared with a chemical-specific toxicity criterion
such as a Reference Dose (RfD) or a Reference Concentration (RfC). EPA recommends using criteria
from the Integrated Risk Information System (IRIS) (U.S. EPA, 1994e) and Health Effects Assessment
Summary Tables (HEAST) (U.S. EPA, 1993e), although values from other sources may be used in
appropriate cases.
The Soil Screening framework differs from a site-specific estimate of risk in that the exposure equations
and models are run in reverse to backcalculate to an "acceptable level" of contaminant in soil. Toxicity
criteria are used to define the acceptable level: a level corresponding to a 10"6 risk for carcinogens and
a hazard quotient (HQ) of 1 for noncarcinogens. The concept of backcalculating to an acceptable level
in soil was presented in RAGS Part B (U.S. EPA, 1991d), and the Soil Screening framework serves to
update Part 8 for addressing residential soils. Site-specific SSLs are consistent with the Superfund
approach to estimating RME on a site-specific basis. Standard default factors are used for the intake and
duration assumptions, site-specific inputs are used in the exposure models, and chemical-specific
concentrations averaged over the exposure area are used for comparison to the SSLs.
Consistent with the site-specific SSLs, generic SSLs use the same intake and duration assumptions and
are compared to area average concentrations. The generic SSLs are based on a hypothetical site model.
In developing the parameters for the hypothetical site, the Superfund program considered the conservatism
inherent in the exposure models (e.g., assumption of an infinite source) and then combined high-end and
central tendency parameters for size, location, and soil characteristics. The resulting generic SSLs should
be protective for most site conditions across the Nation.
OERR performed a sensitivity analysis to determine which parameters most influenced the output of the
volatilization and fugitive dust models used to calculate SSLs for the inhalation pathway. For fugitive
dusts, the paniculate emission factor (PEF) was most sensitive to threshold friction velocity, which was
set at a "high-end" value. For calculation of the volatilization factor (VF), soil moisture content was set
1-6
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Review Draft—Do Not Cite or Quote—December 1994
at a conservative value because it drives the air-filled soil porosity that, in turn, provides the pathway for
chemicals to volatilize from soils. Climatic conditions have a significant impact on dispersion of both
volatile and participate emissions and were set at high-end values to be protective for conditions at most
sites. Different high-end meteorological data sets were selected to calculate 90th percentile dispersion
coefficients for the VF and for the PEF (see Section 2.3).
For the migration of contaminants from soils to ground water, only average soil conditions are used to
calculate generic SSLs because of the conservatism inherent in the partition equation. The generic DAF
for this pathway was developed using a weight-of-evidence approach to be protective under most
hydrogeologic conditions across the country as described in Section 2.4.
1.4.3 Risk Level. The SSLs for direct ingestion and inhalation correspond to a 10*6 risk level for
carcinogens and an HQ of 1 for noncarcinogens (Section 2.1). This "target" hazard quotient is used to
calculate a soil concentration below which it is unlikely for even sensitive populations to experience
adverse health effects. The potential for additive effects has not been "built in" to the SSLs through
apportionment For carcinogens, EPA believes that setting a 10"6 risk level for individual chemicals and
pathways generally will lead to cumulative risks within the risk range (10"4 to 10"6) for the combinations
of chemicals typically found at Superfund sites. For noncarcinogens, additive risks can be considered only
for chemicals with RfDs based on toxic effects on the same target organ (see Section 2.1).
For the migration to ground water pathway, SSLs are backcalculated from acceptable ground water
concentrations, which are (in order of preference): nonzero maximum contaminant level goals (MCLGs),
maximum contaminant levels (MCLs), or health-based limits (HBLs) calculated at the target risk levels
described in the previous paragraph (see Section 2.1).
1.5 How To Use the Soil Screening Framework
As described previously, the Soil Screening framework provides one of three options for determining
SSLs: a simple site-specific method, a detailed site-specific method, or a list of generic SSLs. Selecting
one of these methods for a specific site and applying the SSLs thereby developed involves several steps
(Highlight 1-3), as described in this section. Part 3 of this document describes these steps in greater detail.
Highlight 1-3: Using the Soil Screening Framework
• Develop site conceptual model and compare with SSL conceptual model to determine
applicability of framework.
• Determine if background contaminant concentrations are above generic SSLs.
• Select approach (simple or detailed site-specific, generic) and develop SSLs.
• Measure average soil contaminant concentrations in exposure areas (EAs) of concern.
• Compare average soil levels with SSLs and eliminate site or area of site where EA mean
concentration is less than SSL.
• Consider further study or use of SSLs as PRGs for sites or site areas with contaminant
concentrations greater than SSLs.
1-7
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1.5.1 Developing a Conceptual Site Model. The primary condition for use of the Soil
Screening framework is that exposure pathways of concern and conditions at the site match those taken
into account by the Soil Screening framework. Thus, at all sites it will be necessary to develop a
conceptual site model to identify likely contaminant source areas, exposure pathways, and potential
receptors to assist in determining the extent to which the framework can be applied at the site.
Comparison of this site-specific model with the Soil Screening framework conceptual model forms the
basis for determining the applicability of the framework at the site, the appropriate method for determining
SSLs for the site, and the need for additional information on the site.
The conceptual model upon which the generic SSLs are based is a 30-acre property that has been divided
up for residential use. Thus, the generic SSLs have been developed to be protective for source areas up
to 30 acres. The contamination is assumed to be evenly distributed (i.e., homogeneous) across the area
of concern and extends from the ground surface to the top of the .aquifer. The soil type is assumed to be
loam that has 50 percent vegetative cover. Three exposure pathways are considered: soil ingestion,
inhalation of volatiles and fugitive dusts, and migration to ground water. For the migration to ground
water pathway, the point of compliance (i.e., receptor well) is assumed to be at the edge of the site. No
attenuation is considered in the unsaturated zone; however, dilution is assumed within the aquifer to the
point of compliance. See Figures 1-3 and 1-4 for a graphic representation of aspects of the Soil Screening
framework conceptual model. Section 3.1 provides additional detail on this conceptual model.
A conceptual site model is developed from available site sampling data, historical records, aerial
photographs, and hydrogeologic information. The model establishes a hypothesis about possible con-
taminant sources, contaminant fate and transport, exposure pathways, and potential receptors. The Data
Quality Objectives (DQO) Guidance for Superfund (U.S. EPA, 1993b) and the RI/FS guidance (U.S. EPA,
1989a) provide discussion on the development of a conceptual site model Section 3.1 discusses this
process within the Soil Screening framework. The rationale for including the contaminant migration to
ground water exposure pathway should be consistent with EPA ground water policy (U.S. EPA, 1988a,
1990b, 1992b, 1992d, 1993d).
During development of the conceptual site model,
the following questions should always be
considered before applying the Soil Screening
framework:
Is the site adjacent to surface water-
bodies where the potential for con-
tamination by overland flow or the
release of contaminated ground water
should be considered?
Are there potential ecological con-
cerns?
Is there potential for land use other
than residential?
Are there other likely human expo-
sure pathways that were not consid-
ered in development of the SSLs (e.g.,
local fish consumption; raising of beef,
dairy, or other livestock)?
SECTION VIEW
Raceftor
Land Surface
Assumptions:
• Infinite source
• Source extends to water table
• Well at downgradient edge of source
• 30-acre source size
Figure 1-4. Migration to ground water
pathway—conceptual model.
1-8
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• Are there unusual site conditions (e.g., area of contamination greater than 30 acres,
unusually high fugitive dust levels due to soil being tilled for agricultural use or heavy traffic
on unpaved roads) that may result in higher exposure concentrations than predicted by the
simple site-specific method or generic SSLs?
Answers to these questions will determine the overall applicability of the Soil Screening framework at the
site and will also help determine the appropriate method for developing SSLs. In general, if the
conceptual site model indicates that residential assumptions are appropriate for the site in question and no
pathways of concern other than those covered by the framework are present, then the framework may be
directly applied to the site. If the conceptual model indicates that the site is more complex than the
scenario outlined in this document, then the framework alone will not be sufficient Additional pathways,
receptors, or chemicals should be evaluated on a site-specific basis.
1.5.2 Considering Background Concentrations. A necessary step in determing the usefulness
of the SSL framework is the consideration of background contaminant concentrations, since the framework
will have little utility where background concentrations exceed the SSLs.
EPA may be concerned with two types of background at sites: naturally occurring and anthropogenic.
Natural background is usually limited to metals whereas anthropogenic (i.e., human-made) background
includes both organic and inorganic contaminants.
Generally, EPA does not clean up below natural background; however, where anthropogenic background
levels exceed SSLs and EPA has determined that a response action is necessary and feasible, EPA's goal
will be to develop a comprehensive response to the widespread contamination. This will often require
coordination with different authorities that have jurisdiction over other sources of contamination in the area
(such as a regional air board or RCRA program). This will help avoid response actions that create "clean
islands" amid widespread contaminatioa The background information and understanding of the site
developed as part of the conceptual model can help determine background concentration.
When considering background, one should also consider the bioavailability and mobility of compounds.
Some compounds may form complexes that are immobile and unlikely to cause significant risk. This
situation is more likely to occur with naturally occurring compounds. Therefore, background
concentrations of compounds exceeding the SSLs do not necessarily pose a threat Alternately, activities
at a site can adversely affect the natural soil geochemistry, resulting in the mobilization of compounds.
Consequently, background contamination should be considered carefully. Regardless, where background
concentrations are higher than the SSLs, the SSLs, generally will not be the best tool for site
decisionmaking.
1.5.3 Sampling Exposure Area. After the conceptual site model has been developed, and the
applicability of the Soil Screening framework is determined, the next step is to collect a representative
sample set for each exposure area. An exposure area is defined as that geographic area in which an
individual may be exposed to contamination over time. Because SSLs are developed for a residential
scenario, EPA assumes the exposure area is a 0.5-acre residential lot.
In those situations where little or no sampling has been done, it will be beneficial to collect the site data
required for the simple site-specific methodology in tandem with the collection of samples to identify
contaminant concentrations. The site manager should work to limit the total number of trips to the site
by maximizing the usefulness of the samples collected. Part 4 of this document provides guidance on
sampling sites under the Soil Screening framework.
1-9
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1.5.4 Comparing Soil Levels with SSLs. The final step is to compare onsite soil contaminant
concentrations with the site-specific (or generic) SSLs. At this point, it is reasonable to review the
conceptual site model with the actual site data in hand to reconfirm the accuracy of the model and the
applicability of the Soil Screening framework. Once this is confirmed, site contaminant levels may be
compared with the SSLs. Generally, this comparison will result in one of three outcomes:
1. Site-measured values indicate that an area falls well below all of the SSLs. Soils from these
areas of the site can be eliminated from further evaluation under CERCLA.
2. Site-measured data indicate that one or more SSLs have clearly been exceeded. In this case,
the SSLs have helped to identify site areas, contaminants, and exposure pathways of potential
concern on which to focus further analysis or data-gathering efforts.
3. A site-measured value exceeds one pathway-specific value but not others. In this case it is
reasonable to focus additional site-specific data collection efforts only on data mat will help
determine whether there is truly a risk posed via that pathway or by a limited set of chemicals
at the site. When an exceedence is marginally significant, a closer look at site-specific
conditions and exposures may result in the area being eliminated from further study.
If more than one exposure pathway is of concern at a site, comparison of contaminant concentrations with
the lowest SSL should be used to eliminate the site or area of the site from further consideration under
CERCLA.
For noncarcinogens, SSLs do not consider the potential for acute exposures or additive effects at a site.
If there is reason to believe that exposures at a site may be significant over a short period of time (e.g.,
extensive soil excavation work in a dry region), depending on the contaminant, the site manager should
consider the potential for acute health effects as well. Additive effects must be considered if several
chemicals at a site show toxic effects on the same target organ (Section 2.1). If more than one chemical
detected at a site affects the same target organ, the SSLs for each chemical in the group should be divided
by the number of chemicals present The concentration of contaminants at the site should then be
compared to the SSLs that have been modified to account for this potential additivity.
1.5.5 Use of SSLs as Preliminary Remediation Goals/Cleanup Levels. SSLs are not
nationwide cleanup levels or standards. Where the basis for response action exists and all exposure
pathways of concern are addressed by the SSLs, the SSLs may serve as PRGs as defined in The Human
Health Evalualtion Manual (HHEM), Part B (U.S. EPA, 1991d). A PRG is a strictly risk-based value that
serves as the point of departure for the establishment of site-specific cleanup levels. PRGs are modified
to become final cleanup levels based on a consideration of the nine-criteria analysis described in the
National Contingency Plan (NCP, Section 300.430 (3)(2)(i)(A)), including cost, long-term effectiveness,
and implementability. See Role of the Baseline Risk Assessment in Superfund Remedy Selection Decisions
(U.S. EPA, 1991e) for guidance on how to modify PRGs to generate cleanup levels.
The SSLs should be used as site-specific cleanup levels only when a nine-criteria evaluation using the
SSLs as PRGs for soils indicates that a selected remedy achieving the SSLs is protective, complies with
the applicable or relevant and appropriate requirements (ARARs), and appropriately balances the other
criteria, including cost. An example is a small site or exposure area where the cost of additional study
would exceed the cost of remediating to the generic SSLs.
1-10
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Review Draft—Do Not Cite or Quote—December 1994
Technical Background Document for
Soil Screening Guidance
Part 2: DEVELOPMENT
Review of risk assessments at hazardous waste sites indicates that the three most common pathways of
human exposure to soil contaminants in the residential setting are direct ingestion of soil, inhalation of
volatiles and fugitive dusts, and ingestion of ground water contaminated by chemicals migrating through
soil to ground water. The Soil Screening framework presented in this document has been developed
considering these three pathways using "reasonable maximum exposure" (RME) assumptions (see Section
1.4.2) for a residential setting. The methodology used to develop Soil Screening Levels (SSLs) under this
framework, including the human health basis, models, and assumptions, is described for each pathway in
the following sections.
2.1 Human Health Basis
Table 2-1 lists the regulatory and human health benchmarks used to develop SSLs for each SSL chemical
including drinking water standards (maximum contaminant level goals [MCLGs] and maximum
contaminant levels [MCLs]); drinking water health-based levels (HBLs); oral cancer slope factors; and
oral, noncancer reference doses (RfDs) for the subject chemicals. The human health benchmarks (i.e.,
HBLs, slope factors, RfDs) were obtained from the Integrated Risk Information System (IRIS) (U.S. EPA,
1994e) unless otherwise indicated. MCLGs and MCLs were obtained from U.S. EPA (1994a). The use
of these values for calculating ingestion, inhalation, and migration to ground water SSLs is described in
the following section, along with how other potential pathways for human exposure to contaminants in
soil were considered in the development of the Soil Screening framework.
2.1.1 Direct Ingestion and Inhalation. For soil ingestion and inhalation of volatiles and fugitive
dusts, SSLs correspond to a 10~6 risk level for carcinogens and a hazard quotient (HQ) of 1 for
noncarcinogens. For carcinogens, the U.S. Environmental Protection Agency (EPA) believes that setting
a 10~6 risk level for individual chemicals and pathways generally will lead to cumulative risks within the
10"4 to 10"6 range for the combinations of chemicals typically found at Superfund sites.
For noncarcinogens, the issue of cumulative risk is much more complex, because risk is evaluated based
on the theory that a threshold exists for noncancer effects. The threshold level, below which adverse
effects are not expected to occur, is the basis for the Agency's RfD and reference concentration (RfC).
Since adverse effects are not expected to occur at the RfD or RfC and the SSLs were derived by setting
the potential exposure dose equal to the RfD or RfC (i.e., an HQ equal to 1), it is difficult to address the
risk of exposure to multiple chemicals at levels where the individual chemicals are not expected to cause
any harmful effect.
The Agency believes, and the Science Advisory Board (SAB) agrees (U.S. EPA, 1993h), that HQs should
be added only for those chemicals with the same toxic endpoint and/or mechanism of action. It will be
necessary to divide SSLs developed under the Soil Screening framework to account for additivity where
several contaminants at a site have RfDs/RfCs based on the same endpoint of toxicity (i.e., has the same
critical effect as defined by the Reference Dose Methodology). Furthermore, for certain noncarcinogenic
organics (e.g., ethylbenzene, toluene), the generic SSLs are not based on toxicity but are determined
instead by a "ceiling limit" concentration (C^) above which these chemicals may occur as nonaqueous
phase liquids (NAPLs) in soil (see Section 2.3.4). Therefore, the potential for additive effects could not
be considered in the development of generic SSLs and must be evaluated at every site. Table 2-2 lists
2-1
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Table 2-1. Regulatory and Human Health Benchmarks Used for SSL Development
Maximum
contaminant
level goal (mg/L)
CAS Chemical Name MCLG
Number (PMCLQ) Ref.*
83-32-9 Acenaphthene
67-64-1 Acetone (2-Propanone)
309-00-2 Aldrln
120-12-7 Anthracene
7440-36-0 Antimony 6.0E-03 3
7440-38-2 Arsenic
7440-39-3 Barium 2.0E-00 3
71-43-2 Benzene zero 3
56-55-3 Benzo(a)anthracene zero 3
205-99-2 Benzo(b)fluoranthene zero 3
207-08-9 Benzo(A)fluoranthene zero 3
65-85-0 Benzole add
50-32-8 Benzo(a)pyrene
7440-41-7 Beryllium 4.0E-03 3
1 1 1 -44-4 Bis(2-chlorethyl)ether
117-81-7 Bis(2-ethylhexyl)phthalate
75-27-4 Bromodichloromethane
75-25-2 Bromoform (tribromomethane)
71-36-3 Butanol
85-68-7 Butyl benzyl phthalate zero 3
7440-43-9 Cadmium 5.0E-03 3
86-74-8 Carbazole
75-15-0 Carbon disulfide
56-23-5 Carbon tetrachloride zero 3
57-74-9 Chlordane zero 3
106-47-8 p-Chloroaniline •
108-90-7 Chlorobenzene 1.0E-01 3
124-48-1 Chlorodibromomethane 6.0E-02 3
67-66-3 Chloroform zero 3
95-57-8 2-Chlorophenol
7440-47-3 Chromium 1.0E-01 3
Maximum
contaminant
level (mg/L)
MCL
(PMCL) Ref.*
6.0E-03 3
5.0E-02 3
2.0E+00 3
5.0E-03 3
(1.0E-04) 3
(2.0E-04) 3
(2.0E-04) 3
2.0E-04 3
4.0E-03 3
6.0E-03 3
1.0E-01 3
1.0E-01 3
(1.0E-01) 3
5.0E-03 3
5.0E-03 3
2.0E-03 3
1.0E-01 3
1.0E-01 3
1.0E-01 3
1.0E-01 3
Water health
based limits
(mg/L)
HBLb Basis
2E+00 RID
4E+00 RID
5E-06 SF
1E+01 RID
1E+02 RID
8E-05 SF0
4E+00 RID
4E-03 SF0
4E+00 RID
1E-01 RID
2E-01 RID
Cancer slope factor *
(mg/kg-d)-1
Care.
Class" SFe Ref.*
D
B2 1.7E+01 1
D
A 1.8E+00 1
A 2.9E-02 1
B2 7.3E-01 4
B2 7.3E-01 4
B2 7.3E-02 4
B2 7.3E+00 1
B2 4.3E+00 1
B2 1.1E+00 1
B2 1 .4E-02 1
B2 1.3E-01 1
B2 7.9E-03 1
D
C
B2 2.0E-02 2
B2 1.3E-01 1
B2 1.3E+00 1
D
C 8.4E-02 1
B2 6.1E-03 1
A
Unit risk factor
(ng/mV
Care.
Class" URF Ref.*
D
B2 4.9E-03 1
D
A 4.3E-03 1
A 8.3E-06 1
B2
B2
B2
B2
B2 2.4E-03 1
B2 3.3E-04 1
B2
62
82 1.1E-06 1
D
C
B1 1.8E-03 1
B2 1.5E-05 1
B2 3.7E-04 1
D
C
B2 2.3E-05 1
A 1.2E-02 1
Reference dose
(mg/kg-d)
RfD Ref*
6.0E-02
1.0E-01
3.0E-05
3.0E-01
4.0E-04
3.0E-04
7.0E-02
4.0E+00 1
5.0E-03 1
2.0E-02 1
2.0E-02 1
1.0E-01 1
2.0E-01 1
5.0E-04 1
1.0E-01 1
7.0E-04 1
6.0E-05 1
4.0E-03 1
2.0E-02 1
2.0E-02 1
1.0E-02 1
5.0E-03 1
5.0E-03 1
Reference
concentration
(mg/m3)
RfC Ref.*
5.0E-04 2
1.0E-02 2
2.0E-02 2
N)
tb
O
3
I
o
(D
O
i
S
I
I
_t
-------
Table 2-1 (continued)
Maximum
contaminant
level goal (mg/L)
CAS Chemical Name MCLQ
Number (PMCLQ) Ref.*
218-01-9 Chrysene zero 3
7440r50-8 Copper 1.3E+00 3
57-12-5 Cyanide (amenable) (2.0E-01) 3
72-54-8 ODD
72-55-9 DDE
50-29-3 DDT
53-70-3 Dibenzo(a,/))anthracene zero 3
84-74-2 Dl-n-butyl phthalate
95-50-1 1,2-Dlchlorobenzene • 6.0E-01 3
106-46-7 1 ,4-Dichlorobenzene 7.5E-02 3
91-94-1 3,3-Dlchlorobenzidlne
75-34-3 1,1-Dichloroethane
107-06-2 1,2-Dlchloroethane zero 3
75-35-4 1.1-D!chloroethylene 7.0E-03 3
156-59-2 c/s-1,2-Dlchloroethylene
156-60-5 frans-1,2-Dichloroethylene 1:OE-01 3
120-83-2 2,4-Dlchlorophenol
78-87-5 1 ,2-Dlchloropropane zero 3
542-75-6 1 ,3-Dichloropropene zero 3
60-57-1 Dieldrin
84-66-2 Dlethyl phthalate
105-67-9 2,4-Dimethylphenol
131-11-3 Dimethyl phthalate
51-28-5 2,4-Dinltrophenol
121-14-2 2,4-Dlnltrotoluene
606-20-2 2.6-Dlnltrotoluene
1 1 7-84-0 Di-n-octyl phthalate
115-29-7 Endosullan
72-20-8 Endrin 2.0E-03 3
100-41-4 Ethylbenzene 7.0E-01 3
Maximum
contaminant
level (mg/L)
MCL
(PMCL) Ref.'
(2.0E-04) 3
(2.0E-01) 3
(3.0E-04) 3
6.0E-01 3
7.5E-02 3
5.0E-03 3
7.0E-03 3
7.0E-02 3
1.0E-01 3
5.0E-03 3
2.0E-03 3
7.0E-01 3
Water health
based limits
(mg/L)
HBL" Basis
4E-04 SF6
3E-04 SF0
3E-04 SF0
4E+00 RfD
2E-04 SF0
4E+00 RfD
1E-01 RID
5E-04 SF0
5E-06 SF0
3E+01 RfD
7E-01 RfD
4E+02 RfD
7E-02 RfD
7E-02 RfD
4E-02 RfD
7E-01 RfD
2E-01 RfD
Cancer slope factor
(ing/kg-d)'1
Care.
Class0 SF0 Ref.*
B2 7.3E-03 4
D
D
B2 2.4E-01 1
B2 3.4E-01 1
B2 3.4E-01 1
B2 7.3E+00 4
D
D
B2 2.4E-02 2
B2 4.5E-01 1
C
B2 9.1E-02 1
C 6.0E-01 1
D
B2 6.8E-02 2
B2 1.8E-01 2
B2 1.6E+01 1
D
D
D
D
Unit risk factor
(ug/mY
Care.
Class0 URF Ref.*
D
D
B2
B2
B2 9.7E-05 1
B2
D
D
B2
B2
C
B2 2.6E-05 1
C 5.0E-05 1
D
B2
B2 3.7E-05 2
B2 4.6E-03 1
D
D
D
D
Reference dose
(mg/kg-d)
RfD Ref.*
2.0E-02 1
5.0E-04 1
1.0E-01 1
9.0E-02 1
1.0E-01 7
9.0E-03 1
1.0E-02 2
2.0E-02 1
3.0E-03 1
3.0E-04 1
5.0E-05 1
8.0E-01 1
2.0E-02 1
1.0E+01 2
2.0E-03 1
2.0E-03 1
1.0E-03 2
2.0E-02 2
6.0E-03 1
3.0E-04 1
1.0E-01 1
Reference
concentration
(mg/m3)
RfC Ref.*
2.0E-01 2
8.0E-01 1
5.0E-01 2
4.0E-03 1
2.0E-02 1
1.0E+00 1
to
U)
J
S
o
9
o
§
i
i
(continued)
-------
Table 2-1 (continued)
Maximum
contaminant
level goal (mg/L)
CAS Chemical Mama MCLQ
Number (PMCLO) Ref.*
206-44-0 Fluoranthene
86-73-7 Fluorene
76-44-8 Heptachlor zero 3
1024-57-3 Heptachlor epoxids zero 3
118-74-1 Haxachlorobenzene zero 3
87-68-3 Hexachloro-1.3-butadiene 1.0E-03 3
319-84-6 ot-HCH (a-BHC)
319-85-7 p-HCH (0-BHC)
58-89-9 rHCH (lindane) 2.0E-04 3
77.47.4 Hexachlorocyclopentadlene 5.0E-02 3
67-72-1 Hexachloroethane
193-39-5 lndeno(1 ,2,3-c,d)pyrene zero 3
78-59-1 Isophorone
7439-97-6 Mercury 2.0E-03 3
72-43-5 Methoxychlor 4.0E-02 3
74-83-9 Methyl bromide
74-87-3 Methyl chloride
75-09-2 Methylene chloride zero 3
95-48-7 2-Methylphenol (o-cresol)
91-20-3 Naphthalene
7440-02-0 Nickel 1.0E-01 3
98-95-3 Nitrobenzene
86-30-6 W-Nitrosodiphenylamine
621-64-7 fV-Nltrosodi-n-propylamlne
87-86-5 Pentachlorophenol zero 3
108-95-2 Phenol
129-00-0 Pyrene
7782-49-2 Selenium 5.0E-02 3
7440-22-4 Silver
100-42-5 Stryena 1.0E-01 3
Maximum
contaminant
level (mg/L)
MCL
(PMCL) Ref.*
4.0E-04 3
2.0E-04 3
1.0E-03 3
2.0E-04 3
5.0E-02 3
(4.0E-04) 3
2.0E-03 3
4.0E-02 3
5.0E-03 3
1.0E-01 3
1.0E-03 3
5.0E-02 3
1 .OE-01 3
Water health
based limits
(mg/L)
HBLb Basis
1E+00 RfD
1E+00 RfD
1E-03 SF0
1E-05 SF0
5E-05 SF0
6E-03 SF0
9E-02 SF0
5E-02 RfD
2E+00 RfD
1E+00 RfD
2E-02 RfD
2E-02 SF0
1E-05 SFa
2E+01 RfD
1E+00 RfD
2E-01 RfD
Cancer slope factor
(mg/kg-d)-1
Care.
Class" SF0 Ref.*
D
D
B2 4.5E+00
B2 9.1E+00
B2 1.6E+00
C 7.8E-02
B2 6.3E+00
C 1.8E+00
B2 1.3E+00 2
D
C 1.4E-02 1
B2 7.3E-01 4
C 9.5E-04 1
D
D
D
B2 7.5E-03 1
C
D
A
D
B2 4.9E-03 1
B2 7.0E+00 1
B2 1.2E-01 1
D
D
D
D
Unit risk factor
(M9/mV
Care.
Class0 URF Ref.*
D
B2 1.3E-03
B2 2.6E-03
B2 4.6E-04
C 2.2E-05
B2 1.8E-03
C 5.3E-04
C
D
C 4.0E-06 1
B2
C
D
D
D
B2 4.7E-07 1
C
D
A 2.4E-04 1
D
B2
B2
B2
D
D
D
D
Reference dose
(mg/kg-d)
RfD Ref.*
4.0E-02
4.0E-02
5.0E-04
1.3E-05
8.0E-04
2.0E-04 2
3.0E-04 1
7.0E-03 1
1.0E-03 1
2.0E-01 1
3.0E-04 2
5.0E-03 1
1.4E-03 1
6.0E-02 1
5.0E-02 1
4.0E-02 6
2.0E-02 1
5.0E-04 1
3.0E-02 1
6.0E-01 1
3.0E-02 1
5.0E-03 1
5.0E-03 1
2.0E-01 1
Reference
concentration
(mg/m3)
RfC Ref.*
7.0E-05 2
3.0E-04 2
5.0E-03 1
3.0E+00 2
2.0E-03 2
1.0E+00 1
to
(continued)
-------
Table 2-1 (continued)
Maximum
contaminant
level goal (mg/L)
CAS Chemical Name MCLQ
Number (PMCLQ) Ref."
79-34-5 1 , 1 ,2,2-Tetrachloroethane
127-18-4 Tetrachloroethylene zero 3
7440-28-0 Thallium 5.0E-04 3
108-88-3 Toluene 1.0E+00 3
8001-35-2 Toxaphene zero 3
120-82-1 1.2,4-Trichlorobenzene 7.0E-02 3
71-55-6 1,1.1-Trlcrtloroethane 2.0E-01 3
79-00-5 1,1,2-Trichloroethane 3.0E-03 3
79-01-6 Trichloroethylene zero 3
95-95-4 2,4,5-Trichlorophenol
88-06-2 2,4,6-Trichlorophenol
7440-62-2 Vanadium
108-05-4 Vinyl acetate
75-01-4 Vinyl chloride (chloroethene) zero 3
1330-20-7 Xylenes 1.0E+01 3
7440-66-6 Zinc
Maximum
contaminant
level (mg/L)
MCL
(PMCL) Ret.*
5.0E-03 3
2.0E-03 3
1.0E+00 3
3.0E-03 3
7.0E-02 3
2.0E-01 3
5.0E-03 3
5.0E-03 3
2.0E-03 3
1.0E+01 3
Water health
based limits
(mg/L)
HBLb Basis
4E-04 SF0
4E+00 RfD
8E-03 SF0
2E-01 RfD
4E+01 RfD
1E+01 RfD
Cancer slope (actor
(ing/kg-d)'1
Care.
Class" SF0 Ref.*
C 2.0E-01 1
5.2E-02 5
D
B2 1.1E+00 1
D
D
C 5.7E-02 1
1.1E-02 5
B2 1.1E-02 1
A 1.9E+00 2
D
D
Unit risk factor
(MO/mY
Care.
Class0 URF Ref.*
C 5.8E-05 1
5.8E-07 5
D
B2 3.2E-04 1
D
D
C 1.6E-05 1
1.7E-06 5
B2 3.1E-06 1
A 8.4E-05 2
D
D
Reference dose
(mg/kg-d)
RfD Ref.*
1.0E-02 1
2.0E-01 1
1.0E-02 1
4.0E-03 1
1.0E-01 1
7.0E-03 2
1.0E+00 1
2.0E+00 1
3.0E-01 1
Reference
concentration
(mg/m3)
RfC Ref.*
4.0E-01 1
9.0E-03 2
1.0E+00 5
2.0E-01 1
3)
3
I
t>
o
o
!
a References: 1 = IRIS. U.S. EPA (1994e)
2 = HEAST. U.S. EPA (1993e)
3 = U.S. EPA(1994a)
4 - OHEA (19930
5 = Interim toxicity criteria provided by Superfund Health Risk Technical
Support Center, Environmental Criteria Assessment Office (ECAO),
Cincinnati, OH (1994).
6 = ECAO, U.S. EPA (19941)
7 = ECAO, U.S. EPA(1994h)
0 Categorization of overall weight of evidence for human carclnogenlcity:
Group A: human carcinogen
Group B: probable human carcinogen
B1: limited evidence from epldemlologlc studies
B2: "sufficient" evidence from animal studies and 'inadequate' evidence or 'no
data* from epidemiologic studies
Group C: possible human carcinogen
Group D: not classifiable as to health carcinogenicity
Group E: evidence of Non-Carclnogenicity for humans
I
_t
u>
Health based limits calculated for 30-year exposure duration.
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Table 2-2. SSL Chemicals with Noncarcinogenic Effects on Specific Target Organs
Target organ/chemical
Effect
Kidney
Acetone
1,1-Dichloroethane
2,6-Dinrtrotoluene
Di-n-octyl phthalate
Dimethyl phthalate
Nitrobenzene
2,4,5-Trichlorophenol
Vinyl acetate
Liver
Acetone
Chtorobenzene
Di-n-octyl phthalate
Nitrobenzene
2,4,5-Trichlorophenol
Central Nervous System
Butanol
2,4-Dichlorophenol
2,4-Dinitrotoluene
2,6-Dinrtrotoluene
2-Methylphenol
Circulatory System
Antimony
Barium
p-Chloroaniline
c/s-1,2-Dichloroethylene
Nitrobenzene
Zinc
Reproductive System
Carbon disutfide
2-Chlorophenol
1,2,4-Trichlorobenzene
Gross Pathology
Diethyl phthalate
2-Methylphenol
Naphthalene
Nickel
Vinyl acetate
Increased weight; nephrotoxicity
Histology (inhalation study)
Histopathology
Increased weight
Kidney effects
Renal and adrenal lesions
Pathology
Altered weight
Increased weight
Histopathology
Increased weight; increased SGOT and SGPT
Lesions
Pathology
Hypoactivity and ataxia
Decreased delayed hypersensrtivrty response
Neurotoxicity
Neurotoxicity
Neurotoxicity
Increased blood glucose and cholesterol
Increased blood pressure
Nonneoplastic lesions of splenic capsule
Decreased hematocrit and hemoglobin
Hematologic changes
Decrease in erythrocyte superoxide dismutase (ESOD)
Fetal toxicity and malformations
Reproductive effects
Increased adrenal weights; vacuolization in cortex
Decreased growth rate and food consumption; altered organ weights
Weight loss
Weight loss
Weight loss
Altered weight
Source: U.S. EPA, 1994e.
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SSL chemicals with RFDs/RFCs, grouping those chemicals whose RfDs or RFCs are based on toxic
effects on the same target organ.
EPA's Office of Emergency Remedial Response (OERR) is evaluating the current SSL approach for
noncarcinogens in light of two related issues: apportionment and fractionation. Apportionment is typically
used as the percentage of a regulatory health-based level that is allocated to the source/pathway being
regulated (e.g., 20 percent of the RfD for the migration to ground water pathway). Apportioning risk
assumes that the applied dose from the source, in this case contaminated soils, is only one portion of the
total applied dose received by the receptor. Traditionally, OERR has focused on quantifying exposures
to a receptor that are clearly site-related and has not included exposures from other sources such as
commercially available household products or workplace exposures. Depending on the assumptions
concerning other source contributions, apportionment may result in more conservative regulatory levels
(i.e., levels that are below an HQ of 1). In contrast to apportionment, fractionation of risk may lead to
less conservative regulatory levels because it assumes that some fraction of the contaminant does not
reach the receptor due to partitioning into another medium.
For example, if only one-fifth of the source is assumed to be available to the ground water pathway, and
the remaining four-fifths is assumed to be released to air or remain in the soil, an SSL for the migration
to ground water pathway would be set at five times the HQ of 1 due to the decrease in exposure (since
only one-fifth of the possible contaminant is available to the pathway). However, the SSLs are, by
definition, conservative screening, or "walk-away," levels for remedial action, and available data do not
support chemical-specific assumptions necessary for partitioning at a national level. Therefore, soil
contaminants were not fractionated and the lowest chemical-specific SSL among each of the pathways was
selected as the SSL for that particular chemical (i.e., the SSL was selected from the pathway that drives
the risk).*
The exposure assumptions used in the SSL framework are representative of a chronic exposure scenario
and do not account for situations where high-level exposures may lead to acute toxicity. For example,
in some cases, children may ingest large amounts of soil (i.e., 3 to 5 grams) in a single event This
behavior, known as pica, may result in relatively high short-term exposures to contaminants in soils. Such
exposures may be of concern for contaminants that primarily exhibit acute health effects. Review of
clinical reports on contaminants addressed in this guidance suggests that acute effects of cyanide and
phenol may be of concern in children exhibiting pica behavior. If soils containing cyanide and phenol
are of concern and pica behavior is expected at a site, the protectiveness of the ingestion SSLs for these
chemicals should be reconsidered.
Although the draft SSL guidance instructs site managers to consider the potential for acute exposures,
there are two major impediments to establishing national acute SSLs. First, although data are available
on chronic exposures (i.e., RfDs, RfCs, cancer slope factors), there is a paucity of data relating the
potential for acute effects for most Superfund chemicals. Specifically, there is no scale to evaluate the
severity of acute effects (e.g., eye irritation vs. dermatitis), no consensus on how to incorporate the body's
recovery mechanisms following acute exposures, and no toxicity benchmarks to apply for short-term
exposures (e.g., a 7-day RfD for a critical endpoint). Second, the inclusion of acute SSLs would require
the development of acute exposure scenarios that would be acceptable and applicable nationally. Simply
put, the methodology and data necessary to address acute exposures in a manner analogous to chronic
exposures have not been developed. Over the next few months, OERR will be investigating the potential
for acute effects at the chronic SSLs.
*For several noncarcinogenic organic compounds, SSLs were based on the saturation limit (C^) (see Section
2.3.4).
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2.1.2 Migration to Ground Water. For the migration to ground water exposure pathway, SSLs
have been developed to reflect levels below which concentration limits in ground water will not be
exceeded. The methodology uses nonzero drinking water MCLGs as the acceptable ground water
concentration limits for each contaminant. If nonzero MCLGs are not available, MCLs are used, and, if
MQLs are not available, health-based limits are derived using Agency toxicity criteria, a target cancer risk
of 10"6, and/or a noncancer HQ of 1.
2.1.3 Other Pathways. Dermal adsorption, consumption of garden vegetables grown in
contaminated soil, and migration of volatiles into basements are additional exposure pathways that may
contribute risk to human health in a residential setting. OERR does not believe that the potential for
migration of volatiles into basements can be reasonably incorporated into the framework at this time (see
Section 2.3.6). Incorporation of dermal exposures into the Soil Screening framework is limited by the
amount of available data to quantify dermal adsorption from soil. EPA's Office of Research and
Development (ORD) evaluated the available data on absorption of chemicals from soil in the document
Dermal Exposure Assessment: Principles and Applications (U.S. EPA, 1992c). This document also
presents calculations comparing the potential dose of a chemical in soil from oral routes with that from
dermal routes of exposure.
These calculations suggest that, assuming 100 percent absorption of a chemical via ingestion, absorption
via the dermal route must be greater than 10 percent to equal or exceed the ingestion exposure. Of the
SSL compounds, available data are adequate to show greater than 10 percent dermal absorption only for
pentachlorophenol (Wester et al., 1993). Limited data suggest that dermal absorption of benzo(a)pyrene
from soil may exceed 10 percent, but the Agency believes that further investigation is needed for this
compound. Therfore, pentachlorophenol is the only chemical for which there are sufficient data to
demonstrate that dermal exposure may equal or exceed oral exposure. The ingestion SSL for
pentachlorophenol is adjusted to account for this additional exposure (i.e., the ingestion SSL has been
divided in half to account for increased exposure via the dermal route).
Analysis of Records of Decision (RODs) from Superiund sites and experience of State and Regional
offices responsible for site cleanups suggest that the ingestion of contaminated produce from homegrown
gardens may be a significant exposure pathway. OERR evaluated empirical data on plant uptake,
particularly the data presented in the Technical Support Document for Land Application of Sewage Sludge,
often referred to as the "Sludge Rule" (U.S. EPA, 1992h).
OERR found that the Sludge Rule derived empirical plant uptake-response slopes for selected metals but
that the data available were insufficient to evaluate plant uptake of organics. Although recognizing the
need to evaluate all contaminants as pan of the home gardening scenario, OERR considers it inappropriate
to flag organic contaminants for the plant exposure pathway without a solid scientific basis to estimate
plant uptake. In an effort to obtain additional empirical data, OERR has jointly funded research with the
State of California on plant uptake of organic contaminants. These studies support ongoing revisions to
the indirect, multimedia exposure model CalTOX.
The Sludge Rule identified six metals of concern with empirical plant uptake data: arsenic, cadmium,
mercury, nickel, selenium, and zinc. Plant uptake-response slopes were given for seven plant categories
such as grains and cereals, leafy vegetables, root vegetables, and garden fruits. OERR evaluated the study
conditions (e.g., soil pH, application matrix) and methods (e.g., geometric mean, default values) used to
calculate the plant uptake-response slopes for each plant category and determined that the geometric mean
slopes were generally appropriate for calculating SSLs for the soil-plant-human exposure pathway.
However, the geometric mean empirical uptake-response slopes developed in the Sludge Rule must be
interpreted with caution for several reasons. First, the dynamics of sludge-bound metals may differ from
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the dynamics of metals at contaminated sites. For example, the empirical data were derived from a variety
of studies at different soil conditions using different forms of the metal (i.e., salt vs. nonsalt). In studies
where the application matrix was sludge, the adsorption power of sludge in the presence of calcium ions
may have reduced the amount of metal that is bioavailable to plants and, therefore, plant uptake may be
greater in non-sludge-amended soils. In addition to these confounding conditions, default values of 0.001
were assigned for plant uptake in studies where the measured value was below 0.001. A default value
was needed to calculate the geometric mean uptake-response slope values. Moreover, considerable study-
to-study variability is shown in the plant uptake-response slope values (up to 3 orders of magnitude for
certain plant/metal combinations). This variability could result from varying soil characteristics or
experimental conditions, but models have not been developed to relate changes in plant uptake to such
conditions. Thus, the geometric mean values represent typical or central tendency values from the
experiments; actual values at specific sites could show marked variation depending on soil composition,
chemistry, and/or plant type.
OERR has developed a draft approach to calculating SSLs for the soil-plant-human exposure pathway.
The approach is based on the methodology and plant uptake factors in the Sludge Rule and other EPA-
approved algorithms for backcalculating acceptable soil concentrations for this exposure pathway.
However, because of uncertainties associated with applying mean sludge-based uptake factors to
contaminated soils discussed above, OERR has not formally incorporated the method into the Soil
Screening framework at this time. The method has been included as Appendix A to this document for
review and comment OERR has used the information in the Sludge Rule to identify six metals (arsenic,
cadmium, mercury, nickel, selenium, and zinc) of potential concern through the soil-plant-human exposure
pathway for consideration on a site-specific basis. The fact that these metals have been identified should
not be misinterpreted to mean that other contaminants are not of potential concern for this pathway.
OERR is also looking at empirical data and models for estimating plant uptake of organic contaminants
from soils and would like comment on their incorporation into future updates to the framework (see
Appendix A).
2.2 Direct Ingestion
Calculation of SSLs for direct ingestion of soil is based on the methodology presented for residential land
use in RAGS Part B (U.S. EPA, 1991d). Briefly, this methodology backcalculates a soil concentration
level from a target risk (for carcinogens) or hazard quotient (for noncarcinogens). A number of studies
have shown that inadvertent ingestion of soil is common among children 6 and younger (Calabrese et al.,
1989; Davis et al., 1990; Van Wijnen et al., 1990). Therefore, the approach uses an age-adjusted soil
ingestion factor that takes into account the difference in daily soil ingestion rates, body weights, and
exposure duration for children from 1 to 6 years old and others from 7 to 31 years old. The higher intake
rate of soil by children and their lower body weights lead to a lower, or more conservative, risk-based
concentration compared to an adult-only assumption. RAGS Part B uses this age-adjusted approach for
both noncarcinogens and carcinogens.
For noncarcinogens, the definition of an RfD (Section 2.1) has led to debates concerning the comparison
of less-than-lifetime estimates of exposure to the RfD. -Specifically, it is often asked whether the
comparison of a 6-year exposure, estimated for children via soil ingestion, to the chronic RfD. is
unnecessarily conservative.
The RAGS Part B guidance (U.S. EPA, 1991d) links the use of chronic toxicity criteria with a 30-year
exposure, whereas in the proposed Hazardous Waste Identification Rule (57 FR 21450), EPA's Office of
Solid Waste (OSW) set the Concentration Based Exemption Criteria for noncarcinogens by comparing the
6-year childhood exposure with chronic toxicity criteria. EPA's OERR has asserted that the chronic RfD
is protective of sensitive individuals such as children and that combining a more conservative, shorter-term
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exposure scenario with chronic toxicity criteria is overly protective. OSW has held the view that the
chronic RfD should not be exceeded during childhood when the intake of potentially contaminated soil
could be much higher than for adults (or for the time-weighted average of intake rates used in RAGS
PartB).
In their analysis of the issue, the SAB indicates that, for most chemicals, the approach of combining the
higher 6-year exposure for children with chronic toxicity criteria is overly protective (U.S. EPA, 1993h).
However, they noted that there are instances when the chronic RfD may be based on endpoints of toxicity
that are specific to children (e.g., fluoride and nitrates) or when the dose-response curve is steep (i.e., the
difference between the no-observed-adverse-effects level [NOAEL] and an adverse effects level is small).
Thus, depending on the contaminant, exceeding the RfD (i.e., the "acceptable" daily level) over a short
period of time may be cause for concern. The latter case may be of concern given the potential for higher,
childhood exposures to soil. Since little is known about the effects of exceeding an RfD for short periods
of time, OERR decided to establish screening levels that are protective of this increased exposure during
childhood. In essence, this method ensures that the chronic reference dose is not exceeded during this
shorter (6-year) time period (Equation 2-1).
Screening Level Equation for Ingestion of Noncarcinogenic Contaminants in Residential Soil
Screening Level (mg/kg) =
THQ x BW x AT x 365 d/yr
l/RfD0 x 10~* kg/mg x EF x ED x IR
(2-1)
Parameter/Definition (units)
THQ/target hazard quotient (unitless)
BW/body weight (kg)
AT/averaging time (yr)
RfD0/oral reference dose (mg/kg-d)
EF/exposure frequency (d/yr)
ED/exposure duration (yr)
IR/soil ingestion rate (mg/d)
Default
1
15
6a
chemical-specific
350
6
200
a For noncarcinogens, averaging time is equal to exposure duration.
Unlike RAGS Part B, SSLs are calculated only for 6-year childhood
exposure.
Source: RAGS Part B (U.S. EPA, 199 Id).
For carcinogens, both the magnitude and duration of exposure are important Duration is critical because
the toxicity criteria are based on "lifetime average daily dose." Therefore, the total dose received, whether
it be over 5 years or 50 years, is averaged over a lifetime of 70 years. To be protective of exposures to
carcinogens in the residential setting, RAGS Part B (U.S. EPA, 1991d) and OERR focus on exposures to
individuals who may live in the same residence for a "nigh-end" period of time (i.e., 30 years). As
mentioned above, exposure to soil is higher during childhood and decreases with age. Thus, Equation 2-2
uses the RAGS Part B time-weighted average soil ingestion rate for children and adults; the derivation of
this factor is shown in Equation 2-3.
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Screening Level Equation for Ingestion of Carcinogenic Contaminants In Residential Soil
Screening Level (mg/kg) =
TR x AT x 365 d/yr
SF0 x 10-* kg/mg x EF x IFsofl/adj
(2-2)
Parameter/Definition (units)
TR/target cancer risk (unitless)
AT/averaging time (yr)
SF0 /oral slope factor (mg/kg-d)'1
EF/exposure frequency (d/yr)
EF^^j /age-adjusted soil ingestion
factor (mg-yr/kg-d)
Default
IV6
70
chemical-specific
350
114
Source: RAGS Part B (US. EPA, 199 Id).
Equation for Age-Adjusted Soil Ingestion Factor, lFsoll/adj
^sofl/adj
(mg-yr/kg-d)
X EP
agel-6
BW
agel-6
X EP
age7-31
BW.
age7-31
(2-3)
Parameter/Definition (units)
/age-adjusted soil ingestion factor (mg-yr/kg-d)
/ingestion rate of soil age 1-6 (mg/day)
EDagei-e /exposure duration during ages 1-6 (yr)
IRsoi]/age7-3i /ingestion rate of soil age 7-31 (mg/day)
EDjug^.jj /exposure duration during ages 7-31 (yr)
BWagel_6 /average body weight from ages 1-6 (kg)
BWage?-3i /average body weight from ages 7-31 (kg)
Default
114
200
6
100
24
15
70
Source: RAGS Part B (U.S. EPA, 1991d).
Because of the impracticability of developing a site-specific methodology (i.e., the cost of developing site-
specific data) for direct soil ingestion, the generic SSLs calculated using the defaults listed in Equations
2-1, 2-2, and 2-3 are the only SSLs for this route. Table 3-9 in Section 3.3 lists these generic SSLs for
direct ingestion of soil. EPA is evaluating the data available to support adjustment of the exposure
frequency term based on regional climatic conditions (e.g., duration of snow cover).
2.3 Inhalation of Volatiles and Fugitive Dusts
Agency toxicity data indicate that risks from exposure to some chemicals via inhalation far outweigh the
risks via ingestion; therefore, the SSLs have been designed to address this pathway as well. The models
and assumptions used to calculate SSLs for inhalation of volatiles are updates of risk assessment methods
presented in RAGS Part B (U.S. EPA, 1991d). RAGS Part B evaluated the contribution to risk from the
inhalation and ingestion pathways simultaneously. Because toxicity criteria for oral exposures are
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presented as administered doses (in mg/kg-d) and criteria for inhalation exposures are presented as
concentrations in air (in pg/m3), conversion of air concentrations was required to estimate an administered
dose comparable to the oral route. However, EPA's ORD now believes that, due to portal-of-entry effects
and differences in absorption in the gut versus the lungs, the conversion from concentration in air to
internal dose is not always appropriate and suggests evaluating these exposure routes separately.
The models and assumptions used to calculate SSLs for the inhalation pathway are presented in Equations
2-4 through 2-12, along with the default parameter values used to calculate the generic SSLs presented
in Section 3.3. Particular attention is given to the volatilization factor (VF), saturation limit (C^), and
the dispersion portion of the VF and paniculate emission factor (PEF) equations, all of which have been
revised since originally presented in RAGS Pan B. Chemical-specific human health benchmarks used in
these equations are presented in Section 2.1. Other chemical-specific input parameters required by these
equations are given in Tables 3-2 and 3-3 in Section 3.2. The development is described in Part 5 of this
document
2.3.1 Screening Level Equations for Direct Inhalation. The equations used to calculate the
SSL for the inhalation of carcinogenic and noncarcinogenic contaminants are presented in Equations 2-4
and 2-5, respectively. The derivations of VF and PEF have been updated since RAGS Part B was
published and are discussed fully in Sections 2.3.2 and 2.3.5, respectively.
Screening Level Equation for Inhalation of Carcinogenic Contaminants in Residential Soil
Screening Level = ^ TR x AT x 365 d/yr
Ong/kg) lJEf x 1000 pg^g x EF x ED x [ 1 + 1 1
VF PEF
(2-4)
Parameter/Definition (units)
Default
TR/target cancer risk (unitless)
AT/averaging time (yr)
URF/inhalation unit risk factor
(jig/m3)-1
EF/exposure frequency (d/yr)
ED/exposure duration (yr)
VF/soil-to-air volatilization factor
(m3/kg)
PEF/particulale emission factor
(m3/kg)
1(T6
70
chemical-specific
350
30
chemical-specific
6.79 x 108
Source: RAGS Part B (U.S. EPA, 1991d).
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Screening Level Equation for Inhalation of Noncarcinogenic Contaminants in Residential Soil
Screening Level
(rag/kg)
THQ x AT x 365 d/yr
EF x ED x
[_LX(JL._L:
[RfC (VF PEFy
(2-5)
Parameter/Definition (units)
Default
THQ/target hazard quotient (unitless)
AT/averaging time (yr)
EF/exposure frequency (d/yr)
ED/exposure duration (yr)
RfC/inhalation reference
concentration (mg/m3)
VF/soil-to-air volatilization factor
(m3/kg)
PEF/particulate emission factor
(m3/kg) (Equation 2-12)
1
30
350
30
chemical-specific
chemical-specific
6.79 x 108
Source: RAGS Part B (U.S. EPA, 1991d).
To calculate inhalation SSLs, the volatilization factor and paniculate emission factor must be calculated.
The VF and PEF equations can be broken into two separate models: models to estimate the emissions
of volatiles and dusts, and a dispersion model (reduced to the term Q/Q that simulates the dispersion of
contaminants in the atmosphere.
2.3.2 Volatilization Factor. The soil-to-air VF is used to define the relationship between the
concentration of the contaminant in soil and the flux of the volatilized contaminant to air. VF is
calculated from Equation 2-6 using chemical-specific properties (see Part 5) and either site-measured or
default values for soil moisture, dry bulk density, and fraction of organic carbon in soil.
Derivation of the Volatilization Factor
VF (m3/kg) = Q/C x
x a x
(2 x Dri x 6a x KJ
lO^m^cm2
(2-6)
where
a =
ea + (Ps)
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Parameter/Definition (units)
Default
Source
VF/volatilization factor (m3/kg)
Q/CAnverse of the mean cone, at
the center of a 30-acre-square
source (g/m2-s per kg/m3)
T/exposure interval (s)
Dei/effective diffusivity (cm2/s)
9/air-filled soil porosity
D/diffusivity in air (cm2/s)
n/total soil porosity 0^^!^)
w/average soil moisture content
(gwate/gsoil or cm3wate/gsoil)
Pb/dry soil bulk density (g/cnr)
Ps/soil particle density (g/cm3)
partition coefficient
(g-soil/cm3-air)
H/Henry's law constant
(atm-m3/mol)
K^/soil-water partition coefficient
(cm3/g)
K^/organic carbon partition
coefficient (cm3/g)
f^organic carbon content of soil
(g/g)
35.10
95 x 108 s
0.28
chemical-specific
0.43 (loam)
0.1 (10%)
1.5
2.65
(H/Kd) x 41 (41 is a
conversion factor)
chemical-specific
Koc x foc
chemical-specific
0.006 (0.6%)
EQ, 1994 (for Los Angeles, CA)
U.S. EPA, 1991d
EQ, 1992
n-wpb
see Part 5
Carsel and Parrish, 1988
EQ, 1994
(1 - n) Ps
U.S. EPA, 1991d
U.S. EPA, 199 Id
see Part 5
U.S. EPA, 199 Id
see Part 5
Carsel et al., 1988
The Hwang and Falco (1986) model was used as the basis for the VF equation presented in RAGS Part
B. This model was derived from methods presented by Farmer and Letey (1974) and Farmer et al. (1980).
Fanner et al. presented the empirical equation together with experimental data involving the volatilization
of pesticides from a soil surface. To simplify the calculations, the effective diffusivity in soil, D^, which
accounts for tortuosity effects (i.e., physical impediments to vapor transport associated with moisture
content and soil porosity) in porous media, was later approximated by Hwang and Falco using the
effective porosity in dry soil, as shown in Equation 2-7.
RAGS Part B Del Equation for Dry Soil
^
(2-7)
where
Dri = effective diffusivity (cm2/s)
Dj = diffusivity in air (cm2/s)
Hg = effective soil porosity (unitiess).
During the revaluation of RAGS Part B, OERR sponsored a study (Appendix C; EQ, 1992) to validate
the VF equation by comparing the modeled results with data from (1) a bench-scale pesticide study
(Farmer and Letey, 1974) and (2) a pilot-scale study measuring the rate of loss of benzene, toluene,
xylenes, and ethylbenzene from soils using an isolation flux chamber (Radian, 1989). The results of the
study verified the need to modify the VF equation in Part B to take into account the decrease in the rate
of flux due to the effect of soil moisture content on effective diffusivity (Dei).
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As used in the SSL calculations of VF, Dd is again used to account for the tortuosity effects in porous
media. However, the equation used to describe the decreased flux is taken from Millington and Quirk
(1961) (Equation 2-8). The flux calculation is refined to give a decreased flux rate due to reduced air-
filled porosity and, therefore, accounts for the effect of soil moisture content on tortuosity. Use of the
Millington and Quirk expression reduces effective diffusivity and, as a result, reduces emissions.
SSL Dei Equation with Soil Moisture Effects
Dei=DiX(0f3/n2) (2-8)
where
Dd = effective diffusivity (cm2/s)
Dj = diffusivity in air (cm2/s)
6a = air-filled soil porosity
n = total soU porosity
In revising the Dd equation, EQ (1992) cited two studies in addition to Millington and Quirk (1961).
Farmer et al. (1980) and Hartley (1964) defined the soil solution/soil air partition coefficient as the ratio
of the solubility of the contaminant in water to the saturation vapor concentration of the contaminant and
used this ratio to estimate the diffusion between the vapor and nonvapor phase. Farmer et al. postulated
that, although air-filled porosity is found to be a major factor controlling volatilization flux through the
soil-water-air system, the apparent vapor diffusion coefficient does not depend solely on the amount of
air-filled pore space. The presence of liquid films on the solid surfaces not only reduces the porosity but
also modifies the pore geometry and the length of the gas passage (i.e., tortuosity).
The assumptions used to estimate Dei with Equation 2-8 are largely the same as those presented in RAGS
Part B (U.S. EPA, 1991d). For example, Equations 2-7 and 2-8 indicate that vapor phase diffusion is
regarded as the only transport mechanism for both methods (e.g., no transport takes place via nonvapor
phase diffusion and there is no mass flow due to capillary action). The net result of the change is a less
conservative but more realistic estimate of the volatilization, due to consideration of the effect of soil
moisture content on the amount and tortuosity of air-filled pore space.
OERR is considering replacing the modified Hwang and Falco equation with the simplified equation
developed by Jury et al. (1984) that calculates the maximum flux of a contaminant from an infinite source
of contaminated soil. This equation is theoretically consistent with the Jury et al. (1990) finite source
volatilization model identified for use in the detailed site-specific method (see Section 3.4.1). In addition,
further review of the modified Hwang and Falco equation has identified inconsistencies in the model's
treatment of soil moisture.
The apparent diffusivity term in the modified Hwang and Falco equation considers the effect of soil
moisture on tortuosity only, and phase partitioning is expressed solely in terms of the sorbed and vapor
phases at local equilibrium. The apparent diffusion coefficient used in the Jury et al. (1984) equation not
only accounts for the effect of soil moisture on tortuosity but also includes the effect of liquid-phase
diffusivity and expresses phase partitioning in terms of sorbed, vapor, and liquid phases. Additionally,
an extra 0a term present in Equation 2-6 appears to be in error. OERR is currently comparing the two
models and is validating both against pilot-scale volatilization studies. If the analyses indicate that the
Jury model is more appropriate, OERR will present the results of these analyses, along with a revised VF
equation and SSLs, in future revisions to this guidance. Preliminary results indicate that, in spite of the
inconsistencies, the models currently agree within a factor of two, with the Jury model estimating lower
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emissions. Initial analyses also suggest that removing the extra 6a term from the modified Hwang and
Falco equation should bring the models into closer agreement
Other than initial soil concentration, air-filled soil porosity is the most significant soil parameter affecting
the final steady-state flux of volatile contaminants from soil (U.S. EPA, 1980). In other words, the higher
the air-filled soil porosity, the greater the emission flux of volatile constituents. Air-filled soil porosity
is calculated as
9a = n - w pb (2-9)
where
9a = air-filled soil porosity
n = total soil porosity
w = annual average soil moisture content (g/g)
pb = soil dry bulk density (g/cm3),
and
n=l-(Pb/Ps) (2-10)
where
ps = soil particle density (g/cm3).
Of these parameters, average annual soil moisture content (w) has the most significant effect on air-filled
soil porosity and hence volatile contaminant emissions. Sensitivity analyses have shown that soil bulk
density (pb) has too limited a range for surface soils (generally between 1.3 and 1.7 g/cm3) to affect results
with nearly the significance of soil moisture conditions. Therefore, a default bulk density of 1.50 g/cm3,
the mode of the range given for U.S. soils in the Superfund Exposure Assessment Manual (U.S. EPA,
1988b), was chosen to calculate generic SSLs. This value is also consistent with the mean porosity (0.43)
for loam soil presented in Carsel and Parrish (1988).
The default value of w (0.10 or 10 weight percent) was chosen as a conservative compromise between
that required to achieve a monomolecular layer of water on soil particles (approximately 2 to 5 weight
percent) and that required to reduce the air-filled porosity to zero (approximately 29 weight percent). In
this manner, nonpolar or weakly polar contaminants are desorbed readily from the soil organic carbon as
water competes for sorption sites. At the same time, a soil moisture content of 10 percent yields a
relatively conservative air-filled porosity (0.28 or 28 percent by volume). However, this moisture content
corresponds to a water-filled soil porosity (6W) of 0. 15, which lies about halfway between the mean wilting
point (0.09) and mean field capacity (0.20) reported for Class B soils by Carsel et al. (1988). Class B
soils are soils with moderate hydrologic characteristics whose average characteristics are well represented
by a loam soil type. The default value of ps (2.65 g/cm3) was taken from U.S. EPA (1988b) as the
particle density for most soil mineral material. The default value for f^ (0.006 or 0.6 percent) is the mean
value for the top 0.3 m of Class B soils from Carsel et al. (1988). In the simple site-specific method,
these default values are replaced with measurements or estimates reflecting actual site conditions (see
Section 3.2).
2.3.3 Dispersion Model. The box model in RAGS Pan B has been replaced with a Q/C term
derived from a modeling exercise using meteorological data from 29 locations across the United States.
The dispersion model used in the Part B guidance is based on the assumption that emissions into a
hypothetical box will be distributed uniformly throughout the box. To arrive at the volume within the box
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(the cubic meter portion of an air concentration expressed as micrograms per cubic meter), it is necessary
to assign values to the length, width, and height of the box. The length (LS) was the length of a side of
a contaminated site with a default value of 45 m; the width was based on the windspeed in the mixing
zone (V) with a default value of 2.25 m (based on a windspeed of 2.25 m/s); and the height was the
diffusion height (DH) with a default value of 2 m.
However, the assumptions and mathematical treatment of dispersion used in the box model may not be
applicable to a broad range of site types and meteorology and do not utilize state-of-the-art techniques
developed for regulatory dispersion modeling. OERR was very concerned about the defensibility of the
box model and sought a more defensible dispersion model that could be used as a replacement in the Part
B guidance that had the following characteristics:
• Dispersion modeling from a ground-level area source
• Onsite receptor
• A long-term/annual average exposure point concentration
• Algorithms for calculating the exposure point concentration for area sources of different sizes
and shapes.
To identify such a model, OERR held discussions with the U.S. EPA Office of Air Quality Planning and
Standards (OAQPS) concerning recent efforts in developing a new algorithm for estimating ambient air
concentrations from low or ground-level, nonbuoyant sources of emissions. The new algorithm is
incorporated into the Industrial Source Complex Model (ISC2) platform in both a short-term mode
(AREA-ST) and a long-term mode (AREA-LT). Both models employ a double numerical integration over
the source in the upwind and crosswind directions. Wind tunnel tests have shown that the new algorithm
performs well with onsite and near-field receptors. In addition, subdivision of the source is not required
for these receptors.
Because the new algorithm provides better concentration estimates for onsite and for near-field receptors,
a revised dispersion analysis was performed for both volatile and paniculate matter contaminants
(Appendix D; EQ, 1994).
In the case of volatile contaminants, the AREA-ST model was run with a full year of meteorological data
for 29 U.S locations selected to be representative of the national range of meteorologic conditions (EQ,
1993). The modeling runs included square area sources of 0.5 and 30 acres in size. As a reasonably con-
servative estimate for developing generic SSLs for volatile emissions, a default site (Los Angeles, CA)
was chosen that best approximated the 90th percentile of the 29 normalized concentrations (kg/m3 per
g/m2-s). The inverse of this concentration produced a revised default VF Q/C value of 35.10 g/m2-s per
kg/m3 for a 30-acre site. For the simple site-specific method, the user may select a VF Q/C value from
the 29 locations modeled that best represents a site's size and meteorological condition (see Section 3.2.3).
2.3.4 Soil Saturation Limit. Because of its reliance on Henry's law, the VF model is applicable
only when the contaminant concentration in soil water is at or below saturation (i.e., there is no free-phase
contaminant present). This corresponds to the contaminant concentration in soil at which the adsorptive
limits of the soil particles, the solubility limits of the available soil moisture, and saturation of soil pore
air have been reached. Above this point, nonaqueous phase liquids (NAPLs) may occur in the soil for
contaminants that are liquid at ambient soil temperature, and pure solid phases of compounds that are solid
at soil temperatures will occur. Under such conditions (i.e., free-phase contaminant present), the partial
pressure of the pure contaminant and the partial pressure of the air in the interstitial pore spaces cannot
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be calculated without first knowing the mole fraction of the contaminant in the soil, and the SSLs cannot
be accurately calculated with the VF model. In addition, when NAPLs are suspected in site soils, further
site-specific study is required and SSLs are not applicable for the NAPL-associated contaminants. Because
of these limitations, the SSL must be compared with the soil concentration at which the soil particles, soil
air, and soil pore water are saturated with contaminant (C^). If the SSL calculated using VF is greater
than C^u, and the contaminant is liquid at soil temperature, the SSL is set equal to C^. This application
of the C^ equation is consistent with OERR's guidance for determining the likelihood of dense
nonaqueous phase liquid (DNAPL) occurrence in the subsurface (U.S. EPA, 1992d), with Csat serving as
a conservative indicator of potential NAPL occurrence. Refer to this guidance if C^ levels are exceeded
at a site for liquid contaminants.
Contaminants that are solid at ambient soil temperature will only show increased mobility in a free-phase
form in soil if they are associated with other NAPL contaminants. Otherwise, their risk to human health
through the inhalation pathway is associated only with solid components and released as fugitive
particulates (i.e., their saturated vapor phase concentration in soil does not pose a significant inhalation
risk). Inhalation SSLs for such contaminants are calculated using the same equations used for nonvolatile
metals: Equation 2-4 or 2-5 with the 1/VF term equal to zero. Section 3.2.5 provides additional guidance
on interpreting soil contaminant levels with respect to C^, including a table providing the physical state
at soil temperature (i.e., liquid or solid) of SSL chemicals whose inhalation SSLs exceed their C^ levels.
The updated equation for deriving Csal is presented in Equation 2-11. This equation takes into account
the amount of contaminant that is in the vapor phase in the pore spaces of the soil in addition to the
amount that is dissolved in the liquid and sorbed to the soil particles.
Derivation of the Soil Saturation Limit
_s^
"pb"
ew + ire.)
(2-11)
Parameter/Definition (units)
Default
Source
saturation concentration
(mg/kg)
S/solubility in water (mg/L-water)
Pb/dry soil bulk density (kg/L)
n/total soil porosity (LpaJL^
Ps/soil particle density (kg/L)
K/soil-water partition coefficient
(L/kg)
K^/soil organic carbon/water
partition coefficient (L/kg)
infraction organic carbon of soil
(g/g)
e^water-filled soil porosity
0,/air-filled soil porosity
w/average soil moisture content
(kgwato/kgsoii or LwatoykgspU)
H'/Henry's law constant (unitless)
H/Henry's law constant
(atm-m3/mol)
chemical-specific
1.5
0.43 (loam)
2.65
Kocx foc (organics)
chemical-specific
0.006 (0.6%)
0.15
0.28
0.1 (10%)
H x 41, where 41 is
a conversion factor
chemical-specific
see Part 5
(1 - n) ps
Carsel and Pairish, 1988
U.S. EPA, 1991d
U.S. EPA, 1991d
see Part 5
Carsel et al., 1988
n - wpb
EQ, 1994
U.S. EPA, 1991d
see Part 5
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2.3.5 Particulate Emission Factor. The paniculate emission factor relates the concentration of
contaminant in soil with the concentration of dust particles in the air. Dust generated from open sources
is termed "fugitive" because it is not discharged into the atmosphere in a confined flow stream. Other
sources of fugitive dusts that may lead to higher emissions due to mechanical disturbances include
unpaved roads, tilled agricultural soils, and heavy construction operations.
Both the emissions portion and the dispersion portion of the PEF equation have been updated since RAGS
PartB.
As in Part B, the emissions part of the PEF equation is based on the "unlimited reservoir" model from
Cowherd et al. (1985) developed to estimate paniculate emissions due to wind erosion. The unlimited
reservoir model is most sensitive to the threshold friction velocity, which is a function of the mode of the
size distribution of surface soil aggregates. This parameter has the greatest effect on the emissions and
resulting concentration. For this reason, a conservative mode soil aggregate size of 500 urn was selected
as the default value for calculating generic SSLs.
The mode soil aggregate size determines how much wind is needed before dust is generated at a site. A
mode soil aggregate size of 500 um yields an uncorrected threshold friction velocity of 0.5 m/s. This
means that the windspeed must be at least 0.5 m/s before any fugitive dusts are generated. The threshold
friction velocity should be corrected to account for the presence of nonerodible elements. In Cowherd
et al. (1985), nonerodible elements are described as
... clumps of grass or stones (larger than about 1 cm in diameter) on the surface (that
will) consume part of the shear stress of the wind which otherwise would be transferred
to credible soil.
Cowherd et al. describe a study by Marshall (1971) that used wind tunnel studies to quantify the increase
in the threshold friction velocity for different kinds of nonerodible elements. His results are presented in
Cowherd et al. as a graph showing the rate of corrected to uncorrected threshold friction velocity vs. Lc,
where Lc is a measure of nonerodible elements vs. bare, loose soil. Thus, the ratio of corrected to
uncorrected threshold friction velocity is directly related to the amount of nonerodible elements in surface
soils.
Using a ratio of corrected to uncorrected threshold friction velocity of 1, or no correction, is roughly
equivalent to modeling "coal dust on a concrete pad," whereas using a correction factor of 2 corresponds
to a windspeed of 19 m/s at a height of 10 m. This means that about a 43-mph wind would be required
to produce any paniculate emissions. Given that the 29 meteorological data sets used in this modeling
effort showed few windspeeds at, or greater than, 19 m/s, OERR felt that it was necessary to choose a
default correction ratio between 1 and 2. A value of 1.25 was selected as a reasonable number that would
be at the more conservative end of the range. This equates to a corrected threshold friction velocity of
0.625 m/s and an equivalent windspeed of 11.3 m/s at a height of 7 meters.
Unlike volatile contaminants, meteorological conditions (i.e., the intensity and frequency of wind) affect
both the dispersion and emissions of paniculate matter. For this reason, a separate high-end Q/C value
was derived for PM10 emissions for the generic SSLs. The PEF equation was used to calculate annual
average concentrations for each of 29 sites across the country. As a reasonably conservative estimate for
calculating generic SSLs, a default site (Minneapolis, MN) was selected that best approximated the 90th
percentile concentration. The results produced a revised default PEF Q/C value of 46.84 g/m2-s per kg/m3
for a 30-acre site (see Appendix D; EQ, 1994). As with the VF model, if another of the 29 modeled
locations better represents site conditions, the PEF QC value for that site location may be used along with
site-specific estimates of V, Um, Ut, and F(x) in the simple site-specific method (see Section 3.2.3).
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The generic paniculate emission derived using the default values in Equation 2-12 is 6.79 x 10s m3/kg,
which corresponds to a receptor point concentration of approximately 1.48 pgAn3. This represents an
annual average emission rate based on wind erosion that should be compared with chronic health criteria;
it is not appropriate for evaluating the potential for more acute exposures.
Derivation of the Paniculate Emission Factor
PEF(m3/kg) = Q/C x
3,600 s/h
0.036 x (1 -V) x (Um/U,)J x F(x)
(2-12)
Parameter/Definition (units)
Default
Source
PEF/particulate emission factor
(m3/kg)
Q/C/inverse of the mean cone, at
the center of a 30-acre-square
source (g/m2-s per kg/m3)
V/fraction of vegetative cover
(unitless)
Ua/mean annual windspeed (m/s)
U/equivalent threshold value of
windspeed at 7 m (m/s)
F(x)/function dependent on \Ja/Ut
derived using Cowherd (U.S. EPA,
1985) (unitless)
6.79 x 108
46.84
0.5 (50%)
4.69
11.32
0.194
EQ, 1994 (for Minneapolis, MN)
U.S. EPA, 1991d
EQ, 1994
U.S. EPA, 1991d
U.S. EPA, 199 Id
2.3.6 Intrusion of Volatiles into Basements: Johnson and Ettinger Model. Concern
about the potential impact of contaminated soil on indoor air quality prompted OEKR to consider the
Johnson and Ettinger (1991) model, a heuristic model for estimating the intrusion rate of contaminant
vapors from soil into buildings. The model is a closed-form analytical solution for both convective and
diffusive transport of vapor-phase contaminants into enclosed structures located above the contaminated
soil. The model may be solved for both steady-state (i.e., infinite source) or quasi-steady-state (i.e., finite
source) conditions. The model incorporates a number of key assumptions, including no leaching of
contaminant to ground water, no sinks in the building, and well-mixed air volume within the building.
To evaluate the effects of using the Johnson and Ettinger model on SSLs for volatile organic contaminants,
OERR contracted EQ to construct a case example to estimate a high-end exposure point concentration for
residential land use (Appendix B; EQ, 1994). The case example models a contaminant source relatively
close or directly beneath a building where the soil beneath the building is very permeable and the building
is underpressurized; tending to pull contaminants into the basement Where possible and appropriate,
values of model variables were taken directly from Johnson and Ettinger (1991). Using both steady-state
and quasi-steady-state formulations, building air concentrations of each of 42 SSL chemicals were
calculated. The inverse of these concentrations were substituted into Equations 2-4 or 2-5 as an indoor
volatilization factor (VF^,,,) to calculate carcinogenic or noncarcinogenic SSLs based on migration of
contaminants into basements (i.e., "indoor inhalation" SSLs).
Results showed a difference of up to two orders of magnitude between the steady-state and quasi-steady-
state results for the indoor inhalation SSLs. Infinite source indoor inhalation SSLs were less than the
corresponding "outdoor" inhalation SSLs by as much as 3 orders of magnitude for highly volatile
constituents. For low-volatility constituents, the difference was considerably less, with no difference in
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the indoor and outdoor SSLs in some cases. In all cases, SSLs based on the migration of volatiles into
basements were more conservative than SSLs based on outdoor exposure (see Appendix B). The EQ
study also indicated that the most important input parameters affecting long-term building concentration
(and thus the SSL) are building ventilation rate, distance from the source (i.e., source-building separation),
soil permeability to vapor flow, and source depth. For lower-permeability soils, the number and size of
cracks in the basement walls may be more significant, although this was not a significant variable for the
permeable soils considered in the study.
Although the Johnson and Ettinger model would have a significant effect on SSLs for volatile compounds,
OERR does not believe it can reasonably be incorporated into the Soil Screening framework at this time.
This screening level model is highly sensitive to either a variable (soil permeability) that can vary by 3
orders of magnitude across a typical residential lot (Johnson and Ettinger, 1991) or, for lower permeability
soils, is sensitive to a variable (the number and size of cracks in basement walls) that is difficult to
estimate generically. Such parameters do not lend themselves to the standardization required for the
evaluation of potential future exposure either site-specifically or generically. In addition, the only formal
validation study identified by OERR compares model results with measured radon concentrations from
a highly permeable soil. Although these results compare favorably, it is not clear how applicable they are
to less permeable soils and compounds not already present in soil as a gas (as radon is). Reports of work
conducted by the State of Massachusetts suggest that the model may overestimate concentrations of
volatile organic compounds in building crawl spaces by a factor of 10 or more.
2.4 Migration to Ground Water
The Soil Screening framework for the migration to ground water pathway was developed to identify
chemical concentrations in soil that have the potential to contaminate ground water. Migration of
contaminants from soil to ground water can be envisioned as a two-stage process: (1) release of
contaminant in soil leachate and (2) transport of the contaminant through the underlying soil and aquifer
to a receptor well. The framework considers both of these fate and transport mechanisms in developing
SSLs that are protective of human health through the migration to ground water pathway.
The Soil Screening framework for the migration to ground water pathway was developed under the
following constraints:
• Because of the large nationwide variability in ground water vulnerability, the framework
should be flexible, allowing adjustments for site-specific conditions if adequate information
is available.
• To reflect early-stage application, the methodology needs to be simple, requiring a minimum
of site-specific data.
• The methodology should be consistent with current understanding of subsurface processes.
• The process of developing and applying SSLs under the framework should generate
information that can be used and built upon as a site evaluation progresses.
Flexibility is achieved by incorporating three methods into the framework (Section 1.3): a simple site-
specific methodology, using readily obtainable site-specific data in standardized equations; a detailed site-
specific methodology, utilizing more complex fate-and-transport models; and generic SSLs developed by
using conservative default inputs in the standardized equations.
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The simple-site specific methodology was designed for use during the early stages of a site evaluation
when information about subsurface conditions may be limited. Because of this constraint, the
methodology is based on very conservative, simplifying assumptions about the release and transport of
contaminants in the subsurface:
• The source is infinite (i.e., steady-state concentrations will be maintained in ground water over
the exposure period of interest).
• Contaminants are uniformly distributed throughout the zone of contamination.
• Soil contamination extends from the surface to the water table (i.e., adsorption sites are filled
in the unsaturated zone beneath the area of contamination).
• There is no chemical or biological degradation in the unsaturated zone.
• Equilibrium soil/water partitioning is instantaneous and linear in the contaminated soil.
• The receptor point is at the edge of the site (i.e., there is no dilution from recharge
downgradient of the site).
• The receptor well is within the plume.
• The aquifer is unconsolidated and unconfined (surficial).
• Aquifer properties are homogeneous and isotropic.
• There is no attenuation (i.e., adsorption or degradation) of contaminants in the aquifer.
• NAPLs are not present at the site.
The methodology incorporates a standard linear equilibrium soil/water partition equation to estimate
contaminant release in soil leachate (see Sections 2.4.1 through 2.4.4) and a simple water-balance equation
that calculates a dilution factor to account for dilution of soil leachate in an aquifer (see Section 2.4.5).
The dilution factor represents the reduction in soil leachate contaminant concentrations by mixing in the
aquifer, expressed as the ratio of leachate concentration to the concentration in ground water at the
receptor point (i.e., drinking water well).
Under the Soil Screening framework, simple site-specific and generic SSLs are backcalculated from
acceptable ground water concentrations (i.e., nonzero MCLGs, MCLs, or HBLs; see Section 2.1). First,
the acceptable ground water concentration is multiplied by the dilution factor (or a dilution/attenuation
factor, DAF) to obtain a target leachate concentration. For example, if the dilution factor is 10 and the
acceptable ground water concentration is 0.05 mg/L, the target soil leachate concentration would be 0.5
mg/L. The partition equation is then used to calculate the total soil concentration (i.e., SSL)
corresponding to this soil leachate concentratioa
The simple site-specific methodology described in this section and in Part 3, although simplified, is
theoretically and operationally consistent with the more sophisticated investigation and modeling efforts
that are conducted to develop soil cleanup goals and cleanup levels for protection of ground water at
Superfund sites. SSLs developed using this methodology can be viewed as evolving risk-based levels that
can be refined as more site information becomes available. The early use of the methodology at a site
will help focus further investigative efforts on areas of true concern with respect to ground water quality
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and will provide information on soil characteristics, aquifer characteristics, and chemical properties that
can be built upon as a site evaluation progresses.
2.4.1 Development of Soil/Water Partition Equation. The methodology used to estimate
contaminant release in soil leachate is based on the Freundlich equation, which was developed to model
sorption from liquids to solids. The basic Freundlich equation applied to the soil/water system is (Dragun,
1988):
Kd= CS/CW11 (2-13)
where
Kd = Freundlich soil/water partition coefficient (L/kg)
Cs = concentration sorbed on soil (mg/kg)
Cw = solution concentration (mg/L)
n = Freundlich exponent (dimensionless).
Assuming that adsorption is linear with respect to concentration (n=l)* and rearranging to backcalculate
a sorbed concentration (Cs):
Cs = (Kd)Cw . (2-14)
For SSL calculation, Cw is the target soil leachate concentration.
Adjusting Sorbed Soil Concentrations to Total Concentrations. To specify a screening
level for soil samples taken at Superfund sites, one must relate the sorbed concentration derived above (C^)
to the total concentration measured in a soil sample (Q). In a soil sample, contaminants can be associated
with the solid soil materials, the soil water, and the soil air (Feenstra et al., 1991). That is
Mt = M,. + N^ + M., (2-15)
where
Mj = total contaminant mass in sample (mg)
MS = contaminant mass sorbed on soil materials (mg)
M^ = contaminant mass in soil water (mg)
M^ = contaminant mass in soil air (mg).
Furthermore
H = q Pb vsp , (2-16)
Ms=CsPbVsp, (2-17)
Mw=Cw9wVsp , (2-18)
and
Ma=Ca6aVsp, (2-19)
*The linear assumption will tend to overestimate sorption and underestimate desorption for most organics at
higher concentrations (i.e., above ItT5 M for organics) (Piwoni and Banerjee, 1989).
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where
pb = dry soil -bulk density (kg/L)
Vsp = sample volume (L)
9W = water-filled porosity (LwateI/LsoiI)
Ca = concentration on soil pore air (mg/Lsofl)
6a = air-filled soil porosity
For contaminated soils (with concentrations below C^), Ca may be determined from Cw and the
dimensionless Henry's law constant (HO using the following relationship:
thus
Substituting into Equation 2-15:
Ca=CwH'
csPb * cwew * cwH-ea
(2-20)
(2-21)
(2-22)
or
- c, - c
(2-23)
v )
Substituting into Equation 2-14 and rearranging:
Soil-Water Partition Equation for Migration to Ground Water Pathway: Inorganic Contaminants
c - c
Ct - *-w
(2-24)
Parameter/Definition (units)
Q/screening level in soil (mg/kg)
C^target soil leachate concentration
(mg/L)
il- water partition coefficient
(L/kg)
0,/water-filled soil porosity
soil porosity
nl total soil porosity
w/average soil moisture content
or
H'/Henry's law constant
(unitless)
H/Henry's law constant
(atm-m3/mol)
Pb/dry soil bulk density (kg/L)
Ps/soil particle density (kg/L)
Default
nonzero MCLG,
MCL, or HBL x 10
DAF
chemical-specific
. 0.3 (30%)
0.13
0.43 (loam)
02 (20%)
H x 41, where 41 is
a conversion factor
chemical-specific
1.5
2.65
Source
U.S. EPA, 1994a (nonzero
MCLG, MCL); Section 2.4.6
(DAF)
see Part 5
n-9w
Carsel and Parrish, 1988
U.S. EPA/ORD
U.S. EPA, 199 Id
see Part 5
(1 - n)ps
U.S. EPA, 199 Id
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Equation 2-24 is used to calculate SSLs (total soil concentrations, C\) corresponding to soil leachate
concentrations (Cw) equal to the target contaminant soil leachate concentration. The equation assumes that
soil water, solids, and gas are conserved during sampling. If soil gas is lost during sampling, 6a should
be assumed to be zero. Likewise, for inorganic contaminants except mercury, there is no significant vapor
pressure and H' may be assumed to be zero.
Section 3.2.1 describes how to develop site-specific estimates of these soil parameters for application in
the site-specific methodology. Default soil parameter values used to calculate generic SSLs for the
migration to ground water pathway are the same as those used for the volatilization pathway (Section
2.3.2) except for average soil moisture content (w). Because the migration to ground water pathway
addresses deeper soil horizons than the volatilization pathway, a higher value (0.2 or 20 percent) was used
to account for the general increase in moisture content with depth. The value chosen corresponds to a
water-filled soil porosity (9W) of 0.30, which is between the mean field capacity (0.20) of Class B soils
(Carsel et aL, 1988) and the saturated volumetric water content for loam (0.43).
Kd varies by chemical and soil type. Because of different influences on Kd values, derivations of K<,
values for organic compounds and metals were treated separately.
2.4.2 Organic Compounds— Partition Theory. Past research has demonstrated that, for
hydrophobia organic chemicals, soil organic matter is the dominant sorbing component in soil and that
Kd is linear with respect to soil organic carbon content (OQ as long as OC is above a critical level
(Dragun, 1988). Thus, Kd can be normalized with respect to soil organic carbon to K^., a chemical-
specific partitioning coefficient that is independent of soil type, as follows:
K, = K,, fx (2-25)
where
= organic carbon partition coefficient (L/kg)
= fraction of organic carbon in soil (mg/mg).
Substituting into Equation 2-24:
Soil-Water Partition Equation for Migration to Ground Water Pathway: Organic Contaminants
N
j + 6w * 9aH/ (2-26)
Pb
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Parameter/Definition (units)
Default
Source
C/screening level in soil mg/kg)
C^/target leachate concentration
(mg/L)
organic carbon/water
partition coefficient (L/kg)
f^/organic carbon content of soil
(kg/kg)
9,/water-filled soil porosity
Oj/air-filled soil porosity
n/total soU porosity
w/average soil moisture content
H'/Henry's law constant
(unitless)
H/Henry's law constant
(atm-m3/mol)
Pb/dry soil bulk density (kg/L)
Ps/soil particle density (kg/L)
nonzero MCLG,
MCL, or HBL x 10
DAF
chemical-specific
0.002 (02%)
0.3 (30%)
0.13
0.43 (loam)
0.2 (20%)
H x 41, where 41 is
a conversion factor
chemical-specific
1.5
2.65
U.S. EPA, 1994a (MCL, nonzero
MCLG); Section 2.4.6 (DAF)
see Part 5
Carsel et al., 1988
n-6w
Carsel and Parrish, 1988
U.S. EPA/ORD
U.S. EPA, 199 Id
see Part 5
(1 - n)ps
U.S. EPA, 1991d
Part 5 of this document provides
development
values for the organic SSL chemicals and describes their
The critical organic carbon content, f^.* , represents OC below which sorption to mineral surfaces begins
to be significant This level is likely to be variable and to depend on both the properties of the soil and
of the chemical sorbate (Curtis et al., 1986). Attempts to quantitatively relate f^* to such properties have
been made (see McCarty et al., 1981), but at this time there is no reliable method for estimating f^.* for
specific chemicals and soils. Nevertheless, research has demonstrated that, for volatile halogenated
hydrocarbons, f^* is about 0.001, or 0.1 percent OC, for many low-carbon soils and aquifer materials
(Schwarzenbach and Westall, 1981; Piwoni and Banerjee, 1989).
If soil OC is below this critical level, Equation 2-26 should be used with caution. This is especially true
if soils contain significant quantities of fine-grained minerals with high sorptive properties (e.g., clays).
If sorption to minerals is significant, Equation 2-26 will underpredict sorption and overpredict contaminant
concentrations in soil pore water. However, this f^.* level is by no means the case for all soils; Abdul
et al. (1987) found that, for certain organic compounds and aquifer materials, sorption was linear and
could be adequately modeled down to f^. = 0.0003 by considering K^ alone.
< 0.001), the following
For soils with significant inorganic and organic sorption (i.e., soils with
equation has been developed (McCarty et al., 1981; Karickhoff, 1984):
where
fio+foc -
Kd =
soil inorganic partition coefficient
fraction of inorganic material
1.
(2-27)
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Although this equation is considered conceptually valid, Kio values are not available for the subject
chemicals. Attempts to estimate KJO values by relating sorption on low-carbon materials to properties such
as clay-size fraction, clay mineralogy, surface area, or iron-oxide content have not revealed any consistent
correlations, and semiquantitative methods are probably years away (Piwoni and Banerjee, 1989). How-
ever, Piwoni and Banerjee developed the following empirical correlation (by linear regression, r2 = 0.85)
that can be used to. estimate Kd values for hydrophobic organic chemicals from Kow for low-carbon soils:
logKd = 1.01 log K^ - 0.36 (2-28)
where
Kow = octanoVwater partition coefficient.
The authors indicate that this equation should provide a Kd estimate that is within a factor of 2 or 3 of
the actual value for nonpolar soibates with log Kow < 3.7. If sorption to inorganics is not considered for
low-carbon soils where it is significant, Equation 2-26 will underpredict sorption and overpredict
contaminant concentrations in soil pore water (i.e., it will provide a conservative estimate).
The use of fixed K^ values in Equation 2-26 is valid only for hydrophobic, nonionizing organic
chemicals. Several of the organic chemicals of concern ionize in the soil environment, existing in both
neutral and ionized forms within the normal soil pH range. The relative amounts of the ionized and
neutral species are a function of pH. Because the sorptive properties of these two forms differ, it is
important to consider the relative amounts of the neutral and ionized species when determining K^. values
at a particular pH. Lee et al. (1990) developed a theoretically based algorithm, developed from thermo-
dynamic equilibrium equations, and demonstrated that the equation adequately predicts laboratory-
measured K^. values for pentachlorophenol (PCP) and other ionizing organic acids as a function of pH.
The equation assumes that sorbent organic carbon determines the extent of sorption for both the ionized
and neutral species and predicts the overall sorption of a weak organic acid (K^ _ ) as follows:
!-„) (2-29)
where
KOCJI' KOC j = sorption coefficients for the neutral and ionized species (L/kg)
On =(1+ lop"-?**)-1
pKa = acid dissociation constant
This equation was used to develop K^. values for ionizing organic acids as a function of pH, as described
in Part 5. Section 3.2.1 provides guidance on conducting site-specific measurements of soil pH for
estimating K^ values for ionizing organic compounds under the. simple site-specific method. Because a
national distribution of soil pH values is not available, the median U.S. ground water pH (6.8) from the
STORET database (U.S. EPA, 1992a) was used to develop ionizing organic K^ values for calculation of
generic SSLs.
2.4.3 Inorganics (Metals)— Partition Theory. Equation 2-24 is used to estimate SSLs for
metals for the migration to ground water pathway. The derivation of Kj values is much more complicated
for metals than for organic compounds. Unlike organic compounds, for which K<, values are largely
controlled by a single parameter (soil organic carbon), Kj values for metals are significantly affected by
a variety of soil conditions. The most significant parameters are pH, oxidation-reduction conditions, iron
oxide content, soil organic matter content, cation exchange capacity, and major ion chemistry. The
number of significant influencing parameters, their variability in the field, and differences in experimental
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methods result in a wide range of Kj values for individual metals reported in the literature (over 5 orders
of magnitude). Thus, it is much more difficult to derive generic K^ values for metals than for organics.
The Kg values used to generate metal SSLs for Ba, Be, Cd, Cu, Hg, Ni, and Zn were developed using an
equilibrium geochemical speciation model (MINTEQ2). The values for As, Cr64" , Se, and Th were taken
from empirical, pH-dependent adsorption relationships developed by EPA/ORD. Metal Kj values for SSL
application are presented in Part 5, along with a description of their development and limitations. As with
the ionizing organics, Kj values are selected as a function of site pH in the simple site-specific method
and metal K,, values corresponding to a pH of 6.8 are used to calculate generic SSLs.
2.4.4 Assumptions for Soil/Water Partition Theory. The following assumptions are implicit
in the SSL partitioning methodology. These assumptions and their implications for SSL accuracy should
be read and understood before using the Soil Screening framework to calculate SSLs.
1. There is no contaminant loss due to volatilization or degradation. The source is considered
to be infinite, i.e., these processes do not reduce soil leachate concentrations over time. This is
a conservative assumption, especially for smaller sites.
2. Adsorption is linear with concentration. The methodology assumes that adsorption is
independent of concentration (i.e., the Freundlich exponent =1). This has been reported to be true
for various halogenated hydrocarbons, polynuclear aromatic hydrocarbons, benzene, and
chlorinated benzenes. In addition, this assumption is valid at low concentrations (e.g., at levels
close to the MCL) for most chemicals. As concentrations increase, however, the adsorption iso-
therm can depart from the linear.
Studies on trichloroethane (TCE) and chlorobenzene indicate that departure from linear is in the
nonconservative direction, with adsorbed concentrations being lower than predicted by a linear
isotherm. However, adequate information is not available to establish nonlinear adsorption
isotherms for the chemicals of interest. Furthermore, since the SSLs are derived at relatively low
target soil leachate concentrations, departures from the linear at high concentrations do not
significantly influence the accuracy of the results.
3. The system is at equilibrium with respect to adsorption. This ignores adsoiption/desorption
kinetics by assuming that the soil and pore water concentrations are at equilibrium levels. In other
words, the pore-water residence time is assumed to be longer than the time it takes for the system
to reach equilibrium conditions.
This assumption is conservative. If equilibrium conditions are not met, the concentration in the
pore water will be less than that predicted by the methodology. The kinetics of adsorption arc
not sufficiently understood for a sufficient number of chemicals and site conditions to consider
equilibrium kinetics in the methodology.
4. Adsorption is reversible. The methodology assumes that desorption processes operate in the
same way as adsorption processes, since most of the K^. values are measured by adsorption
experiments rather than by desorption experiments. In actuality, desorption is slower to some
degree than adsorption and, in some cases, organics can be irreversibly bound to the soil matrix.
In general, the significance of this effect increases with K^.
This assumption is conservative. Tailing and irreversible sorption will result in lower pore-water
concentrations than that predicted by the methodology. Again, the level of knowledge on
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desorption processes is not sufficient to consider desorption kinetics and degree of reversibility
for all of the subject chemicals.
2.4.5 Dilution/Attenuation Factor Development
As contaminants in soil leachate move through soil and ground water, they are subjected to physical,
chemical, and biological processes that tend to reduce the eventual contaminant concentration at the
receptor point (i.e., drinking water well). These processes include adsorption onto soil and aquifer media,
chemical transformation (e.g., hydrolysis, precipitation), biological degradation, and dilution due to mixing
of the leachate with ambient ground water. The reduction in concentration can be expressed succinctly
by the DAF, which is defined as the ratio of contaminant concentration in soil leachate to the
concentration in ground water at the receptor point Under the Soil Screening framework, the DAF is used
to backcalculate the target soil leachate concentration from an acceptable ground water concentration (e.g.,
MCLG). For example, if the acceptable ground water concentration is 0.05 mg/L and the DAF is 10, the
target leachate concentration would be 0.5 mg/L.
The simple site-specific methodology addresses only one of these dilution-attenuation processes:
contaminant dilution in ground water. A simple equation derived from a geohydrologic water-balance
relationship has been developed for the methodology, as described in the following subsection. The ratio
factor calculated by this equation is referred to as a dilution factor rather than a DAF because it does not
consider processes that attenuate contaminants in the subsurface (i.e., adsorption and degradation
processes). This simplifying assumption was necessary for several reasons.
First, the infinite source assumption results in all subsurface adsorption sites being eventually filled and
no longer available to attenuate contaminants. Second, soil contamination extends to the water table,
eliminating attenuation processes in the unsaturated zone. Additionally, the receptor well is assumed to
be at the edge of the source, minimizing the opportunity for attenuation in the aquifer. Finally, chemical-
specific biological and chemical degradation rates are not known for many of the SSL chemicals; where
they are available they are usually based on laboratory studies under simplified, controlled conditions.
Because natural subsurface conditions such as pH, redox conditions, soil mineralogy, and available
nutrients have been shown to markedly affect natural chemical and biological degradation rates, and
because the national variability in these properties is significant and has not been characterized, OERR
does not believe that it is possible at this time to incorporate these degradation processes into the simple
site-specific methodology for national application. OERR is considering whether a finite source option
and adsorption processes could be incorporated into the simple site-specific methodology.
If adsorption or degradation processes are expected to significantly attenuate contaminant concentrations
at a site (e.g., for sites with deep water tables or small contaminant source areas), the site manager is
encouraged to consider the detailed site-specific option of using more sophisticated, "full-scale" fate and
transport models. Many of these models can consider adsorption and degradation processes and can model
transient conditions necessary to consider a finite source size. Section 3.4 of this document presents
information on the selection and use of such models for SSL application.
The dilution factor model assumes that the aquifer is unconfined and unconsolidated and has homogeneous
and isotropic properties. Unconfined (surficial) aquifers are common across the country, are vulnerable
to contamination, and can be used as drinking water sources by local residents. Unconsolidated sand and
gravel aquifers are the most common aquifer type for the 364 sites in the HGDB hydrogeologic database
(Newell et al., 1990). Dilution model results may not be applicable to fractured rock or karst aquifer
types.
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In addition, the simple dilution model does not consider facilitated transport. This ignores processes such
as colloidal transport, transport via solvents other than water (e.g., NAPLs), and transport via dissolved
organic matter (DOM). These processes have greater impact as Kow (and hence, K^) increases. However,
the transport via solvents other than water is operative only if certain site-specific conditions are present
Transport by DOM and colloids has been shown to be potentially significant under certain conditions in
laboratory and field studies. Although much research is in progress on these processes, the current state
of knowledge is not adequate to allow for their consideration in SSL calculations.
