WESTERN ENERGY RESOURCE DEVELOPMENT
   A Network.For Monitoring The Impact On
           Surface Water Quality
                    by

               R_ W. Thomas
Environmental Monitoring Systems Laboratory
         Las Vegas, Nevada  89114
ENVIRONMENTAL MONITORING SYSTEMS LABORATORY
     OFFICE OF RESEARCH AND DEVELOPMENT
    U.S. ENVIRONMENTAL PROTECTION AGENCY
          LAS VEGAS, NEVADA 89114

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                                  DISCLAIMER

    This report has been reviewed by the Environmental Monitoring Systems
Laboratory, U.S. Environmental Protection Agency, and approved for
publication.  Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.

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roR~O~
Protection of the environment requires effective regulatory actions based
on sound technical and scientific data. The data must include the
quantitative description and linking of pollutant sources, transport
mechanisms, interactions, and resulting effects on man and his environment.
Because of the complexities involved, assessment of exposure to specific
pollutants in the environment requires a total systems approach that
transcends the media of air, water, and land. The Environmental Monitoring
Systems Laboratory at Las Vegas contributes to the formation and enhancement
of a sound monitoring-data base for exposure assessment through programs
designed to:

. develop and optimize systems and strategies for monitoring
pollutants and their impact on the environment
. demonstrate new monitoring systems and technologies by
applying them to fulfill special monitoring needs of the
Agency's operating programs

This report presents the design and rationale for a network for monitoring
the impact on surface-water quality of western energy resource development.
It proposes trend-monitoring strategy that will permit monitoring agencies to
detect water quality trends in energy resource development areas. Intense
surveys or other studies will be necessary to determine the causitive factors
for observed trends.
The recommendations may affect Federal, State and local water-quality
monitoring agencies, particularly those concerned with the impact of western
energy resource developments. Further information on this report and the
network it describes may be obtained from the Environmental Monitoring
Systems Laboratory in Las Vegas, Nevada.
Glenn E. Schweitzer
Director
Environmental Monitoring Systems
Las Vegas, Nevada
Laboratory
iii

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SUMMARY
Development of fossil fuel, uranium, and other energy reserves located in
the Western United States is essential. These resources, located primarily in
the Northern Great Plains and the Colorado Plateau, consist of extensive gas
and oil fields and coal, oil shale, and uranium deposits. Because of our
national dependence on oil and gas, conversion of coal to liquid and gaseous
forms is anticipated.

Resource development cannot be accomplished without some adverse
environmental impact. The potential for significant degradation of air, land,
or water quality exists. Pollution may occur during any or"all stages of the
extraction, refining, transportation, conversion, or utilization process.
Secondary impacts resulting from increased population pressures, water
management, and development of supportive industries are expected. The
proximity of energy resources to recreational areas (frontispiece) will result
in some impact, even if only to visual aesthetics. With careful planning,
management, and regulation, these impacts can be minimized and held to"
tolerable levels.
The objective of this report is to develop a monitoring network to assess
the impact of energy resource development on water quality in the Western
Mountain States (Montana, Wyoming, Colorado, Utah, New Mexico, and Arizona).
Such a network is required to determine the impact of cumulative effects from
many simultaneous resource development efforts and to detect changes brought
about by pollution from unregulated or unconsidered parameters in time to
impose any necessary mitigation measures. Present monitoring activities are
inadequate to detect subtle changes in most parameters over a 2-to 4-year
period in western rivers and streams.
A monitoring network designed to detect trends in surface-water quality is
proposed on the basis of our present knowledge. In such a network, the total
number of observations from the network is minimized at the expense of the
number of stations in order to provide statistically valid data. This network
consists of 25 stations:
USGS Station Number
09380000
09368000
09379500
09180500
09152500
09132500
09217000
Description

Colorado River at Lees Ferry, Ariz.
San Juan River at Shiprock, N. Mex.
San Juan River near Bluff, Utah
Colorado River near Cisco, Utah
Gunnison River near Grand Junction, Colo.
North Fork Gunnison River near Somerset, Colo.
Green River near Green River, Wyo.
iv

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09224700
09251000
09260000
09306500
09306222
09314500
09328500
09315000
06635000
06438000
06337000
06130500
06214500
06294700
06308500
06326500
06329500
08251500
Blacks Fork near Little America, Wyo.
Yampa River near Maybell, Colo.
Little Snake River near Lily, Colo.
White River near Watson, Utah
Piceance Creek at White River, Colo.
Price River at Woodside, Utah
San Rafael River near Green River, Utah
Green River at Green River, Utah
Medicine Bow River above Seminoe Res., Wyo.
Belle Fourche River near Elm Springs, S. Oak.
Little Missouri River near Watford City, N. Oak.
Musse1she1l River at Mosby, Mont.
Yellowstone River at Billings, Mont.
Bighorn River at Bighorn, Mont.
Tongue River at Miles City, Mont.
Powder River near Locate, Mont.
Yellowstone River near Sidney, Mont.
Rio Grande River near Lobatos, Colo.
These include 17 National Stream Quality Accounting Network (NASQAN)
stations and 8 other U.S. Geological Survey (USGS) stations. Stations are
located near the mouths of rivers that drain areas where energy resource
development is presently occurring. A similar network for ground-water
monitoring needs to be implemented, but available data are insufficient to
adequately determine station locations.

Biological, physical, and chemical parameters likely to be affected by
energy resource development activities were determined. Physical and chemical
water quality parameters recommended for monitoring are listed below.
Parameters marked with * should be measured in sediment samples as well as in
water.
Alkalinity, total
Aluminum, dissolved
Ammonia - nitrogen
Arsenic, tota1*
Barium, tota1*
Biochemical oxygen demand, 5-day*
Bicarbonate, dissolved
Boron, total
Carbon, dissolved organic
Carbon, total organic*
Cadmium, total
Calcium, dissolved
Carbonate, dissolved
Chloride, dissolved or ionic
Chromium, tota1*
Coal content
(sediment samples only)
Conductance, speci fi c @25C
Copper, total *
Cyanide, tota1*
Flow
Fluoride, dissolved
Iron, total*
Kjeldah1 nitrogen
Lead, total*
Magnesium, dissolved
Molybdenum, tota1*
Nickel, total*
Nitrate nitrogen
Nitrite nitrogen
Oxygen, dissolved
0i1*
pH
Phenols*
Sediment grain size
(sediment samples only)
Selenium, tota1*
Sodium, dissolved
Solids, total suspended
Solids, total dissolved
v

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Sulfate, dissolved
Temperature
Turbidity
Uranium, total*
Vanadium, total*
Zinc, total*
Biological monitoring is presently considered the most feasible method of
assessing the impact of introducing numerous organic chemicals into the
environment. Unfortunately, biological monitoring techniques are not well
developed at present; nevertheless samples should be collected and preserved
for future analyses. Sampling and analysis of carp and catfish (or similar
species) for bioaccumulation of toxic elements and compounds, fecal coliform
and fecal streptococci analyses, and collection and analyses of periphyton and
macrobenthos should be conducted. Portions of the fish samples, macrobenthos,
and a special small-fish collection (dace or gambusia) should be preserved for
future analysis.
In order to obtain sufficient data for trend analysis, water samples
should be collected about every 10 days (between 7 and 14 days). Temperature,
flow, pH, and specific conductance data should be collected continuously.
Sediment should be sampled monthly; biological samples seasonally or
semiannually (except for monthly bacteriological analyses). Collection of
semiannual water samples for organic analyses is recommended.

The network described will detect trends in monitored parameters, but it
will not provide adequate data to determine the causes of these trends.
Network data will need to be augmented by data from other stations or from
intense surveys to define the source of degradation. In addition, research on
biological monitoring techniques, organic chemical analyses, data
interpretation techniques, and development of monitoring instrumentation are
necessary. Further definition of a trend-monitoring network for ground water
is required. .
Data collected should be stored in a computerized data base. All data
should be stored either within the same data base or within the same computer
system to facilitate merging physical, chemical, and biological data. EPA's
STORET data base and computer system are recommended for this purpose.
Because of the large volume of data collected by the USGS, software and other
procedures should be developed to facilitate data accessibility and transfer
between EPAls STORET system and USGS's WATSTORE data base. Close attention to
quality assurance should be paid at all stages of collection, analysis, data
reduction, and data entry into computer data bases.
The network described is of value only if it is implemented. Present data
and monitoring activities are not adequate to permit detection of subtle
trends in most parameters over a 2-to 4-year period in western waters. The
network described could be implemented by eliminating other stations or
reducing the number of parameters or sampling frequency at other stations,
especially those near the energy resource monitoring network stations. The
early implementation of such a network is strongly recommended.
vi

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CONTENTS
Foreword.
Summa ry
Fi g ures
Tab 1 e s
Introduction.
Conclusions
Recommendat ions
Background.
Energy Resource
Development
Coal.
Oil shal e
Gas and oil
Other energy resources.
Transportation.
Conversion and refining
Power generation.
Waste di sposal .
Other.
Water availability.
Elemental pollutants.
Possible organic pollutants
Present Water Monitoring System
Network Des i gn for Monitori ng Energy'Deve 1 opment
Monitoring network design considerations.
Parameter selection
Sampling frequency.
Number and location of stations
Recommended Design.
References.
Appendix.
vii
 Page
 i i i
 iv
 .viii
 ix
 1
 3
 5
 7
 10
 10
 20
 22
 24
 26
 28
 28
 28
 29
 32
 32
 41
 55
Impact 61
 61
 62
 69
 75
 81
 83
 92

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FIGURES
Page
Number
Frontispiece Decker Strip Mine and Tongue River
1 Western energy resource region. .
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Reservoir, Montana
. . . . . . . . . . . . .
1
Coal resources in Montana, Wyoming, and western North and
South Dakota. . . . . . . . . . . . . . . . . . . . . . . 11
Coal resources in Utah, Colorado, and northern Arizona and

New Mexico. . . . . . . . . . . . . . . . . . . . . . . . 12
Existing and proposed coal mines in the Yellowstone, Little
Missouri, Belle Fourche, and North Platte River systems. . 13

Existing and proposed coal mines in the San Juan, Colorado,
and Green River systems. . . . . . . . . . . . . . . . . . 14

Fossil fuel generating plants in the Yellowstone, Little
Missouri, Belle Fourche, and North Platte River systems. . 18

Fossil fuel generating plants in the San Juan, Colorado,
and Green River systems. . . . . . . . . . . . . . . . . . 19

Oil shale reserves in the Western United States. . . . . . . 21
Old oil pump showing leakage. . .
. . . . . . . . . .
. . . 23
San Juan River 1962 oil spill from ruptured pipeline. . . . 23
Uranium reserve regions, Western United States.
. . . .
. . 25
Stack plume from coal-fired power plant emission. . . . . . 29
Uranium tailing pond failure. . . . .
. . . . . . . .
. . . 30
(A) Water supply truck .(B) Oil from crankcase leak. . . . .30
Number of samples vs. allowable error for selected
standard deviations. . . . . . . . . . . . . . . .
. . . . 72
Recommended stations for monitoring western energy resource
development impact on surface-water quality, Missouri River

system. . . . . . . . . . . . . . . . . . . . . . . . . . . 78
Recommended stations for monitoring western energy resource
development impact on surface-water quality, Colorado River

sys t em . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
viii

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Number
1
TABLES
Page
2
Recommended Stations for Monitoring Western Energy Resource
Development Impact on Surface-Water Quality. . . . . . . . . . .

Physical/Chemical Parameters to be Monitored for Assessment
of Energy Resource Development Impact on Water Quality. . . . .

Active and Planned Coal Mines in the Western Energy Resource

Region. . . . . . . . . . . . . . . . . . . . . . . . . .
15
5
6
3
4
Minimum Estimates of Nonfue1 Mineral and Material Requirements
for Energy Production in the United States, 1975-90 ...... 31

Elemental Composition of Coal from a Number of Coal Fields
Throughout the Western Energy Development Areas. . . . . . . .. 33

Average Effluent Concentrations from Western Coal Stockpiles. .. 35

Elemental Composition of Ashes from U.S. Coals. . . . . . . . .. 36

Maximum Concentrations of Elements in Various Water Samples. . .. 37

Trace Elements in Oil Shale Retort Waters and Spent-Shale

Leachates. . . . . . . . . . . . . . . . . . . . . . . . . . .. 40
5
6
7
8
9
10
Compounds and Organic Compound Classes Identified in Oil Shale
and Coal Hydrogenation Process Streams and Products. . . . . .. 42

Observed Concentration Ranges of Organic Chemicals From Coa1-
Hydrogenation Streams and Waste-Water Removal Effectiveness. . . 43

Maximum Values of Semivo1ati1e Organic Compounds Identified in
Western Energy Resource Development Samples. . . . . . . . . . 44

Maximum Values of Volatile Organic Compounds Identified in
Western Energy Resource Development Samples. . . . . . . . . . 49

Compounds Occurring More Than 15 Times at Concentrations of
100 ppb or More. . . . . . . . . . . . . . . . . . . . . . . . 54
11
12
13
14
15
Parameters Monitored by the Existing Sampling Network in the San
Juan Basin and Their Average Annual Frequency of Measurement. . 57

High Priority Physical/Chemical Parameters and Parameters
Presently Sampled at NASQAN Stations. . . . . . . . . . . . . . 64

STORET Data Statistics for Selected Parameters at Three
Representative Stations. . . . . . . . . . . . . . . . . . . . . 73
16
17
18
. . . . . . . 80
NASQAN Station Descriptions. . . . . . . . . . . . .
ix

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r~
- -
- .-.. - -
-l
I
1
I
I

I
I
I
Frontispiece. Decker Strip Mine and Tongue River Reservoir, Montana.
Although this is a well-planned facility, the potential for serious
pollution to the reservoir from seepage or a spill exists because of
the mine's size and proximity to the reservoir.

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INTRODUCTION
The United States has embarked on a program to reduce its dependency on
foreign fuels. To accomplish this, large fossil fuel and uranium reserves
located in the northern Great Plains and the Colorado Plateau (Figure 1) must
be developed. These resources cannot be developed without some environmental
disruption. However, close regulation and proper planning can minimize this
disturbance and reduce it to tolerable levels.
The potential exists for serious environmental impact during exploitation
of energy resources. Pollution of air, land, and water resources may occur
during any or all of the necessary extraction, refining, transportation,
conversion, and utilization processes. Secondary impacts from increased
population pressures, water management, and development of supporting
r----TC- ---- -- - T- ---,--"-

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'--....l.J--\--- - l I I (
, ,\, I
'\ ) "-- ---\..---
'", ,--,
~ \
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'.....
Figure 1. Western energy resource region.
1

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industries (e.g., cement manufacture, rail lines, etc.) promise additional
environmental insults. Even the most closely controlled and well-planned
development will have some impact; carelessly or improperly planned
development could result in catastrophe.
In order to assess the impact of energy resource development in the
western energy resource region, it is essential to develop a comprehensive
monitoring program. The objective of this report is to describe a monitoring
network that can be used to assess the impact of energy resource development
on water quality in the western energy resource region. A monitoring network
was selected which is designed to detect trends in surface-water quality.
Biological, physical, and chemical parameters likely to be affected by energy
resource development activities are identified. Radiological parameters were
not included in this investigation because an existing network for
radiological monitoring already exists.

The recommendations' pertain to monitoring the impact of energy resource
development on water quality in the Western Mountain States. They do not
exclude the addition of other parameters to detect trends caused by other
activities such as urban development, agricultural practices, etc. Neither do
they exclude the operation of other stations or monitoring networks for other
purposes; indeed, such activities are to be encouraged. The network design
proposed herein is based upon knowledge and technology available today. As
our understanding of the impacts, affected parameters, and stream modeling
increases, and as technological innovations add to the monitoring
instrumentation available, this design will necessarily be modified. These
considerations, however, do not lessen the need for implementation of such a
network at this time.
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CONCLUSIONS
The follo~ng conclusions have been reached regarding the monitoring of
energy resource development impact upon western water quality:
1. Western energy resource development has great potential for degrading
the quality of both surface and ground waters. Water quantity will be
affected more severely than water quality, but consumptive use imposes its own
effect on water quality. In addition, modern technology will produce large
quantities of toxic and carcinogenic chemicals that could have subtle, but
devastating, environmental impact.

2. Existing water-quality monitoring activities are inadequate to detect
subtle trends in many water-quality parameters over relatively short 5
years) time periods.
3. Ground-water monitoring networks are only now being implemented.
survey data are useful only in determining baseline conditions or
investigating specific local problems.

4. Impact assessment may best be accomplished by monitoring to detect
short-term (2 to 4 years) trends in water-quality parameters likely to be
affected by energy resource development activities. (This should not be
confused with monitoring for only 2 to 4 years.) This trend period is
adequate to identify short-term effects from activities such as road
construction near the river, one-year droughts, etc. and will permit
identification of activities likely to pose a problem in the future. It will
also permit detection of small-magnitude changes in observed concentrations.
Past
5. Existing monitoring stations, especially National Stream Quality
Accounting Network (NASQAN) stations, are suitably located for monitoring
trends in energy resource development impact on surface-water quality.
Ground-water monitoring networks are only now being established.

6. A minimum monitoring frequency between 7 and 14 days is necessary
to obtain satisfactory statistical precision for assessing most
parameters.
7. Trend monitoring can only detect changes in water quality; it cannot
determine the causes for those changes. Data from trend-monitoring stations
must be augmented by data from other stations and from intensive surveys in
order to determine causes for discerned trends.
8. Monitoring on a regular basis for the large number of organic
compounds that may be released into the environment is not cost-effective.
3

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The impact of these, and other perturbations, may be assessed through
biological monitoring. However, techniques are not well developed and need
further research. In particular, techniques need to be developed to assess
the impact on biota of numerous potential carcinogenic compounds present in
minute quantities. In addition to laboratory studies (assays, etc.),
monitoring techniques that use naturally occurring populations are needed in
order to provide an early warning for human exposure.

9. Trend monitoring for energy resource development impact can also
provide data on trends caused by other activities (e.g., agriculture,
recreational development, etc.) in energy resource areas.
4

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RECOMMENDATIONS
A water-quality trend-monitoring network consisting of relatively few
stations with increased sampling frequencies is recommended to assess the
impact of western energy resource development on surface-water quality. This
would permit the detection of trends in water quality over a 2- to 4-year
period.

Twenty-five existing stations should be included in this network (Table
1). Seventeen existing NASQAN stations and eight other USGS stations are
located in downstream reaches of rivers draining areas where energy resources
are located and development activities have begun.
TABLE 1.
RECOMMENDED STATIONS FOR MONITORING WESTERN ENERGY RESOURCE
DEVELOPMENT IMPACT ON SURFACE-WATER QUALITY
I .
USGS Station Number
09380000
09368000
09379500
09180500
09152500
09132500*
09217000*
09224700*
09251000
- 09260000
09306500*
09306222*
09314500*
09328500*
09315000
06635000*
06438000 .
06337000
06130500
06214500
06294700
06308500
06326500
06329500
08251500
*Not NASQAN stations
Description

Colorado River at Lees Ferry, Ariz.
San Juan River at Shiprock, N. Mex.
San Juan River near Bluff, Utah
Colorado River near Cisco, Utah
Gunnison River near Grand Junction, Colo.
North Fork Gunnison River near Somerset, Colo.
Green River near Green River, Wyo.
Blacks Fork near Little America, Wyo.
Yampa River near Maybell, Colo.
Little Snake River near Lily, Colo.
White River near Watson, Utah
Piceance Creek at White River, Colo.
Price River at Woodside, Utah
San Rafael River near Green River, Utah
Green River at Green River, Utah
Medicine Bow River above Seminoe Res., Wyo.
Belle Fourche River near Elm Springs, S. Dak.
Little Missouri River near Watford City, N. Oak.
Musselshe1l River at Mosby, Mont.
Yellowstone River at Billings, Mont.
Bighorn River at Bighorn, Mont.
Tongue River at Miles City, Mont.
Powder River near Locate, Mont.
Yellowstone River near Sidney, Mont.
Rio Grande River near Lobatos, Colo.
5

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Sampling for most parameters should be conducted routinely every 8 to 13
days at each station. Continuous monitoring of flow, conductivity, pH, and
temperature is recommended with data subsequently reduced to daily mean
values. Sampling a~d analysis of sediment samples should be on a quarterly
basis. Table 2 lists physical and chemical parameters that should be
routinely monitored. It is recommended that all water-quality parameters be
monitored at all network stations.
Biological monitoring is the most direct monitoring technique for
assessment of energy resource development impact. Biological monitoring
techniques should be developed, especially those utilizing natural stream
populations to assess subtle, long-term effects. Biochemical uptake and
periphyton sampling and analysis should be immediately implemented.
Collection and storage of other biological samples for future analysis is also
highly recommended.
TABLE 2. PHYSICAL/CHEMICAL PARAMETERS TO BE MONITORED FOR ASSESSMENT
OF ENERGY RESOURCE DEVELOPMENT IMPACT ON WATER QUALITY
Alkalinity, total
Aluminum, dissolved
Ammonia - nitrogen
Arsenic, total*
Barium, total*
Biochemical oxygen demand, 5-day*
Bicarbonate, dissolved
Boron, total
Carbon, dissolved organic
Carbon, total organic*
Cadmi urn, total
Calcium, dissolved
Carbonate, dissolved
Chloride, dissolved or ionic
Chromium, total*
Coal content
(sediment samples only)
Conductance, speci fi c @25C
Copper, total *
Cyanide, total*
Flow
Fluoride, dissolved
Iron, total*
Kjeldahl nitrogen
Lead, total*
Magnesium, dissolved
Molybdenum, total*
Nickel, total*
Nitrate nitrogen
Nitrite nitrogen
Oxygen, dissolved
011*
pH
Phenols*
Sediment grain size
(sediment samples only)
Selenium, total*
Sodium, dissolved
Solids, total suspended
Solids, total dissolved
Sulfate, dissolved
Temperature
Turbidity
Uranium, total*
Vanadium, total*
Zinc, total*
*Measure in sediment samples also.
6

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BACKGROUND
With the development of the "energy crisis," the fossil fuel reserves of
the Western United States suddenly gained increased importance. Throughout
the 1950's, the United States was effectively energy self-sufficient,
satisfying its needs with abundant reserves of domestic fuels such as coal,
oil, gas, and hydroelectric power. However, energy consumption has been
increasing during the past 10 years at an annual rate of 4 to 5 percent, a per
capita rate of consumption eight times that of the rest of the world (U.S.
Bureau of Reclamation 1977a). The Federal Energy Administration (1974) in its
"Project Independence" report states:
- By 1973, imports of crude oil and petroleum products accounted
for 35 percent of total domestic consumption.
Domestic coal production has not increased since 1943.
Exploration for coal peaked in 1956, and domestic production
of crude oil has been declining since 1970.

Since- 196R, natural gas consumption in the continental United
States has been greater than discovery.
The United States now relies on oil for 46 percent of its energy needs,
while coal, our most abundant domestic fossil fuel, serves only 18 percent of
our total needs (U.S. Bureau of Reclamation 1977a). Foreign oil imports have
risen from $3 billion to $45 billion in six years (1971 to 1977) and are the
largest contributor to the U.S. trade deficit (Hayes 1979). To reduce the
United States vulnerable economic and military dependency on foreign oil, the
Federal government is promoting the development of untapped national energy
resources. Included among these resources are the abundant western energy
reserves. Over half of the Nation's potential coal and effectively all the
potential uranium, oil shale, and geothermal resources are located in the
Western United States (Harza Engineering Company 1976).

Western energy resources are located primarily in a belt along the Rocky
Mountains and Colorado Plateau extending from the Northern Great Plains
(Montana, North and South Dakota, and Wyoming) into Arizona and New Mexico
(Figure 1). This region is characterized by sparse annual rainfall, generally
less than 50 centimeters (cm) per year except at higher elevations (Geraghty
et a1. 1973). Annual surface-water runoff is less than 3 cm over most of the
area. Numerous mountain streams rapidly combine into larger rivers or
disappear into desert sands. Flows are erratic, with maximum flows generally
occurring during the snowmelt period, April to July. Episodic storms can
result in substantial discharge and flooding, especially in local areas. At
7

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lower elevations most small streams are intermittent or ephemeral in nature.
In this water sparse region, energy resource development will have an impact
on both water quality and quantity.
Water resources are highly managed in order to provide dependable,
year-round supplies. In 1965 more than 580 reservoirs with capacities greater
than 61,000 cubic meters (m3) and more than 40 transmountain diversions
existed in the Upper Colorado River Basin alone (Water Resources Work Group
1971). Evaporation from these impoundments ranges from 75 cm to about 150 cm
per year and is second only to agriculture as a consumptive user of water in
the region.

Water rights in this semiarid region are based largely on the
appropriation doctrine. The doctrine has two fundamental principles: (1)
first in time is first in right, and (2) beneficial water use is the basis of
that right. According to Gould (1977), appropriation of water rights is
controlled by the States through permits and generally proceeds in five steps:
(1) declaration of an intent to appropriate water; (2) notification to others
of this intent; (3) compliance with prescribed State formalities; (4)
diversion of water; and (5) application of the water to beneficial water use.
Historically, the requirement of diversion essentially ruled out any basic
instream flow rights over and above those necessary to supply downstream
appropriations. Recently, however, both legislative actions and court rulings
have recognized that minimum flows to maintain fisheries are bona fide
beneficial uses and that diversions are not required to establish water rights
(Gould 1977; Cox and Walker 1977).
The impact of this departure from historical appropriation doctrine is
immense, particularly in areas where water rights are already over-
appropriated. Shupe (1978) conducted computer simulations of potential
reservoirs and proposed instream flow requirements in the Powder River Basin.
He found that instream flow requirements necessitated continuous discharge of
a portion of the storage capacity of reservoirs and reduced water availability
for industrial use 53 to 86 percent. An assessment of the planned Middle Fork
(of the Powder River) Reservoir found that without instream flow criteria the
reservoir would be capable of providing 53 x 106 m3 of'water annually on a
constant withdrawal basis. With the instream flow requirements implemented,
he found that the reservoir would operate nearly empty much of the time and
could only provide 20 x 106 m3 of water annually for diversion.
Ground-water resources, although substantial in the study area, are
limited geographically (U.S. Geological Survey 1970) and often are of poor
quality. Sufficient and suitable ground water is unavailable in many areas of
exploitable energy resources, thus increasing the importance of surface water
(University of Oklahoma and Radian Corporation 1977).
Water rights to both surface-and ground-water supplies are further
complicated by Federal and Indian claims, interstate compacts, conflicting
legal decisions, and the rapid disappearance of unallocated waters (Native
American Natural Resources Development Federation of the Northern Great Plains
1974). Changing social outlooks with increased emphasis on aesthetic and
recreational considerations are also of importance (Cox and Walker 1977).
8

-------
Energy resource development is anticipated to be a large consumptive user of
water, especially on the local level, and production will ultimately be
limited by available supplies in many areas.
9

-------
ENERGY RESOURCE DEVELOPMENT
COAL
Energy resource development in the western energy resource region will be
predominantly the exploitation of coal reserves (Figures 2 and 3) and
construction of coal-fired powerplants (University of Oklahoma and Radian
Corporation 1977). Figures 4 and 5 and Table 3 indicate the extent and nature
of major ongoing and planned coal mining activities in the Rocky Mountain
region. Many of these mines are tied to the development of coal gasification
or liquefaction plants. Established markets exist for the Rocky Mountain
region's abundant low-sulfur coal in local, Midwest, and Southwest coal-fired
generating plants. There is active construction of many new plants in the
Western States. In 1976 a total of 48R new generating units were planned for
States west of the Mississippi (Corsentino 1976), of which about 20 percent
were planned for the study area. Locations of present and proposed
fossil-fuel electrical generating plants in the study area are shown in
Figures 6 and 7.
. Figures 4 through 7 indicate that the development of coal and coal-fired
powerplants largely occurs in clusters. The Colstrip-Sheridan area, the
Gillette area and its southward extension, the Seminoe Reservoir region, the
Rock Springs and Naughton areas, the White-Yampa River region, the Gunnison
area, the Huntington Canyon area, and the San Juan Basin area are
concentrations of coal-mining and powerplant development. Figures 4 and 5 and
Table 3 show that nearly all coal explotation is strip mining in the Montana
and Wyoming regions. As one moves south into Colorado, increased numbers of
underground mines are found. In the Gunnison and Huntington Canyon areas,
mining operations are primarily underground. Mines along the Utah-Arizona and
Colorado-New Mexico borders are primarily strip mines, except near Raton where
underground mines are common.
Coal liquefaction is also planned. Corsentino (1976) lists 22 proposed
plants. Commercial development has been slow, and significant development of
liquefaction facilities is not now anticipated prior to 1985. Liquefaction
plants will probably be located near coal mines in areas where sufficient
water is available (Gold and Goldstein 197R).
Strip mining and surface disposal of wastes may have impact on visual
aesthetics. In addition, the disruption of natural surfaces may lead to
increased erosion and loading of nearby streams with suspended sediments and
may remove land from agricultural or other uses. Poor spoil-pile disposition
may lead to unstable and potentially catastrophic situations. Disruption of
soil and rock layers exposes fresh surfaces to chemical weathering and results
in greatly increased dissolved solids concentrations in both surface and
ground waters.
10

-------
,-
......
......
t
')
'"",(,
\: ,,-- 1"-', .....J\ .
," ~

'.....-....
D A H 0 G...~,,,
TVlon ...~
NOtIiOflit' ~:
.~~T~'.

