Draft
Regulatory Impact Analysis
for the Final Rulemaking on Corrective Action
for Solid Waste Management Units
Proposed Methodology for Analysis
APPENDICES
Office of Solid Waste
U.S. Environmental Protection Agency
March 1993
Printed on Recycled Paper
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TABLE OF CONTENTS
EXECUTIVE SUMMARY
1. INTRODUCTION 1-1
1.1 The Need for Regulation 1-1
1.2 Description of Corrective Action Regulatory Impact Analysis 1-5
1.3 R1A Organization 1-6
2. REGULATORY OPTIONS 2-1
2.1 Description of the Baseline 2-1
2.2 Description of the Subpart S Proposed Rule 2-2
2.3 Other Options to be Examined 2-3
3. SAMPLE SELECTION, FACILITY CHARACTERIZATION, AND MODELING OF
RELEASES 3-1
3.1 Approach 3-1
3.2 Results 3-27
3.3 Limitations 3-39
4. REMEDY SELECTION AND MODELING OF REMEDY EFFECTIVENESS ... 4-1
4.1 Background 4-1
4.2 Approach 4-2
4.3 Results .- 4-14
4.4 Limitations 4-22
5. COSTS 5-1
5.1 Approach 5-1
5.2 Results 5-7
5.3 Sensitivity Analysis 5-23
5.4 Largest Federal Facility Cost Analysis 5-29
5.5 Limitations 5-32
6. OVERVIEW OF BENEFITS 6-1
6.1 Potential Benefits 6-1
6.2 Studies Conducted for this RIA 6-6
7. HUMAN HEALTH BENEFITS 7-1
7.1 Background 7-1
7.2 Approach 7-4
7.3 Results 7-27
7.4 Limitations 7-56
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TABLE OF CONTENTS (CONTINUED)
Page
9. AVERTED WATER USE COSTS . . 9-1
9.1 Economic Framework 9-2
Analytic Approach
Results
Limitations
10.1 Approach
10.2 Results
10.3 Sensitivity Analyses
10.4 Limitations
11. RESIDENTIAL PROPERTY ANALYSIS
11.1 Expected Linkages Between TSDFs and Residential
Property Markets
11.2 Statistical Specifications and Data Employed in -‘
Current Analysis
11.3 Overview of Findings
11.4 Conclusions
12. CHANGES IN ThE VALUE OF FACILITIES
12.1 Economic Framework
12.2 Analytic Approach
12.3 Example of Facility Value Benefit Calculations
12.4 Results
12.5 Sensitivity Analysis
12.6 Limitations
13. COMPARISON OF BENEFITS AND COSTS.
13.1 Introduction
13.2 Review of Costs and Benefits
13.3 Comparison of Costs and Benefits
13.4 Limitations
BIBLIOGRAPHY
• 11-1
11-1
11-5
11-10
11-17
12-3
12-2
12-7
12-15
12-17
12-20
12-22
8. ECOLOGICAL BENEFITS
8.1 Approach
8.2 Results
8.3 Discussion
8.4 Limitations
9.2
9.3
9.4
• 8-1
• 8-1
8-6
8-14
8-14
10. NONUSE BENEFITS OF GROUND WATER REMEDIATION
9-8
9-15
9-29
10-1
10-2
10-29
10-31
10-36
13-1
13-1
13—2
13-6
13-9
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TABLE OF CONTENTS (CONTINUED)
Page
APPENDIX A. DEVELOPMENT OF FACILITY SAMPLE..
Al Creating the Federal Frame
A.2 Creating the Non-Federal Sample Frame
A.3 The Full Corrective Action Sample
A-I
A-I
A-3
A-8
APPENDIX B. PREDICTING RELEASES AND EXPOSURES WITH MMSOILS..
B.I Model Selection
B.2 Parameter Selection and Assumptions
B.3 Model Application Assumptions and Limitations
B.4 Post-MMSOILS Processing
APPENDIX F. ECOLOGICAL THREATS: METHODOLOGIES AND
CASE STUDIES
F.I Methodology for Proximity Analysis
F.2 Methodology for Deriving Screening Ecological
Benchmark Levels
F.3 Methodology for Estimating Extent of Contamination
F.4 Proximity Analysis Results
F.5 Concentration-Based Screening Analysis Results
F.6 Time Sequence Results
F.7 Extent of Contamination Results
F.8 Qualitative Case Studies
APPENDIX G. KEY PARAMETERS MATRIX G-I
APPENDIX H. FACIUTY AND SWMU DATA FORMS H-I
APPENDIX I. CHARACTERISTICS OF FACILITY AND SWMU
POPULATIONS
1.1 Facility Characteristics
1.2 SWMU Characteristics
B-I
B-I
B-3
B-I3
B-17
C-I
C-i
C-3
C-7
APPENDIX C. SIMULATION OF REMEDY EFFECTIVENESS
C.I Source Control Technologies
C.2 Waste Treatment Technologies
C.3 Ground-Water Remediation Technologies
APPENDIX D. COST ANALYSIS
D.I Expert Panel Cost Estimation
D.2 Additional Analysis of Results
APPENDIX E. HUMAN HEALTH BENEFITS ANALYSIS
E.I Hazard Identification and Dose-Response Assessment
E.2 Exposure Analysis
E.3 Risk Characterization
D-I
D-I
D-13
E-I
E-i
E-6
E-25
F-I
F-I
F-I
F-8
F-9
F-9
F-9
F-9
F-17
I—i
I—I
1-17
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APPENDIX A. DEVELOPMENT OF FACILITY SAMPLE’
This appendix provides additional detai’ on the process EPA used to identify the frame of
facilities subject to the Subpart S Corrective Action requirements. It documents the federal and
non-federal facility sampling frame creation, the sampling design, and the sample allocation and
selection processes.
Al Creating the Federal Frame
In the winter of 1991, EPA used the 1990 Inventory of Federal Agency Hazardous Waste
Facilities (Inventory) to identify the universe of federal facilities potentially subject to corrective
action under RCRA. Based on this universe (i.e., the sampling frame), EPA selected the federal
facility sample for the corrective action RIA.
According to facility responses to Part Ill of the Inventory 2 , 395 federal faciLities were
identified as being potentially subject to corrective action. To verify this characterization of the
sampling frame. EPA presented the preliminary list of facilities to officials in each of the relevant
Federal departments to identify any facilities that should not be listed among this universe (i.e., are
not subject to corrective action). As a result of this verification process, 36 of the 395 facilities were
deleted for a corrected population of 359 federal facilities potentially subject to corrective action.
Because the Inventory represents data from 1990 and appeared to be more complete than the
Hazardous Waste Data Management System (HWDMS), EPA used the Inventory-derived list as the
sampling frame for the corrective action RIA.
EPA grouped the 359 facilities in the universe into three size categories, (very large, large,
and other) defined by the magnitude of potential costs of corrective actions. Facilities with
estimated costs greater than $1 billion were classified as “very large”; those with estimated costs
between $100 million and $1 billion were classified as “large”. The remaining facilities were classified
as “other.” Exhibit A-I characterizes the 359 federal facilities subject to corrective action by these
three size categories and Federal departments.
A.1.1 Determining the Federal Sample Size and Allocation
The selection of the federal facilities sample underwent several changes befoTe arTivng at
the final sample size. From the 359 federal facilities in the universe, 30 were initially randomly
selected, across strata, for the analysis.
For more information on the sample plan see Selecting Sample Facilities for the Corrective
Action Regulatory Impact Analysis: Draft Report prepared for U.S. EPA by Research Triangle
Institute. Research Triangle Park, NC: June 1991.
2 Part Ill contains information on RCRA treatment, storage, and disposal facilities that
managed hazardous waste on or after November 19, 1980.
-- March 24, 1993 * * *
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A-i
EXHIBIT A-i
DISTRIBUTION OF FEDERAL FACILITIES POTENTIALLY
SUBJECT TO CORRECTIVE ACTION BY SIZE AND DEPARTMENT
Department
Size Category
Very Large
Large
Other Total By
Department
Defense
2
9
284
295 (82%)
Energy
7
13
14
34 (10%)
Other
0
0
30
30 (8%)
Total by Size Category
9
(3%)
22
(6%)
328
(91%)
359
(100%)
Following the selection of the 30 federal facilities, a pilot phase of the study was conducted.
The experiences gained during the pilot phase convinced the Agency of the impracticability of
providing expert panel review for a large number of facilities given the time constraints of the study.
Furthermore, it became clear that the number of facilities for which adequate data existed for the
analysis had been overestimated and that several facilities that may not be subject to RCRA
authority had been inaccurately included in the population.
The Agency determined that inadequate data existed for the analysis if, after contacting the
appropriate regional office or federal agency, either no facility documents were available or the
existing documents were of poor quality. For example, lists of the SWMUs and their locations were
often unavailable.
The facilities which may have been inaccurately included in the population fell into two
categories (1) protective filers, and (2) ninety-day converters. Protective filers represent those firms
that did not manage hazardous wastes as of May 19, 1980, and were therefore not required to submit
a RCRA Part A permit application notifying EPA of hazardous waste activity, but that nevertheless
filed a Part A application to EPA as a legal precaution. Ninety-day converters represent those
facilities that stored hazardous wastes for longer than 90 days at the time of their permit application
(and were therefore subject to permitting as a storage facility), but who subsequently changed their
waste storage practices to less than 90 days and no longer require a hazardous waste storage permit.
While protective filers and ninety-day converters are not subject to the RCRA permitting
requirements, some of these facilities may be subject to RCRA corrective action authorities under
some circumstances.
*** DRAFT -- March 24, 1993 * * *
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A .3
To ensure that this RIA does not understate the population of facilities that may be required
to conduct corrective action, protective filers and ninety-day converters were included in the universe
but were excluded from the sample. Facilities that did not have adequate data to conduct the
analysis were also left in the population but were excluded from the sample. Excluding the facilities
with inadequate data decreased the sample size and therefore allowed the Agency to better analyze
the facilities in the sample. As a result, however, error was introduced into the sample in the form
of non-response error. The exclusion of these facilities from the sample resulted in a final federal
sample of 9 facilities.
A.1.2 Description of the Federal Sample
The 9 federal facilities in the final sample included:
• 0 “Very Large” DOD/DOE Facilities;
• 3 “Large” DOD/DOE Facilities; and
• 6 “Other” Facilities.
No “Very Large” DOD/DOE facilities were included in the final sample for two reasons.
First, because of their large size and complexity, their inclusion in the analysis would have greatly
reduced the number of non-federal and other federal facilities that the Agency would have been able
to include in the RIA. Second, the agencies responsible for these nine facilities are currently
expending a high level of effort towards their characterization and remediation, and therefore, are
best equipped to estimate the potential corrective action costs and benefits at each of these facilities.
In order to maximize the sample size, these facilities were excluded from the sample of facilities
analyzed.
EPA weighted the sample with a higher percentage of “large” facilities (33 percent) than is.
present in the universe (6 percent). This weighting was done for two reasons. First, the universe
of facilities that will require corrective action is likely to contain a larger proportion of facilities in
this size category than the universe of all federal facilities. Second, because these “large” facilities
are expected to drive the cost and benefit analyses, sampling them at a higher rate provides more
precision in these groups, thus making the analysis more accurate.
Al Creating the Non-federal Sample Frame
Creating the sampling frame involved obtaining lists of facilities from EPA databases,
evaluating the suitability of the resulting set of facilities, and revising both the databases and the
selection criteria used. During this exploration, several databases were accessed to locate facilities
belonging to the sampling frame. The components of this frame were three EPA databases:
HWDMS; the Corrective Action Reporting System (CARS); and the Resource Conservation and
Recovery Information System (RCRIS) which now encompasses both HWDMS and CARS.
* * * DRAFT -- March 24, 1993 * * *
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A-4
HWDMS, CARS, and RCRIS were accessed to identify the universe of facilities potentially
subject to corrective action. Facilities in one or more of the following seven categories were
considered potentially subject to corrective action and assigned to the sampling frame:
• Treatment, storage, or disposal facilities;
• A facility that previously stored and/or treated hazardous waste but is no longer part
of the universe;
• Permit by rule facilities (e.g., underground injection facilities and publicly owned
treatment works (POTWs));
• Clean-closed facilities; and
• Other facilities with specific permit status not listed above.
Applying these criteria yielded a universe of 8,284 facilities potentially subject to corrective action.
A flag variable was used to identify and delete Federally-owned facilities. The resulting sampling
frame of non-federal facilities potentially subject to corrective action contained 5,432 facilities.
£2.1 Choosing the Non-federal Sampling Design
Selecting a sample of facilities from the sampling frame involved several steps. First, the
Agency developed a sample design based on the key population parameters of interest. Then, given
specified precision requirements for the selected parameter estimates, the sample allocation was
determined. Finally, EPA used a randomization procedure based on the sampling design to obtain
the distribution of sample facilities specified in the allocation.
EPA considered several factors in deciding to employ a stratified, one-stage cluster design
for the non-federal facility sample. First, greater precision was desired in the parameter estimates
for large facilities, which were expected to incur higher corrective action costs ceterisparibus. At the
same time, EPA wanted to minimize the cost of collecting the data needed to make parameter
estimates for each type of facility. For these reasons, EPA decided to use strata defined by facility
size and data availability.
In order to stratify by data availability, EPA decided to make use of data collected during
RCRA facility assessments (RFAs). Typically, before a permit is issued, EPA or an authorized state
would conduct a RCRA Facility Assessment (RFA) to determine whether a potential problem exists
(the RFA is analogous to the Superfund Preliminary Assessment/Site Investigation [ PA/SI]). If a
likely release is found, the permit would contain a schedule of compliance requiring the
owner/operator to conduct a Remedial Field Investigation (Rfl) to characterize the nature and
extent of contamination. Because much of the information required for this RIA is collected during
an RFA, data collection costs were expected to be much lower for facilities with RFAs completed.
Furthermore, since EPA regions tend to complete RFAS for the highest priority facilities first, the
* * * DRAFF -- March 24, 1993 * * *
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A-5
facilities with RFAs completed could be assumed to be among those most likely to trigger corrective
action. Following this rationale, the Agency established three strata based on size and RFA status.
Within each stratum, sample facilities were selected with equal conditional probabilities, given the
stratum, and without replacement.
The facilities identified as “Large” formed one stratum. These are the facilities that were
identified by EPA Regions as being the largest (with estimated costs between $100 million and 1$
billion). The remaining 5,345 “Not Large” facilities were divided into two strata:
• Those with RFAs completed; and
• Those that do not have RFAs completed.
For these two strata 1,711 facilities were “Not Large” and had a completed RFA, and 3,634 facilities
were “Not Large” and had no RFA.
To further characterize the sampling frame facilities, EPA also profiled them according to
industry group, using the facilities’ 2-digit Standard Industrial Classification (SIC) code. However,
SIC code 49, was divided into 4953, Refuse Systems, and 49 Other as SIC 4953 is frequently used
by commercial hazardous waste management facilities. Facilities for which there was no SIC code
information were grouped into “Missing/Other.”
Although stratifying by SIC code was not practical given the overall sample size planned for
the survey, EPA wanted to control the distribution of the sample with respect to the industries
represented in each stratum. To provide this control, a sequential selection procedure was used to
select the sample. That is, dividing the sampling frame into strata, each stratum was sorted by the
SIC code groupings (industry groups) shown in Exhibit A-2. Then the Agency used a selection
procedure that selected facilities over the entire sequence of SIC codes within each stratum. This
assured a proportional representation of industries in the sample frame up to the limitations imposed
by the stratum.
- A.2.2 Determining the Non.federal Sample Size and Allocation
The optimal sample allocation procedure minimizes the total variable costs of the study,
while meeting predetermined precision requirements (variance constraints). EPA produced
estimates of the average cost of collecting data from a facility for each of the strata. In addition,
EPA determined the level of precision required for estimates for facilities in the “Large” stratum
and overall. Using this information, a system of equations was developed that described the variable
costs and the variances in terms of the characteristics of the sampling design and the sample sizes
of the strata. These equations were solved simultaneously to determine the sample size for each
stratum that would minimize the total variable cost of conducting the study while achieving the
desired level of precision. The solution of this system of equations resulted in the stratum-level
sample sizes shown in Exhibit A-3.
* * * DRAFT -- March 24, 1993***
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EXHIBIT A-2
NON-FEDERAL SAMPLING FRAME BY SIC CODE
INDUSTRY GROUP
lARGE
‘NOT IARGr
RFA NO RFA
UJDUSThY TOTAL
(INDUSTRY PERCI 41)
Mlss lnglOlher
1?
(0 . 6)b
(13.8)’
(02)d
515
(28.8)
(30.1)
(93)
1262
(703)
(34.7)
(232)
1789
(32.93)
2$
Chemise! Produds Marnftdurlng
35
(3.0)
(40.2)
(0.6)
430
(37.3)
(25.1)
(7.9)
689
(59.7)
(18.9)
(12.7)
1154
(2124)
29
Pdroleui. and Can! Prududa
16
(6.9)
(18.4)
(0.3)
123
(52.8)
(7.2)
(2.3)
94
(403)
(2.6)
(1.7)
233
(129)
33
Primary Metals ManItadurlng
5
(1.4)
(5.8)
(0.1)
161
(43.9)
(9.4)
(2.9)
201
(54.8)
(3.5)
(3.7)
367
(6.76)
34
Fabdantad Metals Manefaduzlng
1
(0.2)
(12)
(0.0)
100
(22 . 8)
(5.8)
(1.8)
338
(769)
(9.3)
(62)
439
(8.08)
35
Noneledrlcal Mathlnery
0
(0.0)
(0.0)
(0.0)
56
(23.9)
(3.3)
(1.0)
178
(76.0)
(4.9)
(3.3)
234
(4.31)
36
Eledilsel and Eledjonlc
M.dilnery and EquIpment
2
(0.4)
(2.3)
(00)
82
(17.6)
(4.8)
(1.5)
383
(82.0)
(103)
(7.1)
467
(8.60)
37
Transportation EquIpment
10
(42)
(113)
(02)
57
(23.8)
(3.3)
(1.0)
173
(72.1)
(4.8)
(3.2)
240
(4.42)
49,Othar
Fiedric, Gas, and Sanitary Sm ises
0
(0.0)
(0.0)
(0.0)
62
(28.8)
(3.6)
( II)
153
(712)
(42)
(2.8)
215
(3.96)
4953
Refuse Systems
6
(3.9)
(6.9)
(0.1)
88
(56.8)
(5.1)
(1.6)
61
(39.4)
(1.7)
( I I)
155
(2.85)
50
ioleiale Trade
0
(0.00)
(0.00)
(0.00)
37
(26.6)
(2.2)
(0.7)
102
(73.4)
(2.8)
(1.9)
139
(236)
S atum Total
87
(1.6)
1711
(313)
3634 1 5432
(66.9) (tOO)
• * DRAFT -. March 24, 1993 * * *
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A-7
in each row, the first number is the number of facilities in the sampling frame.
bIn each row, the second number represents that cell’s percentage of the row, for example, 12/1789.
in each row, the third number represents that cell’s percentage of the column, for example, 12/87.
dIn each row, the fourth number represents that cell’s percentage of the table, for example, 12/5432.
* * * DRAFT -- March 24, 1993 * * *
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A-8
EXHIBIT A. .3
INITIAL NONFEDERAL SAMPLE ALLOCATION
Stratum
Number of Facilities
“Large” facilities
56.13
“Not Large” facilities with RFAs
54.86
“Not Large” facilities without
RFAs
56.56
Total
167.55
Following the random selection of the 167 non-federal facilities, EPA conducted the pilot
phase of the study to test the methodology. As described in Section A.1.1 (Determining the Federal
Sample Size and Allocation), the experiences gained during the pilot phase convinced the Agency
of the impracticability of providing expert panel review for a large number of facilities given the time
constraints of the study. Subsequently, the number of non-federaj facilities was randomly dropped
to 70, distributed across the three strata.
A.2.3 Description of the Non-federal Sample
The sample of 70 non-federal facilities was allocated among the three strata and selected as
described in Section A.2.1. The sample of 70 included 26 facilities from the “Large” stratum, 27
from the stratum of facilities that are “Not Large” and have a completed RFA, and 17 from the
stratum of facilities that are “Not Large” and do not have a completed RFA.
As is the case among hazardous waste management facilities in general, the largest share of
the sample came from SIC code group 28, Chemicals Manufacturing. SIC code 29, Petroleum and
Coal Products, accounted for the next largest share. And, roughly one-fourth of the sample either
had no known SIC code or fell into an industry group other than the ones specified by EPA. Thus,
the distribution of the sample facilities among SIC codes reflected the distribution of hazardous
waste management facilities generally.
A.3 The Full Corrective Action Sample
The sample of facilities studied in analyzing the impact of the Subpart S Corrective Action
Rule comprises two separately selected frameworks: the federal facility frame and the non-federal
facility frame. The sample frames were constructed separately for these two samples, and the
samples were selected using different sampling designs and sample allocations. Of the 359 federal
and 5,432 non-federal facilities in the overall population, 9 federal and 70 non-federal facilities were
• DP’ .Ma h24,1993 S
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A-9
selected for the final corrective action sample. Exhibit A-5 summarizes the number of facilities in
the corrective action sample and the associated number in the overall population. For population-
level data, the designation of facilities either requiring corrective action or needing no further action
was estimated based on the sample results. The exhibit presents the facility weights for each stratum
that were used to extrapolate the facility-level data to national results.
*• *DP. FI’...Ma h24,I993*s *
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A-tO
EXHIBIT A-4
FACILITIES SUBJECT TO CORRECTIVE ACTION BY STRATA
Very
Large
DOD/DOE
Large
DOD/DOE
Other
Federal
Total
Large
Not
Large
RFA
Not Large
No RFA
Non-
Federal
Total
TOTAL
Sample
CAR’
0
3
5
8
26
19
6
51
59
NFA 4
0
0
1
1
0
8
11
19
20
Total
0
3
6
9
26
27
17
70
79
Population Weight per
Sample Facility
n/a
7.3
54.7
n/a
3.3
63.4
213.8
n/a
n/a
Population
CAR.
9
22
273
304
87
1204
1283
2574
2878
NFA
0
0
55
55
0
507
2351
2858
2913
Total
9
22
328
359
87
1711
3634
5432
5791
Designates facilities where corrective action was required.
Designates facilities requiring no further action.
‘ DRAFT -- March 24, 1993
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B. PREDICrING RELEASES AND EXPOSURES WITH MMSOILS
Appendix B provides supplementaiy information concerning the prediction of constituent
releases, transport, and exposures. Section 1 discusses the model selection process. Section 2
discusses MMSOILS parameter selection and assumptions. Section 3 discusses model application
assumptions and limitations. Finally, Section 4 discusses post processing of the model outputs,
particularly the aggregation of SWMU-speciflc data into facility-wide estimates. This appendix
does not provide a detailed explanation of the MMSOILS model. For further information on
the MMSOILS model, refer to the model documentation. 1
B.I Model Selection
This section discusses the criteria and process for selecting a fate and transport model for
the RIA analyses. 2 EPA reviewed many models for potential use in the RLA:
• LLM;
• MMSOILS;
• SLUDGEMAN/SLAPMAN;
• EPACMLIEPACMS;
• UST;
• TRAM;
• FECrUZISAPT3D;
• ISC;
• MICHRIV;
• EXAMS; and
• RAMMS.
The Agency evaluated the models using three broad, interrelated criteria to determine the
appropriate level of complexity for the corrective action RIA: objectives criteria, implementation
criteria, and technical criteria. The MMSOILS model was selected for several reasons:
• The model comprehensively addresses multimedia chemical release, fate,
transport, and exposure;
• The model can simulate failure and constituent release from a variety of waste
management units found at RCRA facilities;
• EPA could obtain the input data required by the model within the Agency’s time
U.S. Environmental Protection Agency. MMSOILS: Multimedia Contaminant Fate.
Transport and Exposure Model — Documentation and Users Guide. Draft . Washington, D.C.:
U.S. Environmental Protection Agency, 1992.
2 See also: U.S. Environmental Protection Agency/ORD/ERL Athens. Summary of Review
and Evaluations of the Technical Approach to Corrective Action Regulatory Impact Analysis:
Fate and Transport . Washington, D.C.: U.S. Environmental Protection Agency, 1992.
DRAFT: March 22, 1993 ‘
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B-2
and budget constraints;
• The screening-level complexity of the model was appropriate for the analysis of a
large sample of facilities; and
• EPA had already applied the model and was familiar with its strengths and
weaknesses.
MMSOILS can simulate releases from five type of units commonly found at RCRA facilities:
• Surface impoundments;
• Landfills;
• Waste piles;
• Tanks; and
• Underground injection wells.
The model simulates the fate and transport of constituents released from these units within four
main pathways — atmospheric, surface water, ground water, and soil erosion. The model also
tracks the mass balance of constituents released through the pathways and accounts for cross-
media transfers, such as ground-water discharge to surface water and atmospheric deposition of
contaminants to the soil. MMSOILS also simulates the foodchain/bioaccumulation pathway for
estimating concentrations of contaminants in fish, crops, meat, and milk.
MMSOILS was initially developed as a screening tool to assist EPA in setting priorities
for hazardous waste management at Superfund sites. It was later modified to model multiple
chemicals released from RCRA land-based units and underground injection wells. Following
extensive review for the corrective action RIA, 3 the Agency made numerous additional
modifications (e.g., improved mass balance) to improve the accuracy and flexibility of the
model. 4
U.S. Environmental Protection Agency/ORD/ERL Athens. Summary of Review and
Evaluations of the Technical Approach to Corrective Action ReRulatory Impact Analysis: Fate
and Tran port . Washington. D.C: U.S. Environmental Protection Agency, 1992.
See also U.S. EPA MMSOILS: Multimedia Contaminant Fate. Transport. and Exposure
Model — Documentation and Users Guide. Draft . Washington, DC: September 1992.
DRAFT: March 22, 1993
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8-3
B.2 Parameter Selection and Assumptions
This section discusses the selection of MMSOILS model parameters for the RIA. The
first subsection provides a listing of all the model parameters. The second subsection discusses
the distinctions between central tendency and high-end parameter values. Finally, the third
subsection discusses the model used to estimate tank release rates.
B.2.1 Input Parameters
Exhibit B-I below presents the parameters contained in the input files to the MMSOILS
model. The parameters are organized into sets corresponding to different input files. Under any
given set, each parameter may fit into one of three categories: (1) SWMU-specific parameters;
(2) facility-specific parameters; or (3) global parameters that are constant over the entire
modeling effort, including those in the chemical property database (see Appendix E) or soil
property table. 5 The list does not include those parameters that are only used for control
purposes (e.g., beginning and ending times) or those that are not used for RIA modeling
calculations. 6
U.S. Environmental Protection Agency. Superfund Exposure Assessment Manual , Draft
Report. Washington, D.C.: U.S. Environmental Protection Agency, 1987.
6 Additional information on input parameters for both transport and risk modeling may be
found in Appendix 0, and facility- and SWMU-specific data collections forms may be found in
Appendix H.
DRAFI: March 22, 1993
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R -4
EXHIBIT Bi MMSOILS INPUT PARAMETERS
Constituent-specific Chemical Properties
• Henz s Law Coeffiaent
• Molecular DifTusivity in Air
• Molecular Diffusivity in Waler
• Vapor Preasure
• Molecular Weight
• Adsorption Coefficaeni on
Organic Carbon, Koc
• Landfill Leadiate Concentration
• Concentration in Soil or Fall
• Concentration in Surface
Pore Liquid
• Reference Dose Level, Oral Route
• Reference Dose Level,
Inhalation Route
• Carcinogen Potency Factor,
Oral Route
• Carcinogen Potency Factor,
Inhalation Route
Skin Permeability Factor
Kd for waste materials
Kd for pound water fl
Decay rate
Concentration
Concentration
in Leadiate
in Influent
Concentration in Injection Well
Bioconcentration in Fish
Transfer Factor for Cattle
Uptake from Soil to Plant
Soil Moisture to Root Factor
Action Level for Air
Action Level for Soil
Action Level for Ground Waler
Action Level for Surface Water
From chemical database.
From chemical database.
From chemical database
From chemical database.
From
chemical
database.
From
chemical
database.
From
chemical
database.
From
chemical
database.
From
SWMU
data form.
From
SWMU
data form.
From
SWMU
data form.
From
chemical
database.
From
chemical
database.
From
chemical
database.
From
chemical
database.
From
chemical
database
From
chemical
database.
From
chemical
database.
From
chemical
database.
Atmospheric Pathway Parameters
• Volatilization Model Flag
• Depth of aean Cover
From SWMU data form. Depends on whether waste is covered or
From SWMU data form.
From chemical
From chemical
From chemical
From chemical
From chemical
From chemical
database.
database.
database.
database.
database.
database.
For the central tendency, EPA used the Organic Leadiate Model (OLM)
to obtain kachate concentrations for organica as a function of the
chemical concentration in the unit (from the SWMU data form) and the
solubility (from the chemical database). For the high-end, EPA used
chemical database values for solubility. For inorganics, EPA took central
tendency and high end values from the database. These values are
entered in the input file as the solubility limit
From SWMU data form. Used for landfills
From SWMU data form. Used for volatilization
S
S
S
S
•
•
S
I
•
•
S
•
S
S
S
Varies with pH.
Varies with pH.
Used only for tank releases.
Used only for surface impoundments.
Used only for injection wells
SOS DRAFT: March 22, 1993
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8-5
EXHIBIT B-I (Cont.)
• Depth of Contamination
• Average Temperature
• Mixing Depth of Site Soil
• Deposition Velocity of Partlixilates
• Wind Velocity
• Threshold Friction Velocity
• Fraction of Vegetative Cover
• Roughness Height
• Sill Content of Road Surface
• Mean Vehide Speed
• Mean Vehide Weight
• Number of Wheels per Vehicle
• Number of Days per Year With
at Least 0.01 Inches Rain
• Vehicle Kilometers on
Unpaved Surface
• Drop Height for Loading
Operations
• Capacity of Loading Machinery
• Daily Load of Material
• Area of Daily Spreading
Operations
• Distance to Model Observation
Point
• Number of Stability Classes
• Stability aass Wind Velocity
• Stability Class T pe
• Stability aass Frequency
From SWMU data form
From facility data form.
Global
Global.
From facility data form
Global
From SWMU data form.
From facility data form.
Global
Global
Global
Global
From facility data form.
From SWMU data form. Function of SWMU area.
Global.
Global.
From SWMU data form Function of SWMU area.
From SWMU data form. Function of SWMU area.
Vanes with facility size, SWMU location, etc.
Global
Global
Global
Global.
Surface Water Pathway Parameters
• Flow Rate in River
• Downstream Distance to
Point of Use
• Average River Velocity
• Distance from SWMU to River
• USLE Rainfall Factor
• USLE Soil Erodibility Factor
• USLE Length-Slope Term
• USLE Cover Factor
• USLE Erosion Control Factor
• Ground Water Discharge to
River Rag
• Sediment Delivery Fraction
to Lake
From facility data form
Global. Equal to point of discharge.
From
From
From
From
From
From
Global.
From facility data form.
From facility data form.
facility data form
SWMU data form.
facility data form.
facility data form
SWMU data form.
facility data form.
Function of soil properties.
Function of SWMU size.
• * DRAFT: March 22, 1993 SOS
-------
B-6
EXHIBIT B-i (Cont.)
• Fraction of Organic Carbon
in Lake Sediments
• Wind Velocity Above the Lake
• Average Water Depth of Lake
• Surface Area of Lake
• Lake Sediment Porosity
• Diffusion Path Length for
Contaminated Sediments
Global.
From fadlity data form.
From ladlity data form.
From faality data form.
Global.
Global.
Ground Water Pathway Parameters
• Gradient
• Hydraulic Conductivity
• Aquifer Porosity
• Aquifer Bulk Density
• Fraction of Organic Carbon
in Aquifer
• Dispersivity in X.Direction
• Dispersivity in Y-Direction
• Dispersivity in Z-Direclion
• Thickness of Aquifer
• Times of Ground Water
Use Points
• Positions of Ground Water
Use Points
• Depths of Ground Water
Infiltration and Meteorological Parameters
• Field Capacity of Surface Soil
• Wilting Point or Surface Soil
• Depth of Root Zone
• Number of Soil Layers in
Unsaturated Zone
• Saturated Conductivity for
Each Soil Layer
• Saturated Water Content for
Each Soil Layer
• Bulk Density for Each Soil Layer
• Esponent ub for Moisture Curve
in Each Soil Layer
From faality data form.
From faality data form.
Global.
From facility data form.
A soil type is spedtied in the faality data form. Each soil type has
global properties.
Global soil properties according to soil type.
Global soil properties according to soil type.
Global soil properties according to soil type.
From
fadlity
data
form.
From
taality
data
form.
From
faality
data
form.
From
fadlity
data
form.
From
faality
data
form.
Use Points
Global
Global.
Global.
From faality data form.
From SWMU data form. Dependi. .upon the start-up time of the aquifer
From SWMU data form. Depends upon the location of the SWMU
relative to the faality boundary.
From faislily data form for the central tendency.
Set to the top of the aquifer (or the high-end.
DRAFI’: March 22, i993
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B-7
EXHIBIT B-i (Cont.)
• Percentage of Organic Matter in
Each Soil Layer
• Percentage of aay in
Each Soil Layer
• Percentage of Silt in
Each Soil Layer
• Percentage of Sand in
Each Soil Layer
• Decey Ratio for Each Soil Layer
• Depth of Each Soil Layer
• Pan Factor for Converting
Ep to PET
• Latitude of Site
• Curve Number for Surface Soil
• Monthly Precipitation
• Number of Da per Month
with Preapitation
• Monthly Average Temperature
• Monthly Pan Evaporation (Ep)
• Starting Month of Growing Season
• Ending Month of Growing Season
Foodchain Pathway Parameters
• Deposition Interception Fraction
• Pasture Production
• Vegetable Production
• Soil Intake Rate for Cattle
• Feed Rate for Cattle
• Fraction of Cattle Feed
from Pasture
• Water Intake by Cattle
• Sediment Delivery Fraction (SDF)
- to Agricultural Field
• Area of Agricultural Field
• SDF to Human Exposure Field
• Area of Human Exposure Field
• Fraction of Organic Carbon in
OlTsite Fields
• Bulk Density of Soil iii
Offaite Fields
• Depth of Applied Irrigation
• Source of Irrigation Waler Front
• Source of Cattle Water
From facility data form.
Global soil properties a rding to soil type.
Global soil properties according to soil type.
Global soil properties according to soil type.
Global.
From facility data form. May be decreased ii SWMU is depressed
Global.
facility data form
facility data form.
facility data form.
From faality data form.
Global.
From facility data form
From faalily data form.
Global.
Global.
Global.
Global
From faality data form.
From
facility
data
form.
From
faality
data
form.
From
facility
data
form
From
facility
data
form.
From facility data form
From faality data form.
facility data form.
From facility data form.
From
From
From
From
From
From
From
facility
facility
facility
facility
data form.
data form.
data form.
data form.
DRAFT: March 22, 1993
-------
B-S
SWMU Description Parameters
EXHIBIT B-i (Cont.)
• Source of Human Waler Intake
• 1 ypeofSWMU
• SWMU Width
• Number of Constituents of Concern
Global.
From SWMU data form.
impoundment, or tank.
From SWMU data form.
From SWMU data form.
Choices are injection well, landfill, surface
Landfill Parameters
• Month when Capture System Fails
• Cwer 1 ,pc
• Thickness of Layer
• Area of Layer
• Hydraulic Conductivity of Layer
• Field Capacity of Layer
• Saturation Limit of Layer
• Capture System Effectiveness
• Flag for Clay or Synthetic Layer
• Month of Failure
• Degree of Failure
• Waste Layer Initial
Moisture Content
• Method of Calculating Laadiate
Concentration
• End of Waste Mditions
• Incremental Area of Waste
Added each Year
• Bulk Density of the Waste
Global.
From SWMU data form.
layers. Each layer of the
below.
From SWMLJ data form
layers. Each layer of the
below.
From SWMLJ data form.
From SWMU data form. Equal to SWMU area.
Based on global soil properties for soil and drainage
effectiveness assumptions for clay pr synthetic layers
Based on global soil properties for-soil and drainage
effectiveness assumptions for day or synthetic layers
Based on global soil properties for soil and drainage
effectiveness assumptions for day or synthetic layers
Global
Global.
From SWMU data form.
From SWMU data form
Global.
Liner ‘I ’pe
Each type of cover has a different number of
liner system has a set of charactcristi described
Each type of liner has a different number of
liner system has a set of characteristies desaibed
layers and on remedy
(see Appendix C)
layers and on remedy
(see Appendix C).
layers and on remedy
(see Appendix C)
From SWMU data form. If a layer is clay or synthetic, it is assigned a
series of five failure events as described below.
The liming and magnitude of a liner or cr failure are based on globally
applied deterministic failure probabilities (see Appendix C).
The timing and magnitude of a liner or cover failure are based on globally
applied deterministic failure probabilities (see Appendix C).
Global
SS DRAFI’: March 22, 1993
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B-9
Surface Impoundment Parameters
• Month when Capture System Fails
• Cover 1)rpe
• Thid ness of Layer
• Area of Layer
• Hydraulic Conductivity of Layer
• Field Capacity of Layer
Saturation Limit of Layer
• Capture System Effectiveness
• Flag for Clay or Synthetic Layer
• Month of Failure
• Degree of Failure
• Year of Conversion to Landfill
• Initial Impoundment Volume
• Maximum Impoundment Volume
• Monthly Influx to Impoundment
• Area of Impoundment Surface
• Yearly Evaporation
• Waste Layer Initial
Moisture Content
• Method of Calculating Leacitate
Concentration
• Bulk Density of the Waste
Tank Release Parameters
• Number of Years with Releases
Release Volume for Each Year
EXHIBIT B-i (Cont.)
Liner Type
Global.
From SWMU data form. The model assumes that when an impoundment
is taken out of use it is similar to a landfill, and may be covered to prevent
infiltration. Each type of cover has a different number of layers. Each
layer of the er system has a set of characteristies described below.
From SWMU data form. Each type of cover has a different number of
layers. Each layer of the cover system has a set of characteristica
described below.
