Vapor Intrusion Modeling: Limitations, Improvements,
and Value of Information Analyses
by
JESSICA M. FRISCIA
B.E. Civil Engineering
The Cooper Union for the Advancement of Science and Art, 2011
Submitted to the Department of Civil and Environmental Engineering in Partial Fulfillment of the
Requirements for the Degree of
MASTER OF ENGINEERING IN CIVIL AND ENVIRONMENTAL ENGINEERING
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
AC HJTEHNSOGYl
June 2014
C 2014 Jessica M. Friscia. All rights reserved.
-r~J~
FIBRA R IE S
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic
copies of this thesis document in whole or in part in any medium now known or hereafter created.
Signature of Author:
Signature redacted_
Department of Civil and Environmental Engineering
May 9, 2014
Certified by: _
Signature redacted
David E. Langseth
Senior Lecturer of Civil and Enviropnental Engieering
Accepted by:
Signature redacted
Heidi
. Nepf
Chair, Departmental Committee for Graduate Students
Vapor Intrusion Modeling: Limitations, Improvements, and Value of Information Analyses
by
Jessica M. Friscia
Submitted to the Department of Civil and Environmental Engineering on May 9, 2014, in Partial
Fulfillment of the Requirements for the Degree of Master of Engineering in Civil and
Environmental Engineering
Abstract
Vapor intrusion is the migration of volatile organic compounds (VOCs) from a subsurface source
into the indoor air of an overlying building. Vapor intrusion models, including the Johnson and
Ettinger (J&E) model, can be used to predict the concentration of VOCs in the indoor air of a
building based on a measured subsurface soil gas concentration or contaminant source
concentrations, either in non-aqueous phase liquid (NAPL), groundwater, or soil. An analysis of
two of the EPA-implemented J&E spreadsheet models, one that considers subsurface soil gas
data and one that considers groundwater data, was conducted. The governing equations,
assumptions, and limitations of these spreadsheet models were investigated. A value of
information (Vol) worksheet was developed that can assist practitioners in deciding what
additional data to collect as part of a remedial investigation. The Vol worksheet calculates how
varying values of model input parameters affect the model-predicted indoor air carcinogenic risk.
The worksheet then compares the user-defined target risk to the range of potential risk values for
different combinations of varying parameters. The results of this analysis allow the user to
determine which groups of parameters have the most impact on the model results. This
information can assist the practitioner in deciding whether or not to collect additional data to
reduce the uncertainty in the input parameters.
The EPA J&E soil gas and groundwater spreadsheet models, as well as the Vol worksheet
developed for each model, were applied to case study data for a trichloroethylene-impacted site
in Rhode Island. The results of the J&E model and Vol worksheet analyses for this case study
predicted incremental carcinogenic risk values for trichloroethylene (TCE) below the risk value
calculated based on measured indoor air data. This comparison suggests the potential for other
sources of TCE within the building. Groups of parameters were identified for each model that
impacted the model-predicted carcinogenic risk. The development of a cost-benefit analysis,
which would be used to quantify the value of obtaining additional data for these critical
parameters, is recommended for future research.
Thesis Supervisor: David E. Langseth
Title: Senior Lecturer of Civil and Environmental Engineering
3
Acknowledgements
I owe thanks to many people for their substantial contributions throughout this process. First and
foremost, I would like to thank my adviser, Dr. David Langseth, for his hard work, guidance, and
insight throughout this endeavor. I would also like to thank Dr. Atul Salhotra for proposing this
topic and for all of his feedback.
This study would not have been possible without the training and advice that was provided to me
by Igor Linkov and Matthew Bates of the United States Army Corps of Engineers. Furthermore,
I am grateful to Daniel Groher, Katherine Malinowski, and Cynthia Colquitt of the Army Corps
of Engineers for providing me with the site data and information needed for the case study.
I would like to thank my partner, Joe Corsello, for his assistance and teamwork. I would also like
to thank Dr. Eric Adams, Lauren McLean, Kiley Clapper, and the entire Master of Engineering
community for an unforgettable year. I am grateful to all of my fellow MEng students for
making this experience so rewarding and enjoyable, both in and out of the classroom.
Finally, I would like to thank my parents, Albert Watkins, my entire family, and all of my friends
in Kings County for their support and encouragement.
5
Contents
Abstract ...........................................................................................................................................
A cknowledgem ents.........................................................................................................................
Contents ..........................................................................................................................................
List of Figures .................................................................................................................................
List of Tables ................................................................................................................................
Abbreviations and Units ...............................................................................................................
1. Introduction...............................................................................................................................
1.1 Background .........................................................................................................................
1.2 Project Purpose and Scope................................................................................................
1.3 Project Approach ................................................................................................................
1.4 Overview of Existing Vapor Intrusion Models...................................................................
1.4.1 Johnson and Ettinger (J& E) M odel...........................................................................
1.4.2 Biodegradation of PHCs and the BioV apor M odel ..................................................
1.5 Introduction to V alue of Inform ation..............................................................................
2. EPA J&E Models: Governing Equations, Assumptions, and Limitations ..............
2.1 Groundwater Source M odel.............................................................................................
2.2 Soil Gas M odel ...................................................................................................................
2.3 M odel A ssum ptions and Lim itations ...............................................................................
3. V alue of Inform ation Analysis..............................................................................................
3.1 Param eter Selection and Relevant Values ......................................................................
3.2 Spreadsheet Operation and Model Results - Groundwater Source Model......................
3.3 Spreadsheet Operation and M odel Results - Soil Gas M odel.........................................
4. Case Study - Form er Nike Battery PR-58 .............................................................................
4.1 Site History and Operations.............................................................................................
4.2 Potential Historic Releases and Source Area..................................................................
4.3 Conceptual M odel and the DPW Facility ...........................................................................
4.4 Soil Gas J& E and Vol Analysis.......................................................................................
4.4.1 Soil Gas Vol Analysis, U sing the 2003 Toxicity V alues ........................................
4.4.2 Soil Gas Vol Analysis, U sing the 2014 Toxicity V alues ........................................
4.5 Groundwater J& E and Vol Analysis ...............................................................................
4.6 Comparison of the Groundwater and Soil Gas Model Results.......................................
5. Conclusions and Opportunities for Future Research .............................................................
6. References.................................................................................................................................
Appendix A: Detailed Equations and Structure of Vol Worksheets ........................................
Appendix B: N ike Battery PR-58 Data.......................................................................................
B-1: Data from the 2014 Draft Stone Environm ental RI/FS ..................................................
B-2: Data from the 2012 USACE Vapor Intrusion Investigation...................
Appendix C: Case Study J& E and Vol Analysis Input Data......................................................
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List of Figures
Figure 1: Vapor Intrusion Conceptual Model Proposed by Johnson and Ettinger (1991)......... 17
Figure 2: Vapor Intrusion Conceptual Model with Biodegradation Proposed by DeVaull (2007)
...............................................................................................................................................
20
Figure 3: Conceptual Model for Groundwater Source Contamination (EQM 2004) .........
24
Figure 4: SCS Soil Triangle with Centroids for Each Textural Class (Nielson and Rogers 1990)
...............................................................................................................................................
27
Figure 5: Conceptual Simplification in EPA J&E Model of van Genuchten Profile (Hers 2003)36
Figure 6: Example Building and Soil Properties Investigated in Vol Spreadsheet .................. 43
Figure 7: Example of User-Defined Cells in Vol Spreadsheet.................................................
44
Figure 8: Example of Random Probability Generator in Vol Worksheet .................................
44
Figure 9: Cumulative Distribution Function of the Pressure Differential .................................
46
Figure 10: Example of Random Parameter Value Generator for Toggle Cell "0110111111" in
V ol W orksheet ......................................................................................................................
47
Figure 11: Example Results for Toggle Cell "0110111111" in Vol Worksheet ......................
48
Figure 12: Example Results Table for 5,000 Simulations for Toggle Cell "0110111111" in Vol
Work sheet .............................................................................................................................
48
Figure 13: Example of Ranking of Toggle Cell Values by Percent of Simulations with Change in
Carcinogenic Hazard Classification in Vol Worksheet ...................................................
49
Figure 14: Example of Ranking of Toggle Cell Values with the Same Number of Experiments 50
Figure 15: Form er Nike Battery PR-58 Location ......................................................................
51
Figure 16: Location of DPW Building (Yellow) and Former Nike Battery PR-58 (Red)...... 56
Figure 17: Soil Gas Vol Analysis Results - Percentage of Simulations with Change in
Carcinogenic Risk Classification versus the Number of Experiments for TCE for Target
Risk of 10-6 and Model-Provided Toxicity Values ..........................................................
63
Figure 18: Frequency of Incremental Carcinogenic Risk (x 10-6) for 5,000 Simulations, as
Compared to Target Risk and Default Value, for Varying Pressure Differential Values and
Model-Provided Toxicity V alues......................................................................................
65
Figure 19: Frequency of Carcinogenic Risk (x 10-6) for 5,000 Simulations, as Compared to
Target Risk and Default Value, for Varying Pressure Differential, Porosity, and Water-filled
Porosity Values and Model-Provided Toxicity Values .....................................................
66
Figure 20: Ranges of Estimated Carcinogenic Risk Values for Eight Combinations of Parameters
- Soil Gas Vol Analysis Results for TCE Using Model-Provided Toxicity Values ......
68
Figure 21: Groundwater Source Vol Analysis Results - Percentage of Simulations with Change
in Carcinogenic Risk Classification versus the Number of Experiments for TCE for Target
Risk of 10-6 and Updated Toxicity Values........................................................................
75
Figure 22: Ranges of Estimated Carcinogenic Risk Values for Eight Combinations of Parameters
- Groundwater Source Vol Analysis Results for TCE Using Updated Toxicity Values ..... 76
Figure 23: Decision Tree for Groundwater Vol Analysis of TCE.............................................
78
Figure A - 1: User-Defined Table in Groundwater Vol Worksheet ..........................................
91
Figure A - 2: Parameter Ranges and Distributions Table - Groundwater Vol Worksheet ..... 92
Figure A - 3: Pressure Differential Range Equations ..............................................................
92
Figure A - 4: Particle Diameter Range Equations .....................................................................
93
Figure A - 5: Soil Stratum A Parameter Range Equations ........................................................
93
Figure A - 6: Perm eability Range Equations ............................................................................
93
Figure A - 7: Soil Strata B and C Parameter Range Equations .................................................
94
8
Figure A - 8: Random Number Generator Equations ...................................................................
Figure A - 9: Default and Random Parameter Value Generator Equations...............
Figure A - 10: Toggle Cell Equations......................................................................................
Figure A - 11: Simulated Pressure Differential Equations ........................................................
Figure A - 12: Simulated Hydraulic Conductivity or Permeability Equations ..........................
Figure A - 13: Simulated Groundwater Concentration, Particle Diameter, and Soil Stratum A
P aram eter Equations .............................................................................................................
Figure A - 14: Simulated Strata B and C Parameter Equations...............................................
Figure A - 15: Chemical Properties Equations ..........................................................................
Figure A - 16: Diffusion Path Length Equation.........................................................................
Figure A - 17: Air-Filled Porosity Equations ..........................................................................
Figure A - 18: Saturation and Permeability Equations .............................................................
Figure A - 19: Capillary Zone Equations..................................................................................
Figure A - 20: Building Parameter Equations...........................................................................
Figure A - 21: Henry's Constant Equations ...............................................................................
Figure A - 22: Diffusion Coefficient Equations ........................................................................
Figure A - 23: Effective Diffusion Coefficient Equation ........................................................
Figure A - 24: Source Soil Gas Concentration Equation...........................................................
Figure A - 25: Building Parameter and Peclet Number Equations.............................................
Figure A - 26: Toxicity V alue Equations....................................................................................
Figure A - 27: Attenuation Factor and Indoor Air Concentration Equations .............................
Figure A - 28: Risk A ssessm ent Equations.................................................................................
Figure A - 29: Q uality Control Equations...................................................................................
Figure A - 30: V ol Evaluation Equations ...................................................................................
Figure A - 31: Simulated Incremental Carcinogenic Risk Distribution Equations ....................
Figure A - 32: H istogram Equations...........................................................................................
Figure A - 33: Target and Default Value Equations ...................................................................
Figure A - 34: Toggle Cell Values and Number of Experiments Table .....................................
Figure A - 35: Toggle Cell and Percent Change Values Data Table..........................................
Figure A - 36: Possible Toggle Cell V alues ...............................................................................
Figure A - 37: Possible Combinations in Vol Worksheet ..........................................................
Figure A - 38: Ranking of Toggle Cells Equations ....................................................................
Figure A - 39: Ranking of Toggle Cells According to the Number of Experiments..................
Figure A - 40: User-Defined Table in Soil Gas Vol Worksheet.................................................
Figure A - 41: Parameter Ranges and Distributions Table - Soil Gas Vol Worksheet..............
Figure A - 42: Default Soil Gas Concentration Equation...........................................................
Figure A - 43: Simulated Soil Gas Concentration Equations .....................................................
Figure A - 44: Source Soil Gas Concentration Equation............................................................
Figure A - 45: Effective Diffusion Coefficient Equation - Soil Gas VOICALCS Worksheet
Figure A - 46: Possible Toggle Cell Values - Soil Gas VOICALCS Worksheet ....................
Figure C - 1: Groundwater DATENTER Worksheet - Target Risk of 10-6................................
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Figure C - 2: Groundwater DATENTER Worksheet - Target Risk of 104................................
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Figure C - 3: Groundwater Vol Worksheet Input Using Model-Provided Toxicity Values ...... 124
Figure C - 4: Groundwater Vol Worksheet Input Using Updated Toxicity Values ................... 124
125
Figure C - 5: Soil Gas DATENTER Worksheet .........................................................................
9
Figure C - 6: Soil Gas Vol Worksheet Using Target Risk of 10-6 and Model-Provided Toxicity
V alues .................................................................................................................................
12 6
Figure C - 7: Soil Gas Vol Worksheet Using Target Risk of 10~4 and Model-Provided Toxicity
V alues .................................................................................................................................
12 6
Figure C - 8: Soil Gas Vol Worksheet Using Target Risk of 10-6 and Updated Toxicity Values
.............................................................................................................................................
12 7
Figure C - 9: Soil Gas Vol Worksheet Using Target Risk of 1 0 -4and Updated Toxicity Values
.............................................................................................................................................
12 7
List of Tables
Table 1: Order of Parameters Included in Vol Analysis Worksheet for a Groundwater Source.. 45
Table 2: Noncarcinogenic Hazard Quotient and Incremental Cancer Risk Values for CHCs at
DP W F acility ........................................................................................................................
59
Table 3: Comparison of RfC and URF Values ..........................................................................
59
Table 4: Revised Noncarcinogenic Hazard Quotient and Cancer Risk Values for CHCs Using
2014 Toxicity V alues........................................................................................................
60
Table 5: EPA J&E Spreadsheet Model Results for PCE and TCE for Soil Gas ....................... 61
Table 6: Soil Gas Vol Analysis Results for TCE for a Target Risk of 10-6 and Model-Provided
T oxicity V alues.....................................................................................................................
63
Table 7: Soil Gas Vol Analysis Results for TCE for a Target Risk of 10-4 and Model-Provided
T oxicity V alues.....................................................................................................................
67
Table 8: Soil Gas Vol Analysis Results for TCE for a Target Risk of 10-6 and Updated Toxicity
V alu es ...................................................................................................................................
69
Table 9: Soil Gas Vol Analysis Results for TCE for Four Toxicity/Risk Scenarios................. 71
Table 10: EPA J&E Spreadsheet Model Results for PCE and TCE for a Groundwater Source.. 72
Table 11: Groundwater Source Vol Analysis Results for TCE for Four Toxicity/Risk Scenarios
...............................................................................................................................................
73
Table 12: Groundwater Source Vol Analysis Results for TCE for a Target Risk of 10-6 and
U pdated Toxicity V alues ...................................................................................................
74
Table B - 1 - 1: Shallow Groundwater Analytical Results at DPW Facility ..............
114
Table B - 2 - 1: Sub-Slab Soil Vapor Analytical Results of Detected Chemicals at DPW Facility
.............................................................................................................................................
118
Table B - 2 - 2: Indoor Air Analytical Results of Detected Chemicals at DPW Facility .....
119
Table B - 2 - 3: Chronic Human Health Risk Estimates Using January 11, 2011 PR-58 Indoor Air
Samples at D PW .................................................................................................................
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10
Abbreviations and Units
AST
ATc
ATNC
atm-m 3/mol
cal/mol
Cbuilding
cm
cm 2/s
CERCLA
Cg
CHCs
CSM
C,
DANC
Deft
DPW
ED
EF
EPA
ft
FS
g/cm-s
GW-ADV
GW-SCREEN
h
HQ
IRIS
J&E
k
K
LEL
MCL
n
NA
Attenuation factor
Aboveground storage tank
Averaging time for carcinogens
Averaging time for noncarcinogens
Atmosphere-cubic meter per mol
Calories per mol
Vapor concentration in the building
Centimeter
Square centimeters per second
Comprehensive Environmental Response, Compensation, and Liability
Act
Concentration of the contaminant in the soil gas
Chlorinated hydrocarbons
Conceptual site model
Concentration of the contaminant in the groundwater
Decontamination agent, non-corrosive
Effective diffusion coefficient for soil stratum i
Department of Public Works
Exposure duration
Exposure frequency
United States Environmental Protection Agency
Foot
Feasibility study
Grams per centimeter-second
Advanced groundwater J&E EPA spreadsheet model
Screening groundwater J&E EPA spreadsheet model
Hour
Hazard quotient
Integrated Risk Information System
Johnson and Ettinger
Permeability
Kelvin (unit of temperature) or hydraulic conductivity
Lower explosion limit
Maximum Contaminant Level
Porosity
Natural attenuation
11
NAPL
NCBC
NPL
AP
Pa
PCE
PCI
PHCs
ppmv
RBC
RCRA
RIEDC
RfC
RI
SG-ADV
SG-SCREEN
SVOCs
TCE
TeCA
THQ
TR
pg/L
pg/m3
URF
USACE
UST
GSA
VI
VOCs
Vol
yr
Ow
Non-aqueous phase liquid
Naval Construction Battalion Center
National Priority List
Pressure differential
Pascal
Perchloroethene
Peabody Clean Industries
Petroleum hydrocarbons
Parts per million by volume
Risk Based Concentration
Resource Recovery and Conservation Act
Rhode Island Economic Development Corporation
Reference concentration
Remedial investigation
Advanced J&E EPA spreadsheet model for a soil gas sample
Screening J&E EPA spreadsheet model for a soil gas sample
Semi-volatile organic compounds
Tricholoroethene
Tetrachloroethane
Target hazard quotient
Task risk level
Micrograms per liter
Micrograms per cubic meter
Unit risk factor
United States Army Corps of Engineers
Underground storage tank
United States General Services Agency
Vapor intrusion
Volatile organic compounds
Value of information
Year
Water-filled porosity
12
1. Introduction
1.1 Background
Vapor intrusion is the migration of volatile organic compounds (VOCs) from a subsurface source
into the indoor air of an overlying building. Vapors may migrate from subsurface contamination
typically in the form of non-aqueous phase liquid (NAPL), contaminants dissolved in
groundwater, or contaminated soil, to the air inside a building. The contaminants of interest are
typically petroleum hydrocarbons (PHCs) or chlorinated hydrocarbons (CHCs). When the indoor
air concentrations exceed the human health-based indoor air regulatory concentrations, vapor
intrusion may be significant. The regulatory levels are based on conservative assumptions that
are protective of both carcinogenic and noncarcinogenic adverse health effects. Examples of two
chemicals that may be considered in a vapor intrusion assessment include benzene and
tetrachloroethylene (PCE) (EPA 2013c). Additionally, methane and certain other volatile
chemicals can pose explosion hazards when they accumulate in confined spaces at levels
between the upper and lower explosion limit (LEL) (EPA 2013a, b). The following subsections
discuss this exposure pathway in more detail, methods of mitigating and characterizing it, and
the purpose of this study.
Vapor intrusion may be an issue at more than 100,000 contaminated sites in the United States
(Colbert and Palazzo 2008). The United States Environmental Protection Agency (USEPA) first
issued draft guidance documents regarding vapor intrusion and the associated potential risks to
human health in 2001 and 2002 (EPA 2001, 2002). These draft documents provided guidance for
the investigation and management of vapor intrusion at Resource Recovery and Conservation
Act (RCRA), Superfund, and Brownfield sites, but did not address vapor intrusion for petroleum
releases at underground storage tank sites (Kalmuss-Katz 2013). Proposed revisions (EPA
2013a, b) to the draft guidance documents have been released for comment. In addition,
beginning in the 2000s, several states began to issue their own guidance documents, many of
which are consistent with the federal guidance. Today, a majority of states have adopted some
form of a vapor intrusion regulatory program (Levy 2012).
If a vapor intrusion pathway is suspected at a site because of historic operations or known
releases, an investigation is required under these federal guidance documents as well as many
13
state and local guidance documents. Practical experience with assessing vapor intrusion has
demonstrated that this pathway can be extremely challenging to assess (Swartjes 2011).
Typically, indoor air regulatory levels are in units of pig/m 3 , so extra care must be taken to avoid
contamination during sampling. Certain chemicals may also be detected in air inside buildings
due to emissions from the use of consumer products, building materials, and even outdoor air
sources, so the contribution from the subsurface is difficult to determine. Furthermore, indoor air
concentrations of VOCs vary with season, location, weather, lifestyle, and building ventilation
rate. These issues contribute to a high level of uncertainty when assessing the potential and
degree of vapor intrusion (Swartjes 2011). This variability in indoor air concentrations and the
large number of controllable and uncontrollable factors that affect indoor air concentrations can
lead to unnecessary remediation- and mitigation-related costs.
The costs associated with vapor intrusion investigation and mitigation can be high. Vapor
intrusion costs at just 19 of the U.S. Navy's contaminated sites totaled $6.9 million (McAlary et
al. 2009). Mitigation strategies and technologies for existing buildings that are affected by vapor
intrusion include sub-slab depressurization, remediation by soil vacuum extraction, building
pressurization, air filtration, and sealing of preferential pathways in the building envelope,
including foundation cracks, sumps, etc. In the case of new construction, vapor barrier and
ventilation layers can be incorporated into the building design (Swartjes 2011).
1.2 Project Purpose and Scope
The cost of these remediation strategies can be compared to the cost to collect data that can
reduce uncertainty and result in a more realistic evaluation of the vapor intrusion pathway. Under
the current regulatory environment, and depending on site conditions, mitigating vapor intrusion
in the absence of sufficient data may be more cost effective than completely evaluating the
pathway through extensive data collection and negotiating with the various stakeholders,
including the regulatory agencies. In other cases, reducing uncertainty in the vapor intrusion
pathway may result in decreased remediation costs. The purpose of this study was to develop a
decision analysis tool that engineers could use to better determine what additional data to collect
following a preliminary vapor intrusion investigation.
As part of this investigation, scientists and engineers often measure subsurface soil, soil vapor, or
14
groundwater concentrations, and may then use a computational model or empirical data to
estimate an attenuation factor (c). The attenuation factor is the ratio of the indoor air
concentration to the sub-slab or soil vapor concentration. Thus the attenuation factor can be used
to estimate an indoor air concentration that can in turn be used to estimate the carcinogenic and
noncarcinogenic risk values.
In the case of new construction, the model-predicted indoor air concentration may be used to
decide whether or not a vapor intrusion mitigation system should be incorporated into the design
of the building. In the case of an existing building, the predicted indoor air concentration can be
compared to indoor air concentrations measured within the building. If the measured indoor air
concentration exceeds the model-predicted indoor air concentration for a contaminant, then there
is the potential that vapor intrusion is not the only source of the contaminant. Furthermore, if the
model-predicted indoor air concentrations for all contaminants are below the regulatory levels,
the model results could potentially serve as a line of evidence to demonstrate that vapor intrusion
mitigation is not necessary.
The computational models used to predict the contaminant concentrations in the indoor air of a
building rely on several simplifications of the vapor intrusion conceptual model (Bekele et al.
2012). These assumptions and limitations were investigated as part of this study. Among these
assumptions is the use of default or suggested values for many soil- and building-related
parameters. The effect that the values of these estimated parameters have on model output was
evaluated.
1.3 Project Approach
Determining the effect these estimated parameters have on model output for a given site may
help engineers determine what additional data to collect following a preliminary vapor intrusion
investigation. As part of this study, a Value of Information (Vol) worksheet, using a Monte
Carlo approach, was developed for two vapor intrusion models to help model users determine
which otherwise-estimated parameters have the greatest impact on model output. Vol is a
decision analytic method for quantifying the potential benefit of additional information in the
face of uncertainty (Keisler et al. 2013). In the case of vapor intrusion modeling, the results of
15
the Vol worksheets can be used to determine which parameters or groups of parameters would
be useful to investigate prior to making a remedial decision.
As part of this study, the EPA Johnson and Ettinger (J&E) spreadsheet models for groundwater
and soil gas sources were investigated. These models do not account for the variability of certain
parameters (e.g. soil moisture, porosity, pressure differential, etc.) that affect vapor transport and
migration. Instead, either the model assigns a value for these parameters or the model user is
required to define one using suggestions from the User's Guide (EQM 2004).
The Vol
worksheets that were developed for each of these models informs the user of which combinations
of otherwise-estimated parameters have the greatest effect on whether or not model-predicted
values of the incremental carcinogenic risk exceed the user-defined target carcinogenic risk. In
developing these worksheets, the governing equations and assumptions of the EPA J&E models
were assumed to be valid.
Following the development of these worksheets, a site affected by CHCs - the Former Nike
Battery PR-58 - was used as a case study. Data from this site were analyzed using the EPA J&E
models and Vol worksheet.
1.4 Overview of Existing Vapor Intrusion Models
Vapor intrusion models have been developed to predict VOC concentrations in indoor air based
on source concentrations, e.g. NAPL, groundwater, soil, or subsurface soil gas. Additionally,
models can be used to back-calculate permissible source concentrations based on target indoor
air concentrations or regulatory standards (Bekele et al. 2012). In the 1980s, studies of
radioactive radon intrusion into buildings were conducted, many of which form the basis for
studies in organic vapor intrusion modeling (Nazaroff et al. 1987). In the early 1990s, onedimensional models were proposed by Johnson and Ettinger (1991) and Jury et al. (1990) to
predict the transport of VOCs in the subsurface. Since then, several models have been developed,
ranging from one-dimensional analytical and semi-analytical models (Sanders and Talimcioglu
1997; DeVaull 2007; Mills et al. 2007; Turczinowicz and Robinson 2007) to three-dimensional
numerical models (Abreu 2005; Bozkurt et al. 2009). Some models may be more appropriate
than others depending on if the contaminants of concern are CHCs or PHCs. Sections 1.4.1 and
1.4.2 discuss two of these models and their applicability in more detail.