'~I

1
,

"-.. ""U"::.LL,'
_.._'0\.f--


G& i I
'-~ i~ ,

UTA H I
.it'
'''''''''.'/~~
.' ")~~
N E
BRASKA
II
IIA
.
Anthracite. medium
- and high-volatile
bituminous coal
Beds 01 commercial
value at present
Areas 01 doubllul
commercial value
Sub bituminous Coal
Beds 01 commercial
value at present
Areas 01 doubllul
commercial value
~
.
Lignite and
Brown Coal
Beds 01 commercial
value al present
Areas 01 doubtful
commercial value
Figure 2.
Coal resources in t1ontana, Wyoming, and western North and South Oakota
(from Trumbull 1960).

-------
I
I
I
i
,
I
,. . ~
fp
-.. j clu.l
~~.
. Sal. l8.. City
u
T
.....
N
A
R
z
o
M
E
N
"'"
..
-------
l
I
!
~I. ~
. -----,------r-----------

- r
0.. ulKIO!'" fill" -'.
\.....-.. ".
"'.1"""1'1
-..
Rapid.
City
~ '::~"~~d;
~ .~:~"~):i*:
I (.-..'
\_--- .-

\
\
LEGEND
ExiltinG 8. Proposed COil Min..
. :: Scrip Min.
. "S Und.rground Min'
----I
...
'0.
,

L___----------r------------------

I
I
,
-~---~~~~~----_.~--
Figure
4. Existing and proposed coal mines in the Yellowstone,
Missouri, Belle Fourche, and North Platte River systems
(Corsetino 1976; U.S. Environmental Protection Agency).
Li ttl e
13

-------
~---
. Rivenon
. Casper
i ~..-
--------i -~
I 2" 27
I ".a-
, 26
.32
lEGEND
Existing and Proposed Coal Mine

8 Strip Min.
. Underground Min.
! .'
I j
L--7------'-

. Salt Lake City
-------------------
.'
"1 821
~ ..' 15 820 .. "

1 ~~~ G.:t..:-::..:o'
9..~' 8'22 ~.t>t.. ~NP.
88 11, 16 ; -. ..
2.~. ,1. :JI .24
. 13 ..i23. q>f\:
. \ ~ r ..!1.;:.: Denver. ".2.

\ ~ ;,-.-,. 1 '~~:~

I 2" .~

_..,~
35
3J~J8 1.\11(1'
328 34"37 ! ".....

~. ."
G....".~J" e......
~ 839 ~~
It.."
Colorado
Springs.
...
..3
...
...
--------- - -. -- -- .1'
Figure 5.
Existing and proposed coal mines in the San Juan,
Colorado, and Green River systems.
A frequently cited 1~73 study by Van Voast of well water collected on the
same day from an unmined area and from an area with overlying old coal spoil
materials is a dramatic example (Harza Engineering Corporation 1976). The
calculated dissolved solids level in ground water beneath the spoils was more
than three times that in the undisturbed area. In the spoils area, levels of
magnesium were seven times those in the undisturbed area; calcium, sulfate,
and iron levels nine times; and nitrate and manganese fifteen times. Slight
decreases in sodium and bicarbonate concentrations were observed.
Contaminated ground water may move through an aquifer and may reach a stream
or river.
The chemical composition of the coal bed, the overlying rock strata and
soils, and the amount and nature of their exposure to water influence the
14

-------
TABLE 3.
ACTIVE AND PLANNED COAL MINES IN THE WESTERN ENERGY
RESOURCE REGION
r.,; ne No.
fran Figs.
4 and 5
Mi ne Name
Type*
Owner
Status/
Operat i ona 1
Date
Market
Max i mum
Out put
(10 kg/year)
~
Remarks
WYOMING
1
2
3
McCartney
Sa~age Mine
Circle West
4
5
6
7
8
9
Sarpy Creek
(.4.bsal oka)
East Sarpy Creek
Rosebud Mine
Big Sky Mine
Spri ng Creek Mi ne
Young Creek,
Tanner Creek
Decker 11i nes
10
PSO Mine
2
2a
3
4
5
6
7
8,13
Whi tney
Welch Strip
Big Horn
Lake De Smet
Buckskin Mine
North Rawhide
Unnamed nr Felix
Belle Ayr (North &
Sout h )
East Gfllette
9
10
11
12
14
Wyodak
Caball 0
Thunderbi rd
Cordero
15
16
17
Jacobs Ranch
Black Thunder
Roche 11 e
18
18a
19
20
21
22
Grass Creek
Dave Johnson Mine
Red Rim
China Butte
Medicine Bow
Vanguard fI2 & 3
23
24
25
Hanna
So renson
El kol
26
27
28
FMC Mi ne
Twin Creek
Long Canyon

Bridger
Stansbury n
Ra inbow *8
Black Butte
29
30
31
32
(s) Clay & McCartney
(s) Knife River Coal Co.
(s) Dryer Bros.
(s) Westmoreland Resources

(s) Amax Coal Co.
(s) Western Energy Co.
(s) Peabody Coal Co.
(s) Pacific Power & Light
(s) Shell 011
Decker Coal Co.
(s) Public Service of
Oklahoma
(s) Peter Kiewit Sons,
(s) Welch Coal Co.
(s) Big Horn Coal Co.
(5) Texaco, Inc.
(s) Shell Oil Co.
(s) Carter Mining Co.
(s) Falcon Coal Co.
(s) Amx Coai Co.

(s) Kerr McGee

(s) Wyodak Resources Devel.
(s) Carter Oil Co.
(s) El Paso Natural Gas Co.
(s) Sunoco Energy Devel.
(s) Kerr McGee

(s) Atlantic Richfield
(s) Peabody Coal

(s) Northwestern Resources
(s) Pacific Power & Light
(s) Rocky Mt. Energy Co.
(s) Rocky Mt. Energy Co.
(s) Medicine Bow Coal Co.

(u) Energy Development Co.

(u) Rocky Mountain
Energy Co.
(5) Kemmerer Coal Co.
(s) Kemmerer Coal Co.

(s) FMC Corp.
(s) Rocky Mountain
Energy Co.
(u) Rocky Mountain
Energy Co.
(s) Bridger Coal Co.
(u) Stansbury Coal Co.
(u) Columbine Mining Co.
(s) Black Butte Coal Co.
Existing
New/198D
Exp/198D
Exp/19BD
Exp/1977
New/198D
Exp/1979
New/1978
Inc. New/?
Exi sting
Exp/1980
New/?
New/198D
New/1976
New/?
Exo/1980

New/1977
Exp/1980
New/1980
New/1979
New/1981
New/1978
New/1980
New/198D

Exp/1980
Existing
New/1980
New/1980
Exp/1980
Exp &
New/198D
flew/1980
Exp/1980
Exp/1980
Exp/1980
rlew/1980
New/?
Exp/1980
New/1980
Exp/1980
Ijew/1977
15
Local
Circle West Ammonia Plant
and possible coal
11 quefact ion
Northern midwestern P.P.
Colstrip P.P.
Minnesota P & L
Austin Texas, Commonwealth
Edison P.P., Detroit, Mich.
Edison P.P.
Oklahoma P.P.
Acme P.P., Sheridan, Wyo.
Midwestern powerplants
Coal gasification plant
Shell Oil, Houston refinery
Midwester~ utilities
Midwest, Southwest, ana
Paci fie Northwest P.?
Ark., La.. and Okla.
utilities
Wyodak P.?

P.P.
San Antonio Public Service
P.? and Tri-State P.P.@
Wheatland
Ark., La., and Okla.
utilities
Midwestern P.P.
Proposed coal gasification
plant
Dave Johnson P. P.
Iowa Public Service
North Indiana Public
Service Co.
Iowa Public Service P.P.
P.P
Naughton P.P.
Allied Chem., Almalgamated
Suga r
FMC Trona PI ant
Jim Bridger P.P.
Ideal Cement
Mine Mouth Coke Plant
[daho Power Co.
?
0.3
27.2
137.9
173.3
24.5
145.1
~9.9
4.5
?
0:2
13.6
?
36.3
77 .1
2.7
90-136

45-100
20.0
18-45
27.2
108.9
122.0
63-91
45-100
6.4
24.5
22.7
9-27
32.7
9-181
22.7
27-43
10.0
9-18
27.2
68.0
12.7
1.8
49.2
Proposed
PropOSed
Proposed
Proposed
Proposed
Proposed
Proposed
Proposed
Proposed
(continued)

-------
    TABLE 3. (Continued)   
Mine No.      Status/  Max i mum  
fn:m Figs.     Operational  Output  
4 and 5 Mi ne Name Type*  Owner  Date Market (10 kg/year) Rema rks
    ...~     
!ill!!.          
 Brazath .3,4,5,6 (u) Brazath Corp.  Exp/1980 Indiana-Michigan Electric Co.& 59.0  
       other midwestern P.P.   
2 Gordon Creek 13 (u) General Exploration Co. New/1977 Local and out-of-state 1.8  
       cQlllTlerci a I   
3 Utah 12 (u) Valley Camp of Utah New/1977 Midwestern and western 6.4  
       c cxmerc i a I   
4 5elina 11 & 2 (u) Valley camp of Utah New/1977 Midwestern and western 19.1  
       commercial   
5 O' Conner il (u) Valley Camp of Utah New/1977 Midwestern and western 1.5  
       ccxmerci a I   
6 Star Point 13 (u) Plateau Mining Co.  New/1977 Rocky Mountain states 9.1  
7 Unnamed n r (u) Island Creek Coal Co. New/1977 Ca lifornia P.? -18.1  
 Sunnys ide         
5 Deer Creek (u) Peabody Coal Co.  Exp(1977 Huntington Canyon P.P. 22.7  
9 Swl sher 15 (u) Swi.sher Coal Co.  New/1978 Local and out-of-state 1.13 Reopen
       commercial  old mine
10 Soldi er Canyon (u) California Portland Exp/1977 California Portland 9.1  
   Cewent Co.   Cement plants   
11 Hunti ngton (u) Swisher Coal Co.  New/1978 Local and out-of-state 1.8 Reopen
 Canyon 14      commercial  old mine
12 Ri I da Canyon (u) Western American  New/?  1.8 PropOSed
   Energy Corp.      
13 WI I berg (u) Peabody Coal Co.  New/1980  20.0  
14 Emery (u) Consolidation Coal Co. Exp/1980 Commerical & P.P. 12.7  
15 Straight Canyon (u) Utah Power & Light Co. New/1980 North Emery P.P. 22.7  
16 Emery Strip (s) Consolidation Coal Co. New/? Commercial & P.P. 4.5 Proposed
17 Ferron Canyon (u) Inspir3tional  New/1980  4.5 Proposed
   Development Co.      
18 South Utah (u) Constal State  Exp/1977 South Jtah markets 13.6  
 Fuels '1  Energy Co.      
19 Unnamed Sev i er (u) Clinton Oil Co.  New/1980 CO!III1erci a I 9.1  
 County         
20 Thompson t1i ne (u) Western American  New/?  1.8 PropOSed
   Energy Corp.      
21 Int. Power Project  ICPA   New/1985 Factory Butte p.P. 90.7  
 Mines         
22 Escalante (u) Utah Power &  New/1981 Garfield P.P. 54.4 Proposed
   Light Co.      
23 Factory Butte (s) Atlas Minerals  New/1977 Power generators 9.1  
24 Kai parowits (u) Kaiser Industries  New/1984 Kaiparowits P.P. 90.7 Plans dis-
         continued
         for present
25 John ~enry I'll ne (u) 51'1 Corp.   New/1977  3.6  
26 Unnamed nr Alton (u) Nevada Power Co.  New/1982 P.P. in St. George, Utah 104.3  
       and Las Vegas. Nev.   
COLORADO          
1 Unnamed nr Savory (u) Kemmerer Coal Co.     PropOSed
2 Unnamed nr Rangely (s&u) Blue Mountain Coal   Export  Proposed
3 Gordon Mine (u) Moon Lake Elec. Co. New/? Mine-mouth P.P. for oil 20.9  
      Late 80' s shale development   
4 Unnamed nr Range1y (s) Midland Coal Co.  New/1981  1.5  
5 Rlenau 12 (u) American Fuels, Inc. Exp/1980 Local 2.7  
6 Unnamed nr Meeker (u&s) Consolidation Coal Co. New/1981  39.9  
7 Colowyo (s) W. R. Grace  Exp/1979 Out-of-state utilities 27.2  
8,16 Unnamed nr Craig (s) Paul S. Coupey  New/1980  9.1 PropOSed
9 Wise Hill (u&s) Empire Energy Corp.  Exp/1980 F & I Steel and out-of- 18.0  
 15,6, & 7      state markets   
10 Unnamed nr Craig (s) Utah Int., Inc.  New/1980 Craig P.P. 26.3  
11 Wilson Creek Mine (s) Utah Int., Inc.   Craig P.P. ? Proposed
12 Unnamed nr Pagoda (s) Amer. Elec.  New/1981 AEP P.P. eastern U.S. 12.7  
   Power System      
13 Sun t1i ne (u) Ruby Construction  New/1977 In-state markets 2.7  
14 Seneca 2-W (s) Peabody Coal  Exp/1977 Hayden P.P. 7.3  
         (continueo)
16

-------
    TABLE 3. (Continued)    
Mine rlo.      Status/    Maximum 
fran i'igs.     Operat ional    Output 
~ and 5 Mine Name Type*  Owner  Date Market (10 kg/year) Remarks~
15 Unnamed nr (u) Coal Fuels  New/1971    18.1 
 Steamoat Spri ngs          
17 Unnamed nr (5) Thanas C. Woodward  New/1980    39.9 
 Steamoat Springs          
18 UnnoDed nr (s) ~rchants Petroleum New/1g80    39.9 
 Steamboat Springs  Co.        
19 Unnamed nr (5) Morgan Coal Co.       Propo.sed
 Steamoat Springs          
20 Unnamed nr Walden (s) Consolidation Coal Co. New/1g79    9.1 
21 Grizzly Creek (s) Sunflower Energy Corp. Exp/1980 Electric utilities  18.1 
22 Energy #1 & 2 (5 u) Energy Fuels Corp.  Exp/1980 Pub I ic service of  40.8 
       Colorado. P.P.   
23 Edna Mine (s) Pittsburgh & Midway Exp/1gaO    10.1 
   Coal Canpany       
24 Lincoln Mine (u) Adolph Coors Co.  rlew/1976 Coors Ind.   1.8 
25 Watki ns Li gni te (s) Cameron Engineers  New/1983 Mine-mouth gasification 90.7 Hi gh un-
           certa i nty
26 Unnamed nr Watkins (s) Kerr McGee   Gasification   Hi gh un-
           certa i nty
27 Station Creek (s) Cameron Engineers  New/1980 Industrial fuel  9.1 
28 Thanpson Creek (u) Anschutz Coal Co.  Exp/1979 Steel mills   9.1 Reopen
 #1 & 3          old mine
29 McKinley #1 (u) Energy & Export Co.  New/1971    0.9 
30 McGinley Mine (ul Eagle Head Coal Co.  New/1976    2.3 
31 CMC Mi ne (u Cambridge Mining Corp. Exp/1977 Cameo P.P.   17.2 
32 Coal by. #2 (u) Coal by Mining Co.  Exp/1978     
33 Ki ng Mine (u) Adolph Coors ~o.  New/1980 Coors Ind.   4.5 Reopen
           old mine
34 Old 81 ue Ribbon (u) Sunflower Energy Corp. Reopeni ngl Local   0.9 
 Mine     1977     
35 Converse Mi ne (u) Colorado Westmoreland Exp/1980 Midwestern utilities  13.6 
36 Fanners Mi ne (u) Pittsburgh & Midway Exp/?    9.1 
   Coal Canpany       
37 Unnamed nr (u) Atlantic Richfield  New/1980 Electric utilities in west- 18.1 
 Somerset      ern Colorado or eastern Utah  
38 Hawks Nest East (u) Western Slope  Exp/1971 CF & I Steel, Pueblo, Colo.; 5.4 
   Carbon, Inc.   Ford Motor Co., Detroit, Mich. 
39 Unnamed nr Cimarron (s) H.W. Siddle       Propo.Sed
40 Unnamed nr Hesperus  Energy Resources, L td. New/?     
41 Mel Martines Mine (s) Milton Fuller  Exp/1978 New Mexico markets  2.3 
42 Unnamed nr (s) Groves/Calder   Local    PropOSed
 Walsenburg          
43 Unnamed nr Pryo.r (u) Tipperary Oil & Gas  flew/1978    4.5 
   Corp.        
44 Maxwell (ul CF & I Steel Corp.  New/1978 CF & I Steel Pueblo, Colo. 5.4 
45 Lorenci to (u Freeport Coal co.  New/1979? Eastern steel mills  9.1 
~           
1 Kayenta (s) Peabody Coal Co.  Exp/1971 Navajo P.P.   72.6 
2 Black Mesa Mine (5) Peabody Coal Co.  Exp/1971 Mohave P.P.   45.4 
NEW MEXICO          
1 San Juall Mf ne (5) Utah Int., Inc.  Exp/1980 San Juan P.P.  52.6 
2 Navajo. Mine (s) Utah Int., Inc.  Exp/? WESCO Gasification, Navajo 371. 9 
       and 4 Corners P.P.   
3 Burnt!- Calplex (s) Consolidation Coal  ~!ew/? Burnham Coa I  26.8 
 Mine  Co.    ~sification Complex  
4 West Yort Stri p (s) Kaiser Steel Corp.  Exp/1976 Fontana, Calif., steel ""II 4.5 
5 York Canyon #1 (u) Kaiser Steel Corp.  Exp/1977 Fontana, Calif., steel mill 6.4 
*(s) . Strip mine          
(u) . Underground mine          
P.P. . Powerplants          
17

-------
-----r------f-~-------- -- -- --- - ----
. \ . ------- \
. '. r \
\:~~.-"~
....' -.......(
0.... .
Rapid.
Cltv
I ;
, ..:i..."""
! .fOof'om:
~ ~:N p <
\
I
\
I i..~_..'
.
\ ---
_.-
Lu"".
LEGEND
Foull Fuel P9werpl.nts
* Proposed Powerplants
.exllting Powerplants
. EJlp8nding Powerplants
----I
,

L____---------r------------'-----

I
I
c-:rte \
-----~~~~~~~~--~--
Figure 6. Fossil fuel generating plants in the Yellowstone, Little
Missouri, Belle Fourche, and North Platte River systems
(Primary source: Federal Power Commission 1977).
18

-------
l
r
. Rivef10n
. C.soer
LEGEND
FosSIl Fuel Powerplants
* Proposed POINerplants
. Existing Povverplan"
. Expanding Powerpl8nts
.L,td8 t
MounUH'l .
I
--~-------------------

f ."., ."""ode

"",mO"i> '~)O"'-=
,.~~ G:::~r~::
!.. ~ .v.......

r<.'. ~ ~':': ':::":~~~7;:
~ ~., .
.' .,...h08
~ ( ~:'"
. 8cnu'IIItloll Clt";'
JOf'd.'W.U uke City
M\lfT."--',
Ci", Oad.Do,
.PM",,,
. Ohv~, 1::'7'
"...~..o.. ..-
~ ~.......
8uMo<:_. Rt'''
......
cOlorado;:'
Springs
*
Dr.e t
IIIII.on
. Alamo..
.Clan. I
Puotoe.
Com.nche
I
I

W.'Mntturve I
Tn..... I
,

.-------- I
----~---
Figure 7. Fossil fuel generating plants in the San Juan, Colorado, and
Green River systems (Primary source: Federal Power Commission 1977).
amount and type of water contamination. In the Rocky Mountain States, coal
deposits generally contain insignificant quantities of pyrite and are found
among alkaline soils (Gluskoter et al. 1977). As a results, the acid mine
drainage problems of the East are not common in the area.

Underground mining avoids or reduces many of the problems associated with
strip mining, but ground-water contamination may still occur. Pumping of
seepage from mining shafts may result in surface discharge. The subsequent
washing, refining, storage, and transportation of coal, oil shale, or uranium
ores can also introduce contaminants into western waters. The use of coal
slurry lines to transport coal provides large quantities of polluted water at
the receiving end and presents the possiblity for a major pollution event in
19

-------
the case of a break in the line. Coal slurries require about one cubic meter
of water to move one metric ton of coal (Jones et a1. 1977), placing
additional demands upon an already scarce commodity.
OIL SHALE
Rich deposits of oil shale occur in the western energy resource
development region. These are primarily associated with the Green River
Formation which underlies nearly 65,000 square kilometers (km2) in Utah,
Colorado, and Wyoming (Figure 8). Of this, about 44,000 km2 are believed to
contain commercially exploitable reserves (U.S. Department of Interior 1973).
To date, six oil shale tracts have been offered for public lease, but only the
Utah and Colorado tracts (Figure 8) have been purchased (Crawford et a1.
1977). Activity on several privately owned oil shale reserves is ongoing.
To date, all efforts are largely experimental.
In addition to mining and surface retort activities, tests of in situ
technology are ongoing. Commercial oil companies have conducted experiments
in the Piceance Creek and Uinta Basins (Crawford et a1. 1977). In situ
fracturing experiments are also being conducted by the government near Rock
Springs, Wyoming, and in the Uinta Basin, Utah (Shih et a1. 1976).

Oil shale development has two major drawbacks. First, it is a large
consumptive user of water (14 x 106 m3 per year for a 159,000-m3-oil-per-day
production plant using the Parahoe retorting process), and second, retort
effluent water contains inorganic salts and complex organic compounds
including some suspected carcinogens (Conkle et a1. 1974). Surface retort
processes also increase the volume of extracted shale from 12 to 60 percent.
The spent shale is high in sodium, calcium, magnesium, sulfate, and other
salts and requires wetting to stabilize it prior to disposal (Hughes et a1.
1974). The most likely disposal method will be backfilling and filling of
nearby valleys and canyons. The potential for pollution of downslope water
resources is very large. Seven elements of particular environmental concern
singled out by Dean (1976) in an assessment of the geochemistry of oil shale
were cadmium, antimony, arsenic, mercury, selenium, fluorine, and boron.
In situ technologies have a very high potential for direct contamination of
ground waters. Research has shown that generally the higher the
carcinogenicity of the compound (polycyclic aromatic hydrocarbons) the more
soluble it is in water and that certain salts enhance this solubility
(Institute for Environmental Studies 1975).
The ultimate limit on the size of the oil shale industry will probably be
determined by the availability of water. Hughes et a1. (1974) estimated
available water to be between 2.4 and 4.0 x 106 m3 per day, enough to
support a 0.24 to 0.40 x 106 m3 per day (1.5 to 2.5 x 106 barrels per day) oil
shale industry. Ground-water resources in the Piceance Creek Basin
are estimated at between 3,084 x 106 m3 and 20,840 x 106 m3. (Institute for
Environmental Studies 1975). An amount of water equal to 12,000 x 106 m3 is
sufficient for a 0.16 x 106 m3 per day industry for about 34 years. However,
not all of the ground water in the Piceance Creek Basin is of sufficient
quality to be suitable for use.
20

-------
I'
;
!
. Riverton
. Casper
LEGEND
Solid Un. :: Outtin. of G..8ft Ri..,.,.t=orrMtion
IUSDI. 1973. USGS. 1970)

O..hedLiM :; btent of oil -"ale -.o8it1
liES. 19781
l' if1"":.-.<>"
--------; ~.
I,a,.
o'
i
, -t~ ,
, "
l___~
o = A_. ......... of p,odUCIno 2S
gaUOM/ton with ... bed. 10
ht8t thick 0' g,....
IUSDI.19731

@ :; CommetCiai oil .... 'KiHtie,
(E~A. 1974)

. = Federal oil aha.. ..... tr8CU
--- - - -- - -----
. Salt Lake City
Den ver.
Colorado
Springs.
- .-.--
Figure 8. Oil shale reserves in the Western United States
(from U.S. Geological Survey 1970; U.S. Department of Interior 1973;
U.S. Environmental Protection Agency 1974; and Institute for
Environmental Studies 1975).
Spiraling development and production costs, coupled with uncertainties in
the national oil policy and in world crude oil prices, have caused interest in
development of oil shal e to fl uctuate. Origi nal pl ans call ed for development
of surface processing supported by mines. Recent advances in technology now
place emphasis on in situ or modified in situ methods (mining and rubbling
followed by in situ retorting) (Crawford et ale 1977). Presently, initial
development of Federal lease tracts Ca and Cb is occurring rapidly using a
mine and underground burn procedure. Development of privately owned tracts is
also underway, but large scale development of oil shale reserves is not
anticipated prior to the late 1980's.
21

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GAS AND OIL
Gas and oil resources are widespread throughout the western energy
resource area but, with some exceptions, exist mainly as relatively small,
isolated fields. For example, the eastern Powder River geologie structural
basin (Wyoming) alone has over 210 fields producing oil and gas (Everett
1979). Oil production from the west in 1973 was about 56 x 106 m 3 with known
reserves estimated at about 400 x 106 m3 while gas production was about 50 x
10~ m3 with reserves estimated at 590 x 109 m 3 (University of Ok1 ahoma and
Radian Corporation 1977). The projected 1985 demand for natural gas is
between 300 and 500 x 106 m 3 per year (U.S. Atomic Energy Commission 1972).
In addition, there are widespread areas of potential oil and gas
reserves that are presently commercially unexp10itab1e because of the host
rocks low permeability (Project Gasbuggy Joint Office of Information 1967).
Experiments with hydrofractionation (the fracturing of bedrocks using
injection of high-pressure water) were conducted in the area. The U.S. Bureau
of Mines estimates that about 8.5 x 109m 3 of natural gas are potenti ally
recoverable by nuclear stimulation (U.S. Atomic Energy Commission 1972).
Project Gasbuggy in northern New Mexico and projects Rio R1anco and Rulison in
west-central Colorado detonated nuclear devices in host rocks to stimulate gas
flow. These experiments were successful to various degrees and others were
planned (U.S. Environmental Protection Agency 1974). Public opposition,
changing political policies, and development of other resources (including the
Alaska North Slope oil reserves) led to a suspension of such activities.
Their eventual resumption is presently a matter of speculation.

Water flooding, a process in which water is used to flush petroleum out of
reservoir rocks, is widely practiced in the area (Taylor 1978). Production
ground-water wells for this purpose are completed in aquifers that range from
shallow, freshwater, alluvial aquifers to deeply buried, highly saline,
bedrock aquifers.
The impacts of the development of oil and gas reserves on the environment
are well documented. Increased salt loadings are expected and are already a
problem in some abandoned fields. Mud pits associated with well-drilling
activities (used for mixing mud and circulating water) can break open and
discharge mud, oil, and contaminated water into nearby water courses. These
also may contribute to local contamination of ground-water supplies. Storage
tanks and pipelines have long and continuing histories of minor leaks and
catastrophic spills (Figures 9 and 10).
Contamination of ground water through brine disposal ponds, evaporation
basins, faulty well casings or completions, or deep-well injection of wastes
is a documented problem. In addition, the use of nuclear explosives adds the
additional concern of man-induced radiation contamination. These problems can
only be controlled by strict enforcement actions and strong incentives for the
industry to utilize proper practices and to maintain equipment in good order
to reduce leakage and failure.