From SWMU data form.
From SWMU data form. Equal to SWMU area
Based on global soil properties for soil and drainage layers and on remedy
effectiveness assumptions for clay or synthetic layers (see Appendix C)
Based on global soil properties for soil and drainage layers and on remedy
effectiveness assumptions for clay or synthetic layers (see Appendix C).
Based on global soil properties for soil and drainage layers and on remedy
effectiveness assumptions for day or synthetic layers (see Appendix C).
Global.
From SWMU data form. If a layer is clay or synthetic, it is assigned a
series of five failure events as described below.
The timing and magnitude of a liner or cover failure arc based on globally
applied deterministic failure probabilities (see Appendix C).
The liming and magnitude of a liner or er failure arc based on globally
applied deterministic failure probabilities (see Appendix C)
From SWMIJ data form
From SWMU data form. Taken as 80% of the maximum.
From SWMLJ data form
From SWMU data form.
From SWMU data form.
From facility data form.
Global.
Global.
Global.
From SWMU data form.
From SWMU data form. This may be calculated using release profile
models thai use the tank volume or other characteristica of the unit. See
Section 83.1.
•“ DRAFT: March 22, 1993 ***
-------
B-b
EXHIBIT B-I (Cout.)
Injection Well Parameters
lype of Well Failure
From
SWMU
data
form.
Difference Between Waler Table
From
SWMU
data
form.
and Injeded Waler Column
•
1nje ion Rite
From
SWMLJ
data
form.
Pump Head
From
SWMbJ
data
form.
1 pe of Well
From
SWMU
data
form.
‘
Flag for Best Case or Worst Case
From
SWMU
data
form.
Solution
B.2.2 Central Tendency and High-end Assumptions
To account for uncertainties in the risk assessment process, EPA estimated risks for both
“central tendency” and “high-end” scenarios when conducting long-term modeling. The following
section discusses the data elements and assumptions that changed between central tendency and
high-end modeling scenarios. Each change falls into one of three categories: global, facility-
specific, and SWMU-specific. For each change, the source of the data and a brief description of
the nature of the change is presented. 7
Global Changes
These global changes affect all of the facilities in a uniform manner.
Soil/Water Partition Coefficient for Inor anics (Kd) : The Agency decreased Kd values
for inorganics from mid-range values supplied by EPA’s Office of Research and
Development (ORD) to lower ORD-supplied values for the high-end. The lower Kd
values will simulate less sorption to subsurface soils and will increase the contaminant
velocity in the unsaturated and saturated zones.
• Landfill Leachate quality : The Agency used water solubilities supplied by ORD as
central-tendency concentration values for inorganics and used estimates from the Organic
Leachate Model for central-tendency values for organics. For the high-end, the Agency
increased the landfill leachate concentrations for all constituents and set them equal to
water solubility values supplied by ORD. Higher leachate concentrations increase the
exposure concentrations, and increase the release rate of constituent mass from the unit.
• Ground-Water Well Depth : The Agency set the vertical location of the ground-water
exposure points to the top of the saturated zone for all cases in the high-end. The
‘ Additional information on central tendency and high-end parameter values may be found
in Appendix G.
**S DRAY!’: March 22, 1993
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0-I I
Agency set central-tendency depths based on facility-specific well data or, if no actual
supply wells were present, set the depths to 75 percent of the depth to the bottom of the
aquifer. The high-end well depth corresponded to the maximum ground-water
concentration because modeled concentrations are highest at the surface of the aquifer.
Saturated Hydraulic Conductivity : The central-tendency value for hydraulic conductivity
was based on the best available facility-specific data (e.g., from RFAs or RFIs). For the
high-end, the Agency increased the hydraulic conductivity at each facility by one order of
magnitude from the central-tendency value. Increasing conductivity increases
contaminant velocities in the saturated zone, but may decrease well concentrations
through increased dilution.
Karst and Fractured Bedrock Simulation : Assumptions for simulating the effects of karst
terrain or fractured bedrock on transport and exposures varied for the central tendency
and high-end cases. A complete discussion of all specific assumptions for karst terrain
and fractured bedrock follows in Section B.3.1 of this appendix.
Facility-Specific Changes
• Percentage of Organic Matter in Soil Layers : For the high-end, the Agency selected a
lower range value for the percentage of organic matter in the unsaturated soil layers
based on ranges obtained from the DBAPE database according to location and soil
type. 8 Decreasing the organic matter content decreases the retardation of organics in the
soils and increasestheir transport velocity.
• Fraction of Organic Carbon in Off-Site Fields : The Agency varied this value based on
ranges of values supplied by DBAPE. Decreasing the fraction of organic carbon (f )
increases the mobility of organic constituents and therefore the lower f was used in the
high-end analysis.
• Sediment Delivery Fraction (SDF) to Off-Site Fields : The Agency varied this value based
on professional judgement using topographic maps. For the high-end, a larger SDF was
employed to increase the simulated amount of contaminated soil delivered to offsite
fields.
• River Flow Rate : The Agency varied the flow rate based on ranges obtained from the
REACH files from the STORET database. For the high-end, a lower range for the flow
rate was employed to increase the constituent concentrations in the river by decreasing
the dilution rate.
U.S. Environmental Protection Agency. Data Base Analyzer and Parameter Estimator
( DBAPE) Interactive Computer Program User’s Manual . EPA/600/3-89/083. Washington, D.C.:
U.S. Environmental Protection Agency, 1990.
SS* DRAFT: March 22, 1993
-------
B-12
SWMU-Specific Changes
Waste Concentration : The Agency varied the waste concentration for each constituent
based on the best available background information (e.g, facility documents, TSDR survey
data, and professional judgement). Where the Agency knew the waste concentrations
with greater certainty (e.g., from waste sampling), the central-tendency values typically
reflected the average of the reported concentrations, and the higli-end values reflected
the maximum of the reported concentrations. The Agency used best professional
judgment and available data where SWMU-specific data were not available. Increasing
the concentration increases the overall constituent mass available for transport and
exposure, and directly corresponds to the leachate concentration for tanks and surface
impoundments.
• Depth of Unit : The Agency varied the unit depth based on the best available background
data, with larger unit depths corresponding to the high-end analysis. For units with
known dimensions, this parameter remained constant in the high-end and central
tendency analyses. Increasing this value increases the constituent mass added to landfills,
decreases the depth to the aquifer for all units, and can increase the volatilization release
rate.
• Area of Unit : The Agency varied the unit area based o the best available background
data, with larger unit areas corresponding to the high-end analysis. For units with known
dimensions, this parameter remained constant in the high-end and central tendency
analyses. Increasing this parameter increases the mass added to landfills, and allows for
greater total leachate volume from all units.
• Tank Release Rates : The Agency varied the tank release rate based on central tendency
and high-end estimates of tank volumes or other parameters (such as the drum capacity
of a storage area or a leakage or spillage rate) for a particular SWMU, with greater
release rates corresponding to the high-end analysis. Increasing the release rate increases
- the total constituent mass available for transport and exposure.
B.2.3 Tank Release Assumptions
The Agency simulated releases from tanks based on the results of EPA’s Hazardous
Waste Tank Failure Model (HWTFM). 9 The HWTFM is a stochastic, Monte Carlo simulation
model that generates release profiles for each of several different tank technologies based on the
probability of events such as overflows, corrosion of tanks and pipes, natural catastrophes, and
accidental spills. For each technology, the HWTFM generated several representative release
profiles. A single profile (with the greatest quantity released for an assumed 20-year operating
U.S. Environmental Protection Agency. “Hazardous Waste Tank Risk Analysis, Draft
Report,” prepared by ICF Incorporated and Pope-Reid Associates Incorporated, June 1986.
DRAFT: March 22, 1993
-------
B-13
life) was then selected from among the representative profiles. The Agency then scaled the
profiles according to the tank volume to generate a release profile for specific tanks. For a given
tank size and tank type, this process created a series of release volumes corresponding to each
year of the operating life (e.g., 0.5 m 3 in the first year, 1.2 m 3 in the second year, etc.). The tank
types addressed by the HWTFM included the following:
• Closed, 3700 gallon, in-ground, concrete/steel treatment tank;
• Closed, 2100 gallon, in-ground, concrete storage or accumulation tank;
• Closed, 5500 gallon, above-ground, cradled, carbon steel storage or accumulation
tank;
• Closed, 210,000 gallon, above-ground, ongrade, carbon steel storage or
accumulation tank;
• Open, 2300 gallon, above-ground, cradled, carbon steel treatment tank;
• Closed, 4000 gallon, underground, carbon steel storage or accumulation tank;
• Closed, 4000 gallon, underground, stainless steel storage or accumulation tank;
• Closed, 2300 gallon, above-ground, cradled, carbon steel treatment tank;
• Closed, 5500 gallon, above-ground, cradled, carbon steel storage or accumulation
tank;
• Closed, 60,000 gallon, above-ground, ongrade, carbon steel treatment tank; and
- • Closed, 4000 gallon, underground, stainless steel storage or accumulation tank.
For the corrective action RIA, the Agency selected the tank type from the list above that
corresponded most closely to each tank being simulated at sample facilities. The release rate
profile for the tank was then used as an MMSOILS input during the operational period of each
tank being modeled. The Agency assumed no further releases after tank closure.
B.3 Model Application Assumptions and Limitations
This section covers specific model application assumptions and limitations. The first
subsection presents the assumptions made to simulate transport in karst or fractured media, the
second covers assumptions made for Non-Aqueous Phase Liquids (NAPLs), and the third covers
assumptions for non-standard waste management units (e.g., container storage areas and process
sewers. The last subsection discusses several other limitations of the MMSOILS model.
‘ DRAFT: March 22, 1993
-------
B44
B.3.1 Assumptions for Karst Terrain and Fractured Bedrock
Karst terrain and fractured bedrock systems pose particular ground-water modeling
problems. Karst aquifers are a special form of carbonate aquifer, and differ from other aquifers
in that they contain an integrated system of pipe-like solution channels that act as underground
drains for the highly localized transport of water. Fractured bedrock systems can occur within
many different types of rock and are characterized by an abundance of cracks, joints, faults and
other fissures in the rock. In both these cases, the permeability of the primaiy material (i.e., the
rock) is usually veiy low. However, the secondaly permeability introduced by fractures or
solution channels is usually several orders of magnitude greater, resulting in relatively high flow
through the system.
Although contaminant transport in fractured/lcarst geologic materials is governed by the
same processes as in granular media (i.e., advection, mechanical dispersion, and chemical
reactions), the results in fractured media can be quite different. Because conventional ground-
water flow equations (such as those in MMSOILS) were developed primarily for homogeneous
and isotropic granular aquifers, they are limited in describing flow in fracturedlkarst rock. For
example, the average velocity of ground water through the aquifer is calculated using Darcy’s law
based on the bulk hydraulic conductivity, the hydraulic gradient, and the bulk porosity. Although
this equation is accurate for granular porous media, it becomes more uncertain as the size and
number of the fractures or channels increase and the flow becomes dominated by secondary
transport through these fractures or channels.
Though mathematical equations developed for homogeneous and isotropic granular
aquifers do not accurately describe flow through karst/fractured systems, they are often used for
lack of suitable alternatives. For the RIA, the Agency developed special assumptions for the
more uncertain karst aquifer conditions, but did not use any alternative approaches for fractured
media.
Additionally, EPA differentiated those sites where karst or fractured bedrock represented
the primary aquifer of concern from those where it did not. For many facilities, while karst or
fractured zones were present, they were either confined or were situated beneath a porous
medium aquifer that dominated regional flow. Based on research of the hydrogeology at each
facility, EPA identified several facilities requiring corrective action with karst or fractured
conditions where the karst or fractured aquifer was not the primary aquifer of concern. In these
cases, the Agency did not model the karst aquifer. EPA also determined that a few facilities are
significantly affected by karst conditions, and that some are significantly affected by fractured
bedrock conditions.
For those facilities where transport was affected by karst conditions, the Agency modeled
them using the following approach:
(1) For high-end estimates, EPA assumed downgradient concentrations were
equivalent to the concentration at the bottom of the unsaturated zone. Exposures
DRAPT: March 22, 1993 ‘
-------
8.15
at all downgradient receptor points at any given year were set equal to
arncentrations equal to those exiting the unsaturated zone in that year. Time
delay and mixing with upgradient flow were not accounted for.
(2) For central-tendency estimates, EPA set porosity equal to 0.01, assumed no
contaminant retardation, and used best estimates for conductivity and dispersion.
The Agency modeled the fractured bedrock facilities using the representative porous
medium methodology, (i.e., used the existing best estimates of porosity, conductivity, and
dispersivity at each facility) for central-tendency calculations, and differentiated high-end
calculations by increasing the conductivity by one order of magnitude. Both approaches are
identical to those used to simulate granular porous media.
B3.2 Non-Aqueous Phase Liquids (NAPLs)
Dense and light non-aqueous phase liquids (DNAPLs and LNAPLS) are believed to be
present at several facilities within the RIA sample. Typically, a DNAPL release migrates through
the unsaturated and saturated zones and forms a lens at the bottom of the aquifer. Constituents
dissolve from the DNAPL-contaminated areas of the aquifer into the surrounding ground water
such that the DNAPL-contaminated area acts as a source of contamination within the aquifer.
LNAPLS, on the other hand, usually migrate through the unsaturated zone and form a floating
lens on the water table. An LNAPL lens also acts as a source of ontamination as constituents
dissolve into the underlying water.
Direct simulation of NAPL behavior requires complex algorithms not usually found in
screening-level models such as MMSOILS. While models have been developed that simulate
transport of LNAPLs adequately, effective simulation of DNAPL fate and transport has been
less successful. MMSOILS does not simulate the multiphase flow that characterizes NAPLs, but
instead simulates the transport of these constituents as if they behave like aqueous leachate
releases. While this method does not duplicate the behavior of lens formation, it does ensure
mass conservation and provides a reasonable screening-level approximation of the extent of
contamination. 10
B.3.3 Non-Standard Waste Management Units
MMSOILS is capable of simulating five different types of waste management units:
landfills, waste piles, surface impoundments, tanks, and injection wells. Most waste management
practices use one of these types of units and may be simulated directly. In other cases, however,
actual disposal practices can only be approximated by these units. For example, often the only
evidence of past waste disposal were areas of soil contaminated by an unknown source. In these
Methods for simulating the effectiveness of remedies at NAPL sites are discussed in
Appendix C.
‘ DRAFI’: March 22, 1993 *0*
-------
B-16
cases, EPA simulated the contaminated soil as a landfill provided that sufficient information was
available concerning the extent of contamination and the concentrations of hazardous
constituents in the soil. Another common source of contamination was releases from container
storage areas. EPA generally modeled these units as tanks due to the similarity of the release
behavior (i.e., each releases liquid-state waste to the ground). For these and similar scenarios,
the Agency used the flexibility of the MMSOILS model to approximate releases from these non-
standard units or releases due to non-standard management practices. In other cases, however,
the waste management practice was too complex or uncertain to be accurately simulated. These
cases included direct dumping of wastes into lakes, rivers, or wetlands, and unknown releases
from process sewers or other systems. Because of the high degree of uncertainty, the Agency did
not model cases such as these for the RIA.
B3.4 Other MMSOILS Limitations
Overall, the principal limitation is that MMSOILS is a screening-level model selected for
reasons including the ability to simulate several pathways, readily obtainable input requirements,
and limited computational requirements. While these characteristics are appropriate in a model
used for a national-level screening analysis, they do contribute to uncertainty in the modeling
process.
Several other general limitations of the MMSOILS model and the modeling process
should also be discussed. The most basic involves uncertainty arid estimation in the model input
data. While the Agency used the best possible data sources available (e.g., RCRA Facility
Investigations (RFIs), and facility-specific hydrogeologic surveys), the best of these investigations
still involve uncertainty in their findings. In other cases, such detailed information did not exist
for some facilities, and the Agency used professional judgement to estimate certain key
parameters. This uncertainty in the input data results in corresponding uncertainty in the model
results.
The level of exposure estimate detail in space and time is limited by computational and
data management restrictions. MMSOILS calculates exposure concentrations at a pre-specified
number of points for each pathway. The RIA risk methodology requires exposure estimates
from 1992 through the end of the modeling period in year 2120. To encompass this large period,
the Agency made estimates at ten-year intervals at each exposure point. Furthermore, ground
water concentrations were calculated out to two miles and atmospheric concentrations were
calculated out to ten kilometers beyond the facility boundary. Exposures were generally
calculated near the unit, at the facility boundary, and at regular intervals out to the desired
distance. If ground water contamination was indicated at two miles, an additional run was
performed to determine concentrations out to five miles. While this approach is useful for
capturing potential exposures at relatively distant points, it provides limited resolution for points
within or near to the facility boundary.
Not all of the MMSOILS pathway algorithms are equally precise or accurate. Precision
in time and space varies from the atmospheric algorithm, which calculates annual concentrations
• • DRAFJ’: March 22, 1993
-------
B-17
and any given point, to the soil transport algorithm, which must use an average (i.e., not time-
variant) source concentration. Accuracy refers to the overall vigor and complexity of the model
equations. The ground-water and atmospheric algorithms are most accurate. Other pathway
algorithms, however, contain certain key limitations. For example, the surface water pathway is
limited because downstream concentration decreases due to sedimentation and degradation are
not accounted for. Additionally, the soil pathway is not capable of simulating local transport of
contaminants within the facility; it is limited to deposition and erosion to off-site fields.
B.4 MM SOIlS Post-Processing
The MMSOILS model simulates releases, transport, and exposure on a SWMU-specific
basis. However, the Agency estimated long-term risk and the extent of contamination at off-site
points on a facility-wide basis because of uncertainty in SWMU and well locations, modeling
resource limitations, and because each facility exposure point was potentially influenced by many
SWMUs. Different methods were used to aggregate SWMU-specitic exposure concentrations for
different pathways, as described below.
B.4.1 Ground-Water Pathway
To aggregate the SWMU-specific ground-water results into facility-wide values, EPA
developed an aggregation scheme encompassing three components: creation of a facility-wide
grid, aggregation of the SWMU-specific plumes, and interpolation of the data to give annual
results.
Downgradient Grid
As depicted in Exhibit B-2, the Agency developed an exposure grid for each facility
extending out to a distance two-miles in the direction of ground water flow from the facility
boundary. The Agency constructed the grid to span a 90 degree sector centered in the direction
of ground-water flow. The Agency divided this grid into four radial sectors of 22.5 degrees and
six rings at distances of 0.25, 0.5, 0.75, 1.0, 1.5 and 2.0 miles from the facility boundary. If initial
modeling indicated exposures beyond two miles, the Agency performed a supplementary
MMSOILS run to estimate exposures out to five miles using a similar grid (depending upon local
hydrogeologic conditions and topography). For each element of the grid, the Agency gathered
residential well information (e.g., number of wells and population served). In addition, the
Agency determined the exact location of public/municipal wells, agricultural wells, and closest
potential receptors in relation to the grid origin.
Plume Aggregation
The Agency aggregated ground-water concentrations at offsite downgradient points using
one method for private/residential wells and another method for the remaining wells. These
methods may be demonstrated using Exhibit B-2. For both cases, the aggregation method
assumed that each SWMU lay on the centerline of the grid. This assumption overestimated
•S DRAFT: March 22, 1993 *S*
-------
8-18
exposures in some cases, while underestimating exposures in others. In general, if the size of the
facility was small compared to the size of the grid, the magnitud? of the error was small because
the actual distance from the SWMU to the centerline would be small compared to the scale of
the off-site grid. If the facility was larger, however, this assumption would tend to underestimate
exposures to points that are actually more directly in line downgradient (e.g., exposures to the
public well from SWMU A in Exhibit B-2), and it would overestimate exposures if the SWMU
and exposure point were actually farther apart (e.g., exposures to the public well from SWMU
B). The Agency assumed that these errors would generally cancel each other when determining
aggregate exposure concentrations from several SWMUs at a facility. In addition, if the facility
was very large and the SWMUs were not evenly distributed, the Agency focused the grid on the
portion of the facility where the SWMUs were located in order to minimize the error.
For public wells, agricultural wells, and other explicitly defined points, the Agency
determined the downgradient distance (specified in the input file) from the unit to the exposure
point by taking the downgradient distance from the unit to the facility boundary (e.g., distance L 1
for SWMU A) and adding the downgradient distance from the facility boundary to the specific
exposure point (e.g., distance L 2 ). The lateral (i.e., distance perpendicular to the gradient)
coordinate of the exposure point was taken as the distance from the grid centerline to the
exposure point (e.g., distance L 3 ). Because the Agency assumed that the SWMUs lay on the
centerline, the lateral distance was not a function of the SWMU location. To determine
aggregated exposures at the explicitly defined points, a post-processor summed the SWMU-
specific concentrations at each point.
For private wells; average exposures were calculated for each ring instead of at each well.
For these calculations, the distances to the exposure points were not determined according to the
exact well locations, but were dictated by the geometry of the data collection grid (as shown in
Exhibit B-2). To represent exposure concentrations within any one ring of the grid (e.g. all
points between 1.0 and 1.5 miles downgradient), two points were defined: the first exposure point
(e.g., C 1 ) lay on the grid centerline at the midpoint of the ring (e.g., at 1.25 miles), and the
second exposure point lay 33.75 degrees off the centerline at the midpoint of the ring. To
calculate average aggregate exposures, post-processing routines summed the SWMU-specitic
concentrations for these points (generated by MMSOILS) to give aggregated concentrations at
the points. The routines then averaged these concentrations I(C1 +2xC2)13J and multiplied the
average concentration by the total ring population to produce an aggregate exposure level for
each ring of the grid. Because of symmetry, the offset point represented concentrations on both
sides of the centerline. Note that the exhibit only shows points within one of the six rings, but
the post-processor made separate calculations for each ring.
DRAFI: March 22, 1993
-------
EXHIBIT B—2
GRID FOR SWMU AGGREGATION
(HYPOTHETICAL EXAMPLE)
Facility o mthy
Shaded area represents one cell of the grid.
2.0
Miles Downgradient
from
Facility Boundry
C = exposure points
-------
8.20
Interpolation
MMSOIIS calculated both private and public well exposure concentrations at ten-year
intervals over the modeling period (i.e., 1992 through 2120). A post-processing routine was used
to interpolate these values to annual values across the modeling period, using a cubic spline
routine based on Press, et al..’ 1 This non-linear routine allowed the interpolation process to
capture concentration peaks that may have occurred between the ten-year time-steps, preventing
the underestimation of peak concentrations due to the ten-year modeling constraints. Additional
routines then used these annual values as inputs to estimate risks associated with each of the
exposure points. The risk calculations are discussed further in Appendix E.
B.4.2 Atmospheric Pathway
Aggregation of SWMU-speciflc atmospheric concentrations was more straight-forward
because the modeling and risk methodology averaged exposures over entire (360 degree) grid
rings instead of over partial (90 degree) sectors (as for ground water). The model calculated
annual exposures at various radial distances from the facility boundary, and the exposure point
distance in the input file were defined as the sum of the distance from the facility boundary to
the desired exposure point and the minimum distance from the SWMU to the facility boundary.
Because only one point was defined for each risk calculation (i.e., for each ring), aggregate
concentration were determined by simply summing the SWMU-specific concentrations at each
exposure point for each constituent. -.
L4.3 Surface Water Pathway
The MMSOILS model calculated surface water concentrations at only one exposure
point. For rivers and streams, annual average concentrations were aggregated simply by adding•
the constituent concentrations by year across all the SWMUs. For lakes, MMSOILS generated a
single concentration already averaged over the exposure period. In this case, aggregation was
performed simply by adding the concentration associated with each unit.
B.4.4 Off-site Soil Pathway
The MMSOILS model calculated a single average concentration at each of the two off-
site soil points (i.e., the agricultural field and the human exposure field). For each point,
aggregation was performed simply by adding the concentration associated with each unit.
“Press, W.H.; Flannery, B.P; Teukolsky, S.A; and Vetterling, W.T. Numerical Recipes in C .
Cambridge: Cambridge University Press, 1988.
*SS DRAFr: March 22, 1993
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C. SIMULATION OF REMEDY EFFECJIVENESS
This appendix provides a detailed discussion of the methodology for simulating the
effectiveness of remedies more briefly addressed in Chapter 4. In order to evaluate the benefits
of the corrective measures implemented under Subpart S, it was necessary to estimate the extent
of contamination and associated human and environmental exposures remaining after
implementation of corrective measures. Because not all corrective measures are equally
effective, the Agency developed approaches for simulating the effectiveness of specific corrective
measures technologies, focusing on several key technologies selected by the expert panels.
The key technologies may be divided into three categories:
(1) Source Control Technologies
• Covers and Liners
• Excavation
(2) Waste Treatment Technologies
• Stabilization/Solidification
• Soil Vapor Extraction (SVE)
• Soil Washing
• Landfarming
• On-Site Incineration
(3) Ground-Water Remediation Technologies
• Pump and Treat Systems
• French Drains
• Slurry Walls or HDPE Barriers
The Agency developed quantitative approaches for simulating each of these remedial
technologies. When available, the Agency used performance levels specified by the expert panels
as the basis for determining the remedy effectiveness. In other cases, the Agency used default
values based on technology-specific performance data. EPA assumed some corrective measures
(e.g., off-site disposal and on-site disposal in a Subpart F unit) to be completely effective and
were not simulated. The technology-specific assumptions are described below.
C.i Source Control Technologies
Source control technologies physically restrict the release of contaminants from a waste
management unit. The source controls simulated in the RIA include covers, liners, and
excavation.
DRAFI’: March 24, 1993
-------
C-2
C.I.1 Covers and Uners
The MMSOILS model directly simulates the installation of covers and liners on landfills
and surface impoundments through water balance routines. Exhibit C-I shows the thicknesses
and hydraulic conductivities of clay and synthetic cover or liner materials simulated in the RIA.
EXHIBIT C-I
PROPERTIES OF COVERS AND LINERS
I Clay
Synthetic
Thickness
60 cm if not specified by the
expert_panel
40 mils
Hydraulic Conductivity
1 x iO- cm/sec ‘
1 x 1O 2 cm/sec 2
In addition, the Agency developed a series of time-dependent efficiencies for each type of cover
or liner. 3 The efficiencies simulate the ability of the liner or cover to effectively block flow after
accounting for failure and aging. In developing these efficiencies;- the Agency assumed that new
caps would be added on top of the old caps after 100 years of operation; therefore the efficiency
values for caps, shown in Exhibit C-2, increase at year 100.
U.S. Environmental Protection Agency. Technical Guidance Document: Final Covers on
Hazardous Waste Landfills and Surface Impoundments . EPA/530-SW-89-047. Washington, D.C.:
U.S. Environmental Protection Agency, July 1989.
2 GeoServices Inc. Liner/Leak Detection Rule Background Document , April 1987.
U.S. Environmental Protection Agency. Indexing of Long-term Effectiveness of Waste
Containment Systems for a Regulatory Impact Analysis (draft). Washington, D.C.: U.S.
Environmental Protection Agency, December 1992.
DRAFT: March 24, 1993
-------
C-3
EXIUBIT C-2
EFFICIENCIES OF CAPS AND LINERS
Clay Cap
Synthetic
Cap
Clay/
Synthetic
Composite
Cap
Clay Liner
Synthetic
Liner
RCRA C
Liner
System
Efficiency
when
installed
80%
90%
95%
70%
85%
98%
Ernaency
after 10
years
75%
85%
92%
60%
75%
95%
Efficiency
after 30
years
60%
75%
80%
40%
35%
85%
Efficiency
after 100
years
85%
90%
98%
5%
0%
60%
C.1.2 Excavation
The Agency simulated excavation by revising the MMSOILS model to allow removal of a
specified percentage of waste mass from a unit at a given time (i.e., the year of excavation). If
the expert panels specified that the entire waste source was excavated, the MMSOILS inputs
were modified to specify that no waste remained in the unit after excavation. Contaminants that
had already entered the unsaturated zone continued to migrate, but no new contamination
entered the subsurface. If the expert panels only excavated hot spots (10 percent of the waste,
for example), the inputs were modified to reflect the percent removal (i.e., the mass would be
reduced to 10% of the original value).
C.2 Waste Treatment Technologies
Waste treatment technologies, by definition, reduce the mobility, toxicity, persistence, or
concentration of contaminants in waste materials. The waste treatment technologies simulated
for this RIA include stabilization/solidification, soil vapor extraction, soil washing, landfarming,
and on-site incineration. The primary effect of stabilization is to reduce the constituent mobility
DRAFT: March 24, 1993 ***
-------
C-4
by reducing the leachate flow and by causing some constituents to bind more tightly to the
stabilized material, thus reducing the leachate concentration. For the remaining technologies,
the primary effect is to reduce the amount of constituent mass in the waste material. Because of
these differences, the Agency adopted different methods for simulating these waste treatment
technologies.
C.2. I Stabilization/Solidification
To simulate stabilization/solidification, the Agency modified the MMSOILS model to
allow changes to the conductivity of the waste (affecting the quantity of leachate generated) and
the concentration of hazardous constituents in the leachate at a specific point in time during the
simulation period. At the time of remediation, the model switches the values of these
parameters from the baseline values to new input values reflecting the effectiveness of the
technology.
Decreasing the conductivity of the waste layer restricts the flow of water through the
waste, and thus decreases the volume of leachate generated by the unit. This serves to limit the
mass flux from the unit after remediation (although it does not affect the total mass within the
unit that may leach out in the future). Where the expert panels specified performance standards
for stabilization, those values were used in the remedy simulation. If standards were not
specified, the Agency used default values. Because of differences in effectiveness between in-situ
stabilization (where the binding material is added to the unit and mixed in with augers or
backhoes) and ex-situ stabilization (where the waste is excavated, mixed with the binding material
in a pug-mill or other mixing device, and then replaced), the Agency established two different
default performance levels based on literature values: lx iO cm/s for in-situ stabilized material
and lx 10.8 cm/s for ex-situ stabilized material . Ex-situ stabilization is generally considered more
effective than in-situ stabilization because of greater mixing efficiency.
For metals, solidification/stabilization has been shown to decrease leachate concentration
from wastes due to chemical bonding with the waste matrix. To simulate this effect, the analysis
employed a one order of magnitude reduction in leachate concentration for inorganic compounds
and no reduction for organic compounds.
The parameter changes used to simulate stabilization are summarized in Exhibit C-3.
U.S. Environmental Protection Agency/ORDIRREL, Handbook on In Situ Treatment of
Hazardous Waste-Contaminated Soils , EPA154012-90/002. Washington, D.C.: U.S. Environmental
Protection Agency, January 1990.
U.S. Environmental Protection AgencyJORDIRREL, The Superfund Innovative
TechnoIo v Evaluation (SITE) Program: Technology Profiles , EPA/540/5-901009. Washington,
D.C.: U.S. Environmental Protection Agency, November 1990.
DRAFT: March 24, 1993 S*S
-------
C.5
EXHIBIT C..3
DEFAULT STABILIZATION PARAMETERS
Treatment
Technology
Waste/Constituent
Type
Parameter
Change
In-Situ Stabilization
All
Conductivity
Set to iO cm/sec
Ex-Situ Stabilization
All
Conductivity
Set to 1O cm/sec
All Stabilization
Inorganics
Organics
Leachate
Concentration
Leachate
Concentration
Decrease 1 order of
magnitude
None
C.2.2 Other Waste Treatment Technologies
The remaining treatment technologies simulated in the RIA (SVE, soil washing, land
farming, and incineration) are all treatment technologies that reduce chemical mass in the waste.
To simulate these treatment technologies, the Agency revised the MMSOILS model to adjust the
constituent concentrations in the unit at a given point in time. At the time of remediation,
constituent concentrations within the unit are reset to target cleanup concentrations specified by
the expert panels. If the expert panels did not specify target concentrations, the model used
default inputs based on other performance data. 5 In general, these types of treatment
Many overlapping sources were used to supply performance data for treatment
technologies. These include the following:
• Environmental Research and Technology (ERT), The Land Treatahilitv of Appendix
VIII Constituents Present in Petroleum Industry Wastes , Februaiy 1984.
• U.S. Environmental Protection Agency/ORD/MERL. Hazardous Waste Land
Treatment . Washington, D.C.: U.S. Environmental Protection Agency, April 1983.
• U.S. Environmental Protection Agency. Seminar Publication — Corrective Action:
Technologies and App! ications . EPA/625/4-89/020. Washington, D.C.: U.S. Environmental
Protection Agency, September 1989.
° DRAFT: March 24, 1993 ***
-------
C-6
technologies are only effective on organic constituents. While these treatment technologies may
have some effects on inorganic constituents, the Agency assumed that these effects would be
negligible and did not change the inorganic concentration values. The default effectiveness
values for each technology are presented in Exhibit C-4.
• U.S. Environmental Protection Agency/ORD,RREL Handbook on In Situ Treatment
of Hazardous Waste-Contaminated Soils , EPA/540,2-90/002. Washington, D.C.: U.S.
Environmental Protection Agency, January 1990.
• U.S. Environmental Protection Agency/OERR,ORD. Engineering Bulletin: Soil
Washing Treatment , EPAJ54012-90/017. Washington, D.C.: U.S. Environmental Protection
Agency, September 1990.
• U.S. Environmental Protection Agency/ORDIRREL The Superfund Innovative
Technology Evaluation (SITE) Program: Technolo v Profiles , EPA/540/5-90/006. Washington,
D.C.: U.S. Environmental Protection Agency, November 1990.
• U.S. Environmental Protection Agency/OS WER/Technology Innovations Office.
Innovative Treatment Technologies: Overview and Status — Preliminary Draft . Washington,
D.C.: U.S. Environmental Protection Agency, March 1991.
• U.S. Environmental Protection Agency/OERR,ORD. Engineering Bulletin: In Situ
Steam Extraction Treatment , EPA/540f2-91/005. Washington, D.C.: U.S. Environmental
Protection Agency, May 1991.
*** DRAFT: March 24, 1993
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C l
EXHIBIT C-4
DEFAULT WASTE TREATMENT EFFICIENCIES
Treatment
Technology
Waste/Constituent
Type
Parameter
Change
Soil Vapor
Extraction (SVE)
VOCs
Waste
Concentration
Decrease by 90%
SVOCs
Waste
Concentration
Decrease by 50%
Soil Washing
VOCs
Waste
Concentration
Decrease by 90%
SVOCs
Waste
Concentration
Decrease by 50%
Landfarming
Organics
Waste
Concentration
Decrease by 95%
On-site Incineration
Organics
Waste
Concentration
(reflects residuals)
Decrease by four
orders of
magnitude.
C.3 Ground-Water Remediation Technologies
Pump and treat systems, french drains, slurry walls, and HDPE barriers are all
technologies that restore or contain contaminated ground water. The approach for simulating
these technologies focused on determining the effectiveness of a particular system at restoring or
containing a dissolved plume, while taking into account hydrologic conditions at the site and the
potential presence of non-aqueous phase liquids (NAPLs). The Agency simulated ground-water
remediation using post-processing routines that modify the extent of contamination profiles
remaining after simulating source controls and waste treatment.
C.3.I Restoration of Dissolved Plumes
For most contaminants in ground-water systems, the majority of the total constituent
mass is not initially dissolved in the water, but is sorbed to soil particles. As contaminated water
is pumped from the aquifer during ground-water remediation, it is replaced by clean water that
has been re-injected into the aquifer or that has naturally recharged the aquifer. As contaminant
mass is removed through pumping, additional mass will desorb from the solid phase into the
aqueous phase. Eventually, little mass will remain in the sorbed phase and the ground-water
DRAFT: March 24,1993
-------
C-8
concentrations will have been reduced.
The effectiveness of this ground-water remediation process can be expressed in terms of a
first order coefficient (k) based on mass conservation, the sorption strength of the individual
chemical, and the rate of water flowing through the contaminated zone. This methodology is
consistent with similar applications presented in EPA guidance sources. 6 ’ By using this
available Agency approach for simulating restoration effectiveness, EPA avoided the additional
modeling and data collection complications required when using more sophisticated numerical
models, and remained consistent with the screening-level complexity of the MMSOILS model.
The coefficient (k) is calculated as follows:
k= - PV (I)
where R equals a retardation factor for the specific chemical in the aquifer, and PV is the pore
volume exchange frequency.
The retardation factor (R) is equivalent to the inverse of the fraction of total contaminant
mass that is in aqueous solution. It is defined as follows:
R=1+ !. (2)
‘1
where lCd equals the partition coefficient between soil and water concentrations, p equals the
bulk density of the aquifer material, and i equals the aquifer porosity.
The pore volume exchange frequency (PV) represents the frequency at which one pore
volume is pumped from the aquifer. The pore volume is defined as the volume of water within
the contaminant plume and may be calculated as the product of the plume area, the
contaminated depth (i.e., the aquifer thickness), and the aquifer porosity. To determine the
exchange frequency, the total pumping rate (specified by the expert panels) was divided by the
pore volume.
The aqueous concentration of a contaminant in ground water (C) as a function of time
6 U.S. Environmental Protection AgencylExposure Assessment Group/Office of Health and
Environmental Assessment. Guidance for Establishin2 Tar2et Cleanup Levels for Soils at
Hazardous Waste Sites , Washington, D.C.: U.S. Environmental Protection Agency, 1988.
‘ U.S. Environmental Protection Agency/Office of Emergency and Remedial Response.
Guidance on Remedial Actions for Contaminated Ground Water at Superfund Sites .
Washington, D.C.: U.S. Environmental Protection Agency, 1988.
•S DRAFt: March 24, 1993 “
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C-9
() may be expressed as:
C=C 0 e (3)
where C 0 equals the base aqueous concentration without pumping, and I equals the time since
pumping began.
These equations are based on several key assumptions presented below.
(1) Any additional contaminant releases from the source must be small compared to the total
initial mass. While source controls were not totally or instantaneously effective, EPA
believes the additional mass contributions were relatively small. The Agency included the
effectiveness of the source controls by adjusting the C 0 term.
(2) All of the constituent mass must be initially distributed between the soil and water only.
This implies that the presence of NAPLs within the system invalidate the equations. As
contaminant mass is removed from the aquifer through extraction, more contaminant
mass will dissolve from the non-aqueous phase into the aqueous phase. As long as
NAPLs are present, the extraction system will likely not have a significant affect on the
aquifer concentrations. The approach for simulating plume restoration when NAPLs are
present is discussed in Section C.3.3.