If there is the potential for the presence of NAPLs in soils at the site or site area in question, SSLs should
not be used for this area (i.e., further investigation is required). The Csat equation (Equation 2-11)
presented in Section 2.3.4 can be used to estimate the contaminant concentration at which the presence
of pure-phase NAPLs may be suspected for contaminants that are liquid at soil temperature. If NAPLs
are suspected in site soils, U.S. EPA (1992d) provides additional guidance on how to estimate the potential
for DNAPL occurrence in the subsurface.
Dilution Model Development. The Agency evaluated four simple water balance models to adjust
SSLs for dilution in the aquifer. Although written in different terms, all four options reviewed can be
expressed as the same simple water balance equation to calculate a dilution factor, as follows:
Option 1 (ASTM):
dilution factor = (1 + U^ d/BL) (2-30)
where
U^ = Darcy ground water velocity (m/yr)
d = mixing zone depth (m)
I = infiltration rate (m/yr)
L = length of source parallel to flow (m).
For Darcy velocity:
U^ = Ki (2-31)
where
K = aquifer hydraulic conductivity (m/yr)
i = hydraulic gradient (m/m).
Thus
dilution factor = 1 + (Kid/IL) . (2-32)
Option 2 (EPA Ground Water Forum):
dilution factor = (Qp + QA)/Qp (2-33)
where
Q = percolation flow rate (m3/yr)
QA = aquifer flow rate (m3/yr).
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For percolation flow rate:
Qp = IA (2-34)
where
A = facility area (m2) = WL.
For aquifer flow rate:
QA = WdKi (2-35)
where
W = width of source perpendicular to flow (m)
d = mixing zone depth (m).
Thus
dilution factor = (IA + WdKi)/IWL
= 1 + (KidflL) . (2-36)
Option 3 (Summers Model):
Cw = (Qp Cp)/(Qp + QA) (2-37)
where
Cw = ground water contaminant concentration (mg/L)
Cp = soil leachate concentration (mg/L)
given that
Cw = Cp/dilution factor
I/dilution factor = Qp/(Qp + QA)
or
dilution factor = (Qp + QA)/Qp (see Option 2) .
Option 4 (EPA ORD/RSKERL):
dilution factor = (Qp + QA)/Qp = RX/RL (2-38)
where
R = recharge rate (m/yr) = infiltration rate (I, m/yr)
X = distance from receptor well to ground water divide (m).
(Note that the intermediate equation is the same as Option 2.)
This option is a longer-term option that is not considered further in this analysis because valid X values
are not currently available either nationally or for specific sites. EPA is currently developing regional
estimates for these parameters.
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Dilution Model Input Parameters. As shown, all three options for calculating contaminant dilution
in ground water can be expressed as the same equation:
Ground Water Dilution Factor
dilution factor = 1 + (Kid/CL) . (2-39)
Parameter/Definition (units)
K/aquifer hydraulic conductivity (m/yr)
i/hydraulic gradient (m/m)
d/mixing zone depth (m)
I/infiltration rate (m/yr)
L/source length parallel to ground water flow (m)
Mixing Zone Depth (d). Because of its dependence on the other variables, mixing zone depth is
estimated with the method used for the MULTIMED model (Sharp-Hansen et al., 1990). The
MULTIMED estimation method was selected to be consistent with that used by EPA's Office of Solid
Waste for the EPA Composite Model for Landfills (EPACML). The equation for estimating mixing zone
depth (d) is as follows:
d = (20^) + da (1 - exp[(-LI)/(Vsneda)]) (2-40)
where
y = vertical dispersivity (m/m)
Vs = horizontal seepage velocity (m/yr)
n,, = effective aquifer porosity (L^^L
da = aquifer depth (m).
The first term, (2ctvL)as, estimates the depth of mixing due to vertical dispersivity (d^) along the length
of ground water travel. Defining the point of compliance with ground water standards at the downgradient
edge of the source, this travel distance becomes the length of the source parallel to flow L. Vertical
dispersivity can be estimated by the following relationship (Gelhar and Axness, 1981):
c^ = 0.056
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Vs = Ki/ne (2-44)
so
dIV = da{l-exp[(-LI)/(Kida)]} . (2-45)
Thus, mixing zone depth is calculated as follows:
d = dav + dlv . (2-46)
Estimation of Mixing Zone Depth
d = (0.0112 L2)0'5 + da {1 - exp[(-LI)/(Kida)]} (2-47)
Parameter/Definition (units)
d/mixing zone depth (m)
L/source length parallel to ground water flow (m)
I/infiltration rate (m/yr)
K/aquifer hydraulic conductivity (m/yr)
da/aquifer thickness (m)
Incorporation of this equation for mixing zone depth into the SSL dilution equation results in five
parameters that must be estimated to calculate dilution: source length (L), infiltration rate (I), aquifer
hydraulic conductivity (K), aquifer hydraulic gradient (i), and aquifer thickness (da). Aquifer thickness
also serves as a limit for mixing zone depth. Section 3.2.4 provides guidance for estimating these
parameters under the simple site-specific method. Parameter definitions and defaults used to develop
generic SSLs are as follows:
• Source Length (L) is the length of the source (i.e., area of contaminated soil) parallel to
ground water flow and affects the flux of contaminant released in soil leachate (EL) as well as
the depth of mixing in the aquifer. The default option for this parameter assumes a square,
30-acre contaminant source. Increasing source length (and thereby area) may result in a lower
dilution factor. Assuming a default 30-acre source is conservative for most Superfund sites
because it is based on the average National Priorities List (NPL) site size, not average source
size.
• Infiltration Rate (I). Infiltration rate times the source area determines the amount of
contaminant (in soil leachate) that enters the aquifef over time. Thus, increasing infiltration
decreases the dilution factor. Two options can be used to generate infiltration rate estimates
under the Soil Screening framework. The first assumes that infiltration rate is equivalent to
recharge. This is generally true for uncontrolled contaminated soil sites but would be
conservative for capped sites (infiltration < recharge) and nonconservative for sites with an
additional source of infiltration, such as surface impoundments (infiltration > recharge).
Recharge estimates for this option were obtained from Aller et al. (1987) by hydrogeologic
setting, as described in Section 2.4.6.
The second option is to use the HELP model to estimate infiltration, as was done for OSW's
EPACML and EPA's Composite Model for Leachate Migration with Tranformation Products
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(EPACMTP) modeling efforts. Section 3.2.4 provides information on obtaining and using the
HELP model under the Soil Screening framework's simple site-specific method.
• Aquifer Parameters. Aquifer parameters needed for the dilution factor model include
hydraulic conductivity (K, m/yr), hydraulic gradient (i, mAn), and aquifer thickness (da, m).
Section 3.2.4 provides guidance on developing aquifer parameter estimates for calculation of
a dilution factor for the simple site-specific method.
2.4.6 Dilution-Attenuation Factor Development: Generic SSLs. For determining generic
SSLs, OERR has selected a DAF of 10 to account for contaminant dilution and attenuation during
transport through the saturated zone to a compliance point or receptor well. At most sites, this adjustment
will more accurately reflect a contaminant's threat to ground water resources than assuming a DAF of 1
(i.e., no dilution or attenuation). OERR selected a DAF of 10 using a "weight of evidence" approach.
This approach considers results from OSW's EPACMTP model as well as results from applying the SSL
dilution model described in Section 2.4.5 to 364 ground water sites across the country.
EPACMTP Modeling Effort. One model used to develop DAFs for generic SSLs is described in
Background Document for EPA's Composite Model for Leachate Migration with Transformation Products
(U.S. EPA, 1993a). EPACMTP has a three-dimensional module to simulate ground water flow that can
account for mounding under waste sites. The model also has a three-dimensional transport module and
both linear and nonlinear adsorption in the unsaturated and saturated zones and can simulate chain decay,
thus allowing the simulation of the formation and the fate and transport of daughter (transformation)
products of degrading chemicals. The model can also be used to simulate a finite source scenario.
EPACMTP is comprised of three main interconnected modules:
• An unsaturated zone flow and contaminant fate and transport module
• A saturated zone ground water flow and contaminant fate and transport module
• A Monte Carlo driver module, which generates model parameters from nationwide probability
distributions.
The unsaturated and saturated zone modules simulate the migration of contaminants from initial release
from the soil to a downgradient receptor well. More information on the EPACMTP model is provided
in Appendix E.
The Agency has extensively verified both the unsaturated and saturated zone modules of the EPACMTP
against other available analytical and numerical models to ensure accuracy and efficiency. Both the
unsaturated zone and the saturated zone modules of the EPACMTP, used for the calculation of DAFs for
generic SSLs, have been reviewed by the EPA Science Advisory Board and found to be suitable for
generic applications such as the derivation of nationwide DAFs.
EPACMTP Model Inputs (SSL Application). For nationwide Monte Carlo model applications,
the input to the model is in the form of probability distributions of each of the model input parameters.
The output from the model consists of the probability distribution of DAF values, representing the
likelihood that the DAF will not be less than a certain value. For instance, a 90th percentile DAF of 10
means that the DAF will be 10 or higher in at least 90 percent of the cases.
For each model input parameter, a probability distribution is provided, describing the nationwide likelihood
that the parameter has a certain value. The parameters are divided into four main groups:
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• Source-specific parameters, e.g., area of the waste unit, infiltration rate
• Chemical-specific parameters, e.g., hydrolysis constants, organic carbon partition coefficient
• Unsaturated zone-specific parameters, e.g., depth to water table, soil hydraulic conductivity
• Saturated zone-specific parameters, e.g., saturated zone thickness, ambient ground water flow
rate, location of nearest receptor well
Probability distributions for each parameter used in the model have been derived from nationwide surveys
of waste sites, such as EPA's landfill survey (53 FR 28692). During the Monte Carlo simulation, values
for each model parameter are randomly drawn from their respective probability distributions. In the
calculation of the DAFs for generic SSLs, site data from over 1,300 municipal landfill sites in OSW's
Subtitle D Landfill Survey were used to define parameter ranges and distributions. Each combination of
randomly drawn parameter values represents one out of a practically infinite universe of possible waste
sites. The fate and transport modules are executed for the specific set of model parameters, yielding a
corresponding DAF value. This procedure is repeated, typically on the order of several thousand times,
to ensure that the entire universe of possible parameter combinations (waste sites) is adequately sampled.
In the derivation of DAFs for generic SSLs, the model simulations were repeated 15,000 times for each
scenario investigated. At the conclusion of the analysis, a cumulative frequency distribution of DAF
values is constructed and plotted.
EPA assumed an infinite waste source of fixed area for the generic SSL modeling scenario. EPA chose
this relatively conservative assumption because EPA is considering no further investigation at sites where
soil contaminant concentrations are below the SSLs. However, the Agency is considering the use of the
finite source scenario for chemicals that degrade rapidly and/or are observed in ground water only at short
distances from the source.
For the SSL modeling scenario, the Agency performed a number of sensitivity analyses consisting of
fixing one parameter at a time to determine the parameters that have the greatest impact on DAFs. The
results of the sensitivity analyses indicate that the climate (net precipitation), soil types, and size of the
contaminated area have the greatest effect on the DAFs. The EPA feels that the size of the contaminated
area lends itself most readily to practical application of the SSLs.
To calculate the DAF for the SSLs, the receptor point was taken to be a domestic drinking water well
located on the downgradient edge of the contaminated area. The location of the intake point (receptor well
screen) was assumed to vary between 15 and 300 feet below the water table (these values are based on
empirical data reflecting a national sample distribution of depth of residential drinking water wells). The
location of the intake point allows for mixing within the aquifer. The Agency believes that this is a
reasonable assumption because there will always be some dilution attributed to the pumping of water for
residential use from an aquifer. The horizontal placement of the well was assumed to vary uniformly
along the center of the downgradient edge of the source within a width of one half of the width of the
source. .Degradation and retardation of contaminants were not considered in this analysis. Figure 2-1 is
a schematic showing aspects of the subsurface SSL conceptual model used in the EPACMTP modeling
effort. Appendix E is the background document prepared by EPA/OSW for this modeling effort.
EPACMTP Model Results. The results of the EPACMTP analyses indicate a DAF of 5 for a 30-
acre source at the 90th percentile protection level (Table 2-3). If a 95th percentile protection level is used,
a DAF of 1 is protective for a 30-acre source.
Dilution Factor Modeling Effort. To gain further information on the national range and distribution
of DAF values, the Agency also applied the Soil Screening framework's simple water balance dilution
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PLAN VIEW
Parameters:
• X (distance from source to well) = 0 ft
• Y (transverse well location) = Monte Carlo within
1/2 width of source
• Z (well intake point below water table) = Monte
Carlo, range 15 -» 300 ft
• Rainfall = Monte Carlo
• Soil type = Monte Carlo
• Depth to aquifer = Monte Carlo
• Assumes infinite source term
Figure 2-1. Migration to ground water pathway—EPACMTP modeling effort.
Table 2-3. Variation of DAF with Size of Source Area for SSL
EPACMTP Modeling Effort
Area
(acres)
0.02
0.05
0.11
0.23
0.50
0.69
1.2
1.6
1.8
3.4
4.6
11.5
23
30
46
69
115
85th
1.09E+06
1.86E+05
2.91 E+04
9.31 E+03
2.08E+03
1.65E+03
870
570
477
237
175
65
32
23
18
13
9
DAF
90th
3.76E+04
9.63E+03
2.00E+03
680
198
155
84
59
51
26
20
9
6
5
4
3
2
95th
609
188
53
23
11
8
5
4
4
3
2
2
1.3
1.4
1.2
1.1
1.1
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model to ground water sites included in two large surveys of hydrogeologic site investigations. These
were American Petroleum Institute's (API's) hydrogeologic database (HGDB) and OERR's database of
conditions at Superfund sites contaminated with DNAPL.
The HGDB contains the results of a survey sponsored by API and the National Water Well Association
(NWWA) to determine the national variability in simple hydrogeologic parameters (NeweD et aL, 1989).
The survey was conducted to validate EPA's use of the EPACML model as a screening tool for the land
disposal of hazardous wastes. The survey involved over 400 ground water professionals who submitted
data on aquifer characteristics from field investigations at actual waste sites and other ground water
projects. The information was compiled into HGDB, which is available from API and is included in
OASIS, an EPA-sponsored ground water decision support system. Newell et al. (1990) also present these
data as "national average" conditions and by hydrogeologic settings based on those defined by Aller et
al. (1987) for the DRASTIC modeling effort. Aller et al. (1987) defined these settings within the overall
framework defined by Heath's ground water regions (Heath, 1984). The HGDB estimates of hydraulic
conductivity and hydraulic gradient show reasonable agreement with those in Aller et al. (1987), which
serves as another source of estimates for these parameters.
The SSL dilution factor model (including the associated mixing zone depth model) requires estimates for
five parameters:
da = aquifer thickness (m)
L = length of source parallel to flow (m)
I = infiltration rate (m/yr)
K = aquifer hydraulic conductivity (m/yr)
i = hydraulic gradient (m/m).
Dilution factors were calculated by individual HGDB or DNAPL site to retain as much site-correlated
parameter information as possible. The HGDB contains estimates of aquifer thickness (da), aquifer
hydraulic conductivity (K), and aquifer hydraulic gradient (i) for_272 ground water sites. The DNAPL
site database contains 92 site estimates of seepage velocity (V), which can be related to hydraulic
conductivity and hydraulic gradient by the following relationship:
V = Ki/ne
where
n,, = effective porosity.
For generic SSLs, ne was assumed to be 0.35, which is representative of sand and gravel aquifers (the
most prevalent aquifer type in the HGDB). Thus, for the DNAPL sites, 0.35 x Vwas substituted for Ki
in the dilution factor equation.
Estimates of the other parameters required for the modeling effort are described below. Site-specific
values were used where available. Because the modeling effort uses a number of site-specific modeling
results to determine a nationwide distribution of dilution factors, typical values were used to estimate
parameters for sites without site-specific estimates.
Source Length (L). The contaminant source (i.e., area of soil contamination) was assumed to be
square. This assumption may be conservative for sites with their longer dimensions perpendicular to
ground water flow or nonconservative for sites with their longer dimensions parallel to ground water flow.
The source length was calculated as the square root of the source area for the source sizes in question.
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To cover the likely range of Superfund contaminated soil source area sizes, five source sizes were
modeled: 0.5 acre, 10 acres, 30 acres, 60 acres, and 100 acres. Because a 30-acre source size is the
"typical" site size under the SSL conceptual model, results for a 30-acre source were reviewed for
estimating the generic DAF.
Infiltration Rate (I). Infiltration rate estimates were not available in either database. Recharge
estimates for individual hydrogeologic settings from Aller et al. (1987) were used as infiltration estimates
(i.e., it was assumed that infiltration = recharge). Because of differences in database contents, it was
necessary to use different approaches to obtaining recharge/infiltration estimates for the HODB and
DNAPL sites.
The HGDB places each of its sites in one of the hydrogeologic settings defined by Aller et aL (1987).
A recharge estimate for each HGDB site was simply extracted for the appropriate setting from Aller et
al. The middle of the recharge range presented was used.
The DNAPL database does not contain sufficient hydrogeologic information to place each site into the
Aller et al. settings. Instead, each of the 92 DNAPL sites was placed in one of Heath's ground water
regions. The sites were found to lie within five hydrogeologic regions: nonglaciated central, glaciated
central, piedmont/blue ridge, northeast and superior uplands, and Atlantic/Gulf coastal plain. Recharge
was estimated for each region by averaging the median recharge value from all hydrogeologic settings
except for those with steep slopes (Table 2-4). The appropriate Heath region recharge estimate was then
used for each DNAPL site in the dilution factor calculations.
Aquifer Parameters. All aquifer parameters needed for the SSL dilution model are included in the
HGDB. Because hydraulic conductivity and gradient are included in the seepage velocity estimates in the
DNAPL site database, only aquifer thickness was unknown for these sites. Aquifer thickness for all
DNAPL sites was set at 9.1 m, which is the median value for the "national average" condition in the
HGDB (Newell et aL, 1990).
Dilution Modeling Results. Table 2-5 presents summary statistics for the 92 DNAPL sites, the 272
HGDB sites, and all 364 sites. One can see that the HGDB sites generally have lower dilution factors
than the DNAPL sites, although the absolute range in values is greater in the HGDB. However, the
available information for these sites is insufficient to fully explain any differences in these data sets. The
wide range of dilution factors for these sites reflects the nationwide variability in hydrogeologic conditions
affecting this parameter. The large difference between the average and geometric mean statistics indicates
a distribution skewed toward the lower dilution factor values. The geometric mean represents a better
estimate of the central tendency of such skewed distributions. Appendix F presents the dilution modeling
inputs and results for the HGDB and DNAPL sites, tabulated by individual site.
Selection Of the Default DAF. The default DAF used to calculate generic SSLs was selected
considering the evidence of the national DAF and dilution factor estimates described above. The
EPACMTP model results showed a DAF of 5 for 30 acres at the 90th percentile. The SSL dilution model
results have geometric mean dilution factors for a 30-acre source of 10 and 5 for DNAPL sites and HGDB
sites, respectively. In a weight of evidence approach, more weight was given to the results of the DNAPL
sites because they are representative of the kind of sites to which the SSL framework is likely to be
applied. Considering the conservative assumptions in the SSL conceptual model (see Section 2.4), the
conservatism of the dilution factor model (see Section 2.4.5), and the conservatism inherent in the soil
partition methodology (see Section 2.4.4), OERR believes (1) that these results support the use of a DAF
of 10 to calculate generic SSLs for the migration to ground water pathway and (2) that these generic SSLs
will protect human health from exposure through this pathway at most Superfund sites across the Nation.
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Table 2-4. Recharge Estimates for DNAPL Site Hydrogeologlc Regions
to
vo
Hydrogeotogic setting
Piedmont/Blue Ridge (Region 8)
Alluvial Mountain Valleys
Alter. SS/LS/Sh.( Thin Soil
Alter. SS/LS/Sh., Deep Regollth
Solution Limestone
Alluvium w/ Overbank Deposits
Alluvium w/o Overbank Deposits
Braided River Deposits
Triassic Basins
Swamp/Marsh
Met./lg. Domes & Fault Blocks
Unconsol./Semiconsol. Aquifers
Glaciated Central (Region 7)
Glacial Till Over Bedded Rock
Glacial Till Over Outwash
Glacial Till Over Sol. Limestone
Glacial Till Over Sandstone
Glacial Till Over Shale
Outwash
Outwash Over Bedded Rock
Outwash Over Solution Limestone
Moraine
Buried Valley
Alluvium w/ Overbank Deposits
Alluvium w/o Overbank Deposits
Glacial Lake Deposits
Thin Till Over Bedded Rock
Beaches, B. Ridges, Dunes
Swamp/Marsh
Min.
0.10
0.10
0.10
0.25
0.18
0.18
0.10
0.10
0.10
0.00
0.00
0.10
0.10
0.10
0.10
0.10
0.18
0.25
0.25
0.18
0.18
0.10
0.25
0.10
0.18
0.25
0.10
Recharge (rn/yr)
Max.
0.18
0.18
0.18
0.38
0.25
0.25
0.18
0.18
0.18
0.05
0.05
Overall Average:
0.18
0.18
" 0.18
0.18
0.18
0.25
0.38
0.38
0.25
0.25
0.18
0.38
0.18
0.25
0.38
0.18
Overall Average:
Avg.
0.14
0.14
0.14
0.32
0.22
0.22
0.14
0.14
0.14
0.03
0.03
0.15
0.14
0.14
0.14
0.14
0.14
0.22
0.32
0.32
0.22
0.22
0.14
0.32
0.14
0.22
0.32
0.14
0.20
Recharge (m/yr)
Hydrogeologic setting
Nongladated Central (Region 6)
Alluvial Mountain Valleys
Regolith
River Alluvium
Mountain Crests
Swamp/Marsh
Northeast & Superior Uplands (Region 9)
Alluvial Mountain Valleys
Till Over Crystalline Bedrock
Glacial Till Over Outwash
Outwash
Moraine
Alluvium w/ Overbank Deposits
. Alluvium w/o Overbank Deposits
Swamp/Marsh
Bedrock Uplands
Glacial Lake/Marine Deposits
Beaches, B. Ridges, Dunes
Atlantic/Gulf Coastal Plain (Region 10)
Regional Aquifers
UnVSemlconsol. Surficial Aquifer
Alluvium w/ Overbank Deposits
Alluvium w/o Overbank Deposits
Swamp
Min. Max.
0.18 0.25
0.10 0.18
0.18 0.25
0.00 0.05
0.10 0.18
Overall Average:
0.18 0.25
0.18 0.25
0.18 0.25
0.25 0.38
0.18 0.25
0.18 0.25
0.25 0.38
0.10 0.18
0.10 0.18
0.10 0.18
0.25 0.38
Overall Average:
0.00 0.05
0.25 0.38
0.18 0.25
0.25 0.38
0.25 0.38
Overall Average:
Avg.
0.22
0.14
0.22
0.03
0.14
0.15
0.22
0.22
0.22
0.32
0.22
0.22
0.32
0.14
0.14
0.14
0.32
0.22
0.03
0.32
0.22
0.32
0.32
0.24
3D
*
I
O
I
s
o
i?
2
I
to
2
Source: Aller et at. (1987); hydrogeologlc regions from Heath (1984).
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For sites where conditions may result in little or no dilution or attenuation of contaminants between the
source and the receptor well, the generic SSLs also include values calculated with a DAF of 1. Section
3.3 presents the generic SSLs, guidance on their use, and limitations to their application.
Table 2-5. SSL Dilution Model Results: DNAPL and HGDB Sites
Source area (acres)
DNAPL Sites (92)
Geomean
Average
Std. deviation
Variance
Minimum
Maximum
HGDB sites (272)
Geomean
Average
Std. deviation
Variance
Minimum
Maximum
0.5
34
321
1,629
3E+06
2
15,112
10
741
7,305
5E+07
1
114,973
10
15
138
700
5E+05
1
6,489
7
642
7,210
5E+07
1
114,973
30
10
80
404
2E+05
1
3,747
5
436
4,655
2E+07
1
71,160
100
6
44
221
5E+04
1
2,053
4
290
2,926
9E+06
1
39,049
600
4
19
90
8E+03
1
839
3
128
1,251
2E+06
1
15,931
DNAPL = Dense nonaqueous phase liquids (OERR).
HGDB = Hydrogeologic database (API).
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Technical Background Document for
Soil Screening Guidance
PartS: DETERMINING SSLs
The Soil Screening framework provides an overall approach for developing contaminant-specific Soil
Screening Levels (SSLs) that represent a level of contamination below which there is no concern under
the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA), provided
certain conditions are met SSLs are designed to accelerate decision making and increase consistency in
decisions regarding soil contamination by facilitating prompt identification of the contaminants and
exposure areas of concern at a site. Generally, if contaminant concentrations in soil fall below SSLs and
there are no human health or ecological exposure pathways beyond those addressed under the framework,
no further study or action is warranted for residential use of that area. Exceeding a screening level
suggests that a further evaluation of the potential risks that may be posed by site contaminants is
appropriate to determine the need for a response action, but does not automatically designate a site as
"dirty" or trigger a response action.
The Soil Screening framework quantitatively addresses three residential exposure pathways: direct
ingestion of soil, inhalation of volatiles and fugitive dusts, and migration of contaminants from soil to
ground water. The framework contains three approaches for establishing SSLs, which are described in
the following sections:
• The option emphasized is the simple site-specific method in which readily obtainable, site-
specific data are input into standardized equations to derive screening levels for the inhalation
and migration to ground water exposure pathways. This method requires a small number of
easily obtained soil parameters (Section 3.2.1), selection of a site-specific inverse concentration
(Q/C) term based on site size and meteorological conditions (Section 3.2.3), and estimation of
hydrogeologic parameters for use in the SSL dilution factor model (Section 3.2.4). In addition,
for the migration to ground water pathway, a leach test may be substituted (under certain
conditions) for the sou/water partition equation to estimate soil leachate concentrations (Section
3.2.2).
• The detailed site-specific method involves using full-scale models to estimate exposure and
is analogous to exposure modeb'ng normally done during the baseline risk assessment. The
full-scale modeling allows the application of complex transport and fate models and allows for
consideration of a finite source term. Applying these models will more accurately define the
risk of exposure via the inhalation or the migration to ground water pathway. However, input
data requirements and modeling costs make this the most costly option to implement. Section
3.4 presents information on the selection and use of fate and transport models for the detailed
site-specific method.
« Generic SSLs are derived using default values in the standardized equations. The generic
SSLs are included in the framework as a default option for those situations in which more site-
specific values are not desired (Section 3.3). Although the default parameters are not
necessarily "worst case," they are conservative. At most sites, the simple site-specific method
will offer less conservative values that are still protective at only a modest increase in cost
However, because of the high cost of developing site-specific input parameters, such as soil
ingestion rates or chemical-specific bioavailability, no simple or detailed site-specific methods
have been developed for direct ingestion of soil, and only generic SSLs are available for this
pathway.
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This overall strategy provides a site manager with the flexibility to consider these three options for
determining SSLs. Generally, the selection of an option will involve balancing the investigative costs
associated with implementing the option against the potential cost savings resulting from less conservative
(but still protective) screening levels. However, an equally important factor to consider is how well site
conditions match the SSL conceptual model on which the standardized equations are based and the
default parameters and assumptions used to develop the generic SSLs.
3.1 Conceptual Site Model
The primary condition for use of SSLs is that exposure pathways of concern and conditions at the site
match those taken into account by the Soil Screening framework. Thus, the first step in applying the
framework at a site is to identify likely source areas, exposure pathways, and potential receptors at the site
and to outline site conditions that will affect exposure concentrations at likely receptor points. This
conceptual site model (CSM) is then compared with the SSL conceptual model. This comparison will
assist in determining the extent to which the framework can be applied at the site, will help in choosing
the appropriate option for determining SSLs, and will help identify additional site information needed to
apply the selected option.
3.1.1 SSL Conceptual Model. The Soil Screening framework has been developed using exposure
assumptions for residential land use activities for three pathways of exposure to site contaminants (Figure
3-1):
• Ingestion of soil
• Inhalation of volatiles and fugitive dusts
• Migration of contaminants through soil to an underlying potable aquifer.
Direct Ingestion
of Ground
Water and Soil
Blowing
Dust and
Volatilization
Not Addressed:
• Ecological effects
• Indoor exposure to volatiles from soil and water
• Consumption of fish, beef, or dairy products
• Land uses other than residential
Assumptions:
• Infinite source
• Source extends to water table
• Well at downgradient edge of source
• 30-acre source size
Figure 3-1. The SSL conceptual model.
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Note that the SSLs developed to address these pathways are not necessarily protective of all known human
exposure pathways, reasonable land uses, or ecological threats.
The default conceptual model upon which the equations in the Soil Screening framework: are based is a
0.5-acre residential property. Because this property could be adjacent to other contaminated properties,
the standardized equations for determining simple site-specific SSLs consider larger source areas that are
likely to contribute to inhalation and ground water exposures at the residence. A default 30-acre source
area is assumed for development of generic SSLs; thus, generic SSLs are protective of source areas up
to 30 acres. For the migration to ground water exposure pathway, this default source area corresponds
to a dilution-attenuation factor (DAF) of 10 to account for dilution of soil leachate by uncontaminated
ground water (see Section 2.4.5).
Source size has significant implications for the development of a DAF. Large sources generally tend to
result in low DAFs, while smaller sources generally justify higher DAFs. Before applying generic SSLs
at a site, the user should determine whether the source of contamination at the site is more or less than
30 acres. Where actual source size differs significantly from the 30-acre assumption, the user should
consider a site-specific evaluation because the default DAF of 10 may not be appropriate.
The contamination is assumed to be evenly distributed across the source area and extends from the ground
surface to the top of the aquifer. This is a reasonable assumption for parts of the country where the water
table is fairly shallow (e.g., 5 to 10 feet below surface). However, in areas where the water table is very
deep, this assumption may not be valid and application of the detailed site-specific method may be
appropriate.
For the migration to ground water pathway, the point of compliance (i.e., receptor well) is assumed to be
at the edge of the site (Figure 3-1) and is assumed to be entirely within the contaminant plume. This
reflects the real possibility that a house with a shallow drinking water well could be placed on the source
area if the site is turned over to residential use. This assumption also has implications for the calculation
of the DAF. The user should consider whether this assumption is valid for the site in question and
whether further evaluation would be appropriate.
Because the contamination extends to the water table and the source is assumed to be infinite, no
attenuation is considered in the unsaturated zone. Again, sites that have a very thick uncontaminated
unsaturated zone should consider using a detailed evaluation because a higher DAF may be justified.
Similarly, no attenuation occurs in the saturated zone, but dilution within the aquifer is considered to the
point of compliance. The aquifer is homogeneous, isotropic, unconfined, and unconsolidated. Sites with
fractured rock or karst aquifers may require a detailed evaluation to calculate an accurate DAF. Additional
detail on assumptions for the migration to ground water pathway and their basis is provided in Section
2.4.
Fractionation of contaminant mass between the inhalation and migration to ground water pathways is not
addressed in the SSL framework because the fate and transport models used to develop simple site-specific
and generic SSLs assume an infinite source. Although the assumption is highly conservative, a finite
source model cannot be applied unless there are accurate data regarding source size and volume.
Obviously, in the case of the generic SSLs, such data are not available. It is also unlikely that such data
will be available from the limited subsurface sampling that is done to apply the simple site-specific
method. Thus, it is most likely that a finite source model would be applied as part of a detailed site-
specific investigation. EPA will continue to seek consensus on the appropriate methods to incorporate
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contaminant partitioning and a finite source into the simple site-specific method. The results of these
efforts may be included in future updates to this guidance.
Section 3.4 presents information on equations and models that can accommodate finite sources and predict
the subsequent impact on either ambient air or ground water. However, when using a finite source model,
the site manager should recognize the uncertainties inherent in site-specific estimates of subsurface
contaminant distributions and use conservative estimates of source size and concentrations to allow for
such uncertainties.
Because of the conservatism of the SSL conceptual model and conservative assumptions inherent in the
standardized equations, a typical soil type was used to develop generic SSLs. The default soil type is loam
with a SO percent vegetative cover. Loam was selected as the most commonly occurring soil type in all
soil horizons in a survey of 24,007 U.S. soil series contained in EPA's PATRIOT database (PATRIOT,
1993).
3.1.2 Developing the Conceptual Site Model. The conceptual site model is a three-
dimensional picture of the site that establishes a hypothesis about possible contaminants and their sources,
contaminant release mechanisms, contaminant fate and transport, exposure pathways, and potential
receptors. The CSM generally includes a written description of site conditions relevant to this hypothesis,
supported by graphics (i.e., maps, cross sections, and diagrams) illustrating contaminant source areas,
migration and exposure pathways, and potential human and environmental receptors. The data quality
objectives (DQO) guidance for Superfund (U.S. EPA, 1993b) and the remedial investigation/feasibility
study (RI/FS) guidance (U.S. EPA, 1989a) provide discussion on the development of a conceptual site
model.
The CSM will help identify what environmental data are needed to apply the Soil Screening framework
and why they are needed. It also provides a framework for identifying data gaps and for establishing the
spatial and temporal boundaries for the field investigation (the fourth step in the DQO process; see Part 4).
The following steps are used to develop the CSM under the Soil Screening framework. If a CSM has
already been developed, compare it to these steps to ensure that it is adequate for SSL purposes.
Collect and Compile Existing Site Data. The CSM is initially based on existing site data that
are collected and compiled into an up-to-date database on the site. Existing data sources to review
include available site sampling data, historical records, aerial photographs, and hydrogeologic information.
This information should be sufficient to prepare written descriptions and graphic diagrams of contaminant
sources, contaminant release mechanisms, contaminant migration and exposure pathways, and potential
human and ecological receptors described below. The RI/FS guidance (U.S. EPA, 1989a) provides
guidance on the collection of existing data, including an extensive list of potential data sources.
Organize, Analyze, and Interpret Existing Site Data. Graphic diagrams and written
descriptions are prepared to condense and document the exposure elements necessary to determine the
applicability of the Soil Screening framework at the site and to facilitate identification of the data needed
to apply the simple site-specific method. These diagrams and descriptions should address:
• Information on the contaminant source(s) and release mechanisms
• Media affected (or potentially affected) by the soil contamination
• Site characteristics that can influence contaminant migration
• Potential exposure pathways and receptors.
One of the most important aspects of this step is to identify potential exposure pathways at the site that
are not addressed under the Soil Screening framework. EPA has identified several chemicals (Table 3-1)
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Review Draft—Do Not Cite or Quote—December 1994
Table 3-1. SSL Chemicals Known To Pose Risk Through Non-SSL Pathways
Dermal exposure Soil-plant-human exposure
Pentachlorophenol Arsenic Nickel
Cadmium Selenium
Mercury Zinc
that can pose potential risks to humans through two residential exposure pathways not quantified in the
framework: dermal exposure and soil-plant-human exposure, representing exposure through consumption
of garden vegetables grown in a residential setting (see Section 1.4.1). Appendix A contains a draft
method for developing SSLs protective of soil-plant-human exposures that OERR is considering for
incorporation into the framework. See Section 2.1.3 for a more complete discussion of OERR's efforts
to address this pathway.
The potential for acute exposures at a site also should be evaluated. The exposure assumptions used in
the SSL framework are representative of a chronic exposure scenario and do not account for situations
where high-level exposures may lead to acute toxicity. For example, in some cases, children may ingest
large amounts of soil (i.e., 3 to 5 grams) in a single event This behavior, known as pica, may result in
relatively high short-term exposures to contaminants in soils. Such exposures may be of concern for
contaminants that primarily exhibit acute health effects. Review of clinical reports on contaminants
addressed in this guidance suggests that acute effects of cyanide and phenol may be of concern in children
exhibiting pica behavior. If soils containing cyanide and phenol are of concern and pica behavior is
expected at a site, the protectiveness of the ingestion SSLs for these chemicals should be reconsidered.
If the site is near a surface waterbody or wetlands, the possibility for contamination of these environs
through overland transport or through ground water discharge to surface water should be evaluated to
determine if there is a potential risk to aquatic organisms. Additionally, depending on the use of the
surface waters in question, and if contaminants that biomagnify through the food chain are present, the
site manager may want to consider potential risks to human health for recreational fishermen or sensitive
subpopulations (e.g., subsistence fishermen). Other indirect human exposure pathways (e.g., consumption
of contaminated milk or meat) also should be considered as appropriate.
For the migration to ground water pathway, development of the CSM should include placing the site in
one of the hydrogeologic settings defined by Aller et al. (1987). A hydrogeologic setting is defined as
a mappable unit with common hydrogeologic characteristics and therefore common vulnerability to
contamination. Each setting is a composite description of hydrogeologic factors that control ground water
movement and recharge. These factors can be used to make generalizations about ground water conditions
relevant to applying the Soil Screening framework. Specifically, placement of a site in a setting will:
• Provide a sound hydrogeologic framework for the CSM by establishing the general stratigraphy
and geologic characteristics underlying the site
• Help direct subsequent geologic investigations toward likely hydrogeologic features that could
influence contaminant migration
• Facilitate identification of similar sites in the region that have existing characterization
information for use in estimating hydrogeologic parameters needed to apply the simple site-
specific method
3-5
-------
Review Draft—Do Not Cite or Quote—December 1994
• Provide two sources of typical estimates for these hydrogeologic parameters for use in the site-
specific method or for checking site-measured values: Aller et al. (1987) and the American
Petroleum Institute's (API's) hydrogeologic database (Newell et al., 1989, 1990).
For ease of application, Aller et al. (1987) developed their settings within the framework of the
hydrogeologic regions developed by Heath (1984) (Figure 3-2). To place a site in a setting, the site must
be first placed into one of Heath's regions. The user should then consider geologic and geomorphic
features of the site and select a setting from Aller et al. that is appropriate for the site. This is a relatively
simple task, although it does require some basic geologic knowledge and information on the regional
geology and geomorphology. This information may be obtained from local geologic professionals,
regional geologic maps, and cross sections, supplemented by a topographic map of the site area. A simple
reconnaissance of the site can also be very useful in this regard. Because the setting names are descriptive
of the settings' general geomorphology (e.g., alluvial mountain valleys, swamp/marsh), a site can
sometimes be placed in a setting based on simple visual observations of site conditions.
Determine if Existing Data Adequately Support the CSM. The data collected in the previous
step should be reviewed to determine if further field investigation is needed to adequately define the CSM
for the Soil Screening framework. A simple site reconnaissance can serve to verify that the CSM
descriptions and diagrams are consistent with current site conditions. The reconnaissance may include
simple sampling and analysis at the site to identify contaminant source areas or to determine soil and
hydrogeologic conditions at a site.
During the reconnaissance, personnel should search for signs of soil contamination such as discoloration
or staining and stressed vegetation. A simple field organic vapor detector can be used to detect volatile
emissions from soil if contamination from volatile organic compounds (VOCs) is suspected. Other
portable field analyzers (e.g., X-ray fluorescence detectors for metals or immunoassay kits for certain
organics) may also be used for quick measurements of soil contamination levels. These field methods can
help identify contaminant source areas and also can be used to help estimate the variance of contaminant
concentrations within an exposure area for determining DQOs for measuring soil levels (see Part 4).
Anecdotal information from workers or nearby residents also can be extremely useful in determining past
waste disposal practices. A topographic map of the site should be taken along to mark suspected source
areas as well as potentially sensitive environs (e.g., wetlands and surface waterbodies).
Define the Conceptual Site Model. As the final step in developing a CSM, the compiled and
interpreted data can be used to develop a diagram that illustrates the CSM. The diagram should represent.
linkages among contaminant sources, release mechanisms, exposure pathways and routes, and receptors
to summarize the current understanding of the soil contamination problem. This diagram should be
supported with a written description and maps and cross sections depicting contaminants, contaminant
distributions, and exposure pathways, as appropriate. Figure 3-3 is a sample CSM diagram for a
hypothetical contaminated soil site.
As investigations proceed under the Soil Screening framework, the CSM description and diagram should
be amended and modified as appropriate to reflect additional knowledge gained about site conditions.
3.1.3 Comparing Conceptual Models. Once the conceptual site model is developed, it should
be compared with the SSL conceptual model to determine the applicability of the Soil Screening frame-
work. As part of this comparison, the following questions should always be considered by the decision
maker before applying the framework:
3-6
-------
2. Alluvial Basins
500 MILES
1 •'. t
Northeast and
.Superior Uplands
7. Glaciated
Central
region
6. Nonglaciated
region
9. Northeast and
Superior Uplands
. Western Mountain
CA
6. Nonglaciated
Central
region
5. High
. Plains
2. Alluvial
Basins
Colorado
Plateau
and
Wyoming
Basin
^0^
. 6. Nonglaciated
Central region
6. Nonglaciated
Central region
Source: Heath (1984).
600 KILOMETERS
Figure 3-2. Ground water regions of the United States
-------
RECEPTOR
oo
PRIMARY
SOURCES
Drums and
Tanks
Lagoon
— >
}
\
PRIMARY
RELEASE
MECHANISM
Spills
Infiltration/
Percolation
Overtopping
Dike
\
— >
/
SECONDARY
SOURCES
Soil
^
SECONDARY
RELEASE
MECHANISM
Dust and/or
Volatile
Emissions
•
Plant Uptake
Infiltration/
Percolation
Storm-Water
Runoff
PATHWAY
^
^
Wind
Garden
Vegetables
Ground Water
i
1
Surface
and Set
^
r
) Water
Jlmenls
p
— H
.
»
HUMAN BIOTA
EXPOSURE ROUTE
Ingestlon
Inhalation
Dermal Contact
Ingestlon
Dermal Contact
Area
Residents
•
•
•
Site
trespassers
•
•
•
Terrestrial
•
•
•
Ingesllon
•
Ingestlon
Inhalation
Dermal Contact
Ingestlon
Inhalation
Dermal Contact
•
•
•
•
•
•
•
Aquatic
•
•
•
•
Review Draft— Do Not Ctte or Quote— December 199
Figure 3-3. Sample conceptual site model diagram for contaminated soil (adapted from U.S. EPA, 19898).
-------
Review Draft—Do Not Cite or Quote—December 1994
• Is the site adjacent to surface waterbodies or wetlands?
- Are there potential ecological concerns?
• Is there potential for land use other than residential?
• Are there other likely human exposure pathways that are not directly addressed by the Soil
Screening framework (e.g., local fish consumption; raising of beef, dairy, or other livestock)?
• Are there unusual site conditions (e.g., area of contamination greater than 30 acres,
unusually high fugitive dust levels due to soil being tilled for agricultural use or heavy traffic
on unpaved roads)?
If the answer to any of these questions is yes, the Soil Screening framework may no longer adequately
address all potential pathways and the site manager should consider conducting a risk assessment specific
to the appropriate additional pathway(s). However, the framework still will be useful in such situations
for eliminating or focusing further investigative efforts on the exposure pathways it does address.
Answers to these questions also will help determine the appropriate method for developing SSLs. For the
exposure pathways considered under the Soil Screening framework, the complexity and protectiveness of
site conditions will influence the selection of the appropriate method for determining SSLs. For example,
if site conditions will result in lower exposure concentrations than the defaults used in the standardized
equations to develop generic SSLs (see Section 3.3), higher but still protective SSLs may be derived using
these equations in the simple site-specific method. (The Office of Emergency and Remedial Response
[OERR] believes that this will be the case at many Superfund sites and is therefore emphasizing the simple
site-specific method.) Similarly, if the models and assumptions used to develop simple site-specific and
generic SSLs are overly conservative with respect to site conditions (e.g., the infinite source assumption
for a small source area), the additional cost and time required to apply the detailed site-specific method
may be offset by the cost savings associated with higher, but still protective, SSLs.
More important, if the conceptual site model indicates that site conditions not adequately addressed by the
assumptions in the standardized equations (e.g., fractured rock or karst aquifers) will result in higher
exposure concentrations than predicted by the equations, neither simple site-specific nor generic SSLs are
applicable at the site. The more detailed site-specific approach will be required to evaluate such site
conditions.
In summary, if the conceptual site model indicates that residential assumptions are appropriate for a site
and no other pathways of concern than those covered by the Soil Screening framework are present, then
simple site-specific or generic SSLs may be applied directly to the site. Conversely, if the conceptual
model indicates that there are significant exposure pathways not accounted for in the framework, or that
the site is otherwise more complex than the scenario outlined in this guidance, then the simple site-specific
or generic SSLs will not be sufficient for a full evaluation of the site. A detailed site-specific approach
will be necessary to evaluate the additional pathways.
3.2 Site-Specific SSLs: Simple Method
The Soil Screening framework emphasizes the simple site-specific method in which site data are input into
standardized equations to derive site-specific SSLs for the inhalation and migration to ground water
pathways. Part 2 of this document describes the development of these standard equations, which are
repeated in Highlight 3-1 for easy reference. The methodology for using these equations for calculating
SSLs, along with their inherent assumptions and limitations, is presented in Part 2, which should be read
and understood before applying this method.
3-9
-------
Review Draft—Do Not Cite or Quote—December 1994
Highlight 3-1: Standardized Equations
Equation 3-1 : Screening Level Equation for
Inhalation of Carcinogenic
Contaminants in Residential Soil
Screening Level _ TR x AT x 365 d/yr
(mg/Kg) URFxIOOOMg/mgxEFxEDxM + 1 1
[VF TEFJ
Parameter/Definition (units)
TR/target cancer risk (unitless)
AT/averaging time (yr)
URF/inhalation unit risk factor
(ng/m3)'1
EF/exposure frequency (d/yr)
ED/exposure duration (yr)
VF/soil-to-air volatilization factor
(m3/kg)
PEF/particulate emissbn factor
(m3/kg)
Default
10-6
70
chemical-specific
350
30
chemical-specific
6.79 x108
Equation 3-3: Derivation of the Volatilization
Factor
. * *M** (3 14 X 06 X T) ^ 2 2
(2 X D.J X 6a X K^)
where:
D,J x 9a
TT^TpsFn -~^y^K
Parameter/Definition (units)
VF/volatilizatbn factor (m3/kg)
Q/C/inverse of the mean cone, at the
center of a 30-acre square source
(g/mz-s per kg/m3)
T/exposure interval (s)
D8i /effective diffusivity (cm2/s)
9a /air-filled soil porosity (L^/L^)
D| /diffusivity in air (cm /s)
n/total soil porosity (Lpgjl^^
w/average soil moisture content
Pb/dry soil bulk density (g/crrr)
ps /soil particle density (g/cm3)
KJJS /soil-air partition coefficient
(g-soil/cm3-air)
H/Henry's law constant (atm-m3/mol)
Kg. /soil-water partition coefficient
(cm3/g)
Kg,. /organic carbon partition coefficient
(crrftg)
foc/organb carbon content of soil (g/g)
Default
35.10
9.5 x 108 s
Di(8a3'33/n2)
0.28 or n-wpb
chemical-specific
0.43 (loam)
0.1 (10%)
1.6or(1-n)p.
2.65
(H/K,,) x 41 (41 is a
conversion factor)
chemical-specific
chemical-specific
0.006 (0.6%)
Equation 3-2: Screening Level Equation for
Inhalation of Noncarcinogenic
Contaminants in Residential Soil
Screening Level = jHQ x AT x 365 d/yr
(mg/K9) EF x ED x I" 1 x f 1 + 1 |
["RlC1 ^W PETj]
Parameter/Definition (units)
THQ/target hazard quotient (unitless)
AT/averaging time (yr)
EF/exposure frequency (d/yr)
ED/exposure duratbn (yr)
RfC/inhalation reference concentration
(mg/m3)
VF/soil-to-air volatilization factor
(m3/kg)
PEF/particulate emissbn factor
(m3/kg)
Default
1
30
350
30
chemical- specific
chemical-specific
6.79 x 108
Equation 3-4: Derivation of the Soil Saturation
Limit
CM, - -L (K,, p,, * a,, + H'ea)
Pb
Parameter/Definition (unHs)
C^/soil saturation concentration
(mg/kg)
S/solubility in water (mg/L-water)
pb/dry soil bulk density (kg/L)
n/total soil porosity (l-pore/L^ii)
ps /soil particle density (kg/L)
K^soil-water partition coefficient (L/kg)
«„,. /soil organic carbon-water partition
coefficient (L/kg)
foc/fraction organic carbon of soil (g/g)
6,/water-filled soil porosity (LwateAsoii)
6a /air-filled soil porosity (1^/1^,1,)
w/average soil moisture content
H'/Henry's law constant (unitless)
H/Henry's law constant (atm-m3/mol)
Default
chemical-specific
1.5 or (1 - n) ps
0.43 (loam)
2.65
K,^ x fg,. (organics)
chemical-specific
0.006 (0.6%)
wpb or 0.15
n - wpb or 0.28
0.1 (10%)
Hx 41, where 41 is
a conversion factor
chemical-specific
3-10
-------
Review Draft—Do Not Cite or Quote—December 1994
Highlight 3-1 (continued)
Equation 3-5: Derivation of the Paniculate
Emission Factor
PFFfm3*,,l.n/Rv 3600S/h
0.036 x (1-V) x (Un/U,)-* x F(x)
Parameter/Definition (units)
PEF/particulate emission factor
(m3/kg)
Q/C/inverse of the mean cone, at the
center of a 30-acre-square source
(g/m2-s per kg/m3)
V/fraction of vegetative cover
(unitless)
Um /mean annual windspeed (m/s)
Ut /equivalent threshold value of wind-
speed at 7 m (m/s)
F(x)/function dependent on Um/Ut
derived using Cowherd (1 985)
(unitless)
Default
6.79 x 108
46.84
0.5 (50%)
4.69
11.32
0.194
Equation 3-6: Soil Screening Level Partitioning
Equation for Migration to Ground
Water
Screening Level
in Soil (nig/kg)
Pb
Parameter/Definition (units)
Cy/larget soil leachate concentration
(mg/L)
K^soil-water partition coefficient (L/kg)
Kg,, /soil organic carbon/Water partition
coefficient (L/kg)
fgc /fraction organic carbon in soil (g/g)
Boater-filled soil porosity
w/average soil moisture content
(kQwateAgsoil or Lwate/kSsoi
Pb/dry soil bulk density (kg/I)
n/soil porosity (I^X^,)
Ps/soil particle density (kg/L)
ea/air-filled soil porosity
H'/Henry's law constant (unitless)
H/Henry's law constant (atm-m3/mol)
Default
nonzero MCLG,
MCLorHBLxlO
DAF
chemical-specific,
KOC x foe (organtes)
chemical-specific
0.002 (0.2%)
0.3 or wpb
0.2 (20%)
1.5 or (1 - n) ps
0.43 (loam)
2.65
0.13 or (n - 6J
Hx41
chemical-specific
(assume to be zero
for inorganic
contaminants except
mercury)
Equation 3-7: Derivation of the Dilution Factor
dilution factor = 1
Kid
IT
Parameter/Definition (units)
dilution factor (unitless)
K/aquifer hydraulic conductivity (m/yr)
i/hydraulic gradient (m/m)
d/mixing zone depth (m)
I/infiltration rate (m/yr)
L/source length parallel to ground water flow (m)
Equation 3-8: Estimation of Mixing Zone Depth
d = (0.0112 Lz)°-5 + da {1 - exp[(-LI)/(Kida)]}
Parameter/Definition (units)
d/mixing zone depth (m)
L/source length parallel to ground water flow (m)
I/infiltration rate (m/yr)
K/aquifer hydraulic conductivity (m/yr)
da/aquifer thickness (m)
3-11
-------
Review Draft—Do Not Cite or Quote—December 1994
The site data needed to apply the simple site-specific method include soil properties required to calculate
the volatilization factor (Equation 3-3) and the soil saturation limit (Equation 3-4) and to apply the
soil/water partitioning equation (Equation 3-6). Section 3.2.1 provides guidance on measuring or
estimating these properties at a site. Section 3.2.2 presents a leach test option to substitute for the
soiVwater partition equation (Equation 3-6). Section 3.2.3 describes the use of Q/C tables to derive site-
specific estimates of flu's inverse dispersed air concentration that is used to develop the volatilization factor
(Equation 3-3) and the paniculate emission factor (Equation 3-5). Section 3.2.4 provides guidance on
estimating hydrogeologic parameters necessary for applying the dilution model (Equation 3-7) and the
mixing zone equation (Equation 3-8) to calculate a dilution factor to account for contaminant dilution in
ground water.
Chemical properties needed to calculate SSLs using these equations include soil organic carbon/water
partition coefficients (f^), dimensionless Henry's law constant (HO, solubility, and air diffusivity (Dj).
Table 3-2 presents these properties for the SSL chemicals. Their sources and derivation are described in
Part 5 of this document For certain ionizable organics, K^. changes with pH (see Part 5); Table 3-3
presents pH-specific K^ values for the five ionizing SSL compounds whose K^. changes significantly over
the range of subsurface pH conditions. Similarly, metal soil-water partition coefficients (Kj) also show
a strong dependence on pH. Figure 3-4 shows this relationship for the SSL metals. These metal Kd
values were estimated using the MINTEQ geochemical speciation model, as described in Part 5. To
determine site-specific values for these pH-dependent properties, soil pH is measured at the site and the
appropriate value is selected from the table or figure.
For noncarcinogens, chemical-specific toxicity criteria for SSL development include oral reference doses
(RfDs) for soil ingestion and inhalation reference concentrations (RfCs) for the inhalation pathway.
Cancer slope factors (SF0) are required to calculate SSLs for direct ingestion and inhalation. SSLs for
both pathways are based on a 10 cancer risk level and a hazard quotient (HQ) of 1. For the migration
to ground water pathway, the acceptable ground water concentration is defined by (in order of preference)
nonzero maximum contaminant level goals (MCLGs), maximum contaminant levels (MCLs), or health-
based limits (HBLs) calculated for a 10"6 cancer risk level or an HQ of 1. Table 2-2, Section 2.1, lists
chemical-specific toxicity criteria and human health benchmarks that can be used to calculate SSLs.
SSL calculation for the migration to ground water pathway differs for the partition equation and leach test
option. Both options require the same dilution factor calculation (Equations 3-7 and 3-8 with inputs
described in Section 3.2.4). For the partition equation option, the acceptable ground water concentration
(i.e., nonzero MCLG, MCL, or HBL in order of preference) is multiplied by the dilution factor to obtain
a target soil leachate concentration (C^), which is input into Equation 3-6, along with chemical properties
and the soil parameters described in Section 3.2.1, to backcalculate the SSL. If the leach test option is
chosen (Section 3.2.2), Equation 3-6 is not used. Instead, the leachate concentration from the test is
divided by the dilution factor to obtain an estimated ground water concentration at the receptor well. This
receptor point concentration is then compared with the acceptable ground water concentration to determine
if the site's soils exceed SSLs.
When determining site parameters for the simple site-specific method, bear in mind that one purpose of
SSLs is to define a level in soil below which no further study or action would be required. Therefore,
SSLs developed using site-specific data should generally be calculated assuming the reasonable maximum
exposure (RME)/"high-end" individual exposure. As described in Section 1.4.2, Superfund's approach
to RME utilizes average exposure concentrations to represent random, chronic exposure to site
contaminants. Therefore, site-specific estimates of soil, aquifer, and meteorologic parameters used in the
standardized equations should reflect average or typical site conditions to calculate average exposure
concentrations at a site.
3-12
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 3-2. Chemical-Specific Properties Used in SSL Calculations
CAS No.
83-32-9
67-64-1
309-00-2
120-12-7
71-43-2
56-55-3
205-99-2
65-85-0
50-32-8
111-44-4
117-81-7
75-27-4
75-25-2
71-36-3
85-68-7
86-74-8
75-15-0
56-23-5
57-74-9
106-47-8
108-90-7
124-48-1
67-66-3
95-57-8
218-01-9
72-54-8
72-55-9
50-29-3
53-70-3
84-74-2
95-50-1
106-46-7
91-94-1
75-34-3
107-06-2
75-35-4
156-59-2
156-60-5
120-83-2
78-87-5
542-75-6
60-57-1
84-66-2
105-67-9
131-11-3
51-28-5
121-14-2
606-20-2
117-84-0
Compound
Acenaphthene
Acetone
Aldrin
Anthracene
Benzene
Benzo(a)anthracene
Benzo(b)fluoranthene
Benzole acid
Benzo(a)pyrene
Bis(2-chloroethyl)ether
Bis(2-ethylhexyl)phthalate
Bromodichloromethane
Bromoform
Butanol
Butyl benzyl phthalate
Carbazole
Carbon disulfide
Carbon tetrachloride
Chlordane
p-Chloroaniline
Chlorobenzene
Chlorodibromomethane
Chloroform
2-Chlorophenol
Chrysene
ODD
DDE
DDT
Dibenzo(a,/))anthracsne
Di-n-butyl phthalate
1 ,2-Dichlorobenzene
1 ,4-Dichlorobenzene
3.3-Dichlorobenzidne
1,1-Dichloroethane
1 ,2-Dichloroethane
1,1-Dichloroethylene
ds-1 ,2-Dichloroethylene
frans- 1 ,2-Dichloroethylene
2,4-Dichlorophenol
1 ,2-Dichloropropane
1 ,3-Dichloropropene
Dieldrin
Diethyl phthalate
2,4-Dimethylphenol
Dimethyl phthalate
2,4-Dinitrophenol
2,4-Dinitro toluene
2,6-Dinitro toluene
Di-n-octyl phthalate
KOC
(L/Kg)
4.90E+03
4.60E-01
4.84E+04
2.12E+04
5.70E+01
3.57E-I-05
8.83E+05
6.00E-01
9.16E+05
7.60E+01
8.74E+04
5.40E+01
1.26E+02
S.OOE-t-00
3.41E+04
2.44E+03
5.20E+01
1.64E+02
5.13E+04
4.10E+01
2.04E+02
7.20E-I-01
5.60E+01
3.91 E+02
3.12E+05
8.49E+04
8.64E+04
2.37E+05
1.80E+06
1.57E+03
3.76E+02
5.16E+02
2.44E+03
5.20E+01
3.80E+01
6.50E+01
2.90E+01
5.00E+01
1.46E+02
4.70E+01
2.60E+01
1.09E+04
8.20E+01
1.26E+02
4.60E+01
1.00E-02
5.10E+01
4.20E401
9.80E+08
Di
(cm2/s)
4.21E-02
1.24E-01
1.32E-02
3.24E-02
8.70E-02
5.10E-02
2.26E-02
5.36E-02
4.30E-02
6.92E-02
3.51 E-02
2.98E-02
1.49E-02
8.00E-02
1.72E-02
3.90E-02
1.04E-01
7.80E-02
1.18E-02
4.83E-02
7.30E-02 ,
2.29E-02
1.04E-01
5.01 E-02
2.48E-02
1.56E-02
1.44E-02
1.37E-02
2.00E-02
4.38E-02
6.90E-02
6.90E-02
1.94E-02
7.42E-02
1.04E-01
9.00E-02
7.36E-02
7.07E-02
3.46E-02
7.82E-02
6.26E-02
1.25E-02
2.56E-02
5.84E-02
5.68E-02
2.73E-02
2.03E-01
3.49E-02
1.51 E-02
S
(mg/L)
4.13E+00
6.04E+05
7.84E-02
5.37E-02
1.78E+03
1.28E-02
4.33E-03
3.13E+03
1.94E-03
1.18E+04
3.96E-01
3.97E+03
3.21E+03
7.47E+04
2.58E+00
7.21E-01
2.67E+03
7.92E+02
2.19E-01
3.36E+03
4.09E+02
3.44E+03
7.96E+03
2.15E+04
1.94E-03
7.33E-02
1.92E-02
3.41 E-03
6.70E-04
1.08E+01
1.25E+02
7.30E+01
3.52E+00
5.16E+03
8.31Ei-03
3.00E+03
4.94E+03
8.03E+03
4.93E+03
2.68E+03
1.55E+03
1.87E-01
8.83E+02
6.25E+03
4.19E+03
5.80E+03
2.85E+02
1.05E-I-03
3.00E+00
H'
(dimensionless)
7.5E-03
1.2E-03
4.2E-03
4.6E-03
2.2E-01
1.5E-04
2.5E-04
1.4E-05
3.4E-05
8.8E-04
3.4E-04
1.3E-01
2.5E-02
3.5E-04
7.8E-05
3.3E-03
5.2E-01
1.2E+00
2.7E-03
4.8E-05
1.8E-01
1.0E-01
1.6E-01
6.8E-04
5.0E-05
2.0E-04
5.1 E-03
2.2E-03
4.6E-07
5.9E-05
8.6E-02
1.2E-01
8.5E-07
2.4E-01
5.2E-02
1.0E+00
1.8E-01
2.3E-01
9.8E-06
1.2E-01
1.2E-01
1.1E-04
2.2E-05
1.3E-04
2.4E-05
2.0E-07
6.0E-06
5.3E-06
3.1E-05
3-13
(continued)
-------
Review Draft—Do Not CRe or Quote—December 1994
Table 3-2 (continued)
CAS No.
115-29-7
72-20-8
100-41-4
206-44-0
86-73-7
76-44-8
1024-57-3
118-74-1
87-68-3
319-84-6
319-85-7
58-89-9
77-47-4
67-72-1
193-39-5
78-59-1
7439-97-6
72-43-5
74-83-9
74-87-3
75-09-2
95-48-7
91-20-3
98-95-3
86-30-6
621-64-7
87-86-5
108-95-2
129-00-0
100-42-5
79-34-5
127-18-4
108-88-3
8001-35-2
120-82-1
71-55-6
79-00-5
79-01-6
95-95-4
88-06-2
108-05-4
75-01-4
1330-20-7
K^Soil
Compound
Endosulfan
Endrin
Ethylbenzene
Fluoranthene
Ruorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachloro- 1 ,3-butadiene
a-HCH (a-BHC)
p-HCH (P-BHC)
Y-HCH (lindane)
Hexachloro cyclopentadiene
Hexachloroethane
lndeno(1 ,2,3-c,d)pyrene
Isophorone
Mercury
Methoxychlor
Methyl bromide
Methyl chloride
Methylene chloride
2-Methylphenol
Naphthalene
Nitrobenzene
/V-Nitrosodiphenylamine
A/-Nitrosodi-n-propylamine
Pentachloropnenol
Phenol
Pyrene
Styrene
i ,1 ,2,2-Tetrachloroethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trichlorobenzene
1,1,1 -Trichloroethane
1 ,1 ,2-Trichloroethane
Trichloroethylene
2,4,5-Trichlorophenol
2,4,6-Trichlorophenol
Vinyl acetate
Vinyl chloride
Xylenes (total)
organic carbon/water partition coefficient
KOC
(L/Kg)
7.38E+02
1.08E+04
2.21 E+02
4.91 E+04
7.96E+03
6.81E-f03
7.24E+03
3.75E+04
6.99E+03
1.76E+03
2.28E+03
1.38E+03
9.59E+03
1.83E+03
4.36E+06
3.00E+01
...
7.79E+04
9.49E+00
6.00E+00
1.60E+Q1
5.40E+01
9.64E+02
1.31E+02
3.27E+02
1.70E-I-01
5.67E+02
2.20E+01
6.82E+04
9.12E+02
7.90E+01
3.00E+02
1.31E+02
5.01 E+02
1.54E+03
9.90E+01
7.60E+01
9.40E+01
1.40E+03
2.83E+02
5.00E+00
1.10E+01
2.60E+02
D;
(cm2/s)
1.15E-02
1.25E-02
7.50E-02
3.02E-02
3.63E-02
1.12E-02
1.22E-02
5.42E-02
5.61 E-02
1.76E-02
1.76E-02
1.76E-02
1.61 E-02
2.49E-03
1.90E-02
6.23E-02
1.30E-01
1.56E-02
7.28E-02
1.26E-01
1.01E-01
7.40E-02
5.90E-02
7.60E-02
2.93E-02
5.13E-02
5.60E-02
8.20E-02
2.72E-02
7.10E-02
• 7.10E-02
7.20E-02
8.70E-02
1.16E-02
3.00E-02
7.8QE-02
7.80E-02
7.90E-02
2.91 E-02
3.14E-02
8.50E-02
1.06E-01
7.20E-02
S
(mg/L)
2.31E-01
2.46E-01
1.73E+02
2.32E-01
1.86E+00
2.73E-01
2.68E-01
8.62E-03
2.54E+00
2.40E+00
5.42E-01
4.20E+00
1 .53E+00
4.08E+01
1.07E-02
1 .20E+04
—
8.84E-02
1.45E+04
6.34E+03
1.74E+04
2.77E-I.04
3.11E+01
1 .92E+03
3.74E+01
1.46E+04
1.34E+01
9.08E+04
1.37E-01
2.57E+02
3.07E+03
2.32E+02
5.58E+02
6.79E-01
3:07E+01
1.17E+03
4.40E+03
1.18E+03
9.65E+02
7.53E+02
2.24E+04
2.73E+03
1 .86E+02
H'
(dimensionless)
9.5E-04
4.9E-05
3.2E-01
3.8E-04
3.0E-03
2.4E-02
3.4E-04
2.2E-02
9.8E-01
2.8E-04
1.4E-05
1.4E-04
7.0E-01
1.5E-01
2.0E-07
2.5E-04
4.7E-01
2.6E-04
5.8E-01
1.9E+00
9.7E-02
6.7E-05
2.0E-02
8.4E-04
2.9E-02
1.7E-03
5.8E-04
2.4E-05
3.4E-04
1.4E-01
1.5E-02
7.1E-01
2.SE-01
1.4E-04
1.1E-01
7.6E-01
4.1 E-02
4.3E-01
1.8E-04
1.7E-04
2.3E-02
3.5E+00
2.5E-01
D{ = Diffusivity in air (25 °C).
S = Solubility in water (20-25 °C).
H' = Dimensionless Henry's law constant (H [atm-m3/mol] • 41) (25 °C).
3-14
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 3-3. K-. Values for Ionizing Organics as Function of pH
pH Benzole acid 2,4-Dichlorophenol
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6.0
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.0
5.74
4.81
4.02
3.36
2.82
2.37
2.00
1.71
1.47
1.27
1.12
0.99
0.89
0.81
0.75
0.70
0.66
0.62
0.60
0.58
0.56
0.55
0.54
0.53
0.53
0.52
0.52
0.51
0.51
0.51
0.51
0.50
159
159
159
159
159
158
158
158
158
158
157
157
156
156
155
154
152
151
149
146
143
140
135
130
125
118
111
103
94
85
76
67
Pentachlorophenol
8395
7333
6340
5433
4621
3908
3293
2770
2330
1965
1663
1417
1216
1053
922
817
732
664
610
567
532
505
483
466
452
441
432
425
419
415
412
409
2,4,5-Trichlorophenol
2359
2353
2347
2338
2328
2314
2298
2278
2252
2222
2184
2139
2084
2019
1943
1855
1756
1645
1524
1395
1262
1127
994
867
748
639
542
457
383
320
268
224
2,4,6-Trichlorophenol
1019
1006
991
973
951
925
894
858
818
773
723
671
616
561
506
454
404
359
319
283
252
226
204
186
171
158
148
140
133
128
124
120
3-15
-------
Review Draft—Do Not Cite or Quote—December 1994
4.5
5.5
6.5
pH
7.5
8.5
4.5
4.5
5.5
6.5
PH
7.5
8.5
Figure 3-4. Metal Kd as a function of pH.
3-16
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 3-4 presents the. parameters that must be measured or estimated to apply the simple site-specific
method. Parameters that are used directly in the SSL equations for a particular exposure pathway are
indicated by a solid circle. Open circles indicate other parameters needed to calculate or estimate SSL
equation parameters. The table also provides a source and methodology for measuring or estimating each
parameter. Source parameters indicated in this table are self-explanatory and require no further discussion.
The remaining parameters are described in the following sections.
3.2.1 Soil Parameters. Soil parameters necessary for SSL calculation are presented in Table 3-4.
Several of the required parameters can be measured in the field, others require laboratory measurement.
Although laboratory measurements of these parameters cannot be obtained under the Superfund Contract
Laboratory Program (CLP), they are readily available from soil testing laboratories across the country at
a relatively low cost Although the U.S. Environmental Protection Agency (EPA) does not require full
CLP sample tracking and quality assurance/quality control (QA/QC) procedures for these measurements,
routine EPA QA/QC procedures are recommended, including a Quality Assurance Project Plan (QAPjP),
chain-of-custody forms, and duplicate analyses.
Samples for measuring soil parameters should be taken along with samples for measuring contaminant
concentrations (see Part 4). If possible, consider splitting single samples for contaminant and soil
parameter measurements. Many soil testing laboratories have provisions in place for handling and testing
contaminated samples. However, if testing contaminated samples is a problem, samples may be taken
from clean areas of the site as long as they represent the same soil texture and series and are taken from
the same depth as the contaminant concentration samples.
The sampling design for measuring soil parameters should be adequate to determine average soil properties
across the source area for use in SSL calculations. If samples for soil properties are taken from the same
locations as those for measuring contamination, the same statistical procedures used to estimate average
contaminant concentrations may be used to estimate average soil properties (see Part 4). Alternatively,
each soil horizon and soil series should be sampled at least three times along a transect across the source
to provide data for estimating average properties.
Soil Texture. Soil texture class (e.g., loam, sand, silt loam) is necessary to estimate average soil
moisture content and to apply the Hydrologic Evaluation of Landfill Performance (HELP) model to
estimate infiltration rates (Section 3.2.4). A soil's texture classification is determined from a particle size
analysis and the U.S. Department of Agriculture (USDA) soil textural triangle shown in Figure 3-5. This
classification system is based on the percentages of sand, silt, and clay particles, as defined by the USDA
soil particle size classification at the bottom of Figure 3-5. The particle size analysis method
recommended (Gee and Bauder, 1986) will provide this particle size distribution. Other particle size
methods may be used as long as they provide the same particle size breakpoints for sand/silt (0.05 mm)
and silt/clay (0.002 mm). Be careful of measurements used for engineering purposes (i.e., American
Society for Testing and Materials [ASTM]) because they are based on different break points (i.e., 0.074
mm for sand/silt and 0.005 mm for silt/clay). Field methods are an alternative for determining soil
textural class; an example from Brady (1990) is also presented in Figure 3-5.
Dry soil bulk density (pb) is used to calculate soil porosity and can be determined for any soil horizon
by weighing a thin-walled tube soil sample (e.g., shelby tube) of known volume and subtracting the tube
weight (ASTM D 2937). A moisture content determination (ASTM 2216) is then made on a subsample
of the tube sample to adjust wet bulk density to dry bulk density. The other methods listed in Table 3-4
(ASTM D 1556, D 2167, D 2922) are applicable only to surface soil horizons. All ASTM soil testing
methods are readily available in the Annual Book of ASTM Standards, Volume 4.08, Soil and Rock;
Building Stones, which is available from ASTM, 1916 Race St., Philadelphia, PA.
3-17
-------
Table 3-4. Parameters for Developing Simple Site-Specific SSLs
Parameter
Source Characteristics
Roughness height
Source area (A)
Source length (L)
Source depth
SSL Pathway
Migration to
ground
Inhalation water
O
Data source
Observation
Sampling data
Sampling data
Sampling data
Method
See Appendix G; used to calculate threshold windspeed
Measure total area of contaminated soil
Measure length of source parallel to ground water flow
Measure depth of contamination; used to check assumption of contamination
extending to water table
Soil Characteristics
Soil texture
Dry soil bulk density (pb)
Soil moisture content (w)
Soil organic carbon (!„,,)
Soil pH
Moisture retention exponent (b)
Saturated hydraulic conductivity (Ks)
Avg. soil moisture content (8W)
Mode soil aggregate
O
O
0
O Lab measurement
• Field measurement
O Lab measurement
• Lab measurement
O Field measurement
O Look-up
O Look-up
• Calculated
Field measurement
Particle size analysis (Gee & Bauder, 1986) and USDA classification; used to
estimate 8W & 1
All soils: ASTM D 2937; shallow soils: ASTM D 1556, ASTM D 2167, ASTM D 2922
ASTM D 2216; used to estimate dry soil bulk density
Nelson and Sommers (1982)
McLean (1982); used to select pH-specific K^ (ionizable organics) and Kd (metals)
Table 3-5; used to calculate Bw
Table 3-5; used to calculate 6W
U.S. EPA (1988b)
Appendix G; used to calculate threshold windspeed
Meteorological Data
Threshold windspeed @ 7 m (U, 7)
Air dispersion factor (Q/C)
Mean annual windspeed (U J
Calculated
Q/C table (Table 3-6)
Nat'l Weather Service
Appendix G
Select value corresponding to site area and city with climatic conditions similar to site
Use most representative NWS surface station
Hydrogeotogic Characteristics (DAF)
Hydrogeologic setting
Infiltration/recharge (1)
Hydraulic conductivity (K)
Hydraulic gradient (i)
Aquifer thickness (d)
Conceptual site model
HELP model;
regional estimates
Field measurement;
regional estimates
Field measurements;
regional estimates
Field measurement;
regional estimates
Place site in hydrogeologic setting from Alter et al. (1987) for estimation of
parameters below
HELP (Schroeder et al., 1984) may be used for site-specific infiltration estimates;
recharge estimates also may be taken from Alter et al. (1987) or may be estimated
from knowledge of local meteorologic and hydrogeologic conditions
Aquifer tests (i.e., pump tests, slug tests) preferred; estimates also may be taken from
Alter et al. (1987) or Newell et al. (1990) or may be estimated from knowledge of local
hydrogeologic conditions
Measured on map of site's water table (preferred); estimates also may be taken from
Newell et al. (1990) or may be estimated from knowledge of local hydrogeologic
conditions
Site-specific measurement (i.e., from borehole logs) preferred; estimates also may be
taken from Newell et.al. (1990) or may be estimated from knowledge of local
hydrogeologic conditions
• Indicates parameters used in SSL equations.
O Indicates parameters/assumptions needed to estimate SSL equation parameters.
-------
Review Draft—Do Not Cite or Quote—December 1994
100
y
Y Y\y
Percent Sand
Criteria Used with the Field Method of Determining Soil Texture Classes
Criterion Sand Sandy loam
1. Individual grains Yes Yes
visible to eye
2. Stability of dry Do not form Do not form
clods
3. Stability of wet Unstable Slightly
clods stable
4. Stability of Does not Does not
"ribbon" when form form
wet soil rubbed
between thumb
and fingers
Loam
Some
Easily
broken
Moderately
stable
Does not
form
Silt loam
Few
Moderately
easily
broken
Stable
Broken
appear-
ance
Clay loam Clay
No No
Hard and Very hard
stable and
stable
Very Very
stable stable
Thin, will Very long,
break flexible
Particle Size, mm
0.002 0.05 0.10 0.25 0.5 1.
U.S.
Department ciay silt
of Agriculture
S2 ».
Med. Coarse
Sand
0 2.0
Very
Coarse Kfaaot
aSource: USDA, Brady (1990).
Figure 3-5. U.S. Department of Agriculture soil texture classification.
*
3-19
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Review Draft— Do Not Cite or Quote— December 1994
Soil organic carbon is measured by burning off soil carbon in a controlled-temperature oven (Nelson and
Sommers, 1982). This parameter is used to determine soil-water partition coefficients from the organic
carbon soil-water partition coefficient, K,^. Soil pH is used to select site-specific partition coefficients
for metals and ionizing organics from Figure 3-3 and Table 3-3. This simple measurement is made with
a pH meter in a sou/water slurry (McLean, 1982) and may be measured in the field using a portable pH
meter.
Average soil moisture content is an important parameter because it defines the fraction of total soil
porosity that is filled by water and air, parameters necessary for determining the volatilization factor (VF),
the soil saturation constant (C^), and sou/water partitioning behavior. It is important that the moisture
content used to calculate these parameters represent the annual average soil moisture conditions. Moisture
content measurements on discrete soil samples should not be used because they are too affected by
antecedent rainfall events and may not represent average conditions. Volumetric average soil water
content (6W) may be estimated by the following relationship developed by Qapp and Hornberger (1978)
and presented in the Superfund Exposure Assessment Manual (U.S. EPA, 1988b):
6W = n (I/Ks) 1/(2b+3) (3-9)
where
n = total soil porosity
1 = infiltration rate (m/yr)
Kj. = saturated hydraulic conductivity (m/yr)
b = soil-specific exponential parameter (unitless).
Total soil porosity (n) is estimated from dry soil bulk density (p^ as follows:
n=l-(Pb/Ps) (3-10)
where
ps = soil particle density = 2.65 kg/L.
Values for K,. and the exponential term l/(2b+3) are shown in Table 3-5 by soil texture class.
Table 3-5. Parameter Estimates for Calculating Average Soil
Moisture Content (6W)
Soil texture
Sand
Loamy sand
Sandy loam
Silt loam
Loam
Sandy clay loam
Silt clay loam
Clay loam
Sandy clay
Silt clay
Clay
Ks(m/yr)
1,830
540
230
120
60
40
13
20
10
8
5
1/(2b+3)
0.090
0.085
0.080
0.074
0.073
0.058
0.054
0.050
0.042
0.042
0.039
Source: U.S. EPA, 1988b.
3-20
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Review Draft—Do Not Cite or Quote—December 1994
Site-specific values for infiltration rate (I) may be estimated using the HELP model, as described in
Section 3.2.4. Alternatively, infiltration may be assumed to be equivalent to recharge. Aller et al. (1987)
give recharge range estimates for hydrogeologic settings across the United States.
Mode soil aggregate size is required to estimate threshold windspeed at 7 meters (Ut 7) for site-specific
estimates of the paniculate emission factor (PEF). Soil aggregate size refers to the diameter of aggregated
soil particles under field conditions and should not be confused with conventional soil particle size
analysis in which soil clumps or clods are disaggregated into the sand, silt, and clay particles of which
they are composed. The size distribution of soil aggregates is measured in the field by hand sieving and
should reflect the aggregated nature of the soil under typical field conditions at the site. This is necessary
so that the PEF calculations will be based on soil particles as they occur at the site. Appendix G presents
a method taken from Cowherd et al. (1985) for determining this field-measured parameter.
3.2.2 Leach Test Option. The user also has the option of using a leach test instead of the
partitioning equation in the simple site-specific method for the migration to ground water pathway. If this
option is chosen, soil parameters are not needed for this pathway, but calculation of a dilution factor is
still required.
The framework also includes the option of using a leach test instead of the partitioning equation. In some
instances a leach test may be more useful than the partitioning method, depending on the constituents of
concern and the possible presence of the Resource Conservation and Recovery Act (RCRA) wastes. This
guidance suggests using the EPA Synthetic Precipitation Leaching Procedure (SPLP, EPA SW-846 Method
1312, Appendix H). The SPLP was developed to model an acid rain leaching environment and is
generally appropriate for a contaminated soil scenario. Like most leach tests, the SPLP may not be
appropriate for all situations (e.g., soils contaminated with oily constituents may not yield accurate .results).
Therefore, discretion is advised when applying the SPLP.
The Agency is aware that there are many leach tests available for application at hazardous waste sites,
some of which may be appropriate in specific situations (e.g., the Toxicity Characteristic Leaching
Procedure, known as the TCLP, models leaching in a municipal landfill environment). It is beyond the
scope of this document to discuss in detail other leaching procedures and the appropriateness of their use.
Stabilization/Solidification ofCERCLA and RCRA Wastes (U.S. EPA, 1989e) and the Science Advisory
Board's (SAB's) review of leaching tests (U.S. EPA, 1991c) contain information on the application of
various leach tests to waste disposal scenarios. The user is encouraged to consult these documents for
further information.
To the extent practicable, soil samples for the leach test option should represent average contaminant
concentrations for each source area with potential to contaminate ground water. Because detailed
information about contaminant distributions with depth will be limited when the Soil Screening framework
is applied, the Agency recommends that leach tests be conducted on soil samples with contaminant
concentrations that are representative of the highest average borehole concentration within the source area.
These samples can be obtained by compositing splits of borehole soil samples taken to measure
contaminant concentrations. Part 4 of this document provides guidance on borehole locations and
sampling intervals for subsurface soils. To ensure adequate precision of leach test results, leach tests
should be conducted in triplicate. See Appendix H for additional quality assurance/quality control
measures for leach tests.
3.2.3 Meteorological Related Variables (Inhalation Pathway). Site-specific variables
necessary for calculating the PEF include the mean annual windspeed (Um) and the threshold windspeed
at 7 meters above ground surface (Ut7). Mean annual windspeed can be obtained from the National
3-21
-------
Review Draft—Do Not Cite or Quote—December 1994
Weather Service surface station nearest to the site. Threshold windspeed at 7 meters is calculated from
the source area roughness height and the mode soil aggregate size as described in Cowherd et al. (1985).
A dispersed air inverse concentration (Q/C), representing the inverse of the mean air concentration at the
center of the source area, is necessary to calculate the PEF and the VF. Q/C values have been developed
for six source sizes (Table 3-6) and 29 cities representative of nine climatic zones across the United States
(Figure 3-6). For estimating VF and PEF, the user should select a Q/C value from Table 3-6 correspond-
ing to the climatic zone where the site is located, the city nearest the site, and the appropriate source area.
3.2.4 Hydrogeologic Variables for Dilution Model (Migration to Ground Water
Pathway). Several site-specific variables are necessary for calculating a dilution factor using the SSL
dilution model (Equations 3-7 and 3-8). These include infiltration rate (I) and the aquifer parameters
hydraulic conductivity (K), hydraulic gradient (i), and aquifer thickness (d.,) (see Table 3-4).
A site-specific infiltration rate (I, m/yr) can be estimated using the HELP model. Although HELP was
originally written for hydrologic evaluation of landfills (Schroeder et al., 1984), inputs to the HELP
program can be modified to estimate infiltration in undisturbed soils in natural settings. HELP uses a
water balance calculation and site-specific values for precipitation and evaporation to calculate infiltration
rates.
The most recent version of HELP (2.05 as of 7/7/94) and the most recent user's guide and documentation
can be obtained by sending your address and two double-sided, high-density, DOS-formatted disks to:
attn. Eunice 8urk
U.S. EPA
5995 Center Hill Ave.
Cincinnati, OH 45224
(513) 569-7871.
A 286 or higher DOS-based personal computer (PC) is sufficient for running HELP (further details can
be found in the U.S. EPA documentation). Installing HELP and learning the input sequences can be
expected to require 3 to 5 hours. The time required to enter input parameters for a single simulation can
vary a great deal. If most input values are supplied by HELP'S default data, then input values can be
entered in less than 5 minutes. If daily rainfall and monthly temperature values are input manually, then
entering input could take more than an hour. Execution of the output program also can vary but the
longest simulation did not take more than 30 seconds on a 486, 50-MHz PC.
Before running an infiltration simulation with HELP, the user must answer the following questions:
• Where is the site located?
• How many years of infiltration should be simulated?
• What is the source of rainfall and temperature data?
HELP contains a meteorological database that has 5 years of measured rainfall and temperature data for
about 100 cities in the United States. Rainfall and temperature data covering 20 years can also be
synthetically generated for many cities. The simulation should be made as long as possible so as to
average out any uncharacteristically dry or wet periods. The measured data generally cover only 5 years,
which limits the simulation to 5 years. The synthetically generated data can cover as many as 20 years.
For the simple site-specific infiltration estimates, HELP default variables are suitable where they are
available. Table 3-7 provides guidance for the assignment of HELP input parameters without default
*
3-22
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Review Draft—Do Not Cite or Quote—December 1994
Table 3-6. Q/C Values by Source Area, City, and Climatic Zone
Q/C (glnf-s per kg/m3)
Zone I
Seattle
Salem
Zone II
Fresno
Los Angeles
San Francisco
Zone III
Las Vegas
Phoenix
Albuquerque
Zone IV
Boise
Winnemucca
Salt Lake City
Casper
Denver
Zone V
Bismark
Minneapolis
Lincoln
Zone VI
Little Rock
Houston
Atlanta
Charleston
Raleigh-Durham
Zone VII
Chicago
Cleveland
Huntington
Harrisburg
Zone VIII
Portland
Hartford
Philadelphia
Zone IX
Miami
0.5-Acre
82.72
73.44
62.00
68.81
89.51
95.55
64.04
84.18
69.41
69.23
78.09
100.13
75.59
83.39
90.80
81.64
73.63
79.25
77.08
74.89
77.26
97.78
83.22
53.89
81.90
74.23
71.35
90.24
85.61
1-Acre
72.62
64.42
54.37
60.24
78.51
83.87
56.07
73.82
60.88
60.67
68.47
87.87
66.27
73.07
79.68
71.47
64.51
69.47
67.56
65.65
67.75
85.81
73.06
47.24
71.87
65.01
62.55
79.14
74.97
2-Acre
64.38
57.09
48.16
53.30
69.55
74.38
49.59
65.40
53.94
53.72
60.66
77.91
58.68
64.71
70.64
63.22
57.10
61.53
59.83
58.13
60.0t
76.08
64.78
41.83
63.72
57.52
55.40
70.14
66.33
5-Acre
55.66
49.33
41.57
45.93
60.03
64.32
42.72
56.47
46.57
46.35
52.37
67.34
50.64
55.82
61.03
54.47
49.23
53.11
51.62
50.17
51.78
65.75
55.99
36.10
55.07
49.57
47.83
60.59
57.17
10- Acre
50.09
44.37
37.36
41.24
53.95
57.90
38.35
50.77
41.87
41.65
47.08
60.59
45.52
50.16
54.90
48.89
44.19
47.74
46.37
45.08
46.51
59.16
50.38
32.43
49.56
44.49
43.00
54.50
51.33
30-Acre
42.81
37.88
31.85
35.10
46.06
49.48
32.63
43.31
35.69
35.49
40.14
51.68
38.80
42.72
46.84
41.56
37.68
40.70
39.68
38.42
39.87
50.45
43.03
27.08
42.34
37.86
36.64
46.38
43.57
3-23
-------
I
O
§
o
Figure 3-6. U.S. climatic zones.
-------
table 3-7. HELP Input Parameters for Determining SSL Site-Specific Infiltration Rates
Parameter
Recommended
value
Notes
Simulation Information
Location of site
Duration of infiltration simulation
Cllmatologlcal Inputs
Grass cover
Soil Inputs
Soil texture
Number of layers
Layer type
Thickness of layer (inches)
Site open or inactive
Fraction of potential runoff allowed
Area of site (ft2)
Site specific
5 or 20 years
Site specific
Site specific
1 layer
Vertical percolation
60"
Active
1.0
Site specific
Measured climatological data are available for 100 U.S. cities.
Synthetic climatological data can be generated for 184 cities. Select
city most representative of site meteorological conditions
5 years if using actual climatological data, 20 years if using synthetically
generated climatological data. 5 years is adequate for SSL application.
Bare, poor, fair, good, or excellent, see Schroeder et al. (1984).
Defined according to USDA textural class (see Section 3.2.1)
Just one soil layer should be used unless hydrogeologic data indicate
otherwise.
No drainage or barrier layers should be used.
Layer thickness does not significantly affect results so long as layer
thickness is greater than evaporative depth.
Assumes site is not located in a pit or depression.
If area is not know, 1 ft2 can be input.
Note: The input parameters listed in this table cannot be assigned default values by HELP. There are additional input parameters not
listed in this table that can be assigned default values by HELP or, alternatively, assigned values individually by the user.
D
3
I
o
9
o
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Review Draft—Do Not Cite or Quote—December 1994
variables in the model. The 5-year measured meteorological data are adequate for SSL application; the
user should select a city in the HELP database that is most representative of meteorological conditions at
the site.
Another alternative is to use infiltration rates determined for a better-characterized site in the same
hydrogeologic setting and with similar meteorological conditions as the site in question. A third
alternative is to assume that infiltration is equal to recharge and obtain recharge estimates for the site's
hydrogeologic setting from Aller et al. (1987). The Aller et al. recharge range also may be used to check
the reasonableness of recharge estimates developed using HELP or obtained from another site.
Aquifer parameters needed to estimate a site-specific dilution factor include hydraulic conductivity (K),
hydraulic gradient (i), and aquifer thickness (da). Available site-measured values for these parameters are
the preferred alternative. Existing site documentation should be reviewed for in situ measurements of
aquifer conductivity (i.e., from pump test data), water table maps that can be used to estimate hydraulic
gradient, and boring logs that indicate the depth of the uppermost aquifer. Detailed information on
conducting and interpreting aquifer tests can be found in Nielsen (1991).
If site-measured values are not available, hydrogeologic knowledge of regional geologic conditions or
measured values in the literature may be sources of reasonable estimates. Values from a similar site in
the same region and geologic setting also may be used, but must be carefully reviewed to ensure that the
subsurface conceptual models for the two sites show reasonable agreement. For all of these options, it
is critical that the estimates and sources be reviewed by an experienced hydrogeologist knowledgeable of
regional hydrogeologic conditions.
A third option is to obtain parameter estimates for the. site's hydrogeologic setting from Aller et al. (1987)
or from the American Petroleum Institute's (API's) hydrogeologic database (HGDB) (Newell et al., 1989,
1990). Under the Soil Screening framework, the site's hydrogeologic setting is defined during
development of the conceptual site model. Aller et al. (1987) present ranges of values for K and i by
hydrogeologic setting. The HGDB contains measured values for these parameters and aquifer depth for
a number of sites in each hydrogeologic setting. If HGDB data are used, the median value presented for
each setting should be used. Aquifer parameter values from these sources also can serve as a check of
the reasonableness of site-measured values or estimates obtained from other sources.
The user should note that the simple dilution factor model assumes a homogeneous unconsolidated aquifer
and may give inaccurate or misleading results when applied to a fracture rock or karst setting.
3.2.5 Considering Csat. Equation 3-4 (see Highlight 3-1, p. 3-10) is used to calculate the chemical-
specific soil saturation limit (Csat), which is the concentration at which the soil pore air, pore water, and
surface sorption sites are saturated with the organic chemical in question. Above this concentration the
contaminant will exist in a pure, free phase, and, as explained in Section 2.3.4, Henry's law and the soil
VF model (Equation 3-3) are no longer valid. Moreover, if the pure phase contaminant is liquid at soil
temperatures, Csat serves as a conservative estimate of the concentration above which nonaqueous phase
liquids (NAPL) may be suspected in a soil.
Table 3-8 lists the SSL organic chemicals by their physical state (liquid or solid) at soil temperatures,
along with their melting points. When calculating site-specific SSLs, site-specific C^ values also should
be calculated for the chemicals of concern using the same site-specific soil characteristics used to calculate
SSLs (i.e., bulk density, porosity, average water content, and organic carbon content). If C^ is lower than
the SSL and the compound is liquid at soil temperature, Csat becomes the SSL. Compounds having soil
concentrations above this level may have the potential for occurring as NAPL at the site and there is a
need for further investigation. The dense NAPL (DNAPL) guidance (U.S. EPA, 1992d) provides more
3-26
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Review Draft—Do Not Cite or Quote—December 1994
Table 3-8. Physical State of Organic SSL Chemicals
Compounds liquid at soil temperatures
Melting
CAS No. Chemical point (°C)
Compounds solid at soil temperatures
Melting
CAS No. Chemical point (°C)
67-64-1 Acetone -94
71-43-2 Benzene 6
111-44-4 Bis(2-chlorethyl)ether -50
117-81-7 Bis(2-ethy1hexyl)phthalate -50
75-27-4 Bromodichloromethane -55
75-25-2 Bromoform 8
71-36-3 Butanol -90
85-68-7 Butyl benzyl phthalate -35
75-15-0 Carbon dsulfide -111
56-23-5 Carbon tetrachloride -23
108-90-7 Chlorobenzene -46
124-48-1 Chlorodibromomethane -20
67-66-3 Chloroform -64
95-57-8 2-Chlorophenol 9
84-74-2 Oi-n-butyl phthalate -35
95-50-1 1,2-Dichlorobenzene -17
75-34-3 1,1-Dichloroethane -97
107-06-2 1,2-Dichloroethane -35
75-35-4 1,1-Dichloroethylene -122
156-59-2 os-1,2-Dichloroethytene -81
156-60-5 frans-1,2-Dichloroethylene -50
78-87-5 1,2-Dichloropropane -70
542-75-6 1,3-Dichloropropene NA
84-66-2 Diethyl phthalate -40
131-11-3 Dimethyl phthalate 6
117-84-0 Di-n-octyl phthalate -25
100-41-4 Ethylbenzene -95
87-68-3 Hexachloro-1,3-butadiene -20
77-47-4 Hexachlorocyclopentadiene 10
78-59-1 Isophorone -8
74-83-9 Methyl bromide -94
75-09-2 Methylene chloride -95
98-95-3 Nitrobenzene 6
100-42-5 Stiyene -30
79-34-5 1,1,2,2-Tetrachloroethane -44
127-18-4 Tetrachloroethylene -19
108-88-3 Toluene -95
120-82-1 1,2,4-Trichlorobenzene 17
71-55-6 1,1,1-Trichloroethane -30
79-00-5 1,1.2-Trichloroethane -35
79-01-6 Trichloroethylene -73
108-05-4 Vinyl acetate -93
75-01-4 Vinyl chloride -154
1330-20-7 Xylenes (total) -47.9 -13.3
83-32-9 Acenaphthene 95
309-00-2 Aldrin 104
120-12-7 Anthracene 217
56-55-3 Benz(a)anthracene 156
205-99-2 Benzo(b)fluoranthene 168
207-08-9 Benzo(/c)fluoranthene 217
50-32-8 Benzo(a)pyrene 179
65-85-0 Benzoic acid 122
86-74-8 Carbazole 245
57-74-9 Chlordane 106
106-47-8 p-Chloroaniline 73
218-01-9 Chrysene 250
72-54-8 ODD 110
72-55-9 DDE 89
50-29-3 DDT 109
53-70-3 Dibenz(a,/7)anthracene 266
106-46-7 1,4-Dichlorobenzene 53
91-94-1 3,3-Dichlorobenzidine 132
120-83-2 2,4-Dichlorophenol 45
60-57-1 Dieldrin 177
105-67-9 2,4-Dimethylphenol 26
51-28-5 2,4-Dinrtrophenol 113
121-14-2 2,4-Dinitrotoluene .70
606-20-2 2,6-Dinitrotoluene 65
115-29-7 Endosulfan 85
72-20-8 Endrin 200
206-44-0 Fluoranthene 107
86-73-7 Fluorene 117
76-44-8 Heptachlor 96
1024-57-3 Heptachlor epoxide 161
118-74-1 Hexachlorobenzene 231
319-84-6 a-Hexachlorocyclohexane (a-BHC) 160
319-85-7 p-Hexachlorocydohexane (p-BHC) 315
58-89-9 y-Hexachlorocyclohexane (Lindane) 113
67-72-1 Hexachloroethane 187
193-39-5 lndeno(1,2,3-c,d)pyrene 162
72-43-5 Methoxychlor 78
95-48-7 2-Methylphenol 31
91-20-3 Naphthalene 81
621-64-7 W-Nitrosodiphenylamine 67
86-30-6 AA-Nitrosodi-n-propylamine NA
87-86-5 Pentachlorophenol 174
108-95-2 Phenol 43
129-00-0 Pyrene 156
8001-35-2 Toxaphene 78
95-95-4 2,4.5-Trichlorophenol 67
88-06-2 2,4,6-Trichlorophenol 69
NA = Not available.
3-27
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Review Draft—Do Not Cite or Quote—December 1994
definitive guidance on estimating the potential for DNAPL for such compounds and should be consulted
if soil concentrations exceed C^ levels for liquid compounds.
The interpretation of C^ results is different for organic compounds that are solid at ambient soil
temperature. If C^ levels below inhalation SSLs indicate that exposure through the inhalation pathway
is not of concern when the soil's air, pore water, and sorption sites are saturated with the contaminant
Since this soil concentration corresponds to the highest possible volatile emission flux (i.e., soil air
concentrations cannot be higher), volatile emissions can be assumed to be of no significant concern at any
soil contaminant concentration. However, the additional solid-phase contaminant present in soils above
the C,^ level could pose risk through inhalation of fugitive dust emissions. Therefore, when C^, levels
are below inhalation SSLs for organic compounds that are solid at soil temperature, inhalation SSLs should
be calculated considering paniculate emissions only by eliminating the VF term (i.e., set 1/VF = 0 in
Equation 3-1 or 3-2). The same approach should be taken for nonvolatile metals (i.e., all metals except
mercury). Similarly, when calculating migration to ground water SSLs for such compounds, the 6aH' term
in Equation 3-6 can be set to zero.
3.3 Generic SSLs
Generic SSLs are derived using default values in the standardized equations shown in Highlight 3-1 (page
3-10) and described in Part 2 of this document Table 3-9 presents the default parameters used to
calculate generic SSLs; Part 2 describes their development. Although the default parameters are not
necessarily "worst case," they are conservative. Table 3-10 provides generic SSLs for 107 chemicals for
three exposure pathways: ingestion, inhalation of volatiles and fugitive dusts, and migration to ground
water. Because a site-specific methodology has not been developed for the direct ingestion pathway,
the generic ingestion SSLs presented in Table 3-10 should be used at all sites evaluated under the Soil
Screening framework.