- Known oil and gas fields in the region are presently well developed.
Little future development is expected except in localized areas. Discovery of
22

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;{ .... '''''~.;-'- .L.~ ..-~~>~~~y~...t~ ~~~.~
Figure 9.
Old oil pump showing leakage.
Figure 10.
San Juan River 1962 oil spill from ruptured pipeline.
23

-------
new, large fields is a possibility but is not considered likely in the area.
New, more efficient recovery methods may result in reopening of abandoned
fields or areas that are presently uneconomical to develop (National Petroleum
Council 1973).
"
OTHER ENERGY RESOURCES
In addition to fossil-fuel resources, the study area contains considerable
amounts of uranium, geothermal, and hydroelectric resources. The latter are
highly developed, and future increases will largely be the result of upgrading
present equipment and installing generators at presently unequipped dams (U.S.
Rureau of Reclamation 1q77b). Some new reservoirs may be constructed, and
increased use of pumped storage to supply peaking power is expected.

Uranium deposits in the study area are estimated at 181 x 106 to 272 X 106
kilograms (kg) yellowcake (University of Oklahoma and Radian Corporation
1977). (Yellowcake is processed uranium ore consisting of R5 to 95 percent
uranium tri-oxide (U30a) (Reed et al. 1976). The locations of uranium
reserves and active mines are indicated in Figure 11.
Impacts from uranium mining are similar to those for coal. Many uranium
mines are located in aquifers. Ground-water seepage into mines is often
discharged to the adjacent land surface or may be utilized in dust control or
mill process waters (Water Resources Work Group 1971).

Large quantities of solid wastes must be disposed of at milling sites;
about 98 percent of all processed ore is discharged as tailings (Reed et al.
1976). Tailings are historically discharged directly into streams or piled on
flood plains near streams. The tailings piles are highly susceptible to
erosion, and even minimal contact with water will produce an effluent in
excess of permissible limits. In addition to radioactive compounds that may
leach or erode from tailing piles, milling wastes are frequently high in total
dissolved solids and are either strongly alkaline or acidic (Water Resources
Work Group 1971). Water seeping from tailing piles may contain trace
elements, nitrates, sulfates, or organic compounds that will degrade or
pollute ground or surface waters (Reed et al. 1970). Other potential sources
of exposure from tailings include inhalation of windblown particulates or
gases diffusing from the piles and external whole-body gamma exposure.
Tailings are presently discharged into impoundments primarily to retain
the solid wastes but also to settle and segregate the liquid wastes. Some
1 iquid wastes have been disposed of by deep-well injection. If properly
conducted, this disposal method is acceptable; however, contamination of
aquifers from injected wastes appears to have occurred in New Mexico (Reed et
al. 1976).' Seepage or spills from tailing ponds may contaminate shallow
aquifers or surface waters. The ponds generally support little aquatic life
because of their high pH and the presence of toxic substances. The effects on
animals such as waterfowl that may inadvertently use the ponds are not known
(Reed et ale 1976).
24

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..
Casper *'
RivertonO t bt
, 1
..
..
Edgemon.
* Active Uranium Mills
.. Areas with production and $35.00 reserves
'i) Greater than 500 tons U308 (Names Shown)
. Other areas with production and $35.00
reserves greater than 10 tons U308
o Major Cities
..
.
. ~Denver
Grand Junction
... .*
Uravan
Mineral
"... . Belt

Gr.n..~ .
. '~Gr.nts
. . Mineral
Bait
o 100 200
I I I
Miles
Figure 11.
Uranium reserve regions, Western United States
(from Larson 1978).
Recently, in situ extraction of uranium has become more common,
particularly in Wyoming and New Mexico. In situ leach mining consists of
injecting a suitable leach solution into the ore zones below the water table
to oxidize, complex, and mobilize the uranium. The resultant solution is
recovered by pumping it to the surface through production wells for further
processing. The leach solution is usually a diluted concentration of ammonium
carbonate-bicarbonate or sulfuric acid; the oxidizing agent is usually
hydrogen peroxide or oxygen. Once the uranium minerals are solubilized and
complexed, they follow the solution flow through the ore zone to a production
well. The uranium in the leach solution is recovered by ion exchange
techniques. The processed leach solution is regenerated and recycled to the
well field. When the absorption resin is sufficiently loaded, the uranium is
stripped from the resin using a suitable chemical, then precipitated from the
solution, thickened, filtered, dried, and packaged for shipment (Larson 1978).
Potential for pollution is present throughout the process. Of special
concern is contamination of ground water, both by escape of the injected
solution and from cross-contamination to different strata via the injection
and production wells.
25

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Reed et al. (1976) provides a very good summary of uranium mining
activities in the United States. They list tailing ponds and the resultant
ponded materials as the primary environmental concern. Both have caused
environmental problems in the past. Deep-well injection disposal and in situ
leach mining activities need to be closely monitored in order to minimize any
problems. I .
Geothermal resources have not been accurately characterized, but most
estimates range from 5,000 to 6,000 megawatts (MW) electrical output capacity.
Activity in the region is primarily exploratory with some experimental
development occurring at a few locations. Development of geothermal resources
is expected to produce saline brines rich in trace elements, large amounts of
waste heat and noise, and, in some locales, large amounts of sulfur-rich water
and fumes. Most areas will require large amounts of water both to produce
steam and to provide for cooling. In the near future, development of these
resources in the study area is expected to be limited to a few experimental
sites (University of Oklahoma and Radian Corporation 1977).
There are also limited amounts of tar sands, gilsonite, and other fossil
fuel reserves that may be exploited in the region. Gilsonite is currently
mined for purposes other than use as a fuel. No exploitation of tar sands is
presently occurring in the United States (Corsentino 1976).
The study area receives a large amount of solar radiation, usually over
400 gram calories per cm2 per year, and is cloud-free much of the time (U.S.
Geological Survey 1970). The large amount of unused land in the region makes
it a prime candidate for large-scale solar radiation facilities in the
long-term future. In the near term it is doubtful that any significant
development of solar energy facilities will occur.
TRANSPORTATION
Facilitation of energy resource development requires sufficient
transportation systems to economically expedite fueling of powerplants and
distribution of electricity. Coal deposits are usually distant from adequate
supplies of water necessary to cool electrical generating powerplants, and the
ultimate consumers of this electrical energy may be equally distant from its
source of generation. Transportation systems that may have an environmental
impact include roads, railroads, gas and oil pipelines, coal slurry pipelines,
and electrical transmission lines.
The University of Oklahoma and Radian Corporation (1977) study points out
that railroad coverage of the major coal deposit areas of the Rocky Mountain
and Northern Great Plains regions is weak. For instance, in the Four Corners
region an average of 240 km of spur-track line is needed to connect any new
mine to a main-truck rail line. As coal deposits become actively mined and
new power generation facilities come on line, the demand to expand rail
service will be severe. Saturation of existing rail lines to handle coal
transport will occur within one decade. If 25 million tons of raw coal per
year were to be transported on one set of double track to supply powerplants
26

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in the region and outside markets, 29,000 km of additional track, at a cost of
$5.5 billion, would be needed by the year 2000.
Only one coal slurry pipeline, that connecting the Black Mesa Mine and the
Mohave Powerp1ant in Arizona, is in operation in the United States at this
time (Jones et a1. 1977). Several pipelines have been proposed to transport
coal both within and outside of the western energy resource region. The Office
of Technology Assessment (197R) reviewed coal slurry pipeline technology,
feasibility, and impacts. They concluded that pipelines can transport coal
more economically than other modes when high annual volumes of coal must be
shipped over long distances from large, closely spaced mines, when terrain
characteristics are favorable to pipeline construction, when adequate low-cost
water and electric power for pumping are available, and when some economic
considerations are satisfied. They found that sufficient water was generally
physically available for planned pipelines but was not necessarily legally
available. Competition for water with other future uses will also occur.
Alternatives such as mine-mouth power generation, coal gasification, and coal
liquefaction all require larger amounts of water for a given amount of coal
than pipelines. Disposal of excess water at the receiving end of the pipeline
will be necessary and will require treatment prior to use or discharge.

Gas and oil pipeline systems are currently adequate in most areas of the
region. It is anticipated that existing oil and natural gas pipelines will
transport sYnthetic hydrocarbon products in the near future as coal
gasification and liquefaction operations begin and natural gas and crude oil
supplies become exhausted. It will be necessary to construct an additional 93
billion m3 pipeline capacity requiring an estimated 215 km2 of land for
right-of-way to meet projected supply deliveries by the year 2000 (University
of Oklahoma and Radian Corporation 1977). Spills from such pipelines have
occurred in the past (Figure 10) and will undoubtedly occur in the future.
As each new mine-mouth electrical generating powerp1ant comes on line, new
transmission lines are required to connect it with major distribution
networks. Substantial links between the Four Corners power facilities and
the Phoenix, Tucson, and Los Angeles markets exist. Although Federal and
private utility distribution networks exist throughout the West,
interconnections of power between different river basins and between distant
regions are poorly developed. Interconnection networks offer reliability of
future electricity supplies and effectively handle seasonal surpluses.
Interconnection capacities are highly desirable from a conservation standpoint
since power generation redundancies can often be eliminated. However,
mechanical, administrative, and planning contingencies currently make
large-scale implementation of the system complex and economically impractical.
Expansion of power transmission systems to meet demands by the year 2000 will
require 21,000 km of new line of a 2,200 MW capacity. Impacts from this
expansion will be minimized by replacing older 230 kilovolt (kV) capacity
grids with. 345 kV, 500 kV, and higher capacity lines installed in existing
transmission corridors (U.S. Bureau of Reclamation 1977b). Concern over the
effects of high frequency radiation from power transmission lines is growing.
Public reaction has already slowed construction or forced relocation of power
lines in several areas.
27

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CONVERSION AND REFINING
Conversion and refining of uranium ores, coal, and oil shale will require
large amounts of water, both for processing and for cooling. Waste disposal
and cooling ponds have potential for both surface- and ground-water pollution.
In situ conversion will introduce organic contaminants to ground-water
systems. Soviet studies on large-scale underground coal gasification projects
have found ground-water contamination that persisted up to five years after
production had ceased (Humenick and Mattox 1978).
POWER GENERATION
Electric power generation may cause pollution in several ways. Waste heat
from combustion must be dissipated into the environment. Coal- or oil-fired
plants produce stack emissions that may cause deposition of volatile trace
elements over wide areas (Figure 12). Combustion of coal acts to concentrate
nonvolatile residues in the ash and slag wastes (Radian Corporation 1975).
The removal of sulfur and particulates from stack gases produces a sludge rich
in sulfates and elemental forms (Wewerka et al. 1976). Disposition of ashes
or sludges may lead to subsequent leaching of contaminants into nearby waters.
Concentration of chemicals in cooling system blowdown waters through
evaporative processes, introduction of biocides or scale removers, etc.
produce low-quality water. Discharge from plant wastes, cooling ponds, or
coal or oil stockpiles into waterways may occur with varying degrees of impact
(University of Oklahoma and Radian Corporation 1977).
WASTE DISPOSAl
Coal gasification and liquefaction, oil shale processing, steam electric
generation, uranium extraction, and other energy resource development
activities all produce process streams and wastes rich in pollutants. Great
care will have to be taken to ensure that final disposition of these effluents
does not introduce contaminants, many of which may be carcinogenic, mutagenic,
or teratogenic, into the environment (University of Oklahoma and Radian
Corporation 1977). Both steady discharge from plant processes and accidental
releases are of concern. One study (University of Oklahoma and Radian
Corporation 1977) notes:

"Holding pond benn designs must be site specific and failures are common
in areas where previous design experience is not available. If
accumulations of wet-solids containing heavy metals, trace elements, and
complex aromatic hydrocarbons are released accidentally, they could
provide acute effects in local surface waters. The quantities involved
are quite large. . . 12.6-101.6 million tons of solids will accumulate
over 25 years from just one faci1 ity at one site."
Another study of nine surface-mine sedimentation ponds by Kathuria et a1.
(1976) in eastern mining areas found that poor construction and inadequate
maintenance were major problems. During a rain the ponds had much lower
removal efficiencies than predicted. On the basis of these two reports, the
28

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--I
---
~
=
- 1IId.t:...
~
I
~<


I


<~:.l
I
:g.
~,
"
~
'.~<
<;i
-------
~,'-"",..,-
.,.'-~ ~~:':1t
'\
, . 1;?
~ ,,~- .. ,.. \'.... t.
$. .....r ~~.,
..~.;:...,-(- ~
..-!,. ,,~..;~.;~, >::~;":J"'~
~,~ "'. ' 1, ~'_',V,"""
,- .", "- ....... .. ~

)..'~~-"'..~'. i~,3~ :~~:, \:"/ ..~~
:~,,'~"~" ~~~~.~ ~L~.~- i~.:~~~]~
Figure 13.
Uranium tailing
pond failure.
Figure 14.
(A)
Water supply truck
(B) Oil
from crankcase leak.
30

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TABLE 4.
MINIMUM ESTIMATES OF NONFUEL MINERAL AND MATERIAL REQUIREMENTS
FOR ENERGY PRODUCTION IN THE UNITED STATES, 1975-90
(Albers et al. 1976)
Commodity
Total Demand
(metric tons)
Largest Energy
Source Users
A 1 umi num
Antimony**
Asbestos
Barite
Bentonite**
Boron
Cadmi um**
Chromium**
Cobalt
Concrete**
Copper
Fl uori te**
Indium
Iron
Lead**
Magnesium
Manganese
Mica
Molybdenum**
Nickel
Niobium
Silicon
S i 1 ve r
Tin
Titanium**
Tungsten**
Vanadi urn
Zinc**
Zirconium
18,400,000
1,070
147,000
29,200,000
12,700,000
41
130
264,000
1,760
348,000,000
4,690,000
1,980,000

145
213,000,000
25,300
1,310,000
2,080,000
5,410
78,800
264,000
933
815,000
1,120
1,540
7,020
40,100
3,810
3,260
27,500
Transmission; coal*; solar
Coal
Oil and gas refining; geothermal; nuclear
Oil, gas, and oil shale development
Oil, gas, and oil shale development
Coal; uranium mining and processing
Coal; uranium mining and processing
Nuclear; oil, gas, and oil shale development
Fossil fuel powerplants
Hydroelectric; nearly all
Transmission; powerplants
Transmission; oil, gas, and oil shale
and development; solar
Nuclear powerplants
All
Nuclear powerplants, uranium and coal mining
All (required for steel production)
Oil, gas, and oil shale development
Hydroelectric
Nuclear powerplants
Nuclear powerplants
Oil, gas, and oil shale development
Oil, gas, and oil shale development;
solar; transmission
Nuclear powerplants
. Nuclear powerplants
Geothermal powerplants
Oil, gas, and oil shale development
Oil and gas refineries
Uranium mining
Nuclear powerplants
*Includes mining and transport
**Ore deposits exist in western energy resource region
31

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WATER AVAILABILITY
Energy resource development activities will require large and continuous
amounts of water. This need, coupled with increasing demand and competition
from other water users, should ultimately result in increased water
management. Construction of new dams, aqueducts, pipelines, wells, and other.
control structures will further disrupt naturally occurring hydrologic cycles.
Evaporation from reservoir surfaces is second only to agriculture in water
consumption. The impact of a reservoir on water quality may be either
positive or negative; a reservoir may act as a settling basin and reduce both
suspended and dissolved solids loads or it may raise dissolved concentrations
through evaporation. Reservoirs, once constructed, often become popular
recreation areas and demands for high-quality water become even more
pronounced.
Surface water presently supplies nearly all of the water needs of the area
(Price and Arnow 1974; Taylor 1978). Although the amount of recoverable
ground water in the upper 30 meters of saturated rocks in the Upper Colorado
River Basin alone is estimated at 142 x 109 m3, ground water supplies less
than 2 percent of the total water withdrawn and consumed (Price and Arnow
1974). Future water requirements in the western energy development region
will necessitate utilization of ground-water resources. Appraisals of these
resources, development scenarios, and potential environmental problems are
discussed by Price and Arnow (1974) and Taylor (1978).
In the western energy resource region water availability may ultimately
limit energy resource, agricultural, and urban development. Water shortages
place even greater importance on surface-water quality; throughout the area
recreational use of natural resources is high and high-quality water resources
are particularly treasured. Energy resource development has potential for
impacting the quality of both surface- and ground-water resources. This
impact may be a direct result of resource development activities or it may be
the result of secondary impacts of population growth resulting from
development-related employment opportunities.
ELEMENTAL POLLUTANTS
A relatively large amount of data has been collected on the elemental
composition of coal and its process streams and wastes. Less data are
available for coal conversion streams and releases during in situ processing.
. A large amount of data is also available for oil shale and spent-shale
elemental compositions. Some data are available for most energy development
processes, including in situ oil shale processes.

The elemental composition of representative western coals is presented in
Table 5. Arsenic, boron, bromine, lithium, molybdenum, niobium, sulfur, and
selenium are concentrated in coals above average crustal-rock levels.
Gluskoter et al. (1977), evaluating U.S. coals, found only arsenic, boron,
chlorine, and selenium in concentrations significantly above those in the
Earth1s crust, but boron was only concentrated in Illinois coals, not western
32

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TABLE 5. ELEMENTAl COMPOSITION OF COAL (mg/kg) FROM A NUMBER OF COAL FIELDS
  THROUGHOUT THE WESTERN ENERGY DEVELOPMENT AREAS   
            Navajo Mine Coals 
  Navajo Mine Coal Navajo          Navajo Mine Calculated
  Average fran Mine  Burnham Bottom Fly Stack Overburden Ground
Ele~nt  7 Seams Comp  Mine Coal  Ash Ash Particulates (Average) Deposition
Ant imony  0.42 0.13  0.3-1.2 0.5-0.8 0.4 0.9   0.46 
Arsenic  1.20 1.1  0.1-3.0 0.8-1.1 11  30   2.0  0.39-0.5
Ba ri um  140.00 1     0.5 1.0 0.5   717  4.5-39.3
Beryllium 3.40 1.5 2-3.0  5 6  5   <1  
Bismuth  <0.10 1 0-0.2  10 10  10   <30  
Boron  75.00 80  60.0-150.0 200 700  300   41  
Bromine  1.70   100            
Cadmium  0.66   0.2-0.4 0.7-3.2 1.6 4   <10  
Ce ri um  15.00   150-200          
Cesi um  0.32               
Chranium  4.60 4      20 60  20   16  
Coba 1 t  1.60 2      10 10  10   23  
Copper  44.00 14      53-57 80  65   40  
Oys pros i um 0.68               0.1-1.1
Erbium  0.24               
Europi um  0.46               
Fl uori ne  210.00 44  100    7-17 100  900   417  
Gadolinium <0.33               53-455
Gall ium  12.00 8  Q.5-8.0  30 400  40   27  
Gennani um 0.90 <6  0.1-0.5  30 30  30   <10  
Gold  <0.10               
nafnium  0.44               
Holmium  <0.11               
Iodine  0.45               
iridium  <0.10               
Lanthanum 10.00               
Lead  5.50 6.3  1.4-4.0  23-26 62  50   36  
Lithium  85.00 50     200 200  200   83  0.04-0.45
Luteci um  <0.35               
Manganese 130.00 40  500   200 300  200 455  
Mercury  0.01 0.08  0.2-0.3 0.3-0.6 0.13 0.30 0.055 
Molybdenum 4.90 0.8     3  10  3   <10  0.1-0.6
Neodymium 13.00               
Nickel  2.90 4  3.0-30.0 20  30  20   19  
Niobium  5.60 <2     10  10  10   <20  
Osmium  <0.10               
Pa 11 adi um <0.10               
Platinum  <0.10               
Praseodymium 3.40               
Rhenium  <0.10               
Rhodium  <0.10               
Rubidium  4.60               
Ruthinium <0.10               
Samarium  0.75               
Scandium  4.00             10  
Selenium  0.74 2.7  0.1-0.2 0.2-1.5 6.6 27   <0.5  
S 11 ver  0.03 <0.2     1  1  1   <5  
Strontium 53.00 40     300  500  300 133  
Tantalum  0.39               0.35-1.96
Tellurium 0.20               
Terbium  <0.11               
Thallium  <0.15               
Thori am  3.60               
Thulium  <0.11               
Tin  1.40 <0.6     3  3  3   <5  
Tungsten  6.90               
Uranium  0.66               
Vandium  21.00 20  300-500  50-70 200  60   35  
Ytterbium <1.10               
Yttrium  13.0               
Zinc  12.00 6  1.1-27.0 10  100  10   38  0.07-0.68
Zirconium 140.00 40     200  300  300 142  
33

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coals. Western coals were found to have higher concentrations of barium,
calcium, sodium, phosphorus, and strontium than eastern coals.

During coal mining, handling, storage, and use, trace elements may enter
the environment through volatilization or solution or become concentrated in
coal wastes. Wewerka et al. (1976) found that effluents from western coals
had relatively low total salt contents, high pH values, and modest
concentrations of metallic elements when compared to eastern acid-spoil
drainages. Wachter and Blackwood (1978), using coals from Montana, New
Mexico, and Wyoming, determined the average effluent concentrations from a
simulated rainfall (Table 6). They found that aged coals not exposed to
recent rainfalls had higher runoff concentrations than new coals or coals
recently exposed to rainfall. These concentrations, diluted as the runoff
flowed across open areas to the nearest waterway, were calculated as ranging
fram one to seven orders of magnitude lower than established water-quality
criteria.
Van Meter and Erickson (1976) estimate that a typical 7 million m3 per day
gasification plant will produce about 400,000 metric tons of slag or ash per
year. They note that "vi rtually all the trace el ements content of the coal
will remain in this solid waste and will probably be more soluble. . . ."
Elutant from wastes contained large amounts of soluble caustics, primarily CaD
and Ca(OH)2. Elutant pHs ranged from 12.5 to 11.2. Metals other than
alkalies and alkaline earths were extremely low; only zinc and manganese were
detected by atomic absorbtion methods. .

Klein et al. (1975) traced the pathways of 37 trace elements through a
coal-fired powerplant in Tennessee. The coals used were from Illinois and
Kentucky, but it is felt that element flows would be similar to those of
western coals. They found that aluminum, barium, calcium, cerium, cobalt,
europium, iron, hafnium, potassium, lanthanum, magnesium, manganese, rubidium,
scandium, silicon, samarium, strontium, tantalum, thorium, and titanium were
readily incorporated into the slag and were partitioned evenly between the
inlet fly ash and the slag. Arsenic, cadmium, copper, gallium, lead,
antimony, selenium, and zinc were concentrated in the fly ash, while mercury,
chlorine and bromine remained in the gaseous state and escaped into the
atmosphere.
Ray and Parker (1977) characterized fly ash composition. They found that
it varied widely but was 95 to 99 percent silicon, aluminum, iron, and calcium
while magnesium, titanium, sodium, potassium, sulfur, and phosphorus comprised
from 0.5 to 3.5 percent. The remainder consisted of up to 50 trace elements.
Bromine, chlorine, fluorine, mercury, sulfur, and selenium were generally
discharged to the atmosphere during coal combustion. Arsenic, cadmium,
copper, gallium, molybdenum, lead, antimony, sulfur, zinc, and, to a lesser
degree, selenium, chromium, and nickel were preferentially concentrated in the
fly ash. Elemental composition of ashes from U.S. coals are presented in
Table 7.
Common disposal of fly ash is by ponding the scrubber or precipitor
effluent on site and later mining the material and depositing it as fill.
Theis et a1. (1976) investigated trace-element migration into ground water
34

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TABLE 6.
AVERAGE EFFLUENT CONCENTRATIONS FROM WESTERN COAL STOCKPILES (mg/l)
(from Wachter and Blackwood 1978)
Element in  Montana Wyoming New lv1exico
Effluent " Coal Coal Coal
Antimony (Sb)  NO 14.0 6.5
Arsenic (As)  1.8 5.6 4.1
Beryll i urn (Be)  NO NO NO
Cadmium (Cd)  NO 0.005 NO
Chromium (Cr)  NO 0.04 NO
Copper (Cu)  NO NO 0.02
I ro n ( Fe)  1.5 8.2 5.5
Lead (Pb)  0.05 0.07 0.05
Manganese (Mn)  0.14 0.4 0.04
Mercury (Hg)  0.003 0.005 0.002
Nickel (Ni)  0.02 0.05 0.03
Selenium (Se)  NO 15.0 21.5
Silver (Ag)  NO NO NO
Thorium (Th)  NO NO NO
Zi nc (Zn)  0.17 0.15 0.04
NO - Not detected    
around a powerplant utilizing Illinois coal. The plant produced between 310
and 365 metric tons of ash per day. They found concentrations of trace
elements to be quite low [none exceeded 4 parts per million (ppm)] in ground
water near the site but to vary in response to ash loading rates and pond
handling procedures.