(3) Contaminant mass is freely able to transfer from the sorbed to the aqueous phase. in
reality, the concentration often does not indefinitely decrease, but approaches a near-
steady concentration determined by kinetic limitations of mass transfer to the aqueous
phase. These limitations are likely to be dependent upon both the constituent of concern
and the physical characteristics of the aquifer media.
(4) The rate of mass removal at a simulated extraction well is proportional to the
concentration at that well without pumping. While this conserves mass within the plume,
it does not allow the possibility that the concentration distribution within the plume may
change as a result of the pumping.
(5) All of the contaminant mass is drawn toward the extraction wells and does not escape the
boundaries of the plume. In the case of karst or fractured bedrock, the effectiveness of
the extraction system in capturing all of the contaminated water may be questionable. In
these cases, the contaminant plume may spread beyond its initial boundaries, or fingers of
contaminated water may escape through fractures or solution channels. The ability of
ground-water remediation systems to contain the plume under different hydrogeologic
conditions is discussed in Section C.3.2.
The Agency applied the exponential remediation equation at each well downgradient
from each facility with ground-water contamination using a synthesis of two methods to estimate
the initial concentration (C 0 ). in the first approach, C 0 varies with time and is set equal to the
•“ DRAFT: March 24, 1993 “
-------
c-b
time-varying concentration at each point after simulating source controls and waste treatment.
As demonstrated by a hypothetical case shown in Exhibit C-5, this approach (depicted as
Approach 1) captures the effects of source controls, but may underestimate the impacts of source
controls and waste treatment in the near-term. The second approach (depicted as Approach 2)
sets C 0 equal to a constant concentration (the concentration at the time of remedy
implementation). While this approach is more-consistent with the assumptions of the
exponential equation (because the total mass in the system is fixed), long-term concentrations
may actually be greater than in the case with source controls alone (e.g., at times after year 2054
in Exhibit C-5).
To minimize the effects of the limitations in these two approaches, the Agency estimated
concentrations using a synthesis of both approaches. The post-processing routine calculates a
ground-water concentration using both equations, and then sets the final concentration at each
point for each time-step equal to the lower of the two estimates (e.g., in Exhibit C-5, Approach 2
was used from years 2000 to 2036, and Approach 1 was used at times after year 2036). When
applying this formula, the Agency assumed that the extraction wells operated until the end of the
simulation period or until the cleanup objective was achieved at each well (whichever came first).
•*S DRAFT: March 24, 1993 ‘‘
-------
Exhibit C-5
Hypothetical Comparison of Different
Restoration Approaches
No Corrective Action
• 2010
Begins
2020
Controls Only
Approach 2
2040
=
0
I t
0
Year
-------
C-12
C.3.2 Containment of Dissolved Plumes
Ground-water plumes can be contained through extraction wells, French drains, HDPE
barriers, or slurry walls, often in conjunction with pumping. The expert panels typically specified
HDPE barriers and slurry walls when the objective was to contain the contamination rather than
to restore the aquifer to a beneficial use. Situations requiring containment were typically the
result of very high concentrations of contaminants in the aquifer. The Agency’s assumptions
concerning the effectiveness of these systems are summarized in Exhibit C-6.
EXIUBIT C-6
DISSOLVED PLUME CONTAINMENT EFFICIENCIES
Effectiveness of
containment in
various media
Extraction wells
French drains
HDPE barrier
with ground-
water extraction
Slurry wall with
ground-water
extraction
Granular
porous media
100%
100%
100%
100%
Fractured rock
75%
NA
‘NA
NA
Karst
50%
NA
NA
NA
NA: Not applicable
For situations with 100 percent effective containment, the Agency assumed that the
plume would not expand downgradient after the year of installation of the containment system.
For karst and fractured systems, after simulating the effects of source controls, the exponential
equation was applied to all potential exposure points within the dissolved plume (including any
points beyond the boundaries of the extraction system). The Agency then multiplied the ground-
water concentrations at exposure points located beyond the initial plume boundary by the
appropriate percent effectiveness (i.e., 25 percent br 1-.75J for fractured and 50 percent for
karst). The Agency assumed that French drains, HDPE barriers, and slurry walls would not be
constructed in fractured bedrock or karst conditions.
French drains decrease ground-water concentrations at wells located downgradient of the
barrier.’ This effect was simulated by using the exponential equation and setting C 0 as a
constant equal to the concentration at the time of remediation. The time term within the
‘This is based on the assumption that low-permeability barriers were installed down-gradient
from the french drains to prevent ground water from being pulled up-gradient into the drains.
•** DRAFT: March 24, 1993
-------
c-I 3
exponential equation was then adjusted to account for delay associated with the time of travel
from the drain to each well. In this manner, the effect of the French drain at each well would
not be apparent until the year when ground water traveling from the drain at the time of its
installation reaches each successive well.
C.3.3 Non-Aqueous Phase Liquids
The MMSOILS model is a semi-analytical solution of the advection-dispersion equation
for a single non-conservative constituent and cannot directly simulate the fate and transport of
NAPLs. Because the MMSOILS model cannot directly simulate the behavior of NAPLs,
particularly when remedial technologies were applied to the contaminated area, the Agency
developed a simplified approach for simulating the effectiveness of NAPL remediation. For light
non-aqueous phase liquids (LNAPLs), the Agency assumed that the floating product could be
recovered based on field experience with numerous hydrocarbon spills. Current field experience
with dense non-aqueous phase liquids (DNAPLs), however, indicates that remediating the
DNAPL source may be technically impracticable in many cases. Because of this difference, a
separate approach was used for cases where DNAPLs were present.
The approach for simulating DNAPL remediation assumed that the DNAPL was the
principal source of contamination. With this approach, source controls, such as caps or
excavation, specified for a unit with DNAPL releases would not be effective at reducing overall
releases to the aquifer if it was assumed that DNAPL had already entered the aquifer. In these
cases, the Agency did not simulate any source controls or waste treatment technologies with
MMSOILS.
This approach for ground-water remediation with DNAPLs required the division of the
site into two regions (see Exhibit C-i). The interior region represents the area surrounding the
DNAPL-contaminated portion of the aquifer. The interior region was separated from the
exterior region by the containment system specified by the expert panels for each facility; the
containment system (whether extraction wells, a slurry wall, or a HDPE wall) represented a
hydraulic barrier between the two regions.
Because MMSOILS cannot directly simulate ground-water remedies, the Agency
simulated the effect of the barrier through the MMSOILS cover routine. In order to determine
the year of installation of the simulated barrier, the travel time of the DNAPL constituent was
estimated from the time of release from the unit to the point at which the actual barrier would
be placed in the ground-water system. MMSOILS was then run with the cover installed in order
to generate a modified set of ground-water concentrations reflecting the barrier. Using this
approach, the effect of the hydraulic barrier would be reached during the year of its installation.
For example, if the expert panels specified installation of a slurry wall in year 2005
located 20 meters from a unit boundary, the Agency estimated the time it would take for the
DNAPL constituent to flow from the SWMU, through the unsaturated zone, and to the slurry
wall located 20 meters from the unit boundary (say 20 years for this example). In this case, the
DRAFT: March 24,1993
-------
/
I
I
I
EXH.B. T
HY ‘OT lET CA
C-7
)NAPL S TE
Aqueous Plume
Extent of
DNAPL Lens
.
Containment
Wells
UNIT
___ S
Interior Zone
/
/
/
Exterior Zone
/
S
Ground Water
-------
c-I 5
Agency simulated the slurry wall by installing a cover in year 1985; the resulting exposure
concentrations would show a concentration decline (attributable to the slurry wall) at the 20
meter point at approximately year 2005. The efficiencies of these barriers (and, therefore the
efficiencies of the covers used to simulate them) varied with the site geology and were based on
containment efficiencies presented previously in Exhibit C-6 (e.g., a slurry wall would have a
100% efficiency, while extraction wells in fractured bedrock would have a 75% efficiency).
After running MMSOILS to simulate the post-remediation scenario, the Agency used a
post-processor to calculate concentrations inside and outside the containment zone. In the
interior zone, the Agency assumed that the DNAPL lens would continue to act as a source of
constituent releases to ground water and that the ground water would not be restored to use
(and thus baseline concentrations would be unaffected). Outside the containment zone, the
Agency used the exponential equation (described in Section C.3.1)to simulate the effectiveness
of any ground-water extraction wells specified by the expert panels for plume restoration.
•* DRAFF: March 24, 1993 “
-------
APPENDIX D. COST ANALYSIS
This appendix addresses the development of cost estimates for remedial activities,
focusing on unit costs estimation.
D.1 Expert Panel Cost Estimation
The costs of corrective action presented in Chapter 5 were based on simulated remedy
selections for the proposed corrective action rule. For each remedy selected at each unit, the
expert panels broke out the detailed components of the remedy. For example, a pump and treat
system to address contaminated ground water might include:
• Extraction well installation;
• Pump installation;
• Pump operation;
• Piping installation (from wells to treatment system);
• Construction of treatment system (i.e., air stripper and vapor phase carbon unit);
• Construction of treatment enclosure;
• Operation of treatment system;
• NPDES permit; and
• Discharge through NPDES outfall.
For each step, the experts developed a cost estimate using either professional judgment to
estimate a lump sum cost or a unit cost calculation. Wherever possible, the experts used unit
cost calculations (e.g., $8 per yd 3 to excavate soil). Examples of costs estimated by lump sum
include permits, debris removal, steam cleaning of equipment and structures, and repair of
existing structures. Where unit costs were applicable and available, the experts were able to base
cost estimates on volumes and areas of waste and contaminated media addressed at each unit.
Unit costs were taken from EPA guidance documents for hazardous waste remediation and
engineering manuals (e.g., Means Site Work Cost Data) .
Exhibit D-1 provides the unit cost ranges used by the expert panels. The expert panels
chose unit costs from the sources listed in Exhibit D-2 based on facility-specific characteristics
and professional judgment. For example, excavation of a veiy deep unit may require different
removal techniques than excavation of a shallow unit, with a corresponding difference in unit
cost. For some frequently used remedies, typical unit costs are not identified in this exhibit due
to the complexity of the cost calculations (i.e., the cost estimate is dependent upon a number of
facility-specific characteristics.) For example, the cost of a ground-water extraction well is
partially determined by the depth of well required, necessary screening precautions, type of well
casing used, and the characteristics of the soil and/or bedrock overlying the aquifer. In such
cases, the expert panels would have first designed the well and then estimated its cost. Finally,
the unit cost ranges in Exhibit D-I may group dissimilar activities together. For example, the
category “Other Object Removal” may include unit costs for activities as different as excavating
and crushing a pipeline and removal of nerve gas canisters.
• * * DRAFT--March 23, 1993 * *
-------
Exhibit D-1
Unit Cost Ranges Used by the Expert Panels
Media = AIR Unit Cost
Low High Median
+ + +
Remedial Activity Cost Type lUnit
+ +
Active landfill Capital blower $50,000.00 $50,000.00 $50,000.00
gas collection + + +
meters $93.21 $93.21 1 $93.21
+ + + + +
Catalytic Capital lunit I $200,000.00 $200,000.00I $200,000.00
oxidation + + + +
0&M lunit I $20,000.00 $200,000.00I $20,000.00
+ + + + +
Tarps Capital square meters I $10.76 $118.40 I $10.76
- - - -- - 1 . ? 1 -- $20 00 $20 00
Vapor phase carbon Capital lunit I $500,000.00 $500,000.00 $500,000.00
+ + + +
0&M Icubic meters I $2,210.53 $2,210.53 $2,210.53
Media • GENERAL Unit Cost
Low ..... ! ! -
RemediaL Activity Cost Type lUn it
+ +
Buildings ICapita l square meters $118.40 $118.40 $118.40
Clear and/or grub iCapital squaremeters $0 27 $2I87038J $3 46
Concrete Capital cubic meters I $523.18 $653.981 $588.58
+ + +
meters I $32.81 $82.02I $57.41
+ + +
square meters I $32.29 $53.82 1 $53.82
+ + + +
+ & 1! ! !
Electromagnetic Capital square meters $10.76 $10.76J $10.76
survey + + + +
Investigation square meters I $1.24 $10.76 1 $6.00
+ + + + +
Fencing Capital meters I $32.81 849.21 1 $49.21
+ + +
unit I $1,000.00 81,000.001 $1,000.00
+ + + + +
Medical monitortng Capital Ihours I $144.23 8144.231 $144.23
+ + + + +
Misc. Capital cubic meters I $741.61 8741.61 1 $741.61
construction/repa- + + +
ir meters I $57.41 81,640.421 $656.17
+ + +
unit I $800.00 $800,001 $800.00
+ + + +
O&M CUCID I ! ! : I
+ + ! ! !. ! ! !
Sewer system study Investigation Ihours I 860.001 8150.001 $105.00
+ + + + +
Site preparation Capital cubic meters I S7.85 810.461 $9.16
+ + +
hours I $125.00 8125.001 $125.00
4. + +
square meters I 80.111 80.111 80.11
+ + + + +
Tank replacement Capital cubic meters I 837.041 8789.471 $37.04
+ + +
hours I 864.00 1 $64,001 $64.00
+ + + + +
Wetlands Capital square meters
restoration $0.00 $17.30 $0.00
-------
Exhibit 0-1
Unit Cost Ranges Used by the Expert Panels
Media = GROUND WATER Unit Cost
Low High Median
+ + +
Remedial Activity ICost Type lunit
+ +
Activated altinina oR,11 Icubic meters $0.61 $0.61 $0.61
+ + + + +
Aerobic biologicaL 0*14 cubic meters
treatment $0.26 $0.26 *0.26
+ + + + +
Air stripping CapitaL blower I *22,000.001 *22,000.001 $22,000.00
+ + +
I !?:?!I
0* 1 4 Icubic meters *0.041 *13. 161 $13.16
+ + + + +
Biostimulation 0*14 cubic meters
bioremediation $0.42 *0.42 *0.42
+ + + + +
Carbon Capital Icubic meters I *13.161 *13.161 $13.16
adsorption/GAC + + + +
0* 7 4 Icubic meters *13.161 *16,578.95 $16,578.95
+ + + 4 +
Catalytic 0*14 cubic meters
incineration
(oxidation) $1,435.41 $1,435.41 $1,435.41
4 + + + +
ChemicaL 0*14 cubic meters
precipitation $0.03 $7.89 $0.03
+ + + + +
Discharge to POTW 0*14 cubic meters
(ground water) $0.26 $0.26 $0.26
+ + + + +
Discharge to Capita l Imeters I $95 . 14 *95.14 1 $95.14
surface water + + + +
(ground water) 0*14 Icubic meters I * 0 .2 6 1 *0.261 $0.26
+ + + * +
Equalization ICapita t Icubic meters I *394-741 39 - ’I $394.74
+ + + + +
Extraction wells Capital meters I *164.041 *738.19 1 $328.08
+ * +
welL I $100.00 *50,000.00 1 $7,500.00
+ + + +
0* 1 4 kilowatt hour $010 1 *0.101 $0.10
+ + +
well I $1 ,000.00 *11,500.001 $1,000.00
+ + + + +
Ground water 0&M cubic meters $0.53 *7.891 *1.97
treatment + + +
(unspecified) hours I *28.851 *28.851 $28.85
+ + +
kiLowatt hour *0.10 1 *0.10 $0.10
+ + + + +
HDPE wall ICapi tat square meters I 3 .8 2I $5382I $53.82
4 + + + +
Hydrocarbon 0*14 cubic meters
col lection/recove-
ry $118.90 $118.90
+ +
Intercept ion Capital
trench/French
drain
4 +
Ion exchange 10* 14 I
+ 4
Leachate Capital
colLection
+ 4
Monitoring Labor 0*14
+
Monitoring wells Capital
meters I *145.461 *5,314.961 $984.25
4 + 4
unit *25,000.001 *105,000.001 $25,000.00
+ 4 +
cubic meters I *0.79 1 *0.791 *0.79
4 + +
meters
$57.41 $984.25 $520.83
+ 4 +
hours I *62.501 *625.001 $125.00
+ + +
saffple I *250.001 *2,500.001 $2,500.00
+ + +
meters I *328.081 *656.171 $328.08
+ + +
weLL I *500.001 *28,250.001 $2,000.00
+ + + +
0*14 I
Offsite RCRA 0* 14 cubic meters
landfill $130.80 $261.59 $261.59
(CONTINUED)
-------
Exhibit D-1
Unit Cost Ranges Used by the Expert PaneLs
Media GROUND WATER Unit Cost
Low - Median
Remedial Activity ICost Type Unit
+ +
Offsite disposal 08CM cubic meters
(ground water) $356.71 $356.71 $356.71
+ + + + +
Offsite 0 8 CM cubic meters
incineration $1,435.41 $1,435.41 $1,435.41
+ + + + +
Pier for wells ICapital meters I $1,640.42! $1,640.42! $1,640.42
+ + + 4 +
Piping for ground- Capital Inieters I $15.00! $3,280.84! $96.78
water extraction + + + +
08CM jmeters I $124.67! $124.671 $124.67
+ + + + +
Product recovery Capital Ip 1 ip I $2,000.00I $10,000.00! $2,000.00
ptz s + + + 4
08CM kilowatt hour I $0.10 ! $010 1 $0.10
+ + +
pu lp I $2,000.00! $2,000.00 1 $2,000.00
+ + + + +
Product recovery Capital Iwell $4,00000 $25,000.00I $25,000.00
wells + + + +
0 8 CM Iwell I $2,000.00! $2,000.00! $2,000.00
+ + + + +
Punps for ground- Capital rxsiq $500.00! $50,000.00! $5,000.00
water extraction + + + +
08CM hours I $2.88! $11.54! $11.54
+ + 4
kilowatt hour $0.10! $0.10! $0.10
+ + +
pu 1 $500.00! $10,000.00I $5,000.00
+ + + + +
Ptmips for CapitaL puiip
monitoring wells $1,200.00 $2,500.00 $1,500.00
+ + + + +
Sampling/analysis 08CM hours I $ 25 0 :?OJ
meters $50&00 $500.00! $500.00
+ + +
report I $1,000.00! $15,000.00! $5,000.00
+ + +
sample I $40.00! $6,200.00! $1,200.00
- - - - -+ - - - 11111 $S00 i - I$ ?0 ( $50000
SeaLing/capping Capital meters
abandoned weLls $2,624.67 $2,624.67 $2,624.67
+ + + + +
Sheet pile Capital cubic meters $1,171 .211 $1,171 .211 $1,171.21
+ + +
square meters I $22.97! $22.97I $22.97
+ + + + +
Sludge Capital Icubic meters $2747 $27.47! $27.47
stabilization/vit + + + +
rification 08CM Icubic meters I $19.62 Q32.331 $156.95
+ + + + +
Slurry waLL ICapital lcubic meters I $1922 $38 34 1 $23.07
+ + + + +
Stabilization/sot- 08CM cubic meters
idification (in-
situ) $1,105.26 $1,105.26 $1,105.26
+ + + + +
Subsurface drains ICapital lunit I $3,425.OOI $3,425.00I $3,425.00
+ + + + +
Truck transport 108CM Iki1o m ters I $2.49! $2.69! $2.49
+ + + + 4
Truck transport 08CM kilometers I $2 .49 1 $2.49 1 $2.49
(offsite + + +
treatment) trip I $600.OOI $60000I $&)0.00
+ + + + +
Ultraviolet 08CM cubic meters
oxidation $0.26 $1,105.26 $029
+ + + + +
Vapor phase carbonlCapitaL lunit I $6,000.MOI $6,000.S0I $6,000.00
(CONTINUED)
-------
Exhibit D-1
Unit Cost Ranges Used by the Expert Panels
Media GROUND WATER
Remedial Activity ICost Type lUnit
+ +
Water infiltration CapitaL meters
basin
Unit Cost
Low Nigh Median
+ 4
$2,210.53 $198,947.37 $2,210.53
Media = SOIL Unit Cost -
Low Nigh Median
+ + +
Remedial Activity ICost Type lUnit
+ +
Asphalt kiLns ICapital Icubic meters $118.90 $118.90 $118.90
+ + 4 + +
BEST solvent Capital cubic meters
extraction . $1,301.95 $1,307.95 $1,307.95
+ + + + +
Backfilling (soil) Capital cubic meters I 13.271 352.32 1 $16.00
+ + +
hours I 1100.001 1100.001 $100.00
+ + +
truck miles I 14.00 1 $4.OOj $4.00
+ + + + 4
Backhoe excavation Capital Icubic meters I $3.27 12,179.051 $7.85
(soil) + + +
hours I 170.001 1100.001 $83.00
+
+ • 1
Consolidation ICapital
+ 4
Disposal in Capital
offsite Subtitle C
landfill
+
Disposal in Capital
offsite subtitle D
landf ill
4 4
Disposal in onsite Capital
subtitle 0
landfiLl
+
Excavation and Capital
hauling (soil)
4
Grade or compact Capital
cubic meters
$10 46
10.881 35.23 1 $5.23
+
Landfarming ICapita l Icubic meters I 111.121 111.121 $11.12
+ 4 + + +
Landfarming (in- CapitaL hours
situ) $25.00 $25.00 $25.00
+ + + + +
Low temperature Capital cubic meters
thermal recovery $118.90 1118.90 1118.90
Biostimulation CapitaL
bioremediation (in
situ)
+
Confirmatory Capital
sampling (soil)
0&M
I 1117.721 1117.721 $117.72
+ + +
hours I $30.37 1125.001 1125.00
1 S1.;O;;0T I °
square miles I 1200.001 11,200.001 1675.00
+ + +
hours I 1125.001 1125.001 $125.00
+ + +
sample 1100.001 12.000.001 $250.00
+ + +
cubic meters I $2.94 310.461 $7.85
+ + +
cubic meters
$130.80 $1,586.54 $261.59
+ + +
cubic meters
$98.10 $163.49 $118.90
+ +
cubic meters
$3.27 $3.27 $3.27
cubic meters
cubic meters
(CONTINUED)
-------
Exhibit D-1
Unit Cost Ranges Used by the Expert PaneLs
Media = SOIL Unit Cost
Low ( High ( Median
+ + +
Remedial Activity (Cost Type (Unit
+ +
Metals recovery Capital cubic meters
(soil) $5.23 $5.23 $5.23
+ + + + +
Offsite disposal ICapital (cubic meters I 8392.39 1 $392.39( $392.39
+ + + + +
Offsite Capital cubic meters
incineration $3.27 $1,046.36 $524.82
+ 4 + + +
Offsite Capital (cubic meters ( $261 .59( $1,04636( $518.60
treatment/disposal + + + +
of haz solids 0&M (cubic meters I $52318( 8523.18 1 $523.18
+ + + + +
On-site treatment Capital cubic meters I $L31 ( $1311 $1.31
plant + + +
meters I $98.43( $98.43 ( $98.43
+ 4 + + +
Onsite disposal (Capital (cubic meters I $327( $5. 13( $4.25
+ + + + +
Onsite Capital cubic meters
treatment/disposal
of haz solids $5.23 $5.23 $523
+ + + + +
Replace in unit Capital cubic meters
(soil) $7.19 $10.46 $7.19
+ + + + +
Replace in unit Capital cubic meters
(waste) $1.31 $1.31 $1.31
+ + + + +
Revegetation Capital cubic meters I $O50( $7.85( $4.17
+ + +
square meters I $O.17( $21,527.M2( $0.49
+ + + + +
Rotary kiln Capital cubic meters
incinerator $980.96 $2,153.11 $1,567.04
+ + + + +
SVE blowers (in- O&M horsepower/hour
situ) $0.10 $0.10 $0.10
+ + + + +
SVE monitoring (in Capital (unit I $50OO0 $1,000OOI $1,000.00
situ) + + + +
O&M sample I $10O.00 $100OOI $100.00
+ + +
unit I $4OO00 $400M01 $400.00
+ + + + +
SVE vents (in- Capital cubic meters I 2 6 . 16( $65 401 $65.40
situ) + + +
meters I $328.O8 8328.081 $328.08
+ + + + +
Sampling/analysis IO&M (sample I $37500( $37500 1 $375.00
+ + + + +
Soil flushing (in- Capital cubic meters
situ) $130.80 $130.80 $130.80
SoiL gas survec yes gato $60001 $250 OO( $187 50
Soil Capital cubic meters
neutralization $26.16 $26.16 $26.16
+ + + + +
Soil vapor Capital meters
extraction (SVE)
piping $57.41 $95.14 $57.41
+ I . + + +
Soil vapor cubic meters I $23.78( $4,784.49( $4,421.05
extraction (in- + + +
situ) we
0&M cubic meters $793 86,0 18.95 1 $1,657.89
+ + +
well ( $1,000.O0( $1,000..00( $1,000.00
+ + + + +
Soil washing (Capital (cubic meters ( $6540( $39239( $261.59
+ + + + +
Stabilization/sol- Capital cubic meters
idification $23.54 $359.69 $113J9
(CONTINUED)
Capital
-------
Exhibit D-1
Unit Cost Ranges Used by the Expert Panels
Media SOIL Unit Cost
Low High Median
4 + +
RemediaL Activity ICost Type lUnit
+ +
StabiLization/soL- CapitaL cubic meters
idification (in-
situ) $2.55 $130.80 $24.20
+ + + + +
Thermal desorbers ICapitaL Icubic meters I 1118.901 1261.591 $118.90
+ + 4 + +
Truck transport ICapitaL IkiLometers I 12.491 12.491 12.49
+ + + + +
Truck transport CapitaL cubic meters I 13.011 14.203.751 $6.54
(soiL) + + +
I 9:t!I
truck miLes I 14.00 1 15.00 1 14.00
+ + + + +
Unspecified CapitaL cubic meters
incinerator $356.71 $951.24 $951.24
+ + + + +
Vitrification ICapitat Icubic meters I 1588.581 1588.581 1588.58
Media = SOURCE CONTROL Unit Cost
Low I High Median
+ + +
RemediaL Activity Icost Type lUnit
+ +
AsphaLt cap CapitaL square meters $0.84 $53.82 132.29
+ + + +
O&M square meters I 10.541 10.541 10.54
+ + + + +
AsphaLt Liner ICapitaL square meters I 121.531 127.021 $27.02
+ + + + +
Backfitfing ICa pitaL Icubic meters I 13.271 1130.801 117.66
+ + + + +
Backhoe excavation CapitaL cubic meters I - 11 .701 1130.801 110.46
+ + +
hours I 175.001 173.001 175.00
+ + + +
0&M Icubic meters $7.85 I 17.85 1 $7.85
+ + + + +
Bags ICapitaL lunit I 150.00 1 150.00 1 150.00
4 + + + +
Berm/contairinent CapitaL cubic meters I $5_ill $1 .307.951 11908
waLL + + +
hours I 1125.001 1125.001 1125.00
+ + + + *
CLay Liner CapitaL square meters I ‘4 -351 1 14.35 1 114.35
+ + + +
0&M square meters I 1 1 4 . 3 51 114.35 1 114.35
+ + + + +
Clay cap CapitaL Icubic meters $18.31 131.39 1 119.62
+ + + +
0&M Icubic meters I 10.591 10.591 10.59
+ + + + +
CLay cap with vent CapitaL square meters I 110.001 150.001 149.50
+ + + +
0& 14 square meters I 10.541 10.541 $0.54
+ + + + +
Coaposite/RCRA Capital Isciuare meters I 110.001 1101.661 164.58
cover + + + +
0&N square meters I 10.271 121.531 11.18
+ + + + +
Conposting/Landfa- Capital cubic meters I 110.461 111.121 111.12
rming (in-situ) + + 4
meters I $9841 19 .841 19.84
+ + + + +
Concrete pad/Liner Capital cubic meters I 1653.981 1653.981 1653.98
+ + +
hours I 164.00 1 1 6 4.001 166.00
+ + +
meters I 14.101 14.101 14.10
+ + 4
square meters I 110.761 164.581 $64.58
(CONTINUED)
-------
Exhibit 0-1
Unit Cost Ranges Used by the Expert Panels
Remedial Activity ICost Type Unit
+ +
?! J square meters
Consolidation ICap itat jcubic meters
+ +
Construction of Capital
orisite Sub C
landfiLL
+
Dewatering Capital
+
Drun removal Capital
Onsite Subtitle C
I andf i LI
Onsite Subtitle D
Iandfi IL
$237.81 $237.81! $237.81
+
$1.70
Si 0.46
Media SOURCE CONTROL Unit Cost
Low High Median
- - - - - - - - I..$ !?tI....$ !7O 1 - -
I $6.54 $20.00 !
+ + +
square meters
$22.00 $83.53 $22.00
-+ + +
Icubic meters I $118.90 $118.90j $118.90
+ + + +
truck miles $4.00! $4.00 ! $4.00
+ + + + +
Equalization ICapital leubic meters I $400.00 $400.00! $400.00
+ + + + +
Excavation and Capital cubic meters
Hauling $10.46 $20.00 $20.00
Floating ;ove: - square meters .“ 2 1 £1
Grade or compact ICapital square meters I $0.50! $32.81! $2.48
+ 4 + + +
Gravel cover Capital cubic meters $1 .311 $1 .311 $1.31
+ + +
+ + !!. ‘ !! !
Landfarming Capital Icubic meters $11.12 $11.12 1 $11.12
+ + + +
O&M Icubic meters I $1.03 $11.12 ! $6.07
+ + + + +
Leak detection Capital hours I $62.50! $125.00! $93.75
+ + +
unit I $3.0 00.00J $3,065.00! $3,000.00!
+ + + + +
Mercury retorting ICapital Icubic meters I $17,003.36 $17,003.36! $17,003.36
+ 4 + + +
Neutralization ICapital Icubic meters I $95.69! $95.69! $95.69
+ + + + +
Offsite Subtitle C Capital cubic meters
landfill $130.80 $356.71 $237.81
+ 4 + + +
Offsite Subtitle 0 Capital cubic meters
Landfill
+ + 4
Offsite disposal IcapitaL Icubic meters I
+ + 4 I
Offsite Capital cubic meters
t reatment/di sposa
of haz liquid $13.16
4
Of fsite
treatment/disposal
of haz solids
• +
Capital Icubic meters
$478.47
$13.16
$1,151.00 $1,151.00 $1,151.00
+ + +
Capital cubic meters I $237.81! $956.941 $478.47
+ + +
hours I $150.00 $150.00! $150.00
+ + + +
Capital Icubic meters I 3 . 2 71 $3.271 $3.27
+ + + +
° Icubic meters I 52.46 1 $2.461 $2.46
+ + + +
Capital Icubic meters I $1.31 $1 .311 $1.31
[ a +“.• “.““ + + +
$0.26 $0.26 $0.26
• 4 + + +
Capital cubic meters I $7.42 $570.74! $227.99
+ + +
meters I M.76 516.73 1 $4.92
+ + +
square meters I 540.36 1 $4,036.47! $45.75
Onsite disposal
Onsite
treatment/disposal
of haz Liquids
Other object
removal
(CONTINUED)
-------
Exhibit 0-1
Unit Cost Ranges Used by the Expert PaneLs
Media SOURCE CONTROL Unit Cost
Low Nigh Median
+ 4 +
Remedial Activity ICost Type lUnit
+ +
Overpack druns ICapital Icubic meters $2,392.34 $2,392.34 $2,392.34
+ + + + +
Pack in drirs ICapitat Icubic meters $2,392.34 $2,392.34 $2,392.34
+ + + + +
Pug miLling Capital cubic meters $i9.62 150:001 $50.00
meters I 14.76 1 14.92 1 14.34
+ + + + +
RCRA vault ICapital Icubic meters I 1 47.901 $4790 $4790
+ + + + +
RemovaL of free Capital cubic meters I 10.26 1 19 .3 7I $0.26
Liquids + + +
hours I 11.501 188.001 $33.44
+ + +
meters $47.41 1328.081 $187.75
+ + +
square meters I 19.15 1 19.15 1 $9.15
+ + +
tank I 16,667.001 16,667.001 $6,667.00
+ + + + +
Replace in unit Capital cubic meters
(waste) $1.31 $1.31 $1.31
+ + + + +
Revegetation Capital square meters I 10.161 10.501 $0.49
+ + + +
O&M square meters I 10.271 10.271 $0.27
+ + + + +
Rip rap ICapita l square meters I 1 67.841 186.111 166.98
+ + + + +
Rotary kiLn Capital cubic meters
incinerator $951.24 $2,049.56 $1,500.40
+ + + + +
Run on/run off Capital cubic meters
controLs $1.37 $352.39 $35.13
+ + + +
Seal coating Capital square meters I 30.89 1 10.891 $0.89
(asphalt) + + + +
O&M square meters I 10.11 1 10.11 1 $0.11
+ + + + +
SoiL cap CapitaL cubic meters I 17.85 1 119.621 111.44
+ + +
square meters 116.44 116.44 I $16.44
+ + + +
O&M cubic meters I 10.14 1 10.591 $0.59
+ + +
square meters I 10.541 11.18 1 $0.54
+ + + + +
Soil vapor Capital cubic meters
extraction (in-
situ) 14,421.05 14,421.05 $4,421.05
+ + + + +
StabiLization/soL- CapitaL Icubic meters I 123.541 12,392.34 1 $24.00
idif ication + + + +
O&M Icubic meters I 152.321 152.321 152.32
+ + + + +
Stabilization/sol- CapitaL cubic meters
idification (in-
situ) $23.54 $130.80 $31.39
+ + + + +
Steam CapitaL hours I 130.001 1203.001 $115.00
cleaning/washing + + +
meters I 16.56 1 16.561 $6.36
+ + IIIItIIII ,1s
Synthetic cap I
0&M cubic meters I 10.14 1 10. 14 1 10.14
+ ñ E r $21 s I __!° 5 t
Synthetic liner ICapital Icubic meters 13.42 I 134.17 1 $7.16
(CONTINUED)
-------
Exhibit 0-1
Unit Cost Ranges Used by the Expert Panels
Media = SOURCE CONTROL Unit Cost
Low High Median
+ 4 +
Remedial Activity ICost Type lUnit
+ 4
Tank integrity Capital stank $5,000.00 $5,000.00 $5,000.00
test + + + +
+ & tank
Tank Location Investigation square meters I $1.24! $1.24I $1.24
Tank re moval - Capital cubic meters - I ! FI 7 7j0J $32 26
hours I $75.00 $150.00! $150.00
+ + +
tank $5,00O.00 $20,000.00! $5,000.00
+ + + + 4
Thermal desorbers ICapital Icubic meters I $555.88 $555.881 $555.88
+ + + + +
Truck transport Capital cubic meters I $5.52! $65.40I $15.70
+ + +
kiLometers I $2.49 $31.07! $2.49
+ + +
truck mites I M00 $5.00! $4.00
+ + + +
0&M kiLometers I $2.49 $2.49I $2.49
+ + +
truck mites I $3.00! $3.00! $3.00
+ + + + +
I !T ! 1.
Unspecified Capital cubic meters
incinerator $57.42 $2,400.00 $1,046.36
+ + + + +
Vacuum extraction Capital hours
(in-situ) $88.00 $88.00 $88.00
+ + + * +
Vegetative cover Capital cubic meters I $19 !!!:t!.t
+ !: ‘ ! ! : I ! ? :t I : tI
D M1 square meters I $0.2fl $0.54 $0.54
+ + + + +
Vitrification (in- O&M cubic meters
situ) $594.52 $594.52 $594.52’
+ + + + +
Waste sampling and Capital cubic meters I $24.71I $239.231 $239.23
analysis + + +
I ! ! :! ? I !1! : ? ? I
report I $3,000.00 $3,000.OOI $3,000.00
+ + +
sample $100.00 $2,000.00! $500.00
+ + + +
D&M hours I $125.00! $125.OOI $125.00
+ + +
sample I $100.00! $1,500.OOI $900.00
+ 4 +
well I $7,000.00 $7,000,001 $7,000.00
-------
Exhibit D-1
Unit Cost Ranges Used by the Expert PaneLs
Media = SURFACE WATER Unit Cost
Low High Median
+ + +
RemediaL Activity ICost Type lUnit
+ +
Diversion and CapitaL meters $9.02 $656.17 $328.08
coLLection + + +
(surface water) p imp I $1,025.OOj $1,025.00I $1,025.00
+ + + +
0&M I m eters I $82.02 $82.02 1 $82.02
+ + + + +
Sediment sat ypting 0&M hours $125.00 8125.00J $125.00
+ + +
sanpLe I $1,000.00 $1,000.00I $1,000.00
+ + + + +
StabiLization/ero- CapitaL square meters
sion prevention $107.64 $107.64 $107.64
+ + + + +
Surface water 0&M hours I $12500 $125.OOI $125.00
monitoring + + +
sanpLe I $1,00000 $1,000.OOI $1,000.00
-------
D-12
Exhibit D-2
Sources for Unit Costs
R.S. Means Company. MEANS Site Work Cost Data . Kingston, Massachusetts: R.S.
Means Company. Published annually.
Sobotka & Company. Experience Curves. Innovation and Diffusion: Application to
RCRA . Washington, D.C.: Sobotka & Company, April 1992.
U.S. Environmental Protection Agency. A Compendium of Technologies Used in the
Treatment of Hazardous Wastes . Washington, D.C.: U.S. Environmental Protection
Agency, September 1987.
U.S. Environmental Protection Agency. Guide to Treatment Technologies for Hazardous
Wastes at Superfund Sites . Washington, D.C.: U.S. Environmental Protection Agency,
March 1989.
U.S. Environmental Protection Agency. Handbook - Remedial Action at Waste Disposal
Facilities . Washington, D.C.: U.S. Environmental Protection Agency, 1985.
U.S. Environmental Protection Agency. In Situ Treatment of Hazardous Waste-
Contaminated Soils . Washington, D.C.: U.S. Environmental Protection Agency, January
1990.
U.S. Environmental Protection Agency. Seminar Publication - Corrective Action:
Technologies and Applications . Washington, D.C.: U.S. Environmental Protection
Agency, September 1989.
U.S. Environmental Protection Agency. Subsurface Contamination Reference Guide .
Washington, D.C.: U.S. Environmental Protection Agency, October 1990.