In situations where the cost of conducting a simple site investigation to capture site-specific variation is
not warranted, generic SSLs can be used rather than developing site-specific screening levels. Generally,
the decision to use generic SSLs will be driven by cost The site manager will have to weigh the cost of
conducting a more site-specific investigation with the potential for deriving a higher SSL that provides
an appropriate level of protection. The generic SSLs may be useful in situations where contamination is
not expected to be a problem and the Soil Screening framework is being used for confirmation.
Although the generic SSLs are conservative, they are not necessarily protective of all known human
exposure pathways, reasonable land uses, or ecological threats. Thus, before applying generic SSLs at a
site, it is extremely important to compare the conceptual site model with the SSL conceptual model to
ensure that the site conditions and exposure pathways match those used to develop generic SSLs (see
Section 3.1 and Table 3-9). If this comparison indicates that the site is more complex than the SSL
scenario, or that there are significant exposure pathways not accounted for in the Soil Screening
framework, then generic SSLs are not sufficient for a full evaluation of the site. A site-specific approach
will be necessary to evaluate the additional pathways.
On Table 3-10, the first column to the right of the chemical name presents levels based on direct ingestion
of soil. The second column presents levels based on inhalation of volatiles or soil particulates. The third
column presents SSL values for migration to ground water pathway developed using a DAF of 10 to
account for natural processes that reduce contaminant concentrations in the subsurface (see Section 2.4.5).
If the SSL is not exceeded for any of these three pathways, the user may eliminate those pathways or
areas of the site from further investigation. If more than one exposure pathway is of concern, the lowest
3-28
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Review Draft—Do Not Cite or Quote—December 1994
Table 3-9. Generic SSLs: Default Parameters and Assumptions
SSL pathway
Parameter
Inhalation
Migration to
ground water
Default
Source Characteristics
Continuous vegetative cover
Roughness height
Source area (A)
Source length (L)
Source depth
•
O
50 percent
0.5 cm for open terrain; used to derive
30 acres (121,457 m2); used to derive L
for MTG
349 m (assumes square site)
Extends to water table (i.e., no
attenuation in unsaturated zone)
Soil Characteristics
Soil texture
Dry soil bulk density (pb)
Avg. soil moisture content (w)
Soil porosity (n)
Vol. soil water content (6W)
Vol. soil air content (6a)
Soil organic carbon (foc)
Soil pH
Mode soil aggregate size
Threshold windspeed @ 7 m
O
•
•
O
O
•
O Loam; defines soil characteristics/
parameters
• 1.5 kg/L
O 10 wt% (INH); 20 wt% (MTG); used to
determine 6W and 6a
O 0.43
• 0.15 (INH); 0.30 (MTG)
• 0.28 (INH); 0.13 (MTG)
• 0.006 (0.6%, INH); 0.002 (0.2 %, MTG)
O 6.8; used to determine pH-specific Kg
(metals) and Koc (ionizable organics)
0.5 mm; used to derive U17
11.32 m/s
Meteorological Data
Mean annual windspeed (Um)
Air dispersion factor (Q/C)
Volatilization Q/C
Fugitive paniculate Q/C
4.69 m/s (Minneapolis, MN)
90th percentile conterminous U.S.
35.10; Los Angeles, CA; 30-acre source
46.84; Minneapolis, MN; 30-acre source
Hydrogeologic Characteristics
Hydrogeologic setting
Dilution/attenuation factor (DAF)
O Generic (national); surficial aquifer
• 10
• Indicates input parameters directly used in SSL equations.
O Indicates parameters/assumptions used to develop SSL input parameters.
INH = Inhalation pathway.
MTG = Migration to ground water pathway.
3-29
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Review Draft—Do Not Cite or Quote—December 1994
Table 3-10. Generic Soil Screening Levels for Superfund*
NOTICE: These values were developed for use in application of the Soil Screening Guidance only. They were
developed for specific exposure pathways constituting a residential scenario and should only be used in that
context.
Pathway-specific values
for surface soils
(mg/kg)
CAS No.
83-32-9
67-64-1
309-00-2
120-12-7
71-43-2
56-55-3
205-99-2
207-08-9
50-32-8
111-44-4
117-81-7
75-27-4
75-25-2
71-36-3
85-68-7
86-74-8
75-15-0
56-23-5
57-74-9
108-90-7
124-48-1
67-66-3
218-01-9
72-54-8
72-55-9
50-29-3
53-70-3
84-74-2
95-50-1
106-46-7
91-94-1
75-34-3
107-06-2
75-35-4
156-59-2
156-60-5
78-87-5
542-75-6
60-57-1
84-66-2
Chemical
Acenaphthene
Acetone
Aldrin
Anthracene
Benzene
Benzo(a)anthracene
Benzo(D)fluoranthene
Benzo(fc)fluoranthene
Benzo(a)pyrene
Bis(2-chlorethyl)ether
Bis(2-ethylhexyl)phthalate
Bromodichloromethane
Bromoform
Butanol
Butyl benzyl phthalate
Carbazole
Carbon disulfide
Carbon tetrachloride
Chtordane
Chlorobenzene
Chlorodibromomethane
Chloroform
Chrysene
ODD
DDE
DDT
Dibenzo(a,/7)anthracene
Di-n-butyl phthalate
1 ,2-Dichlorobenzene (o)
1 ,4-Dichlorobenzene (p)
3,3-Dichlorobenzidine
1,1-Dichloroethane
1 ,2-Dichloroethane
1 ,1-Dichloroethylene
c/s-1 ,2-Dichloroethylene
frans-1 ,2-Dichloroethylene
1 ,2-Dichloropropane
1 ,3-Dichloropropene
Dieldrin
Diethyl phthalate
Ingest ion
4,700 b
7,800 b
0.04 e
• 23,000 b
22"
0.9 e
0.9°
9"
0.09 e'f
0.6 B
46 e
5e
81 e
7,800 b
1 6,000 b
32 e
7.800 b
5e
0.5 e
1,600"
8e
1108
88 e
3e
2e
2e
0.09 '•'
7,800 b
7,000 b
27 e
1e
7,800 b
7e
1 e
780 b
1,600b
9e
4e
0.04 e
63,000 b
Inhalation
c
62,000 d
0.5 e
c
0.5*
c
c
e
c
0.3 ••'
210 d
1,800d
46 e
9,700 d
530 d
c
11"
0.2 e
10"
94 b
1,900 d
0.2 e
c
c
c
80 e
-_ c
100 d
300 d
7,700 b
c
980 b
0.3 e
0.04 e
1,500d
3,600 d
11"
0.1 e
2e
520 d
Migration to ground
water pathway levels
(mg/kg)
With 10
DAF
200 b
8b
0.005 e
4,300 b
0.02
0.7
4
4
4
3E-4 e-f
11
0.3
0.5
8b
68
0.2 e'f
14"
0.03
2
0.6
0.2
0.3
1
0.7 e
0.5 e
1e
11
120 b
6
1
0.01 e'f
11 b
0.01 '
0.03
0.2
0.3
0.02
0.001 e'f
0.001 *•'
110 b
WHh1
DAF
20 b
0.8 b
5E-4 e-f
430 b
0.002 f
0.07 '
0.4
0.4
0.4
3E-5 e-'
1
0.03
0.05
0.8 b
7
0.02 «•'
1b
0.003'
0.2
0.06
0.02
0.03
0.1'
0.07 e
0.05 B
0.1 e
1
12 b
0.6
0.1'
0.001 e'f
1b
0.001 '
0.003 '
0.02
0.03
0.002 f
1E-4e-'
1E-46''
11 b
3-30
(continued)
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 3-10 (continued)
Pathway-specific values
for surface soils
(mg/kg)
CAS No.
131-11-3
121-14-2
606-20-2
117-84-0
115-29-7
72-20-8
100-41-4
206-44-0
86-73-7
76-44-8
1024-57-3
118-74-1
87-68-3
319-84-6
319-85-7
58-89-9
77-47-4
67-72-1
193-39-5
78-59-1
72-43-5
74-83-9
75-09-2
91-20-3
98-95-3
1336-36-3
129-00-0
100-42-5
79-34-5
127-18-4
108-88-3
8001-35-2
120-82-1
71-55-6
79-00-5
79-01-6
108-05-4
75-01-4
1330-20-7
65-85-0
106-47-8
Chemical
Dimethyl phlhalate
2,4-Dinrtrotoluene
2,6-Dinftrotoluene
Di-/K>ctyl phthalate
Endosulian
Endrin
Ethyl benzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachloro-1 ,3-butadiene
a-HCH (a-BHC)
P-HCH (p-BHC)
y-HCH (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
lndeno(1 ,2,3-c,d)pyrene
Isophorone
Methoxychlor
Methyl bromide
Methylene chloride
Naphthalene
Nitrobenzene
Polychlorinated biphenyls (PCBs)
Pyrene
Stryene
1 ,1 ,2,2-Tetrachloroethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trichlorobenzene
1,1,1-Trichloroethane
1 ,1 ,2-Trichloroethane
Trichloroethylene
Vinyl acetate
Vinyl chloride
Xylenes (total)
lonizable Organics
Benzoic acid
p-Chloroaniline
Ingestion
7.8E+5 b
160 b
78 b
1,600 b
470 b
23 b
7,800 b
3,100 b
3,100 b
0.1 e
0.07 e
0.4 e
8e
0.1 e
0.4s
0.5 e
550 b
46 e
0.9 e
670 e
390 b
110b
85 e
3,100 b
39 b
1h
2,300 b
1 6,000 b
3e
12 e
1 6,000 b
0.6 e
780 b
c
11e
58 e
78,000 b
0.3 e
1.6E+5b
3.1E+5"
310 b
Inhalation
1,600d
c
c
c
c
c
260 d
c
c
0.3 e
18
1e
1e
0.9 e
16"
e
2b '
49s
c
3,400 d
c
2b
7e
c
110"
._c.h
c
1,400d
0.4 e
11"
520 d
5d
240 b
980 d
0.8 e
3e
370 b
0.002 e'f
320 d
c
... c
Migration to ground
water pathway levels
(mg/kg)
With 10
DAF
1,200b
0.2 b-f
0.1 W
g
4b
0.4
5
980 b
160 b
0.06
0.03
0.8
0.1 f
4E-4 e-f
0.002s
0.006
10
0.2 e'f
35
0.2 e'f
62
0.1 b
0.01 '
30 b
0.09 w
._ h
1,400b
2
0.001 "•'
0.04
5
0.04 f
2
0.9
0.01 '
0.02
84 b
0.01 '
74
280 "•'
0.3 Mi
WHh1
DAF
120"
0.02 blf
0.01 w
-_9
0.4 b
0.04
0.5
98 b
16 b
0.006
0.003
0.08 '
0.01 f
4E-5e'f
2E-4 "•'
6E-4'
1
0.02 e'f
3
0.02 e'f
6
0.01 w
0.001 '
3"
0.009 "•'
__ h
140 b
0.2
1E-4e'f
0.004 f
0.5
0.004 f
0.2 '
0.09
0.001 '
0.002 '
8b
0.001 '
7
28b,i
0.03 b'fj
3-31
(continued)
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 3-10 (continued)
Pathway-specific values
for surface soils
(mg/kg)
Migration to ground
water pathway levels
(mg/kg)
CAS No.
95-57-8
120-83-2
105-67-9
51-28-5
95-48-7
86-30-6
621-64-7
87-86-5
108-95-2
95-95-4
88-06-2
7440-36-0
7440-38-2
7440-39-3
7440-41-7
7440-43-9
7440-47-3
7439-92-1
7439-97-6
7440-02-0
7782-49-2
7440-22-4
7440-28-0
7440-62-2
7440-66-6
57-12-5
Chemical
2-Chtorophenol
2,4-Dichlorophenol
2,4-Dimethylphenol
2,4-Dinttrophenol
2-Methylphenol
AAN'rtrosodiphenylamine
A/-Nitrosodi-o-propylamine
Pentachlorophenol
Phenol
2,4,5-Trichlorophenol
2,4, 6-Trichlorophenol
Inorganics
Antimony
Arsenic
Barium
Beryllium
Cadmium
Chromium (6+)
Lead
Mercury
Nickel
Selenium
Silver
Thallium
Vanadium
Zinc
Cyanide
Ingest ion
390"
240 b
1,600b
160 b
3,900 b
130 "
0.09 e'f
3e.j
47,000 b
7,800 b .
58 e
31 b
0.4 e
5,500 b
0.1 e
39 b
390 b
400 '
23 b
1,600b
390 b
390 b
c
550 b
23,000 b
1,600 b
Inhalation
53,000 d
c
c
c
__ c
c
__ c
c
c
c
210 e
c
380 e
3.5E+5 b
690°
920 e
140 e
—
7b.i
6,900 e
c
c
« c
c
c
c
With 10
DAF
2 ».i
0.5 w
gb.i
0.1 W
6W
0.2 e-f>i
2E-5 *w
0.01 «
49b,i
120"-'
0.06 •*'
._ k
15s
32*
180 '
6'
19'
—
31
21'
3j
k
0.4'
__ k
42,000 b>i
__ k
Wrthi
DAF
0.2 "•«
0.05 «'
0.3 b'fj
0.01 Ml
0.6 "-i
0.02 e>ti
2E-6 •*
0.001 (>i
5b-j
12 w
0.006 e'f-i
k
1 '
3j
18j
0.6 '
2'
—
0.3 '
2' .
0.3 '
._ k
0.04 '
k
4,200 w
k
DAF = Dilution and attenuation factor.
f Screening levels based on human health criteria only.
Calculated values correspond to a noncancer hazard quotient of 1.
No toxicity criteria available lor that route of exposure.
Soil saturation concentration (C^,).
Calculated values correspond to a cancer risk level of 1 in 1,000,000.
Level is at or below Contract Laboratory Program required quant it at ion limit for Regular Analytical Services (RAS).
Chemical-specific properties are such that this pathway is not of concern at any soil contaminant concentration.
A preliminary remediation goal of 1 ppm has been set for PCBs based on Guidance on Remedial Actions for
Superfund Sites with PCB Contamination (U.S. EPA, 1990a) and on Agency-wide efforts to manage PCB
contamination.
SSL for pH of 6.8.
Ingestion SSL adjusted by a factor of 0.5 to account for dermal exposure.
Soil-water partition coefficients not available at this time.
A preliminary remediation goal of 400 mg/kg has been set for lead based on Revised Interim Soil Lead Guidance
for CERCLA Sites and RCRA Corrective Action Facilities (U.S. EPA, 1994g).
3-32
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SSL should be used. The lowest SSL of the three pathways (ingestion, inhalation, and migration to
ground water with DAF of 10) is highlighted in bold for each contaminant
The fourth column contains the SSL developed assuming no dilution or attenuation between the source
and the receptor well (i.e., a DAF of 1). These values should be used at sites where little or no dilution
or attenuation of soil leachate concentrations is expected at a site (e.g., sites with shallow water tables
fractured media, karst topography, or source size greater than 30 acres).
The user should note that several of the SSLs for the inhalation pathway in Table 3-9 are determined by
the soil saturation concentration (C^). As explained in Sections 2.3.4 and 3.2.5, the soil VF model is
based on Henry's law, and therefore is not applicable if free-phase contaminant is present in soil. C^
defines a contaminant-specific concentration at which a soil's pore water, pore air, and surface sorption
sites are saturated with contaminant Above this concentration, the contaminant will exist in a pure free
phase, and an SSL cannot be accurately calculated with the VF model. If the SSL calculated using the
VF model is greater than Csal, and the pure compound is liquid at soil temperatures, the SSL is set equal
to Csal. For these chemicals, the generic Csat serves as a conservative indicator of a concentration in soil
above which NAPLs may be present. If NAPLs are suspected at a site (i.e., if C^ is exceeded for liquid
compounds), further investigation is required and SSLs are no longer applicable. The DNAPL guidance
(U.S. EPA, 1992d) should be consulted if C^ levels are exceeded for liquid compounds. For compounds
that are solid at soil temperature, whose Csat level is less than inhalation SSLs, inhalation SSLs are
calculated based on fugitive paniculate emissions only (see Section 3.2.5).
A further implication of the C^-based SSLs arises from the fact that such levels are not based on toxicity
(i.e., they are not risk-based numbers). Csat SSLs therefore should not be used in evaluations of additive
risks for noncarcinogens at a site. Site-specific evaluation of additive risks should be based only on risk-
based SSLs for chemicals with the same toxic endpoint or mechanism of action (see Section 2.1).
3.4 Detailed Site-Specific Method
In the detailed site-specific method, site-specific data are collected and used in "full-scale" fate and
transport models to develop SSLs for the inhalation and migration to ground water pathways. This
method is similar in scope to modeling efforts used to determine site-specific cleanup goals during the
RI/FS phase of the remedial process. Consequently, it represents the highest level of site-specificity in
evaluating the exposures for these pathways. The advantage of this approach is that it accounts for site
hydrogeologic, climatologic, and contaminant source characteristics and may result in fully protective but
less stringent remediation goals. However, the additional cost of collecting the data required to apply the
model should be factored into the decision to conduct a detailed site-specific investigation.
3.4.1 Inhalation Pathway. For the inhalation pathway, developing SSLs using the detailed site-
specific method includes calculating a site-specific volatilization factor (VF) and paniculate emission factor
(PEF). A detailed site-specific VF may be derived from a site-specific volatilization model, which may
consider a finite source and/or a site-specific determination of the inverse concentration factor for air
dispersion (Q/Q. A detailed site-specific PEF involves only site-specific determination of Q/C along with
site-specific inputs to the PEF equation (e.g., fraction vegetative cover, mean annual windspeed, and
threshold friction velocity). This section provides a brief description of finite source volatilization models
with potential applicability to SSL development that should not be viewed as an official endorsement of
these models (other volatilization models may be available with applicability to SSL development). The
following discussion also provides information on site-specific application of the AREA-ST dispersion
model for estimating the Q/C values needed to calculate both VF and PEF.
3-33
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Review Draft—Do Not Cite or Quote—December 1994
Finite Source Volatilization Models. To identify suitable models for addressing a finite
contaminant source, OERR contracted Environmental Quality Management, Inc. (EQ), to conduct a
preliminary evaluation of a number of soil volatilization models, including volatilization models developed
by Hwang and Falco (1986) as modified by EQ (1992) and by Jury et al. (1983, 1984, and 1990) and
VLEACH (Dynamac, 1993), a multipathway model developed primarily to assess exposure through the
ground water pathway. Study results (EQ and Pechan, 1994) show reasonable agreement (within a factor
of 2) between emission predictions using the modified Hwang and Falco or Jury models, but consistently
lower predictions from VLEACH. Based on these results and the limited validation of the modified
Hwang and Falco model (see Section 2.3.2), OERR is concerned that VLEACH may underestimate the
volatilization from contaminated soils and consequently does not consider VLEACH appropriate for
estimating VF at this time. Of the modified Hwang and Falco or Jury finite source models, Jury et al.
(1990) is the easiest to apply. The application, assumptions, and input requirements for this model are
described in the following paragraphs.
Jury et al. (1990) presents a simplified equation (Jury's Equation Bl) for estimating the flux of a
contaminant from a finite source of contaminated soil. The following assumptions were used to derive
this simplified flux equation:
• Uniform soil properties (e.g., homogeneous average soil water content, bulk density, porosity,
and fraction organic carbon)
• Instantaneous linear equilibrium adsorption
• Linear equilibrium liquid-vapor partitioning (Henry's law)
• Uniform initial contaminant incorporation at t=0
• Chemicals in a dissolved form only (i.e., soil contaminant concentrations are below C^)
• No boundary layer thickness at ground level (no stagnant air layer)
• No water evaporation
• No chemical reactions, biodegradation, or photolysis
• ds » (4DAt)^ (ramifications of this are discussed below).
Under these assumptions, the Jury et al. (1990) simplified finite source model is:
Js = C0(DA/7Ct)*[l-exp(-ds2/4DAt)] (3-11)
where
Js = contaminant flux at ground surface (g/cm2-s)-
C0 = uniform contaminant concentration at t=0 (g/cm3)
DA = apparent diffusivity (cm2/s)
TC = 3.14
t = time (s)
ds = depth of uniform soil contamination at t=0 (cm),
3-34
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Review Draft—Do Not Cite or Quote—December 1994
and
DA = [(9a10/3 Di H' + ew10/3 Dw)/n2]/(pb Kd + 6W + 6a H') (3-12)
where
6a = air-filled soil porosity (Lau/Lsoil) = n - 6W
n = total soil porosity (L^^^) = 1 - (Pb/ps)
6W = water-filled soil porosity (Lwat(H/Lsoil) = wp,,/pw
w = average soil moisture content (g/g)
pb = soil dry bulk density (g/cm3)
ps = soil particle density (g/cm3)
pw = water density (g/cm3)
Dj = diffusivity in air (cm2/s)
Dw = diffusivity in water (cm2/s)
H' = dimensionless Henry's law constant = 41 x H
H = Henry's law constant (atm-m3/mol)
Kd = soil-water partition coefficient (cm3/g) = K^ f^.
K^ = soil organic carbon partition coefficient (crrr/g)
f^ = organic carbon content of soil (g/g).
To estimate the average contaminant flux over 30 years, the time-dependent contaminant flux must be
solved for various times and averaged. A simple computer program or spreadsheet can be used to
calculate the instantaneous flux at set intervals over a 30-year time period as long as ds remains larger than
(4DAt)^. Once the average contaminant flux is calculated, VF is calculated as
VF = (Q/C) x (C^ x (l/Jsave) x ID'4 m2/cm2 (3-13)
where
VF = volatilization factor (m3/kg)
Q/C = inverse concentration factor for air dispersion (g/m2-s per kg/m3)
C0 = uniform contaminant concentration at t=0 (g/cm3)
pb = soil dry bulk density (g/cm3)
Jsave = average rate of contaminant flux (g/cm2-s).
Air Dispersion Models. The inverse concentration factor for air dispersion, Q/C, is used in the
determination of both VF and PFJF. For a detailed site-specific assessment of the inhalation pathway, a
site-specific Q/C can be determined using the Industrial Source Complex Model (ISC2) platform in the
short-term mode (AREA-ST). Only a very brief overview of the application, assumptions, and input
requirements for ISC2 AREA-ST model as used to determine Q/C is provided in this section.
The AREA-ST model FORTRAN code, executable versions, sample input and output files, description,
and documentation can be downloaded from the Other Models section of the Office of Air Quality
Planning and Standards' (OAQPS) Support Center for Regulatory Air Models bulletin board system
(SCRAM BBS). Accessing information is as follows:
OAQPS SCRAM BBS
(919) 541-5742 (24 hours/day, 7 days/week except Monday AM)
1,200-9,600, 14,400 baud
Line Settings: 8 bits, no parity, 1 stop bit
Terminal Emulation: VT100 or ANSI
System Operator (919) 541-5384 (normal business hours EST)
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Review Draft—Do Not Cite or Quote—December 1994
The user registers in the first call and then has full access to the BBS.
The ISC2 AREA-ST model will output an air concentration (in pg/m3) when the concentration model
option is selected (e.g., CO MODELOPT DFAULT CONC rural/urban). The surface area of the
contaminated soil source must be determined. For the ISC2 AREA-ST model, the source location of an
area source is defined by the coordinates of the southwest comer of the square (e.g., SO LOCATION
sourcename AREA -^length -^width height=0). For the source parameter input line, the contaminant's
area emission rate (in units of g/m2-s) must be entered. The area emission rate is the site-specific average
emission flux rate, as calculated in Equation 3-11, converted to units of g/m2-s (i.e., Aremis = Jsave x 104
cm2/m2). Alternatively, an area emission rate of 1 g/m2-s can be assumed. A grid or circular series of
receptor sites should be used in and around the area source to identify the point of maximum contaminant
air concentration. Hourly meteorological data (*.MET files) for the nearest city (i.e., airport) of similar
terrain and the preprocessor PCRAMMET also can be downloaded from the SCRAM BBS.
The ISC2 AREA-ST model output concentration is then used to calculate Q/C as follows:
Q/C = (Jsave x 104 cnrVm2)/^ x 10'9 kg/ug) (3-14)
where
Q/C = inverse concentration factor for air dispersion (g/m2-s per kg/m3)
Jsave = average rate of contaminant flux (g/cm2-s)
Cjjj = ISC2 output maximum contaminant air concentration (pg/m3).
Note: If an area emission rate of 1 g/m2-s is assumed, then (Jsave x 104 cm2/m2) = 1, and Equation 3-14
simplifies to simply the inverse of the maximum contaminant air concentration (in kg/m3).
3.4.2 Migration to Ground Water Pathway. Choosing a model for site-specific application for
. the migration to ground water pathway is integral to an accurate evaluation of potential concern.
However, equally as important are the data used in the application and interpretion of results. In an effort
to provide some useful information for a model application, EPA's Office of Research and Development
(ORD) Laboratories in Ada, Oklahoma, and Athens, Georgia, conducted an evaluation of nine unsaturated
zone fate and transport models (Nofziger et al., 1994, and Criscenti, 1994). The results of this effort are
summarized below. The models reviewed are only a subset of the potentially appropriate models available
to the public and are not meant to be construed as having received EPA approval. Other models also may
be applicable to SSL development, depending on site-specific circumstances. EPA also has developed
guidance for the selection and application of saturated zone transport and fate models and for interpretation
of model applications. The user is referred to Ground-Water Modeling Compendium, Second Edition 1994
(U.S. EPA., 1994c) and Framework for Assessing Ground-Water Model Applications (U.S. EPA, 1994b)
for further information.
Unsaturated Zone Models. Each of the unsaturated zone models selected for evaluation are
capable, to varying degrees, of simulating the transport and transformation of chemicals in the subsurface.
Even the most unique site conditions can be simulated by either a single model or a combination of
models. However, the intended uses and the required input parameters of these models vary. The models
evaluated include
• RITZ (Regulatory and Investigative Treatment Zone model)
• VIP (Vadose zone Interactive Process model)
• CMLS (Chemical Movement in Layered Soils model)
• HYDRUS
• SUMMERS (named after author)
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Review Draft—Do Not Cite or Quote—December 1994
• MULTIMED (MULTIMEDia exposure assessment model)
• VLEACH (Vadose zone LEACHing model)
• SESOIL (SEasonal SOIL compartment model)
• PRZM-2 (Pesticide Root Zone Model).
RTTZ, VIP, CMLS, and HYDRUS were evaluated by Nofziger et al. (1994). SUMMERS, MULTIMED,
VLEACH, SESOIL, and PRZM-2 were evaluated by Criscenti et al. (1994). These documents should be
consulted for further information on model application and use.
The applications, assumptions, and input requirements for the nine models evaluated are described in this
section. The model descriptions include model solution method (i.e., analytical, numerical) and the
purpose of the model. Included also are descriptions of the methods used by the model to simulate
water/contaminant transport and contaminant transformation. Each description is also followed by a table
of required input parameters for the respective model. The input parameters discussed include soil
properties, chemical properties, meteorological data, and other site information. In addition, certain input
control parameters may be required such as time stepping, grid discretization information, and output
format
RITZ. Information on the RTTZ model was obtained primarily from Nofziger et al. (1994). RTTZ is a
steady-state analytical model used to simulate the transport and fate of chemicals mixed with oily wastes
(sludge) and disposed of by land treatment. RITZ simulates two layers of the soil column with uniform
properties. The soil layers consist of: (1) the upper plow zone where the oily waste is applied and (2) the
treatment zone. The bottom of the treatment zone is the water table. It is assumed in the model that the
oily waste is completely mixed in and does not migrate out of the plow zone, which represents the
contaminant source at an initial time. RITZ also assumes an infinite source. The flux of water is assumed
to be constant with time and depth and the Clapp-Homberger constant is used in defining the soil water
content resulting from a specified recharge rate. Sorption, vapor transport, volatilization, and biochemical
degradation are also considered (van der Heijde, 1994). Partitioning between phases is instantaneous,
linear, and reversible. Input parameters required for the RITZ model are presented in Table 3-11.
Biochemical degradation of the oil and contaminant is considered to be a first-order process, and
dispersion in the water phase is ignored.
VIP. Information on the VIP model was obtained from Nofziger et al. (1994). The VIP model is a one-
dimensional, numerical (finite-difference) fate and transport model also designed for simulating the
movement of compounds in the unsaturated zone resulting from land application of oily wastes. Like the
RITZ model, VIP considers dual soil zones (a plow zone and a treatment zone) and considers the source
to be infinite. VIP differs from RITZ in that it solves the governing differential equations numerically,
which allows variability in the flux of water and chemicals over time. Advection and hydrodynamic
dispersion are the primary transport mechanisms for the contaminant in water (van der Heijde, 1994).
Instead of assuming instantaneous, linear equilibrium between all phases, VIP considers the partitioning
rates between the air, oil, soil, water, and vapor-phase transport. Contaminant transformation processes
include hydrolysis, volatilization, and -sorption. Oxygen-limited degradation and diffusion of the
contaminant in the air phases are also considered. Sorption is instantaneous as described for the RITZ
model. The input parameters required for the VTP model are presented in Table 3-12.
CMLS. Information on CMLS was obtained from Nofziger et al. (1994). CMLS is an analytical model
developed as a management tool to describe the fate and transport of pesticides in layered soils and
estimates the amount of chemical at a certain position at a certain time. The model allows designation
of up to 20 soil layers with uniform soil and chemical properties defined for each layer. Water in the soil
system is "pushed ahead" of new water (recharge) entering the system. The water content is reduced to
the field capacity after each infiltration event, and water is removed from the root zone in proportion to
3-37
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Review Draft—Do Not Cite or Quote—December 1994
Table 3-11. Input Parameters Required for RITZ Model
Soil properties
Site characteristics Pollutant properties
Oil properties
Percent organic carbon Plow zone depth
Bulk density
Saturated water
content
Saturated hydraulic
conductivity
Clapp-Homberger
constant
Treatment zone depth
Recharge rate
(constant)
Evaporation rate
(constant)
Air temperature
(constant)
Relative humidity
(constant)
Sludge application rate
Diffusion coefficient
(water vapor in oil)
Concentration in
sludge
KOW
Henry's law constant
Degradation half-life
(constant)
Diffusion coefficient
(in air)
Concentration of oil in
sludge
Density of oil
Degradation half-life of
oil
the available water stored in that layer (Nofziger et al., 1994). CMLS assumes movement of the chemical
in liquid phase only and allows a finite source. Chemical partitioning between the soil and the water is
assumed to be linear, instantaneous, and reversible. Volatilization is not considered. Dispersion and
diffusion of the chemical is ignored and degradation is defined as a first-order process. The input
parameters required for the CMLS model are presented in Table 3-13.
HYDRUS. Information on the HYDRUS model was obtained from Nofziger et al. (1994). HYDRUS
is a finite-element model for one-dimensional solute fate and transport simulations. The boundary
conditions for flow, as well as soil and chemical properties, can therefore vary with time. A finite source
also can be modeled. Soil parameters are described by the van Genuchten parameters. The model also
considers root uptake and hysteresis in the water movement properties. Solute transport and
transformation incorporates molecular diffusion, hydrodynamic dispersion, linear or nonlinear equilibrium
partitioning (sorption), and first-order decay (van der Heijde, 1994). Volatilization is not considered. The
input parameters required by HYDRUS are presented in Table 3-14.
SUMMERS. Information on the SUMMERS model was obtained from Criscenti et al. (1994).
SUMMERS is a one-dimensional analytical model that simulates one-dimensional, nondispersive transport
in a single layer of soil from an infinite source. It was developed to determine the contaminant
concentrations in soil that would result in ground water contamination above specified levels for evaluating
geothermal energy sites. The model assumes steady-state water movement and equilibrium partitioning
of the contaminant in the unsaturated zone and performs a mass-balance calculation of mixing in an
underlying aquifer. For the saturated zone, the model assumes a constant flux from the surface source and
instantaneous, complete mixing in the aquifer. The mixing depth is therefore defined by the thickness of
the aquifer. The model does not account for volatilization. The input parameters required for SUMMERS
are listed in Table 3-15.
3-38
-------
Table 3-12. Input Parameters Required for VIP Model
Soil properties
Site characteristics
Pollutant properties
Chemical properties
Oil properties
lfc>
OJ
Porosity
Bulk density
Saturated hydraulic
conductivity
Clapp-Hornberger constant
Plow zone depth
Treatment zone depth
Mean daily recharge
rate
Temperature
(each layer)
Sludge application
rate
Sludge density
Application period and
frequency in period
Weight fraction water
in sludge
Weight fraction oil in
waste
Concentration in sludge
Oil-water partition
coefficient3
Air-water partition
coefficient3
Soil-water partition
coefficient3
Degradation constant in oil3
Degradation constant in
water8
Dispersion coefficient
Adsorption-desorption rate
constant (water/soil)
Adsorption-desorption rate
constant (water/oil)
Adsorption-desorption rate
constant (water/air)
Oil-air partition coefficient9 Density of oil
Water-air partition
coefficient8
Oxygen half-saturation
constant in air phase3
Oxygen half-saturation
constant in oil phase3
Oxygen half-saturation
constant in water phase3
Oxygen half-saturation
constant (oil degradation)
Stoichiometric ratio of
oxygen to pollutant
consumed
Stoichiometric ratio of
oxygen to oil consumed
Oxygen transfer rate
coefficient between oil and
air phases
Oxygen transfer rate
coefficient between water
and air phases
Degradation rate
constant of oil
3 Parameters required for plow zone and treatment zone.
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 3-13. Input Parameters Required for CMLS
Soil properties
Site characteristics
Chemical properties
Depth of bottom of layers
Organic carbon content
Bulk density
Saturated water content
Field capacity
Permanent wilting point
Daily infiltration
Daily evapotranspiration
Degradation half-life
(each soil layer)
Amount applied
Depth of application
Date of application
KOC
Table 3-14. Input Parameters Required for HYDRUS
Soil properties Site characteristics Pollutant properties
Root uptake
parameters
Depth of soil layers
Saturated water
content
Saturated hydraulic
conductivity
Bulk density
Retention
parameters
Residual water
content
Uniform or stepwise
rainfall intensity
Molecular diffusion
coefficient
Dispersivity
Decay coefficient
(dissolved)
Decay coefficient
(adsorbed)
Freundlich isotherm
coefficients
Power function in
stress-response
function
Pressure head where
transpiration is
reduced by 50%
Root density as a
function of depth
Table 3-15. Input Parameters Required for SUMMERS
Parameters required
Target concentration in ground water
Volumetric infiltration rate into aquifer
Downward porewater velocity
Ground water seepage velocity
Void fraction
Horizontal area of pond or spill
Thickness of aquifer
Width of pond/spill perpendicular to flow
Initial (background) concentration
Equilibrium partition coefficient
Darcy velocity in aquifer
Volumetric ground water flow rate
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MULTIMED. Information on the MULTIMED model was obtained from Criscenti et al. (1994) and
Salhotra et al. (1990). MULTIMED was developed as a multimedia fate and transport model to simulate
contaminant migration from a waste disposal unit For this review, only the fate and transport of
pollutants from the soil to migration to ground water pathway was considered in detail.
In MULTIMED, infiltration of waste into the unsaturated or saturated zones can be simulated using a
landfill module or by direct infiltration to the unsaturated or saturated zones. Flow in the unsaturated zone
and for the landfill module is simulated by a one-dimensional, semianalytical module. Transport in the
unsaturated zone considers the effects of dispersion, sorption, volatilization, biodegradation, and first-order
chemical decay. The saturated transport module is also one-dimensional, but considers three-dimensional
dispersion, linear adsorption, first-order decay, and dilution due to recharge. Mixing in the underlying
saturated zone is based on the vertical dispersivity specified, the length of the disposal facility parallel to
the flow direction, the thickness of the saturated zone, the ground water velocity, and the infiltration rate.
The saturated zone module can simulate steady-state and transient ground water flow, and thus can
consider a finite source assumption through a leachate "pulse duration." The parameters required for the
unsaturated and saturated zone transport in MULTIMED are presented in Table 3-16.
VLEACH. Information on the VLEACH model was obtained from Criscenti et al. (1994). VLEACH is
a one-dimensional, finite difference model developed to simulate the transport of contaminants displaying
linear partitioning behavior through the vadose zone to the water table by aqueous advection and diffusion.
Multiple layers can be modeled and are expressed as polygons with different soil properties and recharge
Table 3-16. input Parameters Required for MULTIMED
Unsaturated zone parameters
Saturated hydraulic
conductivity
Porosity
Air entry pressure head
Depth of unsaturated zone
Residual water content
Number of porous materials
Number of layers
Alpha coefficient
van Genuchten exponent
Thickness of each layer
Longitudinal dispersivity
Percent organic matter
Soil bulk density
Biological decay coefficient
Acid, base, and neutral
hydrolysis rates
Reference temperature
Normalized distribution
coefficient
Air diffusion coefficient
Reference temperature for air
diffusion
Molecular weight
Infiltration rate
Area of waste disposal unit
Duration of pulse
Source decay constant
Initial concentration at landfill
Particle diameter
— •
Saturated zone parameters
Recharge rate
First-order decay coefficient
Biodegradation coefficient
Aquifer thickness
Hydraulic gradient
Longitudinal dispersivity
Transverse dispersivity
Vertical dispersivity
Temperature of aquifer
PH
Organic carbon content
Well distance from site
Angle off-center of well
Well vertical distance
—
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Table 3-17. Input Parameters Required for VLEACH
Soil properties Chemical characteristics Site properties
Dry bulk density Koc Recharge rate
Total porosity Henry's law constant Depth to ground water
Volumetric water content Aqueous solubility Dimensions of "polygons"
Fractional organic carbon Free air diffusion coefficient
rates. Water flow is assumed to be steady state. Linear equilibrium partitioning is used to determine
chemical concentrations between the aqueous, gaseous, and adsorbed phases (sorption and volatilization),
and a finite source can be considered. Chemical or biological degradation is not considered. The input
parameters required for VLEACH are presented in Table 3-17.
SESOIL. Information on the SESOIL model was obtained from Criscenti et al. (1994). SESOIL is a
one-dimensional, finite difference flow and transport model developed for evaluating the movement of
contaminants through the vadose zone. The model contains three components: (1) hydrologic cycle, (2)
sediment cycle, and (3) pollutant fate cycle. The model estimates the rate of vertical solute transport and
transformation from the land surface to the water table. Up to four layers can be simulated by the model
and each layer can be subdivided into 10 compartments with uniform soil characteristics. Hydrologic data
can be included using either monthly or annual data options. Solute transport is simulated for ground
water and surface runoff including eroded sediment Pollutant fate considers equilibrium partitioning to
soil and air phases (sorption and diffusion), volatilization from the surface layer, first-order chemical
degradation, biodegradation, cation exchange, hydrolysis, and metal complexation and allows for a
stationary free phase. The required input parameters for SESOIL are presented in Table 3-18 for the
monthly optioa
PRZM-2. Information on PRZM-2 was obtained from Criscenti et al. (1994). PRZM-2 is a combination
of two models developed to simulate the one-dimensional movement of chemicals in the unsaturated and
saturated zones. The first model, PRZM, is a finite difference model that simulates water flow and
detailed pesticide fate and transformation in the unsaturated zone. The second model, VADOFT, is a one-
dimensional finite element model with more detailed water movement simulation capabilities. The
coupling of these models results in a detailed representation of contaminant transport and transformation
in the unsaturated zone.
PRZM has been used predominantly for evaluation of pesticide leaching in the root zone. PRZM uses
detailed meteorological and surface hydrology data for the hydrologic simulations. Runoff, erosion, plant
uptake, leaching, decay, foliar washoff, and volatilization are considered in the surface hydrologic and
chemical transport components. Chemical transport and fate in the subsurface is simulated by advection,
dispersion, molecular diffusion, first-order chemical decay, biodegradation, daughter compound progeny,
and soil sorption. The input parameters required for PRZM are presented in Table 3-19.
VADOFT can be run independently of PRZM and output from the PRZM model can be used to set the
boundary conditions for VADOFT. The lower boundaries could also be specified as a constant pressure
head or zero velocity. Transport simulations consider advection and diffusion with sorption and first-order
decay. The input requirements for VADOFT are presented in Table 3-20.
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Table 3-18. Input Parameters Required for SESOIL (Monthly Option)
Climate data
Soil data
Chemical data
Application data
Mean air
temperature3
Mean cloud cover
fraction3
Mean relative
humidity8
Short wave albedo
fraction3
Total precipitation
Mean storm duration
Number of storm
events
Number of layers and Solubility in water
sublayers
Thickness of layers Air diffusion
coefficient
pH of each layer Henry's law constant Spill index
Application area
Site latitude
Bulk density
Intrinsic permeability
Pore disconnected-
ness index
Effective porosity
Organic carbon
content
Cation exchange
capacity
Freundlich exponent
Silt, sand, and clay
fractions
Soil loss ratio
Organic carbon
adsorption ratio
Soil adsorption
coefficient
Molecular weight
Valence
Hydrolysis constants
(acid, base, neutral)
Biodegradation rates
(liquid, solid)
Ligand stability
constant
Moles ligand per
mole compound
Molecular weight of
ligand
Ligand mass
Pollutant load
Mass removed or
transformed
Index of volatile
diffusion
Index of transport in
surface runoff
Ratio pollutant cone.
in rain to solubility
Washload area
Avg. slope and slope
length
Erodibility factor
Practice factor
Manning coefficient
SESOIL uses these parameters to calculate evapotranspiration if an evapotranspiration value is
not specified.
Considerations for Unsaturated Zone Model Selection. The accuracy of a model in a site-
specific application depends on simplifications and assumptions implicit in the model and their relationship
to site-specific conditions. Additional error may be introduced from assumptions made when deriving
input parameters. Although each of the nine models evaluated has been tested and validated for simulation
of water and contaminant movement in the unsaturated zone; they are different in purpose and complexity,
with certain models designed to simulate very specific scenarios.
A model should be selected to accommodate a site-specific scenario as closely as possible. For example,
if contaminant volatilization is of concern, the model should consider volatilization and vapor phase
transport. After a model is determined to be appropriate for a site, contaminant(s), and conditions to be
modeled, the site-specific information available (or potentially available) should be compared to the input
requirements for the model to ensure that adequate inputs can be developed.
3-43
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Table 3-19. Input Parameters Required for PRZM
Dally climate data
Pan evaporation and pan factor
Temperature
Precipitation
Monthly daylight hours
Windspeed
Solar radiation
Snowmett factor
Min. evap. extraction depth
Erosion data
Topo. factor/soil erodibility
Average duration of rainfall
Field area
Practice factor
3)
*
fr
a
i
o
Crop data
Surface condition of crop
Max. dry weight of crop after
harvest
Max. interception storage
Max. rooting depth
Emergence, maturation, and
harvest dates
Max. canopy coverage
Pesticide data
Application quantity
Foliar extraction coefficient
Diffusion coefficient in air
Initial concentration levels
No. applications (50 max.)
Incorporation depth
Enthalpy of vaporization
Parent/daughter transform rates
No. of chemicals (3 max.)
Plant uptake factor
Kd and K^
Aqueous, sorbed, vapor decay
rates
Application dates
Foliar decay rates
Henry's law constant
Soil data
Compartment thicknesses
Soil drainage parameter
Wilting point
Runoff curve numbers
Hydrodynamic dispersion
Percent organic carbon
Core depth
Bulk density
Field capacity
No. and thickness of horizons
Initial soil water content
I
J8
Soil temperature
Heat capacity per unit volume
Thermal conductivity of horizon
Albedo
Avg. mo. bottom boundary
temperature
Reflectivity of soil surface
Initial horizon temperature
Height of windspeed meas.
Sand and clay content
Blodegradatlon and Irrigation parameters (not presented)
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Review Draft—Do Not Cite or Quote—December 1994
Table 3-20. Input Parameters Required for VADOFT
Pesticide data Soil data
Number of chemicals Number of soil horizons Relative permeability vs.
saturation
Aqueous decay rate Horizon thicknesses Pressure head vs. saturation
Initial concentration Saturated hydraulic Residual water phase
conductivity saturation
Longitudinal dispersivrty Effective porosity Brooks and Corey n
Retardatbn coefficient Air entry pressure head van Genuchten alpha
Molecular diffusion
Cone, flux at first node '"P"* *lux or nead at first nocle W independent of PRZM)
(if independent of PRZM)
The unsaturated zone models addressed in this study use either analytical, semianalytical, or numerical
solution methods. Analytical models represent the simplest models, requiring the least number of input
parameters. They use a closed-form solution for the pertinent equations. In analytical models, certain
assumptions have to be made with respect to the geometry of the system and external stresses. For this
reason, there are few analytical flow models (van der Heijde, 1994). Analytical solutions are common,
however, for fate and transport problems by solution of convection-dispersion equations. Analytical
models require the assumption of uniform flow conditions, both spatially and temporally.
Semianalytical models approximate complex analytical solutions using numerical techniques (van der
Heijde, 1994). Transient or steady-state conditions can be approximated using a semianalytical model.
However, spatial variability in soil or aquifer conditions cannot be accommodated.
Numerical models use approximations of pertinent partial differential equations usually by finite-difference
or finite-element methods. The resolution of the area and time of simulation is defined by the modeler.
Numerical models may be used when simulating time-dependent scenarios, spatially variable soil
conditions, and unsteady flow (van der Heijde, 1994).
In certain cases, input parameters to be used in a model are not definitively known. Some models allow
some input parameters to be expressed as probability distributions rather than a single value, referred to
as Monte Carlo simulations. This method can provide an estimate of the uncertainty of the model output
(i.e., percent probability that a contaminant will be greater than a certain concentration at a depth), but
requires knowledge of the parameter distributions. Alternatively, a bounding approach can be used to
estimate the effects of likely parameter ranges on model results. These approaches can be used in
situations where there is uncertainty in input parameter values.
Model Applicability. Table 3-21 summarizes characteristics and capabilities of the models evaluated
for this study. The table addresses only unsaturated zone fate and transport model components, although
two models (MULTIMED and SUMMERS) have saturated zone flow and transport capabilities. The
following text highlights some of the differences between the models, outlines their advantages and
disadvantages, and describes appropriate scenarios for model application.
RITZ. RITZ is appropriate for sites where oily wastes are present because it includes sorption on an
immobile oil phase as well as onto soil particles. However, the oil phase can be omitted for simulations
•
3-45
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Review Draft—Do Not Cite or Quote—December 1994
Table 3-21. Characteristics of Unsaturated Zone Models Evaluated
Model
RITZ
VIP
CMLS
HYDRUS
MULTIMED
SUMMERS
PRZM-2
SESOIL
VLEACH
Type
Analytical
•
•
•
Semi-analytica
•
Numerical
•
•
•
•
•
Fate and Transport Processes Considered
o
a
o
(O
0>
in
•
•
•
•
•
o
CO
(0
Q.
'6
Partitioning witl
•
•
Volatilization
•
•
• •.
•
•
•
•c
&
IB
Vapor phase tr
•
•
c
o
p
0>
Q.
CO
T>
Hydrodynamic
•
•
•
•
Diffusion
•
•
•
.0
1
•
•
•
•
•
•
•
o>
o
.tr
I
Nonequilibrium
•
•
f
o
*
0)
•5
Hydrolysis (1st
•
•
•
•
Biodegradation
•
CO
8
£
I
•
•
^j£
S
Q.
O
1
1
•
IT
• •
o
U)
Q
UJ
•
Other
"S
^
"o
J —
Saturated zone
•
•
(0
a
o
8
1
o
•
£
_g
a
o
CD
u
Water balance
•
of scenarios without oily materials. Fate and transport parameters such as sorption, degradation,
volatilization, and first-order decay are considered in the subsurface simulations. The most significant
drawback for the model is the limit on the number of soil layers. Optimally, RITZ would be
recommended for modeling chemical migration in a uniform unsaturated zone as a result of land
application. The required input parameters are typical of those collected during an early-stage site
investigation and therefore RITZ could be used as a screening model.
VIP. VIP also is appropriate for sites where release of oily wastes has occurred. Some of the limitations
described in RITZ also apply to the VIP model. VIP could be used as a followup model to RITZ since
variable chemical and water fluxes can be simulated. In this case, significant additional input parameters
are required to simulate transient partitioning between the air, soil, water, and oil phases.
CMLS. CMLS differs from RITZ and VIP in that it allows designation of up to 20 soil layers with
different properties. However, it does not consider nonaqueous phase liquids, dispersion, diffusion, or
vapor phase transport. A finite source also can be modeled. CMLS estimates the location of the peak
concentration of contaminants through a layered soil system. A limitation of the CMLS model for SSL
application is that it does not calculate leachate concentrations. Instead, it calculates the amount of
chemical at a certain depth at a certain time. The user must estimate the concentration based on the
amount of chemical present and the total flux of water in the system (Nofziger et al., 1993). The model
is typically used to estimate the time for a chemical entering the unsaturated zone to reach a certain depth.
HYDRUS. Like CMLS, the HYDRUS model can also simulate chemical movement in layered soils and
can consider a finite source, but also includes dispersion and diffusion, as well as sorption and first-order
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Review Draft—Do Not Cite or Quote—December 1994
decay. In addition, HYDRUS outputs the chemical concentration in the soil water as a function of time
and depth. In addition, the amount of chemical remaining in the soil is also output. The model also
considers root zone uptake, but other models such as PRZM should be used if the comprehensive effects
of plant uptake are to be considered in the simulations. Because it estimates contaminant concentrations,
HYDRUS is preferable over CMLS as an initial screening tool.
SUMMERS. The SUMMERS model is a relatively simple model designed to simulate leaching in the
unsaturated zone. It is appropriate for use as an initial screening model where site data are limited and
where volatilization is not of concern. However, since attenuation processes such as biodegradation, first-
order decay, volatilization, or other attenuation processes (other than sorption) are not considered, it is a
quite conservative model. Since volatilization is not considered, it cannot be used to simulate migration
of volatile compounds to the atmosphere.
MULTIMED. MULTIMED simulates simple vertical water movement in the unsaturated zone. Since an
initial soil concentration cannot be specified, either the soil/water partition equation (Equation 3-6) or a
leaching test (SPLP) must be used to estimate soil leachate contaminant concentrations. MULTIMED is
appropriate for simulating contaminant migration in soil. In addition, since it links the output from the
unsaturated zone transport module with a saturated zone module, it can be used to determine the
concentration of a contaminant in a well located downgradient from a contaminant source. MULTIMED
is appropriate for early-stage site simulations because the input parameters required are typically available
and uncertainty analyses can be performed using Monte Carlo simulations for those parameters for which
reliable values are not known.
VLEACH. In VLEACH, biological or chemical degradation is not considered. It therefore provides
conservative estimates of contaminant migration in soil. This model may be appropriate as an initial
screening tool for sites for which there is little information available.
SESOIL. SESOIL was designed as a screening tool, but it is actually more complex than some of the
models described. Some of the input data would be cumbersome to obtain, especially for use as an initial
screening tool. It is applicable for simulating spill sites since it allows consideration of surface transport
by erosion and runoff and can utilize detailed meteorological information to estimate infiltration. In the
soil zone, several fate and transport options are available such as metal complexation, hydrolysis, cation
exchange, and degradation. This model would be especially applicable to sites where organic compounds
have been spilled or land-applied and significant site and meteorological information is known.
PRZM-2. PRZM-2 is a relatively detailed model as a result of the coupling of the two models PRZM
and VADOFT. Although PRZM is predominantly used as a pesticide leaching model, it could also be
used for simulation of transport of other chemicals. Because detailed meteorology and surface application
parameters can be included, it is appropriate for simulation of surface spills or land disposal scenarios.
In addition, uncertainty analyses can be performed based on Monte Carlo simulations. Numerous
subsurface fate and transport options exist in PRZM. Water movement is somewhat simplified in PRZM,
and it may not be applicable for low-permeability soils (Criscenti et al., 1994). However, water flow
simulation is more detailed in the VADOFT module of the PRZM-2 program. The combination of these
programs makes PRZM-2 a relatively complex model. This model would be especially applicable to sites
where organic compounds have been spilled or land-applied and significant site and meteorological
information is known.
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Technical Background Document for
Soil Screening Guidance
Part 4: MEASURING CONTAMINANT CONCENTRATIONS IN SOIL
In order to compare site soil concentrations with the SSLs, it is important to develop a sampling strategy
that will result in an accurate representation of site contamination. This Soil Screening Guidance
recommends that site managers use the Data Quality Objectives (DQO) process to develop a sampling
strategy that will satisfy Superfund program objectives. The site manager can use the DQO process to
conveniently organize and document many site-specific features and assumptions underlying the sampling
plan. In the last step of the DQO process, "Optimize the Design for Obtaining Data," the site manager
can choose between two alternative approaches to measuring surface soil contaminant concentrations. The
first is a site-specific strategy that uses site-specific estimates of contaminant variability to determine how
many samples are needed to support the screening decision. The second is a fairly prescriptive approach
that can be used in lieu of the site-specific strategy. Recommendations for subsurface sampling that can
be modified to accommodate site-specific conditions are also included in the guidance.
As described in the supplemental guidance to RAGS (U.S. EPA, 1992g), chronic exposure to site
contaminants is best represented by an average concentration. If an individual is assumed to move
randomly across an exposure area over time, spatially averaged soil contaminant concentrations can be
used to estimate average exposure concentrations. In cases where an individual finds one area of a site
more attractive than others, leading to nonrandom exposure, the risk assessor should consider weighing
time spent in different areas or dividing large areas into several exposure areas for separate evaluation.
However, for most current and future use scenarios, reliable information about specific patterns of
nonrandom activity is not available. Therefore, random exposure appears to be the most reasonable
assumption for a residential scenario. Given this assumption, the U.S. Environmental Protection Agency
(EPA) has decided that at National Priorities List (NPL) sites, comparison of soil contaminant levels to
Soil Screening Levels (SSLs) should be based on average soil concentrations.
Section 4.1 presents general DQOs for the Soil Screening framework and describes activities for
completing site-specific DQOs. The last two sections describe two approaches for determining the number
of samples required to estimate average contaminant concentration levels to compare with SSLs: a site-
specific approach (Section 4.2) and a prescriptive approach (Section 4.3). All recommendations in these
sections assume that the Superfund quality assurance program requirements are followed in accordance
with Superfund policy (U.S. EPA, 1993g).
This document does not provide guidance on sampling to determine background contaminant
concentrations. If there is existing information on background concentrations of SSL constituents at a site,
then these data may be compared to the SSLs. If it appears that background levels are higher than certain
SSLs, this guidance may not be applicable to these constituents at the site and the user should consult U.S.
EPA (1994f) and consult with authorities that have jurisdiction over other sources of contamination in the
area (such as the regional air board or Resource Conservation and Recovery Act [RCRA] program). If
information on background concentrations is not available, then apply the Soil Screening framework to
the site. If all or most of the areas of the site exceed SSLs, then further investigation may be necessary
to determine whether contaminant concentrations in these areas simply reflect elevated background levels.
4.1 Data Quality Objectives for the Soil Screening Framework
The DQO process is a systematic data collection planning process developed by EPA to ensure that the
right type, quality, and quantity of data are collected to support Agency decision making. The DQO
process generates quantitative and qualitative statements (DQOs) that clarify the purpose of the data
4-1
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collection effort; define the most appropriate type of data and the conditions under which the data should
be collected; and specify quantitative performance criteria for using the data. This process is based on
the scientific method and usually results in a statistical sampling plan that allows the site manager to draw
valid inferences about contamination levels over areas of the site.
The DQO process employs statistical concepts for developing either a probabilistic or nonprobabilistic
sampling plan. Consequently, the DQO process is most successful when the scoping team includes a
member who is knowledgeable in statistics. This will help ensure that existing data and other information
about the site are used most effectively in designing the sampling plan.
Most of the key outputs of the DQO process already have been developed as part of the Soil Screening
framework. Consequently, the DQO activities required for a particular soil contamination site will involve
clarifying site-specific conditions. The DQO activities addressed in this section are described in detail in
the Data Quality Objectives Process for Superfund: Interim Final Guidance (U.S. EPA, 1993b) and the
Guidance for Data Quality Objectives (U.S. EPA, 1994d); refer to these documents for more information
on how to complete each DQO activity.
Table 4-1 summarizes the activities for applying the DQO process, which leads into either a site-specific
or prescriptive sampling methodology for measuring soil contaminant levels under the framework. For
each activity, a Soil Screening DQO output is specified. Most of the site-specific tasks have been
completed during other stages of the Soil Screening framework.
4.1.1 State the Problem. The main site-specific activities involved in this step include identifying
the data collection planning team (including technical experts and key stakeholders), and specifying the
available resources. The list of technical experts and stakeholders should contain all key personnel who
have been involved with the Soil Screening framework at me site. Other activities in this step include
developing the conceptual site model and identifying exposure scenarios. Both of these activities already
have been addressed in the Soil Screening framework. This step will then provide an opportunity for
ensuring that this work is adequately documented.
4.1.2 Identify the Decision. The decision within the Soil Screening framework is to determine
whether mean soil concentrations exceed the SSLs for specific contaminants. If so, the site will be
investigated further. If not, no further action will be taken under the Comprehensive Environmental
Response, Compensation, and Liability Act (CERQLA). .
4.1.3 Identify Inputs to the Decision. Here, the site-specific contaminants to be measured at
the site, the method for developing the SSL, and a list of feasible analytical methods are identified. The
first two of these activities already have been addressed in previous sections of the Soil Screening
framework. Therefore, the remaining task is to verify that a Contract Laboratory Program (CLP) method
or a field method for analyzing the samples exists and that the analytical method detection limit or field
method detection limit is appropriate for the site-specific or generic SSL.
4.1.4 Specify the Study Boundaries. This.step of the DQO process will define the sample
population of interest, subdivide the site into appropriate averaging areas, and specify temporal or practical
constraints on the data collection.
The description of the population of interest must include the depth considered to be surface soil. When
measuring soil contamination levels at the surface for the inhalation and ingestion pathways, soils should
be sampled through the top 6 inches for the prescriptive approach. Generally, this range of depth should
account for recent spills as well as older spills where contaminant leaching or transport has occurred.
Additional sampling beyond this depth may be appropriate in areas where soil disturbances are reasonably
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Table 4-1. Soil Screening Framework DQOs
DQO process
SSL Decisions
State the Problem
• Identify scoping team
• Develop conceptual site
model
• Define exposure
scenarios
• Specify available
resources
Identify technical experts and key stakeholders.
Identify and document sources and types of contaminants,
contaminated media, migration pathways, potential physical targets or
receptors.
Verify residential land use, other possible exposure pathways,
potential for ecological impacts.
Identify sampling and analysis budget, scheduling constraints,
available personnel.
Identify the Decision
• Identify decision
• Identify alternative
actions
Determine whether mean soil contaminant levels for particular
contaminants exceed screening levels.
• Eliminate area from further consideration under CERCLA; or
• Investigate further.
Identify Inputs to the Decision
• Identify inputs Specify contaminants to be measured and partitioning equation
parameters (for site-specific approach).
• Define basis for action Either generic, simple site-specific, or detailed site-specific method.
level
• Identify analytical List feasible analytical methods consistent with the program-level
methods requirements.
Specify the Study Boundaries
Define geographic
areas of field
investigation
Define population of
interest
Define scale of decision
making
Subdivide site into EAs.
Define temporal
boundaries of study
Identify practical
constraints
Specify spatial dimensions of field investigation area (or operable unit)
and delineate on site map.
Mean surface soil (0-6 inches) contaminant concentration.
Exposure area (EA) size is 0.5 acre for residential land use.
Subdivide each region into 0.5-acre EAs. Delineate site regions where
contaminant patterns are likely to be different, based on process
knowledge, records, prior sampling, etc.
Identify any known temporal (cyclical) variations in contaminants;
specify sampling schedule.
Identify potential impediments to sample collection, such as access,
health and safety issues, etc.
Develop a Decision Rule
• Specify parameter of
interest
• Specify screening level
Specify "if..., then..."
decision rule
A mean.
If applicable, and if soil parameter data are available, calculate SSLs
for site-specific approach. Otherwise, use generic SSL for planning
purposes.
If the mean concentration of a contaminant within the exposure area
exceeds the screening level, then investigate that EA further.
(continued)
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Table 4-1 (continued)
DQO process SSL Decisions
Specify Limits on Decision Errors
Define baseline The site is contaminated.
condition (null
hypothesis)
Define both types Type I (false positive): do not investigate further ("walk away from") a
of decision errors potentially hazardous EA;
Type II (false negative): investigate further a EA which falls below the
SSLs.
Define the gray Gray region ranges from 1/a the SSL to 2 times the SSL.
region
Assign acceptable Type I: 0.05 (5%);
probabilities of Type II: 0.20 (20%).
Type I and Type II
decision errors
Optimize the Design
Evaluate costs and Evaluate cost and schedule impacts of developing a site-specific
benefits of sampling design that explicitly controls decision errors versus the cost
alternative and schedule impacts of implementing the prescriptive sampling
sampling design approach.
methods
Select sampling Either site-specific sampling method or prescriptive method.
design method
expected as a result of construction practices. For example, in the Northeast, soil may be excavated to
IS feet before the foundation of a home can be laid, and this soil usually is used as fill material around
the property. As a result, contaminants that were at depth can be moved to the surface. Thus, it is
important to be cognizant of construction practices in the area around a site when determining the depth
of sampling.
The first step in subdividing the site into exposure areas is to delineate areas where contaminant patterns
are likely to be different based on process knowledge, historical records, or prior sampling. The goal of
this step is to partition the site into regions where the contaminant variability is likely to be similar within
each region, which leads to more efficient site-specific sampling designs for the site as a whole. Each
region can then be subdivided further into exposure areas. Since all of the exposure areas within a given
region should exhibit similar contaminant variability, one site-specific sampling design can be developed
for all of the exposure areas within that region. Some regions will have relatively low variability and
other regions will have relatively high variability. Consequently, a different sampling design can be
developed for each region, based on the region-specific estimate of the contaminant variability within a
typical exposure area. For example, existing information about a site may allow the site manager to
identify regions as highly likely to be contaminated. Such regions should already be targeted for further
investigation. On the other hand, the site manager may have documentation that large regions of the site
never been used for waste disposal activities. Thus, these regions would be expected to exhibit relatively
low variability and the sampling design could involve a relatively small number of samples per exposure
area. The greatest intensity of sampling effort would be expected to focus on regions of the site where
there is greater uncertainty or variability associated with contamination patterns. This is because the
expectation of relatively large variability in contaminant concentrations requires more samples to determine
with confidence whether the exposure area should be screened out or investigated further.
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After the site has been partitioned into relatively homogeneous regions, each region can then be subdivided
into exposure areas (EAs). An EA is the smallest area over which an individual can reasonably be
expected to move over a period of time and be exposed to contaminants through certain pathways. For
the purposes of this SSL guidance, the Superfund program chose the size of a typical residential lot as an
appropriate averaging area. Therefore, each EA should be no larger than 0.5 acres.
The rationale for subdividing a site into EAs is based on analysis of previous Superfund sites. In support
of RAGS Part A, EPA's Exposure Assessment Group (EAG) conducted a number of simulation studies
using large data sets from NPL sites. The goals of the studies were to examine the use of data sets of
varying sizes to: (1) estimate the true site mean and (2) compare the upper confidence limit on the
arithmetic mean to the true site mean. A limitation of the EAG study was that the subsets of sample data
(either 10,20, or 30 samples) were randomly selected from widely different areas of the site. The random
selection of samples from widely different areas may have led to large statistical variances within each
subset of data. For the SSLs, EPA wants to limit the size of the averaging area, which is expressed as
the EA. Reducing the size of the EA should tend to reduce the variance, since a smaller area is less likely
to contain major differences in contamination conditions (i.e., sampling locations that are close to each
other are more likely to have similar characteristics). Reducing the variance will reduce the number of
samples required to achieve a reasonably good estimate of the true mean concentration of contaminants
at a site.
4.1.5 Develop a Decision Rule. The Soil Screening framework decision rule is:
If the mean concentration of a contaminant within an exposure area exceeds the screening
level, then investigate that EA further.
The screening level is the actual numerical value used to compare against the site contamination data.
The screening level will depend upon the approach used to develop the SSL and the methods used to
develop a sampling design (see Sections 4.2 and 4.3 for more details).
4.1.6 Specify Limits on Decision Errors. EPA recognizes that data obtained from sampling
and analysis can never be perfectly representative or accurate, and that the costs of trying to achieve near-
perfect results can outweigh the benefits. Consequently, EPA acknowledges that uncertainty in data must
be tolerated to some degree. The DQO process attempts to control the degree to which uncertainty in data
affects the outcomes of decisions that are based on data. Within the Soil Screening framework, the data
will be used to support a decision about whether an EA requires further study. Because of variability in
contaminant concentration levels within an EA and because all measurement data contain some amount
of error, the data may mislead the decision maker into making an incorrect decision, called a decision
error. Specifically, a decision error occurs when error in the data mislead the decision maker into
choosing a course of action that is different or less desirable than the course of action that would have
been chosen if the data had been "perfect." Usually the correct decision will be made, but sometimes the
variability in the data masks the true conditions in a way that leads to a decision error. This step of the
DQO process allows the decision maker to set limits on the probabilities of making an incorrect decision.
The Soil Screening framework has provided a complete set of outputs for this step. These results are
described below and are summarized concisely in Table 4-1.
The site-specific strategy presented below uses a statistical sampling design, which generates data that can
be used to perform a hypothesis test. When performing a hypothesis test, it is necessary to state the
presumed or baseline condition, which the statistician calls the "null hypothesis." This baseline condition
is presumed to be true unless the data conclusively demonstrate otherwise, which is called "rejecting the
null hypothesis" in favor of an alternative hypothesis. The baseline condition for the Soil Screening
framework is that the site is presumed to be contaminated.
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When the true mean of an EA is compared with the screening level, there are two possible decision errors
that may occur. (1) decide not to investigate an EA further (i.e., "walk away") when the correct decision
would be to "investigate further"; and (2) decide to investigate further when the correct decision would
be to "walk away." Within the Soil Screening framework, a site is presumed to be contaminated, so the
data must provide clear evidence that it would be acceptable to "walk away." This presumption provides
the basis for classifying the two types of decision errors. The "incorrectly walk away" decision error will
be designated as the Type I or false positive decision error because it has incorrectly rejected the baseline
condition (null hypothesis). Correspondingly, the "unnecessarily investigate further" decision error will
be designated as the Type II or false negative decision error.
To complete the specification of limits on decision errors for the Soil Screening framework, the Type I
and Type H decision error probability limits must be defined in relation to the SSL. This is accomplished
by specifying a "gray region" with respect to the parameter of interest (in the Soil Screening framework,
this parameter is the mean contaminant concentration within an exposure area). The gray region represents
a set of values near the screening level where uncertainty in the data (i.e., the variability) can make the
decision "too close to call." In other words, it would be too expensive to generate a data set of sufficient
size and precision to resolve what the correct determination should be (i.e., "above" or "below" the
screening level) when the average of the data values is very close to the screening level. The Soil
Screening framework establishes a default range for the width and location of the "gray region," which
is the minimum detectable difference of the statistical test. The default gray region ranges from one-half
the SSL to two times the SSL.
By specifying the upper edge of the gray region as twice the SSL, it is possible (though unlikely) that
exposure areas with mean values slightly higher than the SSL may be screened from further study. The
Superfund program believes that this will still be protective given: (1) the exposure scenario and
assumptions used to derive the SSLs are conservative; and (2) the sample data are analyzed in such a way
that the \JCL95 on the mean concentration is compared to the screening level, not the arithmetic mean
itself.
On the lower side of the gray region, the consequences of specifying a Type n decision error rate at one-
half the SSL are primarily financial. If the lower edge of the gray region were to be moved closer to the
SSL, then more exposure areas that were truly below the SSL would be screened out, yet more money
would be spent on sampling to make this determination. If the lower edge of the gray region were to be
moved closer to zero, then less money could be spent on sampling, but fewer exposure areas that were
truly below the SSLs would be screened out In other words, such exposure areas would yield
inconclusive results, hence they would have to be investigated further. The choice of one-half to two
times the SSL as the gray region represents the Superfund program's judgment as to where to balance the
costs of sampling with the costs of making incorrect decisions for most sites.
The Soil Screening framework establishes default values for the tolerable Type I and Type n decision
error rates, based on an overall assessment of the consequences of potential decision errors and consistency
with other relevant EPA guidance and programs. The default Type I decision error rate is 0.05 (5 percent)
at the screening level (two times the SSL), and the default Type n decision error rate is 0.20 (20 percent)
at one-half the SSL. This means that there should be no more than a 5 percent chance that the site
manager will "walk away" from an EA where the true mean concentration is greater than twice the SSL.
Additionally, there should be no more than a 20 percent chance that the site manager will unnecessarily
investigate an EA that did not warrant further study.
The Soil Screening framework quantitative criteria are depicted graphically using a decision performance
goal diagram in Figure 4-1.
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Q) Q)
1 I
a, I
1 *
Q? Q}
= 8
5 S
Tolerable
False
Positive (Type I)
Decision
Error Rates
Gray Region
(Relatively Large
Decision Error
Rates are
Considered
Tolerable.)
0.5 X SSL SSL
2XSSL
J
Screening Level
True Mean Contaminant Concentration
Figure 4-1. Decision performance goal diagram.
1.0
0.95
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
4.1.7 Optimize the Design. For subsurface soil, use the method described below. For surface soil,
choose either the site-specific sampling approach (Section 4.2) or the prescriptive sampling approach
(Section 4.3). These two sections will provide a basis for developing the Sampling and Analysis Plan.
For nonvolatiles, first decide whether or not to composite samples. The site-specific sampling approach
should be used for nonvolatiles if compositing samples is not feasible (see below). Otherwise, compare
the cost, performance, and scheduling impacts of developing a site-specific sampling design with the cost,
performance, and schedule impacts of implementing the prescriptive approach.
Some sampling design considerations which apply to both the site-specific sampling approach and the
prescriptive sampling approach are discussed below. These considerations include sample analysis,
sampling patterns, geostatistics, and quality assurance.
Subsurface Sampling. For the migration to ground water pathway, the soil column should be
sampled to gather information on contaminant distribution with depth. EPA suggests taking split spoon
or Shelby tube samples in at least two boreholes for each source area. Samples in each borehole should
begin at six inches below ground surface and continue at 2-foot intervals until no further contamination
is encountered. Subsurface sampling depths and intervals can be adjusted at a site to accommodate site-
specific information on subsurface contaminant distributions and geologic conditions.
The average soil concentration for each borehole should be compared to the SSLs. If any borehole
average exceeds the SSL, then further site-specific study is warranted. The borehole average can be
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obtained by averaging analyses of the discrete samples taken along the entire length of the borehole or
by combining discrete samples into a composite sample for analysis. If using the leach test, the latter
procedure is preferred. However, note that the compositing approach will prevent the evaluation of vertical
stratification of contaminants (i.e., contaminant concentration trends with depth).
The number and location of subsurface soil sampling (i.e., borehole) locations should be based on
knowledge of likely surface soil contamination patterns and subsurface conditions. This usually means
that core samples for evaluating the migration to ground water pathway should be taken directly beneath
areas of high surface soil contamination. If surface soils are of concern for the ingestion and/or inhalation
pathways as well, then surface soils may be sampled first to provide information on source areas and high
contaminant concentrations to help target subsurface sampling efforts. Note that there may be sources
buried in subsurface soils that are not discernible at the surface. Information on past practices at the site
included in the conceptual site model can help identify such areas.
The intensity of the subsurface soil sampling required to implement the Soil Screening framework
typically will not be sufficient to fully characterize the extent of contamination presenting a potential threat
to ground water. In these cases, conservative assumptions should be used to develop hypotheses on likely
contaminant distributions (e.g., assume that the highest average borehole concentration underlies the entire
source area). Along with knowledge of subsurface hydrogeology and stratigraphy, geostatistics can be a
useful tool in developing subsurface contaminant distributions from limited data and can provide
information to help guide additional sampling efforts.
Finally, soil investigation for the migration to ground water pathway should not be conducted
independently of ground water investigations. Ground water should be sampled to determine whether
there is existing ground water contamination and the results should be considered in the holistic
application of the Soil Screening framework.
Compositing of Samples. EPA recognizes that the sampling and analysis requirements for discrete
sampling may not be feasible within budgetary and scheduling constraints at many sites. For many
nonvolatile contaminants, compositing of discrete samples is an acceptable strategy for reducing sampling
and analysis costs. This is because the Superfund program is interested in ensuring that the mean
contaminant concentration does not exceed the screening level, so the physical "averaging" that occurs
during compositing is consistent with the intended use of the data. Compositing allows a larger number
of locations to be. sampled while controlling analytical costs, since several discrete samples are physically
mixed (homogenized), and one or more subsamples are drawn from the mixture and submitted for
analysis.
A practical constraint to compositing in some situations is the heterogeneity of the soil matrix. The
efficiency and effectiveness of the mixing process may be hindered when soil particle sizes vary widely
or the soil matrix includes foreign objects, organic matter, viscous fluids, or sticky material. Samples may
be composited only if the soil is relatively homogeneous in contamination, the contamination is expected
to be limited, and there is not a long list of target contaminants. If any of these assumptions are violated,
compositing of samples may lead to matrix interference or dilution of the concentrations of some of the
contaminants.
Compositing may mask contaminant levels that are slightly higher than the SSL, but areas of extremely
high contamination will still have a high probability of being detected, provided that an appropriate
sampling grid density is chosen to cover each exposure area and measurement quality assurance is
adequate. This guidance may be used to determine if the mean concentration levels at a site exceed the
screening levels. It does not consider searching for hot spots. If relatively small hot spots are a concern,
then a hot spot search sampling approach should be used (see Chapter 9 of U.S. EPA, 1989c). However,
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if visual evidence implies that a hot spot exists, it is recommended that some samples be selected from
these areas and evaluated separately from the approach described in this guidance.
Sample Analysis. Field methods, such as soil gas surveys, immunoassays, or X-ray fluorescence, can
be used if the field method detection limit is below the SSL. For compounds other than volatiles,
compositing samples is acceptable as long as it is consistent with the field methodology and it does not
dilute the contaminant concentration to levels below the field method detection limit or the analytical
method detection limit. A percentage of both the discrete samples and the composites must be analyzed
by CLP methods. At least 10 percent of field samples should be split and sent to a CLP laboratory for
confirmatory analysis (U.S. EPA, 1993g).
Sample Pattern. A systematic grid pattern, such as a triangular or square/rectangular grid, is
recommended to establish sample locations for each exposure area. Step-by-step procedures for laying
out systematic sampling grids can be found in Chapter 5 of U.S. EPA (1989c) and Chapter 5 of U.S. EPA
(1994f). The starting point of the grid should be randomly selected so as to preserve certain desirable
statistical qualities of the sample pattern; this is referred to as "randomized systematic sampling" in this
document Discrete sampling for both the site-specific and prescriptive approaches should be based on
a randomized systematic grid. The sample pattern for composite sampling may differ depending on
whether the site-specific or prescriptive approach is used.
If the site contains areas where there is evidence of high levels of contamination, such as stained soils,
special measures must be taken to ensure that such suspect areas are properly sampled and evaluated (i.e.,
compared to SSLs). Because the systematic sampling recommended herein is based on a random starting
point, the sampling grid could miss a suspect area if it is small. On the other hand, the results from
samples that were purposefully taken in a particular location (such as judgmental samples taken from a
stained area) must not be combined with results from statistically based samples (such as randomized
systematic samples) because this produces biased estimates of the mean and variance, which confound the
analysis and interpretation of the data. An acceptable approach under these conditions is to use prior
information and professional judgment to locate the boundary of the suspect area so it can be sampled
separately. Because the goal is to include within this boundary only the contaminated area, randomized
systematic sampling within this suspect area should properly capture any higher levels of contamination
while preserving desirable statistical qualities in this data subset At least two discrete samples should be
taken within the suspect area regardless of how small the suspected area is. In general the suspect area
should be sampled at a density at least as high as that used for other areas of the site: There are two ways
in which the sampling results from the suspect area can be compared to the SSL:
• Decide to investigate that exposure area further if the highest sample result exceeds the SSL;
or
• If the site-specific approach is being used and the location of the suspect area is known during
planning, use this information as the basis for a stratified sampling desiga A stratified
sampling design allows the sampling results from the suspect area (stratum) to be combined
with results from other parts of the exposure- area to produce an estimate of the mean
contaminant concentration across the entire exposure area in addition to producing an estimate
of the mean contaminant concentration for the suspect area. More information about stratified
sampling can be found in Chapters 4 and 6 of U.S. EPA (1989c).
GeostatistiCS. If a detailed site-specific approach is anticipated for developing SSLs, the site manager
may want to consider using geostatistics to estimate contaminant concentrations across the site.
Geostatistics is a field of study involving statistical analyses of spatially related data, such as geologic or
environmental data. Geostatistics differs from classical statistics in its treatment of variability and
%
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probabilistic independence. Classical statistics typically models variability and independence among
samples as random phenomena, while geostatistics assumes that there is some spatial continuity between
samples that can be modeled using geostatistical concepts supported by scientific knowledge of the
medium under study. Usually this approach assumes that probabilistic independence is a function of
distance between samples; samples located close to each other will exhibit properties that are more similar,
whereas samples that are separated by greater distances will tend to exhibit properties mat are more
independent of each other. Consequently, the variability in data must be viewed within an inherently
spatial framework.
Geostatistics includes tools such as kriging that can be used to estimate contaminant concentrations at
unsampled points and estimate average contaminant concentrations across areas of the site. However, the
results depend heavily on the scientific and geostatistical judgments made in modeling how the data vary
spatially. Consultation with skilled geostatisticians is recommended prior to initiating any sampling plan
to ensure that the sampling strategy will capture the critical data necessary for the geostatistical analyses.
Software packages have been developed to facilitate geostatistical analyses. One such package is GEO-
EAS, developed by EPA's Environmental Monitoring Systems in Las Vegas, Nevada.
Quality Assurance. Regardless of whether the prescriptive approach or the site-specific approach to
sampling is used, the Superfund quality assurance program requirements must be followed (U.S. EPA,
1993g).
4.2 Site-Specific Sampling Approach
This section considers two basic sampling designs for surface soil sampling: simple random sampling,
and composite simple random sampling (composite sampling). For each design, the sample size required
and a method for analyzing the data are given. The method for analyzing the data given below is based
on a confidence interval for the mean of a lognormal distribution.
One important piece of information required for this step is an estimate of the variance within an EA.
This information can be obtained from (in descending order of desirability):
• Data from a pilot study
• Prior sampling data from the site (but only if the samples were taken randomly within an area
roughly equivalent to the EA)
• Data from similar sites
• Professional judgment
The estimate of variability required for this section is in terms of logarithmic units. Therefore, if existing
data are used to compute this estimate, take the natural logarithms of the data then compute the sample
standard deviation. However, do not develop an estimate of the standard deviation, then take the natural
logarithm of this estimate. For more information on estimating variability, see U.S.EPA (1989c, Section
6.3.1).
The procedures used in this section assume that the contaminant concentrations are distributed lognormally
within each exposure area. The lognormal distribution is the most commonly used probability density
model for environmental contaminant data (Gilbert, 1987, p. 164). In addition, this assumption is
consistent with other Superfund guidance documents (U.S. EPA, 1989d; U.S. EPA, 1992e). The
lognormal distribution provides a reasonable approximation of contamination distribution at Superfund
sites because the lognormal distribution contains only positive values, which is consistent with
concentration data from contaminated sites. Also the lognormal distribution also is unimodal and
*
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Table 4-2. Sample Sizes for
Site-Specific Approach
n
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
4
5
5
6
7
8
10
12
14
17
20
24
29
34
39
45
52
60
positively skewed, so that there is a long right
"tail." This is consistent with data from most
contaminated sites. The data from any particular
site may not strictly follow a lognormal
distribution, but the assumption of lognonnality
should provide an adequate model for analyzing
the data from most sites. The assumption of
lognonnality can be tested if the data appear to
depart significantly from the lognormal form; see
U.S. EPA (1992f) for procedures on how to test
for lognonnality. If the data appear to depart
significantly from the assumption of
lognonnality, consult a statistician for advice on
how to proceed.
4.2.1 Determining Sample Size for
Simple Random Sampling. Use the
estimate of variability within an EA in Table 4-2
to determine the sample size. The sample sizes
in Table 4-2 are based on simulations using the
decision error rates defined in Section 4.1.6 and
the confidence interval for the mean of a log-
normal distribution described in Gilbert (1987,
pp. 169-171). A description of the simulation
procedures is contained in Appendix I.
4.2.2 Determining Sample Size for Composite Sampling. Composite sampling can be an
.acceptable method for reducing variability in sampling data, as long as the sampling objective is to
estimate the mean contaminant concentration, and the soil matrix is not so heterogeneous as to pose severe
problems with obtaining well-mixed composite samples. In general, sample size calculations for
composite sampling involve calculating both the number of composite samples to be analyzed (m), and
the number of aliquots within each composite sample (k).
The procedures used in this section assume that a composite sample is lognormally distributed. If
contaminant concentrations over the site have a skewed distribution with a long right tail, then it is
reasonable to assume that the distribution of an average of a small number of samples from the site will
also be skewed with a right tail. However, the distribution of the average will tend to be less skewed than
the original distribution and have lesser variability. Directions for calculating the sample size for a
composite sample are given in Highlight 4-1. This method assumes that the user specifies k (the number
of aliquots in each composite sample) and then modifies the estimate of variability so that the procedure
for determining the sample size for simple random sampling may be used. A derivation of the
development of the modified estimate of variability is contained in Appendix I.
The sampling pattern used to locate the m discrete samples for each composite sample is important, and
the approach recommended here differs from the prescriptive compositing approach. For composite
sampling under the site-specific approach, the composite samples must be formed in a manner that is
consistent with the assumptions underlying the sample size calculations above. Therefore, each composite
sample must represent an estimate of the mean contaminant concentration over the entire exposure area
In other words, each composite sample must be formed from m discrete samples so that all parts of the
EA have an equal probability of being included in the composite sample. While simple random sampling
would be consistent with this requirement, simulation studies using surface soil data from selected
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Highlight 4-1: Procedure for Determining Composite Sample Size
1. Estimate total variance, d2T.
2. Estimate ratio of measurement error variance to total variance, r2 = d*ME I d2-,- and calculate
the inherent EA variability o2^ =
3. Choose the number of discrete samples per composite, m.
z °"-
4. Calculate dCm = ^ ln[1 +(eU£*-1)/m]+aM£ .
5. Calculate the square root of 62Cm, i.e., calculate
°Cm
6. Substitute 6Cm for d in Table 4-2 to determine the number of composite samples, k, needed to
satisfy the DQOs.
7. To determine the most cost-effective combination of m and k, calculate the cost of collecting
and analyzing k composite samples of size m. Repeat steps 3 through 7 for different feasible
values of m, and choose the combination with the lowest total cost.
Superfund sites indicate that partitioning the EA into m sectors and using one sample from each sector
to form a composite sample will provide better estimates of the mean contaminant concentration in an EA.
Highlight 4-2 illustrates this approach to determining sample locations for a composite sampling design.
Figure 4-2 is an example of sample locations for six composite samples consisting of four aliquots each
where a randomized grid was placed in each of the six sectors.
4.2.3 Analyzing the Data. Directions for analyzing the data for both the composite sample and the
simple random samples are given in Highlight 4-3. This method for analyzing the data is based on an
upper 95% confidence interval for the mean of lognormally distributed data (see Gilbert, 1987, pp. 169-
171). This procedure is consistent with exposure assessment assumptions described in other Superfund
guidance (U.S. EPA, 1989d, 1992g). Although the method is applied in the form of a confidence interval,
if the proper sample size procedures were followed (Section 4.2.1 and 4.2.2) this method is equivalent to
a hypothesis test that controls both the Type I and Type n error rates.
4.3 Prescriptive Sampling Approach
The prescriptive sampling approach described in this section is intended for NPL sites where the site-
specific sampling approach is not used. The prescriptive approach represents a strategy that can be
implemented easily and applied consistently from site to site. This strategy consists of collecting a fixed
number of samples within each exposure area where the number of samples depends on the type of the
contaminant. Although this approach does not use site-specific information nor account for the probability
limits on decision errors, the format of the DQO process is still useful to organize data collection
activities.
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Highlight 4-2: Procedure for Determining Sample Pattern fora Composite Sample Under
the Site-Specific Approach
1. Divide the EA into m sectors of equal area and shape. This may be done based on geometric
criteria (such as checkerboard or striped patterns), but should be consistent with what is
known about contaminant deposition patterns. Number the sectors 1 through m.
2. Create a randomized systematic sampling grid for collecting k discrete samples within each of
the sectors or randomly select sampling locations. Step-by-step procedures for laying out
systematic sampling grids can be found in Chapter 5 of U.S. EPA (1989c). Number the
discrete sampling nodes 1 through k (or use corresponding letters).
3. Form each composite sample by taking one discrete sample from each of the m sectors.
Thus, each composite sample will contain m discrete samples, for a total of k composite
samples after each of the (m x k) nodes have been sampled. Although the order in which the
discrete sampling grid nodes are sampled is not important, all nodes must be sampled once
and only once (except for duplicate samples taken for quality control purposes). To simplify
implementation, it is acceptable to form each composite sample from discrete sampling nodes
in the same relative location (i.e., composite number 1 is formed using discrete node location
1 in all m sectors, composite number 2 is formed using node location 2, etc., until all k nodes
have been sampled).
Figure 4-2. An example of six composite samples of four aliquots each for
site-specific approach.
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Highlight 4-3: Procedure for Analyzing Data Collected Using Site-Specific Approach
Let xv Xg xn represent the n data points collected for an EA. Note that xv Xg xn may
represent composite samples.
1. Take the natural logarithm of each of the n data points and label these points yv y2, .... yn
where ys = ln(Xj).
2. Compute the sample mean Y and the sample standard deviation Sy where
- 1 "
y= YV
-r Z- // •
n w
m
f°r simple random sampling,
1.12 -L£(yrYf
N n-'M
sv = 1.12 _l_]P(yry)2 for composite sampling.*
3. Use sy and n in Table 4-3 to find H095. If sy and n are not contained in Table 4-3, let s1
represent the closest number less than Sy, and s2 represent the closest number greater than sy
in the table. Similarly, let n1 represent the closest number less than n, and n2 represent the
closest number greater than n in the table. Compute:
sv - S1 ., n - "l
p = JL - 1 and q = - L .
% - «i "2 - "i
Let h(1,1) correspond to the value in Table 4-3 for s1 and nv h(1,2) correspond to the value in
Table 4-3 for st and r^, h(2,1) correspond to the value in Table 4-3 for $2 and n1f and h(2,2)
correspond to the value in Table 4-3 for s2 and r^. Then
"0.95 = (1-P)(1-Q)/7(1,1) + P(1-fl)AK2,1) + (1-p)q/»(1,2) + pqh(2,2) .
4. Calculate the upper one-sided 95% confidence limit:
+ 0.5sg +
5. If UCLo 95 > 2*SSL, there is not enough evidence to reject the null hypothesis. Therefore, the
EA requires further investigation.
If UCLo 95 < 2*SSL, reject the null hypothesis and conclude that the EA mean does not exceed
the Soil Sreening Level, therefore, the EA is clean.
* It was found in simulation studies that the coverage probabilities of the confidence intervals deviated from
the intended level of 0.95 in a way that was approximately compensated for by the adjustment factor of
1.12. Further research is under way to determine the underlying reasons for this deviation.
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Table 4-3. Values of H(0.95) for Computing a One-Sided Upper 95%
Confidence Limit on a Lognormal Mean
n
"y
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.25
1.50
1.75
2.00
2.50
3.00
3.50
4.00
4.50
5.00
6.00
7.00
8.00
9.00
10.00
3
2.750
3.295
4.109
5.220
6.495
7.807
9.120
10.43
11.74
13.05
16.33
19.60
22.87
26.14
32.69
39.23
45.77
52.31
58.85
65.39
78.47
91.55
104.6
117.7
130.8
5
2.035
2.198
2.402
2.651
2.947
3.287
3.662
4.062
4.478
4.905
6.001
7.120
8.250
9.387
11.67
13.97
16.27
18.58
20.88
23.19
27.81
32.43
37.06
41.68
46.31
7
1.886
1.992
2.125
2.282
2.465
2.673
2.904
3.155
3.420
3.698
4.426
5.184
5.960
6.747
8.339
9.945
11.56
13.18
14.80
16.43
19.68
22.94
26.20
29.46
32.73
10
1.802
1.881
1.977
2.089
2.220
2.368
2.532
2.710
2.902
3.103
3.639
4.207
2.795
5.396
6.621
7.864
9.118
10.38
11.64
12.91
15.45
18.00
20.55
23.10
25.66
12
1.775
1.843
1.927
2.026
2.141
2.271
2.414
2.570
2.738
2.915
3.389
3.896
4.422
4.962
6.067
7.191
8.326
9.469
10.62
11.77
14.08
16.39
18.71
21.03
23.35
15
1.749
1.809
1.882
1.968
2.068
2.181
2.306
2.443
2.589
2.744
3.163
3.612
4.081
4.564
5.557
6.570
7.596
8.630
9.669
10.71
12.81
14.90
17.01
19.11
21.22
21
1.722
1.771
1.833
1.905
1.989
2.085
2.191
2.307
2.432
2.564
2.923
3.311
3.719
4.141
5.013
5.907
6.815
7.731
8.652
9.579
11.44
13.31
15.18
17.05
18.93
31
1.701
1.742
1.793
1.856
1.928
2.010
2.102
2.202
2.310
2.423
2.737
3.077
3.437
3.812
4.588
5.388
6.201
7.024
7.854
8.688
10.36
12.05
13.74
15.43
17.13
51
1.684
1.718
1.761
1.813
1.876
1.946
2.025
2.112
2.206
2.306
2.580
2.881
3.200
3.533
4.228
4.947
5.681
6.424
7.174
7.929
9.449
10.98
12.51
14.05
15.59
101
1.670
1.697
1.733
1.777
1.830
1.891
1.960
2.035
2.117
2205
2.447
2.713
2.997
3.295
3.920
4.569
5233
5.908
6.590
7.277
8.661
10.05
11.45
12.85
14.26
Source: After Land, 1975.
The prescribed number of samples to be collected in each exposure area is summarized as follows:
• Nonvolatile contaminants—4 composite samples of 5 aliquots each
• Volatile contaminants—10 discrete samples.
For some sites, the prescribed number of samples will be adequate to ensure that the mean concentration
of contaminants does not exceed the SSL. However, the prescriptive approach does not account for site-
specific contaminant variability. Consequently, the prescribed number of samples may be larger than
necessary to adequately characterize the mean for some sites or may insufficient for others.
4.3.1 Composite Sampling for Nonvolatile Contaminants in Surface Soils. The
prescriptive approach allows for compositing of discrete samples, thereby saving analytical costs while
4-15
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still achieving adequate sampling coverage of the site. EPA believes that 20 discrete samples can be
composited down to 4 composite samples, while maintaining reasonable confidence that the area average
is not being grossly underestimated. However, the compositing of samples is only feasible under certain
conditions. Samples may be composited only if the soil is homogeneous both in composition and in
contamination, the contamination is expected to be limited, and there is not a long list of target
contaminants. If any of these assumptions are violated, compositing of samples may lead to matrix
interference or dilution of the concentrations of some of the contaminants.
The recommended prescriptive approach to compositing is as follows:
Divide each 0.5 acre exposure area into quadrants. Within each quadrant, collect five
discrete samples (using the method described in Section 4.1.7, Sample Pattern) and
composite them. If the resulting single composite sample concentration within the
quadrant exceeds the SSL, then designate that part of the exposure area for further
investigation.
This approach has certain practical and economic advantages, but the sampling design is not based on
statistical criteria. In preliminary simulation studies based on selected existing Superfund data sets, this
prescriptive compositing decision rule performed comparably to the statistical decision rule whereby the
upper 95% confidence limit of the mean of 20 discrete samples is compared to the SSL. However, further
investigation is planned to determine how well the prescriptive approach performs in relation to the DQOs
over a variety of site conditions. Collecting all five discrete samples within a quadrant and analyzing only
one composite sample per quadrant represents a compromise between the desire to efficiently locate areas
of high contamination and the need to keep sampling and analysis costs under control. However, this
approach imposes constraints on the statistical analysis and interpretation of the resulting data, since the
design lacks sufficient information about variability in the estimate of the mean. If the site manager wants
to quantify the statistical performance of the sampling design, then the site-specific sampling approach
described in Section 4.2 should be used.
4.3.2 Discrete Sampling of Volatile Contaminants in Surface Soils. Composite sampling
of volatile organic compounds (VOCs) usually is not appropriate because contaminants may volatilize
during homogenization of the samples. Generally, volatilization of VOC contaminants is a significant
source of error that can lead to underestimation of contaminant concentrations (U.S. EPA, 1989c, 1992e).
Recent studies by EPA indicate that methanol may .be useful as a preservative and extractant that can in
some instances, reduce the volatilization of VOCs.
When using the prescriptive approach, EPA recommends taking 10 discrete VOC samples per EA. The
discrete samples should be taken at the nodes of a randomized systematic grid in accordance with Section
4.1.7. EPA has decided that the results of the 10 discrete samples should not be averaged when
implementing the prescriptive approach to the Soil Screening framework, since EAG's simulation studies
showed that the average of subsets with 10 samples frequently underestimated the true mean.
Consequently, EPA has determined that if any one individual VOC sample result within an EA exceeds
the SSL, then that EA should be investigated further.
4-16
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Review Draft—Do Not Cite or Quote—December 1994
Technical Background Document for
Soil Screening Guidance
Part 5: CHEMICAL-SPECIFIC PARAMETERS
5.1 Introduction
Chemical-specific parameters required for calculating Soil Screening Levels (SSLs) include the organic
carbon normalized soil-water partition coefficient for organic compounds (K^.), the soil-water partition
coefficient for inorganic constituents (Kj), water solubility (S), Henry's law constant (H), and air
diffusivity (Dj). In addition, the octanol-water partition coefficient (K^) and vapor pressure (VP) are
needed to calculate K^ and H values, respectively. This part of the background document describes the
collection and compilation of these parameters for the SSL chemicals.
An initial review of literature and database sources revealed considerable variability in the reported values
of each parameter for any given chemical. Potential sources of this variability include:
• All parameters: analytical error, procedural error, differences in the performance of various
analytical techniques and differences in the methodologies or procedures used to measure the
parameters
• Calculated H: variability in the solubility and vapor pressure values used in calculations
• Measured K^. values: variability in soil properties affecting sorption
• Calculated K^ values: differences in equations (e.g., see Lyman et al., 1982) and variability
in the parameters used in calculations (e.g., Kow and solubility).
Existing compilations of chemical parameters also were reviewed as possible sources of definitive values.
However, values extracted from equally reputable sources did not agree for certain chemicals and no
source was complete in its coverage of SSL chemicals. In addition, further investigation uncovered cited
references that were two to three references removed from the original source in some compilations,
adding misquotation as another potential source of error.
Calculation of an SSL requires a single chemical-specific value for each relevant parameter. Generally,
the approach taken was to determine a value that represents the central tendency of the full distribution
of reported values compiled from an extensive review of the literature. For K,,w, solubility, vapor
pressure, and K,,,., measured values were compiled; in addition, calculated values were collected for K^
and vapor pressure. Because of the wide range in values for certain chemicals, the geometric mean of
each chemical-specific parameter was taken as the best estimate of a central tendency value. pH-specific
KQC values for ionizing organic compounds were estimated from reported values and a theoretically based
pH-dependent relationship. Because measured values of the Kj for inorganics exhibit exceptionally high
variability and determination of central tendency was problematic, metal Kd values were estimated using
an equilibrium chemical speciation model. The procedures used for determining chemical-specific values
for each parameter are described below.
5.2 Solubility and K^
Solubility and K^ values were collected and compiled. Values were then reviewed to identify and
eliminate outliers. Because it was not possible to systematically evaluate each reference for accuracy or
5-1
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Review Draft—Do Not Cite or Quote—December 1994
to determine sources of variability for each reported value, outliers were identified by judgment rather than
by using a statistical procedure. Values were judged to be outliers when they originated from sources that
consistently reported exceptionally low or high values or were themselves exceptionally low or high
relative to other reported measurements for a given compound. Since the variability in solubilities
measured between 20 and 25 °C was well within the overall range of measured values, no temperature
corrections were made. Summary statistics for reported solubility values are shown in Table 5-1, and
those for Kow are presented in Table 5-2. The values collected for these parameters and their sources are
listed in RTI (1994).
5.3 Vapor Pressure and Air Diffusivity
Variability in reported vapor pressure values was somewhat lower than that for other parameters and
published values were not as numerous. Therefore, the search for vapor pressure data relied more heavily
on standard sources such as handbooks and databases rather than original references.