Chu et al. (1976) found that alkaline fly ash did not release cadmium,
iron, mercury, manganese, or lead into alkaline or neutral waters but arsenic,
boron, barium, chromium, copper, nickel, selenium, and zinc did solubilize in
alkaline sluice water. Barium, boron, chromium, nickel, and selenium
concentrations exceeded water-quality criteria. Libicki (1978) investigated
changes in ground-water composition over a 2~-year period from an unlined
coal-waste disposal pit located in the roof of an aquifer layer in Poland. He
found the following increases in ground-water concentrations compared to
unaffected aquifer samples: total dissolved solids, 10 times; chlorine, 40
times; sulfate, 10 times; sodium, 100 times; calcium, 6 times; magnesium, 2
times; ammonia, 4 times; phosphate, 8 times; cadmium, 3 times; strontium, 5
times; copper, 6 times; molybdenum, 15 times; boron, 25 times; cyanide, 10
times; and phenols, 2 times.
Pellizzari (1978) analyzed process streams from in situ oil shale and coal
gasification experiments and from low-Btu gasification processing of Rosebud
coal. The results are summarized in Table 8. Cushman et ale (1977) listed 35
elements for potential release into the environment from coal conversion
35

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TABLE 8. MAXIMUM CONCENTRATION (ppm) OF ELEMENTS IN VARIOUS WATER SAMPLES
(from Pellizzari 1978)
Element
Aluminum (Al)
Antimony (Sb)
Arsenic (As)
Barium (Ba)
Beryll i urn (Be)

Bismuth (Bi)
Boron (B)
Bromine (Br)
Cadmium (Cd)
Calcium (Ca)
Cerium (Ce)
Cesium (Cs)
Chlorine (Cl)
Chromium (Cr)
Cobalt (Co)

Copper (Cu)
. Oys pros i urn (Oy)
Erbium (Er)
Europium (Eu)
Fl uorine (F)
Gadolinium (Gd)
Gall i urn (Ga)
Germani urn (Ge)
Go 1 d (Au)
Hafnium (Hf)

Ho 1 mi urn (Ho)
Indi urn (In)
Iodi ne (I)
Iridium (Ir)
Iron (Fe)
Lanthanum (La)
Lead (Pb)
Li th i urn (Li)
Lutetium (Lu)
Magnesium (Mg)
In Situ
Oil Shale
Process
Water
0.5
0.24
2
0.5
<0.001
<0.001
1.5
0.3
<0.001
2 (MC)*
0.002
.01
(MC)
.005
0.01-

.6
0.045
<0.001
0.001
8 (MC)
0.001
0.015
0.015
<0.001
<0.001
<0.001
Trace
0.02
<0.001
5.5
0.001
0.06
1.2
<0.001
4 (MC)
Low Btu Gas-
ification of
Rosebud Coal
Process
Water
1.2
.06
2.5
0.07
<0.04
<0.04
2
.04
<0.04
4.2 (MC)
<0.04
<0.04
1.5
0.47
0.015
0.6
<0.04
<0.4
<0.4
0.6
<0.04
0.06
0.04
<0.004
<0.04
<0.04
Trace
<0.04
<0.04
2.5 (MC)
< .04
0.25
0.125
<0.04
7 (MC)
Pre-
In Si tu
Gasification
Well Water
0.7
0.003
0.01
0.2
<0.004
<0.004
0.02
0.25
.009
(MC)
<0.004
0.002
4
0.04
0.008

0.19
<0.004
<0.004
<0.004
~2
<0.004
0.005
0.005
<0.004
<0.004
<0.004
Trac-e
.003
<0.004
0.6 (MC)
<0.004
0.02
0.5
<0.004
0.009
37
In Situ
Gasification
Process
Product
Waters
7.5
0.01
0.05
0.25
<0.008
0.006
0.4
0.03
0.025
24 (MC)
0.003
0.05
o . 75 ( MC )
0.45
0.005

0.75
<0.008
<0.008
<0.008
~1.4 (MC)
<0.008
0.1
0.14
<0.008
<0.008
<0.008
Trace
<0.003
<0.008
7 ( MC )
0.003
0.55
0.01
<0.008
9
Post-
In Situ
Gasification
Well Water
2
0.005
0.025
4.0
<0.01

<0.012
0.6
0.15
<0.01
(MC)
0.008
0.009
6 ( MC )
0.06
0.02

0.5
<0.01
<0.01
0.02
~5 (MC)
<0.01
0.05
0.015
<0.009
<0.01

<0.01
Trace
0.02
<0.01
8
<0.002
0.12
3.2
<0.01
6
(MC)
(conti nued)

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    TABLE 8. (Continued)    
    Low Btu Gas-   In Situ  
  In Situ ification of Pre-  Gasification Post-
 Oil Shal e Rosebud Coal In Situ  Process In Situ
Elanent  Process Process  Gasification  Product Gasifi cati on
  Water Water  Well Water  Waters Well Water
Manganese (Mn) 0.06  <0.4  0.5  0.2 0.95 (MC)
Mercury (Hg) 0 .001  0.005  <0.009  0.066 <0.23 
Molybdenum (Mo) 0.6  <0.04  0.3'  0.5 0.07 
Neodymium (Nd) 0.003  <0.04  <0.004  0.01 <0.01 
Nickel (Ni) 0.08  1.85  0.07  0.35 0.07 
Niobium (Nb) 0.003  <0 . 04  0.002  0.002 <0.09 
Osmium (Os) <0.001  <0.04  <0.004  <0.008 <0.01 
Pall adi um (Pd) <0.001  <0.04  <0.004  <0.008 <0 . 01 
Phos phoru s (P) 2.5  0.55  0.14  5  0.3 
Platinum (pt) <0.001  <0 .04  <0.004    <0.01 
Potassium (K) 3 (MC) 3.1  >6 (MC) 4.5 8 (MC)
Praseodymium (pr) 0.001  <0.04  <0.004  0.001 <0.01 
Rubidium (Rb) 0.15  0.05  0.06  0.1 0.3 
Samarium (Sm) 0.002  <0.04  <0.004  <0.008 <0.01 
Scandi urn (Sc) <0.006  0.05  0.007  0.3 0.03 
Selenium (Se) 0.003  0.6  0.008  0.05 0.02 
Silicon (Si) 2.5  2  4 (MC) 18  7 (MC)
Silver (Ag) < .0001 <0.04  <0.004  0.1 <0.009 
Sodium (Na)   (MC) 8.5  3.5 (MC) 77 (MC) 7 (MC~
Strontium (Sr) 5  0.65  2  0.3 7 (MC
Sulfur (S)   (MC) 4 (MC) 7 (MC) 10.5  (MC)
Tantalum (Ta) 0.003  0.7  <0.004  <0.008 <0.01 
Tellurium (Te) <0.001  <0.04  <0 . 004  <0 .008 <0.01 
Terbium (Tb) <0.001  <0.04  <0.004  <0.008 <0.01 
Tha 11 i urn (Tl) <0.001  <0.04  <0 . 004  <0 . 008 <0.01 
Thorium (Th) <0.001  <0.04  <0.004  <0 . 004 <0.01 
Tin (Sn) 0.04  0.04  0.006  0.6 0.01 
Titanium (Ti) 0.5  0.25  0.5  3  0.5 
Tungsten (W) 0.002  0.07  0.01  0.08 0.03 
Uranium (U) 0.06  0.07  <0.004  0.004 0.004 
Vanadi um (V) 0.015  0.06  0.005  0.01 0.007 
Ytterbium (Yb) 0.001  <0.04  <0.004  <0.008 <0.01 
Yttrium (Y) 0.002  0.002  0.001  0.001 O. 004 
Zi nc (Zn) 0.12  0.5  0.35  0.06 0.15 
Zirconium (Zr) 0.3  0.06  0.008  0.006 0.015 
*(MC) - Major Component, present in concentration that exceeded upper 
analytical quantification limits.      
      38      

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and lead to serious problems of leaching of soluble materials from the lower
shale formations. They also note that sour water decanted from the product
oil will pose a disposal problem. Ringrose et a1. (1976) found oil shales
tended to be enhanced in arsenic, mercury, and selenium and, to a lesser
degree, in fluorine, lithium, and molybdenum over the surrounding soils. Dean
(1976) noted seven elements of particular environmental interest in oil shale
samp1es--arsenic, boron, cadmium, fluorine, mercury, antimony, and selenium.
These are present in 1-ppm ranges but are volatilized during pyrolysis or
concentrated in the spent shale.
Bates and Thoem (1979) led a review of environmental impacts from oil
shale. Elemental concentrations of raw shales, process waters, and spent-
shale 1eachates are summarized in Table 9. Of prime interest are the
relatively high levels of boron, barium, chlorine, fluorine, sodium, silicon,
and strontium in 1eachates. In addition, surface runoff waters from spent oil
shale test plots contained higher levels of all test elements, and
bicarbonate, chloride, and sediment.
Reed et a1. (1976) found both alkaline and acid' leaching processes in use
during uranium milling. Wastes from the acid process had a pH of 1.5 to 2.0
and contained calcium, sodium, magnesium, and iron cations and chloride and
sulfate anions with small amounts of other elements. Alkaline leach-process
waste solutions contained fewer elements. A cited analysis shows the
alkaline-leach mill-tailings effluent to be high in uranium, sulfate,
carbonate, bicarbonate, thorium, and sodium. Reed et a1. found that
magnesium, copper, manganese, barium, chromium, molybdenum, selenium, lead,
arsenic, vanadium, iron, cobalt, nickel, zinc, thorium, uranium, and radium'
was leached from the ore and appeared in the effluent streams. These
effluents are discharged to tailings ponds. Excess water may be discharged
directly to streams following a neutralization process. They also cited a
number of cases where ground-water contamination from tailings had occurred.
The primary pollutants cited were selenium, nitrates, and sulfates.
In a series of reports on inactive mill-tailing sites, Ford, Bacon and
Davis Utah, Inc. (1977a, 1977b, 1977c and 1977d) found ground waters near mill
tailings to be enhanced in barium, chromium, selenium, lead, arsenic,
vanadium, and cadmium. In the case of cadmium, high ambient levels were
observed throughout the area, and the tailings contribution was believed to be
insignificant.
These studies indicate that a large number of elements and inorganic ions
may be released into the environment by energy resource development
activities. The major ions--ca1cium, magnesium, sodium, sulfate, chloride,
and bicarbonate--are likely to be affected. Cyanides, nitrates, and ammonia
are also cammon products of energy development activities. Minor elements
imported by at least two researchers at elevated or potential problem levels
in coal, oil shale, uranium ore, or their process streams are:
Aluminum
Ant imony
Arsenic
Barium
Boron
Brami ne
Cadmium
Chromium
Copper
Fluorine
Gallium
Iron
Lead
Li thi um
Manganese
39

-------
  TABLE 9.  TRACE ELEMENTS IN OIL SHALE RETORT WATERS AND SPENT-SHALE LEACHATES 
                  Surface Runoff
   Retort Waters (mg/I) (from Bates and Thoem 1979)   Spent-Shale Leachates (mg/I) . (mgl1) 
                  Tosco II \lSBM
                  Spent- Spent-
   In Situ  In Situ    FI scher Pa rahoe     Surf ace Shale Shale
 Element T osco II Tosco II  LETC LETC llL Assay Indirect So11 Parahoe T osco II USBM Retorts Plot Plot
 Aluminum (AI)            0.05 0.05     
 Ant lmony (Sb)  0.007   0.004-0.036 0.047          
 Arsenic (As) 1.0 0.26  2.0 0.26-10 1.8 0.0-4.2 1.0 0.02 <0.007 0.005  0.1  
 Barium (Ba) 0.09 .0.03   0.002-0.081 <0.3   2.0 0.07 0.12   4.0  
 Beryllium (Be)            0.005 0.01     
 Boron (B) 0.44 0.26   0.26-8.8  0.55-2.8 5.0 1.14 0.8-1.6 4.6  2-12  
 Bromine (Br)      0.019-0.66 0.1   0.009       
 Cadmium ~Cd!     0.001     0.00   <0.04    ..... 
 Calcium Ca      0.41-36   1.4 >10.0 166.4 421.4  42 42-114 140.4 61.1
~ Chlorine (CI)      0.023    2.0 4.2B 526  13 5-33 42.0 62.6
a Chromium (Cr) 0.007 0.012   0.011-0.037 0.08  0.004 0.03 0.01 <0.04     
 Cobalt (Co) 0.005 0.37   0.002-0.65 0.23 0.002-0.005 <0.04       
 Copper (Cu) 0.16 0.003  0.2 0.003-0.016   0.16 0.2 0.02 0.05     
 F1 uorlne (F) 0.3     14-54  2.6-3.5 7.0 1.31 11.9 16.6  3.4-60  
 Iron (Fe) 5.7 0.49   0.49-17 14.0  1.0 5.0 0.01 0.03     
 Lead (Pb) <0.002 0.01 , 0.03 0.01-0.37 0.0  0.2 0.005 0.01      
 Lithium (U) 0.006     0.004-0.75    1.0  6.8     
 Magnesium ~Mg!      3.2-350    >10.0 190.3 7.74  3.5 3.5-91 53.4 19.2
 tlanganese Mn 0.019 0.023   0.023-0.14   0.02 0.3 0.05 <0.14     
 Mercury (lIg)  0.01  0.01 0.01-0.39 0.024   <0.01  <0.003     
 Molybdenum (Mo) 0.006 0.47   0.056-0.47  0.006-0.13 0.1       
 Nickel (NI) 0.03 0.26   0.26-2.6  0.034-2.3 0.2 0.01 0.01     
 Potassium (K~      3.4-70   20.0 >10.0 0.02 834.0 77.2 72 10-625 38.7 45.1
 Selenium (Se 0.096 0.005   0.003-0.10 1.7 0.1-3.1 0.1 0.02 0.03 0.02  0.05  
 Slit eon (SI)      1 .7-48    >10.0  8.1     
 Stiver iA9!            0.001 0.001     
 Sodium Na      8.3-1300 20.0  43.0 >10.0 712 5,591 10,700 225 165-2,100 47.6 27.3
 Strontium (Sr)      0.003-0.48   0.06 3.0 2.77 10.4     
 Sulfur (S)      14-310    >10.0       
 Tin (Sn)      0.11-100      0.28     
 Uranium (U)      0.010-4.6           
 Vanadium (V) 0.002 1.2   0.004-11 <0.03  0.0 0.03       
 Zinc (Zn) 0.045 0.04  5.0 0.037-0.47  0.045-0.2 0.4 0.04 0.15     
 Zirconium (Zr) 0.003 0.02   0.008-0.39 14.4          
 LLL = Lawrence Livermore Laboratory              
 LETC D Laramie Energy Technology Center             
 USBH D U.S. 8ureau of Hines               

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Mercury
Molybdenum
Nickel
Selenium
S i1 ve r
Strontium
Sulfur
Thori urn
Uranium
Vanadium
Zinc
POSSIBLE ORGANIC POLLUTANTS
The pollution of water resources by organic compounds derived from energy
development activities is of great concern. Little is actually known about
what compounds are released, their fate, or their effect on the environment.
The extreme difficulty in conducting organic analysis has discouraged most
researchers and has largely limited monitoring efforts to organic carbon,
phenols, oils and grease, and similar substances. Goldstein and Yung (1977)
and Gehrs (1976) reviewed available data and identified the compounds and
compound classes presented in Table 10 as being present in oil shale and
coal-hydrogenation process waters and products. In some cases, particularly
ammonia and phenol, the compounds are major by-products of the process.

Gehrs (1976) states that the phenols are high in acute toxicity but
generally low in known deleterious chronic effects and bioaccumulation
potential. Although little is known about the thiophenes and aromatic amines,
it appears the opposite is true; they seem to have high chronic toxicity and
bioaccumulation potential with low removal and degradation rates. Observed
concentrations of these compounds and assessment of waste-water removal
effectiveness are shown in Table 11.
Wachter and Blackwood (1978) determined concentrations of several selected
organic chemicals in simulated rainfall leachates from coal stockpiles of
Montana coal. They found no compound present at a concentration above 100
parts per billion (ppb) and concluded that levels of organic toxins entering
adjacent waterways could be from 6 to 11 orders of magnitude less than
established criteria.
In a recent study, Pellizzari (1978) reported the elemental and organic
chemical substances found in process and effluent streams in several western
energy developments. More than 50 samples from oil shale process waters, tars
and condensates from the 10w~Btu gasification of coal, and water and tar
samples from in situ coal gasification experiments were analyzed for both
volatile and semivolatile organic compounds.

Pellizzari's (1978) methods provided for the analysis for volatile and
semivolatile compounds to levels of 1 ppb [1 microgram/kilogram (~g/kg)J. The
procedure was quantitive for volatile compounds with solubilities less than 2
percent in water and boiling points less than 220C and for semi polar
compounds with solubilities less than 10 percent and boiling points less than
150C. The technique was not quantitative for highly water-soluble compounds
(e.g., acetonitrile, formaldehyde, etc.). The procedure for semivolatile
compounds gave quantitative recoveries for organic acids, neutral compounds,
and bases for compounds with boiling points greater than 60C and less than
270C. Compounds with zwitterions (dipolar ions) were not recovered and
analyzed, nor were compounds with boiling points greater than 275C.
41

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TABLE 10.
cm1POUNDS AND ORGANIC COMPOUND CLASSES IDENTIFIED IN OIL SHALE
AND COAL HYDROGENATION PROCESS STREAMS AND PRODUCTS
(Goldstein 1977; Gehrs 1977)
acridi ne
hydroxylbenzaldehyde
.!!.-al kanes
nitrosamines
ammonia
anil ine
1-2 benzanthracenes
7,12-dimethyl
benz(a)anthracene
benzo-a-anthracene
phenanthrene
3-4 benzphenanthrenes
fl uoranthene
benzene
alkyl benzenes
dihydroxylbenzene
Cl-dihydroxylbenzene
C2-di hydroxyl benzene
1.2,3-trimethylbenzene
1.2.4-trimethylbenzene
1.3.5-trimethylbenzene
benzoic ac i ds
butenes
n-butane
t-butane
carbazole
chrysenes
coumarone
methyl coumarones
.Q.-cresol
!!!...e.-c reso 1
creosotes
cyanide
thiocyanide
sul focyanides
ethane
acetyl ami nofl ourene
benzofuranols
dfbenzofurans
hydroxybenzofuran
H2S
dihydrics
fonna 1 dehyde
i ndans
indanols
indenes
indenol
lactones
lutidines
methane
naphthalene
1-ethylnapthalene
2-ethylnapthalene
1-methylnaphthalene
2-methylnaphthalene
acenaphthenes
acenaphthylenes
benzonitrile
paraffins
monocycloparaffins
olefins
dicycloparaffins
tricycloparaffins
perylene
benzo(g.h.i)perylene
phenol
C2-phenols
C3-phenols
bi phenol
diphenol
!!!...e.. and .Q..-ethylphenols
3-ethyl-S-methylphenol
benzothiophenols
acetophenones
.'~-picoline
propane
propylene
pyrenes
benzo-a-pyene
pyridine
C2. C3. and C..-pyridine
2-ethylpyridine
quinaldine
quinoline
isoquinoline.
methylquino11ne
N-methylsuccinamide
tetralins
benzothiophene
benzyl thiophene
dibenzothiophene
benzo- def d1benzothiophene
naphthobenzothiophene
methylbenzothiophene
dimethylbenzothiophene
methyldibenzothiophene
tetrahydrobenzothiophene
methyl thiophene
dinaphthothiophene
to 1 uene
!!!...e.-ethyltoluine
.Q...e.. and !!!.-toluidines
o-xylene
m:..e.-xylene
2.3-xylenol
2.4-xylenol
2.S-xylenol
2.6-xylenol
3.4-xylenol
3.5-xylenol
2.4-xylidine
2.S-xylidine
2.6-xylidine
Pellizzari's data demonstrate several important features. First, each
sample has a very unique organic composition. Second, most of the commonly
identified compounds are present in very small 100 ppb) quantities. Third,
the state-of-the-art analyses still have many compounds identified only in the
form "CnHn i somer" and many others as i somers of only somewhat better
defined compounds (e.g., benzofuran isomer). Finally, the number of compounds
identified is staggering.

Tables 12 and 13 summarize these data, indicating the number of times a
compound was reported as exceeding an arbitrary limit of 100 ppb (column N in
the tables) and the maximum value reported for that compound in each of six
sample groupings. The groupings represent: oil shale process effluents
(O.S.); tars and condensates from low Btu coal gasification of Rosebud coal
(LBtu); in situ gasification well samples (Pre I.S.); samples from several
stages of processing of both the Gillette (Gillette) and Hanna II (Hanna) in
situ gasification studies; and postgasification well samples (Post I.S.).
Few organics were present in the pregasification in situ experiments
("native" ground-water samples). Benzene, benzylamine, toluene, n-hexanal,
and isomers of dimethyl phenol , c~esol, methyl thiophene, and C -alkylbenzene
were reported, but at concentrations less than 400 ppb. Ethyl acetate,
however, reached a concentration of 630 ppb. Well samples collected following
42

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TABLE 11. OBSERVED CONCENTRATION RANGES OF ORGANIC CHEMICALS FROM COAL-
HYDROGENATION STREAMS AND RELATED WASTE-WATER REMOVAL EFFECTIVENESS
(Gehrs 1976)
Compound Cl ass

Phenols
Aromatic amines
MonoarOOlatic .hydrocarbons
Thiophenols
Polycyclic hydrocarbons
Anticipated Range (mg/l)

1,000 - 10,000
100 - 1,000
10 - 100
1 - 10
0.1 - 1
Waste Water Removal
Effectiveness (%)
99.9
30 - 50
90 +
30 - 80
in situ gasification contained a few more organic compounds, which were
present in generally larger concentrations than in pregasification samples.
Ethyl acetate, ethyl formate, formic acid, in-pentanel, and cresol and phenol
isomers were present in the higher concentrations; a few other compounds were
present at concentrations less than 200 ppb. Of the compounds found in
postgasification well waters, phenol and dimethyl phenol isomers are of
special concern. Phenol has been selected by the U.S. Environmental
Protection Agency (EPA) as a priority point-source effluent-discharge toxic
pollutant and is a suspected carcinogen (Christensen and Fairchild 1976).
Several dimethyl phenol isomers (xylenols) are known carcinogens or
neoplastens (produce benign tumors); a dimethyl phenol isomer was reported
present at a concentration of 1,500 ppb in a postgasification well sample.
Many of the more complex phenols, cresols, and formic-acid compounds are also
carcinogens and most have toxic qualities as well. The various process
streams, tars, and other samples contained both more varieties and higher
concentrations of organic compounds than well-water samples. Semivolatile
compound concentrations in product waters and tars during in situ gasification
were particularly high, in a few cases saturating the analytical capability to
quantify them. A C2-alkyl analine isomer, Cz-alkyl benzene, cyanobenzene, a
methylbenzofuran isomer, cresol i~omers, o-cresol, naphthalene, phenol,
Cz- and Cq-alkyl phenol isomers, an ethyl phenol isomer, a dimethyl phenol
isomer, methyl pyridine, and 2,3-benzothiophene all exceeded a concentration of
10,000 ppb in one or more samples. Of these, napthalene, phenol, and several
dimethylphecol isomers are suspected carcinogens (napthalene is a neoplasten)
and have been reported as toxic to aquatic organisms at concentrations of
10,000 ppb or less (Christensen and Fairchild 1976).

In order to reduce the lengthy lists of Tables 12 and 13, compounds
occurring in 15 or more of Pellizzari's (1978) samples at concentrations
greater than 100 ppb are indicated in Table 14. Naphthalene commonly occurred
in both sets of analyses, is a suspected carcinogen, and has an aquatic
toxicity of 1 to 10 ppm (Christensen and Fairchild 1976). The cresol and
phenol compounds are common, usually fairly toxic, and often carcinogenic.
Benzene, ethyl benzene, and toluene are suspected carcinogens with aquatic
toxicities in the 10 to 100 ppm range. The cresols, phenols, and naphthalene
are of particular concern since they may also occur in high concentrations.
43

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TABLE 12. MAXIMUM VALUES OF SEMIVOLATILE ORGANIC COMPOUNDS IDENTIFIED IN
WESTERN ENERGY RESOURCE DEVELOPMENT SAMPLES (Pellizzari 1978)
Compound
ethel acetate
biphenyl acetylene
isopropyl alcohol
benzal dehyde
Cz-alkyl benzaldehyde
isomer
C3-alkyl benzaldehyde
C3-alkyl benzaldehyde
dimethyl benzaldehyde isomer
3,4-demethylbenzaldehyde
ethyl benzaldehyde isomer

tolualdehyde
benzylamine
analine
Cz-alkyl analine isomers
methylaniline isomers
anisole
anthracene or phenanthracene
benzene
Cz-alkyl benzene
C3-alkyl benzene isomers
C4-alkyl
C4-alkyl
Cs-alkyl
Cs-alkyl
C7-al kyl
benzene
benzene isomers
benzene isomers
benzene isomers
benzene isomers
Ca-alkyl benzene isomers
C9-al kyl benzene isomers
n-butybenzene
cyanobenzene
ethyl benzene

triethylbenzene isomer
trimethylbenzene isomers
ds-nitrobenzene
.!!.-pentyl benzene
.!!.-popyl benzene
benzefuran
dibenzefuran
methylbenzufuran isomers
benzoic acid
dimethylbenzoid acid isomers

CsHaOz isomer
CsHaO isomer
CSH1Z0 isomer
CSH1Z0 (Ketone 1)
C7H9N

C7H100 isomer
C7H1Z0 isomer
C7H14 isomer
C7H140 isomer
C7H140Z acid
N
16
1
1
2
1
1
2
1
1
1
5
9
4
4
2
37
4
4
9
5
11
4
2
1
3
3
3
5
1
8
1
9
2
7
6
11
4
1

1
3
1
1
3
57
1
1
1
1
493
0.5.
L8tu
377
880
4,030
7,770
44
Concentration in ppb (~g/kg)
497
154
188
110
234
Pre 1.5.
630
275
2,270
3,080
2,310
630
5,100
2,370
590*
760*
Gillette
1,320
522
8,914
4,200
360
9,970
150
157

430
190
150
1,460
10,225
3,214*

1,760*
2,981
3,215
2,180
580
2,050*
11,231*
1,210

5,500
8,391
146
1,839
420
9,150
4,391
11 ,231*
417
170
169
762
7,921
Hanna
Post 1.5.
6,800
153
1,725
640
3,460
3,540*
24,200
1,075
156
693
4,420
540
1,140
340*
330
113
1,350
320
5,100*
370
2,300
640
2,907*
325
259
1,340
1,267
172
180*
218
244
(continued)

-------
  TABLE 12. (Continued)   
      Concentration in ppb (\lg/kg)  
 Compound N 0.5. LBtu Pre 1.5. Gillette Hanna Post 1.5.
CsHuN isomer 11 621   7,936 740 
CaH120 isomer 3     4,231  
CaH11+0 isomer 1   760    
CaH16 isomer 1     150  
CaH160 isomer 2     5,260  
CgH 13N isomer 4 422   761  
CgH11+0 isomer 1 175     
C9H18 isomer 3      380 
CgH180 isomer 2     1,310  
C~20 isomer 1      1,100 
C9H200 isomer 1     421  
C1oH12 isomer 9   380  1,921 1,780 
C1oH1SN isomer 1     3,762  
C1oH16 isomer 1      425 
C loH20 isomer 4     550 280 
C1oH22 isomer 3      11 ,000 
C llH 12 isomer 3   480  540  
C 11M 120 isomer 1  1,410    
C llH 21+ isomer 8   420  3,989 1,040*
CllH21+ isomer 7   230  5,620 1,160 
CllH11+ isomer 2     371 640 
C12H11+ isomer 1     230  
C12H16 isomer 7  2,410*  2,424 880* 
C12H21+ isomer. 6  2,100  3,850* 1,720 
C 12H26 isomer 2      420 
n-C 12H26 isomer 1     1,240  
C13H100 isomer 2  1,190  550  
C13H18 isomer 6  5,130  490 113 
C 13H26 isomer 7  2,050  2,050* 2,500 
C13H28 isomer 6   410  540 513 232
C1l+H10 isomer 1     560 340 
C1l+H11+ isomer        
C 1t+H20 isomer 11   370  1,470 780 
C 1l+H28 isomer 6   590*  5,100 440 
C1t+H3o isomer 1      640* 
C1sH22 isomer        
C1sH28 isomer 4      620 
C1sH30 isomer 7   760*  2,982* 500 
C1SH;2 isomer 7     1,760* 960* 
n-C1 H32 isomers 1      860 
C16H11+ isomers 1     1,410  
C 16H 32 isomers 2  2,310  250*  
C16H31+ isomers 6     1,930 1,660 
n-C16 H31+ isomers 1     2,190  
C17H36 isomers 4     2,370 632 
ClaH36 isomers 4     2,030 840 
n-C18H38 isomers 2     170 440 
C19H&.O isomers 2     170 520 
n-C19Hl+o isomers 2     140 3,100 
C2oH42 isomers 1      780 
        (continued)
   45     

-------
  TABLE 12. (Continued)   
      Concentration in ppb (lig/kg)  
Compound N O.S. LBtu Pre I.S. Gillette Hanna Post 1.S.
CzoH..z isomers 3  11.000   3.140* 
n-Czo H..z isomers 5   510  5.160 1.960 2.615
CZl H.... isomers 2      200* 
n-C21H.... j somers 1  1.710    
CSH100Z carboxylic acid 2      510 
C6H1ZOZ carboxylic acid 1      143 
creso I isomers 43 779 35.000 189 89.600* 270.000* 1.572
O-creso 1  2  2.534   17.760* 
n-decanal  1      440 
n-decane  7   129 635  3.520 
n-dodecane  10  6.560  2.315 5.040 
n-heptadecane 2      280* 
n-hexadecane  4  4.250*   1.600 139
n- nonadeca ne  7  12.110  2.100 1.540 
n-octadecane  3     333 320 
n-pentadecane 6  3.590*  3.989* 1.240 
n-tetradecane 10  12.270  2.976 3.540 
n-tridecane  13  12,100  2.730* 5.100* 127
n-undecane  9  1,650  5.976 3,747 
decanoic acid 1      1.600 
n-eicosane  2      3.100 
n-heneicosane 3      1,140 
methyl cresyl ether '     476  
.      
dimethyl ether 1     135  
fi ourene  8   470  8.200 580 
ethyl formate 13  1,200  720  930
fonnic acid  1       306
phenyl-n-heptane        
n- heptano 1  2 165     280 
heptanoic acid 1     376  
n-heptano1d acid 1     145 8.870 
2-heptanone  1      225 
n-hexanal  2    120  160 
phenyl-n-hexane 6  3.150  342* 1,100 
2-hexanone  2      275 
cycYohexanone  1      210 
1 ndan  8   940*  6.921 3.740 
alkyl 1ndan isomer 5  2,370*  660 560 
methyl1ndan isomer 5  3.590  510 3.540* 
dimethyl1ndan isomer 6  1.570  130 1.160 
tetrementhyl 1ndan 1  1.550    
tr1methyTfndan isomer        
pentamethylinden 6  6.270  8,800* 100 
pentamethylinden isomer 1     4,190  
tetramethyl indanone isomer 1   100    
i ndene  10  7,100  1.440 2.540 
methylindene isomer 10  8.250*  450 2.907* 
indo I e  1      275 
methyl ethyl ketone 1      246 
naphthalene  16  21.350  72 .500 16.400 
        (continued)
    46     

