906R87104
CHAPTER 4
HUMAN HEALTH AND ENVIRONMENTAL HEALTH
RISK ASSESSMENT
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NOTICE
This document is an interim draft and is not yet complete. It is being
circulated for initial critical review of the scenario development methods and
results (Chapter 3), the risk estimation methods (Chapter 4), and the
qualitative assessments (Chapters 6 and 7).
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TABLE OF CONTENTS
EXECUTIVE SUMMARY ...[Reserved]
1. INTRODUCTION
2. OVERVIEW OF THE APPROACH ..
2.1 Scope of This Study ..
2.2 Overview of the Quantitative Risk Assessment Methodology
2.2.1 Model Scenarios
2.2.2 Modeling Procedures
2.2.3 Input Data
2.2.4 Outputs
2.3 Overview of the Qualitative Assessment Methodology
2.3.1 Assessment of Waste Disposal on Alaska's
North Slope
2.3.2 Assessment of Sensitive Environments
2.3.3 Extrapolation from Damage Cases
3. OIL AND GAS: MODEL SCENARIOS
3.1 Approach for Developing Model Scenarios .
3.1.1 Waste Types/Constituents
3.1.2 Waste Management Practices
3.1.3 Environmental Settings
3.1.4 Development of Composite Scenarios
3.2 Estimation of Frequencies for Model Scenarios ,
3.2.1 Waste Type and Waste Management Practice Scenarios
3.2.2 Environmental Setting Scenarios
3.3 Results of Model Scenario Development ,
3.3.1 Waste Types
3.3.2 Selection of Model Constituents
3.3.3 Waste Management Practices
3.3.4 Environmental Settings
4. OIL AND GAS: QUANTITATIVE MODELING METHODS
4.1 Chemical Releases
4.1.1 Underground Injection
4.1.2 Surface Pits
4.1.3 Direct Discharge to Surface Water
4.2 Chemical Transport
4.2.1 Subsurface Transport Submodel ..,
4.2.2 Surface Water Transport Submodel
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TABLE OF CONTENTS
4.3 Potential Effects
4.3.1 Human Health Risks
4.3.2 Aquatic Toxicity
4.3.3 Environmental Resource Damage
4.4 Chemical Parameters Used in Modeling ...
5. OIL AND GAS: QUANTITATIVE MODELING RESULTS .
5.1 Health Risk Estimates ...[Reserved]
5.2 Potential Aquatic Effects ...[Reserved].
5.3 Potential Resource Damages ...[Reserved]
6. OIL AND GAS: ENVIRONMENTAL ASSESSMENT OF WASTE DISPOSAL ON
ALASKA'S NORTH SLOPE
6.1 Introduction
6.1.1 Purpose
6.1.2 Scope
6.1.3 Methods
6.1.4 Organization of This Assessment
6.2 Description of Oil and Gas Activity
6.2.1 Exploration Activities
6.2.2 Development Operations
6.2.3 Production Operations
6.2.4 Waste Generation and Waste Management
6.3 The North Slope Environment ...
6.3.1 Climate and Meteorology
6.3.2 Demography
6.3.3 Land Uses
6.3.4 Geology
6.3.5 Surface Waters
6.3.6 Ground Water
6.3.7 Biota
6.3.8 Sensitive Environments .
6.4 Potential Human Health and Environmental Impacts
of Waste Disposal ,
6.4.1 Airborne Discharges
6.4.2 Solid Wastes
6.4.3 Liquid Wastes
6.5 Conclusions
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TABLE OF CONTENTS
7. OIL AND GAS: ADDITIONAL QUALITATIVE ASSESSMENTS
7.1 Locations of Oil and Gas Activities in Relation to
Sensitive Environments
7.1.1 Introduction
7.1.2 Endangered and Threatened Species Habitats .
7.1.3 Wetlands
7.1.4 National Forest System Lands
7.1.5 National Park System Lands
7.1.6 Conclusions
7.2 Analysis of Damage Case Results
8. GEOTHERMAL ENERGY: METHODS AND RESULTS ...[Reserved]
9. CONCLUSIONS AND RECOMMENDATIONS ...[Reserved]
REFERENCES
APPENDIX A -- DOCUMENTATION OF CHEMICAL PARAMETERS
A. 1 Parameters Used in Risk Modeling
A.2 Parameters Used in Selecting Model Constituents ..
APPENDIX B -- FREQUENCY TABLES FOR MODEL SCENARIOS
B.I Waste Management Practices
B.2 Environmental Settings ...
APPENDIX C -- RISK MODELING OUTPUT: RAW DATA ..[Reserved]
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LIST OF FIGURES
Number Page
2-1 Overview of the Quantitative Risk Assessment
Methodology
2-2 Zones Used for Scenario Development ,
3-1 Location Sampling Strategy ,
3-2 Process for Weighting Environmental Setting Results
3-3 Sample Aggregation of Environmental Setting Results
4-1 Potential Contaminant Release Pathways from Underground
Injection Wells Into Surface Aquifers
4-2 Eight Generic Flow Fields Used in Saturated Zone
Transport Modeling
4-3 Summation of Individual Breakthrough Curves
4-4 Processes Affecting the Transport and Fate of
Contaminants in Surface Water
6-1 Typical Land Based Mud System
6-2 Standard Arctic Drill Site Layout
6-3 Typical North Slope Drill Site
6-4 North Slope Enhanced Oil Recovery Injection Well Design ...
6-5 Alaska's North Slope
6-6 Tundra Polygons
6-7 Generalized Stratigraphic Column of Arctic Alaska
6-8 Tundra Soils
7-1 Relative Abundance of Wetlands in the U.S. in 1984
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LIST OF TABLES
Number
2-1 Measures of Effect for the Quantitative Risk Modeling .
3-1 Categories of Variables Used to Characterize Environ-
mental Settings
3-2 Frequently Occurring Analytes in Drilling Muds and
Produced Liquids
3-3 Environmental Fate and Mobility Screening of Frequently
Occurring Analytes
3-4 Reference Concentrations
3-5 Ranking of Mobile Constituents .,
3-6 List of Model Constituents
3-7 Model Constituent Concentrations
3-8 Waste Management Practice Scenarios: Oil and Gas
Drilling Operations (Exploration and Development)
3-9 Waste Management Practice Scenarios: Oil and Gas
Production Operations
3-10 Distribution of Sample Sites for Drilling Activity
3-11 Distribution of Sample Sites for Production Activity ...
3-12 Values for Variables Used to Characterize Environmental
Settings
4-1 Model Input Parameters for Mud Reserve Pits
4-2 Default Values for Surface Water Parameters Used in
the Oil and Gas Risk Analysis
4-3 Physical/Chemical Parameters Used in Ground-Water
Modeling
4-4 Physical/Chemical Parameters Used in Surface Water
Modeling
4-5 Toxicity Parameters and Effects Thresholds
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LIST OF TABLES
Number
6-1 Conventional Parameters for Reserve Pit Liquids
6-2 Metal Analyses (mg/1) for Reserve Pit Liquids .,
6-3 Organic Analyses for Reserve Pit Liquids
6-4 Constituents of Arctic Reserve Pit Solids
6-5 Conventional Parameters for Produced Waters
6-6 Metal Analyses (mg/1) for Produced Waters ,
6-7 Organic Analyses for Produced Waters
6-8 Effluent Limitations for Tundra Discharge of Reserve
Pit Fluids
7-1 Overlap of Historical Ranges and States with High Oil
and Gas Activity
7-2 Overlap of Critical Haoitats and Counties with High
Oil and Gas Activity
7-3 Percent of Sampled Quad Maps Containing Floodplains
and Wetlands
7-4 National Forest System Units With Significant Oil
and Gas Exploration and Production
7-5 National Park System (NFS) Units With Active Oil and
Gas Operations Within Their Boundaries
7-6 National Park System (NPS) Units With Potential Oil
and Gas Operations Within Their Boundaries
7-7 Prevalent Types of Damage Cases in Each Zone
7-8 Oil and Gas Damage Case Characteristics
A-l Documentation of Physical/Chemical Parameter Values
for Benzene
A-2 Environmental Fate and Mobility Screening of Frequently
Occurring Analytes
A-3 Ionic Composition of Natural Water From Several
Sources
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LIST OF TABLES
Number Page
B-l On-Site Reserve Pits: Estimated Frequency
Distribution of Pit Sizes
B-2 Underground Injection Wells: Estimated Frequency
Distribution of Injection Rates
B-3 Weighted Distribution of Hydrogeologic Settings for
Drilling Sites
B-4 Weighted National Distribution of Hydrogeolgic Settings
for Drillings Sites
B-5 Weighted Distribution of Surface Water Settings at
Drilling Sites
B-6 Weighted Distribution of the Distance to Nearest
Exposure Wells From Drilling Sites
B-7 Weighted Distribution of Hydrogeologic Settings for
Production Sites
B-8 Weighted National Distribution of Hydrogeolgoic
Settings for Production Sites
B-9 Weighted Distribution of Surface Water Settings at
Production Sites
B-10 Weighted Distribution of the Distance to Nearest
Exposure Wells from Production Sites
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EXECUTIVE SUMMARY
[Reserved]
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CHAPTER 1
INTRODUCTION
Section 8002(m) of the Solid Waste Disposal Act, as amended in 1980,
requires EPA to conduct a detailed and comprehensive study of drilling fluids,
produced waters, and other wastes associated with exploration, development, or
production of crude oil, natural gas, or geothermal energy. Section
8002(m)(1)(C) directs EPA to analyze the potential danger to human health and
the environment from surface runoff or leachate resulting from these
activities. This report describes the approach and preliminary results for a
risk analysis to help fulfill the requirements of Section 8002(m)(1)(C). The
approach is applicable to both oil and gas operations and geothermal
operations, although the input data for the analysis differ for the two
industry categories.
The objectives of the risk analysis are to: (1) characterize and classify
the major risk-influencing factors (e.g., waste types, disposal technologies,
environmental settings) associated with current waste management practices at
oil and gas and geothermal energy facilities;1-1 (2) estimate distributions
of risk-influencing factors across the population of oil and gas geothermal
energy facilities within various geographic zones; (3) rank these factors in
terms of their effect on relative risks; and (4) develop, for different zones
of the U.S., initial quantitative estimates of the range of baseline health
and environmental risks for the variety of waste types, management practices,
and environmental settings that exist.
To meet these objectives, the risk analysis has been divided into two main
parts: a quantitative modeling analysis and a qualitative assessment of
issues that are not amenable to modeling. The quantitative modeling estimates
health and environmental risks from fully specified model scenarios that
represent the range of wastes, release sources, and environmental settings
typical of onshore oil and gas and geothermal energy operations. Because
these waste streams, waste management practices, and environmental settings
are highly varied across the U.S., the nation has been divided into zones and
the distribution of model scenarios has been estimated for each zone. The
model scenarios are based on review and analysis of available data on actual
oil and gas and geothermal energy facilities within each of the different
zones, including the information obtained from EPA's waste sampling efforts.
The quantitative risk analysis produces initial estimates of health risks and
potential environmental damage, identifies low-risk and high-risk scenarios,
and ranks major risk-influencing factors, consistent with the purpose of the
1J All references in this report to oil and gas and geothermal energy
facilities or sites refer to exploration, development, and production
operations.
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Section 8002(m) requirements. In addition, although the quantitative modeling
methods used in this study do not produce site-specific risk estimates or
estimates of national population risks, the methods, modeling techniques, and
data base used could be adapted for that purpose.
The quantitative risk assessment is supplemented with a qualitative
evaluation of certain issues that do not lend themselves to a modeling
analysis. Therefore, the second major portion of the risk assessment involves
a qualitative evaluation of: results of the damage case study conducted as
part of the Section 8002(m) study; the locations of oil and gas and geothermal
energy activities relative to sensitive environments; and oil and gas waste
disposal on the North Slope of Alaska.
As with any national assessment of risk from waste-generating activities,
whether based on specific real facilities or model facility scenarios, many
assumptions were necessary for this analysis. Assumptions were necessary for
at least three reasons: (1) lack of important data about waste generating and
management practices and environmental conditions, coupled with the cost of
obtaining such data; (2) significant limitations of available methods for
modeling chemical release, transport, fate, and effects; and (3) modeling
feasibility and practicality, essential considerations to any broadly-scoped
national risk analysis. In addition to the assumptions required for these
reasons, the scope of the analysis was limited in accordance with the
directive of Section 8002(m) and EPA policy decisions. The scope and major
limitations of the risk assessment are summarized ir. Section 2.1 of this
report. The basis for and implications of specific assumptions are discussed
in the relevant portions of the report (i.e., methods and results discussions)
where the assumptions are-made.
The remainder of this report is divided into eight chapters. Chapter 2
provides an overview of the risk assessment approach, giving separate
summaries of the methods used for the quantitative risk modeling and the
qualitative assessments. The oil and gas industry assessment is covered by
Chapters 3 through 7, while Chapter 8 describes the analysis of the geothermal
energy industry. Chapter 3 describes the procedures used to develop model
scenarios for the quantitative analysis and to estimate the frequencies for
those scenarios. Chapter 4 presents the quantitative risk modeling methods,
and Chapter 5 presents the modeling results. Chapters 6 and 7 outline the
methods and results of the qualitative portions of the risk assessment.
Specifically, Chapter 6 provides an assessment of oil and gas waste disposal
on the North Slope of Alaska, and Chapter 7 provides an assessment of the
proximity of oil and gas operations to sensitive environments and an analysis
of available damage case results. Finally, Chapter 8 covers the risk
assessment for geothermal energy activities, and Chapter 9 provides overall
conclusions and recommendations.
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CHAPTER 2
OVERVIEW OF THE APPROACH
This chapter summarizes the specific risk assessment methodology used in
the oil and gas and geothermal energy risk assessment. The chapter is divided
into three main sections: Section 2.1 defines the scope and highlights major
limitations of the overall project; Section 2.2 provides an overview of the
quantitative modeling methods; and Section 2.3 summarizes the approach for the
qualitative portion of the risk assessment.
2.1 SCOPE OF THIS STUDY
As stated in Chapter 1, this study was designed to fulfill the mandate of
Section 8002(m)(1)(C) of the Solid Waste Disposal Act, which directs EPA to
analyze the potential danger to human health and the environment from surface
runoff or leachate resulting from waste management activities related to
exploration, development, and production of crude oil, natural gas, and
geothermal energy. It was not designed to meet other purposes, such as
estimation of benefits as part of a regulatory impact assessment (RIA) or
estimation of actual risks associated with specific sites. Although the study
is broad in scope, EPA did not attempt to analyze every waste type, waste
management practice, environmental setting, exposure pathway, or effect type
that could potentially be important; instead, EPA opted for coverage of the
predominant wastes, management practices, and settings and analyzed the
exposure pathways and effect types considered to be most significant.
As a practical matter, EPA believes it was necessary to limit the scope of
this study. Some of these limitations may be addressed by EPA in additional
studies. Important limitations in the scope of this study (i.e., areas that
EPA has not attempted to model) include:
(1) Risks/impacts from activities other than waste management
(e.g., site development, road building);
(2) Risks/impacts from non-exempt wastes (i.e., wastes already
covered by RCRA regulations);
(3) Risks/impacts due to non-compliance with applicable state
regulations (i.e., full compliance assumed);
(4) Risks/impacts associated with various alternative waste
management practices (i.e., this study addressed only
current practices);
(5) Risks/impacts resulting from past disposal (i.e., this
study assessed potential future risks/impacts resulting
from new activities);
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(6) Site-specific risks/impacts for real facilities (i.e., this
study was based on model scenarios);
(7) Population risks (i.e., potentially exposed populations
were not estimated);
(8) Risks/impacts resulting from short-term (acute or
subchronic) exposures;
(9) Risk/impacts resulting from accidents or low frequency
events;
(10) Risk/impacts associated with certain specific exposure
pathways, wastes, management practices, or environmental
settings, generally due to lack of data or modeling methods
(specific activities not modeled but potentially important
are noted in the appropriate sections of the report); and
(11) In general, risks/impacts of lower frequency wastes,
practices, or environmental settings (i.e., this study
focused on "typical" scenarios).
2.2 OVERVIEW OF THE QUANTITATIVE RISK ASSESSMENT METHODOLOGY
Potential health and environmental risks associated with waste management
activities depend on the types and quantities of wastes being managed; the
storage, treatment, and disposal technologies being used; and the
environmental settings in which the waste management activities are carried
out. These factors determine the degree to which receptors (human or
environmental) may be exposed to harmful constituents of the waste through
various exposure pathways. Risk is estimated by combining exposure
information with data on the toxicity of specific chemicals and information on
the characteristics of receptor populations.
The Agency has conducted a generic, as opposed to site-specific, risk
assessment of onshore oil and gas and geothermal energy operations. A
schematic overview of the approach is given in Figure 2-1. As shown in this
figure, the quantitative risk assessment approach can be broken down into two
main steps leading up to the actual risk estimation: development of model
scenarios and development of modeling procedures. These two steps are
described in Sections 2.2.1 and 2.2.2, respectively. Sections 2.2.3 and 2.2.4
describe the procedures for developing needed input data and the type of
outputs obtained from the modeling approach.
2.2.1 Model Scenarios
A key part of the generic approach is development and specification of
model scenarios (i.e., hypothetical facilities) to cover the range of
important risk-influencing variables. Essentially, scenarios are unique
combinations of subcategories of important variables that are specified in
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sufficient detail to allow risk estimation. The model scenarios for this
analysis were derived from actual data on wastes, management practices, and
environmental settings of the oil and gas geothermal energy industries.
Approximtely 5,000 model scenarios were developed and analyzed as part of this
analysis.
Although the model scenarios do not represent individual real sites, they
represent groups of similar real facilities in the analysis. Generally, the
more one disaggregates an analysis of this type (i.e., the more variables one
considers and the more subcategories they are divided into), the more precise
the results will be. A larger number of variable subcategories means that
each subcategory can better represent a smaller number of real facilities.
However, the data input requirements, modeling complexity, and analytical
requirements also increase substantially with the level of disaggregation.
Therefore, the design of a generic risk analysis such as this must account for
the tradeoffs between analytical precision requirements and project scope.
The proposed generic risk assessment framework provides an appropriate level
of detail and disaggregation to address the objectives listed in Section 1.1
An important consideration in the development of model scenarios is that
an analysis based on the nation as a whole would overlook some of the
intrinsic risk-bearing characteristics of different areas. Therefore, even
though only one set of model scenarios was developed for the nation, the
frequency distribution was estimated across different zones of the U.S. that
have generally similar characteristics (i.e , the fraction of facilities
represented by each model scenario was estimated by zone). It was recognized
that analyzing smaller areas (e.g., individual states) would yield more
accurate scenarios and risk estimates. However, a balance had to be struck
between the desired level of accuracy and the resources that would need to be
expended to achieve that accuracy. The Agency decided that, for the oil and
gas assessment, a reasonable balance between accuracy and cost could be
achieved by analyzing the zones that were used in the selection of sites for
waste sampling. These zones, shown in Figure 2-2, represent clusters of
states that have generally similar geological formations, drilling and
production activities, and climates (EPA, 1987a). Only one modification to
these zones was made for the purpose of this risk assessment: Alaska was
divided into the North Slope portion (Zone 11A) and the portion surrounding
the Cook Inlet and Kenai Peninsula (Zone 11B). Because of the unique
conditions of the North Slope, that area was analyzed separately through a
qualitative approach.
As part of the model scenario development, the frequency of occurrence of
each model scenario was estimated for the different zones. For example, it
was estimated that 44 percent of drilling sites in Zone 2 are in the shallow
ground-water setting and 56 percent are in the deep ground-water setting.
Estimating the frequency distribution allowed the Agency to evaluate the
representativeness of the scenarios and to weight the risk estimation results
by frequency.
2.2.2 Modeling Procedures
In parallel with the development of model scenarios, the Agency developed,
refined, and integrated the analytical tools necessary to quantitatively
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estimate chemical release, transport, and exposure and effects. Rather than
analyze the release, transport, and potential effects of all possible
constituents of oil and gas and geothermal energy wastes, only a few model
constituents were selected and examined.
Three mechanisms for chemical release were modeled: release to ground
water from surface pits; release to upper layers of ground water from
injection wells; and direct discharge to surface water. For surface pit
releases, deterministic models were used to estimate leachate flux from both
operating and closed pits. The chemical quality of leachate was estimated
using empirically derived waste concentrations. Deterministic models and
EPA's waste characterization data were also used to estimate the flux and
quality from injection well casing and grout seal failures. The flow rate and
quality of direct surface water discharges from stripper wells were estimated
based on average waste generation quantities and EPA's concentration data.
Chemical transport was modeled for ground water and surface water.
Ground-water flow and mass transport was modeled using OSW's Liner Location
Risk and Cost Analysis Model (LLM) (EPA, 1986e). The LLM uses a series of
predetermined flow field scenarios to model ground-water flow and a modified
version of the Random Walk Solute Transport Model (Prickett ejt al. , 1981) to
model chemical transport. Chemical transport in rivers was modeled using
equations given in EPA (1984b).
Human exposures and health risks were estimated using the LLM risk
submodel. To estimate environmental effects, only the potential effects to
ground water, surface water, and aquatic organisms were examined. These
effects were assessed by using the ground- and surface water models described
above to estimate volumes of water contaminated above certain criteria,
including ambient water quality criteria.
2.2.3 Input Data
Input data required for the quantitative modeling can be broken down into
three main categories: data on waste volumes and constituents; data on waste
management practices; and data on environmental settings. Data on waste
volumes and constituents were obtained from EPA's research on sources and
volumes of wastes and EPA's waste stream chemical analysis study, both of
which were conducted specifically as part of the Section 8002(m) project. The
results of EPA's research on waste management practices, also conducted as
part of the Section 8002(m) study, were used to define necessary input
parameters concerning waste management practices. Most data needed to
characterize environmental settings were obtained from an analysis of actual
locations sampled from areas containing high levels of oil and gas or
geothermal activity. Once the sample locations were identified, values for
environmental variables were first developed using U.S. Geologic Survey (USGS)
quadrangle maps and various data bases, and then weighted according to the
relative level of oil and gas activity represented by the sample location.
The particular data inputs and methods for obtaining values for those inputs
are described in detail in Chapter 3 of this report.
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2.2.4 Outputs
The first level of results of the quantitative modeling are a set of fully
specified scenarios defining realistic combinations of waste streams, waste
management practices, and environmental settings. The frequency of occurrence
of each scenario in the various zones shown in Figure 2-2 was also estimated.
There are two types of final outputs from the modeling analysis. First,
for each model scenario, up to seven different measures of potential health
and environmental effects are determined, as shown in Table 2-1. Each of
these measures is chemical-specific (i.e., each of the waste constituents in a
scenario is modeled separately). The second type of final output is a ranking
of the relative risks posed by the different scenarios and an analysis of the
major risk-influencing factors within each scenario. For example, the risk
measures for all scenarios are analyzed to determine which waste streams,
waste management practices, environmental settings, exposure pathways, zones,
and combinations thereof present relatively greater or lesser risks.
2.3 OVERVIEW OF THE QUALITATIVE ASSESSMENT METHODOLOGY
There are three components of the risk assessment that, because of the
nature of the issues involved and information available, could be evaluated
more meaningfully through a qualitative approach rather than the quantitative
modeling procedures described above. These components are: (1) assessment of
the potential impacts resulting from oil and gas waste disposal on the North
Slope of Alaska; (2) evaluation of the locations of oil and gas activities in
relation to sensitive environments; and (3) extrapolation from and
interpretation of damage cases.
2.3.1 Assessment of Waste Disposal on Alaska's North Slope
The models used to assess chemical release and transport in other areas
are not applicable to the North Slope because of the unique operating and
environmental conditions in the Arctic. Consequently, the risks from waste
streams on the North Slope were evaluated qualitatively based on information
in the general literature, comments submitted on the October Technical Report
(EPA, 1986d), and the damage case information collected from Alaska. In
addition, conversations were held with representatives of industry, the Alaska
state government, and various offices of the federal government with
information on the North Slope. The results from this assessment provide
descriptive information on the oil and gas waste streams, management
practices, and environmental characteristics of the North Slope, and provide a
basis for qualitative conclusions about the potential for human health and
environmental impacts.
2.3.2 Assessment of Sensitive Environments
The proximity of oil and gas activities to four categories of sensitive
environments was examined: endangered and threatened species habitats,
wetlands, National Forest System lands, and National Park System lands.
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TABLE 2-1
MEASURES OF EFFECT FOR THE
QUANTITATIVE RISK MODELING
Exposure
Medium
Effect Measures
Human Health
Non-health
Ground water
Surface water
o Cancer risk
o Chronic noncancer risk
o Cancer risk
o Chronic noncancer risk
o Water volume conta-
minated above various
resource damage
criteria
o Water volume contami-
nated above aquatic
toxicity criteria
o Water volume contami-
nated above various
other resource damage
criteria
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Because there are hundreds of thousands of onshore oil and gas sites, a
detailed analysis of these sensitive environments around individual sites was
not possible. Instead, the occurrence of sensitive environments was examined
in numerous sample areas known to contain high levels of exploration and/or
production activity. The results from this sampling approach were
supplemented, where possible, with available summary statistics on the number
and type of sensitive environments that are overlapped by oil and gas
operations.
2.3.3 Analysis of Damage Cases
As a separate part of the overall Section 8002(m) study, information was
collected on documented cases of environmental damage resulting from releases
of oil and gas wastes. This damage case information includes data on the
location of releases, the affected media (i.e., land or water), the type of
release source and waste stream involved, and other relevant topics. To
incorporate the damage case results into the risk assessment, they were
organized and evaluated in two different ways. First, the damage cases were
correlated to the modeling scenarios described above in order to form
conclusions about which scenarios appear to be most risky from the standpoint
of observed land and water damages. Second, in order to analyze the relative
environmental impacts across zones of the U.S., the data on individual damage
cases were combined into aggregate descriptions of damage in various zones.
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CHAPTER 3
OIL AND GAS: MODEL SCENARIOS
This chapter describes the specific methods used to develop the model
scenarios used in the quantitative risk analysis. It also describes the
results of the scenario development and presents the final model scenarios,
The chapter is divided into three sections discussing:
Approach for developing model scenarios (Section
3.1);
Methods for estimating the frequency of scenarios
(Section 3.2); and
Results of scenario development (Section 3.3).
3.1 APPROACH FOR DEVELOPING MODEL SCENARIOS
This section explains the methods EPA used to develop the model site
scenarios analyzed in this study. The scenarios are unique combinations of
important risk-influencing factors (i.e., waste, environmental setting, and
disposal method) that are intended to represent, on average, the predominant
types of sites that exist. The industry-specific data gathered by EPA as part
of the overall oil and gas and geothermal energy study were the primary basis
for developing the waste and disposal practice scenarios; environmental
setting scenarios were based on the Agency's analysis of a sample of drilling
and production sites. Subsections 3.1.1, 3.1.2, and 3.1.3 below describe the
methods followed for developing waste stream, waste management practice, and
environmental setting scenarios. Subsection 3.1.4 outlines the approach used
to aggregate the waste, disposal practice, and environmental setting scenarios
into composite scenarios.
EPA had three major objectives that guided the development of model
scenarios:
(1) Representativeness -- scenarios based on actual industry wastes,
practices, and locations;
(2) Broad coverage -- scenarios based on prevelant industry
characteristics, but not necessarily all-inclusive (i.e.,
breadth of coverage favored over depth); and
(3) Feasibility -- number of scenarios limited by modeling and data
collection feasibility.
Obviously, the more model scenarios analyzed in a study such as this one, the
more precise the results. Feasibility considerations, however, limit the
number of variables included and the number of subcategories of each variable
to those deemed necessary to represent the heterogeneity of the industry.
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3-2
Essentially, the development of model scenarios was as follows. First,
the major categories of risk-influencing factors were determined. In this
case, three major categories were developed: waste stream, waste management
practice, and environmental setting. Next, the significant risk-influencing
variables within each major category were identified; for example, disposal
technology type and disposal amount per site were identified as the
significant variables under the waste management practice category. Then, the
minimum number of subcategories of each variable necessary to represent the
industry was determined; for example, three technology (lined and unlined
on-site pits, lined central pits) and three size (small, medium, large)
subcategories were developed for drilling sites. The number of subcategories
is dependent on the heterogeneity of the industry, the sensitivity of risk to
that variable, and the quality of data available. Finally, specifications are
given to the subcategories, based on the industry characteristics; for
example, the small size subcategory for drilling waste was defined as 1,984
barrels per well drilled disposed in a reserve pit with dimensions of 30 x 100
x 6 feet.
3.1.1 Waste Types/Constituents
EPA depended entirely on its own waste characterization done as part of
this study as the basis for development of waste scenarios for risk modeling
(EPA, 1987b). Following a literature review of other waste characterization
information, it was determined that previously available data were inadequate,
especially with regard to the number and types of chemicals measured and the
quality assurance methods used. The EPA characterization study focused on the
two large-volume waste types, drilling pit wastes and produced fluids,
although a few other waste types were analyzed at a limited number of sites.
Almost all of the drilling waste samples were from operations using
water-based drilling muds.
Originally, EPA planned to develop three subcategories of drilling
wastes: wastes from water-based mud drilling, from oil-based mud drilling,
and from air drilling. There were insufficient analytical data, however, on
which to base characterizations of oil-based mud and air drilling wastes.
Thus, the drilling waste characterization for this study represents only
water-based mud drilling operations, which are by far the most prevalent
drilling operations. The produced fluid waste characterization, which was
based on a cross-section of production operations across the nation, was
assumed to be representative of all production sites.
EPA recognizes that a number of other types of waste are generated by oil
and gas activities, and that management of these wastes may potentially result
in adverse effects on human health and the environment. EPA did not, however,
have adequate data on chemical characteristics, sources and volumes, and
management practices of the other wastes to assess their risks.
For the purpose of quantitative risk modeling, it is necessary to specify
certain waste characteristics. Given the risk modeling methods used in this
analysis, it was necessary to specify for each waste type the key chemical
constituents that influence risk (referred to hereafter as model constituents)
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3-3
and their concentrations. EPA used its waste characterization data to
identify chemicals present in the wastes, and then applied a systematic
methodology to select model constituents. The major chemical factors
considered in the selection process were: (1) concentration in the waste; (2)
frequency of detection in the waste samples; (3) mobility in ground water; and
(4) effects threshold, either for human health, aquatic toxicity, or resource
damage effects. The specific selection procedures and results are explained
step-by-step in Section 3.3.2. Primarily for reasons of modeling feasibility,
EPA limited the number of chemicals selected to five per waste type.
EPA's characterization data were also used to determine model constituent
concentrations. The 50th percentile was used to set constituent concentrations
for a "best-estimate" waste characterization, while the 90th percentile was
used for a "conservative" waste characterization. Because operations on the
North Slope of Alaska were not included in the quantitative risk modeling,
samples from those operations (one for produced fluids, two for drilling
wastes) were excluded from the estimation of constituent concentrations.
Also, data for two other drilling waste sites were excluded, one because it
was an air drilling site and one because it was a site using oil-based muds.
Thus, the drilling waste characterization used in this study represents wastes
from sites using water-based muds.
Because the number of samples was limited to no more than three per zone,
EPA did not develop separate waste scenarios for the various zones. One set
of waste scenarios was developed and used to represent the nation. No attempt
was made co vary the waste characterization by geographic zone.
3.1.2 Waste Management Practices
For risk modeling purposes, it is necessary to specify waste management
practices in sufficient detail to allow estimation of chemical releases to
environmental media of concern. At a minimum, identification of the principal
treatment/disposal technologies, basic design and operating information, and
data on unit size/waste throughput are needed. For this analysis, EPA
developed separate model scenarios for drilling wastes (i.e., wastes from
exploration and development) and produced fluids (i.e., wastes from
production) because of the different management practices.
EPA characterized the waste management practices in great detail for 13
states having high activity (see Part ). EPA also estimated, by state,
the volumes of drilling wastes and produced fluids generated annually for the
period of 1981-1985 (see Part ). The results of these two analyses were
the primary basis for development of waste management practice scenarios for
the risk modeling. Based on these data collection efforts, EPA identified the
predominant waste management practices for both drilling wastes and produced
fluids, and then developed scenarios to represent these predominant
practices. No attempt was made to be comprehensive or to develop model
scenarios for alternative practices.
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3.1.3 Environmental Settings
Ranges of values for key environmental variables, including hydrogeologic,
surface water, and exposure point characteristics, were developed for the
nation as a whole. The frequency distributions of these variables at oil and
gas drilling and production sites was estimated for each of the zones used in
this project. These values were developed based on the environmental
characteristics at a sample of actual sites located in areas that have the
highest level of drilling and production activity within each zone.
The approach for characterizing environmental settings can be broken down
into three steps. A location sampling strategy was first used to identify and
select sample locations for detailed examination. Second, values for the
environmental characteristics needed to specify model scenarios were estimated
for each of the sample locations. Last, values for environmental variables
determined for the various sample locations were aggregated into zone and
national distributions. These three steps are described separately in the
sections that follow.
3.1.3.1 Location Sampling Strategy
An outline of the process used to identify and select locations for
detailed examination is presented in Figure 3-1. The overall objective of
this process was to select general locations within each zone where the
greatest amount of drilling and production wastes are generated and disposed.
For each state in the different zones, two weighting factors were
developed: one representing the fraction of national drilling activity made
up by drilling activity in the state; and the other representing the fraction
of national oil and gas production made up by production in the state. Data
on the number of wells drilled in each state in 1985, obtained from Petroleum
Information (1986), were used to develop the drilling weighting factors. The
production weighting factors were developed based on data from the American
Petroleum Institute (1986) on the volume of crude oil produced in each state
in 1985. It is recognized that data on other parameters (such as footage of
wells drilled, drilling waste volumes, and produced water volumes) would yield
different weighting factors and, thus, the selection of possibly different
"high activity" areas. The consequences of using data other than the number
of wells drilled and the volume of oil produced are discussed in Section
3.3.4.
Next, the weighting factors were multiplied, respectively, by the total
number of drilling sites (100) and the total number of production sites (200)
that could be examined within the resource constraints of this project. This
multiplication gave a number of drilling and production sites to be sampled in
each state based on the relative levels of activity in the different states
(i.e., a larger number of samples was allocated to states having higher levels
of activity). A constraint was imposed on this step to ensure that at least
one sample site would be selected from each of the oil and gas zones that have
significant activity. Through this weighting process, it was determined that
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Figure 3-1
LOCATION SAMPLING STRATEGY
t
Determine State Weighting
Factor (Swp):
Total State Activjtv0
Total National Activity
Determine Number of Sites to
Examine in a State*:
SWF * Total Number of Sites
That Could Be Examined0
Rank the Counties in Each
State by Their Levels of
Oil and Gas Activity
Select Quad Maps for
Counties with the
Highest Activity d
a Drilling and production activity considered separately.
b A constraint was imposed on this step to ensure that at least one map would be selected from each
zone that has significant activity.
c Sufficient resources were available to examine a total of 300 sites; 200 for production activity and 100
for drilling activity.
d The number of quadrangle maps selected from each county was based on the county's relative level of
drilling and production activity.
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3-6
34 of the 200 production sites to be examined should be selected from the
North Slope of Alaska. While these 34 sites were examined as described below
to develop qualitative information on the North Slope environment, they were
not used for the purpose of model scenario development because Zone HA (the
North Slope) was not evaluated through the quantitative modeling approach (see
Chapter 6). Thus, a total of 166 production sites were selected in order to
specify model scenarios for production activity. All of the 100 drilling
sites were examined in order to specify model scenarios for drilling activity
(i.e., none of the selected drilling sites were on the North Slope).
After the number of sites to be analyzed within each state was determined
through the above weighting scheme, the counties in each of the states
selected for analysis were ranked in terms of their relative levels of
drilling and production activity. The ranking according to drilling levels
was based on county-specific data from Petroleum Information (1986) on the
number of wells drilled in 1985. County-specific data on produced oil, gas,
and, where available, water volumes were obtained from each of the states
selected for analysis and used to rank the counties according to production
level.
Finally, USGS quadrangle maps were selected from areas within each county
that are most likely to have oil and gas activity, as indicated on the
Potential Gas Committee Map (1982) showing oil and gas production areas. To
determine the appropriate number of maps to select from a given county, a
"unit value" (i.e., the number of drilling sites or production amount to be
represented by a single map) was determined by dividing the total onshore
production amount by 200 and the total number of onshore drill sites by 100.
The number of maps selected from each county corresponds to the number of
"unit values" in each county. A separate set of maps was selected in this way
for drilling and production activity. As described below, these maps served
as the basis for characterizing most of the parameters used to specify
environmental settings. It is important to note that the maps selected in
this way were for oil and gas drilling and production sites themselves -- not
for off-site locations where oil and gas wastes may be treated and disposed,
such as off-site injection wells. Therefore, for those cases where oil and
gas wastes are managed at nearby off-site locations, the environmental setting
at those off-site locations is assumed to be roughly the same as that at the
oil and gas site.
3.1.3.2 Estimation of Values for Key Environmental Variables
Values for three categories of environmental characteristics needed to
specify model scenarios were developed for each sample location. These
categories are: hydrogeologic variables; surface water variables; and
exposure point characteristics.
Hydrogeologic Variables. There are four hydrogeologic variables used to
specify model scenarios for this project: saturated zone flow field type; net
recharge; depth to saturated ground water; and unsaturated zone permeability
(i.e., hydraulic conductivity).
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The saturated zone ground-water flow characteristics at a site are modeled
in the Liner Location Model (LLM) by a series of flow field settings. These
settings are defined by various combinations of ground-water velocities,
saturated zone thicknesses, and aquifer configurations (e.g., confined vs.
unconfined conditions). See Section 4.2 of this report for additional
discussion on the LLM's flow field settings. Each of the sample locations was
assigned to the appropriate LLM flow field setting using information from the
DRASTIC characterization system developed by the National Water Well
Association (NWWA, 1985). The DRASTIC system defines for different regions of
the U.S. values for several variables related to ground-water contamination
potential, including the depth to ground water, topography, unsaturated zone
soil type, saturated hydraulic conductivity, and annual recharge. In most
cases, the sample sites were assigned a distribution of LLM flow fields due to
insufficient information to assign just one flow field to a site.
As part of previous work using the LLM, the nation has been divided into
four net recharge zones: 0.25 in/yr, 1 in/yr, 10 in/yr, and 20 in/yr (EPA,
1986e). For this project, the 0.25 in/yr category was dropped (i.e., the 0.25
in/yr zones were considered to have a recharge rate of 1 in/yr) because the
results for this low recharge rate have differed little from those in 1 in/yr
in previous risk analyses using the LLM. In addition, the DRASTIC system does
not significantly distinguish low recharge areas to support selection of 0.25
in/yr. The lowest recharge rate given for the DRASTIC subregions is the range
of 0-2 in/yr, which does not permit selection between 0.25 and 1 in/yr.
Therefore, each of the sample sites selected for this project was assigned a
recharge rate of either 1, 10, or 20 in/yr based on the site's location
relative to these various recharge zones.
Values for the depth to ground water and unsaturated zone permeability for
each site were determined based on values given for the DRASTIC subregions.
To determine permeability values, the DRASTIC system was used to identify the
unsaturated zone media for each sample site. A typical permeability value for
each media type was then selected using data in Freeze and Cherry (1979).
Surface Water Variables. The surface water variables used to specify
model scenarios for this project were: the average distance to flowing water
(i.e., river or creek) and the average flow rate. When numerous oil and gas
wells were distributed over a selected quad map (which was the case for a
great majority of the maps), the average distance to flowing waters was
determined by measuring the distances to both upgradient and downgradient
surface waters from the approxiate mid-point of the oil and gas field
displayed on the map. For those cases where only one or a few oil and gas
wells were displayed on a map, the average distance to surface water was
determined by measuring only the downgradient distances from the individual
oil and gas well(s). In making these determinations, only those surface
waters that appeared to be permanent and of sufficient size to be used by
humans, used by livestock, or ecologically significant were considered. Flow
rates were then taken from USGS hydrologic survey files for each qualifying
surface water that exited the quad map. In the case of multiple streams or
rivers, the flow rates were averaged using a weighted average reflecting the
relative area of each affected watershed within the quad map.
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Exposure Point Characteristics. Two parameters concerning human exposure
points were used in specifying model scenarios: the distance to the closest
drinking water well and the distance to the closest surface water intake. The
distance to the nearest drinking water well was estimated by determining the
distance to residences in downgradient directions (assumed to be downhill
directions as determined from the USGS topographic maps). The USGS maps were
used to first identify residences or other buildings where drinking water is
likely provided and, then, to measure the distance between the residences and
oil and gas wells shown on the map. Only residences and other buildings that
were situated outside of corporate boundaries were considered. Therefore, the
residences identified in this way were assumed to rely on private drinking
water wells rather than community water supplies.
The downstream distance from a discharge point to the nearest surface
water intake for human consumption was a parameter used only in the analysis
of model scenarios involving direct surface water discharges (i.e., only
stripper well scenarios).1-1 A set of values for the distance from stripper
well discharge points to surface water intake points was assumed. No data on
which to confidently base an estimate of this distance were located, and the
USGS maps could not be used to derive such a measure. Therefore, for the
purpose of specifying model scenarios, two downstream distances to a surface
water intake were simply assumed: 1 kilometer and 10 kilometers.
3.1.3.3 Aggregation of Results
After values for environmental variables were estimated for each of the
sample sites, the results were aggregated into a distribution of values for
each zone and for the nation as a whole. This aggregation can be considered a
three part process, as described in the following paragraphs.
First, the overall distribution of results (excluding the results from
Zone HA, which was evaluated separately) was used to develop typical values
for the needed environmental input variables. Categories representing the
range of values that exists for each variable were established as shown in
Table 3-1. For example, rather than use a single value for the depth to
ground water, two categories of values representing a typical deep and a
typical shallow water table were established. Values were assigned to each of
the categories shown in Table 3-1 for the nation as a whole based on a
compilation of all the county values. Zone-specific values for each category
were not developed.
Second, the results for individual sample locations were weighted
according to the relative level of drilling and production activity at that
location. Figure 3-2 provides an overview of this weighting process. The
IJ Human exposure pathways involving surface water contaminated by
ground-water seepage (as opposed to direct discharge) were not modeled in this
study; instead, the more conservative pathway involving direct human exposure
to the contaminated ground water was evaluated.
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TABLE 3-1
CATEGORIES OF VARIABLES USED TO
CHARACTERIZE ENVIRONMENTAL SETTINGS
Variable Categories of Values
Ground-water flow field type A,B,C,D,E,F,J,K a/
Net recharge High (20 in/yr), medium
(10 in/yr), low (1 in/yr)
Depth to saturated ground water Deep, shallow
Unsaturated zone permeability High, low
Distance to surface water Close, medium, far
.Surface water flow rate High, low
Distance to nearest exposure well Close, medium, far
Distance to nearest surface water Close (1 km),
intake far (10 km)
a/ Letters correspond to LLM generic flow field categories;
see Section 4.2 of this report for a description.
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Figure 3-2
PROCESS FOR WEIGHTING
ENVIRONMENTAL SETTING RESULTS
Develop Initial County
Distribution of Values
Derive State/Zone
Weighting Factor:
Total State Activity
Activity in Portion of
Region Sampled0
Derive County
Weighting Factor:
Total County Activity
Activity in Portion of
State Sampled0
Determine Weighted County Distribution
of Environmental Characterisitcs
County Cpunty State/Zone
Distribution * Weighting * Weighting
Factor Factor
a
For example, the total activity in all the counties that were examined in a given state was used, rather
than the total state activity (i.e., the activity in those counties that were not examined was excluded).
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3-11
results of each site examined within the various sample counties were
initially aggregated into a county distribution. This distribution was then
multiplied by two weighting factors: one representing the level of drilling
or production activity in the county relative to the level of activity in the
sampled portion of the state; and the other representing the level of drilling
or production activity in the state relative to the level of activity in that
portion of the zone that was sampled. A similar scheme was used to weight the
county distributions according to the relative level of activity in the state
compared to level of activity in the nation as a whole. The result of this
process was a set of weighted county distributions for key environmental
variables, with the values from counties and states with higher activities
weighted more heavily than those from counties and states with lower levels of
activity.
Third, the frequency of occurrence of the values for each variable was
estimated for the different zones. This frequency for each zone was based on
a summation of the weighted results for just those counties located within
that zone. Thus, while the values for each variable were held constant for
the nation as a whole, the frequency of occurrence of those values varied
appropriately from one zone to the next. Figure 3-3 provides an example of
the aggregation procedures.
3.1.4 Development of Composite Scenarios
EPA combined the separate waste stream, management practice, and
environmental setting scenarios into composite model scenarios, for which
quantitative risks were estimated. Each composite scenario was specified in
specific detail to allow risk modeling. The theoretical total number of
combinations (i.e., model scenarios) would be the product of the number of
waste streams times management practices times environmental settings;
however, because some combinations are not realistic, the actual number of
composite scenarios that was modeled was lower. Approximately 5,000 different
model scenarios were analyzed in this study.
3.2 ESTIMATION OF FREQUENCIES FOR MODEL SCENARIOS
In a generic, industry-wide risk analysis such as this one, it is
desirable to evaluate how "representative" of real conditions the model
scenarios developed and analyzed are. It also is desirable to account for the
differential distribution of real site conditions among the model scenarios
(i.e., the fact that some model variables "represent" more sites than others)
when analyzing the results. For these two reasons EPA estimated the site
frequency associated with each variable subcategory (e.g., ground-water flow
field type, net infiltration subcategory, waste management unit size
subcategory) and, by making a few assumptions, derived estimates of site
frequency associated with each composite scenario modeled. Because of major
differences in wastes, waste management practices, and locations, separate
model scenarios and frequency estimates were developed for oil and gas
drilling operations and oil and gas production operations. The approach for
estimating frequencies is described below, first for waste type and waste
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FIGURE 3-3
SAMPLE AGGREGATION OF
ENVIRONMENTAL SETTING RESULTS
STEP 1: Assume a Zone X made up of 3 states: State 1, State 2, and State 3.
Based on the total number of wells drilled in each of these states relative to
the total number of wells drilled nationally, it is determined that 3 maps
should be examined from State 1, 2 maps should be examined from State 2, and 1
map should be examined from State 3 (see Section 3.1.3.1 for a description of
this step).
STEP 2: Examine the relative levels of drilling activity in each county of
the different states and identify that: the top 3 counties in terms of
drilling activity in State 1 are County 1-1, County 1-2, and County 1-3; the
top 2 counties in State 2 are County 2-1 and County 2-2; and the top county in
State 3 is County 3-1 (see Section 3.1.3.1). Assume that one map is allotted
to each of these counties.
STEP 3: Determine the unweighted distribution of ground-water flow field
types (assume three possible types A, B, and C for this example) for each
county individually and the zone as a whole (see Section 3.1.3.2):
Ground-water
Flow Field
Type
A
B
C
Unweighted
County
1-1
.6
.3
.1
County
1-2
.2
.2
.6
County
1-3
.7
.2
.1
Frequency
County
2-1
.1
.1
.8
Distribution
County
2-2
.2
.3
.5
County
3-1
.5
.4
.1
Zone
X
.383
.250
.367
STEP 4: Derive county weighting factors for drilling activity (see Section
3.1.3.3)
County
No. of
Wells Drilled
County
Weighting Factor
State 1
County 1-1
County 1-2
County 1-3
30
150
20
.15 (=30/200)
.75
.10
State 2
County 2-1
County 2-2
State 3
County 3-1
20
80
50
.20
.80
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FIGURE 3-3
SAMPLE AGGREGATION OF
ENVIRONMENTAL SETTING RESULTS
(continued)
STEP 5: Derive state/zone weighting factors (see Section 3.1.3.3):
Zone X
States
State 1
State 2
State 3
No. of
Wells Drilled
200
100
50
State/Zone
Weighting Factor
0.57 (= 200/350)
0.29
0.14
STEP 6: Derive weighted county and zone distributions of ground-water flow
field types (see Section 3.1.3.3):
Ground-water
Flow Field
Type
Weighted Frequency
County
1-1
County
1-2
County
1-3
County
2-1
Distribution
County
2-2
County
3-1
Zone
X
A
B
C
.051
.026
.009
.086
.086
.257
,040
.011
.006
.006
.006
.046
.046
.070
.116
.070
.056
.014
.299
.255
.448
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management practice scenarios and then for environmental setting scenarios.
Results of the frequency estimation are summarized in tables in Appendix B.