U.S. Environmental Protection Agency. The Superfund Innovative Technolo v
Evaluation Program: Technology Profiles . Washington, D.C.: U.S. Environmental
Protection Agency, November 1990.
* * * DRAFT--March 23, 1993 * * *
-------
D-13
D.2 Additional Analysis of Results
Exhibit D-3 presents the total projected cost of corrective action broken out by media
and general activity type.
EXHIBIT D-3
CORRECTIVE ACTION COSTS BY ACTIVITY AND MEDIA
Total Net
Present Value Percent of Total
Activity Type Media (millions of $) Cost
RCRA Facility Investigation
Other
1,800
9.7%
RCRA Facility Investigation
Soil
1.3
0.01%
Corrective Measures Study
Other
210
1.12%
Containment
Waste
220
1.16%
Containment
Ground Water
1,400
7.6%
Containment
Surface Water
26
0.14%
Containment
Air
4.1
0.02%
Removal/Treatment of
Media
Ground Water
5,800
30.6%
RemovallTreatment of
Media
Surface Water
________________
0.4
<0.01%
Removal/Treatment of
Media
Air
15
0.08%
Removal Treatment of
Media
Soil
4,000
21 .62%
Disposal
Waste
160
0.83%
Disposal
Ground Water
360
1.94%
Disposal
Soil
160
0.88%
Institutional Controls
Other
11
0.06%
Other Activity
Waste
0.3
<0.01%
Other Activity
Ground Water
31
0.16%
Other Activity
Air
4.5
0.02%
Other Activity
Other
91
0.49%
* * * DRAFI’--March 23, 1993 * * *
-------
D44
EXHIBIT D-3
CORRECTIVE ACTION COSTS BY ACTIVITY AND MEDIA
Total Net
Present Value Percent of Total
Activity ‘Fype Media (millions of S) Cost
Removal [ Freatment of
Waste
Waste
1,400
7.3%
Monitoring
Waste
16
0.09%
Monitoring
Ground Water
1,400
7.36%
Monitoring
Surface Water
130
0.7%
Monitoring
Air
2.0
0.01%
Monitoring
Soil
26
0.14%
Capping
Waste
1,500
7.97%
TOTAL
18,700
100%
* * * DRAFT--March 23, 1993 * * *
-------
APPENDIX E
HUMAN HEALTH BENEFITS ANALYSIS
This appendix discusses in detail the methodology EPA used for the human health
benefits analysis. It lays out the analytical framework that the Agency used to arrive at
quantitative estimates of human health risk at the corrective action facilities, and lists all major
assumptions and values for variables used to quantify risk. This appendix supplements the
summaiy of approach presented in Chapter 7.’
This appendix is organized by major steps of EPA’s risk assessment methodology.
Section E.1 presents topics related to the hazard identification and dose-response assessment.
Section E2 presents topics of the exposure analysis. Topics related to the risk characterization
are presented in Section E.3.
El Hazard Identification and Dose-Response Assessment
In this section, EPA first presents the basis for determining which chemicals posed a
hazard at the corrective action facilities, and, therefore, are included in the human health risk
assessment. Next, the Agency discusses measures of hazard associated with each of these
chemicals. The discussion includes the types of hazard measures or health criteria used in the
RIA, and the sources that EPA used to obtain values for these toxicity measures.
E.l.1 Selecting Chemicals of Concern
EPA selected the chemicals to be evaluated for risk at each facility from a set of
approximately 220 chemicals. This set represents chemicals from Appendices VII (56 f , 7568,
Feb. 25, 1991) and IX (52 fi 25946, July 9, 1987) that also have toxicity data (e.g., action levels,
maximum contaminant levels (MCLs), slope factors, and reference doses). Of these, about 100
chemicals were identified as major contaminants in SWMUs of concern at the 52 facilities
modeled for this benefits analysis. These constituents, along with their toxicity data, are listed in
Exhibit E-1. The Agency used action levels from the corrective action proposed rule (55
30798, July 27, 1990) when available, or derived them using the assumptions given in the
proposed rule. The MCLs listed in Exhibit E-1 are as promulgated under the Safe Drinking
Water Act, up to July 1992.
Exhibit E-1 also lists the health criteria values representing the dose-response
information, i.e., slope factors and reference doses, that EPA used in the risk assessment. EPA
did not develop new dose-response assessments for this RIA, but instead, relied on the Agency’s
existing standardized, peer-reviewed dose-response data. The slope factors and reference doses
are taken from EPA’s Integrated Risk Information System (IRIS), or, if not listed in IRIS, from
EPA’s Health Effects Assessment Summary Tables (HEAST, March 1992). The following
section explains these measures of dose-response in greater detail.
‘Related discussions of the MMSOILS model and its input parameters are presented in Appendix B.
Draft -- March 23, 1993 °
-------
EXHIBIT E-I:
Chemicals Modeled and Health. Criteria Values Used
CAS No Chical MCL AL AIR AL 1 120 AL SOIL RFD 0 RFD £ SF 0 SF I
630206 1,1,1,2-TETRACHL OR OETHANE -- 1.OE+0O 1.OE-02 3.OE+02 3.OE-02 2.6E-02 2.6E-02
79005 1,1,2-TRICKLOROETHANE 5.OE-03 6.OE-01 6.OE-03 1.OE+02 4.OE-03 -- 5.7E-02 5.7E-02
75343 1,1-DICHI. OROETHANE -- 3.5E.02 3.5E+0O 8.OE+03 1.OE-O1 1.OE-01 -- --
75354 1 ,1-DICHL OROETHYLENE 7.OE-03 3.OE-02 5.8E-04 1.OE+O1 9.OE-03 -- 6.OE-O1 1.8E-O1
120821 1,2 ,4-TRICHLOROBENZENE 7.OE-02 1.OE+O1 7.OE-01 2.OE+03 1.OE-O2 2.6E-03 -- --
95501 1 ,2-DICHL OR OBENZENE 6.OE-O1 1.4E402 3.2E+OO 7.2E+03 9.OE-02 5.7E-02 -- --
107062 1 ,2-DICHL OROETHANE 5.OE-03 4.OE-02 3.8E-04 8.OE+OO -- -- 9.1E-02 9.1E-02
156605 1,2-DICHLOROETHYLENE (trans-DCE) 1.OE-01 -- 7.OE-O1 1.6E+03 2.OE-02 -- -- --
78875 1,2-DICHLOR OPROPANE 5. OE-03 -- 5.1E-04 1.OE+01 -- 1.1E-03 6.8E-02
106467 1,4-DICHLOR OBENZENE ?.5E-02 2.5E+03 1.5E-02 2.9E+02 -- 2.OE-01 2.4E-02 --
1746016 2,3 1 7,8-TETRACHL ORODIBEN2 O-p-DIOXIN 3.OE-08 2.3E-08 2.2E-10 4.5E-06 -- - - 1.6E405 1.SE+O5
88062 2,4,6-TRICHIOROPHENOL 2.OE-01 2.OE-03 4.OE+01 -- -- 1.1E-02 1.1E-02
120832 2 ,4-DICHL OR OPHEN OL -. -- 1.OE-01 2.OE+02 3.OE-03 -- --
105679 2,4-D IMETHYLPHENOL -- -- 7.OE-O1 1.6E+03 2.OE-02 -- --
121142 2,4-DINITROTOLUENE -- -- 5.1E-05 1.OE+00 2.OE-03 -- 6.8E-01 --
91941 3 ,3’-DICI IL OROBENZID lNE -- -- LOE-05 2.OE+0O -. -- 4.5E-01 --
108101 4-METHYL-2-PENTANONE - - 7.OE+01 2.OE+00 4.OE+03 5.OE-02 2.3E-02 -- --
83329 ACENAPHTHENE -- -- 2.IE+00 4.8E+03 6.OE-02 -- --
67641 ACETONE -- -- 4.OE+O0 8.OE+03 1.OE-01 -- --
75058 ACEIONITRILE; METHYL CYANIDE -- 3.5E+01 2.OE-01 S.OE+02 6.OE-03 1.OE-02
107028 ACROLEIN -. 3.5E-01 1.OE+02 -- 2.OE-02 6.OE-06 -- -.
107131 ACRYLONITRILE -- I.OE-02 6.OE-05 1.OE+00 -- 5.7E-04 5.4E-01 2.4E-O1
107186 ALLYL ALCOHOL - - - - 2.OE-01 4.OE+02 5.OE-03 -- - - - -
62533 ANILINE -- LOE+00 6.OE-03 LOE+02 -- 29E-04 5.TE-03 --
120127 ANTHRACEHE -- -- L1E+O1 2.4E+04 LOE-01 -- -- --
7440360 ANTIMONY 6.OE-03 -- 1.OE-02 3.OE+O1 4.OE-04 -- -- --
11141165 AROCLOR 1232 5.OE-04 -- 5.OE-06 9.OE-02 -. -- ?.7E+OO --
12672296 AROCLOR 1248 5.OE-04 • 5.OE-06 9.OE-02 -- -- 7.7E+QO --
11097691 AROCLOR 1254 5.OE-04 - - 5.0E06 9.OE-02 -- -- 7.TE+OO - -
7440382 ARSENIC 5.OE-O2 7.OE-05 20E-05 8.OE4O1 3.OE-04 -- 1.7E+OO 1.5E+O1
7440393 BARIUM AND COMPOUNDS 2.OE+OO 4.OE-O1 1.7E+OO 4.OE+O3 7.OE-02 1.OE-04 -- --
71432 BENZENE 5.OE-03 1.2E-O1 1.2E-03 2.4E+O1 -- -- 2.9E-02 2.9E-02
92875 BENZIDINE -- 2.OE-05 2.OE-07 3.OE-03 3.OE-03 -- 2.3E+02 2.3E+02
7440417 BERYLLIUM 4.OE-O3 4.OE-04 8.OE-06 2.OE-O1 5.OE -03 -- 43E+OO 8.4E+OO
117817 BIS(2-ETHYLHEXYL)PHTHALATE -- -- 3.OE-03 5.OE+O1 2.OE-O2 -- 1.4E-02 --
75274 BROM( )ICHLOROMETHANE 1.OE-O1 •- 3.OE-05 5.OE-O1 2.0E02 -- L3E-O1 --
75252 BROMOFORN 1.OE-O1 9.OE-O1 70E-O1 2.QE O3 2.OE-02 -- -7.9E-03 3.9E-03
85687 BUTYL BENZYL PHTHALATE - - - 70E+OO 2.OE+O4 2.OE-O1 - - -- - -
7460439 CADMIUM 5.OE-03 6.OE-04 1.8E-02 6.OE+O1 5.OE-04 -- -- 6.1E+OO
75150 CARBON DISULFIDE - 1.OE+01 4.OE+OO 8.OE+03 1.OE-O1 2.9E-03 -- --
56235 CARBON TETRACHLORIDE 5.OE-03 3.OE-02 3.OE-04 5.OE+O0 7.OE-04 -- 1.3E-O1 5.3E-02
57749 CHLORDANE 2.OE-03 3.OE-03 3.OE-O5 5.OE-O1 6.OE-05 -. 1.3E400 1.3E.OO
108907 CHLORO BENZENE 1.OE-O1 2.OE O1 7.OE-O1 2.OE+O3 2.OE-02 6.OE-03 -- -.
67663 CHLOROFORM 1.OE-O1 4.OE-02 6.OE-03 1.OE+02 1.OE-02 -- 6.1E-03 8.1E-02
-------
EXHIBIT E-I:
Chemicals Modeled and Health Criteria Values Used (cont.)
CAS No thicat MCL AL AIR AL H 2 0 AL SOIL RFD 0 RFD I SF 0 SF I
7440473 CHROMIUM CVI) 1.OE-O1 9.OE-05 1.8E-01 4.01+02 5.01-03 -- -- 4.2E+01
1319773 CRESOLS -. 2.OE+OO 4.01+03 -. -. - - - -
57125 CYANIDES (SOLUBLE SALTS AND COMPLEXES) 2.OE-O1 - - 7.01-01 2.OE+03 2.OE-02 -. - -
72548 DDD -- -- 1.01-04 3.OE+00 -- - - 2.4E-01 --
72559 DDE -- -- 1.OE-04 2.OE+OO -- -- 3.4E-01
50293 DDT -- 1.OE-02 1.OE-04 2.OE+O0 5.OE-04 -- 3.4E-O1 3.4E-01
84742 DIBUTYL PHTHALATE -- -- 4.01+00 8.OE+03 1.01-01 -- -.
60571 D IELDRIN -- 2.01-04 2.OE-06 4.OE-02 5.01-05 -- 1.6E+O1 1.6E+01
122394 DIP 1IENYLAMINE -- - - 9.OE-01 2.01+03 2.5E-02 -- - -
145733 ENDOTHALL 1.OE-01 -- 7.01-01 2.OE+03 2.OE-O2 -- -- - -
72208 ENDRIN 2.OE-03 -- 1.1E-02 2.OE+O1 3.OE-04 -- - . --
100416 ETHYLBENZENE 7.OE-01 1.01+03 4.01+00 8.01+03 1.01-01 2.91-01 -- --
106934 ETHYLENE DIBROMIDE 5.OE-05 5.OE-03 4.OE-07 8.OE-03 -- -- 8.5E+01 7.6E-01
75218 ETHYLENE OXIDE -- 1.OE-O2 1.OE+02 -- -- -- 1.01+00 3.51-01
86737 FLUORENE - - - - 1.41+00 3.21+03 4.OE-02 - . - .
7782414 FLUORINE - - - 2.1E+0O 4.81+03 6.01-02 -- -- --
50000 FORMALDEHYDE - - 8.01-02 7.OE+O0 1.6E+04 2.01-01 -- 4.5E-02
118741 HEXACHLOROBENZENE 1.01-03 2.2E-03 22E-05 4.4E-01 8.OE-04 -- 1.61+00 1.6E+00
302012 HYDRAZINE - . 2.OE-04 1.01-05 2.OE-01 -- - - 3.01+00 1.71+01
74908 HYDROGEN CYANIDE - - - - 7.01-01 2.OE+03 2.01-02 - - - . - -
78831 ISOBUTYL ALCOHOL - - -- 1.OE+01 2.OE+04 3.01-01 -- - - --
78591 ISOPHOROtIE - - - - 9.01-02 2.OE+03 2.OE-O1 -- 9.5E-04 --
7439921 LEAD 1.5E-02 1.SE+00 1.OE+02 5.OE+02 -- - - - - --
58899 LINDANE 2.01-04 -- 2.71-05 5.01-01 3.01-04 -- 1.31+00 --
108316 MALEIC ANHYDRIDE -- - - 4.01+00 8.01+03 1.OE-01 -- -- --
7439976 MERCURY 2.01-03 -- 1.11-02 2.OE+01 3.01-04 -- - - --
16752775 METHOMYL -- - - 9.OE-01 2.OE+03 2.5E-02 -- -. - -
72435 METHOXYCHLOR 4.01-02 -- 1.8E-01 4.OE#02 5.OE-03 -- - - --
74873 METHYL CHLORIDE - - 5.6E+O0 2.71-02 5.4E+02 -- -- 1.3E-02 6.3E-O3
71556 METHYL CHLOROFORM 2.OE-01 1,OE+03 3.OE+0O 7.OE+03 -. 2.91-01 - - --
78933 METHYL ETHYL KETONE -- 3.OE+02 2.01+00 4.01+03 5.01-02 29E-O1 -- - -
75092 METHYLENE CHLORIDE 5.01-03 3.OE-O1 5.OE-03 9.01+01 6.01-02 86E-01 7.5E-03 1.6E-O3
86306 N-NITROSODIPHENYLAJ4INE -- - . 7.OE-03 1.OE+02 -- - - 4.9E-03 --
91203 NAPHTHALENE - - - . L4E-01 3.2E+02 4.01-02 -- - - --
7440020 NICKEL 1.01-01 4.2E-03 7.01-01 20E O3 2.01-02 -- -- 8.4E-O1
98953 NITROBENZENE - - 2.01+00 20E-02 4OE+01 5.OE-04 6.OE-04 -. - -
56382 PARATHION - - - - 20E-O1 5.01+02 6.OE-O3 -- - . - -
82688 PENTACHLORONITROBENZENE (PCNB) - - 1.OE-01 1.01-01 2.OE+02 3.01-03 -- 2.61-01 --
87865 PENTACHLOROPHENOL LOE-O3 -- 1.OE+00 2.01+03 3.01-02 -- 12E-01
108952 PHENOL - - - - 2.OE+01 50E+04 6.OE-O1 - - - - -
85449 PHTHALIC ANHYDRIDE - - - - 7.01+01 2.OE+05 2.OE+OO -- - - - -
1336363 POLYCHLORINATED BIPHENYLS (PCBS) 5.OE-04 -- 4.5E-06 91E-02 -- - - 7.71+00 --
129000 PYRENE -- - - 1.11+00 2.4E+03 30E-02 -- - - --
110861 PYRIDINE - - - - 40E-02 80E+01 1.01-03 -- - -
7782492 SELENIUM 5.01-02 3.5E+0O L1E-Ol 24E+02 5OE-O3 1.OE-03 -- --
7640224 SILVER 50E-02 -- 1.1E-01 2.01+02 5.01-03 -- -- --
93721 SILVEX (2,4 5-TP) 5.01-02 -- 2.81-01 6.41+02 1.01-02 -- - - --
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EXHIBIT E-I:
Chemicals Modeled and Health Criteria Values Used (cont.)
CAS No Cti ica1 NCL AL AIR AL H 2 0 AL SOIL RFD 0 RFD I SF 0 SF I
100425 STYRENE 1.OE-01 1.7E400 ?.OE+0O 2.OE’04 2.OE-01 2.9E-O1 -- --
127184 TETRACHIOROETHYLENE 5.OE-03 1.OE+OO 7.OE-04 1.OE+01 1.OE-02 -• -- --
78002 TETRAETHYL LEAD -- -- 4.OE-06 8.OE-03 1.OE-07 -- -- --
108883 TOLUENE 1.OE+O0 7.OE+03 1.OE+O1 2.OE+04 2.OE-01 1.IE-01 -- --
79016 TRICHIOROETHYLENE 5.OE-03 2.1E-O1 3.2E-03 6.OE+01 -- - -- --
7440622 VANADIUM AND COMPOUNDS - - -. 2.5E-O1 5.6E+02 7.OE-03 -• - -
1314621 VANADIUM PENTOXIDE - - -- 3.OE-O1 7.OE+02 9.OE-03 -• --
108054 VINYL ACETATE -- 2.OE+02 3.5E+01 8.OE+04 1.OE+00 5.7E-02 -- --
15014 VINYL CHLORIDE 2.OE-03 1.2E-02 1.8E-05 3.7E-01 -- -- 1.9E+O0 3.OE-01
1330207 XYLENES (MIXED) 1OE+O1 1.OE+03 7.OE+01 2.OE’05 2.OE400 -- -- --
7440666 ZINC •- -- 7.OE+OO 1.6E+04 3.OE-O1 -- - - --
319846 atpha-BHC -- 5.6E-04 5.6E-06 1.1E-01 -- -- 6.3E+O0 6.3E+O0
319857 beta-BHC 1.9E-02 1.9E-04 3.9E+00 -- -- 1.8E+OO 1.8E+O0
108394 m-CRESOL - - 2.OE+O0 4.OE+03 5.OE-02 - - -- -.
MCL = Maximum Contaminant Level (mg /L)
AL AIR = Action level for air (uglm 3 )
AL H 2 0 = Action level for water (mgfL)
AL SOIL = Action level for soil (mg/kg)
RFD 0 = Oral reference dose (mg/kg-day)
RFD I = Inhalation reference dose (mg/kg-day)
SF 0 = Oral slope factor (mg / kg-day)’
SF I = Inhalation slope factor (mg/kg-day)’
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5
E.1.2 Selecting Health Criteria Values
For risk assessment purposes, individual chemicals are separated into two categories
based on whether they elicit non-carcinogenic or carcinogenic adverse health effects upon
exposure. This classification of chemicals into carcinogens and non-carcinogens (systemic
toxicants) is rooted in the concept of dose threshold. For non-carcinogenic or systemic effects,
protective physiological mechanisms exist that must be overcome before the adverse effect is
manifested. Such a threshold, however, is thought to be absent in the case of carcinogens, i.e.,
any level of exposure, however small, could result in cancer.
Non-carcinogenic Chemicals
To quantitatively assess risk for non-carcinogenic effects, EPA uses a hazard ratio
approach based on the reference dose (RfD). The RID for a particular chemical is defined as an
estimate of the maximum daily exposure level, for humans, that is likely to be without
appreciable risk of adverse health effects during a lifetime. EPA currently develops RfDs for
oral exposure routes, and a closely related parameter, the reference concentration (RfC), for
inhalation exposure effects. For many chemicals, the RID approach has produced useful
quantitative estimates of the toxicity threshold, and thus, has been used as a “benchmark on
which to consider regulatoiy decisions in relation to potential impacts on human health. In
essence, the purpose of the RID is to provide a safe” level to which chemical intakes into the
body (e.g., those projected from human exposure to contaminated environmental media) might
be compared. Intakes that are less than the RID are not likely to be of concern. Intakes that
are greater than the RID indicate an increased probability of adverse effects; however, that
probability is not a certainty. (Note that the ratio of the intake to the RID is flQ.t a measure of
the probability of the adverse effect.)
Carcinogenic Chemicals
Because the hypothetical mechanism for carcinogenesis is referred to as “nonthreshold, TM
and no dose is thought to be risk-free, RIDs cannot be calculated for carcinogens. Instead,
lifetime cancer risks associated with various levels of exposure to carcinogens are estimated by
using a value called a cancer slope factor (SF) expressed in units of mg chemical/kg body
weight/day]-’. The slope factor is a plausible upper-bound estimate of the probability of an
individual developing cancer over a lifetime as a result of exposure to a potential carcinogen.
This probability is referred to as the excess lifetime cancer risk because it is in addition to cancer
risks the individual incurs that are not related to the chemical. The actual risk can vary from a
lower bound of zero to the upper bound represented by the slope factor. Upper bound in this
case denotes that the risk estimate is unlikely to be underestimated although it may very well be
overestimated.
Lead
In current risk assessments, EPA considers lead to be in a unique category of chemical
• toxicity. Although lead is a non-carcinogen, EPA has determined that an RID should not be
developed for lead for two primary reasons. First, an appreciable threshold is absent for many of
SS* Draft -- March 23, 1993 *SS
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6
the noncancer effects of lead. Second, humans are exposed to lead in a variety of multimedia
exposure scenarios.
(a) Absence of an appreciable threshold . Although there is no widely accepted
theoretical basis for the absence of an appreciable threshold for many of the
health effects associated with lead exposure, the data currently available are not
sufficient to adequately define the dose-response relationship for many of the
toxic effects of lead. That is, even though there are considerable human exposure
and health effects data for lead, it is unclear what exposure level is without
appreciable risk of adverse health effects.
(b) Multimedia exposure to lead . Humans are exposed to lead from a variety of
media. The relative contribution of each medium to total lead uptake changes
with age and can vaty greatly in magnitude on a site-specific basis. Because they
are route-specific, RfDs cannot incorporate either site-specific information on
lead exposure sources (e.g., from multiple sources) or age-related data (e.g.,
varying susceptibility with age). For example, an inhalation RfC is an estimate of
the air concentration to which human populations can be exposed for a lifetime,
in the absence of exposure from other sources, without appreciable risks of
adverse health effects. Inhaled lead, however, generally contributes only a
fraction of the total lead intake; most adults are exposed primarily from dietary
sources and through occupational exposures. The RfC method would not be able
to consider these additional intakes.
Although EPA has elected not to calculate an RfD for the noncancer effects of lead
exposure, analyses showing correlations between human blood levels and health effects indicate
that associations may persist through the lowest blood lead levels tested ( 10-15 ug/dL).
Therefore, it is possible that if a threshold for the noncancer effects of lead were to exist, it
would lie within or below the 10-15 ugfdL range. Note, however, that the range 10-15 ug/dL is
regarded as a “level of concern,” warranting attention from a medical viewpoint, and not a dose
level or threshold below which no adverse health effects would be expected to occur (i.e., it is
not strictly parallel to the definition of an RiD). For this RIA, the Agency used 10 ugldL as the
effects threshold for lead.
E.2 Exposure Analysis
In the context of estimating risk at the corrective action facilities, the key steps for
exposure assessment include:
• identifying principal exposure pathways and routes;
• characterizing exposed human populations;
• selecting appropriate exposure assumptions and contact rates; and
• calculating intakes at exposure points.
Draft -. March 23, 1993
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7
Discussed below are methods and sources that the Agency relied on to complete each of the
above steps.
E.2.1 Identilying Exposure Pathways, Exposure Routes, and Exposure Points
At the offset of this RIA, EPA identified a variety of human exposure pathways and
exposure routes that could possibly contribute to risk at the corrective action facilities. The
Agency selected five pathways for analysis: ground water, surface water, air, soil, and foodchain.
Within each of these pathways, EPA evaluated multiple exposure subpathways (e.g., beef
ingestion within the foodchain pathway) and exposure routes (e.g., ingestion, inhalation, and
dermal contact in the ground water pathway) as relevant. Note that not all pathways are
applicable at all facilities. Exposure pathways, exposure routes, and exposure points are shown in
Exhibit E-2.
Once the exposure pathways and exposure routes were identified, EPA determined the
exposure point locations at each facility using best available facility-specific data. Exposure
points occur wherever humans ingest, inhale, or make dermal contact with contaminated media.
The exposure routes and exposure point locations for each pathway are discussed below.
Ground Water
EPA estimated risks from ground water as the sum of risks from three exposure routes:
• ingestion of contaminated drinking water,
• inhalation of volatile compounds during household use of ground water, and
• dermal exposure due to direct contact while showering.
Ground water may also contribute to contamination of surface water (via ground water discharge
to surface water bodies), and foodchain contamination (via use of contaminated ground water for
irrigating crops and/or watering cattle). EPA estimated these risks under the secondary
pathways.
Exposure points for this pathway were confined to a 900 sector for up to 2 miles in the
downgradient direction from the facility. (Plumes were assumed to attenuate and dilute beyond
2 miles, except at a few facilities where information to the contrary was available 2 .) Essentially,
exposures occurred wherever there was a ground-water well in this sector. EPA determined the
number and general location of private wells within 2 miles downgradient of the facility and the
number of residents that they serve, as well as the general location of’ and population served by
public water supply wells. EPA assumed that any wells outside of this 90° sector would not be
affected by contaminated ground water.
At facilities where the plume concentrations exceeded action levels at the two-mile boundary, EPA
determined the number and location of public and private wells up to five miles downgradient of the
facility. The Agency then considered these as additional exposure point locations, and estimated
concentrations of contaminants at these points as well.
Draft -- March 23, 1993
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EXHiBIT E-2
EXPOSURE MEDIA, PATHWAYS, AND ROUTES
Exposure
Route
At Point of
- Exposure
Releases
on-Site or
Adjacent
F2OO2I 3
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9
EPA defined the exposure point for calculating individual risk as the well (private or
public) located closest to the facility within the 900 sector. Exposure concentrations at all wells
within any distance range were estimated as being equal to the average concentration over that
distance range. 3 Exposure point locations for calculating population risk were defined as the
center points of the six distance ranges, and, where applicable, as the exact locations of public
wells. For public wells, all persons served were assumed to be exposed at the well location.
EPA estimated individual and population risks only where facility-specific data indicated
that wells are actually present and the ground water is being used.
Air
The only exposure route EPA considered for the air pathway was inhalation of airborne
contaminants. To evaluate individual and population risk via air exposures, EPA identified
several exposure point locations for this pathway.
First, EPA defined the exposure point for individual risk as the location of the residence
determined to be closest to the facility. Next, data were collected for populations within a 10
Icilometer radius of each facility, sectored into 8 directions and 5 distance ranges. EPA assumed
every person within each range to be exposed to the concentration at the center point of that
range. (Note that MMSOILS predicts an average air concentration at a specific distance, and is
not direction-specific, i.e., the same concentration would be predicted at any point on the circle
defined by that distance.) Distances from sources (i.e., SWMUS with releases to air) to the
center points of distance ranges were calculated by adding the minimum distance between the
SWMU and facility boundary to the distance from the facility boundary to the center point of
each range.
Surface Water
EPA evaluated surface water risks for two different exposure scenarios: (I) recreational
water use, and, where applicable, (2) domestic water use. Surface water is also an intermediate
transport route for contaminants in several other exposure pathways, such as ingestion of
contaminated fish and ingestion of agricultural products that have been irrigated with
contaminated surface water. These risks are accounted for in the foodchain pathway.
Exposure locations for the surface water pathway (recreational use) were detennined
based on known uses (swimming and/or fishing) of surface waters downstream of a facility. In
most cases, EPA defined the closer use point as the exposure location for both swimming and
fishing. In cases where facility-specific information on the distance to these use points was not
‘Note that the highest individual ground water risk could occur at residences other than the one
nearest the facility boundary. For example, plumes resulting from past releases could have moved
downgradient beyond the nearest residence. Alternatively, households more distant than the nearest
household could be located closer to the downgradient direction of the contaminant plume.
**S Draft -. March 23, 1993
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10
available, EPA assumed swimming and fishing to occur at a default distance of 10 meters
downstream from the point where the surface water received contamination from the facility.
For recreational use, the Agency estimated individual risk from surface water exposure
caused by swimming only. This risk was calculated as the sum of two exposure routes:
• dermal absorption via direct contact with surface water, and
• incidental ingestion of surface water while swimming.
As mentioned above, individual risk due to ingestion of fish from the contaminated surface water
is calculated under the foodchain pathway. Population risks for recreational uses (swimming and
fishing) were not calculated because the populations exposed through those pathways are usually
small, and it is difficult to devise reasonable assumptions for the number of people that utilize
surface water bodies for recreation.
For domestic water use, the exposure routes are the same as those considered for ground
water. In this case, the exposure point for both individual and population risk (for all affected
populations) is defined as the point of intake for a municipal water supply identified on the
surface water (i.e., exposure concentrations are estimated at the intake point).
Soil
EPA calculated risks through the soil pathway due to both incidental ingestion and direct
contact (dermal absorption). The contaminated soil transport .páthway also played an
intermediate role in foodchain, surface water, and air pathways. These secondary exposures were
accounted for in the respective pathways.
EPA defined two separate exposure point locations for calculating individual risk via the
soil pathway. The nearest off-site field is identified as the exposure point located at the distance
from the facility to the nearest field likely for human exposure to soil (e.g., parks, playgrounds,
and residential yards). The second exposure point, nearest off-site agricultural field, is the
nearest field located at the distance from the facility to the nearest field where human exposure
to soil through agricultural or gardening activities could take place. This field could be actual
agricultural land or a vegetable garden at the closest residence.
EPA did not calculate population risk from exposure to contaminated soil due to the
complexity of estimating spatial distribution of populations around these fields and likelihood of
population members using these fields for various activities.
Foodchain
The foodchain pathway accounts for exposure due to ingestion of contaminated
vegetables, milk, beef, and fish.
Exposure concentrations in contaminated vegetables, milk, and beef are estimated at the
agricultural field identified as being closest to the facility for the soil pathway. Individual risk is
•*S Draft -. March 23, 1993 ‘
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11
calculated for a person ingesting the contaminated vegetables, beef, and milk originating” from
this field. EPA estimated individual risk for a person ingesting contaminated fish caught from
surface water contaminated by releases from a facility where recreational water use is identified.
The Agency did not calculate population risks for the foodchain pathway because of the
difficulties in estimating the number of persons potentially exposed.
E.2.2 Characterizing Exposed Populations
There are two categories of exposures that EPA characterized for risk assessment in this
RIA: exposures to individuals and exposures to populations. As noted in Section E.2.1 above,
population exposures are evaluated only for the ground-water, air, and surface water pathways.
For this analysis, EPA assumed that both current and future populations would be potentially
exposed to contamination from the facilities.
Current Populations
Based on topographic maps and interviews with municipal officials, EPA determined the
number people served by public and private wells in 1992 near the corrective action facilities.
EPA used these sources to identify numbers of public and private wells and the number of
people served by public wells. To estimate numbers of people served by private wells, the
Agency multiplied the number of private wells by an assumed average of 2.6 persons per well
that is based on the 1990 Census (2.6 persons per residence). The total population estimated for
public and private wells was assumed to be the current population potentially exposed via ground
water.
Likewise, for the air pathway, the population residing within a 10-kilometer radius of
each facility was considered to be potentially exposed via air pollution. This population was
identified using the Graphical Exposure Modeling System (GEMS) to count all individuals
residing within 10 kilometers of the facility boundaries (based on 1980 U.S. census; counts were
updated to 1992 numbers — see next section) and using topographic maps for distances closer to
the facility (i.e., individual residences were identified within a two-mile boundary of the facility).
Finally, EPA determined the population served by municipal water supplies with intakes on
contaminated surface waters using Federal Reporting Data System (FRDS). These populations
were then assumed to be potentially exposed via household use of surface water.
Future Populations
EPA predicted the size of future populations that would potentially be exposed via the
ground water, air, and surface water pathways based on the size of current exposed populations.
This was accomplished by multiplying the current population counts in local areas surrounding
facilities (population within 10 kilometers of the facility or population served by private and/or
public wells, henceforth referred to as local populations”) by population growth rates.
The Agency used commercially available population forecasts to calculate growth rates
for local populations. These forecasts contained county-level population estimates for the years
‘ Draft — March 23, 1993 ‘
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12
1970 through 2015. The county-level population estimates were used to calculate population
growth rates between years 1980 and 2120. Because the population forecasts available extend
only to 2015, the Agency calculated population growth rates for 1980 through 2015 differently
from the growth rates for years beyond 2015 (i.e., 2016 through 2120). For the years prior to
and including 2015, the growth rate for any year i was calculated as the ratio of population count
in year I to the population count in year i-i. For example, the growth rate for the year 2001
equaled:
Population count,, 1 + Population count
EPA estimated the annual growth rate for years 2016 through 2120, using the average of all
annual growth rates between 1970 and 2015.
This method assumes that local populations grow at the same rates as the entire
populations of the counties in which the facilities are located. Many of the facilities are located
near (i.e., within 5 miles ot) county boundaries. EPA purchased county-level population data for
all counties within 5 miles of any facility, as determined using U.S.G.S quadrangle maps and the
Rand McNally 1990 Road Atlas of the United States, Canada, and Mexico (66th edition). For
facilities with multiple counties, EPA first calculated growth rates for each county using the
method described above. The growth rates were then averaged using total county populations as
weighting factors, and this average was considered the growth rate for the facility.
County Population Projection Method
This section discusses the underlying data that EPA used for calculating growth rates.
The discussion includes models and assumption employed in the original forecasting.
Population growth rates used in the human health risk assessment are based on
demographic data that the Agency purchased from Woods & Poole Economics, Inc. These data
contain population estimates by county for each year from 1969 to 2015. The basis of annual
population counts varies as follows:
• 1969 to 1980: Population estimates from the U.S. Census Bureau;
• 1981 to 1990: U.S. Census data adjusted by Woods & Poole
Economics, Inc. to reflect 1990 Census counts; and
• 1991 to 2015: Forecasts by Woods & Poole Economics, Inc.
Population estimates for the years 1981 to 1990 are adjusted estimates based on 1980 and
1990 census counts. The Census Bureau used 1980 census results to estimate populations for the
years 1981 to 1989. Woods & Poole Economics, Inc. adjusted the Census Bureau estimates
using 1990 census results. The purpose of this adjustment was to provide a smooth trend
between 1980 and 1990 and to increase the accuracy of population estimates for these years.
Draft -- March 23, 1993 °
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13
The Census Bureau population statistics, on which all population estimates are based, are
residential populations (i.e., counts of persons residing in census areas). The census bureau
definition of residential population includes civilians, militaiy personnel, college residents, institu-
tional residents (i.e., prison inmates, mental institution residents, hospital patients, and nursing
home residents). These counts include estimated numbers of undocumented aliens and exclude
U.S. citizens living in foreign countries.
The county-level population data purchased included projections for the years 1991 to
2015. The Woods & Poole projections integrate historical data, macroeconomic modeling, and
demographic modeling to simulate population and economic trends for the U.S., economic
regions, and counties. Three sets of models are used for these estimates. County models
estimate population based on county-level demographic and employment statistics. Regional
models estimate populations using county-level demographic and employment statistics. A
national macroeconomic model produces national totals for demographic and economic statistics
(e.g., population, GNP, inflation, employment). National totals are used as upper bounds for
aggregated regional model results, and regional estimates are used as upper bounds for
aggregated county results. All counties are modeled simultaneously to allow trends in one
county to influence trends in other counties. Comparison of forecast results to historical data
show that national and regional statistics produced by Woods & Poole have an average root-
mean-squared error under 3%.
The county level demographic model forecasts population trends on the basis of rates of
birth, death, and migration. Migration patterns are related to projected employment and
earnings. The Woods & Poole demographic model is a traditional cohort-component model. In
such models, the population at a given time is disaggregated by demographic sub-groups (e.g.
age, sex, and race) into a matrix of population counts. The population in the next time step is
calculated by multiplying each sub-population by natality, fertility, and migration rates specific to
that sub-population. Differential rates for sub-groups result in population trends over a number
of subsequent time steps. Woods & Poole disaggregates population counts by five-year age
groups, sex, and race (black, white, and other). Population changes are modeled with a one-year
time step.
Highly-Exposed Subpopulations
When applicable, EPA considered three special cases for individual risk: a subsistence
farmer, a subsistence fisherman, and a pica child. These individuals were assumed to represent
subpopulations whose exposure was expected to be significantly different from the larger
population due mainly to lifestyle and economic factors. EPA defined subsistence farmers as a
subpopulation having higher intake of contaminated vegetables, beef, and milk (i.e, a higher
percentage of their daily consumption of these foods is from the contaminated source). Subsis-
tence fishermen were defined as a subpopulation having a higher consumption of fish from the
contaminated source. The pica child represents a subpopulation of children having an
abnormally high soil ingestion rate.