16
1.4.1 Johnson and Ettinger (J&E) Model
In practice, simplified models are preferred for their user-friendliness and their lesser number of
input parameters. In fact, the EPA as well as several state agencies has issued their own versions
of the J&E model, which will be discussed in more detail in Section 2. This model considers the
following processes: contaminant partitioning from the source to the subsurface soil gas; 1dimensional upward diffusion from a subsurface source through the vadose zone; predominantly
advective flow into the building through a crack in the foundation due to building underpressurization; and mixing and dilution within the building due to ventilation (Johnson and
Ettinger 1991; EQM 2004). Figure 1 depicts this conceptual model.
build"
A ir S
urcei uniino
C
ernlin e s / '
Cv= C suc
Contaminant Vapor Source
(soil or groundwater)
Figure 1: Vapor Intrusion Conceptual Model Proposed by Johnson and Ettinger (1991)
To predict the attenuation factor and indoor air concentrations of organic vapors, models like the
J&E model typically require inputs that include the source concentration; soil-specific
parameters that affect the transport and migration of these vapors through the subsurface; and
building-related parameters that affect the indoor air concentration (Bekele et al. 2012).
Several governmental agencies, including the USEPA, have utilized the J&E model in
developing screening concentration levels (Tillman and Weaver 2006). Additionally, the EPA
commissioned the production of spreadsheet versions of the J&E model (EQM 2004).
Spreadsheets have been developed for NAPL, groundwater, soil, or subsurface soil gas sources
to predict indoor air concentrations for a given source concentration. Unlike the original J&E
17
model, which provides the attenuation factor as the model output, the spreadsheets provide the
incremental carcinogenic risk and noncarcinogenic hazard quotient as the model output, and the
attenuation factor is only an intermediate calculation. Furthermore, in the EPA version,
parameters that are user-defined in the 1991 model are either calculated (for example, the soilgas entry flow rate into the building) or assigned values through built-in tables (for example, the
capillary zone moisture content and capillary zone thickness) (Johnson 2002, 2005).
The potential for conservative predictions of models like the J&E model and EPA spreadsheets
are of concern and merit further investigation. Inaccurate estimates are the result of a
combination of factors including but not limited to the assumption of homogeneous soil in the
subsurface; a lack of consideration for the biodegradation of organic vapors; and simplified
representations of natural attenuation (NA) processes, when they are considered at all (Bekele et
al. 2012). NA processes include biodegradation, physical processes (for example, dilution,
dispersion, and sorption), and chemical reactions. In the case of PHCs, biodegradation can be
significant and models that ignore this process, including the J&E model, may predict overly
conservative estimates of the attenuation factor.
In the case of CHCs, microbial degradation and co-metabolic degradation processes may occur
under predominantly anaerobic conditions (English and Loehr 1991). Despite experimental data
and field assessments confirming the dechlorination of CHCs under certain conditions in the
subsurface (Little et al. 1988; English and Loehr 1991; Barbee 1994; Davis et al. 2002; Chen
2004; Haest et al. 2010), limited effort has been made to include these processes in vapor
intrusion models (Bekele et al. 2012).
However, the J&E model may still provide a useful
screening level representation of the vapor intrusion pathway for CHCs, provided it is
implemented with inputs representative of site conditions.
1.4.2 Biodegradation of PHCs and the BioVapor Model
Unlike for CHCs, biodegradation of PHC vapors in the subsurface has been described in several
field studies (Ostendorf and Kampbell 1991; Hers et al. 2000; Davis et al. 2002, 2009a; Morrill
et al. 2005; Johnson et al. 2009), laboratory investigations (Jin et al. 1994; Pasteris et al. 2002;
Ghazali et al. 2004; H6hener et al. 2006), and modeling studies (Karapanagioti et al. 2003;
Abreu and Johnson 2006; DeVaull 2007; Abreu et al. 2009).
18
In order for biodegradation to occur, sufficient oxygen, hydrocarbons, nutrients, moisture, and
microbial populations must be present. In the case of PHC vapors, aerobic biodegradation is the
primary mode of natural attenuation. Published research and case studies have established that
aerobic biodegradation is limited by the availability of oxygen, and not bacteria, in the vadose
zone because microorganisms capable of degrading PHCs under aerobic conditions are abundant
in almost every type of soil (Weidemeir et al. 1999; DeVaull et al. 2002; Davis et al. 2009a). The
diffusion and transport of oxygen in the vadose zone is influenced by numerous factors,
including soil moisture, porosity, diffusion coefficients, and the uptake of oxygen by soil organic
matter and the biodegradation of PHCs (Bekele et al. 2012). Factors such as air-filled porosity,
water-filled porosity, chemical diffusivity in air, Henry's law constant, and chemical diffusivity
in water are used to estimate the effective diffusion coefficient using the Millington and Quirk
equation (1961).
Efforts to incorporate biodegradation of PHCs were undertaken by Johnson (1998, 1999, and
2002) to improve on the 1991 J&E model. Such an improvement, however, was not incorporated
into the EPA-implemented J&E spreadsheets. Oxygen-limited biodegradation was, on the other
hand, included in the BioVapor model (DeVaull 2007). Figure 2 depicts the conceptual model
that was proposed as part of the development of the BioVapor model.
19
indoor
C
]
aerobic
I
ground
surface
zone
A
Lb
anaerobic zone
source zone
Figure 2: Vapor Intrusion Conceptual Model with Biodegradation Proposed by DeVaull
(2007)
This model does not account for spatial and temporal variability of the soil temperature and
moisture, which affect the distribution of oxygen and organic vapor transport. The model also
does not consider the heterogeneity of soils, which can also affect oxygen and organic vapor
transport.
1.5 Introduction to Value of Information
Vol, which will be discussed in detail in this section and in Section 3, is one tool practitioners
can use to analyze model sensitivity to unmeasured input parameters. Previous studies in vapor
intrusion modeling have not included the use of Vol. Several studies, instead, have used other
tools to determine the sensitivity of the original J&E (1991) model to parameter inputs that
largely go unmeasured, including crack factor, building air exchange rate, porosity, and soil
moisture (Hers et al. 2003; Johnson 2002, 2005; Tillman and Weaver 2006). Johnson (2002,
2005) in particular developed a parametric analysis of the original J&E model using three
dimensionless parameters that influence the attenuation factor. This method allowed for the use
of a flowchart to determine which parameters are critical for a given simulation.
20
Tillman and Weaver (2006) developed a Java package that allows for a synergistic uncertainty
analysis of the original and variants of the J&E model, including the EPA-implemented
spreadsheets. This software considers uncertainty in four building parameters (mixing height,
floor-wall crack width, air exchange rate, and floor depth below grade) and five soil parameters
(porosity, residual moisture content, modeled moisture content, soil gas flow rate, and
temperature).
The study revealed that, for the examples provided in the paper, the greatest
attenuation factor was produced when the lowest values were used for building mixing height,
air exchange rate, residual moisture content, and percent effective saturation along with the
highest values for soil porosity and soil gas flow rate. High or low values of the floor-wall crack
width and subsurface temperature had no effect on the worst-case scenario results (i.e. the results
with the greatest attenuation factor) (Tillman and Weaver 2006). These two parameters were
later found to have no effect on the best-case scenario results (Tillman and Weaver 2007). For
the examples studied, the synergistic effect of the uncertainty of the 14 parameters was found to
cause as large as a 3042% difference in the predicted attenuation factor as compared to the
attenuation factor obtained using default values (Tillman and Weaver 2006).
This synergistic sensitivity analysis is similar in principle, though not identical, to Vol analyses.
Vol can be thought of as the amount that could be paid to obtain information, whereby the
decision with information plus the cost of obtaining the information is equal in value to the
decision to not obtain the information (Keisler et al. 2013). Vol analyses have been used to
investigate the cost-effectiveness of site investigations for remediation purposes (Back et al.
2007; Dakins et al. 1994; Dakins et al. 1996; Rautman et al. 1993; James et al. 1996; Kaplan
1998; Demougeot-Renard et al. 2004; Norberg et al. 2006; Back 2007). However, little effort has
been made to conduct Vol analyses in the field of vapor intrusion (Collier 2011). Furthermore,
Vol has yet to be applied to vapor intrusion modeling to determine if reducing parameter
uncertainty can change model output.
There are several methods of conducting a Vol analysis, including equation-based computation
of the probability distribution of the model output based on the probability distributions of the
input parameters (Meltzer et al. 2011). Another method of conducting a Vol analysis is to use a
Monte Carlo sampling algorithm (Brennan et al. 2007). This method was selected in this study
because the complexities of vapor intrusion models make an analytical approach to Vol very
difficult to conduct.
21
A generalized approach to conducting a Vol analysis using a Monte Carlo sampling algorithm is
as follows. First, a decision model must be set up which includes a decision rule. This rule is
used to evaluate the results of the Monte Carlo simulations. For example, in the case of vapor
intrusion, this rule may be the equation for carcinogenic risk. Next, uncertain parameters must
be identified and their probability distributions must be characterized. Commonly used
distributions include normal, triangular, lognormal, and uniform, among others.
Key
characteristics for each distribution must be identified, which may include the mean, lognormal
mean, mode, range, standard deviation, and lognormal standard deviation. Next, a specified
number, L, of Monte Carlo sample sets of these uncertain parameter values must be simulated.
Finally, for each simulation, the decision rule must be evaluated (Brennan et al. 2007).
The decision rule can then be evaluated using "default" or "typical" values for the uncertain
parameters. The default or typical result of the decision rule may then be compared to the L
randomized results of the decision rule. In the case of risk analysis, one may then compare the
default risk result and the randomized risk results to the target risk. This comparison is one
method of quantifying if there is value in reducing parameter uncertainty. For example, if the
default risk result exceeds the target risk, then it may be useful to quantify the percentage of
randomized simulations in which the risk result does not exceed the target risk. A threshold
percentage, 30% for example, may be chosen above which additional data may need to be
collected to reduce parameter uncertainty.
22
2. EPA J&E Models: Governing Equations, Assumptions, and Limitations
The models that were evaluated were the EPA J&E models for a groundwater source and for a
soil gas sample. More advanced models were not considered in this study because they are less
likely to be used in practice. Furthermore, Vol analyses using a Monte Carlo approach can only
be applied to a relatively simple set of governing equations. A simple model is more appropriate
for this first attempt at using a Vol analysis for vapor intrusion. The following sections discuss
the governing equations of the two models that were evaluated, as well as the assumptions and
limitations of these models.
2.1 Groundwater Source Model
The User's Guide for the EPA-issued J&E model spreadsheet recommends using the
groundwater source models (GW-ADV and GW-SCREEN) in cases where the source of the
volatile contaminant is dissolved in the groundwater (EQM 2004). The GW-SCREEN model
allows for only one soil layer in the subsurface. The GW-ADV model should be used if more
than one soil layer is present. Additionally, the GW-ADV model allows the user to specify more
parameters than the GW-SCREEN model, including pressure differential, the enclosed space
dimensions, the indoor air exchange rate, etc.
Both of these models may be used in either of two ways:
" the user defines the source concentration and the model calculates an indoor air
concentration and associated risk; or,
" the user defines a risk and the model back-calculates the allowable source concentration.
The GW-ADV and GW-SCREEN models are also similar in that both spreadsheet models are
comprised of five worksheets:
" DATENTER: the worksheet where the user defines the relevant parameter values;
" CHEMPROPS: a table of important chemical properties for the contaminant identified in
DATENTER;
-
INTERCALCS: where intermediate calculations are stored;
23
m
RESULTS:
the
final
risk-based
groundwater
concentration
or
carcinogenic/noncarcinogenic risk calculations (depending on which option the user
selects in DATENTER); and,
VLOOKUP: where a table of soil properties for all 12 Soil Conservation Service (SCS)
-
soil classes and a table of chemical properties for all possible contaminants are stored.
Figure 3 provides the conceptual model for both of these versions of the groundwater model.
Figure 3: Conceptual Model for Groundwater Source Contamination (EQM 2004)
The top of the water table is separated from the bottom of the building by a distance LT. The
capillary fringe exists above the water table. Chemicals dissolved in the groundwater volatilize,
and the relationship between the groundwater concentration (C,) and the vapor concentration at
the source of the contamination (Csource) is defined by Henry's law:
source
where
I
I
(1)
wsC
is the dimensionless Henry's law constant (H) for the contaminant at the system
(groundwater) temperature (Ts). If H at a reference temperature is known, H may be estimated
at the system temperature using the Clapeyron equation:
exp [Hs=
vTs
- RT
24
I
HR
(2)
where
AH,= Enthalpy of vaporization at the system temperature (cal/mol)
TR= Henry's law constant reference temperature (K)
HR= Henry's law constant at the reference temperature (atm-m 3/mol)
Rc = Gas constant (1.9872 cal/mol-K)
R = Gas constant (8.205 x 10-5 atm-m 3/mol-K)
The enthalpy of vaporization can be calculated as follows (Lyman 1990):
AH,
I
= AH
IT(,(3)
_1 - T /T( _
where
AH, = Enthalpy of vaporization at the normal boiling point (cal/mol)
Tc = Critical temperature (K)
TB= Normal boiling point (K)
n = Constant (unitless); value depends on value of TB/ Tc
The user inputs the groundwater concentration of a contaminant of interest as well as the system
temperature. The model, using the aforementioned equations and chemical properties stored in
the VLOOKUP and CHEMPROPS worksheets, thereby calculates the vapor concentration at the
source.
Following volatilization, the vapor diffuses through the capillary zone and through the remaining
soil strata. Diffusion through the capillary zone is affected by the following: water-filled
porosity; total porosity; capillary zone thickness; and diffusivity of the contaminant in water and
air.
The water-filled porosity in the capillary zone, 0 ,,, can be determined using the van Genuchten
equation (1980):
+
0"C =0±
where
0-0G
01
0-0
0
1
jmN
[+(alh
_+
__(4
2
0,. = Residual soil water content (cm 3 /cm 3)
0, = Saturated soil water content (cm 3/cm
3
a, = Point of inflection in the water retention curve where
maximal (cm 1)
h = Air-entry pressure head (cm) = 1/a,
25
d0G
is
N
=
van Genuchten curve shape parameter (dimensionless)
M= 1 -(1/N)
In the DATENTER worksheet, the user defines an SCS soil class for the soil stratum
immediately above the water table. The model includes a database of default soil properties for
each SCS soil class in the VLOOKUP worksheet. These values are based on the work of Hers
(2002). Using the default values for each of the aforementioned properties for each soil class,
the model generates a water-filled porosity for the capillary zone. The air-filled porosity in the
capillary zone (0,,,) is then calculated as follows:
(5)
1, = n., -Owac
where
n
=
Soil total porosity in the capillary zone (cm 3 /cm 3)
Porosity values for each SCS textural class are stored in the soil properties table in the
VLOOKUP worksheet. However, the user also has the option of defining the porosity of each
soil strata in the DATENTER worksheet if that information is available.
The effective diffusion coefficient across the capillary zone (DJ"f) is then calculated using the
Millington and Quirk (1961) equation:
D
where
-
D,(3/n
2
/H7,X O/nf. )
)+(D
(6)
D, = Diffusivity in air (cm 2/s)
D, = Diffusivity in air (cm 2 /s)
Using Fick's law of diffusion, the mass transfer rate across the capillary zone can be calculated
by the expression:
W
E =A(C.,.-C
E A(
where
ource -
gO
/L
AC
cz
Df IL
sourc
rC
(7)
cC
E = Rate of mass transfer (g/s)
A
Cross-sectional area through which vapors pass (g/cm 3 _v)
Cgo = A known vapor concentration at the top of the capillary zone (g/cm 3_
v); assumed to be 0 as diffusion proceeds upward
Lz = Thickness of capillary zone (cm)
The thickness of the capillary zone can be calculated using the following equation (Fetter 1994):
Z 0.15
2a 2 cosA
p~gR
26
R
where
U2
=
Surface tension of water (g/s) = 73
k = Angle of the water meniscus with the capillary tube (degrees);
assumed to be 0
Pw =
Density of water (g/cm3 ) = 0.999
g
Acceleration due to gravity (cm/s2) = 980
R
Mean interparticle pore radius (cm)
The mean interparticle pore radius can be calculated using the following relationship (Fetter
1994):
R = 0.2D
where
(9)
D = Mean particle diameter by weight (cm)
The model stores a default mean particle diameter for each SCS soil class in the VLOOKUP
worksheet. Values for the arithmetical mean particle diameter for each textural class were
determined by Nielson and Rogers (1990) using the SCS classification triangle.
The
mathematical centroid of the area of each textural class was determined, as shown in Figure 4.
100
90
80
70
~\
-
'lay
~
4
60
50
40
4
Ca
Clay
Omay Lo
Sily I aY,
Percent Sand
Figure 4: SCS Soil Triangle with Centroids for Each Textural Class (Nielson and Rogers
1990)
An arithmetic mean diameter was determined for sand, silt, and clay from a geometric
distribution with a geometric standard deviation (GSD) of 2.5.
27
A weighted average of the
particle diameter was then computed for the centroid of each textural class using the percentage
of sand, silt, and clay at that point (Nielson and Rogers 1990). Using the textural class of the soil
stratum immediately above the water table, the model then calculates the thickness of the
capillary zone and the mass transfer rate across the capillary zone.
In the vadose zone, the effective diffusion coefficient (D, f) of a given soil stratum, i, is
calculated similarly to Equation 6, except using parameter values for the vadose zone layer:
D(33 3 /n 2)+(D' /HqO
3
/n )
(10)
For each stratum defined by the user, the model calculates an effective diffusion coefficient. The
total overall effective diffusion coefficient (D'f ) across the capillary zone and vadose zone can
be calculated as a harmonic mean:
LT
f=
(11)
(Li,/ Def)
I
i=0
where
Li = Thickness of soil layer i (cm)
LT=
Distance between the source of contamination and the bottom of the
enclosed space floor (cm)
Following diffusive transport across the subsurface from the groundwater source to the bottom of
the slab, pressure-driven advective transport of the vapor across the slab is assumed to dominate.
This assumption is tested by calculating the dimensionless Peclet (Pe) number for transport
across the slab:
Pe =
where
LcrackQoi,
DcrackA crack
(12)
QO;1= Volumetric flow rate of soil gas into the enclosed space (cm 3 /s)
Lcrack= Enclosed space foundation or slab thickness (cm)
Derack =Df, the
effective diffusion coefficient of the soil stratum
immediately beneath the building (cm 2 /s)
Acrack =
Area of total cracks (cm)
If the value of Pe calculated in Equation 12 is greater than 1, then advection dominates. It should
be noted that a gap is assumed to exist at the junction between the floor and the foundation along
the perimeter of the floor. Therefore,
Acrack
is considered a strip of floor-wall seam crack width,
28
w, and length equal to the perimeter of the floor. The value of w and the dimensions of the floor
are user-defined in DATENTER worksheet.
The volumetric flow rate of soil gas entering the building may be user-defined. If the user does
not define the flow rate in the DATENTER worksheet, it is instead calculated using the
following equation (Nazaroff 1988):
_
2TAPkvXcrack
(13)
"pln(2
Zracklrak)
AP = Pressure differential between the soil surface and the enclosed space
where
(g/cm-s2)
kv = Soil vapor permeability (cm 2)
Xcrack=
Floor-wall seam perimeter (cm)
p = Viscosity of air (g/cm-s)
Zcrack =Crack depth below grade (cm)
rcrack
Equivalent crack radius (cm)
Equation 13 requires the following:
" the pressure within the building is less than atmospheric;
m
the soil column properties within the zone of influence of the building are homogeneous;
and,
" the soil is isotropic with respect to soil vapor permeability.
Per Nazaroff's (1988) model, Equation 13, advective vapor flow from the soil into the building is
represented as an idealized cylinder buried below grade. This cylinder represents the total area of
the structure below the soil surface through which vapors pass. Equation 13 was determined for
this idealized cylinder buried some distance (Zcrack) below grade. The length of the cylinder is
assumed be equal to the building floor-wall seam perimeter (Xcrack). The original J&E model
determined the equivalent radius crack as follows (Johnson and Ettinger 1991):
'.rack =
where
7(A8 /X-rack)
(14)
(15)
71= A,,klAu (unitless)
AB= Area of the enclosed space below grade (cm 2
29
In addition to these building-related parameters, the effective soil vapor permeability (ky) of the
stratum immediately below the slab (stratum A) affects the value of Qsil. k, may be user-defined
in the DATENTER worksheet, or it may be calculated based on the user-defined soil
classification of the stratum immediately beneath the slab (stratum A).
kv is calculated as
follows:
k, =k1 . k,,
where
(16)
ki = Intrinsic soil permeability (cm2)
krg = Relative air permeability (unitless)
The intrinsic soil permeability is calculated using the equation:
k,=
(17)
P.9
where
K, = Soil saturated hydraulic conductivity (cm/s)
pw = Dynamic viscosity of water (g/cm-s)
The model assigns a value of K, based on the SCS classification of stratum A. Possible values of
Ks are provided in the VLOOKUP worksheet. The relative air permeability is calculated as
follows:
krg =
where
(
- Ste)
Effective total fluid saturation (unitless) =
Ste
(18)
(1 - SteAI)2A
01 -or
Or
n -O,.
(19)
M = van Genuchten shape parameter (same as in Equation 4)
Class-average values, provided in the VLOOKUP worksheet, for Ow, O,, and n are used based on
the user-defined soil textural classification of stratum A provided in DATENTER.
In addition to Qso
0 i, the building ventilation rate (Qbuilding) affects advective flow across the slab
or foundation. This value can be calculated as follows:
Qhuddn
LWHBER
W
building=
-3,600s/h
where
LB = Length of the building (cm)
WB
=
Width of the building (cm)
HB = Height of the building (cm)
ER = Indoor air exchange rate (h-1)
30
(20)
The values of LB, WB, HB, and ER are all user-defined in the worksheet DATENTER.
Under the assumption of an infinite source and a steady-state mass transfer, the model computes
the attenuation coefficient (a) as follows:
Del/A
B
L QhdinIT
L
exp
DelA1B
Q+odLcrack
DcracAcrack
K Q, udingL
exp
Qr 1 Lcrack
xDrackAcraAck
L
+ DR7I A
KQ
exp
I
IL)
(21)
Q. ILcrack
-
D rackA crack
However, as the value of Pe calculated in Equation 12 approaches infinity, i.e. advective
transport is much more dominant than diffusive transport, then the attenuation coefficient is
instead calculated as:
D" AB
a
QuicinT
(22)
The indoor air concentration within the building can then be calculated using the attenuation
coefficient from either Equation 21 or 22, depending on which is applicable, using the following
equation:
Cuding= aC,rc
where
Cbuilding =
(23)
Vapor concentration in the building (mg/m 3 per pg/L-water)
The user has the option to either define a target risk and allow the model to back-calculate the
allowable source concentration, or define the source concentration and allow the model to
calculate an indoor air concentration and associated risk. If the user selects the first option in
DATENTER, then the maximum allowable groundwater concentration (Cc) for a user-defined
target incremental carcinogenic risk level is calculated as follows:
C( - TR x AT x 365days/yr
URF x EF x ED x Cuiig(
where
TR = Target risk level (unitless)
ATc = Averaging time for carcinogens (yr)
31
(24)
URF = Unit risk factor (m3/ptg)
EF = Exposure frequency (days/yr)
ED
Exposure duration (yr)
The model calculates a value of
Cbuilding
by using Equation 23 and calculating the attenuation
coefficient exactly as discussed previously. The value of Csource in Equation 23 is determined by
assuming an initial groundwater concentration of 1 pg/L-water. The values of TR, ATC, EF, and
ED in Equation 24 are all user-defined in the DATENTER worksheet. Typical values for these
parameters are provided in the User's Guide. In particular, the User's Guide suggests a typical
value for the target risk as 10-6. The model provides a value of URF based on the contaminant of
concern. A table of the URF value for each possible chemical is provided in the VLOOKUP
worksheet.
In the case of noncarcinogens, the risk-based source concentration (CNC) is calculated as follows:
THQx AT,(. x 365days/yr
EF x EDx RfC
where
X
(25)
C
"uldn
THQ = Target hazard quotient (unitless)
ATNC= Averaging time for noncarcinogens (yr)
RfC = Reference concentration (mg/m 3 )
The values of THQ, ATNC, EF, and ED are all user-defined in the DATENTER worksheet. The
model provides a value of RfC based on the contaminant of concern. A table of the RfC value for
each possible contaminant is provided in the VLOOKUP worksheet.
Finally, the risk-based source concentration is then determined to be either Cc or CNC, whichever
value is smaller. If the calculated risk-based source concentration exceeds the solubility of the
contaminant, then the final model output will be the value of the solubility. Pure component
water solubility values for all possible contaminants are stored in the VLOOKUP worksheet.
If the user chooses the second option for model output, i.e. the risk associated with a given
source concentration, the following calculation is instead used for carcinogens:
32
Risk
(26)
URF x EF x ED x Cbuiding
"'"(6)
=
AT x 365dayslyr
This value can then be compared to the value of TR defined in the DATENTER worksheet. In
the case of noncarcinogens, the hazard quotient (HQ) can be calculated as:
EF x ED x
HQ=
1
R
x
Coudin
RIC
(27)
ATN( x 365dayslyr
This value can then be compared to the value of THQ defined in the DATENTER worksheet. It
should be noted that the RfC and URF values were last updated in the model in December 2003
(EQM 2004). These values are updated periodically on the EPA Integrated Risk Information
System (IRIS) Database, and the User's Guide strongly encourages users to use the latest toxicity
values when conducting a risk assessment (EQM 2004).
2.2 Soil Gas Model
When the user of the EPA J&E models has measured contaminant concentrations in the soil gas,
then the soil gas models (SG-SCREEN and SG-ADV) may be used.
Similarly to the
groundwater models, SG-SCREEN allows the user to classify only one soil stratum whereas SGADV allows the user to classify up to three soil strata.
In either the advanced or screening models, the user defines a soil gas concentration at a
specified depth in the DATENTER worksheet. This concentration replaces the value of
that was previously calculated in the groundwater models.
Csource
Aside from this difference, the
governing equations in the soil gas models are largely the same as those in the groundwater
models. In the soil gas models, however, Equations 5, 6, 8, and 9 are no longer calculated. Soil
gas samples should be collected above the capillary zone. Therefore, any calculations that
characterize the diffusion of contaminants across the capillary zone are no longer necessary. As
a result, the soil properties that are no longer included in the calculations are the mean grain
diameter of stratum A, OwCZ, and
0
a,cz.
Another difference between the soil gas and groundwater models is that the only model output
option in SG-SCREEN and SG-ADV is the calculated risk, and the back-calculation option
provided in the groundwater models is not available.