3.2.1 Waste Type and Waste Management Practice Scenarios
EPA's data gathering efforts for waste types and waste management practices
were not designed specifically to produce frequency distributions of various
scenarios. Instead, they were targeted to a more general characterization of
the industry. In some cases, however, frequency distributions for waste
management practices could be estimated from the data collected. Two major
waste types were modeled in this study; subcategories of these major types
were not developed. Because only one waste type was developed for each of the
two kinds of sites modeled (drilling, production), it was unnecessary to
estimate relative frequencies for waste types.
As indicated in Section 3.1.1, EPA actually developed two characteriza-
tions for each waste type: best-estimate, based on median constituent
concentrations, and conservative, based on 90th percentile concentrations.
The intent of developing these two characterizations was to allow an initial
assessment of the effects of waste quality variations on risk. No attempt was
made to estimate relative frequencies or combine risk modeling results for the
two characterizations.
EPA did develop and estimate frequencies for a limited number of
subcategories of waste management practices. For oil and gas drilling sites,
EPA modeled both on-site reserve pits, where most waste is managed (
percent), and centra], reserve pits, which receive wastes from multiple sites.
Two variables were modeled for on-site pits: pit size and presence/absence of
a synthetic liner. In the sources and volumes portion of this 8002(m) study
(see Part _), EPA developed three representative pit sizes (plus two others
for Alaska only) and estimated the numbers of pits of each size constructed
each year for oil and gas producing states for the five-year period,
1981-1985. These pit sizes were used as subcategories in the risk modeling,
and the estimated numbers of each (i.e., 5-year averages) were used to develop
frequency estimates for the nine producing zones and the U.S. as a whole. In
the waste management practices part of this study (see Part ), information
was sought on the number of synthetic-lined versus unlined pits. Specific
quantitative estimates were generally unavailable, but available information
indicated that the great majority of on-site pits were unlined. Thus, EPA
assumed 90 percent were unlined and 10 percent were synthetic-lined for
purposes of estimating frequencies. To allow estimation of overall management
practice scenarios (combined size and presence/absence of liner) for on-site
pits, it was assumed that size and presence/absence of a liner were
independent.
One central drilling waste pit type was modeled as an illustrative example
of this management practice. Because of limited data on wastes, numbers,
designs, and sizes of central pits and because the overall volume of drilling
waste managed this way is relatively small, EPA did not develop subcategories
or estimate frequencies for this practice.
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For production sites EPA modeled underground injection of produced fluids,
which is by far the most prevalent disposal option. In addition, for stripper
oil wells EPA modeled direct discharge to surface water. Because the chemical
release point modeled for underground injection failures is the injection well
itself and because most injection wells serve multiple oil and gas production
wells, EPA used the number of underground injection wells as the basis for
estimating frequencies (not the number of producing oil and gas wells). Three
subcategories of underground injection well were modeled, high, medium, and
low injection rate. Information on total number of Class II underground
injection wells per state was collected from EPA's Office of Drinking Water
(Underground Injection Control Branch) and from state agencies administering
underground injection control programs (see Part ). Zone totals were
developed by summing the numbers for appropriate states. Specific data on
numbers of high, medium, and low rate wells per zone were not available. For
purposes of this study, EPA estimated these frequencies based on the zone
average injection rate. For example, assuming two injection rate
subcategories of 30,000 barrels/year and 300,000 barrels/year and a zone
average injection rate of 200,000 barrels/year, the frequencies would be:
[30,000 x] + [300,000 (1-x)] = 200,000
where
x = fraction of wells at 30,000 bbl/day =0.37
(1-x) = fraction of wells at 300,000 bbl/day =0.63
EPA estimated zone average injection rates by summing for all the states in a
zone the 5-year average annual volumes of produced water that is injected,
then dividing by the number of Class II injection wells in that zone.
Two stripper well/direct discharge subcategories also were developed, high
rate and low rate. The number of stripper wells in each state and zone was
taken from EPA's sources and volumes analysis (see Part ). The actual
number of stripper wells discharging to streams was unknown, as were the
fractions of high and low rate discharges. Thus, frequency estimates were not
developed for the stripper well direct discharge subcategories.
3.2.2 Environmental Setting Scenarios
EPA's approach for characterizing environmental settings at oil and gas
sites, as described in detail in Section 3.1.3, was a sampling approach
designed to produce both environmental variable subcategories and frequency
distributions. The four hydrogeologic variables included in the study (net
recharge, depth to saturated zone, unsaturated zone permeability, and
saturated zone type) were assessed for sample sites; then, the distribution of
sample site locations according to all four variables considered together was
determined (as opposed to determining distributions of each variable
individually and assuming independence to create the overall distribution).
The hydrogeologic data from each sample site location were weighted by
activity level prior to development of the final distribution (see Section
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3-16
3.1.3). By using data from appropriate sites and zone-specific weighting
factors, frequency distributions were estimated for each of the nine producing
zones as well as the nation as a whole. The activity-weighted frequency
distribution of sample sites among the hydrogeologic variable subcategories
was used to represent the actual distribution of hydrogeologic variables at
oil and gas sites.
One additional variable, distance to nearest potential exposure well, was
included in the ground-water analysis. The frequency distribution of sample
sites among the three distance subcategories was estimated separately; it was
assumed that exposure distance is independent of the hydrogeologic variables.
The two surface water variable subcategories (flow rate, distance from site)
were analyzed similarly. A joint distribution of size/distance was determined
for the sample sites, then weighted according to activity. The activity-
weighted sample site distribution was assumed to be representative of the
actual distribution of these two variables at oil and gas sites.
Frequencies for overall environmental setting scenarios (combined surface
and ground water) were estimated by assuming that the separate distributions
of sites among hydrogeologic and surface water variable subcategories were
independent. This overall frequency distribution is applicable only to the
surface water results, which are affected both by ground-water and surface
water setting type. The overall distribution is not applicable to the
analysis of ground-water results, which are not affected by surface water
setting type.
3.3 RESULTS OF MODEL SCENARIO DEVELOPMENT
This section describes the final model scenarios developed on the basis of
the industry characterization. As in the methods section, the discussion has
been divided into waste types, waste management (i.e., disposal) practices,
and environmental settings.
3.3.1 Waste Types
This study focused on the two high-volume waste types generated by oil and
gas operations and exempt from RCRA:
Produced fluids: waste fluids separated from crude oil or
natural gas at production facilities; and
Reserve pit waste from well drilling (i.e., drilling wastes):
wastes associated with well drilling that are generally disposed
in reserve pits, such as drilling muds and cuttings.
Thus, the major exempt waste type from exploration and development activities,
drilling wastes, and the major exempt waste type from production activities,
produced fluids, were included in the study. EPA did not develop
subcategories of these two waste types f.or the risk analysis. EPA specified
model constituents and concentrations for the waste scenarios on the basis of
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3-17
its field sampling study, which also focused on these two waste types. The
selection of model constituents is described in detail in Section 3.3.2.
The produced fluid waste category represents the actual waste stream at
the endpoint of the separation process (after it is separated from the
product, either crude oil or natural gas). It is at this point that the
produced fluids become wastes that need to be managed.
In contrast, the drilling waste category does not represent one specific
process waste, but the combined wastes associated with drilling that generally
are disposed in reserve pits. This definition was necessary because all of
the waste characterization data was for reserve pit solids or liquids, not for
individual waste streams prior to placement in pits. Also, because the
characterization data were almost exclusively from water-based mud drilling
sites, the drilling waste category is representative of these types of
operations, not air or oil-based mud drilling.
3.3.2 Selection of Model Constituents
This section presents the model constituents EPA selected to represent the
two major oil and gas production waste stream categories: drilling wastes
(water-based) and produced fluids. EPA identified those constituents that it
believed would dominate both human and environmental risk estimates based on
the EPA's field sampling data for these waste streams.
In this section, the steps included in the approach for selecting model
constituents and the results of each step are described. The first step was
the selection of appropriate field sampling data for analysis. Next,
infrequently detected constituents were removed from further consideration.
The third step was to screen the remaining constituents on the basis of
environmental mobility. The next step was to rank the remaining constituents
according to the ratio of their concentrations in the sampled media to health
and environmental risk-based reference thresholds. A large portion of the
effort in this step involved obtaining or developing the reference
thresholds. The final two steps were the actual selection of model
constituents and the determination of their concentrations for the model waste
characterizations.
3.3.2.1 Step 1: Selecting Analytical Data for Waste Streams
EPA's Industrial Technology Division used contractors to sample and to
analyze wastes from oil and gas drilling and production sites (EPA 1987a).
For four of the sample types, the number of samples taken nationwide exceeded
10. These sample types and the codes used to refer to them in this section
are:
(1) Drilling Pit Liquids (direct extraction) DPL
(2) Drilling Pit Solids (TCLP extraction) TCLP
(3) Drilling Pit Solids (direct extraction) DPS
(4) Production Endpoint Liquids (direct extraction) PELD
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3-18
EPA examined the original data for the onshore sites for the purpose of
screening chemicals. First, one oil and one air-based drilling site and two
drilling and one production site from the north slope of Alaska were
excluded. For the remaining sites, assumed to be representative of
water-based drilling and production sites in the lower 48 states and southern
Alaska, EPA calculated the unweighted median and maximum concentration
reported for each analyte. For a few sites and analytes with duplicate
samples, EPA used the average of the two to represent the site.
3.3.2.2 Step 2: Screening Analytical Data for Frequency of
Detection by Analyte
To eliminate constituents that were not considered representative of a
sampling category, each analyte was screened for its frequency of detection.
The Agency used the Technical Report's (EPA, 1987a) values for (1) the
weighted frequency of detection and (2) the unweighted frequency of detection
(equal to the number of samples in which the analyte was detected divided by
the total number of samples). EPA selected for further consideration analytes
that satisfied either of the following criteria:
(1) Detected in at least 40 percent of the total number of samples,
or
(2) Weighted frequency of detection of at least 50 percent AND
detection in at least 20 percent of the total number of samples.
Table 3-2 lists the 61 chemicals selected for further consideration and
their unweighted frequency of occurrence by sample category. Analytes not
included because they do not represent individual chemical species are: total
volatile organic carbon, biochemical oxygen demand, chemical oxygen demand,
hydrogen ion, salinity (from chloride), salinity (from sodium), oil and
grease, residues (total, non-filterable, filterable), specific conductivity,
total organic carbon, and oil and grease (retort). In addition, more than 45
chemicals were eliminated on the basis of low frequency of occurrence, and two
were eliminated because they were either field or laboratory contaminants of
the samples.
3.3.2.3 Step 3: Screening Frequently Occurring Chemicals for
Environmental Fate
Because the focus of the risk analysis is on ground-water contamination,
chemical mobility in ground water exerts a major influence on the selection of
model constituents.2-1 Therefore, several factors relevant to contaminant
migration and fate were considered in screening chemicals for selection for
the risk analysis. These factors are described below:
2J EPA modeled contaminant transport in ground water and in surface
water. Contaminants reached surface water through ground-water discharges.
EPA did not model direct discharges to surface water (except for one site
category, stripper oil wells).
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3-19
TABLE 3-2
FREQUENTLY OCCURRING ANALYTES IN DRILLING MUDS
AND PRODUCED LIQUIDS
Unweighted Frequency of
Detection in Sampled Media a/
CAS No.
1-000
1-003
1-005
57125
71432
78933
85018
91203
91576
95487
99876
100414
105679
106445
108101
108883
108952
112403
112958
117817
124185
544763
593453
629594
629970
630013
630024
638686
646311
7429905
7439896
7439921
7439932
7439954
7439965
Chemical Name
Combined nitrogen
Chloride
Nitrate/nitrite (as N)
Cyanides (soluble salts & complexes)
Benzene
2-Butanone (methyl ethyl ketone)
Phenanthrene
Naphthalene
2-Methylnaphthalene
o-Cresol
p-Cymene
Ethylbenzene
2 ,4-Dimethylphenol
p-Cresol
Methyl isobutyl ketone
Toluene
Phenol
n-Dodecane
n-Eicosane
bis(2-Ethylhexyl) phthalate
n-Decane
n-Hexadecane
n-Octadecane
n-Tetradecane
n-Docosane
n-Hexacosane
n-Octacosane
n-Triacontane
n-Tetracosane
Aluminum
Iron
Lead
Lithium
Magnesium
Manganese
DPL
1.00
.82
.38
.20
.33
.40
.20
.40
.38
.47
.73
.40
.67
.60
.53
.33
.33
.94
.94
.71
.41
1.00
1.00
TCLP DPS
1.00
.70
.39
.33
.28 .37
.50 .47
.39
.67
.44 .50
.68
.74
.44 .47
.47
.84
.79
.74
.53
.42
.32
.32
.47
.52 1.00
.95 1.00
.38 .57
1.00 1.00
1.00 1.00
PELD
1.00
.71
.76
.76
.57
.52
.48
.52
.43
.48
.76
.48
.71
.52
.71
.62
.57
.52
.52
.48
.38
.33
.48
.96
.67
1.00
.96
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3-20
TABLE 3-2
(continued)
FREQUENTLY OCCURRING ANALYTES IN DRILLING MUDS
AND PRODUCED LIQUIDS
Unweighted Frequency of
Detection in Sampled Media a/
CAS No.
7439987
7440020
7440097
7440213
7440224
7440235
7440246
7440315
7440326
7440382
7440393
7440417
7440428
7440439
7440451
7440473
7440484
7440508
7440622
7440655
7440666
7440702
7664417
7704349
7723140
16984488
a/ Sample
DPL :
TCLP:
DPS :
PELD:
Chemical Name
Molybdenum
Nickel
Potassium
Silicon
Silver
Sodium
Strontium
Tin
Titanium
Arsenic
Barium
Beryllium
Boron
Cadmium
Cerium
Chromium
Cobalt
Copper
Vanadium
Yttrium
Zinc
Calcium
Ammonia (as N)
Sulfur
Phosphorus
Fluoride
media code:
Drilling Pit Liquids
Drilling Pit Liquids
Drilling Pit Solids
Production Endpoint
DPL TCLP
.76
.88
.88
.94
.41
1.00 1
1.00
.71
.71
.35
1.00 1
.53
.94
.76
.82
.53
.76
.65
.59
1.00 1
1.00 1
1.00
.88 1
.76
1.00
(direct extraction)
(TCLP extraction)
(direct extraction)
Liquids (direct extraction)
.52
.48
.67
.00
.62
.00
.76
.48
.33
.29
.00
.00
.00
.
DPS
.71
.48
.71
1.00
1.00
.52
1.00
.86
.62
.95
.86
.71
1.00
1.00
.90
1.00
.71
1.00
PELD
.83
.92
.46
1.00
.96
.83
.38
.88
1.00
.25
1.00
1.00
.90
.88
1.00
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3-21
It is assumed that surface waters and the upper
layers of ground water will be reasonably well
oxygenated. This means that most ammonia and nitrites
will be oxidized to nitrates and most sulfides will be
oxidized to sulfates.
Most of the sampled phosphorus, sulfur, boron, and
molybdenum is assumed to be in the forms of
phosphates, sulfates, borates and boric acid, and
molybdate.
Chromium is assumed to be in the hexavalent form (Cr
VI).
Constituent retardation relative to a moving mass of
ground water depends directly upon the constituent's
soil/water partition coefficient (Kd value or
distribution coefficient).
Based on these factors, the potential for ground-water transport was
evaluated for the 61 chemicals considered for selection in the risk analysis
by a comparison of Kd values. Kd values were identified for most chemicals,
primarily from the LLM data base. Mobility of chemicals for which Kd values
were unavailable was evaluated qualitatively, based on physical/chemical
properties or structure-activity relationships.
Table 3-3 presents the list of chemicals selected for further
consideration in the risk analysis based on mobility in groundwater, and also
the list of chemicals eliminated on this basis. Given the modeling methods
used, a Kd value in excess of 20 indicates that the chemical is of
sufficiently low mobility to be unable to reach distances of interest within
the modeling time period of 200 years. In addition to those chemicals with Kd
values less than 20, some chemicals for which qualitative data indicated
probably high mobility in ground water were selected in the screening
process. As indicated in Table 3-3, 33 chemicals were considered to be of
sufficient frequency of occurrence and mobility to warrant further
consideration.
3.3.2.4 Step 4: Screening Chemicals for Potential Impact on Human
Health, Aquatic Ecosystems, and Resource Quality
In identifying those constituents most likely to be of concern in ground
and surface water, unweighted median and maximum constituent concentrations
were compared to reference concentrations (RCs) based on human health effects,
aquatic toxicity, and in a few cases resource quality. The derivation of
reference concentrations for human health effects (HRCs), for aquatic toxicity
(ARCs), and for resource quality (RRCs) are described below.
Human Health Reference Concentrations (HRCs). HRCs are the concentrations
in drinking water corresponding to an individual upper bound excess lifetime
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * *
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3-24
TABLE 3-3 (continued)
ENVIRONMENTAL FATE AND MOBILITY
SCREENING OF FREQUENTLY OCCURRING ANALYTES
Notes and References
Notes:
EST-1: Assume all nitrogen is in the form of nitrate.
EST-2: Koc estimated by the following equation: log Koc = (-0.55 * log S) +
3.64, where S = solubility. Solubilities were obtained from the
following sources: Dawson et al. (1980), ICF (1985a, 1985b), Mabey et
al. (1982), Verschueren (1983), Weast (1974).
EST-3: Kd for organics based on assigned Koc value and the assumption of
0.1% for fraction organic carbon in the saturated zone (default value
for the LLM) by the following equation: Kd = Koc * fraction of
organic carbon.
EST-4: Koc assigned 10s because all reported water solubility values were
"insoluble".
References:
(1) ICF Draft Report (1986). Sources reviewed in that report include: Lyraan
et al. (1982), Mabey et al. (1982), Hazardous Substance Data Book (1985),
Organic Chemicals in the Soil Environment, Vol. 1 (1972), Baes et al. (1984),
and Tyler and McBride (1982).
(2) Donahue et al. (1977).
(3) Kabata-Pendias and Pendias (1984).
* * *
April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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3-25
-6
cancer risk of 10 for carcinogens, or an acceptable daily intake for
noncarcinogens (also known as a reference dose, RfD). To calculate drinking
water concentrations, EPA assumed a 70 kg human receptor drinking 2 liters of
water per day.
For chemicals without an EPA-reviewed cancer potency factor or RfD, other
sources were used to estimate an HRC. Although none of these chemicals were
expected to be acutely toxic, some were present in sampled drilling muds and
produced fluids in very large quantities. The following sources were reviewed
for human toxicity effects: Clayton and Clayton (1981), ICF 1985a,
ICF/Clement 1986, McKee and Wolf 1963, NAS (1977-1983), NAS/NAE (1972), and
U.S. EPA (1980a,b, 1985a,b, and 1986a,b,c).
Table 3-4 lists the reference concentrations for human health and briefly
notes the source if other than described above. More complete descriptions of
HRC sources and derivations are available in Appendix A.
Aquatic^ Ecosystem Reference Concentrations (ARCs). ARCs were set equal to
the EPA's Ambient Water Quality Criterion for chronic exposures (Criterion
Continuous Concentration, or CCC) when available. For metals with hardness
dependent toxicities, the CCC at 50 mg/1 CaC03 was used (metals generally
exhibit higher toxicities in softer water). The toxicity of most metals
increases approximately four-fold between 10 and 100 mg/1 CaC03 (EPA 1986a).
For many of the constituents without established CCC values, similar
values have been estimated using largely the Michigan Department of Natural
Resources (MNDR) methodology for developing criteria in the absence of
sufficient data to meet EPA requirements for AWQC (ICF/Clement, 1986). These
criteria, prepared for the National Industrial Environmental Services, are
referred to as NIES values. All relevant NIES CCC values were reviewed and
some modified based on professional judgment.
In the absence of either AWQC or NIES CCC values, the following sources
were reviewed for pertinent studies: Birge (1978), Buccafusco et al. (1981),
EPA (1975, 1980a,b, 1985a, 1986a), Kenaga (1982), LeBlanc (1980), McKee and
Wolf (1963), NAS (1972), Roush et al. (1985), and Verschueren (1983). ARCs
were then estimated using both MDNR's and ICF/Clement's techniques for
estimating a CCC with limited data. For several of the metals, there were
only one or two published toxicity tests, which were not sufficient evidence
even for screening purposes. The implications of these studies are discussed
in a later section.
Table 3-4 lists the reference concentrations for aquatic toxicity and
identifies their origin. For more complete descriptions of sources and the
MDNR methodology, see Appendix A.
Resource Quality Reference Concentrations (RRCs). While several of the
contaminants do not pose a human health or aquatic toxicity hazard at certain
levels, they nonetheless can render ground and/or surface water less
valuable. EPA did not review the constituents in a systematic fashion,
however, for resource quality thresholds. Instead, case studies of ground-
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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3-27
TABLE 3-4
REFERENCE CONCENTRATIONS
(continued)
Methods and Sources
Aquatic Reference Concentrations:
AWQC: EPA (1986a) Ambient Water Quality Criterion Continous Concentration
NIES: ICF/Clement (1986) Criterion Continuous Concentration (CCC), developed
for the National Industrial Environmental Services.
/10/ or /5/: Extrapolation to sensitive species using 5 if the lowest
acute toxicity value is from a sensitive species (e.g., trout),
or 10 otherwise.
7/3,4,25,45: Acute to chronic ratio used to extrapolate a CCC from acute
toxicity data (see Appendix A for toxicity tests and
methodology).
Human Health Reference Concentrations:
RfD: Verified Reference Dose
PF: Potency Factor (from EPA's Carcinogen Assessment Group)
NAS: National Academy of Sciences
LLM: Liner Location Risk Analysis Model
Others: See Appendix A for more complete information.
* April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
-------
3-28
and surface-water contamination were consulted, the potential for
"fertilizing" surface waters with plant nutrients contained in drilling wastes
was considered, and AWQC based on irrigation standards were consulted (EPA,
1986a). Table 3-4 lists the resource damage criteria used in the selection of
model constituents.
Contamination of drinking water wells was frequently cited in case study
reports. Of the salt ions, chloride appears to be responsible for a large
portion of the human perception of a salty taste (EPA 1984a). It is therefore
appropriate to model chloride as potentially reducing the value of potable
water.
In surface waters, chloride is the major conservative anion contributing
to salinity; sodium, potassium, magnesium, calcium, carbonates, and sulfates
also contribute to total dissolved ions in fresh waters (Reid and Wood, 1976;
EPA, 1986a). In general, freshwater organisms can "tolerate" dissolved salts
in higher concentrations than normally experienced (EPA, 1986a; Macan 1963).
However, relative species competitive success can shift, particularly where
invasion of fresh water by brackish water species is possible.
Only surface waters of less than 0.5 ppt salinity are considered fresh
(Macan 1963, NAS 1977, Pennak 1953), and 95 percent of fresh-water species
exist in waters of less than 0.4 ppt (Hart et al., 1945, reported in NAS/NAE,
1972). The average background concentration of inorganic salts dissolved in
hard water is 300 mg/1 and in soft water is 65 mg/1 (Reid and Wood, 1976).
EPA considers the following ions present in drilling muds and produced
fluids to be the major potential contributors to mobile salinity: sodium,
chloride, potassium, magnesium, calcium, and sulfate. To prevent average
surface water from exceeding 400 mg/1 (to reflect the natural range of
salinities for 95 percent of fresh-water species), given a background level of
65 mg/1 in soft water, requires that the ARC equal 335 mg/1 for dissolved
salts for use in the Liner Location Model which does not model background
concentrations. A more conservative approach would be to use the background
level of 300 mg/1 for hard water and set the ARC to 100 mg/1 dissolved salts.
Nitrogen and phosphorus are the two essential plant nutrients most often
limiting to primary production in surface waters (Krebs, 1972; NAS/NAE,
1972). Phosphorus is well recognized'for its contribution to nuisance algal
blooms and eutrophication (EPA 1986a), but is probably not very mobile in
ground water (Donahue et al., 1977). Both ammonia and nitrites are oxidized
to nitrates in oxygenated ground and surface waters, and hence contribute to
the total nitrogen available to plants. Moreover, all forms are reasonably
mobile in ground water. The summed contributions of nitrogen from ammonia and
nitrites/nitrates equal "combined nitrogen" for consideration as a surface
water "fertilizer".
Ranking Chemicals by their Concentrations Relative to the Reference
Thresholds. Constituents were ranked according to the ratio of their median
and maximum concentrations for the four sample categories (DPL, TCLP, DPS,
PELD) to the reference doses for human health, aquatic toxicity, and resource
damage as described above. Table 3-5 ranks the drilling muds (DPL, TCLP, and
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
* * *
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3-29
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DPS), and produced liquids (PELD) for human health, aquatic toxicity, and
environmental damage based upon median sample concentrations.
For most chemicals and sample categories, the ranking of the median
concentration ratio and the maximum concentration ratio were similar. A
notable exception is arsenic in produced liquids. Although detected in only
38 percent of samples (Table 3-2), the maximum concentration exceeded the
human health reference concentration by more than 6 orders of magnitude.
3.3.2.5 Step 5: Selecting Model Constituents
Table 3-6 lists the constituents that EPA believes will pose the highest
human and environmental risks from each of the two generic waste streams,
drilling muds and produced liquids. For drilling muds, all three sample
categories (liquids, solids, and TCLP extracts) were considered in making the
recommendations.
For produced fluids, arsenic was chosen because of its high concentration
in the samples in which it was detected. For potassium and magnesium, the
apparent high potential for aquatic toxicity is based on only a few toxicity
studies. Moreover, as cations contributing to total dissolved salts,
potassium and magnesium are likely to contribute to adverse salinity effects
at lower levels than required for toxic effects. They have therefore been
included in the category of total mobile salts.
For drilling wastes, calcium, potassium, and magnesium are included in the
total mobile salts for the reasons described above. Calcium contributes to
water hardness, but its contribution to dissolved salts is probably equally
important. Although fluoride appeared high in the DPS ranking, the reference
dose for human health of 1 ppm is based on the cosmetic problem of dental
fluorosis; a threshold of 4 ppm is adequate to prevent adverse health effects
(EPA RMCL).
3.3.2.6 Step 6: Determining Concentrations of Model Constituents
The drilling waste scenario represents water-based mud drilling sites
excluding the North Slope. To calculate the median and upper 90th percentile
concentrations, therefore, two samples from the North Slope of Alaska, one
oil-based, and one air-based drilling sample were removed. One sample from
the North Slope of Alaska was removed from the produced fluid data as well.
Table 3-7 gives the model constituent concentration data for the two waste
types.
In order to calculate the median and upper 90th percentile concentrations
for total mobile salts, it was necessary to sum the measured concentrations of
the six contributing chemicals (Na, Cl, K, Mg, Ca, and SO ) on a site by site
4
basis before calculating a median and a mean. Sulfate was not measured in the
waste characterization study, but total sulfur was. Sulfate was estimated by
assuming all sulfur was in the form of sulfate and converting the reported
total sulfur concentrations based on relative molecular weights. As a
conservative modeling approach, despite the somewhat different mobilities of
these ions, the group was modeled as though the Kd for all ions equalled 0.01.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
3-37
TABLE 3-6
LIST OF MODEL CONSTITUENTS
Waste Type
Constituent
Effect Type Being Modeled a/
EC HN AT RD
Produced fluids
Benzene
Arsenic
Sodium
Chloride
Boron
Total mobile salts b/
X
X
X
X
X
X
X
Drilling wastes
(water-based)
Arsenic X
Cadmium
Sodium
Chromium VI
Chloride
Total mobile salts b/
X
X
X
X
X
X
X
X
X
a/ HC = human health, cancer
HN = human health, noncancer
AT = aquatic toxicity
RD = resource damage
b/ Includes chloride, sodium, potassium, calcium, magnesium, and sulfate.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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3.3.2.7 Limitations of the Selection Methodology
There are several necessary assumptions and resulting limitations of
the selection approach. Some significant limitations are listed below.
All aquatic toxicity thresholds are derived from single
species laboratory toxicity tests. None of the tests
predict the effects of chronic low level exposure to
toxicants upon species susceptibility to disease, which in
natural populations might be the most common cause of death
in moderately polluted environments. None of the tests
predict aquatic community effects either.
Several constituents of drilling muds and produced fluids
might affect surface water conditions in ways that change
the competitive balance between native species, or
contribute to emigration, at levels lower than EPA has
considered; for example, calcium and the ions contributing
to salinity.
The shape of the dose-response curve has not been
considered for either human or aquatic responses; potential
for adverse effects has been represented by a threshold.
Infrequently used but toxic drilling additives such as
diesel oil have not been considered. Chemicals detected in
a few samples but excluded as unrepresentative included
several phenols, phthalates, and polynuclear aromatic
hydrocarbons, several chlorinated hydrocarbons, and metals
such as mercury and selenium.
Several aspects of soil chemistry have been ignored. In
the Technical Report it was noted that the pH values
reported were invalid because of unavoidable delays between
obtaining and analyzing samples for pH. The influences of
pH on solubility and mobility of metals or on complex
precipitation equilibria have been ignored.
All chromium was assumed to be in the more toxic
hexavalent form, when much of it would probably be
converted over time to trivalent chromium.
Finally, there are several chemicals present in large
quantities in drilling muds and produced liquids for which
little is known about the long term effects of chronic
exposure, or in some cases, short term toxicity. For
example, strontium might be acutely toxic to aquatic life
(Birge 1978), and little is known about constituents such
as 2-methylnapthalene and phenanthrene.
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE *
-------
3-40
3.3.3 Waste Management Practices
EPA developed management practice scenarios separately for the two
site/waste types. For exploration and development drilling sites, 14 waste
management scenarios were developed (Table 3-8). All scenarios were for
water-based muds, and all but two were for on-site reserve pits, which receive
the great majority of wastes. Three different pit size/waste amount
combinations were specified for on-site pits; the sizes correspond to those
developed for the analysis of waste volumes generated (see Part ). The
estimated frequency distribution of the different pit sizes is shown in Table
B-l of Appendix B. Two technologies, unlined and synthetic lined, were
included. For each of the six size/technology combinations, "best-estimate"
and "conservative" scenarios were developed. Thus, there were 12 on-site
waste management scenarios for drilling pit wastes. The two remaining
scenarios were best-estimate and conservative central treatment pit scenarios;
in these scenarios the pits were larger and were operated for a longer time
period.
For oil and gas production sites, EPA developed 16 scenarios (Table
3-9). Twelve scenarios were for underground injection as the disposal
practice: four each for high, medium, and low volume injection wells. EPA
selected injection rates of 48,000 m3/year, 4,800 m3/year, and 480
m3/year (corresponding to 300,000; 30,000; and 3,000 bbl/year) as high,
medium, and low values based on the distribution of state and zone average
injection rates, which were estimated as described in Section 3.2.1. The
estimated frequency distribution of the three injection rates is given in
Table B-l (Appendix B). Because of the extreme uncertainty in failure and
release modeling for underground injection, four separate scenarios
representing different failure/release mechanisms and assumptions were modeled
for each injection rate subcategory. In addition four scenarios representing
stripper oil wells (defined as producing less than 10 barrels per day)
discharging directly to surface water were modeled.3-1
3.3.4 Environmental Settings
As discussed in Section 3.1.3 above, values for three categories of
environmental variables were developed for the purpose of defining model
scenarios. These variables are: hydrogeologic variables (i.e., LLM flow
field type, recharge, depth to ground water, and unsaturated zone
permeability); surface water variables (i.e., average distance to surface
water and the average flow rate of surface water); and exposure point
characteristics (i.e., distance to the nearest drinking water well and surface
water intake). Nation-wide scenarios involving combinations of these
variables were developed and each scenario was assigned a frequency of
occurrence for the various zones. A separate set of environmental setting
3J There are no federal NPDES regulations on stripper wells, while most
other onshore oil and gas production facilities are subject to the federal
zero discharge requirement. A few other subcategories of the onshore oil and
gas industry, such as coastal and agricultural and wildlife use, are allowed
to discharge to surface water under NPDES permits and are not included in this
study.
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * *
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3-43
scenarios was developed for drilling sites and production sites, based on
sample locations selected from areas containing high levels of drilling and
production activity.
The distributions of sample locations used to characterize environmental
settings at drilling and production sites are given in Tables 3-10 and 3-11.
As shown, a total of 100 sample drilling locations and 200 sample production
locations were examined. The sample locations were spread across a total of
26 states. For drilling activity, over 40 percent of the sample locations
were from Zone 7 (Texas and Oklahoma), approximately 20 percent were from Zone
2, and the remainder were spread across the remaining zones. For production
activity, approximately 32 percent of the sample locations were from Zone 7,
approximately 20 percent were taken from Zone 11A (the North Slope), 20
percent were from Zone 4, and roughly 13 percent were from Zone 10
(California).
As described in Section 3.1.3, the distribution of drill sites shown in
Table 3-10 is based on the relative number of wells drilled in the various
states, and the distribution of production sites shown in Table 3-11 is based
on the relative volumes of oil produced in the various states. EPA did a
preliminary analysis of how these distributions would have differed if
parameters other than the number of wells and volume of oil were used. If the
footage of wells drilled was used rather than the well count, there would have
been fewer drill sites selected for analysis from Zone 2 (Appalachia) where
the wells are relatively shallow, and slightly more sites would have been
selected from Zones 4, 7, and 9. In general, the differences in the drill
site sample distribution based on well count and well footage did not appear
to be significant.
However, the drilling and production site sample distributions would have
been very different from that shown in Tables 3-10 and 3-11 if certain other
parameters were used. For example, using data on the volumes of drilling
waste generated rather than the numbers of wells drilled would have resulted
in no (as opposed to 13) sample sites being selected from Zone 2 and 68
(versus 44) sites being selected from Zone 7. Also, if the relative volumes
of produced water were used as the basis of the production site sample rather
than the volumes of oil, more sample sites would have been selected from Zone
6 (in particular from Kansas), more sites would have been selected from
California and Oklahoma, and a smaller number of sites would have been
selected from Alaska and Louisiana. The Agency believes that using the number
of wells drilled, as opposed to other indicators, yielded the best sample site
distribution for the purpose of scenario development. Using the number of
wells drilled resulted in a sample that was more broadly distributed
geographically, resulting in a better cross-section of the environmental
settings for the industry as a whole.
Table 3-12 presents the values for the categories of environmental
variables listed in Table 3-1. As described in Section 3.1.3, these values
(with the exception of the distance to the nearest surface water intake) were
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
* * *
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3-44
TABLE 3-10
DISTRIBUTION OF SAMPLE SITES
FOR DRILLING ACTIVITY
Zone State
2 Kentucky
New York
Ohio
Pennsylvania
Tennessee
Virginia
West Virginia
4 Alabama
Arkansas
Florida
Louisiana
Mississippi
5 Illinois
Indiana
Iowa
Michigan
Minnesota
Missouri
Wisconsin
6 Kansas
Nebraska
North Dakota
South Dakota
7 Oklahoma
Texas
8 Idaho
Montana
Wyoming
Number of
Counties
Selected
3
1
8
5
1
0
3
21
1
1
0
6
1
9
3
1
0
1
0
0
0
5
8
0
1
0
9
11
33
44
0
1
2
3
Number of
Quad Maps
Examined
3
1
8
5
1
0
3
21
1
1
0
6
1
9
3
1
0
1
0
0
0
5
8
0
1
0
9
11
33
44
0
1
2
2
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3-45
TABLE 3-10
DISTRIBUTION OF SAMPLE SITES
FOR DRILLING ACTIVITY
(continued)
Number of Number of
Counties Quad Maps
Zone State Selected Examined
9 Arizona 0 0
Colorado 2 2
Nevada 0 0
New Mexico 2 2
Utah _1 _1
5 5
10 California 3 3
Oregon 0 0
Washington 0 0
3 3
11A
11B
Alaska-North Slope
Alaska-Kenai Area
National Total
0
1
100
0
1
100
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3-46
TABLE 3-11
DISTRIBUTION OF SAMPLE SITES
FOR PRODUCTION ACTIVITY
Zone State
2 Kentucky
New York
Ohio
Pennsylvania
Tennessee
Virginia
West Virginia
4 Alabama
Arkansas
Florida
Louisiana
Mississippi
5 Illinois
Indiana
Iowa
Michigan
Minnesota
Missouri
Wisconsin
6 Kansas
Nebraska
North Dakota
South Dakota
7 Oklahoma
Texas
8 Idaho
Montana
Wyoming
Number of
Counties
Selected
0
0
1
0
0
0
0
1
1
1
1
22
2
27
2
0
0
2
0
0
0
4
5
0
3
0
8
10
41
51
0
2
6
8
Number of
Quad Maps
Examined
0
0
1
0
0
0
0
1
1
1
1
31
2
36
2
0
0
2
0
0
0
4
5
0
3
0
8
10
54
64
0
2
8
10
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3-47
TABLE 3-11
DISTRIBUTION OF SAMPLE SITES
FOR PRODUCTION ACTIVITY
(continued)
Zone
State
Number of
Counties
Selected
Number of
Quad Maps
Examined
10
Arizona
Colorado
Nevada
New Mexico
Utah
California
Oregon
Washington
0
2
0
3
_3
8
9
0
0
9
0
2
0
5
_3
10
26
0
_0
26
11A Alaska-North Slope
11B Alaska-Kenai Area
National Total
1
3
120
34 a/
7
200 b/
a/ These 34 maps were analyzed in order to develop qualitative information
on the North Slope environment. However, these maps were not used for
the purpose of model scenario development because Zone 11A was not
examined through the quantitative modeling approach.
b/ The total number of maps that was examined for the purpose of model
scenario development was 166: 200 minus the 34 maps for the North Slope
(see footnote a/).
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3-48
TABLE 3-12
VALUES FOR VARIABLES USED TO
CHARACTERIZE ENVIRONMENTAL SETTINGS
Variable
Values
Ground-water flow field type
Net recharge
Depth to ground water
Unsaturated zone permeability
Distance to surface water
Surface water flow rate
Distance to nearest exposure well
Distance to nearest surface water intake
A, B, C, D, E, F, J, K a/
High = 20 in/yr
Medium = 10 in/yr
Low = 1 in/yr
Deep = 21 meters (drilling)
= 18 meters (production)
Shallow =6.1 meters (drilling)
= 4.6 meters (production)
-2
High = 10 cm/sec
-7
Low = 10 cm/sec
Close = 60 m
Medium = 200 m
Far = 1,500 m
High = 850 cfs
Low = 40 cfs
Close = 60 m
Medium = 200 m
Far = 1,500 m
Close = 1 km
Far = 10 km
a/ Letters refer to LLM generic flow field categories; see Section 4.2 of
this report for a description.
* * *
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3-49
derived for the nation as a whole*-1 based on an analysis of all the
individual results for each sample site. The values for downstream distance
to the nearest surface water intake were simply assumed and designed to
reflect a range of distances that would result in a range of risk estimates.
The values in Table 3-12 were combined into hydrogeologic, surface water, and
human exposure settings, and the frequency of occurrence of the settings was
estimated for the different zones and for the nation.
The weighted distribution of hydrogeologic settings developed for drilling
sites is shown for the nine producing zones in Table B-3 in Appendix B. Table
B-4 in Appendix B is the hydrogeologic setting distribution for drilling sites
across the nation as a whole.
Surface water settings were defined by various combinations of the
categories for flow rate and distance to surface water. The weighted
distribution of surface water settings for drilling sites in each of the nine
producing zones and in the nation is shown in Table B-5 in Appendix B. As
indicated in this table, low flow was between 0.14 and 450 cfs and high flow
was greater than 450 cfs. For the distances to surface waters, sites were
considered close to surface waters if they were within 0 to 130 m, medium if
they were within 130 to 850 m, and far if they were within 850 to 2,000 m of a
surface water body. Table B-5 shows that there were numerous drilling sites
that were greater than 2,000 m (i.e., "very far") away from a surface water.
While these sites were accounted for, distances greater than 2,000 m could not
be modeled with the LLM and were not used in the risk modeling.
Table B-6 presents the weighted distribution of distances from drilling
sites to the nearest exposure well. Downgradient distances of up to 2,000 m
were examined; greater distances cannot be modeled by the LLM. The distance
ranges used to define close, medium, and far exposure wells, as well as the
single values used to model these distances (see Table 3-12), were the same as
those used to define close, medium, and far surface waters.
The distributions of hydrogeologic, surface water, and human exposure
settings for production sites are shown in Tables B-7 through B-10 in Appendix
B. These distributions were developed using the same criteria and assumptions
as those used in developing the distributions for drilling sites. Tables B-7
and B-8 give the weighted distributions for hydrogeologic settings at
production sites for the nine producing zones and the nation, respectively.
Table B-9 gives the weighted surface water setting distributions at production
sites, and Table B-10 gives the weighted distributions of distances from
production sites to the nearest exposure well.
"J That is, Zones 2, 4, 5, 6, 7, 8, 9, 10 and 11B; Zones 1 and 3 were
excluded because they have relatively very low levels of oil and gas activity,
and Zone 11A was excluded because it was not included in the quantitative
modeling analysis (see Chapter 6).
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CHAPTER 4
OIL AND GAS: QUANTITATIVE MODELING METHODS
This chapter describes the methods used to estimate health and
environmental risks for the model scenarios described in Chapter 3. Methods
are described and references given for estimating chemical releases, chemical
fate and transport, and potential effects.
4.1 CHEMICAL RELEASES
Three mechanisms for waste constituents to be released into the
environment were modeled: underground injection failures; leachate from
reserve pits; and direct discharge to surface water. The procedures used to
model each of these release mechanisms are described in Sections 4.1.1, 4.1.2,
and 4.1.3, respectively.
4.1.1 Underground Injection
The great majority of produced water wastes from oil and gas production
are disposed by underground injection. This section describes the methodology
for modeling releases from Class II underground injection wells used to
dispose of produced fluids (e.g., brines).
While evidence suggests that releases from failed underground injection
wells can be a source of ground-water contamination, few previous attempts
have been made at modeling the magnitude of these impacts. Because of
technical difficulties in measuring releases to ground-water through failed or
abandoned wells in the field, little empirical data exists to support efforts
at quantifying those failures. Consequently the approach described here for
modeling releases from Class II underground injection wells is based upon
limited empirical evidence and best professional judgment.
Figure 4-1 shows four release pathways from underground injection that can
lead to contamination of surface aquifers. The Agency considered two of these
release pathways when modeling releases from injection wells to upper
aquifers: (1) release through failure of the well casing in the upper
aquifer; and (2) release due to failure of the grout seal separating the
injection zone from the upper aquifer. While the Agency recognizes that
migration through abandoned wells may be an important source of contamination
of upper aquifers, this pathway was not modeled due to technical constraints
and data limitaitons.
The underground injection methodology models four release event scenarios:
Well casing failure -- best-estimate;
Well casing failure -- conservative;
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4-2
Figure 4-1
Potential Contaminant Release Pathways From Underground
Injection Wells into Surface Aquifers
Injection Well
Improperly Plugged
Well
.Well
Casing Failure
Grout Seal
Failure
Surface
Aquifer
Injection Zone
Migration Migration
ป Via Through
Abandoned Fractures
Well 4 or Faults
J/L
TI I ,, ;,
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4-3
Grout seal failure -- best-estimate; and
Grout seal failure -- conservative.
Release estimates are generated for high, medium, and low brine injection
volumes (for grout seal failures) and injection pressures (for well casing
failures) for each scenario. The approaches for estimating well casing and
grout seal failure release rates are described below, along with a brief
discussion of alternative approaches that were considered but rejected.
A properly constructed Class II injection well has the following
components: (1) an outer surface casing, cemented from the ground surface to
below the lower extent of any underground source of drinking water; (2) an
intermediate casing within the surface casing, cemented up to at least the
bottom of the grout seal for the surface casing; and (3) an interior injection
tube. The annular space between the intermediate casing and the injection
tube is sealed from the injection zone by a packer. Injection wells at some
oil and gas sites do not meet these design criteria, making casing and grout
seal failures more likely.
Well Casing Failure. For a properly designed injection well, a release
through a failed well casing to the uppermost aquifer can take place only when
the following situation occurs: (1) the waste leaks into the annular space
between the injection tubing and the intermediate casing (either through a
failed packer or from a failed injection tube); (2) the intermediate casing
fails (either due to corrosion or improper installation); and (3) the surface
casing fails within the uppermost aquifer (either due to corrosion or improper
installation). For wells meeting less stringent design criteria, however,
casing failure becomes more likely.
Release rates through failed well casings are estimated using Darcy's
equation for saturated flow:
Q = K (dh/dl) A [Equation 4-1]
where
Q = release rate into the aquifer (length3/time)
K = hydraulic conductivity of the aquifer (length/time)
dh = pressure head drop from inside well to aquifer at the
point of casing failure (length)
dl = radius of influence in aquifer over which the pressure drop
occurs (length); and
A = area of failed casing hole (length2).
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4-4
The LLM provides a value for hydraulic conductivity (K) for each generic flow
-1 -5
field within the model, ranging from 5x10 to 5x10 cm/sec.
Darcy's Law states that flow through a porous medium depends upon the
pressure gradient (dh/dl), the hydraulic conductivity of the aquifer (K), and
the cross-sectional area over which the flow occurs. EPA developed this new
approach for estimating release rates from failed well casings due to a lack
of strong precedent in modeling this phenomenon.
The force driving the release is the pressure gradient (dh/dl) from within
the injection well at the point of failure to a point within the aquifer far
enough from the point of release to be unaffected by the well pressure. Thus,
the pressure drop (dh) is assumed to be the difference between the hydrostatic
pressure within the aquifer located at the depth of the point of failure, and
the pressure within the injection well at the point of failure (which equals
the pressure head supplied by the pump, plus the hydrostatic pressure of the
liquid column from the point of release to the ground surface). The
differential distance over which the pressure drop dissipates (dl) is
estimated based upon the general physical relationship described by the
Dupuit-Forscheimer well hydraulics equation (which applies to unconfined
aquifers as modeled here). This equation could not be applied directly
because it applies to fully penetrating and screened wells, rather than a
discrete point leak. Using an analogy to the radius of influence from a fully
penetraing injection well, the distance dl is assumed to be a function of the
hydraulic conductivity of the aquifer. Although dl may also vary as a
function of injection pressure, this factor was not considered in EPA's
approach.
The distance dl is assumed to increase between two aquifers with differing
hydraulic conductivities at a rate proportional to the ratio of the square
roots of their hydraulic conductivities:
[Equation 4-2]
Kx(2)
For this modeling analysis, EPA assumed that the minimum distance for dl, 1
-5
meter, will be associated with the slowest flowfield (10 cm/sec). EPA
believes it would be unlikely for the pressure resulting from the point leak
to dissipate within the aquifer at a distance less than 1 meter from the well,
supporting this assumption for a minimum distance for dl. Using equation 4.2,
the estimated distances for dl will vary from 1-100 meters.
Casing failure may result from several mechanisms, including corrosion of
the well casing or cracks/holes resulting from improper installation.
Although the size of a failure in a well casing may vary dramatically, the
model assumes a hole size of 25 square centimeters.
Two approaches for the timing of well casing failures are included in this
methodology: (1) annual releases over the entire twenty year operating period
of the injection well, simulating the case in which integrity tests do not
detect failure; and (2) annual releases over two five-year periods beginning
in years 5 and 15, based on the common frequency of well integrity tests.
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4-5
Based on this modeling approach and the underground injection model scenarios
(see Section 3.3.3), releases through failed well casings may range from .04
percent to 25 percent of the total annual injected volumes.
Grout Seal Failures. Surface aquifers can be contaminated by injected
fluids that migrate from the injection zone along and through cracks in the
outer cement seal of the injection well. Such releases can be caused by a
poor cement job, deterioration of the cement over time, excessive injection
pressures, or any combination of these mechanisms. The approach for
simulating releases due to grout seal failures is described below.
The approach for calculating injection volumes released to the upper
aquifer from grout seal failure is based upon results from a three-dimensional
finite difference flow and transport model developed to investigate impacts
from underground injection (Ward et al., 1986). These results indicated that
a release of 0.00025 percent of the injected produced waters represents a
medium release rate due to grout seal failure. A high (or conservative)
release rate to an upper aquifer associated with this kind of failure was
calculated to be 0.05 percent of the injected fluids. Releases from grout
seal failures were assumed to go undetected for the entire opeating period,
because well integrity tests to detect grout seal failures are infrequently
conducted. Table 3-9 displays the injection rates used to calculate release
volumes due to grout seal failure for the scenarios modeled in this study,
which include both high, medium, and low rate injection volumes.
Alternative Approaches Considered for Modeling Underground Injection. An
alternative approach to modeling both grout seal and casing failures would be
to simulate these failures stochastically. This approach was rejected as too
data intensive and unvalidated, given the limited availability of empirical
data. A stochastic approach has been developed for Class I injection wells,
but these results are not directly transferable.