Because vapor pressures are significantly affected by temperature, one of three methods was used to
correct vapor pressure values to 25 °C. The Clausius-Clapeyron relationship was used to adjust vapor
pressure values when a reference contained more than one value measured over a relatively small
temperature range near 25 °C. When only one vapor pressure was reported in a source, the Clausius-
Clapeyron equations could not be used and temperature adjustments were based on the compound's boiling
point using one of two equations, depending on the physical state of the chemical (i.e., solid or liquid)
at the measurement temperature. When the necessary chemical-specific constants were reported, the
empirically based Antoine equation was used. The equations used for these corrections are presented in
RTI (1994), along with the collected vapor pressure values for each chemical. Summary statistics for the
collected vapor pressure values are presented in Table 5-3.
For air diffusivity values, few published values were available for the subject chemicals, so the
CHEMDAT7 model database (U.S. EPA, 1989b) was used as a consistent source for estimated and
measured values. Table 5-4 presents the values used for air diffusivity.
5.4 Henry's Law Constant
Only a limited number of measured Henry's law constants (H) were found in the literature review.
Reported values for a given chemical sometimes show significant variability because of differences in
experimental methods. Calculated values were also inconsistent because of differences in solubility and
vapor pressure values used in the calculations. To develop consistent values, H was calculated from the
vapor pressure and water solubility values described above as follows:
H = (VP)(M) (5-1)
S
where
H = Henry's law constant (atm-m3/mol)
VP = vapor pressure (atm)
M = molecular weight (g/mol)
S = solubility (mg/L or g/m3).
5-2
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Review Draft—Do Not Cite or Quote—December 1994
Table 5-1. Range, Geometric Mean, and Number of Reported Solubility Values
(20-25 °C)
Solubility (mg/L)
Compound
Acenaphthene
Acetone
Aldrin
Anthracene
Arodor 1016
Aroctor 1254
Arochlor 1260
Benzene
Benzo(a)anthracene
Benzo(o)fluoranthene
Benzo(a)pyrene
Bis(2-chbroethyl)ether
Bis(2-ethylhexyl)phthalate
Bromodichlorometnane
Bromoform
Butanol
Butyl benzyl phthalate
Carbazole
Carbon disuKide
Carbon tetrachloride
Chlordane
Chlorobenzene
Chtorodibromomethane
Chloroform
Chrysene
DDD
DDE
DDT
Dibenzo(a,ft)anthracene
Di-n-butyl phthalate
1 ,2-Dichbrobenzene (o)
1 ,4-Dichbrobenzene (p)
3,3-Dichbrobenzidine
1,1-Dichloroethane
1,2-Dichbroethane
1 , 1 -Dichbroethylene
c/s-1 ,2-Dichloroethylene
frans-1 ,2-Dichtaroethylene
1 ,2-Dichbropropane
1 ,3-Dichloropropene
Dieldrin
Diethyl phthalate
Dimethyl phthalate
2,4-Dinilrotoluene
2,6-Dinrtrotoluene
Di-n-octyl phthalate
Endosulfan
Geometric
mean
4.13E+00
6.04E+05
7.84E-02
5.37E-02
5.71 E-01
5.15E-02
4.47E-02
1.78E+03
1.28E-02
4.33E-03
1.94E-03
1.18E+04
3.96E-01
3.97E+03
3.21 E+03
7.47E+04
2.58E+00
7.21 E-01
2.67E+03
7.92E+02
2.19E-01
4.09E+02
3.44E+03
7.96E+03
1.94E-03
7.33E-02
1.92E-02
3.41 E-03
6.70E-04
1.08E-I-01
1.25E+02
7.30E+01
3.52E+00
5.16E+03
8.31 E+03
3.00E+03
4.94E+03
8.03E+03
2.68E+03
1.55E+03
1.87E-01
8.83E+02
4.19E+03
2.85E+02
1.05E+03
3.00E+00
2.31 E-01
Average
4.24E+00
6.04E+05
1.21 E-01
5.64E-02
6.05E-01
7.04E-02
5.25E-02
1.78E+03
1.47E-02
7.68E-03
2.51 E-03
1.20E+04
4.08E-01
4.06E+03
3.22E+03
7.47E+04
2.60E+00
7.21 E-01
2.69E+03
7.96E+02
5.51 E-01
4.28E+02
3.57E+03
7.97E+03
2.15E-03
9.25E-02
5.45E-02
7.30E-03
8.09E-04
1.09E+01
1.28E+02
7.50E+01
3.55E+00
5.17E+03
8.32E+03
3.19E+03
5.26E+03
8.89E+03
2.71 E+03
1.73E+03
1.89E-01
8.95E+02
4.20E+03
2.85E+02
2.19E+03
3.00E+00
2.93E-01
Standard
deviation
1.12E+00
O.OOE+00
8.09E-02
1.70E-02
2.15E-01
7.43E-02
2.75E-02
5.78E+01
1.01E-02
6.33E-03
1.48E-03
2.67E+03
1.03E-01
7.74E+02
2.75E+02
2.29E+03
3.56E-01
O.OOE+00
3.49E+02
8.09E-H01
7.51 E-01
9.57E+01
9.05E+02
3.39E1-02
1.22E-03
4.97E-02
5.24E-02
1.11E-02
6.24E-04
1.42E+00
2.35E+01
1.48E+01
4.49E-01
3.13E+02
4.30E+02
1.22E+03
1.81 E+03
4.51 E+03
3.70E+02
8.32E+02
2.93E-02
1.43E+02
1.39E+02
1.50E+01
1.92E+03
O.OOE+00
1.74E-01
Minimum
3.47E+00
6.04E+05
1.70E-02
3.00E-02
4.20E-01
1.20E-02
2.50E-02
1.65E+03
8.60E-03
1.20E-03
5.00E-04
1.02E+04
3.00E-01
2.97E+03
3.01 E+03
7.24E+04
2.00E+00
7.21 E-01
2.20E+03
6.00E+02
5.60E-02
1.00E+02
2.31 E+03
7.22E+03
1.02E-03
2.00E-02
1.20E-03
1.00E-03
4.50E-04
9.20E+00
9.23E+01
3.09E+01
3.10E+00
4.59E+03
7.20E+03
2.23E+03
3.50E+03
6.26E+03
2.07E+03
1.00E+03
1.50E-01
6.80E+02
4.00E+03
2.70E+02
2.70E+02
3.00E+00
6.00E-02
Maximum
7.37E+00
6.04E+05
2.00E-01
7.90E-02
9.06E-01
3.00E-01
8.00E-02
1.93E+03
4.40E-02
1.40E-02
4.30E-03
1.72E+04
6.00E-01
4.70E+03
3.93E+03
7.70E+04.
2.90E+00
7.21 E-01
2.94E+03
1.16E+03
1.85E+00
5.03E+02
4.40E+03
8.67E+03
6.00E-03
1.60E-01
1.40E-01
3.74E-02
2.20E-03
1.30E+01
1.56E+02
1 .OOE+02
4.00E+00
5.56E+03
8.80E+03
5.50E+03
7.70E+03
1.67E+04
3.57E+03
2.80E+03
2.50E-01
1.08E+03
4.30E+03
3.00E+02
4.12E+03
3.00E+00
5.30E-01
Sample
size
9
1
5
26
3
11
4
30
10
4
10
5
5
3
8
2
4
1
3
29
4
21
3
23
13
4
7
12
6
4
15
20
2
10
14
5
4
4
13
5
8
4
3
2
2
1
8
...
-
...
...
-
,..
...
(continued)
/KTi
11/94
5-3
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-1 (continued)
Solubility (mg/L)
Compound
Endrin
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachloro-1 ,3-butadiene
a-HCH (a-BHC)
P-HCH (p-BHC)
Y-HCH (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
lndeno(1 ,2,3-c,d)pyrene
Isophorone
Methoxychlor
Methyl bromide
Methyl chloride
Metfiylene chloride
Naphthalene
Nitrobenzene
Pyrene
Styrene
1 ,1 ,2,2-Tetrachforoethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trichlorobenzene
1,1,1 -Trichloroethane
1 , 1 ,2-Trichloroethane
Trichloroethylene
Vinyl acetate
Vinyl chloride
Xylenes (total)
lonizable Organ ics
Benzoic acid
p-Chloroaniline
2-Chlorophenol
2,4-Dichlorophenol
2,4-Dimethylphenol
2,4-Oinitrophenol
2-Methylphenol
A/-Nitrosodiphenylamine
/V-Nitrosodi-n-propylamine
Pentachlorophenol
Phenol
2,4,5-Trichlorophenol
2,4,6-Trichlorophenol
Geometric
mean
2.46E-01
1.73E+02
2.32E-01
1.86E+00
2.73E-01
2.68E-01
8.62E-03
2.54E+00
2.40E+00
5.42E-01
4.20E+00
1.53E+00
4.08E+01
1.07E-02
1.20E+04
8.84E-02
1.45E+04
6.34E+03
_______
3.11E+01
1.92E+03
1.37E-01
2.57E+02
3.07E+03
2.32E+02
5.58E+02
6.79E-01
3.07E+01
1.17E+03
4.40E+03
1.18E+03
2.24E+04
2.73E+03
1.86E+02
3.13E+03
3.36E+03
2.15E+04
4.93E+03
6.25E+03
5.80E+03
2.77E+04
3.74E+01
1.46E+04
1.34E+01
9.08E+04
9.65E+02
7.53E+02
Average
2.47E-01
1.74E+02
2.34E-01
1.87E+00
7.52E-01
2.75E-01
1.50E-02
2.59E+00
3.37E+00
1.63E+00
5.11E+00
1.63E+00
4.24E+01
3.19E-02
1.20E+04
1.11E-01
1.46E+04
6.42E+03
T.77E+04
3.12E+01
1.92E+03
1.38E-01
2.66E+02
3.08E+03
2.58E+02
5.73E+02
9.32E-01
3.19E+01
1.32E+03
4.41 E+03
1.20E+03
2.25E+04
2.73E+03
1.89E+02
3.18E+03
3.38E+03
2.24E+04
4.97E+03
6.36E+03
5.80E+03
2.79E+04
3.75E+01
1.51E+04
1.35E+01
9.23E+04
9.65E+02
7.78E+02
Standard
deviation
1.25E-02
1.81E+01
3.32E-02
1.70E-01
8.98E-01
6.12E-02
1.54E-02
"sToalSn""
3.33E+00
2.14E+00
2.59E+00
4.90E-01
1.07E+01
3.01 E-02
O.OOE+00
7.61 E-02
1.85E+03
9.56E+02
2.85E+03
2.39E+00
1.02E+02
1.81 E-02
6.52E+01
3.01 E+02
1.22E+02
1.82E+02
9.32E-01
8.65E+00
7.87E+02
2.84E+02
2.52E+02
2.50E+03
3.15E+01
4.07E+01
5.71 E+02
3.70E+02
5.80E+03
6.46E+02
1.18E+03
2.00E+02
2.90E+03
2.50E+00
3.66E+03
1.82E+00
1.76E+04
1.70E+01
1.76E+02
Minimum
2.30E-01
1.31E+02
1.66E-01
1.62E+00
5.60E-02
2.00E-01
1.20E-03
"2".bo"ET66""
1.21E+00
1.30E-01
1.21E+00
8.05E-01
2.72E-I-01
1.85E-03
1.20E+04
4.00E-02
1.29E+04
4.80E+03
1.30E+04
2.04E+01
1.78E-f03
1.05E-01
1.60E+02
2.85E+03
1.50E+02
4.70E+02
4.00E-01
1.90E+01
4.80E-f02
3.70E+03
7.43E+02
2.00E-I-04
2.70E+03
1.34E+02
2.70E+03
3.10E+03
1.14E+04
4.46E+03
5.00E+03
5.60E+03
2.50E+04
3.50E+01
9.90E+03
9.59E+00
7.80E+04
9.48E+02
4.34E+02
Maximum
2.60E-01
2.08E+02
2.83E-01
2.23E-I-00
2.02E-I-00
3.50E-01
4.70E-02
3"23E+bd
1.00E+01
5.00E+00
7.80E+00
2.10E+00
5.00E+01
6.20E-02
1.20E+04
2.50E-01
1.75E+04
7.40E+03
2"OOE+b4'
3.44E-I-01
2.09E+03
1.75E-01
3.30E+02
"3."85'ET63
4.84E-I-02
1.55E+03
3.00E+00
4.88E+01
4.40E+03
4.80E+03
1.82E+03
2.50E+04
2.76E+03
3.68E+02
4.20E+03
3.90E+03
2.85E+04
6.20E+03
7.87E+03
6.00E+03
3.08E+04
4.00E+01
T77E+04
1.54E+01
1.22E+05
9.82E+02
9.00E+02
Sample
size
3
24
18
12
3
3
11
3
5
7
11
4
3
2
2
5
8
7
„
27
7
19
5
...„.„
26
31
6
12
20
9
18
2
2
32
5
3
6
5
3
2
2
2
3
6
4
2
5
/KTI
11/94
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-2. Summary Statistics for Kow Values
K,,w (dimensionless)
Compound
Geometric
mean
Average
Standard
deviation
Minimum
Maximum
Sample
size
Acenaphthene
9,215
9,257
896
8,318
10,715
Acetone
0.6
0.6
0.02
0.5
0.6
Aldrin
1,514,459
7.203,068
10,411,175
200,000
25,118,864
Anthracene
29,517
30,317
6,741
15,849
42,658
16
Arochlor 1016
Arochlor1254
Arochlor 1260
Benzene
Benzo(a)anthracene
Benzo(b)fluoranthene
253,488
1,611,131
8,341,144
137
477,042
1,592,720
388,173
1,779,820
9,263,105
138
497,081
1,912,938
232,404
831,855
3,999,911
8
159,199
1,096,742
23,988
1,100,000
4,073,803
126
398,107
602,560
758,578
2,951,209
14,000,000
158
812,831
3,715,352
5
3
5
17
5
5
Benzo(a)pyrene
1,346,731
1,403,698
412,296
933,254
2,187,762
11
Bis(2-chlorethyt)ether
Bis(2-ethylhexyl)phthalate
Bromocfichloro methane
Bromoform
Butanol
Butyl benzyl phthalate
20
160,325
106
224
7
25,874
21
164,176
108
225
7
42,867
7.
35,351
23
18
0.5
30,778
13
128,825
76
200
7
3,715
29
199,526
126
240
8
81,283
3
2
3
3
2
7
Carfoazole
3,758
4,085
1,515
1,950
5,754
Carbon disuffide
100
107
38
69
145
Carbon tetrachloride
521
527
81
437
676
Chlordane
Chlorobenzene
Chlorodibromomethane
Chloroform
Chrysene
ODD
865,813
616
154
89
548,238
1,318,796
1,211,057
644
156
90
564,492
1,349,412
889,665
171
23
6
137,117
282,213
300,000
288
123
79
407,380
977,237
2,630,268
955
174
100
812,831
1,648,162
5
18
3
9
7
6
DDE
1,803,999
3,949,681
4,057,985
430,000
10,000,000
11
DDT
1,174,230
1,383,607
705,253
370,000
2,398,833
11
Dibenzo(a,/))anthracene
Di-n-butyl phthalate
1,2-Dichlorobenzene (o)
1 ,4-Dichlorobenzene (p)
3,3-Dichlorobenzidine
1,1-Dichloroethane
1 ,2-Dichloroethane
1 ,1 -Dichloroethylene
3,527,582
27,332
2,788
2,584
3,758
62
29
132
4,647,258
36,442
2,842
2,635
3,800
62
29
132
2,724,236
20,670
564
541
565
1
0.8
3
933,254
5,495
2,188
1,738
3,236
60
28
129
7,585,776
61,660
3,715
3,715
4,365
63
30
135
4
6
13
19
2
3
4
2
c/s-1,2-Dichloroethylene
48
52
20
32
72
117
....„.„..
67
"39"
30
"98
234
"191™
5
......
1,2-Dichoropropane
1 18
1,3-Dichloropropene
Dieidrin
5.6
"l85",727"
65
"5l"2,602"
33
"626,255"
26
'"i'2,303"
100
T,584,T893"
4
.......
Diethyl phthalate
218
222
46
174
295
Dimethyl phthalate
43
47
.19
30
79
2,4-Dinitrotoluene
2,6-Dinitrotoluene
Di-n-octyl phthalate
Endosulfan
Endrin
99
77
2,136,698,525
3,020
77,934
99
82
3,272,217,731
3,827
85,297
3
30
2,934,932,395
2,108
31,048
95
52
831,760,000
832
36.308
102
112
7,400,000,000
6,761
119,375
3
2
3
4
4
(continued)
5-5
/nn
11/94
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-2 (continued)
Compound
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Geometric
mean
1,326
121,486
14,723
103,513
56,234
318,224
K
Average
1,329
127,092
14,745
166,822
138,048
350,537
DW (dimensionless)
Standard
deviation
81
36,056
781
124,993
101,756
140,006
Minimum
1,170
79,000
13,183
25,119
4,467
100,000
Maximum
1,413
165,959
15,136
338,844
251,189
562,341
Sample
size
11
5
5
7
3
15
Hexachloro-1,3-butadiene
53,817
62,518
30,712
19,953
109,648
g-HCH(g-BCH)
6,297
6,313
450
5,623
7,079
P-HCH
'fHCH"(indane)
6,808
™~~-
6,930
..„_.
1,353
------
5,623
.„.._.„.„..
9,120
7,943"
4
Ti
Hexachlorocydopentadiene .80.662 128.196 102.78.0 9772 323,594 6
"'9",'661 l'o,036 2,724 6760713£04T
IndenoQ ,2,3-c,d)pyrene
8,223,373
17,570,238
19,898,499
3,200,000
45,708,819
Isophorone
50
50
47
54
Methoxychlor
33,561
45,278
38,945
18,621
120,000
Methyl bromide
13
14
1
12
15
Methyl chloride
Methylene chloride
Naphthalene
Nitrobenzene
Pentachlorobenzene
Pyrene
8
18
2,356
68
122,334
100,474
8
18
2,386
69
126,555
104,292
0.2
0.2
415
9
31,073
29,800
8
18
1,950
50
75,858
75,858
9
18
3,890
78
158,489
165,959
3
4
21
7
11
11
Stryerte
849
892
302
575
1,460
1 ,1 ,2,2-Tetrachloroethane
Telrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trichlorobenzene
1,1,1 -Trichloroethane
245
357
465
1,841
9,734
264
245
358
482
1,847
10,042
273
0
28
111
149
2,427
63
245
338
162
1,698
5,370
148
245
398
631
1,995
15,000
320
2
3
23
2
14
5
1.1,2-Trichloroethane
125
126
16
112
148
Trichloroethylene
271
279
71
195
407
Vinyl acetate
0.2
Vinyl chloride
14
17
24
1,301
133
1,148
1,504
lonizae Organics
Benzole add
76
76
74
78
p-Chloroaniline
2-Chlorophenol
2,4-Dichlorophenol
74
145
1,085
75
145
1,167
15
9
439
55
132
562
105
158
1,995
7
9
11
2,4-Dimethylphenol
314
350
171
200
589
2,4-Dinitrophenol
33
33
32
35
2-Methylphenol
105
106
.17
89
132
AANitrosodiphenylamine
1,064
1,137
370
617
1,445
.^^.^.•{JrPrPPy1.?!]™!?6. ?*. ??. .5. .?? .?! 3.
Pentac'hToro'p'h'e'n'oi l"2"6','28'i' 'i'22','254" 23i436" 1Qo',ob"o' i'73,780 "J"
Phenol
2,4,5-Tricnlorophenol
30
------
30
"8,266
2
"4,236'
26
"5,248"
32
•--•—•-
9
...„_
2,4,6-Trichlorophenol
5,150
5,514
2,394
3,715
11,220
5-6
11/94
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-3. Summary Statistics tor Reported Vapor Pressure Values (25 °C)
Vapor pressure (atm)
Compound
Acenaphthene
Acetone
Aldrin
Anthracene
Arodor 1016
Arodor 1254
Arochlor 1260
Benzene
Benzo(a)anthracene
Benzo(b)fluoranthene
Benzo(a)pyrene
Bis(2)anthracene.
Di-n-butyl phthalate
1 ,2-Dichlorobenzene
1 ,4-Dichlorobenzene
3,3-Dichlorobenzidine
1,1-Dichloroethane
1 ,2-Dichloroethane
1,1-Dichloroethylene
c/s-1 ,2-Dichloroethylene
f/ans-1 ,2-Dichloroethylene
1 ,2-Dichloropropane
1 ,3-Dichloropropene
Dieldrin
Diethyl phthalate
Dimethyl phthalate
2,4-Dinitrotoluene
2,6-Dinitrotoluene
Di-n-octyl phthalate
Endosulfan
Endrin
Ethylbenzene
Geometric
mean
4.93E-06
2.99E-01
2.20E-08
3.35E-08
9.37E-07
1.16E-07
2.18E-08
1.25E-01
2.03E-10
1.06E-10
6.43E-12
1.76E-03
8.49E-09
7.68E-02
7.82E-03
1.58E-08
3.50E-07
4.47E-01
1.48E-01
3.55E-08
1.59E-02
4.10E-02
2.69E-01
1.03E-11
1.14E-09
7.45E-09
5.17E-10
2.70E-14
5.55E-08
1.79E-03
1.39E-03
2.89E-10
3.00E-01
1.07E-01
7.88E-01
2.30E-01
4.63E-01
6.66E-02
4.11E-02
1.31E-09
2.17E-06
1.25E-05
2.29E-07
7.47E-07
5.88E-09
1.31E-08
7.68E-10
1.26E-02
Average
9.35E-06
2.99E-01
3.56E-08
1.16E-07
1.05E-06
1.26E-07
3.95E-08
1.25E-01
2.42E-10
4.56E-10
8.30E-12
1.79E-03
8.49E-09
7.72E-02
7.83E-03
1.63E-08
3.50E-07
4.51 E-01
1.48E-01
1.62E-07
1.59E-02
4.10E-02
2.70E-01
T.48E-11
1.28E-09
8.32E-09
2.23E-09
1.01E-13
6.32E-08
1.81E-03
1.47E-03
3.52E-10
3.00E-01
1.08E-01
7.88E-01
2.34E-01
4.66E-01
6.66E-02
4.11E-02
3.06E-09
2.17E-06
3.70E-05
2.29E-07
7.47E-07
8.84E-09
1.31E-08
2.35E-09
1.26E-02
Standard
deviation
1.42E-05
5.66E-03
2.96E-08
1.55E-07
5.22E-07
5.14E-08
3.62E-08
3.24E-03
1.45E-10
5.00E-10
7.61 E-1 2
3.18E-04
O.OOE+00
8.08E-03
4.61 E-04
3.83E-09
O.OOE+00
5.40E-02
1.63E-03
2.56E-07
1.27E-03
O.OOE+00
2.41 E-02
1.43E-11
6.60E-10
4.04E-09
4.31 E-09
1.09E-13
2.83E-08
1.96E-04
5.01 E-04
2.01 E-1 0
5.40E-03
1.19E-02
7.48E-03
4.32E-02
5.82E-02
1.91E-03
O.OOE+00
3.06E-09
O.OOE+00
3.48E-05
O.OOE+00
O.OOE+00
6.64E-09
O.OOE+00
2.96E-09
2.66E-04
Minimum
2.83E-06
2.88E-01
7.89E-09
7.40E-09
5.26E-07
7.90E-08
1.97E-09
1.17E-01
7.20E-11
4.93E-12
2.15E-12
1.26E-03
8.49E-09
6.58E-02
7.37E-03
1.09E-08
3.50E-07
3.19E-01
1.45E-01
1.12E-08
1.41 E-02
4.10E-02
2.56E-01
5.63E-12
7.80E-10
4.79E-09
1.32E-10
3.65E-15
2.66E-08
1.35E-03
8.90E-04
1.51 E-1 0
2.92E-01
1.01 E-01
7.78E-01
1.73E-01
4.14E-01
6.54E-02
4.11 E-02
1.32E-10
2.17E-06
2.17E-06
2.29E-07
7.47E-07
1.58E-09
1.31E-08
2.63E-10
1.21 E-02
Maximum
4.41 E-05
3.04E-01
7.82E-08
4.14E-07
1.97E-06
1.97E-07
1.18E-07
1.30E-01
5.36E-10
1.15E-09
3.31 E-1 1
2.04E-03
8.49E-09
8.30E-02
8.29E-03
1.90E-08
3.50E-07
4.79E-01
1.50E-01
6.05E-07
1.98E-02
4.10E-02
3.24E-01
3.95E-11
2.21 E-09
1.40E-08
1.19E-08
2.69E-13
9.57E-08
2.05E-03
2.40E-03
5.53E-10
3.08E-01
1.37E-01
7.94E-01
2.66E-01
5.64E-01
6.99E-02
4. 11 E-02
8.05E-09
2.17E-06
7.18E-05
2.29E-07
7.47E-07
1.76E-08
1.31E-08
6.53E-09
1.32E-02
Sample
size
7
6
4
12
5
3
8
12
7
3
13
4
1
3
2
3
1
7
5
4
12
2
6
. 4
3
3
6
4
3
9
10
2
7
7
3
3
4
4
1
6
1
2
1
1
3
1
3
12
(continued)
as.
11/9
I
4
5-7
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-3 (continued)
Vapor pressure (atm)
Compound
Fluoranthene
Fluorene
Heptachtor
Heptachlor epoxide
Hexachlorobenzene
Hexachloro-1 ,3-butadiene
a-HCH (o-BHC)
p-HCH{"p-BHC)
•y-HCH (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
lndeno(1 ,2,3-c,d)pyrene
Isophorone
Methoxychlor
Methyl bromide
Methyl chloride
Methylene chloride
Naphthalene
Nitrobenzene
Pyrene
Styrene
1 ,1 ,2,2-Tetrachloroethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trtchlorobenzene
1,1,1 -Trichloroethane
1 , 1 ,2-Trichloroethane
Tr'ichtoroethylene
Vinyl acetate
Vinyl chloride
Xylenes
lonizable Organics
Benzole Acid
Butanol
p-Chloroaniline
2-Chbrophenol
2,4-Dichlorophenol
2,4-Dimethylphenol
2,4-Dinitrophenol
2-Methylphenol
W-Nrtrosodiphenylamine
W-Nitrosodi-n-propylamine
Pentachlorophenol
Phenol
2,4,5-Trichlorophenol
2,4,6-Trichlorophenol
Geometric
mean
1.07E-08
8.17E-07
4.29E-07
5.71 E-09
1.62E-08
2.33E-04
5.61 E-08
6.45E-10
4.89E-08
9.63E-05
6.21 E-04
1.88E-13
5.38E-04
1.62E-09
2.16E+00
""~5"68E+0
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-4. Air Diffusivity (D,) Values for SSL Chemicals (25 °C)
CAS No.
83-32-9
67-64-1
309-00-2
120-12-7
12674-11-2
11096-82-5
71-43-2
56-55-3
205-99-2
65-85-0
50-32-8
111-44-4
117-81-7
75-27-4
75-25-2
71-36-3
85-68-7
86-74-8
75-15-0
56-23-5
57-74-9
106-47-8
108-90-7
67-66-3
95-57-8
218-01-9
124-48-1
72-54-8
72-55-9
50-29-3
53-70-3
84-74-2
95-50-1
106-46-7
91-94-1
75-34-3
107-06-2
75-35-4
156-59-2
156-60-5
78-87-5
542-75-6
120-83-2
60-57-1
84-66-2
131-11-3
105-67-9
Compound
Acenaphthene
Acetone
Aldrin
Anthracene
Arochlor1016
Arochlor 1260
Benzene
Benzo(a)anthracene
Benzo(fe)fluoranthene
Benzole acid
Benzo(a)pyrene
Bis(2-chloroethyl)ether
Bis(2-ethylhexyl)phthalate
Bromodichloromethane
Bromoform
Butanol
Butyl benzyl phthalate
Carbazole
Carbon bisulfide
Carbon tetrachloride
.Chtordane
p-Chteroaniline
Chlorobenzene
Chloroform
2-Chtorophenol
Chrysene
Chtorodibromomethane
ODD
DDE
DDT
Dibenzo(a,/i)anthracene
Di-n-butyl phthalate
1 ,2-Dichlorobenzene
1 ,4-Dichlorobenzene
3,3-Dichbrobenzidene
1,1-Dichloroethane
1 ,2-Dichloroethane
1 ,1 -Dichloroethylene
c/s-1 ,2-Dichloroethylene
trans-1 ,2-Dichloroethylene
1 ,2-Dichloropropane
1 ,3-Dichloropropene
2,4-Dichlorophenol
Dieldrin
Diethyl phthalate
Dimethyl phthalate
2,4-Dimethylphenol
DJ (cmz/s)
4.21 E-02
1.24E-01
1.32E-02
3.24E-02
2.05E-02
1.27E-02
8.70E-02
5.10E-02
2.26E-02
5.36E-02
4.30E-02
6.92E-02
3.51 E-02
2.98E-02
1.49E-02
8.00E-02
1.72E-02
3.90E-02
1.04E-01
7.80E-02
1.18E-02
4.83E-02
7.30E-02
1.04E-01
5.01 E-02
2.48E-02
2.29E-02
1.56E-02
1.44E-02
1.37E-02
2.00E-02
4.38E-02
6.90E-02
6.90E-02
1.94E-02
7.42E-02
1.04E-01
9.00E-02
7.36E-02
7.07E-02
7.82E-02
6.26E-02
3.46E-02
1.25E-02
2.56E-02
5.68E-02
5.84E-02
CAS No.
51-28-5
121-14-2
606-20-2
117-84-0
115-29-7
72-20-8
100-41-4
206-44-0
86-73-7
76-44-8
1024-57-3
118-74-1
87-68-3
319-84-6
319-85-7
58-89-9
77-47-4
67-72-1
193-39-5
78-59-1
7439-97-6
72-43-5
74-83-9
74-87-3
75-09-2
95-48-7
91-20-3
98-95-3
86-30-6
621-64-7
87-86-5
108-95-2
129-00-0
100-42-5
79-34-5
127-18-4
108-88-3
8001-35-2
120-82-1
71-55-6
79-00-5
79-01-6
95-95-4
88-06-2
108-05-4
75-01-4
1330-20-7
Compound
2,4-Dinrtrophenol
2,4-Dinhrotoluene
2,6-Dinitrotoluene
Di-n-octyl phthalate
Endosutfan
Endrin
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachloro-1 ,3-butadiene
a-HCH (a-BHC)
P-HCH (P-BHC)
Y-HCH (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
lndeno(1 ,2,3-c,d)pyrene
Isophorone
Mercury
Methoxychlor
Methyl bromide
Methyl chloride
Methylene chloride
2-Methylphenol
Naphthalene
Nitrobenzene
/V-Nitrosodiphenylamine
AANitrosodi-n-propylamine
Pentachlorophenol
Phenol
Pyrene
Styrene
1 ,1 ,2,2-Tetrachloroethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trichlorobenzene
1,1,1 -Trichloroethane
1 ,1 ,2-Trichloroethane
Trichloroethylene
2,4,5-TrichIorophenol
2,4,6-Trichlorophenol
Vinyl acetate
Vinyl chloride
Xylene
D; (cm2/s)
2.73E-02
2.03E-01
3.49E-02
1.51 E-02
1.15E-02
1.25E-02
7.50E-02
3.02E-02
3.63E-02
1.12E-02
1.22E-02
5.42E-02
5.61 E-02
1.76E-02
1.76E-02
1.76E-02
1.61 E-02
2.49E-03
1.90E-02
6.23E-02
1.30E-01
1.56E-02
7.28E-02
1.26E-01
1.01E-01
7.40E-02
5.90E-02
7.60E-02
2.93E-02
5.13E-02
5.60E-02
8.20E-02
2.72E-02
7.10E-02
7.10E-02
7.20E-02
8.70E-02
1.16E-02
3.00E-02
7.80E-02
7.80E-02
7.90E-02
2.91 E-02
3.14E-02
8.50E-02
1.06E-01
7.20E-02
CAS = Chemical Abstracts Service.
5-9
-------
Review Draft — Do Not Cite or Quote — December 1994
The SSL equations require the dimensionless form of H, H', which is calculated from H (atm-m3/mol) by
multiplying by 41. The calculated values of H and its dimensionless form (H') for each SSL compound
are shown in Table 5-5. Table 5-6 compares the calculated H values with available measured values,
showing good agreement for most compounds.
5.5 Soil Organic Carbon/Water Partition Coefficients (Koc)
Application of SSLs for the inhalation and migration to ground water pathways requires K^ values for
each organic chemical of concern. K^ values are also needed for site-specific exposure modeling efforts.
An initial review of the literature uncovered significant variability in this parameter, with reported
measured values for a compound sometimes varying over several orders of magnitude. This variability
can be attributed to several factors, including actual variability due to differences in soil or sediment
properties, differences in experimental and analytical approaches used to measure the values, and
experimental or measurement error. To resolve this difficulty, an extensive literature review was
conducted to uncover all available measured values and to identify approaches and information that might
be useful in developing valid K^. values.
The soil-water partitioning behavior of nonionizing and ionizing organic compounds differs because the
partitioning of ionizing organics can be significantly influenced by soil pH. For this reason, different
approaches were required to estimate K^ values for nonionizing and ionizing organic compounds.
5.5.1 l^ for Nonionizing Organic Compounds. For notarizing hydrophobia organic
compounds, an extensive literature search was conducted to collect all available measured K^. values for
the subject organic compounds. To minimize misquotation errors, original references were obtained where
possible. Values from compilations and secondary references were used only when the original references
could not be obtained. Redundancy of values was avoided, although in rare instances it was not possible
to determine if compilations included such values, especially when data were reported as "selected" values.
In certain references, soil-water, partition coefficients (e.g., Kd or 1C) were reported along with the organic
carbon content of the soil. In these cases, K^. was computed by dividing Kd by the fractional soil organic
carbon content (f^., g/g). If the partition coefficient was normalized to soil organic matter (i.e., K^), it
was converted to K,,,. as follows (Dragun, 1988):
KOC - 1-724 Kom (5-2)
where
1.724 = conversion factor from organic matter to organic carbon (fom = 1.724 f^. )
K^ = partition coefficient normalized to organic matter (L/kg)
fgn = fraction organic matter (g/g).
Once collected, K^ values were reviewed. It was not possible to systematically evaluate each source for
accuracy or consistency or to analyze sources of variability between references because of wide variations
in soil and sediment properties, experimental and analytical methods, and the manner in which these were
reported in each reference. This, and the limited number of K^. values for many compounds, prevented
any meaningful statistical analysis to eliminate outliers.
Collected values were qualitatively reviewed, however, and some values were excluded. Values measured
for low-carbon-content sorbents (i.e., f^ < 0.001) are generally beyond the range of the linear relationship
between soil organic carbon and Kj and were rejected in most cases. Some references produced
consistently high or low values and, as a result, were eliminated. The final values used are presented in
RTI (1994) along with their reference sources.
5-10
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-5. Henry's Law Constants - SSL Chemicals (25 °C)
Compound
Acenaphthene
Acetone
Aldrin
Anthracene
Benzene
Benzo(a)anthracene
Benzo(o)fluoranthene
Benzole acid
Benzo(a)pyrene
Bis(2-chloroethyl)ether
Bis(2-ethylhexyl)phthalate
Bromodichloromethane
Bromoform
Butanol
Butyl benzyl phthalate
Carbazole
Carbon disulfide
Carbon tetrachloride
Chlordane
p-Chloroaniline
Chlorobenzene
Chlorodibromomethane
Chloroform
2-Chlorophenol
Chrysene
ODD
ODE
DDT
Dibenzo(a,/7)anthracene
Di-n-butyl phthalate
1 ,2-Dichlorobenzene (o)
1 ,4-Dichtorobenzene (p)
3,3-Dichlorobenzidene
1,1-Dichbroelhane
1 ,2-Dichloroethane
1 , 1 -Dichloroethylene
c/s-1 ,2-Dichloroethylene
frans-1 ,2-Dichloroethylene
1,2-Dichloropropane
1 ,3-Dichtoropropene
2,4-Dichlorophenol
Dieldrin
Diethyl phthalate
2,4-Dimethylphenol
Dimethyl phthalate
2,4-Dinrtrophenol
H
atm-m3/mol
1.84E-04
2.88E-05
1.03E-04
1.11E-04
5.47E-03
3.61 E-06
6.17E-06
3.34E-07
8.36E-07
2.14E-05
8.36E-06
3.17E-03
6.15E-04
8.54E-06
1.91 E-06
8.12E-05
1.27E-02
2.88E-02
6.65E-05
1.17E-06
4.37E-03
2.48E-03
4.02E-03
1.66E-05
1.21 E-06
4.96E-06
1.24E-04
5.37E-05
1.12E-08
1.43E-06
2.10E-03
2.81 E-03
2.08E-08
5.76E-03
1.28E-03
2.54E-02
4.51 E-03
5.59E-03
2.81 E-03
2.95E-03
2.38E-07
2.67E-06
5.47E-07
3.25E-06
5.78E-07
4.84E-09
H'
7.54E-03
1.18E-03
4.22E-03
4.55E-03
2.24E-01
1.48E-04
2.53E-04
1.37E-05
3.43E-05
8.77E-04
3.43E-04
1.30E-01
2.52E-02
3.50E-04
7.83E-05
3.33E-03
5.21 E-01
1.18E+00
2.73E-03
4.80E-05
1.79E-01
1.02E-01
1.65E-01
6.81 E-04
4.96E-05
2.03E-04
5.08E-03
2.20E-03
4.59E-07
5.86E-05
8.61 E-02
1.15E-01
8.53E-07
2.36E-01
5.25E-02
1.04E+00
1.85E-01
2.29E-01
1.15E-01
1.21 E-01
9:76E-06
1.09E-04
2.24E-05
1.33E-04
2.37E-05
1.98E-07
Compound
2,4-Dinitrotoluene
2,6-Dinitrotoluene
Di-n-octyl phthalate
Endosulfan
Endrin
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachtor epoxide
Hexachlorobenzene
Hexachloro-1 ,3-butadiene
a-HCH (a-BHC)
P-HCH (P-BHC)
y-HCH (lindane)
Hexachlorocycbpentadiene
Hexachloroethane
lndeno(1 ,2,3-c,d)pyrene
Isophorone
Mercury
Methoxychlor
Methyl bromide
Methyl chloride
Methylene chloride
2-Methylphenol
Naphthalene
Nitrobenzene
AANitrosodiphenylamine
AANitrosodi-n-propylamine
Pentachlorophenol
Phenol
Pyrene
Styrene
1 ,1 ,2,2-Tetrachtoroethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trichlorobenzene
1,1,1-Trichloroethane
1 ,1 ,2-Trichloroethane
Trichloroethylene
2,4,5-Trichlorophenol
2,4,6-Trichlorophenol
Vinyl acetate
Vinyl chloride
Xylenes (total)
H
atm-m3/mol
1.47E-07
1.30E-07
7.66E-07
2.31 E-05
1.19E-06
7.75E-03
9.33E-06
7.29E-05
5.87E-04
8.29E-06
5.35E-04
2.39E-02
6.79E-06
3.46E-07
3.39E-06
1.72E-02
3.60E-03
4.85E-09
6.19E-06
1.14E-02
6.33E-06
1.42E-02
4.52E-02
2.37E-03
1.64E-06
4.82E-04
2.06E-05
6.97E-04
4.14E-05
1.42E-05
5.95E-07
8.27E-06
3.33E-03
3.72E-04
1.73E-02
6.14E-03
3.36E-06
2.62E-03
1.86E-02
1.00E-03
1.06E-02
4.39E-06
4.06E-06
5.50E-04
8.42E-02
6.04E-03
H'
6.03E-06
5.33E-06
3.14E-05
9.47E-04
4.88E-05
3.18E-01
3.83E-04
2.99E-03
2.41 E-02
3.40E-04
2.19E-02
9.80E-01
2.78E-04
1.42E-05
1.39E-04
7.05E-01
1.48E-01
1.99E-07
2.54E-04
4.67E-01
2.60E-04
5.82E-01
1.85E+00
9.72E-02
6.72E-05
1.98E-02
8.45E-04
2.86E-02
1.70E-03
5:82E-04
2.44E-05
3.39E-04
1.37E-01
1.53E-02
7.09E-01
2.52E-01
1.38E-04
1.07E-01
7.63E-01
4.10E-02
4.35E-01
1.80E-04
1.66E-04
2.26E-02
3.45E+00
2.48E-01
/HT1
11/94
5-11
-------
Review
Table 5-6. Comparison
Draft—Do Not Cite or Quote—December 1994
of Calculated and Measured Henry's Law Constants (25 °C)
Compound
Acenaphthene
Acetone
Anthracene
Benzene
Benzo(a)anthracene
Bromodichloromethane
Bromoform
Carbon tetrachloride
Chtorobenzene
Chtorodibromomethane
Chloroform
1,2-Dichtorobenzene (o)
1,4-Dichlorobenzene (p)
1,1-Dichloroethane
1,2-Dichloroethane
1 ,1-Dichloroethylene
c/s-1 ,2-Dichtoroethylene
f/ans-1 ,2-Dichloroethylene
1 ,2-Dichloropropane
1 ,3-Dichloropropene
Ethylbenzene
Fluorene
Heptachtor epoxide
Hexachlorobenzene
Hexachlorocyctopentadiene
Hexachloroethane
lndeno(1 ,2,3-c,d)pyrene
Methyl chtoride
Methylene chloride
Naphthalene
Pyrene
1 ,1,2,2-Tetrachloroethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trichlorobenzene
1,1,1 -Trichloroethane
1 , 1 ,2-Trichloroethane
Trichloroethylene
Vinyl chtoride
Xylenes (total)
lonizable Organ ics
Butanol
N-Nrtrosodiphenylamine
Calculated H
1.8E-04
2.9E-05
- 1.1E-04
5.5E-03
3.6E-06
3.2E-03
6.1E-04
2.9E-02
4.4E-03
2.5E-03
4.0E-03
2.1E-03
2.8E-03
5.8E-03
1.3E-03
2.5E-02
4.5E-03
5.6E-03
2.8E-03
2.9E-03
7.7E-03
7.3E-05
8.3E-06
5.4E-04
1.7E-02
3.6E-03
4.9E-09
4.5E-02
2.4E-03
4.8E-04
8.3E-06
3.7E-04
1.7E-02
6.1E-03
3.4E-06
2.6E-03
1.9E-02
1.0E-03
1.1E-02
8.4E-02
6.0E-03
8.5E-06
7.0E-04
Geometric
Mean
2.2E-04
3.9E-05
3.5E-05
5.5E-03
8.0E-06
1.8E-03
4.6E-04
2.6E-02
2.0E-03
9.4E-04
4.0E-03
2.0E-03
1.9E-03
5.4E-03
1.4E-03
2.3E-02
4.0E-03
9.4E-03
2.8E-03
2.0E-03
8.1E-03
9.1E-05
1.5E-03
9.8E-04
2.1E-02
8.3E-03
7.0E-08
8.2E-03
2.8E-03
4.6E-04
1.3E-05
2.5E-04
1.5E-02
6.5E-03
3.3E-02
1.7E-03
1.6E-02
9.1E-04
1.2E-02
2.5E-02
6.7E-03
8.8E-06
6.6E-04
Measured H
Minimum
6.4E-05
3.9E-05
1.9E-05
5.3E-03^
8.0E-06
1.6E-03
4.6E-04
1.9E-02
3.8E-04
8.5E-04
2.5E-03
1.6E-03
1.9E-03
4.1E-03
1.4E-03
1.8E-02
3.3E-03
8.0E-03
2.8E-03
1.2E-03
7.9E-03
6.4E-05
1.5E-03
5.3E-04
1.6E-02
8.3E-03
7.0E-08
8.2E-03
2.7E-03
4.4E-04
1.1E-05
2.5E-04
1.1E-02
6.4E-03
4.8E-03
1.5E-03
1.3E-02
9.1E-04
1.0E-02
2.0E-02
4.9E-03
8.8E-06
6.6E-04
Maximum
1.4E-03
3.9E-05
6.5E-05
5.8E-03
8.0E-06
1.9E-03
4.6E-04
3.7E-02
3.8E-03
1.0E-03
9.3E-03
2.4E-03
1.9E-03
6.2E-03
1.4E-03
2.6E-02
4.5E-03
1.1E-02
2.8E-03
3.6E-03
8.4E-03
1.2E-04
1.5E-03
1.3E-03
2.7E-02
8.3E-03
7.0E-08
8.2E-03
3.0E-03
4.8E-04
1.9E-05
2.5E-04
2.2E-02
6.7E-03
8.8E-02
1.9E-03
1.9E-02
9.1E-04
1.5E-02
3.0E-02
7.5E-03
8.8E-06
6.6E-04
Sample
Size
5
1
2
4
1
5
1
9
4
5
12
2
1
5
1
4
4
4
1
2
2
3
1
3
2
1
1
1
2
2
3
1
8
4
. 3
2
5
1
3
5
4
1
1
/ten
11/94
5-12
-------
Review Draft—Do Not Cite or Quote—December 1994
Summary statistics for the measured K^. values are presented in Table 5-7. The geometric mean of the
I^ for each nonionizing organic compound is used as the the central tendency K^. value because it is a
more suitable estimate of the central tendency of a distribution of environmental values with wide
variability.
Regression Analysis. To estimate the K^ of chemicals for which no published values were found,
a linear regression was performed to determine the relationship between K^ and Kow for all nonionizing
organic compounds with measured K^. values. Correlation between K^. and Kow improved considerably
when separate linear regressions were performed on two chemical groups:
• Group 1, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and
phthalates
• Group 2, volatile organic compounds (VOCs) and chlorinated pesticides.
The linear regressions for Groups 1 and 2 are plotted in Figure 5- 1, along with the original regression line
developed using the 30 SSL chemicals in the draft SSL guidance (identified as "old line" in Figure 5-1).
The regression equation for Group 1 (PAHs, PCBs, and phthalates) is:
0.97 log Kow - 0.094 (^ = 0.99) . (5-3)
The regression equation for Group 2 (VOCs and chlorinated pesticides) is:
log K^ = 0.78 log Kow + 0.151 (r2 = 0.98) . (5-4)
Figure 5-2 compares the geometric means of the measured values with the values calculated from the
regressions for each compound included in the regression analysis. Table 5-8 compares the geometric
means of Kow and K^ for each compound summarized with the corresponding K^ calculated from the
appropriate regression. The equation (i.e., Group 1 or Group 2) used to calculate K^ from Kow for
chemicals without measured K^, values also is noted in this table. Although an exceptionally strong
correlation was observed for each compound group, the measured values are recommended for use where
they are available.
The strong linear relationship between K^. and Kow values for specific groups of hydrophobic organic
compounds has been noted by a variety of researchers (e.g., see Lyman et al., 1982). The high correlation
coefficients observed for the two groups of compounds evaluated in this study confirm this linear
relationship. They also provide further evidence of the accuracy of the geometric means of the collected
measured values as central tendency estimates of K^ for nonionizing hydrophobic organic compounds.
5.5.2 l^ for Ionizing Organic Compounds. Sorption models used to describe the behavior
of nonionizing hydrophobic organic compounds in the natural environment are not appropriate for
predicting the partitioning of ionizable organic compounds. Certain organic compounds such as amines,
carboxylic acids, and phenols contain functional groups that ionize under subsurface pH conditions
(Schellenberg et al., 1984). Because the ionized and the neutral species of such compounds have different
sorption coefficients, sorption models based solely on the partitioning of the neutral species may not
accurately predict soil sorption under different pH conditions.
To address this problem, a technique was employed to predict K^ values for the 15 ionizing SSL organic
compounds over the pH range of the subsurface environment. These compounds include:
5-13
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-7. Summary Statistics for Measured K^ Values:
Nonlonizing Organlcs
Compound
Acenaphthene
Aldrin
Anthracene
Arochlor 1016
Arochlor 1242
Arochlor 1254
Benzene
Benzo(a)anthracene
Benzo(a)pyrene
Bis(2-chtorethyl)ether
Bis(2-ethylhexyl)phthalate
Bromoform
Butyl benzyl phthalate
Carbon tetrachloride
Chlordane
Chlorobenzene
Chloroform
DDE
DDT
Dibenzo(a,/7)anthracene
Di-n-butyl phthalate
1,2-Dichlorobenzene (o)
1 ,4-Dichlorobenzene (p)
1,1-Dichtoroethane
1,2-Dichloroethane
1,1-Dichloroethylene
1 ,2-Dichloropropane
1 ,3-Dichloropropene
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Endrin
Ethylbenzene
Fluoranthene
Fluorene
Heptachtor
Hexachlorobenzene
a-HCH (a-BHC)
P-HCH (p-BHC)
y-HCH (lindane)
Methoxychlor
Methyl bromide
Geometric
mean
4,898
48,394
21,222
107,285
48,147
810,071
57
356,938
915,911
76
87,420
126
34,087
164
51,310
204
56
86,405
237,194
1,804,926
1,568
376
516
52
38
65
47
26
10,856
82
46
10,811
221
49,096
7,961
6,810
37,501
1,762
2,275
1,381
77,936
9
Average
5,028
48,394
22,207
118,140
65,391
2,374.385
62
458,795
1,056,733
76
87,420
126
42,675
198
51,797
228
60
86,405
308,883
2,030,256
1,581
384
551
59
44
65
47
26
12,594
83
79
11,422
228
49,433
9,750
6,810
45,809
1,835
2,432
1,499
78,916
10
KocMcg)
Standard
deviation
1,138
0
6,590
47,296
48,764
4,264,933
24
292,749
551,866
0
0
0
25,675
130
7,087
104
20
0
230,054
832,305
195
81
190
30
23
0
0
3
7,091
14
69
3,707
56
5,640
5,500
0
25,500
510
886
602
12,553
0.5
Minimum
3,890
48,394
14,125
54,167
14,791
66,000
26
150,000
478,947
76
87,420
126
17,000
77
44,710
83
28
86,405
63,096
565,014
1,386
310
273
30
22
65
47
23
3,960
69
8
7,724
165
41,687
2,906
6,810
13,694
1,022
1,156
632
63,140
9
Maximum
6,166
48,394
33,884
171,250
151,250
15,918,571
100
840,000
1,800,000
76
87,420
126
68,350
439
58,884
407
81
86,405
897,826
3,059,425
1,775
529
911
100
76
65
47
32
27,399
97
192
15,885
331
54,954
16,218
6,810
80,000
2,891
4,254
2,983
97,724
10
Sample
size
2
1
9
4
8
24
17
4
3
1
1
1
2
5
2
9
6
1
29
15
2
5
17
3
3
. 1
1
4
15
2
4
4
6
3
6
1
4
12
17
59
4
2
(continued)
/Ttn
11/94
5-14
-------
Review Draft—Do Not Cite or Quote—December 1994
Table 5-7 (continued)
Compound
Methyl chloride
Methylene chloride
Naphthalene
Nitrobenzene
Pentachlorobenzene
Pyrene
Stryene
1 ,1,2,2-Tetrachloroethane
Tetrachtoroethylene
Toluene
1 ,2,4-Trichlorobenzene
1,1,1-Trichloroethane
1 ,1 ,2-Trichloroethane
Trichloroethylene
Xytenes (total)
Geometric
mean
6
16
964
131
32,354
68,157
912
79
300
131
1,544
99
76
94
260
Average
6
1,065
156
36,365
70,590
912
79
317
139 "~
1,702
110
76
98
271
K«(L/lcg)
Standard
deviation
0
18
473
87
14,170
20,299
0
0
101
46
712
47
0 '
27
81
Minimum
6
9
414
31
11.381
47,487
912
79
~ "~ii6~"~"
56
700
35
76
48
204
Maximum
6
47
2,400
370
55,176
133,590
912
79
495
247
3,011
179
76
150 .
384
Sample
•ize
1
.„_.._.
32
12
5
29
1
1
ZI?§I"I
17
13
1
25
3
7.00
6.00
5.00 --
4.00
3.00
2.00
1.00 -
0.00 •
- o - Group 1 : tog (^.(O.g?) bg K^-0.094
(^.0.99)
— • - Group 2: tog (^-(0.78) tog K^+0.151
OLD LINE: tog ^-(0.88) tog K^+0.114
(^.96)
0.00
1.00 2.00 3.00 4.00 5.00 6.00
7.00
Figure 5-1.
correlation plot.
5-15
11/94
-------
Review Draft—Do Not Cite or Quote—December 1994
1 ;
: ! :
A*
r\i ..
i j • Q:
. i • •
d«-
! •[...: ...1 T^
• i • f •
ik
5-16
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Review Draft—Do Not Cite or Quote—December 1994
Table 5-8. Comparison of Measured and Calculated K^ Values1
Compound
Acenaphlhene
Acetone
Aldrin
Anthracene
Arochlor1016
Arochlor 1254
Arochlor 1260
Benzene
Benzo(a)anthracene
Benzo(b)fluoranthene
Benzo(a)pyrene
Bis(2-chlorethyl)ether
Bis(2-ethylhexyl)phthalate
Bromodichloromethane
Bromoform
Butanol
Butyl benzyl phthalate
Carbazole
Carbon bisulfide
Carbon tetrachloride
Chlordane
p-Chloroaniline
Chlorobenzene
Chtorodibromomethane
Chloroform
Chrysene
ODD
DDE
DDT
Dibenzo(a,/))anthracene
Di-n-butyl phthalate
1,2-Dichlorobenzene (o)
1,4-Dichlorobenzene (p)
3,3-Dichlorobenzidine
1 ,2-Dichloroethane
1,1-Dichloroethane
1,1-Dichloroethylene
c/s-1 ,2-Dichloroethylene
frans-1 ,2-Dichloroethylene
1 ,2-Dichloropropane
1 ,3-DichIoropropene
Dieldrin
Diethyl phthalate
Dimethyl phthalate
2,4-Dinitrotoluene
Chemical
groupb
1
1
2
1
1
1
1
2
1
1
1
NA
1
2
2
1
1
1
2
2
2
2
2
2
2
1
2
2
2
1
1
2
2
1
2
2
2
2
2
2
2
2
1
1
2
Kow
9,215
1
1,514,459
29,517
253,488
1,611,131
8,341,144
137
477,042
1,592,720
1,346,731
20
160,325
106
224
7
25,874
3,758
100
521
865,813
74
616
154
89
548,238
1,318,796
1,803,999
1,174,230
3,527,582
27,332
2,788
2,584
3,758
29
62
132
48
96
118
56
185,727
218
43
99
Calculated 1C.
(Meg)
5,846
0.46
94,623
18,162
147,410
892,520
4,425,557
66
272,847
882,588
749,569
—
94,361
54
97
5
15,975
2,441
52
187
61,155
41
213
72
47
312,425
84,937
108,469
77,577
1,914,389
16,851
693
653
2,441
20
35
64
29
50
59
33
18,388
152
32
51
Measured K-.
(L/kg)
4,898
—
48,394
21,222
107,285
810,071
—
57
356,938
—
915,911
76
87,420
—
126
—
34,087
—
—
164
51,310
—
204
—
56
—
—
86,405
237,194
1,804,926
1.568°
376
516
—
38
52
65
—
—
47
26
10,856
82
46
._
(continued)
/ttn
11/94
5-17
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Review Draft—Do Not Cite or Quote—December 1994
Table 5-8 (continued)
Compound
2,6-Dinftrotoluene
Di-n-octyl phthalate
Endosulfan
Endrin
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachtor epoxide
Hexachloro-1 ,3-butadiene
Hexachlorobenzene
a-HCH (o-BHC)
P-HCH (P-BHC)
rHCH (lindane)
Hexachlorocyctopentadiene
Hexachloroethane
lndeno(1 ,2,3-c,ef)pyrene
Isophorone
Methoxychtor
Methyl bromide
Methyl chloride
Methylene chloride
W-Nitrosodiphenylamine
A/-Nitrosodi-n-propylamine
Naphthalene
Nitrobenzene
Pentachlorobenzene
Pyrene
Stryene
1 , 1 ,2,2-Tetrachloroethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2,4-Trichlorobenzene
1 , 1 ,2-Trichloroethane
1,1,1 -Trichloroethane
Trichtoroethylene
Vinyl acetate
Vinyl chloride
Xylenes (total)
Chemical
groupb
2
1
2
2
2
1
1
2
2
2
2
2
2
2
2
2
1
2
NA
2
2
2
2
2
1
NA
2
1
1
2
2
2
2
2
2
2
2
2
2
2
Kow
77
2,136,698,525
3,020
77,934
1,326
121,486
14,723
103,513
56,234
53,817
318,224
6,297
6,808
4,947
80,662
9,661
8,223,373
50
33,561
13
8
18
1,064
24
2,356
68
122,334
100,474
849
245
357
465
1,841
9,734
125
264
271
5
14
1,295
Calculated K^
(L/Kg)
42
980,081,531
738
9,335
388
72,025
9,226
11,651
7,236
. 6,992
27,996
1,310
1,392
1,085
9,589
1,829
4,364,700
30
—
11
7
13
327
17
1,549
—
13,274
59,865
573
104
139
171
501
1,840
61
110
112
5
11
381
Measured K__
-------
Review Draft—Do Not Cite or Quote—December 1994
Organic Acids Organic Bases
• Benzole acid • p-Chloroaniline
• 2-Chlorophenol • Af-Nitrosodiphenylamine
• 2,4-Dichlorophenol • Af-Nitrosodi-/z-propylamine.
• 2,4-Dimethylphenol
• 2,4-Dinitrophenol
• 2-Methylphenol
• Pentachlorophenol
• Phenol
• 2,3,4,5-Tetrachlorophenol
• 2,3,4,6-Tetrachlorophenol
• 2,4,5-Trichlorophenol
• 2,4,6-Trichlorophenol.
Estimation of K^. values for these chemicals involves two analyses. First, the extent to which the
compound ionizes under subsurface conditions must be determined to estimate the relative proportion of
neutral and ionized species under the conditions of concern. Second, the K^ values for the neutral and
ionized forms (K^ and K^) must be determined and weighted according to the extent of ionization at
a particular pH to estimate a pH-specific K^ value. For organic acids, the ionized species is an anion (A")
with a lower tendency to sorb to subsurface materials than the neutral species. Therefore, K^ for
organic acids is likely to be less than K^,^. In the case of organic bases, the ionized species is positively
charged (HB4) so that K^ is likely to be greater than K^^.
It should be noted that this approach is based on the assumption that the sorption of ionizing organic
compounds to soil is similar to hydrophobia organic sorption in that the dominant sorbent is soil organic
carbon. Shimizu et al. (1993) demonstrated that, for several "natural solids," pentachlorophenol sorption
correlates more strongly with cation exchange capacity and clay content than with organic carbon content.
This suggests that this organic acid interacts more strongly with soil mineral constituents than organic
carbon. The estimates of K^, developed here may overpredict contaminant mobility because they ignore
potential sorption to soil components other than organic carbon.
Extent Of Ionization. The sorption potential of ionized and neutral species differs because most
subsurface solids (i.e., soil and aquifer materials) have a negative net surface charge. Therefore, positively
charged chemicals have a greater tendency to sorb than neutral forms and neutral species sorb more readily
than negatively charged forms. Thus, predictions for the total sorption of any ionizable organic compound
must consider the extent to which it ionizes over the range of subsurface pH conditions of interest.
Consistent with the U.S. Environmental Protection Agency/Office of Solid Waste (EPA/OSW) Hazardous
Waste Identification Rule (HWIR) proposal (U.S. EPA, 1992a), the 7.5th, 50th, and 92.5th percentiles (i.e.,
pH values of 4.9, 6.8, and 8.0) for 24,921 field-measured ground water pH values in the U.S. EPA
STORET database are defined as the pH conditions of interest for SSL development.
The extent of ionization can be viewed as the fraction of neutral species present that, for organic acids,
can be determined from the following pH-dependent relationship (Lee et al., 1990):
>nKld - i^J = (1 » 10>- •—) (5-5)
([HA] * [A-])
5-19
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Review Draft—Do Not Cite or Quote—December 1994
where
<&n,acid = fraction of neutral species present for organic acids
[HA] = equilibrium concentration of organic acid (mol/L)
[A"] = equilibrium concentration of anion (mol/L)
pKa = acid dissociation constant
Using Equation 5-5, one can show that, at pH values more than 1.5 units less than the pKa, the neutral
species predominates, and at pH values more than 1.5 log units above the pKa, the ionized species
predominates. At pH near the pKa, a mixed system of both neutral and ionizing components occurs.
The fraction of neutral species for organic bases is defined by:
( [B°] * [HB *])
where
^n,base = fraction of neutral species present for organic bases (-)
[B°] = equilibrium concentration of neutral organic base (mol/L)
[HB+] = equilibrium concentration of ionized species (mol/L).
As with organic acids, pH conditions will determine the relative amounts of neutral and ionized species.
However, unlike organic acids, the neutral species will predominate at high pH, with ionized species more
prevalent under low pH conditions. For the SSL organic bases, A^nitrosodi-n-propylamine and
AT-nitrosodiphenylamine have very low pKa values and the neutral species are expected to prevail under
environmental pH conditions. The pKa for p-chloroaniline, however, is 4.15 and, at low subsurface pH
conditions (i.e., pH = 4.9), roughly 15 percent of the compound will be present as the less mobile ionized
species.
Table 5-9 presents pKa values and fraction neutral species present over the ground water pH range for the
SSL ionizing organic compounds. This table shows that ionized species are significant for only some of
the constituents under normal subsurface pH conditions. The pKa values for phenol, 2-methylphenol, and
2,4-dimethylphenol are 10 or greater. Hence, the neutral species of these compounds predominates under
typical subsurface conditions (i.e., pH = 4.9 to 8). The pKa value for 2,4-dinitrophenol is less than 4 and
the ionized species of this compound predominates under subsurface conditions. However, the pKas for
2-chlorophenol, 2,4-dichlorophenol, 2,4,5-trichlorophenol, 2,4,6-trichlorophenol, 2,3,4,5-tetrachlorophenol,
2,3,4,6-tetrachlorophenol, pentachlorophenol, and benzole acid fall within the range of environmentally
significant pH conditions. Mixed systems consisting of both the neutral and the ionized species will
prevail under such conditions with both species contributing to total sorption.
Prediction of Soil-Water Partition Coefficients. Lee et ai. (1990) developed a relationship
from thermodynamic equilibrium considerations to predict the total sorption of an ionizable organic
compound from the partitioning of its ionized and neutral forms:
where
= soil organic carbon/water partition coefficient (L/kg)
= partition coefficient for the neutral species (L/kg)
5-20
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Review Draft—Do Not Cite or Quote—December 1994
Table 5-9. Degree of lonization (Fraction of Neutral Species,
as a Function of pH
Compound
Benzole acid
p-Chloroanilinea
2-Chlorophenol
2,4-Dichlorophenol
2,4-Dimethylphenol
2,4-Dinitrophenol
2-Methylphenol
/V-Nitrosodiphenylaminea
A/-Nitrosodi-n-propylaminea
Pentachlorophenol
Phenol
2,3,4,5-Tetrachlorophenol
2,3,4,6-Tetrachlorophenol
2,4,5-Trichlorophenol
2,4,6-Trichlorophenol
pKa
4.20 .">
4.15
8.55
7.85
10.1
3.94
10.3
<0
< 1
4.74
10.0
6.35
5.4
6.94
6.15
pH = 4.9
0.17
0.85
1.00
1.00
1.00
0.10
1.00
1.00
1.00
0.41
1.00
0.97
0.76
0.99
0.95
pH = 6.8
0.003
1.00
0.98
0.92
1.00
0.001
1.00
1.00
1.00
0.01
1.00
0.26
0.04
0.58
0.18
pH = 8.0
0.00
1.00
0.78
0.42
0.99
0.001
1.00
1.00
1.00
0.001
0.99
0.02
0.003
0.08
0.01
a Denotes that the compound is an organic base.
<£n = fraction of neutral species present for acids or bases
KOCJ = partition coefficient for the ionized species (L/kg).
This relationship defines the total sorption coefficient for any ionizing compound as the sum of the
weighted individual sorption coefficients for the ionized and neutral species at a given pH. Lee et al.
(1990) verified that this relationship adequately predicts laboratory-measured K^ values for
pentachlorophenol.
A literature review was conducted to compile the pKa and the laboratory-measured values of K^^ and
K^ shown in Table 5-10. Data collected during this review are presented in RTI (1994), along with the
references reviewed. Sorption coefficients for both neutral and ionized species were reported for only 4
of the 15 ionizable organic compounds of interest Sorption coefficients reported for the remaining
compounds were generally K^, and estimates of K^ were necessary to predict the compound's total
sorptioa The methods for estimating K^ for organic acids and organic bases are discussed separately
in the following subsections.
Organic Acids. Sorption coefficients for both the neutral and ionized species have been reported for
two chlorophenolic compounds: 2,4,6-trichlorophenol and pentachlorophenol. For 2,4,5-trichlorophenol
and 2,3,4,5-tetrachlorophenol, soil-water partitioning coefficient (KJ data in the literature were adequate
to allow calculation of K^ from IL, and soil f^. (Lee et al., 1991). From these measured values, the
5-21
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Review Draft—Do Not Cite or Quote—December 1994
Table 5-10. Soil Organic Carbon/Water Partition Coefficients and pKa Values for
15 Ionizing Organic Compounds
Compound
Benzoic acid
p-Chloroaniline
2-Chlorophenol
2,4-Dichlorophenol
2,4-Dimethylphenol
2,4-Din'rtrophenol
2-Methylphenol
/V-Nftrosodiphenylamine
/V-Nitrosodi-n-propylamine
Pentachlorophenol
Phenol
2,3,4,5-Tetrachlorophenol
2,3,4,6-Tetrachlorophenol
2,4,5-Trichlorophenol
2,4,6-Trichlorophenol
KOC n (L/kg)
32a .
41C
398*
159e
126°
0.8f
54e
327°
17°
19.9539
22h
17,916'
6,190k
2,380k
1,070k
KOC.I c-fcg)
0.5b
41a
6.0"
2-4b
1.89b
0.01b
6.8b
327d
17"
3989
0.3b
67'
100b
36'
107"1
pKa
4.20
4.15
8.55
7.85
10.1
3.94
10.3
<0
< 1
4.74
10.0
6.35
5.4
6.94
6.15
a Meylan et al. (1992).
b Estimate based on the ratio of Koci/Kocn for compounds for which data exist; Kocj was estimated to be
. 0.015%^
Calculated as log K,,c = 0.78 log K^ + 0.151 from linear regression of the geometric areas of K^. and
for VOCs and chlorinated pesticides (r2 B 0.98) from this work.
d Since the pKas for the organic bases are low (< 4.15), the neutral species is expected to predominate.
Therefore, no attempt was made to develop an estimate for K^, and K^ was assumed to equal
e Calculated using data (Kp = 0.62, foc = 0.0039) contained in Lee et al. (1991); agrees well with Boyd
(1982) reporting measured K^ = 126 L/kg.
f Kolligetal. (1993).
9 Lee etal. (1990).
h Hodson and Williams (1988).
[ Average of values reported for two aquifer materials from Schellenberg et al. (1984).
Calculated using data (1C = 0.26, foc = 0.0039) contained in Lee et al. (1991).
K Schellenberg et al. (1984).
' Calculated using data (1C, = 0.14, foc = 0.0039) contained in Lee et al. (1991).
m Kukowski (1989).
ratios of K^ to K^ are: 0.1 (2,4,6-trichlorophenol), 0.02 (pentachlorophenol), 0.015 (2,4,5-
trichlorophenol), and 0.051 (2,3,4,5-tetrachlorophenol). A ratio of 0.015 (1.5 percent) was selected as a
conservative value to estimate K^ for the remaining phenolic compounds and benzoic acid.
Organic Bases. No measured soiption coefficients for either the neutral or the ionized species were
found for the three organic bases of interest (N-nitrosodi-n-propylamine, Af-nitrosodiphenylamine, and
p-chloroaniline). Generally, the sorption of ionizable organic bases has not been as well investigated as
that of the organic acids, and there has been no relationship developed between the sorption coefficients
of the neutral and ionized species. EPA is currently initiating research on models for predicting the
soiption of organic bases in the subsurface.
5-22
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Review Draft— Do Not Cite or Quote— December 1994
for N-nitrosodi-/i-propylamine, AT-nitrosodiphenylamine, and p-chloroaniline were estimated from
octanol-water partitioning coefficients (K^) using Equation 5-4 developed for VOCs and chlorinated
pesticides (Section 5.5.1). K^ was assumed to be equal to K^ for organic bases. Because K^ is
expected to be significantly greater than K^.^, for these compounds, this assumption is conservative.
Partition coefficients for the neutral and ionized species (K^.^ and K^ ^, respectively) and pKa values for
the 15 ionizable compounds are provided in Table 5-10. These parameters can be used in the above
equations to compute K^. for organic acids at any given pH. K^ values for each ionizable organic acid
compound of interest are presented in Table 5-11 for pHs of 4.9, 6.8, and 8.0. Appendix J contains pH-
specific KOJ. values for ionizable organics over this entire pH range.
5.6 Soil-Water Distribution Coefficients (Kj) for Inorganic Constituents
As with organic chemicals, development of SSLs for inorganic chemicals (i.e., toxic metals) requires a
soil-water partition coefficient (K,,) for each constituent However, the simple relationship between soil
organic carbon content and sorption observed for organic chemicals does not apply to inorganic
constituents. The soil-water distribution coefficient (Kj) for metals and other inorganic compounds is
affected by numerous geochemical parameters and processes, including pH; sorption to clays, organic
matter, iron oxides, and other soil constituents; oxidation/reduction conditions; major ion chemistry; and
the chemical form of the metal. The number of significant influencing parameters, their variability in the
field, and differences in experimental methods result in as much as seven orders of magnitude variability
Table 5-11. Predicted Soil Organic Carbon/Water Partition Coefficients (K^, L/kg)
as a Function of pH: Ionizing Organics
Compound
Benzole acid
p-Chloroaniline
2-Chlorophenol
2,4-Dichlorophenol
2,4-Dimethylphenol
2,4-Dinrtrophenol
2-Methylphenol
/V-Nitrosodiphenylamine
AWMitrosodi-n-propylamine
Pentachlorophenol
Phenol
2,3,4,5-Tetrachlorophenol
2,3,4,6-Tetrachlorophenol
2,4,5-Trichlorophenol
2,4,6-Trichlorophenol
pH = 4.9
5.7
41
398
159
126
0.09
54
327
17
8,395
22
17,304
4,727
2,359
1,019
pH = 6.8
0.6
41
391
146
126
0.01
54
327
17
567
22
4,742
333
1,395
283
pH = 8.0
0.5
41
312
67
125
0.01
54
327
17
409
22
458
115
224
120
5-23
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Review Draft—Do Not Cite or Quote—December 1994
in measured metal Kd values reported in the literature (Table 5-12). This variability makes it much more
difficult to derive generic Kd values for metals than for organics.
Because of their great variability and a limited number of data points, no meaningful estimate of central
tendency Kd values for metals could be derived from available measured values. For this reason, an
equilibrium geochemical speciation model (MINTEQ) was selected as the best approach for estimating
Kd values for the variety of environmental conditions expected to be present at Superfund sites.
Table 5-12. Summary of Collected Kj Values Reported in Literature
AECL
(1990)a
Metal
Range
Arsenic (+3)
Arsenic (+2)
Arsenic'
Barium
Beryllium
Cadmium
250-3,000
2.7-17,000
Chromium (+2)
Chromium (+3)
Chromium (+6)
-
—
Copper
Mercury*
Nickel
Selenium
60-4,700
150-1,800
Thallium
Zinc
0.1-100,000
Baes
Average6
3.3
6.7
and Sharp (1983)b
Range
1.0-8.3
1.9-18
No. Values
19
37
—
-
„
6.7
2,200
1.26-26.8
470-150,000
28
15
_
37
22
1.2-1,800
1.4-333
18
55
-
"
..
-
16
0.1-8.000
146
Coughtrey
etal.
(1985)°
Range
~
—
—
~
32-50
--
~
—
-
—
-20
<9
-
>20
Battelle
(1989)d
Range
—
5.86-19.4
530-16,000
70-8,000
14.9-567
--
168-3,600
16.8-360
41 .9-336
322-5,280
12.2-650
5.9-14.9
0.0-0.8
_
a The Atomic Energy of Canada, Limited (AECL, 1990) presents the distribution of Kd values according to
four major soil types—sand, silt, clay, and organic material. Their data were obtained from available
literature.
b Baes and Sharp (1983) present Kd values for approximately 220 agricultural soils in the pH range of 4.5 to
9. Their data were derived from available literature and represent a diverse mixture of soils, extracting
solutions, and laboratory techniques.
0 Coughtrey et al. (1985) report best estimates and ranges of measured soil Kd values for a limited number
of metals.
d Battelle Memorial Institute (Battelle, 1989) reports a range in Kd values as a function of pH (5 to 9) and
sorbent content (a combination of clay, aluminum and iron oxyhydroxides, and organic matter content).
The sorbent content ranges were <10 percent, 10 to 30 percent, and >30 percent sorbent. Their data were
based on available literature.
6 Represents the median of the logarithms of the observed values.
' The valence of these metals is not reported in the documents.
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This approach and model were also used by EPA/OSW to estimate generic Kd values for metals proposed
for use in the HWIR proposal (U.S. EPA, 1992a). The HWIR MINTEQA2 analyses were conducted
under a variety of geochemical conditions and metal concentrations representative of solid waste landfills
across the Nation. The metal Kj values developed for this effort were reviewed for SSL application and
were used as preliminary values to develop the September 1993 draft SSLs.
Upon further review of the HWIR MINTEQ modeling effort, OERR decided it was necessary to conduct
a separate MINTEQ modeling effort to develop metal Kj values for SSL application. Reasons for this
decision include the following:
• It was necessary to expand the modeling effort to include other metal contaminants likely to
be encountered at Superfund sites (i.e., beryllium, copper, and zinc).
• HWIR work incorporated low, medium, and high concentrations of dissolved organic acids that
are present in municipal solid waste (MSW) leachate. These organic acids are not expected
to exist in high concentrations in pore waters underlying Superfund sites; therefore, their
inclusion in the Superfund contaminated soil scenario is not warranted.
• The HWIR modeling simulations for chromium (+3) were found to be in error. This error has
been corrected in subsequent HWIR modeling work but corrected results were not available
at the time of preliminary SSL development
For these reasons, a MINTEQ modeling effort was expanded to develop a series of metal-specific
isotherms for several of the metals expected to be present in soils underlying Superfund sites. The model
used was an updated version of MINTEQA2 obtained from Allison Geoscience Consultants, Inc. The
following text describes the important assumptions and limitations of this modeling effort; more detail is
provided in RTI (1994).
5.6.1 Modeling Scope and Approach. New MINTEQA2 modeling runs were conducted to
develop sorption isotherms for arsenic (+5), barium, beryllium, cadmium, chromium (+3), copper, nickel,
and zinc. The general approach and input values used for pH, iron oxide (FeOx) concentration, organic
matter concentration, and background chemistry were unchanged from the HWIR modeling effort (U.S.
EPA, 1992a).
The HWIR MINTEQA2 analyses were conducted under a variety of geochemical conditions and metal
concentrations. Three types of parameters were identified as part of the chemical speciation modeling
effort: (1) parameters that have a direct first-order impact on metal speciation and are characterized by
a wide range in environmental variability; (2) parameters that have an indirect, generally less pronounced
effect on metal speciation and are characterized by a relatively small or insignificant environmental
variability; and (3) parameters that may have a direct first-order impact on metal speciation but neither
the natural variability nor its significance is known.
In the HWIR modeling effort, parameters of the first type ("master variables") were limited to those
having a significant effect on model results, including pH, concentration of available amorphous iron oxide
adsorption sites (i.e., FeOx content), concentration of solid organic matter adsorption sites (with a
dependent concentration of dissolved natural organic matter), and concentration of leachate organic acids
expected to be present in MSW leachate. High, medium, and low values were assigned to each of the
master variables to account for their natural environmental variability. The SSL modeling effort used this
same approach and inputs except that anthropogenic organic acids were not included in the model
simulations.
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Parameters of the second type constitute the background pore-water chemistry, which consists of chemical
constituents commonly occurring in ground water at concentrations great enough to affect metal speciation.
These constituents were treated as constants in both the SSL and HWIR effort The third type of
parameter was entirely omitted from consideration in the modeling effort in both efforts due to poorly
understood geochemistry and the lack of reliable thermodynamic data. The most important of these
parameters is the oxidation-reduction (redox) potential. To compensate, both modeling efforts incorporated
an approach that was most protective of the environment with respect to the impact of redox potential on
the partitioning of redox-sensitive metals (i.e., each metal was modeled in the oxidation state that most
enhances metal mobility).
For the HWIR modeling effort, metal concentrations were varied from the maximum contaminant level
(MCL) to 1,000 times the MCL for each individual metal. This approach also was taken for SSL
modeling, although for certain metals the concentration range was extended to determine the metal
concentration at which the sorption isotherm departed from linearity.
Sorption isotherms for arsenic (+3), chromium (+6), selenium, and thallium are unchanged from the
previous effort and are based on laboratory-derived pH-dependent sorption relationships developed for
HWIR. Using these relationships, the Kj distribution as a function of pH is presented for each of these
four metals in Figure 5-3.
Sorption isotherms for mercury (+2) could not be estimated from MINTEQA2 because the thermodynamic
database currently does not include the required reactions and associated equilibrium constants for mercury.
species. Kj values developed in U.S. EPA (1992a) from a preliminary mercury database were used for
SSL application. These mercury values have been checked against measured Kd values and show
reasonable agreement (Allison, 1993).
5.6.2 Input Parameters. Table 5-13 lists high, medium, and low values for pH, iron oxide, and
natural organic matter used for both the HWIR and SSL MINTEQ modeling efforts. Sources for these
values are as follows (U.S. EPA, 1992a):
• Values for pH were obtained from analysis of 24,921 field-measured pH values contained in
the EPA STORET database. The pH values of 4.9,6.8, and 8.0 correspond to the 7.5th, 50th,
and 92.5th percentiles of the distribution.
• Iron oxide contents were based on analysis of six aquifer samples collected over a wide
geographic area, including Florida, New Jersey, Oregon, Texas, Utah, and Wisconsin. The
lowest of the six analyses was taken to be the low value, the average of the six was used as
the medium value, and the highest was taken as the high value.
• Organic matter concentration was based on the distribution of solid organic matter in the
unsaturated zone assuming a silt loam soil type. The distribution of solid organic matter was
from a distribution developed for EPA/OSW's Composite Model for Landfills (EPACML).
The values used in the modeling effort correspond to the 7.5th, 50th, and 92.5th percentiles
of the distribution. Corresponding dissolved organic matter concentrations were obtained by
calculating the ratio of the dissolved organic matter concentration to the solid organic matter
concentration in the saturated zone and applying this ratio to the unsaturated zone.
The development of the values presented in Table 5-13 is described in more detail in U.S. EPA (1992a).
Thirteen chemical constituents commonly occurring in ground water were used to define the background
pore-water chemistry for HWIR and SSL modeling efforts (Table 5-14). Because these constituents were
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2.5
2 -
~ 1.5
* ,
0.5 -
4.5
5.5
6.5
pH
7.5
8.5
Relationships:
Arsenic (+3)
Chromium (+6)
Selenium
Thallium
log K,, = 0.0322 pH + 1.24
log Kj - -0.177 pH + 2.07
log Kd . -0.296 pH + 2.71
logKd-0.110pH+ 1.102
I
I
o
3
i
o
3
5
Figure 5-3. Empirical pH-dependent adsorption relationship: arsenic (+3), chromium (46), selenium, thallium.
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Review Draft—Do Not Cite or Quote—December 1994
Table 5-13. Summary of Geochemical Parameters Used in
the SSL MINTEQ Modeling Effort
Organic matter concentration
(weight percent)
Value
Low
Medium
High
pH
4.9 .
6.8
8.0
Dissolved
organic matter
0.02
0.06
0.17
Solid
organic matter
0.03
0.11
0.32
Iron oxide content
(weight percent)
0.01
0.31
1.11
Table 5-14. Background Pore-Water Chemistry Assumed
for the SSL MINTEQ Modeling Effort
Parameter
Aluminum
Bromine
Calcium
Carbonate
Chlorine
Iron (+3)
Magnesium
Manganese (+2)
Nitrate
Phosphate
Potassium
Sodium
Sulfate
Concentration (mg/L)
0.2
0.3
48
187
15
0.2
14
0.04
1
0.09
29
22
25
treated as constants, a single total ion concentration, corresponding to the median total metal concentration
from a probability distribution obtained from the STORET database, was assigned to each of the
background pore-water constituents (U.S. EPA, 1992a).
5.6.3. Assumptions and Limitations. The SSL MINTEQ modeling effort incorporates several
basic simplifying assumptions. In addition, the applicability and accuracy of the model results are subject
to limitations. Some of the more significant assumptions and limitations are described below.
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• The system is assumed to be at equilibrium. This assumption is inherent in geochemical
aqueous speciation models because the fundamental equations of mass action and mass balance
are equilibrium based. Therefore, any possible influence of adsorption (or desorption) rate
limits is not considered.
This assumption is conservative. Because the model is being used to simulate metal desorption
from the solid substrate, if equilibrium conditions are not met, the desorption reaction will be
incomplete and the metal concentration in pore water will be less than predicted by the model.
• Redox potential is not considered. The redox potential of the system is not considered due
to the difficulty in obtaining reliable field measurements of Eh, which are needed to determine
a realistic frequency distribution of this parameter. Furthermore, the geochemistry of
redox-sensitive species is poorly understood. Reactions involving redox species are often
biologically mediated and the concentrations of redox species are not as likely to reflect
thermodynamic equilibrium as other inorganic constituents.
To provide a conservative estimate of metal mobility, all environmentally viable oxidation
states are modeled separately for the redox-sensitive metals; the most conservative was selected
for defining SSL metal K,, values. The redox-sensitive constituents that comprise the
background chemistry are represented only by the oxidation state that most enhances metal
mobility (U.S. EPA, 1992a).
• Potential sorbent surfaces are limited. Only metal adsorption to FeOx and solid organic
matter is considered in the system. It is recognized that numerous, other natural sorbents exist
(e.g., clay and carbonate minerals); however, thermodynamic databases describing metal
adsorption to these surfaces are not available and the potential for adsorption to such surfaces
is not considered. This assumption is conservative and will underpredict sorption for soils with
significant amounts of such sorption sites.
• The available thermodynamic database is limiting. As metal behavior increases in
complexity, thermodynamic data become more rare. The lack of complete thermodynamic data
requires simplification to the defined system. This simplification may be conservative or
nonconservative in terms of metal mobility.
• Metal competition is not considered. Model simulations were performed for systems
comprised of only one metal (i.e., the potential for competition between multiple metals for
available sorbent surface sites was not considered). Generally, the competition of multiple
metals for available sorption sites results in higher dissolved metal concentrations than would
exist in the absence of competition. Consequently, this assumption is nonconservative, but is
significant only at metal concentrations much higher than the SSLs.
Other assumptions and limitations associated with this modeling effort are discussed in RTI (1994).
5.6.4 Results and Discussion. MINTEQ model results indicate that metal mobility is most
affected by changes in pH. Based on this observation and because iron oxide content is not routinely
measured in site characterization efforts, pH-dependentKjS for metals were developed for SSL application
by fixing iron oxide and organic carbon at their medium values. For arsenic (+3), chromium (+6),
selenium, and thallium, the empirical pH-dependent KjS were used. MINTEQ-derived Kd values for
arsenic (+5) and chromium (+3) were not used because the empirically developed values for these
elements were significantly lower. Because it is difficult to accurately speciate these metals during soil
analysis, the more conservative empirically derived values were used.
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Table 5-15 shows the SSL Kg values at high, medium, and low subsurface pH conditions. Figure 5-4
plots MINTEQ-derived metal Kj values over this pH range. Figure 5-1 shows the same for the
empirically derived metal Kds. These results are discussed below by metal and compared with measured
values. See RTI (1994) for more information.
Arsenic. Kd values developed using the empirical equation for arsenic (+3) range from 25 to 31 L/kg
for pH values of 4.9 to 8.0, respectively. These values correlate fairly well with the range of measured
values reported by Baes and Sharp (1983)—1.0 to 8.3 L/kg—and Battelle (1989)—5.86 to 19.4 L/kg.
Barium. For ground water pH conditions, MINTEQ-estimated Kj values for barium range from 0.6 to
17 L/kg. Battelle (1989) reports a range in Kj values for barium from 530 to 16,000 L/kg for a pH range
of 5 to 9. Thus, the predicted Kd values for barium are several orders of magnitude less than the
measured values, possibly due to the lower soiptive potential of iron oxide, used as the modeled sorbent,
relative to clay, a sorbent present in the experimental systems reported by Battelle (1989).
Beryllium. The Kj values estimated for beryllium range from 2.8 to 466,000 L/kg for the conditions
studied. AECL (1990) reports medians of observed values for Kd ranged from 250 L/kg for sand to 3,000
L/kg for organic material. Battelle (1989) reports a range of Kj values from 70 L/kg for sand to 8,000
L/kg for clay. MINTEQ results for medium ground water pH (i.e., a value of 6.8) yields a Kd value of
4,600 L/kg. Hence, there is reasonable agreement between MINTEQ-predicted and measured Kj values
for beryllium.
Table 5-15. Estimated Metal Kd Values for SSL Application
Estimated K,, (L/kg)
Metal
Arsenic (+3)a
Barium
Beryllium
Cadmium
Chromium (+6)a
Copper
Mercury (+2)
Nickel
Selenium3
Thallium3
pH = 4.9
25
0.6
2.8
0.9
31
40
0.1
0.9
18
44
pH = 6.8
29
1.4
4,600
120
19
10,000
145
21
5.0
71
pH = 8.0
31
17
466,000
4,500
14
27,500
209
144
2.2
96
Zinc 1.6 420 16,000
a Determined using an empirical pH-dependent relationship.
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Copper (II
Zinc (II)
4.5
5.5
6.5
PH
7.5
8.5
-1
4.5
5.5
6.5
PH
7.5
8.5
Note: Conditions depicted are medium iron oxide content (0.31 wt. %) and medium paniculate
organic matter (0.1 wt. %).
Figure 5-4. Metal Kd as a function of pH.
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Cadmium. For the three pH conditions, MINTEQ Kj values for cadmium range from 0.9 to 4,500
L/kg. The range in experimentally determined Kd values for cadmium is as follows: 1.26 to 26.8 L/kg
(Baes and Sharp, 1983), 32 to 50 L/kg (Coughtrey et al., 1985), 14.9 to 567 L/kg (Battelle, 1989), and
2.7 to 17,000 L/kg (AECL, 1990). Thus the MINTEQ estimates are generally within the range of
measured values.
Chromium (+6). Chromium (+6) Kj values estimated using the empirical pH-dependent adsorption
relationship range from 31 to 14 L/kg for pH values of 4.9 to 8.0. Baes and Sharp (1993) report a range
of 1.2 to 1,800 L/kg and Battelle (1989) reports a range of 16.8 to 360 L/kg for chromium (+6). The
predicted chromium (+6) Kd values thus generally agree with the lower end of the range of measured
values. These values represent conservative estimates of mobility for the more toxic of the chromium
species.
Copper. MINTEQ-estimated Kd values for copper range from 40 to 27,500 L/kg. Baes and Sharp
(1983) report a range of 1.4 to 333 L/kg and BatteUe (1989) reports a range of 41.9 to 336 L/kg for
copper. Thus, the MINTEQ-predicted Kd values corresponding to conditions of low pH agree fairly well
with the measured values reported in the literature.
Mercury (+2). The MINTEQ Kd values are those computed for use in the finite source methodology
for metals (U.S. EPA, 1992a) and thus incorporate mercury reactions with low concentrations of
anthropogenic dissolved organic acids. This is conservative because the organic acids have the potential
to increase mercury mobility. Furthermore, preliminary sensitivity analyses indicated that the influence
of the organic acids in metal sorption was negligible relative to that of pH, organic matter content, and
soil iron oxide content SSL mercury Kj values presented range from 0.1 to 209 L/kg. Because these
estimates were based on a limited thermodynamic database and do not consider other mercury oxidation
states, they were the subject of some uncertainty. However, Allison (1993) found reasonable agreement
between model-predicted values and the measured values (322 to 5,280 L/kg) reported in Battelle (1989),
given the uncertainty associated with laboratory measurements and model precision.
Nickel. MINTEQ-estimated Kd values for nickel range from 0.9 to 144 L/kg. These values agree well
with measured values of 20 L/kg (mean) and 12.2 to 650 L/kg, reported by Coughtrey et al. (1989) and
Battelle (1989), respectively. However, the predicted values are at the low end of the range reported by
the AECL (1990)—60 to 4,700 L/kg.
Selenium. Empirically derived Kj values for selenium range from 2.2 to 18 L/kg for pH values of 8.0
to 4.9. The range in experimentally determined Kd values for selenium is as follows: less than 9 L/kg
(Coughtrey et al., 1985), 5.9 to 14.9 L/kg (BatteUe, 1989), and 150 to 1,800 L/kg (AECL, 1990).
Although they are significantly below the values presented by the AECL (1990), the MINTEQ-predicted
Kd values correlate well with the values reported by Coughtrey et al. (1985) and Battelle (1989).
Thallium. Empirically derived K,, values for thallium range from 44 to 96 L/kg for pH values of 4.9
to 8.0. Generally, these values are about an order of magnitude greater than those reported by Battelle
(1989>-0.0 to 0.8 L/kg.
Zinc. MINTEQ-estimated Kd values for zinc range from 1.6 to 16,000 L/kg. These estimated Kd values
are within the range of measured Kj values reported by the AECL (1990) (0.1 to 100,000 L/kg) and Baes
and Sharp (1983) (0.1 to 8,000 L/kg). Coughtrey et al. (1985) reported Kd values for zinc of greater than
or equal to 20 L/kg.
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Technical Background Document for
Soil Screening Guidance
Part 6: REFERENCES
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U.S. EPA (Environmental Protection Agency). 1989c. Methods for Evaluating the Attainment of Soil
Cleanup Standards. Volume 1: Soils and Solid Media. EPA 230/02-89-042. Statistical Policy
Branch, Office of Policy, Planning and Evaluation, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1989d. Risk Assessment Guidance for Superfund, Volume
1: Human Health Evaluation Manual, Part A, Interim Final. EPA/540/1-89/002. Publication
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with PCB Contamination. EPA 540G-90/007. Office of Emergency and Remedial Response,
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Water Remediation Options. Directive 9283.1-03. Office of Emergency and Remedial Response,
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Rationale for Analysis of Contaminant Release by the Environmental Engineering Committee.
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Response, Washington, DC. NTIS PB92-963333.
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Remedy Selection Decisions. Publication 9355.0-30. NTIS PB91-921359/CCE. Office of
Emergency and Remedial Response, Washington, DC.
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Methodology for Wastes Containing Metals. HWEP-S0040. Office of Solid Waste, Washington,
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Remedial Response, Washington, DC. NTIS PB91-238584/CCE.
U.S. EPA (Environmental Protection Agency). 1992c. Dermal Exposure Assessment Principles and
Applications. Interim Report. EPA/600/8-91/01 IB. Office of Research and Development,
Cincinnati, OH.
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(Part A). Publication 9285.7-09A. Office of Emergency and Remedial Response, Washington,
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Concentration Term, Volume 1, Number 1. Publication 9285.7-081. Office of Emergency and
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of Sewage Sludge. Volumes 1 and 2. EPA 822/R-93-001A.B. Office of Water, Washington, DC.
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Model for Leachate Migration with Transformation Products, EPACMTP. Office of Solid Waste,
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Reference Fact Sheet Office of Emergency and Remedial Response, Washington, DC. NTIS
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(HEAST): Annual Update, FY1993. Environmental Criteria and Assessment Office, Office of
Health and Environmental Assessment, Office of Research and Development, Cincinnati, Ohio.
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Assessment of Polycyclic Aromatic Hydrocarbons. Office of Health Effects Assessment,
Washington, DC.
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Data Collection Activities. Quick Reference Fact Sheet Office of Emergency and Remedial
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U.S. EPA (Environmental Protection Agency). 1993h. Science Advisory Board Review of the Office of
Solid Waste and Emergency Response draft Risk Assessment Guidance for Superfund (RAGS),
Human Health Evaluation Manual (HHEM). EPA-SAB-EHC-93-007. Science Advisory Board,
Washington, DC
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Advisories. Office of Water, Washington, DC.
U.S. EPA. (Environmental Protection Agency). 1994b. Framework for Assessing Ground Water
Modeling Applications. EPA-500-B-94-004. Resource Management and Information Staff, Office
of Solid Waste and Emergency Response, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1994c. Ground Water Modeling Compendium, Second
Edition. EPA-500-B-94-003. Resource Management and Information Staff, Office of Solid Waste
and Emergency Response, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1994d. Guidance for the Data Quality Objectives. EPA
QA/G-4. Quality Assurance Management Staff, Office of Research and Development,
Washington, DC.
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Duluth, MN. December.
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Standards—Volume 3: Reference-Based Standards for Soils and Solid Media. EPA 230-R-94-
004. Environmental Statistics and Information Division, Office of Policy, Planning, and
Evaluation, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1994g. Revised Interim Soil Lead Guidance for CERCLA
Sites and RCRA Corrective Action Facilities. OSWER Directive #9355.4-12. Office of Solid
Waste and Emergency Response, Washington, DC.
U.S. EPA (Environmental Protection Agency). 1994h. Risk Assessment Issue Paper for: Derivation of
a Provisional RfD for 1,1-Dichloroethane (75-34-3). Environmental Criteria Assessment Office,
Cincinnati, OH.
U.S. EPA (Environmental Protection Agency). 1994i. Risk Assessment Issue Paper for: Provisional Oral
RfD for Naphthalene (91-20-3). Environmental Criteria Assessment Office, Cincinnati, OH.
U.S. EPA (Environmental Protection Agency). 1994j. Role of the Ecological Risk Assessment in the
Baseline Risk Assessment. OSWER Directive No. 9285.7-17. Office of Solid Waste and
Emergency Response, Washington, DC. August 12.
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Van Wijnen, J.H., P. Clausing, and B. Brunekreef. 1990. Estimated soil ingestion by children.
Environmental. Research, 51:147-162.
Wester, R.C., H.I. Maibach, and L. Sedik. 1993. Percutaneous absorption of pentachlorophenol from soil.
Fundamentals of Applied Toxicology, 20.
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APPENDIX A
DEVELOPMENT OF A SOIL SCREENING LEVEL METHODOLOGY
FOR THE SOIL-PLANT-HUMAN EXPOSURE PATHWAY
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RESEARCH TRIANGLE INSTITUTE
Superfund Soil Screening Levels
Development of a Draft Soil Screening Level Methodology for the
Soil-Plant-Human Exposure Pathway
EPA Contract 68-W1-0021
Work Assignment D2-24, Task 6
RTI Project No. 5629-241
Submitted to:
Janine Dinan
Work Assignment Manager
U.S. Environmental Protection Agency
Office of Emergency and Remedial Response
Washington, DC
Submitted by:
Stephen M. Beaulieu
Task Leader
and
Robert S. Truesdale
Work Assignment Leader
Center for Environmental Analysis
Research Triangle Institute
November, 1994
3040 Cornwallis Road • Post Office Box 12194 • Research Triangle Park, North Carolina 27709-2194 USA
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Introduction
This appendix presents a report on incorporating the soil-plant-human exposure
pathway into OERR's draft Soil Screening framework. It contains a summary and analysis of
empirical data on contaminant uptake by plants from the Technical Support Document for
Land Application of Sewage Sludge (U.S. EPA, 1992a), hereafter referred to as the "Sludge
Rule", and describes how these results may be incorporated into the Soil Screening
framework.
In addition to the Sludge Rule, this analysis was based on several key references on
plant uptake of soil contaminants and exposure through the ingestion of contaminated plants.
The major sources consulted include: Current Studies on Human Exposure to Chemicals with
Emphasis on the Plant Route (Paterson & Mackay, 1991), Uptake of Organic Contaminants
by Plants (McFarlane, 1991), Air-to-Leaf Transfer of Organic Vapors to Plants (Bacci and
Calamari, 1991), Paradigm for Soil Risk Assessment: A Soil Scientist's Perspective (Ryan,
1991), Estimating Exposure to Dioxin-Like Compounds (U.S. EPA, 1992b) and Comparison of
Risk Assessment Methodologies for Selected Metals in Sewage Sludge (RTI, 1991).