-------
   TABLE 12. (Continued)   
       Concentration in ppb (llg/kg)  
Canpound  N 0.5. LBtu Pre 1.5. Gi 11 ette Hanna Post I.S.
      I    
acenaphthalene  10  4,380  2,560 2,040 
alkyl naphthalene isomers 2     876  
C2-alkyl naphthalene isomers 7  3,879  1,410* 5,540 
C3-alkyl naphthalene isomers 8  7,810  1,560 580 
C4-alkyl naphthalene isomers 2   430   1,200 
Cs-alkyl naphthalene isomers 2      180 
ethyl naphthalene isomers 8  4,030  1,570 3,400 
methyldihydronaphthalene isomers 2  2,810   340* 
methyl naphthalene isomers 13  17,200  9,200 5,720 124
dimethyl naphthalene isomers 10 199 18,230  8,200* 6,420 115
trimethylnaphthalene isomers 1   470    
isopropylnaphthalene isomers 1  1,780    
n-hexylnitrile  1      250 
n-pentyl nitril e  1      462 
n- propi on i tril e  1      130 
n-nonanal   3      820 
n-nonane   5     520 2,267 
phenyl-n-nonane  1  1,550*    
n-hexylnitrile  1      250 
n-octanal   1      1,140 
n-octane   1      800 
phenyl octane  1      280* 
phenyl-n-octane 2    230   340* 
n-octanoic acid  5 115     1,230 120
n- pentana 1  1       505
n-pentanoic acid  6     138 4,825 
i sopentanoi c aci d 3     400  
2-methylpentanoic acid 2      1,150 
B-methylpentanoic acid 1      725 
2-pentanone  3      1,170 
3-methyl-2-pentanone 2      110 
cyclophntanone         
2-alkyl cyclopentanone 1      650 
2-methylcyclopentanone 3      2,075 
3-methylcyclopentanone 1      2,100 
dimethylcyclopentanone 1      375 
phenol   31 461 41,000  89,600* 270,000* 232
C2-alkyl phenol isomer 20 337 11,000  105,100 14,500 
C3-al kyl phenol i some r 15  1,000  10,500 296,725 2,560*
C4-alkyl phenol isomer 8   110  8,200* 296,725* 
Cs-alkyl phenol isomer 1     470*  
tri-butylphenol isomer 2      4,760 
di-sec-butylphenol isomer 1      3,440* 
tri-sec-butylphenol isomer 5     360 3,840 
ethyl phenol   2   110  4,770*  
ethyl phenol isomer 23 321 5,000  8,800* 74,700 279
methyl ethyl phenol isomer 1     33,625  
dimenthylphenol isomer 34 1,011 28,800 360 48,000* 7,800 1,510
trimethylphenol isomer 1      180 
acetophenone  3     9,327  
          (continued)
     47     

-------
TABLE 12.
(Continued)
Concentration in ppb (~g/kg)
Compound
LBtu
Pre I.S.
Gillette
N
0.5.
bi phenyl
methyl bi phenyl
methyl biphenyl isomers
biphenylene
alkyl pyridine isomer

C-alkyl pyridine isomer
C-alkyl pyridine isomer
C-alkyl pyridine isomer
ethyl pyridine isomer
methyl pyridine isomer
1,390
8
1
2
6
3
3
1
1
1
2
8,210
410
290
960*
6,777
1,010
176
329
8,140
1,020
4,050
20,000
3-methylpyridine isomer
4-methylpyridine isomer
dimethyl pyridine
dimethyl pyridine isomer
trimethylpyridine isomer

pyrro1e
qui no 11 ne
2-alkylquinoline
C-alky1quinoline
tetrahydroquinoline
1
1
2
5
2
2
2
1
4,200
6,310
762
5,160*
487
1
6
1
2
1
7
11,482

130
3,667*
3,820
methyl quinoline isomer
dimethylquinoline isomer
methyl styrene isomer
benzothi ophene
2,3-benzothiophene

to 1 uene
ethyl toluene
ethylto1uene isomer
toluic acid isomer
xylene isomers
210*
4,770*
326
1,880
465
120*
m-xy1ene
'[-xylene
:2:-xylene
13
4
5
1
6
1
2
2
117
760
Hanna
Post I.S.
680
540
300
740
196
250
100
499
973
270
1,087
100
3,000
280*
200
227
2,667
1,540
3,747
490
1,760
150
1,334
5,040
1,400
*More than one compound identified in analysis.
Columns indicate the maximum reported concentration in ppb (~g/kg) for all samples in: 1) oil shale
effl uents (0.5.); low Btu gasi fication of Rosebud coal effl uents and tars (LBtu); in situ
pre-gasification (Pre I.S.); process streams and tars from in situ gasification experiments at Gillette
(Gillette) and Hanna (Hanna), Wyoming; and well-water samples collected after in situ gasification
well-water samples (Post I.S.). N indicates the number of samples in which the indicated compound
exceeded the 100 ppb level; values less than 100 ppb were not reported.
48

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TABLE 13. MAXIMUM VALUES OF VOLATILE ORGANIC COMPOUNDS IDE NTIF I ED IN
WESTERN ENERGY RESOURCE DEVELOPMENT SAMPLES (Pellizzari 1978) 
      Concentration .in ppb (~g/k9)  
Compound  N 0.5. LBtu Pre I.S. Gillette Hanna Post 1.5.
acemaphthene   3      1.690 
acetal dehyde   2      2.033 
methyl phenyl acetylene  1      119 
acetone   17 200 259  2.226 9.330 181
aniline   3  1.143    1.330* 
benza'l dehyde   1      1.340 
benzene   25 134 1.457 192 4.318 7.670* 150
C-alkylbenzene homer 1  222     
C2 alkylbenzene isomer 1      180 
C3 alkylbenzene isomer 7  144  835 1.200 
C~ alkylbenzene isomer 10  250  1.068 2 .100 
Cs alkylbenzene isomer 29  390*  716 2.400 
C6 alkyl benzene isomer 4      1.000* 
C7 alkylbenzene isomer 2      1.200 
C7H lS benzene   1  1.180     
cyanobenzen   3 235     2.670 
methyl cyanobenzene isomer 2      830 
methyl butyl benzene isomer 1      1.200 
n-butyl benzene   1      3.800* 
ethyl benzene   15  160  1.423* 1.660 
f-di etyl benzene  1      3.800* 
d i ethyl benzene   1      1.800 
dimethyl ethyl benzene  isomer 2    100 540 
n-heptyl benzene  1    100*  
isobutyl benzene  1  111*     
isobutyl benzene isomer 1      193 
diisopropyl benzene isomer 1      400* 
methyl propyl benzene isomer 1      152 
tetramethyl benzene isomer 1      1.400 
1.2.3.5-tetramethyl benzene 1    182  
1.2.4.5-tetramethyl benzene 1    168  
1.2.3-trimethyl benzene 10  361  833 3.000 
1.2.4-trimethyl benzene 11  234*  928 6.670 
1.3.5-trimethyl benzene 4  139    622 
n-pentyl   3    125 800* 
dimethylbenzimidazole isomer 1  571     
benzofuran   13  267     
dibenzefuran   1      167 
. dimethyl benzofuran isomer 7  657  2.532 400 
4.7-dimethylbenzofuran 2      1.000 
methyl benzofuran isomer 8  186  2.371 3.000 
2-methyl benzofuran  4  391    150 
t-butano 1   1 130   250* 1.330* 
2-methyl but anal   1      115 
3-methyl-2-butanone  1    3.259*  
.!!!-creso 1   2      2.000 
o-cresol   3      10.000 
2-creso 1   2      4.000 
.2.-cymene   1  111*     
CSH3 isomer   1      127* 
         (continued)
     49     

-------
  TABLE 13. (Continued)   
      Concentration in ppb (jig/kg)  
 Compound N O.S. LBtu Pre I.S. Gillette Hanna Post LS.
CSHIO ; somer 1       
CSH12 ; some r 1   154  750*  
CSH32         
C7li12 isomer 2      115 
C7Hllt isomer 2      240 
C7H12 0 isomer 1      131* 
Ce HIS isomer 2      330 
CgHle isomer 5     191 3,000* 
CglileO isomer 1   660*    
C9H20 isomer 2      3,000* 
CloH12 isomer 5     785 840 
CloH20 isomer 12   234*  892* 3,864* 
CloH22 ; somer 7   200  668* 3,500* 150
CllH12 isomer 2      800 
CllH14 isomer 3     209  
CllHn isomer 7   190  2,171 3,650 
CllH24 i some r 7   660*  122 2,200 260
C12H16 isomer 2      312 
C12H24 i some r 5  1,040  217 1,700* 
C l2H26 isomer 7  1,860  480* 1,520* 
C l3H 16 isomer 1      400 
C13H20 i some r 1      700 
C 1 3H 26 isomer 4     480* 140 
ClltH28 i some r 4   340  933 110 
C litH 30 .isoJmer 4  1,070  100* 200 
ClS~6 isomer 1  1,720    
ClsH30 1 some r 1  1,860    
C1sHn ; somer 3  1,250  110 670 
C1sHn i some r 1      1,700 
C16H34 1 somer 1     126  
n-decane 8   134  1,572 1,910* 
2,6 dimethyl-n-decane 1      420 
n-hexadecane 1      2,660 
7 -methyl tridecane 1      910 
i-pentadecane 1     200  
!!.-tetradecane 7   185  625 2,330 
n-tridecane 1      900 
T-decene 1      1,400* 
l-tetradecene 2      1,000 
l-tr1decene 9  1,267*  675* 5,000 
2-decanone 1     100  
3-dodecane 1   139    
n-dodecane 11   560  1,915 7,600* 
nundecane 11   320  1,227 400 
2,6,10-trimethyldodecane 1      110 
1 -dodecene 1      200 
menthyl-n-propylether 1      210 
n-heptane 1      240 
2-heptanone 5 244 195   151* 
isopropylcyclohexane 1      520 
        (continued)
   50     

-------
  TABLE 13. (Continued)   
      Concentration in ppb (\lg/kg)  
Compound  N 0.5.  LBtu Pre 1.5. Gill ette Hanna Post 1.5.
    A     
n-hexane  7   400  250  
I-phenyl hexane  2      500 
trimethylcyclohexane isomer 2      1,920* 
cycloltexanone  1      170 
2-hexanone  8   202  604 626 
3-hexanone  6   117   300* 
2-methylcyclohexanone  1      330 
3-methylcyclohexanone  1      134 
i ndan  16   247  2,766 3,200 
3-alkyl indane isomer  1      900 
dimethyl indan isomer  4   1,110  107* 1,000 
methyl indan isomer  7   114  300 1,230* 
C2-alkyl indan  2   390*    
C3-alkyl indan isomer  2   189   800* 
trimethyl i ndan  3     128* 140 
l-methyl i ndane  1      1,400 
2-methyl i nda ne  1      1,100* 
i ndene  16   375 140 833 3,158 
dimethylindene isomer  1      1,660 
3,3-dimethylindene  1      800 
methylindene isomers  8   889  317 4,200 
methyl ethyl ketone  11 152    498 13,230 
methyl isopropyl ketone 5     196 7,670* 
methyl-n-propyl ketone 1      125 
Ct; H1Z 0 Ketone isomer  1     107  
naphthalene  22   7,250  13,920 23,584 100
dimethyl naphthalene isomers 4      4.000 
2.6-dimethyl-1,2,3.4-tetrahydro- 1     210  
naphthal ene         
ethylnaphthalene isomer 2      1.670 
methyl naphthalene isomers 1       279
~-methylnaphthalene  17   3,160  2,505 3,200 111
s-methylnaphthalene  17   3,162  2,826 4,200 169
methyldihydronaphthalene isomer 4   171*   670 
3-methyl-1.2-dihydronaphthalene 1     116  
acetonitril e  7     1,246 1,340 
ethane nitrile  2      6.456* 
n-hexyl ni tril e  1      4.000 
i sobutyronitrile  1      127* 
.!!.-pentylnitrile  4     331 1.670* 
.!!.-butyroni tril e  4      1.000 
~-methylbutyronitrile  1      167 
propane nitrile  2      1.492 
propionitrile  5     175 2.000 
.!!.-nonane  4     200 1.400 
3-methyl nonane  1      360 
2-nonanane  2   156  100  
nonene isomer  1      440 
n-octane  4     170 820 
cyclooctatetraene  1      1,400 
        (continued)
    51    

-------
  TABLE 13. (Continued)   
     Concentration in ppb (\lg/kg)  
Canpound  N 0.5. LBtu Pre 1.5. Gi 11 ette Hanna Post 1.5.
2,3-dimethyloctane  1    180  
2 ,6-dimethyl octane   1     421 
2-methyloctane  1    140  
2-octanone  4 126 188   130 
3-methylpentanal  2  209    
.!l-pentane  2  167    130
methyl cycl opentane   1 680     
2-pentanone  15 123 260  846 4,700 
3-pentanone  5    297 2,330 
eycl opentanone  4     1,670 
dimethylcyclopentanone isomer 1     830 
methyleyclopentanone  1     100 
2-methylcyclopentanone  5     3,000 
2-methyl-3-pentanone  2     1,549 
3-methyl-2-pentanone  1  466    
4-methyl-2-pentanone  2     167 
phenol  4     6,700 
C3-alkylphenol isomers  3     800 
C~-alkylphenol isomer  1     537 
biphenol  3     5,330 
ethyl phenol isomer  1     4,200 
2-ethyl phenol  2     1,000 
3-ethylphenol  1     5,000 
dimethyl phenol isomer  1     1,330 
2,4-dimethylphenol  1     5,330 
methyl ethyl phenol   1     690 
trimethylphenol isomer  1     237 
acetophenone  3  660*  100 340 
biphenyl ene  2     1,700* 
propano 1  1    300  
i sopropano 1  1     367* 
n-methylpyrazole  1 177     
pyridine  6 185   707 2,420 
dimethylpyridine isomer  6     2,170 
methyl pyridine isomer  4     1,000 
2-methylpyridine  1     143 
3-methyl pyridi ne  2     670* 
methyl ethyl pyridine isomer 1     300* 
C2-alkylpyridine isomers 1 240     
pyrrole  9    111 6,661 
dimethylpyrrole isomer  2     1,330 
2-methyl pyrrole  3     2,000 
3-methyl pyrro' e  3     2,330 
styrene  10    107 2,200 
2-alkylstyrene  1     120 
ethyl styrene isomer  1     439 
dimethyl styrene isomer  1     2,200 
methyl styrene isomer  3  356   1,100* 
amethylstyrene  5     2,500 
dimethyl sul fide  1     668 
        (cont i nued)
   52     

-------
 TABLE 13. (Continued)   
    Concentration in ppb (~g/kg)  
Compound N 0.5. LBtu Pre 1.5. Gillette Hanna Post 1.$.
thiophene 5    241 620 
C3-a1kylthiophene isomers 2    1,202* 200 
C4-alkylthiophene isomers 1    718*  
benzothiophene 3     1,340* 
d1methy1thiophene isomers 4    2,000* 1,130* 
ethy1thiophene isomer 2    1,423* 157* 
2-isopropy1thiophene 1     200 
methyl thiophene isomers 2   120  556* 
2-methylthiophene 8  134  150 1,000 
3-methylthiophene 7  107  170* 445 
ter-butylthiophene isomer 1     200 
trimethylthiophene isomer 1     1,330 
2,3,4-trimethy1thiophene 2     420 
toluene 22 300 1,334 150 1,554 2,145 
!!l-ethy lto 1 uene 9  278  367 3,864* 
.Q.-ethyl to 1 uene 8  148  571 2,800 
j!,-ethyltoluene 2     3,500* 
j!,-or methyl to 1 uene 3  189  175 620 
.Q.-propylto1uene 1     800 
j!,-propyltol uene 2    167 1,500* 
!!l-+j!,-xylene 21  210  2,000* 4,848 
.Q.-xy1ene 19  312  1,215 1,670 
*More than one compound identified in analysis.
Columns indicate the maximum reported concentration in ppb (~g/kg) for all samples in: 1) oil shale
effluents (0.5.); low Btu gasification of Rosebud coal effluents and tars (LBtu); in situ
pre-gasification well-water samples (Pre 1.5.); process streams and tars from in situ gasification
experiments at Gillette (Gillette) and Hanna (Hanna), Wyoming; and well-water samples collected
after in situ gasification (Post 1.5.). N indicates the number of samples in which the indicated
compound exceeded the 100 ppb level; values less than 100 ppb were not reported.
In summary, there are little data regarding identification of organic
pollutants in energy resource development processes. Post-in situ
gasification water samples analyzed by Pellizzari (1978) contained cresol and
phenol isomers; one dimethyl phenol isomer was reported at a level of 1.5 ppm.
Various process tars and waters also contained high levels of cresols,
phenols, naphthalenes, and other compounds that are both toxic and
carcinogenic. Although reported concentrations rarely exceeded established
toxic levels in water samples for a given compound, they frequently approached
these levels. If a chemical class (e.g., phenols) were grouped,
concentrations would cumulatively exceed toxic levels.
Reed et al. (1976) reviewed uranium milling. They found that the major
organics present were kerosine, amines, and isodecanal. These are primarily
associated with the acid leach process during solvent extraction and are
commonly discharged in the tailing effluents.
53

-------
TABLE 14.
COMPOUNDS OCCURRING MORE THAN 15 TIMES AT CONCENTRATIONS
OF 100 ppb OR MORE (Pellizzari 1978)
Volatile Compounds
Number
of
Occurrences
Semivolatile Compounds
Number
of
Occurrences
Cs-alkyl benzene isomers
*benzene
*napthalene
*to 1 uene
m and .E-xylene
o-xylene
C4-alkyl benzene isomers
a-methyl naphthalene
B-methylnaphthalene
*acetone
i nda n
i ndene
*ethyl benzene
2-pentanone
29
25
22
22
21
19
18
17
17
17
16
16
15
15
C7HIO isomer
*cresol isomer
benzene
*dimethylphenol isomer
*pheno 1
ethyl phenol isomers
C2-alkyl phenol isomer
dimethyl naphthalene
ethyl acetate
*naphthalene
C3-alkyl phenol isomers
57
43
37
34
31
23
20
20
16
16
15
*Listed as suspected carcinogens (Christensen and Fairchild 1976)
54

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PRESENT WATER-MONITORING SYSTEM
Monitoring to assess the impact of western energy resource development on
water quality is presently being conducted by a multitude of Federal, State,
local, and private organizations. The primary data collector is the U.S.
Geological Survey (USGS). USGS maintains more stations and for longer than
any other organization (Iorns 1965). Their data are entered into two Federal
computer data bases, WATSTORE, maintained by USGS, and STORET, maintained by
the U.S. EPA. Much of the WATSTORE data is eventually entered into STORET by
USGS. However, ground-water data and daily records are usually not available
in STORET. STORET is widely used by other Federal and State agencies to store
their own data as well. STORET is the primary data base from which western
energy water quality impact assessments are made.

Most existing water-monitoring stations and networks were designed for
purposes other than assessment of energy resource development impact. Some
stations have only been maintained for a year or two, others are located in
areas of little concern (with regard to energy impact), and still others
monitor only one or two parameters. Ground-water quality records are
noticeably scarce, and attempts to establish monitoring networks of any size
have only recently been initiated. An inventory of available STORET data was
made to identify those stations currently in operation with data records for
several years for a large number of parameters (Environmental Monitoring and
Support Laboratory Las Vegas, 1977). Using data from these stations and an
empirical approach, Thomas et ale (in press) attempted to define long-term
trends for 20 parameters in selected major rivers impacted by energy resource
development. They found data records for many parameters to be inadequate to
detect trends at even the "best" stations.
Wolman (1971) conducted a comprehensive survey to determine trends in the
quality of U.S. rivers. He concluded that existing data were generally
inadequate to identify trends in water quality. Specifically, existing data
were insufficient in volume and the parameters being measured were inadequate
to identify trends in river quality.

Steele et al. (1974) defined areal and temporal changes based on
temperature and conductivity data from 88 USGS stations. They found that for
other parameters the water-quality records available in computerized form were
generally insufficient in terms of areal coverage, frequency of sampling, and
period of record for their purposes. Cleary (1977) complained that, with few
exceptions, the assemblage of river water-quality data by State and Federal
agencies is of limited usefulness in discerning trends or for other
comparative purposes.
55

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On a more Sfecific basis, Judy and Gartner (1977) reviewed water-quality
data in the Yel owstone-Tongue River (Montana) area. They state that water-
quality or stream-flow data have been collected at 176 sites, but most of
these have only one year or less of available data. They further note that
much known data are not available in STORET. Thomas et al. (in press) also
noted this ~ta lack aod found published USGS ground-water records for
Piceance Creek (Weeks and Welder 1974) unavailable in STORET.

Table 15 (Melancon et ale 1979a) presents a summary from STORET of
pertinent energy resource development impact parameters monitored with some
degree of frequency at stations in the San Juan River Basin. Biological and
organic analysis data are almost totally lacking. Most trace elements are
sampled on an intermittent or quarterly basis by USGS. A wide variety of
analytical species (i.e., dissolved, total, etc.) were measured for many
parameters. The difference in the elemental analyses reported at USGS and
Colorado State stations makes simultaneous use of data from the two sources to
detect trends nearly impossible. These data are typical of those reported for
western streams. They are generally inadequate both in available parameters,
and frequency of collection for meaningful trend analysis.
Data quality control is yet another concern. In working with data in the
San Juan Basin it was noted that some USGS dissolved elemental values were
consistently higher than comparable total elemental values reported by
Colorado State Department of Natural Resources. Telephone calls to the
respective agency laboratories were unable to resolve this problem. Several
colleagues working with data in the White and Yampa River Basins have found
discrepancies between data published in USGS Quality of Surface Water Papers
and STORET listings. IIWildli data, such as a reported pH of 170, have been
noted in the STORET records on numerous occasions. Because large
order-of-magnitude fluctuations are common in western rivers, (Thomas et al.
in press) distinguishing between "wild" data and valid outliers poses a major
problem. STORET also does not discriminate between composite, grab, or
averaged samples and provides no method to identify what kind of sample a data
entry represents.
Western energy resource development may impact the aquatic environments
through a multitude of pathways and with a wide variety of pollutants. It is
only the effect of these impacts on living things, particularly man (including
emotional issues), that is of concern. Yet monitoring of these impacts is not
a routine portion of many monitoring programs.

Biological parameters are seldom monitored on a continuous basis. An
exception occurs at NASQAN stations where phytoplankton and periphyton samples
are collected on a monthly and quarterly basis, respectively. Phytoplankton
samples are analyzed for total cells found, identification of the three most
numerous forms, and the percent of the total community that each represents.
Periphyton samples are collected quarterly, and dry and ash free weights
determined. Chlorophyll a and b content is also determined in periphyton
samples (Ficke and Hawkinson 1975). Bacteria analyses (fecal coliform and
fecal streptococci) are also conducted monthly at NASQAN stations and at other
sel ected sites.
56

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TABLE 15.
PARAMETERS MONITORED BY THE EXISTING SAMPLING NETWORK IN THE SAN JUAN BASIN AND
THEIR AVERAGE ANNUAL FREQUENCY OF MEASUREMENT
Parameter
USGS Station Numbers
o 0 0 0 0 0 0 0 0 0
o 0 0 0 0 ~ 0 0 0 0
NOM q M q 0 q N ~
.~ M M M ~ q ~ ~ ~ ~
q ~ q q q q q q q ~
MMMMMMMMMM
0\0\0\0'10\0'\0\0\0\0\
o 0 0 0 0 0 0 0 0 0
o 0 0 0 0 0
000000
enU>U>Menu>
N ~ U1 ,..., co ,....
U1U1U")\OU1U1
MMMMMM
0\0\0\0\0'10\
000000
Colorado Station Numbers
000000 ;
o 0 0 0 0 0
U1U10U1C)U1 N
MqU'lU)CI)m 0
\0\0\0\0\0,..., .-4...-4
MMMMMM 0
~g;~~~~ g
co 0\ ,..., ...-4 ~ q to M ~ M N
\0 \D \D co \D 0 \D 0 U) \D \D
o 0 000 ...-4 0 ...-400 0
o 0 0 0 0 0 0 000 0
o 0 0 000 000 0 0
o 0 0 000 000 0 0
5 588 758 5 466 7 8
U1
....
00010 Water temperature (OC)
00011 Water tenperature (oF)
00060 Stream flow (cfs)
00070 Turbidity (JTU)
00095 Conductivity at 25C (pmho)

00300 Dissolved oxygen (mg/I)
00310 Blochenlcal oXYgen demand
5-lIay (mgl1)
00335 Cobalt, dissolved
low level (mgl1)
00400 lIydrogen-lon concentration (SU)
00410 Total alkalinity
calcium carbonate (mg/I)
00440 Bicarbonate Ion (mg/I)
00445 Carbonate ion (mg/I)
00530 Residue, total nonfl1tered (mgl1)
00600 Total nitrogen (mg/I)
00610 AmnDnla nitrogen, total (mgl1)

00615 Nitrogen dioxide, total (mg/I)
00620 Nitrate nitrogen, total (mg/I)
00625 Total kjeldahl nitrogen (mg/I)
00630 Nitrite nitrate nitrogen
lotal (mg/l)
00665 Phosphorus, total (mg/I)
00671 Phosphorus dIssolved ORTIIO (mg/I)
00900 Total hard calcium
carbonate (mg/I)
00902 NC hard calcium carbonate (mg/I)
00915 Calchnn, dissolved (mgl1)
00980 Calcium carbonate (mg/I)

00925 Magnesium, dissolved (mg/I)
00927 ~Iaglles lum, total (mgl1)
00930 Sodium, dissolved (mg/I)
00929 Sodium, total (mgl1)
00931 Sodium absorption ratio
00935 Potassium, dissolved (mg/I)
00940 Chloride (mg/I)
12 3 12 12 4 12 12 12 12 12 12 12 12 12 12 12 6 12 24 12 24 36
12 2 1 24 1 1 6 12 12 12 12 12 24 12 12 12 6 24 52 12 52 52
12 24 12 12 12 9 6 6 5 12 1* 6 9 12 10 12 12 4 4* 6* 6* 6* 4* 6* 5 3 5* 5 6* 5"
12 12 24 12 12 12 12 12 12 12 12 12 24 12 12 12 6 36 52 12 52 52 5 5 8 8 7 5 8 5 4 5 5 7 8

6 1 1 1 1 1 4 6 6 8 12 8 12 12 6 6 6 12 24 4 12 12 5 5 4 4 4 4 4 5 4 4 5 5 4
6 4 4 12 4 12 1 6 12 24 12 4 4 4 4 4 3 3 4 3 4 3 3 4
6 4
4
4 12 1
6 12 24
12
12 6 6 4 4 6 9 12 10 12 12 12 24 12 12 12 6 36 52 8 52 36 4 4 7 7 7 5 7 4 4 5 6 6 5
10 8 6 6 8 6 6 6 10 6 12 8 12 12 9 12 24 52 4 52 52
10 8 6 6 8 6 6 6 10 6 12 8 24 12 9 12
10 4 6 4 4 6 6 6 6 6 9 8 12 12 8 6
          1   
 1 2 1 1 1 4 4  2  2 6 
 1 2 1 1 1 4 4  4  2 12 
    1  I   I  2 1 
    1  6 12  10  5 6 12 
 1 2 1 1 1 4 2  4  4 12 
 1 2 1 1 1 4 2  4  2 12 
 1 2 1 1 1 4 4  4 24 4 12 
 8 6 6 8 6 6 4 10 4 12 4 12 2 12
10 8 6 6 8 6 6 6 10 6 12 8 24 12 12
10 8 6 6 8 6 6 6 10 6 12 8 24 12 9 12
10 8 6 6 8 6 6 6 10 6 12 8 24 12 9 12
10 8 6 6 8 6 6 6 10 6 12 8 24 12 9 12
10 8 6 6 8 6 6 6 10 6 12 8 24 12 9 12
10 8 6 6 8 6 6 6 10 6 12 8 24 12 9 12
10 8 6 6 8 6 6 6 10 6 12 8 6 12 9 12
10 8 6 6 8 6 6 6 10 6 12 8 6 12 9 12
34 36
24 52
2 2
6 6
6 12

1 1
4 12 52
12 6
12 12
4 52 52
4 52 52
2
12 12
-12
4 4 4 4 5 4 4 4 4 4 345
4 4 5 5 5 4 5 4 554 5 5
: 1 4 466 5 4 5 4 554 5 5
52 24 4 4 6 6 5 4 5 4 5 5 4 5 5
'12 12
12 12

12 12 6 4 6 6 6 4 6 5 5 5 5 5 5
12 6
12 12 4 12 12
36 52 4 52 52 4 4 6 6 5 4 5 4 6 5 5 5 5

36 52 4 52 52
36 52 4 52 52
4 466 5 4 5 4 5 5 5 5 5
36 52 4 52 52
36 52 4 52 36
4 4 6 6 5 4 5 4 555 5 5
4 6 6 5 4 5 555 5 5
36 52 4 52 36 4 4 6 6 5 4 5 4 5 5 5 5 5
12 24 4 24 24
12 52 . 4 52 52 4 4 6 6 5 4 5 4 5 5 5 5 5
(cont t nuell)