Migration through abandoned and improperly plugged wells in the vicinity
of an injection well may be an important release mechanism in some areas
(e.g., oil fields that have a long history of production and exploration). As
discussed before, this release pathway was not modeled here due to the lack of
sufficient information needed to define the necessary model parameters.
4.1.2 Surface Pits
This section describes EPA's approach for modeling releases to ground
water from drilling mud reserve pits used during oil and gas exploration and
development (i.e., drilling). Approaches were developed for modeling both
typical and conservative release scenarios from centralized and on-site
reserve pits. The model has been developed based on common design practices
in the oil and gas industry, which includes using both lined and unlined
drilling mud reserve pits.
The model calculates leachate release rates for three pit design sizes
under each of the different scenarios simulated:
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4-6
On-site mud reserve pits: synthetic-lined and
unlined; and
Centralized mud reserve pits: synthetic-lined.
The model is based on typical industry practices for the use and disposal
of drilling muds. On-site pits generally operate between 30 to 90 days during
drilling operations; thus, the model assumes a 60-day operating period. State
regulations vary on closure requirements, but most states require reserve pits
to be dewatered and the residuals buried within six months to one year after
the end of operation. The model assumes that on-site pits will be dewatered
by the end of the year in which the pit operates (i.e., model assumes a total
fill period of one year). Centralized mud reserve pits are assumed to operate
for twenty years. Centralized reserve pits typically are not dewatered in the
same manner as on-site pits; the remaining free liquids are injected into the
subsurface at closure. The approaches for modeling leachate release rates and
leachate quality from on-site and centralized mud reserve pits are described
below; alternative approaches considered but rejected are also described below.
Leachate Volume for On-Site Mud Reserve Pits. The model uses one basic
algorithm for simulating leachate release during the operating phase for each
of the on-site model scenarios, although the presence of liners varies. The
approach adopted in this model simulates the steady-state flux of leachate
through the pit bottom during its operating phase based upon a modified form
of Darcy's Law (McLin, no date):
q = (HI + H2 + Lt + Lc - hi) / ((Lc/Kc) + (Lt/Kt)) [Equation 4-3]
where:
q = seepage rate (length/time, or volume/time-wetted surface area)
Kc = saturated hydraulic conductivity of intact synthetic liner
(length/time)
Kt = average vertical hydraulic conductivity of bottom sediments
(length/time)
HI = depth of free liquids above mud layer (length)
H2 = depth of mud layer (length)
Lc = liner thickness (length)
Lt = bottom sediment thickness (length)
hi = negative pore water pressure head at liner bottom (length)
Table 4-1 lists the parameters selected for each of the reserve pit
scenarios. The muds are assumed to have a density of 1.3 g/cm3, and the
sediments a density of 1.6 g/cm3.
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4-7
TABLE 4-1
MODEL INPUT PARAMETERS FOR MUD RESERVE PITS
Depth (m) Negative
Pit Free Liner Pore Water
Pit Type Dimensions (m) Liquids Muds Sediments Thickness a/ Pressure b/
(size) (L) (W) (D) (HI) (H2) (Lt) (Lc) (hi)
Onsite
(large) 76 76 2.4 0.4 1.2 0.8 20 mil 0
Onsite
(medium) 53 38 1.8 0.3 0.9 0.6 20 mil 0
Onsite
(small) 30 9.1 1.2 0.2 0.6 0.4 20 mil 0
Central 2 6 4 20 mil 0
a/ Applies only to lined mud reserve pits.
b/ Assumed to be zero due to insufficent data.
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4-8
On-site burial of drilling muds and cuttings takes place at the end of the
operational phase. Two approaches are followed for disposing of free liquids
at closure of a reserve pit: (1) free liquids are allowed to evaporate (in
the arid climate setting); or (2) free liquids are allowed to percolate or are
landspread in the immediate vicinity of the pit (in the moderate and wet
climate settings). Leachate release rates resulting from disposal of free
liquids are calculated using Equation 4-3 in the arid climate, based upon the
assumption that some release to ground water will occur during evaporation of
the free liquids. In the moderate and wet climates, all of the free liquids
in the pit (HI times pit surface area) are available for release to ground
water, based upon the assumption that they either percolate or are land spread
over a sufficiently large surface area.
After on-site burial of the drilling muds (i.e., at the end of model year
1), leachate release volumes are governed by net infiltration through the
closed unit and the integrity of the unit. Release volumes from unlined pits
after closure will be equal to the net infiltration rate, based upon the
assumption that the buried sediments will be likely to crack and will not
prevent infiltration to the subsurface.
The model incorporates two scenarios for simulating releases after closure
from synthetic lined reserve pits. Before liner failure, the release rate
will be limited by the permeability of the synthetic liner. After the liner
begins to fail, it is assumed that complete failure occurs within ten years
(10 percent additional failure per year), at which time the release rate will
be equal to the net infiltration rate. In the typical case, liner failure
begins in year twenty-five,1J while in the conservative case, liner failure
begins in year five. The model assumes a linear increase in the release rate
between the year of initial failure and the year in which the liner totally
fails.
Leachate Volume for Centralized Mud Reserve Pits. The approach for
simulating leachate release volumes from centralized mud reserve pits differs
from the on-site pit approach in two primary ways: (1) the operating life of
the model centralized pit is assumed to be 20 years; and (2) the free liquids
are assumed to be reinjected through underground wells at closure, not
landspread or allowed to evaporate.
In the conservative liner failure scenario (initial failure in year 5 with
complete failure in year 15), a centralized mud reserve pit will be
operational when the liner begins to fail. In order to simplify this modeling
approach, it is assumed that the synthetic liner fails completely in year 10
(midpoint between years of initial and complete failure). Release rates
between failure and closure are estimated with Equation 4-3 by treating the
pit as unlined (setting Lc equal to zero). After closure, the leachate
release rate is assumed to be equal to the net infiltration rate.
1J Typical liner failure rate based upon LLM results for Subtitle C
facilities (EPA 1986e).
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4-9
In the typical liner failure scenario (initial failure in year 25 with
complete failure in year 35), centralized mud pits will have closed before
liner failure begins. Release rates will be calculated in the same manner
followed for lined on-site pits, where the release rate will be limited by the
synthetic liner permeability until the initial year of liner failure,
increasing linearally to the net infiltration rate over a ten-year period.
Leachate Quality. Estimates of leachate quality are based upon reserve
pit sampling data collected by EPA for this project (EPA, 1987b) . Samples of
free liquids and sediments from drilling mud reserve pits were analyzed for
numerous organic and inorganic parameters. The sediments were analyzed for
both total constituent concentrations and extractable concentrations (using
the toxicity characteristic leachate procedure (TCLP)). A representative
characterization of drilling waste reserve pit chemical constituents and their
concentrations in free liquids, sediments, and TCLP leachate was developed
based on these data (see Section 3.3.2).
In the model, leachate concentrations during the operating phase are equal
to the concentrations in the pit free liquids, which would be the primary
source of contaminated leachate. After closure, leachate concentrations are
simulated by the extractable concentrations from the pit sediments (i.e., TCLP
values). Because the sediments were not analyzed for extractable chloride
concentrations, an alternative approach for estimating chloride leachate
concentrations was developed. A mass balance algorithm in the model ensures
that the chemical mass released through leachate never exceeds the total
amount originally available in the pit sediments.
Alternative Approaches Considered for Modeling Surface Pits. EPA
considered several additional approaches for modeling leachate quality and
quantity from drilling mud reserve pits. One approach examined was adapting a
sophisticated numerical unsaturated zone flow model to simulate seepage, which
was rejected due to its complexity and input data requirements. Another
alternative included modifying the Failure/Release Submodel of EPA's Liner
Location Model (LLM), which simulates leachate release from hazardous waste
surface impoundments stochastically. This approach was also rejected because
of the complexity of modifying the submodel and because the relative
simplicity of the release mechanisms being modeled in this study did not
warrant a stochastic modeling approach.
As an alternative approach to modeling leachate quality, the approach
employed in the LLM Subtitle C landfill and surface impoundment models was
examined. This approach uses regression equations developed by OSW for
simulating TCLP concentrations based upon waste concentrations. Because the
sampling data included actual TCLP concentrations for the wastes of interest
(i.e., reserve pit sediments), the more indirect LLM approach was not
considered necessary.
4.1.3 Direct Discharge to Surface Water
Direct discharge to surface water was modeled only for one category of
production facility, stripper oil wells (direct discharges of produced fluids
to coastal waters or to inland waters as a result of agricultural and wildlife
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use are regulated under the Clean Water Act NPDES permit program and were not
considered within the scope of this study). Releases to surface water were
estimated as follows:
(1) For major producing states, the average annual crude
oil produced per stripper well was calculated based on
EPA's industry characterization data (see Part );
(2) A produced fluid:crude oil ratio of was used to
estimate volumes of produced fluids per stripper well
on a state-by-state basis; and
(3) The distribution across states of volume per stripper
well was used as a basis for selecting two
representative discharge (i.e., waste generation)
rates.
By following the above steps, two stripper well scenarios having different
annual discharge rates to surface water were developed and modeled.
4.2 CHEMICAL TRANSPORT
Contaminants released from oil and gas waste management units may be
transported to potential exposure points by several pathways, including
migration in ground water and surface water. In this section, we present
brief descriptions of the submodels that are used to predict transport and
fate of chemicals in the ground-water and surface waste pathways. The section
is divided into two subsections: (1) an overview of the subsurface transport
submodel, including unsaturated and saturated zone components; and (2) a
description of the surface water submodel.
4.2.1 Subsurface Transport Submodel
The subsurface transport submodel of the risk model predicts the mass
transport of contaminants through both the unsaturated and saturated zone. It
traces contaminants from their mass loading into the unsaturated zone through
the saturated zone, and ultimately estimates concentration over time at wells
or surface water discharge points downgradient from a waste management unit.
The unsaturated zone and saturated zone components of the submodel are
described below, along with the approach for estimating contaminated plume
width. 2J
2J The subsurface transport models used in the oil and gas risk
assessment have been adapted from the EPA Liner Location Risk Analysis Model.
For a more detailed description of the model, the reader is referred to the
Liner Location Risk and Cost Analysis Model, Phase II Draft Report, U.S.
EPA-Office of Solid Waste, March 14, 1986 (EPA, 1986e). Appendix B of that
report documents the subsurface transport models in detail.
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Unsaturated Zone. The McWhorter-Nelson wetting front equation (McWhorter
and Nelson, 1979) is used to simulate travel time from the waste management
unit to the water table.3-1 In this equation, unsaturated flow is
represented as one dimensional downward movement of a sharp wetting front
created by leakage from a waste management unit. Contaminant transport in the
unsaturated zone is related to the movement of the wetting front and is
described by the following equations.
t = [L * (9f - 9r)/q] * Rd [Equation 4-4]
where:
t = contaminant travel time from the base of the unit
to the top of the saturated zone (T);
L = distance from the base of the unit to the top of the
saturated zone (L);
9f = water content above the wetting front (dimensionless)
9r = water content below the wetting front (i.e., field capacity)
(dimensionless)
q = liquid (leachate) flux at the release point (L/T); and
Rd = retardation factor (dimensionless)
The first term of the equation, [L * (8f - 9r)/q], defines the
transport time of water over the distance L, and is largely a function of q,
the leakage rate (Darcian flux) from the waste management unit. The transport
time predicted for the wetting front is then adjusted for a specific
contaminant by multiplying it by the contaminant's retardation factor, Rd.
This chemical-specific adjustment is necessary to account for the differential
mobility of various contaminants in ground water.
Retardation factors depend both on chemical characteristics, such as
hydrophobicity and polarity, and on soil characteristics, including fraction
of organic carbon, porosity, and bulk mass density. The following equation is
used to calculate Rd:
Rd = 1 + [(p/n) * Kd * (9f/n)] [Equation 4-5]
where:
Rd = retardation factor (dimensionless);
3J The unsaturated zone component was not used for releases from
underground injection wells; it was assumed that releases were directly into
the saturated zone.
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p = bulk mass density of the earth material (M/L3);
n = porosity of the earth material (dimensionless);
Kd = distribution coefficient for a specific chemical (L3/M);
and
6f = water content above the wetting front (dimensionless).
For organic chemicals, Kd = Koc * foe, where Koc is the organic carbon
adsorption coefficient and foe is the fractional organic carbon content of the
soil. For inorganic chemicals, Kd does not depend so directly on foe, and a
single generalized value is used in the model to represent Kd for inorganics.
Contaminant degradation in the unsaturated zone is also predicted by the
model. Degradation is assumed to be first-order, with the rate constant equal
to (0.69/half-life). The degradation processes modeled for the unsaturated
zone are chemical hydrolysis and aerobic degradation.
The unsaturated zone component simulates only homogeneous, isotropic
hydrogeologic scenarios, and contaminant transport is modeled only in the
vertical direction. Dispersion is not considered, and it is also assumed that
the earth material in the unsaturated zone is the same as that in the top
layer of the saturated zone. Other.key assumptions include: (1) no
contaminant interactions occur (e.g., mobilization of hydrophobic organics by
solvents is not considered); (2) adsorption is considered to be linear, rapid,
and reversible, as described by a Freundlich isotherm; and (3) mass loading is
applied to an area large enough to allow valid one-dimensional (vertical
downward) modeling of transport in the unsaturated zone.
Saturated Zone. A modified version of the Prickett-Lonnquist Random-Walk
Solute Transport Model (Prickett, Naymik, and Lonnquist, 1981) is used in the
oil and gas risk analysis to predict contaminant migration in the saturated
zone. The random-walk model is a two-dimensional, finite-difference model
based on a numerical ground-water flow model and a stochastic "particle
transport" submodel. Particles are used in the model as a surrogate for
chemical mass and are transported in two stages: (1) the advective stage, as
predicted by ground-water flow velocity and contaminant mobility; and (2) the
dispersive stage, in which the particle is transported randomly based on
dispersivity, contaminant velocity, and mobility. Contaminant concentrations
over time are determined by counting the number of particles passing an
exposure point (e.g., downgradient well or ground water-surface water
boundary) during each time step and converting this number to a mass flux.
This mass flux is based on the input mass from the unsaturated zone (i.e., is
"scaled" for the source) and is divided by the volumetric flux across the
boundary of interest to yield a contaminant concentration.
To account for differences in ground-water conditions at oil and gas
facilities, eight generic ground-water flow fields were used to represent the
range of diverse hydrogeologic conditions found in the United States.
Developing these flow fields involved a three-step process: (1) review of
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4-13
ground-water supply reports to determine representative ranges for important
hydraulic parameters; (2) identification of the sequence of aquifers and
nonaquifers to develop typical configurations; and (3) definition of generic
flow fields from data compiled during the first two steps. The eight generic
ground-water flow fields used in the oil and gas project are illustrated in
Figure 4-2.*J
Applications of the model for the oil and gas risk analysis involve a
source (e.g., disposal pit) that is releasing contaminants at a variable rate
for a duration greater than one year. In these situations, the raw
random-walk model output data, which correspond to mass loading for a one-year
period, must be adjusted to account for the longer duration of the source. To
do this, the model calculates a time profile of concentration (i.e., a
breakthrough curve) at the exposure point separately for each year of
contaminant input to the saturated zone. These individual profiles are then
summed to produce the overall time profile of concentration resulting from the
combined effect of releases over multiple years. This summation procedure is
illustrated in Figure 4-3.
The standard breakthrough curves generated by the random-walk model are
adjusted for several factors not rigorously modeled. Adjustments are made for
three factors related to the source and for four physical/chemical processes.
Adjustment factors related to the source include:
1) Source strength: Standard breakthrough curves, which correspond to a
source loading of 1 kg/year in a one-meter wide strip, are adjusted
for the actual source loading.
2) Source duration: Standard breakthrough curves correspond to a
one-year source duration. They are adjusted for the actual source
duration by the summation procedure described in the preceding
paragraph and illustrated in Figure 4-3.
3) Source width: Standard breakthrough curves, which correspond to a
source width of one meter, are adjusted for the width of the source
being modeled.
Adjustment factors for physical/chemical processes not rigorously modeled
include:
1) Degradation: For chemicals that are non-persistent, standard
breakthrough curves are adjusted for degradation due to chemical
hydrolysis and anaerobic biodegradation. First-order, exponential
decrease in concentration is assumed:
toj A total of eleven generic flow fields has been developed for the
Liner Location Model. The three flow fields representing multiple aquifer
systems were not used in this project because of the lack of hydrogeologic
data necessary to determine occurrence at sample sites.
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FIGURE 4-2
EIGHT GENERIC FLOW FIELDS
USED IN SATURATED ZONE TRANSPORT MODELING
30M 1 M/Y-
30M
B
10 M/Y
30M
C
100 M/Y-.
30M
D
1,000 M/Y-*
sow 10.000 M/Y-*
15M
30M
0.05 M/Y
100 M/Y
0.5 M/Y
ISM
30M
Qnm Aป/v ซ
M/Y *'//
O.SM/Y!
il fV ซ ซ..
EXPLANATION
Average Linear Groundwater Velocity Vectors
Water Table Boundary (Meters/Year) Through Each Layer of Saturated
Outflow *^ Material with Constant Thickness (Meters).
Boundary ^^
30M
10 M/Y-
M inflow Cross-Hatch Lines Indicate Layer is Non-Aquifer
Boundary
No-Flow/
Boundary
* *
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FIGURE 4-3
SUMMATION OF INDIVIDUAL BREAKTHROUGH CURVES
SUM OF K> IKG-IYR
BREAKTHROUGH CURVES
NOTE: Individual I yr input curves
have not been scaled to any
particular facility size/unit
mass load
to o
TIME, YEARS
20
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Ct = Co * exp (-k * t) [Equation 4-6]
where Ct = concentration at any time, Co = unadjusted concentration,
k = decay constant (k = 0.69/half-life), and t = elapsed time.
2) Retardation: Standard breakthrough curves are based on an assumption
of no retardation of the contaminants. Adjustments to both timing
and concentration are based on the retardation factor of the specific
chemical being analyzed. For multi-layer flow fields, the
retardation factor is assumed to be constant across all layers.
3) Transverse dispersion: Standard breakthrough curves, which assume no
transverse horizontal dispersion, are adjusted downward to account
for this process by a factor derived from the ratio between the
three-dimensional and two-dimensional analytical solutions of Hunt
(1978).
4) Well dilution: Standard breakthrough curves assume there is no
dilution due to pumping at exposure wells. Adjustments can be made
to the standard curves to account for dilution resulting from mixing
of the contaminated plume with uncontaminated ground water, depending
on the approximate pumping rate of the well being analyzed relative
to aquifer flow and facility size.
The saturated zone component estimates concentrations over a 200-year time
period for each of the well distance/source length/flow field combinations
considered in the oil and gas risk analysis. However, it is important to note
that the model does not use site-specific hydrogeologic parameter values, but
rather the generic well distance/source length/flow field combination that
most closely matches conditions at a site. Other simplifying assumptions that
limit the saturated zone component include the following:
Earth materials in the saturated zone are assumed to
be homogenous and isotropic;
Contaminant mass in an aquifer is evenly distributed
vertically;
Mobility of contaminants is based on a single
estimated Kd value for each chemical;
Degradation in the saturated zone occurs only by
chemical hydrolysis and anaerobic degradation; and
Exposure points are located directly downgradient
from the contaminant source.
In general, the assumptions outlined above tend to result in conservatively
high concentrations at exposure points.
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Plume Width. The LLM includes an algorithm that estimates, as a function
of time and distance, the width of a ground-water plume contaminated above a
pre-specified threshold concentration. The algorithm was derived directly
from the LLM saturated zone transport modeling approach, in particular the
transverse dispersion adjustment factor (see EPA, 1986e for a detailed
description and equations). EPA used the LLM plume width component to
estimate volumes of ground water contaminated above various resource damage
thresholds for the oil and gas development and production model scenarios.
For a given downgradient distance from a source, the LLM plume width
algorithm calculates for each model year the horizontal distance from the
centerline of a contaminant plume to the point where ground-water
concentration equals a pre-specified value. The total plume width is twice
this horizontal distance from the centerline. The equations used by the LLM
to adjust concentrations for transverse dispersion are adapted for this
distance calculation. The factors that affect plume width, as estimated by
the LLM, are source width, centerline concentration, threshold concentration,
downgradient distance, ground-water velocity, dispersivity, and the chemical's
retardation factor.
4.2.2 Surface Water Transport Submodel
To evaluate contaminant transport in surface water, EPA used a one-
dimensional, non-steady state model that simulates advective and dispersive
transport and also accounts for other processes affecting the fate of
contaminants in surface water, such as sediment-water partitioning,
volatilization, degradation, and sedimentation. The model is suitable for
estimating contaminant concentrations over time and distance at locations
downstream from a contaminant source for streams and well-mixed estuaries.
The equation used has been adapted from the instantaneous source (one-time
event loading) transport model described in the EPA report "Technical Guidance
Manual for Performing Waste Load Allocations: Book 2, Streams and Rivers,
Chapter 3, Toxic Substances" (EPA, 19S4b), and is presented below:
C(x,t) =
M
exp
/(4 IT Dxt)'
(x-vt)2
4Dxt
- Kit
[Equation 4-7]
where:
C(x,t) = contaminant concentration in water body at time t
and distance x (M/L3)
M = mass flux of contaminant (M/L2)
Dx = dispersion coefficient (L2/T)
t = time (T)
x = distance (L)
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v = stream velocity (L/T)
Kl = combined first-order loss rate coefficient for all loss
mechanisms (settling, volatilization, degradation) (1/T)
This equation predicts contaminant concentrations over time at locations
downstream from a contaminant source for one-time (event) loading. However,
in the oil and gas risk analysis, most sources release contaminants at a
variable rate for a time period of greater than one year. In these
situations, model output is converted to mass loading, and "breakthrough
curves" with corresponding values for time and distance (i.e., same x and t
values) for different mass loading events are summed to estimate a total
concentration for that time and location. This summation procedure is
identical to the procedure described in the previous section on ground-water
modeling.
The surface water transport model accounts for several transport
mechanisms and environmental fate processes that may affect the distribution
of contaminants in rivers (Figure 4-4). Contaminants are partitioned between
dissolved and adsorbed phases and can volatilize or degrade by photolysis,
hydrolysis, or biodegradation. The degradation mechanisms can occur with
dissolved contaminants, suspended sediments, or in the bed sediments.
Settling of suspended sediments and accumulation in the bed is also included
in the model.
Input variables required by the model include: contaminant and sediment
loadings to the water body; background concentrations of contaminants and
sediment; steady-state velocity and cross-sectional area of the river or
estuary; depth of water column and bed sediments; settling and sedimentation
velocities; and partition coefficients and individual volatilization and
degradation rate constants for each contaminant of concern. For the oil and
gas risk analysis, EPA assumed steady-state conditions exist in the river with
respect to stream velocity, background chemical conditions, cross-sectional
area, depth of water column and bed sediments, and settling and sedimentation
velocities (note that mass input was not assumed to be steady-state). These
parameters are defined for the two generic surface water model scenarios that
were developed for this analysis (see Section 3.1.3 for a more detailed
discussion of the development of model scenarios) and are given in Table 4-2.
Partition coefficients and volatilization and degradation rate constants are
chemical-specific input parameters; values for these parameters for the model
constituents are given in Table 4-4 (see Section 4.4).
As noted, Equation 4-7 is the basis for the surface water transport model
and predicts total water column concentrations of contaminants over time at
locations downstream from a contaminant source. However, for the purpose of
evaluating potential human and environmental exposure, total water column
concentrations may be misleading in that only a portion of the total
contaminant mass may be present in the dissolved phase. Also, contaminants
associated with suspended sediment may settle and accumulate in bed sediment,
where they are lost from the water column but may affect benthic (i.e.,
bottom-dwelling) organisms. Because the distribution of contaminants between
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FIGURE 4-4
PROCESSES AFFECTING THE TRANSPORT AND FATE
OF CONTAMINANTS IN SURFACE WATER
AIR
WATER
Settling/
SEDIMENT
Volatilization
CO,
Chemical and Biological
Degradation
Uptake by Organisms/
Toxicity
Cs=Soluble concentration ; Cp=Farticulate concentration ;
Catm?Atmospheric concentration
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various phases is very important in evaluating toxicity, bioavailability, and
ultimate fate, EPA adapted the surface water transport model to predict
dissolved phase concentration, suspended sediment phase concentrations, and
bed sediment concentrations for each contaminant in addition to predicting
total water column concentrations. These concentrations are calculated as
follows:
Dissolved and suspended sediment phase concentrations
-- These concentrations are calculated using an
equilibrium partitioning model:
Kd = Cp/Cs [Equation 4-8]
where Kd = contaminant-specific partition coefficient,
Cp = suspended sediment phase concentration (mg/kg);
and Cs = dissolved phase concentration (mg/1). In the
model, a fractional distribution of dissolved and
suspended sediment phase contaminants is calculated
based on Kd values and suspended sediment levels, as
follows:
Fd = I/O + KdM) [Equation 4-9]
Fp = KdM/Cl + KdM) [Equation 4-10]
where:
Fd = fraction of total contaminant mass dissolved
in water column
Fp = fraction of total contaminant mass associated
with particular phase in water column
Kd = partition coefficient (1/mg)
M = suspended sediment concentration (mg/1)
The fractional distribution coefficients for the two
phases are then multiplied by the total water column
concentration predicted by the surface water transport
model to estimate dissolved and suspended sediment
phase contaminant concentrations.
Bed sediment concentrations -- This concentration is
estimated using a simple mass balance approach. For
each distance step of the model, the mass of suspended
sediment lost from the water column by settling is
calculated, and the contaminant mass associated with
the sediment is calculated based on the equilibrium
partitioning approach described above. This
contaminant mass is then "diluted" into a bed sediment
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TABLE 4-2
DEFAULT VALUES FOR SURFACE WATER
PARAMETERS USED IN THE OIL AND GAS RISK ANALYSIS
Small Stream Large Stream
Parameter Setting Setting
Flow rate (m3/sec) a/
Width (m) b/
Depth (m) b/
Stream velocity (m/sec) b/
Suspended sediment concentra-
tion (mg/1) c/
Depth of active bed (cm) d/
Settling velocity (m/day) e/
Sedimentation velocity (m/day) f/
Longitudinal dispersion
coefficient (mz/sec) g/
Sediment fractional organic carbon
content (dimensionless) h/
1.1
7.7
0.3
0.5
50
10
0.1
-5
3.9 x 10
130
0.014
24
35
0.9
0.8
50
10
0.1
-5
3.9 x 10
1200
0.014
a/ Flow rate information was compiled for streams located near 100
oil and gas exploration sites and 200 production sites. Values
for the small and large stream settings represent the 25th and
75th percentile, respectively, of all flow rates determined for
the 300 facilities.
b/ Width, depth, and steady-state stream velocity were calculated
based on flow rate using the following empirical relationship
(Leopold and Haddock, 1953; Dunne and Leopold, 1978):
0.5
w = 4.0 Q
0.4
d = 0.198 Q
0.1
u = 1.22 Q
where:
Q = flow rate (cfs)
w = width (ft)
d = depth (ft)
u = velocity (ft/sec.)
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TABLE 4-2 (continued)
DEFAULT VALUES FOR SURFACE WATER
PARAMETERS USED IN THE OIL AND GAS RISK ANALYSIS
FOOTNOTES
c/ This parameter varies widely over time in all surface water
systems and has been given a single value, based on professional
judgment, to simplify the analysis (i.e., to limit the number of
variables considered so that the number of results obtained can be
maintained at a reasonable level).
d/ Value estimated based on information from Manhattan College (1980).
e/ Calculated using Stoke's Law, assuming a particle size of 0.001 mm
and a particle density of 2.65 gm/cm3. Particle size assumption
is important and is based on the fact that finer-grained materials
such as silts and clays are responsible for most adsorption, based
on more favorable surface-to-volume ratio, mineralogy, and surface
activity compared to sand-sized particles.
f/ Calculated from w = w C /Pb, where w = sedimentation
2 1 ss 2
velocity, w = settling velocity, C = suspended sediment
1 ss
concentration, and Pb = bulk mass density of sediment.
g/ Calculated from the empirical equation (Fischer, 1979):
2 2
K = 0.11 u w_
d u*
where:
k = longitudinal dispersion coefficient (L2/T)
u = stream velocity (L/T)
w = width (L)
d = depth (L)
u* = shear velocity (L/T); estimated as 0.1 times u
h/ Value reported is an average of values reported by Means et al.
(1982). Range reported was 0.0015 to 0.0238.
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volume described by the length of the model's distance step, the
width of the surface water body, and the depth of the bioturbation
layer. Total sediment mass in this volume is calculated based on an
average mass bulk density of bed sediments and the bed sediment
contaminant concentration is then expressed as a mass/mass ratio
(i.e., mg/kg), using the following equation:
C = (M * C )/(P * L * W * D ) [Equation 4-11]
B P b b
where:
C = bed sediment contaminant concentration (mg/kg)
B
M = mass of suspended sediment lost from water
column over distance L (mg)
C = mass fraction of contaminant associated with
P
suspended sediment (dimensionless)
P = bulk mass density of bed sediment (kg/m3)
b
L = length of distance step (m)
W = stream width (m)
D = depth of active layer (m)
b
For example, mass loading of cadmium to a stream may result in an initial
water column concentration of 50 mg/1. Based on a Kd value of 12,000 I/kg and
a suspended sediment concentration of 50 mg/1, values for Fd and Fp are
calculated at 0.625 and 0.375, respectively, Thus, the model predicts a
dissolved concentration of cadmium in the water column for 50 mg/1 * 0.625, or
approximately 31 mg/1, and a particulate concentration of 50 mg/1 * 0.375, or
19 mg/1. The particulate phase cadmium concentration is then converted to a
mass-to-mass ratio and a bed sediment cadmium concentration is estimated based
on the stream morphometry and velocity, particle settling velocity, and the
depth of the active bed, as illustrated in Equation 4-11.
In addition to estimating total water column, dissolved, particulate, and
bed sediment concentrations, the surface water model may be used for
predicting the total volume of water in a surface water system that has been
contaminated above a threshold (e.g., drinking water standards, water quality
criteria for the protection of aquatic life, taste/odor thresholds, and
agricultural criteria). Contaminated volumes are estimated by the model for
each year of the analysis by comparing predicted concentrations at each
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distance step to applicable thresholds, and determining contaminated volume
based on stream cross-sectional area multiplied by the distance over which the
threshold is exceeded. Contaminated volume of surface water may be used to
estimate environmental damage and as a measure of resource loss.
The surface water transport model describes many of the most important
processes affecting the migration and fate of contaminants in rivers, but it
is limited by two major assumptions. First, steady-state flow conditions are
assumed, which allows potential human health risks and environmental damage to
be evaluated only under a single set of conditions. Second, the model
considers several contaminant loss mechanisms that are reversible, but does
not account for the reverse process in most situations. For example,
contaminants removed from the water column by adsorption, settling, and
sedimentation may be transferred back to the water column by reverse processes
(e.g., resuspension, desorption). This assumption that reverse processes are
insignificant sources of contaminants is necessary due to the lack of
quantitative data available that would allow us to model processes such as
desorption, resuspension, and diffusion of contaminants between bed sediments
and the water column. It is likely to have the greatest effect in evaluating
potential exposure and risk for non-degradable, low mobility contaminants that
would tend to accumulate in sediments (e.g., cadmium), and considerably less
effect for more mobile contaminants that are subject to degradation and
volatilization (e.g., benzene).
4.3 POTENTIAL EFFECTS
This section describes the methods used to estimate potential effects
based on the outputs of the chemical transport models. Several types of
effects were estimated quantitatively for the model scenarios, including human
cancer risk, human chronic noncancer risk, aquatic toxicity, and environmental
resource damage. The chemical-specific toxicity parameters and thresholds
used in this analysis are given in Table 4-5 in the next section; Appendix A
provides documentation for the values used.
4.3.1 Human Health Risks
For all drinking water exposure pathways EPA estimated human intakes by
assuming an ingestion rate of 2 liters per day and an average body weight of
70 kilograms. For example a drinking water concentration of 5 mg/1 would be
multiplied by 2 I/day and divided by 70 kg, resulting in an intake estimate of
0.14 mg/kg-day. For estimation of both cancer and chronic noncancer risk,
intakes were averaged over a lifetime (assumed to be 70 years).
Carcinogenic Effects. EPA estimated risk for potential carcinogens using
the one-hit equation:
R = 1 - exp (-H * I) [Equation 4-12]
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4-25
where:
R = lifetime individual risk (i.e., probability of an individual
developing cancer over a lifetime)
-1
H = potency, or unit risk (mg/kg-day)
I = lifetime average intake (mg/kg-day)
This equation assumes no threshold (i.e., there is a finite risk for any dose
level) and is linear at low doses, an approach consistent with EPA's cancer
risk assessment guidelines (51 Federal Register 33992-34003, September 24,
1986). The intake, I, used in this equation is based on the output of the
release and transport submodels. The potency, H, is chemical-specific and
represents the response per unit dose of a chemical. Higher H values are
indicative of more potent carcinogens, which produce higher risks than less
potent chemicals at the same dose. Upper-bound unit risk parameters (ql*)
calculated by EPA's Carcinogen Assessment Group using the linearized
multistage model and the most appropriate set of experimental data were used
as H values in this analysis.
Noncarcinogenic Effects. EPA used the Weibull equation with a threshold,
as shown below, to estimate risks of noncarcinogenic effects:
K
R = 1 - exp [-H * (I-t) ] [Equation 4-13]
where:
R = lifetime individual risk (i.e., probability of the effect being
modeled occurring in an individual over a lifetime)
-K
H = Weibull dose-response parameter (mg/kg-day)
I = lifetime average intake (mg/kg-day)
t = effect threshold (mg/kg-day)
K = Weibull shape parameter
This equation incorporates an effect threshold, below which risk equals zero.
At intakes above the threshold, risk is a function of intake and three
chemical-specific parameters, H, t, and K. The lifetime average intake, I,
for the equation is defined and calculated in an identical way to intake for
the carcinogenic risk equation. The threshold, t, is chemical-specific and is
derived from the same experimental data set used to develop H. The shape
parameter, K, which theoretically could vary for different chemicals or
different effects, is set to 2 for all noncarcinogenic effects modeled in this
study. Assigning a value for K is an approximation, but is necessary because
of the lack of sufficient dose-response data for noncarcinogenic effects to
derive a unique value of K for many specific chemicals. The primary basis for
assigning K=2 is a statistical analysis of a limited number of data sets
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4-26
obtained from the primary literature.SJ Sixty-three sets of animal
dose-response data for 15 chemicals were included in the study. The 95
percent confidence range for K in the study was approximately 1 to 4, with an
overall median K value of 1.74.
The key chemical-specific parameter defining the dose-response curve for
noncarcinogenic effects is H. For each chemical, values for H were estimated
from three toxicological parameters extracted from an appropriate study: (1)
the chronic minimum effective dose (MED); (2) the response threshold (t); and
(3) the response fraction at the MED (termed Ro). Both the MED and threshold
were converted from animal values to human equivalent values based on relative
surface area. For chemicals with multiple studies, the study reporting an
adverse effect at the lowest dose was selected as the basis for H. Specific
quality criteria were not applied in study selection, although judgment was
used to eliminate studies having very few dose levels or subjects, or studies
for which the reports were inconsistent or unclear.
When chronic studies were unavailable, MED and threshold values from
subchronic studies were divided by 5 to convert to equivalent chronic values
(based on a study of McNamara, 1976, which reported that in approximately 95
percent of the comparisons made, a difference in no-effect level of five-fold
or less between chronic and subchronic studies was observed). For many
chemicals the effect threshold was not determined specifically from the
experimental data but was estimated by dividing the human equivalent chronic
MEF by a factor of 100 (10 to convert from chronic human equivalent MED to
chronic human equivalent no-effect level, 10 to account for intraspecies
variability). Many studies are reported with insufficient detail to allow
estimation of Ro, and in these cases Ro was set to 10 percent for dichotomous
(quantal) effects and 90 percent for continuous effects.6-1
After values for MED, t, and Ro are determined from an appropriate study
or estimated as described above, calculation of H for a chemical is relatively
straightforward. Rearranging the Weibull equation and substituting K = 2, R =
Ro, and I = MED given the following calculation form for H:
H = -In (1 - Ro) [Equation 4-14]
(MED - t)2
5J This study was done by Science Research Systems, Inc. (now K.S. Crump
and Co.). The complete study report is reproduced in Appendix D of the draft
Liner Location Risk and Cost Analysis report (EPA 1986e).
6J Dichotomous effects either occur or don't occur in each test subject,
and the response frequency is simply the number with the effect as a fraction
of the total. Continuous effects, such as organ weights or enzyme levels, are
often reported as average values for the total test group without data on
individual response. Continuous effects data were "converted" to dichotomous
data by assuming that most (90 percent) of the test individuals are affected
at the MED.
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4-27
The toxicity studies used to derive H values for chemicals included in
this analysis are documented in individual toxicity profiles that are a
supplement to this report (Section A.1.2, Appendix A).
4.3.2 Aquatic Toxicity
As a means of estimating potential effects on aquatic organisms, EPA
estimated for each model scenario involving surface water the volume of water
contaminated above an aquatic effects threshold. Using the surface water
transport model (see Section 3.3.2.2), the downstream distance to the point
where the chemical concentration equals the threshold was calculated. The
flow and velocity, as defined by the model scenario, were used to estimate
contaminated volume from distance.
The aquatic effects thresholds used are given in Table 4-5, and are
documented more fully in brief profiles that are a supplement to the report
(Section A.1.3, Appendix A). These thresholds are based on increased
lethality of sensitive aquatic species, and thus do not represent the
possibility of more subtle ecosystem-level effects.
4.3.3 Environmental Resource Damage
Chemicals can cause environmental damage other than that associated with
increased chronic human health risks or species-level aquatic toxicity. EPA
assessed these other resource damages by estimating the volume of either
ground water or surface water contaminated above specified effects thresholds.
Surface water volumes were calculated using the surface water model and
appropriate thresholds, as described in Section 3.3.3.2. Contaminated
ground-water volumes were estimated using the LLM ground-water transport
component, which can calculate the width of a plume contaminated above a thres-
hold. The width was converted to a volume based on ground-water velocity,
aquifer depth, and porosity values, as defined by the model scenarios.
Environmental damage thresholds for the chemicals modeled are listed below.
Chloride: 250 mg/1, which is EPA's secondary
drinking water standard. It is based on the potential
for corrosion effects and the human taste threshold,
above which water becomes less desirable as a drinking
water source.
Boron: 1 mg/1, which is a limiting value for
irrigation water; levels above this cause damage to
semi-tolerant and sensitive crops (e.g., grains and
citrus crops).
Total Mobile Ions: 335 mg/1, which is the
incremental salinity level associated with ecosystem
effects (e.g., changes in species composition) in
fresh water, assuming a background level of 65 mg/1.
As a way to estimate conservatively the potential
salinity-modifying effects of waste disposal, the
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4-28
masses of mobile ions in the waste (i.e., sodium,
chloride, potassium, magnesium, calcium) were summed
and modeled. The resulting constituent group,
referred to as total mobile ions, was assumed to have
identical environmental transport characteristics to
chloride.
4.4 CHEMICAL PARAMETERS USED IN MODELING
The modeling methods used to predict chemical transport and potential
effects require a number of chemical-specific parameters. Values for the
physical/chemical parameters used in modeling transport and fate are given in
Tables 4-3 (ground water) and 4-4 (surface water). Values for toxicity
parameters and effects thresholds are given in Table 4-5. Section A.I in
Appendix A presents documentation and references for the values used.
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4-29
TABLE 4-3
PHYSICAL/CHEMICAL PARAMETERS USED
IN GROUND-WATER MODELING a/
-1
Degradation Rate Constant (yr ) c/
Model
Constituent
Sodium
Chloride
Arsenic
Benzene
Cadmium
Boron
Chromium VI
Organic/
Inorganic
Inorganic
Inorganic
Inorganic
Organic
Inorganic
Inorganic
Inorganic
Kd or Koc b/
d/kg)
0.01
0.01
5
83
6.5
3
20
Unsaturated
Zone
NA
NA
NA
3.4
NA
NA
NA
Saturated
Zone
NA
NA
NA
0
NA
NA
NA
a/ See Appendix A for sources and documentation.
b/ Values listed are Kd's for all inorganics and Koc's for organics. For
organics, Kd is obtained by multiplying Koc by the fractional organic
carbon content (foe) of the earth materials (i.e., Koc * foe = Kd).
c/ NA = not applicable. The inorganic contaminants considered in the
analysis are not subject to degradation.
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4-30
TABLE 4-4
PHYSICAL/CHEMICAL PARAMETERS USED IN
SURFACE WATER MODELING a/
Model
Constituent
Sodium
Chloride
Arsenic
Benzene
Cadmium
Boron
Chromium VI
Organic/
Inorganic
Inorganic
Inorganic
Inorganic
Organic
Inorganic
Inorganic
Inorganic
Volatilization
Kd or Koc b/ Rate Constant c/
(I/kg) (year -1)
0.01
0.01
5
83
1,000
3
20
NA
NA
NA
1,200
NA
NA
NA
Degradation
Rate Constant d/
(year -1)
NA
NA
NA
5 e/
NA
NA
NA
a/ See Appendix A for sources and documentation.
b/ Values listed are Kd's for all inorganics and Koc's for organics. For
organics, Kd is obtained by multiplying Koc by the fractional organic
carbon content (foe) of suspended sediments (i.e., Koc * foe = Kd).
c/ NA = not applicable. The inorganic contaminants considered in the
analysis are not volatile.
d/ NA = not applicable. The inorganic contaminants considered in the
analysis are not subject to degradation.
e/ Value represents a generalized biodegradation rate constant for surface
water. Chemical degradation processes (hydrolysis, photolysis) are
assumed to be insignificant compared to biodegradation and volatilization
(EPA, 1979).
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CHAPTER 5
OIL AND GAS: QUANTITATIVE MODELING RESULTS
[Reserved]
* * *
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CHAPTER 6
OIL AND GAS: ENVIRONMENTAL ASSESSMENT OF
WASTE DISPOSAL ON ALASKA'S NORTH SLOPE
6.1 INTRODUCTION
In 1968, the presence of extensive onshore oil and gas reserves was
confirmed on the northern edge of the Arctic coastal plain of Alaska. Large
scale efforts have been underway since that time to produce the reserves from
the Prudhoe Bay oil field and surrounding areas. Today, approximately 20
percent of U.S. crude oil production is from Alaska's North Slope (AOGA,
1985). The North Slope fields are also now estimated to contain over 33
percent of the proven U.S. oil reserves and over 12 percent of the U.S. gas
reserves (Standard Oil, 1987).
The severe environmental conditions of the Arctic create oil and gas
operating conditions and waste disposal practices that are different from
those in the continental United States. Unlike other areas in the U.S. where
oil and gas exploration and production occurs, the ground and much of the
wastes on the North Slope are frozen for about nine months of the year. In
addition, the design of drill sites and support facilities generally differs
from that in other areas in order to minimize surface disturbance and to
prevent the permafrost from thawing and losing its support capabilities. For
these reasons, the conditions affecting waste releases, contaminant transport,
and potential health and environmental impacts are significantly different
from the conditions in Alaska's Cook Inlet-Kenai Peninsula area as well as
those in the lower 48 states. Consequently, the quantitative modeling
procedures used in this project to assess risks from oil and gas waste
disposal in other areas are not as applicable to the North Slope.
6.1.1. Purpose
The purpose of this analysis is to examine the human health and
environmental risks associated with oil and gas waste management practices on
the North Slope. This analysis is presented separately from the rest of the
risk assessment because the approach for studying the North Slope, and thus
the type of findings made, are different from those presented previously in
the risk assessment of other areas. Nevertheless, the analysis of North Slope
operations focuses on the same basic measures of effects (i.e., human health
risks, and potential impacts to water, land, and biota) to allow a qualitative
comparison of the North Slope risks relative to the risks identified for other
parts of the country.
6.1.2 Scope
Unlike the risk assessment for other areas, this analysis of the North
Slope is primarily qualitative; it provides descriptive information on oil and
gas waste streams, waste management practices, and environmental
characteristics, and draws qualitative conclusions about the potential for
human health and environmental impacts. Otherwise, however, the scope of this
analysis is defined similarly to the scope for the rest of the risk
assessment. In particular, the analysis does not attempt to determine:
* * *
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6-2
impacts resulting from single, real facilities; absolute levels of risks or
impacts; impacts resulting from accidents or low frequency events; impacts
from offshore activities; impacts resulting from activities other than waste
management (e.g., site development and road construction); or impacts
resulting from waste streams that are not currently exempt from RCRA (e.g.,
waste solvents, paints, lubricants, and motor oil that are not uniquely
associated with oil and gas exploration and development). In addition, the
analysis focuses on potential impacts assuming full compliance with state
regulations1-1 and does not address the impacts that could result from
adoption of regulatory alternatives. Finally, the assessment only addresses
potential impacts from existing operations at the Prudhoe Bay and surrounding
oil fields. The impacts resulting from past Navy and USGS operations in the
National Petroleum Reserve-Alaska, as well as the potential impacts resulting
from proposed exploration within the Arctic National Wildlife Refuge, are not
addressed.
6.1.3 Methods
This assessment was prepared based on available documentation and on
conversations with knowledgeable personnel. No site visits or field studies
at the North Slope were conducted as part of this environmental assessment.
The written information that was used was collected through: a general
literature review; a search of the docket file containing comments on the
October Technical Report (EPA, 1986d); and the study of damage cases and waste
management practices in Alaska conducted as part of the overall Section
8002(m) study. Personal conversations were held with several individuals
knowledgeable of North Slope operations including representatives of industry
(Sohio), the Alaska Department of Environmental Conservation, and the
Anchorage Offices of the Bureau of Land Management and Fish and Wildlife
Service. In many cases, these individuals submitted or identified additional
documentation to support this assessment. All references used are cited in
the text and fully referenced at the end of the assessment.
1J The status of existing state regulations is somewhat difficult to
define at this time. The Alaska Department of Environmental Conservation
(ADEC) and the Alaska Oil and Gas Conservation Commission administer existing
regulations governing solid waste management, water quality, wastewater
disposal, injection wells, reserve pits, and other issues pertaining to the
disposal of oil and gas wastes. However, on October 31, 1986, ADEC published
a "final draft" of proposed amendments to the state's solid waste management
regulations, which would result in a significant change in requirements for
the handling and disposal of drilling wastes. These proposed amendments are
expected to be finalized by early 1988 (or possibly sooner) without
significant modification, and recent permits issued by the state have taken
into account the proposed regulatory changes (personal communication with
Stanley Hungerford, Supervisor of the ADEC Air and Solid Waste Section). For
the purpose of this assessment, the regulatory baseline is considered to be
only the existing regulations that are currently in effect; however, the
proposed amendments also are discussed to illustrate the state's proposed
controls for oil and gas wastes.
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6-3
6.1.4 Organization of This Assessment
The remainder of the assessment is organized into four main sections.
Section 6.2 describes the exploration, development, and production activities
and the common waste management practices on the North Slope. In Section 6.3,
the environmental characteristics of the North Slope are briefly described.
The potential human health and environmental impacts resulting from waste
management practices are discussed in Section 6.4. Finally, Section 6.5
provides some brief conclusions.
6.2 DESCRIPTION OF OIL AND GAS ACTIVITY
Oil and gas operations on the North Slope are concentrated around the
Prudhoe Bay field, the largest oil field yet discovered in the U.S. In total,
this field contains an estimated 9.6 billion barrels of recoverable oil2-1
and over 26 trillion cubic feet of saleable gas (ARCO, 1985). The Prudhoe Bay
field has been split into the Eastern Operating Area, operated by ARCO Alaska,
Inc., and the Western Operating Area, operated by Sohio Alaska Petroleum
Company (Standard Oil). Taken together, operations from these two companies
have developed approximately 200 square miles of the Prudhoe Bay reserves,
which encompass a total area of about 400 square miles (DOI, 1986).
There are other, relatively smaller producing fields surrounding Prudhoe
Bay. The Kuparuk field, located about 40 miles west of Prudhoe Bay, contains
an estimated 1.5 billion barrels of recoverable crude oil (AOGA, 1985). Since
ARCO began crude oil production at Kuparuk in December 1981, development has
expanded to the point where Kuparuk is presently the second largest U.S. field
in terms of daily oil production. Another field, named Milne Point, is
located north of Kuparuk. The Milne Point Field came online in November 1985
and is operated by Conoco Inc. There are two additional fields in the North
Slope vicinity that are also important to note: Lisburne (operated by ARCO)
and Endicott (operated by Sohio). Field startup at Lisburne was scheduled for
late 1986 or early 1987, while pumping is not expected to start at Endicott
until 1988 (DOI, 1986). A portion of the Lisburne production area and all of
the Endicott production area is located about 2.5 miles offshore; however,
operations at both of these fields will involve an expansion of onshore
support facilities.