33
2.3 Model Assumptions and Limitations
Several assumptions were made in the development of the J&E conceptual model that can impact
the values of the calculated indoor air concentration and the carcinogenic/noncarcinogenic risk
values. The EPA lists the following 11 assumptions and limitations of the J&E model (EQM
2004):
" Mode of vapor entry: Contaminant vapors enter the structure primarily through cracks
and openings in the walls and foundation. In particular, the model assumes that the total
area of cracks may be approximated using the perimeter of the floor and a specified crack
width.
" Advection zone of influence: Advective transport occurs primarily within the building
zone of influence. Vapor velocities decrease rapidly with increasing distance from the
building.
" Diffusion across the subsurface: The migration of contaminant vapors from the
subsurface source of contamination to the bottom of the slab is diffusion-dominated.
" Contaminantfate: All vapors originating from below the building will enter the building
unless the floors and walls are perfect vapor barriers. Lateral migration of vapors is not
considered.
-
Homogeneity within each soil stratum: All soil properties (e.g. porosity, permeability,
etc.) in any horizontal plane are homogeneous. Horizontal spatial variability can only be
accounted for by using numerical models that are not typically used outside of academia.
Zones of higher or lower porosity, water content, and permeability are ignored in the
EPA J&E models. In the User's Guide, the user is encouraged to be conservative in
selecting an SCS soil type for each strata, i.e. the user is encouraged to select the coarsest
possible soil type. Therefore, regions of differing porosities within soil strata are likely to
be ignored.
" Contaminant distribution: The contaminant is assumed to be homogeneously distributed
within the zone of contamination and no spatial variability is considered. In the case of a
heterogeneously distributed contaminant, the User's Guide recommends that the model
user identify the average concentration within the zone of contamination in the
34
DATENTER worksheet. However, if descriptive statistics are not available to quantify
the uncertainty in the average value, the User's Guide recommends using the maximum
contaminant concentration as an upper bound estimate. This approach likely results in
higher levels of predicted indoor air concentrations and higher associated risks.
m
Plume dimensions: The areal extent of contamination is assumed to be greater than that
of the building.
Sites where the extent of the contamination is smaller than the
dimensions of the building footprint are not well represented by this model.
" No moisture movement or mixing in the vadose zone: The conceptual model assumes an
absence of convective water movement within the soil column (i.e., no infiltration or
evaporation), and an absence of mechanical dispersion. A time-averaged constant
moisture content is assumed to exist in each stratum of the subsurface, regardless of
weather patterns.
" No natural attenuation: The model does not account for abiotic or biotic transformation
processes (e.g., hydrolysis, biodegradation, etc.). This omission may lead to conservative
results, particularly when modeling vapor intrusion of petroleum hydrocarbons.
-
Soil isotropy: The soil layer in contact with the structure floor and walls is isotropic with
respect to permeability. The calculated permeability is based on a horizontal air
conductivity, which is assumed to be equal to the vertical air conductivity.
m
Constant airflow through the building: Both the building ventilation rate and the pressure
differential across the foundation or slab are constant values. However, studies have
shown that the following may influence the pressure differential over time:
o short-term barometric pressure changes as a result of regular oscillations in
atmospheric winds and pressure fields, often referred to as atmospheric tides
(Hintenlang et al. 1992), or tidal-induced water table fluctuations in coastal areas
(Li et al. 2002);
o longer-term meteorologically-induced barometric pressure changes (Keskikuru et
al. 2001, Patterson et al. 2006);
o rainfall events (Li et al. 2002);
" thermal differences between indoors and outdoors (Garbesi et al. 1989);
35
o wind loading on the structure (Garbesi et al. 1989, Keskikuru et al. 2001); and,
o imbalanced building ventilation (Garbesi et al. 1989).
Variations in these environmental and building conditions are not taken into effect in the
EPA J&E spreadsheet models.
In addition to these assumptions and limitations outlined by the EPA, the following have also
been identified for the soil gas and groundwater source models as part of this study:
-
Limited number of soil strata: Only three strata of soil may be identified in either the
groundwater source or soil gas advanced models (GW-ADV and SG-ADV).
Sites with
more complex stratigraphy may not be well characterized using these models.
" Simplified van Genuchten moisture profile: In the groundwater source EPA J&E model,
the soil moisture profile is not treated as a true van Genuchten profile. Each soil strata has
its own constant water content and the capillary zone has its own constant water content.
A comparison of the van Genuchten water retention curve to the conceptual model
stepwise function is shown in Figure 5.
A
WATER RETENTION
CURVE
Point of inflection where
dew/dh is maximal.
0
C,,
L)
CONCEPTUAL
SIMPLIFICATION
0
1
c
.0-
-..._._._._._._
-
0
W.R
eWFc
6
W.CZ
eW.S
Water-filled Porosity
Figure 5: Conceptual Simplification in EPA J&E Model of van Genuchten Profile (Hers
2003)
36
The conceptual simplification identifies a constant water-filled porosity in the capillary
zone, which is equal to the water-filled porosity at the point of inflection in the van
Genuchten water retention curve. This simplification likely predicts a lower than actual
water-filled porosity for the capillary zone because, as shown in the van Genuchten
profile, the water-filled porosity must approach saturation at the base of the capillary
zone. As a result, model predictions of the attenuation factor, indoor air concentration,
and associated risk may be conservative (Hers 2003).
*
Capillary zone thickness calculations: The groundwater source model does not account
for the possibility that the capillary zone may extend into more than one soil strata. As a
result, the possibility exists that the calculated thickness of the capillary zone is too thin
or too thick. In general, the finer the soil, the thicker the calculated capillary zone will be
(see Equations 8 and 9).
If the soil stratum immediately above the water table is
classified as very fine, then the calculated capillary zone has the potential to be thicker
than the stratum of soil itself. If the soil stratum above that stratum is coarser, then the
calculated capillary zone will likely be too thick. In general, the diffusion coefficient
across the capillary zone is smaller than the diffusion coefficient elsewhere in the
subsurface due to the higher water content (Equation 6). Therefore, a thicker capillary
zone will lead to a lower predicted indoor air contaminant concentration and associated
risk (Equation 11). On the other hand, if the second stratum of soil above the water table
is finer than the first, then the calculated capillary zone thickness may be too small, and
the associated indoor air concentration may be larger.
*
Need for updated toxicity values: The VLOOKUP spreadsheet currently includes toxicity
values that have not been updated since December 2003.
Users conducting a risk
assessment on the predicted indoor air concentration for either the groundwater source or
soil gas model likely need to update these values.
In order to do so, the user must
unprotect the worksheets and change the values.
m
Use of typical/default values ofparameters: Default values of soil properties for all 12
SCS soil textural classes are used. These properties are: porosity; water content; residual
water content; the three van Genuchten parameters; hydraulic conductivity; mean grain
diameter; and bulk density. With regard to building-related parameters, the User's Guide
37
provides typical values for the following: soil-building pressure differential; floor-wall
seam crack width; indoor air exchange rate; and the building mixing height (EQM 2004).
The use of default and typical values for all of these parameters may greatly impact
model results, depending on the sensitivity of the model to these parameter values.
These final two simplifications will be investigated further as part of the value of information
analysis discussed in the following sections of this study.
38
3. Value of Information Analysis
The governing equations and assumptions in the original EPA J&E spreadsheet models were
used as part of the Vol analysis. Despite the limitations of this approach, the Vol worksheets
were developed in order for those already using the EPA J&E spreadsheets to determine if
additional data in the face of uncertainty may change model results. The following sections
discuss how Vol was applied in this study to the EPA J&E vapor intrusion models. First, the
parameters that were selected as part of the analysis are discussed in detail in Section 3.1. Next, a
discussion of how the user operates the Vol worksheets and can analyze the results is provided in
Sections 3.2 and 3.3.
3.1 Parameter Selection and Relevant Values
Uncertain parameters included as part of the Vol analysis were selected using the following
method. First, the DATENTER and INTERCALCS worksheets in the EPA J&E spreadsheets
were inspected to find what soil and building parameters are referenced in the calculations.
Parameters were not selected that relied on the values of other parameters that were already
being investigated. For example, the bulk density was not included in the analysis because it
relies on the density of the solid particles (which is assumed to be a constant - 2.65 g/cm 3) and
the porosity (which was already being investigated). Next, parameters were selected that could
be measured either directly in the field or in the laboratory. Several building parameters were
not included in the analysis, including:
" indoor air exchange rate (ER);
" vapor flow rate into the building (Qso
0 i); and,
" mixing height.
Though these parameters may be measured in the field using a tracer test, this test is costly and
difficult and, therefore, unlikely to be conducted in the field for a typical vapor intrusion
assessment.
If these parameters have a large impact and are highly uncertain, however, Vol
could still show it is worth conducting the test. For the purpose of this initial Vol analysis, these
parameters were not included.
The following list provides the seven parameters that were selected for investigation in the Vol
spreadsheets. For each parameter, the relevant statistical data as well as the typical or default
39
value is discussed. These default values are either built into the model or are user-defined, and
are used to calculate the baseline J&E model output prior to conducting the Monte Carlo
simulations. Should the Vol analysis indicate that there is high model sensitivity to and
uncertainty in any of the parameters, the user may choose to collect additional data. Methods of
collecting data for each parameter are discussed below.
Average Concentration of the Groundwater or Soil Gas Samples, Cw or Cg
The average groundwater or soil gas concentration, depending on the model that is being used,
was included in the analysis because it directly impacts the concentration of the indoor air via
Equation 23. In the Vol worksheet, the user is required to define the contaminant of concern, the
mean measured source concentration (which serves as the default value), and a standard
deviation of the concentration. The method chosen to determine the mean or standard deviation
is at the discretion of the model user, and a normal or lognormal distribution may be appropriate.
Should the Vol analysis indicate that there is high model sensitivity to and uncertainty in the
sample concentration, the user may choose to collect additional samples to refine the data.
Pressure Differential Across the Slab, AP
The pressure differential across the slab was included because of its effect on the value of QsOij.
The user-defined value in the DATENTER worksheet is treated as the default value. The User's
Guide recommends a typical value of 4 Pa and a practical range of values as 0 to 20 Pa (EQM
2004; Eaton and Scott 1984; Loureiro et al. 1990; Grimsrud et al. 1983). This range was used in
the model, and the distribution was assumed to be triangular, with a peak value of 4 Pa. Should
the Vol analysis indicate that there is high model sensitivity to and uncertainty in the pressure
differential, the user may choose to use a manometer in the field to obtain a more accurate value
of AP. However, this will only be possible if a structure or slab already exists on-site.
Permeability of the Soil Stratum Immediately Below the Slab, kv
The permeability of the soil stratum immediately below the slab was included in the analysis
because the value of the permeability of this layer impacts the value of QsOij, as shown in
Equation 17, and the advective transport of vapors across the slab. The user-defined value in the
DATENTER worksheet is treated as the default value. The User's Guide provides practical
40
ranges of the soil vapor permeability depending on if the soil is classified as medium sand, fine
sand, silty sand, or clayey silt. Should the user define a value of the permeability within one of
these ranges, then that range would be used in the Vol worksheet. In all cases, the distribution of
values was assumed to be uniform. Should the user decide to investigate this parameter further,
the user may drill through the slab, collect an undisturbed sample of the soil below the slab, and
have the sample analyzed for this parameter.
Saturated Hydraulic Conductivity of the Soil Stratum Immediately Below the Slab, Ks
Should the permeability not be user-defined, the value of K, impacts the value of kv, as shown in
Equation 13. The typical value of Ks for the user-defined SCS textural classification provided in
DATENTER is treated as the default value in the Vol analysis. These typical values can be
found in the VLOOKUP worksheet as well as in the User's Guide. Schaap and Leij (1998)
determined these values using a lognormal distribution. This same study also provided the
lognormal standard deviations of K, for each textural class, which were included in the Vol
worksheet. Should the user decide to investigate this parameter further, the user may choose to
collect a sample and have it analyzed for this property. Another way of reducing the uncertainty
in this parameter is to conduct a slug or pumping test. However, these tests could only be done
to determine the value of Ks in the soil stratum immediately below the slab if this stratum
extended to below the water table.
Mean Particle Diameter of the Soil Stratum Above Water Table
The mean particle diameter of the soil stratum immediately above the water table was included
in the analysis because the value of this parameter impacts the thickness of the capillary zone.
This value, in turn, impacts the diffusive transport of contaminant vapors across the subsurface,
as shown in Equations 8 and 9. The typical value of the mean diameter for the user-defined SCS
textural classification provided in DATENTER is treated as the default value in the Vol analysis.
These typical values can be found in the VLOOKUP worksheet as well as in the User's Guide.
Minimum and maximum values were calculated for each SCS textural class using the method
described by Nielson and Rogers (1990) (see Section 2.1). Should the user decide to investigate
this parameter further, the user may choose to collect a sample from this stratum and have a sieve
analysis conducted.
41
Porosity, n
The porosity of each soil stratum was included in the analysis because it affects the diffusion
coefficient of each stratum, as shown in Equation 10, and therefore the overall diffusive transport
of contaminant vapors across the subsurface. The typical value of the porosity for the userdefined SCS textural classification provided in DATENTER is treated as the default value in the
Vol analysis. These typical values can be found in the VLOOKUP worksheet as well as in the
User's Guide. Schaap and Leij (1998) determined these values and used a normal distribution.
This same study was used to derive the standard deviations of the porosity for each SCS textural
classification. Should the user decide to investigate this parameter further, the user may choose
to collect a sample from this stratum and have it analyzed for porosity.
Water-Filled Porosity, 0,
The water-filled porosity of each soil stratum was included in the analysis because it affects the
diffusion coefficient of each stratum, as shown in Equation 10, and therefore the overall diffusive
transport of contaminant vapors across the subsurface. The typical value of the water-filled
porosity for the user-defined SCS textural classification provided in DATENTER is treated as
the default value in the Vol analysis, which is equal to the average of the residual water content
(Or) for that SCS textural class and the field capacity at 1/3 bar (EQM 2004). In developing the
Vol spreadsheet, the range of values of the water-filled porosity for each SCS textural class was,
therefore, chosen to be between 0 , and the field capacity. Based on the manner in which the
EPA J&E model calculates the water-filled porosity average, a uniform distribution was assumed
to exist between the minimum and maximum values.
Should the Vol analysis indicate that there is high model sensitivity to and uncertainty in the
water-filled porosity of any particular soil stratum, the user may choose to collect a sample from
this stratum and have it analyzed for this parameter.
However, analysis results may be
influenced by recent weather events and, therefore, not be representative for the entire year. The
User's Guide recommends using the HYDRUS model, developed by the U.S. Department of
Agriculture (USDA), to instead determine long-term average water-filled porosities (EQM
2004). This model uses actual daily precipitation data as well as soil hydraulic properties,
including the van Genuchten properties, to estimate soil water content.
42
3.2 Spreadsheet Operation and Model Results - Groundwater Source Model
Given the parameters listed above, the Vol worksheet was developed to analyze the effect of
each individual or group of parameters on model output. The discussion below outlines the
structure and operation of the Vol worksheet as well as methods of data presentation for the
groundwater 'source model only. Equations imbedded in the worksheet are provided in more
detail in Appendix A. Section 3.2.2 will discuss the Vol worksheet for the soil gas sample model.
In the top left corner of the Vol worksheet (cells Al:F27), a table is provided that contains the
parameters that are analyzed in the Vol worksheet, the default values for these parameters,
minimum and maximum values, and the distribution of values. As discussed in Section 3.1, these
distributions and ranges were determined from the EPA spreadsheet User's Guide and the
supporting literature cited therein. Figure 6 provides an example of this table.
Building properties
default
min
AP
40
0
3
max
200
Distribution
Triangular
4
Soil properties
10
ii Particle Diameter of
12
13
15
fs
0.04
0.038
0 048
Uniform
avg
0375
0,054
min
max
0.055
Distribution
Normal
Uniform
N/A
Log Normal
N/A
Stratum Above
Water Table
A
1
n
o
-
0.053
kv
2678
N/A
no
0481
-
01
0.216
0,110
K,
-
N/A
20
21
2
8
B
23
0,320
Normal
Uniform
24
2
2?
C
nc
039
oC
0.076
-
0.049
-
Normal
0.1
Uniform
Figure 6: Example Building and Soil Properties Investigated in Vol Spreadsheet
Note that Ks and kv are not analyzed for soil strata B and C. These parameters are only analyzed
for the top-most soil stratum, A, because they only affect the advective flow of vapor across the
slab.
43
Next, the highlighted cells (cells Ki:K9) are values that must be user-defined within the Vol
worksheet. These include the contaminant of interest, the arithmetic or geometric mean of the
groundwater concentrations for that contaminant, the arithmetic or geometric standard deviation
of the concentrations, whether the property is new or existing construction, and the updated URF
and RfC values (if applicable). The user may choose to either provide the updated toxicity
values from IRIS or use the values stored in the model. Figure 7 provides an example table.
P
0
IAN
L
K
Pj
a
R
HighlIghted celf must be Ned In.
2
Chemical of Concern
|I7KI1Trichloroethylene
Arithmetic mean groundwater concentration, Cw (ug/L)
8
Arithmetic Standard Deviation of C. (ug/L)
5 Geometric mean groundwater concentration, Cw (ug/L)
r Geometric Standard Deviation of Cw (ug/L)
7 Updated URF (ug/m3)l
a Updated RfC (mg/m 3)
s New Construction? (Y or N)
0.4
OR the geometric meatanndard
deviation
Leave K7 and K8 blank if values in
VLOOKUP are up-to-date
00000041
0.002
N
Figure 7: Example of User-Defined Cells in Vol Spreadsheet
Next, a list of 5,000 simulations of random numbers between 0 and 1 (cells 112:S5014) is
provided for each parameter. These random numbers are regenerated each time the model is
operated. Differing numbers of simulations (500, 1,000, 2,500, and 5,000) were tested during
the development of the worksheet. It was observed that, when 5,000 simulations are used, Vol
analysis results were stable, i.e. not changing by more than 1%. Figure 8 provides an example of
the random probability generator.
I
K
__
16
AP
17
Simulation
Is
Default
19
1
0187
20
0204
0,696
27
2
3
4
5
6
7
8
9
28
10
21
n
2
24
2s
26
Ks
CW
0.678
0.064
0,748
0.489
0,391
0.728
0.751
0.764
0.885
0.212
0.786
S
0
N0P
IA
Random Number Generatlon
.A
nA
Particle Diameter
no
6,
8
nc
oc
0.660
0.726
0.885
0-111
0 416
0.525
0.716
0.304
0.206
0.225
0 951
----
0.648
0.005
0.541
0.056
0.374
0.010
0,462
0.155
0.102
0.525
0 155
0.124
0.510
0.837
0.034
0.496
0.495
0.310
0288
0.210
0.583
0.945
0.659
0.849
0.497
0.931
0.747
0.520
0.376
0.889
0.200
0.638
0.640
0.529
0.298
0816
0.638
0.365
0.891
0.618
0.005
0.961
0.944
0.890
0.391
0.587
0412
0.621
0869
0 525
0.507
0.912
0.870
0 143
0.196
0.611
0 572
0,198
0.095
0.659
0.082
0.600
0,896
0,445
0,311
0.343
0 460
0.916
0.605
0,435
0 575
0.945
0.259
0,794
0.550
Figure 8: Example of Random Probability Generator in Vol Worksheet
To the right of the 5,000 random numbers, a table (cells V12:AE5014) that provides either
default values or random values for each parameter for each simulation was created using the
44
following method. The user has the ability to turn parameters "on" (i.e., allow the table to
produce a random value for that parameter) or "off' (i.e., force the table to produce the default
value for that parameter). This is achieved by entering a sequence of O's and l's in the toggle
cell, A1l3. Parameters may not be included in the analysis at all, regardless of if they are
assigned a 0 or 1, if certain parameters are user-defined in the DATENTER worksheet, as
discussed in Table 1. The order of parameters for which either 0 or 1 is assigned is as follows:
Table 1: Order of Parameters Included in Vol Analysis Worksheet for a Groundwater
Source
Position in Toggle
Cell Value
(From Left to Right)
Parameter
When is Parameter
Included in Analysis?
1
AP
Only included in the analysis in the
case of an existing building because,
in the case of new construction, there
is no slab across which to measure the
pressure differential.
2
kv (if this is user-defined in
DATENTER) or Ks (if kv is
calculated) of soil stratum A
Only included in cases when Qsoil is
not user-defined in the DATENTER
worksheet because these parameters
are only used when the model must
calculate Qsoii.
3
CW
4
Always included.
Mean particle diameter of the
Always included.
soil stratum immediately
above the water table
5
nA
Always included.
6
nW^
Always included.
7
n
8
6second
9
n
10
10 owOnly
6third
Only included if the user defines a
second soil layer in DATENTER.
B
Only included if the user defines a
soil layer in DATENTER.
B
Only included if the user defines a
third soil layer in DATENTER.
c
45
included if the user defines a
soil layer in DATENTER.
Given this order of parameters, for example, a toggle cell value of "0110111111" generates
random values for all parameters, with the exception of AP and the mean particle diameter. Each
value of 1 in the toggle cell is referred to as an "experiment" in the spreadsheet. Therefore, in the
example provided, there are a total of eight experiments.
The random numbers between 0 and 1 in cells 119:S5018 provide the cumulative distribution
probabilities of a value being obtained in the field for that parameter. The inverse of this
cumulative probability and the statistical characteristics of that parameter then provide a random
value for that given parameter, as shown in the example in Figure 9.
.6
E 04
0
20
40
80
95
100
1o
140
160
IM
200
AP
Figure 9: Cumulative Distribution Function of the Pressure Differential
In the example of a cumulative probability of 0.65, the spreadsheet would calculate a pressure
differential of 95 g/cm-s 2 or 9.5 Pa. For parameters that are normally or lognormally distributed,
depending on the magnitude of the user-defined standard deviation, there may be negative
concentration values generated.
To account for this error, the worksheet ignores those
simulations in the analysis. Figure 10 provides an example of the random parameter value
generator based on the random probability generator in Figure 8.
46
V
13
X
Y
Z
AA
AB
AC
AD
AE
0
1
1
0
1
1
1
1
1
1
AP
Ks
C.
owo
nc
owc
40
40
40
40
40
40
40
40
40
40
40
2.68E+01
8.0
8.5
7.7
83
8.0
76
75
8.0
8.4
7.3
8.0
0.076
0.098
14
is
Thee columns are alway
16
17
1s
20
21
22
23
24
25
26
27
28
5.02E+01
3.40E+00
6.64E+01
2.58E+01
1.84E+01
609E+01
6.72E+01
7.11E+01
6.74E+00
4.77E+00
Soccupied.
Particle Olameter
n^A
eA
no
0.04
0.040
0.375
0.054
0.054
0.054
0.055
0.054
0.053
0.055
0.055
0.055
0.054
0.054
0481
0.463
0.216
0.39
0.293
0.419
0506
0.482
0.504
0.496
0,413
0.376
0.514
0.582
0.470
0.220
0.151
0.432
0.474
0.305
0.375
0.394
0.127
0.430
0.236
0 175
0.182
0.354
0.332
0.040
0.374
0.348
0.040
0.344
0.040
0.040
0.331
0.387
0.463
0.398
0.432
0.375
0.456
0.040
0.040
0.040
0.040
0.040
0.302
0.293
0.140
0.337
0.072
0.096
0.080
0.071
0.078
0.097
0.062
0.090
0.077
Figure 10: Example of Random Parameter Value Generator for Toggle Cell "0110111111"
in Vol Worksheet
In Figure 10, the parameters that are turned "on" or "off' are controlled by the cells in row 13.
For example, X13 has a value of 1 and, therefore, randomized C, values are generated for all
5,000 simulations. However, Y13 has a value of 0 and, therefore, the default model particle
diameter is provided for all 5,000 simulations.
From the 5,000 simulations of random or default values, the model provides values of the
following: the attenuation factor (a); the concentration of the contaminant of interest in the
indoor air of the building (Cbuilding); the carcinogenic risk; if the carcinogenic risk exceeds the
target (value of 1 provided) or not (empty cell provided); the noncarcinogenic risk; and if the
noncarcinogenic risk exceeds the target (value of 1 provided) or not (empty cell provided). The
intermediate calculations that produce these results are provided in the VOICALCS worksheet.
Figure 11 provides example results for the default parameter values (row 18) and ten of the 5,000
simulations (rows 19 through 28).
47
17
1s
-
0.0004
21
0,0004
0.0002
0.0003
22
0,0003
23
27
0.0004
0.0006
0.0005
0.0006
0.0003
2s
0.0003
19
2c
24
2.
26
AM
AK
AHAJ
1
Cbudng
Carcinogen
Non-Carcinogen
Incremental Risk Risk > Target? Hazard Quotient Quotient > Target?
0.67
0.62
0 30
0.57
051
0.64
0.95
0.82
0.95
0,43
0,46
1 13E-06
1,04E-06
5.06E-07
9.62E-07
8,60E-07
1 08E-06
1.60E-06
1 38E-06
160E-06
7,23E-07
771E-07
1
1
1
1
1
1
3.20E-01
297E-01
1.44E-01
2.74E-01
2.45E-01
3.09E-01
4.55E-01
3.93E-01
4.57E-01
2.06E-01
2-19E-01
Figure 11: Example Results for Toggle Cell "0110111111" in Vol Worksheet
One way to assess the potential value of information is to consider the carcinogenic hazard
classification, i.e. whether or not the calculated carcinogenic risk exceeds the target risk. If the
default option result (cell AJ 18) exceeds the carcinogenic target risk, then the spreadsheet counts
the number of simulations for which no exceedance is detected. If, however, the default option
does not exceed the target risk, then the spreadsheet counts the number of simulations for which
an exceedance of the target is detected. A percentage of the 5,000 simulations for which this is
the case is provided in cell AP19, "% of Simulations with Change in Carcinogenic Hazard
Classification", as shown in Figure 12.
AO
18
19
20
21
22
AP
Average Increase in Indoor Air Concentration
% of Experiments with Change in Carc. Hazard Classification
Average Difference in Carcinogenic Risk
% of Experiments with Change in Noncarc Hazard Classification
Average Difference in Noncarcinogenic Risk
-0.10
53.9
-1 62E-07
0.0
-4.60E-02
Figure 12: Example Results Table for 5,000 Simulations for Toggle Cell "0110111111" in
Vol Worksheet
The table in cells A018:AP22 provides other methods of evaluating the value of information
given a specific toggle cell value. For example, the average difference in carcinogenic risk is
calculated using the following equation:
J(Risk, -Risk laUl)
Average difference in risk =
48
n
(28)
where
Riski = Incremental carcinogenic risk for simulation, i
RiskDefauft
= Incremental carcinogenic risk for the default simulation
n = total number of simulations for which there are no errors in Excel
(< 5,000)
A similar equation was used to determine the average difference in the noncarcinogenic HQ. A
graphical representation of the distribution of the incremental carcinogenic risk values for a
given toggle cell value is also provided in the worksheet.