-------
TABLE 15.
(Continued)
Parameter
USGS Station Numbers
a C) a
a C) C)
N C) M
.... M M
~ ~ ~
M M M
en en en
a a a
aoOoooC)
cooC)C)oC)
tnMO'I&nLnLOO
Lt') ,.... CO ,.... M 0II:f" an
&nt.n&ntnt.O\OU)
f'I1 C"') M M M M ,..,
~~~g;~gj~
Colorado Station Numbers
oC)o~
o C) 0
U'I 0 U'I
 Ln 0 C)
.q M q 0 q-
M ~  000 0
0000
COOO
~ ~ ~ ~
c:t' c:t U') U')
M M M M
~cnO\O\
0(:)0 0
00945
00950
00951
00955
01000
Sulfate, total (mgl1)
fluoride, dissolved (mg/l)
f1 uoride, total (m9/1)
Silica, dissolved (mg/I)
Arsenic, dissolved (pg/l)
01001 Arsenic, suspended (pg/l)
01002 Arsenic, total (lig/l)
01005 Barium, dissolved (pg/I)
01010 Beryllium, dissolved (pg/l)
01015 Bismuth, dissolved (lig/1)
U1
00
01020
01022
01025
.01027
01030

01032
01034
01037
01040
01042
Boroo, dissolved (lig/1)
Boron, total (lig/l)
Cadmium, dl ssolved (pgl1)
Cadmium, total (pgl1)
Chrontum, dissolved (pgl1)

Chronium, hexavalent (pgl1)
Chromium, total (pg/l)
Cobalt, total (pgl1)
Copper, dissolved (pg/l)
Copper, total (lIgl1)
01045 Iron, total (lIgl1)
01046 Iron, dIssolved (lig/l)
01049 lead, dissolved (pg/l)
01051 lead, total (~9/1)
01054 Manganese, suspended (~g/l)
01055
01056
01060
01062
01065
Manganese (pg/I)
Manganese, di s so lved (pg/l)
Molybdenum, dissolved (119/1)
Molybdenum, total (lig/1)
NIckel, dissolved (pg/I)
01075 Silver, dissolved (pg/l)
01077 Silver, total (pg/l)
01080 Strontium, dissolved (pg/l)
010B5 Vanadium, dIssolved (lig/1)
01092 Zinc, total (119/1)
10 B 6 6 8 6 6 6 10 6 12 8 6 12 9 12
10 8 6 6 8 6 6 6 10 6 12 8 6 12 9 12
12 52 4 52 52 4 4 6 6 5 4 5 4 5 5 4 5 5
12 24 52 24
444 4 4 4 4 4 4 5 344
10 8 6 6 8 6 6 6 10 6 12 8 6 12 9 12  12 52
1 2 1 1 1 2 6  4 1 4 3  1 7 2 2
           2    1 I
         4  2 I I  I 4
I 2 I   I I  I 4 I 4  I  3 I
      I  I  I I  I  I 
      I  I  I I  I  I 
10 8 6 6 8 6 6 4 9 4 4 4 6 8 2 6  12 12
I           2 I I  I I
 2 I  I 2 2  1 1 1 4  I 5 2 3
         4  2  I  I I
     I 1  1 I 1 1  I 2 I I
I 2 I I        2    3 2
           2    I I
           2    I I
I 2 I  I 2 2  1 1 1 4   7 2 3
         4  2    I I
10 8 6 6 8 6
1 2 I 1 I
4
6 4 9 4 12 4
2 I 1 1 1
1 5
2
4 I I
9 3 12 12
I 5 I I
4 4
I 1
2 1
6 2 12
1 1
3 1 1
2
      9 4  2
10 I 2 1 1 2 2 2 18 1 3
      I I I 1 1
     2 1 1 I 1 
 I 2 1    I I I 4
      I   I 1
     2 1  1 1 1
        4  2
1 9 6 1 1
12 4 2 1
I I
6
52 52
2 6

24 6
24 6 4 4 4 4 4 4 4 4 4 5 5 4 4
2
2
2
36 24
4 4 4 4 4 4 4 4 4 544 5
2 6
464 4 6 6 6 4 5 4 5 4 355
2 6
6 4 4 4 444 4 344 4 4
4664 4114454
4 6
2 6
46444 4 4 4 4 4 4 5 344

464 4 6 6 6 4 6 4 5 5 5 5 5
12 2
2 6
464 4 4 4 4 4 4 4 4 5 344
6 6
I ~ 4 4 6 6 6 4 5 4 5 4 555
2 2
I
3433343434323
2
2 2 3 3 3 2 2 2 2 2 223
I
I
6 6 4 4 6 6 6 4 5 4 5 4 555
(conti nued)

-------
TABLE 15.
(Continued)
Parameter
USGS Stat Ion IIUlooers
00000
o 0 CoO
NOMoq-M
~MMMq-
q- -=t q q- q-
MMMMM
0\0.0\0\0\
00000
o 0 00
o 0 00
In In M 0\
.., U'\ ,.... co
U') U') U'\ U')
MMMM
~~~g;
o 0 0 0
o 0 0 0
'" '" U') 0
,...., M "it' .."
U') 1.0 \0 1.0
M M M M
0\ en en en
o 0 0 0
Colorado Station Numbers
ooo~
000
In C '" N
\0 co at 0
\0 1.0,...., .....
(Y) M'" 0
cncncn 0
000 0
m O't ~ ~ ~ ~ U') M .., M N
to 1.0 \0 co \DO \00 \01.0 \0
ggggSc:gC;ggg
00000000000
o 0 coo 0 0 0 000
000000
IJ"') C) 0 0 0 0
:3f5~~~g:
.., q- .., .q- .., In
MMMMMM
g;g;~~~g;
01100 Tin, dissolved (pg/I)
01105 Aluminum, total (pg/l)
01120 Gall lum, dissolved (P',l/l)
01125 Germanium, dl sso Ived (pg/l)
01132 Lithium, total (pg/l)
01135
01145
01146
01141
01150
Rubidium, dissolved (pg/I)
Selenium, dissolved (pg/I)
Selenium, suspended (pg/I)
Selenium, total (pg/I)
Titanium, dissolved (pg/I)
c.n
\D
01160 Zirconium, dissolved (pg/I)
01300 Oil-9rease severity
31501 Total coliform MFIHENDO (100 ml)
31505 Total colHorm IWIICOIIF (100 ml)
31616 Fecal coliform MFM-FCBR (100 ml)

31615 Fecal coliform MPNECHED (100 ml)
10299 Residue suspended at
180C (mg/I)
10300 nes idue dl sso Ived at
1800t (mg/I)
10301 Dissolved solids SUM (mg/I)
10302 Dissolved solids (tons/day)
11890 Mercury, djssolved (pg/I)
12895 11ercu,'y, suspended (pg/l)
11900 t1ercury, tot a I (pg/I)
80154 Suspended sediment
concentrate (pg/I)
80155 Suspended sediment
discharge (tons/day)
          1   1 
          1   1 
          1 1  1 
          I I  1 
            1 1 
           1   
  2 1 1       4   
           2   
         4  2 1 'I 
           1  1 
      1  1  1 1  1 
2 1 1  1 2 4 1 4 2 4 12,12 2 2
     4 6  6 12 6 24 6  6
 1 2 I I 4 6  6 12 8 12 6  6
6 1
I 1
2 12
1
I 1
1
I
3
I
4
4 6
4 6
46444 4 4 4 4 4 5 4 555
I
2 8 6 6 8 6 6 6 10 6 4 6 6 1 2 2

10 8 6 6 8 6 6 6 10 6 12 6 12 12 12 12
10 B 6 6 8 6 6 6 10 6 9 6 12 12 12 12
I 2
4 24
4 24
2
2 1
2 4 1 1
4 12 6 12 12 12
24
24
24
1
,12 '
12
12 12
541 1 1 464 6 5 5 6 6
1 1 1
3 12 12 6
3 12 12 6
4 12 6 12
12 12
6
541 1 146 465 566
4
52 1 36 52 4 4 1 7 1 4 6

52 4 52 24
52 4 52 52
65666
1
I
4
6 6
6 6
6 6 4 4 4 12 6 4 2 1 4 4 3 4 2
12 26

12 26
Parameters are listed by STORET code, name, form, and unit.
*Indicates lIach turbidity untt~ 1 .. intermittently sampled.
MF1HENDO .. memhrane ftlter, immediate, endo agar.
MPNCONF .. most probable number, confirmed test (second stage).
MfH-FCBR .. mrnnhrane filter, MF-C Broth (media grown in).
MPIIECHEO .. most probable number, EC medium.

-------
Monitoring of macrobenthic organisms, fishes, macrophytes, or other
biologic forms is conducted on a sporadic basis, usually for a special study.
The data are seldom readily available to researchers other than the original
collectors.

Phytoplankton, although comprising a large proportion of the aquatic biota
in standing or sluggish water, are usually a small component of the biologic
community in western streams. Indeed, a large proportion of the phytoplankton
in western streams is, in reality, detached periphyton. Taylor et al. (in
press) have investigated phytoplankton and water-quality relationships in U.S.
lakes. They found few usable relationships on individual species or genera
levels, although community structure has shown correlations with water-quality
parameters. Indices of organic contamination or presence of toxins in rivers
are primarily based on periphyton forms. Therefore, the effort presently
placed on phytoplankton analyses in flowing streams would be better spent in
determining benthic algae community composition.
Recent studies with macrobenthic forms in western rivers have indicated
their potential for use in monitoring the effects of changes in water quality.
Again, community-structure studies rather than individual forms appear to hold
the most promise as monitoring tools. Such efforts are considered to be in a
developmental stage at this time.
Use of fish as biological monitors does not appear to be widespread and is
usually limited to species inventories or bioaccumulation studies. Biological
monitoring of effluent streams using caged populations is an increasing
practice. Use of natural populations for monitoring is further complicated by
restrictions imposed by State agencies on collecting fish.

Biological monitoring is the most direct method of assessing impact of
western energy development upon aquatic resources. Development of economical,
standardized methodologies for biological monitoring should be encouraged.
Although biological monitoring, except for the algae forms, may not be
practical at a water-quality station, nearby areas should be suitable for such
purposes.
In summary, present water-quality monitoring networks generally contain
stations suitably situated to monitor the impact of energy resource
development upon western surface-water quality. Existing ground-water quality
monitoring networks are generally quite local in extent and very recent in
implementation. The parameters presently being measured vary widely and, with
few exceptions, are not sampled frequently enough to identify trends. Data on
many parameters of interest, continuous records, and ground-water data are not
generally available in STORET. Data outliers and quality control are concerns
when utilizing the data.
60

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NETWORK DESIGN FOR MONITORING ENERGY DEVELOPMENT IMPACT
MONITORING NETWORK DESIGN CONSIDERATIONS
In order to assess the impact of western energy resource development upon
water quality, water-quality trends, both temporal and spatial, in regional
water resources must be determined. When trends indicate a potential problem,
the next component of the assessment is to locate its cause.

Design of surface-water monitoring or water-quality surveillance systems
and networks have been discussed by many researchers in recent years (NUS
Corporation 1970; Beckers et al. 1972; Beckers and Chamberlain 1974; Sherwani
and Moreau 1975; Lettenmaier 1975; Lettenmaier 1978; Berndt 1975; Lewis 1976).
All agree that careful definition of monitoring objectives is a necessary
first step in designing a network. The primary objectives of a survei1lance-
or enforcement-oriented network are to collect data to effectively enforce
compliance with water-quality standards either through prevention or abatement
(Beckers et al. 1972). Measurement of extremes (pollution events) is an
objective, and sampling frequency is a function of the duration and number of
expected events. Sampling stations are usually located within or near
pollution sources (Beckers et al. 1972; Beckers and Chamberlain 1974; Sherwani
and Moreau 1975). The primary objective of a trend-monitoring network is to .
characterize the water quality of a water body, usually through mean values
(Lettenmaier 1975; Ward 1973). They are designed to collect data adequate to
identify trends in water quality over time or space; detection of pollution
events is not a primary consideration. Stations should be sited outside of
the immediate influence of a point-source pollutant (Lettenmaier 1975).
Although either type of network may satisfy the objectives of the other, the
different considerations for sampling frequency and station location make it
probable that a system designed for surveillance purposes will not provide
adequate data for trend detection and vice versa.
The network design itself consists of specification of the parameters to
be measured, the frequency of sampling, and the number and locations of
sampling sites (Sherwani and Moreau 1975).
Selection of parameters should not be made capriciously. Careful
consideration must be given to ambient concentrations, expected changes from
activities in the area, impacts from present or projected concentrations and
leadings, the analytical methodologies available, sampling considerations, and
available resources. The desirability of monitoring a given parameter must be
weighed against the difficulties in collecting, preserving, and analyzing the
samples. Conversely, the ease with which some measurements can be made should
be weighed against the value of the resultant data.
61

-------
The design is always constrained by limitations on available resources of
time, money, manpower, or technology. Therefore, networks should be optimized
to obtain the maximum amount of pertinent information for the least (or
available) amount of effort or cost. Often the design limit is determined by
the number and nature of the water-quality laboratory analyses that can be
performed given finite resources. It is necessary to compromise between the
sampling frequency at any given station, the number of stations, and the
number and nature of the parameters for which analyses are required. For any
single parameter, the question is how to distribute the samples obtained.
Should a large number of stations be sampled relatively infrequently or vice
versa? Although they approach the problem from different theoretical bases
and with different objectives, nearly all of the reviewed researchers conclude
that the more efficient system consists of few stations with relatively large
numbers of samples from each.
A monitoring network should be able to detect changes in stream water
quality but will usua11y"re1y on other work to determine the causes of such
changes. Beckers et a1. (1972) state that "definition of local problems is
not a monitoring (network) function; it requires, in general, the
implementation of a special purpose, intensive water quality survey. . ." and
that a monitoring network ". . . will be employed to ascertain the status of
the river basin as a whole. . . it is not intended that the system be used to
determine the precise cause and extent of water quality degradation."
Sherwani and Moreau (1975) add that ". . . accidental or intentional spills
cannot be dealt with economically by a monitoring network and should not
influence its design." Berndt (1975), speaking on design of an enforcement
monitoring system, also states that "where a problem area is discovered,
repeated short surveys must be undertaken to identify the specific nature of
the prob1em." Site specific data are available from stations that are
required by mining lease regulations and research activities in the resource
development areas. These data may be used in conjunction with the network to
aid in determining the causes of observed trends. It is important to remember
that trends exist in both time and space. The objective of assessing the
impact of western energy resource development upon water quality is best
satisfied by designing a monitoring network that would identify trends in
water quality over a period of 3 to 4 years.
PARAMETER SELECTION
The long and seemingly all-inclusive list of parameters to be derived
from western energy resource development activities is too large for each to
be included in a monitoring effort. Some method is needed to reduce the total
amount of effort required for sample analysis.

Kinney et a1. (1979) identified 44 physical and chemical parameters as
priority A (high priority) parameters for monitoring oil shale development
impact on surface waters. These were: (1) Parameters that had, in the past,
equalled or exceeded acceptable limits with respect to beneficial water uses
and whose ambient levels in surface waters are likely to be altered by
activities associated with the development and operation of an oil shale
industry to the point where further impairment of beneficial water uses will
62

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result; (2) those parameters for which water-quality criteria must be
established for receiving waters based on tolerance levels of important,
sensitive species in those waters and whose ambient levels in receiving waters
are likely to be altered by activities associated with the development and
operation of an oil shale industry to the point where the biota may be
adversely impacted; or (3) parameters whose measurement is essential for
purposes of interpreting water-quality data.

Melancon et al. (1979a, 1979b) used a similar set of criteria to identify
34 "must-monitorll parameters for moni tori ng energy impact on water qual ity in
the San Juan River Basin and 38 "must-monitorll parameters in the Tongue-Powder
River Basin. Table 16 presents a listing of identified priority
physical/chemical parameters for monitoring energy resource development and
identifies those presently being monitored at NASQAN stations. Even these
seemingly similar criteria did not provide identical parameter lists.
Monitoring of chemicals in the aquatic environment poses a question as to
which form should be measured--total*, dissolved or filterable**, suspended or
nonfilterable, or other forms. Various arguments for and against measuring
each form are still being discussed (Hem 1970).
In natural waters elements can occur as free ions; as inorganic ion pairs
or complexes; in organic complexes, colloids, microcrystalline suspension,
precipitates, and sediments; adsorbed onto colloids, seston, or sediments; and
as part of biological organisms. Strumm and Morgan (1970) discuss the various
states, equilibriums, and interactions at great length. Most toxicity studies
have been carried out on metal aquo ions. Data available for elements in
suspension or as precipitates are meager and insufficient to conclude whether
dissolved or suspended metals are more toxic or whether the toxicity of the
various states is dependent upon the metal (Clarke 1974). Elemental levels
are affected by the physical, chemical, and biological conditions in the water
and in many cases are poorly understood.

In general, excluding sediment and biological analyses, water-quality
measurements are either ionic, total, or dissolved (filterable). In some
cases suspended (nonfilterable) measurements are conducted. It would be
highly desirable to have information on both total and dissolved (and, by
subtraction, suspended) forms, but available resources rarely permit this
during routine monitoring operations. U.S. Environmental Protection Agency
criteria (U.S. Environmental Protection Agency 1976b) are based on the total.
concentration of a substance because an ecosystem can produce chemical,
physical, and biological changes in the element's state that may be
*The total quantity of a given material present in an unfiltered water sample
regardless of the form or nature of its occurrence (i.e., dissolved,
suspended, adsorbed, etc.). This value is approximated by "total
recoverable" analyses, which determine the amount of'material that is an
analYtically detectable form (U.S. Geological Survey 1977).
**Dissolved or filterable forms are those that pass through a 45-micron pore-
size filter of a material that does not chemically affect (including by
sorbtion) the parameter being investigated (U.S. Geological Survey 1977).
63

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TABLE 16. HIGH PRIORITY PHYSICAL/CHEMICAL PARAMETERS AND PARAMETERS
  PRESENTLY SAMPLED AT NASQAN STATIONS 
     Melancon Melancon NASQAN
    Kinney et al. et a 1 . M-Monthly
    et al. 1979a 1979b Q-Quarterly
    1979 (San Juan) (Tongue-Powder) C-Continuous
Alkalinity, total  X X 
Aluminum, dissolved X   
A 1 umi num, total X  X 
Ammonia, total as N  X X M
Arsenic, total    Xa xa Q
Beryl 1 i urn, total   X 
Bicarbonate ion X X X M
Biological oxygen dema nd , Xb Xb 
5-day      
Boron, dissolved X   
Boron, total    X X 
Cadmi urn, tot a 1    X X Q
Carbon, total organi c  Xb Xb M
Calcium, dissolved  X X M
Chlorides, dissolved X X X M
Chromium, total  Xa Xa Q
Specific conductance X X X C
Copper, dissolved X   Q
Copper, total   X X X Q
Cyanide, total   X Xa Xa 
Flow   X X X C
Flourides, di 5',SO 1 ved X  X M
Hardness   X   M
Iron, dissolved X   Q
Iron, total   Xa Xa Xa Q
Lead, dissolved X   Q
Lead, total   Xa Xa Xa Q
Magnesium, dissolved X X X M
Magnes i urn, tot a 1 X   
Manganese, dissolved X   Q
Manganese, total X Xa Xa Q
Mercury, total   Xa Xa Xa Q
Molybdenum, dissolved X   
Molybdenum, total  X X 
       (cont i nued)
     64  

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  TABLE 16. (Continued) 
   Melancon Melancon NASQAN
  Kinney et a 1 . et a 1 . M-Monthly
  et al. 1979a 1979b Q-Quarterly
  1979 (San Juan) (Tongue-Powder) C-Continuous
Nickel, dissolved  X   
Nickel, total    X 
Pesticides  Xa X X 
Phenols  X X X 
pH   X X M
Phosphorus, total   Xa Xa M
Potassium, dissolved  X X X M
Oxygen, dissolved   X X M
Selenium, total   Xa Xa Q
Silicon, dissolved  X   M
Sil icon, total  X   
Sodium, dissolved  X X X M
Solids, dissolved total X X X M
Solids, total dissolved X   
Sulfate, dissolved  X X X M
Suspended sediments, total X X X M
Suspended sediments, fixed X   
Temperature  X X X C
Turbidi ty  X   M
Vanadium, dissolved   X  
Zinc, d i s so 1 ved  X   Q
Zinc, total   X  
Nitrate + nitrate - N     M
Cobalt     Q
Total Kjeldahl nitrogen    M
aOetermine in both water and bottom-sediment samples.
bOetermine in sediment samples only.
65

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detrimental to organisms living in or using the water. In this. report, this
rationale is generally followed for those parameters that are llkely to cause
toxic effects. For the major ions (calcium, magnesium, sodium, sulfate,
chloride, and bicarbonate) and some other parameters, dissolved determinations
are recommended. Where available, the use of specific ion sensors or similar
electrodes is encouraged, especially if continuous measurements are possible.
However, few of these sensors are sufficiently reliable or free from
interferences to use with much confidence in natural waters, and backup
analytical methodologies should also be employed.
The major ions--calcium, magnesium, sodium, sulfate, chloride, and
bicarbonate--are likely to be affected by energy resource development
activities. High levels of calcium, sodium, sulfate, carbonates, and
chlorides are commonly found in various process and waste streams. Changes in
geochemical conditions as the result of mining activities or releases of
ground water may be indicated by either increased concentrations or changes in
the ionic ratios of surface waters. These six parameters shoul'd be measured
in the dissolved form. All of these parameters were mentioned by several
researchers as potential pollutants from energy development waste disposal.

For purposes of monitoring energy resource development impact on water
quality, the following elemental analyses are recommended:
aluminum, dissolved
arsenic, total
boron, total
ba ri urn, total
cadmi um, total
chromium, total
copper, total
fluoride, ionic
iron, total
mercury, total
molybdenum, total
nickel, total
lead, total
se 1 en i um, total
urani urn, total
vanadium, total
zi nc, tot a 1
These elements were selected from those identified as being enhanced in coals,
oil shale, uranium ores, or their process streams or wastes. The rationale
for each element is presented in Appendix A. Of the elements listed, only
barium and uranium were not included on the lists of Kinney (1979) and
Melancon (1979a and 1979b).
In addition to the elemental analyses, monitoring of various organic and
inorganic compounds will be required. Ammonia and sulfuric acids are both
process fluids and by-products of several resource development activities.
Hydrogen peroxide is used during .in situ uranium extraction but is so reactive
and short-lived that monitoring for environmental reasons is not warranted.
Cyanides and thiocyanides are compounds common to fuel processing activities.
Explosives used in extraction and pollution from uranium tailings commonly
increase nitrates and nitrites in waters (Reed et al. 1976). Monitoring for
energy development impact on water quality should include collection of data
on ammonia, sulfate, cyanide, nitrate, and nitrite concentrations.
Bicarbonate and carbonate concentrations, pH, conductivity and, total
alkalinity are likely to be affected by releases of alkalies or acid wastes in
effluent streams, either directly or through subsequent chemical reaction
66

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and should be monitored. Total and dissolved solids concentrations are
expected to increase as a result of energy resource development activities and
should also be measured.
Pollution by organic compounds produced during extraction, conversion, and
refinery operations is of intense concern. Two different approaches to
organic pollutant analysis exist; analyses may be made for selected compounds
or may attempt to detect and quantify all compounds present. Pellizzari's
(1978) data indicate that each sample is unique in its organic composition and
that monitoring for individual compounds would not be effective. In addition,
monitoring for individual compounds assumes that environmentally hazardous
compounds have been identified. In order to obtain data upon which future
monitoring requirements can be based, it is recommended that surveys of all
organic compounds present in energy resource effluents be made on an annual
basis or in conjunction with resource development experiments. The high cost
of such analyses--$2,OOO per sample or more--precludes their incorporation
into a trend-monitoring network at the present time.
It is recommended that available analytical technology be utilized
whenever possible to monitor phenols, oil in water, and selected organic
compound classes. In particular, phenol and oil-in-water measurements are now
possible on a routine basis, and these two parameters should be included at
network monitoring stations. Total organic carbon and dissolved organic
carbon should also be monitored. Because organic debris can place a large
demand upon the oxygen budget, biochemical oxygen demand and total oxygen
concentrations are also recommended as monitoring parameters.
Some basic physical and chemical parameters provide data that can be used
to analyze the other results. These parameters--flow, pH, conductivity,
temperature, and turbidity--should also be routinely monitored. All but
turbidity can be monitored effectively on a continuous basis with tOday's
technology. It is recommended that continuous monitoring be accomplished.
Dissolved oxygen concentration and biochemical oxygen demand are indicative of
the organic pollution loading of a stream and its overall ability to support
1 ife. Both parameters should be monitored.

Sediment analyses should also be made at or near water-quality stations.
Because metals and organics are sorbed onto sediment particles, they are often
more detectable in the sediments than in the water. Analyses should be made
for the toxic metals, cyanides, and total organic carbon. Examination for
coal particles or specific-gravity separation of coal particles is also
recommended. These analyses should be made in the main rivers in areas where
fine sediments collect. Semiannual sampling in spring and fall is
recommended.
Sediment samples from organic-rich backwater areas should be collected
annually. These should represent the upper centimeter or so of the sediment
layer. These samples should be split, one portion analyzed for organic
content, and the other portion preserved for future analysis. The rationale
for this sample is that organic debris often provides a sorption surface for
toxic contaminants and helps to concentrate them. In addition, because oil is
67

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known to persist in sediments, these samples may provide evidence of events
missed by grab samples.
Energy resource development threatens to release into the environment
chemicals whose effects are long-term, cumulative, and subtle. To completely
assess the impact of these chemicals and their potential threat to humans, it
is recommended that biological monitoring of natural populations be
implemented. Presently, biomonitoring technology is not well developed. Yet
it offers the most promise in detecting long-term perturbations in water
quality. It may be utilized to integrate parameters over time or as an
indication of short-lived, toxic releases that occur between grab-sampling
intervals or involve parameters not currently being monitored. Unfortunately,
except for observing gross effects (i.e., death of large numbers of
individuals), few techniques have been developed for utilizing naturally
occurring populations as monitoring tools.
Sampling to determine the bioaccumulation of toxic pollutants should be
conducted on an annual or semiannual basis. Bioaccumulation may occur in any
biota, but fish are recommended as sample organisms because they are
relatively high on the food chain. It is recommended that tissue analysis of
fish samples obtained near network water-monitoring stations be conducted for
EPA-specified toxic pollutants. Because uptake may vary between species,
attempts to obtain the same species previously utilized should be made.
Because they are found throughout the area, carp and catfish are recommended
as sample organisms. Since the costs of tissue analysis are high, three or
four fish should be homogenized, creating a composite sample, prior to
analysis. This reduces the chance of obtaining a single abnormal specimen. A
portion of each sample should be preserved (freeze-dried or other suitable
technique) for possible future analysis.

Samples for fecal coliform and fecal streptococci should be collected and
analyzed on a monthly basis.
The collection of phytoplankton samples from western streams is not likely
to provide meaningful information, and it is recommended these samples not be
obtained at monitoring net stations.
Collection of periphyton samples is recommended, however. Samples should
be collected using an artificial substrate and a standardized technique.
Analyses should include dry and ash-free determinations for biomass estimates
and species identifications and counts. This should be done on a seasonal or
semiannual basis.
Collection of macrobenthos samples, using standardized techniques
appropriate for each sampling locale, should be conducted from nearby riffie
areas on a seasonal or semiannual basis (summer and late fall). Samples
obtained should be split and preserved for future reference. If funds penmit,
community composition (taxonomic enumeration) should be determined to the
lowest possible taxonomic level.

A similar collection of small fish from a limited (three or less) number
of species (dace, gambusia, etc.) should be obtained on an annual or
68

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every-other-year basis. At least 50 specimens should be obtained. These
should be preserved for future analysis. It is hoped that rapid techniques
for identifying tumor development or other evidence of carcinogenic presence
will be developed in the near future. These samples would provide a reference
"library" to obtain baseline data prior to widespread energy resource
development.