The following discussion of oil and gas operations at these North Slope
fields is divided into four sections. Sections 6.2.1, 6.2.2, and 6.2.3
describe exploration, development, and production activities, respectively.
Section 2.4 briefly describes the wastes that are generated from the
exploration, development, and production activities and outlines how these
wastes are treated, stored, and disposed.
2J By comparison, a "giant" oil field in the lower 48 states has 100
million barrels of recoverable oil.
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6-4
6.2.1 Exploration Activities
Exploration activities can involve either seismic testing or the drilling
of exploratory wells. Seismic testing usually consists of generating sound
waves in the ground and analyzing returning echoes for clues to the nature and
extent of geological formations. On the North Slope, sound waves are
typically generated by filling bore holes with explosives or by dragging
portable trailers over the ground in a criss-cross pattern. While these
processes can be physically disruptive, they generate very little, if any,
wastes of interest to this assessment.
Seismic tests can provide indications of the presence of hydrocarbons, but
exploratory wells are needed to confirm that oil and gas actually exist. The
process used to drill an exploratory well is basically the same as that used
for development drilling (see Section 6.2.2 for a description of development
drilling techniques). The biggest difference is that, unlike the clustered
drilling that occurs during development, exploratory wells are usually drilled
by themselves or together with just a few other wells in remote areas.
Otherwise, however, exploratory well drilling and waste management is very
similar to that for development operations as described below. The principal
exploration waste streams of interest for this study are waste drilling muds
and cuttings, rig wash water, spilled liquids, and any workover fluids; all of
which are released into an adjoining reserve pit.
In gene.ral, very little exploration on the North Slope is currently taking
place and has taken place in the recent past. There were only about two or
three onshore exploratory wells drilled on the North Slope in 1986 (personal
communication with John Catlin, Bureau of Land Management). Fairly extensive
exploration within the National Petroleum Reserve in Alaskan (NPRA) has been
conducted in three different time frames: between 1923 and 1926 by the USGS;
from 1944 to 1953 by the Navy; and from 1974 to 1981 by the USGS. Even though
the NPRA was opened for competitive oil and gas leasing in 1980, very little
exploration has been conducted since that time due to reduced oil prices and
the relatively low value of oil and gas found there. As long as prices remain
low, the level of exploration on the North Slope is also expected to remain
low. The principal area that may be targeted for future exploration is the
Arctic National Wildlife Refuge to the east of Prudhoe Bay.
6.2.2 Development Operations
In order to develop a field that has shown positive results from
exploration, a series of wells to be used for oil and gas production is
installed. The primary method of drilling on the North Slope is rotary
drilling. In this operation, a drill bit is extended downhole at the end of a
pipe and slowly rotated, gouging and chipping away at the rock at the bottom
of the hole. A weighted agent called drilling mud is circulated throughout
the well bore. Drilling mud serves several purposes, which include: removing
drilled solids (cuttings) from the bottom of the hole and transporting them to
the surface; lubricating and cooling the drill bit and pipe; developing a
hydrostatic pressure to prevent formation fluids from entering the well bore;
and other important functions (API, 1983).
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6-5
A typical land based mud system is shown in Figure 6-1. As shown in the
exhibit, drilling mud is circulated down the inside of the drill pipe, out the
bit at the bottom of the hole, and back to the surface through the annulus
(i.e., the space between the well bore wall and the drill pipe). As the mud
travels up to the surface, it carries the drill cuttings in a suspension that
is passed through a shale shaker. Mud and cuttings removed by the shale
shaker are discharged into a reserve pit; the mud that is not released into
the reserve pit is recirculated. Muds are recycled as much as possible in the
Arctic in order to conserve space and to save costs. On the average, the mud
used in drilling a North Slope well is recycled 100 to 300 times (Standard
Oil, 1987).
Unlike drill sites in other parts of the country, North Slope development
sites generally consist of multiple wells clustered together on a gravel pad.
The gravel, which is anywhere from three to seven feet thick, serves to
insulate the underlying permafrost and prevent thawing. A consolidated drill
site may start out with 8 to 16 wells; however, the well count on a single pad
may exceed 64 as development of the reservoir matures (Bell, 1987). Typical
drill pad designs for the North Slope are shown in Figures 6-2 and 6-3. As
shown in these exhibits, centralized reserve pits that hold cuttings and
drilling mud are also located on the gravel pads of the drill sites. Each
drill site is typically served by two to four reserve pits, although some
sites may contain as many as eight pits (ARCO, 1985).
If preliminary tests show that the formation(s) penetrated by a bore hole
will be commercially productive, the well is prepared for the production of
oil or gas. This preparation first involves well completion, in which casing
and tubing is installed downhole and a network of valves and controls is
installed at the wellhead. If necessary to enhance the flow of reservoir
fluids into the completed well, the well may also be "stimulated." Well
stimulation may involve the introduction of an acid to increase pore space for
the flow of fluids. A variety of additional chemicals may also be introduced
into the well bore for other purposes (e.g., to inhibit corrosion and to
reduce friction) (EPA, 1986d).
6.2.3 Production Operations
Following the well preparation activities described above, the reservoir
fluids are brought to the surface. In the Arctic, each well at a drill site
is connected to a manifold, which combines the produced fluids from several
wells for delivery to a separation plant. At the separation plant, produced
fluids are separated into crude oil, gas, and water streams. Both ARCO and
Sohio each operate three oil-gas-water separation plants within the Prudhoe
Bay field, with each plant capable of processing more than 300,000 barrels of
oil per day (DOI, 1986). Once separated and treated, the oil is pumped to a
pump station for delivery via the trans-Alaska (Alyeska) pipeline. The
produced gas is generally compressed and injected into the subsurface, but it
may also be conditioned and used as fuel. Produced waters, as described in
Section 6.2.4.2 below, are also injected.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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6-6
FIGURE 6-1
TYPICAL LAND BASED MUD SYSTEM
MUD AND
CUTTINGS
TO RESERVE
PIT
Source: API, 1983.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
6-7
FIGURE 6-2
STANDARD ARCTIC DRILL SITE LAYOUT
FLOWLJNES
GRAVEL PAD
+ + + HฃATE
MANIFOLD
AND
SEPARATOR
RESERVE PIT
RESERVE PIT
PUMP-' I ^-TANK
METHANOL
EQUIPMENT
WELL
200'
O
200'
SCALE IN FEET
Source: Bell, 1987.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
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-------
6-9
The first 16 or so wells on a brand new drilling pad on the North Slope
would be for the primary recovery of oil, in which natural reservoir pressure
drives the reservoir fluids through the rock pores to the producing wells.
However, as the natural reservoir pressure declines, water is injected into
the petroleum-bearing formation to help maintain pressure and to displace a
portion of the remaining crude oil toward the production wells. On the North
Slope, both produced waters and treated seawater are injected for these
reasons. Injection wells for water flooding purposes are drilled between the
existing producing wells on a drill site. As the field matures, the well
density at a drill site increases to help improve the overall extent of
recovery.
In addition to water flooding, the operators at the Prudhoe Bay field have
undertaken an enhanced oil recovery project. This project involves the
injection of enriched miscible gas to help increase recovery from the field.
The enhanced oil recovery is anticipated to increase Prudhoe Bay production by
115 million barrels of oil (DOI, 1985). In addition, the operators of the
other North Slope fields have been experimenting with enhanced oil recovery
using heated produced waters; future successes may produce rounds of
additional development drilling in the other major Arctic oil fields.
6.2.4 Waste Generation and Waste Management
The primary oil and gas wastes that are currently excluded from regulation
as hazardous waste can be divided into three main categories: exploration/
development wastes, production wastes, and associated wastes. The generation
of and common management practices for these waste streams on the North Slope
are discussed separately below.
6.2.4.1 Exploration/Development Wastes
By far the largest portion of exploration/development wastes is made up by
cuttings and drilling fluids or "muds." Cuttings are made up of chunks of
rock, soil, and other naturally occurring solids that are removed during
drilling. Waste drilling mud used onshore at the North Slope consists
primarily of a water-based mixture of the removed solids; however, oil-based
muds may also be used onshore. Of the 39 wells drilled during 1986 in the
Prudhoe Bay western operating area, only four were drilled using oil-based
muds (Standard Oil, 1987).
Drilling muds contain chemical additives designed to yield desired fluid
properties. Common additives include: barite and other mineral ores used to
control mud density; bentonite used to limit the escape of formation fluids
into the well bore; diesel oil, grease, glycerols, and a variety of other
additives used to lubricate the drill bit and pipe; chromium used as a
filtrate reducer and shale inhibitor; paraformaldehyde used as a filtrate
reducer; and various biocides (e.g., 2,4-dichlorophenol) (ARCO, 1985). Waste
drilling muds are also usually contaminated with various levels of
hydrocarbons and metals.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * *
-------
6-10
Available data on the volume of muds and cuttings produced on the North
Slope are varied. Information from ARCO (Bell, 1987) states that drilling may
generate 190 barrels (30 m3) of cuttings in a Kuparuk well or over 440
barrels (70 m3) of cuttings in a Prudhoe Bay well. The ARCO information
further states that approximately 30,000 to 50,000 gallons (113-190 m3) of
water are added to the mud system each day at Prudhoe bay and Kuparuk in order
to maintain the necessary drilling mud properties while drilling in either
unit. According to data from Standard Oil (1987), each well in the Prudhoe
Bay western operating area generates abut 15,000 barrels (2,400 m3) of mud
and 3,000 barrels (480 m3) of cuttings, for a total of about 1,000,000
barrels (160,000 m3) of drilling waste from the 62 wells drilled there in
1986. An Ad Hoc Task Group (1986) formed to examine liquid waste disposal
options on the North Slope reports that approximately 5,000,000 barrels
(800,000 m3) of waste mud and cuttings are generated each year from drilling
operations at Prudhoe Bay.
Cuttings and drilling muds on the North Slope are initially passed through
a shale shaker, a desander and/or desilter, and sometimes a centrifuge.
Water-based muds are then discharged into a reserve pit. When used, oil-based
muds are stored in tanks and eventually disposed of by underground injection.
Discharges to reserve pits from the occasional oil-based mud system on the
North Slope are limited to drill cuttings (Standard Oil, 1987). In addition
to drill muds and cuttings, the reserve pits also receive any fluids released
in the event of a blowout or other spill, as well as miscellaneous other
waster, associated with oil and gas operations such rig washing fluids, well
completion fluids, and workover fluids (see Section 6.2.4.3).
Data from Standard Oil (1987) on the quality of the liquids in four
reserve pits are provided in Tables 6-1, 6-2, and 6-3. Although not shown in
these tables, data provided by Standard Oil also indicate that reserve pit
fluids contain total dissolved solid levels in the range of 2,000 to 4,700
mg/1. Table 6-4 shows the concentration of constituents, as measured by
direct methods and EPA's proposed Toxicity Characteristic Leaching Procedure
(TCLP), in reserve pit solids. The data in Table 6-4 were obtained as part of
the EPA waste sampling program and represent the average concentrations found
in two separate North Slope reserve pits.
As mentioned in Section 6.2.1, the reserve pits associated with
development and production in the Arctic are centralized at the drill sites to
serve all the wells on a given drilling pad. The pits are constructed above
ground, typically with a gravel layer underneath and gravel berms
approximately five feet high. In most cases, the edge of a drill site forms
one of the berms of a reserve pit (ARCO, 1985). Reserve pits associated with
exploration activities are designed similarly to those for development and
production, except that exploration pits are much smaller and may also be
excavated below-ground. Although existing state regulations (20 AAC 25.047)
state that "special precautions must be taken to render impervious the
confining surface of a reserve pit," most pits are unlined. Some of the newer
reserve pits are, however, being constructed with liners (Hungerford, 1987).
In total, there are an estimated 264 reserve pits in the North Slope area
(Versar, 1987a).
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
-------
6-11
TABLE 6-1
CONVENTIONAL PARAMETERS FOR RESERVE PIT LIQUIDS
pH TSS C1 N03 BOO COO TOC NH4 OtG CN
mfl/1 mg/1 ng/1 mg/1 mg/1 mg/1 mg/1 mg/1 mg/1
A Pad
U Pad
S Pad
G Pad
Avtragt
8
7
8
8
8
.02
.99
.53
.18
.18
250
32
532
4900
1429
1960
2650
4360
810
2445
NO
0.1
0.2
NO
0.08
4
9
9
37
15
250
260
390
140
260
218
362
268
31
220
0.2
NO
0.6
0.2
0.3
NO
1.9
0.6
3.5
1.5
0.01
NO
NO
NO
<0.01
BOO - Biochemical Oxygtn Dtmand
C1 Chloridt
CN Cyanidt
COO Chtmical Oxygtn Dtmand
NH - Ammonia as N
NO Not Dtttcttd (No rtsponst on instrumtnt)
NO - Nitratt and Nitritt as N
O&G Oil and Grtast
TSS - Total Susptndtd Solids
Source: Standard Oil, 1987.
Note: These data are preliminary; before using them in the final report,
documentation of the sampling and analytical procedures will be
obtained and reviewed.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
6-12
TABLE 6-2
METAL ANALYSES (mg/1) FOR RESERVE PIT LIQUIDS
TOTAL MPALS
AlurtnuM
AntlMny
Art wile
BaHuป
Bซry1Hiป
Boron
CadHlM
Calclui
ChrortM
Cobalt
Copptr
Iron
Load
Magntiitfli
Hanganoso
Horeury
Nolybdtnu*
Nlektl
Phosphorus(P)
PotassfuM
StltfllUM
SHvซr
Sodtun
StrontfuM
Thai Hun
Tin
TUaniun
Vanadlun
Zinc
A PAD nr
UATO
s.o
NO
0.004
3.1
NO
0.3
NO
73
0.45
NO
NO
11
NO
10
0.31
NO
0.077
NO
0.3
13
NO
NO
2200
0.86
NO
NO
0.090
0.02
0.21
(0.2S)
(0.23)
(0.002)
(0.02S)
(0.003)
(0.23)
(1)
(D
(0.023)
(0.013)
(0.03)
(0.23)
(0.1)
(0.3)
(0.023)
(0.0001)
(0.023)
(0.05)
(0.3)
(1.5)
(0.04)
(0.013)
(2.5)
(0.025)
(0.02)
(0.15)
(0.01)
(0.01)
(0.05)
U PAD PIT
WATE*
0.31
NO
O.OM
0.29
NO
0.3
NO
52
0.11
NO
NO
1.6
NO
5.1
0.30
NO
0.083
NO
NO
9.0
NO
NO
2900
0.56
NO
NO
0.02
NO
0.07
(0.23)
(0.23)
(0.002)
(0.023)
(0.003)
(0.23)
(1)
(1)
(0.025)
(0.015)
(0.05)
(0.25)
(0.1)
(0.5)
(0.025)
(0.0001)
(0.025)
(0.05)
(0.3)
(1.5)
(0.02)
(0.015)
(2.5)
(0.025)
(0.04)
(0.15)
(0.01)
(0.01)
(0.05)
S MO PIT
UATCR
7.3
NO
0.016
4.4
NO
3.6
NO
42
0.22
NO
NO
9.S
NO
6.7
0.1B
NO
0.078
0.06
0.5
6.2
NO
NO
2100
0.30
NO
NO
0.088
0.02
0.18
(0.23)
(0.2S)
(0.002)
(0.023)
(0.003)
(0.23)
(1)
(1)
(0.023)
(0.013)
(0.03)
(0.2)
(0.1)
(0.5)
(0.023)
(0.0001)
(0.023)
(0.05)
(0.3)
(1.5)
(0.04)
(0.015)
(2.5)
(0.025)
(0.04)
(0.15)
(0.01)
(0.01)
(0.05)
6 MO PIT
MATtt
18
NO
0.004
3.9
0.01
1.5
NO
120
0.53
0.06
0.12
140
NO
30
2.2
0.0004
NO
0.2
4
18
NO
NO
680
0.84
NO
NO
0.12
0.16
1.1
(0.3)
(0.3)
(0.002)
(0.03)
(0.01)
(0.04>
(0.04)
(1)
(0.05)
(0.03)
(0.03)
(0.5)
(0.2)
(1)
(0.05)
(0.0001)
(0.05)
(0.01)
(0.6)
(3)
(0.004)
(0.03)
(5)
(0.05)
(0.004)
(0.3)
(0.02)
(0.02)
(0.04)
Otttetion limitl may vary with thซ standard ustd and instrumtnt iปnปH
-------
6-13
TABLE 6-3
ORGANIC ANALYSES FOR RESERVE PIT LIQUIDS
VOLATILE
ORGANICS
BASE/NEUTRAL
ORGANICS
ACID
ORGANICS
A Pad
U Pad
S Pad
G Pad
BOL for all B
-------
6-14
TABLE 6-4
CONSTITUENTS OF ARCTIC RESERVE PIT SOLIDS a/
Constituent /Parameter
Acetone
Aluminum
Arsenic
Barium
Benzene
Biological Oxygen Demand (BOD)
Boron
Cadmium
Calcium
Cobalt
Chemical Oxygen Demand (COD)
Copper
Chloride
4-Chloro-3-methylphenol
Chromium
p-Cymene
n-Decane
n-Dodecane
Ethylbenzene
bis(2-Ethylhexyl) phthalate
Direct
(mg/kg)
0.7
7,620
7
12,500
0.4
30
81
3
29,600
14
9,420
27
29,750
NA
27
2
11
4
3
2
TCLP b/
(ug/1)
438
200
NA c/
1,885
10
NA
363
NA
232,000
NA
NA
NA
NA
32
30
NA
NA
NA
48
8
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
6-15
TABLE 6-4
CONSTITUENTS OF ARCTIC RESERVE PIT SOLIDS a/
(continued)
Constituent /Parameter
Fluoride
n-Hexadecane
Iron
Lead
Magnesium
Manganese
2-Methylnaphthalene
Naphthalene
Nickel
Nitrate/Nitrite
n-Octadecane
Oil and Grease
Phosphorus
Potassium
Silicon
Silver
Sodium
Sulfur
n-Tetradecane
Tin
Direct
(mg/kg)
350
5
24,100
84
5,715
668
0.3
6
33
170
2
121,100
3,642
10,500
2,925
5
4,745
10,610
4,747
30
TCLP b/
(ug/1)
NA
NA
8,535
371
20,300
1,530
52
57
71
NA
NA
NA
NA
4,575
13,465
NA
1,400,000
20,770
NA
NA
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
-------
6-16
TABLE 6-4
CONSTITUENTS OF ARCTIC RESERVE PIT SOLIDS a/
(continued)
Constituent Parameter
Titanium
Total Organic Carbon (TOC)
Toluene
Vanadium
Zinc
Direct
(mg/kg)
126
27,350
2
26
179
TCLP b/
(ug/1)
NA
NA
50
NA
1,500
a/ Average values from two reserve pits.
b/ Toxicity Characteristic Leaching Procedure.
c/ Not available.
Source: EPA, 1987a.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
* * *
-------
6-17
The contents of the reserve pits remain frozen for about nine months a
year. During the summer thaw, the reserve pits are dewatered and the liquids
are often disposed of on-site by annular injection or by injection in a
dedicated disposal well. All of the underground injection is done in
accordance with permits issued by the Alaska Oil and Gas Conservation
Commission. In addition, the Alaska Department of Environmental Conservation
(ADEC) has issued a general wastewater disposal permit that allows, with
restrictions, the release of reserve pit liquids into surface waters or onto
the tundra (ADEC, 1986a). Reserve pit fluids may also be applied to roads in
accordance with an ADEC permit if the fluids exceed the stricter quality
criteria specified in the tundra discharge permit. Such surface discharges
are only allowed for relatively uncontaminated precipitation (e.g., snow
melt); the more highly contaminated liquids from reserve pits that exceed
permitted quality criteria must be discharged by underground injection
(Hungerford, 1987).
Sparse data are currently available on the relative amounts of reserve pit
fluids that are disposed of in different ways. Data from ARCO (1983) show
that, of the 1,180,403 barrels of fluid removed from ARCO's Prudhoe Bay
reserve pits in 1983, approximately 52 percent was applied to roads, 38
percent was injected, and 10 percent was discharged to the tundra. ADEC
estimates that 900,000 barrels of reserve pit fluids were discharged to the
tundra in 1985, while 200,000 barrels of reserve pit fluids were applied to
roads during the same year (Versar, 1987a). ADEC is currently re-evaluating
the effectiveness of road watering as a disposal method (Hungerford, 19f.7).
The drilling mud solids and cuttings settle to the bottom of the reserve
pits. The freeze-thaw cycle helps concentrate the solids in the bottom of the
pit due to the preferential exclusion of solids as the front of frozen
material moves down through the pit (Griles, 1987). At closure, pits
containing the drained solids will be covered with earth materials, graded,
and vegetated. Presently, all drill mud and cuttings disposal is controlled
in accordance with permits issued by ADEC and state regulations. Post-closure
responsibilities (see 18 AAC 60.410) in Alaska's existing solid waste
management regulations include requirements for owners of closed disposal
sites to: maintain the integrity of the disposal site; continue monitoring
for at least 10 years after the site closes; and correct any resulting water
or air quality violations. ADEC's proposed amendments to the solid waste
regulations include additional closure and post-closure responsibilities
including requirements to: meet water quality standards 50 feet away; ensure
that the waste will not thaw after closure or cause thawing of the permafrost;
and meet certain design specifications.
6.2.4.2 Production Wastes
Production wastes on the North Slope of interest to this study consist
primarily of produced water. This water is produced simultaneously with the
oil and gas and is separated at a separation plant. More than 86,000,000
barrels of produced water were generated in 1986 from the Prudhoe Bay field
(AOGA, 1986 cited in Versar, 1987a). This generation rate of produced water
is expected to increase as the field ages. For example, about twice as much
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
6-18
water was produced in 1986 than in 1985 and, by 1990, the generation rate at
Prudhoe Bay is expected to exceed 1,000,000 barrels per day (Standard Oil,
1987). Tables 6-5, 6-6, and 6-7 provide concentration data for conventional
pollutants, metals, and organics, respectively, in samples of produced waters
from the North Slope.
After it is separated from the oil and gas, produced water on the North
Slope is held in above-ground, closed tanks and then injected. The water is
mainly injected into wells designed for enhanced oil recovery (water
flooding), although some water is also injected into dedicated disposal
wells. In total, approximately 73 percent of the produced water is injected
into about 300 enhanced oil recovery wells; the remaining 27 percent is
injected into approximately nine dedicated disposal wells (AOGA, 1986 cited in
Versar, 1987a).
A typical Prudhoe Bay enhanced oil recovery injection well is shown in
Figure 6-4. Except for two differences, disposal wells on the North Slope are
designed basically the same as enhanced oil recovery wells. First, the
injection zone for disposal wells is approximately 6,000 feet below the
surface, whereas injection from enhanced oil recovery wells occurs at a depth
of approximately 9,000 feet. Second, unlike injection wells in other parts of
the country, both types of wells in the Arctic are equipped with a special
insulative fluid ("Arctic Pac" and diesel) that prevents the frozen tundra
from thawing and prevents the injection fluid from freezing. Disposal wells,
however, also contain a circulating glycol solution, which, prevents fluid from
freezing inside the well when the well is not active. Enhanced oil recovery
wells operate essentially continuously and therefore do not need the
additional glycol solution (Bell, 1987).
The operation of both types of wells must be permitted by the Alaska Oil
and Gas Commission, which has primacy over the federal Underground Injection
Control Program. There are several requirements in Alaska's existing
regulations and numerous operating practices designed to assure that produced
waters are injected safely. For example, Alaska's regulations in 20 AAC
25.412 require the following design characteristics: injections wells must be
cased and tubed to prevent leakage and to protect freshwater sources; wells
must be appropriately pressure tested; and wells must be equipped with tubing
and packer to isolate pressure to the injection interval. Operating
specifications in the Alaska regulations (20 AAC 25.252) require: monitoring
during operation to assure mechanical integrity; monthly monitoring reports to
be submitted to the state; and notification to the state and corrective action
in the event of excessive pressure readings.
6.2.4.3 Associated Wastes
A variety of other, relatively smaller volume waste streams are generated
during exploration, development, and production activities. These wastes
include such things as rig washing fluids, well completion fluids, well
stimulation fluids, workover fluids, produced water tank bottoms, and a
variety of oily wastes (e.g., oily sorbents, rags and dirt; waste lubricating
oil; and waste crude oil).
April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
-------
6-19
TABLE 6-5
CONVENTIONAL PARAMETERS FOR PRODUCED WATERS
GC 1
GC 1 Dup
GC 2
GC 3
Average
BOO
CI
CN
COD
NH4 -
NO
N03 -
O&G
TSS
pH TSS CI N03 BOO COD TOC NH4
mg/1 mg/1 mg/1 mg/1 mg/1 mg/1 mg/1
6.91 24 9000 NO 705 1430 293 24
6.89 19 12300 NO 535 955 357 23
6.93 92 12000 NO 290 490 120 21
7.12 29 10800 NO 390 810 359 23
6.96 41 11025 NO 480 921 282 23
Biochemical Oxygen Demand
Chloride
Cyanide
Chemical Oxygen Demand
Ammonia as N
Not detected (No response on instrument)
Nitrate and Nitrite as N
Oil and Grease
Total Suspended Solids
OiG CN
mg/1 mg/1
252 0.01
247 NO
34 NO
256 NO
197 <0.01
Source: Standard Oil, 1987.
Note: These data are preliminary; before using them in the final report,
documentation of the sampling and analytical procedures will be
obtained and reviewed.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
6-20
TABLE 6-6
METAL ANALYSES (mg/1) FOR PRODUCED WATERS
TOTAL METALS
Aluminum
Antimony
Arsenic
Barium
Beryllium
Boron
Cadmium
Calcium
Chromium
Cobal t
Copper
Iron
Lead
Magnesium
Manganese
Mercury
Molybdenum
Nickel
Phosphorus
Potassium
Selenium
Silver
Sodium
Strontium
Thallium
Tin
Titanium
Vanadium
Zinc
* Detection
there was
Source: Standard
NO
NO
NO
3.0
NO
130
NO
260
NO
NO
NO
5.7
NO
85
NO
NO
NO
NO
1.3
73
NO
NO
8500
30
NO
NO
ND
NO
0.1
GC 1
(0.5)
(0.5)
(0.004)
(0.05)
(0.01)
(0.5)
(0.04)
(2)
(0.05)
(0.03)
(0.1)
(0.5)
(0.2)
(1)
(0.05)
(0.0001)
(0.05)
(0.1)
(0.6)
(3)
(0.08)
(0.03)
(5)
(0.05)
(0.08)
(0.3)
(0.02)
(0.02)
(0.1)
limits may vary w
no response of the
Oil,
1987.
GC 1
NO
NO
NO
3.0
NO
130
NO
250
NO
NO
NO
5.6
NO
84
NO
NO
ND
NO
1.2
74
NO
NO
8500
29
NO
NO
NO
NO
0.1
OOP
(0.5)
(0.5)
(0.01)
(0.05)
(0.01)
(0.5)
(2)
(2)
(0.05)
(0.03)
(0.1)
(0.5)
(0.2)
(1)
(0.05)
(0.0001)
(0.05)
(0.1)
(0.6)
(3)
(0.08)
(0.03)
(5)
(0.05)
(0.08)
(0.3)
(0.02)
(0.02)
(0.1)
ith the standard used
instrument.
Note: These data are preliminary;
before
GC 2
NO
NO
NO
0.61
NO
no
NO
220
NO
NO
NO
6.3
ND
220
NO
ND
NO
NO
NO
100
NO
NO
7700
23
ND
NO
NO
NO
0.07
and inป
using
(0.5)
(0.5)
(0.02)
(0.05)
(0.02)
(0.04)
(0.04)
(D
(0.05)
(0.03)
(0.03)
(0.5)
(0.2)
(1)
(0.05)
(0.0002)
(0.05)-
(0.1)
(0.6)
(3)
(0.2)
(0.03)
(5)
(0.05)
(0.04)
(0.3)
(0.02)
(0.02)
(0.04)
GC
NO
NO
0.017
3.8
NO
140
NO
170
ND
NO
NO
3.2
NO
24
ND
NO
NO
ND
1
68
NO
ND
8700
22
NO
NO
ND
NO
0.1
trument sensitivity.
them in
3
(0.5)
(0.5)
(0.002)
(0.05)
(0.01)
(0.5)
(2)
(2)
(0.05)
(0.03)
(0.1)
(0.5)
(0.2)
(1)
(0.05)
(0.0001)
(0.05)
(0.1)
(0.6)
(3)
(0.08)
(0.03)
(5)
(0.05)
(0.1)
(0.3)
(0.02)
(0.02)
(0.1)
"NO" mears
the final rep<
documentation of the sampling and analytical procedures will be
obtained and reviewed.
* April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
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6-21
TABLE 6-7
ORGANIC ANALYSES FOR PRODUCED WATERS
GC 1
GC 1
Oup
GC 2
VOLATILE
ORGANICS
Benzene -
Ethyl benzene
Toluene -
Benzene -
Ethyl btnztnt
Toluene -
Benzene -
Ethlybenzene
Toluene -
19.0 mg/1
- 2.4 ng/1
20.0 119/1
18.0 ng/1
- 2.2 mg/1
18.0 mg/1
4.3 mg/1
- 0.32 mg/1
3.3 mg/1
BASE/NEUTRAL
ORGANICS
CIO to C30 - 4.0 mg/1
p-Cymtne - 0.11 mg/1
2.4 Dimethyl phenol -
0.81 mg/1
Fluorene - 0.016 mg/1
Naphthalene - 0.36 mg/1
Phenanthrene - 0.03 mg/1
Phenol - 1.4 mg/1
CIO to C30 - 7.19 mg/1
p-Cymene - 0.12 mg/1
Bi phenol - 0.057 mg/1
2,4 Dimethyl phenol -
1.0 mg/1
Fluorene - 0.017 mg/1
Naphthalene - 0.35 mg/1
Phenanthrene - 0.033 mg/1
Phenol - 1.6 mg/1
CIO to C30 7.70 mg/1
2,4 Dimethyl phenol -
0.56 mg/1
Naphthalene - 0.33 mg/1
Phenanthrene - 0.021 mg/1
Phenol - 1.40 mg/1
ACID
ORGANICS
BDL
for, all
compounds
BDL
for all
compounds
BDL
for all
compounds
GC 3 Benzene - 12.0 mg/1
Ethyl benzene - 1.2 mg/1
Toluene - 9.3 mg/1
CIO to C30 - 3.55 mg/1 BDL
2,4 Dimethyl phenol - for all
0.90 mg/1 compounds
Phenol - 1.20 mg/1
* Only compounds with values above detection limits are reported
BDL - Below Detection Limit (No response on instrument)
Source: Standard Oil, 1987.
Note: These data are preliminary] before using them in the final report,
documentation of the sampling and analytical procedures will be
obtained and reviewed.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
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6-22
FIGURE 6-4
NORTH SLOPE ENHANCED OIL RECOVERY
INJECTION WELL DESIGN
TREE CAP
SWAB VALVE
INJECTION
STREAM
SURFACE SAFETY
VALVE
WING
VALVE
LOWER MASTER
VALVE
GROUND LEVEL
PERMAFROST
SUBSURFACE CHECK VALVE-/;-;
PREVENTS BACKFLOW -X
THE SURFACE #>XvX'>Xซ:
>20 INCH
:'CONDUCTOR PIPE:
:-CEMENTED IN PLACE TO
:-80 FEET BELOW GROUND LEVE
13 3/8 INCH SURFACE PIPE
CEMENTED AT 2700 FT.
9 5/8 INCH INTERMEDIATE
CASING^'" " "' " ' '
PACKER THAT SEALS THE 9 5/6
INCH X 7 INCH ANNULUS
LIQUID BEING DISPOSED
PERFORATIONS THROUGH
STEEL LINER AND
INCH LINER CEMENTED IN PLACE AT
A TOTAL DEPTH AVERAGING 9800 FT
Source: Standard Oil, 1987.
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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6-23
It appears that the largest portion of these "associated" wastes is made
up of workover fluids. A well workover involves operations to clean a clogged
well to correct a mechanical or formation failure that results in decreased or
halted production. The fluids generated from this process include spent acids
and miscellaneous cleaning, packing, and bailing fluids. Approximately
200,000 to 250,000 barrels of workover fluids are produced each year at
Prudhoe Bay (Ad Hoc Task Group, 1986). Workover fluids are generally disposed
of by underground injection (Standard Oil, 1987).
The other associated wastes are managed in a variety of ways. Untreated
emulsions, cooling waters, engine waters, and produced water tank bottoms are
disposed of in Class II injection wells. Waste lubricating oils, hydraulic
fluids from drilling equipment, waste crude oil, and pigging trap solids are
recycled through the production stream. Oily debris that is combustible is
incinerated at a municipal incinerator, and oily dirt and gravel (contaminated
from any spills) are disposed of in a municipal landfill. Finally, rig
washdown water is generally released to reserve pits (Standard Oil, 1987).
6.3 THE NORTH SLOPE ENVIRONMENT
The North Slope of Alaska extends north from the crest of the Brooks Range
to the Beaufort Sea, and east from the Chukchi Sea to the Canadian border (see
Figure 6-5). It has approximate maximum dimensions of 200 miles north-south
and 600 miles east-west (Smith, 1986). Figure 6-5 also shows that the North
Slope consists of three physiographic provinces: the Brooks Range mountains,
the foothills of the Brooks Range, and the Arctic Coastal Plain.
The consideration of the North Slope environment is, for the purpose of
this assessment, limited to the area in which the vast majority of oil and gas
activity is occurring. As Figure 6-5 illustrates, this smaller area is
bounded on the north by the Beaufort Sea, where an increasing but still
relatively small amount of activity is taking place; on the south, by the
limits of the Arctic Coastal Plain (the 75-meter elevation contour); on the
east, by the Arctic National Wildlife Refuge (ANWR), where oil and gas
activity is currently prohibited; and on the west by the National Petroleum
Reserve-Alaska (NPRA), where there is presently very limited exploration and
development (competitive oil and gas leasing in the NPRA began in 1980). The
"oil and gas activity area" thus delineated has approximate maximum dimensions
of 110 miles north-south and 180 miles east-west, although most oil and gas
activity currently takes place within 10 miles of the coast along a 60-mile
stretch of coastline (Standard Oil, 1987). It includes the principal North
Slope oil and gas production areas (Prudhoe Bay, Kuparuk, and Milne Point) as
well as some surrounding areas for which leases have been sold but in which
there is no production at present. Unless stated otherwise, use of the term
"North Slope" in this section refers to the oil and gas activity area defined
above.
The remainder of this section briefly discusses the climate, demography,
land uses, geology, surface waters, ground water, biota and "sensitive
environments" (such as endangered species habitats) of the North Slope. The
discussion for each of these environmental characteristics is rather general;
more detailed information may be found in the references cited.
* April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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6-24
FIGURE 6-5
ALASKA'S NORTH SLOPE
CHUKCHI
SEA
Borrow
BEAUFORT
SEA
OIL AND GAS
ACTIVITY AREA
BROOKS RANGE
1 ARCTIC COASTAL PLAIN
2 FOOTHILLS
N
50
km
100
Source: Hobble, 1984.
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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6-25
6.3.1 Climate and Meteorology
The North Slope lies in the arctic climatic zone, which is characterized
by long, extremely cold winters; short, cool summers; and low precipitation.
The principal reason for this climate is the extreme northerly latitude of the
area (it lies on and around the 70ฐ North parallel), which results in
continuous darkness during the winter; continuous daylight in the summer; and
alternating periods of darkness and daylight in the spring and fall. Based on
data collected at Prudhoe Bay from 1970 to 1973, the coldest month is
February, with a mean temperature of -25ฐF; the warmest is July, with a mean
temperature of 44ฐF (Brown et. al., 1975, cited in Hobbie, 1980). The mean
annual temperature is 9ฐF (Standard Oil, 1987). Actual temperatures may vary
substantially from these values, occasionally dropping as low as -60ฐF in
winter and reaching as high as 70ฐF in summer (Sohio, 1984). As a rule, the
moderating influence of the Beaufort Sea keeps temperatures along the
coastline milder in the winter and cooler in the summer than temperatures
inland (Smith, 1986).
Precipitation (rain and snow) on the North Slope is low, due largely to
the established presence of a fair weather high pressure system covering the
polar cap (Degler, 1984). On average, the equivalent of only five inches of
rain falls each year (COE, 1980). More than half of this amount actually
falls as rain in the summer; the remainder falls as 2-3 feet of snow. Winds
are persistent and generally range from 10 to 14 knots, but wide stretches of
flat, featureless terrain allow wind speeds to exceed 100 knots occasionally
(Degler, 1984). High winds can result in wind chill readings equivalent to
-115ฐF (Sohio, 1984), and can combine with accumulated snow in the fall,
winter, and spring to create blowing snow conditions that greatly reduce
visibility. In the summer, visibility is reduced by coastal fogs that occur
approximately one day in every three (Hobbie, 1984).
6.3.2 Demography
The North Slope is a vast, largely unpopulated wilderness. The 1980 U.S.
Census reports the population of the entire 90,955-square-mile North Slope
Borough (which includes but is much larger than the oil and gas area) as only
4,199. Well over half of this population resides outside of the oil and gas
area, in Barrow (pop. 2,700) and in three Eskimo communities with an average
population of 300 people each (State of Alaska, 1984).
The area of the North Slope in which oil and gas activity is occurring has
an even smaller population. The developed area consists largely of the
unincorporated, self-contained Prudhoe Bay/Deadhorse industrial complex. Most
of the several thousand persons working in the complex are transient, with
permanent homes in more populated areas of Alaska, such as Anchorage (Sohio,
1984). Some of the oil and gas workers are natives employed seasonally on a
full-time or part-time basis (State of Alaska, 1981).
April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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6-26
The population of the North Slope Borough grew almost 22 percent between
1970 and 1980, and it is likely that this population (including that of the
oil and gas area) has continued to grow in the 1980's. However, increases in
oil and gas activity bring largely transient groups of workers to the area.
6.3.3 Land Uses
Land uses in the North Slope area under consideration are of two basic
types: oil and gas activity, and the traditional use of the land by small
numbers of Inupiat Eskimos for subsistence. Oil and gas activity is confined
to the relatively small areas containing and immediately surrounding oil and
gas wells. In addition to the wells, oil- and gas-related land use includes
drill pads, pipelines, roads, airfields, marine terminals, gravel mines, and a
variety of buildings and other structures that support exploration,
development and production efforts. Subsistence use of the undeveloped tundra
wilderness by the Inupiat consists of traditional activities such as fishing
(e.g., for arctic cod, arctic char, whitefish, and bowhead whale), hunting
(e.g., for caribou, seals, and waterfowl), and trapping (e.g., for arctic fox).
6.3.4 Geology
The North Slope area under consideration lies wholly in the physiographic
province known as the Arctic Coastal Plain, a flat, low-lying terrain
dominated by wet tundra vegetation; large lakes; small, shallow ponds;
meandering, braided streams; a small number of relatively large rivers; and
drained lake basins (COE, 1980; Hobbie, 1980). For 9 to 10 months of the
year, the terrain is covered by ice and approximately 16 inches of snow;
during the short summer, it is saturated with water. Lake shores are often
indeterminate: lakes gradually give way to standing water in adjacent wet
tundra. In many areas, there is more water surface than land surface. The
portion of the tundra that thaws during the summer (and in which all
vegetation occurs) is known as the active layer, and is approximately 2 feet
thick. This layer is underlain by a layer of permanently frozen ground known
as permafrost, which is generally up to 2,000 feet thick.
The tundra is broken into "frost polygons" (see Figure 6-6). These
polygons form as the tundra (particularly drained lake bottoms) contracts and
cracks during freezing. Water fills the cracks during the summers, thawing
the frozen ground surrounding the cracks and widening them. Water in the
cracks freezes into "ice wedges" (see Figure 6-8) during the rest of the year
(Degler, 1984). Over many years, small ponds form along the edges of these
polygons and in their depressed centers. As thawing of the ground and wave
action (caused by winds) enlarge adjacent ponds, they coalesce into lakes.
These lakes grow until they are breached by a stream (or river) and emptied.
The drained lake bed is then subject to contracting and cracking once again.
This recurring process is known as the thaw-lake cycle (Hobbie, 1980).
The other prominent feature of the North Slope physiography is the
elongation of many lakes along a north-northwest axis (Smith, 1986). Although
this axis is approximately perpendicular to the prevailing northeasterly winds
of the area, the connection between the winds and the directional wave erosion
of the lake shores is not fully understood (Hobbie, 1980).
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
6-27
FIGURE 6-6
TUNDRA POLYGONS
TUNDRA
POLYGONALLY
PATTERNED GROUND
STREAM
Source: Degler, 1984.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
6-28
The stratigraphy of the North Slope is characterized by a sequence of
folded, inclined sedimentary strata, principally sandstone, shale, limestone
and conglomerate (AOGA, 1986). As shown in the generalized stratigraphic
column of arctic Alaska presented in Figure 6-7, the deepest strata of this
sequence (several thousands of feet below the surface) are over 400 million
years old. In fact, rocks dating as far back as the Precambrian period
(greater than 570 million years ago) have been identified in outcrops in the
Arctic Coastal Plain (AOGA, 1986). The surface layer of this sequence is the
Gubik Formation, a layer of unconsolidated silty sand and gravel that was
deposited in a shallow sea during the Quaternary period (Black, 1964, cited in
Robbie, 1980). The oil and gas extracted at Prudhoe Bay is found in the
sandstone and gravel of the Sadlerochit formation (see Figure 6-7) about 9,000
feet underground.
A substantial portion of the stratigraphic sequence occurs as permafrost.
The permafrost usually begins 2 to 3 feet below the surface (deeper under
large lakes and rivers) and continues to depths of up to 2,000 feet.
Occasional patches of unfrozen ground, known as talik, occur within the
permafrost (Keller, 1979). Thickness of the permafrost depends on a number of
factors including the proximity to the coastline, where it is approximately
400 feet thick. Because permafrost is defined as any naturally occurring
material that has a temperature below 32ฐF for two or more years -- regardless
of moisture content -- it includes cold but ice-free rock (Smith, 1986).
North Slope soils are generally shallow, moist, high in organic matter,
strongly acid, and not very fertile (Hobbie, 1980; Degler, 1984). The
shallowness of the soils that are unfrozen for some part of the year is due to
the underlying permafrost. The soils are moist, even wet, because the
permafrost prevents subsurface drainage, while the cold air temperatures
severely limit evaporation; however, the soil on and very near the surface
does dry in the summer. Cold temperature and a lack of oxygen (due to high
water content) limit decomposition, keeping the soil acid and high in peat and
other organic matter (Hobbie, 1980). The layer of organic soils is underlain
by a layer of fine- to medium-grained unconsolidated mineral soils, as shown
in Figure 6-8 (Smith, 1986).
6.3.5 Surface Waters
Flat terrain, cold air temperatures, and the permafrost barrier to
subsurface drainage result in abundant surface water on the North Slope
despite low annual precipitation. Surface water occurs mostly in numerous,
generally shallow ponds and lakes, in the cracks between the tundra polygons
described earlier, and as standing water in the tundra vegetation. Surface
water also occurs in the few large rivers of the area (the Colville, the
Kuparuk, the Sagavanirktok, and the Canning), and in small, meandering braided
streams. Small streams are common only in areas without polygonally patterned
ground (Hobbie, 1980). In general, water covers 30 to 90 percent of the
tundra surface during the short ice-free season (Standard Oil, 1987).
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
6-29
FIGURE 6-7
GENERALIZED STRATIGRAPHIC COLUMN
OF ARCTIC ALASKA
LTTHOSTRAT1GRAPHY
82UZU NORTH
SEQUENCE
HYDROCARBON
DSCOVERE8
ฃ CANADIAN BEAUFORT
3fc TAQLU
(MACKENZIE DELTA)
UONU
WESTSAK
UMIAT
PARSONS
* (MACKENZIE DELTA)
Jfc POINT THOMSON
a, KUPARUK
* MILNE PT./
QWYDYR BAY
fit- BARROW GAS FIELD
_ PRUDHOE BAY
r SEAL ISLAND
NORTH STAR
* PRUDHOE BAY
J^ENOICOTr
OLFELD
^ QAS FIELD
$. OIL AND QAS
FIELD
DEPOSmONAL
UNCONFORMITY
EROSIONAL
UNCONFORMITY
J SANDSTONE |0j SHALE
2 CONGLOMERATE ง LIMESTONE
%[ AROUJTE B DOLOMITE
Source adapted from Geologic Report
for the Beaufort Sea Planning
Area (MMS 85-0111, 1965)
* * *
April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * *
-------
6-30
FIGURE 6-8
TUNDRA SOILS
TUNDRA
'"-'. , 'v -*, '"',,. ',>//. ''i,-'f ,'.'j-M', > -;ซ'/.<--* ';,/; /"/v/'v?^ttB"/1 ""-t^*' -''-."' -'C ^ '/,: '*"*-Tr'
^"ygr^^^v^^^fe^ LA YE.
ป'ป' -*-0' \ ป ' ' .'o .*';.ฐ-'.'. o'ป' ".'ฐ .'?'...'.ป o ',"0-"- fa< ' ' 'ฐ ."!
'.MINERAL SOILS;:;;;;ฐ,:/"- -ฐ * - - ?D;:* E^
UP TO 3 FT. DEEP
ICE WEDGE
PERMANENTLY FROZEN SOILS - PERMAFROST
TO DEPTHS UP TO 2000 FT. BELOW THE SURFACE
Source: Degler, 1984.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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6-31
For most of the year, surface waters exist as ice and accumulated snow.
Annual snowfall and maximum snowpack are highly variable; they averaged about
36 inches and 16 inches, respectively, at Barrow, Alaska from 1964 to 1973
(Hobbie, 1980). The small loss of surface waters, principally by drainage and
evaporation, begins with the onset of snowmelt in late May and early June. As
the combination of warmer air temperatures and stronger sunlight begins to
melt the snow, its reflectivity decreases rapidly, bringing about a
corresponding rapid increase in the energy absorbed by the snow as heat. The
strong sunlight then melts large quantities of snow and ice very quickly:
40-60 percent of the meltwater runoff occurs during a period of about four
days in early June, flooding the tundra. Melting of the ice in shallow ponds
and breakup of ice in rivers and streams occurs in several days; complete
melting of the ice in larger lakes requires several weeks (Hobbie, 1980).
Because the surface water in the Brooks Range foothills melts sooner (the
foothills are further south), meltwater washing down from the foothills aids
the thawing of the coastal area (Degler, 1984).
The seasonal runoff basically ends later in June; rivers then become
sluggish and streams virtually stop flowing, except during occasionally heavy
summer precipitation (Hobbie, 1984). The dominant mechanism for loss of the
quantities of water that remain due to poor drainage is summer evaporation.
One study estimated the water loss due to evaporation to be roughly equal to
the 2 to 3 inches of summer precipitation (Brown et. al, 1968, cited in
Hobbie, 1980).
Surface waters are used for drinking, bathing, fishing, and other
activities. The oil and gas industry uses surface waters for these purposes
and others necessary to support oil and gas activity, including discharges of
wastewater. Any discharges to surface waters are regulated by the EPA through
the National Pollutant Discharge Elimination System (NPDES), by the Alaska
Department of Environmental Conservation (ADEC), and by stipulations in leases
granted by the Alaska Department of Natural Resources.
6.3.6 Ground Water
Ground water under the North Slope occurs in a number of geologic
formations up to several thousands of feet deep. However, it is not used for
drinking because it appears that virtually all ground water is either
immobilized as permafrost or contaminated by high levels of dissolved solids.
Ground water from aquifers below the permafrost is saline, naturally contains
from 1,000 to more than 30,000 milligrams per liter of dissolved solids, and
is normally unsuitable for drinking (Heffner, 1987; Standard Oil, 1987).
Produced waters from oil and gas activity at Prudhoe Bay are injected into one
of these aquifers at about 6,000-9,000 feet below the surface (Sohio, 1984).