The body of this report is divided into three sections: Section 1 summarizes the
empirical data and methods used to calculate plant uptake of soil contaminants from the
Technical Support Document for Land Application of Sewage Sludge (U.S. EPA, 1992a);
Section 2 describes the assumptions and input parameters necessary to incorporate the soil-
plant-human pathway into the Soil Screening framework; and Section 3 presents a brief
discussion of the implications of introducing the plant exposure pathway into the framework
with respect to pathways already addressed.
Section 1. Methodology for Calculation of Plant Uptake Rates in the Sludge Rule
1.1 Overview of Sludge Rule Approach to the Plant Pathway
In the Sludge Rule, EPA evaluated fourteen indirect exposure pathways, including
three pathways involving the accumulation of contaminants from the soil into plant tissue.
For the purposes of developing SSLs for a plant exposure pathway, Pathway 2 from the
Sludge Rule (sludge-soil-plant-human) has been selected as the exposure scenario analogous
to a home gardener in a residential setting. In the Sludge Rule, this pathway was developed
for the home gardener who amends garden soil with sewage sludge and then consumes fruits
and/or vegetables grown on the sludge-amended soil.
Based on a screening level analyses, the constituents of concern presented in Table 1-1
were identified for Pathway 2 in the Sludge Rule. For each of these constituents, uptake-
response slopes (termed "UC") were estimated for seven plant groups, including: potatoes,
leafy vegetables, fresh legumes, root vegetables, garden fruits, sweet com, and grains and
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cereals. For metals, the uptake-response slopes are in units of (ug-pollutant/g-plant tissue
DW)(kg-pollutant DW/ha)"1, where DW is dry weight arid ha is hectare. For organic
compounds, a default uptake-response slope of 0.001 in units of (ug-pollutant/g-plant tissue
DW)(ug-pollutant/g-soil DW)"1 was used due to the paucity of data on plant uptake of organic
compounds.
Table 1-1. Constituents of Concern for the Sludge-Soil-Plant-Human Pathway
Inorganics
Arsenic
Cadmium
Mercury
Nickel
Selenium
Zinc
Organics
Aldrin/Dieldrin
Benzo(a)pyrene
Chlordane
DDD/DDE/DDT
Heptachlor
Hexachlorobenzene
Hexachlorobutadiene
Lindane (gamma-HCH)
N-Nitrosodimethylamine
Polychlorinated biphenyls (PCBs)
Toxaphene
Trichloroethylene
Note: N-Nitrosodimethylamine is hot included in the SSL methodology.
1.2 Study Selection and Evaluation
The Sludge Rule lists a step-by-step approach for the selection of studies used to
derive plant uptake-response slopes. All references used in the data base were obtained from
EPA's archives or from other sources and, whenever possible, secondary references were
replaced with primary references. In addition, substantial quality assurance/quality control
measures were taken to ensure that data were not repeated, reference citations were correct,
and qualitative information was recorded. Studies in the data base were placed into one of
three categories:
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• Type A studies were those conducted in the field with sewage sludge (non-spiked).
• Type B studies were studies other than field studies conducted with sewage sludge
(e.g., greenhouse studies where plants were grown in pots, field studies which used
sewage sludge spiked with additional metals).
• Type C studies are primarily those which used metal salts or metal-contaminated
soils or mine tailings.
Study data were evaluated that documented the contaminant concentrations in plant
tissues and associated contaminant loading rates or soil concentrations of the sludge-amended
field. For organic chemicals found in sludge, response slopes could not be calculated because
of a paucity of data on the uptake of organic compounds by plants. For metals, uptake
studies were evaluated with respect to soil chemistry parameters (e.g., cation exchange
capacity and soil pH), parameters affecting bioavailability (e.g., elapsed time after
application), and chemical-specific parameters such as the form of the metal (i.e., salt vs. pure
metal). The most important examples of each of these parameters is discussed below.
Bioavailabilitv - The relationship between bioavailability and metals loading was
delineated from several studies indicating that the bioavailability of metals was highest in the
first year following sludge application. Using an uptake-response curve generated from first.
year data (following sludge application) would overestimate metal accumulation in plants
growing on soils that had been sludge-treated for a longer period of time. Therefore, data
from studies with more than 1 year of sewage sludge application were selected over those of
1 year or less (although most of the available data came from studies of less than 5 years).
Cation Exchange Capacity - The Sludge Rule reviewed several studies investigating
the role of cation exchange capacity (CEC) on metal uptake by plants. Results showed the
relationship between CEC and metal uptake to be inconsistent and often conflicting. Based
on the results in the literature surveyed by the Sludge Rule and a recommendation by
Sommers et al. (1987), the effect of CEC on plant uptake of metals was omitted from the risk
assessment
Soil pH - EPA recognized that soil pH is one of the strongest influences on the
ability of plants to absorb metals from the sewage sludge/soil mixture. Several studies
documented a consistent and significant effect of soil pH on plant uptake of metals; as pH in
the studies decreased, metal bioavailability rose sharply. In fact, agriculture practices
maintain a soil pH of 5.5 or greater to protect plants against natural aluminum and manganese
phytotoxicity, since naturally occurring metals tend to be more mobile in acidic soils. As a
result, the Sludge Rule reported that, because agricultural land will seldom have a pH less
than 5.5, the data set used to determine plant uptake factors included only studies with soil
pH values from 4.5 to 8 (40% of the data set were from studies which used a pH less than 6).
The authors maintained that limiting the study soil pH range would ensure that acidic soils
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were well represented within the data set. However, the inclusion of 40% of the studies with
soil pH levels below 6 may result in uptake slopes that are artificially skewed (i.e., higher
uptake-response slopes).
Chemical Form - Another factor which heavily influences plant uptake of metals is
the chemical form of the metal. Researchers have observed that plant uptake rates of metal
salts tend to be higher than plant uptake rates in studies on elemental metals. This increased
uptake of metal salts has been attributed to the capacity of sewage sludge to selectively
adsorb heavy metals in the presence of Ca2+, an ion usually present in the soil solution of
most fertile soils. Apparently, the adsorption power of sludge reduces the concentrations of
metals available for plant uptake. As a result, the plant uptake-response curves have a
tendency to be curvilinear, eventually reaching a plateau as sludge metal loading is increased.
In contrast, metal salts do not adsorb to sludge the same way as "metals in non-salt forms"
and, therefore, metal salt data were used only when data on non-salt form metal uptake was
unavailable.
1.3 Estimating Plant Uptake-Response Slopes for Metals
Table 1-2 summarizes all the uptake-response slopes for metals of concern according
to number of observations, study type, plant category, pH, concentration range, and geometric
mean. As already discussed, the uptake-response slope for organics was set at the default
value of 0.001 (ug-pollutant/g-plant tissue)(ug-pollutant/g-soil DW)"1 due to insufficient study
data on organics. However, some interpretation of study data was required in order to
estimate uptake-response slopes for metals since the studies (1) often contained incomplete
metal-loading information or (2) presented sludge concentrations in units of pg-pollutant/kg-
sludge instead of application rates in units of kg-pollutant/ha. Therefore, the Sludge Rule
used the following steps for data interpretation and estimation of uptake-response slopes:
1. If sewage sludge loading and sewage sludge metal concentration data were given in
metric tons (mt)/ha, the inorganic loading rate (kg/ha) was calculated by the formula:
Metal load (kg/ha) = sludge applied (mt DW/ha) x [sludge] (mg/kg) x 10~3
2. If the metal concentration in soil was given, the metal loading rate (kg/ha) was
calculated from the formula:
Metal load (kg/ha) = soil metal concentration (mg/kg) • 2(conversion factor)
(The conversion factor was based on the assumption that the soil in which the sewage
sludge is incorporated weighs 2,000 mt DW/ha based on an assumed average bulk
density of 1.33 g/cm3 and a soil incorporation depth of 15 cm.)
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3. The plant uptake-response slope (UC) in units of (pg-pollutant/g-plant tissue DW)(kg-
pollutant DW/ha)"1 was calculated for each study. For studies in which one metal
application rate and one plant tissue concentration were given, the uptake slope is:
UC = Plant Concentration (ug-pollutant/g-plant tissue DW)
Metal load (kg-pollutant/ha)
For studies in which multiple metal loads and tissue concentrations were given, the
slope was determined by least squares linear regression.
5. Where calculated uptake slopes were negative or less than 0.001 (for metals), the
value of the slope was set equal to 0.001 as a conservative default.
6. For each metal of concern, the geometric mean -of the uptake-response slopes was
calculated for each plant group (i.e., potatoes, leafy vegetables, etc.).
The uptake-response slope for each plant group was multiplied by the fraction of food
produced on sludge amended soil (F) and the daily dietary consumption of the food group
(CR) and summed (E UC • F • CR) to estimate the reference cumulative application rate of
pollutant in kg-pollutant/ha.
Section 2. Calculation of SSLs for the Soil-Plant-Human Pathway
2.1 Plant Uptake and Development of SSLs
The Soil Screening framework was developed as a first step toward standardizing the
evaluation and cleanup of contaminated soils under Superfund. Within this framework, soil
screening levels (SSL's) are chemical concentrations in soil that represent a level of
contamination above which there is sufficient concern to warrant further site-specific study.
Currently, the framework considers three pathways for contaminant exposure in a residential
setting:
• Ingestion of soil.
• Inhalation of volatiles and fugitive dusts.
« Migration of contaminants through soil to an underlying potable aquifer.
Considered to be the most common routes of exposure for residential land use, these
pathways have been the focus of a significant effort to develop an acceptable methodology for
estimating potential risks from contaminant exposure. However, the analysis of Records of
Decision (RODs) from Superfund sites as well as the experience of state and regional
environmental offices responsible for site cleanups suggests that the ingestion of contaminated
fruits and vegetables from homegrown gardens may also be a significant exposure pathway.
Since the soil screening levels address the risks of human exposure from contaminants in the
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soil, this pathway is being considered as part of the initiative to incorporate additional
exposure pathways relevant to residential exposures into the Soil Screening framework.
Based on the plant exposure pathways evaluated in the Sludge Rule, data from Pathway 2
were selected as the most appropriate plant uptake-response slopes for possible use in the SSL
methodology.
As discussed in Section 1.3, the Sludge Rule used field study data (e.g., measured
concentrations in plant tissues) to calculate plant uptake-response slopes. The plant uptake
slopes were used to estimate the maximum contaminant levels or cumulative application rates
allowable for sewage sludge, given the conditions of the plant exposure pathway assumed in
the Sludge Rule (e.g., highly exposed individual or HEI). Sections 2.2 through 2.4 illustrate a
draft approach to back-calculating SSLs for the soil-plant-human pathway using the empirical
uptake-response slopes, or UCs, identified in the Sludge Rule.
2.2 Overview of EPA-Approved Algorithms
Current methods approved by the EPA for back-calculating acceptable soil
concentrations for the soil-plant-human exposure pathway take the general form of the
equation cited in Estimating Exposure to Dioxin-Like Compounds (U.S. EPA, 1992b):
Csoii = [Cplant • K,, • 0.15]/[RCF • VGbg] (Equation 2,1)
where
Csoi] = Contaminant concentration in soil (mg/kg or pg/g)
C_lant = Risk-based acceptable plant concentration (mg/kg DW or pg/g DW)
Kd = Soil-water partition coefficient (L/kg)
0.15 = Factor used to convert wet weight to dry weight (g DW/g WW)
RCF = Root concentration factor (mg pollutant/kg plant tissue WW)(pg
pollutant/mL pore water)"1
VGbg = Empirical correction factor of 0.01 for lipophilic contaminants (unitless)
Several of the inputs to Equation 2.1 are derived from other equations as described in
Estimating Exposure to Dioxin-Like Compounds (U.S. EPA, 1992b). For convenience, these
equations are discussed briefly below.
Acceptable plant concentration (Cplant)
The acceptable contaminant concentration in plant tissues (Cplant) for a particular plant
group (e.g. root vegetables, leafy vegetables, etc.), is back-calculated using the following
general equation:
Cplant = (I'BW)/(F-CR) (Equation 2.2)
where
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C lant = Acceptable concentration in plant tissue (pg/g DW)
I = Acceptable daily intake of contaminant (pg/kg-day)
BW = Body weight (70 kg default for adults)
F = Fraction of plant grown in contaminated soil (unitless)
CR = Consumption rate of plant (g-plant DW/day)
For carcinogens, the acceptable daily intake (I) is calculated at the target risk level, frequently
using default assumptions for exposure duration, exposure frequency, and averaging time.
I = (TR AT 365 1000)/(ED EF CSForal) (Equation 2.3)
where
I = Acceptable daily intake of contaminant (pg/kg-day)
TR = Target risk level (unitless, usually 1 x 10"°)
AT = Averaging time (70 yr default)
365 = Conversion factor (day/yr)
1000 = Conversion factor (pg/mg)
ED = Exposure duration (30 yr default)
EF = Exposure frequency (350 day/yr default)
CSForal = Oral cancer slope factor (mg/kg-day)"1
For non-carcinogens, the acceptable daily intake (I) is calculated at the hazard quotient using
the following general equation:
I = HQ-RfD 1000 (Equation 2.4)
where
I = Intake of contaminant (pg/kg-day)
HQ = Target hazard quotient (unitless, usually 1)
RfD = Oral reference dose (mg/kg/day)
1000 = Conversion factor (pg/mg)
Soil-water partition coefficient
The soil-water partition coefficient describes the partitioning of a contaminant between
soil pore water and soU particles and strongly influences the release and movement of a
contaminant into the subsurface and underlying aquifer. Values for Kd derived for calculating
SSLs for the inhalation and migration to ground water pathways can also be used to calculate
SSLs for the soil-plant pathway. K,j values for organic chemicals are linear with respect to
the amount of organic carbon in the soil, therefore, Kd values were derived differently for
organics than for inorganic contaminants.
For organic chemicals, Kd (ml/g) is related to the amount of organic carbon in the soil
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by the equation:
KJ = KO,. x f^. (Equation 2.5)
where
= Organic carbon partition coefficient (ml/g)
foc = The fraction of organic carbon in soil (unitless)
In deriving organic SSL Kj values for the ground- water pathway, OERR assumed that
a constant f^ value of 0.002 was representative of the organic carbon content in the vadose
zone. Although this assumption is valid for the vadose zone, the K^ value used in Equation
2.1 should be derived for the root zone (i.e., the upper layer of soil in which roots are
exposed). As a result, Kd values for the root zone should be estimated with alternative foc
values that more accurately reflect the organic fraction in the root zone and surface soil. For
example, a central tendency foc value of 0.006 and a high end value of 0.001 may be used for
the root zone. K^ values for most organics were estimated from the current literature or
from linear regressions of similar compounds correlating Koc and the octanol/water partition
coefficient, K<,w.
For inorganic chemicals, SSL Kd values were estimated using an equilibrium
geochemical speciation model, MINTEQ2, since subsurface behavior of metals is heavily
influenced by other soil conditions, such as pH, oxidation-reduction conditions, iron oxide
content, cation exchange capacity and major ion chemistry. Because the most significant
variable affecting Kd is pH, SSLs for metals were developed for soil pH values of 4.9, 6.8,
and 8.0 representing the 10th, 50th, and 90th percentile pH conditions for U.S. ground waters,
respectively. For the other variables listed, MINTEQ2 modeling runs were fixed at medium
values.
Root Concentration Factor (RCF)
The root concentration factor is the ratio of the contaminant concentration in the roots
versus the concentration in the soil solution. Because the RCF is heavily dependent upon the
uptake, activity of the plant, the uptake-response slopes (UC) calculated from empirical data in
the Sludge Rule can be used to calculate the RCF. The relationship between RCF and UC is
represented by an equation presented by McFarlane (1991):
UC = RCF/K^ x ^(Equation 2.6)
where
UC = plant uptake-response slope (pg-pollutant/g-plant tissue DW)(pg-pollutant/g-
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soil DW)'1
RCF = root concentration factor, a ratio of the contaminant concentration in the
roots versus the concentration in the soil water (ug-pollutant/g-plant tissue
WW)(ug-pollutant/ml pore water)"1
Koc = organic carbon partition coefficient (ml/g-soil)
foc = fraction of organic carbon in the soil (unitless)
Since Kd = Koc x foc, solving the equation for RCF and substituting Kd for (K^ x f^.) in the
denominator gives:
RCF = UC x Kj (Equation 2.7)
Using the K
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OSWER Directive 9285.6-03 (U.S. EPA, 1991) or by using the methods presented in the
Sludge Rule. The OSWER Directive (U.S. EPA, 1991) recommends that the "typical"
consumption rates are 140 g/day for fruits and 200 g/day for vegetables with the "worst case"
proportion of homegrown produce set at 30 and 40 percent, respectively. In the Sludge Rule,
homegrown food groups were evaluated with both plant uptake-response slopes (i.e, UC) and
data on dietary consumption from the USD A or the Development of Risk Assessment
Methodology for Land Application and Distribution and Marketing of Municipal Sludge (U.S.
EPA, 1989). As a result, consumption rates and homegrown fractions were presented for
seven plant categories: potatoes, leafy vegetables, fresh legumes, root vegetables, garden
fruits, sweet com, and grains and cereals. The uptake-response slopes (UC), homegrown
fraction (F), and consumption rates (CR) were multiplied and summed for each of the plant
categories as shown in Section 1.3 (i.e., £ UC • F • CR). An analogous method for
combining UC, F, and CR is shown below by combining terms in Equations 2.2 and 2.7 into
Equation 2.1 as follows:
Csoii = £Cpiant' Kd • 0.15]/[RCF • VGbg] (Equation 2.1)
Substituting Equation 2.2 for Cplant in Equation 2.1 gives:
Csoa = [{a ' BW)/(F • CR)} • K,, • 0.15]/[RCF • VGbg] (Equation 2.8)
Substituting Equation 2.7 for RCF into equation 2.8 gives:
C^ = [{a ' BW)/(F • CR)} • KJ • 0.15]/[UC • ^ • VGbg] (Equation 2.9)
Since VGb is equal to 1 and the Kd terms cancel, Equation 2.9 can be simplified to:
csoii = KG ' BW)/(F • CR)} • 0.15]/[UC] (Equation 2.9a)
Rearranging Equation 2.9a and summing the terms for homegrown fraction (Fi), consumption
rate (CRi), and uptake-response slope (UCi) for each of the seven produce groups (denoted by
"i") yields the following equation for Csoil:
Csoj, = [(I BW 0.15)/(£ Fi • CRi • UCi)] (Equation 2.10)
This equation may be used to calculate an SSL for the soil-plant-human exposure pathway as
described below in Section 2.4. Since the Kd terms cancel out in Equation 2.9, the inputs
summarized in Table 2-1 are generally independent of site-specific factors such as soil type.
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Table 2-1. Input Parameters for Calculating SSLs for Soil-Plant-Human Pathway
Input
parameter
I
BW
Fi
CRi
UCi
Description
acceptable daily intake (mg/kg-day)
body weight (kg)
fraction plant grown on-site
consumption rate of plant (g-plant
DW/day)
plant uptake-response slope
(ug-pollutant/g-plant tissue DW)
(ug-pollutant/g-soil DW)'1
Value
chemical-specific
70
plant specific
plant specific
plant & chemical
specific
Source
equations 2.3 & 2.4
Soil Screening
framework
Table 2-2
Table 2-2
Table 3-2
2.4 Example Calculation of SSL for Cadmium
As Section 2.3 suggests, the calculation of Csoil (i.e., the SSL for the soil-plant-human
pathway) involves the calculation and identification of several input parameters. This section
walks through an example calculation of the SSL for cadmium exposure for the homegrown
garden scenario and includes a discussion of the calculations and inputs parameters.
Acceptable Daily Intake (I)
Equations 2.3 and 2.4 are used to calculate the acceptable daily intake (I) of
concentration in plants consumed by humans. Since cadmium is considered a noncarcinogen
for the oral pathway with an RfD of 0.001 mg/kg-day for dietary exposure, the daily intake at
a hazard quotient of 1 is given by:
I =
I =
I =
HQ RfD 1000 ug/mg
1 • 0.001 mg/kg-day • 1000 ug/mg
1 Mg/kg-day
Fraction of Homegrown Plants (Fi)
For each of the seven plant categories listed in Section 2.3 (e.g., potatoes, leafy
vegetables, root vegetables, etc.), the fraction grown in contaminated soil (Fi) can be
estimated using the analysis presented in the Sludge Rule or the general figures suggested in
the Oswer Directive 9285.6-03 (U.S. EPA, 1991). For this analysis the homegrown fractions
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presented in the Sludge Rule were used for the seven plant categories as shown in Table 2-2
below.
Consumption Rate (CRi)
The consumption rates selected for this example are also presented in Table 2-2 and,
like the homegrown fractions, were taken from the Sludge Rule for each of the seven plant
categories. It should be noted that the plant groups and consumption rates used in the Sludge
Rule were essentially developed in earlier work by EPA, including the Development of Risk
Assessment Methodology for Land Application and Distribution and Marketing of Municipal
Sludge (U.S. EPA, 1989) and the Methodology for Assessing Health Risks Associated with
Indirect Exposure to Combustor Emissions (U.S. EPA, 1990b) and subsequent addenda.
Table 2-2. Summary Table for Plant-Specific Inputs for
plant category
potatoes
leafy vegetables
fresh legumes
root vegetables
garden fruits
sweet corn
grains & cereals
homegrown
fraction (Fi)
(unitless)
0.37
0.59
0.59
0.59
0.59
0.59
0.0043
consumption rate
(CRi)
(g DW/day)
16
2.0
3.2
1.6
4.2
1.6
89
£ Fi -CRi -UCi
uptake-response slope
(UCi)
(ug-cd/g-plant DW)
(pg-cd/g-soil DW)'1
0.008
0.364
0.004
0.064
0.09
0.118
0.036
0.89
Uptake-Response Slopes (UCi)
The uptake-response slopes for metals presented in the Sludge Rule were in units of
(pg-metal/g-plant DW)(kg-metal/ha)"1 (i.e., plant concentration divided by metal load).
However, UC should be in units of (pg-metal/g-plant DW)(ug-metal/g-soil DW)"1 in order to
be used to calculate Csoil (as opposed to an acceptable cumulative application rate for sludge).
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Therefore, the units for the uptake-response slope for cadmium were converted into (pg-cd/g-
plant DW)(pg-cd/g-soil DW)"1 by dividing the denominator (i.e., metal load) by a factor of 2.
This conversion is simply the reverse of step 2 listed for data interpretation in Section 1.3 in
which the soil metal concentration (in mg/kg or ug/g) was converted to a metal load (in
kg/ha) assuming a certain bulk density, soil incorporation depth, and soil weight (see Section
1.3). The converted uptake-response slopes (UCi) for cadmium are shown above in Table 2-2
for each plant category.
Soil Screening Level (SSL) for Cadmium for the Soil-Plant-Human Pathway
Using the inputs defined above and assuming an adult body weight of 70 kg results in
the following SSL for cadmium for the soil-plant-human exposure pathway:
SSLcd = [(I • BW • 0.15)/(E Fi CRi • UCi)] (Equation 2.10)
SSLcd = [(1 jig/kg-day • 70 kg • 0.15)/0.89 g-soil DW/day]
SSL^ = [(1 Mg/kg-day 70 kg 0.15)/0.89 g-soil DW/day]
SSLcd = 12 Mg/g-soil DW
or equivalently,
SSLcd = 12 mg/kg-soil DW
Section 3. Including Soil-Plant-Human Pathway in Draft SSL Framework
3.1 Empirical Uptake-Response Slopes From Sludge Rule
Sections 1 and 2 described how the empirical, uptake-response slopes were derived in
the Sludge Rule and how those slopes might be used to derive SSLs for the soil-plant-human
exposure pathway. The methods presented in those sections, and Equation 2.10 in particular,
may be used to estimate SSLs for the soil-plant-human exposure pathway. These methods
notwithstanding, empirical uptake-response slopes should be interpretated with caution since
the dynamics of sludge-bound metals may differ from the dynamics of metals at contaminated
sites. For example, the data were derived from a variety of studies at different pH conditions
using different forms of the metal (i.e., salt vs. non-salt). In some cases, default values for
UC of 0.001 were assigned in studies in which the measured UC was lower than 0.001. In
studies where the chemical form was sludge, the adsorption power of sludge in the presence
of Ca2+ may reduce the amount of metal that is bioavailable to plants in these studies.
Contaminated soils at Superfund sites may not possess the same degree of "adsorption
potential" and, therefore, plant uptake may be greater in non-sludge amended soils. In
addition, farming practices such as off-season planting of forage grasses (e.g., rye grass) may
also serve to mitigate potential plant uptake since the forage grasses would be expected to
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adsorb some portion of the metals, possibly making them less available to crops during the
growing season. Therefore, serious consideration of the uncertainties surrounding the
empirical plant uptake data should be an integral part of developing SSLs for the plant
exposure pathway.
Below, Table 3-1 compares the lowest generic SSLs for metals via direct exposure
pathways to the SSLs derived for the soil-plant-human exposure pathway. The review of the
empirical uptake-response slopes for the six constituents with empirical data (geometric
means, Table 1-2) suggests that the UC values are generally suitable for use in deriving the
plant pathway SSLs. The uptake-response slopes were often identified at soil pH values that
are within the range evaluated in the ground-water pathway, albeit on the acidic end of the
scale. The organic carbon fraction (foc) may be higher in the sludge studies than the f^ likely
to be found in the soil of the typical residential home gardener. However, it is unlikely that
the variation in foc will impact die plant uptake of metals to the same degree that it impacts
the plant uptake of organic constituents (see discussion on
Table 3-1. Comparison of Generic SSLs with SSLs for Plant Exposure Pathway
[generic SSL (mg/kg)
SSL for plant exposure
pathway (mg/kg)
arsenic
0.4
0.15
cadmium
6
12
mercury
3
32
nickel
21
870
selenium
3
136
zinc
23,000
4,400
These results also suggest that the soil-plant-human exposure pathway may be of
concern for some contaminants at the SSLs developed for the direct pathways. Although
most generic SSLs are less than the soil-plant-human SSLs, other contaminants with higher
potential for plant uptake (e.g., semi-volatile, lipophilic organics) may have SSLs for the plant
pathway an order of magnitude (or more) below the direct exposure pathways. The results
for metals underscore the need for more empirical data on the uptake-response slopes for
organic chemicals of concern.
3.2 Developing Uptake-response Slopes for Organic Chemicals
The lack of uptake-response data on organics observed in the Sludge Rule has also
been confirmed in several other sources. For example, the status of empirical data on plant
uptake and accumulation of organics was recently evaluated for a data base on
uptake/accumulation, translocation, adhesion, and biotransformation of chemicals in plants
(Nellessen and Fletcher, 1993). This data base, referred to as UTAB, is one of the most
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comprehensive data sources available on chemical processes in plants and contains over
42,000 records taken from more than 2,100 published papers. The authors found that, with
the exception of pesticides, uptake-response data for organic chemicals is available for
roughly 25% of the chemicals monitored by EPA. Given the comprehensive nature of the
UTAB data base, modeling may be the only alternative to evaluating the soil-plant-human
pathway in the near future for many organic chemicals.
Recently, several authors have developed models to predict the uptake and
accumulation of organic chemicals in plants (e.g., Paterson et al., 1991; McKone, 1994; Trapp
et al., 1994; Matthies and Behrendt, 1994). One of the most promising models for use as a
risk assessment tool is PLANTX, a peer-reviewed partitioning model that describes the
dynamic uptake from soil, or solution, and the metabolism and accumulation of xenobiotic
chemicals in roots, stems, leaves, and fruits (Trapp et al., 1994). Unlike a number of other
models used to estimate plant uptake, PLANTX is not based on regression equations that
correlate log K,,w with plant bioconcentration; it is a mechanistic model that accounts for
major plant processes and requires only a few well-known input data. Moreover, it was
designed as a risk assessment tool and has been validated for the herbicide bromicil and
several nitrobenzenes. A follow-on model (PLANTE) has recently been made available
which also incorporates plant uptake during transpiration (i.e., accumulation directly from the
air). The results on bromocil, nitrobenzene, etc. as well as ongoing validation studies suggest
that the PLANT_ models may be a scientifically defensible alternative to the uptake-response
slopes generated by log Kow regressions.
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REVIEW DRAFT - DO NOT CITE OR QUOTE - November, 1994
References
Bacci, Eros and Davide Calamari. (1991). Air-to-Leqf Transfer of Organic Vapors to Plants.
In: Municipal Waste Incineration Risk Assessment C.C. Travis editor, Plenum Press,
New York.
Matthies, M., and H. Behrendt. 1994. Dynamics of Leaching, Uptake, and Translocation: The
Simulation Model Network Atomsphere-plant-soil (SNAPS), in Plant Contamination:
Modeling and Simulation of Organic Chemical Processes. Stefan Trapp and J. Craig
Me Farlane eds, Lewis Publishers, Michigan.
McKone, T. 1994. Uncertainty and Variability in Human Exposures to Soil Contaminants
through Home-grown Food: A Monte Carlo Assessment. Risk Analysis. Vol. 14, No.
4; pp 449-463.
McFarlane, Craig. (1991). Uptake of Organic Contaminants by Plants. In: Municipal Waste
Incineration Risk Assessment. C.C. Travis editor, Plenum Press, New York.
Nellessen, I.E., and J.S. Fletcher. 1993. Assessment of Published Literature Pertaining to the
Uptake/Accumulation, Translocation, Adhesion and Biotransformation of Organic
Chemicals by Vascular Plants. Environmental Toxicology and Chemistry. Vol. 12, pp.
2045-2052.
Paterson, Sally and Donald Mackay. (1991). Current Studies on Human Exposure to
Chemicals with Emphasis on the Plant Route. In: Municipal Waste Incineration Risk
Assessment. C.C. Travis editor, Plenum Press, New York.
Research Triangle Insititute. (1994). Development of Human Health-Based Exit Criteria for
the Hazardous Waste Identification Project: Phase III Analysis. Prepared for the
Office of Solid Waste, U.S. EPA by the Research Triangle Institute under contract 68-
WO-0032.
Research Triangle Insititute. (1991). Comparison of Risk Assessment Methodologies for
Selected Metals in Sewage Sludge. Prepared for the Office of Solid Waste, U.S. EPA
by the Research Triangle Institute under contract 68-WO-0032.
Ryan, J.A. (1991). Paradigm for Soil Risk Assessment: A Soil Scientists Perspective.
Sommers, L.E., V.V. Volk, P.M. Giordano, W.E. Sopper, and R. Bastian. (1987). Effects of
Soil Properties on Accumulation of Trace Elements in Crops. In Land Application of
Sludge: Food Chain Implications. A.L. Page, T.J. Logan and J.A. Ryan, eds. Lewis
Publishers, Michigan.
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REVIEW DRAFT - DO NOT CITE OR QUOTE - November, 1994
Trapp, S., C. Me Farlane, and M. Matthies. (1994). Model for Uptake ofXenobiotic into
Plants: Validation with Bromocil Experiments. Environmental Toxicology and
Chemistry, Vol. 13, No. 3; pp. 413-422.
U.S. Environmental Protection Agency. (1992a). Technical Support Document for Land
Application of Sewage Sludge; Volumes I and II. U.S. EPA Office of Water, EPA
822/R-93-001a,b.
U.S. Environmental Protection Agency. (1992b). Estimating Exposure to Dioxin-Like
Compounds. U.S. EPA Office of Research and Development, EPA/600/6-88/005B.
U.S. Environmental Protection Agency. (1991). Human Health Evaluation Manual,
Supplemental Guidance: "Standard Default Exposure Factors". U.S. EPA Office of
Solid Waste and Emergency Response, Directive 9285.6-03.
U.S. Environmental Protection Agency. (1990a). Exposure Factors Handbook. U.S. EPA
Office of Health and Environmental Assessment, EPA/600/8-89/043.
U.S. Environmental Protection Agency. (1990b). Methodology for Assessing Health Risks
Associated with Indirect Exposure to Combustor Emissions. U.S. EPA Office of
Health and Environmental Assessment, EPA/600/6-90/003.
U.S. Environmental Protection Agency. (1989). Development of Risk Assessment
Methodology for Land Application and Distribution and Marketing of Municipal
Sludge. U.S. EPA Office of Health and Environmental Assessment, EPA 600/6-
89/001.
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APPENDIX B
EVALUATION OF THE EFFECT OF THE DRAFT SSLs IF THE
JOHNSON AND ETTINGER (1991) MODEL FOR THE INTRUSION OF
CONTAMINANT VAPORS INTO BUILDINGS
ENVIRONMENTAL QUALITY MANAGEMENT MEMORANDUM
-------
ENVIRONMENTAL QUALITY MANAGEMENT, INC.
MEMORANDUM
TO: Janine Dinan
SUBJECT: Evaluation of the Effect on the Draft SSLs
of the Johnson and Ettinger (1991) Model
for the Intrusion of Contaminant Vapors
Into Buildings
FILE: 5099-3
DATE: October 7, 1994
FROM: Craig S. Mann
cc:
Under U.S. Environmental Protection Agency (EPA) Contract No. 68-D3-0035, Task
order No. 0-25, Environmental Quality Management, Inc. (EQ) was directed to evaluate
the effect on the draft soil screening levels (SSLs) of employing the Johnson and Ettinger
(1991) model for estimating the intrusion rate of contaminant vapors from soil into
buildings. This memorandum summarizes the evaluation.
Model Review:
Johnson and Ettinger (1991) is a closed-form analytical solution for both convective
and diffusive transport of vapor-phase contaminants fully incorporated in soil into
enclosed structures. The nondimensionalized mass balance is written as:
(r)
\LDJ
= v
where
= Nondimensional variables
= Volume fraction of phase i, unitless
= Concentration of contaminant in phase i, g/cm3
= Time, s
= Convection path length, cm
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Janine Dinan . 2 October 7, 1994
LD = Diffusion path length, cm
P = Pressure in vapor-phase, g/cm-s2
v = Del operator, 1/cm
Q, = Contaminant concentration in vapor phase, g/cm3
D"ff = Effective diffusion coefficient, cnf/s
fj = Vapor viscosity, g/cm-s
K, = Soil permeability to vapor flow, cm2
A Pr = Reference indoor-outdoor pressure differential, g/cm-s2
R = Formation rate of contaminant in phase i, g/cm3-s
and,
Q* = Q/Q
P* = P/APr
t* =t(K, A
where Q, Lp and LD are characteristic concentration, convection pathway length, and
diffusion pathway length, chosen to give the dependent concentration variable and
derivatives of Q* and P* magnitudes of order unity.
The mass balance solution includes the following assumptions:
1. The soil column is isotropic within any horizontal plane.
2. The effective diffusion coefficient is constant within any horizontal plane.
3. Concentration at the soil-air interface is zero (i.e., boundary layer resistance
is zero).
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Janine Dinan 3 October 7, 1994
4. No loss of contaminant occurs across the lower boundary (i.e., no
leaching).
5. Source degradation and transformation are not considered.
6. Convective vapor flow near the building foundation is uniform.
7. Contaminant vapors enter the building primarily through openings in the
walls and foundation at or below grade.
8. Convective velocities decrease with increasing contaminant source-building
distance.
9. All contaminant vapors directly below a basement will enter the basement,
unless the floor and walls are perfect vapor barriers.
10. The building contains no other contaminant sources or sinks, and the air
volume is well mixed.
Therefore,
^building ~ ^
where Q^id™, 0^,^, and E represent the volumetric flow rate or ventilation rate of the
building (crrr/s), contaminant concentration within the building (g/cm3), and rate of
contaminant entry (g/s), respectively.
Also,
where Csource is the vapor-phase contaminant concentration within the soil at the source,
and a represents the attenuation coefficient. Csource is written as:
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Janine Dinan
October 7, 1994
smote
HQ
(4)
where H = Henry's law constant, unitless
Cs = Soil bulk concentration, g/g
/>b = Soil dry bulk density, g/cm3
6W = Soil water-filled porosity, unitless
\^ = Soil-water partion coefficient, cm3/g
Ga = Soil air-filled porosity, unitless.
The authors derive a solution fora for both steady-state conditions (i.e., depth of
contamination, z = «) and for quasi-steady-state conditions (0 < z < L). For steady-
state conditions a is written as:
a =
[buUdnig
exp
creck
' erode. >
exp
00*
croct
emd
aaek
-1
(5)
where
= Effective diffusion coefficient, crr^/s
= Area of basement, cm2
= Source-building separation, cm
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Janine Dinan 5 October 7, 1994
= Volumetric flow rate of soil gas into the building, cm3/s
L^ack = Building foundation thickness, cm
DF""* = Effective diffusion coefficient through crack, crrfVs (Cfrack = ffff)
A:rack = Area of crack, cm2
ing = Building ventilation rate, cm3/s.
For quasi-steady-state conditions the long-term average attenuation coefficient
is:
(6)
= _ (p2 + 2
f -< I l^r T
where pb = Soil dry bulk density, g/cm3
CR = Average contaminant level in soil, g/g
A He = Thickness of depth over which contaminant is distributed, cm
AU = Area of basement, cm2
Q.uiiding = Building ventilation rate, cm3/s
Csource = Vapor-phase soil concentration at source, g/cm3
T = Exposure averaging period, s
LT° = Source-building separation at t=0, cm
and,
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Janine Dinan 6 October 7, 1994
(7)
P=|-^r^Il -expl- v»a1^
(8)
The time required to deplete a finite source (rD) of depth A He is given as:
(9)
If the exposure period (r) is greater than rD) the average emission rate into the building
is given as a simple mass balance:
= p, CK Affc AB I T (10)
and the average building concentration (C^,^) is:
cbuiui*g = IQ^~ <11)
Evaluation
in order to evaluate the effects of using the model on the SSLs for volatile
contaminants, a case example was constructed which best estimates a reasonable high
end exposure point concentration for residential land use. Where possible, values of
model variables were taken directly from Johnson and Ettinger (1991).
The case example assumes that a residential dwelling with a basement is
constructed within the area of homogeneous residual contamination such that the
contaminant source lies directly below the basement floor at t = 0. Therefore, the
-------
Janine Dinan 7 October 7, 1994
diffusion and convection path lengths were set equal to the thickness of the basement
slab (15 cm). Soil permeability to vapor flow from the basement floor to the bottom of
contamination was set equal to 1.0 x 1fJ8 cm2 (1 darcy) which is representative of silty to
fine sand. Soil column-building pressure differential was set equal to 1 pascal (10 g/cm-
s2) as a reasonable long-term average value (Johnson and Ettinger, 1991). Values for
all other soil properties were set equal to those of the Generic SSLs in the July 1994
Technical Background Document for Draft Soil Screening Level Framework (TBD).
Building variables, i.e., basement area, ventilation rate, etc., were taken from Johnson and
Ettinger (1991).
In the analysis, the values for Ceding (kg/m3) were calculated for the 42 chemicals
in the TBD for which human health benchmarks are available. Please note that the values
of G-ource and Q,ui|ding were calculated for an initial soil concentration of 1 mg/kg instead
of 1 x 106 g/g. This was done to facilitate reverse calculation of the SSL in units of
mg/kg. Therefore, these values are artificially high by a factor of 1 x 106. The inverse
of the value of C^u^ (m3/kg) was used as the indoor volatilization factor (VFindoor) and
substituted into Equations 2-4 or 2-5 of the TBD as appropriate to calculate the resulting
carcinogenic and noncarcinogenic inhalation SSLs. SSLs were calculated for both steady-
state conditions (infinite source depth) and quasi-steady-state conditions (finite source
depth). In each case were the exposure period exceeded the time required for source
depletion (finite source depth), the volatilization factor was normalized to an average
contaminant level in soil (Q) of 1 mg/kg. For quasi-steady-state conditions, the depth to
the bottom of contamination was set equal to 2 meters below the basement floor.
The value of the indoor SSL for each contaminant was compared to the respective
SSL calculated for outdoor exposures of the same duration using the Generic SSL
calculations found in the TBD. The outdoor SSLs were computed for a 30 acre square
area source of emissions. Table 1 summarizes the results of this comparison. The
attachment to this memorandum gives the detailed computations for this evaluation.
As can be seen from Table 1, results on a chemical-specific basis indicate a rate
of change as high as three orders of magnitude between the outdoor SSL and the infinite
source indoor SSLs in the case of highly volatile contaminants. For very persistent
contaminants, the relative difference was considerably less, and in some cases there was
no difference in SSL concentrations.
This variability is due to: 1) the variability in the human health benchmarks used
to calculate the risk-based SSLs, and 2) the apparent diffusion coefficient of each
compound. The apparent diffusion coefficient can be expressed as the effective diffusion
coefficient through soil divided by the liquid-phase partition coefficient (Jury et at., 1983).
The apparent diffusion coefficient (DA) is given here so as not to be confused with the
effective diffusion coefficient (D6*) from Johnson and Ettinger (1991):
-------
Janine Dinan
8
October 7, 1994
TABLE 1. SUMMARY OF INDOOR AND OUTDOOR INHALATION SSLs FOR VOLATILE
CONTAMINANTS
Chemical
Aldrin
Benzene
Bis(2-chloroethyl)ether
Bromoform
Carbon disulfide
Carbon tetrachloride
Chlordane
Chlorobenzene
Chloroform
DDT
1 ,2-Dichlorobenzene
1 ,4-Dichlorobenzene
1 . 1-Dichloroethane
1.2-Dichloroethane
1 , 1 -Diehloroethylene
1 ,2-Dichloropropane
1 ,3-Oichloroprapene
Diddrin
Ethytbenzene
Heptachlor
Heptachlor epoxide
Hexachloro-1 ,3-butadiene
Hexachlorobenzene
HCH-alpha(alpha-BHC)
HCH-beta(beta-BHC)
Hexachlorocydopentadiene
Hexachloroethane
Methyl bromide
Methylene chloride
Nitrobenzene
Styrene
1 . 1 .2.2-Tetrachloroethane
Tetrachloroethylene
Toluene
Toxaphene
1 ,2.4-Trichlorobenzene
1,1,1 -Trichloroethane
1,1,2-Trichloroethane
Trichloroethylene
2,4,6-Trichlorophenol
Vinyl acetate
Vinyl chloride
Indoor Indoor Outdoor
SSL, SSL, SSL.
infinite finite infinite
source source source
(mg/kg) (mg/kg) (mg/kg)
0.4
0.002
0.02
0.8
0.03
0.0007
51
0.7
0.001
5*
26
102
4
0.002
0.0001
0.06
0.0007
3
21
0.04
1
0.03
0.3
0.5
.7*
0.06
0.6
0.01
0.04
9
185
0.007
0.05
6
2 .
6
5
0.009
0.01
64
5
0.00002
0.4
0.02
0.05
0.9
0.7
0.01
53
2
0.007
5'
65
235*
35
0.007
0.003
0.3
0.004
4
69
0.04
1
0.05
0.6
0.6
71
0.07
0.6
0.3
0.3
25
472
0.02
0.3
28
2
9
69
0.02
0.09
94
14
0.002
0.5
0.5
0.3
43
11
0.2
54
87
0.2
5'
297*
235*
939
0.3
0.04
10
0.1
2
257'
0.3
1
1
1
0.9
7'
2
45
3
7
100
1439'
0.4
11
521'
2'
214
980'
1
3
190
351
0.01
' = SSL based on Cut.
-------
Janine Dinan 9 October 7, 1994
where DA = Apparent diffusion coefficient, cnf/s
Ga = Air-filled soil porosity, unitless
Da = DiffusMty in air, crrf/s
H = Henry's law constant, unitless
Ow = Water-filled soil porosity, unitless
Dw = Diffusivity in water, cnf/s
0, = Total soil porosity, unitless
pb = Soil dry bulk density, g/cm3
1^, = Soil-water partition coefficient,- cm3/g.
With all nonchemical-specific variables held constant, Rgure 1 shows the
exponential relationship between the apparent diffusion coefficient and the building
concentration for quasi-steady-state conditions (finite source).
For nonchemical-specific variables, a sensitivity analysis was performed for soil
permeability to vapor flow (kj, soil-building pressure differential (AP), depth of
contamination (AH,.), source-building separation att = 0 (L,-0), crack-to-total area ratio (7),
and building ventilation rate
Table 2 shows the results of the sensitivity analysis for the quasi-steady-state
condition (finite source). As can be seen from Table 2, the effect of the building
ventilation rate is linear if the value of Csat is not included in limiting the value of the SSL
Depth of contamination (AHL) has the greatest effect for contaminants with higher
apparent diffusion coefficients (e.g., benzene, chloroform, vinyl chloride, etc.), in that as
AHc increases, the time required for source depletion (rD) also increases. Therefore, with
greater initial contaminant mass in the soil, these compounds are emitted for a longer
period of time thus reducing the SSL For the more persistent contaminants, an increase
in K, or AP produces the greatest results. This is to be expected as values of TD for these
contaminants exceed the exposure duration. Table 2 also indicates that an order of
magnitude change in values of LT° and rj produce same order of magnitude results. It
must be remembered, however, that in the case of LT°, the model assumes isotropic soil
-------
1.00E+01 -,
1 OOE-01
•a.
|
i
1 1 OOE-03 •
i
§
l
1.00E-O5
1 mfjr? .
,«
' >
4
4
,
4
«
«]
^ 4
*
tt
|>*
•
4
)
' >
«
•^P—
^
—
4
4 "
I
*
c_
0)
CD
D
"
0
s
o
CT
1.00E-09
1.00E-07
1.00E-05
Apptrtnt Olffuilon Cotfllctont (cm'/s)
1. OOE-03
1. OOE-01
Figure 1. Building concentration versus apparent diffusion coefficient.
-------
Janine Dinan
11
October 7, 1994
TABLE 2. MODEL SENSITIVITY TO NONCHEMICAL SPECIFIC VARIABLES
Ratio of Variabte-to-Test Condition SSL
Chemical
DDT
Dieldrm
HCH-beta(beta-BHC)
Cnlofdane
Mdiat-w-ft-fc-r MvivviA
rwpin"w*^"D"r o~iMU0UQ
Aldnn
HCH-rtpha(«lpr»-eHC)
Toxaphene
2,4,6-Trichloraphenol
Hexachtofobenzene
HeptacNor
Hexachtoroethane
• i*i ii iii • n • i ii
rUiuuenzeno
B«<2-c-htoroethyl)ether
Hexachtorocydopentadiane
1^.4-TricNonbenzene
Bromofomn
Hexaclriui ix1,3**DutBdMne
Styrme
1.1^2-Tetrachloroethane
1O fVl-Mi-ilinli • mi.nn
,A~UlLIMJIUQentB1l0
1 ,4-DicNarot)enzene
1.1.2-TricNoroelhane
Chtorobenzene
Vmyl acetate
1 ,2-DichtafDethane
Ethylbenzene
— ^. . .
1.2-DicntocDpropane
Toluene
Tetrachloroethylene
Chloroform
1.1-Dchloroethane
Benzene
T ' lili mi • Hi tin.
Tncniofootnyiene
Methytonechtoride
1.1,1-Trichkxoethane
Carbon totracNoride
C-Brbon dtsuKido
1.1-Oichloroethylene
•j.iLiijI hrniiiiiln
M6tnyi ofomoo
Vinyl ecocide
Apparent
diffusion
ooofficiont,
0A
(on-Vs)
1.16E-09
1.59E-09
3.54E-09
5.63E-09
e 7OCJXI
9. 1 OC^J9
1.03E-06
2.81 E-08
3.69E-08
1.81E-07
2.84E-07
3.S2E-07
4 DflCJV
i.oUc-Oo
•» O9C_/¥i
•J.oZcH-Jb
5.94E-06
1Aec^*-c
.OOCHA
1.89E-05
2.32E-05
6.97E-OS
9.50E-05
9.89E-05
1.JXC -A4
.O4c*v4
1.38E-04
3.04E-O4
5.18E-O4
7.79E-04
8.57E-04
8.64E-04
1*>JC_/V5
,Z4c^J3
1.25E-03
ItACJn
.o4cHJo
1 -^^g^va
l.^-^C"VO
1.91E-03
Z08E-03
Z12E-03
<5 JJC_AQ
*-44c--O-3
Z45E-03
3.79E-03
3.82E-03
5.67E-03
7.09E-03
A '•tRC-'/n
O.SDC'UO
Z40E-02
Test
oonditxx)
SSL.
(mg/ke)
5*
4
7*
53
0.4
0.6
2
94
0.6
0.04
n £
O.o
oc
^9
0.05
n m
O.O/
9
0.9
0.05
472
0.02
£C
DO
235'
0.02
2
14
0.007
69
n *s
0.3
28
n "J
O.--?
n nfw
u.uw*
0.007
35
0.02
n no
u.u?
0.3
69
0.01
0.7
0.003
n i
U.o
0.002
Soil vapor
permeability,
IwxIO
1
0.1
0.8
0.1
n 1
U. I
0.1
0.1
0.1
0.1
0.1
0.1
n *s
0.9
A -1
O.I
0.1
f\ i
0.2
0.1
0.2
0.1
0.1
0.2
A "5
0.2
0.2
0.4
0.8
1
0.9
1
1
1
1
1
1
1
1
1
1
1
1
1
soiHiwe
pressure
differential,
APxIO
1
0.1
0.8
0.1
A 1
U.I
0.1
O-J
0.1
0.1
0.1
0.1
n i
0.9
A 4
0.1
0.1
A *>
0.2
0.1
0.2
0.1
0.1
0.2
f\ O
O.Z
0.2
0.4
0.8
1
0.9
1
1
1
1
1
1
1
1
1
Depth to
source
lower
boundary,
AHex10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.8
A C
0.6
0.7
A C
0.5
A A
v«*
0.5
0.4
0.4
n i
u.3
0.4
0.2
0.2
0.2
0.1
A 1
0.1
0.1
Source-
bldg.
S6p8T3t)On
att=0.
U°x10
1
1.2
1
1.3
14
.O
1.2
1.2
1.1
1.1
1.1
1.3
1
*i •>
1.2
1.1
1.2
1.1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Inverse of
crack-
to-total
area ratio,
1/n.xlO
1
1.5
1
1.3
« e
1.9
1.5
1.5
1.1
1.5
1.5
1.5
1,|
.4
•f e
1 .3
1.5
4 e
1.3
1.5
1.5
1.5
1.5
1.5
1C
.3
1
1.5
1.5
1.5
1.5
1.2
f
1
1
1
1
1
1
1
1
1
1
1
BUg.
ventilation
rate,
QttM-oXlO
1.0
3.4
1.0
1.3
A A.
O.1--*
10
10
1.1
10
3.2
10
in
ID
in
in
10
in
1U
10
10
10
3.0
10
A ^
4.O
1.0
10
10
10
10
3.7
•I ft
lU
10
1 A
1U
10
IV
10
10
10
in
1U
10
10
10
10
10
in
IV
10
'= SSL based CM.
-------
Janine Dinan 12 October 7, 1994
conditions from the point of building entry to the bottom of contamination. As LT°
increases, a decreases until diffusion not convection limits the rate of contaminant vapor
transport. The effect of changes in the value of n decrease as values of K decrease such
that for very permeable soils and convection-dominated vapor transport, the effect of
crack size is relatively insignificant.
Conclusions
Use of the Johnson and Ettinger (1991) model to calculate SSLs based on indoor
chronic exposures can have significant impacts on the values of the SSLs for
contaminants with high apparent diffusion coefficients. When comparing the infinite
source indoor model to the infinite source outdoor model for these contaminants, values
of the SSL differ by orders of magnitude for case example conditions. Under these
conditions, diffusion is the limiting transport mechanisms for all but one contaminant for
both steady-state and quasi-steady-state conditions. To effect case example conditions,
the following must be true:
1. The contaminant source must be relatively close or directly beneath the
structure.
2. The soil between the structure and the source must be very permeable (K,
> 1fJ8 cm2).
3. The structure must be underpressurized.
4. The air within the structure must be well mixed (i.e., little or no soil-air
boundary layer resistance).
5. The combination of diffusion coefficient through the cracks, area of the
cracks, and building underpressurization must offer no more resistance than
the soil column beneath the structure.
From this evaluation, the four most important factors affecting the average long-
term building concentration and thus the SSL are building ventilation rate, source-building
separation, soil permeability to vapor flow, and source depth. If the source of
contamination is relatively deep and close to the building, and if the soil between the
source and the building is very permeable, building concentrations of contaminants with
relatively high apparent diffusion coefficients will increase dramatically.
It should be noted, however, that soil permeability, K,, is the most variable
parameter at any given site, and may vary by three orders of magnitude across a typical
residential lot (Johnson and Ettinger, 1991). For this reason, the overall effective diffusion
coefficient should be determined by integration across each soil type. Overall
-------
Janine Dinan . 13 October 7, 1994
diffusion/convection vapor transport will therefore be limited by the soil stratum offering
the greatest resistance to vapor flow.
References
Johnson, Paul C., and Robert A. Ettinger. 1991. Heuristic model for predicting the
intrusion rate of contaminant vapors into buildings. Environ. Sci. Technol., 25(8): 1445-
1452.
Jury, W. A., W. J. Farmer, and W. F. Spencer. 1983. Behavior Assessment Model for
Trace Organics in Soil, I, Model description. J. Environ. Qual., 12:558-564.
Attachment
-------
ATTACHMENT
DETAILED MODEL EVALUATION
-------
COMPARISON OF INDOOR AND OUTDOOR INHALATION SSU FOR VOLATILE CONTAMINANTS
Soil
bulk
Soil
Sot
density, umtetuw, inuhriuie,
Chemical
Aldrin
Benzene
BI>(2-chloroethyQether
BrornofOnn
Carbon disulfide
Carbon tetrachlorkle
Chlordane
Chlorofaenzene
Chloroform
DDT
1.2-Dlchlorobenzene
1.4-Dichlorobcnzenc
1,1-Dlchloroethane
1.2-Dfchtoroethane
1.1-Dlchloroethylcne
1.2-Diehtoropropane
1 , ^Dichlotopropcrw
Dieldrin
Ethytbenzene
Heptachlor
Hcptochkx cpoxfdo
Hexachloro-1 ,3-butadtene
Mexschtofobonzcnfl
HCH-alpha(alpha-BHC)
HCH-beta(beta-BHC)
Hexachloroeyelopentadiene
Hexachloroethane
Methyl bromide
Methyiene chloride
Nitrobenzene
Styrene
1,1,2,2-Tetrachloroetfiane
Tetrachloroethylene
Toluene
Toxaphene
1.2.4-Trlchlorobenzene
1,1,1-Trichtoroethane
1.1,2-Trichtoroethane
Ttlchloroethyleni
2.4.6-Trtchtorophenol
Vinyl acetate
Vinyl chloride
NA o not applicable.
• = SSL baaed on C*.
CAS No.
30940-2
71-43-2
111-44-4
75-2S-2
75-154
56-23-9
57-74-9
108-80-7
67-68-3
50-29-3
95-50-1
108-46-7
75-34-3
107-06-2
75-35-4
78-87-5
542-75-6
60-57-1
100-41-4
76-44-8
1024-57-3
87-68-3
118-74-1
319-844
319*5-7
77-47-4
67-72-1
74-83-9
7509-2
98-95-3
100-42-5
79-34-5
127-18-4
10848-3
8001-35-2
120-82-1
71-5M
7940-5
79-01-6
6846-2
10845-4
7941-4
Pi
(a/cm1)
1.5
1.9
1.9
1.5
1.9
1.5
1.5
1.5
1.5
1.5
1.S
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.S
1.5
1.5
1.5
1.S
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.9
1.5
1.5
1.5
1.5
1.5
1.3
w
flW
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
e.
(em'ton1)
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.1S
0.15
0.15
0.15
0.15
0.15
0.15
0.15
015
0.15
0.15
0.15
0.15
0.15
0.15
0.19
0.19
0.15
0.15
015
0.19
0.15
0.15
0.15
0.15
0.15
0.15
0.15
Soil
total
poroalty,
n
(imltten)
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
Soil
alr-fllted
porosity,
e.
(unKtott)
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0284
0.284
0.284
0.284
0.284
0.284
0.284
0284
0.284
0.284
0.284
0284
0284
0.284
0.284
0.284
0.284
0.264
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.264
0.284
0.284
0.2S4
0.284
Soil
water-Oiled
porosity.
e.
(unMeft)
0.150
0.190
0.150
0.150
0.150
0.150
6.150
0.150
0.150
0.150
0.150
0.150
0.150
0.150
0150
0.150
0.150
0.150
0.150
0.150
0.150
0.150
0.150
0.150
0150
0.150
0.150
0.150
0.150
0.150
0.150
0.150
0.150
0150
0.150
0.150
0150
0150
0150
0.150
0.150
0.150
DDTUtMtr DWtrtMty
In air, to water,
D. D.
(em1*) jemVi)
1.32642 4.866-08
8.70E-02 9.80E-06
6.92E42 7.S3E46
1.49E-02 1.03E45
1.04E41 1.00E45
7.80E42 8.BOE48
1.18E42 4.37E46
7.30642 8.70E46
1.04E41 1.00645
1.37E42 4.95648
690642 7.90E46
690E42 7.90E46
7.42E42 1.04E45
1.04E41 9.90E48
9006-02 1.04E45
7.82E42 8.73646
6.26E42 1.00645
1.25E42 4.74E46
7.SOE42 7.80E46
1.12E42 5.69E46
1.22E42 4.68E4S
S.61E42 6.16E46
5.42E42 S.91E48
1.76E42 5.57E48
1.76E42 5.57E46
1.61E42 7.21E48
2.49643 680648
7.28E42 1.21E45
1.01E41 1.17E45
7.60E-02 8.60E46
7.10E42 800646
7.10E-02 7.90E46
7.20E42 8.20E48
8.70E42 860646
1.16E42 4.34E46
3.00642 8.23E48
7.80642 8.80E46
7.80642 6.80E46
7.90642 9.10648
3.14E42 638E48
850642 9206-08
1.06E41 1.23E-05
Effective
dtnusJon
cocfflctont.
D.
(om'/t)
1.0SE-03
6.99E43
353643
1.19E43
8.31643
623643
9.43E44
5.83643
8.31643
1.09643
9.91E43
S.S1E-03
S.93E43
831E43
7.19E43
6.25643
5.00E43
9.99E44
5996-03
8.95E44
9.75E44
4.48E43
4.33643
1.41E43
1.41E43
1.29643
1.99E44
582643
6.07E43
6.07E43
5.67E43
S.67E43
S.7SE43
6.95E43
927644
2.40E43
6.23E43
6.23E-03
6.31643
2.51E43
6.79E43
8.47E43
SoD vapor
peini68bfllty,
K.
(em1)
1.00648
1.00E-06
1.00E48
1.00E48
1.00648
100648
1.00648
1.00648
1.00648
100648
100648
1.00648
1.00648
100648
100646
1.006-08
. 1 00648
1.00E48
1.00E48
1.00E48
1.00E48
100648
100646
1.00E48
1.006-08
1.00E48
1.00E48
1.00E48
1.00E48
1.00648
100648
100648
1.00648
1.00E-08
1006-08
100648
1006-06
100646
1.00E48
1.00E48
1.OOE48
1.00E-08
Soll-bldo.
preasure
differential,
AP
(Ofcnw1)
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Oflutton
path
length,
L.
(em)
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
IS
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
Convection
path Vapor
length,
L,
(em)
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
1.5
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
viscosity.
u
(0/cm-t)
1.60E-04
180E44
1.80644
180644
1.80E44
1.80644
180644
1.60E44
1.80E44
1.80644
180644
1.80E44
1.80E44
180644
1.80E-O4
1.60E44
1 80644
1.80E44
1.80E44
1.80E44
1.80E44
1.80E44
1.80644
1.80644
1.80E-O4
1.80E44
1.80644
1.80E44
1.80644
1.80E44
1.80E44
1.80E44
1.80644
1.80E44
1.80644
1.80E-04
1.80E-04
1.80E-04
1.80644
1.80644
1.80E44
1.80E-04
Peclet
number.
Pe
(unities*)
0.53
008
010
047
0.07
0.09
0.59
0.10
0.07
0.51
010
0.10
009
007
0.08
0.09
0.11
0.56
009
062
0.57
0.12
0.13
039
039
0.43
279
0.10
007
009
010
0.10
010
0.08
060
0.23
009
009
009
0.22
008
007
Hemys
law constant.
H
(unitleu)
4.20E43
2.20E41
8.80E44
2.50642
5.20641
1.20E«00
2.70643
1.80E41
1.60E41
2.20643
8.60642
1.20E41
2.40E41
5.20642
1.00E*00
1.20641
1.20641
1.10E44
320E41
2.40642
3.40644
9.80641
2.20E42
2.80644
1.40E45
7.10E41
1.50641
5.80641
9.70E42
8.40E44
1.40641
1.50E42
7.10641
2.50E41
1.40E44
1.10E-01
7.60E-01
4.10E42
430641
1.70644
2.30E42
3.50E»00
Organic
carbon
partition
coefficient.
K«
(cm'/B)
4.84E«04
5.70E»01
7.60E«01
1.266*02
5206*01
1.646*02
5.13E«04
2.046*02
5.606*01
2.37E«05
3.766*02
5.166*02
5.20E«01
3.806*01
6.506*01
4.70E*01
2.60E*01
1.09E»04
2.216*02
6.816*03
7.246*03
6.996*03
3.756*04
1.766*03
2.286*03
9.59E*03
1.836*03
9.006*00
1.60E«01
1.31E»02
9126*02
7.90E*01
3.00E«02
1.316*02
5.016*02
1.54E»03
9.90E*01
7.60E*01
9406*01
2.83E*02
5.00E-00
1.10E«01
-------
COMPARISON OF INDOOR AND OUTDOOR INHALATION SSU FOR VOLATILE CONTAMINANTS
Chemical
Aldrtn
Benzene
8fe(2-ch!oreethyOether
Bfonrofofm
Carbon dttuffld*
Cflrbon tctrscnlocldc
Chlordane
Chlofobenzeno
Chloroform
DOT
1,2-Dtehtorobefuene
1.4-Ocntorobenrene
1.1-Dtahloroethane
1.2-Diehkxoethane
1.1-Dlchloroethylene
1,2-Dichloropropane
1.3-Dlchloropropene
Dieldrin
Elhylbeniene
Heptachlor
Heptachlof epoxlde
Hex8chioro-1.3-bu1«a'lene
Hexachlorobenzene
HCH-alpha(alpha-BHC)
HCH-beta(beta-BHC)
Hexachtorocyclopentadiene
Hexachloroethane
Methyl bromide
Methylene chloride
Nitrobenzene
Styrene
t , 1 ,2,2-Tetmchloreethane
Tetrachtoroethylene
Toluene
Toxaphene
1,2,4-Trtchtoroberaene
1.1.1-Trtchloroetnane
1.1.2-Trichlonethane
Trichtoroethylene
2.4,6-Trichloropnenol
Vinyl acetate
Vinyl chloride
NA * not applicable.
' • SSL bated on C.,.
Soil
• orQante
Carbon
fraction.
foe
(unltteM)
0.000
0.006
0.006
0.008
0.006
0.006
0006
0.006
0.006
0.006
0.006
0.006
0006
0.006
0006
0006
0.006
0006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
0.006
Son-
water
partition
• « .
oodncmn.
K«
fcm'/B)
2.90E«02
3.42E-01
4.566-01
7.S6E-01
3.12E-01
9.84E-01
308E»02
1.22E»00
336E-01
142E»03
2.26E»00
3.10E»00
3.12E-01
2.28E-01
3.90E-01
2.82E-01
1.S6E-01
6.54E«01
1.33E»00
4.09E»01
4.34E»01
4.19E»01
2.25E»02
i.o8E*oi
1.37E»01
5.7SE«01
1.10E*01
5.40602
S.60E-02
7.86E-01
S.47E«00
4.74E-01
ieoe»oo
7.66E-01
301E»00
9.24E+00
394E-01
4.56E-01
S.64E-01
1.70E»00
300E-02
6.60E-02
Ftoor-
tnltlal Source will
toll vapor tatrn
cone., cone., perimeter,
C, C^.
(mgncB) (o/cm1)
1.00E»00 1.4SE-OS
1.00E»00 4 55E-01
1.006*00 1.58E-03
1.00E»OO 290E-02
1.00E-OO 1.02E*00
1.006*00 9.15E-01
1.00E*00 6.77E-06
1.00E*00 1.33E-01
1.00E»00 3.436-01
1.00E»00 1.S5E-08
1.00E«00 3.83E-02
1.00E»OO 3.73E-02
1.006*00 3.25EO1
1.00E*OO 1.S4E-Q1
1.006*00 1.47E»00
1.006*00 2.97E41
1 OOE'OO 431E-01
1.00E«00 1.68E-06
1.00E«OO 2.15E-01
1 OOE'OO 5.86E-W
1 OOE«00 7.81E-06
1.00E»00 2.32E-02
1.00E«00 9.77E05
1.00E»00 2.63E-05
I.OOE'OO 1.02E-06
LOOE'OO 1.23E-02
1.00E«OO 1.3SE-O2
1 OOE'OO 2 20E«00
1 OOE*00 453E-01
1.00E«00 948E-04
1.00E«00 2.SOE-02
1.00E«00 2.60E-02
1.00E»00 3.49E-01
I.OOE'OO 2.66E-01
1.00E«00 4.S1E-05
1.00E«00 1.1BE-02
1.00E«00 9.07E-01
1.00E»00 7.27E-02
1.00E»00 3.77E-01
1.00E«00 945E-05
1.00E«00 1.71E-01
1.00E*00 4.22E»00
XM
(cm)
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
3400
Crack
depth
below
grade.
ZM.
(cm)
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
Crack
radlut.
rna
(cm)
4.06
4.06
4.08
4.06
4.08
4.06
4.06
4.06
4.06
4.06
4.06
4.08
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.08
4.06
4.06
4.06
4.06
4.06
4.06
4.06
4.06
Average Souroa-
vapor Mdo.
flow rate separation
IntobMo., a»M).
QM
(em1/*)
2.59
2.59
2.S9
2.S9
2.99
2.59
2.S9
2.59
2.59
2.59
2.99
2.59
2.59
2.59
2.59
2.59
2.S9
2.59
2.59
2.59
259
2.59
259
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.59
2.S9
It
(em)
15
15
15
15
15
15
15
15
15
15
IS
15
15
15
15
15
15
15
15
15
15
15
15
IS
IS
15
15
15
15
15
15
15
15
15
IS
15
15
15
15
15
15
15
tnooof
V
4.S2E-11
9.37E-06
259E-08
1.02E-07
2.S1E-05
1.69E-05
2.45E-11
229E-06
8.45E-06
S.02E-12
5.92E-07
6.09E-07
9.22E-08
3.79E-06
3.UE-05
S49E-06
6.38E-06
4.97E-12
362E-06
155E-09
2.26E-11
3.08E-07
1.25E-09
1.09E-10
423E-12
4.69E-08
7.96E-09
379E-05
1.0BE-05
1.71E-06
4.20E-07
437E-07
3.95E46
9.S2&06
1.24E-10
8.35E-08
1.88E-05
1.3XE-06
1.06E-05
7.03E-10
345E-08
1.06E44
Crack
Bkhj. eftecHve
Area of foundation' dlfnnkm
basement, thlckneaa, coefficient.
A.
(em')
138E»06
138E«06
1.38E»06
138E«06
1.38E*06
138E+06
138E»08
138E»06
1.38E»06
1.38E»08
1.38E'06
1.38E«06
1.38E*06
138E»06
1.38E-06
1.3BE*06
1.38E->06
138E«06
138E*06
1.38E»06
1.38E-06
138E»06
138E-06
1.38E*06
1.38E«08
1.38E»08
1,38E»06
1.36E«06
138E»06
1.36E»06
1.36E*06
138E«06
1.38E»06
1.38E«06
1.36E«06
138E»06
1.38E»06
138E«06
1.36E«06
1.38E»06
138E«06
1.38E«06
Um>
(cm)
IS
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
13
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
DM
(em'/t)
1.05E-03
6956-03
S.S3EO3
1.19E-03
831E-03
6.23E-03
9.43E-04
563E-03
831E-03
1.09E-03
551E-03
551E-03
S.93E-03
831E-03
7.19E03
6.2SE-03
SOOE-03
9.99E-04
5.99E43
895E-04
975E-04
4.48E-03
433E-03
1.41E«
1.41E-03
1.29E-03
1.99E-04
5.82E-03
807E-03
607E-03
367E«J
S.67E-03
3.75E-03
6.95E-03
9.27E-04
2.40E-C3
623E-03
6.23E-03
631E-03
2.51E-03
6.79E-03
8.47E-03
Depth to
Crack- BWfl. source
tototal Area of ventilation lower Exposure
area crack. rate. boundary, duration.
ratio. A«a Indoor O***,
n (cm*) B (cm'/s)
0.01 1.38E»04 3.65E«01 2 90E*04
001 1.38E«04 2.48E»02 2.90E»04
0.01 1.38E»04 1 96E«02 2.90E<04
0.01 1.38E»04 4.34E«01 2.90E>04
0.01 1.38E*04 2.97E«02 290E»04
0.01 1.38E»04 2.23E+02 2.90E»04
001 1 38E«04 3.46E»01 2.90E»04
0.01 1.38E*04 2 09E»02 2.90E«04
0.01 1 38E»04 2.97E*02 2.90E>04
0.01 1.38E«04 4.00E»01 290E»04
0.01 1.38E»04 1.97E»02 2.90E»04
0.01 1.38E«04 1 97E»02 290E«04
0.01 1.38E«04 2 12E-02 2.90E*04
0.01 1.38E«04 297E-02 290E»04
0.01 1.38E*04 2.57E«02 2.90E«04
001 1.38E*04 2 23E«02 2.90E>04
0.01 1.38E»04 1.79E»02 2.90E*04
0.01 1.38E*04 366E»01 2.90E*04
001 1.38E»04 2 14E«02 2.90E*04
001 1.36E«04 3 29E»01 2 90E*04
0.01 t.38E*04 357E-01 290E-CM
0.01 1.38E«04 1 61E»02 2 90E«04
0.01 1.38E*04 1 55E»02 2.90E*04
0.01 1.38E«04 5 11E-01 2.90E->04
0.01 1.38E»04 5.11E»01 2.90E«W
0.01 1.38E»04 4.68E»01 290E'04
001 1.38E»04 808E<-00 290E»W
0.01 1.38E»04 2.08E«02 2.90E«04
0.01 1 38E«04 2.88E»02 2.90E*04
0.01 1.38E»04 2 17E»02 290E-W
0.01 1 38E»W 2.03E-02 2 .90E»04
0.01 1 38E»04 2.03E»02 2.90E«04
0.01 1 38E*04 206E»02 290E«04
0.01 1 38E+04 2.48E»02 2 .90E«04
0.01 1.38E»04 340E»01 290E«04
0.01 1 38E»04 8 63E«01 2.90E.M
0.01 1 38E«M 2 23E«02 290E'M
0.01 1.38E»04 2.23E«02 2.90E*04
0.01 1.38E>04 2.26E«02 2.90E«04
0.01 1.38E«04 903E»01 2 90E«W
0.01 1.36E»04 2.43E*02 290E-W
0.01 1.36E*04 302E*02 290£«04
4H,
(cm)
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
i
(sec)
9.46E«08
9.46E*08
916E-08
9.46E«08
9.46E<08
946E«08
946E«08
946E«08
9.46E«08
946E«08
9.46E«08
946E-08
946E«08
9.46E*08
946E«08
946E«08
946E.08
9.46E-08
946E«08
946E«08
946E-08
946E»08
946E>08
946E»08
946E>08
946E«08
9.46E-08
9.46E.08
9.46E»08
9.46E«08
9.46E«08
9.46E«08
9.46E*OB
946E*08
9.46E-08
946E»08
946E-08
9.46E«08
946E'08
9.46E*08
946£'03
9.46E-08
-------
COMPARISON OF INDOOR AND OUTDOOR INHALATION SSU FOR VOLATILE CONTAMINANTS
Chemical
Aldrin
Benzene
Bis(2-chloroethyl)ether
Bromoform
Carbon bisulfide
Carbon tetrachloride
Chlordane
Chlorobenzene
Chloroform
DDT
1,2-Dichloroben2ene
1.4-Dlchlorobenzene
1.1-Oichloroethane
1.2-Oichloroethane
1,1-Dlchloroethylene
1 . 2-Dlchloropropane
1 . 3-Dichloropropene
Oieldrin
Ethylbenzene
Heptachlor
Heplachlof epoxide
Hexachloro-1,3-butad!ene
Hexachlorobenzene
HCH-alpha(alpha-BHC)
HCH-beta(beta-BHC)
Hexachtorocyclopentadiene
Hexachloroethane
Methyl bromide
Methylene chloride
Nitrobenzene
Styrene
1.1.2.2-Tetrachloroethane
Tetrachloroethylene
Toluene
Toxaphene
1 .2.4-Trichlorobenzene
1.1.1-Trichloroethane
1.1,2-Trichloroethane
Trichloroethylene
2.4.6-Trichlorophenol
Vinyl acetate
Vinyl chloride
NA • not applicable.
' * SSL based on CM.
Infinite
tource
Indoor
attenuation
coefficient.
a
(unit less)
932E-05
265E-04
221E-04
9.59E-09
3.07E-04
2.43E-04
9.11E-05
2.30E-04
3.076-04
9.40E-05
2.20E-04
2.20E-04
2.33E-04
3.07E-04
2.72E-04
. 2.43E-O4
2.05E-04
9.216-05
2.3SE-04
9.02E-05
916E-05
1.89E-O4
1.84E-O4
1.01E-04
1.01E-04
9.80E-05
7.816-05
2.30E-04
300E-04
2.38E-04
2.256-04
2.25E-04
2.26E-04
2.65E-04
908E-05
1.276-04
2.43E-04
2.43E-04
2.456-04
1.30EO4
2.60E-04
3.12E-04
Finite
tource
Inrtnnt
mOOOf
attenuation
coefficient.
a
(unlttett)
868E-05
832E-05
8.87E-03
8.SOE-09
7.936-05
7.786-05
8.66E-05
8.666-05
8.516-05
8.696-05
8-81E-O5
881E-OS
8.15E-05
8.71E-05
7.47E-OS
848E-OS
8.16E-09
8.87E-05
8.SSE-OS
8.64E-OS
8.67E-OS
881E-05
886E-O5
8.74E-09
8.74E-OS
864E-05
7.41E-05
6.75E-05
8.40E-05
8.87E-OS
883E-05
883E-05
8.35605
8.536-05
8.656-05
8.77E-05
7.796-05
8.766-05
8.13E-05
8.82E-05
8.6SE-OS
6.38E-O5
Time for
source
deptetlufi,
Indoor to
(tec)
1.33E«13
363E*08
LOSE* 11
6.51E*09
1.616*08
1.81E*08
2.246*13
1.256*09
4.79E*08
1.24E»14
4.S9E*09
4.46E*09
3.166*08
1,076*09
1.12E*08
5.59E*08
3.88E*OB
1.16E»14
7.71E»08
3.39E*11
2.SOE*13
7.23E*09
1.726*12
7.03E*12
1.82E*14
1.52E*10
2.47E*10
7.55E«07
3.636*08
1.75E*11
6.656*09
6.396*09
4.766*08
6.166*08
4.386*12
1.496*10
1.83E«08
2.28E*09
2.676*08
164E»12
9.65E»C6
3.896*07
Exposure
duration >
time for
depletion
(yes/no)
no
yes
no
no
yes
yes
no-
no
yes
no
no
no
yes
no
yes
yes
yes
no
yes
no
no
no
no
no
no
no
no
yes
yes
no
no
no
yes
yes
no
no
yes
no
yes
no
no
yes
InflnRe Finite
source source
Mog, bW0.
oonc., oonc.,
CM*, CM**
(kQ/lffl j (KflAnj
1.35E-06 1.26E-06
1.20E-01 1.51E-02
3.SOE-04 1.40E-04
2.796-03 2.47E-03
3.13E-01 1.51E-02
2.22E-01 1.51E-02
7.99E-07 7.59E-07
305E-02 1.15E-02
1.056-01 1.51E-02
1.45E-07 1.34E-07
7.99E-03 3.19E-03
8.22E-03 3.28E-03
1 22E-01 1.51E-02
4.73E-02 1.34E-02
4.01E-01 1.516-02
7.21E-02 1 51E-02
8.82E-02 1.31E-02
1.55E-07 1.46E-07
5.066-02 1.516-02
929E-OS S06E-OS
7.16E-O7 6.77E-07
4.38E-03 2.04E-C3
1 BOE-05 866E-06
265E-06 2.30E-06
1.02E-07 8.B8E-08
1.30E-03 1.06E-03
1.05E-03 1.00E-03
5.05E-01 1.51E-02
1.36E-01 1.51E-02
2.256-04 8.41E-05
5.646-03 2.21E-03
5.866-03 2.306-03
7.95E02 1.51E-02
7.106-02 1.516-02
4.096-08 3.90E-06
1.49E-03 1.03E-03
2.206-01 1.51E-02
1.76E-02 6.37E-03
1.416-01 1.516-02
1.23E-05 8.34E-06
445E-02 1.48E-02
1.32E*00 1.51E-02
Inflfrtto
source
Indoor
volattllztlon
factor,
VFMOT
(mVkg)
7.42E«05
8.306*00
2.86E*03
3.596*02
3.20E*00
4.506*00
1.25E*06
3.286*01
9.496*00
6.88E*08
1.25E«02
1.22E*02
8.176*00
2.12E«01
2.496*00
1.39E*01
1.13E«01
6.47E*06
1976*01
1.896*04
1.40E*08
2.28E»02
5.55E«04
3.78E«05
9.76E«06
8.306*02
9.48E*02
1.986*00
7.38E»00
4.446*03
1.776*02
1.71E»02
1.266*01
1.416*01
2.44E«05
6.70E*02
4.546*00
5.67E+01
7.07E»00
813E«04
2.25E«01
7.59601
FMe
source
Indoor
votatliWIon
factor,
VFkA.
(m'/kg)
787E«05
6.636*01
7.13E»03
4.05E»02
6.63E*01
6.63E«01
1.32E*06
8.71E«01
6.636*01
7.44E«06
3.13E*O2
3.05E«02
6.636*01
7.46E*01
6.636*01
8S3E*01
6.636*01
6876*06
6.63E*01
1.98E*04
1.48E*08
4.89E«02
1.16E*OS
4.36E«05
1.136*07
9.426*02
1.006*03
6636*01
6.636*01
1.19E*04
4.53E*02
4.36E*02
6.63E»01
663E«01
2.56E*05
9.71E*02
6.63E*01
1.57E«02
6.636*01
120E*05
6.76E+01
6.636*01
Unit
risk Reference
factor, oonc.,
URF RIC
(pp/hiV (mo/m1)
4.90C-03 NA
8.30E-06 NA
3.30E-04 NA
1. 106-06 NA
NA 1.00E-02
1.50E-05 NA
600E-05 NA
NA 2.00E-02
2.306-05 NA
9.70E-09 NA
NA 2.00E-01
NA 8.00E-01
NA 5.00E-01
2.60E-05 NA
5.00E-05 NA
NA 4.00E-03
3.70E-05 2.006-02
460E-03 NA
NA 1.00E*00
1 30E-03 NA
2.60E-03 NA
220E-05 NA
4.60E-04 NA
180E-O3 NA
5.306-04 NA
NA 7.00EO5
4.00E-06 NA
NA SOOE-03
4.706-07 3.00E-00
NA 2.006-03
NA 1.006*00
5 BOE-05 NA
5.80E-07 NA
NA 4.006-01
3.20E-04 NA
NA 9.006-03
NA 1.00E*00
1.60E-05 NA
1.70E-08 NA
3.10E-OS NA
NA 2.00E-01
8.40E-OS NA
Infinite
source
Indoor
SSL.
carcinogen
(mo/kg)
3.69E-01
2.43E-03
2.11E-02
7.94E-01
NA
7.316-04
5.08E*01
NA
100E-O3
1.73E*02
NA
NA
NA
198E-03
1.216-04
NA
7.466-04
3.42E«00
NA
3.54E-O2
1.31E*00
252E-02
2.94E-01
511E-01
4.48E*01
NA
5.77E-01
NA
3.82E-O2
NA
NA
7.16E-03
5.28E-02
NA
1.86E'00
NA
NA
862E-03
1.01E-02
6.38E*01 .
NA
2.20E05
Infinite
source
Indoor
SSL.
non-
carcinogen
(mo/K9)
NA
NA
NA
NA
3336-02
NA
NA
6.83E-01
NA
NA
2.816*01
1.02E*02
4.26E*00
NA
NA
5.79E-02
2.376-01
NA
2.066*01
MA
NA
NA
NA
NA
NA
6066-02
NA
1.036-O2
2.3tE*01
9.26E«00
1.856*02
NA
NA
5886*00
NA
6.29E*00
4.74E«00
NA
NA
NA
4.69E*00
NA
Finite
source
Indoor
SSU
carcinogen
(mo/kg)
3.96E-01
1.94E-02
526E-02
8.96E-01
NA
1.08E-02
S.34E*01
NA
7.01E-03
1.87E«02
NA
NA
NA
698E-03
323E-03
NA
4.366-03
3.63E*00
NA
3.70EO2
1.36E*00
S.41E-02
611E-01
5.89E-01
5.17E»01
NA
608E-01
NA
3.43E-01
NA
NA
1.83E-02
2.78E-01
NA
1.95E*00
NA
NA
2.39E-02
9.49E-02
9.42E*01
NA
1.92E-03
Finite
source
Indoor
SSL.
non-
carcinogen
(mg/kg)
NA
NA
NA
NA
6.91E-01
NA
NA
1.82E*00
NA
NA
6.53E*01
2.54E*02
3.46E*01
NA
NA
2.76E-01
1.38E*00
NA
691E*01
NA
NA
NA
NA
NA
NA
6.88E-02
NA
3.46E-01
2076*02
2.48E«01
4.726*02
NA
NA
2.766*01
NA
9.11E*00
6.91E*01
NA
NA
NA
1.41E*01
NA
Infinite Finite
source source Outdoor
risk- risk- apparent
based based diffusion
indoor indoor coefficint.
SSL, SSL. a
(mg/kg) (mg/kg) (cm'/s)
3696-01 3966-01 1.99E-09
2.43E-03 194E-02 5 82E-04
2.11E-02 5.26E-02 1.39E-08
7.94E-01 896E-01 5 13E-06
3.33E-02 6.91E-01 1.80E-03
7.31E-04 1.08E-02 9.90E-04
5.08E*01 534E»01 1.08E-09
6.83E-01 1.82E«00 1.12E-04
1.00E-03 701E-03 5.15E-04
1.73E»02 1 87E»02 2.21E-10
2.61E*01 6.53E*01 . 2.74E-05
1.02E»02 2.54E*02 2.78E-05
4.266*00 3.46E*01 5.94E-04
1.98E-03 6.98E-03 2.47E-04
1.21E-04 323E-03 2.40E-O3
5.79E-02 J.76E-01 3 46E-04
7.46E-04 4.36E-03 5.01E-O4
342E»00 363E»00 2.19E-10
2.06E*Ot 691E«01 1.88E-04
3.54E-02 370E-02 6.84E-08
1.31E«00 1 38E*00 9.93E-10
2.52E-02 5.41E02 1.36E-05
2.94E-01 6.11E-01 S.S1E-08
5.11E-01 589E-01 485E-09
4.48E»01 517E»01 1.87E-10
6.06E-02 688E-02 207E-06
5.77E-01 608E-01 354EO7
1.03E-02 346E-01 8.13E-03
3.82E-02 3.43E-01 1.06E-03
9.26E*00 2.48E«01 8.4SE-07
1.85E«02 4.72E*02 1.89E-05
7.16E-03 1.83E-02 2.34E-05
5.28E-02 2.78E-01 2.95E-04
5.88E*00 2.76E*01 2.88E-04
1.866*00 1.95E*00 5.62E-09
6.29E*00 9.11E*00 3.72E-06
4.74E*00 6.91E*01 1.04E-03
8.62E-03 2.39E-02 7.30E-05
1.01E-02 949E-02 6.27E-04
6.38E«01 9.42E«01 3.27E-08
4.69E*00 1.4tE«01 6.78E-04
2.20E-O5 1.92E-03 585E-02
Outdoor
volatilization
factor.
VF.M.
(m'/kg)
9.ME«05
1.82E»03
3.72E»rj4
1.94E*04
1.03E*03
1.39E«03
1.34E*06
4.15E«03
1.93E*03
2.95E»06
8.3BE*03
832E-03
1.80E*03
2.79E«03
895E-03
236E*03
1.96E-03
2.97E*OS
3.20E*03
168E-05
1.39E«08
1.19E*04
1.87E«05
6.29E<05
320E*08
3.05E«04
7.37E*04
4.86E*02
1.35E«03
4.776*04
1.01E*04
907E»03
2.556*03
2.596*03
5.856*05
2.286*04
1.366*03
5.13E»03
1.75E«03
2.43E*05
1.68E<03
1.81E«02
-------
COMPARISON OF INDOOR AND OUTDOOR INHALATION SSLa FOR VOLATILE CONTAMINANTS
Chonricsl
Aldtln
Benzene
Bfe(2-chloroethyr)ether
Bromoform
Carbon dteulflde
Carbon tetrachlortde
Chtordane
Chiofobc nzc no
Chloroform
DDT
1.2-Dlchlorobenzene
1.44Xchlonibenzene
1 .2-dchkxtwthanc
1.1-Dtehloroethyfene
1.2-Otehtoropropane
1.3-OfcMoropfopene
Diddrin •
Ethytbenzena
Heptachlor
HcptBChtw cpoxfdo
Hexaehtoro-1 ,3-butadlene
Hexachlorobenzene
HCH-alpha(alpria-BHC) -
HCH-beta(beta-BHC)
Hexachlorocyctopentsdiene
HeracMoroethane
Methyl bromide
Melhylene chloride
Nitrobenzene
Styrene
1,1,2.2-Tetrachloroethane
Tetrachloroethylene
Toluene
ToxBphene
1.2.4-Trichlorobenzerie
1,1,1 -Trtchloroelriane
1.1,2-Trlehloroethane
2.4.8-Tnchtorophenol
Vinyl acetate
Vinyl chlorld0
NA • not applicable.
••SSL based on CM.
Outdoor
SSL.
c&rcinoQGfl
(mo/kg)
4.89E-01
533E-01
2.74E-01
4.29E«01
NA
2.26E-01
5.42E»01
NA
2.04E-01
7.41E«01
NA
NA
MA
rw
261E-01
4.38E-02
NA
1.29E-01
1.57E»00
NA
3.14E-01
1.30E-00
1.31E»00
988E-01
851E-01
1.47E»01
NA
4.48E»01
NA
6.97E«00
NA
NA
3.81E-01
1.07E»01
NA
4.45E*00
NA
NA
7.81E-01
1.90E«02
NA
5.25E-03
Outdoor
SSL.
non-
carcinogen
(mgftg)
NA
NA
NA
NA
1.08E»01
NA
NA
8.66E»01
NA
NA
e.S«
NA
NA
9.83E«00
4.09E«01
NA
333E»03
NA
NA
NA
NA
NA
NA
NA
2.54E*00
4.21E«03
9.95E«01
1.05E»04
NA
NA
108E«03
NA
2.14E«02
142E»03
NA
MA
rW
NA
3.51E»02
NA
Rtok-
based
outdoor
SSL
(rng/kg)
4.89E-01
333E-01
2.74E-01
4.29E»01
1.08E»01
2.26E-01
5.42E»01
866E*01
2.04E-01
7.41E»01
1.75E*03
8.94E«03
2.61E-01
4.36E-02
983E«00
129E-01
157E«00
333E«03
3.14E-01
1.30E»00
1.31E»00
988E-01
8.51E-01
1.47E«01
2.23E*00
4.48E*01
254E»00
697E'00
995E«01
1.05E«04
3.81E-01
1.07E»01
10BE»03
4.45E«00
2.14E«02
142E»03
7.81E-01
1.90E»02
351E»02
5.25E-03
Pure
oonipoi wilt
solubility.
S
(mpA)
7.84E-02
1.78E>03
1.18E«04
3.21E«03
2.67E«03
792E«02
2.19E-01
4.09E«02
7.96E»03
3.41E-03
t OKCAlM
1.29E*OZ
831E»03
3.00E»03
288E«03
1.55E«03
1.87E-01
1.73E«02
2.73E-01
268E-01
2.54E«00
862E-03
2.40E*00
9.42E-01
1 .53E*00
4.08E«01
145E-04
1.74E»04
1.92E»03
2.57E*02
3.07E»03
2.32E»02
5.58E*02
0.79E-01
3.07E*01
1.17E»03
4.40E«03
7.53E*02
2.24E»04
2.73E«03
Son
saturation
cone..
CM
(mgflqj)
2.28E»01
6.61E«02
656E«03
276E-03
1.36E»03
1.04E«03
6.74E»01
5.59E«02
3.71E«03
4.85E*00
2.33E»02
2.81E»03
2.04E*03
1.0BE«03
4.32E«02
1.22E«01
2.57E»02
1 12E»01
1.17E*01
1.07E*02
1.94E«00
2.56E«01
747E-00
4.53E«02
383E'03
3.73E«03
1.70E»03
1.44E»03
1.77E*03
4.72E»02
521E»02
2.11E«00
2.87E«02
9806*02
2.48E*03
1.3SE«03
3.01E«03
2.28E«03
Indoor
SSL.
Infinite
source
(mg/Hg)
0.4
0.002
0.02
0.8
0.03
0.0007
51
0.7
0.001
5 '
26
102
0.002
0.0001
0.06
00007
3
21
004
1
0.03
03
0.5
7 •
0.06
0.6
0.01
0.04
9
189
0.007
0.09
6
2
6
9
0.009
n fii
U.Ul
64 .
5 -
0.00002
Indoor
SSL,
finite
source
(mg/kg)
0.4
002
005
0.9
0.7
• 0.01
53
2
0.007
5 '
65
235 '
*e
oO
0007
0.003
0.3
0.004
4
69
0.04
1
0.05
06
0.6
7 •
A f!7
U.Uf
0.6
03
0:3
25
472
0.02
0.3
. 28
2
9
69
0.02
n no
u.tw
94
14
0.002
Outdoor
SSL.
Infinite
source
(rngfltg)
05
OS
0.3
43
11
0.2
54
87
02
5 '
297 *
235 *
0.3
0.04
10
0.1
2
257 •
03
1
1
1
0.9
7 *
2
45
3
7
100
1439 •
0.4
11
521 '
2 '
214
980 *
1
3
190
351
0.01
-------
APPENDIX C
LIMITED VALIDATION OF THE HWANG AND FALCO MODEL FOR
EMISSIONS OF SOIL-INCORPORATED VOLATILE ORGANIC
COMPOUNDS
-------
LIMITED VALIDATION OF THE HWANG
AND FALCO MODEL FOR EMISSIONS
OF SOIL-INCORPORATED VOLATILE
ORGANIC COMPOUNDS
by
Environmental Quality Management, Inc.
3109 University Drive, Suite B
Durham, North Carolina 27707
Contract No. 68-D8-0111
Work Assignment No. 92-10
Subcontract No. 0111-EQ1
PN5055
Janine Dinan, Work Assignment Manager
U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF SOLID WASTE AND EMERGENCY RESPONSE
WASHINGTON, D.C. 20460
September 1992
-------
DISCLAIMER
This project has been performed under contract to The Cadmus Group, Inc. It
was funded with Federal funds from the U.S. Environmental Protection Agency under
Contract No. 68-D8-0111. The content of this publication does not necessarily reflect
the views or policies of the U.S. Environmental Protection Agency nor does mention of
trade names, commercial products, or organizations imply endorsement by the U.S.
Government.
-------
CONTENTS
Figures iv
Tables v
Acknowledgment vi
1. Introduction 1
Soil PRGs 2
Project objectives 2
Technical approach 3
2. Review of the RAGS/HHEM, Part B Volatilization Model 4
Model derivation 6
Summary of model assumptions and limitations 11
3. Model Validation 14
Bench scale validation 15
Pilot scale validation 25
4. Parametric Analysis of the Hwang and Falco Model 38
Affects of soil parameters 38
Affects of nonsoil parameters 41
5. Conclusions 44
References 47
Appendices
A. Bench-Scale Model Validation Data A-1
B. Pilot-Scale Model Validation Data B-1
ni
-------
FIGURES
•
Number Page
1 Predicted and measured emission rates of lindane versus time 18
2 Comparison of modeled and measured emission rates of lindane 19
3 Predicted and measured emission rates of lindane versus time
employing the Millington and Quirk expression of D^ 21
4 Predicted and measured emission rates of dieldrin versus time 22
5 Predicted and measured emission rates of dieldrin versus time
employing the Millington and Quirk expression of D^ 23
6 Comparison of modeled and measured emission rates of dieldrin 24
7 Predicted and measured emission rates of benzene versus time 29
8 Predicted and measured emission rates of toluene versus time 30
9 Predicted and measured emission rates of ethylbenzene versus time 31
10 Comparison of modeled and measured emission rates of benzene,
toluene, and ethylbenzene 33
11 Predicted and measured emission rates of benzene versus time
employing the Millington and Quirk expression of D. 35
12 Predicted and measured emission rates of toluene versus time
employing the Millington and Quirk expression of D^ 36
13 Predicted and measured emission rates of ethylbenzene versus time
employing the Millington and Quirk expression of D^ 37
IV
-------
TABLES
•
Number
1 Summary of Statistical Analysis of Bench-Scale Validation 26
2 Summary of Statistical Analysis of Pilot-Scale Validation Using
the Default Hwang and Falco Effective Diffusion Coefficient 32
3 Summary of Statistical Analysis of Pilot-Scale Validation Using
the Millington and Quirk Expression for Effective Diffusivrty 34
-------
ACKNOWLEDGMENT
This report was prepared for the U.S. Environmental Protection Agency by
Environmental Quality Management, Inc. of Durham, North Carolina under contract to
The Cadmus Group, Inc. Dr. Joanne Wyman with The Cadmus Group, Inc. served as
the project technical monitor and Craig Mann with Environmental Quality Management,
Inc. managed the project and was author of the report. Janine Dinan of the U.S.
Environmental Protection Agency's Toxics Integration Branch provided overall project
direction and served as the Work Assignment Manager.
VI
-------
SECTION 1
INTRODUCTION
The Risk Assessment Guidance for Superfund: Volume I - Human Health
Evaluation Manual (RAGS/HHEM) Part B provides guidance on using the U.S.
Environmental Protection Agency (EPA) toxitity values and exposure information to
derive risk-based preliminary remediation goals (PRGs). In general, PRGs provide
remedial design staff with long-term cleanup level targets to use during analysis and
selection of remedial alternatives.
The National Contingency Plan which is found in 40 CFR, Part 300, mandates
that the selected remedial alternative meet all applicable or relevant and appropriate
requirements (ARARs), and provide protection of human health and the environment.
PRGs are developed to quantify the standards that remedial alternatives must meet in
order to achieve these two threshold criteria." Of major concern in establishing PRGs
are "long-term effectiveness and permanence" of the remedy. These balancing criteria
for remedy selection are used to establish the risk posed to the community once the
remediation is complete. Risk-based PRGs quantify the degree of residual risk after
cleanup has been completed. If ARARs do not exist for the contaminant of concern or
for the media of concern, risk-based PRGs are developed to protect human health.
PRGs are typically developed during the scoping phase or concurrent with initial
phases of the Remedial Investigation/Feasibility Study (RI/FS). Risk-based PRGs are
considered initial guidelines developed with readily available information and can be
modified as additional site data are obtained. A risk-based concentration is considered
a final remediation level only after appropriate analysis in the RI/FS and in the Record
of Decision (ROD).
-------
1.1 SOILPRGs
PRGs for the soil medium are calculated for carcinogenic and noncarcinogenic
contaminants from standard residential and commercial/industrial land-use equations
given in RAGS/HHEM. Part B. Integral to these equations is the soil-to-air volatilization
factor (VF) which defines the relationship between the concentration of contaminants in
soil and the volatilized contaminants in the air. The VF (m3/kg) is calculated as the
inverse of the ambient air concentration at the center of a ground-level, nonbouyant
area source of emissions. The RAGS/HHEM, Part B equation for calculating the VF
consists of two parts: 1) a volatilization model, and 2) a dispersion model.
The volatilization model mathematically predicts volatilization of contaminants fully
incorporated in soils as a diffusion-controlled process. The basic assumption in the
mathematical treatment of the movement of volatile contaminants in soils under a
concentration gradient is the applicability of the diffusion laws. The changes in
contaminant concentration within the soil as well as the loss of contaminant at the soil
surface by volatilization can then be predicted by solving the diffusion equation for
different boundary conditions.
This document reports on several studies in which volatilization of contaminants
from soils was directly measured and from which data were obtained necessary to
calculate emissions of contaminants using the RAGS/HHEM, Part B volatilization model.