All possible toggle cell values are provided in cells BD14:BD145. There are 128 possible
combinations provided for cases when all 10 parameters are relevant (see Table 1 for details as to
when parameters are included in the analysis or not). Porosity and water-filled porosity for each
stratum are considered one option, i.e. if values of the porosity of A are varied, then so are values
of the water-filled porosity of A. Therefore, there are not 210 possible combinations, but 27 = 128
possible combinations instead.
The number of possible combinations is decreased based on
whether or not certain parameters are included in the Vol analysis. For each possible toggle cell
value, the "% of Simulations with Change in Carcinogenic Hazard Classification" is listed in
column BE. All possible combinations are then ranked based on this value, as shown in Figure
13. This ranking allows the user to determine which combinations of parameters have the most
influence on model output and which require further data collection.
Rank
17
% Change # Experiments
0110111111
1
53.9
8
19 0100111111
53.6
53.6
53.4
49.4
49.1
48.9
48.6
47.7
47.4
7
6
5
9
7
8
6
9
7
18
21
0110111100
0100111100
22
0111111111
23
24
0111111100
0101111111
25
0101111100
26
1110111111
2
3
4
5
6
7
8
9
27
1110111100
10
20
Figure 13: Example of Ranking of Toggle Cell Values by Percent of Simulations with
Change in Carcinogenic Hazard Classification in Vol Worksheet
49
The combinations of parameters are then grouped by the number of experiments. Combinations
within each group are then ranked based on the percent change, as shown in Figure 14.
SN
16
17
13
20
21
24
25
80
5P
1 Expenment
Sequence % Change
0010000000
15
0001000000
0.0
1000000000
6.1
0100000000 33.2
Rank
3
4
2
1
s
as
ST
2 Expenments
Sequence % Change Rank
0011000000
08
8
30.1
0101000000
2
34.3
1
0110000000
7
1001000000
4,6
0000110000
29.3
3
0.3
9
0000000011
4
1100000000
23.8
1010000000
70
6
0000001100
20.3
5
5R
27
28
29
30
BV
50
SW
3 Expenments
Sequence % Change
0.2
0001000011
ex
Rank
16
0111000000
0001001100
0001110000
0100000011
30.4
17,8
22.8
337
4
12
8
3
0010000011
0010110000
1110000000
1101000000
2.2
30.4
24.3
211
15
5
7
11
1011000000
1000110000
0100110000
1000001100
0100001100
0010001100
1000000011
5.4
267
46.0
21 7
44 6
224
64
14
6
1
10
2
9
13
Figure 14: Example of Ranking of Toggle Cell Values with the Same Number of
Experiments
3.3 Spreadsheet Operation and Model Results - Soil Gas Model
The Vol worksheet that was developed for the soil gas model is similar in structure to the Vol
worksheet discussed in Section 3.2. However, the mean particle diameter is not considered in
this analysis. The value of the particle diameter is used only to calculate the thickness of the
capillary zone, which is only necessary for the groundwater source model. By not including one
parameter, the cell locations of several of the model outputs are different from those provided in
the Section 3.2. There are also only 26 = 64 possible toggle cell combinations, instead of 128.
Another key difference is the calculation of the randomized concentration values. The user is
allowed to define the concentration and the standard deviation in either of two units, pIg/M 3 or
ppmv.
50
4. Case Study - Former Nike Battery PR-58
The following section discusses the case study that was investigated as part of this research. A
site with vapor intrusion risks from CHC contamination only was selected because vapor
intrusion of PHCs may be better evaluated using models that consider biodegradation, such as
BioVapor. The Vol worksheet, however, was developed only for a model that did not consider
biodegradation.
The Former Nike Battery PR-58, or "Property," is located in North Kingstown, Washington
County, Rhode Island. Figure 15 provides the site location.
Figure 15: Former Nike Battery PR-58 Location
For this case study, site history and sources of contamination are discussed. The relevant EPA
J&E models and Vol worksheet were applied to the site data, and the results are described in the
following sections. It should be noted that the primary source of information for this case study,
the 2014 Supplemental Remedial Investigation/Feasibility Study (RI/FS) Report prepared by
Stone Environmental, Inc. (Stone), is still in draft form. All information discussed in this case
study was derived from the Stone RI/FS (2014), unless otherwise cited.
4.1 Site History and Operations
Prior to 1942, the 44-acre Property was a mostly-wooded area surrounded by farmland. In 1942,
the U.S. Navy established Camp Thomas, which occupied approximately 142 acres, including
51
the Property. Following World War II, Camp Thomas was demolished and the Property was not
used again until 1954.
Between 1954 and 1956, the U.S. Army constructed Nike Battery PR-58 on the Property. A
large area on the western half was developed, but the eastern half remained wooded. The battery
consisted of two geographically separated areas: a battery control area and a launch area, which
included an adjacent assembly/service area. The Property consisted of the latter of these two
areas.
Operations at the site included assembling, servicing, maintaining, and preparing the
missiles for the firing battery. Use of the battery by the Army ended in 1962.
The Property was transferred from the Army to the Navy in 1964. From 1964 to 1974, the Navy
used the Property for disaster recovery training. The Navy constructed five to six buildings for
chemical, biological, and nuclear warfare training. In 1974, the Property was transferred to the
U.S. General Services Agency (GSA) and, in 1978, the GSA transferred ownership to the Rhode
Island Economic Development Corporation (RIEDC).
The Property was unused by RIEDC until 1980, when 2.2 acres of the Property were leased to
Peabody Clean Industries (PCI) for two years. During that time, the area was used for oil-water
separation (with an on-site leachfield for disposal of the water), hazardous waste storage, and as
a transfer facility. Three areas in particular were used for PCI activity, including: an area with
four 20,000-gallon aboveground storage tanks (ASTs) used for hazardous materials storage and a
leachfield; an area with two buildings used to store non-hazardous equipment and supplies; and a
temporary drum storage area.
Starting in 1981, buildings associated with the former Nike
Battery PR-58 were demolished. Currently, only one building associated with military activity
remains on the Property.
To the north and east of the Property, the Navy established the former Davisville Naval
Construction Battalion Center (NCBC) in February 1942. The NCBC provided assembly and
storage space for materials shipped to the Navy's advance bases. From the end of World War II
until 1951, the site was inactive, at which point the site became the Headquarters NCBC. In
1989, the NCBC facility was placed on the CERCLA National Priority List (NPL). Operational
closure of the site was obtained in April 1994.
52
4.2 Potential Historic Releases and Source Area
Investigations conducted at the site include the 2008 Remedial Investigations (RI) by the
Johnson Company (JCO), the 2009 to 2010 RI by JCO, a 2010 and 2011 U.S. Army Corps of
Engineers (USACE) investigation of potential soil gas impacts at the downgradient Town
Department of Public Works (DPW) facility, and the 2014 Draft Supplemental RI/FS by Stone
Environmental (Stone 2014).
According to the RI/FS, potential sources of contamination
include:
-
Solvent usage;
" Missile refueling and maintenance operations;
-
Navy disaster recovery training activities;
-
PCI hazardous waste storage and transfer; and,
" Unsecured Property conditions leading to illegal dumping.
Potential hazardous substances associated with these historical activities and operations include:
VOCs, particularly CHCs; semi-volatile organic compounds (SVOCs); PHCs; polychlorinated
biphenyls (PCBs); and metals (Stone 2014). During the 2010 and 2011 soil vapor investigation,
the primary contaminants of concern were CHCs. Previous site investigations had shown that
dissolved concentrations of CHCs in groundwater had migrated from the source area on the
Property to the Town of North Kingstown DPW facility (USACE 2012). The tables in Appendix
B provide the analytical results of groundwater, sub-slab soil vapor, and indoor air sampling at
the DPW facility. The remainder of this study will focus on the potential for vapor intrusion of
CHCs at the DPW facility.
Eight CHCs have been consistently detected above the applicable screening and regulatory levels
in soil and groundwater samples collected at the Property, and in soil gas, groundwater, surface
water, and sediment samples collected in its vicinity: TeCA, PCE, TCE, 1,1,2-TCA, 1,1-DCE,
cis-1,2-DCE, trans-1,2-DCE, and vinyl chloride (VC).
Site operations that may have caused
releases of these CHCs at the Property are discussed herein. During operation of the Nike Battery
PR-58, chlorinated organic solvents, including carbon tetrachloride, PCE, and TCE, were
reportedly used for cleaning of grease spills, parts cleaning, and general cleaning prior to
painting.
Waste solvents and cleaners were typically disposed of by pouring them into the
ground or into a sump, though the location of the disposal area and sump (if one existed) is not
known. However, the three missile magazines on the Property included a sump pit to collect
53
accumulating liquids. These sump pits were pumped and the contents were directly discharged
into a ditch that ran along the eastern side of the magazines. The drainage ditch emptied into a
catch basin that was then connected to the stormwater drainage system.
During Navy occupancy of the Property, disaster recovery training included the use of tear gas,
smoke pots, and biological agent simulants. The burial and disposal of used decontamination
supplies and gear in an area east of the magazine area may have occurred. It is not known if
actual chemicals were used in the training and decontamination, however, an empty 5-gallon can
labeled as "DANC" was found on the Property. DANC (decontamination agent, non-corrosive)
is a mixture of TeCA and 1,3-dichloro-5,5-dimethylhydantoin.
During PCI operations, four ASTs containing water, oil, and some solvent waste were located in
one area of the Property. The liquids from at least one AST were dispensed to an oil-water
separator and the water was then sent to a leachfield. The area surrounding the AST, oil-water
separator, and leachfield was reportedly saturated to ground surface with water and oils from the
tank. In 1983, RIEDM directed a cleanup of contaminated soil related to PCI activities.
Based on the evidence to date, a CHC source area has been determined by Stone Environmental.
The source area was delineated using the criteria of soil containing a total CHC concentration in
excess of 10 mg/kg and groundwater concentrations in excess of 1% of their single constituent
aqueous solubility. The approximately 15,039-ft2 source area extends from the upper 60 ft of
competent bedrock through approximately 12 ft of the basal glacial till/weathered bedrock (STG)
unit.
In the STG unit, comparison of the fractions of the contaminants in the source area shows strong
zonation vertically and horizontally with regard to TeCA versus PCE and TCE. In particular,
soil samples from the upper portion of the source area contain 77 to 99% TeCA whereas soil
samples from the deeper portion of the unconsolidated source area contain 16 to 44% TeCA.
This vertical zonation reflects the historical operations in the source area. PCE and TCE were
used primarily during Nike operations, whereas TeCA was used later during Navy operations
and, therefore, appears above PCE and TCE.
DNAPL has not been directly observed in the bedrock, nor has sampling and analysis of the rock
matrix been conducted.
Instead, delineation of the source area in the bedrock is based on
groundwater analytical results from 13 wells screened in bedrock, five of which exhibit CHC
54
concentrations greater than 1% of their single-component aqueous solubilities. However, the
presence of DNAPL in the bedrock in the source area is expected based on the groundwater
analytical results, the increase in groundwater concentrations with depth, and the presence of
DNAPL in the deeper portion of the STG unit.
4.3 Conceptual Model and the DPW Facility
The current conceptual site model (CSM) interpretation, according to the Stone Environmental
Draft Supplemental RL/FS, is that waste containing non-aqueous phase PCE, TCE, and TeCA
was released to ground surface east of the Nike missile magazines.
The waste percolated
through the vadose zone until it reached the water table, at which point the DNAPL continued
downward through groundwater. DNAPL continued to migrate through the STG unit into
bedrock fractures beneath the center of the source area. A trail of residual DNAPL was initially
left behind, but likely dissolved away over time, particularly in the more hydraulically
conductive units.
Some residual DNAPL exists as discontinuous droplets and blobs in the
portion of the source area within the STG unit. Most of the contaminant mass in the source area
in the STG unit is in the dissolved form in the lower permeability material. A significant fraction
is also sorbed to the organic matter on the soil particles. A similar phase distribution is expected
in the bedrock.
The source area, including the DNAPL, dissolved, and sorbed contaminant mass, are assumed to
be the source of groundwater CHC contamination hydraulically downgradient of the site. The
DPW facility is located approximately 0.25 miles southeast and downgradient from the CHC
source area on the Property. Figure 16 shows the location of the facility in relation to the Former
Nike Battery PR-58.
55
Figure 16: Location of DPW Building (Yellow) and Former Nike Battery PR-58 (Red)
The DPW building was constructed sometime prior to Spring 2002 and, according to the
Director of Public Works and staff, no engineered vapor barrier was installed as part of the
foundation (USACE 2012).
The CHC groundwater plume, originating from the source area, extends to the area below the
DPW facility.
Two shallow wells (SEI-01 and SEI-02) were installed in 2013 directly
upgradient and downgradient of the DPW facility. Additionally, a deeper well (OB-ED), located
in the vicinity of the facility, has been routinely sampled, and concentrations of TeCA, PCE,
TCE, and cis-1,2-DCE have been detected in exceedance of the maximum contaminant levels
(MCLs). For the purpose of this vapor intrusion modeling investigation, however, only data from
the shallow groundwater zone will be investigated. Based on the data obtained during the Stone
Environmental RI/FS, TeCA, PCE, TCE, cis- 1,2-DCE, and trans- 1,2-DCE have all been detected
in the shallow groundwater in the vicinity of the DPW facility. Of these, only TCE was detected
in excess of the EPA MCL of 5 [tg/L. 55.9 tg/L of TCE was detected at the northern well, SEI01, and 19.4 pg/L of TCE was detected at the southern well, SEI-02. Data tables for the one
round of sampling at SEI-01 and SEI-02 are provided in Appendix B-1.
To evaluate the potential for vapor intrusion as a result of the underlying groundwater
contamination, the USACE completed a vapor intrusion investigation at the DPW facility. Nine
sub-slab soil gas samples (eight locations and a duplicate) were collected in 2010 and 2011. In
56
2011, indoor air samples were also collected within the breathing zone at the same locations as
the sub-slab samples, but at a different time. The samples were analyzed for VOCs by EPA
Method TO-15. The analytical results are provided in Appendix B-2.
Indoor air sample analytical results detected 1,2,4-trimethylbenzene, chloroform, BTEX, and
TCE in all nine indoor air samples. PCE was detected in only five of the indoor air samples. It
should be noted that indoor air samples were collected without all potential VOC-containing
materials being removed due to the enormity of that effort as well as the disruption to DPW
activities that would result. Prior to the investigations, findings indicated that the building has
numerous products containing petroleum and non-chlorinated paint, ink, and cleaning solvents.
Additionally, several spray cans of a TCE-containing brake cleaner were found in the garage bay
in non-vented flammable storage cabinets. USACE requested that this product be removed and
not used prior to the sampling event. Cans of spray cleaner containing PCE were also observed
in the police repair bay storage room, however, these were not removed prior to the sampling
event.
To evaluate which contaminants were of concern in the indoor air data, the USACE used the
Risk Based Concentrations (RBCs) from the Tri-Service Environmental Risk Assessment
Workgroup (Tri-Service 2009). In the case of TCE, the RBC used was derived from the EPA
IRIS 2011 Guidance Toxicity Criteria (EPA 2014). Several CHCs were detected in excess of the
RBCs, including chloroform and TCE and, to a lesser extent, PCE.
Both PCE and TCE
concentrations tended to increase at downgradient locations. In the case of PCE at least, this
trend may be the result of the DPW activities because the highest PCE concentration (6 pig/m 3),
which is at least 10 times greater than the next highest PCE concentration, was detected in a
sample collected in the aforementioned police bay storage room. In the case of TCE, indoor air
concentrations ranged from 0.349 to 10.7 pig/m 3.
Sub-slab soil gas analytical results detected CHCs, including PCE and TCE and, to a lesser
extent, chloroform. Generally, concentrations of TCE in the sub-slab soil gas samples decreased
from north to south, with a range of 198 to 366 pg/m 3 in the northern portion of the facility and a
range of 9.8 to 39 [g/m3 in the southern portion of the facility. A less clear trend is noted for
PCE, where concentrations across the facility range from 11.3 to 50.5 pg/m 3, with the exception
of one non-detect sample. Using an attenuation factor of 0.1 and the aforementioned indoor air
57
RBCs, an estimated RBC for the sub-slab soil gas samples was calculated for each chemical.
Using these RBCs, concentrations of PCE and TCE were detected in exceedance of the RBCs in
eight of the sub-slab soil gas samples.
The USACE conducted a risk evaluation of the chronic environmental exposures to the
occupants of the DPW facility. Based on site reconnaissance, exposures were evaluated for an
adult worker at the DPW facility, with an exposure duration and frequency of:
*
8 hours per day;
m
250 days per year; and,
25 years duration of exposure.
0
For non-cancer effects, the USACE calculated the hazard quotient for each contaminant detected
using Equation 27 provided in Section 2.1. For cancer effects, the USACE calculated the excess
lifetime cancer risk using Equation 26.
According to the Comprehensive Environmental
Response, Compensation, and Liability Act (CERCLA), threshold levels for the incremental
carcinogenic risk ratio are between 1:10,000 and 1:1,000,000.
The USACE used the lower
bound incremental risk ratio of 1:10,000 for cancer effects and a hazard quotient value of one for
noncarcinogenic effects as the limit above which remediation would be required. The USACE
compared both the maximum measured indoor air concentration and the 95% Chebyshev upper
confidence limit (UCL) for each contaminant to these remediation goals. Table 2 provides the
results of this risk evaluation for CHCs that were detected in the indoor air samples. It should be
noted that the EPA has not determined an RfC value for chloroform and, therefore, no HQ could
be calculated.
58
Table 2: Noncarcinogenic Hazard Quotient and Incremental Cancer Risk Values for CHCs
at DPW Facility
Chemical
Chloroform
PCE
TCE
Indoor Air Concentration (tg/m
95% UCL
0.16
0.24
Maximum
2
95% UCL
Maximum
6
3)
Noncarcinogenic HQ
N/A
N/A
0.0018
0.0051
Cancer Risk*
3.0 x 10'
4.5 x 101.0 x 10-6
2.9 x 10-6
95% UCL
7
0.83
2.4 x 10-6
Maximum
11
1.2
3.6 x 10-6
*Note: The Risk Assessment Guidance for Superfund states that the resulting cancer risk estimates should be expressed using one significant figure only (EPA 2009).
However, the USACE report used two significant figures, which have been preserved in this table.
All of the 95% UCL non-cancer HQ values were below one. The cancer risk values associated
with the 95% UCL were also all below the target of 10-4. However, if one applies a cancer risk
target of 10-6, which the EPA recommends in the J&E user's manual, the cancer risk of PCE and
TCE exceed the target (EQM 2004).
These HQ and risk values were derived using RfC and URF values that were current on IRIS as
of 2012 (USACE 2012). The risk assessment calculations were revised using RfC and URF
values that are current as of 2014 (EPA 2014). Table 3 provides a comparison of the updated
RfC and URF values and the values that were current at the time of the vapor intrusion
investigation.
Table 3: Comparison of RfC and URF Values
RfC (mg/in3)
URF (m3/tg)
2012
2014
2012
2014
Chloroform
0.023
0.023
N/A
N/A
PCE
0.271
0.04
5.9 x 10-3
2.6 x 10-7
TCE
0.002
0.002
6.1
10-6
4.1 x 10-6
x
Table 4 provides risk assessment results using the 2014 toxicity values. The URF value for
chloroform has not changed; therefore, this chemical is not included in this table.
59
Table 4: Revised Noncarcinogenic Hazard Quotient and Cancer Risk Values for CHCs
Using 2014 Toxicity Values
Chemical
PCE
TCE
Indoor Air Concentration (ptg/n 3)
Non-Cancer HQ
Cancer Risk
95% UCL
2
0.011
4 x 10-8
Maximum
6
0.034
1
95% UCL
7
0.8
2 x 10-6
Maximum
11
1.3
4 x 10-6
x
10-7
For this revised set of toxicity values, the carcinogenic risk for PCE is no longer in excess of
either target risk value. The carcinogenic risk for inhalation of TCE, however, exceeds the more
stringent target risk value of 10-6. Therefore, further investigation of the exposure pathway for
this chemical is warranted.
As discussed previously, on-site activities may have impacted indoor air concentrations and the
associated cancer risks of PCE and TCE. The EPA J&E spreadsheet models may be used to help
determine the potential contribution vapor intrusion has to the indoor air concentrations.
The
J&E spreadsheet models and the Vol worksheet were applied to the soil gas and groundwater
data for PCE and TCE. The following sections discuss the results.
4.4 Soil Gas J&E and Vol Analysis
First, site information, including the SCS soil type below the slab, average soil temperature, and
the dimensions of the DPW facility, were defined in the DATENTER worksheet. The values of
these and other parameters are provided in Appendix C. Next, geometric means of the sub-slab
soil gas concentrations for PCE and TCE were calculated. Table 5 provides the EPA J&E
spreadsheet model results for the geometric mean soil gas concentration for the two
contaminants of concern. The HQ and carcinogenic risk values provided by the model are based
on toxicity values that were current as of 2003. Therefore, a second set of HQ and risk values
were calculated, as well, that used the current toxicity values.
60
Table 5: EPA J&E Spreadsheet Model Results for PCE and TCE for Soil Gas
Geometric
Mean of the
Chemical
Sub-slab
Soil Gas
Samples
Predicted
Predicted
a
Indoor Air
Concentration
carcnogeni
HQ (2014)
carcinogenic
HQ (2003)
Carcinogenic
Risk (2003)
1.1 x 10 5
1 x 10-8
1.6 x 10~4
7 x 10-7
3
Carcinogenic
Risk (2014)
(pg/m 3 )
(tg/m)
PCE
TCE
23.7
69.1
0.00119
0.0012
0.0282
0.0827
4.7 x 10-
4
9.4 x 10
6 x 10 0
3 x 10-
The predicted indoor air concentrations and associated HQ and risk values are at least one order
The cause of this discrepancy is likely the
of magnitude less than the measured values.
contribution of DPW facility activities and materials to the indoor air concentrations. For both
chemicals, the predicted noncarcinogenic HQ does not exceed the target of one and the
carcinogenic risk does not exceed the EPA-recommended target of 10-6.
As part of the Vol analysis, therefore, the potential for the predicted model output to exceed the
EPA-recommended carcinogenic risk targets was investigated. This analysis would help
determine if a human health risk still may exist due to model output variability. Furthermore, if
the Vol analysis results in a high percentage of simulations for which the risk is still below the
target, then the Vol results could potentially be used as another line of evidence for why
mitigation may not be necessary at the facility.
The noncarcinogenic HQ for PCE and TCE were both much less than one and, therefore,
noncarcinogenic effects were not considered further in this analysis.
However, carcinogenic
effects were considered. The Vol worksheet, using the soil and building parameters defined in
the DATENTER worksheet and the calculated
geometric
standard deviation of the
concentrations, calculated which groups of parameters, when varied, produce the greatest
percentage of simulations that exceed the target incremental carcinogenic risk. This criterion will
be referred to henceforth as the "percent change."
Both the 2003 and 2014 toxicity values were used in this analysis. Although the updated toxicity
values are presumably more accurate, the original EPA J&E spreadsheet model only uses the
2003 values in its calculations. Therefore, a Vol analysis that considers the 2003 toxicity values
is of interest. In the case of PCE, no group of parameters produced a percent change greater than
0% for either the 2003 or 2014 toxicity values; therefore, the detailed results of this analysis will
61
not be discussed further. In the case of TCE, however, percent changes greater than 0% were
produced using both the 2003 and 2014 toxicity values. Sections 4.4.1 and 4.4.2 provide the
results of these analyses.
4.4.1 Soil Gas Vol Analysis, Using the 2003 Toxicity Values
The Vol analysis for TCE produced combinations of parameters that yielded percent changes
greater than 40% when the 2003 toxicity values were used. A total of 16, as opposed to 64,
combinations were investigated because there is only one soil stratum between the sub-slab soil
gas sampling depth and the bottom of the building slab. Table 6 and Figure 17 provide the results
of the Vol analysis. Possible parameter combinations are ranked from greatest to lowest percent
change. It should be noted that the percent change values adjust slightly (<1%) each time the
Vol spreadsheet is operated, and the values provided in the following table may change. This
variability in the results is due to the fact that the randomized cumulative probability for each
parameter changes each time the worksheet is operated.
However, the ranking of the
combinations is assumed to be constant when the percent change values for different
combinations are not within 1%
of each other. This has been verified by comparing results
produced each time the worksheet is operated.
62
Table 6: Soil Gas Vol Analysis Results for TCE for a Target Risk of 10- and ModelProvided Toxicity Values
Parameters in Combination*
Rank
AP
1
2
3
4
5
X
X
X
X
X
6
X
7
8
9
10
X
X
11
n, 0,
C9
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
12
13
14
15
16
*X
Ks
X
X
X
X
Number of
Percent Change in
Experiments
Risk Category**
1
3
2
4
2
65.5
64.9
55.3
55.1
54.6
4
54.1
3
5
2
4
52.3
52.2
45.5
45.5
1
41.4
3
1
3
2
0
41.3
40.4
40.2
0.0
0.0
Parameter is varied in the combination.
**The default parameter values produce a model output that is below the target risk of
10-.
Therefore, this column provides the percentage of simulations for which
the model output exceeds the target risk of 10-6.
70
60
*
40
A
30
320
E 10
3
0
0 Exp.#lmt
Figure 17: Soil Gas Vol Analysis Results - Percentage of Simulations with Change in
Carcinogenic Risk Classification versus the Number of Experiments for TCE for Target
Risk of 10' and Model-Provided Toxicity Values
63
It should be noted that the top ranking combination is not the combination that includes all of the
parameters.
In fact, this combination is ranked
8 th
in Table 6.
One explanation for this
observation is the difference in how each parameter is distributed and defined by the user or
model. For all parameters, except for the pressure differential, the default model parameter value
is the mean (either arithmetic or geometric). Therefore, when a parameter is not turned "on" for
a given combination of parameters, the Vol analysis uses the mean value as the default.
However, in the case of the pressure differential, the user must define the default value. Unless
the user has evidence to the contrary, the user is likely to assign a value of 4 Pa based on the
instructions provided in the User's Guide. This value has been determined to be the mode, not
the mean value, of the pressure differential. As a result, the default model output that all 5,000
simulations is compared to is based on both mean and mode parameter values. If the default
pressure differential had instead been chosen to be the mean, then the top ranking combination
would be the one with all of the parameters turned "on." The mean of a triangular distribution is
calculated as follows:
Mean = Minimum + Mode + Maximum
3
(29)
Although using the mean would produce the expected ranking of combinations, the user-defined
pressure differential, whether it is the mode or mean, is more appropriate for this Vol analysis.