Individual risks to both subsistence lifestyles were calculated using the same exposure
concentrations in food as for the larger population, but intake assumptions such as exposure
Draft -- March 23, 1993
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14
frequency and amount ingested were increased to account for higher exposures. Similarly, pica
children were assumed to be exposed to the same soil concentrations as other children, but their
ingestion rates were higher.
E.2.3 Selecting Exposure, Uptake, and Transfer Factors
In order to quantil r human exposure to chemicals from the corrective action facilities,
EPA selected and used (1) rates at which the exposed populations were expected to be in contact
with each contaminated medium, (2) human uptake factors (e.g., soil-to-skin absorption rate),
and (3) food transfer factors that would determine the amount of a chemical that is transferred
from an environmental media to a vehicle of exposure (e.g., from surface water to fish). These
rates and factors were used either in risk equations to calculate intake at media exposure points
or in the MMSOILS model to predict concentrations in the foodchain pathway.
Exposure Factors
The Agency identified exposure rates (i.e., data describing the extent, frequency, and
duration of exposure) for the ground water, surface water, air, soil, and foodchain pathways.
These exposure rates and other exposure assumptions such as the body weight and uncovered
skin area of the exposed individual, are referred to in this document as exposure factors. Values
that EPA used for the standard exposure factors (such as exposure frequency, average body
weight, and exposure duration), and their sources are listed by pathway in Exhibit E-3. Values
and sources for less standard factors (e.g., skin surface area available for exposure and soil-to-
skin adherence factor) are stated in the notes to Exhibit E-3. For the central tendency risk
analysis, exposure factor values used were either the average or the 50th percentile, depending
on availability. Exposure factor values used in computing risks to highly-exposed subpopulations
were the 90th or 95th percentile estimates.
•$S Draft — March 23, 1993
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T E-3
EXPOSURE PARAMETER VALUES USED FOb .RECTIVE ACTION RIA RISK ASSESSMENT (cont.)
Exposure Averaging
Exposure Duration: Averaging Time:
Frequency of Duration: Non Body Time: Non-
Exposure Pathway’ Exposure Rate Exposure Time 2 Exposure Carcinogen carcinogen Weight Carcinogen carcinogen
Ground Water
Ingestion of Contaminated 1 4 liters/day - 350 days ,carh 9 years 9 years’ 70 kg’ 25,560 days 3,285
Drinking Water (avg ) C (501h%) ’ (avg.) (70 years) days 3
(9 years)
Inhalation of Volatile 063 ni 3 /hour - 3 0 days/yearh 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
Contaminants from Indoor Air (15 m’/day) ( S Oth%) ’ (avg.)
(avg ) C
Dermal Absorption of - 0 116 hours/day 350 days/year” 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
Contaminants from Water while (7 minutes) (S Oth%)a (avg.)
Showering 4 (50%) ’
Air
Inhalation of Contaminants 0.83 m 3 /hour 24 hours/day 350 days/year” 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
from Air (20 m 3 /day) ( S Oth%) ’ (avg.)
(avg.)’
Surface WaterS
Dermal Absorption of 2.6 hours/day 26 days/year 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
Contaminated Surface Water (hours/event) (avg.)’ 6 ( S Oth%) ’ (avg.)
while Swimming 4 (avg)’ ‘‘S
Ingestion of Contaminated 0.05 2.6 hours/day 26 days/year 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
Surface Water while Swimming liters/hour’ (hours/event) (avg.)’ 6 ( S Oth%)a (avg.)
(avg.)’
Soil
Ingestion of Contaminated Soil 100 mg/day’ - 350 days/year 1 ’ 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
(Adult) (S Oth%) ’ (avg)
Draft — March 22, 1993
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EXHIBITE-3
EXPOSURE PARAMETER VALUES USED FOR CORRECTIVE ACTION RIA RISK ASSESSMENT (cont.)
Exposure Averaging
Exposure Duration: Averaging Time:
Frequency of Duration: Non- Body Time: Non-
Exposure Pathway’ Exposure Rate Exposure Time 2 Exposure Carcinogen carcinogen Weight Carcinogen carcinogen
Ingestion of Contaminated 200 mg/day 350 days/year” S ycarsc 5 ycarsc 16 kg’ 25,560 days 1,825 days
Soil (Child) (avg.)ac (SOth%) (5 years)
Ingestion of Contaminated 800 mg/day 365 days / yearb S yearsc S yearsc 16 kg’ 25,560 days 1,825 days
Soil (Pica Child) (high end)c (501h%)
Dermal Absorption from Soil - - 350 days/year” 9 years 9 years 1’ 70 kg 1 ’ 25,560 days 3,285 days
(Adult) 7 ( soth%r (avg.)
Dermal Absorption from Soil - - 350 days/year” 5 yearsc 5 16 kg 25,560 days 1,825 days
(Child) 7 (S Oth%)
Foodcha ln
ingestion of Contaminated 46 glday 350 days/year” 9 years 9 years 1’ 70 kg’ 25,560 days 3,285 days
Homegrown Root Vegetables (avg.) 8 (S Oth%) ’ (avg.)
ingestion of Contaminated 65 glday - 350 days/year” 9 years 9 years 1’ 70 kg’ 25,S60 days 3,285 days
Homegrown Leaf Vegetables (avg.) (SOth%) ’ (avg.)
ingestion of Contaminated 44 g/day 350 days/year” 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
Homegrown Beef (avg.) (S Oth%) ’ (avg)
Ingestion of Contaminated Dairy 160 glday - 350 days/year” 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
Products (avg.) ° (SOth%) ’ (avg.)
Ingestion of Contaminated 7.6 glday 350 days/year” 9 years 9 years’ 70 kg’ 25,560 days 3,285 days
Recreationally Caught Fish ( S Oth%) 1 ’ei (S Oth%) ’ (avg.)
Subsistence Farmer
Ingestion of Contaminated 74 gldayd - 365 days/year’ 40 yearsr 40 yearsr 70 kg’ 25,560 days 14,600
Homegrown Root Vegetables: (avg.) days
Sub ustence Farmer
Ingestion of Contaminated Home- 103 g/day 165 days/year’ 40 ycarsr 40 yearsr 70 kg’ 25,560 days 14,600
grown Leaf Vegetables (avg.)c (avg.) days
Subsistence Farmer
Draft — March 22,
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lIT F.-3
EXPOSURE PARAMETER VALUES USED I SRRECTIVE ACTION RIA RISK ASSESSMENT (cant.)
Exposure Averaging
Fxposure Duration: Averaging Time:
Frequency ol t)tiration: Non. Body Time: Non-
Exposure Pathway t Exposure Rate Exposure Time 2 Exposure Carcinogen carcinogen Weight Carcinogen carcinogen
Ingestion of Contaminated 75 g/dayc g - 365 daysi ’car 40 years 1 40 years 1 70 kga 25,560 days 14,600
Homegrown Beef: (avg.) days
Subsistence Farmer
Ingestion of Contaminated Dairy 300 glday . 365 days ,cara 40 years 1 40 years 1 70 kg 25,560 days 14,600
Products: (95 1h%r’° (avg.) days
Subsistence Farmer
Subsistence Fisherman
Ingestion of Contaminated 99 g/day - 365 days’ ,eara 30 years 30 yearsa 70 kg 25,560 days 10,950
Recreationally Caught Fish: (951h%)a (9 Oth°%r (avg.) days
Subsistence Fisherman
SOURCES:
(Note that the sources listed below may in turn refer to secondary documents as the original source for some of the parameter values.)
(a) USEPA 1989. Risk Assessment Guidance for Superfund Volume I Human Health Evaluation Manual (Part A . Office of Emergency and Remedial
Response. EPA/540/l -89/002.
(b) USEPA 1991. Human Health Evaluation Manual, Supplemental Guidance: Standard Default Exposure Factors . Office of Emergency and Remedial
Response. OSWER Directive: 9285.6-03.
(C) USEPA 1989. Exposure Factors Handbook . 0( 11cc of Health and Environmental Assessment. EPA/600/8-89fl)43.
(d) USEPA 1991. Interim Guidance for Dermal Exposure Assessment . 0 1 11cc of Research and Development EPN600IS-911011A.
(g) LJSEPA 1992. Dermal Exposure Assessment: Principles and Applicalions Office of Ilcalih and Fnvironmcntal Assessment EPA-600/8-91/Ollll
Draft — March 22, 1993
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EXJIIBrr E-3
EXPOSURE PARAMETER VALUES USED FOR CORRECTIVE ACTION RIA RISK ASSESSMENT (cont.)
NOTES:
1. Intake via each exposure pathway is calculated using the general equation described in Section E.2.4.
2. The general equation is modified for the inhalation and dermal exposure pathways to include the “Exposure Time.” This parameter (in hours/day) is included as
a multiplicand in the numerator of the equation.
3. The averaging time to calculate noncancer hazard is equal to the exposure duration expressed in days.
4. The general equation is modified to calculate intake via dermal absorption in the water pathway:
Intake = CWxCFxSA xPCxETxEFxED
SW x AT
where factors unique to this pathway indude:
CF = Conversion factor (1 liter! 1000cm’)
SA = Skin surface area available for contact (cm 2 /day)
(19,400 cm 2 for adults (central tendency, source (g)))
PC = Chemical-specific dermal permeability constant
ET = Exposure time (hours/day)
5. Where applicable, exposure due to use of surface water as municipal water supply is evaluated using the same exposure pathways, mutes, and parameter values
as for ground water.
6. The exposure frequency value for dermal absorption and ingestion of contaminated surface water while swimming was provided by EPA’S Office of Research
and Development. This assumes that an individual swims 2 times a week during the summer (3 months) only.
7. The general equation is modified to calculate intake via dermal absorption in the soil pathway:
Intake = CS x CF x SA x AF x ABS x EF x ED
BW x AT
where factors unique to this pathway include:
CF = Conversion factor (10 mgAcg)
SA = Skin surface area available for contact (cm 2 /day)
(5,000 cm 2 for adults (central tendency, source (g)), and 2,500 cm 2 for children (default value, source (d)))
AF = Soil-to-skin adherence factor (mg/cm 2 )
(0.2 mg/cm’ (central tendency, source (g)))
ABS = Absorption factor (chemical specific constant)
Draft - March 24,
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EXPOSURE PARAMETER VALUES USED FOR ( .CTIVE ACTION RIA RISK ASSESSMENT (cont.)
8. The general equation is modified for the foodchain pathways to include “fraction from contaminated source.” This parameter is included as a multiplicand in
the numerator of the equation, and modifies the exposure rate. Thus, the exposure rate accounts for the fraction of total vegetables consumed that conies
from the contaminated source. The proportion of contaminated vegetables is assumed to be 25 percent for the general population (average, source (C)) and
40 percent for subsistence farmers (reasonable worst case, source (C)). For example, the 65 g/day exposure rate for contaminated leafy vegetables represents
25 percent of the total daily consumption of leafy vegetables of 260 g.
9. Exposure rate accounts for the fraction of total beef consumed that comes from the contaminated source. The proportion of contaminated beef is assumed
to be 44 percent for the general population (average, source (C)) and 75 percent for subsistence farmers (reasonable worst case, source (C)).
10. Exposure rate accounts for the fraction of total dairy products consumed that comes from the contaminated source. The proportion of contaminated dairy
products is assumed to be 40 percent for the general population (average, source (C)) and 75 percent for subsistence farmers (reasonable worst case, source
(C)).
11. Exposure rate accounts for the fraction of total fish consumed that comes from the contaminated source. The proportion of contaminated fish is assumed to
be 20 percent for the general population (average, source (c)) and 75 percent for subsistence fishermen (reasonable worst case, source (c)).
Draft — March 24, 1993
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20
Dermal Uptake Factors
Availability of a chemical to cause harm to humans after exposure or contact is
dependent upon uptake into the body. To further quantify human exposure through dermal
contact, the Agency calculated the portion of each chemical in the environment that would be
absorbed through a person’s skin. To do this EPA considered two factors: the skin permeability
coefficient (SPC) for uptake from contact with water and the dermal absorption factor (DAF)
for uptake from soil. Recent EPA guidance 4 was used in developing chemical-specific values for
these factors for use in this RIA.
EPA guidance documents provide specific SPCs for several inorganics and many organics.
In cases where an inorganic chemical is not specifically listed, EPA used a default value of 0.001
cm/hr. For non-specified organics, the Agency calculated SPCs using the following equation
Log SPC = - 2.72 + 0.71 log K. , - 0.0061 x (molecular weight)
where K is a unitless partition coefficient that represents the ratio of the concentration of the
penetrant in octanol to the concentration of the penetrant in water under equilibrium conditions.
A DAF is a fraction expressing how much of a chemical will be absorbed when applied to
the skin in a soil matrix. The main factors controlling the DAF are (1) the chemical’s physical
properties; (2) soil loading or the soil adherence factor; (3) concentration of the chemical in the
soil; (4) exposure duration; and (5) organic content of soil.
There are only three chemicals for which EPA has made recommendations for the range
of DAFs to be used in risk assessments. The upper end of the ranges for each of these three
chemicals have been used for analysis in this RIA:
Chemical DAF
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) 0.03
3,3’,4,4’-Tetrachlorobiphenyl (TCB) 0.06
Cadmium 0.01
Most chemicals are not specifically identified in the EPA guidance document, although
there are two chemicals for which experimentation supports a veiy wide range of DAF values.
DAFs range up to 1.0 for benzo [ ajpyrene and up to approximately 0.3 for DDT, among the
chemicals evaluated in the EPA guidance. In both cases upper end values were utilized in this
R1A. In most cases even ranges are not available and DAF values are inferred from data
collected for analogue chemicals. EPA used TCDD data for all chlorinated dioxins and furans,
TCB data for all PCBs, and Benzo [ a]pyrene data for all PAHs. EPA used the McKone model
U. S. EPA (1992) Dermal &posureAssescment: Pruzcjples and Applicalion& Exposure
Assessment Group, Office of Health and Environmental Assessment, Washington, D.C.
EPA/600/8-91/01 lB.
‘ Draft -. March 23, 1993
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21
for all organics which do not come under the prior categories. 5 The model uses the K and the
dimensionless form of the Henry’s Law Constant (Ks) to determine which DAF is most
appropriate for each chemical category. Using this model for chemicals with Kb between 0.01
and 0.1, and K , greater than 10 the DAF was estimated at 0.2. For those organics not covered
by the model (i.e., those organics with K 1 , between 0.001 and 0.01, or with both K 1, less than 0.001
and K.. 1 , greater than 106), and for inorganic chemicals, EPA used a DAF default value of 1.0.
Foodchaiu Transfer Factors
Transfer factors describe, in mathematical terms, the availability of chemicals for uptake
by way of the foodchain. For this B.IA, transfer factors collectively refer to bioaccumulation
factors, partition coefficients, and food transfer factors. For environment-to-aquatic biota
transfers, bioaccumulation factors are used to estimate the rate at which contaminants will collect
in the lipids of animals (in this RIA, finfish were used to represent all aquatic fauna). Chemical
transfers from soil-to-plant can be estimated using separate partition coefficients for leaf
vegetables and root vegetables. Further up the foodchain, food transfer factors are used to
estimate the rates at which chemicals are passed on through consumption. For this RIA, this is
limited to the assessment of chemical transfers to beef and milk.
Transfer rates vary by chemical and according to placement along the foodchain.
Detailed below are the methods and sources that the Agency used to determine chemical-specific
transfer factors.
For inorganic chemicals in fin fish, the bioaccumulation factors used in this analysis were
those listed in the Multi-media Environmental Pollutant Assessment System (MEPAS) data base.
(MEPAS in turn references original literature for these values, e.g., Napier, 1980, Strenge, 1986,
or Spehar, 1980.) EPA calculated bioaccumulation factors for organic chemicals in tin fish based
on Veith’s (1980) formula presented in MEPAS:
BAF = 10(076 .0.23)
where iç , is the octanol-water partition coefficient
Chemicals taken up by crops accumulate at different rates within the anatomy of the
plant; therefore, the Agency calculated separately partition coefficients for vegetative (vine and
leaf) and root parts of the plant. For organic chemicals, partition coefficients specific to
vegetative plants were calculated as the arithmetic average of the values generated by the Topp
(1988), and Travis and Arms (1988) equations:
Topp (1988): PC 1 (8.77 x 10 ) x MW (2 )
where MW = molecular weight
Note that the guidance document states that EPA is in the process of evaluating the
reasonableness of the generalizations made in the Mckone model.
Draft -. March 23, 1993 S**
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22
Travis & Arms (1988): 10 (1 0.578 I
The final partition coefficient (P ) was calculated as (PC 3 + PC 2 )12. EPA derived partition
coefficients for organics specific to root parts using Briggs’s (1982) equation, modified to be
applicable to concentrations of the chemical in soil rather than in soil solution:
PC, = 10.03 x (K..)°”J + 0.82
K x fe ,,,
where K , = organic carbon partition coefficient
and f .. = fraction organic carbon in soil
The Agency employed the constituent-specific partition coefficients for uptake of inorganic
chemicals compiled by Baes (1984), for both vegetative and root parts of plants.
The Agency calculated food transfer factors between feed and beef using the Travis and
Arms (1988) equation for organic chemicals, and directly used the Baes (1984) values for most
inorganics. The Travis and Arms equation calculates transfer factors to beef for organics based
on the chemical-specific octonol-water partition coefficient K .. . , such that:
TRF,, ,f = 10 (7.6 + IoiK )
Mercury is the only inorganic chemical for which the Baes value was not incorporated; its food
transfer factor was based on Ng et. al., (1982).
Similarly, feed-to rnilk transfer factors for inorganic chemicals were taken from Baes
(1984), while values for organics were calculated according to the Travis and Arms (1988)
equation:
TRF j = 10b0
Note that the transfer factors discussed above were inputs required for MMSOILS, and were not
used external to the model.
E.2.4 Calculating Intakes at Exposure Points
Once the exposure points were selected and exposure assumptions defined, the Agency
proceeded to calculate chemical-specific human intakes at the exposure points. An intake, often
referred to as a chronic daily intake (CDI), is the measure of exposure expressed as the mass of
a chemical in contact with the exchange boundary per unit body weight per unit time (mg of
chemical/kg of body weight-day). The generic equation for calculating chemical intakes is as
follows:
Intake = C x CR x EFD x OF
(mg/kg-day) BW x AT
Draft -. March 23, 1993
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23
Where:
C = chemical concentration contacted over the exposure period (e.g.,
mg/liter water)
CR = contact rate; the amount of contaminated media contacted
per unit time or event (e.g., liters/day)
EFD exposure frequency and exposure duration; describe how long and
how often exposure occurs (calculated as EF x ED, dayst rear and
years)
OF = other pathway-specific exposure factors; adjust the intake as neces-
sary, e.g., skin surface area available for dermal contact via soil or
surface water pathways; fraction ingested from contaminated
source for foodchain pathway
BW = body weight; the body weight of the individual exposed over the
exposure duration (kg)
AT = averaging time; period over which the exposure is averaged (days)
Essentially, the intake equation transforms the concentration of the chemical in an exposure
medium to human daily intake. Note that the intake was calculated from an average concentra-
tion over the duration specific to that intake. For example, the exposure duration used to
evaluate risk for this RIA is equal to 9 years, which is the national median time of residence at
one particular location based on Census Bureau data. An average concentration applicable to all
9 years was used in calculating the intake for this exposure duration.
For this analysis, EPA evaluated exposures from 1992 through 2120. The Agency used
MMSOILS to predict concentrations in the exposure media and pathways throughout this time
period. Concentrations of chemicals are predicted as steady-state in the soil and foodchain
pathways; therefore, the exposure concentration is constant over the entire exposure duration.
Concentrations in ground water, air, and surface waters, however, were time-varying. For these
three pathways, the Agency used a methodolo to calculate 9-year moving averages from the
concentration profile from 1992 through 2120. An example of this is shown in Exhibit E-4. Such
a 9-year average concentration was then used to calculate the intake for that particular year.
E.23 Assessing Lead Exposure
To assess lead exposure, EPA used a mode! known as the Uptake/Biokinetic (UBK)
model developed by the Office of Air Quality Planning and Standards. The LEAD (version 5)
program, a PC software application of the UBK model, was used to estimate blood lead levels in
Draft .. March 23, 1993 S**
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EXHIBIT E-4
CALCULATION OF 9YEAR AVERAGE CONCENTRATION
0 Actual concentration for any gIven year
(e.g., • for 2018)
Con apondlng 9-year average concentration for
that year (e.g., A for 2018)
9-year period over which concentration for that
year Is averaged (e.g., — — — for 2018)
I — — — — — — — — — — — — — — —
I=H=l=I=I=1 I=1
I I I I I I I I I 3 3
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
lime
F2002e-2
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25
the target population (included in the model as children of ages 0-6) based on the concentration
of lead in each exposure media at the facilities. This modeling approach provides estimates of
blood levels of lead based on facility-specific lead concentrations via a variety of pathway
categories (e.g., air, diet, water, indoor dust, soil, paint, and mother’s blood lead concentration).
With this information, the model can be used to estimate blood lead levels based upon
total lead uptake via the various pathways. For this analysis, EPA first used the default values
built in to the program to estimate a baseline concentration of lead in the blood. Then the
Agency added facility- and media-specific lead concentrations to each default, and ran the model
again. The Agency kept the default values in as baselines, because background lead concentra-
lions (due to both natural and anthropogenic sources) were not available for the facilities. If the
Agency had used only the lead concentrations directly attributable to the facility, the estimated
blood concentrations would likely have been understated.
The Agency estimated concentrations of lead in air, groundwater, soil, homegrown
vegetables, home-raised beef, and locally caught fish using MMSOILS, based on central tendency
assumptions of waste release to and fate and transport in environmental media. Values
calculated by MMSOILS were added to the UBK model defaults for the air, soil, and drinking
water exposure pathways. At facilities where ground water was not used as a drinking water
source, only the program default was used. At most facilities, the MMSOILS-predicted values
for air and drinking water concentrations of lead were too low to raise the program defaults, i.e.,
no additional risk would result due to facility-related lead exposure via these pathways. For the
diet section, the Agency directly input the concentrations of lead predicted by MMSOILS for
specific food types, i.e., homegrown vegetables, home-raised beef, and locally-caught fish. In the
program, the default concentration and percent diet for these individual food types are both
zero, and, therefore, the Agency did not need to add to any default lead intake via these
pathways.
In addition to the above lead concentrations input into the program, the model accepts
user input of variables pertaining to facility-specific exposure to lead through paint and through
placental transfer to the infant (maternal route). The Agency did not change the default values
for these parameters. The default uptake from paint was 0.0 ug/day, and the maternal blood
concentration, which determines the mother’s contribution to the child’s lead levels at birth, was
7.50 ugfdL.
E.3 Risk Charactenzauon
The human health benefits analysis for this RIA includes a quantitative characterization
of carcinogenic and non-carcinogenic human health risks for closest exposed individuals and for
populations living near corrective action facilities. Individual and population risks are estimated
for four media pathways, the foodchain pathway, and three highly-exposed subpopulations. For
each pathway, the Agency aggregated risks resulting from each chemical to which the individual
was exposed and aggregated risks from each exposure route within the exposure pathway.
However, the Agency did not aggregate risks across exposure pathways.
Draft -. March 23, 1993 SS*
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26
EPA based its individual and population risk characterizations on exposures modeled at
the facility level. In particular, the Agency estimated baseline risks for facilities among a
stratified random sample of all RCRA facilities in the U.S. The Agency then extrapolated risk
characterizations for sample facilities to determine the aggregate national human health risks in
the absence of corrective action. This process was then repeated for the corrective action
scenano to simulate the effect of the rule on human health risks.
E.3.1 Characterizing Off-site Individual Risk
For each sample facility, the Agency estimated cancer and noncancer health effect’ for
an individual located at the closest off-site point where actual exposure is likely given current
land uses. This “closest individual” may be located at different points for each exposure
pathway. 7 The Agency used different methods to calculate cancer risks and noncancer health
effects to individuals because carcinogens and non-carcinogens have unique relationships between
contaminant exposure level and health effect. Many chemicals have carcinogenic non-
carcinogenic effects. The Agency included both effects of such chemicals in the risk characteriza-
tion (i.e., the contaminants’ contribution to each type of health effect is considered). Also, for
exposures via ground water, surface water, and air, EPA calculated the individual risk based on a
time-vaiying concentration profile. Exposure concentrations at a given exposure point in any of
these media va!y from 1992 through 2120 (the modeling period), leading to time-vaiying risks.
Therefore, EPA calculated both 130-year average and 9-year peak individual cancer risks and
noncancer effects in the ground-water, air, and surface water pathways. 8 An average risk is the
130-year arithmetic average of all the 9-year risks over the modçling period. Peak risk is the
highest 9-year risk. Methods used to characterize the cancer risks and noncancer effects are
detailed below.
Individual Cancer Risk
For this RIA, the Agency estimated “excess cancer risk,” the increase (i.e., above
background) in the probability of developing cancer over an individual’s lifetime in response to
contaminant exposures. To estimate excess cancer risks, the Agency multiplied the daily intake
(i.e., exposure level) of each carcinogen by the sl’-’e factor. Daily intakes differ for each
exposure pathway depending on the exposure rou ..s included and the exposure assumptions.
6 For this RIA, the Agency refers to noncancer human health effects as “noncancer effects”
for convenience. In past RIM and other documents, the Agency has often referred to these
effects as” noncancer hazards.”
See section E.2.1 for descriptions of these points.
8 Risks from all other pathways are not subdivided into average and peak risks because
MMSOILS models steady state (i.e., constant) contaminant concentrations in these pathways
over the modeling period.
Draft -- March 23, 1993
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27
Individual risk for each exposure pathway is the sum of cancer risks calculated for each
carcinogen to which the “closest individual” is exposed, i.e., risk aggregated across chemicals and
across exposure routes within a pathway.
Individual Noncancer Health Effects
The Agency evaluated noncancer effects by determining whether contaminant exposures
exceed the RfD. Ratios of contaminant exposure level to the RfD for that contaminant, called
“hazard quotients,” greater than one indicate increased likelihood of a non-carcinogenic health
effect. The Agency calculated total individual noncancer effects by adding the chemical-specific
hazard quotients. The sum of all hazard quotients is called the “hazard index,” or HI, and is
aggregated across chemicals and across all exposure routes within pathways.
High-End and Central Tendency Risks
For this RIA, the Agency characterized individual cancer risks and noncancer effects for
two contaminant release scenarios. These scenarios, the “high-end” and “central tendency”
scenarios, differ in assumptions regarding the mass of contaminants in SWMUs, rate of contami-
nant releases from SWMUs, and their fate and transport in the environment. For example, the
“high-end” scenario assumes a faster rate of ground water transport than the “central tendency”
scenario. As a result of these different assumptions, the two scenarios present different exposure
concentrations reaching the exposed individual through each pathway. All other features of the
risk characterization including exposure routes and assumptions, individual exposure point loca-
tions, and risk calculations, are identical under the high-end and central tendency scenarios. 9
E.3.2 Characterizing Future On-site Individual Risk
Using central tendency exposure assumptions, EPA also calculated potential risk under
alternative assumptions about future use of on-site land at the facilities. EPA defined this risk as
the risk to a hypothetical individual who is assumed to reside or grow food on-site at some point
in the future. The Agency assessed on-site future risk to individuals due to exposures via the
ground-water, air, and soil, and foodchain pathways, and to pica children and subsistence
farmers.’°
E.3.3 Characterizing Population Risk
The population risk characterization estimates the number of people living near correc-
tive action facilities who will have a cancer or noncancer effect due to exposure. The Agency
expressed population cancer risks as the number of excess cancer cases expected in the exposed
population over the 129-year modeling period. Similarly, the Agency expressed population
See also Exhibit 7-4 and Appendix 0.
‘° See also Section 7.2.3 in Chapter 7.
Draft .. March 23, 1993 S**
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28
noncancer effects as the number of people exposed to contamination exceeding noncancer effects
thresholds (i.e., hazard index or HI greater than 1) during the 129-year modeling period.
The Agency estimated total population risks attributable to three exposure pathways:
ground water, air, and surface water. To do so, the Agency used the same exposure routes and
exposure assumptions that it used to characterize central tendency individual risks from these
pathways. Because the population risk characterization counts numbers of people affected over a
129-year modeling period, the Agency estimated risks for populations currently living near
corrective action facilities and also for future populations. Section E.2.2 describes the approach
for forecasting future exposed populations.
Risk Cohorts
The Agency calculated population risks based on 9 years of exposure using a cohort-
averaging approach. Population risk in any given year is related not to the total exposed
population in that year but to that portion of the total population that has received the full nine-
year exposure. This portion of the population is defined as the “risk-cohort” for that year. The
size of a risk-cohort for a given year, which remains constant through time, is equal to the size of
the cohort which entered (or was born into) the area nine years ago and is now leaving. See
Exhibit E-5 for a graphic representation of a risk-cohort ‘filling up” with exposure years as it
moves through time, until it has completed nine years of exposure and is ready to have its risk
calculated.
For the population risk characterization, the Agency assumed that exposures begin in
1992 and continue only to the end of the modeling period (i.e., until 2120). The Agency did not
include risks due to exposures occurring prior to 1992 (“sunk risks”) in the risk characterization.
Population Cancer Risk
The Agency measured population cancer risk as the number of people in exposed
populations that develop cancer in response to exposure to facility contamination. Total
population cancer risk for a facility over the 129 years is equal to the sum of the cohort popula-
tion risk from 1992 through 2120. EPA estimated cohort population risk or ‘excess cancer
incidence in a risk cohort for any given year by multiplying the individual cancer risk (i.e.,
average lifetime excess cancer risk) for that year by the cohort size. The result of this multipli-
cation is the number of cancer cases expected in the risk cohort.
Population Noncancer Effects
The Agency measured population noncancer effects as the number of people living near
facilities with an individual HI above 1. To estimate population noncancer effects, the Agency
calculated the individual HI and assigned that individual HI to all individuals in the risk cohort.
Thus, if the individual HI exceeded 1, indicating that the individual’s exposure exceeded the
threshold, EPA counted the entire risk cohort as having a HI greater than 1. Again, the total
population for a facility having an exceedence of the HI over the 129 years is equal to the sum of
the risk cohorts with HI greater than I from 1992 through 2120.
“ Draft -- March 23, 1993 *SS
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EXHIBIT E-5
USE OF RISK COHORTS TO CALCULATE POPULATION RISK
2019
A cohort that has r.c.lv.d ths full nli* y.r.
ixposur. (isv.l 01 shading kidicat.. th. niimb.r
of .xposui. yws)
• Populst1onrl forys.r201$
= t [ AC oie
int i&Exposure
Fadors
I —
Cohort movss Into
arsa at th.b.ginning
of 2010
rcA population ii given
y. consists of 9 cohorts
2010
:.j.
2011
2012
2013
2014
2017
2018
cohort starts In 2010, and at
th. .nd of 2018, r.c.iv.s th. 9-y.ar
av.rag. conc.ntratlon (AC
La., this is ths “risk cohoir for 2018.
— Cohort I.av.s arsa aftsr
9 ysars of rssldsncs
Toxicity
Fartor
• Population wth Is mflsctsd In larger alas of nsw cohort. ov.r tim.
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30
E.3.4 Extrapolating to the National Level
The Agency characterized aggregate national human health risk by estimated risks at a
sample of real facilities and extrapolating to the national level. Specifically, the Agency
characterized individual and population risks at the RIA sample facilities that trigger corrective
action. These facilities belong to a stratified random sample of RCRA facilities. Each facility in
the sample has a “facility weighting factor” that reflects the relative frequency with which that
type of facility occurs in the U.S. For example, a facility with a weighting factor of 3.3 represents
3.3 similar facilities that exist in the U.S, and the sum of all facility weighting factors for the
facilities equals the number of facilities in the U.S. that would trigger corrective action. EPA
used facility weighting factors to extrapolate individual and population risks from the facility level
to the national level. The Agency used slightly different approaches to extrapolate population
risks and to extrapolate individual risks; these approaches are described below.
Extrapolating Population Risk
To extrapolate population risks, the Agency multiplied the estimated the number of
people living near each facility that bear cancer or noncancer effects by the facility weighting
factors. This extrapolation of facility-level population risks results in the total number of people
affected at all facilities in the U.S. over the modeling period. Thus, EPA added the extrapolated
facility-specific risks for all sample facilities to estimate the number of people in the U.S.
expected to have cancer or non-carcinogenic effects (i.e., have hazard indices above 1) at all
RCRA facilities that trigger corrective action.
Extrapolating Individual Risk
Because individual and population risks are measured differently, the Agency used a
different approach to extrapolate individual risks to the national level than it used to extrapolate
population risks.
EPA expressed national-level individual risks by determining how many facilities in the
U.S. would cause individuals to incur the various levels of cancer risks and noncancer effects.
The Agency calculated the distributions of facilities at various risk levels by adding the facility
weighting factors of facilities with similar risk levels or hazard indices. For example, a facility
that has an individual cancer risk of 2.3 x iO and a facility weighting factor of 3.3 is counted as
3.3 facilities in the U.S. that have (or cause) individual cancer risks at the iO level. This
approach produced a distribution that shows how many facilities at the national-level would have
each cancer risk or noncancer effect level.
E.3..S Cbaracterizing Effects from Lead Exposure
EPA characterized effects from lead exposure separately from cancer risks and
noncancer effects due to other chemicals. The Agency used the statistical function within the
See Chapter 3 for a discussion of the weighting factors.
SSS Draft -. March 23, 1993 °°
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31
UBK LEAD program to create graphs of the “Distribution Probability Percent Around a Mean
Value” based on the calculated blood concentrations for the target population at each facility.
These graphs show the mean blood lead concentrations, and also report the percent of the target
population estimated to have blood lead levels above the 10 ug/dL cutoff point. The calculated
geometric mean blood lead concentration when the input variables are left as program defaults is
3.23 ug/dL (based on a normal distribution).
EPA determined incremental effects due to contamination from the facility by comparing
the predicted geometric mean blood concentration of lead with the threshold blood level of 10
ug/dL.
•S* Draft -. March 23, 1993
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APPENDIX F
ECOLOGICAL BENEFITS: METHODOLOGIES AND CASE STUDIES
This Appendix provides additional information on both the methods and results of the
ecological analyses described in Chapter 8. Specifically, the methodology for identifying lower
and higher-threat facilities based on the proximity analysis (section F.I), the method for deriving
screening ecological benchmark levels for surface waters and soils (section F.2), and the
methodology for estimating the length of a river or stream contaminated at levels above
ecological benchmarks (section F.3) are described. More detailed data for the results of each
analyses are provided in sections F.4 through F.8.
F.1 Methodology for Proximity Analysis
The proximity analysis described in sections 8.1.1 and 8.2.1 addressed the potential of
releases of hazardous substances from RCRA-regulated facilities to reach valuable or vulnerable
ecological resources. The first step in dividing facilities into potentially higher- and lower-threat
groups on the basis of proximity to RCRA-regulated facilities was to sort the facilities according
to total acreage of habitat types considered more valuable and more vulnerable to releases of
hazardous substances. Two of the land-use categories (i.e., surface water, terrestrial
environments) were considered more valuable than the other three categories (i.e., agricultural,
residential, industrial/other) because the former areas generally are more likely to include
sensitive, diverse and productive ecosystems than the latter areas. It is recognized that specific
surface water bodies and/or terrestrial environments in the vicinity of the sample facilities may be
disturbed or polluted significantly; moreover, this disturbance or pollution may not be related to
the facility. For this analysis, however, surface water and terrestrial environments were generally
assumed to be more valuable than the other habitat types. Of these two categories, surface
waters were considered more vulnerable to contamination because substances released to surface
water can quickly spread to contaminate large areas and expose all members of an aquatic
community. Releases to land tend to contaminate confined areas and expose only a portion of
the community.
Facilities were sorted initially by surface water acreage to reflect an emphasis on areal
extent of vulnerable habitat types in evaluating relative threats. The sorted facility data indicated
that a group of about 30 percent of the sample facilities had relatively small areas of surface
water nearby (i.e., less than 100 acres) compared with the acreage of nearby surface waters for
remaining facilities (i.e., greater than 100 acres). Those facilities with smaller areas of surface
water nearby were categorized as lower-threat and those with larger areas of nearby surface
water as higher-threat. Other characteristics of the environmental setting (e.g., sensitive
environments and the proportion of the surrounding area that consisted of relatively undisturbed
terrestrial ecosystems) also were considered, and a few facilities recategorized as described in
section 8.2.1.
F l Methodology for Deriving Screening Ecological Benchmark Levels
For the concentration-based screening analysis, benchmark levels were derived for the
protection of aquatic life as described in section F.2.1. For the case study analyses, three
additional types of ecological benchmarks were estimated: ambient levels for sediments (benthic
* * * DRAFT -. March 24, 1993 * * *
-------
F-2
invertebrates; section F.2.2), drinking water or dietary intake levels (birds and mammals; section
F.2.3), and ambient levels for soil (plants).
F.2.1 Benchmarks for Aquatic Life
The primary objectives of estimating benchmarks for the protection of aquatic life were:
(I) to identify threshold concentrations above which adverse impacts to aquatic organisms are
likely, and (2) to provide a reasonably uniform basis for evaluating the relative ecological threats
associated with each of the sample facilities. Criteria and extrapolation factors used to derive
benchmark levels are discussed first, followed by data sources for toxicity and to estimate
bioconcentration factors (BCFs).
Criteria and Extrapolation Factors
The starting point was EPA’s chronic ambient water quality criteria (AWQC), which are
intended to be protective of 95 percent of the species in an aquatic ecosystem. When a chronic
AWQC was not available for a particular hazardous substance, an ecological benchmark level
that was roug$ily equivalent to a chronic AWQC was derived by applying one or more
extrapolation factors to toxicity values from the readily available literature. To summarize, the
following criteria and toxicity values (in descending order of preference) were used to derive
ecological benchmark levels:
• EPA chronic Ambient Water Quality Criteria (AWQC);
• Lowest observed effect levels (LOELs), no observed effect levels (NOELs), maximum
acceptable toxicant concentrations (MATCs), or other chronic effect or no-effect values
from AWQC documents or the literature;
• EPA acute AWQC; and
• LC values from AWQC documents or the literature.