6.3.7 Biota
The North Slope lies in the arctic tundra ecosystem, which is
characterized by simplified food webs and a marked lack of diversity in both
flora and fauna. The ecosystem is dominated by aquatic habitats and water-
related birds (Bergman et al., '1977). Not many species can survive the bitter
cold, limited sunlight, low precipitation, and permafrost conditions of the
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE *
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6-32
tundra. As a result, the ecosystem of the North Slope is fragile and easily
disturbed (Miller, 1982). Net annual primary production is low (about 800
kilocalories per square meter; Brewer, 1979) because the harsh conditions make
only a small part of the abundant soil nutrients available to plants (Hobbie,
1980).
As in other northern ecosystems, a large portion of the tundra's nutrients
and primary production occurs below ground. This characteristic can be best
expressed by the ratio of the nitrogen content of plant roots to the nitrogen
in above-ground plant shoots. Whereas such root-to-shoot ratios can be as low
as 0.3 in the tropics, they can exceed 1.2 in the arctic tundra (Odum, 1971).
The flora of the North Slope is dominated by a dense, low-growing,
interwoven mat of grasses, sedges, mosses, and lichens. Ground-clinging woody
shrubs, such as dwarf willows and birches, also occur; these seldom grow more
than a few inches in height, except in river bottoms. A variety of colorful
wildflowers, such as anemones, poppies, roses and asters, blossom during the
brief summer. Many plants have adapted to the severe tundra climate by
reproducing vegetatively rather than sexually.
Due to the severe climate, the fauna of the area are generally migratory;
they inhabit the North Slope mainly during the summer. A handful of animals
with adaptations for surviving the winter remain year-round. Rodents such as
lemmings and ground squirrels burrow into the shallow soil, while snowy owls
and arctic hares survive with the aid of insulating coats (feathers and fur)
and protective white coloration.
Most other animal species migrate southward for the winter. Dominant
mammals of the North Slope include (in addition to lemmings and hares) larger
herbivores such as caribou, moose, reindeer, and musk oxen. Other mammals
include carnivores such as wolves and arctic foxes, and omnivores such as
river otters (Miller, 1982).
Birds common to the North Slope include the Lapland longspur, the snow
bunting, and the long-tailed jaeger. Waterfowl, such as the Canada goose, and
shorebirds, such as the pectoral sandpiper, also summer on the North Slope.
Although mammals generally migrate to and from more southerly portions of
Alaska, birds arrive from all over the world. Northern wheatears travel from
southern Asia and Africa, tundra swans from the Chesapeake Bay, and Arctic
terns from Antarctica (Troy, 1985). The birds feed mostly on large numbers of
just a few species of insects. Swarms of mosquitoes, black flies, and
deerflies inhabit the tundra in the summer months (Miller, 1982).
Both anadromous and freshwater fish are found in the surface waters of the
North Slope. Anadromous species, which live in the Beaufort Sea but return to
freshwater rivers (such as the Colville) each fall to spawn and overwinter,
include arctic char, least and arctic cisco, and whitefish. Principal
freshwater fish include northern pike, arctic grayling, ninespine stickleback,
and lake trout (COE, 1980; Degler, 1984). Because most lakes, ponds, and
parts of streams and rivers freeze to the bottom each winter, fish overwinter
in deep holes found in stream and river channels (Degler, 1984).
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6-33
Other, smaller organisms live in the soil and surface waters of the
tundra. A variety of microbial decomposers (bacteria and fungi) inhabit the
active layer of the soil. Soil invertebrates, which principally include
nematodes, annelid worms, mites, springtails, and dipteran larvae, are found
mostly in the aerobic layer (the top inch or so) of the soil (Hobbie, 1980).
Tundra lakes and ponds contain a number of phytoplankton, zooplankton, and
benthic algae species (Hobbie, 1984).
6.3.8 Sensitive Environments
The most environmentally sensitive areas of the North Slope are its
wetlands, which cover 50 to 75 percent of the Arctic Coastal Plain (Black and
Barksdale, 1949, cited in Bergman et al., 1977). Although the ice-free season
is short, these wetlands provide important nesting, feeding, and staging
habitat for migratory waterfowl, seabirds, and shorebirds. Among the
seasonally breeding species of greatest numerical importance are the Red and
Red-necked Phalaropes, Lapland Longspur, Northern Pintail, Oldsquaw, King
Eider, Arctic and Red-throated Loon, Greater White-Fronted Goose, Semipalmated
and Pectoral Sandpipers, and Dunlin (Woodward et al., 1986; ADNR, 1986). All
of these species of birds rely upon aquatic resources for their food, many
feeding in large measure on the aquatic stages of a few insect species.
Prudhoe Bay wetlands support the only U.S. breeding colony qf Snow Geese
(Woodward et al., 1986). More generally, a thorough analysis of published
data through 1974 (Pitelka, 1974) indicates that of 97 species known to breed
on the entire North Slope, 44 breed regularly in the coastal zone.
In terms of state and federal designations, the most significant
environmentally sensitive area of the entire North Slope is the Arctic
National Wildlife Refuge (ANWR), which lies immediately to the east of the oil
and gas activity area across the Canning River. The ANWR includes both refuge
and wilderness lands used by many of the same animals that inhabit the oil and
gas area (State of Alaska, 1981).
Some North Slope species are listed as threatened or endangered under the
federal Endangered Species Act; these include the Arctic peregrine falcon, the
Eskimo curlew, and the arctic pennycress (COE, 1984). In addition, anadromous
rivers and streams, such as the Colville, the Sagavanirktok, and the Canning,
are protected and managed under Alaska Statute Title 16. The Heritage
Conservation and Recreation Service of the U.S. Department of the Interior has
identified the Sagavanirktok and the Canning Rivers as having scenic value,
and the Alaska Division of Parks has determined that some areas near the oil
and gas activity area have potential archeological value (State of Alaska,
1981).
6.4. POTENTIAL HUMAN HEALTH AND ENVIRONMENTAL IMPACTS OF WASTE DISPOSAL
This section addresses the potential for human health and environmental
impacts resulting from oil and gas waste management practices on the North
Slope. The discussion is divided into three sections addressing potential
impacts from airborne releases, solid wastes, and liquid wastes, respectively.
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6.4.1 Airborne Discharges
The major airborne release mechanisms for wastes of interest in this study
are volatilization and fugitive dust emission. There are no waste treatment
or disposal activities that result in significant forced air discharges. For
example, drill muds and cuttings and produced waters are not disposed of by
incineration on the North Slope, although contaminated trash (e.g., rags, oily
debris) is incinerated. In addition, the large quantities of NOx (and any
other contaminants) that are released from gas fired turbines used to generate
electricity on the North Slope are outside the scope of this assessment. Such
emissions in Alaska are regulated under the Prevention of Significant
Deterioration program of the Clean Air Act.
Evaporation of volatile constituents from open reserve pits is limited
because the pit contents are frozen for most of the year. Even during the
summer thaw, the rate of evaporation should be low due to the cold
temperatures. Although the temperatures vary widely, the average temperature
during July, the warmest month, is approximately 40ฐF. Evaporation from
reserve pits should also be small because, based on available sampling results
from EPA (1987b) and Standard Oil (1987), reserve pit liquids on the North
Slope contain very low concentrations of volatile organics. While the EPA and
Standard Oil results show that reserve pit solids contain measurable
concentrations of volatile compounds (e.g., benzene, ethylbenzene, and
toluene), direct evaporation from the solids should be small because they are
almost always under water during the thaw period. In general, evaporation has
not been an effective means of dewatering pits on the North Slope as the pits
generally receive a net gain of precipitation even though the annual
precipitation rate is small.
As shown in Table 6-7, produced waters on the North Slope contain
measurable concentrations of the volatile organics benzene, ethylbenzene, and
toluene. However, there should not be significant evaporation from produced
water because it is typically stored in closed tanks prior to injection. The
tanks in the western operating area of Prudhoe Bay are equipped with vapor
recovery systems used to reclaim volatile hydrocarbons (Standard Oil, 1987).
In principle, contaminated dusts can be released due to wind or heavy
equipment activity at a site. While such emissions can and probably do occur
on the North Slope, the extent of dusting and its resulting impacts are
expected to be minor. The bulk of the wastes of interest are either frozen
(for about nine months of the year) or exist as liquids or liquid-solid
mixtures during the summer thaw. Fugitive dust emissions from these forms
should not be a problem. Additionally, reserve pits on the North Slope must
be covered with soil and vegetation at closure, and existing state regulations
require owners of closed pits to maintain the integrity of this cover.
Therefore, the potential for fugitive dust emissions from closed pits, which
contain dewatered solids, is small.
The greatest potential for dusting problems appears to be from droplets
and dried residue left on the tundra or roads that have received reserve pit
discharges. The release to air of contaminated particles should be small
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because, even after the top couple of feet of the tundra thaw in the summer,
the ground generally remains moist or covered by a system of small ponds or
puddles as described in Sections 6.3.4 and 6.3.5. A recent road application
permit for ARCO specifies concentration criteria that must be met at the point
where the receiving road meets "... the lands or waters of the state" (ADEC,
1986b). However, criteria are not specified for several constituents present
in reserve pit fluids in fairly high concentrations (e.g., aluminum, iron,
magnesium, sodium, and calcium) and, conceivably, the levels of these
constituents could accumulate on roads after repeated applications.
Subsequent contaminated dust emissions from these roads could occur, but the
quantities of contaminants released in this way and the resulting exposures of
plants and wildlife are unknown.
6.4.2 Solid Wastes
As discussed in Section 6.2.4, the principal solid wastes of interest in
this assessment are drilling mud solids and cuttings. These solids usually
are discharged into above-ground reserve pits located on the drill site pads
where the wastes are generated.
The mud and cuttings are discharged directly to centralized reserve pits
at the drdll site; they are not transported over the tundra where occasional
spills or releases might be expected to occur. Once in the reserve pits,
there are three foreseeable mechanisms for release of chemicals from solids.
First, contaminants from the solids caa be released from an operating pit by
leaching through the pit floor and walls or by becoming suspended in the pit
liquids that are removed and discharged to the environment. Second,
contaminants from the solids contained in a closed pit can leach into the
ground as precipitation percolates through the closed pit contents. Third,
the mud and cuttings themselves could spill onto the ground if an operating
pit's berm fails during spring "breakup" or in the event of erosion and
weathering of a closed pit's cover. The first and second mechanisms for
release, because they mainly involve releases of liquids, are discussed in
Section 6.4.3 below. Potential impacts resulting from the third mechanism for
release are addressed in the following paragraphs.
There are several precautions in place that reduce the potential for mud
solids and cuttings to escape the containment of the reserve pits. First,
Alaska regulations state that drill mud disposed of on land must be confined
in a manner that prevents mass flow of the mud (see 18 AAC 60.085), and
require the owner of closed pits to maintain the integrity of the soil cover,
slopes, and vegetation (see 18 AAC 60.410). In response to these
requirements, the berms of reserve pits are generally constructed of gravel
rather than ice-rich soils that may thaw and slump during the summer months.
Moreover, the muds and cuttings remain frozen for most of the year and, when
thawed, they line the inside of the pit and prevent the flow of bulk solids
through the gravel berms. In addition, as a condition of wastewater disposal
permits, ADEC requires that dewatering devices be positioned sufficiently
above the bottom of reserve pits to prevent withdrawal and carryover of solids
to the receiving environment (ADEC, 1986a).
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Occasional slumping of an operating pit's walls nevertheless occurs during
the summer thaw and it is inevitable that the cover of closed pits will
deteriorate over the years. In these cases, mud and cuttings (if not frozen)
may spill onto the surrounding tundra. Such a release would result in
physical damage to vegetation, contamination of soil, contamination and/or
killing of vegetation, and associated habitat modifications. Permits from the
U.S. Army Corps of Engineers, which are needed to construct reserve pits in
wetlands, require that a minimum distance of 100 feet be maintained between
the toe of a pit's gravel pad and the ordinary high water mark of adjacent
lakes and streams on the North Slope. Therefore, in the event that a pit's
wall is breached it is unlikely that significant quantities of muds and
cuttings could spill into nearby waterways.
If promptly cleaned up, the spilled solids (and their contaminants) should
not migrate far from the point of release, and vegetation in the spill area
would probably slowly recover. Pollen (1986) and McKendrick (1986) report on
vegetation damages resulting from breached reserve pits in the NPRA that had
been abandoned for 3 to 8 years.3-1 Most of the pits studied by Pollen and
McKendrick were constructed so that the top of the mud and cuttings was below
the land surface, thus preventing the flow of solids that might occur in a
breached above-ground pit. However, drainage channels were deep enough in
some of the abandoned pits to allow small quantities of muds to be released,
which would likely be similar to the situation that would exist today if
spilled solids were substantially cleaned up. In these cases, Pollen and
McKendrick found that the exposed soil was generally discolored and putrefied,
but that the vegetation was generally recovering. Several natural species of
vegetation were observed to be recolonizing the area and there were numerous
signs that wildlife had also re-inhabitated the area.
If spilled solids were not cleaned up, the solids could spread (or their
contaminants could leach) to wider areas, and a lasting impact to the
vegetation would be expected. Accompanying this impact would be contamination
of wildlife that tried to use the area and possible displacement of wildlife
to other areas. Two of the well drilling sites studied by Pollen and
McKendrick did not use reserve pits; the muds and cuttings were released
directly onto the tundra. At these sites Pollen and McKendrick found that,
even after 4 to 6 years, vegetation had still not recolonized the areas where
muds and cuttings had been discharged.
3J It is important to clarify that the abandoned reserve pits in the
NPRA were left open and had accumulated much more standing water than could
accumulate in closed pits on the North Slope today, which are required to be
covered, sloped, and vegetated. Therefore, the frequency of closed pit
failure in the NPRA is not necessarily indicative of failures that may occur
when operating pits are closed in the future.
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6.4.3 Liquid Wastes
As discussed in Section 6.2.4, there are a variety of mechanisms for
liquid wastes from oil and gas operations to enter the North Slope
environment. The following sections discuss the releases and potential
impacts associated with:
direct discharge of reserve pit fluids onto the
tundra and roads;
seepage of contaminated fluids from operating
reserve pits;
leaching of contaminants from closed reserve pits;
and
underground injection of reserve pit fluids and
produced waters.
6.4.3.1 Direct Discharge of Reserve Pit Fluids Onto the
Tundra and Roads
Releases. Direct discharge of reserve pit fluids onto the tundra and
roads is practiced on the North Slope because the frozen conditions and low
evaporation rates reduce natural losses of reserve pit fluids, while
repetitive use of the same reserve pit for multiple wells and snow melt each
year continue to add volume to most pits. Surface discharges of pit fluids
occur between June and September of each year. These discharges are regulated
by ADEC and must meet specified water quality criteria. According to Alaska's
General Wastewater Disposal permit (ADEC, 1986a), direct discharge onto the
tundra is permitted for pit fluids meeting the effluent limitations shown in
Table 6-8. Monitoring at the point of release is required to demonstrate
compliance with these limits and permittees are required to report their
monitoring results to ADEC at the end of each year (after the discharges are
completed). The General Wastewater Disposal Permit further specifies that
only pits that have not received any inputs for about a year can be dewatered
onto the tundra. This requirement to release only "aged" pit fluids assures
that the fluids have gone through at least one freeze-thaw cycle, which helps
concentrate the contaminants lower in the liquid column.
ADEC's wastewater quality criteria for road disposal are similar to those
for tundra discharge. A sample road application permit for ARCO (ADEC, 1986b)
specifies the same effluent limitations noted above, except that the limits
for settleable solids and total aromatic hydrocarbons do not have to be met.
In addition, fluids applied to roads do not have to be aged and the compliance
point for the effluent limitations is the point where the road meets the land,
rather than the point of release.
According to reports filed with the ADEC for 1985, approximately 900,000
barrels of liquids were discharged onto the tundra and 200,000 barrels of
liquids were applied to roads from 43 reserve pits on the North Slope (Versar,
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TABLE 6-8
EFFLUENT LIMITATIONS FOR TUNDRA DISCHARGE
OF RESERVE PIT FLUIDS a/
Parameter
Limit
Chemical oxygen demand
pH
Salinity
Settleable Solids
Total oil and grease
Total aromatic hydrocarbons
Arsenic (Total)
Barium (Total)
Cadmium (Total)
Chromium (Total)
Lead (Total)
Mercury (Total)
Copper (Total)
Zinc (Total)
Aluminum (Total)
200 mg/1
between 6.0 and 8.5 pH units
or within 0.5 units of the pH
of the receiving waters
3 parts per thousand
0.2 mg/1
15 mg/1
10 ug/1
0.050 mg/1
1.000 mg/1
0.010 mg/1
0.050 mg/1
0.050 mg/1
0.002 mg/1
b/
b/
b/
a/ As specified in ADEC's General Wastewater Disposal Permit
(ADEC, 1986a).
b/ Effluent limitations are being evaluated for these parameters,
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1987a). ADEC states that, of these 43 pits, 35 were reported to have exceeded
permit effluent limits; however, of the pits in violation, 16 were solely in
violation of a manganese limit that was subsequently deleted by ADEC because
of the naturally high levels of manganese found in waters on the North Slope.
ADEC further states that "... in instances where the exceedance was for a
parameter that was periodically monitored onsite, such as salinity or
suspended solids, the discharge was stopped. Some parameters were sampled for
during discharge, with analysis occuring after completion of the discharge. A
number of the reserve pits contained liquids which exceeded limits for these
parameters, but were discharged from anyway" (ADEC, 1986a).
Environmental Impacts. Reserve pit fluids discharged onto the tundra and
roads are thought to represent a potential threat to the environment for three
reasons. First, there are currently no effluent limits for copper and
aluminum which, based on the data shown in Table 6-2, are present in reserve
pit fluids in potentially harmful concentrations. Both the copper and
aluminum concentrations given in Table 6-2 exceed EPA's acute and chronic
toxicity criteria for the protection of freshwater aquatic life (51 FR 8362).
When the reserve pit fluids are discharged, however, the copper and aluminum
concentrations should be diluted by the large amount of water standing on the
tundra. As indicated in Table 6-8, ADEC is evaluating effluent limitations
for copper, aluminum, and zinc.
Second, although the same reserve pit is typically not dewatered onto the
tundra and roads year-after-year, it appears that the same areas receive
discharges over several years from the different pits at a consolidated drill
site. Because reserve pits are planned to operate for about 30 years, a
discharge area that is used repeatedly could receive a large volume of
liquids, and a large chemical mass loading, over the operating life of a pit.
A large portion of the released constituent mass is expected to flush from the
receiving area each year due to the tremendous on-rush of water that
accompanies spring break up. Simmons et al. (1983) (cited in Standard Oil,
1987) report that conductivity levels in a wet tundra experimental site at
Prudhoe Bay returned to background levels within 30 days after being flooded
with seawater containing a salinity greater than 29,000 mg/1 (which is much
higher than would be expected in reserve pit fluids). However, the results of
this study do not address the rate of flushing of other reserve pit
constituents, some of which are more likely to sorb to soil or accumulate in
plant tissue. In addition, the Simmons et al. study examined the effects of a
one-time spill; it is not clear if the same rate or extent of flushing would
be observed at a site that receives repeated discharges over a period of 30
years, or at other North Slope sites.
Third, many of the individual chemical constituents found in reserve pit
fluids are known to be acutely or chronically toxic at certain concentrations
to aquatic, wildlife, and plant species. For example, salts, boron, and a
variety of hydrocarbons typical of diesel fuel (and found in pit fluids) are
proven phytotoxicants. Further, many of the inorganics found in reserve pits,
such as chromium, lead, copper, aluminum, and mercury, have demonstrated
toxicity in a variety of aquatic organisms, as well as in waterfowl and other
birds. In addition, some pit fluid constituents such as certain metals are
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known to bioaccumulate, potentially magnifying adverse effects through the
food chain. It is also important to consider that multiple constituents, when
released together, can potentially cause greater toxic effects than would be
expected if the constituents were released alone. For instance, the data in
Table 6-2 suggest that reserve pit fluids on the North Slope are fairly hard
(i.e., have substantial calcium and magnesium concentrations). Total hardness
is known to influence the toxicity of other constituents. Woodward et al.
(1986) assert that "...current discharge permits do not address the impact of
toxic organic and inorganic materials interacting together in a complex waste
effluent."
West and Synder-Conn (1986) examined water quality and invertebrate
communities in tundra ponds receiving discharges from reserve pits, and in
hydrologically-connected distant ponds. Hardness, alkalinity, turbidity,
chromium, barium, arsenic, and nickel were detected at statistically
significant elevated levels when compared to concentrations in control ponds.
In addition, alkalinity, chromium, and aliphatic hydrocarbons were elevated in
hydrologically-connected distant ponds when compared to controls. The mean
chromium (total) concentration in receiving ponds and hydrologically-connected
distant ponds exceeded the EPA water quality criterion for chromium(III) for
chronic exposures. Accompanying these water quality variations was a decrease
from control ponds to receiving ponds in invertebrate taxonomic richness,
diversity, and abundance. Although the nature of the data and the statistical
analyses preclude a definitive determination of cause and effects, the data
suggest that pit fluid discharges were affecting water quality and
invertebrate community structure.
Woodward et al. (1986) also examined water quality and toxicological
effects associated with discharge and seepage from North Slope reserve pits.
Nine tundra ponds located near to (within 50 m) and distant from (within
100-200 m) Arctic reserve pits were examined and compared to tundra ponds
located in background locations. Woodward e_t al. found that all near and
distant ponds associated with reserve pits had concentrations of inorganics in
water that were greater than those found at the background sampling sites.
The ions most frequently present at concentrations at least three times
greater than that of background were chloride, sulfate, bromine, boron,
barium, potassium, manganese, sodium, and silicon. Barium, chromium, and lead
were also frequently elevated in sediment samples in near and distant tundra
ponds.
Woodward &t a1. (1986) also reported that reserve pit fluids, when tested
at 100 percent strength (i.e., not necessarily in compliance with the ADEC
tundra discharge limits), did not result in acute toxicity to Daphnia
magna. '
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A separate study by Snyder-Conn (1987) examined the acute toxicity of
North Slope reserve pit fluids to another species of Daphnia that is
indigeneous to the North Slope, Daphnia middendorffiana. In this study,
reserve pit fluids were found to produce significantly significant increases
in acute toxicity to the test species. Therefore, it is uncertain if the
acute toxicity results from the Woodward et al. (1986) study, summarized
above, are representative of potential effects in Arctic species.
The implications of such effects on the tundra ecosystem are not clear.
Invertebrates constitute the major food source for many bird species on the
Arctic coastal plain (Bergman et al., 1977), particularly during the breeding
and brooding seasons, which coincides with the June through September period
that reserve pits are dewatered. Such distributional shifts in invertebrate
community structure may adversely affect water bird foraging and/or
reproductive success. Also, inorganics such as arsenic, chromium, and nickel
are known to bioaccumulate in some invertebrate species, and accordingly, may
pose increased threats to water birds because of concentrated levels in food
sources. This possible concern is exacerbated by the observation that birds
frequently congregate in areas on or adjacent to roads because these areas are
among the first to thaw in the spring (personal communication with Elaine
Snyder-Conn, U.S. Fish and Wildlife Service). ADEC's effluent limitations for
road discharges are more lenient than those for tundra discharges.
In summary, the precise environmental impacts of discharged reserve pit
fluids onto the tundra are not known. Preliminary field and laboratory data
from relatively short-term studies are inconclusive but suggest that potential
impacts to aquatic, wildlife, and plant species may occur, although these
impacts may not be permanent.
Human Health Impacts . The human health risks associated with tundra and
road discharges are expected to be small because the potential for human
exposure is small. For example, the receiving tundra wetlands and small ponds
surrounding drilling and production operations are not used as a source of
drinking water, reserve pit fluids are not released directly into surface
waters used for human consumption, and the probability of horizontal migration
of pit fluids released onto the tundra to distant deep lakes used for drinking
water is small. Because ground water is not a source of drinking water in the
Arctic oil and gas areas, contamination of the ground-water layer would not
present a human health threat even should it occur. Direct human contact with
contaminated wetlands is also anticipated to be minimal because human use of
the tundra during the thaw period is limited by the extremely wet condition of
the land. In addition, the potential for human impacts through the food chain
is considered small because food crops are not grown on the receiving tundra
or nearby areas, and because fishing mainly occurs at distant locations.
6.4.3.2 Seepage of Fluids From Operating Pits
Releases. The reserve pits associated with oil production on the North
Slope are generally above-grade basins within gravel drilling pads.
Typically, the walls and bottoms of the reserve pits are constructed of
unlined, "pit-run" (as mined) gravel, comprised of coarse and fine pieces of
rock. In instances where the pits are constructed of larger gravel, the
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initial permeability of new pit walls and bottoms may be relatively high and
chemical constituents of drilling mud, during the period that they are thawed,
and other wastes disposed in the pits may seep onto the surrounding tundra.
Eventually, the clays and other drilling mud solids disposed in the reserve
pits clog the pore space in the gravel and, during the summer, form the
primary barrier to the movement of solids and leachate through the pit walls.
Although the permeability of pit walls theoretically decreases over time
with increased mud/clay clogging, the walls do not provide a 100 percent
effective barrier to leachate movement. Thus, fluids from operating pits,
when not frozen, may seep onto the tundra. The seepage is thought to occur
primarily through the pit walls above the solid level where most of the fluids
are standing.
ARCO (1985) states that, of the 65 reserve pits5-1 in the eastern
operating area of Prudhoe Bay, "some" of the reserve pits are known to seep.
ARCO further states that it has been working with the ADEC concerning the
reserve pit seepage since October 1984. Many of the abandoned reserve pits in
the NPRA, which contain predominantly gravel berms like the pits in the
Prudhoe Bay area, are also known to be seeping (Pollen, 1986; McKendrick,
1986). However, other than the recognition that the pit designs permit
seepage and that some pits do indeed seep during the summer, no information
has been obtained on the seepage volumes, rates, or concentrations.
Environmental Impacts. The significant difference between reserve pit
seepage and purposeful tundra discharges is that reserve pit seepage should be
more highly contaminated (tundra discharges are supposed to meet permitted
quality criteria), but the seepage volumes should be considerably smaller.
Theoretically, however, the types of impacts from pit seepage could be the
same as those associated with tundra or road discharge. For example,
migrating pit leachate entering tundra wetlands could adversely affect
invertebrate community viability or structure, and heavy metals could
accumulate in plants and invertebrates and adversely affect foraging water
birds. However, such impacts have not yet been documented, and it is not
known if pit liquids leach in sufficient volume or concentration to elicit
such toxic effects. Studies concerning the impacts of reserve pit fluids to
vegetation are summarized below.
McKendrick (1986) linked damage to various plant species surrounding
reserve pits in the NPRA to salts and hydrocarbons escaping from the pits.
Evidence indicated that the persistence of such vegetation damage may be
relatively short-term, and that recolonization of disturbed areas occurs over
time.
Although not conducted specifically using Arctic tundra organisms, a study
by Strosher et al. (1980) documents the impacts on vegetation of pit fluids
5J Note that there are close to a total of 300 reserve pits currently
located on the North Slope generally; there are approximately 65 pits in the
eastern operating area of the Prudhoe Bay field alone.
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generated from three different drilling mud systems commonly used in Alberta.
Release rates into the test plots were designed to simulate actual field
operation levels. One of the mud systems, a potassium chloride/polymer
system, was found to be most toxic to plants and one year after application
resulted in the death of 25 to 30 percent of treated plants when compared to
controls. Damage appeared to be related to both physical contact with and
plant uptake of contaminants. In four plots receiving the highest volume of
potassium chloride/polymer wastes (100 liters per square meter), plant tissue
levels of chlorides, sulfates, or sodium reached potentially toxic or
growth-reducing concentrations. Salinity (total dissolved ion) levels in the
discharged pit fluids were 847, 2,236, 2,001 and 33,313 ppm. The current
salinity requirement for tundra discharge of pit fluids on Alaska's North
Slope is 3,000 ppm. Thus, the salinity levels causing effects in this study
were below the existing Alaska standard.
Soil and plant tissue salinity levels decreased in the Strosher et al.
study after the second year following application, and were equal to controls
three years following application. Therefore, given sufficient time, soils
and plants effectively recover from high salinity inputs. However, soils and
plants exposed to reserve pit seepage over a period of 30 years (the expected
operating period for a Prudhoe Bay pit) may not recover as quickly.
In general, it is currently uncertain how far seepage, in sufficient
concentrations to damage vegetation, could migrate from a reserve pit. During
spring breakup, there is a significant movement of water that could transport
released contaminants to downgradient locations. However, following breakup,
migration through the thin active soil zone should be limited by the generally
flat gradient and by the lack of available water to create a head. The
proposed amendments to ADEC's solid waste management regulations require the
state's water quality criteria to be met at a distance of 50 feet from the
edge of reserve pits. It is unrealistic, though, to assume that any
contamination of ground water, soil, and vegetation will extend to 50 feet and
then stop; once contamination is detected, it would likely take a significant
period of time to take corrective measures.
Human Health Impacts. The potential for human health impacts from reserve
pit seepage is very small for the same reasons noted above for tundra and road
discharges. In fact, although reserve pit seepage may be more highly
contaminated, it should not be distributed over broad areas like tundra
discharges and therefore should have less opportunity to migrate to potential
human exposure points.
6.4.3.3 Leachate From Closed Reserve Pits
Releases. Leaching from the dewatered solids in a closed pit is expected
to be very minor for several reasons. The wastes and any precipitation that
might percolate through the wastes are frozen for all but three months of the
year; as such, the waste could possibly be in contact with liquids for only
short periods at a time. In addition, the Arctic coastal plain is essentially
a desert with annual precipitation (mostly as snow) averaging around five
inches (COE, 1980). Therefore, windblown snow may accumulate around a closed
pit, but there should be little precipitation available to seep into a closed
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pit. This situation results in less ground-water recharge directly under the
pit than in immediately surrounding areas, which would induce ground-water
flow toward the pit and hence reduce the outflow of leachate. Finally, there
are several provisions in Alaska's solid waste management regulations (18 AAC
60.015-60.910) that, if complied with, should reduce leaching of contaminants
from closed pits.6J
Impacts. The potential for seepage from closed reserve pits to affect the
environment or human health is, in the short term, much less than that created
from operating pits. As mentioned above, any seepage from closed pits should
be low in volume and should be mostly contained to areas near the site.
However, while these closed pits will be covered, sloped, and vegetated, they
will not provide a weather- or time-resistant encapsulation of the waste
solids. Therefore, over the years that the closed pits are exposed to the
harsh Arctic conditions, low-volume leachate and residual contamination can be
expected to occur.
6.4.3.4 Underground Injection of Wastes
Releases. Underground injection is another mechanism for the disposal of
reserve pit fluids, and is the primary mechanism for disposal of produced
waters and other waste streams. As discussed in Section 6.2.4.2, the largest
portion of produced waters are injected into wells designed for enhanced oil
recovery, although some water is also injected into primary disposal wells. A
large number of design regulations have been promulgated by the State of
Alaska to ensure that a well will not rupture, contaminate surface water or
ground water, or thaw permafrost.
Impacts. Disposed fluids are injected below the permafrost layer at a
depth of approximately 9,000 feet to ensure that no water will migrate
vertically and contaminate surface water. The receiving formation is not an
underground source of drinking water and is effectively sealed from the
surface by the permafrost (which is up to 2,000 feet thick). Consequently,
the potential for environmental or human health impacts associated with
underground injection is very small under normal operating conditions. The
primary threat to the environment would be associated with a well failure at
the land surface or a failure of surface tanks and pumps used in conjunction
with injection wells. As described in Section 6.2.4, there are several
requirements and operating practices that reduce the likelihood that such a
failure would occur. As part of the damage case study for the overall 8002(m)
study, no damage cases involving injection well failures on the North Slope
were identified (Versar, 1987b).
SJ In the state's proposed revisions to the solid waste management
regulations, there are new requirements involving monitoring, closure, and
permit application requirements for drilling waste disposal sites. Some of
these establish varying degrees of containment and monitoring based on the
distance to surface waters, proximity to humans and public water systems, and
existence of continuous permafrost (Hungerford, 1987).
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6.5 CONCLUSIONS
[Reserved]
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CHAPTER 7
OIL AND GAS: ADDITIONAL QUALITATIVE ASSESSMENTS
In addition to the qualitative assessment of oil and gas waste disposal on
Alaska's North Slope, two other qualitative assessments were performed as part
of this risk analysis. The first is an analysis of the proximity of oil and
gas extraction activities to sensitive environments, and the second is an
evaluation of the damage case study results. EPA considers these assessments
to be screening-level analyses of the potential for environmental harm. The
methods and results of these qualitative assessments are outlined in Sections
7.1 and 7.2 below.
7.1 LOCATIONS OF OIL AND GAS ACTIVITIES IN RELATION TO SENSITIVE ENVIRONMENTS
7.1.1. Introduction
The term "sensitive environment" refers to many types of environmental
areas that are either ecologically critical, unique, or vulnerable; are of
particular cultural or historical significance; or are set aside for the
purpose of conservation. Actual or potential impacts to sensitive
environments are generally valued more highly by society than comparable level
impacts in other environmental areas.
Although the proximity of oil and gas exploration, development, and
production activities to sensitive environments is not an explicit criteria
for designating oil and gas wastes as hazardous, the potential impact to
sensitive environments is an important consideration in evaluating the
environmental risks of wastes from oil and gas operations. For example, the
extent of overlap between sensitive environments and the locations of oil and
gas activities is an important factor in determining whether existing waste
management practices are appropriately protective.
Four categories of sensitive environments were considered for this
assessment: endangered and threatened species habitats; wetlands; National
Forest System lands; and National Park System lands. Only those endangered
and threatened species habitats, forests, and parks that are federally
designated were considered; such areas designated on the state and local
level, although also recognized as sensitive, were not taken into account. In
addition, the National Forest and Park Systems comprise a wide range of
environment types (e.g., preserves, seashores, wild and scenic rivers, and
monuments); however, there are other categories of sensitive environments in
addition to the four categories considered here.
The locations of oil and gas activities in relation to endangered and
threatened species habitats, wetlands, National Forest System lands, and
National Park System lands are discussed in Sections 7.1.2 through 7.1.5,
respectively. Within each section, the methods used in the analysis are
described and results are provided and evaluated. The results include a
qualitative description of the sensitive environment that is overlapped by oil
and gas activity and, where possible, a measure of the extent of overlap. In
Section 7.1.6, the overall implications of the results are discussed.
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7-2
7.1.2 Endangered and Threatened Species Habitats
The Nature Conservancy and State Natural Heritage Programs Offices
maintain data bases that could be used to identify endangered and threatened
species habitats around actual oil and gas sites. While these data bases are
recognized as valuable sources of information, they could not be used in this
analysis because of resource constraints. Instead, a sample of areas having
high exploration and production activity was selected and the endangered and
threatened species habitat within each area was examined using information
readily available in the literature. If needed to supplement the findings
from this sampling approach, use of the Nature Conservancy and State Data
Bases could be examined.
Briefly, the procedure used to select sample areas (which is described in
detail in Section 3.1.3 of this risk assessment report) first involved ranking
states in terms of their relative levels of exploration and production
activity. The top 26 states were selected for analysis (see Section 3.3.4).
The counties within each of these 26 states were then ranked in terms of their
relative levels of activity and, from this ranking, 190 counties with the
greatest level of exploration and/or production activity in several states
were selected. The number of counties selected from a given state was based
on the relative amount of activity within that county. The states and
counties selected in this way served as sample areas for the purpose of
evaluating endangered and threatened species habitats.
The U.S. Fish and Wildlife Service's regulations in 50 CFR Part 17 were
used to determine the endangered and threatened species habitats in each of
the sample states and counties. These regulations identify approximately 400
endangered and threatened species in the U.S. and their historical ranges,
which are broadly defined areas over which the species may be distributed.
The regulations also delineate critical habitats for 96 of the endangered and
threatened species. Critical habitats, which are much smaller and more
rigorously defined than historical ranges, are areas containing physical or
biological factors essential to the conservation of the species.
Table 7-1 shows the number of endangered and threatened species whose
historical range overlaps with each of the 26 states identified as having high
oil and gas activity. Historical ranges are generally very broad and, for
most endangered and threatened species, are defined in terms of multiple
states or large regions of the U.S. Therefore, as demonstrated in Table 7-1,
virtually all of the 26 states are within the historical ranges of numerous
endangered and threatened species. It is important to recognize, however,
that the figures in Table 7-1 do not measure the actual degree to which oil
and gas activities are co-located with endangered and threatened species
habitat; they only provide a rough approximation of the potential for such
co-location.
Using the figures in Table 7-1, the greatest numbers of designated
historical ranges for endangered and threatened species are in California,
Texas, and Tennessee. Both California and Texas also have relatively high
numbers of oil and gas wells and produce relatively high volumes of oil and
gas waste. Tennessee, on the other hand, has a relatively low level of oil
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7-3
TABLE 7-1
OVERLAP OF HISTORICAL RANGES AND STATES
WITH HIGH OIL AND GAS ACTIVITY
I/
Zone State
2 KY
NY
OH
PA
TN
WV
4 AL
AR
FL
LA
MS
5 IL
IN
MI
6 KS
NE
ND
7 OK
TX
8 MT
WY
No. of Species
Whose Historical
Range Includes State
19
8
11
8
51
16
52 b/
35
18
39
15
20
68
12
14
9
18
11
10
8
11
15
47
49
11
12
15
No. of Animals
18
7
10
8
46
16
45
34
18
28
15
20
56
11
14
8
17
11
10
8
11
15
33
35
11
12
15
No. of Plants
1
1
1
0
5
0
7
1
0
11
0
0
12
1
0
1
1
0
0
0
0
0
14
14
0
0
0
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TABLE 7-1 (continued)
OVERLAP OF HISTORICAL RANGES AND STATES
WITH HIGH OIL AND GAS ACTIVITY
a/
Zone State
9 CO
NM
UT
10 CA
11 AK
No. of Species
Whose Historical
Range Includes State
19
27
24
46
75
10
No. of Animals
12
17
13
24
51
10
No. of Plants
7
10
11
22
24
10
a/ Zones 1 and 3 have essentially no oil and gas exploration, development, or
production (includes the states ME, NH, VT, MA, CT, RI, NJ, DE, MD, NC, SC,
and GA). The remaining states are within the zones specified above, but
were excluded from analysis because they have low levels of oil and gas
activity.
b/ Because there are duplicate endangered and threatened species that exist
among states in a given zone, the zone totals are less than the sum of the
individual state totals.
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7-5
and gas activity. In comparing the various zones, the one zone with the
largest number of historical ranges is Zone 10 because of the large number of
historical ranges designated for California. A relatively large number of
historical ranges also have been designated in Zones 4, 2, 7, and 9;
relatively few historical ranges lie in Zones 5, 6, 8, and 11.
To obtain a better indicator of the potential for oil and gas activities
to affect endangered and threatened species, the 190 counties identified as
having high levels of exploration or production were examined to determine if
they contain any designated critical habitats. Of the 190 counties, 13t(or 7
percent) have federally designated critical habitats within their boundaries
(see Table 7-2). These 13 counties encompass critical habitats for a total of
10 different species, or about 10 percent of the species for which critical
habitats have been designated. However, as shown in Table 7-2, only five of
the species have 100 percent of their critical habitat within these counties;
the remainder also have designated critical habitats outside of the 190 county
sample.
The results in Table 7-2 should not be interpreted to mean that oil and
gas activities definitely are infringing on the critical habitats of 10
endangered and threatened species. The specific location of oil and gas sites
in relation to each of the critical habitats is not known, although all of the
190 counties were selected because they have high levels of, and probably
widespread, activity. Thus, similarly to the figures in Table 7-1, the
results in Table 7-2 only provide an indication of the potential for oil and
gas wastes to affect endangered and threatened species.
There are several other important limitations to the above analysis that
should also be mentioned. For one, the federal list of endangered and
threatened species is extensive but not all-inclusive. Many would-be-
endangered species may not yet have been evaluated for inclusion on the list,
and some species are now on the list due to strong state environmental efforts
or other political considerations. There are also numerous species that have
been designated endangered or threatened on the state level. Second, those
species for which critical habitats have been designated are not necessarily
the "most endangered" or "most threatened." Finally, the mere existence of
oil and gas activity (and therefore waste management activity) in the vicinity
of an endangered or threatened species habitat will not necessarily result in
an adverse effect for the species; the actual effects will depend on the
species in question as some species may be unaffected by oil and gas wastes.
7.1.3 Wetlands
Wetlands include a wide variety of marshes, swamps, and bogs. The U.S.
Fish and Wildlife Service specifically defines wetlands as "... lands
transitional between terrestrial and aquatic systems where the water table is
usually at or near the surface or the land is covered by shallow water."
According to the Fish and Wildlife Service definition, a wetland must also
meet one or more of the following criteria: (1) at least periodically, the
land must support vegetation adapted for life in water or saturated soil; (2)
the substrate must be predominantly undrained hydric soil; and (3) the
substrate must be saturated with water or covered by shallow water at some
time during the growing season of each year (Cowardin et al., 1979).
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TABLE 7-2
OVERLAP OF CRITICAL HABITATS AND COUNTIES
WITH HIGH OIL AND GAS ACTIVITY
a/
County
Species
Percent of Critical
Habitat Overlapping
With County b/
Fresno, CA
Kern, CA
Los Angeles, CA
San Luis Obispo, CA
Santa Barbara, CA
Ventura, CA
Escambia, FL
Barton, KS
Eddy, NM'
Morgan, TN
Burleson, TX
Pecos, TX
Refugio, TX
Fresno Kangaroo Rat
California Condor
California Condor
Palos Verdes Blue Butterfly
California Condor
Morro Bay Kangaroo Rat
California Condor
California Condor
Perdido Key Beach Mouse
Whooping Crane
Gympsum Wild Buckwheat
Spotfin Chub
Houston Toad
Leon Springs Pupfish
Whooping Crane
100
30
5
100
10
100
15
15
50
5
100
10
50
100
5
a/ None of the 96 critical habitats established to date by the U.S. Fish and
Wildlife Service overlap with the remaining 177 high-activity counties
selected for this analysis.
b/ The figures in this column were derived by dividing the number of
designated critical habitat areas within a county by the total number of
designated critical habitat areas in all counties. The figures do not
necessarily indicate the percent of land area of a critical habitat that
overlaps with a given county. For example, two areas have been designated
critical habitat for the Houston Toad; one in Burleson County and one in
Bastrop County. Therefore, 50 percent of the number of critical habit
areas lies in Burleson County; however, 50 percent of the total land area
designated critical habitat for the Houston Toad does not necessarily lie
in Burleson County.
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7-7
Wetlands are very important, and thus "sensitive," for several reasons.
First, wetlands across the country create habitats for many forms of wildlife
including fish, shellfish, waterfowl and other birds, muskrats, beavers, and
an array of reptiles and amphibians. Wetlands also provide other environ-
mental benefits such as purifying natural waters by removing nutrients,
sediment, and pollutants. Finally, wetlands have socioeconomic value by
providing flood and storm damage protection, erosion control, water supply and
ground-water recharge, recreation, and other benefits (Tiner, 1984). Because
of these values, there are several programs in place, on both the federal and
state levels, to acquire (preserve) wetlands and to regulate wetlands uses.
A detailed analysis of the locations of individual oil and gas sites in
and around wetlands was not carried out because there are hundreds of
thousands of oil and gas sites spread across the country. In addition, a good
inventory does not exist on the locations of wetlands in many areas. The
National Wetlands Inventory group within the Fish and Wildlife Service has
prepared a set of detailed wetlands maps; however, maps are presently
available for only Hawaii (a non-oil and gas producer), approximately 30
percent of the lower 48 states, and 6 percent of Alaska. Information on the
locations of wetlands from state governments, the U.S. Army Corps of
Engineers, and the Soil Conservation Service is inconsistently available or
not in a readily accessible form. Therefore, two general approaches were
adopted for analyzing the locations of oil and gas sites relative to
wetlands. First, the general distribution of oil and gas activities and known
wetland areas were examined to obtain a gross indication of the potential for
overlap. Second, the amount of wetlands at a sample of sites with high levels
of oil and gas activity was evaluated.
Figure 7-1 illustrates the estimated extent of wetlands in the U.S. in
1984. Wetlands exist in every state but, as shown in Figure 7-1, the states
that contain the most wetland acreage are Alaska, Louisiana, and Florida. In
Alaska, 50 to 75 percent of the North Slope, where the Prudhoe Bay and
surrounding oil fields are located, is classified as wetlands (Bergman et al.,
1977). Wetlands are also abundant but not nearly as prevalent in the Cook
Inlet-Kenai Peninsula area, the other area in Alaska containing significant
onshore oil and gas activity. Because Louisiana also contains abundant and
widespread onshore oil and gas activity, there also is likely to be a
relatively large amount of oil and gas operations in or proximate to wetlands,
particularly in the coastal regions of that state. Although wetlands are
distributed in virtually all areas of Florida, only those in the pan-handle
area (where the oil and gas activity is concentrated) are likely to have oil
and gas wastes disposed in their vicinity.
Figure 7-1 also shows that a relatively large percentage of the nation's
wetlands is located in states that have very little onshore oil and gas
activity (i.e., North Carolina, South Carolina, Georgia, New Jersey, Maine,
and Minnesota). Moreover, some of the states with relatively large amounts of
oil and gas exploration and production (e.g., Texas, Oklahoma, California,
Kansas, and Wyoming) have, at least on a state-wide basis, relatively small
amounts of wetlands.
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7-8
FIGURE 7-1
RELATIVE ABUNDANCE OF WETLANDS
IN THE U.S. IN 1984
,. oK M E X / C O
G t L I-
LEGEND
Less than 5%
5-15%
15-25%
Greater than 25%
Source: Tiner, 1984.
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7-9
In order to obtain more site-specific results, the 190 sample counties
(selected as described in Section 3.1.3 above) were examined for the presence
of wetlands. In particular, a total of 300 USGS quandrangle maps selected
from areas having the highest level of oil and gas activity within each county
was analyzed to determine the percent of the quad map area that lies in either
floodplains1J or wetlands. Floodplains and wetlands were delineated on the
quad maps as: any area with designated swamp symbols; playa lakes; areas
within the designated drawdown segments of reservoirs; and other areas that,
based on topographical clues available on the quad map (e.g., near water and
little topographical relief; agricultural areas near water), appeared to be
either a floodplain or wetland. The results of this analysis are given in
Table 7-3.
The results in Table 7-3 generally mirror the conclusions stated above.
Specifically, based on an analysis of the high oil and gas activity areas of
each state, the greatest potential for overlap between oil and gas operations
and wetlands appears to be in Louisiana and Alaska (particularly the North
Slope). As a result, the zones where wetlands may be at greatest risk caused
by oil and gas wastes are Zones 4, HA, and 11B. The other states and other
zones, except for Zone 5, have much smaller amounts of wetlands in areas
surrounding high oil and gas activity. Based on ~;he sample of sites examined,
oil and gas activities in Zone 5 (especially in Illinois) also appear to be
concentrated in areas with abundant wetlands.
7.1.4 National Forest System Lands
The lands of the National Forest System are administered by the U.S.
Forest Service (within the U.S. Department of Agriculture). The National
Forest System is comprised of 282 National Forests, National Grasslands, and
other areas in the United States, Puerto Rico, and the Virgin Islands and
includes a total area of approximately 191 million acres (USFS, 1985).
National Forest System lands are considered "environmentally sensitive"
because they contain valuable wildlife, recreation, and watershed resources.
These environmental resources are managed together with the National Forest
System's economic resources -- timber, rangeland, and minerals (including oil
and gas) -- according to the principles of multiple use and sustained yield,
as required by federal regulation (36 CFR 200.1). Some particularly sensitive
environments within the National Forest System have been specially designated
1J A floodplain is a flat expanse of land bordering an old river. While
there may be portions of a floodplain that are classified as wetlands, not all
lands falling within a floodplain are necessarily classified as such.
Floodplains may never, or only occasionally, be flooded and Cowardin et a1.
(1979) assert that it is ground water (not surface water) that controls to a
great extent the presence of wetlands. Nevertheless, floodplains were
considered in this analysis in order to account for palustrine wetlands (i.e.,
non-tidal wetlands dominated by trees, shrubs, and emergent vegetation) that
are not readily apparent on USGS quad maps. It should be noted, however, that
considering an entire floodplain to be a wetland will often result in an
overestimate of the extent of wetlands.