In conjunction with the foregoing sampling program, research efforts to
develop techniques for biological monitoring of energy resource development
impact should be conducted. In addition, because of the presence of numerous
known and suspected carcinogens, it is recommended that carcinogenic studies
be instituted both using oral doses to standard test animals and using aquatic
forms exposed to various organic compound solutions. Field monitoring
techniques to determine the extent and increase in cancer or tumor incidence,
mutagenic or teratogenic effects, or other long-term consequences of sustained
low-level pollutants need to be developed and, once developed, applied.
Survey-type biological sampling programs should also be conducted to help
establish a reliable data base upon which to build a biological monitoring
program. It is emphasized that biological monitoring must be accomplished in
conjunction with physical chemical monitoring efforts (and vice versa).
Neither monitoring technique will serve all purposes.
Arguments may be made for determining interparameter correlations and if a
strong correlation exists between two or more parameters, only monitoring one
of them. To successfully use this procedure, the correlation must be strong
enough to provide the desired accuracy and precision of the unmonitored
parameter and the cause of the relationship must be well understood. If any
activity in the area is likely to change the concentration of one parameter
preferentially, then the concentration of the other can no longer be safely
estimated. Preferential concentrations of many elements are known to occur in
the various stages of processing. Extraction methods commonly result in a
changed geochemical environment and in shifts in solution susceptibilities.
Chemicals used by the energy industry to extract and process both the fuel and
necessary water supplies may increase an element's concentration either
directly or through subsequent reactions. It is therefore advised that all
recommended water-quality parameters be measured at each monitoring station.

It should be stressed that the parameters discussed and recommended are
for use in monitoring energy resource development impacts on water quality in
Rocky Mountain States. As local conditions or circumstances dictate, other
parameters may be of interest. Changes in technology, unsuspected problems,
or other considerations may also result in adding to the parameter list. The
recommended parameters are those that will permit monitoring of energy
resource development impact. The list is not intended to preclude addition of
other parameters.
SAMPLING FREQUENCY
A major component of sampling system design is the frequency of
measurements needed to satisfy the system objectives. Because operational
constraints on available resources usually place a limit upon the total number
69

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of analyses, a compromise must be made between the number of stations to be
sampled and the number of samples to be taken at each station.

It would seem desirable to obtain continuous records of all parameters at
all stations. However, few reliable field sensors exist, thus limiting
~ continuous sampling to flow, temperature, conductivity, pH, dissolved oxygen,
chloride ion, and a few other parameters. In addition, practical limits exist
with regard to handling and storing voluminous records. In most cases
continuous records are reduced to daily, weekly, or monthly means. In lieu of
continuous sampling, grab samples, representing a discrete point in time, or
composite samples, representing an average water sample over a period of time,
are necessary.
Sampling frequency for trend monitoring has been addressed directly by
Ward (1973), Ward and Nielsen '1978), Lettenmaier (1975, 1978), Sanders and
Adrian (1978), and, to a lesser extent, Sherwani and Moreau (1975) and Berndt
(1975). Most other workers have been concerned with surveillance monitoring
(i.e., detection of water-quality criteria violations). In nearly all cases,
although different assumptions, objectives, and statistical approaches were
used, the conclusion is that the most efficient system is one that has few
stations with a relatively large number of samples (for a given parameter).

Before trend analysis can be attempted, the influence of hydrologic
variability must be removed from the data. Sherwani and Moreau (1975) state
that the sampling frequency to accomplish this is a function of:
1 )
2)
3)
4)
the response time of the system;
the expected variability of the parameter;
the half-life and response time of the constituents;
seasonal fluctuations and random effects (i.e., meteorlogical,
a n a 1 yt i c a 1, e tc . ) ;
5)
6)
7)
8)
representativeness under different conditions of flow;
frequency and duration of short-term pollution events;
magnitude of response; and
variability of the inputs.
Frequent sampling of a large lake or large, sluggish flowing river, where
the rate of change in concentration of the parameter being investigated is
slow, will provide little additional useful information above that provided by
a few samples. Conversely, where concentration changes are rapid as may be
the case in a small stream, frequent sampling is required. Similarly, highly
variable parameters must be sampled more frequently than those with little
change in order to attain a desired confidence level. It is apparent that the
most efficient sampling frequency will vary from parameter to parameter and
70

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will require various amounts of preexisting data to calculate.
matter, however, these calculations may be unnecessary.
As a practical
Ward (1973) calculated curves of the number of samples per unit time
necessary to obtain an allowable error in the calculated mean value for
normally distributed, randomly sampled populations with differing standard
deviations. The curves are inverse power function hyperbolae that flatten out
between -20 and 40 samples per time interval (Figure 15). Ward computed
examples using dissolved oxygen, pH, and total dissolved solids data from
Colorado rivers and arrived at a figure of 25 samples per year to attain an
"accuracy" in which the calculated mean is within 10 percent of the true mean
95 percent of the time.

Lettenmaier (1975) utilized both parametric and nonparametric statistics
to evaluate trend-monitoring designs. He found that the flex point in his
curves (after which increases in sample numbers contributed relatively little
new information) was between 30 and 50 samples per year for a 95 percent
confidence level.
To aid in determining an optimum sampling frequency, confidence intervals
for a number of parameters were estimated. The confidence interval is that
range within which the true mean can be expected to fall within a specified
level of confidence. For a 95 percent confidence level, assuming a normal
distribution, this interval at a given station and for a given parameter is
expressed as:
x-
1.96a
ilJ~X+
1.96 a
vn
vn
where n =
x =
the number of samples,
the sample mean,
a = the standard deviation of the population,
lJ
= the mean of the population.
Table 17 presents data from three stations. The standard deviation was
calculated from available STORET data and is assumed to approximate the
standard deviation of the population. (Where n is large, this assumption
should be true; where n is small 30), it is more questionable.)
Calculations of the precision term above (~1.96a/vrn) were made assuming
weekly sampling over a one-year period and are tabulated in Table 17.
The precision term for the above assumptions reduces to ~0.27a. Over a
three-year period of weekly sampling, the precision term would be reduced to
approximately two-thirds of its original value (~0.16a). Reducing the
sampling rate to biweekly or monthly increases the annual precisions roughly
to half again and double the weekly sampling values, respectively
71

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*


700 \
600 \
500
400
300
~ 200
~
E 100
...
o
:: 100
w
Qj
:a
III
~
.2
Ct
Figure 15.
.
,
..,.
.
I
I
I




:: \\
40 \ \''-.--

. 8"""",,-
20 '. ~8--
............... -
a 500
o
o
20
0=100   
-A    
  A 
0=50   
8    
a 25 8 
.  . 
40 60 80 100 120
Number of Samples Per Unit Time Interval
Number of samples vs. allowable error for selected standard
deviations (cr) (Ward 1973).
(fO.38cr for n = 26 and fO.57cr for n = 12). To obtain a 99 percent confidence
level, the constant becomes 2.58, and the precision term for weekly sampling
over a one-year period becomes fO.36cr. .
Table 17 indicates that a weekly-to-biweekly sampling frequency will
provide acceptable precisions for detecting trends in most of the parameters
investigated in surface waters. Flow, iron, and arsenic are possible
exceptions. Daily flow records would provide markedly improved precisions of
f87.3, f184.1, and f153.9 cubic feet per second (cfs)* for stations 06308500,
09368000, and 09247600, respectively. USGS maintains daily flow records, so
this does not seem an unreasonable monitoring requirement. The iron and
arsenic data reflect large variances (standard deviations), but additional
sampling for these would require substantial additional resources and would
probably not provide a cost-effective return. The cause of these high
variances should be examined to determine if they result from sampling or
analytical procedures or whether they are the result of natural causes. If
the result of sampling or analytical problems, corrective actions may be
taken. Similarly, cadmium also has a high variance.
*Non-metric units are used in the STORET data base.
72

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TABLE 17.
Parameter
00060
Flow (cfs)
00095
Conduct fvity (llmho)
00300
D1 sse I ved oxygen (mg/l ) 52
OD31D
B1ochen1cal oXygen
demand, 5-day (mg/l)
00400
pH (SU)
00410
Total alkalinity (mg/l) 232
00915
Cal cium (mg/l)
00930
Sodium (mg/l)
00931
Sodium absorption ratio 313
00945
Sulfate (mg/l)
01002
Arsenic (lig/1)
01022
Boron (lig/1)
01025
Cadmium (llg/1)
01040
Copper (lig/1)
01046
Iron (lig/1)
01051
Lead (lig/1)
01145
Selenfum IIg/l)
71900
Mercury (lig/1)
70300
Residue @180oF (mg/l) 267
70301
Dissolved solfds (lig/1) 110
STORET DATA STATISTICS FOR SELECTED PARAMETERS AT THREE
REPRESENTATIVE STATIONS
Tongue River at
Miles City, Montana
06308500
n
<:lean
(x)
Std
dev
(cr)
227.1
2.2
1.9
0.3
52.7
15.5
24.0
0.5
82.8
7.8
106.1
.1.4
3.4
48.6
77.2
50.4
40.3
Preci-
sion
(0.270)
t229 .7
:t61 .3
to.6
1.3
to.1
tl4.2
t22.3
t2.1
t2B.6
1.8
2.5
to.7
:1:13.6
San Juan Rfver at
Shi prock, Ilew 11exico
09368000
to.4
t4.2
t6.5
~.1
to.5
~.1
~.05
n
mean
(x)
Std
dev
(cr)
995
976
1931.2 1794.0
Preci.
sian
(0.27,,)
t484.4
t95.0
1.2
tl4.2
t41.3
t5.1
t28.4
t8.0
:5.3
tl8.6
1.6
0.09
t78.1
:56.5
to.5
to .3
10.1
19.2
t8.3
to.3
d.1
10.4 .
to.02
Yampa River below
Craig, Colorado
09247600
n
mean
(x)
Std
dev
(0)
28
34
831.9
348.2 . 120.7
1500.1
28
98
1.8
Preci -
sian
(0.27e-)
1405.0
t32.6
1.4
~.4
227
597.4 850.7
0.5
0.09
167.8
169.5
tl0.9
0.6
0.20
t45.3
t43.1
109
46
749.6
9.6
351.8
1.9
1.6
0.4
33.9
76.7
70.7
30.9
52.7
34
32
8.0
106.7
0.4
35.5
10.0
12.0
0.3
33.8
. 1.0
31.0
0.5
2.4
107.2
46.3
0.5
0.00
78.3
:0.1
:9.5
t2.7
13.2
to.l
t9.1
tZ.7
t8.4
0.5
2.1
to.l
~.6
314 830.3
10.5
14
314
8.0
227.3
948
589
7.8
121.6
1.1
152.8'
18.7
105.1
10.3
4.3
29.8
4.0
32
31
30.9
25.8
t28.9
:tl2.5
0.8
0.00
to.2
o
140
314
62.9
62.4
939
781
925
926
1.8
238.4
19.7
69.0
2.5
0.06
289.4
209.2.
31
32
1.0
64.0
*Precisions are based on 95 percent conffdence level and an assumed weekly sampling schedule over a one-year period.
121.1.
312
17
1.5
226.6
15
14.1
110.7
7
. 1.3
70.9
However, analytical resolution in most cases is known to be 10 ~g/liter or
multiples thereof, so the indicated mean precisions are acceptable.
2
17
5.9
165.0
606*
20
21
156
15
17.7
114.5
32*
8
8
33
8
117.0
75.0
The statistical procedures utilized thus far assume that the individual
data observations are independently distributed, i.e., observation B is not
dependent upon the magnitude of observation A. Hydrologic data, including
water-quality data, are a continuum and thus, for at least a finite period, do
not meet this assumption. Lettenmaier (1976) computed correlation
coefficients for lag 1 Markov models using daily data. For the data analyzed,
these ranged from about 0.75 to 0.9. Using a variety of nonparametric tests
for trend detection, he concluded that the point of diminishing returns IIfor
the lag 1 correlation coefficients observed, where little additional power in
trend detection was gained for additional sampling effort, appeared to be
17
79
19
17
18
559.2
558.9
73
15
15
527
510
548.0
446.4
8
8
31
215.4

-------
in the range of 40 to 90. samples per year.1I In a later work, Lettenmaier
(1978) concluded that for detection of a step trend lithe optimal data
collection frequency. . . appears to be in the range from biweekly to
monthly. . . .11 However, he also noted that lIan attempt to estimate
correlation structures for a number of parameters. . . was largely
unsuccessful because the avail ab 1 e sampfi ng i nterv.a 1 s are too 1 argell and that
frequently sampled data are necessary to adequately estimate persistent
structure.
Optimum sampling frequency is further complicated by the question of how
to distribute the number of samples. Should samples be collected at equal
time intervals, on a semi random basis, or on the basis of hydrographic
considerations? In theory, it would be best to maximize the information value
of any individual data point by collecting samples during periods of high
variability in parameter concentrations. Methods to accomplish this have been
proposed by several workers (Berndt 1975; Sh~rwani and Moreau 1975; Sanders
and Adrian 1978). In each case a large amount of preexisting data is required
to apply the sophisticated techniques used to determine when to sample.

There are two basic problems with this IIstratified samplingll approach.
One is that the highest variability in parameter concentration occurs at
different times for different parameters and at different stations. The
practical considerations of sampling logistics require either considerable
compromise, large resources, or both. The second problem is that a paradox
exists. If a rigorous statistical design is used to maximize the stratified
sampling schedule, a great deal of data is required for the calculations.
Moss et al. (1978) note that if the prerequisite design parameters are known
with certainty, there is probably no need to collect the data. In order to
implement stratified sampling based on variability, even on a less rigorous
basis, a large amount of prior knowledge is required.
Stratified sampling is based on one other assumption--that parameter-
concentration variability will continue to follow preestablished patterns.
Pollutants generated by energy resource development activities mayor may not
follow historical distribution patterns. The environmental disturbances may
be expected to more closely follow the level of mining or processing activity
than hydrographic cycles. In addition, stream flows are increasingly
subjected to manipulation through operation of dams, diversions, discharges,
etc. These manipulations impose additional elements into the naturally
occurring flow regimes and parameter concentration patterns.

Data are not presently adequate to determine optimum sampling times except
for a few parameters such as temperature and conductivity. Sanders and
Adrian's (1978) method requires an extensive period of daily parameter records
in order to determine the harmonic components of variability. Such records
are generally unavailable although an argument may be advanced for using
conductivity to estimate the variability of ionic constituents.
Lettenmaier (1978) observed that for purposes of trend detection, whether
using an independent observation or an autocorrelated data assumption, it is
important for data to be collected uniformly in time. The stratified sampling
74

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strategy was generally found to be much less efficient, requiring two to three
times as many samples to obtain equivalent confidence levels, than uniform
sampling strategies.

The advantage of frequently sampling one parameter during periods of low
flow highly variable concentration are offset by other parameters that exhibit
low variability in concentration under low-flow conditions and large
concentration changes during high-flow periods. A fixed sampling interval
greatly simplifies logistics. The selection of an interval without a seven-
day component will help assure that man's weekly work schedule will not lead
to biased data. (In addition, the hour of sampling should not be consistently
the same; ideally it would be distributed over the 24-hour period. If this is
done, the existence of naturally occurring daily cycles such as those commonly
observed for dissolved oxygen, pH, and weather/solar data must be acknowledged
and allowed for in data interpretation.) A recommended sampling frequency
uniformly spaced over 8- to 13-day intervals will provide between 25 and 50
samples per year and should yield acceptable precisions for subsequent
analyses and interpretation.
NUMBER AND LOCATION OF STATIONS
As previously indicated, the number of stations to be included in a
network is more often dictated by available resources than by theoretical
considerations. Resource constraints demand a compromise between the number
of stations and the frequency of sample collection at each. Nearly all
researchers have concluded that the more efficient network consists of
relatively few stations with relatively large numbers of samples from each.
Available resources for monitoring energy resource development impact on
water quality are not known and will assuredly fluctuate from year to year.
For this reason it was decided to select only the minimum number of stations
necessary to identify trends in energy resource development areas. Further
definition and resolution of the causes of observed trends may be accomplished
through use of data from other sources or by intensive survey efforts.

Water-quality monitoring stations in the study area are generally sited on
flowing streams. Sampling of lakes or reservoirs requires collection of
samples at various depths in the water column to detect vertical variations in
parameter concentrations. In most western streams, turbulent flow is expected
to homogenize the water column. Thus a single sample can be representative of
water quality at that point. (This assumption is not always true and should
be tested occasionally at all monitoring streams.) Larger reservoirs also act
as settling basins for suspended materials; horizontal gradients may be
substantial within the reservoir, and sediments may act as sinks for some
materials. These materials are potentially available for reentry into the
water with changes in environmental conditions. For trend-monitoring
purposes, reservoirs have been avoided as station locations in favor of
flowing streams. Although this decision simplifies the monitoring system, it
neglects the potential impact of toxic compound buildup in reservoir
sediments. Evaluation of reservoirs as sinks for toxic materials should be
made as a special study or separate monitoring effort.
75

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Location of stations for monitoring energy impact trends on water quality
must be carefully selected so that water-quality parameter levels will be
representative for that water body. Spatial distribution within a water body
will differ from parameter to parameter because of differences in their source
locations and the transformations they undergo in the environment. Methods to
accomplish effective station siting were advanced by many authors (NUS
Corporation 1970; Sharp 1971; Beckers et al. 1972; Sherwani and Moreau 1975;
Berndt 1975).
Sharp (1971) proposed an approach that determined the approximate center
of a stream network by making equivalent stream order numbers at the outlet of
each subnetwork approximately equal. A monitoring station would be located at
the outlet of each subbasin, and the process reiterated. Beckers et al.
(1972) utilized quantitative methods to develop a preliminary design for a
surveillance network. They first compute numerical priorities for each
stretch of river for each water-quality parameter of interest. From these
data, they calculate the preferred location for sampling each parameter.

Sherwani and Moreau (1975) developed a surveillance-station siting
technique that first determined the number of stations required to achieve a
desired precision based on a normal distribution of variances of parameter
concentrations in an area. The first station was chosen so that the excess
concentration (the difference between the natural background concentration and
that at the end of the mixing zone below a point source) was reduced 50
percent. To locate this site, they determined concentration gradients of
parameters of interest, selected the gradient with the greatest rate of decay,
and calculated the initial sampling location. The distance between subsequent
station spacings were doubled each time. They then use interstation
correlations and principal component techniques to reduce the size of the
network and obtain the optimum density.
Lettenmaier (1975) presented a very good review of work on sampling system
designs. He noted a 1974 study by Sanders in which, following a development
utilizing Sharp's (1971) methods to establish macroscopic station locations,
Sanders used mixing length theory to determine "microscopic" locales. Sanders
found that even when mixing was far from complete, high correlations existed
between water-quality parameter concentrations in stream cross sections.
Lettenmaier (1975), in his development of trend-monitoring design, found
the network design efficiency to be relatively insensitive to sample station
location. In a later paper (Lettenmaier 1978), he recommends a subjective
approach to selecting station locations that includes the following:
1.
Consideration of using existing stations
2. Location of stations to monitor a substantial proportion of the
total runoff from a river basin (i.e., stream mouth locations)
3. Location so that data obtained at trend-monitoring stations may
be combined with data from adjacent stations to isolate effects of suspected
trend cau se s
76

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4. Location such that the best available estimate of cross-sectional
stream quality is given by a single grab sample

5. Avoidance of sites where local effects, such as planned highway
construction, stream channelization, etc., would produce spurious results
6.
requirements
Location to facilitate access to minimize sampling-team travel
Trend-monitoring stations may be minimized by siting these on larger
streams or rivers downstream from areas experiencing energy resource
development. This is feasible because of the geographic distribution of
energy reserves and development activities (Figures 2, 3, 4, 5,8, and 11).
Such a network should be sufficient to detect trends in surface-water quality
caused by development of energy resources. They will, however, also detect
trends resulting from other activities in the upstream drainage. In order to
determine the cause of an observed trend, it will be necessary to utilize
other sources of data.
In general, National Stream Quality Accounting Network (NASQAN) stations
are suitably located to serve for monitoring western energy resource
development impact. NASQAN is a nationwide system of water-quality monitoring
stations located at major hydrographic subdivisions of river basins. The
network's objectives include determining areal variability and detection of
long-term (75 year) changes in stream quality. The location of stations
suitable for monitoring energy resource development impact on water quality is
indicated in Figures 16 and 17, and tabulated in Table 18 (Ficke and Hawkinson
1975).
In addition to the NASQAN stations above, stations near the mouths of the
Blacks Fork, White, San Rafael, and Price Rivers, Piceance Creek, the Green
River at Green River, Wyoming, the North Fork of the Gunnison River near
Somerset, Colorado, and the Medicine Bow River above Seminoe Reservoir,
Wyoming, need to be added. Existing USGS water-quality stations are
satisfactorily situated for use at each of these locales. These 25 stations
would be sampled on an 8-to 13-day basis. The data obtained will provide for
trend analyses but will have to be augmented by data from other stations to
determine the cause of any observed trends.
77

-------
I
,
I
I
I
. Proposed Water Monitoring
Network Station
"".".1 lit
, -_._-!----~f-' --~,~~. -. -'
..... \, /~
", Glee... iIII P ...

\'"'''''''' ...,
~... .
G.b-
".
,f./ ',.,\ )
;,
\
"
(
"\
...'" .J'
'\ { '\'
'..,(--_:._.-.".~ '1 ",

,I ","Iow.f_flt" ,
1--......._--....J
\ 1:~1;
! :j~.~D7)
I <,4""~
1
-'--1
,
I
1
,

L'___--'------r------------------

i
I
I
~__._~~n~~_____..~--
Figure 16. Recommended stations for monitoring
development impact on surface~water quality,
western energy resource
Missouri River system.
78

-------
. Rivano"
. C..per
. Proposed Water Monitoring
Network Station
A
-------------------
. Salt ~ke City
Denver.
Tavlor
Po'"
A...
Colorado
Spring' .
----------~--------
Figure 17. Recommended stations for monitoring western energy resource
development impact on surface-water quality, Colorado River system.

79

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TABLE 18.
NASQAN STATION DESCRIPTIONS
USGS    
Station No. Station Name State Latitude Longitude
09380000 Colorado River at Lees Ferry AZ 36521 111351
08251500 Rio Grande near Lobatos CO 37651 105451
09152500 Gunnison River near   
 Grand Junction CO 38591 108271
09251000 Yampa River near Maybe11 CO 40301 108021
09260000 Little Snake River near Lily CO 40331 108251
06130500 Musse1she11 River at Mosby MT 47001 107531
06214500 Yellowstone River at Billings MT 45481 108281
06294700 Bighorn River at Bighorn MT 40691 107281
06308500 Tongue River at Miles City MT 46221 105481
06326500 Powder River near Locate MT 46  27 1 105191
06329500 Yellowstone River near Sidney MT 47411 104091
09368000 San Juan River at Shiprock NM 36481 108441
06337000' Little Missouri River near   
 Watford City NO 47351 103151
06438000 Belle Fourche River near   
 E1 m Spri ngs SO 44221 102341
09180500 Colorado River near Cisco UT 38491 109181
09315000 Green River at Green River UT 38591 110091
09379500 San Juan River near Bluff UT 37091 109521
80

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RECOMMENDED DESIGN
A monitoring network designed to detect trends in water quality over a
two- to four-year period is recommended in order to assess the impact of
energy resource development. The network is specifically designed to address
ongoing and projected developments in the Rocky Mountain and Upper Colorado
Basin States (Figure 1). Radiological parameters are not included in this
design.

Energy resource development in the area will largely be the exploitation
of fossil fuels and uranium reserves. In particular, extraction and
conversion of coal and oil shale kerogens is expected. Known resources are"
limited to a relatively few watersheds, and their development tends to cluster
activities into fairly small geographic areas. Because of this, monitoring
efforts can be limited to a small number of stations. Twenty-five stations
were selected from existing USGS monitoring stations for inclusion into a
monitoring network. These stations were selected on major streams draining
areas of ongoing development and are listed in Table 1 and indicated in
Figures 16 and 17.
Continuous monitoring of flow, pH, conductivity, temperature, and, if
possible, dissolved oxygen and chloride is recommended. Data obtained may be
expressed as daily means. Grab samples of water should be collected every 8
to 13 days and analyzed for the chemicals indicated in Table 2.
Research on the nature and amount of organic compounds produced and
released by energy resource development activities must continue. Although
present state-of-the-art does not permit routine monitoring of most organic
compounds or compound classes, identification of chemicals of primary concern
must be accomplished and monitoring techniques developed. Those compounds or
compound classes (i.e., phenols) for which analytical techniques are available
should be monitored on a regular basis. It is recommended that surveys of all
organic compounds present in energy resource development effluents be made on
an annual basis or in conjunction with specific experiments.
Sediment samples containing significant amounts of the silt-clay fraction
should be collected at or near water-quality stations at least each quarter.
Desired analyses are indicated in Table 2. In addition, sediment samples
should be collected annually from organic-rich backwater areas near the site.
These should be subjected to organic analyses.
Biomonitoring should be a major component of the network. Fish tissues
should be analyzed for EPA-specified toxic pollutants. Fecal coliform and
fecal streptococci levels should be monitored monthly. Periphyton production
and taxonomy should be determined seasonally using artificial substrates.
81

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Monitoring of macrobenthic communities and populations should be conducted
seasonally in riffle areas near the stations. A small fish collection should
also be made on an annual or every-other-year basis. Representative
biological samples should be preserved and stored for future,
yet-to-be-developed analyses.
In order to implement the sampling program outlined above, it may be
necessary to drop stations located elsewhere. Modern monitoring theory
supports this decision--it is more efficient to collect many samples at fewer
stations than to collect the same number of samples distributed over a large
number of stations. Careful consideration as to which stations to drop is
essential because nonnetwork station data are needed to determine the cause of
any detected trends.

Finally, any data collected are only useful if they are available. It is
highly recommended that careful, timely storage in a unified cqmputer data
base be accomplished. Development of data analysis and interpretation
procedures should occur concurrently with data collection and be made readily
available to area researchers. A document outlining procedures to identify
data trends or other interpretive outputs should be prepared and routine
methods applied to all monitoring network stations on a regular basis.
Storage of data obtained in readily accessible computer data storage- banks
is necessary for a meaningful monitoring program. STORET is recommended for
storage of water-quality data. Biological or other data unacceptable to
STORET should be stored elsewhere on the EPA computer to allow convenient
interface with the water-quality data. Software and other necessary
procedures to facilitate merger of STORET data with those in other Federal
data bases (e.g., WATSTORE) should be developed and made accessible to
researchers.
This paper describes a water-sampling network designed to assess energy
resource development impact on water quality through detection of temporal
trends. Stations and parameters were held to a minimum to accomplish this
task. Addition of stations or parameters is to be encouraged. At specific
areas, local conditions may well require additional parameters.
82

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Lettenmaier, D. P., 1975. "Design of Monitoring Systems for Detection of
Trends in Stream Quality," Univ. of Washington, Dept. of Civil Engineering
Tech. Rept. 39, Seattle, Wash. 217 pp. NTIS PB 272 814.
Lettenmaier, D. P., 1976. "Detection of Trends in Water Quality Data From
Records With Dependent Observations. II In: Water Resources Research, Vol.
12, No.5, pp. 1037-1046, Oct. 1976.
Lettenmaier, D. P., 1978. "Design Considerations for Ambient Stream Quality
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August 1978. American Water Resources Assn., Washington, D.C.

Lewis, D. H., 1976. "Optimization of State Water Quality Monitoring Systems."
In: Jour. of Computers and Operations Research, Vol. 3, No. 2-3, pp.
127-143. Pergamon Press, August 1976. Also as EPA Report
EPA-600/J-76-029, Corvallis, Ore. NTIS PB 265 313.
libicki, J., 1978. "Effects of the Disposal of Coal Waste and Ashes in Open
Pits," U.S. Environmental Protection Agency, Ind. Env. Rsch. Lab.,
EPA-600/7-78-067, Cincinnati, Ohio. 298 pp. NTIS PB 284 013.
Loogna, G. 0., 1972. liThe Carci nogeni c Properties of Oil Shal e Products and
the Prophylaxis of Cancer." In: Naachi-prakt Deyatel. Inst. Eksp, Klino,
Tallin, pp. 105-113. Translation by Leo Kanner Assn., U.S. Environmental
Protection Agency, EPA-TR76-54, Washington, D.C. 16 pp. NTIS: PB 258
920-T.
Mason, B., 1952. "Principles of Geochemistry," John Wiley and Sons, Inc., New
York, N.Y. 310 pp.
McKee, J. E., and H. W. Wolf, 1963. "Water Quality Criteria," State Water
Quality Control Board, Resources Agency of California, Pub. No.3-A,
Sacramento, Calif. 548 pp.
Melancon, S. M., 1. Simonian, and R. W. Thomas, 1979a. "Assessment of
Energy Resource Development Impact on Water Quality: The San Juan River
Basin," U.S. Environmental Protection Agency, Environmental Monitoring and
Support Laboratory, EPA-600/7-79-235, Las Vegas, Nev. 151 pp.
87

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Melancon, S. M., B. C. Hess, and R. W. Thomas, 1979b. tlAssessment of
Energy Resource Development Impact on Water Quality; The Tongue and
River Basins,tI U.S. Environmental Protection Agency', Environmental
Monitoring and Support Laboratory, EPA-600/7-79-249, Las Vegas, Nev.
222 pp.
Powder
Moss, M. E., D. P. Lettenmaier, and E. F. Wood, 1978. liOn The Design Of
Hydrologic Data Networks," pp. 772-775. In: EOS Trans. AGU, Vol. 59,
No.6, Washington, D.C.