When the mode is used as the default pressure differential, the most critical parameter appears to
be the pressure differential across the slab, as shown in Table 6. The eight combinations that
produce the highest percent change in carcinogenic risk classification all include the pressure
differential. No information regarding the value of the pressure differential was provided in the
2014 Draft RI/FS (Stone 2014) or the 2012 vapor intrusion evaluation (USACE 2012).
Therefore, the EPA-suggested value of 4 Pa was used. There is likely, however, great variability
in this value across the DPW facility. As part of the 2012 vapor intrusion evaluation, the
USACE reviewed the June 2002 Test, Adjust, and Balance Report, prepared by Votta
Associates, to assess the heating, ventilation, and air conditioning (HVAC) at the DPW Facility.
Based on this report, the office spaces appear to be maintained at a slight positive pressure,
whereas the garages, bays, and shops may be maintained at a slight negative pressure. It is
unknown, however, if the system adjusts for winter versus summer loads (USACE 2012).
64
Figure 18 provides a histogram of the estimated carcinogenic risk values for the 5,000
simulations in the Vol worksheet when only the pressure differential is varied. The model output
for the default value of 4 Pa is provided in green and the target risk of 10.6 is provided in red.
140
120
100
>-80
S-Simulations
~60
a Target Risk
a Default Value
-- - - -
40
20
vvEstimated Incremental Carcinogenic
Risk (Per Millon)
Figure 18: Frequency of Incremental Carcinogenic Risk (x 10-6) for 5,000 Simulations, as
Compared to Target Risk and Default Value, for Varying Pressure Differential Values and
Model-Provided Toxicity Values
As the histogram shows, the default value (7 x 10~7) is below the target risk. However, as was
shown in the previous table, approximately 65.5% of simulations were found to produce a
calculated carcinogenic risk exceeding the target.
Based on these results, it may be useful to determine the pressure differential at the sample
locations throughout the DPW facility. This parameter, which largely affects the advective
transport of vapors across the slab, can be measured in the field using a manometer for little to
no cost. With a more accurate value of this parameter, the EPA J&E model would predict the
contribution that vapor intrusion has on the carcinogenic risk of TCE at the DPW facility with
reduced uncertainty. The predicted risk associated with using a pressure differential of 4 Pa is
almost one order of magnitude less than the actual risk associated with the indoor air
concentration of TCE. However, as the previous figure shows, there is the potential for this
65
contribution to be larger. When the pressure differential is varied, the indoor air concentration of
TCE for all 5,000 simulations ranged from approximately 0.0024 to 0.33 pg/m3. These values
are less than the 95% UCL of the indoor air sample TCE concentrations.
The second-ranked combination, which varies the pressure differential, porosity, and water-filled
porosity, produces a percent change within 1% of the top-ranked combination. As has been
discussed, the values provided in Table 6 may change slightly (<1%) between model runs.
Therefore, any combinations that have results within 1% of each other should be considered as
essentially equal. The distribution of possible carcinogenic risk values for the second-ranked
combination is provided in Figure 19.
140
120
100
N Simulations
8 Target Risk
a Default Value
40
20
Estimated Incremental Carcinogenic Risk (Per Million)
Figure 19: Frequency of Carcinogenic Risk (x 10~6) for 5,000 Simulations, as Compared to
Target Risk and Default Value, for Varying Pressure Differential, Porosity, and Waterfilled Porosity Values and Model-Provided Toxicity Values
In order to obtain more accurate values of the porosity and water-filled porosity, one must drill
through the slab at the facility, collect an undisturbed soil sample, and analyze the sample for
these parameters. Mathematical simulations may also help predict the value of the water-filled
porosity, as was discussed in Section 3.1. Even if the porosity and water-filled porosity values
obtained are assumed to be accurate, there is less value in collecting these additional data. The
66
additional costs of labor, drilling, and soil testing in order to obtain these values do not produce a
different result than obtaining an improved value by investigating the pressure differential only.
The analysis described above was for a target risk of 10-6. If, however, a target risk of 10-4 is
used, as the USACE used in the vapor intrusion investigation, the following Vol results are
obtained.
Table 7: Soil Gas Vol Analysis Results for TCE for a Target Risk of 10-4 and ModelProvided Toxicity Values
Number of
Percent Change in
AP
Ks
C9
n, O,
Experiments
Risk Category**
X
X
X
X
3
2.7
X
5
2
4
2.7
2.1
2.0
Parameters in Combination*
Rank
1
2
3
4
X
X
X
X
X
X
X
*X = Parameter is varied in the combination.
**The default parameter values produce a model output that is below the target risk of
10-4.
Therefore, this column provides the percentage of simulations for which
4
the model output exceeds the target risk of 10- .
The remaining 12 possible combinations all yielded percent changes below 1%. The top ranking
combinations when a target risk of 10-4 is used are different from the top ranking combinations
when a target risk of 10-6 is used. Figure 20 illustrates this observation.
67
1OE-02
LOE403
1.OE.04
Target Risk
LOE-05
Model Output
.Default
----
Measured
indoor Air Risk
LOE-06
C
-..
L E1
M axim um
--
I
75th Percentile
25th Percentile
iOE09
Mwnimumn
LOE-10
AP
AP, n, e
AP, K, n, 0
AP, K, C
AP, K
K, C
K, C, n, 0
All
Parameters In Combination
Top Ranking Combinations
for Target Risk of W'%
I
Top Ranking Combinations
for Target Risk of 10D
Figure 20: Ranges of Estimated Carcinogenic Risk Values for Eight Combinations of
Parameters - Soil Gas Vol Analysis Results for TCE Using Model-Provided Toxicity
Values
As expected, the figure demonstrates that when a different combination of parameters is used,
different ranges of carcinogenic risk are produced.
By comparing the top four ranking
combinations for each target risk, one may observe the top ranking combinations for the less
stringent target risk (10 -4) all include Cg. On the other hand, the top four ranking combinations
for the more stringent target risk (10-6) do not include this parameter.
The wider range of values produced by including this parameter is likely caused by the great
variability in the sub-slab soil gas concentrations detected at the DPW facility. The smaller
range of estimated risk values for the remaining four combinations do not include values in
excess of 10~4. Therefore, no percent change in carcinogenic risk category would be observed for
this target value. It should be noted that the top four combinations for a target risk of
1 0 -4
produce percent changes in excess of 40% when the more stringent target risk of 10-6 is used. In
other words, Cg may be a valuable parameter to investigate further in the case of a target risk of
106 , as well. Even though combinations including this parameter do not rank in the top four for
the more stringent risk, the actual percent change in risk category for the simulations is greater
using a risk of 10~6 than when using a risk of 1 0 ~4.
68
The results for all eight combinations can be compared to the indoor air carcinogenic risk
calculated by the USACE based on the indoor air concentrations of TCE, as represented by the
orange line in the figure. For consistency, this value was adjusted using the 2003 toxicity values.
The median value of the carcinogenic risk for each combination lies below the calculated indoor
air risk. Moreover, for the top four combinations for a target risk of 10-6, the entire range of
possible carcinogenic risk values lies below the calculated indoor air risk. The majority of the
simulations, therefore, produced a carcinogenic risk below the calculated risk associated with the
indoor air concentrations. This observation suggests that the J&E Vol worksheet for a soil gas
sample largely produced results within an acceptable range of values.
4.4.2 Soil Gas Vol Analysis, Using the 2014 Toxicity Values
If, instead, the updated URF value for TCE is used in the analysis, different results emerge. In
the default scenario, the model predicts a carcinogenic risk below 10-6 . Therefore, the percent of
simulations in which the model predicts a carcinogenic risk in excess of the target would be an
appropriate value to investigate. When a target risk of 10- 4 is used, no combinations yield a
percent change greater than 1%. When a target of 10-6 is used, however, the results in Table 8 are
produced.
Table 8: Soil Gas Vol Analysis Results for TCE for a Target Risk of 10~6 and Updated
Toxicity Values
Rank
1
2
3
4
AP
X
X
Parameters in Combination*
C9
Ks
X
X
X
X
X
X
X
X
5
X
X
6
7
X
X
X
8
X
n, 0W
X
X
X
X
X
Number of
Experiments
3
5
2
4
Percent Change in
Risk Category**
10.1
9.9
7.5
7.5
2
1.7
4
3
1.6
0.6
1
0.6
*X = Parameter is varied in the combination.
**The default parameter values produce a model output that is below the target risk of 10- . Therefore, this column provides the percentage of simulations for which
the model output exceeds the target risk of 10-'.
The remaining eight possible combinations all yielded percent changes equal to 0%. It should be
noted that these remaining eight combinations all excluded Cg. The user may decide, therefore,
that this parameter is the most critical parameter. The greatest impact on the percent change in
69
risk category occurs when all the parameters, with the exception of the porosity and the waterfilled porosity, are varied. However, based on the percent change values in the table, the cost in
collecting these data will likely not be offset by an improved understanding of the inhalation
carcinogenic risk for TCE. A rigorous cost-benefit analysis was not included in the scope of this
work, however, one could be included in future research on this topic.
The rankings and percentages provided in Table 8 are different from those provided in Table 6.
The only difference between the model inputs for each set of results was the URF value for TCE.
As shown in Table 5, the default risk using the 2003 URF value was greater than the default risk
using the 2014 URF value. This decrease in risk values that is observed using the updated URF
value would cause all 5,000 simulations to have decreased risk values, as well. As a result, a
smaller percentage of simulations with a calculated risk value that exceeds the target of 10-6
would be observed.
It should also be noted that different parameters were determined to be critical depending on the
URF value used. When the 2003 URF value is used, the critical parameter for a target risk of 10
6 was
determined to be AP. When the 2014 URF value is used, the critical parameter for a target
risk of 10-6 was determined to be Cg.
The potential cause of this observation has not been
determined. However, this observation illustrates the impact that the URF value has on
determining what data to collect, and the importance of updating the URF value prior to
operating the J&E model and Vol worksheet.
In summary, the four toxicity/risk scenarios considered in the Vol analysis are as follows:
1.
Target risk of 10-6, using the model-provided toxicity values;
2. Target risk of 10-4, using the model-provided toxicity values;
3. Target risk of 10-6, using the updated toxicity values; and,
4. Target risk of 10-4, using the updated toxicity values.
A summary of these four scenarios is provided in Table 9. Results for 15 combinations are
considered because the 16th combination is the case when no parameters are varied.
70
Table 9: Soil Gas Vol Analysis Results for TCE for Four Toxicity/Risk Scenarios
URF
Value
Source
ModelProvided
(2003)
Updated
(2014)
Calculated
Indoor Air
Risk
6 x 10
5
Model
Default
Risk
100
Default
Risk is
Above/
Below
Target
Below
Simulations
Considered
if Above/
Below
Target
Above
# of
Combinations
with Percent
Change in Risk
Category >1%
14
Approx. Range
of Percent
Change Values
for all 15
Combinations
0-65.5
104
Below
Above
4
0-2.6
10-
B
Below
Below
Above
Above
6
0
0-10.1
All 0
Target
Risk
7 x 10-7
1
2 x 10-6
3 x 10-
10
In all cases, as would be expected, the carcinogenic risk calculated using the indoor air analytical
results is greater than the predicted contribution of risk due to vapor intrusion. This result is
likely due to activities and materials at the DPW facility contributing to the indoor air
concentrations in addition to the contributions from vapor intrusion. The closer the default risk
prediction is to the target risk value, the greater the percent of simulations with a change in
carcinogenic risk category. The difference between the default risk and the target risk depend
largely on the user-defined URF value and the user-defined target risk. The parameters that are
most critical change depending on the toxicity/risk scenario.
For example, when the model-
provided URF value and a target risk of 10~6 are used, the pressure differential is most critical.
However, in other toxicity/risk scenarios, combinations including this parameter have less of an
impact on the percent change.
4.5 Groundwater J&E and Vol Analysis
A similar process to the one described above was used to predict the indoor air carcinogenic risk
associated with vapor intrusion from a groundwater source. The same input parameters were
defined in the DATENTER worksheet for the groundwater source J&E model as in the soil gas
sample J&E model, as shown in Appendix C.
A geometric mean of the groundwater
concentrations for PCE and TCE at the two shallow wells, SEI-01 and SEI-02, were calculated.
Table 10 provides the EPA J&E spreadsheet model results for the two contaminants of concern.
71
Table 10: EPA J&E Spreadsheet Model Results for PCE and TCE for a Groundwater
Source
Chemical
Geometric
Mean of the
Grondte
uwaer
Samples
Predicted
Peitd
Indoor Air
a
Concentration
3
(Pg/m
Noncarcinon
nogeni
HQ (2003)
Predicted
Carcinogenic
Noncarcinogenic
Risk (2003)
HQ (2014)
Carcinogenic
Risk (2014)
(pg/L)
*
PCE
0.77
0.000486
0.126
4.8 x 10-
6 x 10-'
TCE
32.9
0.000517
3.50
0.02
3 x 10-
This value rounds to 1x 10-6, but is calculated as 1.2 x
7.2 x
10-4
0.398
3 x 10-9
1
x
10-6 *
10-6.
The predicted HQ and carcinogenic risk values for PCE are at least one order of magnitude less
than the values calculated using the indoor air concentrations (see Table 4 for comparison). The
predictions for the HQ and carcinogenic risk for TCE, however, are less than, but within, one
order of magnitude of the values calculated using the indoor air concentrations. This difference
between PCE and TCE could potentially be due to a greater use of PCE than TCE at the facility.
Similarly to the Vol analysis described in Section 4.4, the Vol worksheet for a groundwater
source was used for both PCE and TCE for the four possible toxicity/risk scenarios. Screenshots
of the Vol worksheet for PCE and TCE are provided in Appendix C.
The default
noncarcinogenic HQ for PCE and TCE (provided in Table 10) were both much less than one and,
therefore, were not considered in this analysis. In the case of PCE, because the default
carcinogenic risk was below the targets of 104 and 10-6, the Vol analysis was conducted to
assess the potential for the model output to be greater than the targets. The Vol worksheet, using
the soil and building parameters defined in the DATENTER worksheet and the calculated
geometric standard deviation of the groundwater concentrations, calculated which groups of
parameters, when varied, produce the greatest percentage of simulations that exceed the target
risk. No group of parameters produced a percent change greater than 1% for any of the four
scenarios listed above for PCE; therefore, the detailed results of these analyses will not be
discussed further.
In contrast to PCE, the Vol analysis results for TCE for two of the four toxicity/risk scenarios
revealed combinations of parameters with percent changes in the carcinogenic risk category
greater than 1%. The Vol analysis results for all four scenarios for TCE are provided in Table
11. In the case of the groundwater source model for the DPW facility, a total of 32 combinations
(including the combination in which no parameters are varied) are possible.
72
Table 11: Groundwater Source Vol Analysis Results for TCE for Four Toxicity/Risk
Scenarios
URF
Value
Source
Calculated
Indoor Air
Risk
Model
Default
Risk
Provided
(2003)
6 x 10-
Updated
Updated)
(2014)
2 x 10
-6
Default
Risk is
Above/
Below
Target
Simulations
Considered
if Above/
Below
Target
# of
Combinations
with Percent
Change in Risk
Category >1%
Approximate
Range of Percent
Change Values
for all 31
Combinations
10-6
Above
Below
0
0-0.1
5
10-4
Below
Above
16
0-4.7
-6
10-6
Above
Below
30
0_All_
0-50.1
Model5
Target
Rgek
Risk
3 x 10-
1 x 10A*
i0 4
Below
Above
0
All 0
* This value rounds to I x 10-6, but is calculated as 1.2 x 10-6. Therefore, it is greater than 10.6, and simulations that are below this target are
considered in Vol analysis.
As would be expected, the calculated indoor air risk is greater than the model default risk for all
toxicity/risk scenarios. The greatest range of percent change values was calculated for the Vol
analysis scenario in which the updated URF value and the more stringent target risk are used.
This scenario will be discussed in more detail. This toxicity/risk scenario not only provides the
greatest range of percent change values; it also includes the URF and target risk values that
would most likely be used in a risk assessment. Table 12 and Figure 21 provide the detailed
results of the Vol analysis for this toxicity/risk scenario.
73
Table 12: Groundwater Source Vol Analysis Results for TCE for a Target Risk of 106 and
Updated Toxicity Values
Number of
Experiments
Percent
Change in
Risk
Category**
X
4
2
5
3
3
50.1
48.1
47.8
45.7
44.8
X
5
42.2
X
X
4
3
41.9
41.0
X
1
6
39.9
39.8
3
1
4
2
39.1
38.6
37.8
37.5
Parameters in Combination*
Rank
AP
I
2
3
4
5
6
X
K
sP
K
C
g
X
X
X
X
X
X
X
X
X
X
X
X
9
10
X
X
X
X
X
X
X
X
X
X
X
X
X
16
17
18
19
20
21
X
X
X
X
X
22
X
23
X
X
24
X
X
X
X
X
X
4
36.4
X
X
X
2
4
4
2
5
5
35.2
34.2
33.9
31.5
31.1
30.9
3
28.5
2
28.4
X
X
X
X
X
X
X
X
X
X
X
X
3
26.8
X
2
21.7
X
3
18.9
X
X
3
4
15.5
15.0
X
26
X
27
28
X
X
X
29
30
X
X
X
1
2
9.6
8.1
X
1
0
31
0
32
=
X
X
X
25
*
X
X
12
13
14
15
n O
'0
X
X
7
8
11
Particle
Diameter
Parameter is varied in the combination.
**The default parameter values produce a model output that is above the target risk of 10-. Therefore, this column provides the percentage of simulations for which
the model output is below the target risk of 106.
74
60
150
+
+
+
+
40
-+
230
0
1
3
2
45
6
# Expeimofts
Figure 21: Groundwater Source Vol Analysis Results - Percentage of Simulations with
Change in Carcinogenic Risk Classification versus the Number of Experiments for TCE for
Target Risk of 10-6 and Updated Toxicity Values
Figure 22 provides the possible ranges of the top 8 combinations along with the model default
risk, the target risk, and the calculated risk associated with the indoor air concentrations.
75
:1-04
.
Target Risk
Default Model Output
Measured Indoor Air Risk
Maximum
11
:-06
75th
Percentile
Median
25th Percentile
E
-
Minimum
--
IE-CS
K, C n, 0
K,
CC,
Diameter, n. e
K, C
Diameter
AP,K C
K, n, 0
n, e
K, Diameter,
n, 8
C n. 0
Parameters in Combination
Figure 22: Ranges of Estimated Carcinogenic Risk Values for Eight Combinations of
Parameters - Groundwater Source Vol Analysis Results for TCE Using Updated Toxicity
Values
For every combination in the figure, the measured indoor air risk is greater than the
7 5 '1
quartile
of the estimated carcinogenic risk values. This observation suggests that, for each combination,
more than half of the J&E Vol worksheet results are within what would be expected for this site.
Furthermore, the median risk value for all of the combinations is approximately equal to the
target risk, as would be expected from the results in Table 12. This observation indicates that,
for the DPW facility, the contribution to the indoor air carcinogenic risk that can be associated
with vapor intrusion from groundwater may be within an acceptable risk range. This finding
may influence decisions regarding how to mitigate or remediate the site.
As part of this decision-making, an engineer may choose to use the EPA J&E groundwater
source default model results to determine if the risk associated with vapor intrusion exceeds the
target risk of 10-6. If it does, as is the case for the DPW facility, the model user may decide to
mitigate this risk. However, as the results for this case study suggest, the Vol analysis may yield
simulation results that are largely below this target risk. Therefore, it may be cost-effective to
refine the parameter values to determine if the risk associated with vapor intrusion from the
76
groundwater actually exceeds the target. If it does not, then mitigation and remediation costs may
decrease.
For this case study, the results indicate that the hydraulic conductivity of the soil stratum
immediately below the slab is likely the most critical parameter. The top seven combinations in
Table 12 and Figure 22 all include this parameter and, when only one experiment is conducted, it
is this parameter that causes the greatest percent change in the carcinogenic risk category. The
user can reduce the uncertainty in the hydraulic conductivity value by either collecting a sample
and analyzing for this parameter, or conducting a slug or pumping test.
The groundwater concentration, however, may also be useful to investigate further. The top four
combinations all include this parameter.
Furthermore, the percent change when only this
parameter is varied is within 1% of the percent change when the hydraulic conductivity is varied.
This result is expected in part because the geometric mean and standard deviation of the
groundwater concentrations were calculated based on only two data points (one round of
sampling at two wells). Therefore, the potential for high variability in the simulated groundwater
concentrations would be expected. The uncertainty in the groundwater concentration could be
reduced with additional rounds of sampling and/or the installation of additional shallow wells.
Depending on the anticipated groundwater monitoring program at the facility, these additional
data points may come at no extra cost. For example, if an additional well is scheduled to be
installed and sampled, then reducing the uncertainty in the groundwater concentration will come
at no additional cost. If that is the case, then combinations that only include the groundwater
concentration may be of interest. Under the assumption that additional groundwater data will be
collected, the decision tree in Figure 23 may aid an engineer in determining which other
parameters may be of interest.
77
55
C,K^n0
50
C
0C,C,,n,
CKD
3 45
C,KAP,n,6
.240
S
C,K,D,
C,KAnO
C
D
AP,n,e
cCK,
35
C
n,O
E
CCAPCP,
30
C,DAP
25
1
2
3
4
5
6
# Experiments
Figure 23: Decision Tree for Groundwater Vol Analysis of TCE
C = groundwater concentration; K = hydraulic conductivity; D = mean particle diameter; n = porosity;
0 = water-filled porosity; AP = pressure differential
Each node in the decision tree represents a combination of parameters that can be investigated.
Starting with the first node, which only considers the groundwater concentration, an engineer can
decide which single parameter to investigate next by considering nodes that contain two
experiments. Based on the results in Figure 23, investigating the hydraulic conductivity yields
the greatest percentage of simulated carcinogenic risks that are below the target. The decision
tree can also be used at later starting points than just the node labeled as "C." For example, if a
slug test has also already been scheduled at the site, then the starting point would be the node
labeled as "C,K." From this node, additional parameters could be selected that would yield
increased percentages of simulated carcinogenic risks that are below the target. In this scenario,
additionally investigating the porosity and water-filled porosity would provide the greatest
percentage of simulated carcinogenic risks that are below the target.
When the cost of groundwater sampling is covered by other items in a project budget, this
analysis can help an engineer determine other parameters to investigate. However, if there is a
78
cost associated with collecting and analyzing additional groundwater samples, then a cost-benefit
analysis would be required to determine which parameters or combinations of parameters should
be investigated further. Under the assumption that the default model output value for the
carcinogenic risk is accurate, a cost estimate of the anticipated remediation or mitigation costs
could be produced. Next, the cost to obtain the value of each parameter as well as the combined
cost of the parameters in each combination would need to be calculated. Combinations that have
costs less than the cost of the anticipated remedial or mitigation design could be analyzed in the
Vol worksheet. The combination that produces the greatest percent change for the lowest cost
would likely be selected as part of a future site investigation.
4.6 Comparison of the Groundwater and Soil Gas Model Results
The results of the groundwater and soil gas J&E and Vol analyses differ. When the soil gas EPA
J&E model was applied to the soil gas data from the site, the model predicted an incremental
carcinogenic risk below the target of 10-6 for TCE. The Vol analysis, therefore, determined
which combinations of parameters produced simulated risk values exceeding this target. When
the groundwater EPA J&E model was applied to the groundwater data from the site, however,
the model predicted an incremental carcinogenic risk above the target of 10-6 for TCE. The Vol
analysis then determined which combinations of parameters produced simulated risk values
exceeding this target.
Both the soil gas and groundwater models rely on the calculation of an attenuation factor, which
provides the ratio of the indoor air concentration of a contaminant to either:
m
the sub-slab soil gas concentration directly at the sample point, in the case of the soil gas
model; or,
" the sub-slab soil gas concentration immediately above the groundwater table, in the case
of the groundwater model.
Both models calculate the attenuation factor using Equation 21 in Section 2.1, which for
reference is reproduced below:
79
SDqf7A
exp
B
DcrackAcrack
Qhu10dinglT ,
a-
'I A 8
exp
QClLDracT
Df A
Q~~rc+
Dcrackcra
(
Q6uilding
Q1ollLcrack
_
A
QiiLcrac
3exp
.+ Pf
T
\
si IT
_
Dcrack Acrack
D_,
D
(21)
-1dcrc
All of the quantities in this equation are the same in the groundwater and soil gas models, except
for LT and Df.
LT,
which is the distance from the bottom of the slab to the sub-slab soil gas
point of reference, would be larger in the groundwater model than in the soil gas model. In the
soil gas model, D', which is the effective diffusion coefficient in the subsurface, would be
calculated based on the diffusion coefficient in the portion of the sand layer below the DPW
facility that is in the vadose zone. In the groundwater model, D 1 would be calculated based on
the diffusion coefficients in the vadose zone and in the capillary zone.
The attenuation factor in both models is multiplied by the source soil gas concentrations to
predict the concentration in the indoor air.
In the case of the soil gas model, the source
concentration is the user-defined mean sub-slab soil gas concentration.
In the case of the
groundwater model, the source concentration is calculated using Henry's Law and the userdefined mean groundwater concentration.
Despite these differences, the predicted indoor air concentration should be the same for both
models. The cause of the discrepancy in the predicted values can be investigated by determining
the soil gas concentration below the slab that is predicted by the groundwater model. This value
can then be compared to the measured sub-slab concentrations.
The EPA J&E groundwater
model does not provide the sub-slab concentration directly; therefore, this value had to be
calculated using the following equation:
Csg
where
c"do"rair
asg
(30)
Csg = predicted soil gas concentration of TCE at the depth that the soil gas
measurements were collected
Cindoor air =
predicted indoor air concentration of TCE by using the
groundwater model
80
asg =
predicted ratio of the indoor air concentration to the soil gas
concentration at the depth that the soil gas measurements were collected
This equation was applied to the indoor air concentration predicted by the EPA J&E groundwater
source spreadsheet using the default parameter values. The predicted sub-slab concentration was
thereby calculated to be 4,160 ptg/m 3 , which is outside the range of the measured values (9.8 to
355 pg/m3 ). The geometric mean of the measured values, 69 tg/m 3, which was used as the
source concentration in the soil gas model, is two orders of magnitude less than the predicted
value in the groundwater model. Therefore, the EPA J&E groundwater model did not accurately
predict the measured concentrations immediately below the slab at the DPW facility. This
inaccuracy could be due to any number of the limitations discussed in Section 2.3.
81
5. Conclusions and Opportunities for Future Research
The investigation described above as applied to the former Nike Battery PR-58 case study
demonstrates how Vol analyses can be used to better predict the EPA J&E spreadsheet model
results. The Vol analysis provides the user with a helpful tool in determining to what potential
degree concentrations of contaminants of concern in the indoor air of a building can be attributed
to vapor intrusion. Vol also allows the model user to better select which otherwise uncertain
parameters to investigate further. When the default model output exceeds the target carcinogenic
risk, the Vol analysis worksheet helps the model user select parameters that, with improved
accuracy, could reduce remediation or mitigation costs. When the model output is below the
target risk, the Vol analysis worksheet helps the model user determine if a human health risk still
may exist due to model output variability. Furthermore, if the Vol analysis results in a high
percentage of simulations for which the risk is still below the target, then practitioners could
potentially use this as another line of evidence for why mitigation may not be necessary.