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TABLE 7-3
PERCENT OF SAMPLED QUAD MAPS
CONTAINING FLOODPLAINS AND WETLANDS
Zone
2
4
5
6
7
8
9
State
Kentucky
New York
Ohio
Pennsylvania
Tennessee
West Virginia
Zone Total:
Alabama
Arkansas
Florida
Louisiana
Mississippi
Zone Total:
Illinois
Indiana
Michigan
Zone Total:
Kansas
North Dakota
Zone Total:
Oklahoma
Texas
Zone Total:
Montana
Wyoming
Zone Total:
Colorado
New Mexico
Utah
Zone Total:
Number of
Quad Maps
Analyzed a/
3
1
9
5
1
3
22
2
2
1
37
3
45
5
1
3
9
13
4
17
21
87
108
3
10
13
4
7
4
15
Average % of Quad Area in
Floodplain or Wetland
Zone Avg:
Zone Avg:
Zone Avg:
Zone Avg:
Zone Avg:
Zone Avg:
Zone Avg:
1
3
3
2
1
1
2 b/
3
3
5
63
1
52
31
12
12
23
2
2
2
4
3
3
1
1
1
2
1
1
1
10
California
29
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TABLE 7-3
PERCENT OF SAMPLED QUAD MAPS
CONTAINING FLOODPLAINS AND WETLANDS
(continued)
Zone
11A
State
Alaska North
Number of
Quad Maps
Analyzed a/
Average % of Quad Area in
Floodplain or Wetland
11B
Slope
Alaska Kenai-
Cook Inlet Area
34
92
42
National Total: 300
National Avg:
21
a/ Quad maps are from sample of high-activity counties. See
text (Sections 3.1.3 and 3.3.4) for detail.
b/ Zone averages represent a weighted average.
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7-12
as wildernesses, primitive areas, wild and scenic rivers, recreation areas,
national monuments, game refuges, and wildlife preserves. Economic activity
is prohibited or severely restricted in these specially designated areas.
National Forest Wilderness areas, in which oil and gas activity is generally
prohibited, cover approximately 32 million acres -- roughly 17 percent of the
National Forest System's total acreage (USFS, 1987).
Information on potential or actual oil and gas activity within the
National Forest System was obtained from telephone communications with Forest
Service personnel, press reports, and reports prepared by public interest
groups.
Although the federal government owns the surface rights to all land in the
National Forest System, private parties and other governments own oil and gas
and other subsurface rights to some of the lands. Where the federal
government owns oil and gas rights, these rights may be leased to private
parties by the U.S. Department of the Interior Bureau of Land Management
(BLM). The terms and conditions of the BLM leases include provisions designed
to safeguard the environment. Where oil and gas rights are owned by
non-federal parties, the USFS may still establish environmentally sensible
restrictions on the use of the rights. If these restrictions are not
followed, and if subsequent efforts at negotiation fail, the government may
acquire the oil and gas rights and compensate the previous owners.
Federal oil and gas leases, for either exploration (drilling) or
production, have been granted for approximately 25 million acres (roughly 27
percent) of National Forest System lands. Actual oil and gas production is
occurring on a smaller acreage involving approximately 1,000 individual
leases. Most of the current production is oil rather than gas (USFS, 1987).
No information was obtained on the acreage on which oil and gas activity is
occurring but under state or private leases.
The vast majority of oil and gas exploration and production within the
National Forest System (over 95 percent) occurs within a handful of units
(USFS, 1987); these units are listed in Table 7-4. The two units with the
greatest production are the Little Missouri National Grassland in North
Dakota, and the Thunder Basin National Grassland in Wyoming; taken together,
they account for more than 90 percent of current production on all National
Forest System lands.
The units listed in Table 7-4 represent 8 of the 26 sample states with
significant oil and gas production. The units are located in 5 of the 11 U.S.
zones defined in this report (Zones 3, 5, 6, 8, and 9). Although most of the
units listed are located in Zones 3 and 5, the greatest production occurs in
Zone 6 (Little Missouri National Grassland) and Zone 8 (Thunder Basin National
Grassland). In terms of the sample counties selected from areas containg high
levels of oil and gas activity, 39 of the 190 sample counties (approximately
20 percent) have a designated National Forest System unit within their
boundaries. This includes Billings and McKenzie Counties in Missouri which
contain portions of Little Missouri National Grassland and Campbell, Converse,
and Crook Counties in Wyoming which contain portions of Thunder Basin National
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7-13
TABLE 7-4
NATIONAL FOREST SYSTEM UNITS
WITH SIGNIFICANT OIL AND GAS EXPLORATION AND PRODUCTION
National Forest System Unit State a/
Hoosier National Forest IN
Huron National Forest MI
Little Missouri National Grassland b/ ND
Mark Twain National Forest MO
Manti-La Sal National Forest UT
Manitee National Forest MI
Ouachita National Forest AR
Ozark National Forest AR
St. Francis National Forest AR
Shawnee National Forest IL
Thunder Basin National Grassland c/ WY
a/ Parts of some of these National Forest System units lie
in other states.
b/ This unit and the Thunder Basin National Grassland
account for more than 90 percent of oil and gas
production occurring on National Forest System lands.
c/ This unit and the Little Missouri National Grassland
account for more than 90 percent of oil and gas
production.
Source: Telephone communications with National Forest
Service personnel, February-March, 1987.
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7-14
Grassland. Several of the counties have more than one unit -- there are a
total of 61 units distributed across the 39 counties, with the largest number
of units located in the sampled portions of California (Zone 10), Wyoming
(Zone 8), and Utah (Zone 9). It should be emphasized, however, that just
because a National Forest System unit is located within a county having
relatively high levels of oil and gas activity, it does not mean that oil and
gas operations are necessarily infringing on those units.
Many other National Forest System lands on which oil and gas leases have
been granted or proposed may be used for oil and gas production in the
future. Possible production in some of these areas is controversial,
particularly in specially designated areas such as the Teton Wilderness of the
Teton National Forest in Wyoming (Wilderness Society, 1987; Chapman, 1986).
7.1.5 National Park System Lands
The National Park System was created in 1916 for the purpose of preserving
and protecting the U.S.'s outstanding natural resources. Today, the system
contains a total of almost 80,000,000 acres made up by 337 units and 30
affiliated areas (NPS, 1986a). Units within the National Park System include
national parks, preserves, monuments, recreation areas, seashores, rivers,
parkways, and several other areas. The system is administered by the National
Park Service within the U.S. Department of the Interior.
Oil and gas exploration, development, and production can take place within
the boundaries of National Park System units because, although the federal
government owns the surface rights to the land in parks, it does not
necessarily own the mineral rights. In total, the federal government does not
own mineral rights on nearly 6,000,000 acres (approximately 8 percent) of the
National Park System (NPS, no date). In addition, there are many parks in
which the Federal government does not own all the land within the park
boundaries; some of the land is owned by either states or private.parties.
There are two basic categories of rights to develop oil and gas on
National Park System Lands: federal mineral leases and non-federal mineral
rights. Federal mineral leases convey the rights to individuals to
temporarily occupy the land surface in order to explore for and extract
federally-owned minerals. Except for four national recreational areas where
mineral leasing has been authorized by Congress and permitted by
regulation,2-1 all National Park System units are closed to new leasing.
However, the holders of valid leases that predate the establishment of a unit
can continue to explore for and extract oil and gas within the unit's
boundaries. This right will continue as long as there is oil and gas
production in paying quantities (NPS, 1986b).
2J Congress authorized continued federal mineral leasing in five
national recreation areas: Glen Canyon, Lake Chelan, Lake Mead, Ross Lake,
and Whiskeytown. However, because of the presence of sensitive environments
and incompatible land uses, the Park Service issued regulations to close all
of Lake Chelan and portions of the other national recreation areas to new
leasing.
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7-15
Non-federal mineral rights are held by states or private parties when the
federal government does not own the subsurface within a park (the federal
government may own just the surface or may own neither the surface nor
subsurface). In these cases, the National Park Service must allow the
minerals to be developed or it must acquire the mineral rights. Congress has
provided that, if the development of non-federal mineral rights poses a
significant environmental threat to a unit, the mineral rights could be
acquired by the federal government without the consent of the owner.
Essentially all lands within a unit that are not possessed by the federal
government, as well as all lands over oil and gas that is not possessed by the
government, are leasable.
Information on the locations of oil and gas operations relative to
National Park System units was obtained from Park Service statistical
summaries, Park Service reference materials, and telephone communications with
Park Service personnel. There are currently 10 units (approximately 3 percent
of the total number of units) that have active oil and gas operations within
their boundaries. These units, the acreage that is leased, and the acreage
that is leaseable are identified in Table 7-5. It is important to note that
active operations are not necessarily located on every acre that is under
lease. Based on the figures in Table 7-5 approximately 23 percent of the land
area comprised by the 10 units is currently under lease; however, 83 percent
of these units is leaseable.
The National Park Service also has identified 32 additional units that do
not have active oil and gas operations currently, but have the potential for
such activities in the future (see Table 7-6). These units were identified as
having the potential for activity because they either: currently have acreage
leased for oil and gas development; are in areas where there is ongoing oil
and gas development which, based on an understanding of the local geology, is
likely to spread inside the park; or are considered by the National Park
Service as candidates for oil and gas development for other reasons (personal
communication with personnel in the Energy, Mining and Minerals Division,
National Park Service). Combining the figures in Tables 7-5 and 7-6, there
are approximately 334,700 acres within the National Park System (or roughly 4
percent of the total) that are currently under lease for oil and gas
exploration or extraction. Three units are, however, essentially 100 percent
under lease (Aztec Ruins, Lake Meredith, and Padre Island).
In addition to the park units noted above that have active or potential
operations within their boundaries, there are currently 43 units that have
active operations within 15 miles and there are 35 other units that have,
according to the National Park Service, the potential for oil and gas
operations within 15 miles.
7.1.6 Conclusions
As would be expected with any large and widespread industry, there are
numerous oil and gas sites around and in each of the sensitive environment
categories considered in this analysis. For each type of sensitive
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
7-16
TABLE 7-5
NATIONAL PARK SYSTEM (NFS) UNITS
WITH ACTIVE OIL AND GAS
OPERATIONS WITHIN THEIR BOUNDARIES
Total Acres
Currently Leased a/
NFS Unit Total Acres Private/State
Alibates (TX)
Aztec Ruins (NM)
Big Cypress (FL)
Big South Fork (KY)
Big Thicket (TX)
Cuyahoga Valley (OH)
Lake Meredith (TX)
Natchez Trace (MS)
Padre Island (TX)
Theodore Roosevelt (ND)
1,
1,371
27
570,000
122,960
85,839
32,460
44,978
50,189
130,697
70,416
108,937
385
0
13,413
53,050
3,171
9,148
44 , 9 / 8
0
130,697
68
254,910
Total Acres
Federal Leaseable in Future
0
27
0
0
0
0
0
187
0
1,308
1,522
385
0 b/
529,062
106,100
85,839
20,319
44,978
1,437
130,697
2,782
921,599
a/ All of the federal lease acreage, as well as the non-federal lease
acreages for Big Thicket, Lake Meredith, and Padre Island, are based on
data from the National Park Service; the non-federal lease acreages in the
other units are best estimates provided by National Park Service personnel.
b/ While certain federally-owned acres are currently leased, they are not
available for future leasing once the terms of the current leasing
arrangement expire.
Source: Personal communication with representatives of the Energy, Mining
and Minerals Division of the National Park Service, March 1987.
* April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE
*
-------
7-17
TABLE 7-6
NATIONAL PARK SYSTEM (NFS) UNITS
WITH POTENTIAL OIL AND GAS
OPERATIONS WITHIN THEIR BOUNDARIES
NFS Unit
Agate Fossil Beds (NE)
Arches (UT)
Bighorn Canyon (MT)
Bryce Canyon (UT)
Capitol Reef (UT)
Carlsbad (NM)
Cahco Culture (NM)
Channel Islands (CA)
Chesapeake and Ohio Canal (MD)
Chiricahua (AZ)
Coulee Dam (WA)
Dinosaur (CO)
Fort Laramie (WY)
Fort Necessity (PA)
Fort Union Trading Post (ND)
Fossil Butte (WY)
Glacier (MT)
Glen Canyon (UT)
Golden Spike (UT)
Grand Teton (WY)
Guadalupe Mountains (TX)
Total Acres
3,055
73,378
120,296
35,835
241,904
46,755
33,974
249,353
20,791
11,985
100,390
211,141
832
903
434
8,198
1,013,572
1,236,880
2,735
310,521
76,293
Acres Currently
Leased
0
240
0
1,504
1,621
0
4,443
0
0
2,496
40
2,451
0
0
0
0
0
61,944
10
0
0
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
7-18
TABLE 7-6
NATIONAL PARK SYSTEM (NFS) UNITS
WITH POTENTIAL OIL AND GAS
OPERATIONS WITHIN THEIR BOUNDARIES
(continued)
NFS Unit
Gulf Islands (FL)
Jean Lafitte (LA)
Lake Mead (AZ)
New River Gorge (WV)
Obed (TN)
Olympic (WA)
Russell Cave (AL)
Saint Croix (WI)
Santa Monica Mountains (CA)
Saratoga (NY)
Zion (UT)
Total Acres
139,775
20,000
1,495,665
62,024
5,005
914,816
310
35
150,000
3,389
146,598
Acres Currently
Leased
0
0
3,197
0
0
290
0
0
0
0
0
Source: Personal communication with representatives of the Energy, Mining
and Minerals Division of the National Park Service, March 1987.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * *
-------
7-19
environment, there are specific programs in place to help assure that any
activity, including oil and gas activity, does not unduly affect the area.
For example, the "consultative process" under the Endangered Species Act is
designed to make sure that endangered species interests are taken into account
when considering proposed actions; the U.S. Army Corps of Engineers is
responsible under the Clean Water Act for controlling activities in wetlands;
and the National Forest Service and National Park Service, in conjunction with
the Bureau of Land Management, have procedures in place for controlling oil
and gas development and production on the public domain. Nevertheless, oil
and gas activities have frequently been permitted to encroach on sensitive
environments, sometimes in the midst of considerable public controversy.
Based on the results of this study, conclusions cannot be drawn with
respect to which category of sensitive environment appears to be at greatest
risk from oil and gas waste management activities. The results of the
screening-level analysis of wetlands indicate that, at least on the national
level, wetlands as a group support relatively lower levels of oil and gas
operations in comparison to the other sensitive environment categories;
however, there are some parts of the nation (e.g., Alaska's North Slope and
Louisiana) where there is considerable overlap between oil and gas activities
and wetlands. In comparing the different zones and states, sensitive
environments in Texas (Zone 7) seem to be at relatively higher levels of risk
from oil and gas activity. There are numerous endangered and threatened
species historical ranges and some critical habitats in Texas, and there are
several National Park System units in Texas that have active oil and gas
operations within their boundaries. Texas, however, has relatively few
national forests and grasslands, and the wetlands in Texas are concentrated
along the Gulf coast and eastern third of the state; thus, the potential for
oil and gas activities to infringe on these two types of sensitive
environments is generally less than that in some other states.
7.2 ANALYSIS OF DAMAGE CASE RESULTS
This section concerns the third of three qualitative, non-modeling
portions of the risk assessment (after the discussions of Alaska's North Slope
and "sensitive environments"): an analysis of documented cases of damage to
human health and the environment caused by oil and gas wastes.
The information contained in each of the damage cases describes the date,
location, type and volume of the wastes(s) released to the environment, the
type of waste management technology from which the release emanated, and the
nature and extent of the resultant damage. The descriptions were gathered
primarily from involved parties (state governments, attorneys, injured
individuals, etc.) in several states with substantial oil and gas exploration,
development, and production. Each of the damage cases investigated was
checked for validity on the basis of scientific evidence, administrative
decisions, or judicial rulings before they were included in this report.
Part of this report contains a complete discussion of the damage case study
methods and results.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
7-20
For the purposes of this risk assessment, the detailed damage case results
were summarized in two ways. First, the various damage case descriptions in
each zone of the U.S. (see Figure 2-2) were aggregated into a single typical
damage case description for each zone. Second, in order to permit a rough
comparison of these data to the modeling results, the damage cases were
grouped according to the specific affected medium, waste type, and waste
management practice involved in each.
In addition to meeting the test of validity for the damage cases discussed
in Part , the damage cases were examined for this risk assessment to
determine if they occurred (or continued to occur) within the last five years
(on or after January 1, 1982), or whether they involved a violation of the
state regulations in effect at the time of release. If there was considerable
uncertainty over whether a given damage case met one or both of these two
additional requirements, it was excluded from consideration. Most of the
damage cases investigated and discussed in Part involve a release that
occurred greater than five years ago and/or a violation of state regultaions.
As a result, the exclusion of such cases here reduced the set of relevant
damage cases to a small number (27), limiting the extent to which
generalizations can be drawn for the different zones. Finally, it should be
noted that the number of damage cases in a zone or for a specific "affected
medium-waste type-waste management practice" scenario does not necessarily
reflect the degree of damage in a particular zone (or for a particular
scenario), and that a single case may involve several related incidents.
Results from the first analysis, identifying typical damage cases for each
zone, are presented in Table 7-7. This table shows that the contamination of
ground water by releases of brine and produced waters from .surface/disposal
pits constitutes the typical damage case scenarios for Zones 5, 6, and 9,
which cover the Midwest, the Plains states, and the Southwest and southern
Rocky Mountain states, respectively. The typical "nature of damage" in these
regions is the contamination of soil and associated well water by the high
levels of chlorides associated with brines. The table also reveals the
prevalence of drilling mud-related damage cases -- and resultant vegetation
damage -- in Zone 2 (New York, Pennsylvania, and the Appalachian states) and
Zone 4 (the south central states). Direct discharge of briny oilfield wastes
to surface waters is the most common type of waste management practice in
coastal Texas, which lies in Zone 7. Finally, Table 7-7 presents a typical
damage case for the entire U.S., in which brine leaking from surface pits
contaminates ground water.
Table 7-8 presents the second of the two summary analyses of damage case
data: a rough correlation of damage cases with individual modeling
scenarios. As would be expected from the preceding discussion of Table 7-7,
the most common damage case scenario is the contamination of ground water by
brines leaking from surface pits. Other relatively common combinations of
scenario elements include the direct discharge of brine to estuaries, and
contamination of ground water by brines leaking from disposal/injection
wells. Brine is the most common waste type for four of the five categories of
affected media.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
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7-24
In addition to the constraints and defining characteristics already noted
for each of the damage case descriptions, there are more general limitations
on the degree to which the data presented here fully represent the nature and
extent of oil and gas related damages in the U.S. Most importantly, it should
be noted that damage cases were collected only from the 13 states with the
most oil and gas activity (out of 33 states with significant oil and gas
activity). Moreover, the regulations defining "compliance" vary considerably
from state to state, as did the cooperation of involved parties in each state
with the data acquisition for this study. Part contains a more detailed
discussion of these limitations.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE *
-------
CHAPTER 8
GEOTHERMAL ENERGY: METHODS AND RESULTS
[Reserved]
* * *
April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
CHAPTER 9
CONCLUSIONS AND RECOMMENDATIONS
[Reserved]
* * *
April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE *
-------
REFERENCES
Ad Hoc Task Group, 1986, Liquid Waste Disposal Options for the North Slope of
Alaska, Final Report.
Alaska Department of Environmental Conservation (ADEC), 1986a, General
Wastewater Disposal Permit and Information Sheet. Permit No. 8640-DB001.
Alaska Department of Environmental Conservation (ADEC), 1986b, Wastewater
Disposal Permit No. 8636-DB003, Issued on May 15, 1986 to ARCO Alaska,Inc.
Alaska Department of Natural Resources (ADNR), 1986, Final Finding and
Decision of the Director and ACMP Consistency Determination Regarding Oil
and Gas Lease Sale 51, Prudhoe Bay Uplands, State of Alaska Department of
Natural Resources, Division of Oil and Gas, Anchorage, Alaska.
Alaska Oil and Gas Association (AOGA), 1986, The Geology of the Coastal Plain
of the Arctic National Wildlife Refuge, Anchorage, Alaska.
Alaska Oil and Gas Association (AOGA), 1985, Alaska Oil and Gas Industry Facts.
American Petroleum Institute, 1986, Basic Petroleum Data Book, Petroleum
Industry Statistics, Volume VI, Number 3, Washington, DC.
American Petroleum Institute (API), 1983, Summary and Analysis of API Onshore
Drilling Mud and Produced Water Environmental Studies, API BUL D19, First
Edition.
APHA-AWWA-WPCF, 1975, Standard Methods for the Examination of Water and
Wastewater, 14th Edition, American Public Health Association, Washington,
D.C.
ARCO, 1985, Report on Releases of Hazardous Waste or Constituents from Solid
Waste Management Units at the Facility -- Prudhoe Bay Unit Eastern
Operating Area, submitted to EPA Region X in support of an Underground
Injection Control permit application.
ARCO, 1983, Prudhoe Reserve Pit Fluids -- 1983 Summary Volume Totals.
Baes, C.F., III, Sharp, R.D., Sjoreen, A.L., Shor, R.W., 1984, A Review and
Analysis of Parameters for Assessing Transport of Environmentally Released
Radionuclides through Agriculture, Prepared for the U.S. Department of
Energy, ORNL-5786, September 1984.
Baes, C.F., III, and R.D. Sharp, 1983, A Proposal for Estimation of Soil Leach-
ing and Leaching Constants for Use in Assessment Models. Journal of
Environmental Quality 12: p.17-28.
Bell, L.N., 1987, Comments from ARCO Oil and Gas Company submitted to Lee
Thomas concerning the EPA Technical Report on Oil and Gas and Geothermal
Energy Wastes (see EPA, 1986d referenced below).
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
-2-
Bergman, R.D., R.L. Howard, K.F. Abraham, and M.W. Weller, 1977, Water Birds
and Their Wetland Resources in Relation to Oil Development at Storkersen
Point, Alaska, U.S. Department of the Interior, Fish and Wildlife Service
Resource Publication 129, Washington, D.C.
Birge, W.J., 1978, Aquatic Toxicology of Trace Elements of Coal and Fly Ash.
In: Energy and Environmental Stress in Aquatic Systems, J.H. Thorp and
J.W. Gibbons (eds.), DOE Symposium Series (CONF-771114).
Brewer, Richard, 1979, Principles of Ecology, Saunders College Publishing,
Philadelphia, Pennsylvania.
Buccafusco, R.J., S.J. Ells, and G.A. LeBlanc, 1981, Acute Toxicity of
Priority Pollutants to Bulegill (Lepomis macrochirus). Bull. Environm.
Contam. Toxicol. 26:446-452.
Chapman, Brian, 1986, "Drilling Still Likely in B-T Grizzly Habitat," Casper
Star-Tribune, Casper, Wyoming, December 5, 1986.
Clayton, G.D. and F.E. Clayton (eds.), 1981, Patty's Industrial Hygiene and
Toxicology, Third Revised Edition, John Wiley and Sons, N.Y.
Corps of Engineers (COE), U.S. Army, 1984, Endicott Development Project, Final
Environmental Impact Statement, Volume I (Summary), Anchorage, Alaska.
Corps of Engineers (COE), U.S. Army, 1980, Final Environmental Impact
Statement: Prudhoe Bay Oil Field Waterflood Project Summary, Anchorage,
Alaska.
Cowardin, L.M., F.C. Golet, and E.T. LaRoe, 1979, Classification of Wetlands
and Deepwater Habitats of the United States, Fish and Wildlife Service,
U.S. Department of the Interior, FWS/OBS-79/31.
Dawson, e_t a_l. , 1980, Physical/Chemical Properties of Hazardous Waste
Constituents, Prepared by Southeast Environmental Research Laboratory for
U.S. EPA.
Degler, Sue A., 1984, The North Slope and Beaufort Sea Environment, Sohio
Alaska Petroleum Company, Anchorage, Alaska.
DOI, 1986, Alaska Summary Report for June 1984 - December 1985, OCS
Information Report MMS 86-0023, Minerals Management Service.
DOI, 1985, Arctic Summary Report -- January 1985, OCS Information Report MMS
85-0022, Minerals Management Service.
Donahue, R.L., R.W. Miller, and J.C. Shickluna, 1977, Soils, Fourth Edition,
Prentice-Hall, Englewood Cliffs, N.J.
Dunne, T. and Leopold, L.B., 1978, Water in Environmental Planning, W.H.
Freeman and Co. New York.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
-3-
Endangered and Threatened Wildlife and Plants, Title 50 Code of Federal
Regulations Part 17.
EPA, 1987a, Technical Report: Exploration, Development, and Production
of Crude Oil and Natural Gas, Field Sampling and Analysis Report, U.S. EPA
530-SW-87-001.
EPA, 1987b, Technical Report: Exploration, Development, and Production
of Crude Oil and Natural Gas, Appendix D, Analytic Methods. U.S. EPA
530-SW-87-005.
EPA, 1986a, Quality Criteria for Water, EPA 440/5-86-001 for [Specific
Chemicals].
EPA, 1986b, Superfund Public Health Evaluation Manual, EPA 540/1-86/060.
EPA, 1986c, Health Effects Assessment, prepared by the Environmental
Criteria and Assessment Office, U.S. EPA, Cincinnati, Ohio, 1985 (updated
1986).
EPA, 1986d, Technical Report: Wastes from the Exploration, Development and
Production of Crude Oil, Natural Gas and Geothermal Energy,
EPA/530-SW-86-051, Office of Solid Waste.
EPA, 1986e, Liner Location Risk and Cost Analysis Model, Draft Phase II Report,
Office of Solid Waste, Washington, DC.
EPA, 1985a, Chemical, Physical, and Biological Properties of Compounds
Present at Hazardous Waste Sites, Prepared by ICF/Clement Associates.
EPA, 1985b, Health Effects Assessment for [Specific Chemical]. [Note:
58 individual documents available for specific chemicals or chemical
groups]. ECAO U.S. EPA, Washington D.C.
EPA, 1984a, National Secondary Drinking Water Regulations, U.S. EPA.
Washington, D.C. EPA 570/9-76-000.
EPA, 1984b, Technical Guidance Manual for Performing Waste Load Allocations:
Book 2, Streams and Rivers.
EPA, 1982, Final, Injection Well Construction Practices and Technology,
October.
EPA, 1980a, Ambient Water Quality Criteria for Phthalate Esters,
Washington, D.C., EPA 440/5-80-067.
EPA, 1980b, Ambient Water Quality Criteria for Polynuclear Aromatic
Hydrocarbons, Office of Water Regulations and Standards, Washington, D.C,
EPA 440/5-80-069.
EPA, 1979, Office of Water Planning and Standards, Water Related Environmental
Fate of 129 Priority Pollutants, Volumes I and II, EPA-440/4-79-029a.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
-4-
EPA, 1975, Supplement to Development Document, Hazardous Substances
Regulations Federal Water Pollution Control Act as Amended 1972, EPA
440/9-75-009.
Fischer, H.B., List, E.J., Koh, R.C.Y., Imberger, J. and Brooks, N.H., 1979,
Mixing In Inland and Coastal Waters, Academic Press Inc., New York.
Freeze, R.A. and J.A. Cherry, 1979, Groundwater, Prentice-Hall, Inc., Englewood
Cliffs, NJ.
Griles, Steve, 1987, Comments from DOI. Land and Minerals Management Division
submitted to Winston Porter concerning the EPA Technical Report and oil
and gas and geothermal energy wastes (see EPA, 1986d referenced above).
Hart, W.B., P. Doudoroff, and J. Greenbank, 1945, The Evaluation of the
Toxicity of Industrial Wastes, Chemicals, and Other Substances to
Freshwater Fishes, Waste Control Laboratory, the Atlantic Refining Co. of
Philadelphia, as reported in NAS 1972.
Hazardous Substance Data Bank (HSDB) NLM, 1985, as cited in ICF Memo 1985.
Heffner, M.T., 1987, Comments from Standard Oil Production Company submitted
to the Office of Solid Waste Docket Clerk concerning the EPA Technical
Report on oil and gas and geothermal energy wastes (see EPA, 1986d
referenced above).
Hobbie, John E., 1984, The Ecology of Tundra Ponds of the Arctic Coastal
Plain: A Community Profile, U.S. Fish and Wildlife Service, Washington,
D.C.
Hobbie, John E., 1980, Limnology of Tundra Ponds (Barrow, Alaska), Dowden,
Hutchinson and Ross, Stroudsburg, Pennsylvania.
Hungerford, Stanley, 1987, Comments from the Alaska Department of
Environmental Conservation submitted to Dan Derkics concerning the EPA
Technical Report on oil and gas and geothermal energy wastes (see EPA,
1986d referenced above).
Hunt, B., 1978, Dispersive Sources in Uniform Ground-Water Flow, Journal of
the Hydraulics Div., ASCE 104:75-85.
ICF Memo, 1987, Criterion Continuous Concentration Protective of Aquatic Life
for Gasoline, March 11.
ICF/Clement, 1986, Alternate Concentration Limit Demonstration Document, Part
II, Prepared for National Industrial Environmental Services, Kansas.
ICF Draft Report, 1986, Development of Soil:Water Distribution Coefficients
for the Liner Location Model Inorganic Chemicals, June 12.
ICF 1985a, RCRA Risk-Cost Analysis Model (Constituent Data Base for OSW,
U.S. EPA).
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
-5-
ICF Memo, 1985b, Verification of Solubility and Koc Values for the Liner
Location Model, September 10.
Kabata-Pendias, A. and H. Pendias, 1984, Trace Elements in Soils and
Plants, CRC Press, Boca Raton, Florida.
Kekker, Marcel. 1972. Organic Chemicals in the Soil Environment, Vol. 1,
Chapter 2 ("Adsorption," by Hamaker and Thompson), Goring and Hamaker.
Keller, Edward A., 1979, Environmental Geology (2nd ed.), Charles E. Merrill
Publishing Company, Columbus, Ohio.
Kenaga, E.E., 1982, Predictability of Chronic Toxicity from Acute Toxicity of
Chemicals in Fish and Aquatic Invertebrates, Environ. Toxicol. Chem. 1:
347-358.
Krebs, C.J. 1972, Ecology, The Experimental Analysis of Distribution and
Abundance, Harper & Row, New York.
Leopold, L.B. and Maddock, T., 1953, The Hydraulic Geometry of Stream
Channels and Some Physiographic Implications, U.S. Geological Survey
Professional Paper 252.
LeBlanc, G. A, 1980, Acute Toxicity of Priority Pollutants to Water Flea
(Daphnia magna), Bull. Environm. Contam. Toxicol. 24:684-691.
Lyman, e_t al. , 1982, Handbook of Chemical Property Estimation Methods,
McGraw-Hill, New York..
Mabey, et al., 1982, Aquatic Fate Process Data for Organic Priority
Pollutants, Prepared by SRI International, EPA Contract Nos. 68-01-3867
and 68-03-2981, prepared for Monitoring and Data Support Division, Office
of Water Regulations and Standards, Washington, D.C.
Manhattan Summer College Notes, 1980, Modeling of Toxic Substances in Natural
Water Systems, Manhattan College, Summer Institute.
Macan, T. T., 1963, Freshwater Ecology, 2nd Edition, John Wiley and Sons, N.Y.
McKee, J.E., and H.W. Wolf, 1963, Water Quality Criteria, California State
Water Resources Control Board, Publication 3-A (Reprint December, 1971).
McKendrick, Jay D., 1986, Final Wellsite Cleanup on National Petroleum Reserve-
Alaska, Volume 3, Recording of Tundra Plant Responses, U.S. Geological
Survey.
McLin, Stephen G., no date, Seepage Assessment Strategy for Drilling Fluid
Reserve Pits, School of Civil Engineering and Environmental Science, The
University of Oklahoma, unpublished.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
-6-
McNamara, B.P., 1976, Concepts in Health Evaluation of Commercial and
Industrial Chemicals. Pages 61-141 in Mehlman, M.A., Shapiro, R.E. and
Blumenthal, H., eds., Advances in Modern Toxicology. Volume 1, Part 1:
New Concepts in Safety Education, John Wiley and Sons, New York.
McWhorter, D.B. and J.D. Nelson, 1979, Unsaturated Flow Beneath Tailings
Impoundments, Jour. Geotech. Eng. Div. ASCE GT 11: 1317-1334.
Means, J.C., Wood, S.G., Hassett, J.J., and Banwart, W.L., 1982, Adsorption of
Amino-Carboxy-Substituted Polynuclear Aromatic Hydrocarbons by Sediments
and Soils, Environmental Science and Technology, 16, pp. 93-98.
Miller, G. Tyler, Jr., 1982, Living in the Environment (3rd ed.), Wadsworth
Publishing Company, Belmont, California.
National Academy of Sciences (NAS), 1977-1983, Drinking Water and Health,
Volumes 1-5.
National Academy of Sciences (NAS), 1972, Water Quality Criteria 1972, U.S.
EPA, Washington, D.C. EPA R3-73-033, March 1973.
National Park Service, (NFS), 1986a, National Park Service Listing of Acreages,
Washington, D.C.
National Park Service (NFS), 1986b, Interim Guidance for Mineral Management
Planning Including Mineral Terms and Authorities, Energy, Mining and
Minerals Division, Denver, CO.
National Park Service (NPS), no date, Final Script: NFS Videotape "Mineral
Development on National Park Lands," Denver, CO.
National Water Well Association (NWWA), 1985, DRASTIC: A Standardized System
for Evaluating Ground-Water Pollution Potential Using Hydrogeologic
Settings, PB85-228146, Worthington, OH.
Odum, E.P., 1971, Fundamentals of Ecology, (3rd ed.), W.B. Saunders Company,
Philadelphia.
Pennak, R.W., 1953, Fresh-Water Invertebrates of the United States, The
Ronald Press Company, N.J.
Petroleum Information, 1986, Resume 85, Denver, CO.
Pitelka, F.A., 1974, An avifaunal review of the Barrow Region and North Slope
of Arctic Alaska. Arct. Alp. Res. 6:161-184.
Pollen, Michael R., 1986, Final Wellsite Cleanup on National Petroleum Reserve-
Alaska, Volume 2, Sampling and Testing of Waters and Bottom Muds in the
Reserve Pits, U.S. Geological Survey.
Potential Gas Committee Map, 1982, Plate 1 in Potential Supply of Natural Gas
in the United States as of December 31, 1982, Colorado School of Mines,
1983, Golden, CO.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
-------
-7-
Prickett, T.A., T.C. Naymik and C.G. Lonnquist, 1981, A "Random-Walk" Solute
Transport Model for Selected Groundwater Quality Evaluations, Bulletin
#65, Illinois State Water Survey, Champaign, IL.
Reid, G.K., and R.D. Wood, 1976, Ecology of Inland Waters and Estuaries,
2nd Edition, D. Van Nostrand Company, N.Y.
Roush, T.H., G.L. Phipps, D.L. Spehar, C.E. Stephan, and Q.H. Pickering,
1985, Effects of Pollution on Freshwater Organisms, Journal WPCF
57:667-711.
Smith, D.W. and T.D.W. James, 1979, ALUR Sump Studies III: Biological
Changes: Report to Department of Indian and Northern Affairs.
Smith, Phillip D.J., 1986, Final Wellsite Cleanup on the National Petroleum
Reserve - Alaska, Volume 1, Final Cleanup at Selected (1975-1981)
Wellsites, U.S. Geological Survey, Washington, D.C.
Sohio Alaska Petroleum Company, 1984, Prudhoe Bay and Beyond (4th ed.),
Anchorage, Alaska.
Snyder-Conn, E., 1987, An i.n situ acute toxicity test with Daphnia a promising
screening tool for field biologist. Draft. U.S. Fish and Wildlife
Service.
Standard Oil Company, 1987, Arctic Oil and Gas: Exploration and Production
Wastes.
State of Alaska (Governor's Agency Advisory Committee on Leasing), 1984, A
Social, Economic, and Environmental Analysis of the Proposed Kuparuk
Uplands Oil and Gas Lease Sale Nos. 47 and 48, Juneau, Alaska.
State of Alaska (Governor's Agency Advisory Committee on Leasing), 1981, A
Social, Economic, and Environmental Analysis of the Proposed Prudhoe Bay
Uplands Oil and Gas Lease Sale No. 34, Juneau, Alaska.
Strosher, M.T., W.E. Younkin and D.L. Johnson, 1980, Environmental Assessment
of the terrestrial disposal of waste drilling muds in Alberta: Chemistry
of sump fluids and effects on vegetation and soils. Prepared for the
Canadian Petroleum Association.
Tiner, R.W., Jr., 1984, Wetlands of the United States: Current Status and
Recent Trends, Fish and Wildlife Service, U.S. Department of the Interior.
Todd, D.K., 1970, The Water Encyclopedia, Maple Press, Cited in U.S. EPA
Supplement to Development Document, 1975.
Troy, Declan M., 1985, Birds of Prudhoe Bay and Vicinity, Sohio Alaska
Petroleum Company, Anchorage, Alaska.
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE *
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-8-
Tyler, L.D. and McBride, M.B., 1982, Mobility and Extractability of Cadmium,
Copper, Nickel, and Zinc in Organic and Mineral Soil Columns, Soil Sci.
134: 198-205
U.S. Forest Service (USFS), 1985, Land Areas of the National Forest System as
of September 30, 1985, U.S. Department of Agriculture, Washington, B.C.
U.S. Forest Service (USFS), 1987, Telephone communications with USFS
personnel, February-March 1987.
U.S. Department of Agriculture, No Date, Washington, D.C.
U.S. Government Printing Office, 1985, U.S. Code of Federal Regulations,
Title 36, Section 200 (U.S. Forest Service), Washington, D.C.
Versar, 1987a, Alaska Oil and Gas Waste Management Practices, Draft report
submitted to EPA's Office of Solid Waste.
Versar, 1987b, Draft Damage Case Report Forms and Summaries.
Verschueren K., 1983, Handbook of Environmental Data on Organic Chemicals,
Second Edition, Van Nostrand Reinhold Company, N.Y.
Weast, R.C. (ed), 1974, Handbook of Chemistry and Physics, 60th Ed.
Cleveland: CRC Press.
West, R.L. and E. Snyder-Conn, 1986, The Effects of Prudhoe Bay Reserve Pit
Fluids on the Water Quality and Macroinvertebrates of Tundra Ponds, Draft,
U.S. Fish and Wildlife Service, Fairbanks, Alaska.
Wilderness Society, 1987, Management Directions for the National Forests
of the Greater Yellowstone Ecosystem, Washington, D.C.
Wiley, J.D. and P.O. Nelson, 1984, Cadmium Adsorption by Aerobic Lake
Sediments, Journal of Environmental Engineering (ASCE) 110(1). p. 227-243.
Woodward, D.F., R.G. Riley, T.R. Garland, and E. Snyder-Conn, 1986, Effects of
Oil and Drilling Fluids and Their Discharge on Fish and Waterfowl Habitat
in Alaska, Draft, U.S. Fish and Wildlife Service, Jackson, Wyoming.
Word, D.S. et al., 1986, Analysis of Potential Failure Mechanisms Pertaining to
Hazardous Waste Injections in the Texas Gulf Coast Region, Journal of the
Underground Injection Practices Council, No. 1.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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APPENDIX A
DOCUMENTATION OF CHEMICAL PARAMETERS
* * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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APPENDIX A
DOCUMENTATION OF CHEMICAL PARAMETERS
This appendix contains the documentation of chemical parameter values used
in the oil and gas and geothermal energy quantitative risk analysis. Section
A.I covers the parameters used in modeling the environmental transport, fate,
and effects of selected chemical constituents (as described in Sections 4.2
and 4.3 of the report). Section A.2 covers the parameters used in the
selection of a subset of model constituents from the waste streams of concern
(as described in Section 3.3 of the report).
A.I PARAMETERS USED IN RISK MODELING
Tables 4-3, 4-4, and 4-5 in Section 4.4 of the report summarize the
chemical parameter values used in the risk modeling. Section A.1.1 provides
documentation for physical/chemical parameter values, Section A.1.2 covers
human toxicity parameter values, and Section A.1.3 covers aquatic toxicity
parameter values.
A.1.1 Physical/Chemical Parameters
Organics. Benzene was the only organic chemical modeled in the analysis.
Table A-l provides references for the Koc, degradation rate constant, and
volatilization rate constant values used.
Inorganics. Several inorganic species were modeled in the analysis; the
distribution coefficient, Kd, was the only chemical parameter used in modeling
inorganics. Values for Kd are documented in brief profiles for individual
inorganic species; these profiles are included at the end of this section.
Because the inorganics modeled do not degrade or volatilize, rate constants
for these processes were set to zero in all cases.
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A-2
TABLE A-l
DOCUMENTATION OF PHYSICAL/CHEMICAL
PARAMETER VALUES FOR BENZENE
Parameter
Value
Reference
Koc 83 I/kg
-1
Volatilization rate 1200 year
constant (surface
water)
Degradation rate constant
-1
surface water 5 year
-1
unsaturated zone 3.4 year
saturated zone
Lyman et al. , 1982
Mackay and Leinonen, 1975,
as cited in Callahan et al.,
1979
Verscheuren, 1983
Patrick and Barker, 1985,
as cited in Mackay, 1985
* * *
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* * *
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A-3
ARSENIC--Kd PROFILE
Arsenic (As) has four stable oxidation states: +5, +3, 0, and -3. The
oxidized forms, As(V) and As(III), are the most common. The free element
As(0) is rarely found, and the reduced form occurs only when the Eh is very
low (Callahan et al. 1979). Oxidized arsenic is found in arsenic acid
(H AsO ) and arsenious (H AsO or HAsO ) acids, as well as in minerals and
34 332
-3 -3
dissolved salts. Metal arsenates (As) ) and arsenite (AsO ) are formed
4 3
with calcium, magnesium, and other uni- or divalent cations. Under mildly
oxidizing conditions in the pH range from -0.9 to 9.2, HAsO is the
2
predominant species. Under more strongly oxidizing conditions, the
-2
equilibrium between H AsO and HAsO determines the predominant arsenic
2 4
form. As the Eh of the solution decreases (i.e., as the solution becomes more
reducing), As(III) becomes the dominant ion. In the environment, as a
solution or soil becomes more anaerobic, arsenate will be reduced to arsenite.
Arsenate can form relatively insoluble complexes with a variety of
cations: consequently, its solubility depends on the ionic composition of the
water. In general arsenic will remain at mg/1 levels (Dawson and Banning
1982; Lemmo ^t al. 1983). Barium is the cation which produces the least
soluble arsenate complex and is likely to control arsenic concentration to
very low levels. In the absence of barium, other metal-arsenate complexes may
control arsenic transport.
In addition to removal by precipitation of salts, arsenic is removed from
solution by adsorbing to iron and aluminum oxides. Adsorption to iron oxide
should be favored by low pH levels. (Lemmo et al. 1983). Therefore, typical
concentrations of arsenic in'freshwater are at yg/liter levels (Woolson
1977). Arsenic can also complex with dissolved organic matter having low
molecular weight. In an estuarine system, these complexes presumably prevent
adsorption and coprecipitation with sediments, thus keeping arsenic in
solution (Waslenchuk and Windom 1978).
Arsenic also can undergo redox reactions under conditions found in some
soils. In aerated soils, arsenate may be formed, while in flooded or
nonoxidized soils, arsenite (a more toxic form) may be produced. Biological
activity may contribute to the oxidation of As(III) back to As(V), and vice
versa (Walsh et al. 1977). The fate of arsenic in soils also depends on the
adsorption of arsenic to soil particles. Korte et al. (1976) found that clay
content, surface area, and free iron oxide content of soil could be used to
predict sorption. Soil pH, however, was not a significant factor. Other
researchers have shown that As(V) has a higher adsorption capacity for
bauxite, alumina, and carbon than does As(III); carbon is the least adsorptive
of the three. Furthermore, adsorption decreases with increasing pH -- above 9
for As(III) and above 7 for As(V) (Gupta and Chen 1978).
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A-4
The environmental behavior of arsenic, including, sorption reactions, is
dependent upon redox conditions as well as pH. When arsenic binds to oxides,
it is rendered immobile. Baes et al. (1984) report a theoretical Kd of 200
ml/g. Baes and Sharp (1983), however, report a range in observed Kds of 1.0
to 8.3 ml/g for As(III); other investigators report Kds in the range of 1.9 to
18 ml/g (Frost and Griffin 1977). In work with sewage sludge, Gerritse et al.
(1982) report ranges of Kd of about 15-30 ml/g for sandy loam soil and 5-10
ml/g for sandy soil. These experimental Kds, which are much lower than the
value given by Baes et al. (1984), may more accurately reflect the mobility of
arsenic in soils. Therefore, the Kd for arsenic has been set at 5 ml/g, a
value near the lower end of the range of reported values.
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A-5
BORON--Kd PROFILE
Boron (B) does not occur naturally in its elemental form and it is
extremely difficult to isolate. Boron is commonly found in the form of boric
acid, borates, borax, and in borosilicates. Boric acid is a weak acid and is
present under environmental conditions as B(OH) . Borate ion, B(OH) , is
3 4
formed at higher pH. The pK of boric acid is 9.2 and this means that at a pH
of 9.2, 50 percent of the boron present is in the form of boric acid. As the
pH drops to 8.3, for example, 83 percent of the total boron is present as
boric acid. Polymeric forms of boric acid can form at high concentrations.
Borax, Na B 0 'lOH 0), dissolves in water to form a buffer solution
247 2
of boric acid and borate (Cotton and Wilkinson 1972, Muetterties 1976).
Boron is very mobile in the environment. The mobility of boron is
generally influenced by water flux (Kabata-Pendias and Pendias 1984). soil
pH, soil texture, anion and cation exchange capabilities, clay minerology and
organic matter content will also influence the mobility of boron (Hatcher et
al. 1967, Biggar and Fireman, 1960). Course textured soils were found to
leach boron more rapidly. Soils in cool humid climates were also found to
leach boron (Kabata-Pendias and Pendias 1984).
Boron adsorption was found to be governed by boric acid at low pH, while
at high pH, the borate anion is believed to contribute to the total boron
adsorbed. Maximum boron adsorption occurs at a pH between 8.5 and 9.1
(Schalscha et al. 1973, Bingham and Page 1971).
A Langmuir adsorption model can be used to characterize the adsorption of
boron in soil (Biggar and Fireman 1960, Schalscha et al. 1973, Singh 1964)
Biggar and Fireman (1960) applied the Langmuir model to four soils with
limited success. They concluded that the adsorption from solution for three
soils for concentrations ranging from 4 mg B/liter to 256 mg B/liter and
adsorbed concentrations were found to range from 60 to 26 mg B/liter.
Schalscha et al. (1973) observed that complete equilibrium between soil
boron and water boron was achieved within 24 hours, and that up to 60 percent
of the boron was adsorbed within the first few seconds. Hatcher e_t al. (1967)
reported similar results for the initial adsorption of boron to aluminum
hydroxide. Their adsorption studies showed that boron was released over time
perhaps as the result of aluminum hydroxide polymerizing. Sims and Bingham
(1968) also found that iron and aluminum oxides had a pH dependent affinity
for boron, with increased adsorption occurring at pH values around 6. The
influence of dissolved organics in water, clays (Carriker and Brezoniky 1978),
and organic matter (Schalscha et al. 1973.) on boron adsorption appears to be
minimal. Bingham and Page (1971) observed that boron adsorption onto soils
2-
was not noticeably influenced by the presence of other anions such as SO
3- 4
and PO .
4
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A-6
Boron was found to be very mobile in sewage sludge equilibrated with both
sandy and sandy loam soils. The Kd was estimated to be approximately less
than or equal to 1 ml/g (Gerritse et al. 1982). Baes et al. (1984) reported a
Kd for boron of 3 ml/g and this is the value that we have adopted. This value
indicates that boron will be mobile in soils although there may be a low
control on its mobility.
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A-7
CADMIUM--Kd PROFILE
Cadmium (Cd) occurs in the +2 oxidation state in the environment (Cotton
and Wilkinson 1972). Redox potential has little influence on Cadmium
speciation in water (Callahan et al. 1979). Rather, the pH of the water will
tend to control speciation of cadmium through the formation of hydroxides:
+ -2 +2
CdOH , Cd(OH) (aq), Cd(OH) , and Cd(OH) . The dominant ion will be Cd at
234
pH levels up to 8 (Pourbaix 1963, Moore and Ramamoorthy 1984). Under reducing
conditions and in the presence of sulfur, the relatively insoluble cadmium
sulfide (CdS) will form. Therefore, under anaerobic conditions, CdS will be
expected to control cadmium solubility (Callahan e_t al. 1979).
Organic matter such as humic acid, fulvic acid, and other natural
substances can influence the speciation of cadmium. Gardiner (1974) found
that the amount of cadium ion in lake water is inversely related to the pH and
the amount of organic matter present. A cadmium-organic complex was found to
bind cadmium to pH levels as low as 3 (Guy and Chakrabarti 1976). Water
hardness limits the extent of complex formation. O'Shea and Mancy (1978)
reported that an increase in hardness resulted in a decrease in complex
formation.