National Academy of Sciences, 1973. "Water Quality Criteria 1972," U.S.
Environmental Protection Agency, EPA/R3/73-003, Washington, D.C. 594 pp.
National Petroleum Council, 1973. "U.S. Energy Outlook: Oil and Gas
Availability," National Petroleum Council, Washington, D.C. 768 pp.
Native American Natural Resources Development Federation of the Northern
Great Plains (NANROF), 1974. "Declaration of Indian Rights to the Natural
Resources in the Northern Great Plains States." u.S. Bureau of Indian
Affairs, Washington, D.C. 25 pp.

NUS Corporation, 1970. "Design of Water Quality Surveillance System,"
Federal Water Quality Administration, U.S. Department of Agriculture
Federal Water Pollution Control Research Series 16090 DBJ 08/70,
Washington, D.C. 301 pp.
Office of Technology Assessment, 1978. "A Technology Assessment of Coal
Slurry Pipelines," Congress of the United States, U.S. GPO, Washington,
D.C. 155 pp.
Pellizzari, E. D., 1978. "Identification of Components of Energy-Related
Wastes and Effluents," U.S. Environmental Protection Agency, Office of
Rsch. and Devl., EPA-600/7-78-004, Athens, Ga. 500 pp.
Price, D., and 1. Arnow, 1974. "Summary Appraisals of the Nation's
Ground-Water Resources - Upper Colorado River," USGS Prof. Paper 813-G,
U.S. Geological Survey, Washington, D.C. 40 pp.

Project Gasbuggy Joint Office of Information, 1967. "project Gasbuggy: A
Government-Industry Natural Gas Production Stimulation Experiment Using
Nuclear Explosives," U.S. AEC-EL Paso Natural Gas Company - Department of
Interior. 20 pp.
Radian Corporation, 1975. "Coal Fired Power Plant Trace Element Study Station
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of Energy Activities, EPA-68-01-2663, Denver, Colo. 294 pp. NTIS PB 283
278.
Ray, S. S., and F. G. Parker, 1977. "Characterization of Ash From Coal-Fired
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EPA-600/7-77-010, Washington, D.C. 130 pp.
88

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Reed, A. K., H. C. Meeks, S. E. Pomeroy, and V. Q. Hale, 1976.
Environmental Aspects of Uranium Mining and Milling," U.S.
Protection Agency, Ind. Env. Rsch. Lab., EPA-600/7-76/036,
Ohio. 59 pp.
"Assessment of
Environmental
Cincinnati,
Ringrose, C. D., R. W. Klusman, and W. E. Dean, 1976. "Soil Chemistry in the
Pi ceance Creek Basi n," pp. 101-111. In: USGS, 1976, Geochemi cal Survey
of Western Energy Regions, Third Annual Progress Report, July 1976, USGS,
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Sanders, T. G., and D. D. Adrian, 1978. "Sampl ing Frequency for River Qual ity
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Sharp, W. E., 1971. "A Topologically Optimum Water Sampling Plan for Rivers
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Dec. 1971.
Sherwani, J. K., and D. H. Moreau, 1975. "Strategies for Water Quality
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Shih, C. C., C. H. Prien, T. D. Nevens, and J. E. Cotter, 1976.
"Technology Overview Reports for Eight Shale Oil Recovery Processes,"
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Shupe, Stephen J., 1978. "Instream Flow Requirements in the Powder River Coal
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April, 1978. American Water Resources Assn., Washington, D.C.
Slawson, G. C. (Ed.), 1979. "Groundwater Quality Monitoring of Western Oil
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Monitoring and Support Laboratory, EPA-600/7-79-023, Las Vegas, Nev.
240 pp.
Steele, T. 0., E. J. Gilroy, and R. O. Hawkinson, 1974. "An Assessment of
Areal and Temporal Variations in Streamflow Quality Using Selected Data
From the National Stream Quality Accounting Network," USGS, Open File
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StUI1111, W., and J. J. Morgan, 1970.
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Taylor, O. J., 1978. "Summary Appraisals of the Nation's Ground-Water
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"Aquatic Chemistry," Wiley-Interscience,
89

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Taylor, W.O., S. C. Hern, L. R. Williams, V. W. Lambou, M. K. Morris, and F.
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Measurements of Trophic State," 1J.S. Environmental Protection Agency,
Environmental Monitoring and Support Laboratory, Las Vegas, Nev.

Theis, T. L., J. D. Westrick, C. L. Hsu, and J. J. Marley, 1976. "Field
Investigation of Trace Metals in Groundwater From Fly Ash Disposal," pp.
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Washington, D.C.
Thomas, R. W., T. L. Thompson, V. W. Lambou, and L. Ze1iph, In Press.
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and Systems Laboratory, Las Vegas, Nev.
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U.S. Atomic Energy Commission, 1972. "Environmental Statement, Wagon Wheel
Gas Stimulation Project, Sublette County, Wyoming," Washington, D.C.,
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U.S. Bureau of Reclamation, 1977a. "E1 Paso Coal Gasification Project, San
Juan County, New Mexico, Final Environmental Statement INTFES 77-03, Vol.
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U.S. Bureau of Reclamation, 1977b. "Report on the Western Energy Expansion
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U.S. Department of the Interior, 1973. "Final Environmental Impact Statement
for the Prototype Oil Shale Leasing Program, Vol. 1, Regional Impacts of
Oil Shale Development," U.S. Environmental Protection Agency, Ind. Env.
Rsch. Lab., EPA-600/7-77-069, Cincinnati, Ohio. 186 pp.
U.S. Environmental Protection Agency, 1974. "Northern Great Plains Resources
Program Accomplishment Plan Region VIII," U.S. Environmental Protection
Agency, Region VIII, Denver, Colo. 189 pp.

U.S. Environmental Protection Agency, 1976a. "Surface Coal Mining in the
Northern Great Plains of the Western United States," Region VIII, OEA
76-1, Denver, Colo. 146 pp.
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EPA-440/9-76-023, Washington, D.C. 501 pp.
U.S. Geological Survey, 1970. "The National At1 as of the United States of
America," U.S. Department of Interior, Washington, D.C. 417 pp.

U.S. Geological Survey, 1977. "National Handbook of Recommended Methods for
Water-Data Acquisition," U.S. Department of Interior, Reston, Va.
191 pp.
90

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Univ. of Oklahoma and Radian Corporporation, 1977. "Energy From the West: A
Progress Report of a Technology Assessment of Western Energy Resource
Development," 2 Vols., U.S. Environmental Protection Agency, Off.
of Energy Development, EPA-600/7-77-072 a and b, Washington, D.C. 805 pp.
Van Meter, W. P., and R. E. Erickson, 1976. "Environmental Effects From
Leaching of Coal Conversion By-Products: Interim Report for the Period
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2019 -3.
Van Voast, W. A., and R. B. Hedges, 1975. "Hydrologic Aspects of Existing and
Proposed Strip Coal Mines Near Decker, Southeastern Montana," Mont. Bur.
of Mines and Geol., Bull. No. 97, Helena, Mont. 31 pp.

Wachter, R. A., and T. R. Blackwood, 1978. "Source Assessment: Water
Pollutants From Coal Storage Areas," U.S. Environmental Protection Agency,
Ind. Env. Rsch. Lab. EPA-600/2-78-004m, Cincinnati, Ohio. 160 pp.
Ward, R. C., 1973. "Data Acquisition Systems in Water Quality Management,"
U.S. Environmental Protection Agency, Office of Rsch. and Devl.,
EPA-R5-73-014, Washington, D.C. 259 pp.

Ward, R. C., and K. S. Nielson, 1978. "Evaluating the Sampling Frequencies of
Water Quality Monitoring Networks," U.S. Environmental Protection Agency,
Environmental Monitoring and Support Laboratory, EPA-600/7-78-169, Las
Vegas, Nev. 40 pp.
Water Resources Work Group, 1971. "Upper Colorado Region Comprehensive
Framework Study, Appendix V, Water Resources," Upper Colorado Region
State-Federal Inter-Agency Group for the Pacific Southwest Inter-Agency
Committee, Water Resources Council. 66 pp.

Weeks, J. B., and F. A. Welder, 1974. "Colorado Water Resources Basic-Data
Release 35: Hydrologic and Geophysical Data From the Piceance Basin,
Colorado," Colorado Dept. of Nat. Resources, Denver, Colo. 121 pp.
Wewerka, E. M., J. M. Williams, P. L. Wanek, and J. D. Olsen, 1976.
"Environmental Contamination From Trace Elements in Coal Preparation
Wastes: A literature Review and Assessment," Environmental Protection
Agency, EPA-600/7-76-007/ERDA-6600-MS, Research Triangle Park, N.C.,
69 pp. and Los Alamos Scientific Lab., University of California, Los
Alamos, N.M.
Wolman, M. G., 1971.
174, No. 4012.
liThe Nation's Rivers," pp. 909-923.
In:
Science, Vol.
91

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APPENDIX
RATIONALE FOR SELECTION OF ELEMENTS AND ELEMENTAL FORMS
FOR MON !TOR I NG
Aluminum is present in high concentrations in minerals associated with oil
shales, and alum may be used as a coagulant during water treatment. Dissolved
aluminum has been reported lethal to fish in concentrations as low as 0.07
milligrams/liter (mg/1) (McKee and Wolf 1963; Clarke 1974). Toxic effects
from suspended aluminum hydroxides were observed in rainbow trout (Salmo
gairdneri) (Clarke 1974). Hem (1970) observed that small aluminum hydroxide
crystals less than 0.10 micron in diameter had considerable chemical and
physical stability and would pass through a 0.45-micron filter. Many aluminum
salts are insoluble, and as a major constituent of the Earth's crust, total
aluminum levels should be relatively high and variable. It is recommended
that dissolved aluminum be monitored.
Arsenic is a cumulative poison to humans and is a suspected carcinogen
(Christensen and Fairchild 1976). Arsenic is concentrated in runoff from
western coal stockpiles (Wachter and Blackwood 1978) and fly ash, oil shales
(Ringrose et ale 1976) and uranium tailings (Ford, Bacon and Davis Utah, Inc.
1977a, 1977b, 1977c, and 1977d). Ferguson and Gavis (1972) reviewed the
arsenic cycle in natural waters but concluded that there is little capability
to predict the fate of arsenic entering a lake or river. Total arsenic should
be monitored at network stations.
Boron has an established criterion of 750 mg/liter for use as irrigation
water (U.S. Environmental Protection Agency 1976b). It is readily solubilized
and was found in high concentrations in ash pond sluice runoff (Chu et ale
1976) and beneath coal waste pits (Libicki 1978). Boron levels in semiarid
western streams and ground waters are often elevated and unsuited for
irrigation of sensitive crops. Weeks and Welder (1974) reported dissolved
boron contents greater than 12,000 ~g/liter from wells in the Piceance Basin.
Boron levels in semiarid western streams and ground waters are often elevated
and unsuited for irrigation of sensitive crops. (Kinney et ale 1979; Melancon
et al. 1979a, 1979b). It is recommended that total boron concentrations be
monitoried at network stations.
Barium should be rapidly precipitated in western waters as insoluble
carbonates or sulfates (U.S. Environmental Protection Agency 1976b). Although
soluble barium salts are poisonous (National Academy of Sciences 1973), their
rapid precipitation, relatively low toxicity to aquatic life (U.S.
Environmental Protection Agency 1976; Clarke 1974), and generally low ambient
concentrations throughout the area (Kinney et a1. 1979; Melancon et a1. 1979a,
1979b) lessen the degree of hazard. Barium is solubilized from fly
92

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ash disposal ponds (Chu et al. 1976) and is used to treat wastes containing
Ra-66 (Clarke 1974). Very high barium levels were reported by Weeks and
Welder (1974) in well waters from the Piceance Basin. Monitoring of total
barium concentrations at network stations is recommended.
Cadmium is concentrated in fly ashes but is not released in alkaline
sluice waters (Chu et al. 1976). Libicki (1978) found cadmium concentrations
increased in ground waters beneath coal waste disposal pits. Attari and
Mesinger (1976) found cadmium in HYGAS quench water, and Pellizzari (1978)
reported levels in excess of criteria in in situ coal gasification process and
product waters. ~admium is a cumulative poison and suspected carcinogen (U.S.
Environmental Protection Agency 1976b). It is toxic to aquatic biota at very
low concentrations (U.S. Environmental Protection Agency 1976b; National
Academy of Science 1973; Clarke 1974). In addition, it acts synergistically
with other metals and cyanides to increase toxicity (McKee and Wolf 1963).
Data on ambient cadmium levels available in STORET are generally reported in
multiples of 10 ~g/liter, the present EPA standard for drinking water.
Because of the present uncertainties regarding ambient levels of cadmium and
its extreme toxicity, total concentrations of this element should be monitored
using the greatest analytical precision and accuracy practical.

Chromium levels were enhanced in ground waters beneath uranium ore
tailings (Ford, Bacon and Davis Utah, Inc. 1977a, 1977b, 1977c, and 1977d, and
dissolved chromium is found in process and effluent streams (Reed et ale
1976). Chu et ale (1976) found that chromium in alkaline fly ash sluice
waters exceeded water-quality criteria. Fish are fairly tolerant of chromium
but some aquatic invertebrates are quite sensitive (U.S. Environmental
Protection Agency 1976b). Total chromium should be monitored at network
stations.
Copper toxicity varies by copper's chemical states, by water conditions,
and by aquatic species. Copper is generally less toxic in highly alkaline
waters. Sublethal effects are seen prior to acute exposure concentrations,
and observed lethal levels for eggs and fry are substantially less than for
adults (Clarke 1974). Copper is insoluble in the presence of iron sulfides;
it will be immobilized if there is an oxygen deficit but will diffuse back
into the water when the surface layers become oxidized (Hutchinson 1957).
Copper was solubilized from fly ash pond effluents (Chu et ale 1976) and was
increased in ground water beneath mine spoil disposal pits (Libicki 1978).
Monitoring of total copper at network sites is recommended.

Fluorides appear to have toxic effects on fish beginning in the 1 to 2
mg/liter concentration range (McKee and Wolf 1963). The minimum drinking
water standard is 1.4 mg/liter for average annual maximum daily temperatures
of 26.3C to 32.5C, well above the area. Fluorides are generally discharged
to the atmosphere during coal combustion (Ray and Parker 1977). Ringrose et
ale (1976) found oil shales to be enhanced in fluoride over surrounding soils,
and Pellizzari (1978) found high fluoride levels in regional ground waters
both before and after in situ gasification. Weeks and Welder (1974) reported
high fluoride content in well waters in the Piceance Basin. Fluoride ion
concentrations should be monitored at network stations.
93

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Iron is the fourth most abundant element by weight in the Earth's crust
(Mason 1952). Criteria for iron are established for water supplies (0.3
mg/liter) to prevent laundry staining (U.S. Environmental Protection Agency
1976b). Iron has been found toxic to fish at concentrations as low as 0.2
mg/liter, but waters that support good fish populations may have
concentrations as high as 0.7 mg/liter (McKee and Wolf 1963). Clarke (1974)
cites several researchers who demonstrated that Fe(OH)2 precipitation on gill
structures is responsible for fish mortality. Hutchinson (1957) discussed the
iron cycle in fresh water. High iron concentrations were found in effluents
from coal stockpiles (Wachter and Blackwood 1978), in in situ post
gasification well waters (Pellizzari 1978), and in some Piceance Basin well
waters (Weeks and Welder 1974). Monitoring of total iron concentrations at
network water-quality stations is recommended.

Gallium is a fairly common rare earth element. Scant toxicity data exist,
but those that do indicate that gallium is relatively untoxic. Although
concentrated in coal ash residues (Klein et ale 1975; Melancon et ale 1979a),
it does not appear at high levels in most effluent or process streams where it
is measured. Monitoring of gallium at network stations is not recommended
unless it can be accomplished as part of a multielement analysis that includes
other elements of interest.
~~
Mercury is an element of great biological concern due to its toxicity at
very low concentrations, its potential for bioaccumulation, its mobility, and
its well documented affects upon humans. It is found at high levels in in
situ post gasifiction ground waters, (Pellizzari 1978). Mercury is
volatilized during coal combustion and much of it escapes to the atmosphere,
although fly ash levels are also high (Ray and Parker 1977; Klein et ale
1975). Oil shales contain high levels of mercury (Ringrose et al. 1976) as do
some retort process streams (Bates and Thoem 1979). Mercury is readily
adsorbed on sediment and organic particles. It is recommended that total
mercury be monitored at network stations.

Lithium has been reported as toxic to some crops at concentrations below
1 mg/liter (McKee and Wolf 1963). Lithium salts have relatively low
toxicities for animals (Christensen and Fairchild 1976). Lithium may be
concentrated in fly ash (Wewerka et ale 1976; Melancon et ale 1979a), and
Pellizzari (1978) found it to be considerably enhanced in some process waters
and in situ post gasification ground water. Because of its low toxicity,
lithium is not recommended as a standard monitoring parameter unless it can be
conveniently determined in multielement water-quality analyses.
Manganese is not considered a problem in natural waters. A criterion of
50 ~g/liter was established for public water supplies for taste
considerations. Although it may be enhanced in coal or oil shale wastes,
manganese does not appear to be present in leachates at concentrations
significantly above those found in natural waters (Bates and Thoem 1979;
Wachter and Blackwood 1978). Manganese is not recommended for monitoring at
network stations unless it can be conveniently determined in multielement
analyses.

Molybdenum is a relatively low toxicity element that is an essential trace
mineral. It may be concentrated in coal ashes (Ray and Parker 1977; Wewerka

94

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et al. 1976) and oil shales (Ringrose et al. 1976). libicki (1978) reported
it beneath coal waste dumps. It is also enhanced in spent-shale leachates
(Bates and Thoem 1979). Because increases in total molybdenum may be
effectively used to detect the presence of leachate or other waters, it is
recommended for monitoring at network stations.
Nickel is found in coal stockpile leachates (Wachter and Blackwood 1978),
in alkaline fly ash sluice water (Chu et al. 1976), and in some process waters
(Pellizzari 1978; Bates and Thoem 1979) at enhanced levels. Nickel has been
found to be toxic to plant life at concentrations as low as 500 ~g/liter (U.S.
Environmental Protection Agency 1976b) and may affect aquatic life at levels
as low as 30 ~g/liter (Clarke 1974). Nickel hydroxide has a low solubility
and is strongly absorbed by iron and manganese oxides (Hem 1970). It is
recommended that total nickel be monitored at network stations.
~ead, .1 ike mercury, is of intense environmental concern. It is a
cumulative poison with a fairly low effects threshold (U.S. Environmental
Protection Agency 1976b). lead compounds generally have low solubilities, and
most lead in natural waters is found adsorbed on clays or complexed with
dissolved and suspended organics or with iron, aluminum, and manganese
hydroxides (Clarke 1974). lead is preferentially accumulated in fiy ash (Ray
and Parker 1977) and in some process waters (Pellizzari 1978). lead
concentrations in spent-shale leachates ranged from 5 to 17 ~g/liter (Bates
and ihoem 1979). . In addition, lead and its compounds are widely used in
construction, in various industrial processes, and as a ruel additive.
Monitoring of total lead is recommended at network stations.

Sulfur is insoluble in water but may be present as colloids. It is
usually present in water as sulfates, sulfites, sulfides, or more complex
salts. In most waters the sulfites and sulfides are rapidly oxidized to
sulfates (Clarke 1974). Sulfides', particularly hydrogen sulfide
(undissociated H2S), are lethal to fish, fry, or eggs at levels of 2 ~g/liter
(U.S. Environmental Protection Agency 1976b). Sulfides may be produced
naturally during anaerobic conditions. Sulfides are also by-products from oil
shale, coal gasification, and other processes (Slawson 1979). The location of
monitoring stations on flowing streams should assure sufficient oxygen to
eliminate most sulfides and convert them to sulfates. Monitoring for total
sulfur, sulfides, or sulfites is not considered effective at network stations.
As noted previously, it is recommended that sulfate be monitored. Monitoring
for sulfides near ongoing fuel extraction or processing installations or in
lakes at stations used to supplement network data is highly recommended.
Antimony tends to be precipitated in natural waters as oxides or sulfides
(McKee and Wolf 1963). Few data are available as to its aquatic toxicity, but
the National Academy of Sciences (1973) reported toxicities occurring at
levels above 10 mg/liter. Antimony is found at high concentrations in coal
stockpile effluent (Wachter and Blackwood 1978) and is concentrated in fly ash
(Ray and Parker 1977; Klein et al. 1975). However, levels are below those
reported as causing effects on biota. Monitoring of antimony is not
recommended at network stations.
95

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Selenium has an established Federal drinking-water standard of 10 ~g/liter
(PL 93-523, Section 1412). Western coals are rich in selenium, most of which
is concentrated in the fly ash (Klein et ale 1975) or discharged to the
atmosphere (Ray and Parker 1977). Chu et ale (1976) found selenium levels in
alkaline fly ash pond sluice water to exceed water-quality standards, and
Bates and Thoem (1979) reported concentrations in spent-shale leachates to
range from 7 to 50 ~g/liter. Selenium levels were found to exceed
water-quality criteria in ground water beneath uranium tailing piles (Ford,
Bacon and Davis Utah, Inc. 1977a, 1977b, 1977c, and 1977d). One case of fish
mortalities caused by selenium released from bottom sediments and passed
through the food chain is reported by the National Academy of Sciences (1973).
Total selenium is recommended for monitoring at network stations.

Silicon is the second most abundant element in the Earth's crust, and
silica is the most abundant mineral (Mason 1952). Silica (silicon dioxide) is
largely insoluble in water but may occur as finely divided or colloidal
suspended material (McKee and Wolf 1963). Hem (1970) and Strum and Morgan
(1970) consider it likely that dissolved silicon is present as silicic acid
ions (H3Si04) or more complex species. Although silicon levels are likely to
be enhanced from energy resource development activities, they are not likely
to pose any environmental hazard. Monitoring of silicon concentrations is not
recommended at network stations.
Strontium concentrations were five times higher beneath coal waste pits
than in unaffected ground water (Libicki 1978). Strontium concentrations were
also considerably greater in post in situ gasification well-water samples
(Pellizzari 1978) and in spent-oit-shale leachates (Bates and Thoem 1979).
Strontium is not absorbed readily by soils and consequently may travel easily
in ground waters (McKee and Wolf 1963). Strontium is closely related to
calcium and barium and commonly replaces them in minerals and bone structures.
The radioisotope, Sr-90 is of environmental concern. Although this report
does not address monitoring for radionuclides, Sr-90 should be monitored in
ground waters near nuclear stimulation experiments. Routine monitoring of
total or dissolved strontiums levels is not recommended at network stations.
Uranium is found both as an ore and concentrated in some coals. It has a
relatively low chemical toxicity to aquatic life (McKee and Wolf 1973).
Uranium may enter the environment from process-stream spills, in situ mining
operations, or leaching of tailings ponds (Reed et ale 1976). Uranium may be
concentrated by algae from several hundred to a thousand times greater than
ambient water concentrations. Uranium levels in water may be the first
indication of contamination from extraction operations. Therefore, monitoring
of total uranium concentrations is recommended at all network stations.
Vanadium is closely associated with uranium deposits. It appears in both
process and effluent streams (Reed et ale 1976) and was found to be enhanced
in ground water beneath tailing piles (Ford, Bacon and Davis Utah, Inc.,
1977a, 1977b, 1977c, and 1977d). Vanadium has low chemical toxicity (McKee
and Wolf 1963) but may be of use as an indicator of tailing-pile leaching or
pond seepage. It is recommended that toal vanadium concentrations be
monitored at network stations.
----go

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Zinc has been found in coal stockpile effluents (Wachter and Blackwood
1978), spent-shale leachates (Bates and Thoem 1979), and ash pond effluents
(Chu et al. 1976). Clarke (1974) reports sublethal effects on reproduction
from zinc at concentrations as low as 180 ~g/liter, and McKee and Wolf (1963)
report lethal effects on trout ova and fry at concentrations as low as 10
~g/liter. These effects are somewhat mitigated in hard waters. Zinc.
concentrations in effiuents and leachates are high enough to be of concern.
It is recommended that total zinc concentrations be monitored at network
stations.
I
I
,.
97

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! to.
.'
l-- -~-

.. -'
          TECHNICAL REPORT DATA        
      (Please read InstrUctions on (he reverse before completing)      
1. REPORT NO.     12.         3. RECIPIENT'S ACCESSION NO.  
4. TITLE ANO SUBTITLE            5. REPORT DATE     
WESTERN ENERGY RESOURCE DEVELOPMENT             
A Network for Monitoring the Impact on      6. PERFORMING ORGANIZATION CODE
Surface Water Quality                  
7. AUTHOR(S)               8. PERFORMING ORGANIZATION REPORT NO.
Robert W. Thomas                   
9. PERFORMING ORGANIZATION NAME AND ADDRESS      10. PROGRAM ELEMENT NO.   
Envi ronmental r~onitori ng Systems Laboratory   IBD625     
Office of Research and Development      11. CONTRACT/GRANT NO.   
U.S. Eni ronmental Protect ion Agency             
Las Vegas, Nevada 89114                 
12. SPONSORING AGENCY NAME AND ADDRESS      13. TYPE OF REPORT AND PERIOD COVERED
U.S. Envi ronmental Protection Agency--Las Vegas" NV Fi nal      
Office of Research and Development      14. SPONSORING AGENCY COOE  
Envi ronrnental r~oni tori ng Systems Laboratory          
Las Vegas, Nevada 89114          EPAj600j07     
15. SUPPLEMENTARY NOTES                   
NTIS-Only distribution                  
16. ABSTRACT                      
 A monitoring network to assess the impact of energy resource development on surface
water quality in Western Mountain States is developed. A literature review of energy
resource development activities, known and potential pollutants, monitoring strategiest
and data requirements for statistical analyses located the river systems most likely to
be affected, determined parameters of interest, and identified deficiencies in existing
monitoring operations. The most cost-effective mo.nitori ng strategy was determi ned to
be a system designed to detect surface water quality trends over a 2- to 4-year period.
 Twenty-five existing U.S. Geological Survey stations located near the mouths of
rivers where energy resource development is occurring, were selected for inclusion in a
monitoring network. For most water quality parameters it will be necessary to sample
approximately every ten days in order to obtain enough data to permit meaningful trend
analyses. Forty-two water quality parameters were identified. In addition, monthly
sediment, seasonal biological, and semiannual organic chemical sampling and analyses
a re recommended. The described system would identify trends in surface water quality
but would require additional efforts to determine the causes of those trends. It would
provide an early warning for regional problems and permit the timely execution of any
necessary mitigation or corrective measures.          
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17.        KEY WORDS AND DOCUMENT ANALYSIS      
a.  DESCRIPTORS      b.IDENTIFIERS/OPEN ENDED TERMS C. COSA TI Field/Group
 5 water qua 1 i ty          Western Energy Resourcp 081   
 4 water pollution and control    Development Area   13B,H  
 2 coal mining                680   
 3 oi 1 shale                   
 1 coal gasification                 
18. DISTRIBUTION STATEMENT        19. SECURITY CLASS (This Report) 21. NO. OF PAG ES
              UNCLASSIFIED   96   
 RELEASE TO PUBLIC        20. SECURITY CLASS (This page) 22. PRICE   
              UNCLASSIF lEO      
EPA Form 2220-1 (Rev. 4-77)
PREVIOUS EDITION IS OBSOI..ETE

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