The J&E spreadsheet model output values rely on the user-defined soil and building parameter
values; the built-in chemical, toxicity, and soil parameter values; and the user-defined target
carcinogenic risk and noncarcinogenic hazard quotient.
The results of the case study
demonstrate that variability in these parameters causes variability in the model-predicted indoor
air concentrations of contaminants and the associated incremental carcinogenic risk.
The Vol worksheets that have been developed for the EPA J&E groundwater source and soil gas
sample models have several features that could be improved.
The following list provides
suggestions for future research:
"
Improved methods of representing cost and value: Section 4.5 closed with a general
discussion of how the user can take the Vol worksheet results to make remedial
investigation decisions. An improved version of the worksheet could include a built-in
cost analysis.
This analysis would require the user to provide an estimate of the
mitigation or remediation costs for the site, based on the default model risk result, as well
as the cost of investigating each parameter or group of parameters. Combinations of
parameters that would cost less than the anticipated remedial cost would be identified.
For these combinations only, a plot of the percent change in the carcinogenic risk
82
category for the 5,000 simulations versus the cost of the investigation would then be
generated.
-
Additionalparameters to investigate: The building parameters that were not included in
the worksheet (i.e. the building mixing height and the indoor air exchange rate) could be
included in a future version of the Vol analysis worksheet.
-
Additional case studies: The Vol worksheets should be applied to other sites to identify
other areas of improvement. Applying data from other sites to the worksheets would also
help to identify trends in analysis results.
*
Expanding VoI to other vapor intrusion models: This study only considered the EPA J&E
spreadsheet models for soil gas and groundwater sources.
Other models, including
BioVapor, should be considered in future research.
-
Improvements to the EPA J&E spreadsheet models: This study identified several of the
key assumptions and limitations of the EPA J&E spreadsheet models for soil gas and
groundwater sources. With these limitations in mind, an improved spreadsheet model
could be developed.
In conclusion, improved applications of Vol to vapor intrusion models, as identified above, can
help engineers and scientists make better remedial investigation decisions and conclusions.
83
6. References
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Biodegradation on Soil Vapor Intrusion into Buildings: Influence of Degradation Rate,
Source Concentration, and Depth." Environmental Science & Technology 40 (7) (April):
2304-2315. doi:10.1021/es051335p.
Abreu, Lilian DV, Robert Ettinger, and Todd McAlary. 2009. "Simulated Soil Vapor Intrusion
Attenuation Factors Including Biodegradation for Petroleum Hydrocarbons."
Groundwater Monitoring & Remediation 29 (1): 105-117.
Back, Pdr-Erik, Lars Rosen, and Tommy Norberg. 2007. "Value of Information Analysis in
Remedial Investigations." AMBIO: A Journal of the Human Environment 36 (6)
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water. Ground Water Monit. Rem. 14:129-140. doi:10. 1111/j.1745-6592.1994.tb00098.x
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84
Davis, G. B., B. M. Patterson, and M. G. Trefry. 2009. "Evidence for Instantaneous Oxygenlimited Biodegradation of Petroleum Hydrocarbon Vapors in the Subsurface." Ground
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Davis, John W., J. Martin Odom, Kim A. DeWeerd, David A. Stahl, Susan S. Fishbain, Robert J.
West, Gary M. Klecka, and John G. DeCarolis. 2002. "Natural Attenuation of
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DeVaull, George E. 2007. "Indoor Vapor Intrusion with Oxygen-limited Biodegradation for a
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DeVaull, George, Robbie Ettinger, and John Gustafson. 2002. "Chemical Vapor Intrusion from
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Eaton, R. S., and A. G. Scott. 1984. Understanding radon transport into houses. Radiation
Protection Dosimetry 7 (1-4):251-253.
English, C. W., and R. C. Loehr. 1991. "Degradation of Organic Vapors in Unsaturated Soils."
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. "OSWER Final Guidance for Assessing and Mitigating the Vapor Intrusion Pathway
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http://www.epa.gov/oswer/vaporintrusion/documents/vaporlntrusion-final-guidance20130411 -reviewdraft.pdf
. "Integrated Risk Information System (IRIS)." Accessed 1 May 2014.
http://www.epa.gov/iris/
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Environmental Quality Management, Inc. (EQM). 2004. "User's guide for evaluating subsurface
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Fetter, C. W. 1994. Applied Hydrogeology, 3rd Ed., Prentice-Hall, Inc. Englewood Cliffs, New
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Ghazali, F.M., R.N.Z.A. Rahman, A.B. Salleh, and M. Basri. 2004. "Biodegradation of
hydrocarbons in soil by microbial consortium." Int. Biodeterior. Biodegrad. 54:61-67.
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Grimsrud, D. T., M. H. Sherman, and R. C. Sonderegger. 1983. "Calculating infiltration:
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Haest, P.J., D. Springael, and E. Smolders. 2010. "Dechlorination Kinetics of TCE at Toxic TCE
Concentrations: Assessment of Different Models." Water Research 44 (1) (January):
331-339. doi:10.1016/j.watres.2009.09.033.
Hers, I., J. Atwater, L. Li, and R. Zapf-Gilje. 2000. "Evaluation of vadose zone biodegradation
of BTX vapours." J. Contain. Hydrol. 46:233-264. doi:10.1016/ S0169-7722(00)00135-2
Hers, I. 2002. "Subsurface Vapor Intrusion to Indoor Air Pathway: Model Predictions and
Comparisons to Field Data." U.S. EPA RCRA National Meeting.
Hers, I., R. Zapf-Gilje, P.C. Johnson, and L. Li. 2003. "Evaluation of the Johnson and Ettinger
model for prediction of indoor air quality." Ground Water Monit. Rem. 23 (2):119-133.
doi:10. 1111/j.1745-6592.2003.tb00678.x
H6hener, Patrick, C6line Duwig, Gabriele Pasteris, Karin Kaufnann, Nathalie Dakhel, and
Hauke Harms. 2003. "Biodegradation of Petroleum Hydrocarbon Vapors: Laboratory
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Hydrology 66 (1-2) (October): 93-115. doi: 10.10 16/SO 169-7722(03)00005-6.
Jin, Yan, Thilo Streck, and William A. Jury. 1994. "Transport and Biodegradation of Toluene in
Unsaturated Soil." Journal of Contaminant Hydrology 17 (2): 111-127.
Johnson, P. C., R. A. Ettinger, J. P. Kurtz, R. Bryan, and J. E. Kester. 2009. "Empirical
Assessment of Ground Water-to-Indoor Air Attenuation Factors for the CDOT-MTL
Denver Site." Groundwater Monitoring & Remediation 29 (1): 153-159.
Johnson, Paul C. 2002. "Identification of Critical Parameters for the Johnson and Ettinger (1991)
Vapor Intrusion Model." American Petroleum Institute 17: 1-N2.
. 2005. "Identification of Application-Specific Critical Inputs for the 1991 Johnson and
Ettinger Vapor Intrusion Algorithm." Ground Water Monitoring & Remediation 25: 6378.
86
Johnson, Paul C., and Robert A. Ettinger. 1991. "Heuristic Model for Predicting the Intrusion
Rate of Contaminant Vapors into Buildings." Environmental Science & Technology 25
(8): 1445-1452.
Johnson, Paul C., Cristin Bruce, Richard L. Johnson, and Mariush W. Kemblowski. 1998. "In
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chemicals residing below the soil surface." Water Resour. Res. 26 (1):13-20.
doi:10. 1029/WR026i00 1 p0 0 0 13
Kalmuss-Katz, Jonathan. "Global Pollution and Prevention News: EPA Finalizes Vapor
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89
Appendix A: Detailed Equations and Structure of Vol
Worksheets
90
Groundwater Vol Worksheet Structure and Governing Equations
The VoL spreadsheet that was developed includes the following worksheets:
" DATENTER: The user-defined worksheet that is included in the original J&E
spreadsheet;
-
CHEMPROPS: The chemical properties worksheet that is included in the original J&E
spreadsheet;
" VLOOKUP: The soil and chemical properties worksheet that is included in the original
J&E spreadsheet;
*
Vol: The worksheet that provides the results of the value of information analysis; and,
" VOICALCS: The worksheet in which all intermediate calculations are made that lead to
the results in the VOI worksheet.
Figure A - 1 provides the table in the groundwater Vol worksheet that contains the user-defined
values. It should be noted that the CAS Registry Number for the chemical of concern is selected
in a drop-down menu, and the equation in cell L2 provides the name of the chemical of concern.
K
-
L
-
-
--
o
P
Higghted ceb must be Md In.
2 Chemical of Concern
SArithmetic mean groundwater concentration, C. (ug/L)
SArithmetic Standard Deviation of C, (ug/L)
s Geometric mean groundwater concentration, Cw (ug/L)
r Geometric Standard Deviation of Cw (ug/L)
7 Updated URF (ug/m 33)
a Updated RfC (mg/M )
s New Construction? (Y or N)
[
8
=VLOOKUP(K2,VLOOKUPIA25:B132,2,FALSE)
0.4
Please provide EITHER the arithmetic OR
the geometric mean/standard deviation.
Leave K7 and K8 blank if values in
VLOOKUP are up-to-date.
0-0000041
0002
N
Figure A - 1: User-Defined Table in Groundwater Vol Worksheet
From the user-defined values in the DATENTER worksheet, the Vol worksheet generates a table
of building and soil parameter values, as demonstrated by the example in Figure A - 2.
91
B
A
0
C
Building properties
default
min
AP
40
0
E
F
max
200
Distribution
Triangular
0.048
Uniform
8
S
8
9
Soil properties
0.038
0,04
Particle Diameter of
Stratum Above
Water Table
avg
min
max
0.375
-
-
oW^
0.054
0 053
0.055
K5
kv
26.78
N/A
-
-
Distribution
Normal
Uniform
Log Normal
N/A
N/A
N/A
nB
ows
0.481
0.216
-
--
0.110
0.320
Normal
Uniform
0 049
0.1
n
A
B
C
nc
0.39
EAC
0,076
--
Normal
Uniform
Figure A - 2: Parameter Ranges and Distributions Table - Groundwater Vol Worksheet
The values provided for the pressure differential in Figure A - 2 are calculated based on the
equations in Figure A - 3.
B
1I
C
Building properties
default
0EF
mm
max
Distribution
=IF(ISBLANK(
=IF(ISBLANK(
=IF(ISBLANK(
=lF(C3="N/A"
DATENTERIM48),IF( DATENTER!M48), DATENTERIM48),
"N/A",
K9="Y" "N/A",
IF(K9="Y",N/A". IF(K9="Y","N/A",
"Triangular")
DATENTER!F48) "N/A")
0),"N/A")
200),"N/A")
Figure A - 3: Pressure Differential Range Equations
92
The values provided for the particle diameter are calculated based on the equations in Figure A 4.
A
F
a
Soil properties
10
=VLOOKUP(DATENTERI =VLOOKUP(DATENTERI =VLOOKUP(DATENTERI
Particle Diameter of
$L$28'VLOOKUP with
$L$28'VLOOKUP with
$L$28,'VLOOKUP with
Stratum Above
ranges'I$A$3:$U$14, 8, ranges'I$A$3:$U$14, 18, ranges'f$A$:$U$14, 19,
Water Table
FALSE)
FALSE)
FALSE)
Uniform
11
Figure A - 4: Particle Diameter Range Equations
The values provided for the soil stratum A parameters are calculated based on the equations in
Figure A - 5 and Figure A - 6.
A
D
C
B
E
min
A
A~
16
o A
=VLOOKUP(DATENTERI
$E$38,VLOOKUP with
ranges'l$A$3:$U$14, 6,
FALSE)
Normal
=VLOOKUP(DATENTERI =VLOOKUP(DATENTERI
$E$38,VLOOKUP with
$E$38'VLOOKUP with
=VLOOKUP(DATENTERI
$E$38,VLOOKUP with
Uniform
ranges'!$A$3:$U$14, 10, ranges'I$A$3:$U$14, 20, ranges'I$A$3:$U$14, 21,
FALSE)
FALSE)
17
K,
Distribution
max
FALSE)
=IF(ISBLANK(
DATENTERtM48),IF(C19
="N/A" VLOOKUP(
DATENTER!$E$38,
VLOOKUP with ranges'!
=IF(C18="N/A",
"N/A,Log
Normal")
$A$3:$U$14, 2, FALSE),
"N/A"),"N/A")
18
Figure A - 5: Soil Stratum A Parameter Range Equations
a
-t
0
f
=lF(ISBLANK(DATENTERIM48), IF(
=IF(ISBLANK(DATENTERIM48),IF(
ISBLANK(DATENTERI$O$28),"NA",IF(
ISBLANK(DATENTER$0$28),"N/A,IF(
=lF(ISBLANK(
DATENTERIM48),
kv
IF(ISBLANK(
DATENTER
$0$28),"NIA",
DATENTERIO28),
"N/A")
DATENTERI$0$28>VLOOKUP with
rangeslB21,IF(DATENTERf$0$28<=
VLOOKUP with ranges'C21.VLOOKUP
with ranges'lB21,IF(DATENTER1$0$28<
-VLOOKUP with ranges'IC20VLOOKUP
with ranges'lB20,F(DATENTERI$0$28<
VLOOKUP with ranges'Cl9'VLOOKUP
with ranges'B19,F(DATENTERI$0$28<
-VLOOKUP with ranges'C18,VLOOKUP
with ranges'1B18,"ERROR")))),IF(
DATENTERI$0$28=VLOOKUP with
ranges'B21,10^-11,"ERROR"))),"N/A")
DATENTERI$0$28<VLOOKUP with
ranges'C18,IF(DATENTERl$0$28>=
VLOOKUP with ranges'1B18,VLOOKUP
with ranges'1C18,IF(DATENTERI$0$28> -IF(C19="N/A",
=VLOOKUP with ranges'B19,VLOOKUP
"N/K,
with ranges'!C19,1F(DATENTER!$0$28>
'UIfform")
=VLOOKUP with ranges'lB20,VLOOKUP
with ranges'1C20,lF(DATENTERl$0$28>=
VLOOKUP with ranges'IB21 VLOOKUP
with ranges'IC21,"ERROR")))),IF(
DATENTERI$O$28=VLOOKUP with
ranges'C18,10^-5,"ERROR"))),N/A")
is
Figure A - 6: Permeability Range Equations
93
The values provided for the soil strata B and C parameters are calculated based on the equations
in Figure A - 7.
A
B
c
8
-- C
0
n1
=lF(DATENTER!$I$28=
0,"N/A",VLOOKUP(
DATENIERI$l$38,
VLOOKUP with ranges'!
$A$3:$U$14, 6, FALSE))
68
=IF(DATENTERI$l$28=0,
"N/A",VLOOKUP(
DATENTERI$l$38,
VLOOKUP with ranges'!
$A$3:$U$14. 10, FALSE))
F
=IF(DATENTERi$I$28= =lF(DATENTERI$l$28= =IF(C22="N/A"
0,"N/A",--")
0,"N/A","--")
"N/A","Normal")
=IF(DATENTER!
$l$28=0,"N/A".
VLOOKUP(
DATENTER!$l$38,
VLOOKUP with
ranges'!$A$3:$U$14,
20, FALSE))
=IF(DATENTER!
$l$28=0,"N/A"
VLOOKUP(
DATENTERI$$38,
VLOOKUP with
ranges'!$A$3:$U$14,
21, FALSE))
=IF(OR(DATENTER!
$J$28=0,DATENTER!
$l$28=0),"N/A","--")
=IF(OR(DATENTERI
$J$28=0,DATENTERI
=lF(C23="N/A",
"N/A","Uniform")
24
C
=IF(OR(DATENTER!$J$28=0,
DATENTER!$l$28=0),"N/A"
nc VLOOKUP(DATENTER!
n~
$M$38.'VLOOKUP with
ranges'!$A$3:$U$14, 6,
FALSE))
$l$28=0),"N/A","---")
=IF(C26="N/A".
"N/A" "Normal")
=IF(OR(DATENTER!
=IF(OR(DATENTER!
=IF(OR(DATENTER!$J$28=0,
$J$28=0,DATENTER! $J$28=0DATENTER!
DATENTERi$l$28=0),"N/A",
$l$28=0),"N/A".
$l$28=0),"N/A",
VLOOKUP(DATENTER!$M$38, VLOOKUP(DATENTER! VLOOKUP(DATENTER! =lF(C27="N/A".
VLOOKUP with ranges'!$A$3. $M$38'VLOOKP with $M$38,'VLOOKUP with "N/A","Uniform")
$U$14, 10, FALSE))
ranges'!$A$3:$U$14,
ranges'!$A$3:$U$14.
20, FALSE))
21, FALSE))
6,c
Figure A - 7: Soil Strata B and C Parameter Range Equations
The 5,000 simulations of random cumulative probabilities for each parameter are created using
the equations in Figure A - 8. It should be noted that the equations in row 19 are representative of
the equations in rows 19 through 5018 in the Vol worksheet.
N
1 Simulation
1s
Default
19
1
AP
Ks
C',
=RAND()
=RAND()
=RAND()
0
Random Number Generation
Particle Diameter
nA
EA
=RAND()
=RAND()
=RAND()
a
n
oo
ow
=RAND() =RAND() =RAND() =RAND()
Figure A - 8: Random Number Generator Equations
94
nc
From these random probabilities, the default (row 18) and simulated parameter (row 19) values
are calculated as shown in Figure A - 9. It should be noted that row 19 is representative of rows
19 through 5018 in the Vol worksheet.
As~
V
W
AP
=IF($C$18=
"N/A",$B$19,
$B$18)
thesa columns are
16
17
=IF(
$G$3=
"N/A","
$C$3)
1s
19
CALCSI
A15
C.
Particle
DIAMeter
=IF(AND(
=IF(
$C$19="N/A",
$C$18="N/A"), ISBLANK
=$C$11
"",IF($W$17= (K3),K5,
"kv"$C$19,
K3)
$C$18))
=Vol C C
CALCSIB15
=Vo1l
=Vo
C 5S
I CALCS!
C15
D15
AC
AD
no
("
nc
=IF(
$C$22=
$C$22)
=lF(
$C$23=
"N/A""",
$C$23)
=IF(
$C$26=
"N/A"
$C$26)
=Vol=Vol=VolCALCSI CALCSI CALCSI
F15
G15
E15
=VolcALcS!
H-15
=VolCALCSI
115
A
always occupled.
A
=$C$16
=$C$17
"N/A",
c
=IF(
$C$27=
"N/A" ,
$C$27)
=VoCALCS!
J15
Figure A - 9: Default and Random Parameter Value Generator Equations
The randomized parameter values are calculated in the VOICALCS worksheet as follows. First,
the toggle cell value in the Vol worksheet is duplicated in the VOICALCS worksheet, as shown
in Figure A - 10.
0
N
a =VOI!A113
Toggle
Figure A - 10: Toggle Cell Equations
95
Next, the parameter values are randomized. In Figure A - 11 through Figure A - 14, the cells in
row 9 determine whether or not a parameter will be randomized based on the toggle cell value.
Row 14 provides the default value of the parameter and row 15 provides the randomized or
default value, depending on the value calculated in row 9. It should be noted that row 15 is
representative of rows 15 through 5014.
A
=1*MID(N9,1,1)
9
10
11
12
AP
13
14
15
=VOI!V18
=IF(VOI!$C$3="N/A",",IF($A$9=0,VOI!$C$3,IF(VOI!
J19<=(40-VO!$D$3)/(VOI!$E$3VOI!$D$3),VOI!$D$3+SQRT(VOI!J19*(VOI!$E$3VOI!$D$3)*(40-VOI!$D$3)),VOI!$E$3-SQRT((1 VOI!J19)*(VOI!$E$3-VOI!$D$3)*(VOI!$E$340)))))
Figure A - 11: Simulated Pressure Differential Equations
a
9
=1*MID(N9,2,1)
10
11
12
13 =IF(VOI!$C$18="N/A",VOi!$B$19,VOIi$B$18)
=VOIIW18
=IF(AND(VOi!$C$19="N/A".VOII$C$18="N/A"
),"".IF(VOI!$W$17="kv" IF($B$9=O,VOII$C$1
9,VOl!$D$19+(VOlIK19*(VOI$E$19VOI'$D$19))),IF($B$9=0,VO!$C$18,LOGINV
(VOlIK19,LN(VOI$C$18),((LN(10)/LOG(1Q))*
VLOOKUP(DATENTERI$E$38,'VLOOKUP
is
with ranges'!$A$3$U$14,12,FALSE))))))
14
Figure A - 12: Simulated Hydraulic Conductivity or Permeability Equations
C0
9
=1MID(N9,3,1)
EF
=1*MID(N9,4,1)
=1*MID(N9,5,1)
=1*MD(N9,6 1)
10
These columns are always occupied.
12
1
1
Cw
=VOIIX18
Particle Diameter
=VOIIY18
nA
=VOI!Z18
OwA
=VOIIAA18
=IF($C$9=0,F(SBLANK(VO
=IF($E$9=0,VOI!$C$16,N
i$K$3),VOI!$K$5,VOII$K$3).l =IF($D$9=0,VOI!$C$ ORMINV(VOIIN19,VOI!$C =IF($F$9=0,VOI!$C$
F(ISBLANK(VOI!$K$3),LOGI 11,VOI!$D$1 1+(VOi! $16,VLOOKUP(DATENTE 17,VOlI$D$17+(VOli
NV(VOIIL19,LN(VOII$K$5),L
M19*(VOII$E$11RI$E$38YVLOOKUP with
019*(VOll$E$17N(VOI!$K$6)),NORMINV(VO!
VOI!$D$1 1)))
ranges'f$A$3:$U$14,15,F
VOI!$D$17)))
L19.VOI!$K$3,VOI!$K$4)))
ALSE)))
Figure A - 13: Simulated Groundwater Concentration, Particle Diameter, and Soil Stratum
A Parameter Equations
96
J
I
9
=1*MID(N9,,1)
=1*M1D(N9,8,1)
=1*M1D(N9,9,1)
=1*MID(N9,10,1)
nB
=VOIIAB18
OwB
=VOltAC18
nC
=VOILAD18
=VOIIAE18
10
11
12
13
14
OwC
=IF(VOI!$C$26=N/K,"'IF($1$9
=1F(VOI$C$27="N/A
=IF(VO1I$c$23="WA
9=0,VOlt$C$22,NORMNV(VOl1 ","",IF($H$9=0,VOII$ =O,VO 1$C$26,NORMINV(VO1 R ,"IIF($J$9-0,VOII$C
19,VO11$C$26,VLOOKUP(DATE
P19,VOI!$C$22,VLOOKUP(DAT
$27,VOI!$D$27+(VOII
C$23,VOI!$D$23+(V
NTERI$M$38,'VLOOKUP with
ENTERI$1$38,'VLOOKUP with
S19-(VOII$E$27011019'(VOlt$E$23ranges1$A$3:$U$14,15,FALSE))
ranges'1$A$3:$U$14,15,FALSE)
VOII$D$27))))
VOII$D$23))))
=IF(VO11$C$22="N/A","IF($G$
Is
Figure A - 14: Simulated Strata B and C Parameter Equations
Chemical properties for the chemical of concern are determined next, using the equations in
Figure A - 15.
V
W
Henry's
Henrys
U
3
Z
Y
X
Enthalpy
of
AC
AA
Organic
Pure
law constant
law constant
vaporization at
Normal
carbon
component
Unit
5
Ditusivity
Ddiusnivy
at reference
reference
the normal
boiling
Cnircal
partition
water
6
in air
tn water,
temperature.
temperature
boiling point
point
temperature
coeticient
solubility,
nsk
factor.
7
D4
D
H
TR
AHv
TB
TC
Koc
S
URF
8
(.m1/)
(cal/moll
(("K)
(NK
(cm3/9)
(mg/L)
(pg/m)-l
4
(cm2/s)
(atryn
3
/tmol)
0
( QC
AD
Reference
conc
RIC
Mm3,
-VLOOKUP(VOK -VLOOKUP(VOIK -VLOOKUP(VOllK -VLOOKUP(VOliK -VLOOKUP(VOi!K -VLOOKUP(VOllK -VLOOKUP(VOlK -VLOOKUP(VO1iK -VLOOKUP(VOlIK -VLOOKUP(VOlIK -VLOOKUP(VOIlK
ChemicalDataL ZChemicalData. 2ChemicalData
Chemica Data ZChemicaLDaa 2ChemicalData 2.ChemicalData
2,ChemicalData 2.ChemicalData. 2.ChemicalData. 2.ChemicaData
t4FALSE)
13,FALSE)
6,FALSE)
IFALSE)
1 .FALSE)
10.FALSE)
12 FALSE)
9.FALSE)
8.FALSE)
5.FALSE)
4,FALSE)
10
Figure A - 15: Chemical Properties Equations
Starting at column M in the VOICALCS worksheet, intermediate values are calculated. Figure
A - 16 through Figure A - 25 provide the equations that determine these intermediate values. It
should be noted that the equations in row 14 are representative of the equations in rows 14
through 5014.
MS
13
Lt
=IF(DATENTER!$G$28-
DATENTER!$F$28<=O,
1,DATENTER!$G$2814
DATENTER!$F$28)
Figure A - 16: Diffusion Path Length Equation
97
C
,
ea
13 8 A
14
p
eaC
=IF(OR(DATENTER!$F$3 =IF(OR(DATENTER!$J$
8="",DATENTER!$G$38= 38="",DATENTER!$K$3
"",DATENTER!$H$38=""), 8=",DATENTER!$L$38
"ERROR",ABS(E14-F14)) =""),"ERROR",G14-H14)
=IF(OR(DATENTER!$N
$38="",DATENTER!$O$
38="",DATENTER!$P$3
8=.""),"ERROR",114-J14)
Figure A - 17: Air-Filled Porosity Equations
a
13
S
R
S of A
ki of A
krg of A
kv of A
=(F14VLOOKUP(DATENTER!$M$2
8,SoilData,7,FALSE))/(E14VLOOKUP(DATENTER!$M$2
8,SoilData,7,FALSE))
=(IF($B$13="Ks",B14,
"")*(1/3600)*(0.01307*
((DATENTER!$E$28+
273.15)/(283.15))A0.5)
)/(0.999*980.665)
=(1-Q14)^0.5*(1Q14A(1NLOOKUP(DATENTE
R!$M$28,SoilData,5,FALSE)
))A(2*VLOOKUP(DATENTER!