The chemistry of cadmium in natural systems is dominated by the ionic,
dissolved form of the metal. In the presence of organic acids, a complex ion
may form. As the pH of the water increase, cadmium hydroxide forms and may
remove some of the cadmium from solution. Carbonate will also precipitate
some of the cadmium at very high pH levels.
Cadmium is among the most mobile of the heavy metals, and its mobility
depends more on sorption processes than on precipitation reactions (Kabata-
Pendias and Pendias 1984). Sorption of cadmium is influenced by the clay
(Korte et al. 1976) and metal oxide (Callahan et al. 1979) content of the soil
and sediments. Sorption is a pH dependent process that increases with
increasing pH (Frost and Griffin 1977). Below pH 6 to 7, desorption is the
dominant process. Huang et al. (1977) found that organic anions enhance
sorption even at low pH levels. The organic acids react with cadmium to form
an organo-cadmium complex, which can then be sorbed to soil or sediment
particles. Nevertheless, cadmium has considerably less affinity for two
sludge-treated soils than do copper, zinc or lead and is expected to be more
mobile than those chemicals (Williams et al. 1984).
Cadmium transport in the Mississippi River was found to be related to
levels of organic materials and metal oxides (Eisenreich et al. 1980). Clay
material also removes cadmium effectively. Cadmium adsorbed to clay, however,
is more available for resuspension than cadmium associated with carbonate
minerals or precipitated as a solid or with hydrous iron oxide. The presence
of phosphate can enhance cadmium removal by processes similar to those
discussed above for organic ions (Farrah and Pickering 1977). Sedimentary
iron oxides and carbonates are thought to play a key role in removing cadmium
from solution.
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A-8
Despite these potential removal processes, cadmium is a relatively mobile
element that tends to remain in solution as the divalent cation. Thus, the Kd
for cadmium is relatively small, and for ground-water transport modeling in
the LLM a Kd value of 6.5 ml/g has been adopted, as reported by Baes et al.
(1984).
For surface water modeling, EPA has adopted a Kd value of 1000 ml/g, due
to cadmium's strong adsorption to organic material and small particles (clays,
hydrous metal oxides, and colloidal material) found in surface water systems.
Although this value is much higher than the Kd value used for ground water in
our analysis, it is typical of the behavior of most heavy metals in surface
water (e.g., copper, lead, zinc) and is consistent with values observed in
several major studies of the distribution of cadmium in surface water (EPA,
1984b; Wiley & Nelson, 1984).
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A-9
CHLORIDE--Kd PROFILE
Chlorine (Cl) is found in the -1 to +7 oxidation states, with the -1 state
-1
being the chloride ion (Cl ). The elemental state of chlorine is Cl .
2
Because of its great oxidizing power, chlorine is highly reactive and can
combine in a variety of ways with both inorganic and organic chemicals. While
there are many compounds in which chlorine has a positive valence, there are
no simple compounds containing positively charged chlorine. Positively
charged chlorine forms part of a radical, as in combination with oxygen. The
electron affinity of chlorine (4.02 eV) is the greatest among all the
halogens, and is greater than that of oxygen. The chloride compounds range in
character from ionic to covalent, with many of them having intermediate
character (Considine 1983).
Cl (aq) in dilute solutions exists only at rather low pH (0 to 2).
2
Addition of Cl to water is accompanied by disproportionation into HOC1 and
2
-1 -1
CIO , which are strong oxidants. Therefore, Cl , HOC1, and CIO are
2
thermodynamically unstable in water becaue these species oxidize water (in the
absence of a suitable catalyst, the reaction is extremely slow). Within the
-1
entire Eh-pH range of natural waters, Cl is the stable Cl species; it cannot
be oxidized by 0 (Stumm and Morgan 1981).
2
In one study landfill leachtes were passed through laboratory colums to
evaluate the potential of clay minerals for attentuating the various chemical
constituents of the leachate (Griffin e_t al. 1976). Chloride attenuation was
relatively low, only 10.7 percent. The low attenuation is attributed to the
fact that in soil systems chloride is a mobile nonintereacting anion. The low
chloride attenuation was not a function of the type or amount of clay mineral
present and was attributed to physical dispersion in the porous column media,
with perhaps a small amount of chemical interaction at anion exchange sites on
the clay.
The Kd for chloride was given as 0.25 ml/g by Baes et. al. (1984); the
representative Kd for the LLM has been assigned a somewhat lower value, 0.01
ml/g, so that chloride will be essentially unretarded in ground water.
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A-10
CHROMIUM--Kd PROFILE
Chromium (Cr) can be found in oxidation states ranging from -2 to +6.
Cr(III) is the most commonly occuring oxidation state, and Cr(VI) is the next
most frequently found. Cr(V) and Cr(IV) are unstable intermediates that occur
during the reduction of Cr(VI) to Cr(III). The lower oxidation states are
only formed under strongly reducing conditions, and of these oxidation states
only Cr II is found in aqueous solutions. (Cotton and Wilkinson 1972, Moore
and Ramamoorthy 1984).
The chemistry of Cr(IH) and Cr(VI) is very different. Cr(VI) is quite
soluble in aqueous solutions and is found as chromates or dichromates, which
tend to form complex ions. Cr(VI) is not sorbed by clays or hydrous metal
oxides. Cr(III) tends to react with hydroxide to form Cr(OH) and can be
3
sorbed by clays and metal oxides (Callahan et al. 1979).
Cr(III) is the predominant form under the pH redox conditions commonly
found in natural waters. Theoretically, oxidation of Cr(III) to Cr(VI) could
occur at redox potentials found in well-aerated soils. Bartlett and Kimble
(1976), however, found no evidence of oxidation regardless of pH or aeration.
Cr(III) tends to form stable complexes with both organic and inorganic anions,
and those anions should remove Cr(III) from solution. In addition, above pH 5,
Cr(OH) will precipitate. Below pH 5, the hydrated ion is stable, while above
3
pH 9 soluble hydroxides are formed (NRCC 1976).
Cr(VI) is found as the oxo-anion in water. The anions chromate
-2 -2 -2
(CrO ), hydrochromate (HCrO ), and dichromate (Cr ) ) are found at
4 4 27
varying pH levels. Chromate is the dominant ionic form in the pH range of
natural water. Because of its anionic nature, it will not bind to negatively
charged particulate matter, resulting in very mobile Cr(VI) complexes. In the
presence of reducing agents, Cr(VI) will be reduced to
Cr(III) (NRCC 1976, Schroeder and Lee 1975).
Cr(III) and Cr(VI) also behave differently in soils. Chromium(III) can be
adsorbed or complexed to soil particles, metal oxides, or organic matter and
is therefore rather immobile. Most of the Cr(III) found in soils is in mixed
Cr(II)) and Fe(III) oxides or in the lattice of minerals, although Cr(III)
complexed with organic ligands may stay in solution for over a year (James and
Bartlett 1983a). Cr(III) is mobilized only in very acidic soil media. Cr(VI),
by contrast, is easily mobilized, independent of the soil pH (Kabata-Pendias
and Pendias 1984). The adsorption of chromium onto clays is pH dependent,
with Cr(III) adsorption increasing with increasing pH and Cr(VI) adsorption
decreasing with increasing pH (Griffin et al. 1977).
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A-ll
-2
Soil pH will influence the mobility of the Cr(VI) species. CrO may
4
-2
behave similarly to other anions, HPO for example, and form a bridged
4
complex with iron or aluminum oxides or other positively charged soil
-2
constituents. The phosphate bridges are very stable and render HPO
4
-1
unavailable for use or movement. Another parallel can be drawn with H PO ,
2 4
-1
which is also tightly held by soils. On the other hand HCrO may behave
4
-1
similar to HCO and remain mobile (James and Bartlett 1983b).
3
Soil pH can also affect the reduction of Cr(VI) to Cr(III). In the
presence of reducing organic compounds, under aerobic conditions, or under
reducing conditions in the presence of ferrous or sulfide ions, reduction of
Cr(VI) will occur. The reaction is fastest under low pH conditions (Bartlett
and Kimble 1976) but was not observed in soils with little organic matter. It
is also possible that Cr(III) will be oxidized to Cr(VI). Bartlett and James
(1979) found that this oxidation occurred in soils high in manganese oxide.
They reported that this reaction was not very efficientonly 7 percent of the
chromium was oxidized--and hypothesized that the remainder forms an insoluble
hydroxide. As can be seen from the above discussion, Cr(III) will be the
dominant oxidation state under most conditions. Cr(VI), however, is the
oxidation state that has been assumed for the purposes of toxicological
assessment in the LLM because it is considerably more toxic to living
organisms.
The chemistry of chromium in soil is complicated by the fact that in a
highly oxidized form, chromium is very mobile, whereas in a lower oxidation
state, its mobility is very low. Generally Cr(III) is expected to be the
predominant form in natural systems. Cr(VI), however, is far more hazardous
to human and biota. Therefore, although 850 ml/g Kd value for Cr(III),
reported by Baes et al. (1984), is probably a realistic, a Kd of 20 ml/g is
used in the LLM to better represent Cr(VI). This value falls within the range
of Kd values for Cr(VI) reported by Baes and Sharp (1983), 1.2 to 1800 ml/g,
and by Gerritse et al. (1982), 10 to 40 ml/g.
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A-12
SODIUM -- Kd PROFILE
Sodium (Na) is found in the +1 oxidation state in the environment.
Because of the ease of removal of its single outer shell electron, sodium is
exclusively monovalent in its compounds, which are electrovalent. Sodium
ranks sixth among the elements occurring in the earth's crust, with an average
of 2.9 percent Na in igneous rocks. Sodium metal does not occur in nature
because of its great chemical reactivity (Merck 1976).
Like the other alkali metals, sodium forms compounds with virtually all
the anions, organic as well as inorganic. Sodium compounds are remarkable for
their great variety, and for the fact that the reactivity of sodium
bicarbonate with many metallic oxides permits the preparation of many
compounds that are unstable in aqueous solution. Sodium reduces most metal
oxides to the elemental state and also reduces metallic chlorides (Considine
1983).
In their evaluation of the potential clay minerals for attenuating
landfill leachate, Griffin et al. (1976) found that sodium attenuation was
relatively low, only 15.4 percent. The attenuation of sodium follows the
+ + + +
general order of cation replaceability in soils: Li Na K Cs . Studies
comparing the exchange of mono-, di-, and trivalent cations on clay have shown
that cations of higher charge are more tightly bound (Bolt and Bruggenwert
1976).
The Kd for sodium was given as 0.25 ml/g by Baes et al. (1984); a somewhat
lower value has been assigned, 0.01 ml/g, as the representative Kd for the LLM.
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A-13
A.1.2 Human Toxicity Parameters
Chronic human health risks were estimated for five chemicals in this
analysis. Potential carcinogenic risks were estimated for benzene and
arsenic, and chronic noncancer risks were estimated for cadmium, chromium, and
sodium. The quantitative toxicity parameters used in the study, as listed in
Table 4-5 of the report, are documented in brief toxicity profiles given at
the end of this section. See Section 4.3 of the report for more details on
the dose-response modeling methods. Refer to the Liner Location Risk and Cost
Analysis Model Draft Report (EPA, 1986d), especially Appendix D and the
supporting toxicity profiles, for complete documentation.
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A-14.
ARSENIC--HUMAN TOXICITY PROFILE
The toxicity of arsenic compounds varies greatly depending on the chemical
form (organic or inorganic) and the oxidation state of arsenic. Trivalent
compounds are more toxic than pentavalent compounds.
Long-term inhalation of inorganic arsenic compounds affects the
respiratory system in humans (including perforation of the nasal septum).
Chronic exposure to arsenic is also associated with skin lesions, peripheral
neuropathy, cardiovascular disorders, and peripheral vascular disorders
(blackfoot disease) (IARC 1980, EPA 1980a). Smelter workers exposed to
arsenic compounds (and possibly other toxic compounds) during pregnancy have
had an increased number of infants with low birth weights and increased
frequencies of abortion and malformations. Arsenic compounds have also
increased the frequency of chromosomal aberrations in humans (IARC 1980, EPA
1980a).
In several mammalian species, arsenate and arsenate compounds are
embryotoxic and/or teratogenic when given orally or by injection (EPA 1980a,
IARC 1980). In addition, enzyme systems in a wide variety of species are
inhibited by trivalent arsenic compounds (EPA 1980a). Arsenic compounds
induce chromosomal aberrations and morphological changes in mammalian cells.
Inorganic arsenic compounds are classified as human carcinogens by IARC
(1980) causing skin cancer in persons exposed to inorganic arsenic through
drugs, drinking water, and pesticides and lung cancer in workers exposed to
airborne arsenic compounds. In addition, excess incidences of liver cancer,
lymphomas, leukemia, myeloma, and colon cancer in various occupationally-
exposed groups occurred (IARC 1980, EPA 1980a). The evidence for
carcinogenicity in animals is limited. Increased incidences of leukemia and
lymphomas occurred in mice given subcutaneous injections of sodium arsenate
during pregnancy and lung tumors developed in rats given a calcium
arsenate-copper mixture by intratracheal administration (IARC 1980, EPA 1980a),
EPA's Carcinogen Assessment Group (CAG 1985) estimates a unit risk for
-1
arsenic of 15 (mg/kg/day) , based on carcinogenicity data from studies
involving human drinking water exposure.
-1
H = 15 (mg/kg/day)
K = 1
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A-15
BENZENE --HUMAN TOXICITY PROFILE
Toxicity to the hematopoietic system from chronic exposure to benzene is
well documented for humans. Among the reported effects are myelocytic and
aplastic anemia, thrombocytopenia, leukopenia, pancytopenia, and leukemia (in
particular acute myeloblastic and monocytic leukemia). Benzene exposure
causes an increased incidence of chromosomal aberrations in peripheral blood
lymphocytes and bone marrow cells. Effects on the immunologic system from
chronic exposure to benzene also occur. Acute exposure to high concentrations
of benzene can produce narcosis (effects on the central nervous system) and
cardiac sensitization (ACGIH 1981, Clayton and Clayton 1981, NAS 1980, EPA
1980b).
The principal effect of chronic benzene exposure in laboratory animals is
leukopenia; hematotoxic effects have been observed by inhalation, injection,
and oral administration. Acute inhalation and intravenous exposures cause
rapid central nervous system effects, hemolysis, and death (Clayton and
Clayton 1981, NAS 1980). Benzene-induced carcinogenicity has not been
conclusively demonstrated in animals (NAS 1980, EPA 1980b).
EPA's Carcinogen Assessment Group assigns an upper-bound unit risk of
-1
0.052 (mg/kg/day) for benzene based on human occupational exposure data (GAG
1985).
-1
H = 0.052 (mg/kg/day)
K = 1
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A-16
CADMIUM--HUMAN TOXICITY PROFILE
Human toxicity from exposure to cadmium is well-documented. Acute toxic
effects of cadmium inhalation include irritation of the upper respiratory
tract, chest pains, nausea and diarrhea, dizziness, and death usually due to
massive pulmonary edema. Major toxic effects of cadmium ingestion include
nausea, vomiting, salivation, diarrhea, abdominal cramps, and in some cases
death (Doull et al. 1980). The principal target organs following chronic
exposure to cadmium are the lungs and kidney. Chronic inhalation of cadmium
fumes and dust can lead to an emphysema-like condition with loss of
ventilatory capacity, increased residual lung volume, and shortness of breath
(Doull et al. 1980). The kidney appears to be the most cadmium-sensitive
organ, primarily because of its prediliction for accumulation of cadmium.
Renal tubular damage is the most important effect of cadmium exposure,
irrespective of the route of exposure. The damage is usually characterized by
proteinurea; other signs of renal tubular damage may include a reduced ability
to concentrate urine, glycosuria, hypercalcinuria, aminoaceduria, and
increased uric acid excretion. Excessive exposure to cadmium through the
ingestion of contaminated food and drinking water results in a disease
characterized by osteomalacia and renal tubular dysfunction (Itai-itai
disease).
The toxic effects of cadmium observed in humans also occur in experimental
animals. Rats exposed to a cadmium aerosol for 15 days develop lung
inflammation followed by emphysema and fibrosis. Signs similar to those of
itai-itai disease develop experimentally in rats given an excess of cadmium in
a calcium deficient diet.. In newborn animals, cadmium causes cerebral and
cerebellar damage. Cadmium is also toxic to the testes of rats and mice, and
causes hyperglycemia and glucose intolerance in animals. Cadmium is
teratogenic and fetotoxic in rats, mice, and hamsters (Doull et al. 1980).
With regard to mutagenicity, cadmium can cause chromosomal or mitotic
aberrations in mammalian cell assays and to increase error frequency in DNA
synthesis. Mixed results have been obtained for cadmium in microbial
mutagenicity assays. Drosophila studies and dominant lethal mutation studies
on cadmium were negative (EPA 1980c).
Several epidemiologic studies of workers exposed to cadmium oxide dust
provide evidence that cadmium increases the risk of prostatic cancer and
respiratory cancer (Clayton and Clayton 1981, EPA 1980c, NAS 1980). IARC
(1976) concludes that cadmium-and certain cadmium compounds are probably
carcinogenic to humans. Animal studies demonstrate that the injection of
certain cadmium compounds causes sarcomas at the site of injection and
testicular tumors (Leydig or interstitial cell tumors). Several long-term
feeding and inhalation studies have been carried out with cadmium compounds;
the induction of tumors in animals by these routes of exposure has not
occurred (EPA 1980c).
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A-17
Ingestion
Cadmium is considered noncarcinogenic by oral exposure in the Liner
Location Model (inhalation exposure routes were not modeled in this study).
To estimate a chronic oral MED for the noncarcinogenic effects of cadmium
lowest-adverse-effect-level (LOAEL) of 0.0043 mg/kg/day was chosen (EPA,
1980c). This value was based on EPA's evaluation of human data from Japan
where itai-itai disease was endemic. The value represents the chronic human
for MED induction of renal tublar proteinuria (EPA 1984e). We assume a 10
percent response at the MED (Ro = 0.1). The national primary drinking water
standard, which is 0.01 mg/1, is based on concerns for the toxic effects of
cadmium and is assumed to be a human no-effect level. Thus, the human
threshold is 0.00029 mg/kg/day.
-2
H = 6.6 x 103 (mg/kg/day)
K = 2
Threshold = 0.00029 mg/kg/day
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A-18
CHROMIUMHUMAN TOXICITY PROFILE
Chromium compounds can occur in any of three oxidation states, 0, +3, and
+6. The biological properties of chromium compounds vary with the oxidation
state. We have chosen the most hazardous form of chromium, which is the +6
oxidation state.
The pulmonary carcinogencity of chromium, presumably chromium (VI), is
well-documented for humans. Increased incidences of lung cancer are
associated with occupational exposure to chromium compounds by inhalation;
there is no evidence of increased risk of cancer from ingestion (IARC 1980,
EPA 1980d). In addition, inhalation exposure to elevated concentrations of
chromium (VI) compounds causes mucosal irritation, ulceration, and perforation
in the respiratory system of humans. Dermal exposure results in primary
irritation and allergic contact sensitization in relatively high numbers of
exposed individuals (EPA 1980d). An increased incidence of chromosomal
aberrations occurred in workers exposed to chromium (VI) compounds (IARC
1980 , EPA 1980d).
Administration of Cr(VI) to experimental animals by various routes causes
liver damage, kidney damage (confined to the proximal convoluted tubules), and
chronic irritation of the respiratory tract. Some embryotoxic and teratogenic
effects developed in mice and hamsters given Cr(VI) at very high doses; it is
unclear whether these effects are due to maternal toxicity (IARC 1980 ).
Chromium compounds cause mutations, cell transformations, and chromosomal
aberrations in numerous prokaryotic and eukaryotic systems, both j.n vivo and
in vitro (EPA 1980d, IARC 1980).
The carcinogenic potential of chromium (VI) in animals either by
inhalation or ingestion has not been successfully demonstrated. Tumors in
experimental animals have been induced only by implantation of pellets
containing chromium (VI) compounds or by injection of chromium (VI) compounds
(IARC 1980, EPA 1980d).
Ingestion
Chromium is considered noncarcinogenic by oral exposure in the Liner
Location Model (inhalation exposure routes were not modeled in this study).
The results used to estimate the chronic MED for chromium by ingestion are by
Gross and Heller (1946, as reported in National Research Council Canada
1976). Rats were administered 134 ppm potassium chromate in drinking water
over a 2-3 month period. At this level, an increase in kidney and liver
lesions occurred. Based on this subchronic study, the chronic animal MED is
estimated to be 6.7 mg/kg/day. The equivalent chronic human MED is estimated
to be 1.0 mg/kg/day.
-2
H = 2.4 (mg/kg/day)
K = 2
Threshold =0.01 mg/kg/day
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A-19
SODIUM (SODIUM CHLORIDE)--HUMAN TOXICITY PROFILE
The low toxicity of sodium chloride (common salt) is evidenced by the fact
that it is a major component of the body (blood plasma contains about 140 mM
sodium, or about 8 g/1 sodium chloride) and a universal dietary component.
The average daily diet contains 100-200 millimoles of sodium chloride (about
6-12 g) (White e_t a_l., 1968), which is without adverse effect in normal
individuals. Individuals with some forms of hypertension, however, are
sometimes advised to reduce their salt intake, but even the most restrictive
diet will contain about 200 mg/day of sodium (about 500 mg/day sodium
chloride) and few individuals can tolerate such a restricted diet (Harvey et
al . , 1976). A recent study of the relation between the intake of various
nutrients and hypertension casts some doubt on the relationship between sodium
intake and high blood pressure since it found that hypertensives (systolic
blood pressure greater than or equal to 160 mm Hg) tended to have lower sodium
intake than individuals with normal blood pressure (McCarron e_t al. , 1984).
In rats, high dietary levels of sodium chloride are related to elevated
blood pressure. In a chronic study, Meneely and Ball (1958) fed rats diets
containing 2.8-9.8 percent sodium chloride. Animals consuming 7 percent
sodium chloride or more developed a syndrome resembling nephrosis with massive
edema, hypertension, anemia, lipemia, hypoproteinemia and azotemia. All of
the affected animals died and showed severe arteriolar disease at necropsy.
Significant hypertension was seen at all dose levels and there was a tendency
for the degree of hypertension to increase with increasing dietary salt
intake. The minimum effective dose was not clearly identified, but is below
2.8 percent in the diet (about 1.4 g/kg/day) .
While the majority of the population may, and indeed do, consume fairly
high levels of sodium chloride without ill effects, lower levels may adversely
affect some individuals with hypertension. It is clear, however, that a
threshold exists even for those susceptible individuals. Since a diet
containing about 500 mg/day (7 mg/kg/day) sodium chloride is the minimum used
to treat hypertension, this level must be below the threshold. A study by
Luft et al . (1979) was used to establish human MED and threshold values for
sodium. In this study, 14 normotensive men were given between 10 and 1,500
meq/day sodium. Blood pressure did not increase until the daily sodium intake
exceeded 800 meq. Thus, the MED is set at 800 meq and, in the absence of more
specific dose-response information, the NOEL is set at 10 meq sodium. These
values correspond to adult intakes of 260 and 3.3 mg/kg/day of sodium,
respectively. The values were divided by 5 to adjust for the fact that this
was a subchronic study. Thus, the chronic human MED was set at 52 mg/kg/day
and the threshold at 0.66 mg/kg day. This threshold is substantially lower
than the 6000 mg/day sodium chloride (86/mg/kg/day, or 34 mg/kg/day as sodium)
that Dahl (1960) reported could be ingested without adverse health effects.
The MED is near the high end of the average dietary range (approximately 70
mg/kg/day sodium) . We assume that 10 percent of the population has
hypertension to which high sodium intake is a contributory factor (Ro = 0.10).
-5 -2
H = 4.0 x 10 (mg/kg/day)
K = 2
Threshold =0.66 mg/kg/day
* * *
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A-20
A.1.3 Aquatic Toxicity Parameters
The Aquatic Reference Concentrations (ARCs) are intended to represent
threshold concentrations below which adverse effects would not be expected in
aquatic ecosystems. ARCs were estimated for benzene, arsenic, cadmium,
chromium, and total mobile salts. The reference concentrations, as listed in
Table 3-3 of the report, and documented in brief aquatic toxicity profiles
given at the end of this section.
The EPA's Ambient Water Quality (AWQ) Criteria have been developed with
the intention of protecting all but a small minority (5%) of aquatic species
from adverse effects from either acute or continuous exposure to pollutants
(EPA, 1986a). Surface water ecosystems are considered sufficiently resilient
under most circumstances to withstand minor changes in species abundance
without major changes in community structure and nutrient cycling. EPA has
therefore adopted the AWQ Criterion Concentration for Continuous Exposures
(CCC) whenever possible as the ARC for modeling the potential impact of
constituents leached from drilling and production wastes over periods of years
on surface- water ecosystems. In the absence of an established AWQ CCC, EPA
has developed ARCs designed to be protective of 95 percent of species from the
data available using similar methodology. In Section A.2, EPA provides a
brief summary of the available data and method of deriving each ARC.
Terminology
The EPA's Ambient Water Quality Criteria consist of two concentrations:
the Criterion Maximum Concentration and the Criterion Continuous
Concentration. The Criterion Maximum Concentration (CMC), which represents
the highest one-hour average concentration that is permissable, -is based on
Species Acute Values derived from acute toxicity tests. For a single species
toxicity test, a Species Acute Value (SAV) is equal to that concentration
producing a 50% effect level, usually lethality, e.g. TLm (median Tolerance
Limit), or LC50 (Lethal Concentration for 50% of test organisms). Species
Acute Values (SAV) can vary depending upon the species, chemical, and
environmental parameters such as pH, temperature, and dissolved oxygen. The
Final Acute Value, (FAV), is based on all available SAVs and is designed to be
lower than the SAV for 95 percent of species. The Criterion Maximum
Concentration equals the FAV divided by two to bring the effect level from 50
to a few percent mortality.
The Criterion Continuous Concentration (CCC), which represents the maximum
allowable four-day average concentration, is equal to the lowest of the Final
Chronic Value, the Final Plant Value, and the Final Residual Value. The Final
Chronic Value (FCV) is designed to protect 95 percent of animal species from
adverse effects from continuous exposure to a pollutant. The Final Plant
Value (FPV) is used instead in the event that aquatic plants are generally
more sensitive to the contaminant than are animals. The EPA intends the Final
Residual Value (FRV) to prevent tainting or exceedance of FDA tissue action
levels in commercially or recreationally important aquatic species and to
protect wildlife, including fish and birds that consume aquatic organisms from
adverse effects.
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A-21
A Species Chronic Value (SCV) is defined as the threshold concentration
above which adverse effects will be seen and is derived using data from a
life-cycle, partial life-cycle, or early-life-stage test. Operationally, a
chronic value is calculated using the geometric mean of the no observable
effect level (NOEL) and the lowest level associated with an adverse effect
(LOAEL).
When several toxicity tests are available for the same species and
chemical, the EPA recommends that one represent the species with a Species
Mean Acute Value (SMAV) or Species Mean Chronic Value (SMCV) equal to the
geometric mean of the individual toxicity test values. For an individual
species, the Acute to Chronic Ratio (ACR) is equal to the SMAV divided by the
SMCV.
Data Requirements and Methodology
In order to calculate a Criterion Continuous Concentration for freshwater,
the EPA requires that either (a) chronic values are available for species in
at least eight families (as described in Appendix A of the 1986 Quality
Criteria for Water), (EPA 1986a), or (b) acute values are available for
species in at least eight families and there is at least one species for which
a corresponding chronic value is available.
The Michigan Department of Natural Resources (1984) developed
modifications of the EPA's methods for developing CCCs to derive aquatic
criteria concentrations in the absence of sufficient data to meet the EPA's
minimum data requirements (reproduced in full in ICF/Clement 1986, Appendix
C).
In general, the MDNR reduces the data requirement to fewer species from
fewer families and provides a conservative method for estimating a CCC from
acute toxicity studies alone. ICF/Clement (1986) developed additional
guidelines for estimating a CCC from extremely limited data for the National
Industrial Environmental Services (NIES). In brief, for chemicals for which
acute toxicity tests are available for fewer than six species form six
families (three invertebrate and three vertebrate), one divides the lowest
SMAV or SAV by a factor of 10 to extrapolate to more sensitive species, or by
a factor of 5 if the SAV is from a species of trout because Salmonids are
generally very sensitive to most contaminants. One then extrapolates a CCC
from this acute value using an ACR based on one or more species if possible,
or a generic ACR is an ACR cannot be calculated for a single species.
The MNDR recommends a generic ACR of 45 based on their analysis of ACR's
for 65 chemicals. The MNDR found that 80 percent of the ACRs tabulated were
under 45; however, their data base included pesticides, other organics, and
heavy metals. Kenaga (1982) tabulated ACRs by chemical type and found
significant differences between compound types as one would expect. EPA
therefore uses the maximum ACR for a given family of compounds instead of the
value 45 whenever it appears appropriate.
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A-22
ARSENICAQUATIC TOXICITY PROFILE
Arsenic is stable in surface water in four oxidation states (+5, +3, 0,
-3) as both inorganic and organometallic species; common species include
arsenate and arsenite (EPA 1980a).
EPA propsed an AWQ CCC of 0.190 mg/1 for inorganic arsenic(III), but
considered the data insufficient to propose a CCC for inorganic arsenic(V)
(EPA 1986a). EPA did note that aquatic plants were affected by concentrations
as low as 0.048 mg/1 arsenic(V).
For inorganic arsenic(III), acute values for 16 freshwater animal species
ranged from 0.81 mg/1 for a cladoceran to 97 mg/1 for a midge, but the three
ACR's only ranged from 4.7 to 4.9 (EPA 1986a). The five acute values for
inorganic arsenic(V) covered about the same range, but the single ACR was 28.7
(EPA 1986a).
The freshwater residue data indicate that arsenic is not bioconcentrated
to a high degree and exhibits a short half-life in fish tissue; therefore,
residues should not be a problem to predators of aquatic life (EPA 1986a).
ARC 0.190 mg/1 based on arsenic(III).
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A-23
BENZENE--AQUATIC TOXICITY PROFILE
Most of the toxicity data concerning the effects of benzene on aquatic
life have been determined using static test conditions without measured
concentrations; the only flow-through test being for a rainbow trout (EPA
1980b). Consequently, these data may underestimate the toxicity of this
volatile chemical (EPA 1980b). The acute toxicity of benzene to freshwater
species has been measured with eight species and the species average LC50
values from static tests range from 17.0 mg/1 for the rainbow trout (Salmo
gairdneri) to 390 mg/1 for the mosquito fish (Gambusa affinis) (EPA 1980b).
Acute-chronic ratios of greater than 2 and 2.1 have been calculated for
Daphnia magna and the rainbow trout respectively (EPA 1980b, Sloof et al.
1983). No data are available for benthic crustaceans, insects, or
detritivores (EPA 1980b).
On the basis of the lowest LC50 value of 17 mg/1 for rainbow trout, an ACR
of 2.1 and a divisor of 5 to extrapolate to more sensitive species,
ICF/Clement (1986) calculated CCC for benzene of 1.6 mg/1.
EPA decided that this value, however, might not be sufficiently protective
because the available data indicated only that the ACR was greater than 2.1.
A chronic test with Daphnia magna (U.S. EPA 1978 reported in EPA 1980b)
indicated that the chronic value for this species was higher than 98 mg/1.
The species mean acute value for D. magna is 380 mg/1 (EPA 1980b). This
implies an upper limit for the ACR for D. magna of 380/98 or 3.8. Moreover,
the only flow-through test with rainbow trout fingerlings yielded an LC50 of
5.3 mg/1 (EPA 1980b), a value lower than any of the static tests. EPA
therefore divided 5.3 by 5 to extrapolate to more sensitive species, and by
the more conservative ACR of 3.8 to estimate the final Reference Concentration
of 0.280 mg/1.
A 50% reduction in cell numbers of the aquatic plant Chlorella vulgaris
was reported at a concentration of 525 mg/1. This concentration is several
orders of magnitude higher than the reference concentration of 0.280 mg/1
based on animal toxicity tests. The log P octanol coefficient for benzene of
2.13 at 20 C (Verschueren 1983) is not sufficiently high for bioaccumulation
to be of serious concern (EPA 1985a).
ARC = 0.280 mg/1
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A-24
CADMIUM--AQUATIC TOXICITY PROFILE
In natural fresh waters, cadmium sometimes occurs at concentrations of
less than 0.01 ug/1 (EPA 1984a). In most well oxygenated fresh waters that
are low in total organic carbon, free divalent cadmium will be the predominant
form (EPA 1984a).
Chronic toxicity tests have been conducted on cadmium with sixteen
species, including four invertebrates and twelve fishes in thirteen genera.
Chronic values are available over a wide range of hardness for two species,
Daphia magna and the fathead minnow. The slope of the regression of the
natural logarithm of the chronic value against the natural logarithm of
hardness gave slopes of .77 and .81 respectively, the pooled slope being 0.785
(EPA 1984a).
Acute to chronic ratios ranged from 0.902 for the chinook salmon to 433.8
for the flagfish, with no apparent pattern to the variation (EPA 1984a).
Thus, the CCC was calculated using the chronic data alone and the slope of
.785 to relate the threshold concentration to hardness. The final freshwater
chronic value for cadmium is 0.66 ug/1 at a hardness of 50 mg/1, and the
equation for relating the final CCC value to water hardness is:
CCC value (ug/1) = exp (0.7852[In(hardness)] - 3.490)
Growth reduction was the major toxic effect observed with freshwater
aquatic plants (EPA 1984a). Because the lowest toxicity values for fish and
invertebrate species are lower than the lowest values for plants, the water
quality criteria which protect freshwater animals should also protect
freshwater plants.
Bioconcentration factors for cadmium in fresh water range from 3 for brook
trout muscle to 12,4000 for whole body of mosquitofish. Usually, fish
accumulate only small amounts of cadmium in muscle as compared to most other
tissues.
ARC = 0.00066 mg/1
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A-25
CHROMIUM--AQUATIC TOXICITY PROFILE
Chromium (III) or chromium (VI) or both can exist in various bodies of
water, and either can be converted to the other under appropriate natural
conditions. The chemical and toxicological properties of the two oxidation
states appear to be quite different, and they will therefore be treated
separately.
The acute toxicity of chromium (VI) apparently increases as pH is lowered
or as hardness is lowered or both (EPA 1984b). The available data, however,
are insufficient to develop criteria on the basis of water quality
characteristics (EPA 1984b).
The Acute to Chronic Ratio for chromium (VI) seems to be lower for
sensitive species (1.1 to 7.0 for Daphnia magna compared to 223 to 261 for two
trout species) and so they were used as a freshwater final ACR. The final CCC
of 10.8 ug/1 was derived by dividing the final acute value of 31.5 ug/1 by the
final ACR of 2.92 (EPA 1984b).
The acute toxicity of chromium (III) increases as hardness is lowered.
For three species, there were sufficient data from a sufficient range of water
hardnesses to regress the natural logarithm of acute toxicity values against
the natural logarithm of hardness. The slopes for all three species (Daphnia
magna, the fathead minnow, and the bluegill) fell between 0.78 and 0.89, the
pooled slope being equal to 0.819.
Both the freshwater chronic values and acute-chronic ratios for chromium
(III) range from about 27 to about 1,300. A chronic value of 68.6 ug/1 is
available for the rainbow trout. Because this is an important and relatively
sensitive spcies, the EPA used it to calculate a final chronic value, or CCC,
form the final acute value. The same slope derived from acute data (0.819)
was applied to the chronic toxicity of chromium (III). The final equation
relating the final CCC value to water hardness is:
CCC value (ug/1) = exp (0.8190[In(hardness)] + 1.561)
At a hardness of 50 mg/1 as CaCoS, this equals 0.120 mg/1.
Toxicity tests with chromium(III) have only been conducted with two
freshwater plant species, but both effect levels were higher than the animal
CCC. No bioconcentration factor has been measured for chromium (III) with
freshwater organisms (EPA 1984b).
EPA uses the CCC for chrornium(VI) as the ARC because it is the lower of
the two values.
ARC = 0.011 mg/1 as chromium(VI)
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A-26
TOTAL MOBILE SALTS--AQUATIC TOXICITY PROFILE
Salinity is an oceanographic term, and although not precisely equivalent
to the total dissolved salt content, it is related to it (Capurro 1970 cited
by EPA 1986a). It is numerically smaller than the filtrable residue and
usually is reported as grams per kilogram or parts per thousand
(APHA-AWWA-WPCF 1975). The principal inorganic anions dissolved in water
include the carbonates, chlorides, sulfates, and nitrates (principally in
ground waters); the principal cations are sodium, potassium, calcium, and
magnesium (EPA 1986a). The total weight of the following ions dissolved in 1
kg of sea water is termed salinity: sodium, potassium, calcium, magnesium,
chloride, sulfate, carbonate (Reid and Wood 1976).
The average salinity of soft fresh water is 0.065 ppt (or 65 mg/1), and of
hard fresh water is 0.30 ppt (300 mg/1) (Hart et al. 1945). Among inland
waters in the United States supporting a good mixed fish fauna, about 5
percent have a dissolved-solids concentration under 72 mg/1; about 50 percent
under 169 mg/1, and about 95 percent under 400 mg/1. The upper limit of what
is considered "fresh water" is 500 mg/1 (the 1958 "Venice System" value
reported in Reid and Wood 1976, Macan 1963, and Pennak 1953).
Salinity constitutes a striking environmental limit on organisms,
primarily through its effect on physiological processes other than the uptake
and utilization of nutrients. The tolerance of fresh-water organisms to
increasing dissolved ions varies widely between species. Some fish are
anadramous, while others "tolerate" brackish salinities despite their tendency
to be associated with less saline waters (Hynes 1970). Other species
"tolerate" more narrow ranges of dissolved salt content (Hynes 1970).
Nonetheless, merely tolerating dissolved ions, and remaining equally
competitive are two different issues. On the basis of available data, it is
not possible to extrapolate the response of an aquatic community to changes in
salinity. The degree of salt addition that could trigger a major shift in
community structure and species composition and abundance would depend upon
background concentrations and the native or current species distributions.
EPA therefore takes a different approach for estimating an ARC for total
dissolved salts. EPA assumes the following:
(1) ions of Ca, Mg, K, Na, Cl, and S04 contribute to total
mobile salinity potentially impacting surface waters,
(2) all ions have the same mobility as chloride,
(3) the average background concentration of dissolved salts
already present in soft water is 65 mg/1
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A-27
(4) the total dissolved ions derived from the background
concentration^) and the added mobile salts cannot exceed
400 mg/1, in order to be protective of 95 percent of
fresh-water species.
Based on these assumptions, the ARC for total mobile salts in surface water is
335 mg/1 (400 mg/1 - 65 mg/1).
ARC = 335 mg/1
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A-28
A.1.4 Resource Damage Parameters
Ground water can be rendered unsuitable for domestic use, irrigation of
crops, or watering of livestock by concentrations of certain contaminants that
do not pose a human health risk. EPA considers the loss of ground water that
can be used for these purposes a significant loss of natural resources. Of
the 33 constituents considered, three are well documented for their ability to
reduce the suitablity of ground water at concentrations lower than those
posing health problems. These are chloride, boron, and total salinity.
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A-29
BORON--RESOURCE DAMAGE PROFILE
Boron is an essential element for the growth of plants. Optimum yields of
some plants are obtained at concentrations of a few tenths mg/1 in nutrient
solutions (NAS/NAE 1972). At higher concentrations, however, boron is toxic
to many plants. Sensitive plants such as citrus trees, plum, pear and apple
trees, grapes, and avacados, exhibit toxic effects below 1 mg/1; semi-tolerant
plants such as tomatoes, cotton, corn, oats, and potatoes, show adverse
effects between 1 and 2 mg/1; tolerant crops such as alfalfa, onions, turnips,
lettuce, and carrots, exhibit toxic effects at 2 to 4 mg/1 (from Eaton 1935,
1944 reported by NAS/NAE 1972).. At concentrations above 4 mg/1, the
irrigation water is generally unsatisfactory for most crops (NAS/NAE 1972).
Bradford (1966, reported in NAS/NAE 1972) concluded that boron
concentrations in excess of 0.75 mg/1 injured citrus fruits, and Chapman (1968
reported in NAS/NAE 1972) stated that citrus trees showed some mild toxicity
symptoms when the boron concentration exceeded 0.5 mg/1.
Based on the preceeding information, NAS/NAE (1972) concluded that boron
concentrations should not exceed 0.75 mg/1 in irrigation waters to protect
sensitive crops, such as grapes and citrus trees, 1.00 mg/1 for semi-tolerant
crops, and 2.00 mg/1 for tolerant crops (EPA 1986a). A threshold value of 1
mg/1 is considered appropriate for screening for resource damage.
RRC = 1.00 mg/1
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A-30
CHLORIDE--RESOURCE DAMAGE PROFILE
The EPA established a secondary maximum contaminant level (SMCL) of 250
mg/1 for chloride to prevent excess corrosion of pipes in hot water systems
and to protect consumers from objectionable taste.
According to Lawrence (1975), the approximate life of domestic water
heaters for 200 mg/1 total dissolved solids (IDS) in water (or 100 mg/1 of
chlorides) ranged between 10 and 13 years. Beyond this level, declining
heater life as a function of increasing TDS was fairly uniform, about 1 year
shortened life per 200 mg/1 additional TDS (100 mg/1 chlorides) (as reported
in EPA 1984c).
The EPA (1984c) used three different studies of human taste thresholds for
various salts to support the SMCL of 250 mg/1 for chloride. Median detection
limits forchloride in the three studies ranged between 160 mg/1 to 395 mg/1 of
chloride.
For comparison, the World Health Organization's (WHO) International
Standard for chloride is 200 mg/1 and the Food and Drug Administration's (FDA)
Bottle Water Standard is 250 mg/1 of chloride (reported in EPA 1984c).
RRC = 250 mg/1
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A-31
TOTAL MOBILE SALTS--RESOURCE DAMAGE PROFILE
Agricultural uses of water are limited by excessive dissolved solids
concentrations. Irrigation use of water depends not only upon the osmotic
effect of dissolved salts, but also on the ratio of the various cations
present (EPA 1986a). The National Technical Advisory Committee (NTAC 1968)
classified salinity hazards as follows: no detrimented effects usually
noticed for water <500 mg/1 and detrimental effects on sensitive crops can
be expected between 500 and 1,000 mg/1 total dissolved salts.
RRC = 500 mg/1
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* A *
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A-32
A. 2 PARAMETERS USED IN MODEL CONSTITUENT SELECTION PROCESS
All chemicals that were frequently found in drilling muds or fluids and
were sufficiently mobile to be of concern were screened for human health
effects, aquatic organism toxicity, and the potential for resource damage.
The basis for the Reference Concentrations (RCs) were from the following
sources whenever possible: for human health effects, verified Reference Doses
(RfDs) and carcinogenic Potency Factors (PF), for aquatic toxicity, Ambient
Water Quality Criterion Continuous Concentration (AWQ CCC), for resource
damage, Secondary Recommended Maximum Contaminant Levels (SRMCLs), or AWQ CCC
based on irrigation standards. When these criteria did not exist for a
chemical, other sources were used. For the Aquatic Reference Concentrations,
the methods of developing an RC based on acute toxicity data are given in
section A.1.3.
For HRCs, EPA assumed a 70 kg human receptor drinking 2 liters of water
per day. The HRCs are the concentrations in drinking water corresponding to
an individual upper bound excess lifetime cancer risk of 10-s for
carcinogens, or an acceptable chronic daily intake for noncarcinogens (RfD).
Table A-2 summarizes the Reference Concentrations based on human health,
aquatic toxicity, and resource damage.
Abbreviations:
ACR: Acute to Chronic Ratio (for aquatic toxicity)
AIC: Acceptable Intake for Chronic Exposure (EPA 1986b)
ARC: Aquatic. Reference Concentration (this memo)
AWQ: Ambient Water Quality (EPA 1986a)
CCC: Criterion Continuous Concentration (for aquatic toxicity)
HEA: Health Effects Assessment (EPA 1986c)
HRC: Human Reference Concentration (this memo)
LC50: Lethal Concentration for 50% of organisms in acute
toxicity test (24 to 96 hours in duration)
LOEL: Lowest Observed Effect Level
NIES: National Industrial Environmental Services Alternate
Concentration Limits developed by ICF/Clement (1986)
NOEL: No Observed Effect Level
MDNR: Michigan Department of Natural Resources
PF: Potency Factor (for human carcinogens)
RfD: Verified Reference Dose (for human noncarcinogens)
RRC: Resource damage Reference Concentration
TLm: Median Tolerance Limit (concentration resulting in 50%
mortality in acute toxicity test, 24 to 96 hour)
NIES CCC Weight of Evidence Classification:
A Ambient Water Quality Criteria available, species from
at least eight aquatic families tested.
Bl LC50/TLm values available for at least six
species from six families; three vertebrate and
three invertebrate.
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A-34
TABLE A-2
REFERENCE CONCENTRATIONS
(continued)
Methods and Sources
Aquatic Reference Concentrations:
AWQC: EPA (1986a) Ambient Water Quality Criterion Continous Concentration
NIES: ICF/Clement (1986) Criterion Continuous Concentration (CCC), developed
for the National Industrial Environmental Services.
/10/ or /5/: Extrapolation to sensitive species using 5 if the lowest
acute toxicity value is from a sensitive species (e.g., trout),
or 10 otherwise.
7/3,4,25,45: Acute to chronic ratio used to extrapolate a CCC from acute
toxicity data (see Appendix A for toxicity tests and
methodology).
Human Health Reference Concentrations:
RfD: Verified Reference Dose
PF: Potency Factor (from EPA's Carcinogen Assessment Group)
NAS: National Academy of Sciences
LLM: Liner Location Risk Analysis Model
Others: See Appendix A for more complete information.
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A-35
B2 LC50/TLm values available for at least three species from
three families; one invertebrate and one vertebrate.
C LC50/TLm available for at least one invertebrate or one
vertebrate.
Considerations:
A serious difficulty in interpreting reports of acute toxicity tests is
that some authors did not specify whether concentrations were expressed in
terms of the element or the compound tested, for example as Ca-H- or Ca-H-C03--.
For several chemicals, the only summary of aquatic effects found was McKee
and Wolf's 1963 Water Quality Criteria for the State of California. The
reports of aquatic toxicity tests cited appear not to have been critically
reviewed. Moreover, the endpoints reported were usually vague, such as
"toxic" or "lethal concentrations". Review of the original literature cited
is beyond the scope of this study, and the EPA therefore does not calculate
ARCs based on these citations.
Toxicity Summaries
Arsenic
-1
Arsenic is an established human carcinogen with a PF of 15 (mg/kg-day)
The form of arsenic present in solution is dependent upon such factors as
pH, organic content, and suspended solids. Data are only sufficient to
establish an AWQ CCC for inorganic arsenic (III) at 0.19 mg/1.
Benzene
Benzene is an established human carcinogen with a PF of 0.052
-1
(mg/kg-day)
On the basis of the only flow-through test with with juvenile rainbow
trout, and an acute to chronic ratio of 3.8 or less for Daphnia magna, (EPA
1980b), and a divsor of 5 to extrapolate to more sensitive species the ARC for
benzene is 0.280 mg/1 (weight of evidence B2).
Boron
Boric acid and its salts such as borax (sodium tetraborate decahydrate)
exhibit very weak acute toxicity. Sodium borate and boric acid are used
medicinally. The average adult daily intake of boron has been variously
estimated at 1 to 20 mg/day, with fruits and vegetables as the major
contributors (Waggott 1969). Canada has established a drinking water standard
of 5 mg/1, with a desirable upper limit of 1 mg/1.
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Wier and Fisher (1972) performed a 2-year chronic ingestion study on dogs
and rats. Calculation of HRC from this study is as follows. The NOAEL for
the dog study was 350 ppm. The dogs were assumed to weigh 10 kg and to
consume 250 g of food/day, or 8.75 mg/kg-day of Boron. The NOAEL for the rat
study was 350 ppm. The rats were assumed to weight 250 gms and to consume 15
g/day of food, or 21 mg/kg-day of boron. An Acceptable Daily Intake (ADI) can
be derived from these values by dividing by 100, by 10 to extrapolate from a
chronic animal study to humans, and by 10 to extrapolate to sensitive members
of the population, or 0.0875 mg/kg-day and 0.21 mg/kg-day respectively.
Assuming a 70 kg man drinking 2 liters of water per day, these values are 3.06
mg B/l and 7.35 mg B/l respectively.