These data are then compared and analyzed by statistical methods to determine the
relative accuracy cf the model.
1.2 PROJECT OBJECTIVES
The primary objective of this project was to assess the relative accuracy of the
RAGS/HHEM, Part B volatilization model using experimental emission flux data from
previous studies as a reference data base.
-------
1.3 TECHNICAL APPROACH
The following series of tasks comprised the technical approach for achieving the
project objective:
1. Review the theoretical basis and development of the RAQS/HHEM, Part B
volatilization model to verify the applicable model boundan/condrtions and
variables, and to document the assumptions and limitations of the model.
2. Perform a literature search and survey (not to exceed nine contacts) for
the purpose of determining the availability of acceptable emission flux data
from experimental and field-scale measurement studies of volatile organic
compound (VOC) emissions from soils. Acceptable data must have
undergone proper quality assurance/quality control (QA/QC) procedures.
3. Determine if the emission flux measurement studies referred to in Task
No. 2 also provided sufficient site data as input variables to the
volatilization model. Again, acceptable variable input data must have
undergone proper QA/QC procedures.
4. Review, collate, and normalize emission flux measurement data and
volatilization model variable data, and compute chemical-specific emission
rates for comparison to respective measured emission rates.
5. Perform statistical analysis of the results of Task No. 4 to establish the
extent of correlation between measured and modeled values and perform
parametric analysis of key model variables.
-------
SECTION 2
REVIEW OF THE RAGS/HHEM, PART B VOLATILIZATION MODEL
The soil-to-air volatilization factor as calculated from Equation No. 8 of the
RAGS/HHEM, Part B incorporates the Hwang and Falco (1986) model for volatilization
of polychlorinated byphenyls (PCBs) incorporated in soils as developed by EPA's
Exposure Assessment Group (EPA, 1986a). The model calculates the instantaneous
emission flux at time, t, as:
2- • CM (1)
a f)°-5 Kd
.
where NA = Instantaneous emission flux, g/cnf-s
E = Soil porosity, dimensionless
D.J = Effective diffusivrty of component i in soil, cmVs (= D, • E333)
D, = Diffusivrty of component i in air, crrf/s
n = 3.1416
t = Time from soil sampling, s
H = Henry's Law constant, dimensionless form
KH = Soil/water partition coefficient, cm3-water/g-soil
C,0 - Initial contaminant soil concentration , g/g-soil
-------
and,
o = . jl (2)
[£ * P, • (1-£) •
where P, is the true density of soil (i.e., particle density, p).
The model assumes that the surface of the contaminated soil column is exposed
to the atmosphere. The initial and boundary conditions are:
. 1. Initial condition Q = (H/^)C.e, at t = 0, L s: 0
2. Boundary condition Q = (H/^,)C.0, at L = «, t > 0
3. Boundary condition Q = 0, at L = 0, t > 0
where Q = concentration of component i in the vapor phase in the air-filled soil pore
spaces, and L = depth of contaminated soil column.
The average flux rate, F^, over exposure interval, T, is time-averaged as:
/v; (7) = 2/V.m (3)
or
N. = 2/V.
To calculate the total average emission rate, Q(g/s), the average flux rate is multiplied
by the area of contaminated soil:
-------
Q = A(N.)
2.1 MODEL DERIVATION
The Hwang and Falco model is derived from the methods presented by Farmer
and Letey (1974) and Farmer, et al. (1980). Farmer, et al. considered a system where
pesticide is uniformly mixed with a layer of soil and volatilization occurs at the soil
surface. If diffusion is the only mechanism supplying pesticide to the surface of an
isotropic soil column, and if the diffusion coefficient, D, is assumed to be constant, the
general diffusion equation is:
_£ - £ = o (5)
3x2 D dt
where c = Soil concentration, g/cm3 total volume
x = Distance measured normal to soil surface, cm
D = Apparent diffusion coefficient in soil, cxrf/s
t = Time, s.
If the pesticide is rapidly removed by volatilization from the soil surface and is
maintained at a zero concentration, the initial and boundary conditions are:
c = C0att = 0,0 < x< L
c = 0 at x = 0 and t > 0
3c/3x = 0 at x = L
where L is the depth of the contaminated soil column (cm) and C0 is the initial
concentration in soil (g/cm3).
-------
Recognizing the analogy between the heat transfer equation (Fourier's Law) and
the transfer of matter under a concentration gradient (Pick's Law), Farmer, et al.
employed the heat transfer equations of Carslaw and Jaeger (1959, page 97, Equation
No. 8) to solve the diffusion equation given these boundary and initial conditions for the
emission flux, f(g/cm2-s), as: ,
CO
AE . + 2 T (-1)" exp(-A7^2/Of) <6)
(/rOf)^ ^
where the following analogies to the Carslaw and Jaeger solution are assumed:
v (temperature) = c
V0 (initial temperature) = C0
k (thermal diffusivity), and
K (thermal conductivity) = D.
The summation term in Equation No. 6 decreases with increasing L and
decreasing D and t. If this term is small enough to be negligible, Equation No. 6 for L
= oo, reduces to:
f = D CJin Of)0-5 (7).
The concentration for the semi-infinite case is given by Crank (1985) as:
C = C0 erf [x/2(Of)°-5] (8)
-------
Equation Nos. 7 and 8 are also applicable to a finite system (0 < x < L) as long as the
concentration at the lower boundary of the soil layer, x = L, is not decreased by
pesticide moving in the upward or downward direction. To estimate the maximum time
at a given set of parameters for which this model is adequate, the critical value of loss
to C0 can be assumed to be set equal to 1 percent. With this assumption, the
boundary conditions used in deriving Equation Nos. 7 and 8 are violated if
f > /.'/14.4 D
(9)
Hwang and Falco (1986a) redefined the general diffusion equation given by
Farmer, et al. in Equation No. 5 as:
(10)
a*2 E • H o. ar
where C = Concentration in soil pore spaces, g/cm3
c = Soil concentration, g/cm3 total volume
P. = True density of soil, P., multiplied by (1-E), g/cm3
K, = Soil/water partition coefficient, cm3-water/g-soil
H = Henry's Law constant, dimensionless form
E = Soil porosity, dimensionless
D.J = Effective diffusivity of component i in soil, cm2/s (=D, • E333)
D, = Diffusivity of component i in air, cnrr'/s
x = Distance measured normal to soil surface, cm
t = Time, s
8
-------
or
(111
a*2 ar
where
[£«• (P,)(1-
(12)
In this manner, Hwang and Falco redefined the general diffusion equation given by
Farmer, et al. for vapor phase diffusion and soil adsorption.
It should be noted that both the Farmer, et al. equation and the Hwang and
Falco equation have many of the same assumptions. Both equations assume that
vapor phase diffusion is the only transport mechanism moving contaminants from the
soil column to the soil surface. This assumes no transport via nonvapor phase
diffusion or mass flow due to capillary action within the soil column. These
assumptions were shared due to the relative insolubilities of hexachlorobenzene
(Farmer, et al. 1980) and PCBs (Hwang and Falco 1986a) which comprised the
contaminants of concern for the respective studies. In addition, the experimental
conditions of Farmer, et al. and the theoretical concerns of Hwang and Falco excluded
mass flow from consideration. Farmer, et al. and Hartley (1964), however, suggest that
a soil solution/soil air partition coefficient defined as the ratio of the solubility of the
contaminant in water to the saturation vapor concentration of the contaminant may be
used to estimate the major mode of diffusion between the vapor and nonvapor phase.
Because the vapor phase diffusion coefficient is approximately 10* larger than the
solution (nonvapor) phase diffusion coefficient, a partition coefficient of 10* may be
considered as a transition point for determining when vapor diffusion or solution
diffusion becomes dominant. Chemicals with partition coefficients much smaller than
10* will diffuse mainly in the vapor phase while those with partition coefficients much
-------
greater than 10* will diffuse mainly in the solution phase. Therefore, chemicals which
diffuse mainly in the solution phase are not considered applicable to either the Farmer,
et al. or Hwang and Falco procedures.
In the Hwang and Falco model, the contaminant concentrations in soil and in
interstitial vapors are assumed to be in local equilibrium and related by the following
equation:
C= \±L\CB (13)
Hwang and Falco thus assume that the concentration of interstitial vapors is a
function of the ratio of the Henry's Law constant and its soil/water partition coefficient.
This relationship holds true below the soil concentration at which the absorptive
properties of the soil and the theoretical dissolution limit of the contaminant in the
available free soil moisture have been reached. The term (H/K^) is thus defined by
Hwang and Falco as the soil/air partition coefficient (K,,.).
Finally, Hwang and Falco substitute the effective dtffustvity is soil, D_, for the
Farmer, et al. apparent diffusion coefficient in soil, D. The effective diffusivrty in soil, D^,
is used to account for tortuosity effects in porous media; however, the effective porosity
for dry soil (D, • E° ") is used for simplicity. Hwang and Falco, however, do reference
the Farmer, et al. 098°) procedures for incorporating the effect of soil geometry and
soil moisture in the porosity term. Farmer, et al. (1980) refined the flux calculations to
give a decreased flux rate due to reduced air-filled porosity taking into account the
effect of soil moisture on tortuosity. The equation from Millington and Quirk (1961) is:
D ,- D (/33/P) (14)
where D^ = Effective diffusivrty in soil, crrr'/s
10
-------
P. = Air-filled soil porosity, dimensionless
P, = Total soil porosity, dimensionless.
Therefore, the Hwang and Falco solution for instantaneous emission flux at the
soil surface (Equation No.1) a is direct derivation of the Farmer, et al. reduced equation
for diffusion-controlled emission flux (Equation No.7). The Hwang and Falco equation,
however, attempts to establish the relationship between the concentration in air and the
concentration adsorbed to soil through the use of the air/soil partition coefficient, K^.
This is equivalent to the Farmer, et al. isotherm coefficient, R,,, and is approximated in
moist soils as the ratio of the Henry's Law constant to the soil/water partition
coefficient, 1^. As an equivalent expression to the Farmer, et al. reduced equation, the
Hwang and Falco equation is valid until time t = L2/14.4a (Equation No. 9).
2.2 SUMMARY OF MODEL ASSUMPTIONS AND LIMITATIONS
The Hwang and Falco volatilization model is analogous to the mathematical
solution for heat flow in an infinite solid (Carslaw and Jaeger, 1959), and as such it's
applicability to diffusion processes is limited to the initial and boundary conditions upon
which the model is derived. The model assumes that there are no other vectors for
movement or loss of contaminant other than vapor phase diffusion from the soil column
to the soil surface (diffusion-controlled). Other vectors such as mass flow due to
capillary action, redistribution of contaminants due to rain events, loss of contaminants
at the lower boundary due to leaching, nonvapor phase or solution diffusion,
biodegradation, photolysis, and possible codistillation at the soil surface are not
considered.
The model also assumes an infinitesimal layer of noncontaminated soil at the
soil-air-interface once local equilibrium has been reached (Q = 0 at L = 0, t > 0). This
implies adequate wind velocity at the boundary layer to completely volatilize
contaminants at the soil surface rendering the process diffusion-controlled. The
boundary conditions also specify that the depth of the contaminated soil column, L, is
11
-------
infinite, thus assuming zero vertical movement Or loss from the lower boundary over an
infinite time period. In this manner, the model is analogous to the Farmer, et al.
reduced flux solution (Equation No. 7). The validity of this argument becomes
questionable with decreasing depth of the actual contaminated soil column, L, and
increasing time. «
The soil/air partition coefficient, K^., assumes that under equilibrium conditions,
contaminants are partitioned only between the aqueous phase, interstitial vapors, and
that adsorbed to soil. The Henry's Law constant employed to derive K^. is the ratio of
the contaminant's vapor pressure and solubility in water. Thus, K. assumes an excess
of water at equilibrium. In this manner, back-calculation of the total concentration in soil
(i.e., the PRO) employing the Hwang and Falco model is invalid for soil concentrations
at and above which the saturation vapor concentration is achieved. This concentration
is estimated in the RAGS/HHEM, Part B as the saturation concentration,
(15)
where Ce- = Saturation concentration, mg/kg
t^, = Soil/water partition coefficient, L/kg
s = Solubility in water, mg/L
r\, = Soil moisture content, kg-water/kg-soil
6m = Soil moisture content, L-water/kg-soil.
As defined in Hwang and Falco (1986), the soil/water partition coefficients for
PCBs, t^,, were experimental values reported in the scientific literature. In the
RAGS/HHEM, Part B equation, t^ is defined as the product of the octanol/water
partition coefficient, K^, and the soil organic carbon fraction. This theoretically derived
value of Ki does not account for the adsorptive properties of the day, silt, or dissolved
12
-------
organic content of the soil which may increase the actual absorptive soil properties,
thereby effecting the time for the flux to arrive at steady-state conditions.
The effective diffusion coefficient, D^, used in the Hwang and Falco model
assumes dry soil, thus maximum diffusion conditions. Use of the Millington and Quirk
expression (Equation No. 14) will reduce the effective diffusivrty. Although air-filled
porosity is found be to the major factor controlling volatilization flux through the soil-
water-air system, the apparent vapor diffusion coefficient does not depend only on the
amount of air-filled pore space (Farmer, et al., 1980). The presence of liquid films on
the solid surfaces not only reduce the porosity, but also modifies the pore geometry
and the length of the gas passage. Farmer, et al. thus recommend use of the
Millington and Quirk expression to derive the effective diffusion coefficient when an
estimate of long-term soil water contents can be made.
In general, the Hwang and Falco model describes the desorption of
contaminants from soil and the vapor phase diffusion of the contaminants to the soil
surface to replace that lost by volatilization to the atmosphere. The model assumes an
exponential decay curve over time once steady-state conditions are achieved. In
actuality, there is a high initial flux rate from the soil as surface concentrations are
depleted. The lower flux rate characteristics of the later portion of the decay curve is
thus determined by the rate at which contaminants diffuse upward. This type of
desorption curve has been well documented in the literature. It is-important to note that
the Hwang and Falco model does not account for the high initial rate of volatilization
before equilibrium is attained and will tend to underpredict emissions during this period.
Finally, the Hwang and Falco model is applicable only to single chemical compounds
fully incorporated into isotropic soils. Effective solubilities and activity coefficients in
multicomponent systems are not addressed in the determination of the air/soil partition
coefficient nor is the effect of nonlinear soil adsorption and desorption isotherms.
However, because of the complexities involved with theoretical solutions to these
effects, their contribution to the relative accuracy of the model is difficult to predict,
especially in multicomponent systems.
13
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SECTION 3
MODEL VALIDATION
To achieve the project objective, Environmental Quality Management, Inc. (EQ)
executed a literature search and a survey of professional environmental
investigation/research firms as well as regulatory agencies to obtain experimental and
field data suitable for comparing modeled emissions with actual emissions. The
literature search uncovered several papers and bench-scale experimental studies
concerned with the volatilization and vapor density of pesticides and chlorinated
organics incorporated in soils (Farmer, et al., 1972, 1974, and 1980; Spencer and
Cliath, 1969 and 1970; and Spencer, 1970).
From the literature search, one study was found which met the data
requirements for this project (Farmer, et al., 1972 and 1974). This study reports the
experimental emissions of lindane (1,2,3,4,5,6-hexachlorocydohexane, gamma isomer)
and dieldrin (1,2,3,4, lO,lO-hexachloro-6,7-epoxy-1,4,4a,5I6,7,8,8a-octahydro-1,4-endo,
exo-5, 8-dimethanonapthalene) incorporated in Gila silt loam. Suitable data for input to
the Hwang and Falco model were also available from this study.
The objective of the survey of professional firms and regulatory agencies was to
find pilot-scale or field-scale studies of volatilization of organic compounds using the
EPA emission isolation flux chamber. The candidate flux chamber studies must also
have provided adequate data for input to the Hwang and Falco model.
Flux chamber studies were chosen to provide pilot-scale or field-scale
measurement data needed for model validation. Rux chambers have been widely used
to measure flux rates of VOCs and inorganic gaseous pollutants from a wide variety of
sources. The flux chamber was originally developed by soil scientists to measure
biogenic emissions of inorganic gases and their use dates back at least two decades
14
-------
(Hill, et al., 1978). In the early 1980's, EPA became interested in this technique for
estimating emission rates from hazardous wastes and funded a series of projects to
develop and evaluate the flux chamber method. The initial work involved the
development of a design and approach for measuring flux rates from land surfaces. A
test cell was constructed and parametric tests performed to assess chamber design
and operation (Kienbusch and Ranum, 1986 and Kienbusch. et al., 1986). A series of
field tests was performed to evaluate the method under field conditions (Radian
Corporation, 1984 and Balfour, et al., 1984). A user's guide was subsequently
prepared summarizing guidance on the design, construction, and operation of the EPA
recommended flux chamber (Keinbusch, 1985). The emission isolation flux chamber is
presently considered the preferred in-depth direct measurement technique for
emissions of VOCs from land surfaces (EPA, 1990).
EQ contacted several environmental consulting firms as well as State and local
agencies. In addition, the EPA data base of emission flux measurement data was
reviewed (EPA, 1991). Although several flux measurement studies were reviewed, only
one applicable study was identified with adequate QA/QC documentation and with the
necessary input data for the Hwang and FaJco model (Radian Corporation, 1989).
3.1 BENCH-SCALE VALIDATION
From Farmer, et al. (1972 and 1974) the influence of pesticide vapor pressure on
volatilization was measured by comparing the volatilization from Gila silt loam of dieldrin
with that of lindane. Volatilization of dieldrin and lindane was measured in a dosed air-
flow system by collecting the volatilized insecticides in ethylene glycol traps. Ten grams
of soil were treated with either 5 or 10>g/g of C-14 tagged insecticide in hexane. The
hexane was evaporated by placing the soils in a fume hood overnight. Sufficient water
was then added to bring the initial soil water content to 10 percent. For the
i
volatilization studies, the treated soil was. placed in an aluminum pan 5 mm deep, 29
mm wide, and 95 mm long. This produced a bulk density of 0.75 g/crrf. The
aluminum pan was then introduced into a 250 mL bottle which served as the
15
-------
volatilization chamber. A relative humidity of 100 percent was maintained in the
incoming air stream to prevent water evaporation from the soil surface. Air flow was
maintained at 8 mL/s equivalent to approximately 0.018 miles per hour. The
temperature was maintained at 30° C. The soil was a Gila silt loam, which contained
0.58 percent organic matter.
«
The volatilized insecticides were trapped in 25 mL of ethylene glycol.
Insecticides were extracted into hexane and anhydrous sodium sulfate was added to
the hexane extract to remove water. Aliquots of the dried hexane were analyzed for
lindane and dieldrin using liquid scintillation. The extraction efficiencies for lindane and
dieldrin were 100 and 95 percent, respectively. The concentrations of volatilized
compounds were checked using gas-liquid chrcmatography. All experiments were r in
in duplicate.
From the experimental data, variables for input to the Hwang and Falco
volatilization model were:
0 Soil porosity (E) = 1 - P;/p
0 Bulk density (F;) = 0.75 g/cm3
0 Effective diffusion coefficient (DJ = D, • E0'33
0 Initial soil concentration (Q,8) = 5 ppm and 10 ppm.
The diffusion coefficient in air, D,, is from Equation No. 2-4 of EPA (1988). The
soil/water partition coefficient, \^, was set equal to the product of the soil organic
carbon content fraction, 0.0058, and the organic carbon partition coefficient, K^, from
EPA (1986). All data were input to a spreadsheet and emission flux rates were
subsequently computed. Emission rates, Q, were computed as the product of the flux
rates and the soil surface area (27.55 cm3). Initial soil concentrations were compared
against calculated values of the saturation concentration, C.M, using Equation No. 15.
Water solubilities used in Equation No. 15 are from EPA (1986); soil water content was
10 percent (w/w) from Farmer (1972 and 1974). Initial soil concentrations of 5 and 10
ppm for dieldrin, and 10 ppm for lindane were above their respective theoretical
16
-------
saturation concentrations by less than or equal to a factor of 50. Appendix A contains
the spreadsheet data for both lindane and dieldrin at initial soil concentrations of 5 ppm
and 10 ppm.
The instantaneous emission rate values predicted by the model for each period
corresponding to measurements of volatilization flux were compared, figure 1 shows
the comparisons of predicted and measured values for lindane at an initial
concentration of 10 ppm. A best curve was fit to both the measured and predicted
values. As expected, both curves indicate an exponential decrease in emission rate
with time.
The ratio of the modeled emission rate to the measured emission rate was
determined as a measure of the relative difference between the two emission rates
(Figure 2). The natural log of this ratio was then analyzed by using a standard paired
Student's t-test. This analysis is equivalent to assuming a lognormal distribution for the
emission rates and analyzing the log-transformed data for differences between modeled
and measured emission rates.
The data were also analyzed by using standard linear regression techniques.
Again, the data were assumed to follow a lognormal distribution. A simple linear
regression model was fit to the log-transformed data and the Pearson correlation
coefficient was determined. The Pearson correlation coefficient is a measure of the
strength of the linear association between the two variables.
From a limited population of 4 observations, the correlation coefficient was
calculated to be 0.998 with a mean ratio of modeled-to-measured values of 5.6. The
actual significance level or p-value of the paired Student's t-test was 0.0163, which
indicates some degree of statistical difference between modeled and measured values
at the 95 percent confidence level. The lower and upper confidence limits were
calculated to be 1.8 and 17, respectively. This indicates that at the 95 percent
confidence limit, the modeled emission rates are 1.8 to 17 times higher than the
measured emission rates. At an initial concentration of 5 ppm, the correlation
17
-------
o>
£
S
CO
.S2
LU
0
03
9
8
7-
6-
MM
E
4-
3-
2-
1-
0
CURVE FIT: Soil Concentration = 10 ppm
y (predicted) - 4.06189E-09 * x"-0.4998
y (measured) = 2.72935E-11 * e"(((x -192.2279) ~2)/10871.8546)
\
-ED
0 20 40 60 80 100 120 140 160 180 200
Time From Sampling (hrs)
Measured -~~ Predicted
D
Figure 1. Predicted end measured emission rates of lindsne versus time.
-------
to
-26
-25
-24 -23
Log Measured Emission Rate
-22
-21
10pprn «--•-«> 5ppm
Figure 2. Comparison of modeled end meeeured emission rat*« of llndan*.
-------
coefficient was 0.99 with a mean ratio of 6.2, p-value of 0.0414, and a 95 percent
confidence interval of 2.0 to 19.
In an attempt to determine the relative significance of the Hwang and Falco
assumption of dry soil on the effective diffusion coefficient and the subsequent effect on
the emission rate, the predicted values were recalculated using the Millincjton and Quirk
expression for effective diffusivity (Equation No. 14). Figure 3 shows the results of this
comparison. As expected, the relative difference between the modeled and measured
emission rates decreased. Statistical analysis at an initial concentration of 10 ppm
indicates a correlation coefficient of 0.99, and mean ratio of 3.7, a p-value of 0.0333,
and a resultant decrease in the 95 percent confidence interval to 1.2 to 11. Pooling
data between measured and modeled values for initial concentrations of 5 ppm and 10
ppm, reduced the correlation coefficient and p-value to 0.93 and 0.0006, respectively;
but the increase in the population to 8 observations decreased the 95 percent
confidence interval to 2.3 to 6.7.
For dieldrin, Figure 4 shows the comparison of modeled to measured values
using the default Hwang and Falco assumption of dry soil, while Figure 5 shows the
comparison using the Millington and Quirk expression .for effective diffusivity. As can be
seen, the relative difference between modeled and measured values in the case of
dieldrin is significantly reduced over that of lindane. In addition, the predicted values
using the Millington and Quirk expression are initially lower than the measured values
and are subsequently higher once equilibrium is achieved. The initially higher
measured emission rates are to be expected as surface contaminants are depleted and
the model boundary conditions are achieved. The relative magnitude between the
difference of modeled and measured emission rates for lindane and dieldrin may be
due to analytical precision and accuracy differences between the two compounds.
Figure 6 shows the comparison of the log-transformed data for the modeled and
measured emission rates of dieldrin at an initial concentration at both 5 ppm and 10
ppm. The modeled data in Figure 6 were calculated using the standard Hwang and
Falco assumption of dry soil. At an initial concentration of 10 ppm, the correlation
20
-------
^
CO O
* V
C LU
o
8
£
5-
4-
LU
O
(0
73
2-
1-
CURVE FIT: Soil Concentration = 10 ppm
y (predicted) - 2.71067E-09 * x^-0.5003
y (measured) « 2.72935E-11*6^ (((x -192.2279) ^ 2)/l 0871.8546)
0 20 40 60 80 100 120 140 160 180 200
Time From Sampling (hrs)
Measured Predicted
D
Figure 3. Predicted «nd measured emission rate* of llndane vertut time employing the Mllllngton and Quirk expression of D.,.
-------
1.6
ro
to
3 ~
CO O
01 " 1
c iiJ V
.2 2
E 1 0.8
LU .t
c t
2 0.6
5
0.4-
0.2
Soil Concentration = 10 ppm
CURVE FIT:
y (predicted) = 7.37639E-10 * (x^-0.4989)
y (measured) - 2.63479E-11 * e A (((x-254.(.880)A 2)/32642.6542)
50 100 150 200
Time From Sampling (hrs)
250
300
Measured --"••- Predicted
Figure 4. Predicted and measured emission rates of dleldrln versus time.
-------
0>
OS
1.4
1.2-
1-
'55 "•" 0.8-
0.6
0.4-
.g
2
0)
5
0.2
0
Soil Concentration = 10 ppm
CURVE FIT:
y (predicted) « 5.29339E-10 * 1.1777 A(1/k) * XA-0.4994
y (measured) - 2.63479E-11 * e ~ (((x-254.6880)A 2)/32642.6542)
50
100 150 200
Time From Sampling (hrs)
250
300
Measured ----- Predicted
Figure 5. Predicted intf measured emtolon rate* of dleMrln versus time employing the Mllllngton end Quirk expression of D.,.
-------
-22.8H
-22.7
-22.8
-22.9
-23.0
Q) ~23'1
j«j -23.2
c -23.3
•§ -23.4
•g -23.5
m -23.8
J> -23.7
-§ -23.8
5 -23.9
-24.0
-24.1
-24.2-
-24.3
-24.4
-24.5
-24.8
5
-26
0,'
-25
-24
-23
-22
Log Measured Emission Rate
10ppm
5ppm
Figure 6. Comparison of modeled end meeeured emits Ion retes of dleldrin.
-------
coefficient was calculated to be 0.98, the mean ratio 1.5, the p-value 0.0023, and the
95% confidence limit 1.2 to 1.8.
After applying the Millington and Quirk expression, pooled data (5 ppm and 10
ppm initial concentrations) indicate a correlation coefficient of 0.97. The p-value of the
paired Student's t-test was calculated to be 0.4133 indicating no statistically significant
difference between modeled and measured values (i.e., a mean ratio of 1).
Table 1 summarizes statistical analysis for the bench-scale comparative validation
of the Hwang and Falco model. As can be seen from these data, a larger population of
observations in conjunction with use of the Millington and Quirk expression of effective
diffusivity reduced the 95 percent confidence interval in the case of lindane and resulted
in no significant statistical difference in the average between modeled and measured
values in the case of dieldrin.
3.2 PILOT-SCALE VALIDATION
From Radian Corporation (1989), a study was designed to determine how
different treatment practices affect the rate of loss of benzene, toluene, xylenes, and
ethylbenzene (BTEX) from soils. The experiment called for construction of four piles of
loamy sand soil, each with a volume of approximately 4 cubic yards (7900 pounds ), a
surface area of 8 square meters, and a depth of 0.91 meters. Each test cell was lined
with an impermeable membrane and the soil in each cell was sifted to remove particles
larger than three-eighth inch in diameter. The contaminated soil for each pile was
prepared in batches using 55-gallon drums. In the 'high level" study, each soil batch
was brought to 5 percent moisture content and 6 liters of gasoline added. Additional
water was then added to bring the soil to 10 percent moisture by weight. The drums
were capped and sat undisturbed overnight. The drums were then opened the next
day and shoveled into the test cell platform. Twenty-two soil batches were prepared for
each soil pile. Each batch consisted of 360 pounds of soil and 6.0 liters of fuel.
Therefore, each soil pile contained 7900 pounds of soil and 132 liters of gasoline. Each
soil pile was then subjected to one of the following management practices:
25
-------
TABLE 1. SUMMARY OF STATISTICAL ANALYSIS OF BENCH-SCALE VALIDATION
Chemical
Lindane (10 ppm)
Lindane (5 ppm)
Lindane (10 ppm,
Millington-Quirk)
Lindane (5 ppm,
Millington-Quirk)
Lindane (pooled data,
Millington-Quirk)
Dieldrin 10 ppm
Dieldrin 5 ppm
Dieldrin (10 ppm,
Millington-Quirk)
Dieldrin (5 ppm,
Millington-Quirk)
Dieldrin (pooled data,
Millington-Quirk
N
4
4
4
4
8
7
7
7
7
14
Correlation
coefficient
0.998
0.99
0.99
0.99
0.93
0.98
0.99
0.98
0.99
0.97
Mean ratio:
Modeled-to-
measured
5.6
6.2
3.7
4.1
3.9
1.5
1.4
1.1
1.0
1.0
p-value
0.0163
0.0414
0.0333
0.0277
0.0006
0.0023
0.0004
0.8367
0.4327
0.4133
95%
confidence
interval
(1.8, 17)
(2.0, 19)
(1.2, 11)
(1.3, 13)
(2.3, 6.7)
(1.2, 1.8)
(1.2, 1.6)
a
a
a
'p-value > 0.05 indicates no significant difference between the average of modeled and
measured values; therefore, no 95% confidence interval is calculated.
0 A control pile that was not moved or treated
0 An "aerated" or "mechanically mixed" pile
0 A soil pile simulating soil venting or vacuum extraction
0 A soil pile heated to 38° C.
Losses due to volatilization during the mixing process reduced the residual BTEX
in soil. For the purpose of this validation study, however, these losses ensured that
initial soil concentrations of benzene, toluene, and ethylbenzene would be below or
26
-------
within a factor of two of their respective saturation concentrations. Because the mixed
pile, vented pile, and heated pile were subject to mechanical disturbances or thermal
treatment, only the control pile data were used in this study.
In general, the test schedule called for collection of soil samples and air emission
loss measurements during the first, sixth, and seventh weeks. Soil samples were
collected randomly within specified grid areas by composite core collection to the
maximum depth of the pile. Emission losses were measured similarly using an
emission isolation flux chamber as specified in Kienbusch (1985). Only data for which
soil samples and flux chamber measurements were taken on the same day were used
for this study.
In addition to total soil concentrations of BTEX, soils were analyzed for moisture
content and bulk density. Over the duration of the experiment, soil moisture, 0W,
averaged 10 percent by weight; bulk density, P7, was measured at 1.5 g/cm3.
Default values given by the RAGS/HHEM, Part B were used in the Hwang and
Falco model for those variables which either could not be calculated from site-specific
data or for which site data were not collected. Particle density, p, was thus set equal to
2.65 g/cm3; total porosity, E, was calculated as E = 1-P7/p; air-filled porosity, P., was
calculated as P. = E- 0mP7, and the soil organic carbon content fraction was set equal
to 0.02. The Henry's Law constant and solubility in water of each compound are from
EPA (1986), and the soil/water partition coefficient for each compound, K^, was
calculated as the product of the soil organic carbon content fraction and the organic
carbon partition coefficient, 1^, from EPA (1986). Diffusivrty in air, D,, for each
compound is from Equation No. 2-4 of EPA (1988).
Analysis of BTEX in soil samples was accomplished by employing the EPA 5030
extraction method and the EPA 8020 analytical method. The BTEX method was
modified to reduce the sample hold time to one day in an effort to improve the
accuracy of the method. Five soil samples were submitted in duplicate. The relative
percent differences (RPD) ranged from 8.0 to 48.9 percent. The average RPD for the
five samples was 26.8 percent. In addition. EPA QC sample analysis indicated average
percent recoveries ranging from 89 percent for m-xylene to 119 percent for toluene.
27
-------
The pooled coefficient of variation (CV) for all the BTEX analysis was 10.5 percent.
Spiked sample recoveries (eight samples) ranged from 75 percent for m-xylene to 168
percent for toluene. The average spike recoveries ranged from 108 percent for
benzene to 146 percent for toluene. Finally, both system blanks and reagent blanks
indicated no contamination was found in the analytical system. .
It should be noted that the standard method used for BTEX analysis was
observed to have contributed to the variabilities in soil concentrations. The EPA
acceptance criteria based on 95 percent confidence intervals from laboratory studies
are roughly 30 to 160 percent for the BTEX compounds during analysis of water
samples. The necessary extraction step for soil samples would increase this already
large variability.
Analysis of vapor phase organic compounds via the emission isolation flux
chamber was accomplished using a gas chromatograph (GC). Gas samples were
collected from the flux chamber in 100 mL, gas-tight syringes and analyzed by the GC
in laboratory facilities adjacent to the test site. During the study, a multicomponent
standard was analyzed daily to assess the precision and daily replication of the
analytical system. The results of the analysis indicated a good degree of reproducibility
with coefficients of variation ranging from 5.1 to 16.3 percent.
From these data, instantaneous emission rates were calculated for benzene,
toluene, and ethylbenzene corresponding to each time period at which flux chamber
measurements were made. Appendix B contains the spreadsheet data for benzene,
toluene, and ethylbenzene at initial soil concentrations of 110 ppm, 880 ppm, and 310
ppm, respectively.
Employing the default assumption of dry soil (i.e., Dd = D, • E° "), Figures 7, 8,
and 9 show the comparison of modeled and measured emission rates for benzene,
toluene, and ethylbenzene, respectively. The Radian Corporation study noted that the
second measured value in each figure represented a data outlier, possibly due to the
formation of a soil fissure, reducing the soil path resistance and increasing the emission
flux.
28
-------
2
1.8
1.6
o> 1.4-
* ^ 12
c UJ
.Q °
w T"~ 1
(0 (/) '
•= Q)
iS .i 0.8
Q) t-
S 0.6
N
I 0.4
Outlyer
Soil Concentration = 110 ppm
CURVE FIT:
y (predicted) - 9.75946E-05 * x~-0.4998
y (measured) = 1.05949E-06 -»- (-8.58711E-10) * x + 0.0001/x
100 200 300 400 500
Time From Sampling (hrs)
600
700
Measured •••-"-•- Predicted
D
Figure 7. Predicted and measured emission rates of benzene versus time.
-------
9
8
7-1
»
•2 6-
g o 5H
'8 \
UJ £
CD ^
c
0)
4-
3-
2-
1-
0
Soil Concentration = 880 ppm
CURVE FIT:
y (predicted) = 4.135E-04 * x A -0.4990
y (measured) « 0.1198E-03 * 0.9976 ^x * x^-0.4163
0 100 200 300 400 500 600 700 800 900 10001100
Time From Sampling (hrs)
Measured —- Predicted
n
Figure 8. Predicted and measured emission rates of toluene versus time.
-------
1.6
1.4-
1.2-
2
£
£ & H
E ^ 0.8-
LLI
0
S B o.e^
N
I 0.4-
LJJ
0.2-
0
0
Soil Concentration = 310 ppm
CURVE FIT:
y (predicted) = 7.24790E-39 * e~ ((lnx-308.0597) ^2/1212.2352)
y (measured) = 0.0007 * 3.24201 E-13 "(1/x) * X^-
r
100 200 300 400 500 600 700
Time From Sampling (hrs)
Measured Predicted
D
Figure 9. Predicted and measured emission rates of ethylbenzent versus time.
-------
Table 2 presents the results of the statistical analysis of the comparison of
modeled and measured values using the default Hwang and Falco effective diffusion
coefficient. For both benzene and ethylbenzene, measured values were below the
detection limits after the fifth observation; measured values for toluene were below the
detection limit after the seventh observation. .
TABLE 2. SUMMARY OF STATISTICAL ANALYSIS OF PILOT-SCALE VALIDATION
USING THE DEFAULT HWANG AND FALCO EFFECTIVE DIFFUSION COEFFICIENT
Chemical
Benzene (1 10 ppm)
Ethylbenzene (310 ppm)
Toluene (880 ppm)
Pooled data
N
5
5
7
17
Correlation
coefficient
0.81
0.98
0.95
0.80
Mean ratio:
Modeled-to-
measured
3.0
3.6
2.9
4.3
p-value
0.0580
0.0050
0.0039
0.0001
95%
confidence
level
(0.94, 9.8)
(1.9, 6.9)
(2.3, 16)
(2.7, 6.8)
Figure 10 shows the comparison of the log-transformed data for the modeled
and measured emission rates of the three compounds indicating some degree of
variability from the expected straight-line exponential decay. As can be seen from
Table 2, correlation coefficients ranged frorrU).81 for benzene to 0.98 for ethylbenzene,
while p-values and 95 percent confidence intervals indicate a significant statistical
difference between modeled and measured values. Pooled data indicate a correlation
coefficient of 0.80, a mean ratio of 4.3, a p-value of 0.0001, and a 95 percent
confidence interval of 2.7 to 6.8.
By applying the Millington and Quirk expression of effective diffusMty to the
Hwang and Falco model, however, statistical analysis indicates no significant difference
between the average of the modeled and measured emission rates. Table 3 presents
the results of the statistical analysis after applying the Millington and Quirk expression
to the modeled emission rates. As can be seen by these data, p-values of the paired
32
-------
-9-f
-10
-11
-12
-13
B.
B
B
B
-15
-14
-13 -12
Log Measured Emission Rate
-11
Benzene *•-•-« Ettiylbenzene T..T..r Toluene
-10
Figure 10. Comparison of modeled and measured emission rates of benzene, toluene, and ethylbenzene.
-------
TABLE 3. SUMMARY OF STATISTICAL ANALYSIS OF PILOT-SCALE VALIDATION
USING THE MILLINGTON AND QUIRK EXPRESSION FOR EFFECTIVE DIFFUSIVITY
Chemical
Bezene (110 ppm)
Ethylbezene (310
ppm)
Toluene (880 ppm)
Pooled data
N
5
5
7
17
Correlation
coefficient
, 0.81
0.98
0.95
0.80
Mean ratio:
Model-to-
measured
1.02
1.22
2.04
1.43
p-value
0.9596 '
0.4342
0.1215
0.1164
95%
confidence
interval
a
a
a
a
•p-yalue >0.05 indicates no significant difference between the average of modeled and
measured values; therefore, no 95% confidence interval is calculated.
Student's t-test for individual compounds and for pooled data are greater than 0.05,
indicating no significant statistical difference.
Figures 11, 12, and 13 show the comparisons of modeled emission rates
employing the Millington and Quirk expression for effective diffusivity and measured
emission rates for benzene, toluene, and ethylbenzene, respectively. Note that in the
case of toluene and ethylbenzene the modeled and measured emission rate curves
intersect as steady-state conditions are achieved; while the curves for the more volatile
benzene indicate that steady-state conditions have been achieved.
34
-------
(0
O)
IB
to
DC
C
.O
'w
E
UJ
-------
.CO
O)
4
3.5
3
2.5
C
CD
0.5-
0
Outlyer
Soil Concentration = 880 ppm
0
CURVE FIT:
y (predicted) = 1.397E-04 * x~ -0.4999
y (measured) = 0.1198E-03 * 0.9976 ^x * x^-0.4163
100 200 300 400 500 600 700 800 900 10001100
Time From Sampling (hrs)
Measured -— Predicted
Figure 12. Predicted and measured emission rates of toluene versus time employing the Mllllngton snd Quirk expression ot D...
-------
C CO
H
I 8
!
0)
N
0)
n
I
5-
4-
2-
1-
Soil Concentration = 310 ppm
CURVE FIT:
y'(predicted) = 2.37669E-05 * x~ -0.4995
y (measured) = 0.0007 * 3.24201 E-13 ~ (1 IK) * x * -1.1464
Outlyer
Measured -•—- Predicted
n
100 200 300 400 500 600 700
Time From Sampling (hrs)
Figure 13. Predicted and measured emission rate* of ethylbenzene versus time employing the Mllllngton and Quirk expression of DM
-------
SECTION 4
PARAMETRIC ANALYSIS OF THE HWANG AND FALCO MODEL
This section presents the results of parametric analysis of the key variables of
the Hwang and Falco volatilization model (Equation No. 1). Because the Hwang and
Falco model is a direct derivation of the model presented in Farmer, et al. (1980), the
parametric observation? of Farmer, et al. are also directly applicable. Hwang and Falco
redefined the general diffusion equation of Farmer, et al. establishing the relationship
between vapor phase diffusion and soil phase adsorption. For this reason, other model
variables related directly to vapor density in interstitial pore spaces and soil adsorption
capacity were further analyzed for this study.
From Farmer, et al., air-filled porosity was found to be the most significant soil
parameter affecting the final steady-state flux through soil. The volumetric water
content of the soil and the soil bulk density determine the air-filled porosity. Other soil
parameters such as soil organic matter content and soil texture were found to have
affected the time for the flux to arrive at steady-state conditions but did not effect the
magnitude of the final flux except as they influenced soil bulk density.
In addition it was found that soil temperature increased volatilization exponentially
due to the effect on the vapor pressure. Farmer, et al. also noted that the chemical
stability and resistance to microbial degradation of the contaminant were directly related
to the maximum rate of flux and the persistence of the contaminant in soil.
4.1 AFFECTS OF SOIL PARAMETERS
In this section, the experimental results of Farmer, et al. (1980) are discussed as
they relate to the effect of soil water content, soil bulk density, air-filled soil porosity,
and temperature on the vapor phase diffusion in soil.
38
-------
Soil Moisture Content
Farmer, et al. indicates that the effect of soil moisture content on the volatilization
flux of contaminants through soils is exponential. Increasing soil water content
decreases the pore spaces available for vapor diffusion and will decrease volatilization
flux. In contrast, increasing soil water content has also been shown fo increase the
volatility of pesticides in soil under certain conditions (Gray, et al., 1965; and Spencer
and Cliath, 1969 and 1970). In essence, the soil water content affects the pesticide
adsorption capacity by competing for soil adsorption sites. Under these conditions, an
increase in soil moisture above a certain point will tend to desorb contaminants,
increasing the flux dependent on the relative water and contaminant adsorption
isotherms.
Bulk Density
Soil compaction or bulk density also determines the porosity of soil and thus
affects the vapor phase diffusion through the soil. Experimental results from Farmer, et
al. indicate that soil bulk density also has an exponential effect on volatilization flux
through the soil. From previous considerations of the effect of soil water content, a
higher bulk density will have similar effects to that of an increased soil moisture content
The higher the soil bulk density, the smaller the steady-state flux.
So/7 Air-Filled Porosity
The effects of soil water content and .soil bulk density on volatilization through
soil can be contributed to their effect on the air-filled porosity, which in turn is the major
soil factor controlling volatilization through the soil. The effect of air-filled porosity is
manifested in the expression of the effective diffusion coefficient. The effective diffusion
coefficient, however, does not depend only on the amount of air-filled pore space. The
presence of liquid film on the solid surfaces not only reduces porosity, but also modifies
the pore geometry increasing tortuosity and the length of the gas passage. Use of the
Millington and Quirk expression of the effective diffusion coefficient better accounts for
39
-------
these effects than does the assumption of dry soil. This assertion is verified by this
study.
Soil Temperature
The effect of soil temperature on the volatilization flux through soil is
multifunctional. The diffusion in air, D,, is theoretically related to temperature, T, and the
collision integral, Q, in the following manner (Lyman, et al., 1990):
jO.S
D, (variation of) (16)
The exponential coefficient for temperature varies from 1.5 to 2 over a wide range of
temperatures. Barr and Watts (1972) found that 1.75 gave the best values for gaseous
diffusion. Farmer, et al. (1980) estimates the effective diffusion coefficient at
temperature T2 as :
where Dj = Diffusion coefficient at T2
D, = Diffusion coefficient at T,
T = Absolute temperature.
Finally, a temperature increase will effect the vapor pressure function of the
Henry's Law constant used to define the soil/air partition coefficient, K^, which causes
an increase in the vapor concentration gradient across the soil layer. In addition, any
additional heat generated inside a landfill due to aerobic decomposition of organic
wastes will have a short-term effect on the temperature of the soil. In actual fact,
temperature gradients will exist across the soil due primarily to seasonal variations.
40
-------
Vapor diffusion is influenced by such gradients; however, these effects of fluctuating
soil temperatures will tend to cancel one another over time. The overall effect of
temperature on volatilization flux can be approximated by use of Equation No. 17 and
an average soil temperature.
*
4.2 AFFECTS OF NONSOIL PARAMETERS
The remaining variables in the Hwang and Falco model that are not related to
soil properties are the initial soil concentration, Cto, the time from sampling, t, the
soil/water partition coefficient, Ka, and the summation expression in the original Farmer,
et al. flux equation (Equation No. 6).
Initial Soil Concentration
The effect of change in the initial soil concentration term in the Hwang and Falco
model is linear; i.e., an increase in C.0 of 100 percent causes an increase in the
emission rate of 100 percent. Probably the greatest degree of uncertainty in the value
of C(0 is likely to be either insufficient soil sampling to adequately characterize site soil
concentrations, or the variability in percent recovery of contaminants as it applies to
existing sampling and analysis methods for organic compounds in soils. Typically,
present extraction and analysis method recovery variability increases the likelihood of
under prediction of the emission rate (i.e., more contaminant is present in the soil than
is reported by sampling and analysis methods).
Time From Sampling
The time variable, t, is an exponential rate operative in the Hwang and Falco
model. When emission rates computed from the model are plotted against the
reciprocal of t05, a straight line is obtained; therefore, when the emission rates are
plotted against t, an asymptotic curve results. In this manner, the steady-state
volatilization rate remains constant while the soil concentration is reduced exponentially
(i.e., never reaching zero).
41
-------
Soil/Water Partition Coefficient
The RAGS/HHEM, Part B defines the soil/water partition coefficient, J^, in the
Hwang and Falco model as the product of the organic carbon partition coefficient, K^,
and the soil organic carbon fraction, OC. By basing the soil/water partition coefficient
on soil organic carbon rather than on total mass, most of the variation in sorption
coefficients between different soils is eliminated. The remaining variation may be due to
other characteristics of soil such as clay content and surface area, cation exchange
capacity, pH, soil moisture salinity, concentration of dissolved organic matter in soil
water, and nonlinear adsorption isotherms (Lyman, et al., 1990). In the Hwang and
Falco model, the effect of a change in the value of J^ on the emission rate is identical
to that of a change in time, t, such that by holding all other variables constant,
increases in the value of K^ will generate the same asymptotic emission rate curve as
increases in the value of t. For this reason, the model is very sensitive to variations in
the value of IV
Farmer, et a/., Summation Expression
Finally, the initial diffusion model (Equation No. 6) from Farmer, et al. (1974)
includes a summation expression which is eliminated by Hwang and Falco by assuming
L = w. in actuality, however, the depth of'the contaminated soil column is finite.
Farmer, et al. explained that the reduced equation diffusion model boundary conditions
were valid until t > L2/14.4 D (Equation No. 9). The same limitation applies to the
Hwang and Falco model at t > L2/14.4 a. Therefore, by derivation, the Hwang and
Falco model equivalent to the Farmer, et al., original solution is:
N
If, a If
*
Z.c.
CO
2 £ (-1f exp(-n2L2/a f)
(18)
42
-------
If the value of the summation term in Equation No. 18 is negligible in comparison to 1,
the equation can be reduced. The summation term will be small if the expression in the
exponential, n2L2/ot, is large, say on the order of 10 or more. The expression rrV/ot
is largely dependent on the value of L. For example, from the lindane experiments of
Farmer, et al. (1972), the boundary conditions of the Hwang and Falco model are
violated for a soil depth, L, of 0.5 cm at t = 11.6 hours (Appendix A, t Max").
However, from the BTEX experiments of Radian Corporation (1989), the boundary
conditions of the model are not violated for ethylbenzene at L = 91 cm until t = 19,204
hours or 2.19 years (see Appendix B, t Max").
If the Hwang and Falco model is applied in its reduced form to ethylbenzene
under the conditions specified in Radian Corporation (1989), the instantaneous
emission rate for the default exposure interval g]ven in RAGS/HHEM, Part B (7.9 x 10*
seconds or 30 years) is 2.26 x 10 "8 g/s. In contrast, if the non reduced solution
(Equation No. 18) is applied, the instantaneous emission rate for the same exposure
interval is 1.00 x 10 "8 g/s, reducing the original solution by a factor of 0.444. It can
then be assumed that over the default exposure period, use of the reduced equation
will tend to overpredict the instantaneous and average emission rates by a factor of
approximately two.
43
-------
SECTION 5
CONCLUSIONS
From the results of this study, it can be concluded that for the compounds
included in the experimental data, there are no statistically significant differences
between the measured emission rates and those predicted by the Hwang and Falco
volatilization model employing the Millington and Quirk expression of effective diffusivity.
These results are valid if the initial soil concentration is within a factor of 10 or below the
theoretical soil saturation concentration, C.M, and if the initial and boundary conditions
of the model are reasonably attained under bench-scale and pilot-scale conditions.
The initial and boundary conditions of the model specify an infinitesimal layer of
uncontaminated soil at the soil/air interface (Q = 0, at L = 0, t > 0) and an infinite
contaminated soil column (Q = H/Ky)CM, at L = », t > 0). In this manner, the model
will tend to underpredict emissions before steady-state conditions are achieved as the
contaminants at the soil surface are depleted. By assuming an infinite contaminated
soil column, the model employs the reduced form of the original Farmer, et al. solution.
With a finite contaminated soil column of approximately 100 cm in depth, however, the
model will tend to overpredict the average emission rate over extended periods of time
(e.g., 30 years) by a factor of approximately two.
As applied in the RAGS/HHEM, Part B, the Hwang and Falco volatilization model
employs the conservative assumption of dry soil when calculating effective diffusivity in
soil (D.j = D, • I?-*3). The results of this study indicate that this assumption tends to
overpredict average emissions by approximately one order of magnitude. On the other
hand, use of the Millington and Quirk expression of effective diffusivity
[DM = D, (P.333/Ff)] indicates no statistically significant difference between modeled
and measured emission rates.
44
-------
In its present form, the Hwang and Falco volatilization model indicates good
agreement between predicted and measured emission rates under controlled
conditions. The following assumptions and simplications, however, have been made;
1. Q and C.0 are related by Henry's Law precluding the presence of
nonaqueous phase contaminants (i.e., free phase contaminants).
2. The soil/air partition coefficient (K,, = H/^) does not take into account
the effects of the relative difference of the Henry's law constant, H, at
temperatures for which values of H are experimentally derived and those
of in situ soils. In addition, the effects of mutticomponent systems on
effective solubilities of contaminants is not addressed in the value of the
Henry's Law constant
3. The soii/air partition coefficient, K*. does not account for the effects of
other soil and soil water properties (e.g., clay/silt content, water salinity,
pH, etc.) on the value of the soil/water partition coefficient, Ky. Because
K^ is the product of the organic carbon partition coefficient, \
-------
9. The model assumes no movement of contaminants occur across the
lower boundary (e.g., leaching).
Emission rates predicted by the Hwang and Falco volatilization model indicate
good correlation to measured emission rates under controlled condition, but predicted
values for field conditions would be subject to error because the boundary conditions
and environmental conditions such as wind velocity, incorporation depth, and water
movement are not as well defined as they are in the laboratory or under pilot-scale
conditions. Nonetheless, results of this study indicate that the model should make
reasonable estimates of loss by vapor-phase diffusion.
46
-------
REFERENCES
Balfour, W. D., B. M. Eklund, and S. J. Williamson. Measurement of Volatile Organic
Emissions from Subsurface Contaminants. In Proceedings of the National Conference
on Management of Uncontrolled Hazardous Waste Sites. September 1984, pp. 77-81.
Hazardous Materials Control Research Institute, Silver-Springs, Maryland.
Barr, R. F. and H. F. Watts. 1972. Diffusion of Some Organic and Inorganic
Compounds in Air. J. Chem. Eng. Data 17:45-46.
Carslaw, H. S., and J. C. Jaeger. 1959. Conduction of Heat in Solids. 2* Edition
Oxford University Press, Oxford.
Crank, J. 1985. The Mathematics of Diffusion. Oxford University Press, New York.
U.S. Environmental Protection Agency. 19861. Superfund Public Health Evaluation
Manual. Office of Emergency and Remedial Response. EPA-540/1-86-060.
U.S. Environmental Protection Agency. 1986 a. Development of Advisory Levels for
Potychlorinated Biphenyls (PCBs) Cleanup. Office of Health and Environmental
Assessment. EPA-600/6-86-O02.
U.S. Environmental Protection Agency. 1988. Superfund Exposure Assessment
Manual. Office of Emergency and Remedial Response. EPA-540/1 -88-001.
U.S. Environmental Protection Agency. 1990. Procedures for Conducting Air Pathway
Analyses for Superfund Activities, Interim Finul Documents: Volume 2 • Estimation of
Baseline Air Emissions at Superfund Sites. Office of Air Quality Planning and
Standards. EPA-450/1-89-002a.
U.S. Environmental Protection Agency. 1991. Database of Emission Rate Measurement
Projects • Technical Note. Office of Air Quality Planning and Standards. EPA-450/1-
91-003.
Farmer, W. J., K. Igue, W. F. Spencer,
-------
Farmer, W. J., and J. Letey. 1974. Volatilization Losses of Pesticides From Soils.
Office of Research and Development. EPA-660/2-74/054.
Farmer, W. J., M. S. Yang, J. Letey, and W. F. Spencer. 1980. Land Disposal of
Hexachlorobenzene Wastes. Office of Research and Development. EPA-600/2-80/119.
Gray, R. A., and A. J. Weierch. 1965. Factors Affecting the Vapor Loss of EPTC from
Soil. Weeds 13:141-147.
Hartley, G. S. 1964. Herbicide Behavior in the Soil. I. Physical Factors and Action
Through the Soil. P. 111-161. In LJ. Audus (ed.) The Physiology and Biochemistry of
Herbicides. Academic Press, London and New York.
Hill, F. B., V. P. Aneja, and R. M. Felder. 1978. A Technique for Measurement of
Biogenic Sulfur Emission Fluxes. J. Env. Sci. Health AIB (3), pp. 199-225.
Hwang, S. T., and J. W. Falco. 1986. Estimation of Multimedia Exposure Related to
Hazardous Waste Facilities. Cohen, Y. (ed). Plenum Publishing Corp.
Kienbusch, M. Measurement of Gaseous Emission Rates from Land Surfaces Using an
Emission Isolation Flux Chamber - User's Guide. Report to EPA-EMSL, Las Vegas
under EPA Contract No. 68-02-3889, Work Assignment No. 18, December 1985.
Kienbusch, M. and D. Ranum. 1986. Validation of Flux Chamber Emission
Measurements On a Soil Surface • Draft Report to EPA-EMSL, Las Vegas, Nevada.
Kienbusch, M., W. D. Balfour, and S. Williamson. The Development of an Operations
Protocol for Emission Isolation Flux Chamber Measurements on Soil Surfaces.
Presented at the 79th Annual Meeting of the Air Pollution Control Association (Paper
86-20.1), Minneapolis, Minnesota, June 22-27, 1986.
Lyman, W. J., W. F. Reehl, and D. H. Rosenblatt. 1990. Handbook of Chemical
Property Estimation Methods. American Chemical Society, Washington, D.C.
Millington, R. J., and J. M. Quirk. 1961. Permeability of Porous Solids. Trans. Faraday
Soc. 57:1200-1207.
Radian Corporation. So/7 Gas Sampling Techniques of Chemicals for Exposure
Assessment - Data Volume. Report to EPA-EMSL, Las Vegas under EPA Contract No.
68-02-3513, Work Assignment No. 32, March 1984.
Radian Corporation. Short-term Fate and Persistence of Motor Fuels in Soils. Report to
the American Petroleum Institute, Washington, D.C. July 1989.
48
-------
Spencer, W. F., M. Cliath, and W. J. Farmer. 1969. Vapor Density of Soil Applied
HEOD as Related to Soil Water Content, Temperature, and HEOD Concentration. Soil
Sci. Soc. Amer. Proc. 33:509-511.
Spencer, W. F., and M. Cliath. 1970. Vapor Density and Apparent Vapor Pressure of
Lindane (y-BHC). J. Agr. Food Chem. 18:529-530.
«
Spencer, W. F. 1970. Distribution of Pesticides Between Soil, Water and Air. In
Pesticides in the So/7: Ecology, Degradation and Movement. A symposium, February
25-27, 1970. Michigan State University.
49
-------
ENVIRONMENTAL QUALITY MANAGEMENT, INC.
MEMORANDUM
TO: Ms. Janine Dinan DATE: July 11, 1994
SUBJECT: Revisions to VF and PEF Equations FROM: Craig Mann /X/
FILE: 5099-3 cc:
Subsequent to the evaluation of the dispersion equations in the RAGS - Part B
performed by Environmental Quality Management, Inc. (EQ, 1993), questions have arisen
as to the accuracy of the modeling protocol used to derive the dispersion coefficient
(Q/C) used in the volatilization factor (VF) and the paniculate emission factor (PEF)
presently employed to calculate the air pathway Soil Screening Levels (SSLs).
EQ, 1993 used the Industrial Source Complex model (ISC2-ST) to derive a
normalized concentration (kg/m3 per g/rrf-s) for a, series of square and rectangular area
sources of differing size. This modeling protocol employed a source subdivision scheme
similar to that recommended in the ISC2-ST Model User's Manual (EPA, 1992) whereby
the source was subdivided into smaller sources closest to the center of the area. The
center of the area was found to represent the point of maximum annual average
concentration for all source shapes analyzed. Consecutive model runs were performed
whereby source subdivision was increased between runs. Final source subdivision was
reached when the model results converged within a factor of three percent or less.
From these data, a simple linear regression was used to evaluate the nature of the
relationship between the normalized concentration and the size of the area. Preliminary
plots of the data indicated that the relationship was exponential. Therefore, the
relationship was linearized by taking the natural logarithms (In) of each variable. The
resulting linear regression for a square area of 0.5 acres resulted in a normalized
concentration (C/Q) of 0.0098 kg/m3 per g/rrf-s; the inverse of the normalized
concentration resulted in a dispersion coefficient (Q/C) of 101.8 g/nf-s per kg/m3.
On May 5,1994 a teleconference was held between representatives of the Toxics
Integration Branch of the Office of Emergency and Remedial Response (OERR) and the
Source Receptor Analysis Branch of the Office of Air Quality Planning Standards (OAQPS)
to discuss the relative merits of the available area source algorithms as applied to near-
field and on-site receptors exposed to ground-level nonbuoyant emissions. The
conclusions drawn from this teleconference were that a new algorithm recently developed
by OAQPS would yield more accurate results for the exposure scenario in question.
-------
Ms. Janine Dinan
July 11, 1994
The new algorithm is incorporated into the 1SC2 model platform in both short-term
mode (AREA-ST) and long-term mode (AREA-IT). Both models employ a double
numerical integration over the area source in the upwind and crosswind directions as
follows:
X =
dyldx
(1)
where QA = Area source emission rate (g/nf-s)
K = Units scaling coefficient
V = Vertical term
D = Decay term.
The integral in the lateral (i.e., crosswind or y) direction is solved analytically as:
/,exp
-0.5
(2)
where erfc is the complementary error function.
The integral in the longitudinal (i.e., upwind or x) direction is solved by using a
weighted average of successive estimates of the integral using a trapezoidal
approximation. The model uses three separate criteria to determine convergence of the
upwind integral. The result of these numerical methods is an estimate of the full integral
that is essentially equivalent to, but much more efficient than, the method of estimating
the integral as a series of line sources, such as the method used by the Point, Area, Line
(PAL 2.0) model. Wind tunnel tests have also shown that the new algorithm performs well
with on-site and near-field receptors.
-------
Ms. Janine Dinan 3 July 11,1994
Because the new algorithm provides better concentration estimates and does not
require source subdivision, a revised dispersion analysis was performed for both volatile
and paniculate matter contaminants using the new algorithm.
The first part of the analysis involved a determination of the relationship between
concentration and source size. In addition, this part of the analysis included a
determination of the point of maximum annual average concentration for a square area
source. This assessment employed the AREA-ST model as acquired from the OAQPS
Technology Transfer Network, Support Center for Regulatory Air Models (SCRAM) Bulletin
Board.
Meteorological data used for this analysis were 1989 hourly data for the Los
Angeles National Weather Service (NWS) surface station, upper air data were from the
Oakland NWS station for the same year. Rural dispersion coefficients were employed and
all regulatory default options used. Modeling assumed flat terrain with no flagpole
receptors; source rotation angle was set equal to zero.
Five source sizes were included in the assessment: 0.5,5,30,200, and 600 acres.
A coarse cartesian receptor grid was employed within and extending beyond the source
perimeter; a discrete receptor was also placed at the center of each source (x,y = 0,0).
Emissions from each source were set equal to 1.0 g/irf-s; concentrations were calculated
in units of kg/m3.
Figure 1 shows the relationship between source size (acres) and annual average
concentration (kg/m3) for the five source sizes modeled. In each case, the point of
maximum concentration was located at the center of the source. As an example,
Attachment A is the model run sheets for the 0.5 acre source. As can be seen from
Figure 1, the relationship between concentration and source size is exponential. Results
also show that the maximum concentration representing the 600 acre source is 2.9 times
higher than that of the 0.5 acre source.
Having established that when using the AREA-ST model the point of maximum
concentration for a square area source is the center receptor, the second part of the
analysis was to determine which of the 29 meteorological sites from EQ, 1993 best
represents the average exposure and the high end exposure to volatile and paniculate
matter emissions. It was determined that the average exposure case should be
represented by the 50th percentile site concentration, while the high end exposure is best
represented by the 90th percentile site concentration.
Each of the 29 sites from EQ, 1993 were subsequently modeled at an emission
rate of 1.0 g/mf-s with a single discrete receptor at the center of the square area source.
Source sizes modeled were 0.5 acres and 30 acres. Hourly meteorological data for each
site were from EQ, 1993. From the set of 29 normalized annual average concentrations,
-------
0.10
^ ^
CO
o
h-
LJJ
O
"Z.
O
o
0.01
0.1
0.04238
0.03680
0.02849
0.02167
0.01453
i r
1 10 100
SOURCE SIZE (Acres)
to
o>
D
Q)
1000
C_
c
Figure 1. Normalized annual average concentration versus source size.
CO
CO
-------
Ms. Janine Dinan
July 11, 1994
the 50th percentile site was determined to be Salt Lake City, Utah; Los Angeles, California
(89th percentile site) was determined to be the closest approximation of the 90th
percentile site. Table 1 shows the resulting dispersion coefficients for the two source
sizes and the percentile ranking of each site.
TABLE 1. VOLATILE DISPERSION SITE RANKINGS
City
Huntington
Fresno
Phoenix
Los Angeles
Winnemucca
Boise
Hartford
Little Rock
Portland
Salem
Charleston
Denver
Atlanta
Raleigh-Durham
Salt Lake City
Houston
Lincoln
Harrisburg
Bismarck
Seattle
Cleveland
Albuquerque
Miami
San Francisco
Philadelphia
Minneapolis
Las Vegas
Chicago
Casper
NWS
surface
station
number
13860
93193
23183
24174
24128
24131
14740
13963
14764
24232
13880
23062
13874
13722
24127
12960
14939
14751
24011
24233
14820
23050
12839
23234
13739
14922
23169
94846
24089
0.5 Acre
(Q/C)
(g/m2-s per
kg/m3)
52.7i
62.00
64.06
68.81!
69.2! >
69.40
71.33
73.37
74.24
73.4£
74.91
75.59
77.16
77.4(5
78.06
79.24
81.63
81.90
83.40
82.71
83.19
84.18
85.40
89.53
90.69
90.74
95.51
97.75
100.00
30 Acre
(Q/C)
(g/m2-s per
kg/m3)
Site
ranking
percentile
(%)
27.08 1 100
31.85
32.63
35.10
35.49
35.69
36.64
37.68
37.86
37.88
38.42
38.80
39.68
39.87
40.14
40.70
41.56
42.34
42.72
42.81
43.03
43.31
43.57
46.06
46.38
46.84
49.48
50.45
51.68
96
93
89
86
82
79
75
71
68
64
61
i_ 57
54
50
46
43
39
36
32
29
25
21
18
14
11
7
4
0
-------
Ms. Janine Dinan
July 11, 1994
In order to determine the average and high end sites for paniculate matter
exposures resulting from wind erosion, a normalized concentration could not be used
because meteorological conditions other than simple dispersion (i.e., wind velocity and
frequency) influence emissions and therefore actual concentrations. For this reason,
actual concentrations were calculated for each site using the existing PEF equation as
follows:
3600 sjh
(3)
where
C
(C/Q)
Um
U,.7
F(x)
= Annual average PM,0 concentration, kg/m3
= Normalized annual average concentration (kg/m3 per
g/rrf-s)
= Fraction of continuous vegetative cover
= Mean annual windspeed, m/s
= Equivalent threshold value of windspeed at 7 m, m/s
= Windspeed distribution function from Cowherd, 1985.
The value of (C/Q) for each site was the normalized concentration previously estimated
for volatile emissions (i.e., the inverse of each dispersion coefficient in Table 1). The value
of V was set equal to 0.5. The mean annual windspeed (Um) for each site was taken from
Weather of U.S. Cities, Second Edition, Volume 2 by J. A. Ruffner and F. E. Bair, Gale
Research Co., Detroit, Michigan. The value of F(x) was estimated for each site from
Figure 4-3 or calculated from Appendix B of Cowherd 1985, as appropriate.
The value of U,_7 was calculated as follows:
-------
Ms. Janine Dinan 7 July 11, 1994
where U,.7 = Equivalent threshold value of windspeed at 7 m, m/s
Zo = Surface roughness height, cm (2; = 0.5 cm for open terrain)
14 = Threshold friction velocity, m/s (4 = 0.625 m/s).
Table 2 gives the results of this analysis and shows the relative PM,0
concentrations for each site by source size and the percentile rankings. As can be seen
from Table 2, the 50th percentile site was Salt Lake City, Utah, while the 89th percentile
site was Minneapolis, Minnesota.
Table 3 summarizes the results of the dispersion coefficient analysis for both the
VF and PEF equations. In addition, Table 3 also gives the default values of the PEF
variables for both average and high end exposures.
-------
TABLE 2. PEF CALCULATIONS AND SITE RANKINGS
City
Casper
Cleveland
Lincoln
Minneapolis
Bismarck
Chicago
Philadelphia
Miami
Atlanta
Seattle
Boise
Las Vegas
Albuquerque
Denver
San Lake City
Portland
Charleston
Hartford
San Francisco
Little Rock
Wlnnemucca
Houston
Raleigh-Durham
Harrisburg
Los Angeles
Salem
Huntington
Fresno
Phoenix
NWS
surface
station
number
24089
14820
14939
14922
24011
94846
13739
12839
13874
24233
24131
23169
23050
23062
24127
14764
13880
14740
23234
13963
24128
12960
13722
14751
24174
24232
13860
93193
23183
Mean
annual
wlndspeed
(mph)
12.9
10.6
10.4
10.6
10.3
10.4
9.6
9.2
9.1
9.1
8.9
9.1
9.0
8.8
M
8.7
8.7
8.6
8.7
8.0
7.9
7.8
7.7
7.7
7.4
7.0
6.5
6.4
6.3
Mean
annual
._ J.,.-*— nmm rl
wncspcM
fcn/s)
5.77
4.83
4.65
4.69
4.60
4.65
4.29
4.11
4.07
4.07
3.98
4.07
4.02
3.93
J.M
3.89
3.89
3.64
3.89
3.56
3.53
3.49
3.44
3.44
3.31
3.13
2.91
2.86
2.82
F(x) <=2 from Cowherd (1965), Figure 4-3.
F(x) > 2 from Cowherd (1985), Appendix B.
NA = Not Applicable.
Roughness
helghLZo
(cm)
0.5
0.5
0.5
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.6
0.5
0.5
0.5
0.5
0.5
O.S
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Threshold
friction
vetocfty^
at surface
(m/s)
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.626
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
0.625
Threshold
friction
velocity.
at7m
(m/8)
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
11.32
X
1.74
2.08
2.16
2.14
2.16
2.16
234
2.44
2.47
2.47
2.52
2.47
2.49
2.55
2.86
2.58
2.58
2.61
2.58
2.80
2.84
2.88
2.01
2.91
3.03
3.21
3.45
3.51
3.56
FM,
x<«2
0.57
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
F(X),
x>2
NA
2.32E-01
1.62E-01
1.94E-01
1.70E-01
1.82E-01
9.93E-02
6.82E-02
6.16E-02
6.16E-02
4.95E-02
6.16E-02
5.S3E-02
4.41 E-02
4.41E-02
3.91 E-02
3.91 E-02
3.45E-02
3.91 E-02
1.45E-02
1.23E-02
1.03E-02
8.60E-03
8.60E-03
4.74E-03
1.87E-03
4.45E-04
3.19E-04
2.25E-04
Vegetative
cover
(fraction)
0.50
0.50
0.50
0.60
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.60
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
PM10
emission
flux
(Btan2-»)
3.77E-07
9.01 E-08
6.30E-06
M2E48
5.73E-06
6.30E-08
2.71 E-08
1.64E-08
1.43E-08
1.43E-08
1.07E-08
1.43E-08
1.24E-08
9.25E-09
•\26E-Ot
7.93E-09
7.93E-09
6.76E-09
7.93E-09
2.29E-09
1.B8E-09
1.51E-09
1.21E-09
1.21E-09
5.92E-10
1.88E-10
3.76E-11
2.58E-11
1.73E-11
0.5 Acre
(Q/C)
(g/m2-«per
kg/m3)
100.00
83.19
81.63
90.74
63.40
97.75
90.09
85.40
77.16
82.71
69.40
95.51
64.18
75.59
71.06
74.24
74.91
71.33
89.53
73.37
69.25
79.24
77.46
81.90
68 82
73.42
52.77
62.00
64.06
0.5 Acre
•nnual
swage
cone.
(ugr~m3)
3.77
1.08
0.77
0.76
0.69
0.64
0.30
0.19
0.19
0.17
0.15
0.15
0.15
0.12
0.12
0.11
0.11
0.095
0.069
0.031
0.027
0.019
0.016
0.015
8.60E-03
2.69E-03
7.13E-04
4.16E-04
2.71 E-04
30 Acre
(Q/C)
(fl/m2-sper
ka/m3)
51.68
43.03
41.56
46.84
42.72
50.45
46.38
43.57
39.68
42.81
35.69
49.48
43.31
38.80
40.14
37.66
38.42
36.64
46.06
37.66
35.49
40.70
39.87
42.34
35.10
37.88
27.08
31.85
32.63
30 Acre
annual
average
cone.
(ug/m3)
7.29
2.09
1.52
148
1.34
1.25
0.58
0.38
0.36
0.33
0.30
0.29
0.29
0.24
0.23
0.21
0.21
0.18
0.17
0.061
0.052
0.037
0.030
0.029
0.017
5.22E-03
1.39E-03
8.09E-04
5.31 E-04
Site
ranking
jiercentile
(%)
100
96
93
89
66
82
79
75
71
68
64
61
57
54
60
46
43
39
36
32
29
25
21
16
14
11
7
4
0
00
-------
Ms. Janine Dinan
July 11, 1994
TABLE 3. VF AND PEF VALUES OF (Q/C) FOR AVERAGE AND HIGH END
EXPOSURES
Site
size
0.5 Acres
30 Acres
Average
annual cone.,
PM10
(ug/m3)
0.12
0.23
High End
annual cone.,
PM10
(ug/m3)
0.76
1.48
PEF
Average
(Q/C),
(g/m2-s per
kg/m3)
78.06
40.14
PEF
High End
(Q/C).
(g/m2-s per
dg/m3)
90.74
46.84
VF
Average
(Q/C),
(g/m2-s per
kg/m3)
78.06
40.14
VF
High End
(Q/C).
(g/m2-s per
kg/m3)
68.82
35.10
Average Site for PM10= Salt Lake City
Average Site for Volatiles = Salt Lake City
High End Site for PM10 = Minneapolis
High End Site for Volatiles = Los Angeles
Average Site for PM10: Mean annual windspeed (Um) = 'J.93 m/s; F(x) = 0.044, at x = 2.55.
High End Site for PM10: Um = 4.69 m/s; F(x) = 0.194, at x = 2.14.
Where:
Vegetative cover (V) = 0.5.
Surface roughness height (Zo) = 0.5 cm.
Threshold friction velocity (Ut) = 0.625 m/s at surface.
Threshold windspeed at 7 meters (Ut-7) = Ut/0.4 x In(700/Zo) = 11.32 m/s.
-------
ATTACHMENT A
AREA-ST MODEL RUN SHEETS FOR A 0,5 ACRE SQUARE AREA SOURCE
-------
CO STARTING
CO TITLEONE AREA SOURCES--- 1/2 acre run
CO HODELOPT 0FAULT CONC RURAL
CO AVERT1NE PERIOD
CO POLLUTID PM10
CO RUNORNOT RUN
CO ERRORFIL AREA1.ERR
CO FINISHED
SO STARTING
•• SRCID SRCTYP XS YS ZS
**
SO LOCATION A1/2 AREA -22.5 -22.5 .0000
•* SRCID OS HS XINIT YINIT
** .... . .....
SO SRCPARAM A1/2 1.0 0.0 45. 45.
SO ENISUNIT .100000E-02
-------
•" AREAST - VERSION TESTA •" *** AREA SOURCES--- 1/2 acre run *"* 05/18/94
TEST OF ST AREA SOURCE ALGORITHM •** •" 16:40:30
PAGE 1
•" MODELING OPTIONS USED: CONC RURAL FLAT DFAULT
•"" MODEL SETUP OPTIONS SUMMARY *•*
"Model Is Setup For Calculation of Average concentration Values.
••Model Uses RURAL Dispersion.
"Model Uses Regulatory DEFAULT Options:
1. Final Plane Rise.
2. Stack-tip Dounuash.
3. Buoyancy induced Dispersion.
4. Use Cains Processing Routine.
5. Not Use Hissing Data Processing Routine.
6. Default Wind Profile Exponents.
7. Default Vertical Potential Tenperature Gradients.
8. "Upper Bound" Values for Super-squat Buildings.
9. No Exponential Decay for RURAL Mode
••Model ASSUKS Receptors on FLAT Terrain.
••Model Assumes No FLAGPOLE Receptor Heights.
••Model Calculates PERIOD Averages Only
••This Run Includes: 1 Source(s); 1 Source Group(s); and 25 Receptor(s)
••The Model Assumes A Pollutant Type of: PM10
••Model Set To Continue Running After the Setup Testing.
••Output Options Selected:
Model Outputs Tables of PERIOD Averages by Receptor
Model Outputs Tables of Highest Short Term Values by Receptor (RECTABLE Keyword)
••VOTE: The Following Flags May Appear Following CONC Values: c for Calm Hours
oi for Missing Hours
b for Both Calm and Missing Hours
••Misc. Inputs: Anem. Hgt. (n) = 10.00 ; Decay Coef. = .0000 ; Rot. Angle = .0
Enission Units =
-------
•** AREAST - VERSION TESTA ***
TEST OF ST AREA SOURCE ALGORITHM
•*• MODEL I HG OPTIONS USED: CONC
SOURCE
ID
NUMBER EMISSION RATE
PART. (USER UNITS
CATS. /METER**2)
••* AREA
»»*
RURAL
SOURCES -
FLAT
***
COORD (SU CORNER)
X Y
(METERS) (METERS)
•• 1/2 acre run
DFAULT
AREA SOURCE DATA '
BASE RELEASI:
ELEV. HEIGHT
(METERS) (METERS;*
*»«
***
»**
X-DIM r-DIM ORIENT. EMISSION RATE
OF AREA OF AREA OF AREA SCALAR VARY
(METERS) (METERS) (DEC.) BY
05/18/94
16:40:30
PACE 2
A1/2
.10000E+01
-22.5
-22.5
.0
.00
45.00
45.00
.00
-------
•** AREAST - VERSION TESTA •*• •** AREA SOURCES--- 1/2 acre run *•* 05/18/94
TEST OF ST AREA SOURCE ALGORITHM **• **« 16:40:30
**• MODELING OPTIONS USED: CONC RURAL FLAT OFAULT P*
••• SOURCE IDs DEFINING SOURCE GROUPS ***
GROUP ID SOURCE IDs
AREA1 A1/2
-------
**• AREAST - VERSION TESTA **• *** AREA SOURCES--- 1/2 acre run
TEST OF ST AREA SOURCE ALGORITHM *••
*•* MODELING OPTIONS USED: CONC RURAL FLAT DFAULT
05/18/94
16:40:30
PAGE 4
( .o.
( -25.0,
( 25.0,
( -25.0,
( -50.0,
( 50.0,
( -50.0,
( -75.0,
( 75.0,
( -75.0,
( -100.0,
( 100.0,
( -100.0,
.0,
-0.
-25.0.
25.0.
•0.
-50.0,
50.0,
.0.
-75.0.
75.0,
.0.
-100.0,
100.0,
*** DISCRETE CARTESIAN RECEPTORS *•*
(X-COORD, Y-COORD, ZELEV, ZFLAG)
(METERS)
.0, .0);
.0, .0);
.0, .0);
.0, .0);
•0, .0);
-0. .0);
•0. .0);
.0, .0);
.0, .0);
.0. .0);
.0. .0);
.0. .0);
.0, .0);
( 25.0,
( 25.0,
( -25.0,
( 50.0,
( 50.0,
( -50,0,
( 75.0.
( 75.0.
( -75.0.
( 100.0,
( 100.0,
( -100.0,
.0,
25.0,
-25.0,
.0,
50.0,
-50.0,
.0.
75.0.
•75.0,
.0,
100.0,
-100.0,
.0,
.0,
.0,
.0,
.0.
.0,
-0.
.0.
.0,
.0,
.0.
• 0,
•0);
• 0);
• 0);
•0);
• 0);
•0);
.0);
.0);
.0);
•0);
•0);
•0);
-------
*** AREAST - VERSION TESTA ••• •*» AREA SOURCES--- 1/2 acre run
TEST OF ST AREA SOURCE ALGORITHM ***
*** MODELING OPTIONS USED: CONC RURAL FLAT
DFAULT
•** METEOROLOGICAL DAYS SELECTED FOR PROCESSING
(1=YES; 0=NO)
STABILITY
CATEGORY
A
B
C
D
E
F
1
.70000E-01
.70000E-01
.10000E+00
.15000E+00
.35000E+00
.55000E+00
WIND SPEED CATEGORY
2 3
.70000E-01 .70000E-01
.70000E-01 .70000E-01
.10000E+00 .10000E+00
.15000E+00 .15000E+00
.35000E+00 .35000E+00
.55000E+00 .55000E+00
4
.70000E-01
.70000E-01
.10000E+00
.15000E+00
.35000E+00
.55000E+00
5
.70000E-01
.70000E-01
.10000E+00
.15000E+00
.35000E+00
.55000E+00
6
.70000E-01
.70000E-01
.10000E+00
.15000E+00
.35000E+00
.55000E*00
VERTICAL POTENTIAL TEMPERATURE GRADIENTS
(DEGREES KELVIN PER METER)
STABILITY
CATEGORY
A
B
C
D
E
F
1
.OOOOOE+00
.OOOOOE+CO
.OOOOOE+00
.OOOOOE+00
.20000E-01
.35000E-01
WIND
2
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.OOCOOE+00
.20000E-01
.35000E-01
SPEED CATEGORY
3
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.20000E-01
.35000E-01
4
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.20000E-01
.35000E-01
5
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.20000E-01
.35000E-01
6
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.OOOOOE+00
.20000E-01
.35000E-01
05/18/94
16:40:30
PAGE S
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1
1
1
1
1
1
1
1
1
1
1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1
1
1
1
1
1
1
1
NOTE: METEOROLOGICAL DATA ACTUALLY PROCESSED WILL ALSO DEPEND OH WHAT IS INCLUDED IN THE DATA FILE.
••* UPPER BOUND OF FIRST THROUGH FIFTH WIND SPEED CATEGORIES ~*
(METERS/SEC)
1.54. 3.09, 5.U. 8.23, 10.80,
•** WIND PROFILE EXPONENTS •*•
-------
*** AREAST - VERSION TESTA *** *** AREA SOURCES--- 1/2 acre run
TEST OF ST AREA SOURCE ALGORITHM "*•
** MODELING OPTIONS USED: CONC RURAL FLAT
05/18/94
16:40:30
PAGE 6
DFAULT
••• THE FIRST 24 HOURS OF METEOROLOGICAL DATA *•*
FILE: C:\CRAIG\23174-89.ASC
SURFACE STATION NO.: 23174
NAME: LOS
YEAR: 1989
FORMAT: <4I2,2F9.4,F6.1,I2,2F7.1>
UPPER AIR STATION NO.: 23230
NAME: OAKLAND
YEAR: 1989
YEAR MONTH DAY HOUR
89 1
89 1
89 1
89 1
89 1
89 1
89 1
89 1
89 1
89 1
89
89
89
89
89
89
89
89
89
89
89
89
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
1 19
1 20
1 21
1 22
89 1 1 23
89 1 1 24
FLOU
VECTOR
251.0
228.0
194.0
143.0
173.0
272.0
265.0
233.0
257.0
261.0
44.0
56.0
83.0
59.0
82.0
74.0
81.0
87.0
154.0
167.0
280.0
252.0
220.0
260.0
SPEED
(H/S)
3.09
3.09
2.57
4.63
2.06
3.09
2.06
2.06
2.06
.00
2.06
3.60
4.12
4.12
4.12
3.60
3.60
3.09
4.12
2.06
2.57
2.06
3.09
1.54
TEMP
(K)
282.6
282.0
282.0
282.0
282.0
280.4
280.4
282.0
283.7
285.9
288.2
289.3
289.3
290.4
287.6
287.6
285.9
284.3
286.5
285.4
285.4
284.3
283.2
283.7
STAB
CLASS
4
4
4
4
5
6
6
5
4
3
3
3
3
3
3
4
5
6
5
6
6
6
6
7
MIXING HEIGHT (M)
RURAL URBAN
533.0
568.6
604.1
639.6
675.2
710.7
746.3
134.9
278.2
421.6
564.9
708.3
851.6
995.0
995.0
995.0
992.3
975.8
959.2
942.7
926.2
909.6
893.1
876.5
533.0
568.6
604.1
639.6
151.0
151.0
151.0
265.4
387.0
508.6
630.2
751.8
873.4
995.0
995.0
995.0
979.1
880.6
782.2
683.8
585.3
486.9
388.4
290.0
NOTES:
STABILITY CLASS 1=A. 2=8. 3=C. 4sO, 5=E AND 6=F.
FLOU VECTOR IS DIRECTION TOUARD WHICH WIND IS BLOWING.
-------
*** AREAST - VERSION TESTA *•• ••• AREA SOURCES-•• 1/2 acre run
TEST OF ST AREA SOURCE ALGORITHM *•••
*** MODELING OPTIONS USED: CONC RURAL FLAT
05/18/94
16:40:30
PAGE 7
DFAULT
THE PERIOD ( 8760 HRS) AVERAGE CONCENTRATION
INCLUDING SOURCE(S): A1/2
VALUES FOR SOURCE GROUP: AREA1
DISCRETE CARTESIAN RECEPTOR POINTS ***
X- COORD (M) Y- COORD (M)
.00
-25.00
25.00
•25.00
•50.00
50.00
•50.00
-75.00
75.00
•75.00
•100.00
100.00
•100.00
.00
.00
•25.00
25.00
.00
-50.00
50.00
.00
-75.00
75.00
.00
-100.00
100.00
•* CONC OF PM10
CONC
.01453
.00594
.00104
.00223
.00158
.00018
.00037
.00078
.00008
.00016
.00047
.00005
.00009
IN KILOGRAMS/CUBIC-METER
X- COORD (M) Y- COORD (M)
25.00
25.00
•25.00
50.00
50.00
•50.00
75.00
75.00
•75.00
100.00
100.00
-100.00
.00
25.00
-25.00
.00
50.00
•50.00
.00
75.00
-75.00
.00
100.00
-100.00
**
CONC
.00679
.00414
.00220
.00175
.00060
.00034
.00076
.00024
.00015
.00041
.00013
.00009
-------
**• AREAS! • VERSION TESTA ••* •** AREA SOURCES--- 1/2 acre run
TEST OF ST AREA SOURCE ALGORITHM ***
MODELING OPTIONS USED: CONC
RURAL FLAT DFAULT
*** THE SUMMARY OF MAXIMUM PERIOD ( 8760 HRS) RESULTS *•*
05/18/96
16:40:30
PAGE 8
•* CONC OF PM10
IN KILOGRAMS/CUB 1C-METER
NETWORK
GROUP ID
AREA1 1ST
2ND
3RD
4TH
5TH
6TN
**» RECEPTOR
AVERAGE CONC
HIGHEST
HIGHEST
HIGHEST
HIGHEST
HIGHEST
HIGHEST
TYPES:
VALUE
VALUE
VALUE
VALUE
VALUE
VALUE
GC -
GP =
DC =
DP =
BO =
IS
IS
IS
IS
IS
IS
GRIDCART
GRIDPOLR
DISCCART
DISCPOLR
BOUNDARY
.01453
.00679
.00594
.00414
.00233
.00220
AT (
AT (
AT {
AT (
AT {
AT (
25
-25
25
-25
-25
RECEPTOR i:XR, YR,
-00.
.00,
.00,
.00,
.00,
.00,
.00,
.00,
.00,
25,. 00,
2S.OO,
-2S.OO,
ZELEV, ZFLAG) OF
.00,
.00,
.00.
.00.
.00.
.00.
.00)
.00)
.00)
.00)
.00)
.00)
TYPE GRID- ID
DC
DC
DC
DC
DC
DC
-------
••* AREAST - VERSION TESTA •** ••• AREA SOURCES--- 1/2 acre run
TEST OF ST AREA SOURCE ALGORITHM ••*
MODEL ING OPTIONS USED: CONC RURAL FLAT
0FAULT
05/18/94
16:40:30
PAGE 9
*** Message Sumary For ISC2 Model Execution
Summary of Total Messages
A Total of 0 Fatal Error Message(s)
A Total of 0 Warning Message(s)
A Total of 653 Informational Message(s)
A Total of 653 Cain Hours Identified
FATAL ERROR MESSAGES
•*• HONE •**
HARMING MESSAGES
•** NONE •**
•»«««•««•««•*•»««»••
ISCST2 Finishes Successfully
-------
APPENDIX E
DETERMINATION OF GROUNDWATER DILUTION
ATTENUATION FACTORS FOR FIXED WASTE SITE AREAS
USING EPACMTP
-------
DETERMINATION OF GROUNDWATER
DILUTION ATTENUATION FACTORS
FOR FIXED WASTE SITE AREAS
USING EPACMTP
BACKGROUND DOCUMENT
EPA OFFICE OF SOLID WASTE
May 11, 1994
R04-94.489
-------
APPENDIX D
REVISIONS TO VF AND PEF EQUATIONS
ENVIRONMENTAL QUALITY MANAGEMENT, INC.
MEMORANDUM
-------
Pt
(unitless)
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
0.434
Pa
(unitless)
0.264
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
0.284
alpha
(cm2/s)
7.36E-05
7.36E-05
7.36E-05
7.36E-05
7.36E-05
7.36E-05
1.11E-03
1.11E-03
1.11E-03
1.11E-03
1.11E-03
1.11E-03
3.14E-04
3.14E-04
3.14E-04
3.14E-04
3.14E-04
3.14E-04
3.14E-04
alpha
(MQ)
(cm2/s)
8.32E-06
8.32E-06
8.32E-06
8.32E-06
8.32E-06
8.32E-06
1.25E-04
1.25E-04
1.25E-04
1.25E-04
1.25E-04
1.25E-04
3.55E-05
3.55E-05
3.55E-05
3.55E-05
3.55E-05
3.55E-05
3.55E-05
Predicted
Emission
Rate
(g/s)
1.45E-05
8.25E-06
6.54E-06
3.16E-06
2.69E-06
2.42E-06
1.99E-05
1.14E-05
901E-06
4.35E-06
3.70E-06
3.33E-06
8.47E-05
4.83E-05
3.84E-05
1 .85E-05
1.58E-05
1.42E-05
1.30E-05
Measured
Emission
Rate
(g/s)
5.72E-06
3.68E-06
2.34E-06
5.42E-07
3.90E-07
O.OOE+00
5.98E-06
1.95E-05
1.97E-06
8.67E-Q7
6.28E-07
O.OOE+00
3.21 E-05
3.75E-05
1 .06E-05
2.90E-06
1.80E-06
7.37E-07
5.63E-07
Predicted
Emission
Rate.(MQ)
(g/s)
4.86E-06
2.77E-06
2.20E-06
1.06E-06
9.04E-07
8.13E-07
6.69E-06
3.82E-06
3.03E-06
1.46E-06
1.24E-06
1.12E-06
2.85E-05
1 .63E-05
1.29E-05
6.22E-06
5.30E-06
4.76E-06
4.37E-06
CsoCsat
(Yes/No)
No
No
No
No
No
No
No
No
No '
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
tMAX
(hrs)
19204
19204
19204
19204
19204
19204
1276
1276
1276
1276
1276
1276
4504
4504
4504
4504
4504
4504
4504
t>tMAX
(Yes/No)
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
-------
Saturation,
Csat
(ug/g)
349.6
349.6
349.6
349.6
349.6
349.6
465.5
465.5
465.5
465.5
465.5
465.5
374.5
374.5
374.5
374.5
374.5
374.5
374.5
Measured
Emission
Flux
(ug/m2-min)
2640
1700
1080
250
180
0
2760
9000
910
400
290
0
14800
17300
4910
1340
830
340
260
KOC
(cm3/g)
1100
1100
1100
1100
1100
1100
83
63
83
83
83
83
300
300
300
300
300
300
300
Kd
(cm3/g)
22.00
22.00
22.00
22.00
22.00
22.00
1.66
1.66
1.66
1.66
1.66
1.66
6.00
6.00
6.00
6.00
6.00
6.00
6.00
Kas
(g/cm3)
0.011983
0.011983
0.011983
0.011983
0.011983
0.011983
0.138066
0.138066
0.138066
0.138066
0.138066
0.138066
0.043528
0.043528
0.043528
0.043528
0.043528
0.043528
0.043528
Di
(cm2/s)
0.0667
0.0667
0.0667
0.0667
0.0667
0.0667
0.0871
0.0871
0.0871
0.0871
0.0871
0.0871
0.0783
0.0783
0.0783
0.0783
0.0783
0.0783
0.0783
Dei
(cm2/s)
0.0472
0.0472
0.0472
0.0472
0.0472
0.0472
0.0616
0.0616
0.0616
0.0616
0.0616
0.0616
0.0554
0.0554
0.0554
0.0554
6.0554
0.0554
0.0554
Dei
(MQ) H
(cm2/s) (atm-m3/mol)
0.005333
0.005333
0.005333
0.005333
0.005333
0.005333
0.006964
0.006964
0.006964
0.006964
0.006964
0.006964
0.00626
0.00626
0.00626'
0.00626
0.00626
0.00626
0.00626
0.00643
0.00643
0.00643
0.00643
0.00643
0.00643
0.00559
0.00559
0.00559
0.00559
0.00559
0.00559
0.00637
0.00637
0.00637
0.00637
0.00637
0.00637
0.00637
t
Cumulative
(sec)
66640
266100
422640
1816200
2506380
3099000
86640
266100
422640
1816200
2506380
3099000
86640
266100
422640
1816200
2506380
3099000
3689400
t
Cumulative
(hrs)
24
74
117
505
696
861
24
74
117
505
696
861
24
74
117
505
696
861
1025
-------
Comparison of Radian Corporation (1989) Measured Emission Rates to Hwang and Falco Model Predicted Emission Rates
Chemical
Ethylbenzene
Ethylbenzene
Ethylbenzene
Ethylbenzene
Ethylbenzene
Ethylbenzene
Benzene
Benzene
Benzene
Benzene
Benzene
Benzene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Sample
Point
3
4
5
6
7
8
3
4
5
6
7
8
3
4
5
6
7
8
9
Cso
(ug/g)
310
310
310
310
310
310
110
110
110
110
110
110
880
880
880
880
880
880
880
Flux Contaminated
Chamber Soil
Area Depth Soil
(m2) (m) Type
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Loamy sand
Bulk
Density
(9/cm3)
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
Particle
Density
(g/cm3)
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
Soil
Moisture
(Wt. %)
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Solubility.
s
(g/cm3)
152.000
152.000
152.000
1 52.000
1 52.000
152.000
1750.000
1750.000
1750.000
1750.000
1750.000
1750.000
535.000
535.000
535.000
535.000
535.000
535.000
535.000
Organic
Carbon.
OC
(fraction)
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
Cso = initial soil concentration
Koc • organic carbon partition coefficient
Kd a soil/water partition coefficient
01 a ditfuslvity In air
H = Henry's Law constant
t 9 time from sampling
Pt - total toll porosity
Pa s air-filled soil porosity
Del « effective diffusion coefficient assuming dry soil
Del (MQ) e effective diffusion coefficient using the Milllngton and Quirk expression
alpha - (Del x E)/IE + P» x (1-E) x Kd/H)]
alpha (MQ) - alpha using the Milllngton and Quirk expression of Dei
Predicted Emission Rate s modeled values assuming dry soil
Predicted Emission Rate, (MQ) = modeled values using the Milllngton and Quirk expression of Dei
tMAX-L~2/14.4 x alpha
-------
APPENDIX B
PILOT-SCALE MODEL VALIDATION DATA
B-1
-------
Measured
Emission
Flux Koc
(ng/cm2-day) (cm3/g)
400
260
140
110
105
90
85
200
115
75
65
60
55
40
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
Kd
(cm3/g)
9.86
9.86
9.86
9.86
9.86
9.86
9.86
9.86
9.86
9.86
9.86
9.86
9.86
9.86
Kas
(g/cm3)
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
0.000002
Di
(cm2/s)
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
4.88E-02
Dei
(cm2/s)
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
4.22E-02
Dei
(MQ) H
(cm2/s) |atm-m3/mol)
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
2.17E-02
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
4.58E-07
t t
Cumulative Cumulative Pt
(sec) (hrs) (unitless)
86400
259200
432000
518400
604800
777600
1036800
86400
259200
432000
518400
604800
777600
1036800
24
72
120
144
168
216
288
24
72
120
144
168
216
288
0.717
0.717
0.717
0.717
0.717
0.717
0.717
0.717
0.717
0.717
0.717
0.717
0.717
0.717
-------
Pa
(unitless)
0.642
0.642
0.642
0.642
0.642
0.642
0.642
0.642
0.642
0.642
0.642
0.642
0.642
0.642
alpha
(cm2/s)
3.24E-08
3.24E-08
3.24E-08
3.24E-08
3.24E-08
3.24E-08
3.24E-08
3.24E-08
3.24E-08
3.24E-08
3.24E-08
3.24E-03
3.24E-08
3.24E-08
alpha
(MQ)
(cm2/s)
1.67E-08
1.67E-08
1.67E-08
1 .67E-08
1.67E-08
1 .67E-08
1 .67E-08
1 .67E-08
1.67E-08
1.67E-08
1 .67E-08
1 .67E-08
1 .67E-08
1 .67E-08
Predicted
Emission
Rate
(g/s)
1.51E-10
8.74E-11
6.77E-11
6.18E-11
5.72E-11
5.05E-11
4.37E-11
7.57E-11
4.37E-11
3.39E-11
3.09E-11
2.86E-11
2.52E-11
2.19E-11
Measured
Emission
Rate
(g/s)
1.28E-10
8.29E-11
4.46E-11
3.51 E-11
3.35E-11
2.87E-1 1
2.71 E-11
6.38E-11
3.67E-11
2.39E-11
2.07E-11
1.91 E-11
1.75E-11
1.28E-11
Predicted
Emission
Rate, (MQ)
(9/s)
1.09E-10
6.27E-11
4.85E-11
4.43E-11
4.10E-11
3.62E-11
3.13E-11
5.43E-11
3.13E-11
2.43E-11
2.22E-11
2.05E-11
1.81 E-11
1.57E-11
Cso>Csat
(Yes/No)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
tMax
(hrs)
290
290
290
290
290
290
290
290
290
290
290
290
290
290
t>tMAX
(Yes/No)
No
No
No
No
No
No
No
No
No
No
No
No
No
No
-------
Comparison of Farmer at al. (1972 and 1974) Measured Emission
Chemical
Dieldrin
Dieldrin
Dieldrin
Oieldrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Cso
(ug/g)
10
10
10
10
10
10
10
5 .