$M$28,SoilData,5,FALSE))
=IF(AND(DATENTER!$
M$28>0,DATENTER!$
O$28>0),"ERROR",IF(
DATENTER!$O$28>0,
B14,S14*R14))
14
Figure A - 18: Saturation and Permeability Equations
L1
ncc,
8
acz
(),CZ
=IF(DATENTER!$
=0.15/ K$28="A"E14EF
(=140.512)AEER
(D14-=.2) DATENTER!K$2 =V14-X14
8="B",G14,114))
14
=lF(DATENTER!$K$28="A" VLOOKUP(DATENTER!$L$28,SoilData,7,FALSE)+((E14VLOOKUP(DATENTER!$L$28,SoilData,7,FALSE))/(2^VLOOKUP(DATENTER!$L$28.
SoilData,5,FALSE))),IF(DATENTER!$K$28="B",VLOOKUP(DATENTER!$L$28
SoilData, 7FALSE)+((G14-VLOOKUP(DATENTER!$L$28,SoilData,7,FALSE))
/(2AVLOOKUP(DATENTER!$L$28,SoilData,5,FALSE))),VLOOKUP(DATENTER!$L$28,
SoilData,7,FALSE)+((114-VLOOKUP(DATENTER!$L$28,SoilData,7,FALSE))/
(2AVLOOKUP(DATENTER!$L28,Soil_Data,5,FALSE)))))
Figure A - 19: Capillary Zone Equations
AA
1
rack
Qbuilding
=IF(DATENTER!$F$28>DATEN
=(2*
=(DATENTER!$G$4
DATENTER! 8*DATENTER!$H$48
$G$48)+(2* *DATENTER!$l$48*
DATENTER! DATENTER!$K$48)*
$H$48)
1/(60*60)
14
AB
AC
Ab
TER!$E$48,(DATENTER!$G$48
*DATENTER!$H$48)+(2*(DATE
NTER!$F$28*DATENTER!$G$4
8))+(2*(DATENTER!$F$28*DAT
ENTER!$H$48)),(DATENTER!$
G$48*DATENTER!$H$48))
=(2*(DATENTE
R!$G$48*DATE
NTER!$J$48)+2 =DATENTER!
*(DATENTER!$
$F$28
H$48*DATENTE
R'$J$48))/AA14
Figure A - 20: Building Parameter Equations
98
AD
AHVTS
13
AE
AG
AT S
H'TS
HTS
=IF($Y$10/$Z$10<0.57,$X$10*((1=IF(DATENTE
((DATENTER!$E$28+273.15)/$Z$10))/(1=EXP(=0.00018*((
($Y$10/$Z$10)))AO.3,IF(AND($Y$10/$Z$10>=0.57 1*((AD14/1.9872)*( R!$E$28="",0/
DATENTER'
(1/(DATENTER! 0,AE14/(0.000
,$Y$10/$Z$10<=0.71),$X$10*((1$E$28+273.1
$E$28+273.15))- 08206*(DATE
((DATENTER!$E$28+273.15)/$Z$10))/(15)1298.15)
($Y$10/$Z$10)))A(0.74*($Y$10/$Z$10)(1/($W$10+273.15) NTER!$E$28+
A0.5
273.15)))
0.1 16),$X$10*((1-((DATENTER!$ES28
))))*$V$10
+273.15)/$Z$10))/(l-($Y$10/$Z$10)))A0.41))
14
Figure A - 21: Henry's Constant Equations
13
DeB
=IF(DATENTER!
=IF(DATENTER!
=($T$10*
$I$28=0,0,($T$10*( $J$28=0,0,($T$10*
(N14A3.33/E14A2))
014A3.33/G14A2))+ (P14A3.33/114A2))+
+(($U$10/AF14)*
(F14A3.33/E14 A2))
AK
A)
Dec
DA
DOOA
(($U$10/AF14)*(
(($U$10/AF14)*(
H14A3.33/G14A2)))
J14A3.33/114A2)))
Deffc
=($T$10*(W14A
3.33N14A2))+
(($U$10/AF14)*
(X14A3.33N14A2))
14
Figure A - 22: Diffusion Coefficient Equations
DeT
13
14
=IF(AND(AH14>0,AI14>0,AJ14>0),M14/(((IF(M14=1,1,DATENTER!$H$28DATENTER!$F$28))/AH14)+(DATENTER!$$28/A114)+((DATENTER!$J$28U14)/AJ14)+(U14/AK14)),IF(AND(AH14>0,AI14>0,AJ14=0),M14/(((IF(M14=1,1,
DATENTER!$H$28-DATENTER!$F$28))/AH14)+((DATENTER!$1$28U14)/A114)+(U4/AK14)),F(AND(AH14>0,A14=0,AJ14=0),M14/(((IF(M14=11,
DATENTER!$H$28-DATENTER!$F$28)-U14)/AH14)+(U14/AK14)))))
Figure A - 23: Effective Diffusion Coefficient Equation
AO
13
cme
=IF(AND(DATENTER!$G$4>0,DATENTER!
$G$8>0),"ERROR",IF(DATENTER!$G$4>0,
AF14*1000,IF(AND(DATENTER!$G$8>0,
OR(VOI!$K$3>0,VOI!$K$5>0)),MIN(C14,
$AB$10*1000)*AF14*1000,"ERRORS-)))
14
Figure A - 24: Source Soil Gas Concentration Equation
99
13
14
AP
AQ
AR
AS
AT
rcrack
Osod
Dorack
Aclack
exp(Pe
=IF(ISBLANK(DATENTER!
=IF(DATENTER!
$M$48),(2*PI()*F(A14="''
=AB14*
"ERROR",
$E$48=""
DATENTER!$F$48,A14)*
AA14/
=AH14 =AA14*
EXP((AQ14*
AB14
T14*Y14)/(AG14*LN(2*
Y14
DATENTER!$E$48)/
AC14/AP14)),DATENTER!
(AR14*AS14)))
$M$48*(1000/60))
Figure A - 25: Building Parameter and Peclet Number Equations
Next, the toxicity values are either provided in the model or are user-defined. The equations in
Figure A - 26 account for either possibility.
13
AU
AV
URF
RfC
=IF(ISBLANK(VOl!$K$7), =IF(ISBLANK(VOI!$K$8),
IF($AC$10=0,"NA",
IF($AD$10=0,"NA",
$AC$10),VOI!$K$7)
$AD$10),VOI!$K$8)
14
Figure A - 26: Toxicity Value Equations
The equations for the attenuation factor and indoor air concentration are provided in Figure A 27.
AW
AX
13
14
Cbudng
=IF(ISERROR(AT14),((AL14*AA14)/(Z14*M14))/(((AL14
*AA14)/(AQ14*M14))+1),(((AL14*AA14)/(Z14*M14))*
EXP((AQ14*DATENTER!$E$48)/(AR14*AS14)))/(EXP((
AQ1 4*DATENTER!$E$48)/(AR14*AS14))+((AL14*AA14)
/(Z14*M14))+((AL14*AA14)/(AQ14*M14))*(EXP((AQ14*
DATENTER!$E$48)/(AR14*AS14))-1)))
=AO14*
AW14
Figure A - 27: Attenuation Factor and Indoor Air Concentration Equations
100
Based on the toxicity values and the predicted indoor air concentration, the equations in Figure A
- 28 calculate the incremental carcinognic risk and the noncarcinogenic hazard quotient.
Columns AZ and BB determine if the predicted risk and hazard quotient exceed the user-defined
target values.
AY
13
14
Target?
Incremental Risk
Be
BA.
AZ
Quotient >
Target?
Risk >
Hazard Quotient
=IF(IF(ISERROR(MATCH(VOI!$K$2,
=IF(IF(ISERROR(MATCH(VOI!$K$2,
CASNo,0)),"ERROR",$AB$10*1000)=" =IF(AY14> CASNo,))"ERROR",$AB$10*1000)="ERRO =IF(BA14>
ERROR","ERROR",IF(DATENTER!
DATENTERI R","ERROR",IF(DATENTER!$G$8="","NA", DATENTER!
IF(AV14="NA", NA",(DATENTER!$H$57*
$G$8="","NA",IF(AU14="NA',"NA",(AU14 $A$57,1,"")
$J$57,1,'"')
' '"
DATENTER!$G$57*(1/AV14)*AX14*0.001 )
*DATENTER!$H$57*DATENTER!$G$57*
(DATENTER!$F$57*365))))
AX14)/(DATENTER!$E$57*365))))
Figure A - 28: Risk Assessment Equations
The randomized parameter values that are calculated in the cells in Figure A - 11 through Figure
A - 14 typically produce risk values that are positive, as would be expected. However, there is
the potential that the randomized parameter values for a given simulation produce an incremental
risk that is either negative or infinite. These simulations should not be included in the Vol
analysis. To account for these errors, the equations shown in Figure A - 29 determine if a
simulation has an error in the calculations, and counts the number of errors for all simulations.
Column BD reproduces the incremental risk calculation (per million) only if there is no error in
the original calculation.
BC
10
BU
=COUNTI F(BCI14: BC5014, "ERROR") # ERRORS
11
12
13
14
Incremental Risk * 10A6
ERROR in alpha calc?
=IF(ISNUMBER(AY14),IF(AY14>0,'"',
"ERROR"),"ERROR")
=lF(BC14='",AY14*
10A6,"")
Figure A - 29: Quality Control Equations
101
The results of the simulations that do not contain errors are evaluated using the equations in
Figure A - 30.
BG
14
Average
BH
=(SUMIF(AW1 5:AW5014,">0",AX1 5:AX5014)/
Increase in Indoor Air Concentration
COUNTIF(AX1 5:AX5014,">0"))-AX14
=100*IF(AZ14=1,(5000-BC1O-COUNTIF(AZ15:
% of Experiments with Change in Carc. Hazard Classification
16
17
18
Average Difference
AZ5014,"1'")),COUNTIF(AZ1 5:AZ5014,"1 "))/(5000-BC1 0)
=(SUMIF(AW15:AW5014,">O",AY15:AY5014)/(5000BC10))-AY14
in Carcinogenic Risk
% of Experiments with Change in Noncarc. Hazard Classification =100I*F(BB14=1,(5000-BC1O-COUNTIF(BB15:BB5014,
"1")),COUNTIF(BB15:BB5014,"1"))/(5000-BC10)
=(SUMIF(AW1 5:AW5014,">0",BA1 5:BA5014)/(5000-
Average Difference inNoncarcinogenic Risk
ABC10))-BA14
Figure A - 30: Vol Evaluation Equations
In order to create a histogram of the simulated incremental carcinogenic risk values in the Vol
worksheet, the equations in Figure A - 31 must be calculated in the VOICALCS worksheet. It
should be noted that the histogram contains 100 risk ranges.
2E
ii
12
BK
25th Percentile =QUARTILE(BD15:BD5014,1)
75th Percentile =QUARTILE(BD1 5:BD5014,3)
=MEDIAN(BD15:BD5014)
Min
=(MIN(BD15:BD5014))
Max
=(MAX(BD15:BD5014))
Avg
=AVERAGE(BD14:BD5014)
Bucket Size
=(BK1 5-BK1 4)/100
13 Median
14
1s
16
17
Figure A - 31: Simulated Incremental Carcinogenic Risk Distribution Equations
From the calculated bucket size, the values in the x- and y-axes in the histogram are calculated
using the equations in Figure A - 32.
8J
22
BK
Bucket Min
1
BL
Max
BM
Bucket Name
=CONCATENATE(
=BK14 =BK23+ ROUND(BK23,2)." $BK$17
ROUND(BL23,2))
23
2
=CONCATENATE(
=BL23 $BK$17 ROUND(BK24,2)" -
ROUND(BL24,2))
24
BN
Count
=COUNTIF($BD$15:
$BD$5014,">="&BK23)-
COUNTIF($BD$15:
$BD$5014,">="&BL23)
BO
Cumulative Density
=BN23/BN123
=COUNTIF($BD$15:
$BD$50142>="&BK24)COUNTIF($BD$15:
$BD$5014,">="&BL24)
Figure A - 32: Histogram Equations
102
=B023+(BN24/$BN$123)
In order to represent the target and default values in the histogram, the equations in Figure A - 33
are calculated.
8P
80
Is Default in Bucket?
Location of Target
=IF(AND((DATENTER!$l$57* =IF(AND($BD$14>=$BK23
10^6)>=$BK23,(DATENTER! =IF(AND$BD$1)=$K23
)<$L23)D$14<$L23),MAX(
$$57*
2 $BN$23:$BN$122)+20,")')
22
Figure A - 33: Target and Default Value Equations
The worksheet also compares the "% of Simulations with Change in the Carcinogenic Hazard
Classification" (cell BH15 in Figure A - 30) for all possible toggle cell values. The table that
includes this comparison in the Vol worksheet pulls information from the VOICALCS
worksheet. The following discussion provides information regarding the tables and equations
that are used to create the comparison table in the Vol worksheet.
A table of all possible toggle cell values and the number of experiments in each toggle cell is
stored in the VOICALCS worksheet, a portion of which is shown in Figure A - 34.
Bu
BV
# Expenmen ts
Sequence
14 0000000000
0
1
1000000000
16 1100000000
2
1
17 0100000000
3
18 1110000000
2
19 0110000000
1
20 0010000000
2
21 1010000000
1
22 0001000000
2
23 1001000000
3
24 1101000000
2
0101000000
25
4
26 1111000000
13
Figure A - 34: Toggle Cell Values and Number of Experiments Table
103
An Excel data table then calculates the "% of Simulations with Change in the Carcinogenic
Hazard Classification" in column BY for each possible toggle cell value, as shown in Figure A 35.
BX
11
BY
DATATABLE
12
13
14
1s
16
=BH15
0000000000
1000000000
1100000000
0
6.08
23.8
17 0100000000
33.24
1110000000
0110000000
0010000000
24.28
34.32
18
19
20
1.54
21 1010000000
6.98
0
4.62
24
0001000000
1001000000
1101000000
25
0101000000
30.1
26
1111000000
21.72
22
23
21.08
Figure A - 35: Toggle Cell and Percent Change Values Data Table
104
A different list of possible toggle cell values is generated, depending on the information provided
in the DATENTER worksheet. For example, if the value of Q,0 jj is provided in DATENTER,
then the analysis does not consider toggle cells that include the pressure differential or hydraulic
conductivity/permeability. These lists are stored in the VOICALCS worksheet, and a portion of
these lists is shown in Figure A - 36.
OA
00
cc
q calc. existing
q caic, existing. no c
IIONODO00o
1100000000
1110000000
1110000000
1010000000
1001000000
1101000000
1111000000
1011000000
1000110000
1100110000
1110110000
1010110000
1001110000
1101110000
1111110000
1011110000
0100000000
0110000000
0101000000
0111000000
0100110000
0110110000
1010000000
1001000000
1101000000
1111000000
1011000000
1000110000
1100110000
1110110000
1010110000
1001110000
1101110000
1111110000
1011110000
0100000000
0110000000
0101000000
0111000000
0100110000
0110110000
0101110000
0111110000
0000001100
1000001100
1100001100
0100001100
1110001100
0110001100
00
o
oc
q calc, oxating, no b or c
or
1100000000
1110GmD0,0
1010000000
1001000000
1101000000
1111000DOm
1011000000
1000110000
1100110000
1110110000
1010110000
1001110000
1101110000
1111110000
1011110000
0100000000
0110000000
0101000000
0111000000
0100110000
0110110000
0101110000
0101110000
0111110000
0111110000
0000001100
1000001100
1100001100
0100001100
1110001100
0110001100
01
q caic, new construct
011GDDmOOO
0101000000
0111000000
0100110000
0110110000
001110000
0111110000
0000001100
0100001100
0110001100
0010001100
0001001100
0101001100
0111001100
0011001100
0000111100
0100111100
0110111100
0010111100
0001111100
0101111100
0111111100
0011111100
0000000011
0100000011
0110000011
0010000011
0001000011
0101000011
Figure A - 36: Possible Toggle Cell Values
105
The VOI worksheet, then, pulls the information in the VOICALCS worksheet to create a table
that contains the percent change values for each possible toggle cell. The equations used to
create this table are provided in Figure A - 37. It should be noted that cells BD18:BD25 provide
toggle cells that are always included in the analysis, i.e. toggle cells that do not consider the
pressure differential, hydraulic conductivity/permeability, soil stratum B parameters, and soil
stratum C parameters. Starting with BD26, toggle cells are only provided if they are included in
the applicable list (see Figure A - 36).
1
BC
Bt
=VLOOKUP(BD18,
VOlCALGS!
$BU$14$8V$141,
-IF(8018='"7
VLOOKUP(8D18,
VOl_CALCS1
# Experiments
000000000
$BX$14:$BY$141 2
FALSE))
2,FALSE)
=IF(AND(VOICALCS?$l$15=",VOICALCS$G$15=""
VolCALCSI$A$15="",VOICALCS'$B$15="),",IF(AND(
VOl_CALCSI$l$15="",VOICALCS!$A$15="",VO_CALCS
$B$15=""),VOl_CALCSIC022,IF(AND(VOCALCS'$A$15
=IF(BD26="".,
=lF(BD26=""
= "",VoICALCS!$B$15=""),V01_CALCSICM22.IF(AND(
VLOOKUP(BD26'
VLOOKUP(BD26
VOICALCSI$l$15="",VOICALCS!$G$15=-VOCALCS
VOl_CALCSI
VOl_CALCS!
$A$15=""),VOI CALCS'CK22,IF(AND(VOI CALCS'$l$15=
$BU$14:$BV$141
$BX$14:$BY$141 2
"",VOlCALCS!$A$15=""),VoCALCSIC22
IF(
2,FALSE))
FALSE))
VOlCALCS!$A$15="",VOICALCSICG22,IF(AND(
VOl_CALCS!$I$15="" VOlCALCSI$G$15="),VOlCALCS
I CE22,IF(VOICALCS!$l$15="" V01_CALCS!CC22,
VoiCALCSCA22))))))))
Figure A - 37: Possible Combinations in Vol Worksheet
The possible toggle cell values are then ranked based on the percent change values, as shown in
Figure A - 38.
Bh
17
=OFFSET(
$BD$17'
MATCH(BJ18,
$BE$18:
$BE$145,O),O)
81
BJ
BK
Rank
% Change
# Experiments
=LARGE(
$BE$18:
$BE$145
BI18)
=IF(ISERROR(OFFSET(
$BC$17,MATCH(BH18,
$BD$18:$BD$145,0),0)),",
OFFSET($BC$17,MATCH(
BH18,$BD$18:$BD$145,0),O))
1
Figure A - 38: Ranking of Toggle Cells Equations
Finally, the toggle cells are grouped according to the number of experiments and then ranked
based on the percent change values. Figure A - 39 provides the equations used to generate the
ranking of toggle cells that include only one experiment. It should be noted that row 18 is
106
representative of rows 18 through 21 in the table that considers toggle cells that include only one
experiment.
814
17
BP
so
I Experiment
16
Sequence
% Change
=IF(ISERROR(VLOOKUP(BN18,
001000
18
0
$BD$18:$BE$145,2,FALSE)),--"'
VLOOKUP(BN18,$BD$18:$BE$145,
2,FALSE))
Rank
=tF(BO18="-" "-",
RANK(BO18,$BO$18:
$BO$21))
Figure A - 39: Ranking of Toggle Cells According to the Number of Experiments
Tables that consider toggle cells with two, three, four, five, six, seven, eight, and nine
experiments are also included in the Vol worksheet. These tables use similar equations to those
shown in Figure A - 39.
107
Soil Gas Vol Worksheet
The soil gas Vol analysis contains the same worksheets as the groundwater Vol analysis. The
table in the soil gas Vol worksheet that contains the user-defined values is shown in Figure A 40.
2
a
4
s
6
7
s
9
10
I1
K
M
N
0
P
Q .
S
Highlighted cells must be filed In.
Chemical of Concern
- =VLOOKUP(K2.VLOOKUP!A25 B132.2. FALSE)
Initial soil gas concentration. C. (ug/m ppmv)
Please provide mean/standard deviation in units
of EITHER ug/m 3 OR ppmv Also. please provide
Standard Deviation of C. (ug/m' ppmv)
EITHER the arithmetic OR geometric mean/SD In
Lognormal Mean of C. (ug/m ppmv)
43
K3 L6. only two cells should be occupied at any
Geometric Standard Deviation of C. (ug /rn ppmv)
12
time
New Construction? (Y or N)
Updated URF (ug/m')-'
4 10E-06
Leave K8 and K9 blank if values
Updated RIC (mg/im3)
in VLOOKUP are up-to-date
0002
Target risk for carcinogens
0000001
Target risk for noncarcinogens
[III]
Figure A - 40: User-Defined Table in Soil Gas Vol Worksheet
An example of the parameter ranges and distributions table is provided in Figure A - 41. Note
that, unlike in the groundwater analysis, the particle diameter is not included in the soil gas
analysis.
B
2
AP
C
E
0
Building properties
default
min
40
0
'
max
200
Distribution
Triangular
max
0,485
0.055
4
12
17
Soil properties
avg
min
0,265
0375
0054
0053
15
nA
6.
16
K,
26,78
kV
N/A
N/A
N/A
Distribution
Normal
Uniform
Log Normal
N/A
0481
0216
0,321
0110
0641
0320
Normal
Uniform
039
025
0 049
0,53
01
Normal
Uniform
13
14
A
-
i0
20
C
in-
C
nc
21
22
23
24
25
0076
Figure A - 41: Parameter Ranges and Distributions Table - Soil Gas Vol Worksheet
108
All of the randomized probabilities and parameter values are calculated in the same manner as
they were in the groundwater analysis, with the exception of the source concentration. The
equations used to determine the default and simulated soil gas concentrations are provided in
Figure A - 42 and Figure A - 43.
14
15
16
These columns are always occupied.
CI
=IF(AND(ISBLANK(K3),ISBLANK(K5)),
IF(ISBLANK(L3),L5,L3),
IF(ISBLANK(K3),K5,K3))
Figure A - 42: Default Soil Gas Concentration Equation
C
13
14
Cg
=VOI!W16
1s
=IF(OR(VOl!$K$3>0,VOI!$K$5>0),IF($C$9=0,IF(ISBLANK(VOI!
$K$3),VOI!$K$5,VOI!$K$3),IF(ISBLANK(VOI!$K$5),NORMINV(
VOl!LI7,VOI!$K$3,VOI!$K$4),LOGINV(VOI!K17,LN(VOI!$K$5),
LN(VOI!$K$6)))),IF($C$9=0,IF(ISBLANK(VOI!$L$3),VOI!$L$5,VOI!
$L$3),IF(ISBLANK(VOI!$L$5),NORMINV(VOI!L17,VOI!$L$3,VOI!
$L$4),LOGINV(VOI!L17,LN(VOI!$L$5),LN(VOI!$L$6)))))
Figure A - 43: Simulated Soil Gas Concentration Equations
All of the intermediate calculations are largely the same in the groundwater and soil gas
In the soil gas VOICALCS worksheet, however, the soil gas
VOICALCS worksheet.
concentration at the source is calculated using the equation in Figure A - 44.
13
Soil Gas Conc.
=lF(OR(VOl!$K$3>0,VOI!$K$5>0),
14
C14,(C14*$Z$10)/(0.00008205*
(DATENTER!$G$24+273.15)))
Figure A - 44: Source Soil Gas Concentration Equation
109
Unlike in the groundwater analysis, the capillary zone is not considered in the soil gas analysis.
The effective diffusion coefficient, therefore, is calculated using the equation in Figure A - 45,
instead.
AG
13
14
DeffT
=IF(AND(AD14>0,AE14>0,AF14>0),Li4/(((IF(L14=1,1,DATENTER!$H$24DATENTER!$E$24))/AD14)+(DATENTER!$1$24/AE14)+(DATENTER!$J$24/AF14)),
IF(AND(AD14>0,AE14>0,AF14=0),Li4/(((IF(L14=1,1,DATENTER!$H$24DATENTER!$E$24))/AD14)+(DATENTER!$1$24/AE14)),IF(AND(AD14>0,AE14=0,
AF14=0),Li4/((IF(L14=1,1,DATENTER!$H$24-DATENTER!$E$24))/AD14),
"ERROR")))
Figure A - 45: Effective Diffusion Coefficient Equation - Soil Gas VOICALCS Worksheet
The final difference between the two analyses is the lists of possible toggle cell values. Because
the particle diameter is not considered in the soil gas analysis, there is half the number of
possible toggle cell values. Figure A - 46 provides a portion of the lists of possible toggle cell
values that are stored in the VOlCALCS worksheet.