-For values other than chronic AWQC, one or more of the following extrapolation factors was
applied to derive an ecological benchmark value that is roughly equivalent to a chronic AWQC:
• A factor of 5 to account for variation in species sensitivity;
• A factor of 10 to extrapolate from an acute to chronic value; and
• A factor of 10 to account for high bioaccumulation potential.
Exhibit F-I presents a summary of the criteria or toxicity values and extrapolation factors
used to derive screening ecological benchmark levels.
• * * DRAFT .- March 24, 1993 * * *
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F .3
EXHIBIT F-i
CRITERIA, TOXICI’IY VALUES, AND EXTRAPOLATION FACIORS USED TO
DERIVE SCREENING ECOLOGICAL BENCHMARK LEVELS
Type of Criterion or Toxicity Value
Extrapolation Factor
EPA Chronic AWOC
I
LOEL, NOEL, MATC, or other effect/no effect value from AWQC
5!’
document or other literature
EPA Acute AWQC
io ’
LC,, from AWOC document or other literature
- substance with log K ,, .. less than 33 or BCF less than 300
50 ’
- substance with log K,., 33 or BCF 300
500 ’
! ‘ A factor of 5 for variation in species sensitivity
‘ A factor of 10 to extrapolate from an acute to a chronic value
‘ A factor of 5 for variation in species sensitivity and a factor of 10 to extrapolate from an
acute to chronic value
g A factor of 5 for variation in species sensitivity, a factor of 10 to extrapolate from an
acute to a chronic value, and a factor of 10 to account for high bioaccumulaiion potential
Data Sources
The following sources (in descending order of preference) were used to obtain chronic or
acute aquatic toxicity values from the literature:
• A database of chronic freshwater toxicity values for approximately 190 air toxics
compiled in June 1991 by ORD’s Environmental Research Laboratory —
Duluth.’ All toxicity values in this database were subject to quality assurance
(QA) reviews by ERL—Duluth staff. Toxicity values were based on data in
EPA’s Aquatic Information Retrieval (AQUIRE) database, ERL-.—Duluth’s
quantitative structure-activity relationship (QSAR) algorithms, and professional
judgment.
ICF Incorporated (ICF). Focus Chemicals for the Clean Air Act Amendments Great
Waters Study . August 15, 1991 draft report prepared for the U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, 1991.
* * * DRAFT -- March 24, 1993 * * *
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F-4
• A database of acute toxicity values (freshwater, saltwater) for 330 chemicals
compiled in April 1991 for the Superfund Hazard Ranking System. 2 All toxicity
values in this database were subject to QA reviews by EPA’s Office of Emergency
and Remedial Response. The primary data source for toxicity values was
AQUIRE.
• The lowest available toxicity value (acute, freshwater for any aquatic organism)
from compilation of freshwater toxicity values for 410 chemicals. 3
The following sources (in descending order of preference) were used to obtain log K,,
and BCF values from the available literature:
• A database of BCF values for approximately 190 air toxics compiled in June 1991
by ORD’s Environmental Research Laboratory — Duluth. 5 All BCF values in
this database were subject to quality assurance (QA) reviews by ERL—Duluth
staff. BCF values were based on data in EPA’s Aquatic Information Retrieval
(AQUIRE) database, ERL—Duluth’s QSAR algorithms, and professional
judgment.
• A database of BCF values (freshwater, saltwater) for 330 chemicals compiled in
April 1991 for the Superfund Hazard Ranking System. 6 All BCF values in this
database were subject to QA reviews by EPA’s Office of Emergency and
Remedial Response. The primary data source for BCF values was AQUIRE.
2 U.S. Environmental Protection Agency (EPA). Superfund Chemical Data Matrix . Data
file compiled in April 1991 by the Office of Emergency and Remedial Response, Site Evaluation
Division, Site Assessment Branch, 1991.
Mayer, F.L., Jr., and Ellersieck, M.R. Manual of Acute Toxicity: Interpretation and Data
Base for 410 Chemicals and 66 Species of Freshwater Animals . Washington, D.C.: U.S.
Department of Interior, Fish and Wildlife Service, 1986.
K,. is defined as the octanol-water partitioning coefficient of a substance. 1_og iç.. values
correlate with log BCF values over a certain range of K. values.
ICF Incorporated (ICF). Focus Chemicals for the Clean Air Act Amendments Great
Waters Study . August 15, 1991 draft report prepared for the U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, 1991.
6 U.S. Environmental Protection Agency (EPA). Superfund Chemical Data Matrix . Data
file compiled in April 1991 by the Office of Emergency and Remedial Response, Site Evaluation
Division, Site Assessment Branch, 1991.
* * DRAFT— March 24,1993 *
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F-i
F.2.2 Benchmarks for Sediments and Benthic Communities
Sediment criteria for the protection of benthic invertebrates are derived based on the
equilibrium partitioning (EP) approach suggested by EPA’s Office of Water: 7
SB = K 1 xSWB,
where SB = sediment benchmark level (mg/kg);
= sediment partition coefficient; and
SWB = surface water benchmark (mg/I).
The sediment partition coefficient (I( ) for a non-ionic organic compound is calculated from its
organic carbon:water partition coefficient (} ) and the site-specific fraction of organic carbon
found in the sediment (f ):
=
For this analysis, K values were obtained from the literature, and f was assumed to be 0.01.
F.2.3 Benchmarks for Drinking Water and Diet for Wildlife
Drinking water or dietary ecological benchmark levels for terrestrial wildlife were derived
based on no-observed-adverse-effect levels or other low-observed-effect levels for oral exposures
reported in the literature. Where appropriate, one or more extrapolation factors were used to
account for uncertainty in applying available toxicity values to the receptors of concern. For
toxicity values reported as dietary concentrations, an additional factor based on reported or
assumed daily intake levels and body mass was used in order to express benchmark levels in
Benchmark Levels for Terrestrial Wildlife
The toxicity of contaminants ingested by wildlife species were derived based on dietary
no-observed-effect levels (NOELs), lowest-observed-effect levels (LOELs), LC values, or LD ,
values reported in the literature; NOELs and, secondarily, LOELs are preferred and were used
when available. Likewise, data on chronic studies are preferred and were used when available,
but most benchmarks are based on subchronic or acute data. Where appropriate, one or more
extrapolation factors were used to account for uncertainty in applying available toxicity values to
the receptors of concern.
A factor of 500 was applied to LC and LD values based on:
- A factor of 5 to extrapolate from 50 percent mortality to a low percent mortality
level (EPA 1986);
‘ U.S. Environmental Protection Agency. Interim Sediment Criteria Values for Nonpolar
Hydrophobic Organic Compounds . Washington, D.C.: Office of Water, 1988.
* * * DRAFT -- March 24, 1993 * * *
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F-6
- A factor of 10 to extrapolate from acute to chronic exposures; and
- A factor of 10 to account for variation in species sensitivity.
• A factor of 50 was applied to chronic LOELs and other effect levels based on:
- A factor of 5 to extrapolate from a LOEL to a NOEL; and
- A factor of 10 to account for variation in species sensitivity.
• A factor of 10 was applied to a subchronic NOEL to account for variation in species
sensitivity.
For toxicity values reported as dietaiy concentrations (e.g., LC values), an additional factor
based on reported or assumed daily intake levels and body mass was used in order to express
benchmark levels in appropriate units (mg/kg body mass).
Drinking Water Intake
The following formula was used to estimate daily drinking water intake for each receptor
of concern:
D = C x I x P,
where D = daily dose (mg/kg body mass);
C = concentration in surface water (mg/I);
I = daily water intake for receptor (I/kg body mass); and
P = proportion of daily water intake obtained from surface water body being
evaluated.
For this analysis, P was assumed to be 1 for each assessment area, and I was estimated separately
for deer, small mammals, and birds from the literature.
Dietary Intake
The following formula was used to estimate daily food chain intake for each receptor of
concern:
D = C x I x F x P,
where D = daily dose (mg/kg body mass);
C = concentration in prey item (mg/kg);
I = daily dietary intake for receptor (kg/kg body mass);
F = fraction of diet comprised of prey item; and
P = proportion of daily food insake obtained from assessment area being
evaluated.
* * * DRAFT -. March 24, 1993 * S *
-------
F-7
Values for C were based entirely on estimated residues in earthworms (for robins and shrews)
and shrews (for red-tailed hawks and barred owls). Residues were estimated based on measured
soil concentrations within each exposure area and on literature values for soil:earthworm and
soil:shrew bioaccumulation factors (BAFs). Note that most of the other prey eaten by hawks and
owls are herbivorous and would not be expected to bioaccumulate significant amounts of soil
contaminants. Values of the exposure parameters (I, F, and P) were estimated separately for the
robin, shrew, barred owl, and red-tailed hawk from the literature.
Aqueous Benchmarks for Piscivorous Wildlife
EPA’s Environmental Research Laboratory — Corvallis developed a procedure for
deriving screening-level surface water criteria protective of wildlife (SLWC) through exposure via
drinking water and consumption of contaminated aquatic prey.’ The method involves back-
calculating a surface water concentration based on a NOEL or other appropriate toxicity value
for the chemical and wildlife species of concern, the aquatic life bioconcentration factor (BCF)
for the chemical of concern, various exposure parameters (e.g., daily food and water consumption
rates, body mass) for the species of concern, and a unitless extrapolation factor to account for
differences in species sensitivity to the chemical of concern. The general formula for deriving an
SLWC is:
SLWC= NOEL xMxSSF
W+(FxBCF)
where: SLWC = screening-level wildlife criterion (mg/L);
W = average daily water consumption of animal (Llday);
F = average daily food consumption of animal (kg/day);
BCF = aquatic life bioconcentration factor (11kg);
M = average body mass of animal (kg);
SSF = species sensitivity factor (unitless values from 0.01 to 1); and
NOEL = no observed effect level or equivalent toxicity value (mg/kg-day).
BCF values for contaminants of concern were obtained by taking the lower of two values:
the BCF value for fish listed in the September 1989 draft of the Chemical Data Bases for the
Multimedia Environmental Pollutant Assess,nenl System (MEPAS): Version i, or the higher of
8 U.S. Environmental Protection Agency. Water Quality Criteria to Protect Wildlife
Resources . Corvallis, OR: Office of Research and Development, Environmental Research
Laboratory, December 1989. EPA Report No. EPA/60013-891067.
° U.S. Department of Energy. Chemical Data Base for the Multimedia Environmental
Pollutant Assessment System (MEPAS): Version 1 . Richland, WA: External Review Draft
prepared for the Assistant Secretary, Office of Environmental Audit by Pacific Northwest
Laboratories, 1989.
* * * DRAFT-. March 24, 1993 * * *
-------
F-8
available freshwater or saltwater BCF values listed in the April 1991 draft of the Superfwzd
Chemical Data Matrix (SCDM). These BCFs do not represent measured values for site-specific
fish likely to be eaten by the receptors of concern. Values in the MEPAS data bases generally
represent whole-body BCFs for freshwater fish calculated from the octanol-water partition
coefficient (organic substances) or by other extrapolation techniques (inorganic substances).
Values in the SCDM represent the highest BCF measured for any aquatic species (i.e., they are
not limited to fish). Because BCF is a term in the denominator of the SLWC methodology,
using the lower of these two values yields a higher SLWC (i.e., a higher BCF would lower the
ecological benchmark level).
Fish Contamination Benchmarks for Piscivorous Wildlife
Dietaiy concentrations in fish flesh protective of piscivorous wildlife (i.e., birds,
mammals) were determined following New York State’s approach.’°
F.2.4 Benchmarks for Soils
Soil ecological benchmark levels for terrestrial plants were derived based on soil
contaminant concentrations associated with LOELs extrapolated from a LOEL to a NOEL
Phytotoxicity data for other soil contaminants were not found in the available literature. Data
also were available on the acute toxicity of contaminated soils based on earthworm toxicity tests.
Finally, the US Fish and Wildlife Service’s data for evaluating soil contamination also were
used.’ 1
F.3 Methodology for Estimating Extent of Contamination
Extent of contamination was estimated for surface water contamination at facilities where
non-metals exceeded ecological benchmarks. The following steady-state decay equation was
used:
CV
Where: A = distance contaminated above benchmark (m)
Cb = ecological benchmark level (mg/I)
10 New York State Department of Environmental Conservation. Niagara River Biota
Contamination Project: Fish Flesh Criteria for Piscivorous Wildlife . Albany, NY: Division of
Fish and Wildlife, Bureau of Environmental Protection, 1987. Technical Report 87-3.
“ Beyer, W.N. Evaluating soil contamination . U.S. Fish and Wildlife Service/DOl.
Biological Report Volume 90 (1990), pp.16-il.
* * * DRAFT-- March 24, 1993 * * *
-------
F.9
C 0 = predicted concentration at point of discharge (mg/I)
V = stream velocity (m/sec)
X = decay coefficient (sec)
In this equation, the decline in contaminant concentrations over distance (i.e., the decay
coefficient or X) is determined solely by the degradation rate of the hazardous substance in
surface water (i.e., it does not account for dilution occurring in the surface water body due to
downstream tributary, runoff, and ground water inputs). Degradation parameters used to
estimate X included aerobic and anaerobic oxidation, photolysis, photooxidation, reduction,
volatilization, and hydrolysis; rates for all of these processes were summed.
F.4 Proximity Analysis Results
Exhibit F-2 summarizes data on estimated total acreage of surface water, terrestrial,
agricultural, residential, and industrial/other environments on-site and within one mile of each
facility in the analysis. Also presented for each facility are distance to the nearest surface water
and terrestrial habitat, number of sensitive environment” types on-site and within one mile, and
distance to the nearest sensitive environment. These results were summarized and discussed in
section 8.2.1.
F.5 Concentration-based Screening Analysis Results
Exhibit F-3 lists the hazard index (I-il) estimated for sample facilities at which maximum
concentrations predicted for nearby surface waters by MMSOILS exceeded the benchmarks for
the protection of aquatic life. Under the central-tendency scenario, the HI for nine of the
sample facilities are predicted to exceed 1.0, for two sample facilities to exceed 10, and for one
sample facility to exceed 100. Under the high-end scenario, the HIs for eighteen of the sample
facilities are predicted to exceed 1.0, for fourteen sample facilities to exceed 10, for six sample
facilities to exceed 100, for three sample facilities to exceed 1,000, and for one sample facility to
exceed 10,000.
F.6 Time Sequence Results
For facilities at which surface water concentrations of constituents exceeded ecological
benchmark levels, predicted concentrations were plotted over the 128-year modeling period.
Results are shown in Exhibit F-4.
F.7 Extent of Contamination Results
The maximum extent of contamination above benchmark levels (capped at 12 miles,
approximately 63,000 feet) for each facility is presented as distance (ft) in Exhibit F-3. In cases
where more than one constituent exceeded benchmark levels, only the constituent with the
greatest estimated extent of contamination is shown in the table. NA” in Exhibit F-3 indicates
that only metals exceed ecological benchmark levels, and extent of contamination could not be
estimated. Under central tendency assumptions, extent of contamination could be estimated for
SS*Dp FJ’....Mareh24,1993SSS
-------
F-b
two of the seven facilities at which estimated environmental concentrations exceeded ecological
benchmark levels. Estimated extent of contamination ranged from 290 feet to 12 miles (cap).
* * * DRAFT -- March 24, 1993 * * *
-------
F-Il
EXHIBIT F-2
NEARBY LAND USE AND SENSITIVE ENVIRONMENTS!’
HIGHER-THREAT FACILITIES
Obs.
No.
Combined Total Area On-site and Within One Mile or Facility (A es) ’
Nearest Distance
(miles)!’
Sensitive Environments
Surface
Waler
Terrestrial
Agricultural
Residential
Industrial!
Other
Total ’
Surface
Water
Terrestrial
Number
Nearest Distance
(miles)!’
008
9,100
1,800
0
330
2,700
13,970
000
0.00
2
0.00
060
6,400
1,000
0
2,300
3,700
13,480
000
0.00
I
0.00
162
3,600
470
560
850
2,600
8,040
0.00
0.00
3
000
109
3,300
13,000
0
130
600
17,180
0.00
0.00
1
0.04
019
3,200
1,500
0
430
1,700
6,880
0.00
0.00
1
0.00
081
2,700
3,000
2,400
1,900
1,900
11,970
0.00
0.00
I
0.00
152
2,100
270
<60
240
3,900
6,500
0.00
0.00
0
—
065
1,800
4,200
180
<90
320
6,600
0.00
0.00
1
0.00
018
1.200
2,200
0
3,200
4,500
11,120
0.00
0.00
1
0.64
063
1,200
2,000
200
<80
500
4,350
0.00
0.00
I
0.03
163
1,200
590
880
290
<20
2,940
0.25
0.00
1
030
104
1,200
14,000
380
2,800
740
18,820
000
0.00
1
0.00
044
910
360
120
670
2,000
4,100
000
0.00
I
0.47
March 24, 1993 Draft
-------
HIGHER-THREAT FACILITIES (continued)
F-12
EXHIBIT F-2 (continued)
Obs.
No.
Combined Total Area On-site and Within One Mile of Facility (Acresr
Nearest Distance
(miles)!’
Sensitive Environments ! ’
Surface
Water
Terrestrial
Agricultural
Residential
Industrial
/Other
Total ’
Surface
Water
Terrestrial
Number
Nearest Distance
(miles)!’
093
820
2,600
6,300
410
2,700
12,720
0.00
0.00
0
—
013
710
1,600
5,700
0
230
8,280
0.00
0.00
0
—
144
690
180
2,300
100
210
3,490
0.00
0.00
1
0.00
041
680
200
0
2,100
470
3,440
0.00
0.00
1
0.00
021
670
2,400
<40
1,400
170
4,690
0.00
0.00
1
0.00
122
610
2,100
1,700
2,900
520
7,870
0.00
0.0
1
0.00
168
580
1,800
200
700
730
4,020
0.01
0.00
0
—
131
450
1,900
960
1,400
930
5,710
0.00
0.00
0
—
025
430
2,500
430
950
240
4,520
0.00
0.00
1
0.85
057
410
3.100
2,500
990
1,800
8,850
0.00
0.00
1
0.00
112
310
3,200
0
3,400
2,100
9,050
0.00
0.00
1
0.42
March. ?3 Draft
-------
HIGHER-THREAT FACILITIES (continued)
F-13
EXHIBIT F-2 (continued)
Obs.
No.
Combined Total Area On-site and Within One Mile of Facility (Acres) ’
Nearest Distance
(miles)!’
Sensitive Environments
Surface
Water
Terrestrial
Agricultural
Residential
Industrial
/Other
TotaI ’
Surface
Water
Terrestrial
Number
Nearest Distance
(miles)!’
029
290
750
0
1,200
1,600
3,790
0.00
0.00
1
0.00
165
290
1,100
<50
380
770
2,570
0.00
0.00
1
0.00
114
240
810
0
4,500
180
5,760
0.00
0.00
1
0.00
161
220
630
2,900
1,700
320
5,730
0.00
0.00
0
—
120
200
620
1,100
1,100
1,100
4,130
0.28
0.00
1
1.00
136
200
710
0
1,500
1,700
4,070
0.13
0.00
0
—
069
200
1,100
<90
500
2,900
4,840
0.00
0.00
1
0.00
039
180
2,100
0
260
130
2,640
0.00
0.00
1
0.34
084
180
2,300
330
2,900
350
6,100
0.00
0.00
1
0.38
082
140
3,200
570
160
140
4,220
0.01
0.00
0
—
132
110
370
1,400
750
1,00(1
3,680
0.00
0.00
1
0.80
046
100
1,200
1,500
5,500
630
8,960
0.00
0.00
0
—
014
<90
2,900
0
310
1,400
4,710
0.00
0.00
1
0.00
078
<30
2,600
160
380
70
3,220
0.00
0.00
1
0.00
‘ March 24, 1993 Draft
-------
F- 14
LOWER-THREAT FACILITIES
EXHIBIT F-2 (continued)
Ohs.
No.
Combined Total Area On-site and Within One Mile of Facility (Acres) ’
Nearest Distance
(mites)!’
Sensitive Environments ’ 4
Surface
Water
Terrestrial
Agricultural
Residential
Industrial!
Other
Total ’
Surface
Water
Terrestrial
Number
Nearest Distance
(miles)!’
125
180
360
300
90
2,100
3,040
0.23
0.00
0
—
124
<90
3,200
460
780
360
4,880
0.00
0.00
0
—
002
<80
100
3,000
780
1,200
5,200
0.00
0.19
0
—
074
<50
160
50
1,800
3,700
5,790
0.16
0.06
0
—
017
<50
<50
0
1,300
920
2,300
0.15
0.87
1
0.99
048
<30
520
0
720
1,300
3,340
0.34
0.00
0
—
146
<30
<60
0
1,700
1,500
3,270
0.70
0.03
0
—
047
<30
1,000
1,400
300
<60
2,760
033
0.00
0
—
080
<20
340
0
1,400
450
2,260
0.00
0.14
0
—
049
2
3,000
30
0
3
2,990
0.00
0.00
0
—
190
1
<70
0
1,500
2,100
3,680
0.25
0.80
0
—
153
1
1,800
200
20
<20
2,040
0.09
0.00
0
—
067
0
<50
0
2,100
480
2,620
-
0.17
0
—
027
0
210
1,300
1000
170
2,850
-
0.00
0
—
!b’ Facilities are grouped according to professional judgment into higher- and lower-threat categories as explained in sections 8.1.1 and F.1; within each categoly,
facilities are sorted by surface water acreage.
‘ For a definition of land-use categories and sensitive environments, see text.
E Zeros in these columns indicate that surface waters, terrestrial habitats, or other sensitive environments are present within the facility boundaiy dashes in this
column indicate that none of these environments were present on-site or within one mile.
sum of areas in the five categories does not match the total column because values for the five categories were estimated to within two significant digits
whereas the value for the total was estimated separately to the nearest 10 acres.
!‘ Dashes in this column indicate that no sensitive environment is present within one mile of the facility.
March 93 Draft
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F-IS
EXHIBIT F-3
FACILITIES AT WHICH MAXIMUM PREDICTED CONCENTRATIONS EXCEEDED
ECOLOGICAL BENCHMARK LEVELS
Hazard Index
Constituent
Maximum Extent of
Contamination ’ (feet)
CP’
HE ’
C T ”
HEW
013
3.5 x 102
1.3 x 10
formaldehyde
(63,0001
(63,000]
021
2.2 x i0
3.2 x 100
mercury
NA
NA
025
7.8 x 10’
2.6 x 102
fluorene
NA
(63,000J
041
1.4 x 100
5.4 x 101
acetone
--
(63,000]
044
2.4 x io
4.0 x 10’
chromium
NA
NA
046
1.1 x 100
5.0 x 100
lead
NA
NA
049
1.8 x 100
43 x 10’
cadmium
NA
NA
060
9.1 x 10’
8.4 x 1O
phenol
NA
163,000]
063
4.2 x 100
2.7 x 102
tetrachloroethylenei
cyanides
200
3,000
069
6.2 x 1O
13 x 10’
chromium
NA
NA
081
4.1 x 100
2.3 x 10’
xylenes
NA
17,300
104
1.7 x 10
5.1 x 100
chloroform
NA
28,400
112
8.2 x 10.2
13 x 10’
metals
NA
NA
114
4.4 x 10 ’
3.0 x 102
pentachlorophenol
NA
163,0001
122
8.9 x 10.2
2.3 x 10’
selenium
NA
NA
124
1.2 x 100
1.2 x iO
metals
NA
NA
144
1.7 x 10.1
1.5 x 101
benzene
NA
8,000
153
4.7 x 100
8.7 x 10’
metals
NA
NA
167
2.6 x 10
1.9 x 100
chromium
NA
NA
Ccniral tendency s nario
! High-end scenario.
!‘ Contaminant for which the calculated extent of contamination was greatest at that facility
gi Extent of contamination was capped at twelve miles (approximately 63,000 feet).
NA = not applicable (no organic chemicals exceeded ecological benchmark levels)
— Unavailable
**S March 25, 1993 Draft
-------
F- 16
EXHIBIT F4
SUMMARY OF TIME COURSE OF PREDICTED EXCEEDANCES OF
ECOLOGICAL BENCHMARK LEVELS
Approximate Duration of
Predicted Exceedance Beyond
1992 (years)W
Overall Pattern of
Predicted
Concentrations ’
C1
HE ’
013
>130
>130
Falling
021
---
80
Rising
025
---
>130
Rising
041
20
>130
Mixed
044
---
>130
Rising
046
>130
>130
Constant
049
>130
---
Constant
060
---
>130
Rising
063
30
>130
- Falling
069
---
>130
Falling
081
---
>130
Mixed
104
---
20
Falling
112
---
>130
Rising
114
---
>130
Rising
122
---
>130
Rising
124
>130
>130
Mixed
144
---
>130
Mixed
153
>130
>130
Constant
167
---
60
Rising
V For substance with greatest exceedance duration
‘ Pattern exhibited by majority of substances that exceeded benchmark levels
W Central tendency scenario
‘ High-end scenario
indicates that no constituent exceeded its ecological benchmark under that scenario
March 24, 1993 Draft
-------
F-17
Under high-end assumptions, extent of contamination could be estimated for nine of the 19
facilities at which estimated concentrations exceeded benchmark levels. Estimated extent ranged
from 2,700 feet to 12 miles (cap).
F.8 Qualitative Case Studies
Qualitative case studies were prepared for three facilities to identify types of risks that
are not accounted for by the proximity and screening concentration-based analyses, as described
in section 8.1.3. The case-study analyses followed the EPA Risk Assessment Forum’s framework
for ecological risk assessment. For each facility, assessment and measurement endpoints were
selected to evaluate ecological threats. Assessment endpoints are the actual entities or
environmental characteristics that are to be protected (e.g., population abundance, biodiversity)
or adverse effects that are to be prevented (e.g., extinction, contamination); measurement
endpoints are measurable environmental characteristics that approximate, represent or lead to
the assessment endpoint(s) using field or laboratory methods (e.g., the chemical concentration
shown to cause a reduction in survival, growth, or reproduction in a standard laboratory toxicity
test).
F.8.1 Facility A
Facility A is a large facility that has manufactured a wide variety of chemicals for several
decades. Roughly one-third of the more than thousand acre facility is developed. The property
boundary is bounded by a forest, a neighboring facility, and a large river. A wide array of
chemicals have been generated as waste during manufacturing operations; the majority of wastes
and residues have been managed, treated, and disposed on-site throughout the site’s history.
Numerous areas on-site, once used for the management and disposal of wastes in the past, have
since been closed or replaced by newer systems. Elevated levels of chlorinated pesticides, volatile
solvents, and inorganic chemicals have been found in ground water, surface water, sediments, air,
and soils.
For terrestrial areas, representative ecological receptors of concern were selected based
on the plant and animal species characteristic of each area, the relative potential for exposure to
DDT and its metabolites (DDTR), and the availability of toxicity data. Terrestrial receptors of
concern included the robin, red-tailed hawk, barred owl, shrew, deer, and other small mammals
and birds. Earthworms also were selected as receptors of concern for soil toxicity tests used to
evaluate the acute toxicity of contaminated soils. For this analysis, assessment endpoints for
terrestrial biota are focused at the population level; measurement endpoints were focused at the
individual level. Assessment endpoints for aquatic biota are focused at the
population/community level; measurement endpoints were mortality and stream benthic
community structure.
The evaluation focused on exposure pathways likely to result in the greatest levels of
exposure for the receptors of concern: soil, surface water, sediment, and terrestrial biota. The
exposure assessment focused on developing estimates of representative and maximum
concentrations and/or exposure levels within each assessment area via the appropriate exposure
pathway(s). Representative concentrations or exposure levels were based on the mean
March 24, 1993 Draft ***
-------
F-18
concentration within the exposure area of concern; maximum concentrations or exposure levels
were based on the highest measured concentration. To estimate daily intake levels for wildlife
from environmental concentration data, diet composition and food and water ingestion rates
were identified from the available literature, including EPA’s Wildlife Exposure Factors
Handbook, currently being developed by the Office of Research and Development. Risk to
receptors of concern was evaluated by comparing average and/or maximum measured
contaminant concentrations (or estimated intake levels, as appropriate) to the appropriate
ecological benchmark levels. The results of the risk characterization are summarized below.
There appears to be little risk to terrestrial wildlife from drinking contaminated surface
water. With the exception of diazinon, maximum estimated intake levels are well below
benchmark levels; average intake levels for all contaminants are below benchmark levels. Robins
and shrews ingesting earthworms and other invertebrates from the contaminated areas may be at
risk for lethal effects from DDTR exposure. Estimated mean and maximum intake levels for
owls and hawks feeding on small mammals from the contaminated area are well below
benchmark levels, suggesting little risk to these species. Mean and maximum concentrations of
arsenic, copper, lead, and DDTR are greater than phytotoxic levels in all exposure areas
evaluated within the floodplain area. However, benchmark levels for the inorganic chemicals are
below site-specific background levels, so evaluating risk from these contaminants is difficult.
Areas of bare soil coincide with high soil contaminant concentrations, suggesting phytotoxic
effects of these contaminants.
Maximum aqueous concentrations of all contaminants are below benchmark levels for the
protection of aquatic life in on-site surface waters; no contaminants were found above
background off-site. There thus is little risk to aquatic life from contaminated surface water.
Sediment concentrations, however, exceed benchmark levels. In an on-site creek, average and
maximum sediment concentrations of ametryn, diazinon, and metolachlor were greater than
ecological benchmark levels. In the off-site river, average and maximum sediment concentrations
of diazinon were greater than ecological benchmark levels. These results suggest that adverse
effects may be occurring in the benthic communities of the creek and river. To the extent that
these communities support part of the entire aquatic community’s food chain, indirect adverse
effects on higher trophic levels also might occur. This conclusion is supported by a survey that
found that the benthic community in the creek is dominated by chironomids, species typically
dominant in stressed waters. However, such a finding could be due to low dissolved oxygen in
the creek.
Approximately 60 acres of soil in the floodplain areas (70 percent of suitable open field
habitat) are contaminated above benchmark levels for terrestrial plants, robins, and shrews. Tens
of acres of the floodplain are devoid of vegetation. A significant proportion of the populations
of the robins and shrews utilizing the floodplain area may be adversely affected. Given the
tendency of DDTR to bioaccumulate in terrestrial biota, impacts beyond the contaminated area
are possible, although estimated intakes for hawks and owls are below benchmark levels.
Sediments in an on-site creek are contaminated above benchmark levels for a distance of
approximately 6,000 ft (1.2 miles). It is possible that benthic communities are altered within this
entire length. Downstream river sediments at sampling points approximately 18,000 ft (3.5 miles)
*** March 24, 1993 Draft
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F-19
apart are contaminated above benchmark level. Benthic life within the river is possibly adversely
affected for at least 3.5 miles, and the zone of impact may extend farther downstream.
The available data do not allow for separation of the effects of SWMU releases from
permitted releases under the facility’s NPDES outfalls.
F .8.2 Facility B
Facility B is a large waste treatment facility sited on approximately several hundred acres
of land bounded on three sides by surface water: two brackish tidal marshes and a brackish tidal
creek. The watershed of the tidal creek is predominantly agricultural. Point source discharges
from other major industrial/commercial facilities in the area may contribute pollutant loadings to
the creek. Stream loadings of various other non-point source pollutants are also attributable to
runoff from agricultural activities. The water quality of the creek is generally poor;
concentrations of ammonia, phosphorus, and copper routinely exceed water quality criteria and
state standards. Historical concentrations of cadmium, mercury, and PCBs have occasionally
exceeded water quality criteria and standards.
Receptors of concern for surface water contamination include fish and other aquatic life
as well as piscivorous wildlife. Representative piscivorous receptors (i.e., great blue heron,
kingfisher, mink) were selected based on likelihood of presence in the area, position in the food
chain (i.e., top carnivores), and availability of toxicity data. Receptors of sediment contamination
include benthic invertebrates. Assessment endpoints for aquatic biota (both pelagic and benthic)
are focused at the population/community level; measurement endpoints were mortality and
stream benthic community structure. Assessment endpoints for ‘piscivorous wildlife are focused
at the population level; measurement endpoints were mortality, reproduction, and sublethal
adverse effects.
The evaluation focused on exposure pathways likely to result in the greatest levels of
exposure for the receptors of concern: surface water, sediment, and aquatic biota. The exposure
assessment focused on developing estimates of representative and maximum concentrations.
Representative concentrations or exposure levels were based on the arithmetic mean of available
concentration data for an exposure area; maximum concentrations or exposure levels were based
on the highest measured concentration. Risk to receptors of concern was evaluated by
comparing mean and/or maximum measured contaminant concentrations to media-specific
ecological benchmark levels. The results of the risk characterization are presented below.
In the tidal creek and north marsh, maximum concentrations of arsenic, chromium,
mercury, and possibly PCBs exceeded chronic benchmark levels. Maximum concentrations of all
except mercury (and possibly PCBs) exceed acute benchmark levels as well. In the north marsh,
average concentrations of many of these substances also exceeded chronic benchmark levels.
Thus there is some risk to aquatic life from contaminated surface water.
Maximum sediment concentrations of arsenic, barium, cadmium, chromium, copper,
mercury, PCBs, and zinc all exceeded benchmark levels in the north marsh and tidal creek;
average and maximum concentrations of many of these substances in the north marsh, south
*** March 24, 1993 Draft
-------
F-2O
marsh, and tidal creek also exceeded guidelines for heavily polluted sediments. These results
suggest that contamination from the facility may be adversely affecting the benthic communities
of the creek and the marshes. This conclusion is supported by a benthic organism survey in the
tidal creek that found no amphipods near the facility outfall, although many were present both
upstream and 2,000 feet downstream from the facility.
There appears to be some risk to piscivorous wildlife from eating contaminated aquatic
organisms. Maximum measured surface water concentrations of mercury, zinc, and possibly
PCBs exceed ecological benchmarks for the protection of piscivorous wildlife. However, the
inability to determine average aqueous concentrations from available data and the uncertainty
involved in developing benchmark levels makes it difficult to characterize risk to these receptors
of concern.
Approximately 45 acres of tidal wetland sediments in marshes and possibly 4,000 feet of
the tidal creek are contaminated with metals and PCBs above ecological benchmarks. Water
quality in the tidal creek is generally within water quality standards for the protection of aquatic
life, except that zinc exceeds both background levels and the ecological benchmarks for the
protection of aquatic life as well as benchmarks for the protection of piscivorous wildlife. The
water-table zone ground water that surfaces in one marsh area is contaminated with mercury and
possibly PCBs above benchmark levels for the protection of aquatic life. Water contamination in
another marsh area is undetermined. Moreover, concentrations of most contaminants in
background samples also exceeded ecological benchmark levels, making it difficult to determine
whether observed contamination can be attributed to the facility.
The available data do not allow for separation of the effects of SWMU releases from
permitted releases under the facility’s NPDES outfalls. However, ground-water flow is the
primary method for migration of contaminants from the site.
F.8.3 Facility C
Facility C is sited in a lightly developed area, and the undeveloped on-site land is either
woodland or field. Off-site land near the facility is predominantly surface water or wetland. The
facility is surrounded by woodland, wetlands, residences, and a lake. Two creeks flowing from
the lake pass the facility.
Most of the wastes generated at the facility are shipped off-site, but some are treated on-
site. Ground water is contaminated with volatile organic constituents (VOCs) and heavy metals.
Lead, zinc, mercury, and other metals are found in the edible portions of fish in the lake.
Ecological receptors of concern include fish and other aquatic life as well as piscivorous
wildlife. Representative piscivorous receptors (i.e., mink, belted kingfisher, and great blue heron)
were selected based on likelihood of presence in the area and availability of toxicity data.
Assessment endpoints for aquatic biota and piscivorous wildlife were focused on the
population/community level. Measurement endpoints include mortality and
morphological/physiological condition of individuals.
*** March 24, 1993 Draft ‘
-------
F-21
The evaluation focused on exposure pathways likely to result in the greatest levels of
exposure for the receptors of concern. The exposure assessment focused on developing estimates
of representative and maximum concentrations. Representative concentrations or exposure levels
were based on the arithmetic mean of available concentration data; maximum concentrations or
exposure levels were based on the highest measured concentrations. Risk to receptors of
concern was evaluated by comparing mean and maximum contaminant concentrations in surface
water and fish tissue to ecological benchmark levels. The results of the risk characterization are
presented below.
In the lake near the facility, maximum and average concentrations of cadmium, copper,
lead, and mercuiy were above ecological benchmark levels for the protection of aquatic life,
indicating that waters of the lake pose a significant threat to these ecosystems. In a nearby
stream/wetland where contaminated ground water discharges, mean and maximum concentrations
of cadmium, copper, lead, and mercury were above ecological benchmark levels. Lead, zinc, and
copper showed slight declines in concentration with increasing downstream distance. In addition,
mean and maximum concentrations of three volatile organic constituents were above ecological
benchmark levels. Concentrations of VOCs were highest at the suspected ground water seeps,
and they attenuated rapidly with downstream distance due to volatilization and dilution.
Additional evidence that Facility C poses a threat to aquatic life is the presence of internal and
external parasites and of fin rot (a fungal disease) in fish from the lake. Parasites and fin rot
were most prevalent among fish collected closest to the facility.
Concentrations of heavy metals were above dietaiy benchmark levels for the protection of
piscivorous wildlife in all three fish species collected from the nearby lake. Two constituents
(i.e., cadmium and mercury) were several orders of magnitude above ecological benchmark levels
in all three fish species collected from the lake. Only one constituent (i.e., lead) was ever found
below dietary benchmark levels. Chromium, copper, and zinc were consistently approximately
one order of magnitude above dietary benchmark levels. Given these results, fish-eating wildlife
in the vicinity of the lake appear to be at risk.
Because heavy metals were found above surface water e ological benchmark levels for the
protection of aquatic life at all stations (including the background station), the entire lake is
likely to exhibit an impaired aquatic ecosystem. Samples of fish tissue clearly suggested that
contaminants enter the food chain and pose risk to fish-eating wildlife species such as mink,
belted kingfisher, or great blue heron. Contaminated waters exit the lake via two streams.