5
5
5
5
5
5
Contaminated
Emitting Soil
Area Depth Soil
(cm2) (m) Type
27.55
27.55
27.55
27.55
27.55
27.55
27.55
27.55
27.55
27.55
27.55
27.55
27.55
27.55
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
Gila silt loam
Gila silt loam
Qila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Rates to Hwang and Falco Model Predicted Emission Rates
Bulk Particle
Density Density
(g/cm3) (g/cm3)
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
2.65
2.65
265
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
Soil
Moisture
(Wt. %)
fo
10
10
10
10
10
10
10
10
10
10
10
10
10
Solubility,
s
(g/cm3)
0.195
0.195
0.195
0.195
0.195
0.195
0.195
0.195
0.195
0.195
0.195
0.195
0.195
0.195
Organic
Carbon, Saturation.
OC Csat
(fraction) (ug/g)
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Cso = initial soil concentration
Koc = organic carbon partition coefficient
Kd = soil/water partition coefficient
Oi = diffusivity in air
H a Henry's Law constant
t s time from sampling
Pt = total soil porosity
Pa = air-filled soil porosity
Dei « effective diffusion coefficient assuming dry soil
Del (MQ) = effective diffusion coefficient using the Millington and Quirk expression
alpha = (Dei x E)/[E + Ps x (1-E) x Kd/H)]
alpha (MQ) « alpha using the Millington and Quirk expression of Dei
Predicted Emission Rate = modeled values assuming dry soil
Predicted Emission Rate, (MQ) - modeled values using the Millington and Quirk expression of Dei
LA2/14.4x alpha
-------
Pa
(unitless)
0.642
0.642
0.642
0.642
0.642
0.642
0.642
0.642
alpha
(cm2/s)
9.73E-07
9.73E-07
9.73E-07
9.73E-07
9.73E-07
9.73E-07
9.73E-07
9.73E-07
alpha
(MQ)
(cm2/s)
4.32E-07
4.32E-07
4.32E-07
4.32E-07
4.32E-07
4.32E-07
4.32E-07
4.32E-07
Predicted
Emission
Rate
(g/s)
8.30E-10
4.79E-10
3.71 E-10
3.14E-10
4.15E-10
2.39E-10
1.86E-10
1.57E-10
Measured
Emission
Rate
(a/s)
3.70E-10
1.02E-10
4.46E-11
2.87E-11
1.59E-10
5.10E-11
1.91E-11
1.28E-11
Predicted
Emission
Rate, (MQ)
(9/s)
5.53E-10
3.19E-10
2.47E-10
2.09E-10
2.76E-10
1.60E-10
1.24E-10
1.04E-10
CsoCsat
(Yes/No)
Yes
Yes
Yes
Yes
No
No
No
No
tMAX
(hrs)
11.16
11.16
11.16
11.16
11.16
11.16
11.16
11.16
tMMAX
(Yes/No)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-------
Measured
Emission
Flux
(ng/cm2-day)
1160
320
140
90
500
160
60
40
KOC
(cm3/g)
1080
1080
1080
1080
1080
1080
1080
1080
Kd
(cm3/g)
6.26
6.26
6.26
6.26
6.26
6.26
6.26
6.26
Kas
(g/cm3)
0.000051
0.000051
0.000051
0.000051
0.000051
0.000051
0.000051
0.000051
Di
(cm2/s)
5.43E-02
5.43E-02
5.43E-02
5.43E-02
5.43E-02
5.43E-02
5.43E-02
5.43E-02
Dei
(cm2/s)
4.69E-02
4.69E-02
4.69E-02
4.69E-02
4.69E-02
4.69E-02
4.69E-02
4.69E-02
Dei
(MQ)
(cm2/s)
2.08E-02
2.08E-02
2.08E-02
2.08E-02
2.08E-02
2.08E-02
2.08E-02
2.08E-02
t
t
H Cumulative Cumulative Pt
(atm-m3/mol)
7.85E-06
7.85E-06
7.85E-06
7.85E-06
7.85E-06
7.85E-06
7.85E-06
7.85E-06
(sec)
86400
259200
432000
604800
86400
259200
432000
604800
(hrs)
24
72
120
168
24
72
120
168
(unitless)
0.717
0.717
0.717
0.717
0.717
0.717
0.717
0.717
-------
Comparison of
Chemical
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Farmer et al. (1972 and 1974) Measured Emission Rates to
Contaminated
Emitting Soil
Cso
(ug/g)
10
10
10
10
5
5
5
5
Area
(cm2)
27.55
27.55
27.55
27.55
27.55
27.55
27.55
27.55
Depth
(m)
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
Soil
Type
Gila silt loam
Gila silt loam
Gila silt loam
Gita silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Gila silt loam
Bulk
Hwang and Falco Model Predicted Emission Rates
Particle
Density Density
(g/cm3)
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
(g/cm3)
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
Soil
Moisture
(Wt. %)
10
10
10
10
10
10
10
10
Solubility,
s
(g/cm3)
7.80
7.80
7.80
7.80
7.80
7.80
7.80
7.80
Organic
Carbon.
OC
(fraction)
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
0.0058
Saturation.
Csat
(ug/g)
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
Cso * Initial soil concentration
Koc » organic carbon partition coefficient
Kd a soil/water partition coefficient
Di « diffusivity in air
H = Henry's Law constant
t = time from sampling
Pt » total soil porosity
Pa * alr-fllled soil porosity
Del - effective diffusion coefficient assuming dry soil
Dei (MQ) => effective diffusion coefficient using the Millington and Quirk expression
alpha = (Del x E)/{E + Ps x (1-E) x Kd/H))
alpha (MQ) = alpha using the Millington and Quirk expression of Dei
Predicted Emission Rate « modeled values assuming dry soil
Predicted Emission Rate, (MQ) = modeled values using the Millington and Quirk expression of Dei
tMAX = L~ 2/14.4 x alpha
-------
APPENDIX A
BENCH-SCALE MODEL VALIDATION DATA
A-1
-------
High Rise Buildings.
(30+Floors)
Suburban
Medium Buildings*
(Institutional )
E
u
o
M
X
O
O
ac
Suburban
Residential Dwellings
Wheat Field
Plowed Field
2.0 (cm)
1000
—800—
—400-
-400-
-200-
100
—80.0-
—60.0-
—40.0—
-20.0-
10.0
Natural Snow
—8.0—
• 2.0-
1.0
-0.8-
-0.6-
-0.4.
-0.2—|
0.1
• Urban Area
Woodland Forest
Grassland
Figure 3-6. Roughness Heights for Various Surfaces (Cowherd and
Guenther, 1976)
27
-------
The emission factor equation for vehicle travel on unpaved surfaces,
as presented in Section 4, requires estimates of site-specific traffic and
surface parameters. Average vehicle speed and number of wheels can be esti-
mated from direct observation of traffic, site inspection of road condition,
and interviews with people living or working near the site. Vehicle weight
can be estimated from vehicle type and number of wheels, using a chart
presented in Section 4. Default values for road surface silt content are
also provided.
28
-------
ro
OJ
4->
U
O)
u
O)
4->
U
U
c
ZJ
10
3 4567891
Figure 3-5. Increase 1n Threshold Friction Velocity with L
-------
The difficulty in estimating I also increases for small non-erodible
elements. However, because small non-erodible elements are more likely to
be evenly distributed over the surface, it is usually acceptable to examine
a smaller surface area, e.g., 30 cm x 30 cm. The photographs of various non-
erodible element distributions presented in Appendix A can be used as an aid
in estimating L for surfaces with small non-erodible elements. These photo-
graphs illustrate the physical appearance corresponding to various values of L .
The least acceptable technique for classifying the credibility of the
surface material is by visual surface examination and matching with the photo-
graphs given in Appendix A. Once again, loose sandy soils fall into the high
credibility ("unlimited reservoir"). These soils do not promote crust forma-
tion, and show only a brief effect of moisture addition by rainfall. On the
other hand, compacted soils with a tendency for crust formation fall into the
low ("limited reservoir") credibility group. Clay content in soil, which
tends to promote crust formation, is evident from crack formation upon drying.
The roughness height, z , which is related to the size and spacing of
surface roughness elements, is needed to convert the friction velocity to
the equivalent wind speed at the typical weather station sensor height of
7 m above the surface. Figure 3-6 depicts the roughness height scale for
various conditions of ground cover (Cowherd and Guenther, 1976). The con-
version to the 7 m value is discussed in Section 4 (Figure 4-2).
In addition to these surface properties, it is also important that the
field personnel note the location and orientation of significant topographic
features that are likely to influence the dispersion of contaminated material
from the site. Significant topographic features will include not only the
terrain of the surrounding area but also the large-scale roughness elements
such as trees and buildings that might enhance or obstruct the wind flow
for the site in question. A consideration of these features is important
in the proper interpretation of the modeling results presented in Section 4.2.
In order to ensure the best possible characterization of the local-scale wind
flow, it is recommended that the response team contact both the nearest Na-
tional Weather Service office and an American Meteorological Society (AMS)
Certified Consulting Meteorologist1.
3.3 CHARACTERIZATION OF MECHANICAL RESUSPENSION BY VEHICLE TRAFFIC
The most typical type of intensive mechanical disturbance occurs with
vehicle travel over the contaminated surface material. The occurrence of
traffic over the site can be determined by inspection of the site for ex-
istence of roads. Other less common forms of mechanical disturbance are
associated with any operation which moves or turns over surface material
(i.e., scraping, grading, tilling, etc.). All of these operations not only
release suspended particulate matter into the air, but greatly increase the
potential for subsequent wind erosion by destroying protective surface crusts
and removing vegetative cover. Because these types of disturbance are rare,
the following discussion is limited to vehicle traffic as the typically sig-
nificant mechanical resuspension process.
A list of Certified Consulting Meteorologists is available from the
American Meteorological Society, 45 Beacon Street, Boston, Massachusetts
02108. Telephone: (617) 227-2425
26
-------
FIELD PROCEDURE FOR DETERMINATION OF THRESHOLD FRICTION VELOCITY*
1. Prepare a nest of sieves with the following openings: 4 mm, 2 mm,
1 mm, 0.5 nm, 0.25 mm. Place a collector pan below the bottom sieve
(0.25 mm opening).
2. Collect a sample representing the surface layer of loose particles
(approximately 1 cm in depth for an uncrusted surface), removing
any rocks larger than about 1 cm in average physical diameter. The
area to be sampled should not be less than 30 cm x 30 cm.
3. Pour the sample into the top sieve (4 mm opening), and place a lid
on the top.
4. Rotate the covered sieve/pan unit by hand using broad sweeping arm
motions in the horizontal plane. Complete 20 rotations at a speed
just necessary to achieve some relative horizontal motion between
the sieve and the particles.
5. Inspect the relative quantities of catch within each sieve and de-
termine where the mode in the aggregate size distribution lies, i.e.,
between the opening size of the sieve with the largest catch and the
opening size of the next largest sieve.
Adapted from a laboratory procedure published by W. S. Chepil (1952).
Figure 3-3.
23
-------
looojt;
f>o
BSIiMHmmM^
o
0)
u
o
"oJ
•r-
•M
o
•o
15
•I
100
10
Aggregate Size Distribution Mode (nm)
Figure 3-4. Relationship of Threshold Friction Velocity
to Size Distribution Mode
-------
1.0 INTRODUCTION
The Agency is developing estimates for threshold values of chemical concentrations in soils at
contaminated sites that represent a level of concentration above which there is sufficient concern
to warrant further site-specific study. These concentration levels are called Soil Screening
Levels (SSLs). The primary purpose of the SSLs is to accelerate decision making concerning
contaminated soils. Generally, if contaminant concentrations in soil fall below the screening
level and the site meets specific residential use conditions, no further study or action is
warranted for that area under CERCLA (EPA, 1993b).
The Soil Screening Levels have been developed using residential land use human exposure
assumptions and considering multiple pathways of exposure to the contaminants, including
migration of contaminants through soil to an underlying potable aquifer. Contaminant migration
through the unsaturated zone to the water table generally reduces the soil leachate concentration
by attenuation processes such as adsorption and degradation. Groundwater transport in the
saturated zone further reduces concentrations through attenuation and dilution. The contaminant
concentration arriving at a receptor point in the saturated zone, e.g., a domestic drinking water
well, is therefore generally lower than the original contaminant concentration in the soil leachate.
The reduction in concentration can be expressed succinctly in a Dilution-Attenuation Factor
(DAF) defined as the ration of original soil leachate concentration to the receptor point
concentration. The lowest possible value of DAF if therefore one; a value of DAF=1 means
that there is no dilution or attenuation at all; the concentration at the receptor point is the same
as that in the soil leachate. High values of DAF on the other hand correspond to a high degree
of dilution and attenuation.
For any specific site, the DAF depends on the interaction of a multitude of she-specific factors
and physical and bio-chemical processes. The DAF also depends on the nature of the
contaminant itself; i.e., whether or not the chemical degrades or sorbs. As a result, it is
impossible to predict DAF values without the aid of a suitable computer fate and transport
1-1
-------
simulation model that simulates the migration of a contaminant through the subsurface, and
accounts for the relevant mechanisms and processes that affect the receptor concentration.
The Agency has developed the EPA Composite Model for Leachate Migration with
Transformation Products (EPACMTP; EPA, 1993a, 1994) to assess the groundwater quality
impacts due to migration of wastes from surface waste sites. This model simulates the fate and
transport of contaminants after their release from the land disposal unit into the soil, downwards
to the water table and subsequently through the saturated zone. The fate and transport model
has been coupled to a Monte Carlo driver to permit determination of DAFs on a generic,
nationwide basis. The EPACMTP model has been applied to determine DAFs for the subsurface
pathway for fixed waste site areas, as part of the development of Soil Screening Levels. This
report describes the application of EPACMTP for this purpose.
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2.0 GROUNDWATER MODEL
2.1 Description of EPACMTP Model
The EPA Composite Model for Leachate Migration with Transformation Products (EPACMTP,
EPA, 1993a, 1994) is a computer model for simulating the subsurface fate and transport of
contaminants that are released at or near the soil surface. A schematic view of the conceptual
subsurface system as simulated by EPACMTP, is shown in Figure 1. The contaminants are
initially released over a rectangular source area representing the waste site. The modeled
subsurface system consists of an unsaturated zone underneath the source area, and an underlying
water table aquifer. Contaminants move vertically downward through the unsaturated zone to
the water table. The contaminant is assumed to be dissolved in the aqueous phase; it migrates
through the soil under the influence of downward infiltration. The rate of infiltration may reflect
the combined effect of precipitation and releases from the source area. Once the contaminant
enters the saturated zone, a three-dimensional plume develops under the combined influence of
advection with the ambient groundwater flow and dispersive mixing.
The EPACMTP accounts for the following processes affecting contaminant fate and transport:
advection, dispersion, equilibrium sorption, first-order decay reactions, and recharge dilution
in the saturated zone. For contaminants mat transform into one or more daughter products, the
model can account for the fate and transport of those transformation products also.
The EPACMTP model consists of three main modules:
• An unsaturated zone flow and transport module
• A saturated zone flow and transport module
• A Monte Carlo driver module, which generates model input
parameter values from specified probability distributions
The assumptions of the unsaturated zone and saturated zone flow and transport modules are
described in Section 2.2. The Monte Carlo modeling procedure is described in Section 2.3.
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Heceptor Well
Leakage from
Contaminated Area
Unsaturated
Saturated ~
Ambient
Groundwater Row
Figure 1 Conceptual view of the unsaturated zone-saturated zone system simulated by
EPACMTP.
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2.2 Fate and Transport Simulation Modules
2.2.1 Unsaturated zone flow and transport module
Details on the mathematical formulation and solution techniques of the unsaturated zone flow
and transport module are provided in the EPACMTP background document (EPA, 1993a). For
completeness, the major features and assumptions are summarized below:
• The source area is a rectangular area.
• Contaminants are distributed uniformly over the source area.
• The soil is a uniform, isotropic porous medium.
• Flow and transport in the unsaturated zone are one-dimensional,
downward.
• Flow is steady state, and driven by a prescribed rate of infiltration.
• Flow is isothermal and governed by Darcy's Law.
• The leachate concentration entering the soil is either constant (with
a finite or infinite duration), or decreasing with time following a
first-order decay process.
• The chemical is dilute and present in solution or soil solid phase
only.
• Sorption of chemicals onto the soil solid phase is described by a
linear or nonlinear (Freundlich) equilibrium isotherm.
• Chemical and biological transformation process can be represented
by an effective, first-order decay coefficient.
2.2.2 Saturated zone flow and transport module
The unsaturated zone module computes the contaminant concentration arriving at the water table,
as a function of tune. Multiplying this concentration by the rate of infiltration through the
unsaturated zone yields the contaminant mass flux entering the saturated zone. This mass flux
is specified as the source boundary condition for the saturated zone flow and transport module.
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Groundwater flow in the saturated zone is simulated using a (quasi-) three-dimensional steady
state solution for predicting hydraulic head and Darcy velocities in a constant thickness
groundwater system subject to infiltration and recharge along the top of the aquifer and a
regional hydraulic gradient defined by upstream and downstream head boundary conditions.
In addition to modeling fully three-dimensional groundwater flow and contaminant fate and
transport, EPACMTP offers the option to perform quasi-3D modeling. When this option is
selected, the model ignores either the flow component in the horizontal transverse (-y) direction,
or the vertical (-z) direction. The appropriate 2D approximation is selected automatically in the
code, based on the relative significance of phone movement in the horizontal transverse versus
vertical directions. Details of this procedure are provided in the saturated zone background
document (EPA, 1993a). The switching criterion that is implemented hi the code will select the
2D areal solution for situations with a relatively thin saturated zone in which the contaminant
plume would occupy the entire saturated thickness; conversely, the solution in which advection
in the horizontal transverse direction is ignored is used in situations with a large saturated
thickness, in which the effect of vertical phone movement is more important.
The saturated zone transport module describes the advective-dispersive transport of dissolved
contaminants in a three-dimensional, constant thickness aquifer. The initial boundary is zero,
and the lower aquifer boundary is taken to be impermeable. No-flux conditions are set for the
upstream aquifer boundary. Contaminants enter the saturated zone through a patch source of
either constant concentration or constant mass flux on the upper aquifer boundary, representing
the area directly underneath the waste site at the soil surface. The source may be of a finite or
infinite duration. Recharge of contaminant-free infiltration water occurs along the upper aquifer
boundary outside the patch source. Transport mechanisms considered are advection,
longitudinal, vertical and transverse hydrodynamic dispersion, linear or nonlinear equilibrium
adsorption, first-order decay and daughter product formation. As in the unsaturated zone, the
saturated zone transport module can simulate multi-species transport involving chained decay
reactions. The saturated zone transport module of EPACMTP can perform either a fully three-
dimensional transport simulation, or provide a quasi-3D approximation. The latter ignores
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advection in either the horizontal transverse (-y) direction, on the vertical (-z) direction,
consistent with the quasi-3D flow solution. In the course of a Monte Carlo simulation, the
appropriate 2D approximations are selected automatically for each individual Monte Carlo
iteration, thus yielding an overall quasi-3D simulation.
The saturated zone and transport module is based on the following assumptions:
• The aquifer is uniform and initially contaminant-free.
• The flow field is at steady state; seasonal fluctuations in
groundwater flow are neglected.
• The saturated thickness of the aquifer remains constant; mounding
is represented by the head distribution along the top boundary of
the modeled saturated zone system.
• Flow is isothermal and governed by Darcy's Law.
• The chemical is dilute and present in the solution or aquifer solid
phase only.
• Adsorption onto the solid phase is described by a linear or
nonlinear equilibrium isotherm.
• Chemical and/or biochemical transformation of the contaminant
can be described as a fust-order process.
2.23 Model capabilities and limitations
EPACMTP is 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 main assumptions embedded in the fate and transport model are gwnmari™d in the previous
sections and are discussed in more detail here. The user should verify that the assumptions are
reasonable for a given application.
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Uniform Porous Soil and Aquifer Medium. EPACMTP assumes that the soil and aquifer
behave as uniform porous media and that flow and transport are described by Darcy's law and
the advection-dispersion equation, respectively. The model does not account for the presence
of cracks, macro-pores, and fractures. Where these features are present, EPACMTP may
underpredict the rate of contaminant movement.
Single Phase Flow and Transport. The model assumes that the water phase is the only mobile
phase and disregards interphase transfer processes other than reversible adsorption onto the solid
phase. For example, the model does not account for volatilization in the unsaturated zone,
which will tend to give conservative predictions for volatile chemicals. The model also does not
account for the presence of a second liquid phase (e.g., oil). When a mobile oil phase is
present, the movement of hydrophobic chemicals may be underpredicted by the model, since
significant migration may occur in the oil phase rather than in the water phase.
Equilibrium Adsorption. The model assumes that adsorption of contaminants onto the soil or
aquifer solid phase occurs instantaneously, or at least rapidly relative to the rate of contaminant
movement. In addition, the adsorption process is taken to be entirely reversible.
Geochemistry. The EPACMTP model does not account for complex geochemical processes,
such as ion exchange, precipitation and complexation, which may affect the migration of
chemicals in the subsurface environment. EPACMTP can only approximate such processes as
an effective equilibrium retardation process. The effect of geochemical interactions may be
especially important in the fate and transport analyses of metals. Enhancement of the model for
handling a wide variety of geochemical conditions is currently underway.
First-Order Decay. It is assumed that the rate of contaminant loss due to decay reactions is
proportional to the dissolved contaminant concentration. The model is based on one overall
decay constant and does not explicitly account for multiple degradation processes, such as
oxidation, hydrolysis, and biodegradation. When multiple decay processes do occur, the user
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must determine the overall, effective decay rate. In order to increase flexibility of the model,
the user may instruct the model to determine the overall decay coefficient from chemical specific
hydrolysis constants plus soil and aquifer temperature and pH.
Prescribed Decay Reaction Stoichiometry. For scenarios involving chained decay reactions,
EPACMTP assumes that the reaction Stoichiometry is always prescribed, and the speciation
factors are specified by the user as constants (see EPACMTP Background Document, EPA,
1993a). In reality, these coefficients may change as functions of aquifer conditions (temperature,
pH, etc.) and/or concentration levels of other chemical components.
Uniform Soil. EPACMTP assumes that the unsaturated zone profile is homogeneous. The
model does not account for the presence of cracks and/or macropores in the soil, nor does it
account for lateral soil variability. The latter condition may significantly affect the average
transport behavior when the waste source covers a large area.
Steady-State Flow in the Unsaturated-Zone. Flow in the unsaturated zone is always treated as
steady state, with the flow rate determined by the long term, average infiltration rate through
a disposal unit, or by the average depth of ponding in a surface impoundment. Considering the
time scale of most practical problems, assuming steady-state flow conditions in the unsaturated
zone is reasonable.
Groundwater Mounding. The saturated zone module of EPACMTP is designed to simulate flow
and transport in an unconfined aquifer. Groundwater mounding beneath the source is
represented only by increased head values on top of the aquifer. The saturated thickness of the
aquifer remains constant in the model, and therefore the model treats the aquifer as a confined
system. This approach is reasonable as long as the mound height is small relative to the
saturated thickness of the aquifer and the thickness of the unsaturated zone. For composite
modeling, the effect of mounding is partly accounted for in the unsaturated zone module, since
the soil is allowed to become saturated. The aquifer porous material is assumed to be uniform,
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although the model does account for anisotropy in the hydraulic conductivity. The lower aquifer
boundary is assumed to be impermeable.
Flow in the Saturated Zone. Flow in the saturated zone is taken to be at steady state. The
concept is that of regional flow in the horizontal longitudinal direction, with vertical disturbance
due to recharge and infiltration from the overlying unsaturated zone and waste site (source area).
EPACMTP accounts for variable recharge rates underneath and outside the source area. It is,
however, assumed that the saturated zone has a constant thickness, which may cause inaccuracies
in the predicted groundwater flow and contaminant transport in cases where the infiltration rate
from the waste disposal facility is high.
Transport in the Saturated Zone. Contaminant transport in the saturated zone is by advection
and dispersion. The aquifer is assunred to be initially contaminant free and contaminants enter
the aquifer only from the unsaturated zone immediately underneath the waste site, which is
modeled as a rectangular horizontal plane source. EPACMTP can simulate both steady state and
transient transport in the Saturated Zone. In tile former case, the contaminant mass flux entering
at the water table must be constant with time. In the latter case, the flux at the water table can
be constant or vary as a function of time. The transport module accounts for equilibrium.
adsorption and decay reactions, both of which are modeled in the same manner as in the
unsaturated zone. The adsorption and decay coefficients are assumed to be uniform throughout
saturated zone.
23 Monte Carlo Module
EPACMTP was designed to perform simulations on a nationwide basis, and to account for
variations of model input parameters reflecting variations in site and hydrogeological conditions.
The fate and transport model is therefore linked to a Monte Carlo driver which generates model
input parameter values from the probability distribution of each parameter. The Monte Carlo
modeling procedure is described in more detail in this section.
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The Monte Carlo method requires that for each input parameter, except constant parameters, a
probability distribution is provided. The method involves the repeated generation of pseudo-
random values of the uncertain input variable(s) (drawn from the known distribution and within
the range of any imposed bounds) and the application of the model using these values to generate
a series of model responses (receptor well concentration). These responses are then statistically
analyzed to yield the cumulative probability distribution of the model output. Thus, the various
steps involved in the application of the Monte Carlo simulation technique are:
(1) Selection of representative cumulative probability distribution
functions for the relevant input variables. |
(2) Generation of a pseudo-random number from the distributions
selected in (1). These values represent a possible set of values (a
realization) for the input variables.
(3) Application of the fate and transport simulation modules to
compute the output(s), i.e., downstream well concentration.
(4) Repeated application of steps (2) and (3) for a specified number of
iterations.
(5) Presentation of the series of output (random) values generated in
step (3).
(6) Analysis of the Monte Carlo output to derive regulatory DAF
values.
The Monte Carlo module designed for implementation with the EPACMTP composite model
performs steps 2-5 above. This process is shown conceptually in Figure 2. Step 6 is performed
as a post-processing step. This last step simply involves converting the normalized receptor well
concentrations to DAF values, and ranking then for high to low values. Each Monte Carlo
iteration yields one DAF value for the constituent of concern (plus one DAF value for each of
the transformation products, if the constituent is a degrader). Since each Monte Carlo iteration
has equal probability, ordering the DAF values from high to low, directly yields their cumulative
probability distribution (CDF). If appropriate, CDF curves representing different regional
distributions may be combined into a single CDF curve, which is a weighted average of the
regional curves.
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Inpu t
Foromtt»r S
n
O
D
E
L
ttode I
Ou tpu t
cr
£
Value
Input Distributions
I
Figure 2
Value
Output Distribution
Conceptual Monte Carlo framework for deriving probability distribution of model
output from probability distributions of input parameters.
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A simplified flow chart that illustrated the linking of the Monte Carlo module to the simulation
modules of the EPACMTP composite model is presented in Figure 3. The modeling input data
is read first, and subsequently the desired random numbers are generated. The generated
random and/or derived parameter values are then assigned to the model variables. Following
this, the contaminant transport fate and transport simulation is performed. The result is given
in terms of the predicted contaminant concentration(s) in a down-stream receptor well. The
generation of random parameter values and fate and transport simulation is repeated as many
times as desired to determine the probability distribution of down-stream well concentrations.
2.3.1 Capabilities and Limitations of Monte Carlo Module
The Monte Carlo module in EPACMTP is implemented as a flexible module that can
accommodate a wide variety of input distributions. These include: constant, normal, log-
normal, exponential, uniform, log,0 uniform,, Johnson SB, empirical, or derived. In addition,
specific upper and/or lower bounds can be provided for each parameter. The empirical
distribution is used when the data does not fit any of the other probability distributions. When
the empirical distribution is used, the probability distribution is specified in tabular form as a
list of parameter values versus cumulative probability, from zero to one.
It is important to realize that the Monte Carlo method accounts for parameter variability and
uncertainty; it does, however, not provide a way to account or compensate for process
uncertainty. If the actual flow and transport processes that may occur at different sites, are
different from those simulated in the fate and transport module, the result of a Monte Carlo
analysis may not accurately reflect the actual variation in groundwater concentrations.
EPACMTP does not directly account for potential statistical dependencies, i.e., correlations
between parameters. The probability distributions of individual parameters are considered to
be statistically independent. At the same time, EPACMTP does incorporate a number of
safeguards against generating impossible combinations of model parameters. Lower and upper
bounds on the parameters prevent unrealistically low or high values from being generated at all.
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1
Read input data
and desired
modelinQ options
Perform Simulation
Post Processing
i
r
Print Results
Figure 3 Flow chart of EPACMTP for Monte Carlo simulation.
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In the case of model parameters that have a direct physical dependence on other parameters,
these parameters can be specified as derived parameters. For instance, the ambient ground water
flow rate is determined by the regional hydraulic gradient and the aquifer hydraulic conductivity.
In the Monte Carlo analyses, the ambient ground water flow rate is therefore calculated as the
product of conductivity and gradient, rather than generated independently. A detailed discussion
of the derived parameters used in the model is provided in the EPACMTP User's Guide (EPA,
1994).
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3.0 MODELING PROCEDURE
This section documents the modeling procedure followed in determining the groundwater
pathway DAF values for the Soil Screening Levels. Section 3.1 describes the overall approach
for the modeling analysis; section 3.2 describes the model options used and summarizes the input
parameter values.
3.1 Modeling Approach
The overall modeling approach consisted of two stages. First, a sensitivity analysis was
performed to determine the optimal number of Monte Carlo repetitions required to achieve a
stable and converged result, and to determine which site-related parameters have the greatest
impact on the DAFs. Secondly, Monte Carlo analyses were performed to determined DAF
values as a function of the size of the source area, for various scenarios of receptor well
placement.
3.1.1 Determination of Monte Carlo Repetition Number and Sensitivity Analysis
The criterion for determining the optimal number of Monte Carlo repetitions was set to a change
in DAF value of no more than 5 percent when the number of repetitions is varied. A Monte
Carlo simulation comprising 20,000 repetitions was first made. The results from this simulation
were analyzed by calculating the 85th percentile DAF value obtained by sampling model output
sequences of different length, from 2,000 to the full 20,000 repetitions. The modeling scenario
considered in this analysis was the same as that in the base case scenario discussed in the next
section, with the size of the source area set to 10,000 m2.
The sensitivity analysis on site-related model parameters was performed by fixing one parameter
at a time, while remaining model parameters were varied according to their default, nationwide
probability distributions as discussed in the EPACMTP User's Guide (EPA, 1993b).
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For each parameter, the low, medium, and high values were selected, corresponding to the 15th,
50th, and 85th percentile, respectively, of that parameter's probability distribution. As a result,
the sensitivity analysis reflects, in pan, the width of each parameter's probability distribution.
Parameters with a narrow range of variation will tend to be among the less sensitive parameters,
and vice versa for parameters that have a wide range of variation. By conducting the sensitivity
analysis as a series of Monte Carlo simulations, any parameter interactions on the model output
are automatically accounted for. Each of the Monte Carlo simulations yields a probability
distribution of predicted receptor well concentrations. Evaluating the distributions obtained with
different fixed values of the same parameter provides a measure of the overall sensitivity and
impact of that parameter. In each case the model was run for 2000 Monte Carlo iterations.
Steady-state conditions (continuous source) were simulated in all cases.
In a complete Monte Carlo analysis, over 20 different model parameters are involved. These
parameters may be divided into two broad categories. The first includes parameters that are
independent of contaminant-specific chemical properties, e.g., depth to water table, aquifer
thickness, receptor well distance, etc. The second category encompasses those parameters that
are related to contaminant-specific sorption and biochemical transformation characteristics. This
category includes the organic carbon partition coefficient, but also parameters such as aquifer
pH, temperature and fraction organic carbon. The sensitivity of the model to the first category
of parameters has examined, by considering a non-degrading, non-sorbing contaminant. Under
these conditions, any parameters in the second category will have zero sensitivity. In addition,
all unsaturated zone parameters can be left out of the analysis, since the predicted steady state
contaminant concentration at the water table will always be the same as that entering the
unsaturated zone. The only exception to this is the soil type parameter. In the nationwide
Monte Carlo modeling approach, different soil types are distinguished. Each of the three
different soil types (sandy loam, silt loam or silty clay loam) has a different distribution of
infiltration rate, with the sandy loam soil type having the highest infiltration rates, silty clay
loam having the lowest, and silty loam having intermediate rates. The effect of the soil type
parameter is thus intermixed with that of infiltration rate. Table 1 lists the input 'low',
'medium' and 'high' values for all the parameters examined.
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Table 1 Parameter input values for model sensitivity analysis.
Parameter
Source Parameters
Source Area (m2)
Infiltration Rate (m/yr)
Recharge Rate (m/yr)
Saturated Zone Parameters
Saturated Thickness (m)
Hydraulic conductivity (m/yr)
Regional gradient
Ambient groundwater velocity (m/yr)
Porosity
Longitudinal Dispersivity (m)
Transverse Dispersivity (m)
Vertical Dispersivity (m)
Low
4.8X104
6.0 xlO4
6.0xl(T»
15.55
1.9X103
4.3 XlO-3
53.2
0.374
4.2
0.53
0.026
Median
2.8X105
6.4 xlO'3
8.0 xlO'3
60.8
1.5 XlO4
1.8 XlO-2
404.0
0.415
12.7
1.59
0.079
High
l.lxlO6
1.7X10-'
1.5x10-'
159.3
5.5x10*
5.0 XlO-2
2883.0
0.455
98.5
12.31
0.62
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3.1.2 Analysis of DAT Values for Different Source Areas
Following completion of the sensitivity analysis discussed above, an analysis was performed of
the variation of DAF values with size of the contaminated area. The sensitivity analysis, results
of which are presented in Section 4.1, showed that the size of the contaminated source area is
one of the most sensitive parameters in the model. For the purpose of deriving DAF values for
the groundwater pathway in determining soil screening levels, it would therefore be appropriate
to correlate the DAF value to the size of the contaminated area. .
The EPACMTP modeling analysis was designed to determine the size of the contaminated area
that would result in DAF values of 10 and 100 at the upper 85th, 90th, and 95th percentile of
probability, respectively. Since it is not possible to directly determine the source area that
results in a specific DAF value, the model was executed for a range of different source areas,
using a different but fixed source area value in each Monte Carlo simulation. The 85th, 90th,
and 95th percentile DAF values were then plotted against source area, in order to determine the
value of source area corresponding to a specific DAF value.
3.1.2.1 Model Options and Input Parameters
Table 2 qnmnarires the EPACMTP model options used in performing the simulations. Model
input parameters used are gimmarired in Table 3. The selected options and input parameter
distributions and values are consistent with those used in the default nationwide modeling, and
are discussed individually in the EPACMTP User's Guide (EPA, 1994). Exceptions to this
default modeling scenario are discussed below.
Source Area
In the default, nationwide modeling scenario, the waste site area, or source area, is treated as
a Monte Carlo variable, with a distribution of values equal to that of the type of waste unit, e.g.
landfills, considered. In the present modeling analyses, the source area was set to a different
but constant value in each simulation run.
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Table 2 Summary of EPACMTP modeling options.
OPTION
Value Selected
Simulation Type
Number of Repetitions
Nationwide Aggregation
Source Type
Unsat. Zone Present
Sat. Zone Model
Contaminant Degradation
Contaminant Sorption
Monte Carlo
15,000
Yes
Continuous
Yes
Quasi-3D
No
No
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Table 3 Summary of EPACMTP input parameters.
Parameter
Value or
Distribution Type
Comment
Source-Specific
Area
Infiltration Rate
Recharge Rate
Leachate Concentration
Chemical-Specific
Hydrolysis Rate Constants
Organic Carbon Partition Coeff.
Unsaturated Zorij Specific
Depth to Water Table
Dispersivity
Soil Hydraulic Properties
Soil Chemical Properties
Saturated Zone Specific
Sat. Zone Thickness
Hydraulic Conductivity
Hydraulic Gradient
Seepage Velocity
Particle Diameter
Porosity
Bulk Density
Longitudinal Dispersivity
Transverse Dispersivity
Vertical Dispersivity
Receptor Well x-coordinate
Receptor Well y-coordinate
Receptor Well z-coordinate
Constant
Soil-type dependent
Soil-type dependent
= 1.0
= 0.0
= 0.0
Empirical
Soil-depth dependent
Soil-type dependent
Soil-type dependent
Exponential
Derived from Part. Diam.
Derived from Conductivity and
Gradient
Empirical
Derived from Part. Diam
Derived from Corosity
Distance-dependent
Derived from Long. Dispersivity
Derived from Long. Dispersivity
= 25 feet
Within plume
Empirical
Varied in each run
default
default
default
Contaminant does not degrade
Contaminant does not sorb
default
default
default
default
default
default
default
default
default
default
default
default
default
default
Set to fixed value
default
default
Note: 'Default' represents default nationwide Monte Carlo scenario as presented in EPACMTP User's
Guide (EPA, 1994).
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Receptor Well Location
In the default nationwide modeling scenario, the position of the nearest downgradient receptor
well in the saturated zone is treated as a Monte Carlo variable. The position of the well is
defined by its x-, y-, and z-coordinates. The x-coordinate represents the distance along the
ambient groundwater flow direction from the downgradient edge of the contaminated area. The
y-coordinate represents the horizontal transverse distance of the well from the plume centerline.
The x-, and y-coordinate in turn can be defined in terms of an overall downgradient distance,
and an angle off-center (EPA, 1994). The z-coordinate represents the depth of the well intake
point below the water table. This is illustrated schematically in Figure 4, which shows the
receptor well location in both plan view and cross-sectional view.
In the default nationwide modeling scenario, the x-, and z-coordinates of the well are determined
from Agency surveys on the distance of residential wells from municipal landfills, and data on
the depth of residential drinking water wells, respectively. The y-coordinate value is determined
so that the well location falls within the approximate area! extent of the contaminant plume (see
Figure 4).
For the present modeling analysis, a number of different receptor well placement scenarios were
considered. These scenarios are summarized in Table 4.
The base case scenario (scenario 1) involved setting the x-distance of the receptor well to 25 feet
from the edge of the source area. Nationwide default options were used for the receptor well
y- and z-coordinates. The y-coordinate of the well was assigned a uniform probability
distribution within the boundary of the plume. The depth of the well intake point (z-coordinate)
was assumed to vary within upper and lower bounds of IS and 300 feet below the water table,
reflecting a national sample distribution of depths of residential drinking water wells (EPA,
1994).
In addition to this base case scenario, a number of other well placement scenarios were
investigated also. These are numbered in Table 4 as scenarios 2 through 6. Scenario 2
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GROUNDWATER FLOW
CONTAMINANTED
AREA
RECEPTOR WELL
LAND SURFACE
GROUNDWATER FLOW
Figure 4 Plan view and cross-section view showing location of receptor well.
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Table 4 Receptor well location scenarios.
Scenario
1 (Base Case)
2
3
4
5
6
Xwell
25 ft from edge of source
area
Nationwide Distribution
0 ft from edge of source
area
25 ft from edge of source
area
100 ft from edge of
source area
25 ft from edge of source
area
Ywell
Monte Carlo within plume
Monte Carlo within plume
Monte Carlo within half-
width of source area
Monte Carlo within half-
width of source area
Monte Carlo within half-
width of source area
Width of source area +
25ft
Zwell
Nationwide
Distribution
Nationwide
Distribution
Nationwide
Distribution
Nationwide
Distribution
Nationwide
Distribution
25 ft below
water table
Xwell = Downgradient distance of receptor well from edge of source area.
Ywell = Horizontal transverse distance from plume centerline.
Zwell = Depth of well intake point below water table.
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corresponds to the default, nationwide Monte Carlo modeling scenario in which the x, y, and
z locations of the well are all variable. In scenarios 3, 4 and 5, the distance between the
receptor well and the source area is varied from zero to 100 feet. In these scenarios, the y-
coordinate of the well was constrained to the central portion of the plume. In scenario number
6, the x-, y-, and z-coordinates of the receptor well were all set to constant values. These
additional scenarios were included in the analysis in order to assess the sensitivity of the model
results to the location of the receptor well.
Aquifer Particle Size Distribution
In the default Monte Carlo modeling scenario, the aquifer hydraulic conductivity, porosity, and
bulk density are determined from the mean particle diameter. The particle diameter distribution
used is based on data compiled by Shea (1974). In the present modeling analyses for fixed
waste site areas, the same approach and data were used, but the distribution was shifted
somewhat to assign more weight to the smallest particle diameter interval. The result is that
lower values of the hydraulic conductivity values generated, and also of the ambient groundwater
seepage velocities, received more emphasis. Lower ambient groundwater velocities reduce the
degree of dilution of the incoming contaminant plume and therefore result in lower, i.e. more
conservative, DAF values. Table 5 «imniarfrMi the distribution of particle size diameters used
in both the default nationwide modeling scenario and in the present analyses.
3-10
-------
Table 5 Distribution of aquifer particle diameter.
Nationwide Default
Particle Diameter Cumulative
(cm) Probability
3.9 104
7.8 10"
1.6 10-3
3.1 lO'3
6.3 10"3
1.25 10-2
2.5 lO"2
5.0 10-2
1.0 ID"1
2.0 10-1
4.0 10-'
8.0 10-'
0.000
0.038
0.104
0.171
0.262
0.371
0.560
0.792
0.904
0.944
0.946
1.000
Present Analyses
Particle Diameter Cumulative
(cm) Probability
4.0 KT4
8.0 1O4
1.6 lO'3
3.1 lO"3
6.3 10-3
1.25 Ifr2
2.5 1(T2
5.0 10"2
1.0 10-'
2.0 10-'
4.0 10-1
7.5 10-'
0.100
0.150
0.200
0.270
0.330
0.440
0.590
0.790
0.880
0.910
0.940
1.000
3-11
-------
4.0 RESULTS
This section presents the results of the modeling analyses performed. The analysis of the
convergence of the Monte Carlo simulation is presented first, followed by the parameter
sensitivity analysis, and thirdly the analysis of DAF values as a function of source area for
various well placement scenarios.
4.1 Convergence of Monte Carlo Simulation
Table 6 summarizes the results of this convergence analysis. It shows the variation of the 85th
percentile DAF value with the number of Monte Carlo repetitions, from 2,000 to 20,000. The
variations in DAF values are shown both as absolute and relative differences. The table shows
that for this example, the DAF generally increases with the number of Monte Carlo repetitions.
It should be kept in mind that the results from different repetition numbers as presented in the
table, are not independent of one another. For instance, the first 2000 repetitions are also
incorporated in the 5000 repetition results, which in turn is in the 10,000 repetition result, etc.
The rightmost column of Table 6 shows the percentage difference in DAF value between
different repetition numbers. At repetition numbers of 14,000 or less, the percentage difference
varies in a somewhat irregular manner. However, for repetition numbers of 15,000 or greater,
the DAF remained relatively constant, with incremental changes of DAF remaining at 1% or
less. Based upon these results, a repetition number of 15,000 was selected for use in the
subsequent runs with fixed source area.
4.2 Parameter Sensitivity Analysis
Results of the parameter sensitivity analysis are summarized in Table 7. The parameters are
ranked in this table in order of relative sensitivity. Relative sensitivity is defined for this
purpose as the absolute difference between the "high" and "low" DAF at the 85th percentile
level, divided by the 85th percentile DAF for the "median" case.
4-1
-------
Table 6 Variation of DAF with number of Monte Carlo repetitions.
No. of Repetitions
2,000
5,000
10,000
11,000
12,000
13,000
14,000
15,000
16,000
17,000
18,000
19,000
20,000
85-th Percentile
DAF
347.8
336.9
354.2
359.2
387.4
369.3
369.1
387.3
387.4
388.0
387.3
390.2
392.8
Difference
-10.9
+17.3
+5.0
+28.2
-18.1
-0.2
+ 18.2
+0.1
+0.6
-0.7
+2.9
+2.6
Relative
Difference (%)
-3.1
+5.1
+ 1.4
+7.9
-4.7
-0.05
+4.9
+0.03
+0.15
-0.18
+0.75
+0.67
4-2
-------
Table 7 Sensitivity of model parameters.
85% DAF Value
Parameter
Infiltration Rate
Saturated Thickness
G.W. Velocity
Source Area
Hydr. Conductivity
Vertical Well Position
G.W. Gradient
Long. Dispersivity
Vert. Dispersivity
Porosity
Receptor Well Distance
Transv. Dispersivity
Receptor Well Angle
Ambient Recharge
Low
4805.4
25.3
7.6
357.1
19.8
49.1
32.4
182.6
179.6
41.3
163.9
156.7
127.3
108.3
Median
418.8
198.5
97.7
85.2
180.4
206.1
168.3
104.2
114.9
49.9
117.9
156.3
130.8
100.0
High
11.6
2096.9
816.3
35.6
660.1
491.4
383.0
78.8
66.6
79.7
84.5
173.5
113.6
114.4
- Relative
Sensitivity*
11.4
10.4
8.3
3.8
3.5
2.1
2.1
1.0
1.0
0.8
0.7
0.1
0.1
0.06
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
* Relative Sensitivity = | High-Low | /Median
4-3
-------
The table shows that the most sensitive parameters included the rate of infiltration, which is a
function of soil type, the saturated thickness of the aquifer, the size of source area, the
groundwater seepage velocity, and the vertical position of the receptor well below the water
table. The least sensitive parameters included porosity, downstream distance of the receptor well
in both the x- and y-directions, the horizontal transverse dispersivity, and the areal recharge rate.
To interpret these results, it should be kept in mind that the rankings reflect in part the range
of variation of each parameter in the data set used for the sensitivity analysis. The infiltration
rate was a highly sensitive parameter since, for a given leachate concentration, it directly affects
the mass flux of contaminant entering the subsurface. The size of the source are would be
expected to be equally sensitive, were it not for the fact that in the sensitivity analysis, the
source area had a much narrower range of variation than the infiltration rate. The "high" and
"low" values of the source area, which were taken from a nationwide distribution of landfill
waste units, varied by a factor of 23, while the ratio of "high" to "low" infiltration rate was
almost 300.
In the simulations performed for the sensitivity analysis, no constraint was imposed on the
vertical position of the well. The well was modeled as having a uniform distribution with the
well intake point located anywhere between the water table and the base of the aquifer. The
aquifer saturated thickness and vertical position of the well were both among the sensitive
parameters, with similar effects on DAF values. Increasing either the saturated thickness, or
the fractional depth of the receptor well below the water table, increases the likelihood that the
receptor well will be located underneath the contaminant plume and sample uncontaminated
groundwater, leading to a high DAF value. The dilution-attenuation factors were also sensitive
to the groundwater velocity, and the parameters that determine the groundwater velocity, i.e.,
hydraulic conductivity and ambient gradient. Table 7 shows that a higher groundwater velocity
results in an increase of the dilution-attenuation factor. Since a conservative contaminant was
simulated under steady-state conditions, variations in travel time do not affect the DAF. The
increase of DAF with increasing flow velocity reflects the greater mixing and dilution of the
contaminant as it enters the saturated zone in systems with nigh groundwater flow rate. Porosity
4-4
-------
also directly affects the ground water velocity, but was not among the sensitive parameters. This
is a reflection of the narrow range of variation assigned to this parameter.
The off-center angle which determines the y position of the well relative to the plume center line
would be expected to have a similar effect as the well depth, but is seen to have a much smaller
sensitivity. This was a result of constraining the y-location of the receptor well to be always
inside the approximate area] extent of the contaminant plume. The effect is that the relative
sensitivity of the off-center angle was much less than that of the vertical coordinate of the well.
The low relative sensitivity of recharge rate reflects the fact that this parameter has an only
indirect effect on plume concentrations.
Overall, the Monte Carlo results were not very sensitive to dispersivity and downstream distance
of the receptor well. The probable explanation for these parameters is that variations of the
parameters produce opposing effects which tended to cancel one another. Low dispersivity
values will produce a compact plume which increases the probability that a randomly located
receptor well will lie outside (underneath) the plume. Higher dispersivities will increase the
chance that the well will intercept the plume. At the same time, however, mass balance
considerations dictate that in this case average concentrations inside the plume will be lower than
in the low dispersivity case. Similar reasoning applies to the effect of receptor well distance.
If the well is located near the source, concentrations in the plume will be relatively high, but so
is the chance that the well does not intercept the plume at all. At greater distances from the
source, the likelihood that the well is located inside the plume is greater, but the plume will also
be more diluted. In the course of a full Monte Carlo simulation these opposing effects would
tend to average out. The much lower sensitivity of transverse dispersivity, aT, compared to aL
and Ov can be contributed to the imposed constraint that the well must always be within the areal
extent of the plume.
The results of the sensitivity analysis show that the site characteristic which lends itself best for
a classification system for correlating sites to DAF values is the size of the contaminated (or
4-5
-------
source) area. In the subsequent analyses, the DAF values were therefore determined as a
function of the source area size. These results are presented in the following section.
4.3 DAF Values as a Function of Source Area
This section presents the DAF value as a function of source area for various well location
scenarios. The results for each of the scenarios examined are presented in tabular and graphical
form. Figure 5 shows the variation of the 85th, 90th, and 95th percentile DAF with source area
for the base case scenario. The source area is expressed in square feet. The figure displays
DAF against source area in a log-log graph. The graph shows an approximately linear
relationship except that at very large values of the source area, the DAF starts to level off.
Eventually the DAF approaches a value of 1.0. As expected, the curve for the 95th percentile
DAF always shows the lowest DAF values, while the 85th percentile shows the highest DAFs.
The DAF versus source area relationship for the other well placement scenarios are shown in
Figures 6 through 10. The numerical results for each scenario are summarized in Tables Al
through A6 in the appendix.
Inspection and comparison of the results for each scenario indicate that the relationship follows
the same general shape in each case, but the magnitude of DAF values at a given source area
can be quite different for different well placement scenarios. In order to allow a direct
comparison between the various scenarios analyzed, the DAF values obtained for a source area
of 150,000 ft2 (3.4 acres) are shown in Table 8 as a function of the receptor well location
scenario.
Inspection of the DAF values shows that the default nationwide scenario for locating the receptor
well results in the highest DAF values, as compared to the base case scenario and the other
scenarios, in which the receptor well location was fixed at a relatively close distance from the
waste source. In the default nationwide modeling scenario, the well location is assigned from
nationwide data on both the distance from the waste source and depth of the well intake point
below the water table. In the default nationwide modeling scenario, the receptor well is allowed
to be located up to 1 mile from the waste source. In the base case (Scenario 1) the well is
4-6
-------
LOE+O/d::::-:::::::::::::;::!:::;::;;;;: :::::;:J:::;::|;::: -A-J-; -'j .™JSn~r
1.0E-f06i
1.0E+05:
1.0E+04= ssss
1.0E+03=
1.0E+02=
1.0E+01 =
I.OE-fOO-
1.0E+03
j Ywtll • Uniform within plum* {
1.0E+04
1.0E+05
AREA OF LANDFILL (sq. (t)
1.0E+06
1.0E+07
85 TH -•- 90 TH -»- 95 TH
Figure 5 Variation of DAF with size of source area for the base case scenario (x=25 ft, y=uniform in plume, z=nationwide
distribution).
-------
1.0E+04q
1.0E+03:
1.0E+02:
f* 1.0E+01r
0
1.0E+00
1.0E+03
1.0E+04
yL ™::::
-1 Yw«ll • Mont* Carlo within plum*
Zvrall - Nationwide dtttrlb.
1.0E+05
AREA OF LANDFILL
J....
1.0E+06
1.0E+07
85 TH
90 TH
95 TH
Figure 6 Variation of DAF with size of source area for the default nationwide scenario (Scenario 2: x=nationwide distribution,
y=uniform in plume, z=nation wide distribution).
-------
1.0E+083
< 1.0E+04
1.0E+00--
1.0E+03
1.0E-f04
1.0E405
AREA OF LANDFILL
1.0E+07
85 TH -f— 90 TH -JK- 95 TH
Figure 7 Variation of DAF with size of source area for Scenario 3 (x=0, y=uniform within half-width of source area,
z=nationwide distribution).
-------
1.0E+063T
1.0E+05=
1.0E+04=
< 1.0E+03=j
1.0E+02=
1.0E-f01 = ":
1.0E+00
1.0E+03
1.0E+05
AREA OF LANDFILL
1.0E+08
1.0E-I-07
85 TH-4-90 TH-^-95 TH
Figure 8 Variation of DAF with size of source area for Scenario 4 (x=25 ft, y=uniform within half-width of source area,
z=nationwide distribution).
-------
1.0E+05=j
1.0E+04:
1.0E+03::::::
1.0E+02=
1.0E+01::::
1.0E4-00
j Yw«ll g 0 to 1/2 landfill width |
1.0E403
1.0E+04
1.0E+05
AREA OF LANDFILL (sq ft)
1.0E+06
1.0E+07
85 TH -H- 90 TH
95 TH
Figure 9 Variation of DAF with size of source area for Scenario 5 (x=100 ft, y=uniform within half-width of source area,
z=nationwide distribution).
-------
1.00E+05;
1.00E+04=
1.00E+03=
1.00E+02=
t
_A
10
1.00E+01:
1.00E+00
1.0E+03
y.::\ ye.
:)Xwell o 25 ft |
Yw«l| - 0 to 1/2 landfill width I
Zwell = 25 ft
i.OE+04 1.0E+05-
AREA OF LANDFILL (sq ft)
^,
I.OE-f-06
1.0E+07
85 TH -•- 90 TH -CS- 95 TH
Figure 10 Variation of DAF with size of source area for Scenario 6 (x=25 ft, y=width of source area -I- 25 ft. z=25 ft).
-------
Table 8 DAF values for waste site area of 150,000 ft2.
Model DAF Percentile
Scenari° 85 90 95_
1 (base case) 237.5 26.4 2.8
2 300.1 114.7 26.8
3 158.8 17.9 1.7
4 132.1 16.6 1.8
5 98.8 15.1 2.0
6 94.7 25.3 4.4
4-13
-------
allowed to be located anywhere within the areal extent of the contaminant plume for a fixed x-
distance of 25 feet. This allows the well to be located near the fringes of the contaminant plume
where concentrations are relatively low and DAF values are correspondingly high. In contrast,
in Scenarios 3, 4, and 5, the well location was constrained to be within the half-width of the
waste source. In other words, the well was always placed in the central portion of the
contaminant plume where concentrations are highest. As a result, these scenarios show lower
DAF values then the base case scenario. The results for Scenarios 3,4, and 5, which differ only
in the x-distance of the receptor well, show that placement of the well at either 25 or 100 feet
away from the waste source results in 85% and 90% DAF values that are actually lower, i.e.
more conservative, than placement of the well directly at the edge oft he waste source. This is
a counter-intuitive result, but may be explained from the interaction between distance from the
waste source and vertical extent of the contaminant plume below the water table. Close to the
waste source, the contaminant concentrations within the plume are highest, but the plume may
not have penetrated very deeply into the saturated zone (Figure 2). Because the vertical position
of the well was taken as a random variable, with a maximum value of up to 300 feet, the
probability that a receptor well samples pristine groundwater underneath the contaminant plume
is higher at close distances from the waste area. Conversely, as the distance from the source
increases, the plume becomes more dilute but also extends deeper below the water table. The
final result is that the overall DAF may actually decrease with distance from the source. The
table also shows that at the 95% level, the lowest DAF is obtained in the case where the well
is located at the edge of the waste source. This reflects that the highest concentration values will
be obtained only very close to the waste source.
The results for the last scenario, in which the x, y, and z locations of the receptor well were all
fixed, show that fixing the well depth at 25 feet ensures that the well is placed shallow enough
that it will be located inside the plume in nearly all cases, resulting in low DAF values at the
85th and 90th percentile values. On the other hand, the well in this case is never placed
immediately at the plume centerline, so that the highest concentrations sampled in this scenario
are always lower than in the other scenarios. This is reflected in the higher DAF value at the
95th percentile level.
4-14
-------
One of the key objectives of the present analyses was to determine the appropriate groundwater
DAF value for a waste area of given size. For the base case scenario, the 90th percentile DAF
value is on the order of 100 or higher for a waste area size of 1 acre (43,560 ft2) and less. For
waste areas of 10 acres and greater, the 90th percentile DAF is 10 or less.
4-15
-------
REFERENCES
Shea, J.H., 1974. Deficiencies of elastic particles of certain sizes. Journal of Sedimentary
Petrology, 44:905-1003.
U.S. EPA, 1993a. EPA's Composite Model for Leachate Migration with Transformation
Products: EPACMTP. Volume I: Background Document. Office of Solid Waste, July
1993.
U.S. EPA, 1993b. Draft Soil Screening Level Guidance Quick Reference Fact Sheet. Office
of Solid Waste and Emergency Response, September 1993.
U.S. EPA, 1994. Modeling Approach for Simulating Three-Dimensional Migration of Land
Disposal Leachate with Transformation Products and Consideration of Water-Table
Mounding. Volume JJ: User's Guide for EPA's Composite Model for Leachate
Migration with Transformation Products (EPACMTP). Office of Solid Waste, May 1994.
R-l
-------
APPENDIX A
-------
Table Al DAF values as a function of source area for base case scenario (x=25 ft,
y=uniform in plume, z-nationwide distribution).
Area
(sqft)
1000
2000
5000
10000
30000
50000
70000
80000
150000
200000
500000
1000000
2000000
3000000
5000000
DAF
85 TH
1.09E+06
1.86E+05
2.91 E+04
9.31 E+ 03
1647.18
869.57
569.80
477.33
237.47
174.86
64.52
32.27
17.83
12.94
8.91
90 TH
3.76E+04
9.63E+03
2.00E+03
680.27
155.21
84.25
59.28
50.56
26.36
20.19
9.12
5.61
3.68
2.94
2.33
95 TH
609.01
187.69
53.02
22.57
7.82
5.41
4.34
3.97
2.77
2.37
1.61
1.32
1.16
1.11
1.06
A-l
-------
Table A2 DAF values as a function of source area for Scenario 2 (x=nationwide
distribution, y=unifonn in plume, z=nationwide distribution).
Area
(sq. ft)
5000
8000
10000
45000
50000
100000
150000
220000
500000
1000000
5000000
6000000
DAF
85 TH
6222.78
3977.72
3215.43
817.66
745.16
424.81
300.12
218.87
110.35
63.45
21.03
19.06
90 TH
2425.42
1573.32
1286.01
315.06
288.27
160.82
114.71
82.30
40.10
23.75
7.85
7.01
95 TH
565.61
371.06
298.78
73.48
67.20
38.11
26.82
20.00
10.92
6.22
2.55
2.39
A-2
-------
Table A3 DAF values as a function of source area for Scenario 3 (x=0 ft, y=uniform
within half-width of source area, z=nationwide distribution).
Area
(sq. ft.)
1000
2000
5000
10000
30000
50000
70000
60000
150000
200000
500000
1000000
2000000
3000000
DAF
65
1.42E+07
9.19E+05
5.54E+04
1.16E+04
1.43E+03
668.45
417.19
350.39
158.76
114.63
40.55
21.13
11.58
8.66
90
2.09E+05
2.83E+04
2.74E+03
644.33
120.42
60.02
37.97
33.16
17.87
12.96
5.54
3.50
2.38
1.98
95
946.07
211.15
44.23
15.29
4.48
3.10
2.53
2.34
1.74
1.56
1.23
1.15
1.08
1.06
A-3
-------
Table A4
°f S°Urce aret for Scenario 4 <*=
witlun half-width of source area, z=nationwide distribution).
ft, y=unifonn
Area
(sq. ft.)
1000
2000
5000
10000
30000
50000
70000
80000
150000
200000
500000
1000000
2000000
3000000
DAF
85
5.93E+05
1.09E+05
1.64E+04
4.89E+03
928.51
490.20
323.42
272.85
132.05
97.94
37.99
20.08
11.35
8.49
90
2.07E+04
4.92E+03
1.03E+03
352.49
93.98
49.78
34.79
29.82
16.55
12.29
5.50
3.50
2.40
2.00
95
348.31
118.11
29.86
13.14
4.73
3.28
2.69
2.47
1.82
1.61
1.29
1.17
1.10
1.07
A-4
-------
Table A5 DAF values as a function of source area for Scenario 5 (x=100 ft, y =uniform
within half-width of source, z=nationwide distribution).
Area
(sq. ft.)
1000
2000
5000
10000
30000
50000
70000
80000
150000
200000
500000
1000000
2000000
3000000
DAF
85
4.24E+04
1.52E+04
4.24E+Q3
1.81E+03
497.27
293.34
207.77
184.57
98.81
74.63
32.99
18.66
11.14
8.33
90
3.43E+03
1.33E+03
437.25
204.29
68.21
40.72
29.89
26.86
15.05
11.55
5.83
3.71
2.53
2.09
95
181.88
74.79
27.23
13.09
5.10
3.71
2.96
2.73
2.03
1.82
1.40
1.26
1.16
1.13
A-5
-------
Table A6 DAF values as a function of source area for Scenario 6 (x=25 ft, y=source
width + 25 ft, z=25 ft).
AREA
(SOFT)
1200
1500
5000
7500
23000
26000
29000
100000
170000
250000
800000
1800000
DAF
85 TH
44247.79
30759.77
4789.27
2698.33
637.76
544.66
482.63
139.66
76.69
50.40
16.10
10.26
90 TH
10479.98
7215.01
1273.40
725.69
155.16
135.91
121.43
35.55
2124
15.04
6.04
3.87
95 TH
1004.72
744.05
140.81
82.51
21.82
18.84
16.52
5.56
3.94
3.19
1.81
1.48
A-6
-------
APPENDIX F
DILUTION FACTOR MODEL RESULTS
-------
Dilution Factor Model Results: DNAPL Sites
Source size (acres) 0.5
Source length (m) 45
Aquifer thickness (m)
10 30 100 600
201 349 636 1,559
9.1
Site Name
Army Creek Landfill
Atlantic Wood Ind.
Adas Tack Corp.
Auburn Rd. Landfill
Baird & McGuire
BaHy Qroundwater
Beacon Hts. Landfill
Bette Sand Pit
Brodhead Creek
Brunswick Naval Air Sta.
Cannon Eng.- Bridgewater
Central Landfill
Centre County Kepone
Chas.-Geo. Redam. Trust
Coakley Landfill
Craig Farm Drum
Davis Liquid Waste
Delaware City PVC
Domey Road Landfill
Dover Mun. Landfill
DuPont-Newport
Dublin TCE Site
Durham Meadows
EastMt.Zton
Elizabethtown Landfill
Gallup's Quarry
Greenwood Chemical
Groveland Wells
Halby Chemical Co.
Harvey & Knott Drum
Havertown PCP
Heleva Landfill
Henderson Road
Hocomonco Pond
Holton Circle
Hunterstown Road
Industri-plex
Kane and Lombard Street
Kearsarge Metallurgical Corp.
Keefe Environmental Services
Keltogg-Deering Well Field
Kimberton Site
Landfill & Resource Recovery
Lindane Dump
State
DE
VA
MA
NH
MA
PA
CT
PA
PA
ME
MA
Rl
PA
MA
NH
PA
Rl
DE
PA
NH
DE
PA
CT
PA
PA
CT
VA
MA
DE
DE
PA
PA
PA
MA
NH
PA
MA
MD
NH
NH
CT
PA
Rl
PA
Infiltration by
Hyd. Region
Region
10
10
9
9
9
6
9
6
7
9
9
9
6
9
9
6
9
10
6
9
10
6
9
6
6
9
8
9
10
10
8
6
6
9
9
6
9
8
9
9
9
8
9
6
(m/yr)
024
024
022
022
022
0.15
022
0.15
020
022
022
022
0.15
022
022
0.15
022
024
0.15
022
024
0.15
022
0.15
0.15
022
0.15
022
024
024
0.15
0.15
0.15
022
022
0.15
022
0.15
022
022
022
0.15
022
0.15
Average GW
Velocity (m/yr)
Seepage Darcy
5,563 1,947
1261 442
3 1
61 21
61 21
3204 1,121
15 5
10 4
11246 3,936
230 81
3 1
223 78
61,189 21.416
34 12
113 40
451 158
169 66
223 78
1,913 670
289 101
33 12
32 11
612 214
1218 426
56 20
67 23
3 1
612 214
5 2
434 152
24 9
28 10
834 292
1,986 695
2,809 983
562 197
289 101
681 238
7 2
12 4
946 331
308 108
244 86
82 29
Mixing zone depth
0.5
5
5
10
5
5
5
6
6
5
5
11
5
5
6
5
5
5
5
5
5
6
5
5
5
5
5
10
5
9
5
6
5
5
5
5
5
5
5
8
7
5
5
5
5
Site size (acres)
10 30 100
21
21
30
23
23
21
27
27
21
22
30
22
21
24
22
21
22
22
21
• 22
25
24
21
21
23
23
30
21
30
22
24
24
21
21
21
21
22
21
29
28
21
22
22
22
37 67
37 68
46 76
40 72
40 72
37 67
44 76
44 76
37 67
38 69
46 76
38 69
37 67
42 74
39 70
37 68
38 69
38 69
37 67
38 69
42 74
41 73
37 68
37 68
39 71
40 72
46 76
37 68
46 76
37 68
41 74
41 73
37 68
37 68
37 67
37 68
38 69
37 68
46 76
45 76
37 68
37 68
38 69
39 70
600
165
166
174
173
173
165
174
174
165
168
174
168
165
174
171
166
169
169
165
168
174
173
166
165
172
172
174
166
174
167
174
173
166
165
165
166
168
166
174
174
166
167
168
170
Dilution factor
0.5
861
197
2
12
12
793
4
4
2,085
41
2
39
15,112
8
21
113
34
36
474
51
7
10
105
303
16
13
2
105
2
69
8
9
208
336
475
141
51
170
3
4
161
78
43
22
Site size (acres)
10 30 100 600
370 214
85 49
1 1
5 4
5 4
341 197
2 2
2 2
895 517
18 11
1 1
17 10
6,489 3,747
3 2
9 6
49 29
15 9
16 10
204 118
22 13
3 2
4 3
45 27
130 76
7 4
6 4
1 1
45 27
1 1
30 18
4 2
4 3
89 52
145 84
204 '118
61 35
22 13
73 43
2 1
2 2
69 40
34 20
19 11
10 6
118 49
27 12
1 1
2 2
2 2
108 45
1 1
1 1
284 116
6 3
1 1
6 3
2,053 839
2 1
4 2
16 7
5 3
6 3
65 27
8 4
2 1
2 1
15 7
42 18
3 2
3 2
1 1
15 7
1 1
10 5
2 1
2 1
29 12
46 20
65 27
20 9
8 4
24 10
1 1
1 1
23 10
11 5
7 3
4 2
7/21/94
-------
Dilution Factor Model Results: DNAPL Sites
Source size (acres)
Source length (m)
AquHer thickness (m)
0.5 10 30 100 600
45 201 349 636 1559
9.1
Site Name
Linemaster Switch Corp.
Maryland Sand, Gravel & Stone
McKinCo.
Metal Banks
Mottoto Pig Farm
MW Manufacturing
NCR Corp. Millsboro
Norwood PCBS
Nyanza Chemicals
O'Connor Company
Old City of York Landfill
Od Soutrington Landfill
Old Springfield Landfill
Osbome Landfill
Ofis Air Nat!. Guard
Ottati & Goss/Kingston Drums
Pease Air Force Base
Peterson/Puritan, Inc.
tecfltoFarm
Dinette's Salvage Yard
PSC Resources
Re-Solve, Inc.
Recticon/Alled Steel
Rhinehart Tire Fire
Saco Tannery Waste Pits
Saunders Supply Co.
Savage Mun. Water Supply
Silresim Chemical Corp.
Somersworth San. Landfill
South Municipal Water Supply
Southern MD Wood Treating
Stamina Mills, Inc.
Std. Chlorine/Tybout's Comer LF
Strasburg Landfill
Sullivan's Ledge
Sussex County Landfill #5
Sylvester's
Tansitor Electronics
TibbetsRoad
US Defense General Supply
US Dover AFB
US Naval Air Development
US Newport Nav. Educ.&Tm. Ctr.
W.R. Grace & CoJActon
State
CT
MD
ME
PA
NH
PA
DE
MA
MA
ME
PA
CT
VT
PA
MA
NH
NH
Rl
Rl
ME
MA
MA
PA
VA
ME
VA
NH
MA
NH
NH
MD
Rl
DE
PA
MA
DE
NH
VT
NH
VA
DE
PA
Rl
MA
Infiltration by
Hyd. Region
Region
9
8
9
6
9
7
10
9
9
9
8
7
9
7
9
9
9
9
9
9
9
9
6
6
9
10
. 9
9
9
9
10
9
10
8
9
10
9
9
9
8
10
6
9
9
(m/yr)
022
0.15
022
0.15
022
020
024
022
022
022
0.15
020
022
020
022
022
022
022
022
022
022
022
0.15
0.15
022
024
022
022
022
022
024
022
024
0.15
022
024
022
022
022
0.15
0.24
0.15
022
022
Average GW
Velocity (m/yr)
Seepage
1,113
2
890
5
131
21,027
223
389
39
214
779
134
30
1,113
312
46
11
56
534
333
45
834
73
1,346
56
28
235
26
139
90
2
2,809
39
2,160
112
198
490
103
28
37
4
56
3
445
Dairy
389
1
312
2
46
7,359
78
136
14
75
273
47
11
389
109
16
4
19
187
117
16
292
26
471
19
10
82
9
49
32
1
983
14
756
39
69
171
36
10
13
1
19
1
156
Mixing zone depth
Site size (acres)
0.5 10
5 21
10 30
5 21
8 29
5 22
5 21
5 22
5 22
5 24
5 22
5 21
5 22
6 25
5 21
5 22
5 24
7 28
5 23
5 22
5 22
5 24
5 21
5 22
5 21
5 23
6 25
5 22
6 25
5 22
5 23
12 30
5 21
6 24
5 21
5 22
5 22
5 22
5 22
6 25
5 23
10 30
5 23
11 30
5 22
30 100
37 68
46 76
37 68
46 76
38 70
37 67
38 69
37 68
41 74
38 69
37 68
38 70
42 74
37 68
38 69
41 73
45 76
40 72
37 68
38 68
41 73
37 68
39 70
37 68
40 72
42 75
38 69
42 75
38 70
39 71
46 76
37 67
41 74
37 67
39 70
38 69
37 68
39 . 70
42 75
40 72
46 76
39 71
46 76
37 68
600
166
174
166
174
170
165
169
167
174
169
166
170
174
166
168
173
174
173
167
167
173
166
171
165
173
174
168
174
170
171
174
165
174
165
171
169
167
171
174
173
174
172
174
167
Dilution factor
OJS
189
2
152
3
24
3.896
36
68
9
38
194
27
7
208
54
10
4
11
92
58
9
142
20
334
11
6
42
6
25
17
2
475
8
535
21
33
84
19
7
11
2
16
2
77
Site size (ac
10 30
81 47
1 1
65 38
1 1
10 6
1,673 966
16 10
29 17
4 3
16 10
84 49
12 7
3 2
89 52
24 14
4 3
2 1
5 3
40 23
25 15
4 3
61 36
9 5
144 83
5 3
3 2
18 11
3 2
11 7
8 5
1 1
204 118
4 2
230 133
9 6
14 9
36 21
8 5
3 2
5 3
1 1
7 4
1 1
33 20
m)
100 600
26 11
1 1
21 9
1 1
4 2
530 217
6 3
10 5
2 1
6 3
27 12
4 2
2 1
29 12
8 4
2 1
1 1
2 2
13 6
9 4
2 1
20 9
3 2
46 19
2 2
2 1
6 3
2 1
4 2
3 2
1 1
65 27
2 1
73 31
4 2
5 3
12 6
3 2
2 1
2 2
1 1
3 2
1 1
11 5
-------
Dilution Factor Model Results: DNAPL Sites
Source size (acres)
Source length (m)
Aquifer thickness (m)
0.5 10 30 100 600
45 201 349 636 1,559
9.1
Site Name
Western Sand & Gravel
Westinghouse Elevator
Wmthrop Landfill
Woodiawn County Landfill
State
Rl
PA
ME
MD
Infiltration by
Hyd. Region
Region (m/yr)
9 022
6 0.15
9 022
8 0.15
Average GW
Velocity (m/yr)
Seepage Darcy
48 17
562 197
16 6
557 195
Mixing zone depth
Site size (acres)
0.5 10 30 100 600
5 24 40 73 173
5 21 37 68 166
6 27 44 76 174
5 21 37 68 166
Dilution factor
0.5
10
141
5
140
Site size (acres)
10 30 100
432
61 35 20
2 2 1
60 35 20
600
1
9
1
9
7/21/94
-------
Dilution Factor Model Results for 271 Sites in the Hydrdgeologic Database (HGDB) - National Average
I Source length (m)
Source area (acres)
0.5
45
10
201
30
349
100
636
600
1.559
Hydrogeologic
Selling
I.I!
Ill
1.3
1.6
1.6
1.7
1.7
1.8
1.9
1.9
2.12
2.12
2.12
2.12
2.12
2.12
2.12
2.12
2.13
2.13
2.13
2.3
2.3
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
Infiltration
(m/y)
0.32
0.32
0.03
0.08
0.08
0.14
0.14
0.03
0.08
0.08
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.05
0.05
0.05
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
Average K
(m/y)
946
63
63
5,676
946
I.92E+05
1.58E+05
63.072
I.26E+05
2.76E+06
1.577
31.536
6
23.652
1.577
126
946
1.388
95
2,838
158
5.992
315
3.3IE+06
315
631
I.07E+05
1,892
3
I.9IE+05
4.100
Hyd. Grad.
(m/m)
I.OOE-02
3.00E-02
8.00E-02
2.00E-03
9.30E-02
l.OOE-02
I.OOE-04
5.00E-03
I.OOE-03
3.00E-02
l.OOE-03
I.OOE-03
3.00E-03
3.00E-03
5.00E-03
2.00E-03
2.00E-03
3.00E-03
3.00E-04
2.00E-03
l.OOE-03
I.OOE-03
5.70E-03
5.00E-03
I.OOE-03
l.OOE-02
S.OOE-03
I.OOE-03
3.00E-03
I.OOE-03
I.OOE-03
Darcy v
(m/y)
9
2
5
11
88
1.924
16
315
126
82.782
2
32
0.02
71
8
0.3
2
4
0.03
6
0.2
6
2
16,556
0.3
6
536
2
0.01
191
4
Aq. Thick.
(m)
305
30
23
21
15
6
3
2
5
23
914
24
8
6
24
5
3
91
9
30
130
183
46
18
15
9
7
37
4
8
3
Mixing Zone Depth (d)
Source Area (acres)
0.5
6
11
5
5
5
5
5
5
5
5
5
5
12
5
5
8
5
5
14
5
18
6
10
5
18
6
5
10
8
5
6
10
28
42
22
23
21
21
23
21
21
21
25
21
29
21
22
26
23
22
30
23
72
28
40
21
37
26
21
38
25
21
24
30
48
«
39
39
37
37
39
37
37
37
43
37
45
37
38
42
39
39
46
40
112
49
64
37
52
44
37
61
41
37
40
100
88
97
70
71
68
67
70
67
68
67
77
68
75
68
69
72
70
71
76
72
170
89
104
67
83
76
68
99
71
68
70
600
213
195
172
173
166
165
168
165
166
165
190
166
173
166
170
170
168
174
174
176
292
213
210
165
180
174
166
201
169
167
168
Dilution Factor (DF)
Source Area (acres)
0.5
5
3
23
18
124
1,459
9
421
169
I.I5E+05
9
133
1
298
35
2
6
19
1
14
2
5
3
8.118
1
5
265
3
1
96
2 .
10
5
2
23
17
89
419
3
95
39
I.15E+05
9
133
1
86
35
1
2
19
1
14
2
5
3
6.978
1
2
91
3
1
35
1
30
5
2
14
10
51
242
2
55
23
71,160
9
88
1
50
23
1
2
19
1
II
2
5
2
4.019
1
2
53
2
1
20
1
100
5
1
8
6
29
133
2
31
13
39.049
9
49
1
28
13
1
1
19
1
6
2
5
2
2,206
1
1
30
2
1
12
1
600
5
1
4
3
12
55
1
13
6
15,931
9
20
1
12
6
1
1
II
1
3
1
4
1
901
1
1
13
1
1
5
1
\df-hgdb.xls 5/19/94
-------
Dilution Factor Model Results for 271 Sites in the Hydrogeologic Database (HGDB) - National Average
1 Source length (m)
Source area (acres)
0.5
45
10
201
' 30
349
100
636
600
1,559
Hydrogeologic
Setting
2.4
2.4
2.4
2.5
2.5
2.5
2.5
2.5
2.5
2.6
2.6
2.6
2.9
2.9
2.9
2.9
2.9
2.9
3.5
3.7
3.7
3.7
4.1
4.2
4.2
4.3
4.4
4.4
5.2
5.3
5.8
Infiltration
(m/y)
0.22
0.22
0.22
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32.
0.03
0.03
0.03
0.03
0.03
0.03
0.08
0.14
0.14
0.14
0.03
0.03
0.03
0.03
0.14
0.14
0.03
0.03
0.03
Average K
(m/y)
16.714
11,038
315
946
1,261
6.938
23.337
4,415
56,134
50,773
13,876
1,577
1.261
22,075
3,469
2.2IE+05
126
3
3
0.3
2.96E+05
2.21E+05
32
22
284
3
946
9,776
2.43E+05
2.32E+06
631
Hyd. Grad.
(m/m)
4.00E-03
2.00E-03
2.00E-03
2.00E-03
3.00E-03
3.00E-03
4.00E-03
7.00E-04
2.00E-03
5.00E-03
2.80E-02
I.OOE-03
I.OOE-04
I.OOE-03
2.00E-02
I.OOE-03
2.00E-03
5.00E-03
I.50E-02
3.00E-02
2.00E-04
2.00E-03
I.OOE-OI
2.80E-02
3.20E-03
7.00E-03
8.00E-03
1.30E-02
2.00E-03
2.00E-03
3.00E-03
Darcy v
(m/y)
67
22
1
2
4
21
93
3
112
254
389
2
O.I
22
69
221
0.3
0.02
0.05
0.009
59
442
3
1
1
0.02
8
127
486
4,636
2
Aq. Thick.
(m)
6
13
3
8
305
23
37
38
10
9
34
12
18
91
IS
IS
II
2
15
9
9
9
21
11
3
2
3
3
17
12
24
Mixing Zone Depth (d)
Source Area (acres)
0.5
5
5
8
10
9
5
5
9
5
5
5
11
12
5
5
5
8
7
20
14
5
5
5
6
6
7
5
5
5
5
S
10
22
23
24
29
38
24
22
37
22
22
21
33
38
22
21
21
31
24
37
30
22
21
23
27
24
24
23
21
21
21
24
30
38
40
40
45
65
42
38
60
38
37
37
49
55
37
37
37
47
39
. 52
46
38
37
40
45
40
39
40
37
37
37
41
100
69
72
70
76
116
75
69
99
69
68
68
79
86
68
68
67
78
70
83
76
69
68
72
77
70
70
70
68
67
67
75
600
168
174
168
173
271
180
170
203
169
167
166
177
183
167
166
165
176
167
180
174
168
165
174
176
168
167
168
166
165
165
179
Dilution Factor (DF)
Source Area (acres)
0.5
35
13
1
2
3
9
33
3
39
87
132
2
2
94
291
922
3
1
1
1
47
336
15
4
3
1
5
63
2.025
19,317
10
10
II
8
1
1
3
8
33
3
19
37
132
1
1
94
208
660
2
1
1
1
20
145
14
2
2
1
2
15
1,596
11,072
10
30
7
5
1
1
3
5
32
2
II
22
119
1
1
94
120
381
1
1
1
1
12
84
9
2
1
1
1
9
919
6,377
6
100
4
3
1
1
3
3
18
2
7
12
66
1
1
94
66
209
1
1
1
1
7
46
5
1
1
1
1
5
505
3.500
4
600
2
2
1
1
3
2
8
1
3
6
27
1
1
52
28
86
1
1
1
1
3
20
3
1
1
1
1
3
207
1.428
2
\df-hgdb.xls 5/19/94
-------
Dilution Factor Model Results for 271 Sites in the Hydrogeologic Database (HGDB) - National Average
| Source length (m)
Source ire* (acres)
0.5
45
10
201
' 30
349
100
636
600
I.5S9
Hydrogeologic
Setting
5.8
6.11
6.11
6.11
6.11
6.12
6.12
6.12
6.12
6.12
6.13
6.13
6.13
6.13
6.14
6.14
6.14
6.14
6.14
6.14
6.2
6.2
6.2
6.2
6.2
6.2
6.3
6.3
6.4
6.4
6.4
Infiltration
(m/y)
0.03
0.22
0.22
0.22
0.22
0.03
0.03
0.03
0.03
0.03
0.32
0.32
0.32
0.32
0.22
0.22
0.22
0.22
0.22
0.22
0.14
0.14
0.14
0.14
0.14
0.14
0.03
0.03
0.08
0.08
0.08
Average K
(m/y)
33,113
4.415
4,415
1.577
81,994
3,154
3
946
315
13
I.58E+Q5
1,577
126
315
1.577
33.113
14,191
O.I
5,676
1.892
9
3
3
2,208
126
1,325
31.536
1,892
9.776
6
3
Hyd. Grad.
(m/m)
2.00E-06
5.00E-03
I.OOE-02
l.OOE-02
3.00E-03
6.00E-03
7.00E-02
8.00E-03
1.70E-02
I.30E-OI
6.00E-03
2.30E-02
5.00E-05
3.30E-02
4.00E-02
I.OOE-02
7.00E-04
I.OOE-02
l.OOE-03
2.00E-03
3.IOE-02
I.OOE-02
l.OOE-03
3.30E-02
4.00E-03
S.OOE-03
1.40E-OI
4.30E-02
I.20E-02
I.50E-02
2.50E-02
Darcy v
(nVy)
0.07
22
44
16
246
19
0.2
8
5
2
946
36
0.01
10
63
331
10
0.001
6
4
0.3
0.03
0.003
73
1
7
4,415
81
117
0.09
0.08
Aq. Thick.
(m)
34
15
21
24
9
3
18
6
9
1
30
144
IS
61
8
23
18
2
6
6
152
5
91
30
8
21
3
6
30
24
12
Mixing Zone Depth (d)
Source Area (acres)
0.5
18
5
5
5
5
5
9
5
5
5
5
5
20
6
5
5
6
6
6
7
25
9
96
5
II
6
5
5
5
24
17
10
51
23
22
24
21
22
34
22
22
22
21
23
37
27
22
21
25
23
26
26
92
26
113
22
29
25
21
21
21
46
33
30
70
40
39
41
37
37
53
38
38
38
37
40
52
47
38
37
43
38
42
43
138
42
128
38
45
43
37
37
37
61
49
100
101
72
70
75
68
68
85
69
70
68
68
73
83
84
69
68
77
69
73
73
199
72
159
69
75
77
67
68
68
92
79
600
199
175
171
179
166
167
183
169
170
166
166
178
180
198
169
166
180
167
171
171
316
170
256
163
173
182
165
165
166
189
177
Dilution Factor (DF)
Source Area (acres)
0.5
2
13
24
10
123
51
3
34
24
2
317
14
1
5
33
164
7
1
5
3
2
1
1
57
2
7
11,774
341
165
2
1
10
1
9
23
10
49
12
2
10
11
1
317
14
1
5
12
164
5
1
2
2"
2
1
1
57
1
6
2,637
98
165
1
1
30
1
5
14
6
29
8
1
6
7
1
257
14
1
5
7
101
3
1
1
1
2
1
1
47
1
4
1,519
57
135
1
1
100
1
3
8
4
16
5
1
4
4
1
142
14
1
4
5
56
2
1
1
1
2
i
1
26
1
3
834
32
75
1
1
600
1
2
4
2
7
2
1
2
2
1
58
12
1
2
2
23
2
1
1
1
1
1
1
11
1
2
341
14
31
1
1
\df-hgdb.xls 5/19/94
-------
Dilution Factor Model Results for 271 Sites in the Hydrogeologic Database (HGDB) - National Average
Source length (m)
Source area (acres)
0.5
45
10
201
' 30
349
too
636
600
1,559
Hydrogeologic
Selling
6.4
6.4
6.5
6.5
6.5
6.5
6.5
6.5
6.5
6.6
6.8
6.9
7.1
7.1
7.1
7.11
7.11
7.11
7.11
7.12
7.12
7.12
7.13
7.13
7.13
7.13
7.14
7.14
7.14
7.14 '
7.14
Infiltration
(m/y)
0.08
0.08
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.14
0.14
0.14
0.14
0.32
0.32
0.32
0.32
0.32
Average K
(m/y)
13
0.03
34,690
31.536
3
315
315
63
189
0.03
2,208
0.6
221
315
915
13
2,523
9
95
116.052
12,614
4,100
0.3
15,453
5.519
3.154
11.038
6,938
6,307
23.652
17,660
Hyd.Grad.
(m/m)
1 .OOE-02
5.00E-02
8.00E-03
5.00E-02
4.00E-02
5.00E-03
2.50E-02
4.00E-02
2.30E-02
4.00E-02
2.50E-02
3.00E-03
2.00E-03
I.80E-02
7.00E-04
7.00E-02
2.00E-02
5.50E-02
6.00E-03
4.00E-03
4.90E-02
3.00E-03
l.OOE-03
6.00E-03
1. OOE-02
1.30E-02
2.50E-OI
4.00E-03
4.90E-02
3.30E-02
2.00E-03
Darcy v
(m/y)
O.I
0.002
278
1.577
O.I
2
8
3
4
0.001
55
0.002
0.4
6
0.6
0.9
50
0.5
0.6
464
618
12
0.0003
93
55
41
2.759
28
309
781
35
Aq. Thick.
(m)
30
15
5
6
9
21
19
20
61
2
2
18
40
3
12
II
3
6
4
76
6
32
1
8
5
17
5
8
5
18
43
Mixing Zone Depth (d)
Source Area (acres)
0.5
23
20
5
5
14
8
6
7
6
7
5
23
17
6
12
12
5
11
9
5
5
6
5
5
5
5
5
5
5
5
5
10
51
37
21
21
30
33
25
30
27
23
22
40
53
24
33
32
22
27
25
21
21
25
22
22
22
22
21
23
21
21
23
30
67
52
37
37
46
53
42
49
47
39
38
55
74
40
49
48
38
43
41
37
37
43
38
37
38
38
37
40
37
37
40
100
97
83
68
67
76
87
76
84
85
69
68
86
107
70
79
78
69
73
71
68
68
77
68
68
69
69
67
72
68
68
73
600
195
ISO
166
165
174
186
180
185
199
167
166
183
205
168
177
176
168
171
169
166
166
183
166
167
168
170
165
172
166
166
177
Dilution Factor (DF)
Source Area (acres)
0.5
2
1
203
1,197
1
3
8
4
5
1
14
1
2
4
2
2
17
1
1
230
305
8
1
72
44
33
884
It
105
262
14
10
1
1
46
343
1
2
6
3
5
1
4
1
2
2
1
1
5
1
1
230
92
8
1
27
12
25
199
4
26
225
14
30
1
1
27
198
1
2
4
2
5
1
3
1
1
1
1
1
3
1
1
230
54
6
1
16
7
15
115
3
15
130
14
100
1
1
15
109
1
1
3
2
4
1
2
1
1
1
1
1
2
1
1
230
30
4
1
9
4
9
63
2
9
72
8
600
1
1
7
45
1
1
2
1
2
1
1
1
1
1
1
1
1
1
1
106
13
2
1
4
2
4
26
1
4
30
4
\df-hgdb.xls 5/19/94
-------
Dilution Factor Model Results for 271 Sites in the Hydrogeologic Database (HGDB) • National Average
Source length (m)
Source area (acres)
0.5
45
10
201
' 30
349
100
636
600
1,559
Hydrogeologic
Selling
7.14
7.14
7.15
7.15
7.16
7.16
7.17
7.17
7.17
7.17
7.17
7.17
7.17
7.17
7.17
7.17
7.17
7.17
7.18
7.2
7.3
7.3
7.3
7.4
7.4
7.4
7.5
7.5
7.6
7.6
7.6
Infiltration
(m/y)
0.32
0.32
0.32
0.32
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.22
0.22
0.22
0.32
0.32
0.14
0.14
0.14
Avenge K
(m/y)
1
14,507
7,253
24,314
3,154
221
946
315
19
21,760
3.469
3,154
126
32
0.2
32
315
63
1.892
12,299
25,544
946
0.001
3.784
2,681
189
11.038
63
63
6,623
126
Hyd.Grad.
(m/m)
6.00E-03
1 .20E-02
6.00E-04
6.80E-03
3.00E-03
4.00E-03
S.OOE-02
I.OOE-03
8.00E-03
4.00E-03
1 .70E-02
I.OOE-02
1 .50E-OI
9.00E-03
2.00E-01
3.00E-02
7.00E-03
2.20E-02
5.00E-03
9.00E-03
9.00E-04
5.00E-03
4.00E-03
4.00E-02
9.00E-03
1.20E-02
5.00E-04
7.00E-03
7.00E-03
2.00E-02
l.OOE-02
Darcy v
(m/y)
0.008
174
4
165
9
1
47
0.3
0.2
87
59
32
19
0.3
0.03
1
2
1
9
111
23
5
S.05E-06
151
24
2
6
0.4
0.4
132
1
Aq. Thick.
(m)
11
18
37
II
9
8
14
12
5
15
55
5
30
3
30
II
23
3
1
18
4
5
6
2
2
61
23
518
4
21
IS
Mixing Zone Depth (d)
Source Area (acres)
0.5
13
5
8
5
5
9
5
15
10
5
5
5
5
8
35
10
7
7
5
5
5
6
11
5
5
9
7
36
9
5
9
10
32
22
33
22
24
29
22
33
27
22
22
22
23
24
52
31
31
24
22
22
22
25
27
22
23
38
30
147
25
21
33
30
48
38
55
38
41
45
38
49
42
37
38
38
39
40
67
48
51
40
38
37
39
41
43
37
39
63
50
236
41
37
SI
|_ 100
78
68
94
68
73
75
69
79
73
68
69
69
72
70
98
78
86
70
68
68
70
72
73
68
70
106
86
371
71
68
82
600
176
168
200
168
173
173
169
177
170
167
169
169
175
168
195
176
188
168
166
167
168
170
171
166
167
221
187
624
169
167
180
Dilution Factor (DF)
Source Area (acres)
0.5
1
60
3
57
9
2
38
2
1
68
47
24
16
1
1
2
4
2
2
86
14
5
1
25
7
3
4
2
1
102
3
10
1
SI
3
29
4
1
24
1
1
48
47
6
16
1
1
1
3
1
1
73
4
2
1
6
2
3
3
2
1
102
2
30
1
30
2
17
3
1
14
1
1
28
47
4
13
1
1
1
2
1
1
43
3
2
1
4
2
3
2
2
1
59
I
100
1
17
2
10
2
1
8
1
1
16
37
3
7
1
1
1
2
1
1
24
2
1
1
3
1
2
2
2
1
33
1
600
1
7
1
5
1
1
4
1
1
7
16
2
4
1
1
1
1
1
1
10
1
1
1
2
1
1
1
1
1
14
1
\df-hgdb.xls 5/19/94
-------
Dilution Factor Model Results for 271 Sites in the Hydrogeologic Database (HGDB) - National Average
| Source length (m)
Source area (acres)
0.5
45
10
201
30
349
100
636
600
1,559
Hydrogeologic
Selling
7.7
7.7
7.7
7.7
7.8
7.8
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
7.9
8.1
8.1
Infiltration
(nVy)
0.14
0.14
0.14
0.14
0.14
0.14
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.22
0.14
0.14
Average K
(nVy)
8,830
158
6
9
3
631
6.307
23,967
29,959
0.3
I.IOE+05
13,245
37,843
2,208
9,776
1,892
34,374
44,150
15,768
7,253
13,876
99.654
6.62E+05
14,822
7,884
6.40E+07
5,676
18.922
3.879
63
3.469
Hyd.Grad.
(m/m)
5.00E-04
3.00E-03
2.70E-02
4.20E-02
2.00E-02
5.00E-03
I.OOE-03
2.00E-03
4.00E-03
8.00E-03
4.00E-03
6.00E-03
3.00E-03
9.00E-04
7.00E-04
3.00E-02
6.00E-03
2.00E-03
I.OOE-03
6.00E-04
2.00E-03
7.00E-04
3.00E-03
I.OOE-03
3.00E-02
9.00E-04
I.OOE-03
5.00E-03
4.00E-03
7.00E-02
3.00E-02
Darcy v
(m/y)
4
0.5
0.2
0.4
0.06
3
6
48
120
0.002
442
- 79
114
2
7
57
206
88
16
4
28
70
1,987
15
237
57,616
6
95
16
4
104
Aq. Thick.
(m)
46
5
4
3
3
8
61
23
19
21
21
12
9
23
15
32
26
19
24
40
122
7
6
61
3
76
6
8
8
12
152
Mixing Zone Deplh (d)
Source Area (acres)
0.5
6
9
8
7
8
7
6
5
5
26
5
5
5
9
6
5
5
5
5
7
5
5
5
5
5
5
6
5
5
6
5
10
27
26
25
24
24
27
28
22
22
43
2!
22
22
35
26
22
21
22
24
30
23
22
21
24
21
21
26
22
24
26
22
30
47
42
41
40
40
44
48
38
38
58
37
38
38
55
45
38
37
38
41
51
40
38
37
42
37
37
42
38
41
44
37
100
84
72
71
70
70
75
86
70
68
89
68
69
68
89
78
70
68
69
75
89
72
69
67
76
68
67
73
69
73
77
68
600
195
170
169
168
168
173
201
171
168
186
166
169
168
188
180
170
167
168
179
199
177
168
165
184
166
165
171
168
172
177
167
Dilution Factor (DF)
Source Area (acres)
0.5
5
1
1
1
1
4
5
25
61
1
218
41
58
3
5
30
103
45
10
4
. 16
36
976
9
75
28,244
5
48
10
5
81
10
5
1
1
1
1
2
5
25
S3
1
218
23
25
2
3
30
103
39
10
4
16
12
280
9
18
28,244
2
18
4
3
81
30
5
1
1
1
1
1
5
16
31
1
126
14
15
2
2
25
73
23
6
3
16
7
162
9
11
28,244
1
11
3
2
81
too
3
1
1
1
1
1
4
9
17
1
70
8
9
1
2
14
40
13
4
2
16
5
89
8
6
28,244
1
6
2
2
81
600
2
1
1
1
1
1
2
4
8
1
29
4
4
1
1
6
17
6
2
2
11
2
37
4
3
13,045
1
3
1
1
74
\df-hgdb.xls 5/19/94
-------
Dilution Factor Model Results for 271 Sites in the Hydrogeologic Database (HGDB) - National Average
| Source length (m)
Source ire* (acres)
0.5
45
10
201
' 30
349
100
636
600
1,559
Hydrogeologic
Selling
81
8.1
8.1
8.6
8.6
9.1
9.1
9.1
9.1
9.1
9.1
9.12
9.12
9.13
9.14
9 14
9.14
9.14
9.14
9.15
9.15
9.15
9.15
9.15
9.15
9.15
9.15
9.15
9.15
9.2
9.2
Infillrallon
-------
Dilution Factor Model Results for 271 Sites in the Hydrogeologic Database (HGDB) - National Average
1 Source length (m)
Source area (acres)
O.S
45
10
201
30
349
100
636
600
1.559
Hydrogeologic
Setting
9.2
9.4
9.5
9.7
9.9
9.9
9.9
9.9
9.9
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.3
10.5
10.5
10.5
Infiltration
(m/y)
0.14
0.14
0.14
0.08
0.22 .
0.22
0.22
0.22
0.22
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.22
0.32
0.32
0.32
Average K
(tn/y)
6
32
32
126
8.830
315
284
9
1
4,415
284
19,552
• 158
315
126
315
32
126
631
3,469
2.208
6.07E+05
2.208
126
3
25
4,415
9
315
4,415
631
Hyd. Grad.
(m/m)
I.OOE-OI
6.00E-02
5.00E-03
3.00E-02
4.00E-03
5.IOE-01
I.OOE-02
3.00E-03
5.00E-03
5.00E-03
I.OOE-02
3.00E-04
6.00E-04
4.00E-03
5.00E-03
I.OOE-02
I.70E-02
3.00E-03
5.00E-03
2.00E-03
I.OOE-05
2.00E-03
I.OOE-02
2.50E-02
I.OOE-02
9.50E-03
1.40E-02
8.00E-03
2.00E-03
2.00E-03
l.OOE-03
Darcy v
(nVy)
1
2
0.2
4
35
161
3
0.03
0.006
22
3
6
O.I
1
0.6
3
0.5
0.4
3
7
0.02
1,214
22
3
0.03
0.2
62
0.08
0.6
9
0.6
Aq. Thick.