110
B xn
t2
q caic existing
100110000
101110000
100000000
011000000
010110000
011110000
010000000
111000000
110110000
111110000
110000000
001001100
000111100
001111100
000001100
101001100
100111100
101111100
100001100
011001100
010111100
011111100
010001100
111001100
110111100
111111100
110001100
001000011
By
q ca~c. existing, no c
q caic, existing, no b or c
101000000
100110000
101110000
100000000
011000000
010110000
011110000
010000000
111000000
110110000
111110000
110000000
001001100
000111100
001111100
000001100
101001100
100111100
101111100
100001100
011001100
010111100
011111100
010001100
111001100
110111100
111111100
110001100
101000000
100110000
101110000
100000000
011000000
010110000
011110000
010000000
111000000
110110000
111110000
110000000
Oz
q caic, new construct
011000000
010110000
011110000
010000000
001001100
000111100
001111100
000001100
011001100
010111100
011111100
010001100
001000011
000110011
001110011
000000011
011000011
010110011
011110011
010000011
001001111
000111111
001111111
000001111
011001111
010111111
011111111
010001111
Figure A - 46: Possible Toggle Cell Values - Soil Gas VOICALCS Worksheet
111
Appendix B: Nike Battery PR-58 Data
112
B-1: Data from the 2014 Draft Stone Environmental
RI/FS
113
Table B - 1 - 1: Shallow Groundwater Analytical Results at DPW Facility
Sample ID
Sample Depth (feet bgs)
CAS#
Sample Date / Time
RIDEM
GA
EPA
MCL
Standard
(pg/L)Stnad
(Ftg/L)
SEI-0117-GW
17
1
9/11/2013
SEI-02-15_0GW
15
9/11/2013
VOLATILE ORGANIC COMPOUNDS (pg/L)
1,1,1 ,2-Tetrachloroethane
630-20-6
NE
NE
1U
1 U
1,1,1-Trichloroethane
71-55-6
200
200
1U
1 U
1,1,2,2-Tetrachloroethane
79-34-5
NE
NE
9.3
3.8
1,1 ,2-Trichloroethane
79-00-5
5
5
0.6J
1U
1,1-Dichloroethane
75-34-3
NE
NE
1U
1 U
1,1-Dichloroethene
75-35-4
7
7
1U
1 U
1,1-Dichloropropene
563-58-6
NE
NE
2U
2U
1,2,3-Trichlorobenzene
87-61-6
NE
NE
1U
1 U
1,2,3-Trichloropropane
96-18-4
NE
NE
1U
1U
1,2,4-Trichlorobenzene
120-82-1
70
70
1U
IU
1,2,4-Trimethylbenzene
95-63-6
NE
NE
1U
U
1,2-Dibromo-3-Chloropropane
96-12-8
0.2
0.2
1,2-Dibromoethane
106-93-4
NE
0.05
1,2-Dichlorobenzene
95-50-1
600
600
1U
1U
1,2-Dichloroethane
107-06-2
5
5
1U
1U
1,2-Dichloropropane
78-87-5
5
5
1U
1U
1,3,5-Trimethylbenzene
108-67-8
NE
NE
1U
1U
1,3-Dichlorobenzene
541-73-1
NE
NE
1U
1U
1,3-Dichloropropane
142-28-9
NE
NE
1U
1U
1,4-Dichlorobenzene
106-46-7
75
75
1U
1U
2,2-Dichloropropane
594-20-7
NE
NE
1U
1U
2-Butanone
78-93-3
NE
NE
IOU
IOU
2-Chlorotoluene
95-49-8
NE
NE
1U
1U
2-Hexanone
591-78-6
NE
NE
IOU
IOU
4-Chlorotoluene
106-43-4
NE
NE
1U
1U
4-Isopropyltoluene
99-87-6
NE
NE
1U
1U
114
Sample ID
Sample Depth (feet bgs)
CAS#
Sample Date / Time
EPA
RIDEM
1-
MCL
(ptg/L)
Standard
17
15
(pg/L)
9/11/2013
9/11/2013
Sample ID
VOLATILE ORGANIC COMPOUNDS (ptg/L)
4-Methyl-2-Pentanone
108-10-1
NE
NE
IOU
IOU
Acetone
67-64-1
NE
NE
IOU
IOU
Benzene
71-43-2
5
5
1U
1U
Bromobenzene
108-86-1
NE
NE
2U
2U
Bromochloromethane
74-97-5
NE
NE
1U
1U
Bromodichloromethane
75-27-4
NE
NE
0.6U
0.6U
Bromoform
75-25-2
NE
NE
1U
lU
Bromomethane
74-83-9
NE
NE
2UJ
2UJ
Carbon Disulfide
75-15-0
NE
NE
1U
1U
Carbon Tetrachloride
56-23-5
5
5
1U
1U
Chlorobenzene
108-90-7
100
100
1U
lU
Chloroethane
75-00-3
NE
NE
2U
2U
Chloroform
67-66-3
NE
NE
1U
1U
Chloromethane
74-87-3
NE
NE
2U
2U
Cis-1,2-Dichloroethene
156-59-2
70
70
9.2
2
Cis-1,3-Dichloropropene
10061-01-5
NE
NE
0.4U
0.4U
Dibromochloromethane
124-48-1
NE
NE
1U
1U
Dibromofluoromethane
1868-53-7
NE
NE
24.3
23.6
Dibromomethane
74-95-3
NE
NE
1U
1U
Dichlorodifluoromethane
75-71-8
NE
NE
2U
2U
Diethyl Ether
60-29-7
NE
NE
1U
1U
Di-Isopropyl Ether
108-20-3
NE
NE
1U
1U
Ethylbenzene
100-41-4
700
700
1U
1U
Hexachlorobutadiene
87-68-3
NE
NE
0.6U
0.6U
Isopropylbenzene
98-82-8
NE
NE
1U
IU
M,P-Xylenes
1330-20-7
10000
10000
2U
2U
Methyl Tert-Butyl Ether
1634-04-4
40
40
1U
1U
Methylene Chloride
75-09-2
5
5
2U
2U
Naphthalene
91-20-3
NE
100
1U
1U
115
Sample ID
Sample Depth (feet bgs)
CAS#
Sample Date / Time
EPA
EA
RIDEM
GA
SEI
17-GW
Sample D
SapeI
MCL
Standard
17
15
(tgJL)
(pg/L)
9/11/2013
9/11/2013
VOLATILE ORGANIC COMPOUNDS (pg/L)
N-Butylbenzene
104-51-8
NE
NE
1U
1U
N-Propylbenzene
103-65-1
NE
NE
1U
1U
O-Xylene
95-47-6
NE
NE
1U
1U
Sec-Butylbenzene
135-98-8
NE
NE
1U
1U
Styrene
100-42-5
100
100
1U
1U
Tert-Butylbenzene
98-06-6
NE
NE
1U
1U
Tetrachloroethene
127-18-4
5
5
1
0.6J
Toluene
108-88-3
1000
1000
1U
1U
Trans-1,2-Dichloroethene
156-60-5
100
100
3.3
1.1
10061-02-6
NE
NE
0.4U
0.4U
Trichloroethene
79-01-6
5
5
Trichlorofluoromethane
75-69-4
NE
NE
1 U
1 U
Vinyl Acetate
108-05-4
NE
NE
5U
5U
Vinyl Chloride
75-01-4
2
2
1U
1 U
Trans-1,3-Dichloropropene
bgs - below ground surface
pg/L - micrograms per liter
(parts per billion)
Bold results indicate detections of the analyte.
Shaded results indicate an exceedance of the Rhode Island Groundwater GA
EPA MCL - U.S. Environmental Protection Agency Maximum Contaminant Levels
RIDEM GA Standard - Rhode Island Department of Environmental Management
U - Analyte not detected, limit of quantitation listed.
UJ - Analyte not detected; limit of quantitation estimated during data validation
J - Results estimated during data validation.
R - Result rejected during data validation.
NE - Not Established.
116
B-2: Data from the 2012 USACE Vapor Intrusion
Investigation
117
00
Ug/M3
ug/m
95-47-6
127-18-4
108-88-3
79-01-6
o-Xylene
p/m-Xylene
Tetrachloroethene
Toluene
Trichloroethene
95-63-6
71-43-2
67-66-3
100-41-4
95-47-6
127-18-4
108-88-3
79-01-6
1,2,4-Trimethylbenzene
Benzene
Chloroform
Ethylbenzene
o-Xylene
p/m-Xylene
Tetrachloroethene
Toluene
Trichloroethene
20-JAN-Il
2.46
3.11
-
19-JAN-Il
22.5
3.98
198
41
4.84
355
25.6
18.8
39
0.11
0.97
730
730
0.41
5200
0.59
-
-
50.5
5.7
13.2
9.77
11.3
17.9
31.8
23.7
11.9
9.8
-
-
-
-
-
7.3
0.31
2.96
4.18
-
-
20-JAN-11
8.62
20-JAN-Il
-
L1100909-08
CENAESSG-08-02
21-JAN-l
20.4
Li 100909-07
CENAE-SSG07-02
21-JAN-l
4.27
L 1100909-06
CENAESSG-06-02
8.21
47.5
11.7
207
3.38
2.81
8.47
24.8
21.8
222
Li 100909-03
CENAESSG-04-02
20-JAN-1I
3.82
2.53
Li 100909-02
CENAE-SSG03-02
20-JAN-l
2.21
1.7
2.91
Sample Locations
-
-
-
L 1100909-01
CENAESSG-02-02
Li 100820-12
CENAE-SSG01-02
L1100909-05
CENAE-SSG05-02
Indoor
Air
RBC'
730
0.41
5200
0.59
730
7.3
0.31
0.11
0.97
Indoor
Air
RBC
7.98
47.1
12
204
3.12
L 1100909-04
CENAE-SSG04-02 DUP
20-JAN-1I
3.79
2.49
'Risk Based Concentrations (RBCs) from Tri-Service Environmental Risk Assessment Workgroup - DoD Vapor Intrusion Handbook (January 2009) Table
B-1. TCE RBC is from updated IRIS 2011 Guidance Toxicity Criteria.
Highlighted cells indicate exceedance of RBC.
U/m3
ug/m3
Ug/M3
ug/m3
In/3
ug/m
Ug/M3
Ug/M3
Ug/M3
CAS No.
VOCs of Concern
Units
ug/m3
ug/m3
Ug/M3
ug/mug/m3
ug/m
ug/mn
95-63-6
71-43-2
67-66-3
100-41-4
1,2,4-Trimethylbenzene
Benzene
Chloroform
Ethylbenzene
Units
CAS No.
VOCs of Concern
Sample Locations
Table B - 2 - 1: Sub-Slab Soil Vapor Analytical Results of Detected Chemicals at DPW Facility
ug/m
95-63-6
71-43-2
67-66-3
100-41-4
95-47-6
127-18-4
108-88-3
79-01-6
1,2,4-Trimethylbenzene
Benzene
Chloroform
Ethylbenzene
o-Xylene
p/m-Xylene
Tetrachloroethene
Toluene
Trichloroethene
127-18-4
108-88-3
79-01-6
p/m-Xylene
Tetrachioroethene
Toluene
Trichloroethene
19
6
22
4.84
18.6
0.556
114
10.7
7.7
-
12.3
0.72
8.81
-
14.5
0.832
730
0.41
5200
0.59
7.3
0.31
0.11
0.97
730
Li 100820-09
CENAE-IA08-02
19-JAN-11
11.1
3.18
0.239
5.31
6.64
Li 100820-08
CENAE-IA07-02
19-JAN-1I
9.2
2.02
0.141
5.39
5.65
L 1100820-07
CENAE-IA06-02
19-JAN-1I
3.74
2.09
0.102
2.58
2.69
Highlighted cells indicate exceedance of RBC.
B-1. TCE RBC is from updated IRIS 2011 Guidance Toxicity Criteria
1Risk Based Concentrations (RBCs) from Tri-Service Environmental Risk Assessment Workgroup - DoD Vapor Intrusion Handbook (January 2009) Table
ug/m
Ug/m3
Ug/M3
Ug/m
ug/m3
Ug/M3
Ug/M3
Ug/M3
Ug/M3
95-63-6
71-43-2
67-66-3
100-41-4
95-47-6
1,2,4-Trimethylbenzene
Benzene
Chloroform
Ethylbenzene
o-Xylene
Li 100820-06
CENAE-IA05-02
19-JAN-1I
4.7
2.55
0.146
2.94
3.11
Indoor
Air
RBC'
Units
CAS No.
VOCs of Concern
0.72
0.94
Sample Locations
23.1
24.5
0.762
39.9
0.349
157
1.32
29.2
0.59
6.25
3.52
4.67
5.08
14.6
7.04
3.71
0.102
5.17
5.61
16
0.156
8
7.35
0.132
8.22
7.62
23
-
68.9
24
0.112
44
52.1
139
0.156
6.18
3.89
0.127
4.92
5.5
15.9
0.183
ug/m
19-JAN-II
19-JAN-1I
19-JAN-Il
19-JAN-1I
19-JAN-11
0.41
5200
7.3
0.31
0.11
0.97
730
730
Li 100820-05
CENAE-IA04-02-DUP
Li 100820-04
CENAE-IA04-02
Li 100820-03
CENAE-IA03-02
L 1100820-02
CENAE-IA02-02
Li 100820-01
CENAE-IA01-02
Ug/M3
ug/m
ug/m'
Ug/M3
ug/m'
Ug/M3
Ug/M3
Units
CAS No.
VOCs of Concern
Indoor
Air
RBC'
Sample Locations
Table B - 2 - 2: Indoor Air Analytical Results of Detected Chemicals at DPW Facility
k)
0.044
95% Chebyshev (Mean, Sd) UCL
1,2,4-Trimethylbenzene
Benzene
Ethylbenzene
25
0.09
0.033
95% Chebyshev (Mean, Sd) UCL
95% Chebyshev (Mean, Sd) UCL
Xylene (m,p)
Xylene (o)
9,125
9,125
9,125
9,125
9,125
250
250
250
250
250
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
8
8
8
8
8
0.03
NA
1
0.271
5
0.002
0.1
0.1
Tetrachloroethene
Toluene
Trichloroethene
Xylene (m,p)
Xylene (o)
25,550
25,550
25,550
25,550
25,550
25,550
25,550
25,550
25,550
(days)
0%
0%
31%
8%
3%
100%
0.0018
0.0042
0.83
0.2
0.076
3
,
53%
5%
NA
0%
1.4
0.12
NA
0.0065
Percent
of
Total
HQ
0.013
10
,
12%
100%
0.0000024
0.00002
,
5%
52%
2%
29%
Percent
of
Total
ELCR
0.0041
1
0.000001
0.00001
0.0000003
0.0000058
Cancer
Risk
0.0061
3
Mutagen ADAF
IUR (mg/M 3)-
for benzene based on high end of inhalation unit risks per USEPA IRIS database.IUR for ethylbenzene based on Tier III toxcity value from CalEPA, since there is none from USEPA IRIS.
HQ = Hazard Quotient; ET = Exposure Time, hours/day; CF = Conversion Factor, day/24 hr; EF = Exposure Frequency, days/year; ED = Exposure Duration, years; APnc = Averaging Period for
noncancer, days; ATc = Averaging Period for cancer, days; RfCinh = inhalation Reference Concentration; IUR = inhalation Unit Risk
IUR
9,125
9,125
9,125
9,125
250
250
250
250
0.0417
8
8
8
8
0.007
1,2,4-Trimethylbenzene
(mg/)(days)
ATnc
EF
EF
(Noncancer
(days
CF
CFdy
(day/24
Benzene
Chloroform
Ethylbenzene
VOLATILESET
DPW Worker
(hr/day)
25
0.007
95% Chebyshev (Mean, Sd) UCL
Trichloroethene
RfCinh
25
0.092
25
25
0.002
95% KM (BCA) UCL
95% Approximate Gamma UCL
Toluene
Tetrachloroethene
HQ
0.0059
25
0.028
95% Chebyshev (Mean, Sd) UCL
Apca
0.023
0.0025
25
RT
0.0078
25
Mutagen
0.016
25
1
0.00016
3
Non
95% KM (BCA) UCL
10
Mutagen ADAF
ED (yr)
95% Chebyshev (Mean, Sd) UCL
25
or Non
Mutagen
Cancer
Non
Chloroform
(Mg/M
USEPA Recommended Metric
VOLATILES
Exposure
Point
Concentration
3)
DPW Worker
Table B - 2 - 3: Chronic Human Health Risk Estimates Using January 11, 2011 PR-58 Indoor Air Samples at DPW
Appendix C: Case Study J&E and Vol Analysis Input
Data
121
Groundwater J&E and Vol Analysis Input
First, the geometric means of the groundwater concentrations were calculated in Excel using the
following equations:
ConcentrationTE = GEOMEAN(55.9,19.4)= 32.9pg/L
ConcentrationPCE= GEOMEAN(, 0.6)= 0.8pg/L
These values were entered into the DATENTER worksheet of the J&E model. Figure C - 1 and
Figure C - 2 provide screenshots of the DATENTER worksheet for TCE using the two target
incremental carcinogenic risk values of interest (10-6 and 10-4, respectively).
F
E
ENTER
ENTER
12
chemt'ca
4ro04w0
1n CAS No
14 040rn0b0
Z no dash,"i
17
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ENTER
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Depth
SoN
24
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1
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for
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Figure C - 1: Groundwater DATENTER Worksheet - Target Risk of 10 6
122
(004m04'
E
F_
ENTER
V
13 CAS No
14
U
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a
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7 00% 320E.01
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t"ETR
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Ao..ogo
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a oer
4.
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41
42
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09100
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ENTER
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of SON
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ENTER
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5010*1.41 50*0*11
40
7
of L- l-4
s
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ENTER
I
l.OE-04
Figure C - 2: Groundwater DATENTER Worksheet - Target Risk of 104
Next, the geometric standard deviations of the groundwater concentrations were calculated.
Because only two data points were available, the geometric standard deviations could not be
calculated directly. Instead, it was assumed that each measured groundwater concentration was
one geometric standard deviation away from the geometric mean.
The geometric standard
deviations were calculated, therefore, as follows:
GSDT(E
=
(3)
eXp(In(55.9)-In(GEOMEAN(55.9,19.4)))= 1 .7pg/L
GSDCE = exp (ln(i)
-
ln(GEOMEAN(1,0.6)))= 1.3pg/L
The values of the geometric mean and geometric standard deviations were entered into the Vol
worksheet. Figure C - 3 and Figure C - 4 provide screenshots of the Vol worksheet for TCE
using the toxicity values from 2003 and 2014, respectively.
123
(4)
Building properties
defaut
40
AP
mm
0
max
200
Distribution
Trianigular
HIghlighted caob must be Ned In
[ Ij
|Trchlroethylome
Chemical ot Concern
Arithmeic mean groundwater concentration, C, (rg/L)
Arithmetic Standard Deviation of C, (ug/L)
Geometrc mean groundwater concentration C, (ug/L)
Geometric Standard Deviation of C, (ug/L)
Updated URF (ug/m)
Updated RfC (mgWn(
New Construction? (Y or N)
Saril properties
Particle Diameter of 0044
0046
Stratum Above
Water Table
A
avg
0375
n'
oj
0054
2678
WA
K.
k,
B
C
no
6r
NiA
NA
n8,
NA
NA
min
0053
0003
Uniform
max
Distribution
-
Normal
Uniform
Log Normal
N/A
0055
-
WA
NWA
NIA
N/A
WA
NIA
N/A
N/A
N/A
WA
n
2
3
AP
0187
0204
069
N/A
4
0648
N/A
5
Simulation
Default
1
N/A
NiA
32,9
A7
6
7
8
0005
0541
0 05
0374
9
0010
Ks
-0678
0064
0748
0489
0,391
0728
0751
0764
0155
C,
0880
0212
0786
0525
0155
0124
0510
0 &37
0034
Random No
Particle Diameter
-0114
0004
0431
0421
0328
0415
0298
0953
0116
Figure C - 3: Groundwater Vol Worksheet Input Using Model-Provided Toxicity Values
Buildirg properties
defaut
AP
40
min
0
Highighted
max
200
Distribution
Triangular
Chemical of Concern
Arthmetic mean groundwater concentration. C, (ug/L)
Arithmetic Standard Deiation of C (ug/L)
Geometric mean groundwater concentration C, (ugao
Geometrec Standard Deviaton Of C, (UL)
Soil
A
8
properties
0044
0 046
0053
Uniform
awg
0,375
0054
2678
NiA
mir
0053
N/A
max
0055
N/A
Distrbution
Normal
'3i9
N/A
N/A
N/A
WA
N/A
N/A
N/A
N/A
r :C
0,
NA
NJA
NA
N/A
N/A
NA
N/A
N/A
n'
9,'
K,
k,
n'
329
17
00000041
0002
r
Updated URF (ug/m)"
Updated RfC (mg/m')
New Construction? (Y or N)
i Particle Diameter (A
q
Stratum Above
Water Table
calve must to 1n6d in.
I1YIZ
Tric hlor eotylenm
Uniform
Simulaori
Log Normal
Default
1
2
3
4
WA
5
6
7
8
9
AP
Ks
C,
Random N
Particle Diameter
0187
0 204
0696
0 48
0005
0541
0056
0374
0010
0678
0064
0748
0489
0391
0728
0751
0764
0155
085
0,212
0786
0525
0155
0124
0510
0837
0034
0114
0564
0431
0421
0328
041S
0298
0953
0116
Figure C - 4: Groundwater Vol Worksheet Input Using Updated Toxicity Values
124
Soil Gas J&E and Vol Analysis Input
First, the geometric means of the soil gas concentrations were calculated in Excel using the
following equations:
ConcentrationTCE
=
GEOMEAN(9.8,13.2, 31.8, 39,198, 207, 222, 355)= 69pg/m3 (5)
ConcentrationPCE=GEOMEAN(6.78,11.3,22.5,23.7,24.8,41,47.5, 50.5)=24pjg/m3 (6)
It should be noted that the PCE concentration of 6.78 pg/m 3 corresponds to the minimum
detection limit (MDL).
These concentration values were entered into the DATENTER worksheet of the J&E model.
Figure C - 5 provides a screenshot of the DATENTER worksheet for TCE.
7
CUM477
a
CASN7
10
- ash"L
C.
Chwifal
pDWNI
G.794
.E.0L
Ewn
*
18
18
S
216
27
28
28
OP
(Mim
12
24
ENTE
I
EWTE"
BNTE
t8807to*
72
7W
747987$
$ 870
AM7&8$
cflw
tu.777874
44
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ENTER
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747$.
E
T,
N747
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strau
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ir Wao-o
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.
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e
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tro t
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bulk dwwft
774777
974777$a"
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ENTER
ENTER
tratumA
SCS
stwo
36
A
Z8
Figure C - 5: Soil Gas DATENTER Worksheet
Next, the geometric standard deviations were calculated using the following equations in Excel:
GSD
TCE
= exp (STDEV(1n(9.8),n(13.2),Jn(31.8), ln(39),n(198),ln(207)jn(222),ln(355)))= 4.1pg/ m3 (7)
GSDCE = exp(STDEV(ln(6.78),ln(l 1.3)ln(23.7) ln(50.5)jln(47.5),ln(24.8),ln(22.5),ln(41)))= 2.Opg / m3 (8)
125
0.1
1
The values of the geometric mean and geometric standard deviations were entered into the Vol
worksheet. Figure C - 6 through Figure C - 9 provide screenshots of the Vol worksheet for TCE
for the following four risk/toxicity scenarios:
5. Target risk of 10-6, using the model-provided toxicity values;
6. Target risk of 10-4, using the model-provided toxicity values;
7. Target risk of 10-6, using the updated toxicity values; and,
8. Target risk of 10~4, using the updated toxicity values.
Highlighted ceO. imet be ied In.
Budding properties
AP
default
40
min
0
max
200
Distrnbution
Trangular
Chemical of Concern
indial sod gas concentration C,
Trichlorleylene
pp nra)
(ug/m,
Standard Deviation of C, (ug/m, ppmv)
Lognormal Mean of C, (ug/m
69
ppmv)
Geometric Standard Deviation of Cr (ug/m
New Construction? (Y or N)
Updated URF (ug/rm
Updated RfC (mg/m)
Target risk for carcinogens
Target risk for noncarcinogens
41
ppmv)
n
0 000001
1
Soil properties
A
aug
n
K,
k,
B
C
no
'e
nc
1,
min
max
0485
0 055
Distnibution
Normal
Uniform
Simulation
-
--
Log Normal
Default
--
N/A
N/A
NIA
1
2
3
4
5
6
7
0375
0054
0265
0053
26,78
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
NA
N/A
N/A
NIA
NA
Randofr
N/A
N/A
8
NWA
N/A
9
AP
Ks
C'
0 173
0512
0341
0469
0212
0 786
0337
0670
0951
0292
0965
0456
0458
0280
0628
0 350
0 182
0 126
0254
0 783
0 583
0682
0 060
0337
0251
0409
0674
Figure C - 6: Soil Gas Vol Worksheet Using Target Risk of 106 and Model-Provided
Toxicity Values
AP
Building properties
default
mm
40
0
max
200
Distribution
Triangular
Highlighted cele nust be ied in.
[ Z"7Trchkomethylene
Chemical of Concem
Initial soil gas concentration. C, (ugfm pp mv)
Standard Deviation of C,
(ug/m
Lognormal Mean of C, (ug/m
ppmv)
ppmv)
69
Geometric Standard Deviation of C, (ugim ppmV)
New Construction? (Y or N)
Updated URF (ug/m)"
Updated RfC (mg/m'/
Target risk for carcinogens
Target risk for noncarcinogens
41
n
0 0001
I
Soil properties
A
B
n
u,-
avg
0375
0054
min
0265
0053
0.485
K,
k,
2678
N/A
0055
-
Distribution
Normal
Uniform
Log Normal
N/A
N/A
N/A
no
0,
max
Rondom
Simulation
Default
N/A
N/A
N/A
NA
1
2
3
4
N/A
N/A
N/A
N/A
5
-
0212
0786
0126
0 254
0 337
0350
0182
0783
0583
0682
0 060
0337
0251
0409
0674
N/A
N/A
N/A
N/A
8
N/A
N/A
N/A
NA
9
0628
7
B,:
-
Ks
0173
0512
0341
0469
0965
0456
0458
0280
6
C
AP
0670
0,951
0,292
Figure C - 7: Soil Gas Vol Worksheet Using Target Risk of 10- 4 and Model-Provided
Toxicity Values
126
Building properties
default
min
AP
4
A
40
Soil properties
min
avg
0265
0.375
0.054
0.053
26.78
N/A
N/A
n'
8,^
K,
k,
it
2
-t
B
2
C
0
max
Distribution
200
Trangular
max
Distribution
Normal
Uniform
Log Nonnal
N/A
0,485
0.055
N/A
rtgigghted asts must he Oud in.
Chemical of Concern
Trchloroeflylne
Iniial soil gas concentration. C, (ug/m ppnw)
3
Standard Deviation of C, (ug/m pprmv)
Lognormal Mean of C, (Ug/mI ppmv)
69
Geometic Standard Deviation of C, (ug/m ppnw)
4A
New Construdtion? (Y or N)
n
3
Updated URF (ug/m y'
4 tOE-OS
3
Updaled RfC (mg/m )
0.002
Target nsk for carcinogens
0000001
Target risk for noncarceogens,
Randow
Simulation AP
Default
Ks
-
1
0.173
0212
2
3
0786
0.126
0,254
no
,'
N/A
N/A
N/A
N/A
N/A
N/A
4
0.512
0.341
0469
N/A
N/A
5
0.965
0,350
nc
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
6
7
8
9
0456
0 458
0280
0628
0.182
0.783
0583
0.682
6
0.337
0,670
0951
0292
0,060
0
337
0-251
0409
0674
Figure C - 8: Soil Gas Vol Worksheet Using Target Risk of 106 and Updated Toxicity
Values
AP
A
1
6
Building properties
min
default
0
40
Soil properties
min
ag
n^
0375
0265
6"
0054
0053
K.
2678
kv
N/A
N/A
Highighted
max
200
Distribution
Trangular
max
0.485
0055
-
Distiution
Normal
Uniform
Log Nonnl
N/A
N/A
of Concern
Initial soil gas concentration C, (ug/M ppM)
Standard Deviation of C, (ug/rm ppnw)
Lognormal Mean of C,(ugm/n ppm)
Geometric Standard Deviaion of C, (ug/m' ppnw)
New Construction? (Y or N)
3
Updaled URF (ug/m y'
Updated RfC (mg/m3)
Target risk for carcinogens
Target rsk for noncarcinogens
24
C
nC
O,,
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Trhiloroehyne
69
41
n
4 ItE-06
0002
00001
I
ReOdom
Simulation AP
Default
1
18
no
05
as must be Md In,
Chemical
N/A
N/A
--
Ks
C.
-
2
3
4
5
0173
0512
0341
0469
0965
6
0.456
0.182
7
8
9
0.458
0.280
0.628
0.783
0.583
0.682
0.212
0.786
0.126
0254
0350
0337
0,670
0.951
0292
0060
0337
0251
0409
0,674
Figure C - 9: Soil Gas Vol Worksheet Using Target Risk of 104 and Updated Toxicity
Values
127