Heavy metals were above benchmark levels in at least 1,560 feet of one of the streams and its
seep tributaries in a nearby wetland. If the surface water flowing from the lake is assumed to be
contaminated above benchmark levels, then an additional 1,580 feet of the stream would be
contaminated for a total of 3,140 feet. The wetland may be contaminated above benchmark
levels by ground water discharge and by surface water overflow from the stream. However, the
extent of this contamination cannot be determined, because wetland sediments were not sampled
and aqueous samples were taken within water courses only.
‘ March 24, 1993 Draft ***
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APPENDIX C
KEY PARAMETERS MATRIX
This appendix presents the values of the parameters used in the human health risk
assessment and their sources. In particular, the parameters are those for the major steps in
EPA’s risk assessment methodology:
• Waste characterization;
• Release analysis;
• Fate and transport analysis;
• Exposure analysis;
• Dose-response assessment; and
• Risk characterization.
Parameters are presented in matrix format; the matrix includes values of the key parameters used
to model the four fundamental risk descriptors:’
• Central tendency individual risk;
• High-end individual risk;
• Risk to highly-exposed subpopulations (i.e., subsistence fishermen, subsistence
farmers, and pica children); and
• Population risk.
In addition, the parameter matrix includes a qualitative description of the relative uncertainty of
each parameter.
Although this Appendix primarily supplements Chapter 7 and Appendix E, which cover
the human health risk assessment, the parameters presented in the matrix are relevant to
Chapters 3, 4, 8, 9, 10, and 12 as well.
To the extent possible, the Agency’s selection of key parameters is consistent with recent
EPA guidance on risk characterization, (i.e., Guidance on Risk Characterization for Risk
Managers and Risk Assessors , dated February 26, 1992). Sources of parameter values are
identified in endnotes to the parameter matrix.
‘See Chapter 7 for a detailed discussion of these risk descriptors.
Draft -- March 23, 1993
-------
High-End
(2)
*** Draft — March 23, 1993
1
Individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Uncertainty
Parameter
Individual Risk
WASTE CHARACTERI2AT1ON
Central Tendency
(1)
Population Risk
(3)
Waste Volume
Best estimate (e.g.,
based on site-specific
SWMU dimensions)
High-end estimate
based on site-specific
SWMU dimensions
Same as (1)
Medium-
High
Constituent Concentration
Midpoint of SWMU-
specific data or TSDR
values
High-end of SWMU-
specific’data or TSDR
values
Same as (1)
Medium-
High
DistrIbutIon Coefficl.nt (Kd)
Non-karat facilities
pH-dependent mid-point
value specified by ORD
pH-dependent low-end
value specified by
ORD
Same as (1)
Low
Solublllty UmIt
Organics
Solubility limit values
from ORD
Same as (1)
Low
Inorganics
Central tendency
solubility limit values from
ORD
High-end solubility
limit values from ORD
Same as (1)
Low
Degradation Rate
pH-dependent best
estimate of hydrolysis
rates from ORD’
Same as (1)
Medium
-------
Parameter
Individual Risk
Population Risk
(3)
individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Uncertainty
Central Tendency
(1)
High-End
(2)
RELEASE ANALYSIS
Containment System
Failure
Site-specific best
estimate based on
SWMU characteristics
Same as (1)
Medium
Leachate Concentration
Surface
Impoundments and
Tanks
Site-specific best
estimate of pond/tank
concentration
Site-specific high-end
estimate of pond/tank
concentration
Same as (1)
Medium
Landfills
Organics: Derived from
initial concentrations and
solubilities from ORD,
using organic leachate
model (OLM);b
Inorganics: Used ORD
central tendency
solubility values’
Organics: Equal to
high-end solubitity
limit;’
Inorganics: Used ORD
high-end solubility
values’
Same as (1)
Medium
Mass Balance
Volatilization
Non-steady state release
Same as (1)
Low
Particulate Emissions
Steady-state release until
mass is depleted°
Same as (1)
Low
Soil Erosion
(Dissolved/
Adsorbed)
Steady-state release until
mass is depletede
Same as (1)
Low
Leachate
Non-steady state release
Same as (1)
Low
Draft — ‘1 23, 1993 ***
-------
Individual Risk Individual Risk to
Highly-Exposed Parameter
Parameter
Central Tendency High-End Population Risk Subpopulations Uncertainty
(1) (2) (3) (4)
FATE AND TRANSPORT ANALYSIS
Recharge
Depth of soil layer
Site-specific best
estimate
Same as (1)
Low
Depth of root zone
10 cm (constant
assumption)c
Same as (1)
Low
Percent Organic
Matter
Site-specific best
estimate
Site-specific high-end Same as (1)
estimate
Low
Field capacity
Site-specific best
estimate
Same as (1)
Low
Wilting point
Site-specific best
estimate
Same as (1)
Low
Saturated
conductivity
Site-specific best
estimate
Same as (1)
Low
Saturated water
content
Site-specific best
estimate
Same as (1)
Low
Exponent b for
moisture curve
Site-specific best
estimate
Same as (1)
Low
Ground Water Pathway
Hydrautic
conductivity
Site-specific best
estimate
Order-of-magnitude
higher than best
estimate
Same as (1)
Medium
Hydraulic gradient
Site-specific best
estimate
Same as (1)
Low-
Medium
Porosity
(Non-karst facilities)
Site-specific best
estimate
Same as (1)
Low
Dispersivity
Scale-dependent
Same as (1)
Medium
Draft — March 23, 1993
3
-------
Parameter
Individual Risk
Population Risk
(3)
Individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Uncertainty
Central Tendency
(1)
High-End
(2)
Non-Karat Facilities
Advection
Dispersion
Derived from central
tendency estimates of
gradient, conductivity,
porosity
Derived from high-end
estimates of
conductivity
Same as (1)
Medium-
High
Derived from central
tendency estimates of
advection, aquifer
thickness, and distance
from unit
Derived from central
tendency estimates of
aquifer thickness and
distance from unit and
high-end estimates of
advection
Same as (1)
Medium-
High
Karst Facilities
Retardation
in ground-
water
pathway
Assumes no contaminant
retardation, simulated by
setting Kd = 0 for
inorganics and by setting
aquifer f = 0 for
organics
Retardation is not
applicable given
assumption of
advection and
dispersion
Same as (1)
Medium-
High
Advection
Dispersion
Derived from central
tendency estimates of
gradient, conductivity,
and porosity set to 0.01
Advection is assumed
to be instantaneous;
downgradient
exposures at’ any point
in any year are
assumed to be at
concentrations equal
to those exiting the
unsaturated zone in
that year
Same as (1)
Medium-
High
Derived from central
tendency estimates of
advection, aquifer
thickness, and distance
from unit
Dispersion is assumed
not to apply
Same as (1)
Medium-
high
Draft rch 23, 1993
4
-------
Individual Risk Individual Risk to
Highly-Exposed Pb. _..ieter
Parameter
Central Tendency High-End Population Risk Subpopulations Uncertainty
(1) (2) (3) (4)
Distance to wells,
surface water
Site-specific best
estimate
Same as (1)
Low
Well Depth
Site-specific best
estimate
Screened at surface of
aquifer
Same as (1)
N/A
Medium
Non-Aqueous Phase
Uquids (NAPL5)
NAPLs modelled using
same
advection/dispersion
approach as for other
contaminants (i.e., there
was no modelling of two-
phase flow for light or
dense NAPLs)
Same as (1)
.
High
Air Pathway
Mixing depth of site
soil
15 cm (constant
assumption)c
Same as (1)
Low
Fraction of vegetative
cover
Site-specific best
estimate
Same as (1)
Medium
Vehicle traffic
parameters
Site-specific best
estimate
Same as (1)
Medium
Stability array data
Wind velocity = 3 m/s,
stability class E, 30%
frequency (per ORD)
Same as (1)
Medium
Distance to
receptors, off-site
fields, and off-site
agilcultural fields
Site-specific best
estimate
Same as (1)
Low
Surface Water Pathway
Flow rate in river
Annual avg. or based on
stream order
Lowest monthly avg.
or based on next
smallest stream order
Same as (1)
Medium
Draft -. March 23, 1993 ***
5
-------
Parameter
Individual Risk
Population Risk
(3)
Individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Uncertainty
Central Tendency
(1)
High-End
(2)
Sediment dilution
ratio_(lakes_only)
Best estimate
High-end estimate
N/A
N/A
Medium
Distance to surface
water intakes and
recreational uses
(swimming, fishing)
Site-specific, nearest
downstream use-point on
topo maps or default of
10 meters downstream
from discharge point
Same as (1)
.
Low
Soil Pathway
Sediment delivery
fraction
Site-specific best
estimate
Site-specific high-end
estimate
N/A
Same as (1)
Medium
Area of field
Site-specific best
estimate
Same as (1)
N/A
Same as (1)
Low
Distance to field
Site-specific best
estimate, or default
distance (at facility
boundary)
Same as (1)
N/A
Same as (1)
Low
Foodchaln Pathway
Sediment delivery
fraction
Site-specific best
estimate
Site-specific high-end
estimate
N/A
Same as (1)
Medium
Area of field
Site-specific best
estimate
Same as (1)
N/A
Same as (1)
Low
Fraction of Organic
Carbon in Field
Site-specific best
estimate
Site-specific high-end
estimate
N/A
Same as (1)
Low
Plant uptake rates
Chemical-specific best
estimate from literature
Same as (1)
N/A
Same as (1)
Medium-
High
Bioconcentration and
uptake
Chemical-specific best
estimate from literature
Same as (1)
N/A
Same as (1)
Medium-
High
Draft — ‘i23, 1993
-------
Individual Risk
High-End
(2)
*** Draft — March 23, 1993
7
Individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Central Tendency
(1)
Population Risk
(3)
.geter
Uncertainty
Distance to off-site
Site-specific. Fishing
Same as (1)
N/A
Same as (1)
Low-
agricultural fields and
location: nearest
Medium
fishing locations
downstream use-point on
topo maps or default of
10 meters downstream
from discharge point.
Agricultural field: closest
agricultural field identified
on topo map.
-------
Parameter
Individual Risk
Population Risk -
(3)
Individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Uncertainty
Central Tendency
(1)
High-End
(2)
EXPOSURE ANALYSIS
General AssumptIons
(apply across all exposure
pathways, except where
noted)
Body Weight
70 kg adult (average)’
Same as (1)
Low
Exposure Duration
9 years (median
residence time)’
Same as (1)
Low
Exposure Frequency
350 days/years
Same as (1)
Low
Ground Water Pathway
Maximum Distance
Modeled (from facility
boundary)
5 miles (or less if site-
specific data indicate
plume intercepted by
surface water)
Same as (1)
N/A
N/A
Ingestion of
Contaminated
Drinking Water
Contact Rate
1.4 1/day (average) th
Same as (1)
N/A
Low
Dermal Absorption of
Contaminants while
Showering
Exposure
Time
7 minutes/day h
Same as (1)
N/A
Low
Surface Area
Exposed
while
Showering
19,400 square cm’
Same as (1)
N/A
Low
Draft — ‘23, 1993
-------
High-End
(2)
Draft — March 23, 1993
9
Individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Individual Risk
Central Tendency
(1)
Population Risk
(3)
Pc . er
Uncertainty
Inhalation of
Contaminants in
Indoor Air from
Contaminated
Ground-water
Contact Rate
15 m 3 /day (average) ’
Same as (1)
N/A
Low
Exposure
Location
Exposure assumed
throughout home to
average concentration in
indoor air.
Same as (1)
N/A
Low
Alt Pathway
Maximum Distance
Modeled (from facility
boundary)
10 kilometers
Same as (1)
N/A
N/A
Inhalation of
Contaminants from
Air
Contact Rate
20 m 3 /day (average)
Same as (1)
N/A
Low
Exposure
Location
Exposure assumed
throughout home to
same concentration as in
outdoor air at that
location
Same as (1)
N/A
Low
Soil Pathway
Maximum Distance
Modeled (from facility
boundary)
Site-specific, depending
on topographic and
meteorological
characteristics of the area
Same as (1)
N/A
Same as (1)
N/A
Exposure Duration
Aduft: 9 years
Child: 5 yearsh
Same as (1)
N/A
Child: 5 years
Low
-------
Parameter
Individual Risk
Population Risk
(3)
Individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Uncertainty
Central Tendency
(1)
High-End
(2)
Exposure Frequency
Adult: 350 daystyear
Child: 350 days/year 9
Same as (1)
N/A
Child: 365 days/year
Low
Ingestion of Soil
Contact Rate
Adult: 100 mg/day
(average)’
ChIld: 200 mg/day
(average)”
Same as (1)
N/A
Child: 800 mg/day
(upper range)’
High
Body Weight
Adult: 70 kg (average)’
Child: 16 kg (median)”
Same as (1)
N/A
Child: Same as (1)
Low
Dermal Absorption
from Soil
Surface Area
Exposed for
Dermal
Absorption
Adult: 5,000 square cm”
Child: 2,500 square cm”
Same as (1)
N/A
Child: Same as (1)
Low
Exposure Location
Site specific estimates;
exposure at off-site fields
(e.g., playgrounds,
athletic fields, and school
yards) and off-site
agricultural fields (e.g.,
field crops, pastures, row
crops, orchards,
vineyards, and large
suburban vegetable
gardens) assumed to
constituent concentration
calculated as an average
over entire field
Same as (1)
N/A
N/A
.
Low
Draft — ‘1 23, 1993
-------
Individual Risk
High-End
(2)
Draft — March 23 1993 ***
Individual Risk to•
Highly-Exposed
Subpopulations
(4)
Uncertainty
Parameter
Central Tendency
(1)
Population Risk
(3)
Surface Water Pathway
Maximum Distance
Modeled (from facility
boundary)
Site-specific (specified
uses within 15 miles)
Same as (1)
N/A
N/A
Ingestion of
Contaminated
Drinking Water
Dermal Absorption of
Contaminants while
Showering
Inhalation of
Contaminants hi
Indoor Air from
Contaminated
Surface Water
Exposure due to
household use of surface
water is evaluated using
the same assumptions as
for the corresponding
ground water exposure
routes
Same as (1)
N/A
N/A
Dermal Absorption
while Swimming
Exposure
Frequency;
Exposure
Time
26 days/yea, ’
2.6 hours/day’
Same as (1)
N/A
N/A
Low
Surface Area
Exposed
19,400 square cm’
Same as (1)
N/A
NIA
Low
Incidental Ingestion
of Water while
Swimming
Exposure
Frequency;
Exposure
Time
26 days/year”
2.6 hours/day’
Same as (1)
N/A
N/A
Low
11
-------
Parameter
individual Risk
Population Risk
(3)
Individual Risk to
Highly-Exposed
Subpoputations
(4)
Parameter
Uncertainty
Central Tendency
(1)
High-End
(2)
Contact Rate
0.05 1/hour’
Same as (1)
N/A
N/A
Low
Exposure location
when swimming
Exposure at recreational
use point to constituent
concentration in surface
water
Same as (1)
N/A
N/A
Low
Foodchaln Pathway
Maximum Distance
Modeled (from facility
boundary)
Site-specific
,
Same as (1)
N/A
Same as (1)
N/A
Exposure Duration
9 years’
Same as (1)
N/A
30 years (90th
percentile residence
time) for subsistence
fishermen and 40
years for subsistence
farmer&
Low
Exposure Frequency
350 days/year’
Same as (1)
N/A
365 days/year’
Low
Exposure Leaf 65 g/day (based on Same as (1) N/A 103 g/day (40% from Low
Rate Veg. assumption that 25% of contaminated
consumption is from source)hm
contaminated source)hm
Root
Veg.
46 g/day (25% from
contaminated source) Im
Same as (1)
N/A
74 g/day (40% from
contaminated
source)”
Low
Beef
44 g/day (44% from
contaminated source)””
Same as (1)
N/A
75 g/day (75% from
contaminated
source)”
Low
Dairy
Products
160 g/day (40% from
contaminated source)0
Same as (1)
N/A
300 g/day (75% from
contaminated
source)”°
Low
Draft — ‘i23, 1993 * *
-------
High-End
(2)
*** Draft — March 23,1993
Individual Risk to
Highly-Exposed
Subpopulations
(4)
Parameter
Individual Risk
Central Tendency
(1)
Population Risk
(3)
Pare. •
Uncertainty
Fish
7.6 9/day (20% from
contaminated source)
Same as (1)
N/A
99 g/day (75% from
contaminated
source)’M
Medium-
High
Exposur. to Lead
N/A
N/A
N/A
Risk to sensitive sub-
population (children)
assessed using Lead
UBK model; calculates
blood lead levels in
children 0-7 years in
age; assumes lead
intake from maternal
blood, inhalation,
water, soil, indoor
dust, and food
ingestion
Low
Future PopulatIon Growth
N/A
N/A
County-level growth
rate’
N/A
High
13
-------
Parameter
Individual Risk
Population Risk
(3)
Individual Risk to
Highly-Exposed
Subpopu lations
(4)
Parameter
Uncertainty
Central Tendency
(1)
High-End
(2)
DOSE-RESPONSE ASSESSMENT
Slop. Factor.
Based on upper 95%
confidence limr
Same as (1)
Low-
High
RfD.
Based on most sensitive
endpoint
Same as (1)
Low-
High
Draft. i 23, 1993 ***
-------
High-End
(2)
Population Risk
(3)
individual Risk to
Highly-Exposed
Subpopulations
(4)
N/A Not Applicable
SOURCES:
(Note that the sources listed below may In turn refer to secondary documents es the original source for some of the parameter values.)
(a) Memo from Gerard Lanlak (EPAJERL-Athens) to Barnes Johnson (EPNOSW/CABD). Review and Recommenth
Impact Analysis (CA RIA)’. December 1990.
Draft — March 23, 1993 **
Related to Chemical Data Used In the Correclive Action Regulatory
Parameter
Individual Risk
Central Tendency
(1)
Pars,. ,eter
Uncertainty
RiSK CHARACTERIZATION
Cancer Risk
Lifetime excess cancer
risk for average individual
Lifetime excess cancer
risk for highly-exposed
individual
Statistical number of
cancer cases in
population
Lifetime excess cancer
risk for individual of
highly-exposed
subpopulation
N/A
Non-Cancer Risk
Hazard index for average
individual
Hazard index for
highly-exposed
individual
Number of people
with hazard index> I
Hazard index for
individual of highly-
exposed
subpoputation
N/A
Risk from Exposur. to Lead
N/A
N/A
N/A
Comparison of
exposure
concentration for
sensitive
subpopulation with
threshold blood lead
level of 10
micrograms/deciliter
N/A
Addftlvity of Risk
Risks added across
chemicals and exposure
routes within a given
pathway (e.g., ingestion
and dermal absorption
via swimming in surface
water pathway)
Same as (1)
N/A
15
-------
(b) OLM final in 51 FR 41084-100. November 13, 1986.
(c) MMSOIL model
(e) Bees. C.F. ill. Sharp, R.D., SJoreen. P.L, and Herman. D.W. November 1984. TERRA: A Computer Code for Simulating the Transport of EnvIronmentally Released Radionuclides through
Agriculture.” Oak Ridge National Laboratorv. ORNL 5785 .
US Department of Energy September 1989. Chemical Data Bases for the Multimedia Environmental Pollutant Assessment System ( MEPAS): Version 1 . Draft for External Review.
Topp. E., Scheunert, I. Altar, k and Korte, F. 1986. Factors affecting uptake of ‘ 4 C-labeled organic chemicals by plants from soil. Ecotoxicol. Environ. 8sf. 11:219-228.
Travis, C.C.. and Anne, A.D. 1988. Blocoricentrationof organics in beef, milk, and vegetation. Environ. Sd. Technol. 22(3)271-274.
(1) USEPA 1989. Risk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part A) . Office of Emergency and Remedial Response. EPA/540/1.89/002.
(g) USEPA 1991. Human Health Evaluation Manual, Supplemental Guidance: Standard Default Exposure Factors . Office of Emergency and Remedial Response. OSWER Directive: 9285.6-
03.
(h) USEPA 1989. Exposure Factors Handbook . Office of Health and Environmental Assessment. EPAI600I8-891043.
(I) The general equation to calculate intake via derinel absorption In the water pathway is:
Intake — CWxCFxSAxPCxETxEFxED
BW x AT
where:
CW - ChemIcal concentration In water (mg/I)
CF — ConversIon factor (1 literIl000cm’)
BA — Skin surface area available for contact (cm ’/day)
(19,400 cm’ for adults (central tendency, source (I)))
PC - Chemical-specific dermal permeability constant
E1 Exposure time (hours/day)
EF — Exposure frequency (days/year)
ED = Exposure duration (years)
BW Body weight (kg)
AT Averaging tIme (days)
(j) Internal communication from ORD.
(k) The general equation to calculate intak, via d.rmal absorption In the soil pathway is:
Intake = CSxCFxSAxAFxABSxEFxED
BW x AT
where:
CS = Chemical concentration in soil
CF — Conversion factor (iO mglkg)
SA = Skin surface area avallable for contact (cm’Fday)
(5,000 cm’ for adults (central tendency, source (f)), and 2,500 cm’ for children (default value, source (d)))
AF — Soil-to-skin adherence factor (mg/cm’)
(0.2 mg/cm’ (central tendency, source (f)))
*** Draft — 23, 1993
-------
ABS = Absorption factor (chemical specific constant)
EF = Exposure frequency (days/year)
ED = Exposure duration (years)
9W = Body we ht (kg)
AT Averaging time (days)
( I) The exposure frequency value for dermal absorption and ingestion of contaminated surface water while swimming was prodded by ORD/OHEA. This assumes that an individual swims 2
times a week during the summer (3 months) only.
(m) Exposure rate accounts for the fraction of total vegetables consumed that comes from the contaminated source. The proportion of contaminated vegetables Is assumed to be 25 percent
for the general population (average, source (c)) and 40 percent for subsistence farmers (reasonable worst case, source (c)). This Is used to adjust chronic daily intake. e.g.. the final intake
used to calculate risk would be (intake x 0.25) for the average population and (intake x 0.4) for subsistence farmers. For example, the 65 g/day exposure rate for contaminated leafy
vegetables represents 25 percent of the total daily consumption of leafy vegetables of 260 g.
(n) Exposure rate accounts for the fraction of total beef consumed that comes from the contaminated source. The proportion of cunlu.minated beef Is assumed to be 44 percent for the general
population (average, source (c)) and 75 percent for subsistence farmers (reasonable worst case, source (c)).
(0) Exposure rate accounts for the fraction of total dairy products consumed that comes from the contaminated source. The proportion of contaminated dairy products Is assumed to be 40
percent for the general population (average, source (c)) and 75 percent for subsistence farmers (reasonable worst case, source (c)).
(p) USEPA 1991. Internal EPA Memorandum dated August 19. 1991.
(q) The general equation Is modified for the fish Ingestion pathway to Include traction from contaminated source. This parameter I. Included as a multiplicand in the numerator of the equation
and is abbreviated Fl.” Fl was estimated as 20 percent for the general population (average, source (c)) and 75 percent for subsistence fishermen (reasonable worst case, source (c))
(r) County-level population growth projections from Woods and Poole Economics, Inc.
(s) The slope factors and reference doses are taken from EPA’s Integrated Risk Information System (iRIS), or. if not listed in IRIS. from EPA’s Health Effects Assessment Summary Tables
(HEAST. March 1992).
*** Draft — March 23,1993 ***
17
-------
APPENDIX H: FACILITY AND SWMU DATA FORMS
-------
FACILITY DATA FORM
-------
FACILITY DATA FORM
A. TRACKING DATA
1. Enter the following information:
PERSON COMPLETING DATA FORM (FULL NAME)
SECTIONS COMPLETED
2. Person entering data to the PC data base (full name): ____________________________
M 3. Date of the topographIc maps used: _______________________________________
B. GENERAL
1. ICF sample number: _____________________
2. EPA facIlIty IdentIficatIon number: _____________________________________
3. Facility name: _______________________________________________________________
Facility address: _______________________________________________________________
Facility city, state, and zip code: ____________________________________________________
Facility county: _____________________________
4 If applicable,
4a. Federal facility identification number: _____________________________
4b. Federal agency and/or department: (e.g., DOE, BLM, etc.): ___________________________
5. SIC code of current industrial activity, if available (use 4 digits even if only 2 or 3 digits are
available, i.e., fill in appropriately with zeros):
5a. Primary: __________ 5b. Secondary: __________
SOURCE:
6. If applicable, describe past industrial activity and period of activity, use SIC code if available (use
4 digits even if only 2 or 3 digits are available, i.e., fill in appropriately with zeros):
SIC
DESCRIPTION
OF ACTIVITY
TIME
PERIOD
CODE
(IF
SIC
CODE IS UNAVAILABLE)
(YEA
R ACTIVITY STARTED TO YEAR ENDED)
SOURCE:
-------
- 2 - Facility:________
7. Number of employees at the facility (most recent estimate): ____________
SOURCE:
8. Year Industrial activity (i.e., not necessarily waste management activity) began at facility:
SOURCE:
9. Year Industrial activity ceased at facility (enter 9999 if still ongoing):
SOURCE:
11. Describe the statutory or regulatory authority under which environmental assessments, remedial
investigations, or corrective actions have occurred (e.g., RCRA 3004(u)):
SOURCE:
12. Permit status:
— 01 Permitted, operating facility (currently managing RCRA-regulated hazardous waste)
— 02 Operating facility seeking permit (interim status)
— 03 Closing facility that needs a post-closure permit
— 04 Closing facility that does not need a post-closure permit
— 05 Closed facility with post-closure permit
— 06 Other, please specify -
SOURCE: ___________________________________
C. PHYSICAL DATA
M 1. Facility longitude: _____________________ Facility latitude:
SOURCE: ____________________________________
2. Facility dimensions: Length: __________ Width:
UNITS: FT. YD, Ml, MT. KM
SOURCE: __________________________
3. Total area of the facility: ________________
UNITS: AC, HA, SY, SM, M 2
SOURCE: ___________________________
M 4. Facility slope (i.e., average grade):
SOURCE: -_____________
-------
-3-
Facility:_________
D. SOIL-SPECIFIC INFORMATION
Variables for Infiltration. Leaching, and Recharge Data File
M 1. Field capacity of surface soil (vol/vol): _______________
SOURCE: __________________________________________
M 2. WIlting point of surface soil (vol/vol): _______________
SOURCE: _______________________________________
H/M 3. Number of subsurface soil/rock layers found at the facilIty
(i.e., number of distinct soil/rock horizons In the unsaturated zone):
SOURCE: _________________________________________
H/M 4. For each soil/rock layer Identified above, specify the following:
LAYER
DEPTH OF SOIL
LAYER (iength)
TEXTURAL
CLASSIFICATION’
PERCENT ORGANIC MAUER
MIDPOINT (CENTRAL
TENDENCV)
LOW (HIGH END)
Subsurface Layer 1
Subsurface Layer 2
-
Subsurface Layer 3
Subsurface Layer 4
Subsurface Layer 5
Subsurface Layer 6
Subsurface Layer 7
UNITS:
CM, IN, FT, MT
Source:
1 Seiect appropriate USDA textural clas
1. sand 6. sandy clay loam
2. loamy sand 7. clay loam
3. sandy loam 8. silty clay loam
4. loam 9. sandy clay
5. silt loam 10. silty clay
u Iu I I.
11. clay 16. limestone
12. sand & gravel 17. karst limestone
13. sand & gravel w/sig. clay 18. basalt layers
14. sandstone 19. hydraulically connected
basalt
15. shale 20. fractured igneous &
metamorphic
21. glacial till
-------
-4-
Facility:_________
MONTH
Variables for Surface Water Pathway Data File
M 5. Obtain the following erosion parameters:
USLE rainfall factor (tonnes-m/ha-hr):
USLE cover factor:
SOURCE: _____________________
M 6. SCS curve number for surface soil:
SOURCE: _____________________
E. METEOROLOGICAL INFORMATION
Variables for Infiltration. Leaching, and Recharge Data File
M 1. Complete the following meteorological parameters:
JANUARY ___________
FEBRUARY ___________
MARCH __________
APRIL ___________
MAY _______________
JUNE _________________
JULY
AUGUST
SEPTEMBER __________________
OCTOBER _______________
NOVEMBER _________________
DECEMBER
ANNUAL
AVERAGE
Units: CM, IN, FT
Source:
AVERAGE
TEMP.
CM, IN, FT
AVERAGE MONTHLY NO. DAYS WITH
PRECIPITATION PRECIPITATION
(length)
AVERAGE MONTHLY
PAN EVAPORATION
(length)
DC, DF
-------
5 - Faciiity ________
M 7. Average wind velocity (length/time): _______ Units: MS. FS, KH, MR
Source:
F. LAND USE INFORMATION
M 1. Identify percentage of each of the following land uses or land covers within the facility boundaries
(total = 100%):
1 a. Pasture: _________ _____ Specify level of grazing
1 b. Meadow: _________ (high, moderate, or low)
ic. Woodland: _________ _____ Specify level of grazing
1 d. Cropland: _________ (high, moderate, or low)
I e. Bare soil or dirt roads:
If. Hard-surface roads:
19 Other paved surfaces:
(including buildings)
1 h. Water (e g., ponds, lakes,
surface impoundments, wetlands):
1 i. Fraction of other vegetative cover:
SOURCE
M 2. Indicate percent of facility that is located in each of these terrains (total=100%):
2a. in 1 00-year floodplain:
2b. Above floodplain in river bottom:
2c. All other above floodplain:
SOURCE:
M 3. Indicate types of environmental settings at facility
01 Seismic impact areas _02 Karst terrain
_03 Floodplains _04 Poor foundation conditions
_05 Complex hydrogeology _06 Areas susceptible to mass movements
07 Ground-water vulnerability/resource value
SOURCE: _________________________
-------
- 6 - Facility:________
M 4. Indicate types of sensitive or valuable environments in vicinity of facility (enter zero for distance, if on-sit or
adjacent):
TYPE OF SENSITIVE OR VALUABLE ENVIRONMENT
DISTANCE FROM FACILITY
— 01
Wetlands
— 02
National Park
— 03
Areas identified under Coastal Zone Management Act
— 04
Scenic areas identified under National Estuary Program
— 05
Critical areas identified under Clean Lakes Program
— 06
National Seashore Recreational Area
— 07
— 08
National Lakeshore Recreational Area
National Forest
UNITS: FT. YD, Ml, MT, KM
SOURCE:
M 5. Land uses within 1 and 10 miles of the facility boundary [ Enter 0 for percent of land area if the percent is
unknown but the lana use is known to occur]:
Percent of Land Area Nearest Distance
Within 1 Mile Within 10 miles: to Facility
5a. Industrial _______ _______ _______
Sb Commercial -
5c. Residential _______ ________ _______
5d. Agricultural _______ _______ _______
5e. Forest/Field _______ _______ _______
5f. Elementary School ______ _______ ______
5g. Other School ______ _______ ______
5h. Wetlands ______ _______ ______
5i. Park ______ _______ ______
5j. Surface Water ______ _______ ______
5k. Other, specify: ________ ______ _______ ______
UNITS: FT. YD, Ml, MT, KM
SOURCE:
M 7. Based on land use, surface roughness height (length): _____ UNITS: CM, IN, FT
SOURCE: __________________________
M 8. StartIng month for growing season:
M 9. EndIng month for growing season:
SOURCE (for 8 and 9): ________ -
* *
-------
- 7 - Facility ________
G. SURFACE WATER INFORMATION
2. ReceivIng water bodies (check one, both, or none, as applicable):
01 _____ River, stream, creek, etc. 03 _____ Ocean, coastal area, etc.
02 _____ Lake, pond, etc. 04 ____ None
SOURCE:
If there are no receiving water bodies, skip to Section H (page 8)
H 3. Does ground water underlying the facility discharge to surface water?
01 ____ Yes 02 ____ No
SOURCE:
Stream/River Parameters
H 4. Name of the nearest streams/rivers downgradient that are likely to receive contamination from tacihty:
(a)____________________________________
(b)____________________________________
SOURCE:
For the streams/rivers Identified above (Question G.4), give the following information:
5. Distance to the streams/rivers from the faculty boundary (length): (a)_____________
(b)______________
UNITS. FT, YD, MT, Mi, KM
SOURCE
M 6. Average flow rate in streams/rivers (vol/time):
central tendency high end
(a) _______________ ____________
(b) _______________ ____________
UNITS: OM, OF, TS, CS, GP
SOURCE: _______________________
M 7. Average stream/river velocity (length/time): (a)_____________
(b)______________
UNITS: MY, FY, MS, FS
SOURCE:
-------
- 8 - Facility:_________
M 7a. If applicable, provide information on the water bodies that either of the streams (identIfied In GA ’
Into:
Name of the water body (either 4a or 4b) flowing into
another water body: ______________________________
Name of the receiving water body within 15 miles: ______________________________
Distance to point of confluence (within 15 miles): __________________ UNITS: MT, FT, MI
SOURCE:
Lake or Coastal Water Body Parameters
H 8. Name of the nearest lake or coastal water body downgradient that is likely to receive contamination from
facility ________________________________
SOURCE:
For the lake or coastal water body identified above (Question G.8), give the following information:
H 9. Distance to the nearest lake or coastal water body from facility boundary (length):_________
UNITS: MT, YD, FT, Ml, KM
SOURCE:
H/M 10. Surface area (only for lakes): ___________
SOURCE:
H/M 11. Average water depth (length): __________
SOURCE:
M 12. Sediment dilution ratios (0 to 1)
Central tendency: ___________ _____________
SOURCE:
UNITS: AC, HA, SY, SM, M 2
UNITS: CM, IN, FT, MT, YD
High end:____________
H. GROUND WATER INFORMATION
H 2. Upper aquifer material: _____________
SOURCE: _______________________________
H 3. Regional hydraulic gradient (length/length): _____________
UNITS:
SOURCE: _____________________________
H 4. Hydraulic conductivIty of upper aquifer (length/time): ____________ UNITS:MY, FY, FS
SOURCE:
-------
- 9 - Facility:_________
1.’ 5• Porosity of upper aquifer material (voi/vol): ____________
SOURCE:
H 6. Bulk density of upper aquifer material (masslvol): ____________ UNITS: GC, PF
SOURCE:
H 7. FractIon of organic carbon in upper aquifer material (model default is 0.001): ____________
SOURCE:
H 8. Saturated thickness of upper aquifer (length): ____________ UNITS: MT, FT. YD
SOURCE:
H/M 9. pH of ground water: _________ SOURCE:____________________________
M 14. II there are ground-water withdrawal wells (i.e., monitoring wells or boreholes) on-site, please give the
following information:
14a. Number of wells: _________
14b. Number of people served: ________
14c. Average well depth (length): UNITS: MT. FT. YD
14e. Total pumprng rate (vol/time): _________ UNITS: OF, QM, TS, CS, GP
14f. Use of water from wells: _________________________________
SOURCE:
H 15. Information on lower aquifer
1 5a. Is there a lower aquifer that is used as a drinking water source?
____ 01 Yes ____ 02 No _____ 03 Unknown
SOURCE:
15b. If 15a. is Yes, depth from ground surface to top of lower aquifer (length):
UNITS: MT. FT. YD
SOURCE:
1 5c. If iSa is Yes, is upper aquifer hydraulically connected with the lower aquifer?
____ 01 Yes — 02 No — 03 Unknown
SOURCE:
-------
- 10 - Facility:________
17. If there are any upgradient monitoring wells that show ground-water contamination, list the
contaminants and their concentrations below (i.e., list background levels):
CHEMICAL NAME
CAS NUMBER (IF AVAILABLE)
CONCENTRATION
UNITS
ML, GL, PP, PB, PG
ML, GL, PP, PB, PG
ML, GL, PP, PB, PG
ML, GL, PP. PB, PG
ML, GL, PP, PB, PG -
ML, GL, PP, PB, PG
SOURCE: __________
I. EXPOSURE INFORMATION
M 1. Distance from facility boundary to the location of nearest person (Maximum Exposed Individual (MEl))
potentially exposed to air releases (i.e., distance from facility boundary to nearest residential point):
___ N ___ E ___ S ___ W
UNITS: FT, YD, MI, MT, KM
SOURCE:
M 2. Size and location of population potentially subject to air exposure (within 50 km of facility). Enter population
for each distance and direction combination:
Direction
Distance Categories (in kilometers from facility boundary)
Oto.5
>5101
> lto2
>2to5
>5tolO
N to NE
NE to E
E to SE
SE to S
S to SW
SW to W
W to NW
NW to N
SOURCE:
-------
- ii - Facility:________
5. Ground-water flow and potential exposures
5a. Direction of ground-water flow beneath facility
SOURCE:
M 5b. Average well screening depth with respect to surface of upper aquifer for
(i) residential use wells:
(ii) public use wells:
(iii) agricultural use wells:
UNITS: MT, FT SOURCE:___________________________
M 5d. Location of the nearest downgradient residential use well (public or private) in the direction of
groundwater flow as identified in l.5a for MEl calculations:
Direction. __________ Distance (length): ______________ UNITS: FT, Ml, MT
SOURCE:
M 5e Private wells within 2 miles of facility potentially subject to ground-water exposure. Enter number of
private wells / number of residents served by private wells (e.g., 3 I 8) in the directions downgradient
from the facility. Specify direction of ground water flow, which is assumed to be the centerline of a
ground-water contamination plume. (The plume will usually be assumed to spread to 45 degrees, but
may be broader in some settings.)