(m)
8
6
6
107
18
6
9
2
6
55
8
21
3
6
2
II
7
4
1
3
8
15
8
12
12
5
9
6
3
20
0
Mixing Zone Depth (d)
Source Area (acres)
0.5
10
7
II
6
5
5
8
7
II
5
8
7
8
10
7
8
II
9
6
6
12
5
5
9
16
9
5
II
8
6
S
10
29
27
27
25
22
22
29
24
27
24
28
30
24
27
24
30
28
25
22
24
29
21
24
31
33
26
22
27
24
27
22
30
45
43
43
44
39
37
46
39
43
42
45
49
40
43
39
47
44
41
38
40
45
37
41
48
49
42
39
43
40
46
37
100
75
73
73
79
71
68
76
70
73
76
75
84
70
73
70
78
74
71
68
70
75
67
73
79
79
72
70
73
70
81
68
600
173
171
171
192
172
167
174
167
171
183
173
186
168
171
167
176
172
169
166
168
173
165
172
177
177
170
170
171
168
184
165
Dilution Factor (DF)
Source Area (acres)
0.5
2
3
1
7
19
81
3
1
1
9
3
4
1
2
1
3
1
1
1
2
1
407
9
3
1
1
23
1
1
5
1
10
1
1
1
7
16
24
2
1
1
9
1
3
1
1
I
2
1
1
1
1
1
291
4
2
1
1
10
1
1
4
1
30
1
1
1
7
10
14
1
1
1
9
1
2
1
1
1
1
1
1
1
1
1
168
3
1
1
1
6
1
1
3
1
100
1
1
1
7
6
8
1
1
1
7
1
2
1
1
1
1
1
1
1
1
1
93
2
1
1
I
4
1
1
2
I
600
1
1
1
4
3
4
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
38
1
1
1
1
2
1
1
1
1
\df-hgdb.xls 5/19/94
-------
Dilution Factor Model Results for 271 Sites in the Hydrogeologic Database (HGDB) - National Average
| Source length (m)
Source area (acres)
0.5
45
10
201
' 30
349
100
636
600
1.559
Hydrogeologic
Selling
II. I
11. 1
II. 1
II. 1
11.3
11.3
11.3
11.4
11.4
11.4
11.4
11.4
11.4
11.4
11.4
11.4
11.4
11.4
11.4
11.4
12.1
12.4
13.4
13.4
Infiltration
(m/y)
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0'.32
0.32
0.32
0.32
0.32
0.32
0.32
0.32
0.08
0.08
Average K
(m/y)
95
0.6
2.176
6.307
7,569
12.614
631
3.154
1.261
32
13,876
2,523
1,261
315
1,577
I.77E+05
315
284
946
8,168
5.52E+05
3.09E+05
7.884
5,361
Hyd.Grad.
(m/m)
I.OOE-02
4.00E-03
4.20E-04
I.20E-OI
6.00E-03
5.00E-03
I.OOE-02
I.50E-OI
2.00E-03
5.00E-03
2.00E-03
2.00E-03
I.70E-02
5.00E-02
2.30E-02
I.90E-02
l.OOE-03
3.00E-03
2.00E-04
3.30E-03
8.00E-03
5.00E-04
2.00E-02
l.OOE-03
Darcy v
(nVy)
0.9
0.003
0.9
757
45
63
6
473
3
0.2
28
5
21
16
36
3.355
0.3
0.9
0.2
27
4.415
155
158
5
Aq. Thick.
(m)
20
18
7
3
46
5
6
6
II
15
61
2
3
2
5
4
24
30
2
6
152
43
3
6
Mixing Zone Depth (d)
• Source Area (acres)
0.5
15
23
II
5
5
5
7
5
9
20
5
6
5
5
5
5
25
18
6
5
5
5
5
5
10
40
39
29
21
23
22
26
21
31
37
24
23
23
23
23
21
46
49
23
23
21
22
21
24
30
57
55
44
37
39
38
43
37
47
52
41
39
39
38
39
37
61
67
39
40
37
38
37
40
100
87
85
75
68
72
70
73
68
78
83
74
69
70
69
71
67
92
98
69
72
67
69
68
72
600
185
183
172
166
175
169
171
166
176
180
180
167
168
167
169
165
189
195
167
171
165
168
166
171
Dilution Factor (DF)
Source Area (acres)
0.5
2
1
1
162
17
21
4
160
3
1
II
2
6
3
13
1.003
2
2
1
II
1.474
54
141
9
10
1
1
1
37
17
6
2
46
1
1
II
1
2
1
4
225
1
1
1
4
1,474
54
32
3
30
1
1
1
22
17
4
1
27
1
1
II
1
2
1
2
130
1
1
1
2
1.474
53
19
2
100
1
1
1
12
II
2
1
15
1
1
9
1
1
1
2
72
1
1
1
2
1,474
34
II
2
600
1
1
1
6
5
2
1
7
1
1
4
1
1
1
1
30
1
1
1
1
1,360
14
5
1
\df-hgdb.xls 5/19/94
-------
Hydrogeologic Settings for HGDB Sites
Region Setting Reference Number
Western Mountain Ranges
Mountain Slopes Facing East 1.1
Mountain Flanks Facing East 1.3
Mountain Flanks Facing West 1.4
Wide Alluvial Valleys Facing East 1.6
Wide Alluvial Valleys Facing West 1.7
Alluvial Mountain Valleys Facing West 1.8
Alluvial Mountain Valleys Facing East 1.9
Coastal Beaches 1.11
Alluvial Basins
Mountain Slopes 2.1
Alternating Sedimentary Rocks 2.3
River Alluvium With Overbank Deposits 2.4
River Alluvium Without Overbank Deposits 2.5
Coastal Lowlands 2.6
Alluvial Fans 2.9
Alluvial Basins with Internal Drainage 2.13
Playa Lakes 2.11
Continental Deposits 2.12
Columbia Lava Plateau
Lava Flows: Hydraulically Connected 3.3
Alluvial Fans 3.5
River Alluvium 3.7
Colorado Plateau and Wyoming Basin
Resistant Ridges 4.1
Consolidated Sedimentary Rocks 4.2
Alluvium and Dune Sand 4.3
River Alluvium 4.4
High Plains
River Alluvium with Overbank Deposits . 5.2
River Alluvium without Overbank Deposits 5.3
Playa Lakes 5.7
Ogalalla 5.8
Non-Glaciated Central Region
Triassic Basins 6.2
Mountain Slopes 6.3
Mountain Flanks 6.4
Alternating Beds of Sandstone, Limestone, or
Shale Under Thin Soil 6.5
Alternating Beds of Sandstone, Limestone, or
Shale Under Deep Regolith 6.6
Alluvial Mountain Valleys 6.8
Braided River Deposits 6.9
River Alluvium with Overbank Deposits 6.14
River Alluvium without Overbank Deposits 6.11
-------
Hydrogeologic Settings for HGDB Sites
Region Setting Reference Number
Unconsolidated and Semi-Consolidated
Aurfers 6.12
Solution Limestone 6.13
Glaciated Central Region
Till Over Solution Limestone 7.1
Outwash Over Solution Limestone 7.2
Till Over Bedded Sedimentary Rock 7.3
Thin Till Over Bedded Sedimentary Rock 7.4
Outwash Over Bedded Sedimentary Rock 7.5
Till Over Sandstone 7.6
Till Over Shale 7.7
Glaciated Lake Deposits 7.8
Outwash 7.9
Till Over Outwash 7.18
Moraine 7.11
Buried Valley 7.12
River Alluvium with Overbank Deposits 7.13
River Alluvium without Overbank Deposits 7.14
Beaches, Beach Ridges, and Sand Dunes 7.15
Swamp/Marsh 7.16
Till 7.17
Piedmont Blue Ridge Region
Thick Regolith 8.1
River Alluvium 8.6
Northeast and Superior Uplands
Glacial Till Over Crystalline Bedrock 9.1
Glacial Lakes/Glacial Marine Deposits 9.2
Bedrock Uplands 9.4
Swamp/Marsh 9.5
Mountain Flanks 9.7
Glacial Till Over Outwash 9.9
Outwash 9.15
Alluvial Mountain Valleys 9.11
River Alluvium with Overbank Deposits 9.12
River Alluvium without Overbank Deposits 9.13
Till 9.14
Atlantic and GuH Coast
Confined Regional Aquifers 10.1
Unconsolidated and Semi-Consolidated
Shallow Surfacial Aquifers 10.2
River Alluvium with Overbank Deposits 10.3
River Alluvium without Overbank Deposits 10.4
Swamp 10.5
Southeast Coastal Plain
Solution Limestone and Shallow Surfacial
Aquifers 11.1
-------
Hydrogeologic Settings for HGDB Sites
Region Setting Reference Number
Swamp 11.2
Beaches and Bars 11.3
Coastal Deposits 11.4
Hawaii
Volcanic Uplands 12.1
Coastal Beaches 12.4
Alaska
Coastal Lowland Deposits 132
Glacial and Glacio-lacustrine Deposits of the
Interior Uplands 13.4
-------
APPENDIX G
CHARACTERIZATION OF WIND EROSION POTENTIAL
(FROM COWHERD ET AL, 1985)
-------
The second step In the estimation of emissions from an abandoned waste
dump or spill site is the determination of the potential for entrainment of
contaminated soil by wind or by mechanical disturbance. This determination
will be based on a visual site inspection coupled with optional hand siev-
ing of surface material.
3.2 CHARACTERIZATION OF WIND EROSION POTENTIAL
With regard to estimating particulate emissions from wind erosion of
contaminated surface material, site inspection can be used to determine the
potential for continuous wind erosion. The two basic requirements for wind
erosion are that the surface be dry and exposed to the wind. For example,
if the contaminated site lies in a swampy area or is covered by unbroken
grass, the potential for wind erosion is virtually nil. The same would be
true if a substance spilled or otherwise applied to the surface solidifies
and acts as impervious binder. If, on the other hand, the vegetative cover
is not continuous over the contaminated surface, then the plants are con-
sidered to be nonerodible elements which absorb a fraction of the wind
stress that otherwise acts to suspend the intervening contaminated soil.
For estimating emissions from wind erosion, either of two emission fac-
tor equations are recommended (Section 4) depending on the credibility of
the surface material. Based on the site survey, the contaminated surface
must be placed in one of two credibility classes described below. The
division between these classes is best defined in terms of the threshold
wind speed for the onset of wind erosion.
Nonhomogeneous surfaces impregnated with nonerodible elements (stones,
clumps of vegetation, etc.) are characterized by the finite availability
("limited reservoir") of erodible material. Such surfaces have high thresh-
old wind speeds for wind erosion, and particulate emission rates tend to
decay rapidly during an erosion event. On the other hand, bare surfaces of
finely divided material such as sandy agricultural soil are characterized
by an "unlimited reservoir" of erodible particles. Such surfaces have low
threshold wind speeds for wind erosion, and particulate emission rates are
relatively time independent at a given wind speed.
For surface areas not covered by continuous vegetation the classifica-
tion of surface material as either having a "limited reservoir" or an
"unlimited reservoir" of erodible surface particles is determined by estimat-
ing the threshold friction velocity. Based on the authors' analysis of wind
erosion research, the dividing line for the two credibility classes is a
threshold friction velocity of about 75 cm/sec. This somewhat arbitrary
division is based on the observation that highly erodible surfaces, usually
corresponding to sandy surface soils that are fairly deep, have threshold
friction velocities below 75 cm/sec. Surfaces with friction velocities
larger than 75 cm/sec tend to be composed of aggregates too large to be
eroded mixed in with a small amount of erodible material or of crusts that
are resistent to erosion (Gillette et al., 1982).
The cutoff friction velocity of 75 cm/sec corresponds to an ambient
wind speed of about 10 m/sec (22 mph), measured at a height of about 7 m.
21
-------
In turn, a specific value of threshold friction velocity for the credible
surface is needed for either wind erosion emission factor equation (model).
Crusted surfaces are regarded as having a "limited reservoir" of erodi-
ble particles. Crust thickness and strength should be examined during the
site inspection, by testing with a pocket knife. If the crust is more than
0.6 cm thick and not easily crumbled between the fingers (modulus of rupture
> 1 bar), then the soil may be considered nonerodible. If the crust thick-
ness is less than 0.6 cm or is easily crumbled, then the surface should be
treated as having a limited reservoir of erodible particles. If a crust is
found beneath a loose deposit, the amount of this loose deposit, which con-
stitutes the limited erosion reservoir, should be carefully estimated.
For uncrusted surfaces, the threshold friction velocity is best esti-
mated from the dry aggregate structure of the soil. A simple hand sieving
test of surface soil is highly desirable to determine the mode of the sur-
face aggregate size distribution by inspection of relative sieve catch
amounts, following the procedure specified in Figure 3-3. The threshold
friction velocity for erosion can be determined from the mode of the
aggregate size distribution, following a relationship derived by Gillette
(1980) as shown in Figure 3-4.
A more approximate basis for determining threshold friction velocity
would be based on hand sieving with just one sieve, but otherwise follows
the procedure specified in Figure 3-3. Based on the relationship developed
by Bisal and Ferguson (1970), if more than 60% of the soil passes a 1-mm
sieve, the "unlimited reservoir" model will apply; if not, the "limited
reservoir" model will apply. This relationship has been verified by Gillette
(1980) on desert soils.
If the soil contains nonerodible elements which are too large to in-
clude in the sieving (i.e., greater than about 1 cm in diameter), the effect
of these elements must be taken into account by increasing the threshold
friction velocity. (JJa.rsna1^ (1971) has employed wind tunnel studies to
quantify the increase in the threshold velocity for differing kinds of non-
erodible elements. His results are depicted in terms of a graph of the rate
of corrected to uncorrected friction velocity versus L (Figure 3-5), where
I is the ratio of the silhouette area of the roughness elements to the total
area of the bare loose soil. I The silhouette area of a nonerodible element
is the projected frontal area normal to the wind direction.
A value for L is obtained by marking off a 1 m x 1 m surface area and
determining the fraction of area, as viewed from directly overhead, that
is occupied by non-erodible elements. Then the overhead area should be
corrected to the equivalent frontal area; for example, if a spherical non-
erodible element is half imbedded in the surface, the frontal area is one-
half of the overhead area. Although it is difficult to estimate L for
values below 0.05, the correction to friction velocity becomes less Sensi-
tive to the estimated value of L..
22
-------
LIST OF TABLES
Page
Table 1 Parameter input values for model sensitivity analysis 3-3
Table 2 Summary of EPACMTP modeling options 3-5
Table 3 Summary of EPACMTP input parameters 3-6
Table 4 Receptor well location scenarios 3-9
Table 5 Distribution of aquifer particle diameter 3-11
Table 6 Variation of DAF with number of Monte Carlo repetitions 4-2
Table 7 Sensitivity of model parameters 4-3
Table 8 DAF values for waste site area of 150,000 ft2 4-13
Table Al DAF values as a function of source area for base case scenario (x=25 ft,
y=unifonn in plume, z-nationwide distribution) A-l
Table A2 DAF values as a function of source area for Scenario 2 (x=nationwide
distribution, y=uniform in plume, /^nationwide distribution) A-2
Table A3 DAF values as a function of source area for Scenario 3 (x=0 ft,
y=uniform within half-width of source area, z=nationwide
distribution) A-3
Table A4 DAF values as a function of source area for Scenario 4 (x=25 ft,
y=uniform within half-width of source area, z=nationwide
distribution) A-4
Table A5 DAF values as a function of source area for Scenario 5 (x=100 ft,
y=uniform within half-width of source, z=nationwide distribution). ... A-5
Table A6 DAF values as a function of source area for Scenario 6 (x=25 ft,
y=source width + 25 ft, z=25 ft) A-6
-------
TABLE OF CONTENTS
Page
PREFACE i
ABSTRACT . ii
1.0 INTRODUCTION 1-1
2.0 GROUNDWATER MODEL 2-1
2.1 Description of EPACMTP Model 2-1
2.2 Fate and Transport Simulation Modules 2-3
2.2.1 Unsaturated zone flow and transport module 2-3
2.2.2 Saturated zone flow and transport module 2-3
2.2.3 Model capabilities and limitations 2-5
2.3 Monte Carlo Module 2-8
2.3.1 Capabilities and Limitations of Monte Carlo Module 2-11
3.0 MODELING PROCEDURE 3-1
3.1 Modeling Approach 3-1
3.1.1 Determination of Monte Cario Repetition Number and Sensitivity
Analysis 3-1
3.1.2 Analysis of DAF Values for Different Source Areas 3-4
3.1.2.1 Model Options and Input Parameters . . 3-4
4.0 RESULTS 4-1
4.1 Convergence of Monte Carlo Simulation 4-1
4.2 Parameter Sensitivity Analysis 4-1
4.3 DAF Values as a Function of Source Area 4-6
REFERENCES '. R-l
111
-------
LIST OF FIGURES
Figure 1 Conceptual view of the unsaturated zone-saturated zone system simulated
by EPACMTP 2-2
Figure 2 Conceptual Monte Carlo framework for deriving probability distribution
of model output from probability distributions of input parameters 2-10
Figure 3 Flow chart of EPACMTP for Monte Carlo simulation 2-12
Figure 4 Plan view and cross-section view showing location of receptor well 3-8
Figure 5 Variation of DAF with size of source area for the base case scenario
(x=25 ft, y=uniform in plume, z=nationwide distribution) 4-7
Figure 6 Variation of DAF with size of source area for the default nationwide
scenario (Scenario 2: x=nationwide distribution, y=uniform in plume,
z=nationwide distribution) 4-8
Figure 7 Variation of DAF with size of source area for Scenario 3 (x=0,
y=uniform within half-width of source area, z=nationwide
distribution) 4-9
Figure 8 Variation of DAF •with size of source area for Scenario 4 (x=25 ft,
y=uniform within half-width of source area, z=nationwide
distribution) 4-10
Figure 9 Variation of DAF with size of source area for Scenario 5 (x=100 ft,
y=unifonn within half-width of source area, z—nationwide
distribution) 4-11
Figure 10 Variation of DAF with size of source area for Scenario 6 (x=25 ft,
y=width of source area -I- 25 ft, z=25 ft) 4-12
IV
-------
PREFACE
The work documented in this report was conducted by HydroGeoLogic, Inc. for the EPA Office
of Solid Waste. The work was performed partially under Contract No. 68-WO-0029 and
partially under Contract No. 68-W3-0008, subcontracted through ICF Inc. This documentation
was prepared under Contract No. 68-W4-0017. Technical direction on behalf of the Office of
Solid Waste was provided by Dr. Z.A. Saleem.
-------
ABSTRACT
The EPA Composite Model for Leachate Migration with Transformation Products (EPACMTP)
was applied to generate Dilution Attenuation Factors (DAF) for the groundwater pathway in
support of the development of Soil Screening Level Guidance. The model was applied on a
nationwide basis, using Monte Carlo simulation, to determine DAFs as a function of the area
of the contaminated site at various probability levels. The analysis was conducted in two stages:
First, the number of Monte Carlo iterations required to achieve converged results was
determined. Convergence was defined as a change of less than 5% in the 85th percentile DAF
value. A number of 15,000 Monte Carlo iterations was determined to yield convergence;
subsequent analyses were performed using this number of iterations. Second, Monte Carlo
analyses were performed to determine DAF values as a function of the contaminated area. The
effects of different placements of the receptor well were evaluated.
11
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APPENDIX A
PHOTOGRAPHS OF NONEROOIBLE ELEMENT DISTRIBUTIONS
A-l
-------
This Appendix presents a series of photographs of nonerodible element
distributions along with the associated multipliers for correcting the thresh-
old friction velocity (u*t) determined only for the erodible material. The
non-erodible elements are generally larger than about 1 cm in equivalent
physical diameter. The appearance of the contaminated surface in question
should be compared to the photographs for the purpose of determining the
appropriate correction factor.
The correction factors for the subsequent figures are as follows:
Figure A-l No correction. L < 10"3
Figure A-2 /V corrected =2 L - 0.01
(u*t) uncorrected
Figure A-3 (u«t> corrected =5 L =0.1
(u*t) uncorrected
The remaining photographs illustrate the appearance of dusted surfaces
and a surface protected by dried vegetation. Figure A-4 shows a dusted sur-
face covered with an appreciable amount of both erodible and nonerodible
particles. Figure A-5 shows a dusted surface with a negligible reservoir
of loose erodible material; the quarter coin in the photograph indicates
approximate scale. Figure A-6 shows a surface that is well protected by
dried vegetation, rendering the surface nonerodible; the white square in
the photograph is of 1 mx 1 m inside dimensions.
A-2
-------
Figure A-3
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^.fSf^Mf\. ••?«£? • rJ*v,:~ lv.-i_'
-------
-------
Figure A-2
-------
Figure A-5
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I
00
Figure A-6
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TECHNICAL REPORT DATA
,'/Vi-«r reaJ Instruction) on tin- rcn'rsc before cumplcliHgJ
1. REPORT NO. 2
EPA/600/8-85/002
4. TITLE AND SUBTITLE
Rapid Assessment of Exposure to Participate Emissions
From Surface Contamination Sites
7. AUTHOR(S)
Chatten Cowherd, Jr., Gregory E. Muleski, Phillip J.
Englehart, and Dale A. Gillette (NOAA)
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Midwest Research Institute
425 Volker Boulevard
Kansas City, MO 64110
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. EPA
Office of Health and Environmental Assessment
Washington, D.C. 20460
3. RECIPIENT'S ACCESSION NO.
W85 192219 flK
5. REPORT DATE
February 1985
6. PERFORMING ORGANIZATION CODE
B. PERFORMING ORGANIZATION REPORT NO
10. PROGRAM ELEMENT NO.
1 1. CONTRACT/GRANT NO.
68-03-3116
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
EPA/600/21
IS. SUPPLEMENTARY NOTES
EPA Project Officer: John L. Schaum
16. ABSTRACT
Emergency response actions at chemical spills and abandoned hazardous waste
sites often require rapid assessment of (a) the potential for atmospheric contam-
ination by the chemical or waste compound and (b) the inhalation exposure of
people living in the vicinity of a surface contamination site. This manual pro-
vides a methodology for rapid assessment of inhalation exposure to respirable
particulate emissions from surface contamination sites. Respirable particulate
matter is defined as airborne particles equal to or smaller than 10 jn aerodynamic
diameter. The methodology consists of a site survey procedure, particulate emis-
sion factor equations for wind and mechanical entrainment processes, procedures
for mapping atmospheric contaminant concentration distributions by scaling the
output of pre-solved computer models of regional atmospheric dispersion, and an
equation for calculation of inhalation exposure. In addition to the components
of the methodology, this manual discusses critical contaminant and site charac-
teristics, describes assumptions and limitations of the procedures, and presents
example applications.
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS
IB. DISTRIBUTION STATEMENT
Distribute to Public
b. IDENTIFIERS/OPEN ENDI-O TERMS
"•SiV&im1""1*-''
"•8ftuj8$fWB'lWfw
c. COSATI Field/Group
21. NO. Of PAGES
1^
22. PRICE
EPA Form 2220-1 (9-73)
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APPENDIX H
SYNTHETIC PRECIPITATION LEACHING PROCEDURE (SPLP)
(SW-846 METHOD 1312)
-------
METHOD 1312
SYNTHETIC PRECIPITATION LEACHING PROCEDURE
1.0 SCOPE AND APPLICATION
1.1 Method 1312 is designed to determine the mobility of both organic
and inorganic analytes present in samples of soils and wastes.
2.0 SUMMARY OF METHOD
2.1 For liquid samples (i.e.. those containing less than 0.5 % dry
solid material), the sample, after filtration through a 0.6 to 0.8 IM glass
fiber filter, is defined as the 1312 extract.
2.2 For samples containing greater than 0.5 % solids, the liquid phase,
if any, is separated from the solid phase and stored for later analysis; the
particle size of the solid phase is reduced, if necessary. The solid phase is
extracted with an amount of extraction fluid equal to 20 times the weight of the
solid phase. The extraction fluid employed is a function of the region of the
country where the sample site is located if the sample is a soil. If the sample
is a waste or wastewater, the extraction fluid employed is a, pH 4.2 solution.
A special extractor vessel is used when testing for volatile amalytes (see Table
1 for a list of volatile compounds). Following extraction, the liquid extract
is separated from the sample by 0.6 to 0.8 pm glass fiber filter.
2.3 If compatible (i.e., multiple phases will not form on combination),
the initial liquid phase of the waste is added to the liquid extract, and these
are analyzed together. If incompatible, the liquids are analyzed separately and
the results are mathematically combined to yield a volume-weighted average
concentration.
3.0 INTERFERENCES
3.1 Potential interferences that may be encountered during analysis are
discussed in the individual analytical methods.
4.0 APPARATUS AND MATERIALS
4.1 Agitation apparatus: The agitation apparatus must be capable of
rotating the extraction vessel in an end-over-end fashion (see Figure 1) at 30
± 2 rpm. Suitable devices known to EPA are identified in Table 2.
4.2 Extraction Vessels
4.2.1 Zero Headspace Extraction Vessel (ZHE). This device is for
use only when the sample is being tested for the mobility of volatile
analytes (i.e., those listed in Table 1). The ZHE (depicted in Figure 2)
allows for liquid/solid separation within the device and effectively
precludes headspace. This type of vessel allows for initial liquid/solid
separation, extraction, and final extract filtration without opening the
vessel (see Step 4.3.1). These vessels shall have an internal volume of
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500-600 ml and be equipped to accommodate a 90-110 mm filter. The devices
contain VITOJT 0-rings which should be replaced frequently. Suitable ZHE
devices known to EPA are identified in Table 3.
For the ZHE to be acceptable for use, the piston within the ZHE
should be able to be moved with approximately 15 psi or less. If it takes
more pressure to move the piston, the 0-rings in the device should be
replaced. If this does not solve the problem, the ZHE is unacceptable for
1312 analyses and the manufacturer should be contacted.
The ZHE should be checked for leaks after every extraction. If the
device contains a built-in pressure gauge, pressurize the device to 50
psi, allow it to stand unattended for 1 hour, and recheck the pressure.
If the device does not have a built-in pressure gauge, pressurize the
device to 50 psi, submerge it in water, and check for the presence of air
bubbles escaping from any of the fittings. If pressure is lost, check all
fittings and inspect and replace 0-rings, if necessary. Retest the
device. If leakage problems cannot be solved, the manufacturer should be
contacted.
Some ZHEs use gas pressure to actuate the ZHE piston, while others
use mechanical pressure (see Table 3). Whereas the volatiles procedure
(see Step 7.3) refers to pounds-per-square-inch (psi), for the
mechanically actuated piston, the pressure applied is measured in torque-
inch-pounds. Refer to the manufacturer's instructions as to the proper
conversion.
4.2.2 Bottle Extraction Vessel. When the sample is being
evaluated using the nonvolatile extraction, a jar with sufficient capacity
to hold the sample and the extraction fluid is needed. Headspace is
allowed in this vessel.
The extraction bottles may be constructed from various materials,
depending on the analytes to be analyzed and the nature of the waste (see
Step 4.3.3). It is recommended that borosilicate glass bottles be used
instead of other types of glass, especially when inorganics are of
concern. Plastic bottles, other than polytetrafluoroethylene, shall not
be used if organics are to be investigated. Bottles are available from a
number of laboratory suppliers. When this type of extraction vessel is
used, the filtration device discussed in Step 4.3.2 is used for initial
liquid/solid separation and final extract filtration.
4.3 Filtration Devices: It is recommended that all filtrations be
performed in a hood.
4.3.1 Zero-Headspace Extraction Vessel (ZHE): When the sample
is evaluated for volatiles, the zero-headspace extraction vessel described
in Step 4.2.1 is used for filtration. The device shall be capable of
supporting and keeping in place the glass fiber filter and be able to
withstand the pressure needed to accomplish separation (50 psi).
1VITON» is a trademark of Du Pont.
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NOTE: When 1t Is suspected that the glass fiber filter has been ruptured, an
In-line glass fiber filter may be used to filter the material within the
ZHE.
4.3.2 Filter Holder: When the sample is evaluated for other than
volatile analytes, a filter holder capable of supporting a glass fiber
filter and able to withstand the pressure needed to accomplish separation
may be used. Suitable filter holders range from simple vacuum units to
relatively complex systems capable of exerting pressures of up to 50 psi
or more. The type of filter holder used depends on the properties of the
material to be filtered (see Step 4.3.3). These devices shall have a
minimum internal volume of 300 ml and be equipped to accommodate a minimum
filter size of 47 mm (filter holders having an internal capacity of 1.5 L
or greater, and equipped to accommodate a 142 mm diameter filter, are
recommended). Vacuum filtration can only be used for wastes with low
solids content (<10 %) and for highly granular, liquid-containing wastes.
All other types of wastes should be filtered using positive pressure
filtration. Suitable filter holders known to EPA are shown in Table 4.
4.3.3 Materials of Construction: Extraction vessels and
filtration devices shall be made of inert materials which will not leach
or absorb sample components. Glass, polytetrafluoroethylene (PTFE), or
type 316 stainless steel equipment may be used when evaluating the
mobility of both organic and inorganic components. Devices made of high-
density polyethylene (HOPE), polypropylene (PP), or polyvinyl chloride
(PVC) may be used only when evaluating the mobility of metals.
Borosilicate glass bottles are recommended for use over other types of
glass bottles, especially when inorganics are analytes of concern.
4.4 Filters: Filters shall be made of borosilicate glass fiber, shall
contain no binder materials, and shall have an effective pore size of 0.6 to
0.8-Mm or equivalent. Filters known to EPA which meet these specifications are
identified in Table 5. Pre-filters must not be used. When evaluating the
mobility of metals, filters shall be acid-washed prior to use by rinsing with IN
nitric acid followed by three consecutive rinses with deionized distilled water
(a minimum of 1-L per rinse is recommended). Glass fiber filters are fragile and
should be handled with care.
4.5 pH Heters: The meter should be accurate to + 0.05 units at 25*C.
4.6 ZHE Extract Collection Devices: TEDLAR*2 bags or glass, stainless
steel or PTFE gas-tight syringes are used to collect the initial liquid phase and
the final extract when using the ZHE device. These devices listed are
recommended for use under the following conditions:
4.6.1 If a waste contains an aqueous liquid phase or if a waste
does not contain a significant amount of nonaqueous liquid (i.e.. <1 % of
total waste), the TEDLAR bag or a 600 ml syringe should be used to collect
and combine the initial liquid and solid extract.
2TEDLAR* is a registered trademark of Ou Pont.
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4.6.2 If a waste contains a significant amount of nonaqueous
liquid In the Initial liquid phase (i.e.. >1 * of total waste), the
syringe or the TEDLAR bag nay be used for both the initial solid/liquid
separation and the final extract filtration. However, analysts should use
one or the other, not both.
4.6.3 If the waste contains no Initial liquid phase (is 100 X
solid) or has no significant solid phase (Is 100 % liquid), either the
TEDLAR bag or the syringe may be used. If the syringe is used, discard
the first 5 ml of liquid expressed from the device. The remaining
aliquots are used for analysis.
4.7 ZHE Extraction Fluid Transfer Devices: Any device capable of
transferring the extraction fluid into the ZHE without changing the nature of the
extraction fluid Is acceptable (e.g.. a positive displacement or peristaltic
pimp, a gas-tight syringe, pressure filtration unit (see Step 4.3.2), or other
ZHE device).
4.8 Laboratory Balance: Any laboratory balance accurate to within ±
0.01-grams may be used (all weight measurements are to be within + 0.1 grams).
4.9 Beaker or Erlenmeyer flask, glass, 500 ml.
4.10 Watchglass, appropriate diameter to cover beaker or Erlenmeyer
flask.
4.11 Magnetic stirrer.
5.0 REAGENTS
5.1 Reagent grade chemicals shall be used in all tests. Unless
otherwise Indicated, it is intended that all reagents shall conform to the
specifications of the Committee on Analytical Reagents of the American Chemical
Society, where such specifications are available. Other grades may be used,
provided it is first ascertained that the reagent is of sufficiently high purity
to permit its use without lessening the accuracy of the determination.
5.2 Reagent Hater. Reagent water is defined as water in which an
Interferant is not observed at or above the method's detection limit of the
analyte(s) of interest. For nonvolatile extractions, ASTM Type II water or
equivalent meets the definition of reagent water. For volatile extractions, it
1s recommended that reagent water be generated by any of the following methods.
Reagent water should be monitored periodically for impurities.
5.2.1 Reagent water for volatile extractions may be generated
by passing tap water through a carbon filter bed containing about 500
grams of activated carbon (Calgon Corp., Filtrasorb-300 or equivalent).
5.2.2 A water purification system (Millipore Super-Q or
equivalent) may also be used to generate reagent water for volatile
extractions.
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5.2.3 Reagent water for volatile extractions may also be prepared
by boiling water for 15 minutes. Subsequently, while maintaining the
water temperature at 90 ± 5 degrees C, bubble a contaminant-free inert gas
(e.g. nitrogen) through the water for 1 hour. While still hot, transfer
the water to a narrow mouth screw-cap bottle under zero-headspace and seal
with a Teflon-lined septum and cap.
5.3 Sulfuric acid/nitric acid (60/40 weight percent mixture) HjSO^/HNQj.
Cautiously mix 60 g of concentrated sulfuric acid with 40 g of concentrated
nitric acid.
5.4 Extraction fluids.
5.4.1 Extraction fluid #1: This fluid is made by adding the
60/40 weight percent mixture of sulfuric and nitric acids to reagent water
(Step 5.2) until the pH is 4.20 ± 0.05. The fluid is used to determine
the Teachability of soil from a site that is east of the Mississippi
River, and the Teachability of wastes and wastewaters.
NOTE: Solutions are unbuffered and exact pH may not be attained.
5.4.2 Extraction fluid #2: This fluid is made by adding the
60/40 weight percent mixture of sulfuric and nitric acids to reagent water
(Step 5.2) until the pH is 5.00 ± 0.05. The fluid is used to determine
the Teachability of soil from a site that is west of the Mississippi
River.
5.4.3 Extraction fluid 13: This fluid is reagent water (Step
5.2) and is used to determine cyanide and volatiles Teachability.
NOTE: These extraction fluids should be monitored frequently for impurities.
The pH should be checked prior to use to ensure that these fluids are made
up accurately. If impurities are found or the pH is not within the above
specifications, the fluid shall be discarded and fresh extraction fluid
prepared.
5.5 Analytical standards shall be prepared according to the appropriate
analytical method.
6.0 SAMPLE COLLECTION, PRESERVATION, AND HANDLING
6.1 All samples shall be collected using an appropriate sampling plan.
6.2 There may be requirements on the minimal size of the field sample
depending upon the physical state or states of the waste and the analytes of
concern. An aliquot is needed for the preliminary evaluations of the percent
solids and the particle size. An aliquot may be needed to conduct the
nonvolatile analyte extraction procedure (see Step 1.4 concerning the use of this
extract for volatile organics). If volatile organics are of concern, another
aliquot may be needed. Quality control measures may require additional aTiquots.
Further, it is always wise to collect more sample just in case something goes
wrong with the initial attempt to conduct the test.
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6.3 Preservatives shall not be added to samples before extraction.
6.4 Samples may be refrigerated unless refrigeration results in
irreversible physical change to the waste. If precipitation occurs, the entire
sample (including precipitate) should be extracted.
6.5 When the sample is to be evaluated for volatile analytes, care
shall be taken to minimize the loss of volatiles. Samples shall be collected and
stored in a manner intended to prevent the loss of volatile analytes (e.g..
samples should be collected in Teflon-lined septum capped vials and stored at
4*C. Samples should be opened only immediately prior to extraction).
6.6 1312 extracts should be prepared for analysis and analyzed as soon
as possible following extraction. Extracts or portions of extracts for metallic
analyte determinations must be acidified with nitric -acid to a pH < 2, unless
precipitation occurs (see Step 7.2.14 if precipitation occurs). Extracts should
be preserved for other analytes according to the guidance given in the Individual
analysis methods. Extracts or portions of extracts for organic analyte
determinations shall not be allowed to come into contact with the atmosphere
(i.e.. no headspace) to prevent losses. See Section 8.0 (Quality Control) for
acceptable sample and extract holding times.
7.0 PROCEDURE
7.1 Preliminary Evaluations
Perform preliminary 1312 evaluations on a minimum 100 gram aliquot of
sample. This aliquot may not actually undergo 1312 extraction. These
preliminary evaluations include: (1) determination of the percent solids (Step
7.1.1); (2) determination of whether the waste contains insignificant solids and
is, therefore, its own extract after filtration (Step 7.1.2); and (3)
determination of whether the solid portion of the waste requires particle size
reduction (Section 7.1.3).
7.1.1 Preliminary determination of percent solids: Percent
solids is defined as that fraction of a waste sample (as a percentage of
the total sample) from which no liquid may be forced out by an applied
pressure, as described below.
7.1.1.1 If the sample will obviously yield no free
liquid when subjected to pressure filtration (i.e.. is 100%
solids), weigh out a representative subsample (100 g minimum) and
proceed to Step 7.1.3.
7.1.1.2 If the sample is liquid or multiphasic,
liquid/solid separation to make a preliminary determination of
percent solids is required. This involves the filtration device
discussed in Step 4.3.2, and is outlined in Steps 7.1.1.3 through
7.1.1.9.
7.1.1.3 Pre-weigh the filter and the container that will
receive the filtrate.
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7.1.1.4 Assemble filter holder and filter following the
manufacturer's Instructions. Place the filter on the support
screen and secure.
7.1.1.5 Weigh out a subsample of the waste (100 gram
minimum) and record the weight.
7.1.1.6 Allow slurries to stand to permit the solid phase
to settle. Samples that settle slowly may be ceritrlfuged prior to
filtration. Centr1fugat1on 1s to be used only as an aid to
filtration. If used, the liquid should be decanted and filtered
followed by filtration of the solid portion of the waste through
the same filtration system.
7.1.1.7 Quantitatively transfer the sample to the filter
holder (liquid and solid phases). Spread the sample evenly over
the surface of the filter. If filtration of the waste at 4*C
reduces the amount of expressed liquid over what would be expressed
at room temperature, then allow the sample to warn up to room
temperature In the device before filtering.
NOTE: If sample material (>1 % of original sample weight) has obviously adhered
to the container used to transfer the sample to the filtration apparatus,
determine the weight of this residue and subtract it from the sample
weight determined In Step 7.1.1.5 to determine the weight of the sample
that will be filtered.
Gradually apply vacuum or gentle pressure of 1-10 psi, until air
or pressurizing gas moves through the filter. If this point is not
reached under 10 psi, and if no additional liquid has passed through the
filter in any 2-minute interval, slowly increase the pressure in 10 psi
increments to a maximum of 50 psi. After each Incremental increase of 10
psi, if the pressurizing gas has not moved through the filter, and if no
additional liquid has passed through the filter in any 2-minute Interval,
proceed to the next 10-psi increment. When the pressurizing gas begins to
move through the filter, or when liquid flow has ceased at 50 psi (i.e..
filtration does not result In any additional filtrate within any 2-minute
period), stop the filtration.
NOTE: Instantaneous application of high pressure can degrade the glass fiber
filter and may cause premature plugging.
7.1.1.8 The material in the filter holder is defined as
the solid phase of the sample, and the filtrate is defined as the
liquid phase.
NOTE: Some samples, such as oily wastes and some paint wastes, will obviously
contain some material that appears to be a liquid, but even after applying
vacuum or pressure filtration, as outlined in Step 7.1.1.7, this material
may not filter. If this is the case, the material within the filtration
device is defined as a solid. Do not replace the original filter with a
fresh filter under any circumstances. Use only one filter.
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7.1.1.9 Determine the weight of the liquid phase by
subtracting the weight of the filtrate container (see Step 7.1.1.3)
from the total weight of the filtrate-filled container. Determine
the weight of the solid phase of the sample by subtracting the
weight of the liquid phase from the weight of the total sample, as
determined in Step 7.1.1.5 or 7.1.1.7.
Record the weight of the liquid and solid phases.
Calculate the percent solids as follows:
Weight of solid (Step 7.1.1.9)
Percent solids - x 100
Total weight of waste (Step 7.1.1.5 or 7.1.1.7)
7.1.2 If the percent solids determined in Step 7.1.1.9 is equal
to or greater than 0.5%, then proceed either to Step 7.1.3 to determine
whether the solid material requires particle size reduction or to Step
7.1.2.1 if it is noticed that a small amount of the filtrate is entrained
in wetting of the filter. If the percent solids determined in Step
7.1.1.9 is less than 0.5%, then proceed to Step 7.2.9 if the nonvolatile
1312 analysis is to be performed, and to Section 7.3 with a fresh portion
of the waste if the volatile 1312 analysis is to be performed.
7.1.2.1 Remove the solid phase and filter from the
filtration apparatus.
7.1.2.2 Dry the filter and solid phase at 100 ± 20*C
until two successive weighings yield the same value within + 1 %.
Record the final weight.
Note: Caution should be taken to ensure that the subject solid will not flash
upon heating. It is recommended that the drying oven be vented to a hood
or other appropriate device.
7.1.2.3 Calculate the percent dry solids as follows:
Percent (Weight of dry sample + filter) - tared weight of filter
dry solids - x 100
Initial weight of sample (Step 7.1.1.5 or 7.1.1.7)
7.1.2.4 If the percent dry solids is less than 0.5%,
then proceed to Step 7.2.9 if the nonvolatile 1312 analysis is to
be performed, and to Step 7.3 if the volatile 1312 analysis is to
be performed. If the percent dry solids is greater than or equal
to 0.5%, and if the nonvolatile 1312 analysis is to be performed,
return to the beginning of this Section (7.1) and, with a fresh
portion of sample, determine whether particle size reduction is
necessary (Step 7.1.3).
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7.1.3 Determination of whether the sample requires particle-size
reduction (particle-size is reduced during this step): Using the solid
portion of the sample, evaluate the solid for particle size. Particle-
size reduction 1s required, unless the solid has a surface area per gram
of Material equal to or greater than 3.1 cor, or is smaller than 1 cm in
Its narrowest dimension (i.e.. is capable of passing through a 9.5 mm
(0.375 inch) standard sieve). If the surface area is smaller or the
particle size larger than described above, prepare the solid portion of
the sample for extraction by crushing, cutting, or grinding the waste to
a surface area or particle size as described above. If the solids are
prepared for organic volatiles extraction, special precautions must be
taken (see Step 7.3.6).
Note: Surface area criteria are meant for filamentous (e.g.. paper, cloth, and
similar) waste materials. Actual measurement of surface area is not
required, nor is it recommended. For materials that do not obviously meet
the criteria, sample-specific methods would need to be developed and
employed to measure the surface area. Such methodology is currently not
. available.
7.1.4 Determination of appropriate extraction fluid: .
7.1.4.1 For soils, if the sample is from a site that is
east of the Mississippi River, extraction fluid II should be used.
If the sample is from a site that is west of the Mississippi River,
extraction fluid 12 should be used.
7.1.4.2 For wastes and wastewater, extraction fluid II
should be used.
7.1.4.3 For cyanide-containing wastes and/or soils,
extraction fluid 13 (reagent water) must be used because leaching
of cyanide-containing samples under acidic conditions may result
in the formation of hydrogen cyanide gas.
7.1.5 If the aliquot of the sample used for the preliminary
evaluation (Steps 7.1.1 - 7.1.4) was determined to be 100% solid at Step
7.1.1.1, then it can be used for the Section 7.2 extraction (assuming at
least 100 grams remain), and the Section 7.3 extraction (assuming at least
25 grams remain). If the aliquot was subjected to the procedure in Step
7.1.1.7, then another aliquot shall be used for the volatile extraction
procedure in Section 7.3. The aliquot of the waste subjected to the
procedure in Step 7.1.1.7 might be appropriate for use for the Section 7.2
extraction if an adequate amount of solid (as determined by Step 7.1.1.9)
was obtained. The amount of solid necessary is dependent upon whether a
sufficient amount of extract will be produced to support the analyses. If
an adequate amount of solid remains, proceed to Step 7.2.10 of the
nonvolatile 1312 extraction.
7.2 Procedure when Volatiles are not Involved
A minimum sample size of 100 grams (solid and liquid phases) is
recommended. In some cases, a larger sample size may be appropriate, depending
on the solids content of the waste sample (percent solids, See Step 7.1.1),
1312 - 9 Revision 0
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whether the Initial liquid phase of the waste will be Mlsdble with the aqueous
extract of the solid, and whether Inorganics, semi volatile organ Ics, pesticides,
and herbicides are all analytes of concern. Enough solids should be generated
for extraction such that the volume of 1312 extract will be sufficient to support
all of the analyses required. If the amount of extract generated by a single
1312 extraction will not be sufficient to perform all of the analyses, more than
one extraction may be performed and the extracts from each combined and all quoted
for analysis.
7.2.1 If the sample will obviously yield no liquid when subjected
to pressure filtration M.e.. is 100 % solid, see Step 7.1.1), weigh out
a subsample of the sample (100 gram minimum) and proceed to Step 7.2.9.
7.2.2 If the sample 1s liquid or multlphaslc, liquid/solid
separation Is required. This Involves the filtration device described In
Step 4.3.2 and Is outlined 1n Steps 7.2.3 to 7.2.8.
7.2.3 Pre-weigh the container that will receive the filtrate.
7.2.4 Assemble the filter holder and filter following the
manufacturer's Instructions. Place the filter on the support screen and
secure. Add wash the filter if evaluating the mobility of metals (see
Step 4.4).
Note: Acid washed filters may be used for all nonvolatile extractions even when
metals are not of concern.
7.2.5 Weigh out a subsample of the sample (100 gram minimum) and
record the weight. If the waste contains <0.5 % dry solids (Step 7.1.2),
the liquid portion of the waste, after filtration, is defined as the 1312
extract. Therefore, enough of the sample should be filtered so that the
amount of filtered liquid will support all of the analyses required of the
1312 extract. For wastes containing >0.5 X dry solids (Steps 7.1.1 or
7.1.2), use the percent solids information obtained in Step 7.1.1 to
determine the optimum sample size (100 gram minimum) for filtration.
Enough solids should be generated by filtration to support the analyses to
be performed on the 1312 extract.
7.2.6 Allow slurries to stand to permit the solid phase to settle.
Samples that settle slowly may be centrifuged prior to filtration. Use
centrlfugation only as an aid to filtration. If the sample Is
centrifuged, the liquid should be decanted and filtered followed by
filtration of the solid portion of the waste through the same filtration
system.
7.2.7 Quantitatively transfer the sample (liquid and solid phases)
to the filter holder (see Step 4.3.2). Spread the waste sample evenly
over the surface of the filter. If filtration of the waste at 4*C reduces
the amount of expressed liquid over what would be expressed at room
temperature, then allow the sample to warm up to room temperature in the
device before filtering.
1312 - 10 Revision 0
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NOTE: If waste material (>1 % of the original sample weight) has obviously
, adhered to the container used to transfer the sample to the filtration
apparatus, determine the weight of this residue and subtract it from the
sample weight determined in Step 7.2.5, to determine the weight of the
waste sample that will be filtered.
Gradually apply vacuum or gentle pressure of 1-10 ps1, until air
or pressurizing gas moves through the filter. If this point if not
reached under 10 psi, and if no additional liquid has passed through the
filter in any 2-minute interval, slowly increase the pressure in 10-psi
increments to maximum of 50 psi. After each incremental increase of 10
psi, if the pressurizing gas has not moved through the filter, and if no
additional liquid has passed through the filter in any 2-minute interval,
proceed to the next 10-psi increment. When the pressurizing gas begins to
move through the filter, or when the liquid flow has ceased at 50 psi
(\te.. filtration does not result in any additional filtrate within a
2-ninute period), stop the filtration.
NOTE: Instantaneous application of high pressure can degrade the glass fiber
filter and may cause premature plugging.
7.2.8 The material in the filter holder is defined as the solid
phase of the sample, and the filtrate is defined as the liquid phase.
Weigh the filtrate. The liquid phase may now be either analyzed (see
Steps 7.2.12) or stored at 4*C until time of analysis.
NOTE: Some wastes, such as oily wastes and some paint wastes, will obviously
contain some material which appears to be a liquid. Even after applying
vacuum or pressure filtration, as outlined in Step 7.2.7, this material
may not filter. If this is the case, the material within the filtration
device is defined as a solid, and is carried through the extraction as a
solid. Do not replace the original filter with a fresh filter under any
circumstances. Use only one filter.
7.2.9 If the sample contains <0.5% dry solids (see Step 7.1.2),
proceed to Step 7.2.13. If the sample contains >0.5 % dry solids (see
Step 7.1.1 or 7.1.2), and if particle-size reduction of the solid was
needed in Step 7.1.3, proceed to Step 7.2.10. If the sample as received
passes a 9.5 mm sieve, quantitatively transfer the solid material into the
extractor bottle along with the filter used to separate the initial liquid
from the solid phase, and proceed to Step 7.2.11.
7.2.10 Prepare the solid portion of the sample for extraction by
crushing, cutting, or grinding the waste to a surface area or particle-
size as described in Step 7.1.3. When the surface area or particle-size
has been appropriately altered, quantitatively transfer the solid material
into an extractor bottle. Include the filter used to separate the initial
liquid from the solid phase.
NOTE: Sieving of the waste is not normally required. Surface area requirements
are meant for filamentous (e.g.. paper, cloth) and similar waste
materials. Actual measurement of surface area is not recommended. If
1312 - 11 Revision 0
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sieving is necessary, a Teflon-coated sieve should be used to avoid
contamination of the sample.
7.2.11 Deternine the amount of extraction fluid to add to the
extractor vessel as follows:
20 x X solids (Step 7.1.1) x weight of waste
filtered (Step 7.2.5 or 7.2.7)
Height of -
extraction fluid
100
Slowly add this amount of appropriate extraction fluid (see Step
7.1.4) to the extractor vessel. Close the extractor bottle tightly (it is
recommended that Teflon tape be used to ensure a tight seal), secure in
rotary extractor device, and rotate at 30 ± 2 rpm for 18 + 2 hours.
Ambient temperature (i.e.. temperature of room in which extraction takes
place) shall be maintained at 23 + 2*C during the extraction period.
NOTE: As agitation continues, pressure may build up within the extractor bottle
for some types of sample (e.g.. limed or calcium carbonate-containing
sample may evolve gases such as carbon dioxide). To relieve excess
pressure, the extractor bottle may be periodically opened (e.g.. after 15
minutes, 30 minutes, and 1 hour) and vented into a hood.
7.2.12 Following the 18 ± 2 hour extraction, separate the material
in the extractor vessel into its component liquid and solid phases by
filtering through a new glass fiber filter, as outlined in Step 7.2.7.
For final filtration of the 1312 extract, the glass fiber filter may be
changed, if necessary, to facilitate filtration. Filter(s) shall be
acid-washed (see Step 4.4) if evaluating the mobility of metals.
7.2.13 Prepare the 1312 extract as follows:
7.2.13.1 If the sample contained no initial liquid phase,
the filtered liquid material obtained from Step 7.2.12 is defined
as the 1312 extract. Proceed to Step 7.2.14.
7.2.13.2' If compatible (e.g.. multiple phases will not
result on combination), combine the filtered liquid resulting from
Step 7.2.12 with the initial liquid phase of the sample obtained
in Step 7.2.7. This combined liquid is defined as the 1312
extract. Proceed to Step 7.2.14.
7.2.13.3 If the initial liquid phase of the waste, as
obtained from Step 7.2.7, is not or may not be compatible with the
filtered liquid resulting from Step 7.2.12, do not combine these
liquids. Analyze these liquids, collectively defined as the 1312
extract, and combine the results mathematically, as described in
Step 7.2.14.
7.2.14 Following collection of the 1312 extract, the pH of the
extract should be recorded. Immediately aliquot and preserve the extract
1312 - 12 Revision 0
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for analysis. Metals aliquots must be acidified with nitric add to pH <
2. If precipitation Is observed upon addition of nitric add to a small
aliquot of the extract, then the remaining portion of the extract for
metals analyses shall not be acidified and the extract shall be analyzed
as soon as possible. All other aliquots must be stored under
refrigeration (4*C) until analyzed. The 1312 extract shall be prepared
and analyzed according to appropriate analytical methods. 1312 extracts
to be analyzed for metals shall be acid digested except In those Instances
where digestion causes loss of metallic analytes. If an analysis of the
undigested extract shows that the concentration of any regulated metallic
analyte exceeds the regulatory level, then the waste Is hazardous and
digestion of the extract is not necessary. However, data on undigested
extracts alone cannot be used to demonstrate that the waste is not
hazardous. If the individual phases are to be analyzed separately,
determine the volume of the Individual phases (to + 0.5 %), conduct the
appropriate analyses, and combine the results mathematically by using a
simple volume-weighted average:
(V,) (C,)'* (V2) (C2)
Final Analyte Concentration -
V, + V2
where:
V, - The volume of the first phase (L).
C, - The concentration of the analyte of concern in the first phase (mg/L).
V2 * The volume of the second phase (L).
C, • The concentration of the analyte of concern in the second phase
7.2.15 Compare the analyte concentrations in the 1312 extract with
the levels identified in the appropriate regulations. Refer to Section
8.0 for quality assurance requirements.
7.3 Procedure when Volatile* are Involved
Use the ZHE device to obtain 1312 extract for analysis of volatile
compounds only. Extract resulting from the use of the ZHE shall not be used to
evaluate the mobility of non-volatile analytes (e.g.. metals, pesticides, etc.).
The ZHE device has approximately a 500 ml internal capacity. The ZHE can
thus accommodate a maximum of 25 grams of solid (defined as that fraction of a
sample from which no additional liquid may be forced out by an applied pressure
of 50 psi), due to the need to add an amount of extraction fluid equal to 20
times the weight of the solid phase.
Charge the ZHE with sample only once and do not open the device until the
final extract (of the solid) has been collected. Repeated filling of the ZHE to
obtain 25 grams of solid is not permitted.
Do not allow the sample, the initial liquid phase, or the extract to be
exposed to the atmosphere for any more time than is absolutely necessary. Any
1312 - 13 Revision 0
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manipulation of these materials should be done when cold (4*C) to minimize loss
of volatlles.
7.3.1 Pre-weigh the (evacuated) filtrate collection container
(see Step 4.6) and set aside. If using a TEDLAR* bag, express all liquid
from the ZHE device into the bag, whether for the initial or final
liquid/solid separation, and take an aliquot from the liquid in the bag
for analysis. The containers listed in Step 4.6 are recommended for use
under the conditions stated in Steps 4.6.1-4.6.3.
7.3.2 Place the ZHE piston within the body of the ZHE (it may be
helpful first to moisten the piston 0-rings slightly with extraction
fluid). Adjust the piston within the ZHE body to a height that will
minimize the distance the piston will have to move once the ZHE is charged
with sample (based upon sample size requirements determined from Step 7.3,
Step 7.1.1 and/or 7.1.2). Secure the gas inlet/outlet flange (bottom
flange) onto the ZHE body in accordance with the manufacturer's
instructions. Secure the glass fiber filter between the support screens
and set aside. Set liquid inlet/outlet flange (top flange) aside.
7.3.3 If the sample is 100% solid (see Step 7.1.1), weigh out
a subsample (25 gram maximum) of the waste, record weight, and proceed to
Step 7.3.5.
7.3.4 If the sample contains <0.5% dry solids (Step 7.1.2), the
liquid portion of waste, after filtration, is defined as the 1312 extract.
Filter enough of the sample so that the amount of filtered liquid will
support all of the volatile analyses required. For samples containing
>0.5% dry solids (Steps 7.1.1 and/or 7.1.2), use the percent solids
information obtained in Step 7.1.1 to determine the optimum sample size to
charge into the ZHE. The recommended sample size is as follows:
7.3.4.1 For samples containing <5% solids (see Step
7.1.1), weigh out a 500 gram subsample of waste and record the
weight.
7.3.4.2 For wastes containing >5% solids (see Step
7.1.1), determine the amount of waste to charge into the ZHE as
follows:
25
Weight of waste to charge ZHE * x 100
percent solids (Step 7.1.1)
Weigh out a subsample of the waste of the appropriate size and
record the weight.
7.3.5 If particle-size reduction of the solid portion of the
sample was required in Step 7.1.3, proceed to Step 7.3.6. If particle-
size reduction was not required in Step 7.1.3, proceed to Step 7.3.7.
7.3.6 Prepare the sample for extraction by crushing, cutting, or
grinding the solid portion of the waste to a surface area or particle size
1312 - 14 Revision 0
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as described in Step 7.1.3.1. Wastes and appropriate reduction equipment
should be refrigerated, if possible, to 4*C prior to particle-size
reduction. The means used to effect particle-size reduction must not
generate heat in and of itself. If reduction of the solid phase of the
waste is necessary, exposure of the waste to the atmosphere should be
avoided to the extent possible.
NOTE: Sieving of the waste is not recommended due to the possibility that
volatiles may be lost. The use of an appropriately graduated ruler is
recommended as an acceptable alternative. Surface area requirements are
meant for filamentous (e.g.. paper, cloth) and similar waste materials.
Actual measurement of surface area is not recommended.
When the surface area or particle-size has been appropriately
altered, proceed to Step 7.3.7.
7.3.7 Waste slurries need not be allowed to stand to permit the
solid phase to settle. Do not centrifuge samples prior to filtration.
7.3.8 Quantitatively transfer the entire sample (liquid and solid
phases) quickly to the ZHE. Secure the filter and support screens into
the top flange of the device and secure the top flange to the ZHE body in
accordance with the manufacturer's instructions. Tighten all ZHE fittings
and place the device in the vertical position (gas inlet/outlet flange on
the bottom). Do not attach the extraction collection device to the top
plate.
Note: If sample material (>1% of original sample weight) has obviously adhered
to the container used to transfer the sample to the ZHE, determine the
weight of this residue and subtract it from the sample weight determined
in Step 7.3.4 to determine the weight of the waste sample that will be
filtered.
Attach a gas line to the gas inlet/outlet valve (bottom flange)
and, with the liquid inlet/outlet valve (top flange) open, begin applying
gentle pressure of 1-10 psi (or more if necessary) to force all headspace
slowly out of the ZHE device into a hood. At the first appearance of
liquid from the liquid inlet/outlet valve, quickly close the valve and
discontinue pressure. If filtration of the waste at 4*C reduces the
amount of expressed liquid over what would be expressed at room
temperature, then allow the sample to warn up to room temperature in the
device before filtering. If the waste is 100 % solid (see Step 7.1.1),
slowly increase the pressure to a maximum of 50 psi to force most of the
headspace out of the device and proceed to Step 7.3.12.
7.3.9 Attach the evacuated pre-weighed filtrate collection
container to the liquid inlet/outlet valve and open the valve. Begin
applying gentle pressure of 1-10 psi to force the liquid phase of the
sample into the filtrate collection container. If no additional liquid
has passed through the filter in any 2-minute interval, slowly increase
the pressure in 10-psi increments to a maximum of 50 psi. After each
incremental increase of 10 psi, if no additional liquid has passed through
the filter in any 2-minute interval, proceed to the next 10-psi increment.
1312 - 15 Revision 0
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When liquid flow has ceased such that continued pressure filtration at 50
ps1 does not result In any additional filtrate within a 2-minute period,
stop the filtration. Close the liquid Inlet/outlet valve, discontinue
pressure to the piston, and disconnect and weigh the filtrate collection
container.
NOTE: Instantaneous application of high pressure can degrade the glass fiber
filter and may cause premature plugging.
7.3.10 The material In the ZHE 1s defined as the solid phase of
the sample and the filtrate is defined as the liquid phase.
NOTE: Some samples, such as oily wastes and some paint wastes, will obviously
contain some material which appears to be a liquid. Even after applying
pressure filtration, this material will not filter. If this Is the case,
the material within the filtration device 1s defined as a solid, and is
carried through the 1312 extraction as a solid.
If the original waste contained <0.5 % dry solids (see Step 7.1.2),
this filtrate 1s defined as the 1312 extract and is analyzed directly.
Proceed to Step 7.3.15.
7.3.11 The liquid phase nay now be either analyzed immediately
(see Steps 7.3.13 through 7.3.15) or stored at 4*C under minimal headspace
conditions until time of analysis. Determine the weight of extraction
fluid 13 to add to the ZHE as follows:
20 x % solids (Step 7.1.1) x weight
of waste filtered (Step 7.3.4 or 7.3.8)
Height of extraction fluid -
100
7.3.12 The following steps detail how to add the appropriate
amount of extraction fluid to the solid material within the ZHE and
agitation of the ZHE vessel. Extraction fluid 13 is used In all cases
(see Step 5.7).
7.3.12.1 With the ZHE in the vertical position, attach a
line from the extraction fluid reservoir to the liquid inlet/outlet
valve. The line used shall contain fresh extraction fluid and
should be preflushed with fluid to eliminate any air pockets in the
line. Release gas pressure on the ZNE piston (from the gas
inlet/outlet valve), open the liquid inlet/outlet valve, and begin
transferring extraction fluid (by pumping or similar means) into
the ZHE. Continue pumping extraction fluid into the ZHE until the
appropriate amount of fluid has been introduced into the device.
7.3.12.2 After the extraction fluid has been added,
immediately close the liquid Inlet/outlet valve and disconnect the
extraction fluid line. Check the ZHE to ensure that all valves are
in their closed positions. Manually rotate the device in an
end-over-end fashion 2 or 3 times. Reposition the ZHE in the
1312 • 16 Revision 0
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vertical position with the liquid inlet/outlet valve on top.
Pressurize the ZHE to 5-10 psi (if necessary) and slowly open the
liquid inlet/outlet valve to bleed out any headspace (into a hood)
that may have been introduced due to the addition of extraction
fluid. This bleeding shall be done quickly and shall be stopped
at the first appearance of liquid from the valve. Re-pressurize
the ZHE with 5-10 psi and check all ZHE fittings to ensure that
they are closed.
7.3.12.3 Place the ZHE in the rotary extractor apparatus
(if it is not already there) and rotate at 30 + 2 rpm for 18 ± 2
hours. Ambient temperature (i.e.. temperature of room in which
extraction occurs) shall be maintained at 23 + 2*C during
agitation.
7.3.13 Following the 18 ± 2 hour agitation period, check the
pressure behind the ZHE piston by quickly opening and closing the gas
inlet/outlet valve and noting the escape of gas. If the pressure has not
been maintained (i.e.. no gas release observed), the ZHE is leaking.
Check the ZHE for leaking as specified in Step 4.2.1, and perform the
extraction again with a new sample of waste. If the pressure within the
device has been maintained, the material in the extractor vessel is once
again separated into its component liquid and solid phases. If the waste
contained an initial liquid phase, the liquid may be filtered directly
into the same filtrate collection container (i.e.. TEOLAR' bag) holding the
initial liquid phase of the waste. A separate filtrate collection
container must be used if combining would create multiple phases, or there
is not enough volume left within the filtrate collection container.
Filter through the glass fiber filter, using the ZHE device as discussed
in Step 7.3.9. All extracts shall be filtered and collected if the TEDLAR*
bag is used, if the extract is multiphasic, or if the waste contained an
initial liquid phase (see Steps 4.6 and 7.3.1).
NOTE: An in-line glass fiber filter may be used to filter the material within
the ZHE if it is suspected that the glass fiber filter has been ruptured
7.3.14 If the original sample contained no initial liquid phase,
the filtered liquid material obtained from Step 7.3.13 is defined as the
1312 extract. If the sample contained an initial liquid phase, the
filtered liquid material obtained from Step 7.3.13 and the initial liquid
phase (Step 7.3.9) are collectively defined as the 1312 extract.
7.3.15 Following collection of the 1312 extract, immediately
prepare the extract for analysis and store with minimal headspace at 4*C
until analyzed. Analyze the 1312 extract according to the appropriate
analytical methods. If the individual phases are to be analyzed
separately (i.e.. are not miscible), determine the volume of the
individual phases (to 0.5%), conduct the appropriate analyses, and combine
the results mathematically by using a simple volume- weighted average:
(V,) (C,) + (V2) (C2)
Final Analyte
Concentration V, + V2
1312 - 17 Revision 0
November 1992
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where:
V, - The volume of the first phases (L).
C1 - The concentration of the analyte of concern in the first phase (mg/L).
V2 * The volume of the second phase (L).
C2 - The concentration of the analyte of concern in the second phase
7.3.16 Compare the analyte concentrations in the 1312 extract with
the levels identified In the appropriate regulations. Refer to Section
8.0 for quality assurance requirements.
8.0 QUALITY CONTROL
8.1 A minimum of one blank (using the same extraction fluid as used for
the samples) for every 20 extractions that have been conducted in an extraction
vessel .
8.2 A matrix spike shall be performed for each waste type (e.g..
wastewater treatment sludge, contaminated soil, etc.-) unless the result exceeds
the regulatory level and the data is being used solely to demonstrate that the
waste property exceeds the regulatory level . A minimum of one matrix spike must
be analyzed for each analytical batch. As a minimum, follow the matrix spike
addition guidance provided in each analytical method.
8.2.1 Hatrix spikes are to be added after filtration of the 1312
extract and before preservation. Matrix spikes should not be added prior
to 1312 extraction of the sample.
8.2.2 In most cases, matrix spike levels should be added at a
concentration equivalent to the corresponding regulatory level. If the
analyte concentration is less than one half the regulatory level, the
spike concentration may be as low as one half of the analyte
concentration, but may not be less than five times the method detection
limit. In order to avoid differences in matrix effects, the matrix spikes
must be added to the same nominal volume of 1312 extract as that which was
analyzed for the unspiked sample.
8.2.3 The purpose of the matrix spike is to monitor the
performance of the analytical methods used, and to determine whether
matrix interferences exist. Use of other internal calibration methods,
modification of the analytical methods, or use of alternate analytical
methods nay be needed to accurately measure the analyte concentration in
the 1312 extract when the recovery of the matrix spike is below the
expected analytical method performance.
8.2.4 Hatrix spike recoveries are calculated by the following
formula:
%R (% Recovery) « 100 (Xs - XJ / K
where :
Xs - measured value for the spiked sample
Xu = measured value for the unspiked sample, and
K - known value of the spike in the sample.
1312 - 18 Revision 0
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K « known value of the spike 1n the sample.
8.3 All quality control measures described 1n the appropriate analytical
methods shall be followed.
8.4 The use of internal calibration quantitation methods shall be
employed for a metallic contaminant if: (1) Recovery of the contaminant from the
1312 extract is not at least 50% and the concentration does not exceed the
appropriate regulatory level, and (2) The concentration of the contaminant
measured In the extract is within 20% of the appropriate regulatory level.
8.4.1. The method of standard additions shall be employed as the
internal calibration quantitation method for each metallic contaminant.
8.4.2 The method of standard additions requires preparing
calibration standards in the sample matrix rather than reagent water or
blank solution. It requires taking four identical aliquots of the
solution and adding known amounts of standard to three of these aliquots.
The forth aliquot is the unknown. Preferably, the first addition should
be prepared so that the resulting concentration is approximately 50% of
the expected concentration of the sample. The second and third additions
should be prepared so that the concentrations are approximately 100% and
150% of the expected concentration of the sample. All four aliquots are
maintained at the same final volume by adding reagent water or a blank
solution, and may need dilution adjustment to maintain the signals in the
linear range of the instrument technique. All four aliquots are analyzed.
8.4.3 Prepare a plot, or subject data to linear regression, of
instrument signals or external-calibration-derived concentrations as the
dependant variable (y-axis) versus concentrations of the additions of
standards as the independent variable (x-axis). Solve for the intercept
of the abscissa (the independent variable, x-axis) which is the concentra-
tion in the unknown.
8.4.4 Alternately, subtract the instrumental signal or external-
calibration-derived concentration of the unknown (unspiked) sample from
the instrumental signals or external-call brat ion-derived concentrations of
the standard additions. Plot or subject to linear regression of the
corrected instrument signals or external-callbration-derived concentra-
tions as the dependant variable versus the independent variable. Derive
concentrations for the unknowns using the internal calibration curve as if
it were an external calibration curve.
8.5 Samples must undergo 1312 extraction within the following time
periods:
1312 - 19 Revision 0
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SAMPLE MAXIMUM HOLDING TIMES fdavsl
Volatiles
Semi-
volatiles
Mercury
Metals,
except
mercury
From: Field
Collec-
tion
To: 1312
extrac-
tion
14
14
28
180
From: 1312
extrac-
tion
To: Prepara-
tive
extrac-
tion
NA
7
NA
NA
From: Prepara-
tive
extrac-
tion
To: determi-
native
analysis
14
40
28
180
Total
Elapsed
Time
28
61
56
360
NA - Not Applicable
If sample holding times are exceeded, the values obtained will be considered
minimal concentrations. Exceeding the holding time is not acceptable in
establishing that a waste does not exceed the regulatory level. Exceeding the
holding time will not invalidate characterization if the waste exceeds the
regulatory level.
9.0 METHOD PERFORMANCE
9.1 Precision results for semi-volatiles and metals: An eastern soil
with high organic content and a western soil with low organic content were used
for the semi-volatile and metal leaching experiments. Both types of soil were
analyzed prior to contaminant spiking. The results are shown in Table 6. The
concentrations of contaminants leached from the soils were consistently
reproducible, as shown by the low relative standard deviations (RSDs) of the
recoveries (generally less than 10 X for most of the compounds).
9.2 Precision results for volatiles: Four different soils were spiked
and tested for the extraction of volatiles. Soils One and Two were from western
and eastern Superfund sites. Soils Three and Four were mixtures of a western
soil with low organic content and two different municipal sludges. The results
are shown in Table 7. Extract concentrations of volatile organics from the
eastern soil were lower than from the western soil. Replicate leachings of Soils
Three and Four showed lower precision than the leachates from the Superfund
soils.
1312 - 20
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10.0 REFERENCES
1.0 Environmental Monitoring Systems Laboratory, "Performance Testing of
Method 1312; QA Support for RCRA Testing: Project Report". EPA/600/4-
89/022. EPA Contract 68-03-3249 to Lockheed Engineering and Sciences
Company, June 1989.
2.0 Research Triangle Institute, "Interlaboratory Comparison of Methods 1310,
1311, and 1312 for Lead in Soil". U.S. EPA Contract 68-01-7075, November
1988.
1312 - 21 Revision 0
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Table 1. Volatile Analytes1
Compound CAS No.
Acetone 67-64-1
Benzene 71-43-2
n-Butyl alcohol 71-36-3
Carbon disulflde 75-15-0
Carbon tetrachloride 56-23-5
Chlorobenzene 108-90-7
Chloroform 67-66-3
1,2-Dichloroethane 107-06-2
1,1-Dichloroethylene 75-35-4
Ethyl acetate 141-78-6
Ethyl benzene 100-41-4
Ethyl ether . 60-29-7
Isobutanol 78-83-1
Methanol .67-56-1
Methylene chloride 75-09-2
Methyl ethyl ketone 78-93-3
Methyl isobutyl ketone 108-10-1
Tetrachloroethylene 127-18-4
Toluene 108-88-3
1,1,1,-Trichloroethane 71-55-6
Trichloroethylene 79-01-6
Trichlorofluoronethane 75-69-4
l,l,2-Trichloro-l,2,2-trifluoroethane 76-13-1
Vinyl chloride 75-01-4
Xylene 1330-20-7
When testing for any or all of these analytes, the zero-headspace extractor
vessel shall be used instead of the bottle extractor.
1312 - 22 Revision 0
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Table 2. Suitable Rotary Agitation Apparatus1
Company
Location
Model No.
Analytical Testing and
Consulting Services,
Inc.
Associated Design and
Manufacturing Company
Environmental Machine and
.Design, Inc.
IRA Machine Shop and
Laboratory
Lars Lande Manufacturing
Hi Hi pore Corp.
Harrington, PA
(215) 343-4490
Alexandria, VA
(703) 549-5999
Lynchburg, VA
(804) 845-6424
Santurce, PR
(809) 752-4004
4-vessel extractor (DC20S);
8-vessel extractor (DC20);
12-vessel extractor (DC20B)
2-vessel
4-vessel
6-vessel
8-vessel
12-vessel
24-vessel
(3740-2);
(3740-4);
(3740-6);
(3740-8);
(3740-12);
(3740-24)
8-vessel (08-00-00)
4-vessel (04-00-00)
8-vessel (011001)
Whitmore Lake, MI 10-vessel (10VRE)
(313) 449-4116 6-vessel (5VRE)
Bedford, MA
(800) 225-3384
4-ZHE or
4 1-liter
bottle extractor
(YT300RAHW)
1 Any device that rotates the extraction vessel in an end-over-end fashion at 30
±2 rpm is acceptable.
1312 - 23
Revision 0
November 1992
-------
Table 3. Suitable Zero-Headspace Extractor Vessels1
Company Location Model No.
Analytical Testing & Harrington, PA C102, Mechanical
Consulting Services, Inc. (215) 343-4490 Pressure Device
Associated Design and Alexandria, VA 3745-ZHE, Gas
Manufacturing Company (703) 549-5999 Pressure Device
Lars Lande Manufacturing2 Whitmore Lake, MI ZHE-11, Gas
(313) 449-4116 Pressure Device
Millipore Corporation Bedford, MA YT30090HW, Gas
(800) 225-3384 Pressure Device
Environmental Machine Lynchburg, VA VOLA-TOX1, Gas
and Design, Inc. (804) 845-6424 Pressure Device
1 Any device that meets the specifications listed in Step 4.2.1 of the method is
suitable.
2 This device uses a 110 mm filter.
1312 - 24 Revision 0
November 1992
-------
Table 4. Suitable Filter Holders1
Company
Nucleopore Corporation
Micro Filtration
Systems
Millipore Corporation
Location
Pleasanton, CA
(800) 882-7711
Dublin, CA
(800) 334-7132
(415) 828-6010
Bedford, MA
(800) 225-3384
Model/
Catalogue #
425910
410400
302400
311400
YT30142HW
XX1004700
Size
142 mm
47 mm
142 mm
47 mm
142 mm
47 mm
1 Any device capable of separating the liquid from the solid phase of the waste
is suitable, providing that it is chemically compatible with the waste and the
constituents to be analyzed. Plastic devices (not listed above) may be used when
only inorganic analytes are of concern. The 142 mm size filter holder is
recommended.
Table 5. Suitable Filter Media1
Company
Millipore Corporation
Nucleopore Corporation
Whatman Laboratory
Products, Inc.
Micro Filtration
Systems
1 Any filter that meets the
Location Model
Bedford, MA AP40
(800) 225-3384
Pleasanton, CA 211625
(415) 463-2530
Clifton, NJ GFF
(201) 773-5800
Dublin, CA GF75
(800) 334-7132
(415) 828-6010
specifications in Step 4.4 of
1312 - 25
Pore
Size
(M"i)
0.7
0.7
0.7
0.7
the Method is suitable.
Revision 0
November 1992
-------
TABLE 6 - METHOD 1312 PRECISION RESULTS FOR SEHI-VOLATILES AND METALS
Eastern Soil (oH 4.2)
FORTIFIED ANALYTES
bis(2-chloroethyl)-
ether
2-Chlorophenol
1 ,4-Dichlorobenzene
1 , 2 -Dichlorobenzene
2-Methylphenol
Nitrobenzene
2,4- Dime thy 1 phenol
Hexaehlorobutadiene
Acenaphthene
2 , 4 - Dinitrophenol
2 ,4-Dinitro toluene
Hexachlorobenzene
gamma BHC (Lindane)
beta BHC
METALS
Lead
Cadmium
Amount
Spiked
(Mg)
1040
1620
2000
8920
3940
1010
1460
6300
3640
1300
1900
1840
7440
640
5000
1000
Amount
Recovered*
(Mg)
834
1010
344
1010
1860
812
200
95
210
896**
1150
3.7
230
35
70
387
% RSD
12.5
6.8
12.3
8.0
7.7
10.0
18.4
12.9
8.1
6.1
5.4
12.0
16.3
13.3
4.3
2.3
Western Soil foH 5.0)
Amount
Recovered*
(Mg)
616
525
272
1520
1130
457
18
280
310**
23**
565
10
1240
65.3
10 N
91
% RSD
14.2
54.9
34.6
28.4
32.6
21.3
87.6
22.8
7.7
15.7
54.4
173.2
55.2
51.7
51.7
71.3
* - Triplicate analyses.
** - Duplicate analyses; one value was rejected as an outlier at the 90%
confidence level using the Dixon Q test.
1312 - 26
Revision 0
November 1992
-------
TABLE 7 - METHOD 1312 PRECISION RESULTS FOR VOLATILE*
Soil
No,
, 1
(Western)
Avg.
Compound Nane
Acetone
Acrylonltrile
Benzene
n- Butyl Alcohol
(1-Butanol)
Carbon dlsulfi.de
Carbon tetrachloride
Chlorobenzene
Chloroform
1,2- Dichloroe thane
1 . 1- Dichloroe thane
Ethyl acetate
Ethylbenzene
Ethyl ether
Isobutanol (4 -Methyl
-1-propanol)
Methylene chloride
Methyl ethyl ketone
(2-Butanone)
Methyl isobutyl
ketone
1.1.1.2 -Tetrachloro-
e thane
1,1.2.2 -Tetrachloro-
e thane
Tecrachloroethene
Toluene
1.1.1-Trichloro-
e thane
1.1.2-Trlchloro-
e thane
Trichloroethene
Trichloro-
fluoronethane
1,1.2-Trlchloro-
tr if luoroe thane
Vinyl chloride
%Rec.
44.0
52.5
47.8
55.5
21.4
40.6
64.4
61.3
73.4
31.4
76.4
56.2
48.0
0.0
47.5
56.7
81.1
69.0
85.3
45.1
59.2
47.2
76.2
54.5
20.7
18.1
10.2
* %RSD
12
68
8
2
16
18
6
8
4
14
9
9
16
m
30
5
10
6
7
12
8
16
5
11
24
26
20
.4
.4
.29
.91
.4
.6
.76
.04
.59
.5
.65
.22
.4
.3
.94
.3
.73
.04
.7
.06
.0
.72
.1
.5
.7
.3
Soil
He,
2
(Eastern)
Avg.
%Rec.* %RSD
43.8
50.5
34.8
49.2
12.9
22.3
41.5
54.8
68.7
22.9
75.4
23.2
55.1
0.0
42.2
61.9
88.9
41.1
58.9
15.2
49.3
33.8
67.3
39.4
12.6
6.95
7.17
2
70
16
14
49
29
13
'16
11
39
4
11
9
ND
42
3
2
11
4
17
10
22
8
19
60
58
72
.25
.0
.3
.6
.5
.1
.1
.4
.3
.3
.02
.5
.72
.9
.94
.99
.3
.15
.4
.5
.8
.43
.5
.1
.0
.8
Soil No. 3
(Western and
Sludge)
Avg.
%Rec . *
116.0
49.3
49.8
65.5
36.5
36.2
44.2
61.8
58.3
32.0
23.0
37.5
37.3
61.8
52.0
73.7
58.3
50.8
64.0
26.2
45.7
40.7
61.7
38.8
28.5
21.5
25.0
» %RSD
11
44
36
37
51
41
32
29
33
54
119
36
31
37
37
31
32
31
25
44
35
40
28
40
34
67
61
.5
.9
.7
.2
.5
.4
.0
.1
.3
.4
.8
.1
.2
.7
.4
.3
.6
.5
.7
.0
.2
.6
.0
.9
.0
8
.0
Soil No. 4
(Western and
Sludge)
Avg.
%Rec.
21.3
51.8
33.4
73.0
21.3
24.0
33.0
45.8
41.2
16.8
11.0
27.2
42.0
76.0
37.3
40.6
39.8
36.8
53.6
18.6
31.4
26.2
46,4
25.6
19.8
15.3
11.8
*** %RSD
71.4
4.6
41.1
13.9
31.5
34.0
24.9
38.6
37.8
26.4
115.5
28.6
17.6
12.2
16.6
39.0
40.3
23.8
15.8
24.2
37.2
38.8
25.4
34.1
33.9
24.8
25.4
* Triplicate analyses
** Six replicate analyses
*** Five replicate analyses
1312 - 27
Revision 0
November 1992
-------
Motor
(30± 2rpm
Extraction Vessel Holder
ULJUU
Figure 1. Rotary Agitation Apparatus
1312 - 28
Revision 0
November 1992
-------
Liquid Inlet/Outlet Valve
Top Flange
Support Screen
Filter
Support Screen
Vrton O-Rings
Bottom Flange
Pressurized Gas
Inlet/Outlet Valve
Pressure
Gauge
Figure 2. Zero-Headspace Extractor (ZHE)
1312 - 29
Revision 0
November 199
-------
METHOD 1312
SYNTHETIC PRECIPITATION LEACHING PROCEDURE
> 3 A*»MbU ftlUr
holder, weigh out
• ubaaaiple. alloe
•olidl to cattle:
transfer lubaaeele
to fill.r h«U«r:
fillet. d*l«rBin« A
••Ud*
3-6 B*
•tlh
' 4 Dry filter and
•olid ph«t«. record
•eight, calculate k
dry >olid>
7 4 S B«g&n again
• ith nm (ubianpl*
No
744 Oiaeard
•olid and l&quid
pha*». »ill u»
n«« liquid pha«» •
oatract
1312 - 30
Revision 0
Noventer 1992
-------
METHOD 1312
SYNTHETIC PRECIPITATION LEACHING PROCEDURE (continued)
' i 2
•!!•••
1 »ll». >*U«
• ( *M*U t«
:• »n«r I*
1312 - 31
Revision 0
Noventer 1992
-------
METHOD 1312
SYNTHETIC PRECIPITATION LEACHING PROCEDURE (continued)
> t 2 2
Qu*nt»tAtivalf
lr«n«f«r
• a»r*»nala M*unl
of •*•»!• I* fill*'
h»ltf*r. «•»!?
pr««»«r« I* fitter
until U«uid fl«»
e»a>a>
» t 2 1
fillral*.
UUrat*
alar* wttl
••tract an
Haiah
A*« ar
alfaia
•f ••traet&*n
t* •tlr*ct*r.
••tract C»r II
1312 - 32
Revision 0
November 1992
-------
METHOD 1312
SYNTHETIC PRECIPITATION LEACHING PROCEDURE (continued)
i 6 S Fii '.ered
itenai i« defined
at extract
7 6 5 filtered
liquid frsoi St«p>
7 6 ' and '623
«r« t«fin«d ••
••tract
766 76'
Record pH of
•ilracl. pr«»«rv«.
analyse by
appropriate ••thadf
7 6 B Compare
centaaAnant
concentration! in
••tract to
appropriate
•.hre»hold»
STOP
1312 - 33
Revision 0
November 1992
-------
METHOD 1312
SYNTHETIC PRECIPITATION LEACHING PROCEDURE (continued)
•*••!• *t*«
2HC
>0 Sft
' 7 4 r>ltr«l« »
defined •• ••tract
lion
1 H..*h
» t C»«l
tnd
partiel* t&s*
•ttheut f«n»r«tinf
hMl
' 7 J Do net
evntrtfiM* •••l«
pr»*r to Iiltrat&vn
1312 - 34
Revision 0
November 1992
-------
METHOD 1312
SYNTHETIC PRECIPITATION LEACHING PROCEDURE (continued)
> ' 3 Tr.o.r.t
•••pi* to :NE
! ' 1 Attach
.nor apply
ivr> ufiUi
flo» eeaaea
? 10 Fi;ir«t« L>
«fm«o •» ••tract
•eieM a(
••traetian fluid
to add lo 2HC
' ' 13 Puap
•traeti«A fluid
into IK
1 1 14 *•»•«• T
h«ad>oa««.
r«pt«>yril. ZHC
> ' IS Rotate ZKC
for 18 Koyra at 22C
No
procedure with r.a«
' ' It Separate
' is
•»pr
lh*di
r«aullt if
7 7 It Crapar*
e*nt«ainant
eone«ntr«ii*R« ID
appropriate
STOP
1312 - 35
Revision 0
Noventer 1992
-------
APPENDIX I
DERIVATION OF SAMPLE SIZES
-------
REVIEW DRAFT - DO NOT CITE OR QUOTE - November, 1994
This appendix presents a technical explanation of the statistical procedures used in the site-specific
sampling strategy.
Let Y be a lognonnally distributed random variable with mean p and variance T2. If X = ln(Y), then
X is normally distributed with a mean of p and a variance of Po = 2SSL vs. Ha: p < p0
with a Type I error rate of a = 0.05 and a Type II error rate of P = 0.20 at one-half the SSL (pj = 0.5
SSL).
Simple Random Sampling
A test of the above hypotheses based on the upper 95% confidence interval for p described in Gilbert
(1987) pages 169-171 was used. However, while this confidence interval controls the Type I error rate
at 0.05, it will not control the Type II error rate unless the proper sample size is specified. Therefore,
simulations were performed to identify the sample size necessary to control the Type n error rate to
0.20 at 0.5 times the SSL. As long as the ratio p0/pl is fixed, the procedure depends only on a. For
different sample sizes and differing values of a, simulated sampling from lognonnal distributions was
used to estimate Type I and Type II error rates. For each sample size and value of o, 1000 simulations
were used. The final sample size was then selected as the smallest sample size, n, which achieved a
power of 0.8 (i.e., a Type n error rate of 0.2) at 0.5 times the SSL.
Composite Simple Random Sampling
A composite of m random samples from the site can be viewed as a physical average with the same
statistical properties as a statistical average. Thus, a composite sample produced by combining m
individual random samples would have a mean p and a variance of T2/m. In addition, the distribution
of this composite would be unimodal, skewed and have a long right tail for small values of m.
Although the composite is not exactly lognormal, it may be approximated by a lognonnal distribution
with a mean p and a variance of T?/m. Therefore, the method described in Gilbert (1987) and in
section 4.3 may be used to test the above hypotheses when composite samples are collected and each
composite is viewed as a random sample from a lognormal distribution.
1-3
-------
REVIEW DRAFT > DO NOT CITE OR QUOTE - November, 1994
To do this, it is necessary to derive the underlying normal parameters p' and a7 which are functions of
p and o and have the following properties:
p = p', and T72 = T?/m.
where m is the number of aliquots within a composite. Now,
i72 = p/2[e°'2-1] .= pV2-1].
This shows that the transformation of the variance may be accomplished solely by altering the a
parameter.
Assume that o'=da for some value of d. Then
=> a7 = da ••
Now, the a-transform must be accompanied by a corresponding p-transform, in such a way that the
lognormal mean is unchanged. Therefore, it is necessary that.
= ln(p) -
It is easy to check that for PQ, ulf o being the parameters governing the null and alternative hypothesis
of interest, i.e.,
, and Pl =
the transformations p-»p' and o-xy' defined above preserve p0 and pl and have the desired effect on
the variances.
Simulations were performed to verify that this method performs as desired. These simulation studies
were performed over a range of site standard deviations (in natural logarithmic units) between 0.5 and
2.0, for either 4 and 10 samples per composite, and for measurement error coefficients of variation of
either 10% or 25%. For each case, 1000 simulations per condition were used to estimate the Type I
error rate and the power against the alternative of interest (.5SSL). The compositing plan sample sizes
calculated by the method above were found to be conservative in all cases, i.e., they controlled the
Type n error rate to 0.2 at .5SSL, when a lognormal distribution over the site is assumed. However,
1-4
-------
REVIEW DRAFT - DO NOT CITE OR QUOTE - November, 1994
in order to obtain a coverage probability of 0.95, it is necessary to adjust the estimate of the standard
deviation in the confidence interval by multiplying by a factor of 1.12 (in most cases). With this
adjustment, the compositing plan sample sizes control error rates at the desired level.
The simulations show that one may transform the estimate of o by:
where OA represents the standard deviation of analytic error and m is the number of aliquots within a
composite sample. Then a7 can then be used in Table 4-2 to derive the number of composites
necessary to achieve the Type I and Type II error rates.
T-5
-------
APPENDIX J
Koc VALUES FOR IONIZING ORGANICS
AS A FUNCTION OF pH
-------
Appendix J. Kge Values for Ionizing Organlcs as a Function of pH
K pH pKa
Benzole Acid
32
32
32
32
32
32
32
32
AA
*Jc
32
OH
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
32
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
5 7
0. 1
5.8
c n
5.9
6.0
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.0
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
A 9
*\.£.
4.2
A O
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
4.2
0.1663
0.1368
0.1118
0.0909
0.0736
0.0594
0.0477
0.0383
00107
U.l/OUr
0.0245
n nine
0.0196
0.0156
0.0124
0.0099
0.0079
0.0063
0.0050
0.0040
0.0032
0.0025
0.0020
0.0016
0.0013
0.0010
0.0008
0.0006
0.0005
0.0004
0.0003
0.0003
0.0002
0.0002
1
1
1
1
1
1
1
1
-
'
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5.74
4.81
4.02
3.36
2.82
2.37
2.00
1.71
1 47
1 •" •
1.27
140
.12
0.99
•
0.89
0.81
0.75
0.70
0.66
0.62
0.60
0.58
0.56
0.55
0.54
0.53
0.53
0.52
0.52
0.51
0.51
0.51
0.51
0.50
0.76
0.68
0.60
0.53
0.45
0.37
0.30
0.23
0 17
V, 1 I
0.10
n AC
0.05
0.00
•0.05
•0.09
•0.13
•0.16
•0.18
-0.20
•0.22
-0.24
•0.25
-0.26
•0.27
•0.27
•0.28
•0.28
-0.29
•0.29
•0.29
-0.29
-0.30
•0.30
Benzole Acid
n An
U.OU i
n An - - \
U.OU V
\
n An \
U.4U V
J \
g, 0.20 N^
nrm - - . x
u.uu N/
^Ssv>^
^ ->
n AC\ \ I i i ' ; f ' -• i • I i • i t 3 : ? I ; - • ! ; i ,
<>cor-x,-mocor^
TJioio-or
-------
Appendix J. K0. Values for Ionizing Organlcs as a Function of pH
pH pKa
K,,
2,4-Dichlorophenol
159
159
159
159
4 ff\
159
159
159
4 C A
159
159
159
4 Cft
159
159
159
ICQ
159 .
159
159
•ICQ
* 1 \K7
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
4.9
5.0
5.1
5.2
C 1
5.3
5.4
5.5
C C
5.6
5.7
5.8
c a
3.9
6.0
6.1
C O
0.&
6.3
6.4
fi 5
O.u
6.6
6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.0
7.85
7.85
7.85
7.85
7QC
.85
7.85
7.85
7OC
.85
7.85
7.85
7 OK
.03
7.85
7.85
7 Q£
f .00
7.85
7.85
7 HI
1 *Uw
7.85
7.85
7.85
7.85
7.85
7.85
7.85
7.85
7.85
7.85
7.85
7.85
7.85
7.85
7.85
0.9989
0.9986
0.9982
0.9978
A firvyn
0.9972
0.9965
0.9956
A finA A
0.9944
0.9930
0.9912
A noon
U.M003
0.9861
0.9825
n O7Q1
U.v7/OI
0.9726
0.9657
OQR79
V,&\JI &
0.9468
0.9339
0.9182
0.8991
0.8762
0.8490
0.8171
0.7801
0.7381
0.6912
0.6401
0.5855
0.5288
0.4712
0.4145
2
2
2
2
2
2
2
2
2
2
2
2
2
b
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
158.82
158.78
158.72
158.65
4 CO CO
ibo.bo
158.45
156.30
4 CD 43
15o.1l
157.90
157.62
1^7 Oft
I0/ .tO
156.82
156.26
4CC £7
133. Of
154.71
153.63
15230
1 WfciVV
150.66
148.65
146.19
143.20
139.62
135.36
130.35
124.57
117.99
110.65
102.63
94.09
85.20
76.20
67.31
2.20
2.20
2.20
2.20
n on
t.dU
2.20
2.20
o on
£.£V
2.20
2.20
o on
c.cU
2.20
2.19
21Q
• 19
2.19
2.19
218
fft 1 V
2.18
2.17
.2.16
2.16
2.14
2.13
2.12
2.10
2.07
2.04
2.01
1.97
1.93
1.88
1.83
2 on
.OU
220 -
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PH
-------
Appendix J. K.,. Values for Ionizing Organlcs as a Function of pH
1C, pH pKa
Pentachlorophenol
Lft
19953
19953
19953
19953
19953
19953
19953
19953
19953
4AACO.
19953
i y"oo
19953
1 VVWW
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
19953
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
C T
5.7
C Q
5.8
C Q
o.y
R n
O.U
61
V« 1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.0
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
A T A
4.74
4-JA
.74
4TA
.ll
A -]A
H. / 1
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
4.74
0.4089
0.3546
0.3039
0.2575
0.2159
0.1795
0.1481
0.1213
n nnoo
0.0988
A ADA1
(J.UoUl
'
UtUuc 1
0.0418
0.0335
0.0268
0.0214
0.0171
0.0136
0.0108
0.0086
0.0069
0.0055
0.0043
0.0035
0,0027
0.0022
0.0017
0.0014
0.0011
0.0009
0.0007
0.0005
398
398
398
398
398
398
398
398
ono
398
OQQ
o9o
O9O
398
398
398
398
398
398
398
398
398
398
398
398
398
398
398
398
398
398
398
398
8394.52
7333.13
6340.20
5432.72
4620.83
3908.22
3293.15
2769.92
ooon on
2330.29
4QCA 71
19o4.n
'
m iv. Ut>
1215.90
1053.32
922.15
816.66
732.02
664.26
610.09
566.85
532.36
504.88
482.99
465.57
451.71
440.69
431.92
424.96
419.42
415.02
411.52
408.74
3.92
3.87
3.80
3.74
3.66
3.59
3.52
3.44
n n*i
3.37
q on
O.c9
'
O. 1 O
3.08
3.02
2.96
2.91
2.86
2.82
2.79
2.75
2.73
2.70
2.68
2.67
2.65
2.64
2.64
2.63
2.62
2.62
2.61
2.61
Pentachlorophenol
4.UU
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O(Niooo--^i(^ocr>-oo
^ioiou50
-------
Appendix J. K^ Values for Ionizing Organlcs as a Function of pH
KMn pH pKa
2,4,5-Trlchlorophenol
o\
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
2380
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6.0
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.0
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
6.94
0.9910
0.9886
0.9858
0.9821
0.9776
0.9720
0.9650
0.9563
0.9456
0.9324
0.9164
0.8970
0.8737
0.8460
0.8136
0.7762
0.7336
0.6863
0.6347
0.5799
0.5230
0.4655
0.4089
0.3546
0.3039
0.2575
0.2159
0.1795
0.1481
0.1213
0.0988
0.0801
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
2358.81
2353.39
2346.60
2338.11
2327.50
2314.29
2297.87
2277.54
2252.45
2221.66
2184.08
2138.58
2083.96
2019.11
1943.09
1855.28
1755.61
1644.66
1523.80
1395.24
1261.89
1127.12
994.46
867.23
748.20
639.42
542.10
456.68
382.95
320.23
267.53
223.71
3.37
3.37
3.37
3.37
3.37
3.36
3.36
3.36.
3.35
3.35
3.34
3.33
3.32
3.31
3.29
3.27
3.24
3.22
3.18
3.14
3.10
3.05
3.00
2.94
2.87
2.81
2.73
2.66
2.58
2.51
2.43
2.35
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^CMiOoO-— ^r^OcO^OO*
jtoioioo^o-or^r^r^r^
PH
-------
Appendix J. Koc Values for Ionizing Organlcs as a Function of pH
K«n pH pKa
2,4,6-Trichlorophenol
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
1070
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6.0
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.0
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
6.15
0.9468
0.9339
0.9182
0.8991
0.8762
0.8490
0.8171
0.7801
0.7381
0.6912
0.6401
0.5855
0.5288
0.4712
0.4145
0.3599
0.3088
0.2619
0.2199
0.1829
0.1510
0.1238
0.1009
0.0818
0.0661
0.0532
0.0428
0.0343
0.0274
0.0219
0.0175
0.0139
107
107
107
107
107
107
107
107
107
107
107
107
107
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
1018.73
1006.33
991.20
972.85
950.81
924.61
893.85
858.26
817.80
772.66
723.38
670.84
616.19
560.81
506.75
454.90
406.41
362.15
322.64
288.06
258.34
233.20
212.24
194.99
180.94
169.63
160.62
153.52
147.99
143.74
140.53
138.16
3.01
3.00
3.00
2.99
2.98
2.97
2.95
2.93
2.91
2.89
2.86
2.83
2.79
2.75
2.70
2.66
2.61
2.56
2.51
2.46
2.41
2.37
2.33
2.29
2.26
2.23
2.21
2.19
2.17
2.16
2.15
2.14
325
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