Boron concentrations should not exceed 0.75 mg/1 in irrigation waters to
protect sensitive crops, such as grapes and citrus trees, 1.00 mg/1 for
semi-tolerant crops, and 2.00 mg/1 for tolerant crops (EPA 1986a). A
threshold value of 1 mg/1 is considered appropriate for screening for resource
damage.
NAS/NAE (1972), in support of a marine concentration threshold for boron,
notes that boron's toxicity is slightly lower in hard water than in distilled
and that boric acid and borates should be more toxic to freshwater than to
marine aquatic life. Based on freshwater studies, including acute 96 hr LCSOs
of 3,600 ppm as sodium borate and 5,600 mg/1 as boric acid for mosquito fish,
they claim that concentrations of 50 mg/1 as boric acid (or 50 x 0.18 = 9 mg/1
elemental boron) and 25 mg/1 as sodium borate (or 25 x 0.22 = 5.5 mg/1
elemental boron) should have minimal effects on marine fauna.
Lewis and Valentine (1981) determined a 48 hr LC50 of 226 mg/1 of boron
for Daphnia magna. They also determined a reproductive depression LOEL and
NOEL in a 21 day study of 13 mg/1 and 6 mg/1 respectively. The geometric mean
of the LOEL and the NOEL provides a point estimate of the chronic effect
threshold, in this case 8.8 mg/1. The Acute to Chronic Ratio for Daphnia
magna exposed to boron is therefore 25.7 The final ARC is 1.76 mg/1, is
based on the lowest acute value of 226 mg/1 divided by the measured ACR of
25.7 and by 5 to extrapolate to more sensitive species assuming D. magna is
relatively sensitive.
Cadmium
The human AIC for cadmium is 0.29 mg/kg-day (HEA). This limits drinking
water concentrations to 0.01 mg/1.
The equation relating chronic aquatic cadmium toxicity to water hardness
is e raised to the (0.7852[In hardness] - 3.490) power. In soft water (50
mg/1 as CaC03), the CCC for cadmium is 0.66 ug/1 (0.00066 mg/1) (EPA 1984a).
Calcium
Water hardness is caused by the polyvalent metallic ions dissolved in
water. In fresh water these are primarily calcium and magnesium. Hardness
commonly is reported as an equivalent concentration of calcium carbonate.
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The World Health Organization 1984 Guidelines for Drinking Water Quality
recommends a limit of 200 mg/1 calcium based on a hardness level of 500 mg/1
calcium carbonate which is 40 percent calcium by weight (cited in ICF/Clement
1986). Higher hardness levels are considered unsuitable for general domestic
purposes (McKee and Wolf 1963).
The EPA did not establish AWQC for calcium. For the mosquito fish the 96
hr and 48 hr LC50 for calcium carbonate and calcium sulfate respectively were
each 56,000 ppm. The LC50 for bluegill tested with calcium nitrate was 10,000
ppm. Calcium is 40 percent of calcium carbonate by weight, 38 percent of
calcium sulfate and 25 percent of calcium nitrate. Assuming that the reported
ppm reflected both anions and cations, the LC50s expressed as mg/1 calcium ion
alone would be 22,400 (from CaC03), 16,240 (from CaS04), and 2,500 ppm
(Ca(N03)2). Using the value for CaCOS, assuming carbonate to be the least
toxic of the associated anions, an ACR of 4, and dividing by 10 to extrapolate
to sensitive species, the ARC equals 560 mg/1 calcium ion.
Unfortunately, fresh-water community species composition might be strongly
affected by calcium concentrations. Calcium is probably more variable in
amount than any other ion in fresh waters; from less than 1 mg/1 in soft
waters to 100 mg/1 in hard. "There is scarcely an animal group in which the
distribution of at least some species has not been related to the calcium
concentration" (Macan 1963). Sufficient information is not available,
however, to model the effect of increasing calcium concentration shifting
species composition in aquatic ecosystems.
Cerium
Metallic cerium does not occur free in nature; the principal source of
this element being monazite sand. Most eerie (+4) and cerous (+3) salts are
soluble, except for the oxide and fluoride. There is conflicting opinion
about the possible deleterious effects of cerium salts, but there is no
evidence of severe toxicity (McKee and Wolf 1963).
There are no established aquatic criterion concentrations for cerium or
cerium salts. McKee and Wolf (1963) cited a few studies of aquatic toxicity.
Daphnia sp. exposed to 1000 mg/1 cerium appeared unaffected. The "lethal
dose" for the fish Tinea vulgaris was reported as 30,000 mg/1. E. coli,
however, was adversely affected by 0.75 mg/1 cerium. Review of the original
literature for these studies is beyond the scope of this project. The results
for Daphnia and Tinea suggest that cerium salts are relatively non-toxic to
aquatic life.
Chloride
The EPA has set a secondary maximum contaminant level (SMCL) of 250 mg/1
for chlorides to prevent most aesthetic effects and to prevent corrosion of
pipes in hot water systems (EPA 1984c).
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The 24 to 96 hr LC50 values for calcium chloride reported in Appendix III,
Table 1 of the 1972 Water Quality Criteria (NAS/NAE 1972) were 3,526 mg/1 for
Daphnia magna, 8,350 for Lepomis macrochirus, and 13,400 mg/1 for Gambusia
affinis. Chloride represents 64 percent of the salt by weight. Assuming
calcium ions to be less toxic than chloride, these LC50s would equal 2,257,
5,344, and 8,710 mg/1 as chloride ions.
Using an ACR of 3 (assuming similarity to sodium), and division of 2,257
by 10 to extrapolate to sensitive species, the ARC for chloride ions equals
75.2 mg/1.
Chromium
Chromium can exist in several valence states; however, in the aquatic
environment, it is virtually always found in the valence states +3 or +6 (EPA
1984b). Hexavalent chromium is a strong oxidizing agent which reacts readily
with reducing agents such as sulfur dioxide to give trivalent chromium, while
trivalent chromium oxidizes slowly to Cr (VI) (EPA 1984b). Trivalent chromium
forms numerous types of hexacoordinate complexes and is largely immobile in
ground water. Hexavalent chromium exists in solution as a component of an
anion, and is far more mobile.
Chromium(III) is less toxic than chromium(VI) both to humans and to
aquatic life. The RfD for chromium III is 1.0 mg/kg-day while the RfD for
chromium(VI) is 0.005 mg/kg-day.
For chromium(III), the AWQ CCC is the numerical value give by e raised to
the (0.8190[ln(hardness)]+1.561). In soft water (hardness of 50 mg/L as
CaCOS), this value is 0.120 mg/1. For chromium(VI), the AWQ CCC is 0.011 mg/1.
Cobalt
Cobalt is part of the vitamin B12 molecule and a wide margin exists
between essential and toxic levels. Cobalt salts have been used
therapeutically at up to 300 mg/day (the equivalent of drinking 2 liters with
150 mg/1 of cobalt (NAS 1977)). However, cobalt salts included in a beer
formulation at concentrations of 1.2 to 1.5 mg/1 were reported to be
responsible for a number of deaths due to congestive heart failure (USFDA 1975
cited in NAS 1977). This concentration is considered "well below" levels that
are safe for people not consuming large amounts of alcohol (NAS 1977). The
USSR has recommended a limit of 1.0 mg/1 in drinking water.
Based on livestock toxicity testing, the EPA set an upper limit of 1 mg/1
for cobalt in livestock waters (NAS/NAE 1972).
There are no established CCCs for cobalt. McKee and Wolf (1963) cited a
report (Schweiger 1961) that cobalt concentrations of 1 mg/1 "were not
harmful" to one-year-old tench, carp, rainbow trout, and char; or to
curstacea, worms, and insect larvae forming the food for these fish.
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o-Cresol
The verified RfD for o-Cresol is 0.05 mg/kg-day. Assuming a 70 kg man
drinking 2 liters of water per day, the HRC equals 1.75 mg/1.
In the absence of either an AWQ or a NIES CCC, EPA calculated an aquatic
reference dose as follows. EPA (1975) reports 96 hr TLm's for goldfish of 19
mg/1, bluegills of 20.8 mg/1, fathead minnows of 13.4 mg/1, guppies of 18
mg/1; a 24 hr TLm of 15 mg/1 for tench and a 24 hr TLm of 2 mg/1 for trout
embryos. Dividing the lowest adult fish TLm of 13.4 mg/1 by 10 to extrapolate
to more sensitive species, and using a generic ACR of 45 (MDNR 1984), the ARC
equals 0.03 mg/1.
p-Cresol
The established human RfD is the same as for o-Cresol, but again, there is
no AWQ or NIES CCC.
Verschueren (1983) reports an LC50 of 5 mg/1 for trout embryos and 19 mg/1
for fathead minnows. EPA (1975) reported 24 hr TLm's of 15.4 mg/1 for tench,
21.2 m/1 for carp; a 96 hr TLm of 10 mg/1 for bluegills and a 48 hr TLm of 24
mg/1 for mosquito fish. Because embryo data are difficult to interpret (EPA
1986a), the lowest adult fish TLm of 10 mg/1 was divided by 10 to extrapolate
to more senstive species and divided by a generic ACR of 45 (MDNR 1984) to
obtain the ARC of 0.22 mg/1 for p-Cresol.
Cyanides (sol salts & complexes)
The RfD for inorganic cyanides not otherwise specified is 0.022
mg/kg-day. Assuming a 70 kg man drinks 2 liters of water daily, the HRC
equals 0.750 mg/1.
The AWQ CCC for cyanides is 0.0052 mg/1, and has not been shown to be
hardness dependent.
2,4-Dimethylphenol
Included in the Liner Location Model (constituent data base) is an
ingestion threshold of 0.0018 mg/kg-day. For a 70 kg person drinking 2
liters/day, the reference human health concentration is 0.063 mg/1.
The established NIES CCC of 0.027 mg/1 carries a weight of evidence
classification of B2 (IGF/Clement 1986). It is based on the lowest available
acute LC50 value of 2.12 mg/1 for Daphnia magna, an ACR of 8 (Kenaga 1982),
and a divisor of 10 to extrapolate to more sensitive species.
Ethylbenzene
The HRC of 3.5 mg/1 is based on the RfD for ethylbenzene of 0.1 mg/kg-day.
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A-40
On the basis of the lowest available LC50 concentration of 45.3 mg/1 for
the guppy, an ACR of 103 for the fathead minnow, and a divisor of 10 to
extrapolate to sensitive species, the NIES CCC was set at 0.44 mg/1 with a
weight of evidence of B2 (IGF/Clement 1986).
Fluoride
The RfD based HRC is 1 mg/1. This level protects against dental
fluorosis. The RMCL and MCL, however, are each 4 mg/1 (the previous MCL was
1.4-2.4 mg/liter dependent on annaual average air temperatures, and the
secondary MCL was 2 mg/1), based on the opinion that the cosmetic effects of
dental fluorosis are not adverse health effects.
An aquatic limit of 1.5 mg/1 fluoride has been cited by several authors
(McKee and Wolf 1963, Todd 1970, EPA 1975). A TLm of 2.3 mg/1 for trout in
one study (EPA 1975), however, would suggest that a lower criterion would be
more in keeping with the intent of the AWQ CCC. Nonetheless, review of the
original literature is beyond the scope of this study, and the ARC is set at
1.5 mg/1.
Lithium
Inhilation exposure to various lithium compounds can cause eye, skin, and
mucous membrane irritation, pulmonary emphysema, blurred vision, coma, and
epileptic seizures. Little information is available on the toxicity of
lithium via oral exposure (EPA 1985a), but it is not considered very toxic .
Daily intake is about 2 mg (Doull et al. 1980), and therapeutic doses for the
treatment of mania and hypomania are at least 900 mg/person-day (Gilraan, et
al. 1985).
Lithium salts are generally soluble in water. McKee and Wolf (1963) noted
"lethal concentration" of lithium chloride for several aquatic species. From
the U.S. literature, these ranged from 1950 to 3750 mg/1 for some fish
species, and 7 to 1000 mg/1 for a few invertebrates and bacteria. One German
publication reported that 33 mg/1 is harmless to fish while 100 mg/1 produces
toxic effects (McKee and Wolf 1963).
Magnesium
Magnesium is rapidly excreted by the kidney, and it is unlikely that
magnesium in food and water is absorbed and accumulated in tissues in
sufficient quantities to induce toxicity. Concentrations of 700 mg/1 produce
a laxative effect. EPA therefore used 700 mg/1 for initial screening purposes
only.
NAS/NAE (1972) Appendix III, Table 1 reports LC50s of 3,390 and 3,800 mg/1
for Daphnia magna exposed to MgC12 and MgS04 respectively, of 19,000 mg/1 for
bluegill sunfish (MgC12), and 15,500 and 17,500 mg/1 for mosquito fish exposed
to MgS04 and MgC12 respectively. Magnesium represents 41.4 percent of
magnesium chloride and 20 percent of magnesium sulfate by weight. The acute
values for D. magna therefore represent 1,389 and 760 mg/1 Mg, the geometric
mean of which equals 1027 mg/1. Using a divisor of 5 to extrapolate to more
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A-41
sensitive species (assuming D. magna is relatively sensitive itself) and an
ACR of 4 CKenaga 1982) results in an ARC of 51 mg/1 as Mg++.
This assumes that Mg is producing the effect. It is possible, however,
that chloride and sulfate are responsible instead, or that magnesium salts
create an osmotic stress at these levels. It appears reasonable at this time
to consider Mg-H- as contributing to total salt content rather than as a toxic
constituent.
Methyl Isobutyl Ketone
The HRC of 1.75 is based on the RfD of 0.05 mg/kg-day.
Verschueren (1983) cites one 24 hr LC50 for goldfish of 460 mg/1, and a
toxicity threshold for the bacteria Pseudomonas putida of 275 mg/1. Using the
goldfish LC50, a divisor of 10 to extrapolate to sensitive species, and a
generic ACR of 45 (MDNR 1984) yields an ARC of 1 mg/1.
Methyl Ethyl Ketone (2-Butanone)
The human reference dose of 1.75 mg/1 is based upon the AIC of 0.050
mg/day.
The aquatic toxicity data are insufficient for an AWQ CCC. Verschueren
(1983) cited two LC50 values, 5,600 mg/1 for mosquitofish and 1,690 mg/1 for
bluegills. Division of 1,690 by 10 to extrapolate to sensitive species, and a
generic Acute/Chronic Ratio of 25 for a non-halogenated organics (Kenaga 1982),
produce a final ARC of 6.76 mg/1.
2-Methylnapthalene
There was insufficient information for either human health effects or
aquatic effects to derive a reference dose. The concentrations of
2-methylnapthalene were therefore compared to the reference doses for
napthalene to estimate the possibility that 2-methylnapthalene could be of
concern. In all cases, the ratio of 2-methylnapthalene's median concentration
in drilling and production wastes to napthalene's reference concentrations
were less than 1.
Molybdenum
The EPA (1985b) has established an adjusted acceptable daily intake for
moybdenum of 0.100 mg/1.
NAS/NAE (1972) cite 96 hr LCSOs for the fathead minnow of 70 and 370 mg/1
in soft (20 mg/1 CaC03) and hard (400 mg/1 CaCOS) water respectively.
Division by 10 to extrapolate to sensitive species, with generic ACR of 45
(MDNR 1984) yields an ARC of 0.155 mg/1 MoO , or 0.10 mg/1 as Mo.
3
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A-42
One additional acute-toxicity test for molybdenum is with embryo and
larval stages of the rainbow trout (Birge 1978). The LC50 equaled 730 ug/1
and the LCI equaled 22 ug/1. Assuming these stages to be the most sensitive,
using the LC50 as the lowest acute value, division by 5 to extrapolate to
more sensitive species, and division by and ACR of 2 (recommended by the EPA,
1986a when the lowest acute value is from an embryo) suggests an aquatic
reference concentration (ARC) of 0.073 mg/1.
An ARC of 0.073 mg/1 of Mo is chosen as the more protective concentration.
Napthalene
The Liner Location Model's threshold value for adverse occular effects of
napthalene is 0.3 mg/kg-day, or 10.5 mg/1 drinking water (EPA 1986d).
The NIES CCC of 0.032 mg/1 carries a weight of evidence of B2 and is based
on the lowest acute value of 1.9 mg/1 for rainbow trout, an average ACR of 12
(11.0 mg/1, EPA 1980e; 13.2 mg/1, Kenaga 1982), and a divisor of 5 to
extrapolate to more sensitive species (IGF/Clement 1986).
Nitrate/Nitrite
Because nitrite is easily oxidized to form nitrate, nitrate predominates
in ground and surface waters. The EPA's Lifetime Drinking Water Health
Advisory (DWHA) for nitrate nitrogen (based on the ten-day HA for a 4 kg
infant) is 10 mg/1.
EPA's AWQ CCC for nitrate nitrogen is 90 mg/1 based on observations by
Knepp and Arkin (1973) that laregmouth bass and channel catfish could be
maintained at concentrations up to 400 mg/1 nitate without significant effect
upon their growth and feeding activities. LCSOs determined for chinook
salmon, fingerling rainbow trout, and bluegill were 1,310, 1,360, and 2,000
mg/1 nitrate nitrogen, respectively. No acute/chronic ratios have been
determined.
Combined Nitrogen
Several contaminants are essential plant nutrients, and under the
appropriate circumstances, can contribute to nuisance algal blooms in
recreationally valuable surface water. Of the essential plant nutrients found
in drilling muds and produced waters, some are major controlling factors on
primary production whereas others are occasionally or rarely controlling.
Major controlling nutrients include nitrogen, phosphorous, and for diatoms,
silicon. Occasionally controlling are the availability of manganese, iron,
and molybdenum. Rarely controlling factors include cobalt, sulfer, potassium,
calcium, sodium, zinc, copper, boron, and vanadium (Krebs 1972). Of these,
only one of the two most common limiting nutrients, nitrogen, was considered.
Phosphates, although well recognized as stimulating algal blooms and
contributing to lake eutrophication, are not mobile enough in ground water to
reach surface waters in the model scenarios.
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A-43
Drilling muds and produced liquids provide two sources of nitrogen,
ammonia and nitrates/nitrites. In well oxygenated surface water, both ammonia
and nitrites are converted to nitrates by a variety of aerobic bacteria. The
combined nitrogen content of the samples therefore equalled the sum of the
nitrogen contained in ammonia and in the nitrates/nitrites.
Examination of Appendix III of the Technical report indicated that the
reported concentrations were for the nitrogen only from ammonia and from
nitrates/nitrites (in contrast to the data presentation in the Technical
Report itself). Combined nitrogen therefore equalled the sum of the ammonia
and nitrate/nitrite concentrations.
Although the EPA's AWQC include a threshold for phophates beyond which
undesirable plant growth is expected, there is no equivalent value for
nitrogen. Moreover, it should be remembered that one or the other but not
both of these nutrients could be limiting at one time. The 1972 Quality
Criteria for Water (NAS/NAE 1972), however, cited Sawyer's (1947)
recommendation that if nitrogen levels exceed 0.30 mg/1 or phosphorous levels
exceed 0.010 mg/1 in Wisconsin lakes, algal blooms would follow. In order to
not be overly conservative, nitrogen levels are set as 0.60 mg/1 for lakes and
reservoirs and as 2.40 mg/1 for rivers. For comparative purposes, EPA's AWQC
(EPA 1986a) for phosphate phosphorous is 0.025 mg/1 within lakes or reservoirs
and 0.10 mg/1 in streams that do not discharge directly to lakes.
Phenanthrene
Phenanthrene is a polycyclic aromatic hydrocarbon (PAH), and is moderately
persistent in natural environments (EPA 1985a). There are no epidemiological
or case studies available suggesting that phenanthrene is carcinogenic in
humans. However, at least two skin painting studies report development of
tumors at the site of application in mice (EPA 1985a).
The International Agency for Research on Cancer (IARC 1983), considers the
evidence inadequate to assess the carcinogenicity of phenanthrene. It is
possible, then, that it is only weakly carcinogenic compared to some of the
other PAHs.
For carcinogenic PAHs, the EPA AWQC for human health associated with a
-6
risk level of 10 equals 0.0028 ug/1. This is based upon the more potent
carcinogens, and is probably inappropriate for phenanthrene.
There are no fresh-water aquatic tests for the toxicity of phenanthrene in
the literature reviewed.
Potassium
Potassium is abundant in the food supply, whereas water generally
contributes little to total potassium intake (NAS 1980). Potassium excess
leading to toxicity is not common and is not incurred through the diet (NAS
1980), which makes it unlikely to be incurred through drinking water either.
NAS (1980) estimated adequate and safe intake levels for potassium ranging
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A-44
from 350 to 925 rug/day for infants under 6 months to 1,875 to 5,600 rug/day for
adults.
NAS/NAE (1972) cited LC50 values for bluegills of 940 mg/1 and of 679 mg/1
for Daphnia magna. The Acute/Chronic Ratio for for bluegill sunfish was
measured as 3 (Kenaga 1982). Using a divisor of 10 to extrapolate to
sensitive species from the cladoceran value, the ARC is set equal to 22.6 mg/1.
It is probably reasonable to consider potassium as contributing to total
dissolved salts.
Salinity
Salinity is an oceanographic term, and although not precisely equivalent
to the total dissolved salt content, it is related to it (Capurro 1970 cited
by EPA 1986a). It is numerically smaller than the filtrable residue and
usually is reported as grams per kilogram or parts per thousand
(APHA-AWWA-WPCF 1975). The principal inorganic anions dissolved in water
include the carbonates, chlorides, sulfates, and nitrates (principally in
ground waters); the principal cations are sodium, potassium, calcium, and
magnesium (EPA 1986a). The total weight of the following ions dissolved in 1
kg of sea water is termed salinity: sodium, potassium, calcium, magnesium,
chloride, sulfate, carbonate (Reid and Wood 1976).
In the process of evaporating seawater in ordfir to weigh the salts,
carbonates are decomposed to oxide, and bromine and some quantity of chlorine
are liberated. Since the salts of the metallic ions, the conservative
elements, occur in very uniform proportions, it is possible to determine the
salinity by measuring the chloride content, or chlorinity, and applying it in
the following relationship:
Salinity ppt = 0.03 + 1.805(chlorinity ppt).
Table A-3 illustrates the ionic composition of two rivers and value means,
medians, values and ranges for river waters comprising over 90 percent of
fresh water rivers.
The average salinity of soft fresh water is 0.065 ppt (or 65 mg/1), and of
hard fresh water is 0.30 ppt (300 mg/1) (Hart et al. 1945). Among inland
waters in the United States supporting a good mixed fish fauna, about 5
percent have a dissolved-solids concentration under 72 mg/1; about 50 percent
under 169 mg/1, and about 95 percent under 400 mg/1 (Hart et al. 1945). The
upper limit of what is considered "fresh water" is 500 mg/1 (the 1958 "Venice
System" value reported in Reid and Wood 1976, Macan 1963, and Pennak 1953).
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A-45
TABLE A-3
IONIC COMPOSITION OF NATURAL WATER FROM SEVERAL SOURCES
Ion
Na
K
Ca
Mg
Cl
S04
C03
(1)
Mean
N.A. Rivers
percent
7.5
1.8
19.4
4.9
7.4
15.3
33.4
(1)
Delaware
River
percent
6.7
1.5
17.5
4.8
4.2
17.5
33.0
(1)
Rio
Grande
percent
14.8
0.9
13.7
3.0
21.7
30.1
11.7
(2)
90 % of
median
tng/1
10
(a)
28
7
9
32
90
(2)
River Waters
range
mg/1
6 - 85
(a)
15 - 52
4-14
3 - 170
11 - 90
72 - 400
N.A. = North American
(1) from Clarke 1924 reported in Reid and Wood 1976.
(2) from Hart et al. 1945 reported in NAS/NAE 1972.
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Salinity constitutes a striking environmental limit on organisms,
primarily through its effect on physiological processes other than the uptake
and utilization of nutrients. The tolerance of fresh-water organisms to
increasing dissolved ions varies widely between species. Some fish are
anadramous, while others "tolerate" brackish salinities despite their tendency
to be associated with less saline waters (Hynes 1970). Other species
"tolerate" more narrow ranges of dissolved salt content (Hynes 1970).
Nonetheless, merely tolerating dissolved ions, and remaining equally
competitive are two different issues. On the basis of available data, it is
not possible to extrapolate the response of an aquatic community to changes in
salinity. The degree of salt addition that could trigger a major shift in
community structure and species composition and abundance would depend upon
background concentrations and the native or current species distributions.
EPA therefore takes a different approach for estimating an ARC for total
dissolved salts. EPA assumes the following:
(1) ions of Ca, Mg, K, Na, Cl, and S04 contribute to total mobile
salinity potentially impacting surface waters,
(2) all ions have the same mobility as chloride,
(3) the average background concentration of dissolved salts already
present in soft surface water is 65 mg/1 (a liberal assumption),
(4) the average background concentration of dissolved salts already
present in hard surface water is 300 mg/1, (a conservative
assumption), and
(5) the total dissolved ions derived from the background concentration(s)
and the added mobile salts cannot exceed 400 mg/1, in order to be
protective of 95 percent of fresh-water species.
Based on these assumptions, a liberal ARC for total mobile salts in surface
water is 335 mg/1 (400 mg/1 - 65 mg/1), and a more conservative ARC is 100
mg/1 (400 mg/1 - 300 mg/1).
Silicon
Sodium silicates have been used as coagulants for the removal of turbidity
and iron, and dosages of 4.0 to 8.0 mg/1 have been used to form protective
coatings on pipes to inhibit corrosion of iron (McKee and Wolf 1963).
An abundance of silica in water, along with other necessary nutrients,
favors the growth of diatoms, although a concentration of at least 0.5 mg/1
might be required for some species to even utilize silica (McKee and Wolf
1963).
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A-47
Sodium
The aquatic toxicity data are insufficient for deriving and AWQC CCC.
Moreover, it is unclear whether the results from tests with sodium chloride
result from toxic effects of sodium, chloride, or osmoregulatory problems.
NAS/NAE (1972) cite several acute toxicity studies, including LCSOs of 14,125
and 6,447 mg/1 of sodium chloride for bluegills and Daphnia magna,
respectively. Sodium represents 39 percent of NaCl. Assuming the reported
concentrations to include both ions and assuming the effect to result from
sodium, LCSOs of 5,509 and 2,514 ppm sodium ion is implied.
The Acute/Chronic Ratio for sodium has been measured as 3 for bluegill
sunfish (Kenaga 1982). Division of 2,514 by 10 to extrapolate to sensitive
species, with an ACR of 3, the ARC equals 83.4 mg/1 sodium ion. It must be
emphasized that this ACR is highly speculative depending upon several
assumptions, and is used in the initial screening of chemicals only.
Strontium
The National Academy of Sciences (1977) estimated a 7-day SNARL of 84 mg/1
for strontium. EPA applied a safety factor of 10 to estimate a lifetime SNARL
of 8.4 mg/i.
EPA (1984d) set an ADI for strontium sulfide of 0.018 mg/kg. Assuming
that strontium does not bioaccumula^e, that a 70 kg individual consumes 2
liters of water per day, and tnat 10% of total strontium is from drinking
water, a protective concentration of strontium sulfide would equal 63 ug/1.
However, strontium sulfide is considerably more toxic than the strontium
cation (ICF/Clement 1986), so the NAS 7-day SNARL has been used instead.
EPA found one acute-toxicity test for strontium with embryo and larval
stages of the rainbow trout (Birge 1978). The LC50 equaled 200 ug/1 and the
LCI equaled 6 ug/1. Assuming these stages to be the most sensitive, and using
the LCI as the LOEL roughly equivalent to a CCC, and division by 5 to
extrapolate to more sensitive species suggests an aquatic reference
concentration (ARC) of 1.2 ug/1.
EPA considers one study insufficient to estimate strontium's toxicity, but
notes that strontium contained in drilling pit liquids and produced fluids
might represent a considerable risk to aquatic ecosystems if this study is
accurate and representative.
Sulphur as Sulphates
Sulfur (as sulfates) mobility in soils depends upon the anion exchange
capacity of the soil. Sulfate is very mobile in spodosols, acidic soils that
provide limited amounts of basic material. Sulfates are moderately mobile in
most other soils.
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A-48
The EPA determined a SMCL for sulfates of 250 mg/1 for sulfates to prevent
most taste effects (EPA 1984c). This level will also prevent laxative
effects. This is equivalent to 82.5 mg/1 of sulfur.
Sulfates are considered part of the total mobile dissolved salts.
Sulfide
EPA assumes that sulfides are oxidized to sulfates in ground and surface
waters.
Toluene
Based on the RfD of 0.03 mg/kg-day, the HRC for toluene is 1.05 mg/1.
On the basis of the lowest acute LC50 value cited by IGF/Clement (1986) of
17 mg/1 for the bluegill, division by 10 to extrapolate to more sensitive
specie?, and a generic ACR of 25 for aromatics (Kenaga 1982), the final ARC is
0.068 -.ซ/!.
Yttrium
EPA did not find information relating to the toxicity of yttrium.
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A-49
REFERENCES FOR APPENDIX A
American Conference of Governmental Industrial Hygienists (ACGIH). 1981.
Documentation of the Threshold Limit Values. Fourth Edition. ACGIH,
Cincinnati, Ohio.
APHA-AWWA-WPCF. 1975. Standard Methods for the Examination of Water and
Wastewater, 14th Edition, American Public Health Association, Washington,
D.C.
Baes, C.F., III, Sharp, R.D. 1983. A proposal for estimation of soil leaching
and leaching constants for use in assessment models. J. Environ. Qual.
12:17-28.
Baes, C.F., III, Sharp, R.D., Sjoreen, A.L., Shor, R.W. 1984. A Review and
Analysis of Parameters for Assessing Transprot of Environmentally Release
Radionuclides through Agriculture. Prepared for the U.S. Department of
Energy. ORNL-5786. September 1984.
Bartlett, R.J. and James, B.R. 1979. Behavior of chromium in soils: III.
oxidation. J. Environ. Qual. 8:31-25.
Bartlett, R.J. and Kimble, J.M. 1976. Behavior of chromium in soils:
1. Trivalent forms. J. Enviror. Qual. 5:379-382.
Biggar, J.W. and Fireman, M. 1960. Boron adsorption and release by soils.
Soil Sci. Soc. Am. Proc. 24:115-120.
Bingham, F.T., and Page, A.L. 1971. Specific character of boron adsorption
by an amorphous soil. Soil Sci. Soc. Am. Proc. 35:892-893.
Birge, W.J. 1978. Aquatic toxicology of trace elements of coal and fly ash.
In: Energy and Environmental Stress in Aquatic Systems, J.H. Thorp and
J.W. Gibbons eds., DOE Symposium Series (CONF-771114).
Bolt, G., and Bruggenwert, B. 1976. Soil Chemistry A: Basic Elements.
New York: Elsevier Publishing Co.
Bradford, G.R. 1966. Boron. In: Diagnostic Criteria for Plants and Soils,
H.D. Chapman ed. University of California, Division of Agricultural
Science, Berkeley, pp. 33-61.
Callahan, M.A., Slimak, M.W., Gabel, N.W., May, C.P., Fowler, G.F., Freed,
J.R., Jennings, P., Durfee, R.L., Whitmore, F.C. Maestri, B., Mahey, W.R.,
Holt, B.R., and Gould, C. 1979. Water-related Environmental Fate of 129
Priority Pollutants, Volume 1, EPA 440/4-79-029a, Office of Water Planning
and Standards, U.S. Environmental Protection Agency, Washington, D.C.
* *
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A-50
Capurro L.R.A. 1970. Oceanography for practicing engineers. Barnes and Noble
Inc., New York. reported in EPA 1986a.
Carcinogen Assessment Group (CAG). 1985. Relative Carcinogenic Potencies
Among 54 Chemicals Evaluated by the Carcinogen Assessment Group as Suspect
Human Carcinogens. Revised May, 1985.
Carriker, N.E., and Brezoniky, P.L. 1978. Sources, levels, and reactions of
boron in Florida water. J. Environ. Qual. 4:516-522.
Chapman, H.D. 1968. Mineral nutrition of citrus. In: The Citrus Industry, W.
Reuther, L.D. Batchelor, and H.J. Webber eds. University of California,
Division of Agricultural Science, Berkeley, vol 2. pp 127-289.
Clarke, F.W. 1924. The composition of river and lake waters of the United
States, U.S. Geol. Surv. Prof. Pap. No. 135, as cited in Reid and Wood
1976.
Clayton, G.D., and Clayton, F.E., eds. 1981. Patty's Industrial Hygiene and
Toxicology. Third Revised Edition. Volume 2: Toxicology. John Wiley
and Sons, New York.
Considine, D.M. ed. 1983. Van Nostrand's Scientific Encyclopedia. 6th Ed.
New York: Van Nostrand Reihold Co.
Cotton, T.A., and Wilkinson, G. 1972. Advanced Inorganic Chemistry. 3rd. ed.
New York: John Wiley and Sons.
Dahl, L.K. 1960. Essential Hypertension, an International Symposium.
Cattier, P.T., and Buck, K.D., eds. Springer-Verlag Berlin, pp. 61-75 as
cited in National Academy of Sciences, 1972. Water Quality Criteria,
Washington, D.C. p. 88.
Dawson, G.W., and Banning, D. 1982. Exposure-response analysis for setting
site restoration criteria. Presented at the Third National Conference on
Management of Uncontrolled Hazardous Waste Sites, Washington, D.C.
November 29-December 1, 1982.
Doull, J., Klaasen, C.D., and Amdur, M.O. 1980. Casarett and Doull's
Toxicology. Second Edition. MacMillan Publishing Co., Inc., New York.
Eaton, F.M. 1935. Boron in soils an irrigation waters and its effects on
plant with particular reference to the San Joaquin Valley of California.
U.S. Dept. Agr. Tech Bull No. 448, 131 pp.
Eaton, F.M. 1944. Deficiency, toxicity, and accumulation of boron in plant.
J. Agr. Res. 69:237-277.
Eisenreich, S.J., Hoffman, M.R., Rastetter, D., Yost, E., and Maier, W.J.
1980. Metal transport phases in the upper Mississippi River. In
Kavanaugh, M.C., and Leckie, J.O., eds. Particulates in Water. American
Chemical Society, Washington, D.C. pp. 135-176.
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A-51
EPA. 1986a. Quality Criteria for Water, EPA 440/5-86-001 for [Specific
Chemicals].
EPA. 1986b. Superfund Public Health Evaluation Manual, EPA 540/1-86-060.
EPA. 1986c. Health Effects Assessment, prepared by the Environmental
Criteria and Assessment Office, U.S. EPA, Cincinnati, Ohio, 1985 (updated
1986).
EPA. 1986d. Liner Location Risk and Cost Analysis Model, Draft Phase II
Report, Office of Solid Waste, Washington, DC.
EPA. 1985a. Chemical, Physical, and Biological Properties of Compounds
Present at Hazardous Waste Sites, Prepared by ICF/Clement Associates.
EPA. 1985b. Health Effects Assessment for [Specific Chemical]. [Note:
58 individual documents available for specific chemicals or chemical
groups]. ECAO U.S. EPA, Washington D.C.
EPA. 1984a. Ambient Water Quality Criteria for Cadmium. Office of Water
Regulations and Standards, Criteria and Standards Division. U.S. EPA,
Washington DC. EPA 440/5-84-032.
EPA. 1984b. Ambient Water Quality Criteria for Chromium. Office of Water
Regulations and Standards, Criteria and Standards Division. U.S. EPA,
Washington DC. EPA 440/5-84-029.
EPA. 1984c. National Secondary Drinking Water Regulations, U.S. EPA.
Washington, D.C. EPA 570/9-76-000.
EPA. 1984d. International Paper ACL Demonstration Memorandum for Lee Thomas,
Assistant Administrator, to Morris Kay, Region VI Administrator. Nov. 19,
1984.
EPA. 1984e. Health Effects Assessment for Cadmium. Draft Report. Environ-
mental Criteria and Assessment Office, Cincinnati, Ohio.
EPA, 1980a. Ambient Water Quality Criteria for Arsenic. U.S. EPA,
Washington DC. EPA 440/5-80-021.
EPA. 1980b. Ambient Water Quality Criteria for Benzene. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-018, PB81-117293.
EPA. 1980c. Ambient Water Quality Criteria for Cadmium. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-025. PB81-117368.
EPA. 1980d. Ambient Water Quality Criteria for Chromium. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-035. PB81-117467.
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A-52
EPA. 1980e. Ambient Water Quality Criteria for Napthalene, Office of Water
Regulations and Standards, Criteria and Standards Division. Washington,
DC. EPA 440/5-80-059.
EPA. 1975. Supplement to Development Document, Hazardous Substances
Regulations Federal Water Pollution Control Act as Amended 1972, EPA
440/9-75-009.
Farrah, H., and Pickering, W.F. 1977. Influence of clay-solute interactions
on aqueous heavy metal ion levels. Water, Air, Soil Pollut. 8:89-197.
Frost, R.R. and Griffin, R.A. 1977. Effect of pH on adsorption of copper,
zinc, and cadmium from landfill leachate by clay minerals. J.
Environmental Science and Health 12:138-156.
Gardiner, J. 1974. The chemistry of cadmium in natural waters - I. A study
of cadmium complex formation using the cadmium specific-ion-electrode.
Water Res. 8:23-30.
Gerritse, K.G., Vriesema, R., Dalenberg, J.W., and DeRoss, H.P. 1982. Effect
of sewage sludge on trace metal mobility in soils. J. Environ. Qual.
11:359-364.
Gilman, A.G., Goodman, L.S., Rail, T.W., and Murad, F. 1985. The Pharmacolo-
gical Basis of Therapeutics. Seventh Edition. MacMillan Publishing
Company, N.Y.
Griffin, R.A, Shimp, N.F., Steel, J.D., Ruch, R.R., White, W.A., and Hughes,
G.M. 1976. Attenuation of pollutants in Municipal landfill leachate by
passage through clay. Environmental Science and Technology. 10:1262-1268.
Griffin, R.A., Au, A.K., and Frost, R.R. 1977. Effect of pH on adsorption of
chromium from landfill-leachate by clay materials. J. Environ. Sci.
Health A12:431-449.
Gupta, S. and Chen, K.Y. 1978. Arsenic removal by adsorption. J. Water
Pollution Control Federation 50:483-506.
Guy, R.D. and Chakrabarti, C.L. 1976. Studies of metal-orgaic interactions in
model systems pertaining to natural waters. Can. J. Chem. 54:2600-2611.
Hart, W.B., Doudoroff, P., and Greenbank, J. 1945. The Evaluation of the
Toxicity of Industrial Wastes, Chemicals, and Other Substances to
Freshwater Fishes, Waste Control Laboratory, the Atlantic Refining Co. of
Philadelphia, as reported in NAS 1972.
Harvey et ai. 1976. The Principles and Practice of Medicine, 19th Ed.
Appleton-Century-Crofts, New York. p. 370-392.
Hatcher, J.T., Bower, C.A. and Clark, M. 1967. Adsorption of boron by soils
as influenced by hydroxy aluminum and surface area. Soil Science
104:422-426.
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A-53
Huang, C.P., Elliott, H.A., and Asmean, R.M. 1977. Interfacial reactions and
the fate of heavy metals in soil-water systems. J. Water Pollut. Control
Fed. 49:745-756.
Hynes, H.B.N. 1970. The Ecology of Running Waters. University of Toronto
Press. Toronto.
ICF/Clement. 1986. Alternate Concentration Limit Demonstration Document,
Parts I and II, and Appendices A-C Prepared for National Industrial
Environmental Services, Kansas.
International Agency for Research on Cancer (IARC). 1983. Monographs on the
Evaluation of the Carcinogenic Risk of Chemicals to Humans. Volume 32:
Polynuclear Aromatic Compounds. Part 1. Chemical, Environmental, and
Experimental Data. World Health Organization, Lyon, France.
International Agency for Research on Cancer (IARC). 1980. IARC Monographs on
the Evaluation of the Carcinogenic Risk of Chemicals to Humans. Volume
23: Some Metals and Metallic Compounds. World Health Organization, Lyon,
France.
International Agency for Research on Cancer (IARC). 1976. IARC Monographs on
the Evaluation of Carcinogenic Risk of Chemicals to Man. Vol. 11:
Cadmium, Nickel, Some Epoxides, Miscellaneous Industrial Chemicals and
General Considerations on Volatile Anaesthetics. World Health
Organization, Lyon, France.
James, B.R. and Bartlett,.R.J. 1983a. Behavior of chromium in soils: V. Fate
of organically complexed Cr III added to soil. J. Environ. Qual.
5:169-172.
James, B.R. and Bartlett, R.J. 1983b. Behavior of chromium in soils: VII.
Adsorption and reduction of hexavalent forms. J. Environ. Qual.
12:177-181.
Kabata-Pendias, A., and Pendias, H. 1984. Trace Elements in Soils and Plants.
Boca Raton, Florida: CRC Press.
Kenaga, E.E., 1982, Predictability of Chronic Toxicity from Acute Toxicity of
Chemicals in Fish and Aquatic Invertebrates, Environ. Toxicol. Chem. 1:
347-358.
Knepp, G.L., and Arkin, G.F. 1973. Ammonia toxicity levels and nitrate
tolerance of channel catfish. The Progressive Fish Culturist 35:221.
Korte, N.E., Skopp, J., Fuller, W.H., Nievla, E.E. and Alesii, B.A. 1976.
Trace element movement in soils: Influence of soil physical and chemical
properties. Soil Sci. 122:350-359.
Krebs, C.J. 1972. Ecology, The Experimental Analysis of Distribution and
Abundance, Harper & Row, New York.
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A-54
Lawrence, C.H. 1975. Estimating Indirect Costs of Urban Water Use. J. of
the Envir. Eng. Div., ASCE, pp. 517-533 (August).
Lemmo, N.V., Faust, S.P., Belton, T., and Tucker, R. 1983. Assessment of the
chemical and biological significance of arsenical compounds in a heavily
contaminted watershed. Part 1. The fate and speciation of arsenical
compounds in aquatic environments: A literature review. J. Environ. Sci.
Health 19:355:387.
Lewis, M.A. and Valentine, L.C. 1981. Acute and chronic toxicities of boric
acid to Daphnia magna Straus. Bull. Environment Contam. Toxical.
27:309-315. Seigel
Luft et
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A-55
National Academy of Sciences (NAS). 1977-1983. Drinking Water and Health,
Volumes 1-5.
National Technical Advisory Committee (NTAC) to the Secretary of the Interior
1968. Water quality criteria U.S. Government Printing Office.
Washington, D.C. as reported in EPA 1986a.
National Research Council Canada. 1976. Effects of Chromium in the Canadian
Environment. NRCC No. 15017. ISSN 0316-0114.
National Academy of Sciences (NAS), National Academy of Engineering (NAE).
1972. Water Quality Criteria 1972. U.S. EPA, Washington, D.C. EPA
R3-73-033, March 1973.
O'Shea, T.A., and Mancy, K.H. 1978. The effect of pH and hardness metal ions
on the competitive interaction between trace metal ions and inorganic and
organic complexing agents found in natural waters. Water Res. 12:703-711.
Pennak, R.W. 1953. Fresh-Water Invertebrates of the United States, The
Ronald Press Company, N.J.
Pourbaix, M. 1963. Atlas D'Equilibres Electrochemiques. Paris: Gauthier-
Villar and Co.
Reid, G.K., and Wood, R.D. 1976. Ecology of Inland Waters and Estuaries,
2nd Edition, D. Van Nostrand Company, N.Y.
Sawyer, C.N. 1947. Fertilization of lakes by agricultural and urban
drainage. T.N. Engl. Water Works Ass. 61:109-127.
Schalscha, E.B., Bingham, F.T., Galindo, G.G., and Galvan, H.P. 1973. Boron
adsorption by volcanic ash soils in southern Chile. Soil Science
116:70-76.
Schroeder, B.C., and Lee, G.F. 1975. Potential transformations of chromium
in natural waters. Water, Air, Soil Pollut. 4:355-365.
Schweiger G. 1961. The toxic action of heavy metal salts on fish and organisms
on which fish feed. Arch. Fisch Wiss 8,54 (1957); Water Pollution
Abstracts 34:9, 1744, Reported in McKee and Wolf 1963.
Sims, J.R., and Bingham, F.T. 1968. Retention of boron by layer silicates,
sesquioxides, and soil materials: II. Sesquioxides and III. Iron- and
aluminum-coated layer silicates and soil materials. Soil Sci. Soc. Am.
Proc. 32:364-373.
Singh, S.S. 1964. Boron adsorption equilibrium in soils. Soil Science
98:383-387.
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Sloof, W., Canton, J.H., and Hermens, J.L.M. 1983. Comparison of the
susceptability of 22 freshwater species to 15 chemical compounds. I.
(sub)acute toxicity tests. Aquatic Toxicology. 4:113-128.
Stumm, W. and Morgan, J. 1981. Aquatic Chemistry. John Wiley and Sons,
N.Y. 1981.
Todd, D.K., 1970. The Water Encyclopedia, Maple Press, Cited in U.S. EPA
Supplement to Development Document, 1975.
Verschueren K. 1983. Handbook of Environmental Data on Organic Chemicals,
Second Edition, Van Nostrand Reinhold Company, N.Y.
Waggott, H.A. 1969. An investigation of the potential problem of increasing
boron concentrations in rivers and water courses. Water Res 3:749-765
Walsh, L.M., Sumner, M.E., and Keeney, D.R. 1977. Occurrence and
distribution of arsenic in soils and plants. Environ. Health Perspect.
249:67-71.
Waslenchuck, D.G. and Windom, H.L. 1978. Factors controlling the estuarine
chemistry of arsenic. Estuarine Coastal Marine Sci. 7:455-464.
White, A., Handler, P., and Smith, E.L. 1968. Principles of Biochemistry.
Fourth Ed. McGrow-Hill, New York.
Wier, R.J. and Fisher, R.S. 1972. Toxicologic studies on borax and boric
acid. Toxicol. Appl. Pharmacol. 23:351-364.
Wiley, J.D. and P.O. Nelson. 1984. Cadmium Adsorption by Aerobic Lake Sedi-
ments, Journal of Environmental Engineering (ASCE) 110(1). p.227-243.
Williams, D.E., Vlamis, J., Pukite, A.H., Corey, J.E. 1984. Metal movement
in sludge-treated soils after six years of sludge addition: 1. Cadmium,
copper, lead and zinc. Soil Science. 137:351-359.
Woolson, E.A. 1977. Fate of arsenicals in different environmental
substrates. Environ. Health Perspect. 19:73-81.
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APPENDIX B
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B-2
APPENDIX B
FREQUENCY TABLES FOR MODEL SCENARIOS
A key part of model scenario development is estimating the frequency of
occurrence of the scenarios in the real world. The procedures used to
estimate frequencies for the model scenarios used in this risk analysis are
described in Section 3.2. This appendix presents tables showing the estimated
frequencies for waste management practice and environmental setting scenarios.
B.I WASTE MANAGEMENT PRACTICES
As described in Chapter 3 of this report, EPA identified the predominant
waste management practices for both drilling wastes and produced fluids and
then developed scenarios to represent these practices. The waste management
practices modeled for drilling waste were on-site reserve pits and off-site
central pits (which receive wastes from multiple sites). Two variables were
modeled for on-site pits: pit size and presence/absence of a synthetic
liner. Table B-l presents the estimated frequency distribution of on-site
reserve pit sizes.
For produced fluids, EPA modeled underground injection as the principal
waste management practice. Three subcategories of underground injection well
were modeled: high, medium, and low injection rate. The estimated frequency
distribution of injection rates in each of the nine producing zones and the
nation is displayed in Table B-2.
B.2 ENVIRONMENTAL SETTINGS
Values for three general categories of environmental variables were
specified for the purpose of defining environmental settings at oil and gas
sites (see Chapter 3). These categories are: hydrogeologic variables;
surface water variables; and variables on exposure point characteristics. The
variables within each of these categories were combined to specify scenarios,
and the frequency of occurrence of the various combinations was estimated for
each zone and the U.S. as a whole. The values used for each of the variables
were developed based on an analysis of the environmental characteristics at a
sample of oil and gas sites.
Tables B-3 through B-6 give the weighted frequency distributions of
environmental setting scenarios for drilling sites. Table B-3 is for
hydrogeologic settings in each of the nine producing zones, Table B-4 is for
hydrogeologic settings for the nation, Table B-5 is for surface water
characteristics, and Table B-6 is for the distance to nearest exposure wells.
An analogous set of tables for production sites is given in Tables B-7 through
B-10.
* * * April 24, 1987 INTERIM DRAFT: DO NOT CITE OR QUOTE * * *
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