Direction
Distance Categories (in miles and fractions of miles from facility boundary)
0 to .25
> .25 to .5
> .5 to .75
> .75 to 1
> ito 1.5
> 1.5 to 2
Second 22 5 °
sector to left of
plume centerline
First 22.5 ° sector
to left of plume
centerline
First 22.5 ° sector
to rIght of plume
centerline
Second 22.5 °
sector to rIght of
plume centerline
M 5e1. Population served by private wells located near the facility boundary:
0 - 50 meters from the facility boundary:
50 - 150 meters from the facility boundary:
150 - 400 meters from the facility boundary:
SOURCE:
-------
- 12 - Facility:________
M 5f. Distance to and population served by public wells (within 2 mile of the facility) located in the groundv
flow direction (i.e., within 450 on either side of centerline of groundwater flow direction)
Direction from Facility Distance from Facility Population Served
a. _________ ______________ ___________
b. _________ ______________ ___________
C. __________ _______________ ____________
d. ________ _____________ __________
e. _________ ______________ ___________
UNITS: MI, KM, FT. YD, MT
SOURCE:
M 3. Nearest agricultural (crop and cattle) field that Is likely to receive maximum (considering predominant
wind direction and topography) contamination from the site:
3a. Direction from facility: _____________
3b. Distance from facility: ____________ UNITS: F , YD, MI, MT, KM
3c. Area of field: ____________ UNITS: AC, HA, SY, SM, M2
3d. Type of crop: _____________
3e. Sediment delivery fraction values for
Central tendency: _________ High end: ____________
3f. Fraction of organic carbon in surface soil
Central tendency: __________ High end: ___________
3g. Bulk density of surface soil: ___________ UNITS: GC, PF
3h Depth of irrigation: ____________ UNITS: MY, FY, IV, CR
3i. Source of irrigation water:
— 01 surface water-river — 02 surface water-lake — 03 ground water
3j. Source of cattle water:
— 01 surface water-river — 02 surface water-lake — 03 ground water
SOURCE:
M 5c. If the source of irrigation for the agricultural field in 1.3 is groundwater, and it this well is located in the
Qroundwater flow direction , give the location of nearest downgradient agricultural use well:
Direction:__________
Distance from facility (length):___________ UNITS: MT. FT. YD, Ml, KM
SOURCE:
-------
- 13 -
Facility ________
4. Nearest off-site field for human exposure via direct contact with soil (e.g., playground, garden):
4a. Direction from facility: _____________
4b. Distance from facility: ___________
4c. Area of field:_______________
4d. Sediment delivery fraction values for
Central tendency:__________
SOURCE:
UNITS: FT, YD, Ml, MT, KM
UNITS: AC, HA, SY, SM, M2
High end: _______________
M
6. Potential surface water exposures
6a. Public drinking water supply intakes located within 15 miles downstream of facility on water bodies
identified in G.4:
Distance from Facility
Water Body
Population Served
ii.
lii.
iv.
M
UNITS:
SOURCE:
Ml, KM. FY, YD, MT
6b. Other water uses within 15 miles downstream of the facility on water bodies identified in G 4 (e.g.,
agricultural uses, swimming, and fishing):
Distance from Facility
Water Body
Water Use
ii
iii.
iv.
Units:
SOURCE
Ml, KM, Fr, YD, MT
M 7. Number of residences within 1/4 mile of facility:
SOURCE: ________________________
J. FOOD CHAIN PATHWAY
M 2. Pasture production (mass/area): __________
SOURCE:
UNITS: KS, KA, PA, PM
M 3. Vegetable production (mass/area): __________
SOURCE:
UNITS: KS, KA, PA, PM
* ** * * *
-------
- 14 - Facility.________
M 4. Is there any evidence of:
4a. Cattle grazing in the nearby agricultural field (Yes/No): _________
4b. Human consumption of vegetables produced in the nearby agricultural
field (Yes/No): —
K. RELEASE INFORMATION
Identify the types of chemical contaminants found at the facility (e.g., from monitoring or sampling data). Similar
questions are asked about each SWMU at the facility in the SWMU data form. If you are unable to answer the
chemical-specific questions at the SWMU-level, please describe the contaminants found at the facility in this
section of the facility data form.
1. Have there been any releases or contamination noted at the facility in the past?
(Yes/Suspected/No/Unknown): —
SOURCE:
2. Describe facility-wide release information:_________________________________________
L. FACILITY-WIDE SWMU INFORMATION
1. Number of solid waste management units or areas of concern identified at facility:
_____ Regulated RCRA Subpart F land disposal SWMUs (i.e., landfill, surface impoundment, waste p
land treatment unit that managed hazardous waste after 7/26/82)
_____ Other SWMUs (e.g., solid waste landfills, other hazardous waste management units requiring permit or
exempt)
_____ Areas of concern (AOC)
SOURCE:
-------
- 15 - Facility:________
2. Complete the following table for the SWMUs identified:
SWMU TYPE
.
TOTAL NUMBER
.
NUMBER UNDER CERCLA
AUTHORITY
NUMBER WrTH MIXED HAZARDOUS
AND RADIOACTiVE WASTES
SUBPART F
OTHER SWMUS
SUBPART F°
OTHER SWMUS
SUBPART F
OTHER SWMUs
Landfills
Land Treatment
Waste Piles
.
Treatment Surface Impoundments
Storage Surface Impoundments
,
Unspecified Surface Impoundments
Treatment Tanks
Storage Tanks
Unspecified Tanks
Incinerators
Injection Wells
Waste Transfer Stations
Waste Recycling Operations
Routine and Systematic Spill Areas -
Other Spill Areas
Accumulation Areas
Process Sewers
Other SWMUs
Areas of Concern 1
Total I I I I I I
SOURCE:
PLEASE COMPLETE ONE SWMU DATA FORM FOR EACH SWMU IDENTIFIED ABOVE.
M. COMMENTS AND FURTHER FACILITY DESCRIPTION (Use this page - - and attach others as’necessary —
to present comments and facility description information that might be relevant for the corrective action RIA but
was not included elsewhere in this form.)
-------
SWMU DATA FORM
-------
A. TRACKING DATA
SWMU DATA FORM
2. Enter the following information:
Person Completing Data Form Sections Completed
1. Person entering data from this form to the PC data base:
3. Is the location of this SWMU identified on available site maps (YIN): _______
B. SWMU IDENTIFICATION
1. ICF number:
2. Facility name: _____________________________________________________
4. SequentIal SWMU number (start W/ 001 for each facility): __________
5. SWMU name (e.g., pond #1):
C. GENERAL DESCRIPTION
1. SWMU Regulatory Status (Check one):
01 Regulated RCRA Subpart F land disposal SWMU (i.e., landfill, surface impoundment, waste
pile, or land treatment unit that managed hazardous waste after 7/26/82)
02 Other SWMU
SOURCE:
I a. Is the SWMU subject to other federal cleanup authorities?
____ 01 Yes
____ 02 No
lb. If yes, what authorities could apply?
____ 01 CERCLA (e.g., whole facility or part of
operable unit is on NPL)
____ 02 RCRA Subtitle I (product storage tanks)
03 TSCA (PCB spills)
04 CWA (Discharges to surface water)
_____ 05 AEA (radionuclides)
06 Other:
3. SWMU type:
01 Landfiii
— 02 Land Treatment
03 Waste Pile
— 04 Treatment Surface impoundment
— 05 Storage Surface impoundment
— 06 Unspecified Surface impoundment
— 18 Other SWMU, specify
SOURCE
07
Treatment Tank
— 13 Waste Recyciing Operation
08
Storage Tank
— 14 Routine & Systematic Spill Area
09
Unspecified Tank
— 15 Other Spiii Area
10
Incinerator
— 16 Accumuiatuon Area
— 17 Process Sewer
11
Injection Wail
12
Waste Transfer St
— 19 Areas of Concern
-------
- 2 - Facility ______
SWMU: ____
ic. Is the SWMU subject to state cleanup authorities?
— 01 Yes, enter name of applicable state authorities:________________________________
02 No
SOURCE (la, lb. and ic):______________________________________
2. Year SWMU was first used: _______
SOURCE: ___________________________________
3. Operating status of SWMU:
01 Currently in service (skip to Question 7)
02 Currently not in service (go to next question)
SOURCE:
4. Complete one of the following if the SWMU is currently pt In service:
Year waste is no longer added to the SWMU: _____
SWMU is temporarily not in service. What year is it expected to resume use? _____
SOURCE:
5. If SWMU has undergone closure, how was unit closed?
_01 Unit dismantled and removed _05 No closure measures taken
_02 Excavation and decontamination _06 impoundment doled with waste in place
_03 Containment system installed _07 Other- Describe ______________________________________
04 Unit capped _08 Unknown or unavailable
SOURCE:
7. Complete the following SWMU dimensions:
Area (length * length) Depth (length)
Central tendency
High End
UNITS
HA, AC, M2, SY, SF
FT. YD, MT, IN, CM
SOURCE:
8. is this a SWMU of concern?
— Yes - Continue filling out the form
— No - Stop here , do not complete remainder of the form (e.g., do not include Subpart F units)
Continue only if this is a SWMU of concern.
-------
- 3 - Facility: ______
SWMU: ____
D. KNOWN. SUSPECTED. OR POTENTIAL RELEASES
1 Has the SWMU released any wastes/constituents? Specify one of the following for each SWMU:
Yes/Suspected/None Documented/No/Unknown
— Ground Water Surface Water
Soil Air
— Other, specify ______________________
SOURCE:
3 Were corrective actions taken for these releases?
01 Yes 02 No 03 Unknown
If so,
3a What year did actions start?
— 01 pre-1984 — 02 post-1984 — 03 Unknown
3b. What media? (check all that apply)
— 01 Soil — 02 Ground water 03 Surface water
04 Air 05 Other 06 Unknown
SOURCE:
5 What activities has an RFA or similar site investigation suggested for this SWMU? (Check all that apply.)
— 01 No further action -
— 02 Additional investigation to determine appropriate action. Describe recommendations:
03 Specific remedial action. Describe recommended remedial activities, including interim
measures: _______________________________________________________________
SOURCES ______________________________________________
6 Likelihood of future releases of concern. Based on information presented in the RFA or other
documentation, indicate the relative potential for future releases to each medium with one of the following
codes.
01 - Release Expected (High) 04 - Release Not Possibie
02- Release Probable (Medium) 05 - Likelihood Unknown
03 - Release Unlikely (Low)
MEDIUM
POTENTIAL FOR
RELEASE
BASIS/SOURCE
Ground Water
Surface Water
Soil
Air
.
-------
- 4 - Facility: ______
SWMU: ____
This data form is now complete and you do not need to proceed to the remaining sections. Fill V
following sections only for the SWMUs that have to be modeled.
E. WASTE TYPE AND QUANTITY
1. List of waste types/codes and quantities contained by the unit:
WASTE TYPE/CODES
QUANTITY (MASS OR VOLUME)
MASS LB. TN, TM, KG
VOLUME GN, LT, CF, M3, CY
2. At this SWMU (landfills, waste piles, and land treatment units) are air emissions due to:
vehicle particulate disturbance possible? (V/N): _____
spreading or material movement operations possible? (V/N): _____
SOURCE:
4. Complete the following table with constituent concentration and physical state of wastes in the
SWMU:
CONSTITUENT
CONCENTRATION (mg/kg for solid or
PHYSICAL STATE
sludge. mg/i for liquid)
(L=iiquid, S=Soiid/
Sludge)
NAME
CAS NUMBER
CENTRAL TENDENCY
HIGH END
SOURCE:
-------
- 5 - Facility. ______
SWMU: ____
F. LOCATION OF SWMU
1. DIstance from edge of SWMU to facility boundary:
la in direction of ground-water flow (length): _____
lb. in direction of surface water runoff (if river is on-site, measure the
distance up to river boundary) (length): _______
1 c. in 4 compass directions (length):
__ N E S ___ W
UNITS: FT. YD, MT. Ml, KM
SOURCE:
G. MODELING INFORMATION
3. Fraction of contaminated area with vegetative cover: ______
SOURCE:
7. Depth of clean cover (i.e., uncontaminated topsoil or other material): _____________
UNlTS FT. YD, MT. iN, CM
SOURCE
9. How will this unit be modeled? (Fill out sections noted for each category below)
— 01 Landfill - Section H
— 02 Surface Impoundment - Section H
— 04 injection Well - Section J
— 05 Tanks - Section K
SOURCE:
10. Relationship to other SWMUs:
ba. Wastes/wastewaters removed/discharged from this SWMU to SWMU #: ______
lOb. Wastes/wastewaters received by this SWMU from SWMU #: ______
lOc. Other (e.g., describe groups of SWMUs, similar or identical SWMUs in parallel waste management
trains):
SOURCE:
if the SWMU is a landfill or a surface impoundment or a surface impoundment converted to a
landfill, complete Section H; injection well - Section J; tank - Section K. (Section I and L are blank.)
-------
- 6 - Facility. ______
SWMLJ: ____
H. LANDFILL/SURFACE IMPOUNDMENT (Si )
1. LandfIll/SI cover type:
_00 Uncovered _02 Clay Cover
_01 Vegetative _03 RCRA Cap
SOURCE: ____________
2. LandfIll/SI liner type:
_01 Unlined 04 Double Clay/Synthetic
_02 Clay Liner _05 MTR Double Composite/Synthetic
_03 Single Synthetic
SOURCE: ________________________
3. Characterize each cover/liner component:
Layers
Thickness (iength)
cover
Vegetation (inciuding top soil)
Drainage
Synthetic
Clay
Liner
Drainage - I
Drainage - 2
Synthetic .1
-
Synthetic -2
Ciay
Thickness Units: FT, YD, MT, IN, CM
SOURCE
4. Describe integrity of cover, liner, and other containment features: __________
SOURCE:
If this is a surface impoundment complete the following:
10. If this is a landfill converted from a surface Impoundment,
year in which It was converted (enter ‘9999’ if still a SI): _______
SOURCE: ______________________
13. Monthly influx to pond (vol): ________ UNITS: M3, GN, LT, CF, CV
SOURCE: -______________________
-------
- 7 - Facility: ______
SWMU: ____
J. INJECTION WELL
1. Difference between water table and injection water column (length).
UNITS. MT, Fr, YD
SOURCE:
2. Injection rate (vol/time):
UNITS: QF, QM, TS, CS
SOURCE:
3. Pump head (length): ______
UNITS: MT, PT, YD
SOURCE:
4. Type of Injection well:
_____ 01 Water flooding (secondary recovery)
_____ 02 Disposal
SOURCE:
5. Type of well failure expected:
______ 01 Grout failure
______ 02 Well casing failure
______ 03 Conservative approach between grout and well casing failures
SOURCE:
-------
- 8- Facility: ______
SWMU: ____
K. TANKS
Tank placement:
______ 01 Aboveground
______ 02 Surface
______ 03 Underground
SOURCE:
2. Construction material:
______ 01 Carbon
______ 02 Steel
______ 03 Concrete
_______ 04 Other, specify:
SOURCE:
3. Describe construction and integrity of containment features (indicate if no containment features
are present):
SOURCE: __________
4. Tank volume:
Central Tendency:
High End.
UNITS: GN, M3, LT
SOURCE:__________
5. Tank Code: _____
SOURCE
-------
- 9 - Facility ______
SWMU:
M. COMMENTS AND FURTHER SWMU DESCRIPTION (Use this space -- and attach other pages
as necessary -- to present comments and SWMU description information that might be relevant for the
corrective action RIA but was not included elsewhere in this form.)
-------
APPENDIX I. CHARACTERISTICS OF FACILITY AND SWMU POPULATIONS
This appendix describes the population of facilities and solid waste management units
(SWMUs) in the United States subject to corrective action requirements. The stratified random
sampling methodology EPA used to select the Corrective Action Regulatory Impact Analysis
(RIA) sample facilities allows the Agency to extrapolate from data collected on these sample
facilities and characterize the national population of facilities and SWMUs subject to corrective
action. The national data presented here are extrapolated from sample facility data using
weights derived from the RIA’s stratified sampling procedure.
EPA used the data forms shown in Appendix H to collect detailed information on all of
the facilities and SWMUs in the RIA sample. Section 1.1 presents characteristics of facilities .
Facility data include general characteristics, size, environmental setting, information about
surface and ground water potentially affected by the facilities, and descriptions of populations
potentially exposed to releases from facilities. Section 1.2 presents SWMU characteristics,
including numbers and types of SWMUs at facilities and the characteristics of wastes managed in
the SWMUs. Throughout the appendix, EPA’s characterization of the regulated population is
based on extrapolation from the sample, rather than compilation of data for the entire
population.
For facilities considered most likely to trigger corrective action, the Agency collected
some information not collected for other facilities. For example, EPA did not collect detailed
information on environmental and waste characteristics for facilities that were not likely to
require remediation. In these cases, the exhibits presented in this section describe the population
of facilities likely to trigger corrective action, rather than the full -population of facilities subject
to corrective action requirements. The data presented in sections 1.1.1, 1.1.2, and 1.2.1 apply to
the full population of facilities subject to corrective action (N = 5,800), while those presented in
sections 1.1.3 through 1.1.7 and 1.2.2 apply only to facilities likely to require remediation (N =
2,600).
1.1 Facility Characteristics
1.1.1 General Characteristics
As described in Chapter 3 and Appendix A, the population of facilities subject to RCRA
corrective action authorities consists of 359 federal facilities and about 5,400 non-federal
facilities, for a total of approximately 5,800 facilities. The nine very large federal facilities in the
population were excluded from the RIA sample because of their size and complexity, and
because better data on the facilities are available from the federal agencies. The population
characterized in this analysis includes the remaining facilities in the population, 5,400 non-federal
and 350 federal. Exhibit I-I shows the distribution of’ facilities by EPA Region, derived by
extrapolating from the sample to the population.
EPA also collected data on the current industrial activity of facilities by SIC code.
Exhibit 1-2 shows the numbers of facilities in different industries at the two-digit SIC code level.
The data show that the largest number of facilities are in the chemicals and allied products
DRAF I’ - March 23, 1993
-------
Exhibit Ii
Facilities by EPA Region
(N = 5,800)
the Virgin Islands
0
1.,
Region
Region 9 also Indudes Guam and American Samoa
—.
Region 9
520 facilIties
9.0%
Region 2
200 facilities
3.6%
Region I
290 facilities
5.1%
Region 3
1,300 facilitIes
22%
Region 6
780 facilities
13%
-------
1-3
EXHIBIT 1-2
INDUSTRIAL AC’FIVITY AT FACILITIES
(N = 5 , 800)
SIC Code
Industry
Number of
Facilities
Percent of
Facilities
28 Chemicals and Allied Products 1,100 19
49 Electric, Gas and Sanitary Services 690 12
38 Instruments and Related Products 640 11
36 Electronic and Other Electrical
Equipment 560 9.7
29 Petroleum and Coal Products 360 6.2
97 National Security and
International Affairs (Federal) 350 6.0
24 Lumber and Wood Products 350 6.0
33 Primary Metal Industries 340 5.9
34 Fabricated Metal Products 340 5.9
20 Food and Kindred Products :210 3.6
35 Industrial Machinery and
Equipment 210 3.6
87 Engineering and Management
Services 210 3.6
42 Trucking and Warehousing 130 2.2
82 Educational Services 130 2.2
30 Rubber and Miscellaneous Plastics
Products 67 1.2
51 Wholesale Trade--Nondurable
Goods 63 1.1
37 Transportation Equipment 6.7 0.12
TOTAL
5,800
100
Totals may not sum due to rounding.
° DRAFT - March 23, 1993 *0*
-------
1-4
industry (SIC Code 28), with significant numbers also in electric, gas and sanitary services (SIC
Code 49), instruments and related products (SIC Code 38), and electronic and other electric
equipment (SIC Code 36).
The Agency’s investigation also provided data on the operating status of facilities,
including the years that industrial activity began and ended at each facility, and whether
industrial activity is still ongoing. Exhibit 1-3 shows the distribution of start dates of industrial
activity for all facilities subject to corrective action. The oldest facility opened in 1775; the
newest is a “future facility” that has yet to be built. Construction was heaviest between 1961 and
1980, when over 100 facilities per year, on average, began activity. It was slowest in the period
including the Depression (1921 to 1940), when only about 13 facilities opened annually. The
Agency’s estimates show that the majority of facilities are currently active (about 4,300). There
are an estimated 1,200 inactive facilities, and the status of 300 facilities is unknown.
Subtitle C of RCRA requires owners or operators of hazardous waste treatment, storage
and disposal facilities (TSDFs) to obtain operating permits. Facilities built before November 19,
1980 are allowed to operate under interim status until EPA makes a permit decision. Several
kinds of permits exist. The most common for operating facilities are treatment, storage and
disposal permits. Closing facilities which leave wastes in place must obtain a post-closure permit
describing the necessary post-closure care. Exhibit 1-4 shows the numbers of facilities in several
categories of permit status, for all facilities subject to corrective action.
1.1.2 Physical Data
EPA’s data collection effort determined the dimensions of facilities and calculated their
total areas. Exhibit 1-5 shows the distribution of facility areas for all facilities subject to
corrective action requirements. The smallest facility is estimated to cover 0.076 acres, the largest
is 8,900 acres, the mean size is 330 acres, and the median size is 20 acres. Almost 30 percent of
facilities are between 10 and 100 acres.
1.1.3 Soil Data
For facilities likely to trigger corrective action, EPA collected information on surface soil
and on up to seven soil and rock layers below the surface soil. These subsurface soil/rock layers
constitute the unsaturated zone, where spaces between soil particles contain both air and water
and water cannot be pumped out. For facilities expected to require remediation, Exhibit 1-6
shows the thickness of the unsaturated zone, summed across all subsurface layers. For the large
majority of facilities, the unsaturated zone is between one and five meters thick. The mean
thickness is 5.4 meters, and the median, 2.6 meters.
1.1.4 Sensitive Environmental Settings
EPA collected information on the occurrence of sensitive environmental settings at
facilities, primarily for those facilities likely to trigger corrective action. Exhibit 1-7 shows the
results for the approximately 2,600 facilities predicted to be likely to trigger corrective action.
Environmental settings with seven characteristics were considered: seismic impact areas,
DRAFI’- March 23, 1993
-------
Exhibit 1-3
Start Dates of Industrial Activity
(N = 5,800)
2,500
2.100
2,000
• —
I ’
.—
o 1,500
0
1,000
E 770
630
500
250 210
130
0 Iii I I I ___ I ___
1775-1900 1901-1920 1921-1940 1941-1960 1961-1980 1981-present Not Available
Year Activity Began
Minimum= 1775
Median= 1963
Mean = 1957
Maximum = 1989
-------
1-6
EXHIBIT 1-4
PERMIT STATUS OF FACILITIES
(N = 5,800)
Permit Status
Number of
Facilities
Percent of
Facilities
Permitted, operating facility
1,900
33
Operating facility seeking
permit (interim status)
1,600
28
Closing f icility that needs a
post-closure permit
250
4.3
Closing facility that does not
need a post-closure permit
210
3.6
Closed facility with post-
closure permit
67
1.2
Other
1,500
26
Not available
270
4.7
TOTAL
5,800
100
Totals may not sum due to rounding.
floodplains, complex hydrogeology, ground-water vulnerability or high resource value, karst
terrain, poor foundation conditions, and susceptibility to mass movements. EPA has considered
special standards for waste management activities at facilities located in or near these sensitive
environments. Each of these settings represents a situation where there is an increased
probability of environmental release, difficulty in monitoring or remediation, or high resource
value. To evaluate the occurrence of these settings, the Agency used a methodology developed
for a draft RIA of location standards for hazardous waste management facilities.’ Note that
more than one of the settings may exist at any facility. The estimates suggest that the most
common of the environmental settings investigated are floodplains and seismic impact areas.
Karst terrain is infrequently found.
‘U.S. Environmental Protection Agency. Summary Regulatory Impact Analysis/Background
Information Document for the Development of Subtitle C Location Standards under Section 3004 (o (7
of RCRA . Washington, D.C.: U.S. Environmental Protection Agency, January 26, 1990.
DRAFT - March 23, 1993 °*°
-------
Exhibit 1-5
Distribution of Facility Areas
(N = 5,800)
0
Minimum = 0.076 acres
Median = 20 acres
Mean = 330 acres
Maximum = 8,900 acres
Area (acres)
2.000
1,500
1,000
500
• —
.
C
a.)
E
z
0-1 >1 -10 >10-100 >100-I .000 >1,000-10.000 Not Available
-------
Exhibit 1-6
Thickness of the Unsaturated Zone
(N = 2,600)
2,500
2,000 1,900
a.)
I-
•‘-
1,500
0
- 1,000
E
z
500
270 210
130
67
0 I I I I I I
0-I >1-5 >5-b >10-30 >30
Thickness (meters)
Minimum =0.0025 meters
Median = 2.6 meters
Mean = 5.4 meters
Maximum =74 meters
-------
EXHIBIT 1-7
ENVIRONMENTAL SETTINGS OF FACILITIES
(N = 2,600)
Environmental
Setting
Number of
Facilities
Percent of
Facilities
Seismic Impact
Area
980
38
Floodplains
1,200
46
Complex
Hydrogeology
260
10
Ground-water
Vulnerability or
Resource Value
910
35
Karst Terrain
20
0.77
Poor Foundation
Conditions
560
22
Susceptibility to
Mass Movements
490
,
19
1.1.5 SurI ce-Water Information
The Agency collected data on the kinds of downgradient water bodies which were judged
to be likely to receive erosion or ground-water releases from the facilities. Water bodies were
classified into several categories: river, stream, creek, etc.; lake, pond, etc.; ocean, coastal area,
etc.; and no receiving water bodies. Exhibit 1-8 shows the number of facilities with each kind of
water body, for facilities likely to trigger corrective action. For any given facility, more than one
kind of water body may be present. For some facilities, there are no receiving water bodies
present.
The Agency’s data collection also provided information on the proximity of water bodies
to facilities, again primarily for facilities likely to trigger corrective action. Exhibit 1.9 shows the
distribution of distances from these facilities’ boundaries to the nearest streams or rivers
downgradient that EPA judged to be likely to receive contamination from the facilities. The
mean distance is about 940 meters from the facility, and the minimum is zero meters (where a
river or stream runs through or adjacent to the facility). For an estimated 290 of the facilities,
there are no streams or rivers likely to receive contamination.
•S* DRAFT - March 23, 1993 °‘°
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I - 10
EXHIBIT I-S
FACILITIES WITH RECEIVING WATER BODIES
(N = 2,600)
Receiving Water Body
Number of
Facilities
Percent of
Facilities
River, Stream, Creek, etc.
Lake, Pond, etc.
Ocean, Coastal Area, etc.
No Receiving Water Bodies
2,300
680
160
280
88
26
6.2
11
The Agency also collected data on the distances from facility boundaries to the nearest
lakes and coastal water bodies downgradient that could potentially receive contamination from
the facilities. Of the facilities likely to trigger corrective action, about 680 have lakes that could
receive contamination. EPA estimates that about 360 of these lakes are within a kilometer of
the facility, while about 320 are more than one kilometer away. About 160 facilities have oceans
or coastal areas potentially receiving contamination; an estimated 20 of these water bodies are
within a kilometer of the facility, while about 140 are more than a kilometer away.
1.1.6 Ground-Water Information
EPA’s data collection effort provided information on the hydrogeological characteristics
of facilities likely to trigger corrective action. The saturated zone is the region below the
unsaturated zone, where spaces between soil particles are filled with water that can be pumped
up for consumption (ground water). The saturated zone may be further divided into layers by
relatively impermeable geologic strata. Exhibit 1-10 shows the thickness of the upper saturated
zone across the 2,600 facilities. The thickest saturated zone is about 230 meters; the thinnest, 1.2
meters. The mean thickness is 26 meters, and the median is 11 meters.
Exhibit I-li shows the average linear velocity of ground water in the upper saturated
zone at these facilities. The mean ground-water velocity is about 95 meters per year. The
minimum velocity observed is close to zero, and the maximum is 1.1 kilometers per year. At 42
percent of facilities, ground water moves at a rate between one and ten meters per year.
1.1.7 Exposure Information
EPA collected data on the distance from facility boundaries to the location of the nearest
person potentially exposed to air releases (the Maximum Exposed Individual, ME!). Exhibit 1-12
shows the results for facilities likely to trigger corrective action. For almost half of these
facilities, the ME! is immediately adjacent to the facility. The mean distance is 86 meters, the
median is close to zero meters, and the maximum is about 2.7 kilometers.
DRAFT - March 23, 1993 “
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rJ)
.—
-4
.
0
a .)
E
z
1,200
1,000
800
600
400
200
0
Minimum =0 meters
Median = 240 meters
Mean = 940 meters
Exhibit 1-9
Distribution of Distances to Nearest Stream or River
(N = 2,600)
0- I >1 - 1,000 >1,000 - 10,000
Distance (meters)
-------
Exhibit 1-10
Thickness of Upper Saturated Zone
(N = 2,600)
Minimum = 1.2 meters
Median = 11 meters
Mean = 26 meters
Maximum = 230 meters
Thickness (meters)
ri)
.—
—
.
0
I-
E
z
1.200
1,000
800
600
400
200
0
0-10 >10-20 >20-40 >40-60 >60
-------
Exhibit 1-11
1.200
1,000
800
600
400
200
0
C l )
.—
.
0
E
z
Minimum = 0.000017 meters/year
Median = 9.6 meters/year
Mean = 95 meters/year
Maximum = 1,100 meters/year
Average Linear
Velocity of Groundwater
(N = 2,600)
0-I >1-10 >10-100 >100-1,000 >1,000
Velocity (meters/year)
-------
Exhibit 1-12
Distance to Maximum Exposed Individual
(N = 2,600)
1.400
1,200 1,200
1,000 970
800
0
E
z 4 °°
280
200 130
0— _____ 13
Adjacent >0-100 >100-500 >500-1.000 >1.000
Distance (meters)
Mean = 86 meters
Maximum = 2,700 meters
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I - 15
EPA also determined the total size of populations that are located within ten kilometers
of facilities and could potentially be exposed to air releases from them. The Agency determined
the population within each of five concentric rings around the facilities. For all facilities likely to
trigger corrective action, Exhibit 1-13 shows the average across facilities of the population in each
of these rings. In addition, the exhibit shows the average of the cumulative populations within
different distances of the facilities. The average across facilities of the total population within 0.5
kilometers is approximately 1,300; within two kilometers, 13,000; within ten kilometers, 200,000.
EXHIBIT 1-13
AVERAGE POPULATION POTENTIALLY EXPOSED
TO AIR RELEASES FROM FACILITIES
(N = 2,600)
Distance
From Facility
Boundary
(kin)
Average
Population
Cumulative
Distance From
Facility
Boundary (kin)
Cumulative
Population
0 to 0.5
>0.5 to 1
>1 to 2
>2 to 5
>5 to 10
1,300
3,000
8,600
48,000
140,000
0 to 0.5
0 to 1
0 to 2
0 to 5
0 to 10
1,300
4,300
13,000
61,000
200,000
The Agency also investigated potential exposure through the ground- and surface-water
routes. For the ground-water route, EPA determined the distance in the direction of ground-
water flow to the nearest public or private residential use well likely to receive contamination. 2
For approximately 640 facilities, the nearest well is less than a kilometer away; for about 140
facilities, it is more than a kilometer away. For the remainder of the 2,600 facilities, there are no
downgradient wells judged to be likely to receive contamination.
For all facilities likely to trigger corrective action, Exhibit 1-14 shows the average across
facilities of the number of private wells that could become contaminated by releases to ground
water. The data include only wells downgradient from the facility. The exhibit also shows the
2 For questions involving the number of wells near facilities, wells that were on the other side of
groundwater divides (e.g., surface water intercepts) were not included.
DRAFF - March 23, 1993
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I- 16
number of residents served by these wells. Data for each distance interval are given, as well as
cumulative numbers within different distances of the facilities. The average number of wells
within two miles of facilities is about 16, and the average population served by wells within two
miles is 43 3
EXHIBIT 1-14
AVERAGE NUMBER OF PRIVATE WELLS POTENTIALLY SUBJECI’
TO CONTAMINATION AND POPULATIONS SERVED
(N = 2,600)
Distance From
Facility
Boundary
(miles)
Average
Number of
Wells
Average
Population
Served
Cumulative
Distance From
Facility Boundary
(miles)
Cumulative
Number of
Wells
Cumulative
Population
Served
0 to 0.25
>0.25 to 0.5
>0.5 to 0.75
>0.75 to 1
>1 to 1.5
>1.Sto2
0.80
0.39
1.2
2.2
3.3
8.2
2.1
1.0
3.2
5.7
8.8
22
0 to 0.25
OtoO.5
0 to 0.75
0 to 1
Oto 1.5
Oto2
0.80
1.2
2.4
4.6
7.9
16
2.1
3.1
6.3
12
21
43
EPA also collected information on public wells near facilities and the populations they
serve. Of the facilities likely to trigger corrective action, EPA estimates that about 200 have
public wells in the ground-water flow direction that are likely to become contaminated. 4 For
these facilities, the mean and median distances to the nearest well are about 1.2 miles, and the
minimum distance is zero miles. The average across facilities of the number of people served by
each of these wells is 4,200, and the median is about 3,500. The minimum number of people
served by a well is 25, and the maximum is 53,000.
3 The calculation of populations served assumes a ratio of 2.63 residents per well, based on 1990
national census data.
‘In general, EPA examined wells within two miles of the facility. However, for facilities for which
modeling results indicated that contamination would spread beyond two miles, the Agency collected data
on wells within five miles.
•* DRAFT - March 23, 1993 ***
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I - 17
Finally, for the surface-water route, EPA collected data on public drinking water supply
intakes and their users. Of facilities likely to trigger corrective action, about 870 are estimated to
have public drinking water supply intakes within 15 miles downstream on one of the streams or
rivers described in section 1.1.5. For these facilities, the mean distance to the closest intake is 8.4
miles, the minimum distance 3.7 miles, and the maximum distance 15 miles. The average
number of people served by these public water systems is 130,000, the minimum number 300, and
the maximum number 340,000.
1.2 SWMU Characteristics
1.2.1 SWMU Identification and General Characteristics
EPA collected data on the characteristics of SWMUs at all facilities subject to corrective
action requirements. Exhibit 1-15 shows the total numbers of SWMUs and the average number
of SWMUs per facility, for federal and non-federal facilities. 5 Exhibit 1-16 shows the same data
for SWMUs in three regulatory status categories:
• regulated RCRA Subpart F land disposal SWMUs (i.e. landfill, surface
impoundment, waste pile, or land treatment unit that managed hazardous waste
after 7/26/82)
• Other SWMUs (e.g., solid waste landfills, other hazardous waste management
units requiring permit, or exempt units)
• Areas of concern
Exhibit 1-17 shows the numbers of SWMUs broken down into detailed categories of SWMU
types. Exhibit 1-18 presents data on the SWMUs in each of these categories that will require
remediation. The exhibit shows the average across SWMUs of the age of the SWMU, from the
year it was first used until the present. It also shows the average length of use of SWMUs (from
the year use began until the year it ended for inactive SWMUs and from the year use began until
the present for active SWMUs). Finally, it shows the area of SWMUs.
1.2.2 Waste Characteristics
EPA also collected data on the constituents of wastes in the SWMUs at facilities likely to
trigger corrective action. Exhibit 1-19 shows the ten most prevalent constituents, and the
numbers of facilities where these constituents are present. The most common constituents are
chromium, lead, arsenic, phenol and tetrachloroethylene, each present at least a thousand of the
2,600 facilities likely to require cleanup.
5 Note that the sample excluded the nine largest Federal facilities and thus underestimates the total
number of SWMUs.
DRAFT - March 23, 1993
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I. 18
EXHIBIT 1-15
TOTAL SWMUS AND SWMUs PER FACILITY
FOR FEDERAL AND NON-FEDERAL FACILITIES
(N = 5,800)
SWMU
Type
Number of
SWMUs
Number of
Facilities
SWMUs
Per Facility
Federal •
Non-Federal
9,700
94,000
350
5,400
28
17
TOTAL
104,000
5,800
18
Totals may not sum because of rounding.
‘Excludes the nine vely large federal facilities.
EXHIBIT 1-16
TOTAL SWMUs AND SWMUS PER FACILITY
BY REGULATORY STATUS OF SWMU
(N = 5,800)
Regulatory Status
- of SWMU
Number of
SWMUs
SWMUs Per
Facility
Subpart F
4,600
0.79
Other SWMUs
97,000
17
Areas of Concern
1,700
0.29
TOTAL
104,000
—
Totals may not sum because of rounding.
° DRAFT - March 23, 1993
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1- 19
Landfihls
Land Treatment
Waste Piles’
Surfice Impoundments’
Tanks
Incinerators
Injection Wells
Waste Transfer Stations
Waste Recycling Operations
Spill Areas
Accumulation Areas
Process Sewers
Other SWMUS
Areas of Concern
Totals may not sum because of rounding.
• Includes both Subpart F units and SWMUs.
EXHIBIT 1-17
NUMBERS OF SWMUS BY SWMU TYPE
(N = 5,800)
SWMU Type
Number of
SWMUs
Percent of
SWMUs
5,400
790
2,800
9,700
30,000
1,600
430
1,600
2,300
5,800
10,000
5,000
26,000
1,700
5.2
0.76
2.7
9.3
29
1.5
0.41
1.5
2.2
5.6
9.6
4.8
25
1.6
TOTAL
104,000
100
“ DRAFT - March 23, 1993 ***
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1-20
EXHIBIT 1-18
AGE, LENGTH OF USE AND AREA
FOR SWMUs REQUIRING REMEDIATION
(N = 5,800)
SWMU Type
Number of
SWMUs
Remediated
Age
(Years)
Length
of Use
(Years)
Surface
Area (M 2 )
Landfills 2,000 40 23 20,000
Land Treatment a 320 18 11 25,000
Waste Piles a 810 34 24 1,700
Surface
Impoundments a 3,500 28 20 18,000
Tanks 2,800 29 25 9,700
Incinerators 10 72 58 200
Injection Wells 37 13 N/A N/A
Waste Transfer
Stations 3 20 20 4
Waste Recycling
Operations 0 —
Spill Areas 1,100 26 21 1,900
Accumulation
Areas 740 37 28 900
Process Sewers 410 25 10 390
Other SWMUs 2,900 41 27 1,800
Areas of Concern 100 15 13 23
TOTAL
15,000
30
21
6,200
NA: Data not available.
Totals may not sum because of rounding.
‘Includes both Subpart F units and SWMUs.
DRAFF - March 23, 1993
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I - 21
EXHIBIT 149
MOST PREVALENT CONSTITUENTS
IN SWMUs
(N = 2,600)
Number of
Facilities
Where
Percent of
Facilities
Where
Constituent
Present
Present
Chromium 1,500 58
Lead 1,500 58
Arsenic 1,100 42
Phenol 1,000 38
Tetrachloroethylene 1,000 38
Benzene 990 38
Cadmium 980 38
Toluene 890 34
Xylenes (Mixed) 800 31
Methyl Chloroform 700 27
DRAFI’ - March 23, 1993 °
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